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BackgroundHuman embryonic stem cells provide access to the earliest stages of human development and may serve as a source of specialized cells for regenerative medicine. Thus, it becomes crucial to develop protocols for the directed differentiation of embryonic stem cells into tissue-restricted precursors.Methods and FindingsHere, we present culture conditions for the derivation of unlimited numbers of pure mesenchymal precursors from human embryonic stem cells and demonstrate multilineage differentiation into fat, cartilage, bone, and skeletal muscle cells.ConclusionOur findings will help to elucidate the mechanism of mesoderm specification during embryonic stem cell differentiation and provide a platform to efficiently generate specialized human mesenchymal cell types for future clinical applications.Lorenz Studer and colleagues describe the use of embryonic stem cells to derive mesenchymal precursors and then fat, cartilage, bone, and skeletal muscle cells.
Embryonic stem (ES) cells are pluripotent cells derived from the inner cell mass of the blastocyst that can be maintained in culture for an extended period of time without losing differentiation potential. The successful isolation of human ES cells (hESCs) has raised the hope that these cells may provide a universal tissue source to treat many human diseases. However, directed differentiation of hESCs into specific tissue types poses a formidable challenge. Protocols are currently available for only a few cell types, mostly of neural identity [1–3], and differentiation into many of the cell types derived from the paraxial mesoderm has not been reported, with the exception of a recent study indicating osteoblastic differentiation [4]. Mesenchymal stem cells (MSCs) have been isolated from the adult bone marrow [5], adipose tissue [6], and dermis and other connective tissues [7]. Harvesting MSCs from any of these sources requires invasive procedures and the availability of a suitable donor. The number of MSCs that can be obtained from a single donor is limited, and the capacity of these cells for long-term proliferation is rather poor. In contrast, hESCs could provide an unlimited number of specialized cells. In this study, we present techniques for the generation and purification of mesenchymal precursors from hESCs and their directed differentiation in vitro into various mesenchymal derivatives, including skeletal myoblasts. Our isolation method for mesenchymal precursors is the first example, to our knowledge, of efficiently deriving structures of the paraxial mesoderm from ES cells, and further highlights the potential of hESCs for basic biology and regenerative medicine.
Cell Culture and FACSUndifferentiated hESCs, H1 (WA-01, XY, passages 40–65) and H9 (WA-09, XX, passages 35–45), were cultured on mitotically inactivated mouse embryonic fibroblasts (Specialty Media, Phillipsburg, New Jersey, United States) and maintained under growth conditions and passaging techniques described previously [3]. OP9 cells were maintained in alpha MEM medium containing 20% fetal bovine serum (FBS) and 2 mM L-glutamine. Mesenchymal differentiation was induced by plating 10 × 103 to 25 × 103 cells/cm2 on a monolayer of OP9 cells in the presence of 20% heat-inactivated FBS in alpha MEM medium. Flow-activated cell sorting (FACS) (CD73-PE; PharMingen, San Diego, California, United States) was performed on a MoFlo (Cytomation, Fort Collins, Colorado, United States). All human ES cell–derived mesenchymal precursor cell (hESMPC) lines in this study are of polyclonal origin. Primary human bone marrow–derived MSCs and primary human foreskin fibroblasts (both from Poietics, Cambrex, East Rutherford, New Jersey, United States) were grown in alpha MEM medium containing 10% FBS and 2 mM L-glutamine. Adipocytic DifferentiationhESMPCs are grown to confluence followed by exposure to 1 mM dexamethasone, 10 μg/ml insulin, and 0.5 mM isobutylxanthine (all from Sigma, St. Louis, Missouri, United States) in alpha MEM medium containing 10% FBS for 2–4 wk. Data were confirmed in hESMPC-H1.1, -H1.2, -H1.3, and -H9.1 (hESMPC-H1.4 was not tested). Chondrocytic DifferentiationDifferentiation of hESMPCs was induced in pellet culture [5] by exposure to 10 ng/ml TGF-β3 (R & D Systems, Minneapolis, Minnesota, United States) and 200 μM ascorbic acid (Sigma) in alpha MEM medium containing 10% FBS for 3–4 wk. Data were confirmed in hESMPC-H1.1, -H1.3, and -H9.1 (hESMPC-H1.2 and -H1.4 were not tested). Osteogenic DifferentiationhESMPCs were plated at low density (1 × 103 to 2.5 × 103 cells/cm2) on tissue-culture-treated dishes in the presence of 10 mM β-glycerol phosphate (Sigma), 0.1 μM dexamethasone, and 200 μM ascorbic acid in alpha MEM medium containing 10% FBS for 3–4 wk. Data were confirmed in hESMPC-H1.1, -H1.3, and -H9.1 (hESMPC-H1.2 and -H1.4 were not tested). Myogenic DifferentiationConfluent hESMPCs were maintained for 2–3 wk in alpha MEM medium with 20% heat-inactivated FBS. More rapid induction was observed in the presence of medium conditioned for 24 h by differentiated C2C12 cells. Coculture of hESMPCs and C2C12 cells was carried out in alpha MEM with 3% horse serum and 1% FBS [8]. Data were confirmed in hESMPC-H1.3, -H1.4, and -H9.1 (hESMPC-H1.1 and -H1.2 were not tested). CytochemistryImmunocytochemistry for all surface markers was performed on live cells. Monoclonal antibodies VCAM, STRO-1, ICAM-1(CD54), CD105, CD29, and MF20 were from Developmental Studies Hybridoma Bank (University of Iowa, Iowa City, Iowa, United States); CD73, CD44, and ALCAM(CD166) were from BD Biosciences Pharmingen (San Diego, California, United States). All other immunocytochemical analyses were performed after fixation in 4% paraformaldehyde and 0.15% picric acid, followed by permeabilization in 0.3% Triton X100. Polyclonal antibodies used were MyoD (Santa Cruz Biotechnology, Santa Cruz, California, United States) and nestin (gift from R. McKay); monoclonal antibodies were vimentin, alpha smooth muscle actin, fast-switch myosin, pan-cytokeratin (all from Sigma), and human nuclear antigen (Chemicon, Temecula, California, United States).Alkaline phosphatase reaction was performed using a commercially available kit (Kit-86; Sigma) and the mineral was stained with silver nitrate according to the von Kossa method. Fat granules were visualized by Oil Red O staining solution (Sigma). Alcian Blue (Sigma) was used to detect extracellular matrix proteoglycans in chondrogenic cultures. Gene-Expression AnalysesRT-PCR analysisTotal RNA was extracted by using the RNeasy kit and DNase I treatment (Qiagen, Valencia, California, United States). Total RNA (2 μg each) was reverse transcribed (SuperScript; Invitrogen, Carlsbad, California, United States). PCR conditions were optimized and linear amplification range was determined for each primer by varying annealing temperature and cycle number. PCR products were identified by size, and identity was confirmed by DNA sequencing. Primer sequences, cycle numbers, and annealing temperatures are provided in Table S1.Affymetrix analysisTotal RNA (5 μg) from primary MSCs, from hESMPC-H9.1, hESMPC-H1.2, and three samples of undifferentiated hESCs (H1; passages 42–46), were processed by the Memorial Sloan-Kettering Cancer Center Genomics Core Facility and hybridized on Affymetrix (Santa Clara, California, United States) U133A human oligonucleotide arrays. Data were analyzed using MAS5.0 (Affymetrix) software. Transcripts selectively expressed in each of the mesenchymal cell populations (MSC, hESMPC-H9.1, and hESMPC-H1.2) were defined as those called “increased” by the MAS5.0 algorithm in each of three comparisons with independent samples of undifferentiated hESCs. A Venn diagram was generated to visualize overlap in gene expression. Further statistical analyses were performed as described below.
Mesenchymal differentiation of hESCs (lines H1 [WA-01] and H9 [WA-09]) [9] was induced by plating undifferentiated hESCs on a monolayer of murine OP9 stromal cells [10], in the presence of 20% heat-inactivated FBS in alpha MEM medium. OP9 cells have been previously shown to induce blood cell differentiation from mouse ES cells [11]. After 40 d of coculture, cells were harvested and sorted by FACS for CD73, a surface marker expressed in adult MSCs [5] (Figure 1A). An average of 5% CD73+ cells was obtained from the mixed culture of OP9 and differentiated hESC progeny. CD73+ cells were replated in the absence of stromal feeders on tissue culture plates and expanded in alpha MEM medium with 20% FBS for 7–14 d. We next established the membrane antigen profile of the resulting population of flat spindle-like cells. The H1- and H9-derived CD73+ cells expressed a comprehensive set of markers that are considered to define adult MSCs, including CD105(SH2), STRO-1, VCAM (CD106), CD29(integrin β1), CD44, ICAM -1(CD54), ALCAM(CD166), vimentin, and alpha smooth muscle actin (Figure 1B and 1C). The cells were negative for hematopoietic markers such as CD34, CD45, and CD14. They were also negative for neuroectodermal, epithelial, and muscle cell markers including nestin, pancytokeratin, and desmin (data not shown). The human identity of these presumed mesenchymal cells (termed hESMPC-H1.1, -H1.2, -H1.3, -H1.4, and -H9.1) was confirmed for all experiments by immunocytochemistry for human nuclear antigen to rule out the possibility of contamination with OP9 cells (Figure S1). To further characterize hESMPCs, we performed genome-wide expression analysis using oligonucleotide arrays (Affymetrix U133A). The expression profiles of hESMPC-H1.2 and hESMPC-H9.1 were compared with that of human primary adult MSCs. Housekeeping genes for each of the mesenchymal cell populations were eliminated by subtracting those transcripts also expressed in at least one of three independent samples of undifferentiated hESCs. Based on this analysis, 1,280 transcripts were selectively expressed in hESMPC-H1.2, 932 transcripts in hESMPC-H9.1, and 1,218 transcripts in primary adult MSCs. A remarkable overlap of 579 transcripts shared among the three mesenchymal populations was observed (Figure 1D). Using the genes that were selected in the initial filter, we performed a statistical analysis on the expression levels to determine whether the genes were expressed significantly differently in the two cell types. We used a Bayesian extension to the standard t-test [12] to assess this difference. Of the 579 genes, 412 of them were significantly different, at a false discovery rate cutoff of 0.05. The relative fold changes were also extremely large in many of the cases. We also looked at the variance of the expression levels within the cell types. For the MSCs, 94% had a coefficient of variation less than 20% for the expression (log transformed); for the ES-derived cells, 72% had a coefficient of variation less than 20%. Numerous known MSC markers were included in the list of 412 genes, such as the mesenchymal stem cell protein DSC54 (13.9-fold increase, p < 0.001), neuropilin 1 (30.4-fold increase, p < 0.001), hepatocyte growth factor (48.1-fold increase, p < 0.001), forkhead box D1 (14.8-fold increase, p < 0.001), and notch homolog 2 (2.9-fold increase, p < 0.001) . Table S2 lists the p-values from the test, the mean and standard deviation of the expression levels, and the relative fold change of all 412 genes between the two types. Known markers of MSCs, such as mesenchymal stem cell protein DSC54, were all included within the 579 shared transcripts. These findings support the immunocytochemical data and suggest that hESMPCs and primary MSCs are highly related. MSCs are characterized functionally by their ability to differentiate into mesenchymal tissues, such as fat, cartilage, and bone. Therefore, we tested whether hESMPCs have the same potential (Figure 2). Adipocytic differentiation of hESMPCs was induced under conditions described previously for primary adult MSCs [5]. Appearance of cells harboring fat granules was observed after 10–14 d in culture. After 3 wk of induction, more than 70% of the cells displayed Oil Red O+ fat granules, and PPARγ, a marker of adipocytic differentiation, was detected by RT-PCR. (Figure 2A). Chondrocytic differentiation was achieved using the pellet culture system [5]. After 28 d in culture, more than 50% of all cells exhibited robust staining for Alcian Blue, a marker specific for extracellular matrix proteoglycans. Chondrocytic differentiation was confirmed by the gene expression of collagen II and aggrecan, two components of extracellular matrix selectively expressed by chondrocytes, using RT-PCR (Figure 2B). Osteogenic differentiation was induced in the presence of β-glycerolphosphate [5]. Osteogenesis was demonstrated by specific staining for calcium deposition in the matrix (von Kossa, Figure 2C; or Alizarin Red, Figure S2A) and increased expression of bone-specific alkaline phosphatase and bone sialoprotein at day 28 of treatment (Figures 2C and S2B). At day 28, Alizarin Red staining was detected in approximately 70% of all cells. Throughout these studies, human adult MSCs and foreskin fibroblasts were used as positive and negative controls, respectively. In addition to adipocytic, chondrocytic, and osteogenic differentiation, reports suggested that adult MSCs can form skeletal muscle [13]. Although generation of skeletal muscle cells from adult MSCs remains controversial, we tested whether hESMPCs exhibit this potential. Under the conditions previously described [13], hESMPC-H1.1 and -H9.1 did not yield significant numbers of MyoD+ cells after 15–20 d in culture. However, when confluent cells were maintained in culture in the presence or absence of 5-AzaC without passage for more than 21 d, expression of specific skeletal muscle markers such as MyoD and fast-switch myosin was observed (Figure 3A). More rapid myogenic differentiation was obtained in the presence of 24-h-conditioned medium from the murine myoblastic cell line C2C12 previously induced to form myotubes [14]. Direct coculture of hESMPCs with C2C12 cells led to the formation of hESMPC-derived myotubes, as visualized by expression of human nuclear antigen (Figure 3B), similar to those formed by host C2C12 cells. After 1 wk of coculture, myotubes composed of human nuclei accounted for more than 10% of the total number of human cells present, and each human myotube was composed of up to ten human nuclei. Human cell contribution to myotubes in coculture was confirmed by expression of human muscle-specific transcripts such as MyoD, myosin heavy chain IIa, and myogenin (data not shown). These data demonstrate that hESMPCs can give rise to mesenchymal derivatives typically obtained from primary adult MSCs, as well as to cells expressing markers of skeletal muscle. One concern for the clinical application of hESC-derived progeny in regenerative medicine is the risk of teratoma formation due to the presence of residual undifferentiated ES cells among the differentiated progeny. We did not detect markers of undifferentiated hESCs, such as Nanog [15] or Oct-4 [16], in any of the hESMPCs by RT-PCR (see Figure 2D) and immunocytochemistry (data not shown), suggesting the lack of any undifferentiated ES cells in hESMPC cultures. However, future in vivo studies are required to rule out the potential of these cells for teratoma formation.
Previous studies have demonstrated the derivation of neural cells [1–3], hematopoietic [17] and endothelial lineages [18], and cardiomyocytes [19] from hESCs. This study presents the induction of paraxial mesoderm with the generation of multipotent mesenchymal precursors. We calculate that under these conditions a single undifferentiated hESC yields an average of one CD73+ cell at day 40 of differentiation, suggesting a balance between cell proliferation and cell selection. There were no obvious differences in marker and gene-expression profile or in differentiation behavior among the five hESMPC lines generated. However, some of the lines (e.g., hESMPC9.1) exhibited a tendency of spontaneous osteogenic differentiation after long-term propagation. Directed differentiation of hESCs into somatic stem-cell-like precursors represents a substantial advancement in harnessing the developmental potential of hESCs. The high purity, unlimited availability, and multipotentiality of hESMPCs will provide the basis for future therapeutic efforts using these cells in preclinical animal models of disease. Such in vivo studies will also be required to properly assess the safety profile of these cells. Furthermore, our system also offers a novel platform to study basic mechanisms of mesodermal induction and differentiation during early human development.
BackgroundUsing antibodies to specific protein antigens is the method of choice to assign and identify cell lineage through simultaneous analysis of surface molecules and intracellular markers. Embryonic stem cell research can be benefited from using antibodies specific to transcriptional factors/markers that contribute to the "stemness" phenotype or critical for cell lineage.ResultsIn this report, we have developed and validated antibodies (either monoclonal or polyclonal) specific to human embryonic stem cell antigens and early differentiation transcriptional factors/markers that are critical for cell differentiation into definite lineage.ConclusionThese antibodies enable stem cell biologists to conveniently identify stem cell characteristics and to quantitatively assess differentiation.
Although the stem cell concept was introduced decades ago, to date, stem cells can only be defined functionally, not morphologically or phenotypically. Two functions define stem cells. Firstly, they are self-renewing, thus able to propagate to generate additional stem cells. Secondly they can differentiate into various progenitor cells, which commit to further maturation along a specific lineage. While stem cells can be best defined functionally, a good number of molecular markers have been used to prospectively identify various stem cell populations. Although the functional importance of many of these antigens remains unknown, their unique expression pattern and timing of expression provide a useful tool for scientists to identify as well as isolate stem cells. Embryonic stem cells (ESC), derived from the inner cell mass of pre-implantation embryos, have been recognized as the earliest stem cell population [1,2]. This pluripotent population can differentiate into all somatic tissue including germ cells. In the case of human ESC, they can differentiate into some extra-embryonic derivatives as well. Like mouse ESC, human ES cells can be maintained and propagated on mouse fibroblast feeders for extended periods in media containing basic fibroblast growth factor (bFGF) [3]. Gene expression of undifferentiated human ES cells has been investigated among several ES cell lines by a variety of techniques. They include comparison with databases, reverse transcriptase-polymerase chain reaction, focused cDNA microarrays, and immunocytochemistry. A list of molecules comprised of known ES-specific or -highly expressed genes and candidates that can serve as markers for human ESCs and may also contribute to the "stemness" phenotype has been established [3-11]. For example, pluripotent ESC can be characterized by high level expression of Oct3/4 (POU domain, class 5, transcription factor 1, Pou5f1) and Nanog, which are a member of POU domain and homeobox transcription factors respectively. A critical amount of Oct3/4 and Nanog expression is required to sustain stem-cell pluripotency and both of these markers are downregulated as cells differentiate in vitro and in vivo [4-9]. Antibodies to Oct3/4 which cross react with human Oct 3/4 have been widely used to monitor the presence of undifferentiated ESC. No single marker however is sufficient or unique for identifying ESCs. Oct3/4 for example is expressed by germ cells and may be expressed by specific populations later in development. Likewise, Nanog has been shown to express in other tissues. We and other have noted however, that while no single marker is sufficient a constellation of positive and negative markers can in concert unambiguously allow one to define the state of ESC cultures and that surface markers in combination can be used to prospectively sort for ESC. Based on published data at the level of gene expression, we have cloned a number of candidate marker genes. We have also expressed the recombinant protein and generated a panel of monoclonal or polyclonal antibodies to these proteins. Using these antibodies we have confirmed the specificity and selectivity of these antibodies on several ESC lines and established their utility as stem cells markers. Our results confirm the expression pattern and timing of these cell markers at the protein level, whereas previous data reported at the level of gene expression.
Characterization of undifferentiated human ES cells and differentiated EBs by antibodiesAll monoclonal antibodies were initially selected for their abilities to recognize recombinant proteins in direct ELISAs. A subset were also tested by Western Blot analysis using recombinant proteins and cell lysate to confirm binding to a single epitope. The best clone was later screened for its applications for immunocytochemistry and flow cytometry using various cell lines. Human peripheral blood platelets were used for screening mouse anti-human CD9 antibody. MCF-7 cells were used for screening mouse anti-human E-Cadherin and PODXL (podocalyxin-like) antibodies. MG-63 cells were used for screening mouse anti-human GATA1 (GATA binding protein 1) antibody. Beta-TC6 cells were used for screening for mouse anti-human/mouse PDX-1 (pancreatic duodenal homeobox-1) antibody. NTERA-2 cells were used for screening mouse anti-human Oct3/4 and SOX2 (sex-determining region Y-box 2) antibodies. All polyclonal antibodies were affinity-purified using recombinant proteins and validated by direct ELISAs and Western. Caco-2 cells were used for validation of goat anti-human GATA6 antibody and NTERA-2 cells were used for validation of goat anti-human Nanog and anti-human Oct3/4 antibodies (Summarized in Table 1).Table 1Summary list of antibody verification by western blot.AntibodySample used for analysisMol. Wt. (KD)Gt × hBrachyurymouse ES-derived EB lysate48Ms × hDPPA5N/AN/AGt × hGATA6Caco2 cell lysate65Gt × hNanogNTERA-2 cell lysate33Gt × hOct 3/4NTERA-2 cell lysate39Gt × hPDX1beta-TC 6 cell lysate32Gt × hSOX17mouse ES-derived EB lysate45Ms × hCD9PBMC25Rt × hGATA-1N/AN/AMs × hE-CadherinMCF-7 cell lysate97Ms × hPODXLMCF-7 cell lysate57Ms × hSOX2NTERA-2 cell lysate36N/A: 1. DPPA5 is still being subcloned. Only Elisa verification is available.2. The clone for GATA-1 (MAB1779) does not work for Western blot application but is useful for IHC, The clone picked for Western blot analysis does not work for IHC (MAB17791, see data in ).After antibodies were validated in direct ELISAs, Western blot or cell lines (Fig. 1 and data not shown), they were used to examine the expression of individual molecules in undifferentiated human ES cells and differentiated EBs. When examined by immunohistochemistry, high level of expressions of Oct3/4, SOX2, E-Cadherin, PODXL and Nanog were observed in undifferentiated human ES cells (Fig. 2A, 2B and 2C). DPPA5 (developmental pluripotency associated 5) expression was also observed in undifferentiated human ES cells (data not shown). We noted that a subset of the proteins used were membrane bound proteins. To test if any of the antibodies generated could recognize an extracellular epitope and thus be used for live cell sorting, we repeated staining of live cells as previously described. The CD9, E-Cadherin and PODXL antibodies recognized an extracellular epitope and their ability to select cells by FACS was confirmed (Fig. 3). Minimal or no expressions of Oct3/4, E-Cadherin, PODXL and Nanog were detected in the differentiated EBs (Fig. 2D, 2E and 2F). However, SOX2 expression, which is observed in neural progenitor cells, is persistent in subsets of EBs.Figure 1Western blot analysis for Gt × hOct3/4 (A), Gt × hNanog (B) and Ms × hSOX2 (C) in NTERA-2 cell lysate, Ms × hE-Cadherin (D) in MCF-7 cell lysate, Ms × hCD9 (E) in PBMC lysate and Ms × hPDX-1(F) in β-TC-6 cell lysate. Numbers indicate the positions of molecular weight markers.Figure 2Undifferentiated human ES cells (A, B, and C) and differentiated EBs (D, E and F) were analyzed using antibodies to indicated molecular markers. Immunostaining with goat anti-human Oct3/4 (Red in A and D), mouse anti-human SOX2 (Green in A and D), goat anti-human E-Cadherin (Red in B and E), mouse anti-human PODXL (Green in B and E), and goat anti-human Nanog (Red in C and F), are contrasted with DAPI nuclear staining (Blue in C-F). Note the dramatic downregulation of ESC specific markers (Oct3/4, E-Cadherin, PODXL, and Nanog) in EBs. However, SOX2 expression is persistent in subsets of EB cells. Scale bars = 100 μm.Figure 3Human embryonic stem cells stained with anti-CD9 (A), anti-E-Cadherin (B), and anti-PODXL (C) and antigen expression detected by a flow cytometer. The specific staining is indicated by green histogram and corresponding isotype control is indicated by black histogram.Suspension culture with FGF withdrawal is known to induce differentiation of ES cells to all three germ layer precursors [12]. The differentiation status of the EB used here was detected to contain all germ cell markers by RT-PCR (Fig. 4). In order to examine how more antibodies can be used for characterization of early differentiation events from human ES cells, we examined the expressions of endodermal markers, SOX17, GATA6 and PDX-1, and mesodermal markers, Brachyury and GATA1, in the undifferentiated human ES cells and differentiated EBs. Expressions of SOX17, GATA6, PDX-1, Brachyury and GATA1 were not detected in undifferentiated human ES cells (data not shown). In contrast to the undifferentiated ES cells, subpopulations of SOX17-, GATA6-, Brachyury- and GATA1-positive cells were observed (Fig 4). These results suggest that both endodermal and mesodermal precursors exist in EBs with FGF withdrawal for 8 days. However, no PDX-1-positive cells were seen in EBs differentiated with the same treatment (data not shown).Figure 4Differentiated EBs were analyzed by either immunocytochemistry or RT-PCR to the indicated molecular markers. (A) Immunostaining with goat anti-human SOX17 (Red), is contrasted with Fluoro Nissl nuclear staining (Green). (B) Immunostaining with goat anti-human GATA6 (Red), is contrasted with DAPI nuclear staining (Blue). (C) Immunostaining with goat anti-human brachyury (Red), is contrasted with DAPI nuclear staining (Blue). (D) Immunostaining with mouse anti-human GATA1 (Red). Note that each antibody recognizes subsets of EB cells. Scale bars = 100 μm. (E) The differentiation status of EB is detected by RT-PCR using different germ layer cell markers. Selected endoderm markers AFP, FoxA2; mesoderm markers Hand1, MSX1 and ectoderm marker Msl1 were all highly expressed in the EB samples while their expression was either undetectable or at low level in the ES samples. G3PDH was a positive control showing similar amount of RNA samples were used for analysis. Examination of cross-reactivity of antibodies on mouse ES and differentiated cellsWe have also examined the cross-reactivities of these antibodies to mouse ES cells using mouse D3 ES cell line and mouse fetal endodermal tissue. Cross-reactivity to mouse of goat anti-Oct3/4, goat anti-PDX-1, goat anti-SOX17 and mouse anti-SOX2 was detected. Minimal cross-reactivity to mouse, measured by 10% intensity to human by higher than control cells, was observed in mouse anti-CD9 and mouse anti-E-cadherin antibodies. Goat anti-Nanog and mouse anti-PODXL antibodies appear to be human-specific as well (data not shown). The subtypes of monoclonal antibodies were also identified in the best clones. These results are summarized in Table 2.Table 2Summary of antibodies detection in ES and EB samples.AntibodyESEBReactivity to mouseIsotype of monoclonal antibody (Clone No.)Gt × hBrachyuryNoYesNTMs × hDPPA5YesNTNTNDGt × hGATA6NoYesNTGt × hNanogYesDownNoGt × hOct 3/4YesDownYesGt × hPDX-1NoNoYesGt × hSOX17NoYesYesMs × hCD9YesNoMinimalMouse IgG2B (clone 209306)Ms × hE-cadherinYesNoMinimalMouse IgG2B (clone 180224)Ms × hGATA1NoYesNTRat IgG2B (clone 234732)Ms × hPODXLYesNoNoMouse IgG2A (clone 222328)Ms × hSOX2YesYesYesMouse IgG2A (clone 245610)*NT, Not tested; ND, Not determined.
The expression patterns detected using antibodies developed in our facility are consistent with data reported using reverse transcriptase-polymerase chain reaction or cDNA microarrays. Moreover several of the monoclonal antibodies have differing heavy chain subunits allowing double labeling using subtype specific markers to be performed. In summary, we have developed a useful collection of antibodies that would be useful for identification of stem cell characteristics and assessment of differentiation. Several additional antibodies to the molecules that have been identified as potential cell lineage markers [13] are currently under development using the same approach.
Cloning and expression of Brachyury, DPPA5, CD9, E-Cadherin, GATA1, GATA6, Nanog, Oct3/4, PDX-1, PODXL, SOX2 and SOX17Brachyury (aa. 1–202), DPPA5 (a.a. 1–116), GATA1 (a.a. 1–413), GATA6 (aa. 1–449), Nanog (aa. 153–305), Oct3/4 (aa. 1–265), PDX-1 (aa. 1–283), SOX2 (aa. 135–317) and SOX17 (aa. 177–414) were expressed in E. Coli and extracellular domains of CD9, E-Cadherin, PODXL were expressed in mouse NSO cells. All proteins were purified and sequenced before they were used as antigens for immunizations and as substrate for antibody screening and subcloning. Production and purification of antibodiesAll monoclonal antibodies were derived from fusions of mouse myeloma with B cells obtained from BALB/c mice which had been immunized with purified antigen. The IgG fraction of the culture supernatant was purified by Protein G affinity chromatography (Sigma). Each panel of antibodies was screened and selected for their abilities to detect purified recombinant antigen in direct ELISA and Western blot. All polyclonal antibodies were derived from sera of goats which had been immunized and boost it with purified antigen. Antibody was purified from the sera by an antigen-affinity chromatography. Cells and cell cultureHuman Caco-2, MG-63, MCF-7, NTERA-2 and mouse D3 cells were purchased from American Type Culture Collection (ATCC). Cells were cultured according to the ATCC instructions. Information regarding human ES cell line HSF-6 (NIH code UC06) can be obtained at the website [14]. Undifferentiated human ES cells were cultured according to the protocol provided by the University of California, San Francisco in human ES culture medium [DMEM supplemented with 20% KnockOut Serum Replacement (Invitrogen) and 5 ng/mL of bFGF (R&D Systems)]. To induce formation of embryoid bodies (EBs), ES colonies were harvested, separated from the MEF feeder cells by gravity, gently resuspended in ES culture medium and transferred to non-adherent suspension culture dishes (Corning). Unless otherwise noted, EBs derived from human ES cell aggregates were cultured for 8 days in ES culture medium deprived of bFGF and used for analysis by immunohistochemistry as described. Western blotCells are solubilized in hot 2× SDS gel sample buffer (20 mM dithiothreitol, 6% SDS, 0.25 M Tris, pH 6.8, 10% glycerol, 10 mM NaF and bromophenyl blue) at 2 × 106 per mL. The extracts are heated in a boiling water bath for 5 minutes and sonicated with a probe sonicator with 3–4 bursts of 5–10 seconds each. Samples are diluted with 1× SDS sample buffer to the desired loading of 1–5 × 103 per lane. Lysates were resolved by SDS-PAGE, transferred to Immobilon-P membrane, and immunoblotted with 0.5 μg/mL primary Abs as described in R&D Systems Website [15]. ImmunohistochemistryAntibodies were used with the appropriate secondary reagents at a concentration of 5 to 10 μg/ml. Cells or sections of EBs were fixed with 4% paraformaldehyde in PBS at room temperature for 20 min, then blocked and permeabilized with 0.1% Triton X-100, 1% BSA, 10% normal donkey serum in PBS at room temperature for 45 min. After blocking, cells were incubated with diluted primary antibody overnight at 4°C followed by coupled anti-mouse or anti-goat IgG (Molecular Probes) at room temperature in the dark for an hour. Between each step cells were washed with PBS with 0.1% BSA. RT-PCRTotal RNA was extracted from EBs using Trizol LS (Invitrogen). cDNA was synthesized by using Superscript II reverse transcriptase (Invitrogen) according to the manufacturer's recommendations. The PCR primers are available upon request. Flow cytometryAntibodies were prepared at the concentration of 0.1 mg/mL. 10 μL of the stock solution was added to 1 – 2.5 × 105 cells in a total reaction volume not exceeding 200 μL. The sample was then incubated for 20 min at 2–8 °C. Following incubation, excess antibody was removed by washing cells twice with FACS buffer (2% FCS and 0.1% sodium azide in Hank's buffer). After wash, cells were resuspend in 200 μL of FACS buffer and the binding of unlabeled monoclonal antibodies was visualized by adding 10 μL of a 25 μg/mL stock solution of a secondary developing reagent such as goat anti-mouse IgG conjugated to a fluorochrome for 20 min at 2–8°C. Following incubation, cells were washed once with FACS buffer, once with PBS. After wash, cells were resuspend in 400 μL of PBS and analyzed on a FACScant flow cytometer (Becton-Dickinson, Mountain View, CA). Five thousand events were collected and analyzed using CELL Quest software.
BackgroundMany novel studies and therapies are possible with the use of human embryonic stem cells (hES cells) and their differentiated cell progeny. The hES cell derived CD34 hematopoietic stem cells can be potentially used for many gene therapy applications. Here we evaluated the capacity of hES cell derived CD34 cells to give rise to normal macrophages as a first step towards using these cells in viral infection studies and in developing novel stem cell based gene therapy strategies for AIDS.ResultsUndifferentiated normal and lentiviral vector transduced hES cells were cultured on S17 mouse bone marrow stromal cell layers to derive CD34 hematopoietic progenitor cells. The differentiated CD34 cells isolated from cystic bodies were further cultured in cytokine media to derive macrophages. Phenotypic and functional analyses were carried out to compare these with that of fetal liver CD34 cell derived macrophages. As assessed by FACS analysis, the hES-CD34 cell derived macrophages displayed characteristic cell surface markers CD14, CD4, CCR5, CXCR4, and HLA-DR suggesting a normal phenotype. Tests evaluating phagocytosis, upregulation of the costimulatory molecule B7.1, and cytokine secretion in response to LPS stimulation showed that these macrophages are also functionally normal. When infected with HIV-1, the differentiated macrophages supported productive viral infection. Lentiviral vector transduced hES cells expressing the transgene GFP were evaluated similarly like above. The transgenic hES cells also gave rise to macrophages with normal phenotypic and functional characteristics indicating no vector mediated adverse effects during differentiation.ConclusionPhenotypically normal and functionally competent macrophages could be derived from hES-CD34 cells. Since these cells are susceptible to HIV-1 infection, they provide a uniform source of macrophages for viral infection studies. Based on these results, it is also now feasible to transduce hES-CD34 cells with anti-HIV genes such as inhibitory siRNAs and test their antiviral efficacy in down stream differentiated cells such as macrophages which are among the primary cells that need to be protected against HIV-1 infection. Thus, the potential utility of hES derived CD34 hematopoietic cells for HIV-1 gene therapy can be evaluated.
Human embryonic stem cells (hES cells) show great promise for many novel cellular therapies due to their pluripotent nature [1]. These cells have the capacity to give rise to mature cells and tissues that arise from all three germ layers during embryonic development [2-4]. Several pluripotent hES cell lines have so far been derived from the inner cell mass of human blastocysts and can be cultured indefinitely in an undifferentiated state [5-7]. Thus, these cells provide a renewable source of pluripotent stem cells from which many types of differentiated cells could be produced for experimental and therapeutic purposes. Cell differentiation protocols currently exist for the derivation of neurons, cardiomyocytes, endothelial cells, hematopoietic progenitor cells, keratinocytes, osteoblasts, and hepatocytes to name a few [2,3,8,9]. In addition to providing for potential cellular replacement therapies, opportunities exist in programming hES cells to correct a genetic defect and/or to express a therapeutic transgene of interest. Using such approaches, many possibilities exist for treating a number of genetic and immune system disorders [1]. Many novel applications can be foreseen for hES cells in infectious disease research. AIDS is a potential disease that can benefit from exploiting hES cells for cell replacement therapy as they have the capacity to differentiate into various hematopoietic cells. HIV continues to be a major global public health problem with infections increasing at an alarming rate [10,11]. Given the present lack of effective vaccines and the ineffectiveness of drug based therapies for a complete cure, new and innovative approaches are essential. Gene therapy through intracellular immunization offers a promising alternative approach and possible supplement to current HAART therapy [12-14]. HIV mainly targets cells of the hematopoietic system, namely, T cells, macrophages, and dendritic cells [15]. As infection progresses, the immune system is rendered defenseless against other invading pathogens and succumbs to opportunistic infections. There is a great deal of progress in the area of stem cell gene therapy for AIDS [12]. A primary goal of many ongoing studies is to introduce an effective anti-HIV gene into hematopoietic stem cells [16-18]. As these cells possess the ability to self renew, they have the potential to continually produce HIV resistant T cells and macrophages in the body thus providing long term immune reconstitution. These approaches use CD34 hematopoietic stem cells for anti-HIV gene transduction via integrating viral vectors such as lentiviral vectors [16-18]. Lentiviral vectors have several advantages over conventional retroviral vectors since higher transduction efficiencies can be obtained and there is less gene silencing. The CD34 cells currently used for many therapies are primarily obtained from bone marrow or mobilized peripheral blood [1,19]. Thus, CD34 progenitor cells are an essential ingredient for HIV gene therapy. In view of the need for CD34 cells for HIV gene therapy as well as for other hematopoietic disorders, if one can produce these cells in unlimited quantities from a renewable source, it will overcome the limitations of securing large numbers of CD34 cells for therapeutic purposes. In this regard, progress has been made in deriving CD34 cells from hES cells (hES-CD34). Different methods currently exist to derive CD34 cells from hES cells with varying efficiencies [20-27]. Recent reports have indicated the capacity of hES cell derived CD34 cells to give rise to lymphoid and myeloid lineages thus paving the way for utilization of these cells for hematopoietic cell therapy [20,27-29]. For the effective utilization of hES-CD34 cells for HIV gene therapy, a number of parameters need to be examined. First, one has to demonstrate that hES-CD34 cells can give rise to macrophages and helper T cells which are the main cells that need to be protected against HIV infection. Recent evidence has shown that hES-CD34 cells can give rise to myelomonocytic cells [21]. However, thorough phenotypic or functional characterization of these cells is lacking. It is also not clear if these cells are susceptible to HIV infection. Similarly, although the hES-CD34 cells were shown to have lymphoid progenitor capacity, only B cell and natural killer (NK) cell differentiation has been examined so far [21,28]. The capacity to generate T cells remains to be evaluated. With this background, as a first step, our primary goal in these studies is to examine the capacity of hES-CD34 cells to give rise to phenotypically and functionally normal macrophages and whether such cells are susceptible to productive HIV infection. Since lentiviral vectors have been shown to successfully transduce hES cells [30-33], we further investigated the ability of transduced hES cells to differentiate into transgenic macrophages that can support HIV-1 infection. Demonstration of HIV-1 productive infection in these cells will permit future efficacy evaluations of anti-HIV genes in this system. Here we show that normal and lentiviral vector transduced hES-CD34 cells can give rise to phenotypically and functionally normal macrophages that support HIV infection thus paving the way for many novel approaches to evaluate their potential for HIV gene therapy.
Derivation of macrophages from hES cellsUndifferentiated hES cell colonies grown in media supplemented with 4 ng/ml bFGF displayed normal morphology of pluripotent human embryonic stem cells with tight and discreet borders on the MEF feeder layers (Fig 1A). Similarly, lentiviral vector transduced hES cell colonies, also displayed normal morphology and growth characteristics (Fig 1A). As expected, the vector transduced colonies displayed green fluorescence due to the presence of the GFP reporter gene. When cultured on irradiated S17 mouse bone marrow stromal cells, both nontransduced and transduced hES cells developed into embryonic cystic bodies (Fig 1A). FACS analysis of single cell suspensions of the cystic bodies showed levels of CD34 cells which ranged from 7–15%. Figure 1B displays a representative FACS profile of hES-CD34 cells. Purified CD34 cells were later cultured in semi-solid methylcellulose medium to derive myeloid colonies. Both nontransduced (denoted as ES in figures) and vector transduced (denoted as GFP ES in figures) hES cell derived CD34 cells gave rise to normal myelomonocytic colonies similar to human fetal liver derived CD34 cells (denoted as CD34 in figures) (Fig 1A). When pooled colonies were cultured further in liquid cytokine media for 12–15 days for differentiation, the cells developed into morphologically distinct macrophages (Fig 1A). When compared, the morphology of macrophages derived from all stem cell progenitor populations appeared similar. These results were found to be consistent in replicative experiments. The transgene GFP expression was also maintained during the differentiation of hES cells into mature macrophages. GFP expression in cystic body derived CD34 cells was around 80% (data not shown) with similar levels seen in differentiated macrophages (Fig 2).Figure 1Derivation of macrophages from lentiviral vector transduced and normal hES cells. A) Transduced and non-transduced H1 hES cells were cultured on mouse S17 bone marrow stromal cell layers to derive cystic bodies. Cystic body derived CD34 cells were purified by positive selection with antibody conjugated magnetic beads and placed in methocult media to obtain myelomonocytic colonies. Pooled colonies were cultured in liquid cytokine media supplemented with GM-CSF and M-CSF to promote macrophage growth. For comparison, fetal liver derived CD34 cells were cultured similarly to derive macrophages. Representative ES cell colonies, cystic bodies, methocult colonies, and derivative macrophages are shown with GFP expressing cells fluorescing green under UV illumination. B) Representative FACS profile of hES cell derived CD34 cells stained with PE conjugated antibodies. Percent positive CD34 cells are shown with isotype control shown in the left panel.Figure 2Phenotypic FACS analysis of hES cell derived macrophages. A) Macrophages derived from transduced and nontransduced hES CD34 and fetal liver CD34 cells were stained with antibodies to CD14, HLA-DR, CD4, CCR5, and CXCR4 and the expression of these surface markers was analyzed by FACS. B) Isotype controls for PE and PE-CY5 antibodies. Percent positive cells are displayed in the plots for each respective cell surface marker staining. Dot plots are representative of triplicate experiments. hES cell derived macrophages display a normal phenotypic profileMacrophages play a critical role in immune system function and are also major target cells for many viral infections including HIV-1. Distinct surface phenotypic markers exist on these cells and, thus far, there has been no thorough evaluation of hES cell derived macrophages. Therefore we analyzed hES cell derived macrophages for the presence of characteristic cell surface markers and compared these to the phenotypic profile displayed on fetal CD34 cell derived macrophages. The surface markers analyzed were CD14, a monocyte/macrophage specific marker, HLA-DR (a class II MHC molecule found on antigen presenting cells), CD4, the major receptor for HIV-1 infection, and CCR5 and CXCR4, chemokine receptors which are critical coreceptors essential for HIV-1 entry. EGFP expression was also analyzed to determine the levels of transduction and any transgene silencing that may occur during differentiation. Fetal liver (CD34), nontransduced (ES), and vector transduced (GFP ES) hES cell derived macrophages were all positive for the monocyte/macrophage marker CD14 (99.3%, 88.7%, and 99.2%, respectively) (Fig 2A). However, the mean fluorescent intensity (MFI) was found to be lower on hES cell derived macrophages. Surface expression of HLA-DR was observed at similar levels between macrophages derived from fetal liver CD34 cells (99.6%), nontransduced hES cells (92.8%), and transduced hES cells (98.2%) (Fig 2A). CD4 levels were comparable for all stem cell derived macrophages (99.2%, 83.3%, and 88.7%, respectively) (Fig 2A). CCR5 and CXCR4 cell surface expression was also observed for fetal liver CD34 cell (99.6% and 99.3%), nontransduced hES cell (91.9% and 92.6%), and transduced hES cell (98.9% and 99.3%) derived macrophages (Fig 2A). As compared to fetal liver CD34 cell derived macrophages, hES cell derived macrophages displayed a higher level of expression of CXCR4. Isotype controls for both PE and PECY5 stains are shown in Fig 2B. The above phenotypic data are representative of triplicate experiments. Transgenic hES cell derived macrophages are functionally normalThe antigen presenting cell surface specific marker HLA-DR (MHC II) on normal macrophages is critical for presenting antigen to CD4 T cells. A second co-stimulatory molecule, B7.1 is present at low basal levels on resting macrophages and is necessary to activate T cells. Its expression is elevated upon activation with certain stimuli such as LPS. Our results of LPS stimulation of respective macrophages have shown upregulation of B7.1 with values for fetal liver CD34 cell (CD34) (27.9% to 75.4%) nontransduced (ES) (17.8% to 49.4%) and transduced (GFP ES) (35.6% to 65.7%) hES cell derived macrophages (Fig 3A). These values represent a significant upregulation of B7.1 for all three macrophage populations.Figure 3Functional analysis of hES cell derived macrophages for B7.1 costimulatory molecule upregulation and phagocytosis of E. coli particles: A) Mature macrophages were stimulated with LPS to determine B7.1 upregulation. Twenty-four hours post-stimulation, macrophages were labeled with a PE-CY5 conjugated anti-B7.1 antibody and analyzed by FACS. B7.1 upregulation data are representative of triplicate experiments. Isotype control is shown in the left panel. B) To assess phagocytic function, E. coli Bioparticles® were added directly to the cultured macrophages. Twenty four hours post-addition, cells were analyzed by FACS. Percent positive cells are displayed in the plots for each experiment. These data are representative of triplicate experiments.Another important function of macrophages is their ability to phagocytose foreign material and present antigenic peptides on their cell surface. To evaluate phagocytic function, fluorescently labeled E. coli Bioparticles® were added to macrophage cultures followed by FACS analysis. Nontransduced (94.6%) as well as lentiviral vector transduced (98.7%) hES cell derived macrophages were found to be capable of phagocytosing the Bioparticles® in comparison to fetal liver CD34 cell derived macrophages (95.8%) (Fig 3B). These values are representative of triplicate experiments. Magi-CXCR4 cells with no phagocytic capacity were used as non-phagocytic cell controls and similarly exposed to E. coli Bioparticles® (Fig 3B). No uptake of the bacteria could be seen. Thus, uptake of E. coli Bioparticles® by macrophages is indicative of active ingestion.Macrophages, as effector cells, play a key role in the inflammatory response. Activated macrophages secrete various cytokines, two of the major ones being IL-1 and TNF-α. To determine if hES cell derived macrophages have such a capacity, cells were stimulated with LPS. On days 1, 2, and 3 post-stimulation, culture supernatants were analyzed by ELISA to detect IL-1 and TNF-α. As seen in figure 4A, there were no significant differences in IL-1 secretion between the three sets of macrophages. Similarly, nontransduced and transduced hES cell derived macrophages were also capable of TNF-α secretion upon LPS stimulation. However, levels of the respective cytokines detected were slightly lower than those from fetal liver CD34 cell derived macrophages (Fig 4B). The values of cytokine secretion levels represent triplicate experiments.Figure 4Cytokine IL-1 and TNFα secretion by stimulated hES cell derived macrophages: Macrophages derived from transduced and nontransduced hES and fetal liver CD34 cells were stimulated with 5 μg/ml LPS. On days 1, 2, and 3 post-stimulation, supernatants were collected and assayed by ELISA for (A) IL-1 and (B) TNFα. Experiments were done in triplicate. hES cell derived macrophages support productive HIV-1 infectionThe above data have shown that hES cell derived macrophages are very similar to normal human macrophages based on phenotypic and functional analysis. In addition to being important cells of the immune system, macrophages are among the major target cells for certain viral infections, particularly for HIV-1. We wanted to determine if hES cell derived macrophages were susceptible to HIV-1 infection compared to standard macrophages. In these studies, we only used an R5-tropic strain of HIV-1 since macrophages are natural targets for this virus. Our results from challenge studies of these cells clearly indicated the capacity of hES cell derived macrophages in supporting a productive infection. Levels of virus increased up to 15 days similar to non-hES derived macrophages showing that the initial viral input was amplified in productive viral infection. However, the levels of viral yield were found to be slightly lower for the ES cell derived macrophages. In the case of GFP-ES macrophages, there was a decline in viral titer. This could be due to possible lower numbers of cells present in the initial cultures.
As a first step towards the use of hES cells for hematopoietic stem cell and HIV gene therapies, we have shown here that phenotypically and functionally normal macrophages could be derived from hES-CD34 cells. Both non transduced and lentiviral vector transduced hES cells were found to be capable of generating CD34 cells that give rise to macrophages which could support productive HIV-1 infection. Current sources of CD34 cells consist of human bone marrow, cytokine mobilized peripheral blood, fetal liver, and cord blood [34]. However, the number of cells that can be obtained for manipulations is not unlimited. Therefore, deriving CD34 cells for therapeutic and investigative purposes from hES cells with unlimited growth potential has the advantage of a consistent and uniform source. The ability to obtain phenotypically normal and functionally competent macrophages from hES cells is important to evaluate their potential therapeutic utilities in the future. Additionally, testing of transgenic hES cells derived via lentiviral vector gene transduction is also helpful to determine the stability of the transgene expression and their capacity for differentiation into end stage mature cells such as macrophages. Based on these considerations, both non- transduced and lentiviral vector transduced hES cells were evaluated for their capacity to give rise to CD34 progenitor cells. In colony forming assays using semisolid methylcellulose medium, the morphology of myelomonocytic colonies derived from hES CD34 cells appeared similar to that of fetal liver CD34 cells. When subsequently cultured in cytokine media that promotes macrophage differentiation, morphologically normal macrophages were obtained with hES-CD34 cells similar to that of fetal liver CD34 cells. At higher magnification, the macrophages displayed flat projecting cellular borders with fried egg appearance with distinct refractory lysosomal granules in the cytoplasm (data not shown). Lentiviral vector transduced hES cells also did not display any abnormal growth or differentiation characteristics as compared to nontransduced hES-CD34 cells indicating no adverse effects due to vector integration and expression. Transduced cells gave rise to cystic bodies with similar CD34 cell content and profiles upon development. The transduced hES-CD34 cells also gave rise to apparently normal macrophages that expressed the transgene as shown by GFP expression. These results are consistent with those of others that showed normal differentiation of hES cells to other cell types following lentiviral transduction [32]. A requirement for successful cellular and HIV-1 gene therapy is that mature end stage cells derived from CD34 progenitor cells be phenotypically and functionally normal to maintain and restore the body's immunological function. Accordingly, hES cell derived macrophages were evaluated to determine if they met these criteria. Macrophages display distinct cell surface markers upon end stage differentiation. To determine whether hES cell derived macrophages display these surface markers, FACS analysis was performed to detect the presence of CD14, HLA-DR (MHCII), CD4, CCR5, and CXCR4. As observed in Fig 2A, both nontransduced and transduced hES cell derived macrophages expressed all of these markers with some differences in their levels of expression. HLA-DR, CD4, and CCR5 expression profiles were comparable between all cell types analyzed. Even though all cell types analyzed stained positive for CD14, relative expression of CD14 was slightly lower on hES cell derived macrophages compared to fetal liver CD34 cell derived macrophages. On the contrary, the levels of CXCR4, a chemokine receptor involved in cellular homing, were found to be higher on hES-CD34 cell derived macrophages. This may be due to inherent differences in the cell types and/or due to their physiological state at the time of harvest [35]. Additional hES cell lines need to be evaluated in the future to establish if these differences are consistent. A major functional role of macrophages in vivo is their ability to serve as professional antigen presenting cells. During this process macrophages present antigen peptide fragments complexed with both classes of MHC molecules and deliver a costimulatory signal through the expression of B7 molecules. Upon stimulation with LPS, hES-CD34 cell derived macrophages had shown upregulation of the costimulatory molecule B7.1 similar to cells derived from fetal liver. Furthermore, the hES-CD34 cell derived macrophages also showed a normal capacity to ingest foreign particles in phagocytosis assays using E.coli Bioparticles®. In addition to antigen presentation and phagocytosis, macrophages also play a critical role in inflammation and secrete cytokines in response to external stimuli. When exposed to LPS, the hES-CD34 cell derived macrophages secreted two important cytokines IL-1 and TNF-α similar to that of fetal liver derived cells. The above data has established that phenotypically and functionally normal macrophages could be derived from hES-CD34 cells. Macrophages in addition to playing important physiological roles are also major cell targets for certain viral infections, particularly HIV-1. Here we evaluated the susceptibility of hES-CD34 cell derived macrophages to be productively infected with HIV-1. Similar to that of fetal liver CD34 cell derived cells, the hES-CD34 macrophages also supported HIV-1 infection although the levels of viral yield differed somewhat. However this should not be a major concern for testing anti-HIV genes in these cells. In all the above experiments, the vector transduced transgenic macrophages also behaved similarly to that of nontransduced cells showing that they were also physiologically normal. The lack of vector toxicity on cellular maturation is encouraging for future work with transduced hES-CD34 cells to derive other important differentiated cells like T cells and dendritic cells relevant for HIV studies. Although there are numerous studies on hES cell differentiation into many important end stage mature cells, systematic work on hES cell hematopoietic differentiation and thorough characterization of end stage mature cells that participate in critical immune responses has just begun [21,27-29]. Our current results established that physiologically normal macrophages could be derived from hES cells and that these cells have the potential for use in cellular and gene therapies. To our knowledge this is the first demonstration that hES cell derivatives can be used for infectious disease research. Due to the extensive ability for hES cells to self-renew, large numbers of differentiated cells can be derived so that infection studies and evaluation tests can be carried out in a more standardized way. Our results showing that both normal and transgenic derivative macrophages support HIV-1 infection points out to their utility for testing anti-HIV constructs transduced into hES-CD34 cells and pave the way for their application in stem cell based HIV gene therapy. So far a number of studies including our own have tested many gene therapeutic constructs in CD34 cells from conventional sources. These constructs include anti-HIV ribozymes, RNA decoys, transdominant proteins, bacterial toxins, anti-sense nucleic acids, and most recently siRNAs [36-50]. In addition, a number of cellular molecules that aid in HIV-1 infection such as cellular receptors and coreceptors CD4, CCR5 and CXCR4 have also been successfully tested in CD34 cell derived macrophages and T cells [16,18,38]. Some of these approaches have progressed into clinical evaluations as well [14,51,52]. Based on our current results, many of these novel anti-HIV constructs can also be tested in hES-CD34 cells for their potential application. Although there are advantages of using hES cell derived CD34 cells for potential cellular therapies, transplantation of these cells constitutes an allogenic source with immune rejection as a major issue. However, a recent study using human leukocyte reconstituted mice suggested that hESCs and their derivative cell types were less prone to invoking an allogeneic response [53]. Other recent studies demonstrated successful engraftment of primary and secondary recipients with hES cell derived hematopoietic cells in both immunodeficient mice and in vivo fetal sheep models adding further support that any obstacles could be overcome [23,54,55]. Moreover, multiple novel strategies to avoid immune-mediated rejection of hES cell-derived cells have been proposed [56,57]. It is not too far in the future that even autologous hES cells may be derived from specific individuals for deriving CD34 cells which can be used for cell replacement therapy.
Phenotypically normal and functionally competent macrophages could be derived from hES-CD34 cells. Since these cells are susceptible to HIV-1 infection, they provide a uniform source of macrophages for viral infection studies. Based on these results, it is also now feasible to transduce hES-CD34 cells with anti-HIV genes such as inhibitory siRNAs and test their antiviral efficacy in down stream differentiated cells such as macrophages which are among the primary cells that need to be protected against HIV-1 infection. Thus, the potential utility of hES derived CD34 hematopoietic cells for HIV-1 gene therapy can be evaluated.
Growth, propagation and lentiviral transduction of hES cellsThe NIH approved human ES H1 cell line was obtained from WiCell (Madison, Wisconsin). hES cell colonies were cultured on mouse embryonic fibroblasts (MEF) (Chemicon, Temecula, CA) in the presence of DMEM-F12 (Invitrogen, Carlsbad, CA) supplemented with 20% KNOCKOUT serum replacement with 1 mM L-glutamine, 1% Nonessential Amino Acids, 0.1 mM β-mercaptoethanol, 0.5% penicillin/streptomycin, and 4 ng/ml human basic fibroblast growth factor. Culture medium was replaced daily with fresh complete DMEM-F12. Mature colonies were subcultured weekly by digesting with collagenase IV as previously described [5]. A VSV-G pseudotyped lentiviral vector (SINF-EF1a-GFP) containing a GFP reporter gene (kindly supplied by R. Hawley, George Washington University) was used for hES cell transductions as previously described (30, 58). Generation of the pseudotyped vector in 293T cells and its concentration by ultracentrifugation were described previously [30,48]. For vector transduction, the undifferentiated hES cells were prepared into small clumps of 50–100 cells with enzyme digestion as done for routine passaging of cells. The cell clumps were incubated with the vector for 2 hrs in the presence of polybrene 6 ug/ml. A secondary cycle of transduction was done by adding fresh vector and incubating for another 2 hrs. The general vector titers were 1 × 107 and the multiplicity of infection was 10. The transduction efficiency was about 50%. The transduced colonies were cultured on MEF like above. Derivation and purification of CD34 cells from hES cellsUndifferentiated hES cells were cultured on S17 mouse bone marrow stromal cell monolayers to derive cystic bodies containing CD34+ hematopoietic progenitor stem cells. hES cell cultures were treated with collagenase IV(1 mg/ml) for 10 minutes at 37°C and subsequently detached from the plate by gentle scraping of the colonies. The hES cell clusters were then transferred to irradiated (35 Gy) S17 cell layers and cultured with RPMI differentiation medium containing 15% FBS (HyClone, Logan, UT), 2 mM L-glutamine, 0.1 mM β-mercaptoethanol, 1% MEM-nonessential amino acids, and 1% penicillin/streptomycin. Media was changed every 2 to 3 days during 14–17 days of culture on S17 cells [20].After allowing adequate time for differentiation, hES cystic bodies were harvested and processed into a single cell suspension by collagenase IV treatment followed by digestion with trypsin/EDTA supplemented with 2% chick serum (Invitrogen, Carlsbad, CA) for 20 minutes at 37°C. Cells were washed twice with PBS and filtered through a 70 uM cell strainer to obtain a single cell suspension. To assess the levels of CD34 cells in the bulk cell suspension, cells were labeled with PE conjugated anti-CD34 antibody (BD Biosciences, San Jose, CA) and analyzed by FACS. To purify the CD34 cells, Direct CD34 Progenitor Cell Isolation Kit (Miltenyi Biotech, Auburn, CA) was used following the manufacturer's protocol. Isolated CD34 hematopoietic progenitor stem cells were then analyzed by FACS as mentioned above to determine cell purity. For comparative experiments, human CD34 hematopoietic progenitor cells were also purified from fetal liver tissue as described above. Derivation of macrophages from hES cell derived and human fetal CD34 cellsCD34 cells were cultured initially in semisolid media to derive myelomonocytic colonies followed by liquid culture in cytokine supplemented media as described below. Purified CD34+ progenitor cells (~2.5 × 105 to 4.0 × 105) were placed directly into Methocult semisolid medium (Stem Cell Technologies, Vancouver, BC), mixed, and cultured in 35 mm plates. Myeloid colonies were allowed to develop for 12–15 days. Upon differentiation and proliferation, myelomonocytic colonies were harvested by the addition of 5 ml DMEM containing 10% FBS, 10 ng/ml each GM-CSF and M-CSF. Cells (~106) were placed in a 35 mm well and allowed to adhere for 48 hours. At two and four days post-harvest, medium was replaced with fresh complete DMEM supplemented with 10 ng/ml GM-CSF and M-CSF. By 4–5 days, cells developed into mature macrophages which were used for subsequent phenotypic and functional characterization. Phenotypic analysis of hES cell derived macrophagesTo determine if nontransduced and lentiviral vector transduced hES cell derived macrophages display normal macrophage surface markers, FACS analysis was performed using respective fluorochrome conjugated antibodies. Fetal liver derived CD34+ cells as well as nontransduced and transduced hES cell derived macrophages were evaluated in parallel. Cells were scraped from their wells, washed two times with PBS, and stained with the following antibodies: PE-CD14, PE-HLA-DR, PECY5-CD4, PECY5-CCR5, PECY5-CXCR4 (BD Biosciences, San Jose, CA). A blocking step was first performed by incubating the cells with the respective isotype control for 30 minutes at 4C before staining with the respective cell surface marker antibodies. Isotype control staining was used to determine background levels. FACS analysis was performed on a Beckman-Coulter EPICS ® XL-MCL flow cytometer with data analysis using EXPO32 ADC software (Coulter Corporation, Miami, FL). A minimum of 8,000 cells were analyzed in each FACS evaluation. Functional analysis of hES cell derived macrophagesPhysiological roles of macrophages include phagocytic and immune related functions. To determine if hES cell derived macrophages were functionally normal, a stimulation assay to determine upregulation of the costimulatory molecule B7.1 was performed. Activated macrophages upregulate the expression of B7.1 upon activation with various stimuli. Accordingly, fetal liver CD34, nontransduced hES, and GFP-alone transduced hES cell derived macrophages were stimulated by the addition of LPS (5 ug/ml) to the cell culture medium. Twenty-four hours post-stimulation, cells were stained with an anti-B7.1 antibody labeled with PE-Cy5 (BD Biosciences, San Jose, CA) and analyzed by FACS. To assess the hES cell derived macrophages' phagocytic function, 5 ug/ml of fluorescently labeled E. coli Bioparticles® (Invitrogen, Carlsbad, CA) were added directly to the cell culture medium. Four hours later, macrophages were washed six times with PBS and fresh medium with 10 ng/ml GM-CSF and M-CSF was added. Twenty-four hours later, cells were analyzed by FACS for the presence of ingested Bioparticles® which can be detected in the PE (FL2) channel. Lentiviral vector transduced Magi-CXCR4 cells, a HeLa cell derivative with no phagocytic capacity, were used as non-phagocytic cell controls and similarly exposed to E. coli Bioparticles®Human ES cell derived macrophages were also analyzed for their ability to secrete two major cytokines, IL-1 and TNF-α, upon external stimulation. Accordingly, macrophages were stimulated with 5 ug/ml of LPS during culture. On days 1, 2, and 3 post-stimulation, cell culture supernatant samples were collected and analyzed by a Quantikine® ELISA kit (R&D Systems, Minneapolis, MN). Non-stimulated supernatants were also analyzed for basal levels of cytokine secretion. HIV-1 infection of hES cell derived macrophagesTo determine if hES cell derived macrophages can be infected with HIV-1 and support viral replication, cells were challenged with a macrophage R5-tropic BaL-1 strain of HIV-1. An m.o.i. of 0.01 in the presence of 4 ug/ml polybrene was used. At different days post-infection, culture supernatants were collected and assayed for p24 antigen by ELISA. To quantify viral p24 levels, a Coulter-p24 kit (Beckman Coulter, Fullerton, CA) was used.
BackgroundIn order to compare the gene expression profiles of human embryonic stem cell (hESC) lines and their differentiated progeny and to monitor feeder contaminations, we have examined gene expression in seven hESC lines and human fibroblast feeder cells using Illumina® bead arrays that contain probes for 24,131 transcript probes.ResultsA total of 48 different samples (including duplicates) grown in multiple laboratories under different conditions were analyzed and pairwise comparisons were performed in all groups. Hierarchical clustering showed that blinded duplicates were correctly identified as the closest related samples. hESC lines clustered together irrespective of the laboratory in which they were maintained. hESCs could be readily distinguished from embryoid bodies (EB) differentiated from them and the karyotypically abnormal hESC line BG01V. The embryonal carcinoma (EC) line NTera2 is a useful model for evaluating characteristics of hESCs. Expression of subsets of individual genes was validated by comparing with published databases, MPSS (Massively Parallel Signature Sequencing) libraries, and parallel analysis by microarray and RT-PCR.Conclusionwe show that Illumina's bead array platform is a reliable, reproducible and robust method for developing base global profiles of cells and identifying similarities and differences in large number of samples.
Embryonic stem cells (ESCs), derived from the inner cell mass of pre-implantation embryos, have been recognized as the most pluripotent stem cell population. Human ES cells (hESCs) can be maintained and propagated on mouse or human fibroblast feeders for extended periods in media containing basic fibroblast growth factor (bFGF) [1-4] while retaining the ability to differentiate into ectoderm, endoderm and mesoderm as well as trophoectoderm and germ cells. Gene expression in hESC has been investigated by a variety of techniques including massively parallel signature sequencing (MPSS), serial analysis of gene expression (SAGE), expressed sequence tag (EST) scan, large scale microarrays, focused cDNA microarrays, and immunocytochemistry [5-7]. Markers for hESCs that may also contribute to the "stemness" phenotype have been established and markers that distinguish ESCs from embryoid bodies (EB) have been developed. Novel stage-specific genes that distinguish between hESCs and EBs have been identified and allelic differences between ESC have begun to be recognized [8-10]. As the potential of hESCs and their derivatives for regenerative medicine is being evaluated, it has become clear that the overall state of the cells, degree of contamination and comparisons of the more than a hundred different newly derived lines will need to be performed. It will be necessary to develop methods to monitor and assess hESC and their derivatives on a routine basis. Since differentiated cells are often scattered within or at the edge of colonies [11] and the differentiation is so subtle that morphological characteristics and even immunohistochemistry are insufficient to detect it, larger scale methods of analysis need to be developed. Our strategy was to compare a variety of different hESC lines that were derived and expanded by three different institutions (WiCell Research Institute, BresaGen, Inc., and Technion-Israel Institute of Technology), and cultured in two separate laboratories (Burnham Institute and NIA) to a baseline set of data against which cell samples can be compared. By using cells grown in different conditions we expected to be able to identify core commonalities and by comparing feeders and embryoid bodies (EB) with hESC identify measures of contamination and early markers of differentiation. Further, by comparing embryonal carcinoma cell (EC) and karyotypically variant lines with hESC, we would be able to directly assess their utility as surrogates (for quality control purposes) for hESC. We employed a pre-commercial prototype of the Illumina HumanRef-8 BeadChip [12], a genome-scale bead based array technology that combines the sensitivity and low cost of a focused array with the coverage of a large scale array, while requiring much smaller sample sizes than MPSS, EST scan or SAGE. We show that the Illumina bead based array correctly identified blinded duplicates as the closest related samples and readily distinguished between hESC lines, as well as between ESCs and EBs derived from them. This array allowed us to estimate the degree of feeder contamination present in the cultures. Similarities and differences between EC line NTera2 and hESC lines could be determined and verified, and the database comparisons allowed us to identify core self-renewal pathways that regulate hESC propagation.
Multiple hESC lines can be assessed by Illumina bead arrayForty-eight samples were selected from multiple laboratories and gene expression profiles were examined using a bead array containing 24,131 transcripts derived from the Human RefSeq database that included full length and splice variants. Each gene was represented by sequences containing an average of thirty beads to provide an internal measure of reliability. Samples included 7 hESC lines BG01, BG02, BG03, I6, H1, H7 and H9, EBs that were differentiated from hESCs of the three BG lines, human fibroblast feeder HS27 (ATCC), hESC-derived fibroblasts, karyotypically abnormal hESC line BG01 Variant (BG01V) [13] and EC line NTera2 [14]. Samples were blinded and biological and technical repeats were examined at the same time. A single slide contained eight replicates and six such slides were used for the present set of samples. Results were normalized to average following Illumina Beadstudio manual and the quality of each sample was assessed by immunocytochemitsry and RT-PCR prior to subjecting them for analysis (data not shown). Results from the entire sample set are available for download as an excel spreadsheet (Additional file 1) and a CD of the results is available upon request. The total number of genes identified as expressed at >0.99 confidence is summarized in Table 1. Intensity results are reported in arbitrary units and ranged from 10 to 20,000 (a two thousand fold range). Although the sensitivity of the array has been reported to be high, in the present report we have restricted our analysis to expression of at least 100 units in any one sample. Using this cutoff, on average cells expressed approximately 8,000 transcripts (Table 1, 2), a number similar to the number detected by SAGE, MPSS and EST analysis [5-7,10,15,16]. As with other analysis, genes with the highest abundance were housekeeping genes, ribosomal genes and structural genes (Table 2 and Additional file 1). These genes were similar in most samples though relative levels varied.Table 1Correlation coefficients of paired samples in this bead array In order to test the reproducibility and reliability of the bead array, duplicate samples of hESC lines H9, I6, and EC line NTera2 and human fibroblast feeders (HS27) were run at the same time and correlation coefficients (R2) of duplicates were generated using the entire data of all genes with expression level >0 (§), or genes with detection confidence >0.99 (), or genes with detection confidence >0.99 and expression level > 100 arbitrary units (#). Note that the correlation coefficients are in the range of 0.9382–0.9761 and the number of genes was in the range of 10,000–14,000.Duplicate SamplesNo. of all genes (expr.>0)§R2of all genes (expr. >0)No. of genes (>0.99)R2 of genes (>0.99)No. (>0.99, level>100)#H918,8990.868113,6720.97087,408I619,1390.866312,5700.97616,826NTera219,1620.874114,0360.93827,147Feeder18,1570.872410,6060.97517,021Table 2Distribution of genes with expression levels <50 and >50–10,000 as detected by Illumina bead array in 8 hESC populations All human ESC samples were hybridized in one experiment and the relative detection levels of genes were binned to obtain a global overview of transcription, approximately 8, 000 genes (~50%) were greater than 100 arbitrary units. The numbers are similar to results obtained by other large scale analysis such as MPSS.Abundance (relative detection levels)H9H9 on human feedersI6BG01BG02BG03BG01VPooled (H1, H7, H9)No.%No.%No.%No.%No.%No.%No.%No.%<502,90921.26,06738.43,55925.34,52830.74,25630.54,70632.24194.35,80334.4>5010,81778.89,74761.610,48474.710,23269.39,69469.59,91567.89,43895.711,08565.6>1008,12659.27,53947.77,40952.87,70352.27,49653.77,43050.87,23073.38,21748.7>5003,07722.42,94718.62,62618.73,06520.82,95021.12,85219.52,85128.92,94117.4>10001,62911.91,62510.31,49010.61,63811.11,58911.41,51710.41,56615.91,5549.2>50002481.82571.62561.82561.72631.92621.82752.82511.5>10000900.7940.61120.8920.61000.71070.71011.0940.6Total No. of genes detected at >0.99 confidence13,72615,81414,04314,76013,95014,6219,85716,888One of the advantages of the Illumina arrays is the ability of running multiple samples simultaneously thus allowing multiple pairwise comparisons to be performed readily. To show the similarity of relative gene expression between samples, we have used Illumina Beadstudio and clustering software packages Pcluster [17] and TreeView [18] to generate a heat-map (Figure 1) and a dendrogram (Figure 2). Based on their properties, we classified some of our samples into four groups, (A) undifferentiated hESCs (including a sample from karyotypically abnormal variant, designated as "ES", n = 11); (B) differentiated ES cells and EBs (designated as "EB", n = 6); (C) hESC derived neural cells (designated as "NS", n = 3); and (D) hESC derived mesenchyme and human fibroblast feeder cells (designated as "FB", n = 5) and these groups were shown in the heat-map. Comparing the overall pattern of expression, we made several important observations: 1) Duplicates clustered close to each other and were more related to each other than to any other sample; 2) ESCs appeared more similar to each other than to EBs; 3) NTera2 cells appeared more similar to ESCs while differentiated NTera2 and EBs can be readily distinguished from their parent populations (Figure 2); 4) BG01V appeared similar to undifferentiated BG01 cells; 5) In general ESC lines grown in one laboratory appeared more similar than samples grown in other laboratories, suggesting that culture conditions affected gene expression but that this effect was much smaller than the effect of differentiation.Figure 1Unsupervised two-way hierarchical cluster analysis of differentially expressed genes illustrated in a heat-map. Each row represents the relative levels of expression of a single gene. Each column represents a sample. The samples include four groups of cells, ES designates 11 samples of hESCs, EB contains 6 samples of differentiated ESCs and EBs, NS consists of 3 hESC derived neural cells and FB is a collection of hESC derived mesenchyme and fibroblasts. High expressions relative to mean are colored red. Low expressions are colored green. Black represents no significant change in expression level between mean and sample. Samples cluster closer within their own group than samples from other groups.Figure 2Dendrogram of unsupervised one-way hierarchical clustering analysis of relative expression of genes in selected samples. The clustering analysis was based on the average linkage and Euclidean distances as the similarity metric using differentially expressed genes identified by ANOVA (p < 0.05). hESCs clustered together and BG lines cultured in the same laboratory shared the largest similarities. EBs were separated from hESCs from which they were derived. EC line NTera2 and feeder cells can be distinguished from hESCs respectively.The global analysis suggested that the bead arrays used were sufficiently sensitive such that individual subsets of genes could be analyzed, different populations of cells could be readily distinguished and that a subset of candidate genes could be sufficient to distinguish between groups of cells. The comparison across multiple samples will allow a set of core stem cell markers to be identified. In subsequent sections we have performed such analysis. Readers are urged to analyze the expression of desired genes directly as it is impossible to test every gene given the large body of data generated. Comparison between MPSS and Illumina bead array resultsWe have previously used EST scan and MPSS to analyze pooled samples of ESCs and EBs from three different WiCell lines (H1, H7 and H9) [5]. Comparison between the two methodologies indicated that while there is good concordance for genes expressed at high levels, this does not hold for genes expressed at lower levels. As a test of the quality of the data generated in these experiments and to evaluate whether comparisons can be made across different methodologies, we re-ran the identical samples on the bead array platform. The complete comparison of gene expression is shown in Additional file 2 and is summarized in Tables 3 and Table 4. Overall, concordance in Illumina array was better than that evident between EST scan and MPSS datasets [9], but clearly showed much wider differences than that seen with running duplicates in the same assay format. Nevertheless, this comparison provides an independent verification of the data and suggests that if a sample is detected in more than one large-scale analysis, the reliability of the gene expression detection is high, which also reduces the number of individual genes needed to be verified. Caution should be observed in comparing different samples run on different platforms, especially when there has not been rigorous bioinformatic matching of the source sequences used to identify genes in the platforms. Often genes called by the same symbol originate from different database records, which may originate from different splice variants or contain sequence differences due to polymorphisms or outright error [19].Table 3Expression of hESC specific markers in pooled hESC sample as detected by Illumina bead array The expression of previously identified hESC markers was examined in all hESC samples (the values displayed represent the expression level of pooled H1, H7 and H9). Most of the genes were also identified using Illumina bead array in all 8 hESC populations in this study (1), the gene CER1 was detected in all except one duplicate of H9 (2), Nanog was not detected in all populations (3) and Sox2, Lin41, NR6A1 and FoxD3 were not detected in the array although they were present in the chips for hybridization (4).AccessionSymbolPooled ES (H1, 7, 9)CommentsNM_003641.1IFITM188441NM_020997.2LEFTB6579.61NM_024674.3LIN283944.11NM_175849.1DNMT3B33911NM_003212.1TDGF131691NM_001769.2CD92930.81NM_000165.2GJA12404.41NM_021195.2CLDN62247.21NM_004360.2CDH11972.91NM_021127.1PMAIP11601.51NM_032805.1ZNF2061504.51NM_003577.1UTF11444.11XM_050625.2SFRP21353.91NM_006548.3IMP-212061NM_152312.2GYLTL1B1066.61NM_015973.2GAL1043.31NM_003240.2EBAF944.11NM_054023.2SCGB3A2890.51NM_020990.2CKMT1742.41NM_033668.1ITGB1694.71NM_003744.3NUMB618.31NM_007015.1LECT1597.41NM_021912.2GABRB3482.71NM_006729.2DIAPH2467.11NM_000222.1KIT188.91NM_005454.1CER1151.52NM_024865.1NANOG56.93NM_002701.1POU5F1694.41NM_003106SOX2ND4NM_006458.2LIN41ND4NM_001489.3NR6A1ND4NM_012183FOXD3ND4Table 4Comparison of MPSS and Illumina bead array results The samples were analyzed by MPSS and bead array. The number of genes detected by each method and the degree of overlap is summarized. Note much higher degree of overlap when the top 2000 hits were compared. : Most of the genes detected by MPSS were novel genes not included in the bead array.ESNo.%Common in both (Top 2000 hits)1,62281.1Common in both (All hits)5,07146.0By bead array only3,46231.4By MPSS only 2,50422.7Total11,037100EBNo.%Common in both (All hits)5,16843.1By bead array only4,13134.4By MPSS only 2,69422.5Total11,993100 Human feeders and hESCs can be readily distinguished and contamination can be readily assessedFor all samples, we conducted an unsupervised one-way hierarchical clustering analysis. The clustering analysis was based on the average linkage and Euclidean distances as the similarity metric using differentially expressed genes identified by ANOVA (P < 0.05). The analysis revealed the underlying features and variation patterns of gene expression in each cell types. Figure 2 shows results of the cluster analysis of relative gene expression in selected samples. As one of our purposes of this study was to distinguish between human fibroblast feeders cells and hESCs and hEBs, wishing to readily detect feeder contamination in hESCs, we included one of the human feeder cells HS27 (ATCC) in this study. We have been using HS27 as feeder cells for H9 hESCs for more than two years and all hESCs grown on HS27 had normal karyotype, expressed all undifferentiated markers, and made teratomas with all germ layers (data not show). The global pairwise comparison clearly showed that human feeders were far more dissimilar to hESCs than hESCs grown in different laboratories, hESCs compared to their differentiated EBs that contained mesodermal tissue, and hESCs compared to the karyotypically variant hESC line BG01V. Pairwise comparisons of human feeders with hESCs resulted in a correlation coefficient of 0.66, which was less than the correlation coefficient of 0.71–0.74 observed between hESCs and their corresponding EBs. The large difference between human feeders and hESCs suggested that it would be possible to identify markers that were robust and reliable in distinguishing the two populations, and these markers would be sufficiently sensitive in detecting contamination of feeders. We examined the data to develop a list of genes that had high levels of expression in human feeder cells maintained in hESC medium but whose expression was low or absent in either ESCs or EBs. The absence of expression in EBs was used as a control for spontaneous differentiation of ESC colonies (including mesodermal differentiation) which may occur and the markers selected should be able to distinguish between these two events. A complete list of genes expressed at least ten-fold higher in human feeders is provided in Figure 3. Quantitative RT-PCR (qPCR) was used to verify the fold change of the expression of 4 genes, including THBS1, MMP3, TNFRSF11B and KRTHA4 (Figure 3C). Further confirmation can also be done using immunocytochemistry, as antibodies against these genes are commercially available.Figure 3Human fibroblast feeder cells can be distinguished from hESCs and EBs. Bead array identified lists of genes that were uniquely expressed in human fibroblast feeders as compared to hESCs (A) and hEBs (B). The four genes whose expression was confirmed by qPCR (C) were in bold. In the graph (C), gene expression of each gene in feeder cells was designated as 1 fold and the bars represented fold decrease for each gene.Thus this comparison allowed us to distinguish between hESCs and human feeders and identify candidate markers that could detect feeder cell contamination should human feeders be used in the propagation of hESCs. hESCs and EBs can be distinguished from each otherIllumina bead array analysis confirmed that hESCs could be readily distinguished from EBs by global analysis. This raised the possibility that specific subsets of markers could be identified. We and others have used MPSS and EST scan and generated array data to make lists of hESC-specific genes [5,9,10,20]. As discussed above, most hESC markers identified by MPSS have been detected in the present bead array analysis (Table 3), confirming the utility of these previously identified markers for use in assessing undifferentiated status of hESCs. In addition, we have generated a list of genes differentially expressed at higher level in EBs than in hESCs, a subset of which is shown in Table 5. These markers were common to all EB samples tested and included genes known to be expressed in ectoderm, endoderm and mesoderm. The entire set of differentially expressed genes is provided in Additional file 3. Thus, the bead array format, which allows multiple pairwise comparisons, can be used to identify genes that are expressed by all differentiating EB samples in the present study. Our data suggested that a core set of limited markers might be sufficient to monitor the process of differentiation. By suitable selection of different germ cell layer specific markers one may also assess the overall quality of differentiation toward germ cells.Table 5Genes which are differentially expressed at higher levels in EBs than in hESCsSymbolAll EBAll ESEB/ESRELN11120.52224.0SST1394.25.5253.5SLC40A11210.19.1133.0IGF24896.938.4127.5SLN2017.817118.7DCN7588.176.299.6ANXA84048.941.996.6AQP11665.420.581.2APOB1256.415.879.5AHSG1414.320.868.0NID21476.731.447.0FGB221052.442.2LUM8439.4205.441.1MGP2772.469.839.7THBD1206.434.435.1SERPINA11255.643.728.7HAND112294437.428.1HBE11106.542.626.0TTR7661.2347.122.1HBG21601.185.618.7COL2A11636.291.817.8KIAA09771370.87817.6AFP8941552.316.2COL3A11355796714.0IGFBP38446.3603.414.0PAX61577.8118.213.3APOA19398.1709.813.2FRZB3523.7315.711.2SPON21548.7159.49.7CEBPD1100.2122.79.0DLK13355.8374.89.0RDC11589.7192.18.3BMP41851.5227.38.1PITX21057.9131.28.1ACTA25045629.88.0GAS11215.61547.9AGTRL11053.8135.17.8COL5A15876.4765.47.7CDKN1C3134.1412.97.6CXCL142220.5312.67.1DOK41011.1145.27.0ARHGDIB1635.3246.56.6FLRT21879.5314.36.0MSX12771.8499.75.5 Smaller but distinct differences among undifferentiated hESC linesOur cluster analysis indicated that BG01, BG02 and BG03 cell lines were overall more similar to each other than to other lines (Figure 1 and 2), but nevertheless showed additional differences than technical or biological repeats of the same sample. This raised the possibility that this microarray strategy may be sufficiently sensitive to identify relatively cell type specific candidate genes that could be used to distinguish one hESC population from another or to identify differences that were due to varied isolation and growth conditions. As a test we looked for differences between BG01, BG02 and BG03, which were grown in the same laboratory under the same conditions. Lists of candidate genes are shown in Figure 4A, C and 4E and the comparison of these three lines are shown in scatter plots in Figure 4B, D and 4F.Figure 4BG lines show small but distinct differences as assessed by bead array. These three hESC lines share high similarities as shown by the scatterplots of BG01 vs BG02 (B), BG01 vs BG03 (D) and BG02 vs BG03 (F). Comparisons of all three lines were made and lists of selected genes that were specifically expressed in BG01 (A), BG02 (C) and BG03 (F) are shown. Correlation coefficients (R2) were generated using all genes with expression level >0 (black and blue dots), or all genes with detection confidence >0.99 (blue dots). Genes outside the two thin red lines were detected at >2.5- fold difference.We reasoned as well that such a global comparison should allow us to distinguish between male and female lines if genes present on the Y chromosome were expressed at high levels in the undifferentiated state and were detected by the bead array. Several such candidate genes were identified. The most robust were RPS4Y, RPS4Y2, and EIF1AY (Figure 5). To confirm that these were useful markers, we designed RT-PCR primers and tested their expression in a male (BG01) and a female (BG03) line (Figure 5B). We noted that several of these continued to be expressed at high levels as ESCs differentiated to form EBs and upon further differentiation (data not shown), suggesting that these markers might be used in adult stem cell and germ cell populations as well.Figure 5Male and female hESC lines can be distinguished by genes identified by bead array. Five potential genes RPS4Y, RPS4Y2, EIF1AY, VCY, and AMELY are located in the Y chromosome. By comparing the expression level of these genes in all hESC lines, we have found that 3 out of 5 were specifically expressed in male hESC lines I6, BG01 and BG02 (A) and this was verified by RT-PCR in male line BG01 and female line BG03 (B). G3PDH was used as an internal control. *: represents the gene expression level is detected at <0.99 confidence.In summary, our data suggest that the bead array format is sufficiently sensitive and global that it can distinguish one cell line from another even if those two cell lines are grown in the same laboratory under virtually identical conditions. Bead array can also be used to distinguish between male and female lines. Comparison of diploid pluripotent cells with NTera2 and BG01 variantOur previous results have suggested that EC lines share many of the properties of hESCs and can be used as a useful model for initial testing of biological questions [21]. More recently we have identified BG01V as a karyotypically abnormal variant that behaves much like its normal counterpart BG01, but is not subject to the same constraints of use as karyotypically normal hESCs [13]. Given the sensitivity of the bead array analysis, we tested its ability to detect the overall similarities and differences between NTera2 and a pooled ESC sample or between the karyotypically abnormal BG01V and its normal parent line (Figure 6).Figure 6Diploid pluripotent EC cell line NTera2 and karyotypically abnormal hESC line BG01V can be distinguished from normal hESCs using Illumina array. Comparison of NTera2 and pooled hESC sample resulted a correlation coefficient of 0.8997. Two lists of genes, which were specifically expressed in NTera2 (C) or in hESCs (E) were identified. Likewise, while sharing similarities with BG01 (B, correlation coefficient= 0.9043), BG01V was different from BG01 in expression for many genes, particularly genes from the TGFβ pathway (D, F). Black dots represent genes that were detected at >0 expression level, blue dots represent genes that were detected both at > 0 expression level and at >0.99 confidence. Genes plotted outside the two thin red lines were detected at >2.5- fold difference.Our results showed that, while NTera2 shared a high similarity with hESCs [21], it did have important differences with hESC lines. Examining these differences (summarized in Figure 6C and 6E), we noted that some reflected the origin of the tumor cells from which this line was derived [14]. Several germ cell markers such as GAGE2, GAGE7 and GAGE8 were highly expressed in NTera2 but were absent (or present at low levels) in any of the hESC lines examined (See Figure 6C and Additional file 1. Note that the GAGE genes are highly similar in sequence, making it difficult to distinguish one family member from another through hybridization; thus, while all of these GAGE gene probes gave positive signal, it is difficult to say if the signal came from the specific gene itself or from cross-hybridization from one of the other family members). None of these were present in BG01V, indicating that the karyotypically abnormal variant is not the equivalent of a teratocarcinoma line such as NTera2. In addition to the expression of germ cell markers, we noticed a significant difference in the expression of genes in the TGFβ pathway, such as GDF3 (Figure 6C), TGFBI, CDKN1A, IGFBP7, IGFBP3, NODAL, CER1 and BMP2 (Figure 6E). This is consistent with the postulated role of this pathway in germ cell differentiation [22,23] and suggests that TGFβ pathway cannot be reliably tested using NTera2 as a model for hESC.The BG01V showed clear differences from its normal counterpart and some major changes are summarized in Figure 6D and 6F. Early markers of differentiation appeared to be present at higher levels in BG01V as compared to any of the hESC lines examined, although hESC specific genes continued to be expressed at high levels (see Additional file 4). In particular, the Wnt pathway and the TGFβ signaling pathway (Figure 6D), both of which involved in the early process of differentiation [24,25], appeared to be activated (Additional file 4), suggesting that the role of growth factors and signaling in these early events cannot be readily studied in this cell line.In summary, the analysis highlighted the utility of the potential reference standards NTera2 and BG01V, demonstrated their general similarity and provided detail on potential caveats to their application. Global arrays provide a snapshot of the state of the cells and identify core self-renewal pathwaysWe have utilized a small fraction of the data to demonstrate the overall utility of this approach and its sensitivity in identifying small differences in cell populations. An additional potential application of such an analysis is the ability to examine the general state of a particular signaling pathway and determine whether it is active. By comparing across many samples, a procedure previously expensive and difficult in terms of the RNA and replicate requirement, one can rapidly identify key regulatory pathways.To test whether we could use such multiple pairwise comparisons to elucidate the major regulatory pathways that may be required for hESC self-renewal, we examined several metabolic pathways. The results of the analysis of the insulin/insulin-like growth factor (IGF) signaling pathway are shown in Figure 7. Using the same 4 groups of samples as in Figure 1, we conducted PAM (Prediction Analysis of Microarray) [26], in search for biomarkers used in diagnostic identification of these four groups, ES, EB, NS, and FB. In PAM, a list of significant IGF pathway genes whose expression characterizes each diagnostic class was obtained. The average gene expression level in each class was divided by the within-class standard deviation. The nearest centroid classification computed took the gene expression profile from a new sample and compared it to each of these class centroids. For cross-validation of prediction results, multiple classification processes were performed on two data sets randomly constructed each time from the entire gene expression dataset. The first dataset, consisting of 70% of the total data, was used as the training dataset, and the other dataset, containing the remaining 30% of data, was used for the data prediction and verification process. The final biomarkers were determined in such a way that the misclassification error rate was minimal. The resulting graph (Figure 7) showed the shrunken class centroids for genes that had at least one nonzero difference in each diagnostic class. The genes with nonzero components in each class were almost mutually exclusive and represented candidate biomarkers for the diagnosis of each class. All data analyses were performed using the bioconductor package [17].Figure 7Identification of diagnostic markers by PAM. The shrunken class centroids for genes which have at least one nonzero difference are shown. The genes with nonzero components in each class were almost mutually exclusive and were the candidate molecular markers for the diagnosis of the four groups of cell populations, including, (from left to right) hESC derived mesenchyme and human fibroblast feeder cells ("FB", n = 5), undifferentiated hESCs ("ES", n = 11), hESC derived neural cells ("NS", n = 3), and differentiated ES cells and EB, ("EB", n = 6). The identified biomarkers can be used to distinguish the four groups of cell populations.
Undifferentiated hESCs have been analyzed by EST scan, MPSS, SAGE and microarray [5,10,16]. The goal of these experiments including our own is to develop a low cost reliable method to assess multiple samples to generate a global database of markers and to provide a method of identifying core measures of similarities and differences across multiple laboratories. We and others have proposed three alternative methods of assessment: Quantitative RT-PCR [9,20], focused arrays [27] or a large scale array with bioinformatics tools being utilized to focus on appropriate subsets of genes [5,7,15,16,28]. Each of these methods has its advantages and disadvantages. The present results suggest that the global Illumina bead array retains the advantages of low cost per sample associated with focused arrays yet still has the strength of the global attributes of MPSS or EST scan while requiring much less RNA and turnaround time. To test this array format we examined samples from a variety of laboratories in a blinded fashion to determine whether the array was sufficiently sensitive and rapid for routine assessment. Duplicates using 100 ng of RNA were run and results obtained forty-eight hours later. The resolution was sufficient that ESC samples could be distinguished from one another and a variant karyotypically abnormal subclone could be distinguished from the parent population (correlation coefficient = 0.9043). Aliquots of the pooled ES and pooled EB samples, which we had prepared for MPSS, were included in this run to compare these two methods directly. The current analysis confirms that comparison across platforms is difficult and that only positive results can be treated with any reliability. The absence of expression cannot be readily interpreted. In particular, genes expressed at low levels (greater than 70% of all genes detected) should not be assessed in cross platform comparisons. The limited concordance at low levels raises a question as to how many genes are actually expressed by any one cell line and whether the cutoff of 3 tpm used for MPSS or 100 intensity units for bead arrays is a reasonable cutoff. We used 100 units for our analysis and we would suggest that readers exercise similar caution. Nevertheless even at this higher cutoff the arrays were remarkably sensitive and allowed us to readily distinguish between samples including cells grown in the same laboratory. The basis of the sensitivity could be attributed to a limited set of genes and those genes could be identified for future use. For example BG01V, while much more similar to BG01 than to any other cell type, could still be distinguished from a biological replicate of BG01 by the expression of a particular subset of differentiation markers (Figure 6). EC cells such as NTera2 could be distinguished from hESCs by the expression of germ cell markers and the presence of a partially inactivated TGFβ (BMP) signaling pathway (Figure 6). Distinguishing ESCs from EBs was relatively straightforward. We have confirmed the utility of previously identified markers for use in this platform as well as identified an additional set of markers that can serve as biomarkers to distinguish between the hESC and EB states. A subset of these markers have been used to develop a qPCR assay that shows such a high sensitivity that changes in cell behavior can be detected after as little as twenty-four hours and the development of EBs can be reliably staged [10,20]. During the identification of ES and EB specific markers, we have noticed that some known hESC markers, such as Nanog, was not detected in all populations of hESCs that were included in this analysis. Several ESC-specific gene, including Lin41, Sox2 and FoxD3, were not detected in the array either (Table 3). We believe that the problem with Lin 41, Sox2 and FoxD3 is a technical one as we were able to confirm expression using alternate methods. We are in progress of redesigning appropriate probes for these genes. In the case of the gene Nanog, there are several pseudo genes in the genome for Nanog and it has been a major technical challenge designing primers or probes that are specific and sensitive. We believe that a partial explanation for the variability in Nanog expression is due to the lack of sensitivity to this gene. However, immunocytochemistry while not strictly quantitative shows similar variability when used to assess Nanog expression in different cell lines [9,27,28]. This large comparison between samples allowed us to identify markers that distinguish human feeder cells from hESC. While we have listed 19 potential markers (Figure 3) and identified several hundred potential markers as shown in Additional file 5, we suggest that as few as 3–4 genes may be sufficient. Previously we found that as few as four were satisfactory to distinguish between hESCs and hEBs, which are two much more closely related samples [9]. In this study we have confirmed by qPCR the differential expression of four genes, THBS1, MMP3, TNFRSF11B and KRTH4, to separate human fibroblast feeders and hESCs (Figure 3). Several markers such as MMP3 and TNFRSF11B have commercially available antibodies (R&D systems) that may be used to further confirm contamination of feeder cells by immunocytochemistry. Efforts to identify other useful antibodies based on these results continue [29]. While we have focused on the immediate utility of the Illumina array platform, it is important to remember that this array provides a global snapshot of cell state and the data obtained can be readily compared in order to determine key signaling pathways. The ability to compare multiple samples in one run enhances data selectivity and reliability. To make such analysis more readily available, we utilized several software tools including the software package available through Illumina. The BeadStudio software provided with the BeadLab and BeadStudio genetic analysis systems for use with the bead array datasets provides a useful set of analytical and presentation tools that allow straightforward comparisons, which are sufficient for average users. For detailed analysis we recommend using more specific commercial tools or software packages developed by NCBI.
In summary, the Illumina bead array has several key strengths including high throughput, low cost and high sensitivity. By using this array, we can readily detect contaminating feeders and spontaneous differentiation, differentiate male and female lines and distinguish between one undifferentiated population and another. Such a global analysis allows us to assess context dependent signaling and identify biomarkers of particular states of cells. Our future efforts will focus on data mining and developing better cross platform comparison tools and generating focused high throughput arrays for quality control in clinical and research settings.
hESC cultureThe hESC lines H1, H7 and H9 (WiCell, Madison, WI) were cultured on feeder layers derived from mitotically inactivated HS27 human fibroblast cells (HS27, ATCC), or mouse embryonic fibroblsts or under feeder-free conditions on Matrigel (BD, Franklin Lakes, NJ) coated plates for at least 10 passages. Culture medium for all cultures was composed of DMEM/F12-Glutamax 1:1, 20% Knockout Serum Replacement, 2 mM nonessential amino acids, 100 μM beta-mercaptoethanol, 50 μg/ml Pen-Strep (all from Invitrogen, Carlsbad, CA), and 4 ng/ml human recombinant basic fibroblast growth factor (bFGF/FGF2; PeproTech Inc., Rocky Hill, NJ.) Feeder-free cultures were prepared for gene expression analysis by manually harvesting individual colonies with uniform typical undifferentiated ESC morphology.BG01 (46, XY), BG02 (46, XY), BG03 (46, XX), I6 (46, XY) and BG01V (BG01 karyotypic variant: 49, XXY, +12, +17): Cells were maintained for 3 (BG01V), 7 (BG02), 8 (BG01), or 21 (BG03) passages under feeder-free condition on fibronectin-coated plates in medium that had been conditioned by mouse embryonic fibroblasts for 24 hours. Culture medium was DMEM/F12, 1:1 supplemented with 20% Knockout Serum Replacement, 2 mM non-essential amino acids, 2 mM L-glutamine, 50 μg/ml Pen-Strep, 100 μM beta-mercaptoethanol, and 4 ng/ml of bFGF.Different hESC lines were grown in slightly different culture conditions as described above. H lines were grown on Matrigel coated dishes, while BG lines on fibronectin treated dishes. These coating substrata supported the growth of hESCs similarly, as evaluated by colony morphology, immunocytochemistry and proliferation rate (data not shown).Embryoid bodies (EBs) were prepared from BG lines as described in [5]. Cells were aggregated and cultured on non-adherent substrata for fourteen days.Other cellsNTera2 cells were purchased from ATCC and cultured in parallel with hESCssamples using protocols described previously [21]. HS27 embryonic human newborn foreskin cells (ATCC CRL-1634) were grown in DMEM with 10%FBS.All samples included in this study can be found in Additional file 6.Bead array gene expression analysisRNA was isolated from cultured cells using the Qiagen RNEasy kit (Qiagen, Inc, Valencia, CA). Sample amplification was performed using 100 ng of total RNA as input material by the method of Van Gelder et al [30]. Amplified RNA synthesized from limited quantities of heterogenous cDNA [30] was performed using the Illumina RNA Amplification kit (Ambion, Inc., Austin, TX) following the Manufacturer instructions. Labeling was achieved by use of the incorporation of biotin-16-UTP (Perkin Elmer Life and Analytical Sciences, Boston, MA) present at a ratio of 1:1 with unlabeled UTP. Labeled, amplified material (700 ng per array) was hybridized to a pilot version of the Illumina HumanRef-8 BeadChip according to the Manufacturer's instructions (Illumina, Inc., San Diego, CA). Amersham fluorolink streptavidin-Cy3 (GE Healthcare Bio-Sciences, Little Chalfont, UK) following the BeadChip manual. Arrays were scanned with an Illumina Bead array Reader confocal scanner according to the Manufacturer's instructions. Array data processing and analysis was performed using Illumina BeadStudio software.Identification of differentially expressed genes and clustering analysisDifferentially expressed genes between ES and EB were identified by ANOVA at p value 0.05 using bioconductor [17]. Unsupervised hierarchical clustering analysis and principal component analysis (PCA) were conducted using software Pcluster [31] and TreeView [18].Identification of diagnostic markersPAM (prediction analysis of microarray) was employed for the identification of diagnostic markers from insulin pathway genes by using the software package bioconductor [17]. PAM is a class prediction method for expression data mining. It can provide a list of significant genes whose expression characterizes each diagnostic class. The average gene expression level in multiple classes, such as ES, EB, NS, and FB, was divided by the within-class standard deviation for that gene. The nearest centroid classification computed by PAM takes the protein expression profile from a new sample, and compares it to each of these class centroids [26].RT-PCR and quantitative real-time PCR analysisTotal RNA was isolated with TRIzol (Invitrogen. cDNA was synthesized using 2.5 μg total RNA in a 20-μl reaction with Superscript II (Invitrogen) and oligo (dT)12–18 (Promega; Madison, WI). One microliter RNase H (Invitrogen) was added to each tube and incubated for 20 minutes at 37°C before proceeding to the RT-PCR analysis. The PCR primers are: RPS4Y-forward: 5' AGATTCTCTTCCGTCGCAG 3', RPS4Y-reverse, 5' CTCCACCAATCACCATACAC 3'; EIFAY-forward, 5' CTGCTGCATCTTAGTTCAGTC 3'; EIFAY-reverse 5' CTTCCAATCGTCCATTTCCC 3'. Quantitative real time PCR gene specific primer pairs and probes were purchased from Applied Biosystems (Foster City, CA) for the following genes: MMP3 (Hs00233962_m1), TFRSF11B (Hs00171068_m1), THBS1 (Hs00170236_m1), KRTHA4 (Hs00606019_gH), and for internal control β-actin (ACTB, Hs99999903_m1).
BackgroundHuman stem cells are viewed as a possible source of neurons for a cell-based therapy of neurodegenerative disorders, such as Parkinson's disease. Several protocols that generate different types of neurons from human stem cells (hSCs) have been developed. Nevertheless, the cellular mechanisms that underlie the development of neurons in vitro as they are subjected to the specific differentiation protocols are often poorly understood.ResultsWe have designed a focused DNA (oligonucleotide-based) large-scale microarray platform (named "NeuroStem Chip") and used it to study gene expression patterns in hSCs as they differentiate into neurons. We have selected genes that are relevant to cells (i) being stem cells, (ii) becoming neurons, and (iii) being neurons. The NeuroStem Chip has over 1,300 pre-selected gene targets and multiple controls spotted in quadruplicates (~46,000 spots total). In this study, we present the NeuroStem Chip in detail and describe the special advantages it offers to the fields of experimental neurology and stem cell biology. To illustrate the utility of NeuroStem Chip platform, we have characterized an undifferentiated population of pluripotent human embryonic stem cells (hESCs, cell line SA02). In addition, we have performed a comparative gene expression analysis of those cells versus a heterogeneous population of hESC-derived cells committed towards neuronal/dopaminergic differentiation pathway by co-culturing with PA6 stromal cells for 16 days and containing a few tyrosine hydroxylase-positive dopaminergic neurons.ConclusionWe characterized the gene expression profiles of undifferentiated and dopaminergic lineage-committed hESC-derived cells using a highly focused custom microarray platform (NeuroStem Chip) that can become an important research tool in human stem cell biology. We propose that the areas of application for NeuroStem microarray platform could be the following: (i) characterization of the expression of established, pre-selected gene targets in hSC lines, including newly derived ones, (ii) longitudinal quality control for maintained hSC populations, (iii) following gene expression changes during differentiation under defined cell culture conditions, and (iv) confirming the success of differentiation into specific neuronal subtypes.
Modern DNA microarrays permit a comprehensive analysis of quantitative and qualitative changes in RNA transcript abundance, outlining the cross-sections of gene expression and alterations of these in response to genetic or environmental stimuli. Genome-scale microarrays (cDNA- or oligonucleotide-based) are most valuable when screening populations of cells for the novel genes reflecting potential diagnostic and prognostic markers or for an identification of novel therapeutic targets. On the other hand, custom microarray platforms that focus on specific pre-selected subset of genes relevant to a particular field of investigation can be less costly and more suitable for detection of smaller gene expression changes. Microarray technology has added important information on both normal development and pathological changes in neurons. This is well illustrated by multiple studies on substantia nigra dopaminergic neurons, which degenerate in Parkinson's disease (PD) [1-5]. The shortcomings of pharmacological therapies in PD have stimulated a search for alternative treatment strategies. In successful cases, transplants of human embryonic mesencephalic dopaminergic neurons can both restore dopaminergic neurotransmission and provide some symptomatic relief [6-8]. A wider application of neural transplantation in PD is, however, currently not feasible due to the unpredictable and variable outcome, the risks of unwanted side-effects (dyskinesias) [9,10] and ethical and practical problems associated with using donor cells obtained from aborted embryos and fetuses [11,12]. Human embryonic stem cells (hESCs) are considered a promising future source of cells for cell replacement therapy in PD and other neurological conditions [13]. They could constitute a virtually infinite source of self-renewing cells that can be persuaded to differentiate into specific types of neural cells, including dopaminergic neurons [14-16]. The molecular mechanisms that govern development of cultured hESCs into specific types of neural cells are not fully understood. To promote our understanding of such mechanisms, it would be valuable to have tools that readily and reproducibly can help to characterize the cells as they differentiate from pluripotent stem cells into post-mitotic neurons. This important issue was addressed in earlier studies by Luo et al. and Yang et al., who designed small-to-moderate scale custom microarray platforms (281 and 755 gene targets, respectively) [17,18]. In addition SuperArray Bioscience Corporation (Frederick, MD, USA) have manufactured a range of small-scale arrays (263 gene targets for human array; [19]). We sought to create an improved and updated microarray platform for hESC/neuronal differentiation-oriented gene expression studies. Therefore, we generated a specialized large-scale DNA microarray platform (the "NeuroStem Chip") that has over 1,300 pre-selected gene targets and multiple controls spotted in quadruplicates (~46,000 spots total). Here we introduce the platform and the advantages it can offers to neuroscientists and stem cell biologists: particularly, in the niche of gene expression-oriented characterization of the samples using an assay of pre-selected, already established gene targets. In the current study, we use the NeuroStem Chip to characterize an undifferentiated population of pluripotent hESCs (cell line SA02, Cellartis AB, Göteborg, Sweden) and compare the gene expression in those cells with that of a hESC-derived cell population rich in neurons, including tyrosine hydroxylase-positive dopaminergic neurons.
Stem cells have unique biological characteristics, but only a limited number of genes are currently recognized as established stem cell markers. Examples include POU domain, class 5, transcription factor 1 (Oct3/4), signal transducer and activator of transcription 3 (Stat3), teratocarcinoma-derived growth factor (Tdgf1), Enk-pending (Nanog), undifferentiated embryonic cell transcription factor 1 (Utf1) and DNA methyltransferase 3B (Dnmt3b) [20]. At the same time, hundreds of genes are suggested as candidate markers for "stemness", but their coupling to the undifferentiated stem cell state is not yet fully verified [21]. The concept of "stemness" (term introduced in 1986 by Grossman & Levine) is defined as "core stem cell properties that underlie self-renewal and the ability to generate differentiated progeny" [22]. Considering the complexity of the processes involved, stemness can hardly be ensured by co-operation of just a few genes. Nevertheless, three stemness genes (namely, Oct3/4, Stat3 and Nanog) are considered "master"-genes that control the self-renewing process [23,24]. Various types of stem cells, such as hematopoietic, mesenchymal and neural (HSCs, MSCs and NSCs, respectively), embryonic germ and embryonic carcinoma cells (EGCs and ECCs, respectively) are all characterized by variations in gene expression profiles, and only a few gene markers are associated with all these cell types [25,26]. We have aimed to embrace the most comprehensive set of those genes into a solitary array, the NeuroStem Chip. Thereby, it is possible to employ it to monitor the relative expression levels of numerous known and candidate stemness genes in a single experiment. Similar to the genetic bases underlying stemness, cell differentiation is associated with altered expression levels of certain recognized or candidate genes [25]. We therefore incorporated gene markers of development and differentiation in general, and that of neuronal and dopaminergic differentiation in particular, into the NeuroStem Chip. Examples include markers for the processes of neuronal maturation, axonal branching, neural/neuronal survival, etc. Finally, we ensured that known markers for specific types of neurons, allowing identification of individual cell types, were present on the chip. We paid special attention to genes associated with the differentiation and maturation of dopaminergic neurons. In many published studies, the expression of only a single (tyrosine hydroxylase, TH) or 2–3 markers for dopaminergic neurons (e.g. amino acid decarboxylase (AADC), dopamine transporter (DAT), vesicular monoamine transporter 2 (VMAT2)) have been used to indicate dopaminergic identity of neurons. In contrast, the NeuroStem Chip includes oligonucleotide probes for 88 genes related to dopaminergic neurons, thus being more comprehensive in this sense, compared to other existing microarray platforms, including focused ones [17,18]. Those entries encompass recognized and candidate markers for dopaminergic neurons (mature and early) and progenitors, as well as markers for the maturation and differentiation of the latter (Table 1). Table 2 represents conditional functional breakdown of genes targeted by the NeuroStem microarray platform. A number of important gene groups that are included in the chip are not mentioned in Table 2. Among these, entries related to Dickkopf gene family, galanin-, melatonin-, vasoactive intestinal peptide (VIP)-, cAMP response element-binding protein (CREB)- and B cell leukemia 2 (Bcl2) oncogene-related are present. Many of them play potentially important, yet undefined, roles in the biology of stem cells. Additionally, we included some genes implicated in disease mechanisms of neurodegenerative disorders (most importantly, Parkinson's disease and Alzheimer's disease) in the chip. Furthermore, we incorporated a number of markers for distinct differentiation pathways (e.g. hematopoietic and pancreatic) and cell types (e.g. cancer subtypes and a range of normal cell types) to serve as essential controls. Taken together, we believe that in its present form NeuroStem Chip represents currently most comprehensive gene expression platform for studies on stem cells, neural/neuronal differentiation, human neurodegeneration and neuronal survival, both in vivo and in vitro. The complete layout of NeuroStem Chip will be disclosed to the academic community, upon request. The microarray format we selected relies on long oligonucleotide molecules (69–71 nucleotides) printed over a solid surface. We spotted the synthesized oligonucleotides (Operon Biotechnologies) with a constant concentration across the slides, and evaluated the quality and consistency of spotting in a series of control experiments. We then illustrated the utility and technical reliability of the NeuroStem Chip by characterizing the gene expression profile of commonly utilized hESC line SA02 (Sahlgrenska 2; [27]), including (i) undifferentiated cells and (ii) cells committed towards neuronal/dopaminergic differentiation pathway. For the first of these, we used total RNA sample purified from hESC colonies that exhibited morphology consistent with cell proliferation and the absence of spontaneous differentiation (Figure 1A). We also evaluated the expression of the cell cycle marker Ki67 and the pluripotency marker OCT3/4 in the sample by immunocytochemistry (Figure 1B–E). Co-culturing of ESCs with murine stromal cells (including PA6 cell line) rapidly generates dopaminergic neurons from ESCs by an unexplained mechanism termed stromal cell-derived inducing activity (SDIA; [28,29]). We therefore committed hESCs toward the neuronal/dopaminergic differentiation pathway by co-culturing with PA6 cells for 16 days, resulting in appearance of cells positive for early and late neuronal markers, including nestin, β-III-tubulin, and TH, the established marker of dopaminergic neurons (Figure 2). To verify the expression of some key stem cell- and neural phenotype-associated genes we performed RT-PCR comparing RNA samples from the undifferentiated hESCs with hESCs of the same line differentiated toward neuronal/dopaminergic pathway, as described above. The expression profile outlined by RT-PCR confirmed the identity of the sample used (Figure 3). After performing RNA integrity tests, we incorporated fluorescent labels to the amplified RNA samples from hESCs (Cyanine 3-CTP (Cy3) and Cyanine 5-CTP (Cy5)), hESC-derived cells containing TH-positive neurons (Cy3 and Cy5) and human universal reference RNA (Cy5), and hybridized aliquots with NeuroStem microarray slides using the following conditions: hESC vs. reference, Cy3 : Cy5 = (i) 20:10 pmol, and (ii) 10:5 pmol; and hESC vs. hESC-derived cells, Cy3 : Cy5 = (iii) 30:20 pmol, respectively. Universal reference RNA has been previously established as a standard reference material for microarray experiments, proving an ability to effectively hybridize to a large fraction of microarray spots [30]. We performed two-color hybridizations (e.g. for the experiment vs. reference) following an established protocol [31], and included dye-flip technical replicates in the analysis (Figure 4). Using the online software program BASE [32] we sequentially filtered the data by background subtraction, negative flagging, negative intensities and for inconsistent data amongst replicates [33]. Figure 5A shows a comparison of the spot intensities prior to normalization (M versus A plot), with the Log2 of the expression ratio between Cy3/Cy5 being plotted as a function of the log10 of the mean of the total expression intensities for Cy3 and Cy5 channels. The deviation of the line from zero revealed a need for normalization, so prior to data analyses we normalized signals using a locally weighted scatterplot-smoothing regression (LOWESS) algorithm (Figure 5A–B; fitted line) implemented in BASE. Since the reproducibility of two-color microarray gene expression data is critically important, we calculated Pearson correlation coefficients of the reporters present in the filtered database comparing the average expression ratios (7005 for hESCs vs. universal reference; 6947 for undifferentiated vs. neuronal/dopaminergic lineage-committed hESCs). Results obtained revealed that data were consistent across technical replicates (dye-swap and amount of loaded material), showing general high reproducibility: e.g., correlation coefficients were greater than 0.96 for technical replicates and 0.78 for dye-swapping samples in hESCs vs. universal reference hybridizations (Table 3). To detect genes with high expression levels in hESC samples, we filtered data for intensity values >100 in the hESC sample and performed clustering analysis using the TIGR MultiExperiment Viewer (MEV; [34]). To visualize variations of spot/reporter per technical replicate, hierarchical clustering was performed by K-means classifier based on the linear-correlation-based distance (Pearson, centred) method. The optimal number of clusters was determined empirically to produce the most balanced ratio of entries to cluster of highly expressed genes. A cluster of 101 genes up-regulated in the hESC sample [see Additional file 1], was plotted in a centroid graph (Figure 5C); the variation across technical replicates was low. We merged technical replicates to generate a list of the most up-regulated genes expressed in the hESC sample compared to the universal reference RNA (Table 4). Standard error of the mean expressed as percentage was calculated for the 4 technical replicates, and was 6.7% for the top 25 genes up-regulated in hESC samples, compared to universal reference RNA. We performed the analysis of microarray data, as described in the Methods, and spot error values were generally in the lower range, indicating high stringency of the signals and low variance. As seen in Table 4 and Table 5, the NeuroStem Chip identified numerous genes associated with stem cells. In particular, homeo box expressed in ES cells 1 (Hesx1) gene was identified as the most up-regulated in the ES cell preparation, compared to universal reference RNA. Highly expressed in pluripotent ESCs, Hesx1 expression is down-regulated upon embryonic stem cell differentiation [35,36], as also clearly seen in differentiation experiment of our own (Table 4). Similarly, Gremlin 1 homolog, cysteine knot superfamily gene (Grem1, also known as Cktsf1b1 and Dand2) is a recognized factor of cell-fate determination of ESCs [37]. Many more genes highly up-regulated in the hESC sample in comparison with universal reference RNA are associated with stem cells: further examples include Gap junction protein α1 (Gja1) and Zic family member 3 heterotaxy 1 (Zic3) (Table 4) [20]. The expression of fibroblast growth factor receptor 2 (Fgfr2) is of particular interest. Basic fibroblast growth factor (FGF2, bFGF) supports hESC proliferation and their ability to maintain undifferentiated phenotype when cultured in vitro [38,39]. Moreover, in some hESC lines a very high concentration of FGF2 could substitute for the need of feeder cells [40]. At the same time, genes listed in Table 4 represent the most highly up-regulated entries in a relatively limited group of genes (Figure 5C). Many other genes involved in maintenance of ESC phenotype (i.e. established or candidate markers of stem cells) have lower levels of expression (Table 5). Examples include undifferentiated embryonic cell transcription factor 1 (Utf1), DNA methyltransferase 3B (Dnmt3b), developmental pluripotency associated 4 (Dppa4, a newly established pluripotency marker [41]) and numerous candidate markers of "stemness": e.g. genes for KIAA1573 protein, forkhead box O1A (Foxo1a), high-mobility group box 1 (Hmgb1), C-terminal binding protein 2 (Ctbp2) and left-right determination factor 1 (Lefty1), as well as others. For numerous established or candidate markers of stem cells the expression levels were not considerably higher (Log2 ratio < 1) in the hESC sample compared to the universal reference RNA. For example, the expression of Nanog, DNA (cytosine-5-)-methyltransferase 3α (Dnmt3a), MutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli) (Msh2), Thy-1 cell surface antigen (Thy1), high-mobility group box 2 (Hmgb2), transcription factor 3 (Tcf3), Nanos homolog 1 (Nanos1), MyoD family inhibitor (Mdfi), Calumenin (Calu) and soluble thymidine kinase 1 (Tk1) was detected in hES SA02 cells with Log2 ratio value < 1. Expression levels of those genes range from being inconsiderably higher to nearly equal to that in universal reference RNA sample. We believe that those findings could be explained by cellular composition of human universal reference RNA sample [42], which includes pooled RNA samples from proliferating cells (e.g., skin and testis cell lines). Thus, the relative difference between gene expression of certain markers of stem cells in undifferentiated hESCs and universal reference RNA is naturally decreased. Taken together, the gene expression signature of hES SA02 cell line profiled by NeuroStem Chip is indeed characteristic for pluripotent stem cells, providing proof-of-concept. Notably, comparison of expression profiles of undifferentiated hESCs and hESC-derived cells committed toward dopaminergic differentiation pathway by co-culturing with SDIA for 16 days have revealed that many of the stem cell marker genes mentioned above were down-regulated in differentiation (Table 5). Expectedly, Hesx1, Grem1, Dnmt3b, Utf1 and Nanog could be listed among these. At the same time, numerous other genes, including Pitx2, Dlk1 and Msx1 were up-regulated in the latter sample ([see Additional file 2], Figure 3). Table 1 lists 24 dopaminergic system-related entries (e.g., Ptx3, Th, Lhx1) with gene expression up-regulated by Day 16 of hESC differentiation protocol; few more genes have demonstrated less prominent up-regulation (Log2 ratio values in the range of 0.7/0.97–1.0). The gene expression profiles generated are therefore consistent with the results of earlier studies utilizing hSC-derived samples with similar characteristics [43,44]. Diversity of NeuroStem Chip entries responsive to hESC commitment toward neuronal/dopaminergic differentiation pathway clearly illustrates the complexity of that pathway. The cell population obtained after 16 day exposure to SDIA is highly heterogeneous. Only around 0.2% of the cells are TH-positive cells (Figure 2). This heterogeneity, with an apparent presence of residual pluripotent cells explains the presence of stem cell marker genes, including homeobox transcription factor Nanog, as revealed by RT-PCR data (Figure 3). It would be therefore impossible to apply the platform to identify novel genes associated with the process of differentiation; for that application, the genome-scale microarray platforms (e.g., Affymetrix) are clearly superior. Nevertheless, being based upon a moderate assay of pre-selected specific gene targets, the comparative analysis of microarray data derived from undifferentiated and dopaminergic differentiate pathway-committed hESCs provides a valuable cross-cut of complex relationship between factors driving or indicative to neuronal/dopaminergic differentiation [see Additional file 2]. RT-PCR analyses have validated the overall reliability of NeuroStem microarray platform: all of the entries detected in the hybridization experiments have demonstrated similar trends when analyzed by RT-PCR means (Figure 3). Those entries include Sox2, En1 and Nanog (ratio of differentiated/undifferentiated hESC sample normalized spot intensity < 0.75, down-regulated), Gadph, Aldh1a1, Sdha, Tubb and Nestin (ratio .1.0, unchanged), Actb, Th, Msx1 and Pitx2 (ratio >1.25, up-regulated). Some of the housekeeping genes (Gapdh, Sdha, Tubb, Actb) have somewhat different expression in undifferentiated vs. differentiated cells, consistent with previous reports on certain established housekeeping genes (including Gapdh) being variable in human samples [45]. Importantly, all the observed gene expression trends were similar in both microarray and RT-PCR. Our experiment therefore confirms that the NeuroStem Chip microarray platform can still identify gene expression changes related to early stages of differentiation of hESC into dopaminergic neurons.
Recent technological advances have led to DNA microarrays which contain over hundred thousand of spots of DNA material, reaching a truly genomic scale. Highly specialized DNA microarrays of smaller scale (e.g. the NeuroStem Chip) still have an important role in the directed studies in particular fields. Since they are significantly less expensive, compared to many recognized large-scale platforms (e.g. Affymetrix Human Genome platforms), they have a clear advantage in routine work involving samples from, e.g., multiple cell culture conditions. While there is a risk that one will miss out on changes in genes previously not believed to be relevant to neural differentiation, the restricted number of genes in the NeuroStem Chip also simplifies analysis and adds power. NeuroStem Chip is comparable to other stem cell-related focused microarray platforms in regards to manufacturing costs and technical simplicity of the recommended hybridization protocols. At the same time, it currently implies an advantage in both the scale and the spectrum of pre-selected, specific gene targets assayed. Some suggested areas of application for NeuroStem microarray platform could be the following: (i) characterization of the expression of established, pre-selected gene targets in human stem cell (hSC) lines, including newly derived ones, (ii) longitudinal quality control for maintained hSC populations, (iii) following gene expression changes during differentiation under defined cell culture conditions, and (iv) confirming the success of differentiation into specific neuronal subtypes. In addition, the NeuroStem Chip can be used to characterize gene changes in intracerebral grafts of human cells, even when they are transplanted into experimental animals. We specifically wish to stress that we are about to make the NeuroStem Chip available at a non-profit cost to the research community. We believe it has the potential to become an important screening tool in the expanding field of hSC studies in application to neurological/neurodegenerative disorders.
Human embryonic stem cell (hESC) culturesUndifferentiated hESCs of SA02 (Sahlgrenska 2) line (Cellartis AB, Göteborg, Sweden; see NIH Human Embryonic Stem Cell Registry at [46]) were maintained over a monolayer of human "feeder cells" (hFCs; human foreskin fibroblasts, ATCC; cell line CCD-1112Sk). Feeder cells were grown in hFC medium (Iscove's modified Dulbecco's medium (IMDM) supplemented with 10% heat-inactivated FCS (Stem Cell Technologies, USA) and 0.5% Penicillin/Streptomycin mix) for 11 passages. One day prior to hESC plating, hFC medium was washed away from the hFCs, the latter were resuspended in a hESC proliferation medium (VitroHES media (Vitrolife AB, Sweden) supplemented with 4 ng/ml human recombinant basic FGF (hrbFGF, Biosource International, USA) and plated in a central ring of gelatinized in vitro fertilization (IVF) dishes with a cell density of 120,000 cells/dish. The outer rings of the IVF dishes were filled with Dulbecco's modified Eagle medium (DMEM) supplemented with 0.5% Penicillin/Streptomycin mix. One half of the culture medium was replaced every other day. The cells were maintained at 37°C, 5% CO2, 95% humidity settings. Every 6 days, fragments of the hESC colonies (around 10–14 colonies per dish, measuring around 0.015 × 0.015 mm) that had an unaltered morphology (indicating lack of spontaneous differentiation) were mechanically cut from dishes using stem cell knives/transfer pipettes (SweMed Lab International AB, Sweden) and then plated on fresh hFCs. Commitment of hESCs towards neuronal/dopaminergic differentiation pathwayCo-culturing with the PA6 stromal cell line (MC3T3-G2/Pa6, from RIKEN Cell Bank Japan (RCB 1127), derived from newborn mouse calvaria rapidly generates high numbers of DA neurons from mouse and monkey ESCs by an unknown mechanism named stromal-derived inducing activity (SDIA; [28,29]). For differentiation experiments, PA6 cells were plated on gelatine-coated T25 flasks at 16 × 103 cells/cm2 (400,000 cells/flask) density 2 days prior to introducing hESCs into the co-culture and cultured at PA6 culturing media (containing minimum essential medium alpha (α-MEM) supplemented with 10% FCS and 0.5% Penicillin/Streptomycin). Alternatively, PA6 cells were plated over Type I collagen-coated glass cover-slips placed in wells of 4-well-plates (50,000 cells/well, for immunocytochemical (ICC) analysis). Three hours prior to initiation of co-culture, PA6 cells were rinsed 3 times with PBS and media was replaced with co-culture media (Glasgow's modified Eagle's media (G-MEM) supplemented with 8% knock-out serum replacement (KSR), 2 mM glutamine, 0.1 mM non-essential amino-acids (NEAA), 1 mM pyruvate, 0.1 mM β-mercaptoethanol (βME) and 4 ng/μl bFGF). Fragments of hESC colonies (80–90 per flask; 4–5 per well of 4-well-plate) presenting undifferentiated morphology were manually passaged onto the confluent PA6 monolayer and cell co-cultures were maintained at 37°C, 5% CO2, 95% humidity settings. One half of the co-culture medium was replaced every other day for first 10 days, and daily onwards. Characterization of hESCs and hESC-derived cells by immunocytochemistry (ICC) and RT-PCRIVF dishes with hESCs grown atop hFCs and 4-well plate dishes with hESCs growing atop PA6 cells were fixed with 4% paraformaldehyde (PFA) for 15 minutes at the day of passage/harvest (Day 6 of hESC/hFC co-culturing) and Day 16 of co-culturing with PA6 cells, respectively. Cells were pre-incubated with blocking solution containing PBS, 0.5% Triton X-100 and 5% of donkey serum. They were then incubated with primary antibodies in blocking solution overnight at room temperature. After three washes with PBS, cells were incubated with the donkey anti-rabbit IgG conjugated with FITC or anti-mouse Cy3 (1:200, Jackson ImmunoResearch Laboratories). Cells were then washed once with PBS, incubated with 1:1000 DAPI in PBS for 10 minutes, followed by another wash with PBS. Coverslips were mounted onto glass slides with PVA mounting medium containing anti-fading reagent DABCO. The following primary antibodies were used: mouse anti-Oct3/4 (1:500, Santa Cruz Biotechnology Inc.); rabbit anti-Ki67 (1:200, Novocastra Ltd.); rabbit anti-TH (1:500, Chemicon). Immunostained cell cultures were visualized using a Zeiss fluorescent microscope attached to a Nikon digital camera.Using RT-PCR, all RNA samples used in this study were tested negative for the presence of gDNA (data not shown). The intron-spanning primers for RT-PCR were selected from published works or designed using Oligo 4.0 software (Molecular Biology Insight) or Clone Manager Suite 7.1 (Sci Ed Software, NC, USA) and ordered from TAG Copenhagen A/S, Denmark, as the following: Sox2, SRY-box 2: 5'-TAC CTC TTC CTC CC CTC CA-3', 5'-ACT CTC CTC TTT TGC ACC CC-3'; En1, Engrailed 1: 5'-AAG GGA CGA AAC TGC GAA CTC C-3', 5'-GAC ACG AAA GGA AAC ACA CAC TCT CG-3' [47]; Nanog: 5'-TGC TTA TTC AGG ACA GCC T-3', 5'-TCT GGT CTT CTG TTT CTT GAC T-3' [48]; Gapdh, glyceraldehydes-3-phosphate dehydrogenase: 5'-GGA AGG TGA AGG TCG GAG TCA A-3', 5'-GAT CTC GCT CCT GGA AGA TGG T-3'; Aldh1A1, aldehyde dehydrogenase 1 family, member A1: 5'-GGG CAG CCA TTT CTT CTC AC-3', 5'-CTT CTT AGC CCG CTC AAC AC-3' [49]; Sdha, succinate dehydrogenase: 5'-TGG GAA CAA GAG GGC ATC TG-3', 5'-CCA CCA CTG CAT CAA ATT CAT G-3' [50]; Tubb, β-tubulin: 5'-CTC ACA AGT ACG TGC CTC GAG-3', 5'-GCA CGA CGC TGA AGG TGT TCA-3'; Nestin: 5'-AGA GGG GAA TTC CTG CT GAG-3', 5'-CTG AGG ACC AGG ACT CTC TA-3' [47]; Actb, β-actin: 5'-CAT CGA GCA CGG CAT CGT CA-3', 5'-TAG CAC AGC CTG GAT AGC AAC-3' [51]; Th, Tyrosine hydroxylase: 5'-CGA GCT GTG AAG GTG TTT G-3', 5'-TTG GTG ACC AGG TGA TGA C-3'; Msx1, homolog of Drosophila muscle segment homolog 1: 5'-CTC AAG CTG CCA GAA GAT GC-3', 5'-TCC AGC TCT GCC TCT TGT AG-3'; Pitx2, paired-like homeodomain transcription factor 2: 5'-ACC TTA CGG AAG CCC GAG TC-3', 5'-TGG ATA GGG AGG CGG ATG TA-3' [49]. cDNA was synthesized from 1 mg of total RNA using SuperScript II (Invitrogen), and RT-PCR amplifications were performed using the MiniOpticon system (Bio-Rad) with REDTaq Polymerase (Sigma-Aldrich) essentially as described by the manufacturer. Following initial denaturation for 5 min at 95°C, DNA amplifications were performed for 35 (En1, Nanog, Aldh1a1), 33 (Sox2, Nestin, Th, Msx1), 32 (Tubb, Pitx2), 27 (Sdha), 25 (Actb) or 22 (Gapdh) cycles of 1 min at 95°C, 1 min at 55°C (En1, Pitx2), 57°C (Sox2, Nanog, Sdha, Nestin), 58°C (Tubb), 58.5°C (Aldh1a1, Th) or 59°C (Gapdh, Actb, Msx1), and 1 min at 72°C. The final extension was 5 min at 72°C. Twenty μl volumes of RT-PCR products were analyzed by electrophoresis at 1% agarose gels and visualized by ethidium bromide staining.RNA purification and fluorescent dye incorporationFor RNA purification of undifferentiated hESCs, the latter were mechanically separated from hFCs, collected in a 500 μl volume of VitroHES media, rinsed in PBS buffer and spun down at 300 rcf for 5 min. hESC-derived cells grown atop PA6 cells were harvested using a papain dissociation kit (Worthington Biochemical Corporation), rinsed in PBS buffer and spun down as described above. The resulting cell pellets were resuspended in RLT buffer (Qiagen, USA), passed through the shredder column (Qiagen) and stored at -80°C until the RNA sample was purified following the RNeasy Micro Kit (Qiagen) protocol (without carrier RNA); with DNase I (Quiagen) treatment incorporated to the latter. RNA integrity was tested using both ND-1000 specrophotometer (NanoDrop, USA) and RNA Nano LabChip/2100 Bioanalyzer system (Agilent Technologies, USA).Fluorescent label (24 nmol of the Cyanine 3-CTP (Cy3); PerkinElmer, USA) was incorporated to 350–500 ng of total RNA amplified using Low RNA Input Fluorescent Linear Amplification Kit (Agilent Technologies), generally following the kit manufacturer's protocol. Similarly, 24 nmols of the Cyanine 5-CTP (Cy5; PerkinElmer) fluorescent label were incorporated to 400 ng sample of Human Universal Reference RNA (Stratagene, USA); in addition, dye-swap replicate amplification were performed. Amplified fluorescent cRNA samples were purified using RNeasy mini-columns (Quiagen), and fluorescence of the eluted products was measured using ND-1000 specrophotometer (NanoDrop). Microarray technologyLong oligonucleotide probes (69–71 nucleotides) matching gene targets of interest were selected from Operon V2 and V3 human AROS sets (Operon Biotechnologies Inc., USA). Arrays were produced by the SweGene DNA Microarray Resource Centre, Department of Oncology at Lund University (Sweden) using a MicroGrid II 600R arrayer fitted with MicroSpot 10 K pins (Harvard BioRobotics, USA). Printing was performed in a temperature- (18–20°C) and humidity- (44–49% RH) controlled area on Corning UltraGAPS aminosilane slides (Corning Inc., USA) with 140 μm spot-to-spot centerdistance and 90–110 μm average spot size. Following printing, arrays were dried for 48 hours andstored in a dessicator until used. Microarray slides were UV-cross-linked (800 mJ/cm2), pre-hybridizedwith fluorescently labeled samples using the Pronto! Universal Microarray Hybridization Kit (Corning) and subsequently hybridized with test (Cy3-labeled)/reference (Cy5-labeled) RNA samples (or in reverse dye-labeling order) at 42°C for 17 h using a MAUI hybridization station (BioMicro Systems Inc., USA) and the Pronto! Universal Microarray Hybridization Kit, generally following manufacturer's instructions, with several minor adaptations [31]. Data acquisition and statistical analysisImmediately following the washing steps, the fluorescence intensities were measured using a confocal laser scanner (G2505B, Agilent Technologies). After image formatting by Tiff Image Channel Splitter Utility (Agilent Technologies) and grid annotation, a complete set of spots was visually inspected for each slide. Using GenePix Pro (Molecular Devices Corp. USA) flags for artifactual spots were annotated for each spot. Median pixel intensity minus the median local background for both dyes was used to obtain a test over reference intensity ratio. Data normalization was performed per array subgrid using LOWESS curve fitting with a smoothing factor of 0.33 [52,53]. All normalizations, filtering, merging of technical replicates and analyses were performed in the BioArray Software Environment database [32]. To visualize sample-dependent variation of spot intensities, data was uploaded to the TIGR MultiExperiment Viewer (MEV; [34]).
This work uncovers novel mechanisms of aging within stem cell niches that are evolutionarily conserved between mice and humans and affect both embryonic and adult stem cells. Specifically, we have examined the effects of aged muscle and systemic niches on key molecular identifiers of regenerative potential of human embryonic stem cells (hESCs) and post-natal muscle stem cells (satellite cells). Our results reveal that aged differentiated niches dominantly inhibit the expression of Oct4 in hESCs and Myf-5 in activated satellite cells, and reduce proliferation and myogenic differentiation of both embryonic and tissue-specific adult stem cells (ASCs). Therefore, despite their general neoorganogenesis potential, the ability of hESCs, and the more differentiated myogenic ASCs to contribute to tissue repair in the old will be greatly restricted due to the conserved inhibitory influence of aged differentiated niches. Significantly, this work establishes that hESC-derived factors enhance the regenerative potential of both young and, importantly, aged muscle stem cells in vitro and in vivo; thus, suggesting that the regenerative outcome of stem cell-based replacement therapies will be determined by a balance between negative influences of aged tissues on transplanted cells and positive effects of embryonic cells on the endogenous regenerative capacity. Comprehensively, this work points toward novel venues for in situ restoration of tissue repair in the old and identifies critical determinants of successful cell-replacement therapies for aged degenerating organs.
Embryonic stem cells (ESCs) are distinguished by their ability to self-renew and to differentiate into any other cell type via asymmetric cell divisions, in which one daughter cell maintains ‘stemness’ while the other daughter cell differentiates into a particular tissue type. ESCs, including those of human origin (hESCs), are derived from the blastocyst and can be propagated in vitro (Evans & Kaufman, 1981; Thomson et al., 1998; Wobus & Boheler, 2005). Their tremendous potential for organogenesis has created a great interest in using hESCs for replacing tissues and organs lost to disease, or old age (reviewed in Wobus & Boheler, 2005). As such, the use of hESCs is particularly important, due to the fact that adult organ stem cells are often limited in number, cell-fate plasticity, expansion capacity, telomere length, and lifespan (Mayhall et al., 2004). The general goal behind most cell-replacement approaches is to expand and then differentiate hESCs in vitro, thus producing a cell type of interest, such as neuronal, blood, endothelial, pancreatic, bone, and others. These differentiated cells are expected to replace their dysfunctional counterparts in vivo. The scope of disorders that can be potentially treated with a neoorganogenesis approach is large and includes many that are currently incurable, such as muscle atrophy, diabetes, Alzheimer's disease, Parkinson's disease, and other degenerative diseases that often accompany human aging (McDonald et al., 1999; Liu et al., 2000; Hori et al., 2002; Kim et al., 2002; Blyszczuk et al., 2003). While many studies have focused on the derivation, propagation, and in vitro differentiation of hESCs (reviewed in Hoffman & Carpenter, 2005; Wobus & Boheler, 2005), relatively few have examined the properties of these cells and their more differentiated progeny in the aged, as opposed to the young, systemic and local organ environments. Recently published data suggest that these extrinsic cues become altered with age in ways that preclude activation of organ stem cells (such as satellite cells), inhibit repair-specific molecular signaling (such as delta-Notch), and interfere with productive tissue repair (Conboy et al., 2003, 2005; Janzen et al., 2006; Krishnamurthy et al., 2006; Molofsky et al., 2006). Furthermore, at least two lines of evidence suggest that stem cell-based tissue-replacement therapies might be hindered in the elderly, because all cells along the developmental lineage (e.g., stem cells, more differentiated progenitor cells or even tissues containing a pool of precursors) might rapidly ‘age’ and fail to contribute to organ repair when introduced into the old organism in vivo. First, in heterochronic tissue-transplantation studies, the age of the host environment determined the regenerative outcome, as both young and old skeletal muscle explants containing differentiated and precursor cells effectively regenerated in young, but not in old animals (Zacks & Sheff, 1982; Carlson & Faulkner, 1989). Second, using parabiotically paired young and old mice, the regenerative potential of muscle and liver was shown to be influenced by the age of the systemic environment (Conboy et al., 2005). Thus, we sought to determine whether key molecular identifiers of stem cell properties, the rate of cell proliferation, and the myogenic capacity would be influenced by the age of extrinsic milieu, regardless of whether stem cells are embryonic or the more differentiated, muscle-specific satellite cells. Satellite cells are muscle stem cells situated in direct contact with myofibers, the differentiated muscle cells. When myofibers are damaged, quiescent satellite cells are activated to proliferate and then differentiate into fusion-competent myoblasts that continue to proliferate and can form primary cultures, but are also capable of producing new, multinucleated myofibers or myotubes in vitro and in vivo (Morgan et al., 2002; Collins et al., 2005; Wagers & Conboy, 2005). Activated satellite cells express myogenic markers, such as Myf5, M-cadherin, and Paired box gene 7 (Pax7); fusion-competent myoblasts express high levels of desmin, and de novo generated myofibers or myotubes express embryonic myosin heavy chain (eMyHC) and continue to express desmin (Schultz & McCormick, 1994; Wagers & Conboy, 2005). While desmin can be also present in smooth and cardiac muscle cells, the isolation of hind limb skeletal muscle with subsequent purification of myofibers away from all interstitial cells, as well as purification of associated muscle stem cells results in primary cultures that are uniformly of skeletal muscle lineage. Every desmin+ cell in such cultures is a fusion-competent myoblast, and is able to produce multinucleated myotubes after 48 h of culture in differentiation-promoting medium [Dulbecco's modified Eagle's medium (DMEM) with 2% horse serum]. Some of these myogenic cells fuse into myotubes, even in the mitogen-rich medium [(Opti-MEM (Invitrogen, Carlsbad, CA, USA) with 5–10% mouse serum or DMEM with 10% fetal bovine serum, FBS] (Conboy & Rando, 2002; Conboy et al., 2003; and see below). An experimental system was developed that (i) provided the ability to study the regenerative response of hESCs and of muscle stem cells in various heterochronic environments in vitro; and (ii) allowed examination of the effects of hESCs on muscle repair, in vivo, after transplantation into young vs. old hosts. This model allowed us to address both the negative effects of the aged niche on key stem cell properties and the positive effects of hESCs on the aged muscle-specific organ progenitor cells in vitro, and on the regenerative capacity of old muscle in vivo. The resulting data demonstrate that the composition of conserved extrinsic cues, regulating stem cell responses, becomes altered with age in ways that inhibit both hESCs and adult stem cell regenerative potential. Specifically, molecular markers of stem cell functionality, e.g. Oct4 (in hESCs) and Myf5 (in muscle stem cells), the rate of cell proliferation, and the capacity for myogenic differentiation are all dominantly inhibited by the aged systemic milieu, and by the old differentiated muscle tissue. However, while satellite cells are unable to deter the inhibitory affects of aged systemic and local niches, hESCs are capable of antagonizing the aged environments, thereby enhancing the regenerative potential of both young and old muscle stem cells in vitro and in vivo. Thus, a complex interplay between negative regulation of hESCs and adult muscle stem cells by the aged niche, and positive regulation of the host's regenerative responses by hESCs will likely determine the success of hESC-based cell-replacement therapies in the old.
Regenerative responses of adult muscle stem cells and hESCs are dominantly inhibited by the aged systemic milieuPrevious work established that the upregulation of repair-specific molecular signaling mechanisms, such as Notch, and successful engagement of resident muscle stem cells in tissue repair are largely determined by the age of the systemic milieu, rather than by the cell-autonomous age of muscle cells, or by the differences in their numbers (Conboy & Rando, 2005; Conboy et al., 2005). Intriguingly, these experiments also hinted at a small but persistent inhibitory effect of the aged systemic milieu on the performance of young stem cells. Exploring this further, we found that young serum permits satellite cells to be myogenic, while old serum inhibits the satellite cell regenerative potential not only alone, but also when mixed with young serum, suggesting a dominant over-riding of ‘young’ serum factors (Fig. 1). Myofiber cultures, in which satellite cells have been activated by injury in vivo, were established from young (2–3 months) and old (22–24 months) C57-BL/6 male mice, as previously described (Conboy & Rando, 2002; Conboy et al., 2005). As previously shown, this method is well suited for the assessment of satellite cell regenerative myogenic capacity (Conboy & Rando, 2002; Wagers & Conboy, 2005). Isolated myofiber explants with associated satellite cells were cultured overnight in the presence of young or old serum (alone at 5% and 10%, and mixed at 5% young + 5% old); bromodeoxyuridine (BrdU) was added for the last 2 h of culture to measure the rate of cell proliferation. The effects of heterochronic systemic milieu on myogenic potential were examined as generation of proliferating myoblasts that express desmin and Myf5, and that spontaneously form multinucleated nascent myotubes. As shown in Fig. 1A and quantified in Fig. 1B, the age of sera clearly determined satellite cell regenerative potential and old serum strongly inhibited the myogenic potential of young satellite cells either when present alone, or when mixed with young sera. Similar data was obtained by using another myogenic marker, Pax7 (Supplementary Fig. S1). Additionally, there were two to three times fewer total cells generated in the presence of aged serum (not shown).Fig. 1The age of sera determined the regenerative potential of satellite cells. (A) Young satellite cells were cultured either in 5% or 10% young (Young), 10% old (Old), or in a 5%+ 5% mouse sera combination (young + old). Cells were analyzed by immunofluorescence microscopy, using anti-BrdU (red), antidesmin (green) or anti-Myf5 antibodies (green, small panels). Similar results are shown for Pax7 immunodetection (Supplementary Fig. S1). Hoechst (blue) labeled nuclei. (B) Three independent experiments were quantified [300 young myofibers per experiment] as percentage of desmin+/Myf5+/BrdU+ de novo generated cells for each age and culture condition. On average, two to three fewer cells were generated when cultured in the presence of old. Shown are identical microscope fields at ×40 magnification. At least three independent experiments produced similar results. () indicates P≤ 0.001 as compared to young sera.Importantly, it was not simply the dilution of young serum factors that resulted in diminished myogenic capacity when young and old sera were mixed, because young sera promoted robust myogenesis both at 10% and 5%. Thus, old serum factors dominantly inhibited the myogenic capacity of young satellite cells even in the presence of young serum. This observation suggests that satellite cells of young mice engage in efficient myogenic responses, in part, because the inhibitory influence of old circulatory milieu is absent.These data reveal that the regenerative potential of young muscle stem cells is determined by the age of the systemic milieu, prompting us to investigate whether hESCs would similarly succumb to inhibitory factors present in the aged circulation.To determine the effects of aged serum on stem cell self-renewal/pluripotency, we analyzed hESC expression of Oct4 and studied the rate of hESC proliferation, by assessing BrdU incorporation (Fig. 2) and Ki67 expression (Supplementary Fig. S2). Specifically, these determinants of hESC regenerative potential were examined in the presence of heterochronic (young vs. old) mouse sera added to typical hESC medium, e.g., MEF-conditioned medium (MCM). Oct4 is expressed by self-renewing, pluripotent ESCs in culture, by the totipotent inner cell mass of the blastocyst and by the germ cells (Nichols et al., 1998; Pesce et al., 1999). Most cells in control cultures or young conditions expressed high levels of this marker of ‘stemness’, and maintained their normal phenotype and morphology throughout the various co-culture experiments performed in this study (see below).Fig. 2The regenerative potential of embryonic stem cells was negatively affected by aged mouse sera. (A) hESCs were cultured in MCM with 10% young (young) or old (old) mouse serum, or in three control media: MCM without mouse sera; GM (myoblast medium of Ham's F10 with 20% FBS) and DMEM/FBS (hESC differentiation medium of DMEM with 10% FBS). BrdU was added for the last 2 h of culture to measure the rate of cell proliferation. Immunodetection assays were performed for BrdU (red), Oct4 (red), and Ki67 (Supplementary Fig. S2). Hoechst (blue) labels nuclei. A high rate of hESC proliferation and Oct4 expression is displayed in all control media and in the presence of young mouse serum. In contrast, hESC proliferation and Oct4 expression are inhibited in the presence of old mouse serum, either alone or when mixed with young serum. MCM with mouse sera at 5% gave results similar to those observed with 10% young mouse sera or in control media (Supplementary Fig. S3). (B) Three independent experiments yielded similar results and were quantified as percentage of BrdU+ and Oct4+ cells for each culture condition. indicates P < 0.001 as compared to young serum.Importantly, at 10% aged serum dramatically inhibited the self-renewal and proliferative potential of hESCs, as judged by highly diminished Oct4 expression and a lack of BrdU incorporation. Again, the inhibitory factors in the aged milieu were dominant over the young, as evidenced by a decline in Oct4 expression, the low rate of BrdU incorporation, and Ki67 expression in young and old mixed environments (5% young + 5% old sera in MCM). Similar to the data shown for adult stem cells (ASCs) (Fig. 1), it was not simply a dilution of young serum factors as hESCs robustly proliferated and expressed high levels of Oct4 when cultured with 5% young sera in MCM (Supplementary Fig. S3). Quantification of multiple independent experiments has demonstrated that hESC expression of Oct4 and BrdU incorporation have been reduced by two- to threefold in the aged milieu (Fig. 2B).As expected, hESCs cultured in control media, including MCM alone that does not contain either young or old serum, also displayed a high rate of proliferation and Oct4 expression (Fig. 2, control medium). Additionally, in this experimental set-up there was no general inhibitory effect of sera per se on hESC proliferation and Oct4 expression, as 10% young mouse sera (young) and 10–20% of FBS (growth medium and DMEM/FBS) allowed for a high rate of cell proliferation and for uniformly high Oct4 levels (Fig. 2).When instead of immediate exposure to aged mouse serum, hESCs were first cultured overnight in MCM, these cells were no longer susceptible to the negative effects of old systemic milieu (Fig. 3), suggesting that hESC-produced factors established an embryonic microniche that may provide temporary protection from the aged environment. It appears that satellite cells do not have such anti-aging ability, because despite an initial activation in entirely young environments, e.g., after muscle injury to young muscle, isolated satellite cells remain susceptible to inhibition by the old mouse serum (Figs 1 and 4C). Similarly, culturing satellite cells isolated from noninjured muscle in growth-promoting medium for 1–2 days does not protect against the inhibitory affects of aged systemic milieu (not shown).Fig. 3Embryonic stem cells produce youthful microniche in culture. (A) As opposed to immediate exposure to old mouse serum after passaging (10% old), preculturing of hESCs for 24 h in feeder-free conditions, e.g., Matrigel™ + MCM, prior to replacing MCM with MCM + 10% old mouse sera, resulted in continuously high BrdU incorporation and Oct4 expression (embryonic microniche + 10% old). BrdU was added for the last 2 h of culture to measure the rate of cell proliferation. Immunodetection of BrdU and Oct4 (both in red) was performed as described in Experimental procedures. Hoechst (blue) labels nuclei. (B) Three independent experiments yielded similar results and were quantified as percentage of BrdU+/Oct4+ for each condition. * indicates P < 0.001 as compared to ‘old + MCM’.Fig. 4Aged muscle niche inhibits the regenerative potential of hESCs and satellite cells. (A) Immunodetection of a mouse-specific M-cadherin (green) or desmin (red; both human and mouse proteins are detected) revealed that hESCs underwent muscle lineage differentiation when co-cultured with young, but not old myofibers. The myogenic progeny of hESCs appears M-cadherin−/desmin+ (white arrow in young), as opposed to M-cadherin−/desmin− hESCs that lack myogenic commitment (white arrow in old). M-cadherin+/desmin+ cells are the myogenic progeny of mouse satellite cells (yellow arrows). To assess the effects of secreted factors produced by young vs. old myofibers on the rate of hESC proliferation, transient, 2 h BrdU incorporation was examined in hESCs cultured for 48 h with supernatants produced by heterochronic myofiber explants (See Experimental procedures for details). As compared to young myofiber-derived supernatants (young myofiber supernant), exposure to old myofiber-derived supernatants (old myofiber supernant) inhibited hESCs proliferation, as judged by BrdU immunodetection (red). As expected, the rate of hESCs proliferation was high in control media (shown in Fig. 2). Hoechst (blue) labels nuclei in all experiments. Quantification of desmin+/BrdU+ hESCs in direct myofiber cocultures, or with muscle supernatants, is shown in (B). * indicates P≤ 0.001 as compared to young. (C) Transwell co-cultures between purified young satellite cells and myofibers isolated from uninjured young (young myofiber) and old (old myofiber) muscle demonstrated that satellite cell regenerative myogenic capacity was inhibited by the aged differentiated muscle. Myogenic potential was determined by the ability of satellite cells to generate proliferating desmin+ myoblasts (immunodetection shown in green) and by rate of proliferation (2 h BrdU incorporation; immunodetection shown in red). (D) Satellite cell regenerative potential was quantified as percentage of desmin+/BrdU+ cells for transwell co-cultures with young or old uninjured myofibers (i.e., RM, resting muscle). n = 3; * indicates P≤ 0.05 as compared to young.Comprehensively, these data establish that the inhibition of stem cell regenerative potential by the aged systemic milieu is conserved between species (mouse vs. human) and cell types (adult vs. embryonic stem cells). As summarized in Table 1, aged mouse sera similarly affected the expression of key molecular identifiers of both embryonic and adult stem cells, e.g., Oct4 in hESCs and Myf5 in mouse ASCs. As expected, adult mouse stem cells did not express Oct4, and hESCs did not express Myf5 in these experimental conditions (not shown). Moreover, aged systemic milieu had similar inhibitory effects on proliferation of hESCs and ASCs, suggesting that not only the regenerative capacity, but also the presence and expansion of stem cells will be significantly restricted in aged organs. Intriguingly, prolonged culturing of hESCs in their preferred in vitro conditions enables generation of an embryonic microniche that antagonizes the inhibitory influences of aged circulatory factors.Table 1Conservation of stem cell aging in the systemic environmentRate of proliferation ESC/ASC (percentage of BrdU)Call-fate identifier ESC (percentage of Oct4)/ ASC (percentage of Myf5)10% young59.5 ± 0.8, 59.3 ± 4.099.0 ± 0.1, 50.7 ± 9.510% old32.7 ± 2.1, 27.3 ± 3.517.6 ± 3.2, 18.1 ± 5.95% young + 5% old31.0 ± 2.6, 38.0 ± 2.020.6 ± 3.5, 17.1 ± 4.2Quantified results from Figs 1, 2 are summarized and presented as mean percentages from experimental replicates ± SE. Rate of proliferation (BrdU) and cell-fate identifier (Oct4 or Myf5) are shown for both ESCs and ASCs cultured in heterochronic systemic conditions of 10% young (young), 10% old (old) or in 5%+ 5% mouse sera combination (young + old). Results for 5% young mouse sera are very similar to those for 10% young mouse sera and are shown in Fig. 1 (ASCs) and Supplementary Fig. S3 (hESCs). The regenerative potential of hESCs and ASCs is inhibited by aged differentiated muscleAfter establishing that the aged systemic niche negatively affects the regenerative capacity of hESCs and of ASCs, we then assessed whether myogenic potential and the rate of cell proliferation would be inhibited in hESCs and ASCs by the aged local muscle niche. Myofibers with associated satellite cells were isolated from young and old injured muscle, and were directly co-cultured with hESCs in typical hESC differentiation medium (DMEM/FBS). Similar to Fig. 1, the myogenic potential in these co-cultures was assayed by the expression of desmin, which is present in both fusion-competent myoblasts and newly formed myotubes. To analyze whether hESCs, mouse myogenic progenitor cells or both could express desmin in direct co-cultures, we costained these cells with a mouse-specific antibody to a myogenic marker, M-cadherin, which does not react with human protein, and a desmin-specific antibody that recognizes both mouse and human proteins. As shown in Fig. 4A, hESCs underwent myogenic differentiation in co-cultures with young myofibers (M-cadherin−/desmin+ mononucleated cells, white arrow in young). These myogenic progeny of hESCs in co-cultures with young myofibers could be of skeletal, smooth or cardiac muscle lineages (Debus et al., 1983; Fischman & Danto, 1985; Schultz & McCormick, 1994). As expected, the young mouse muscle progenitor cells (M-cadherin+/desmin+) were more advanced in their degree of myogenic differentiation, which was of skeletal muscle lineage, as judged by the formation of large, multinucleated de novo myotubes (yellow arrow in young). In addition to the myogenically differentiated human cells, co-cultures with young myofiber explants also contained some small undifferentiated hESC colonies, as determined by immunoreactivity to a human-specific antibody to the nuclear mitotic apparatus protein, NuMA and Oct4 expression (Supplementary Fig. S4).In contrast, when co-cultured with the aged mouse myofibers, only mouse cells appeared desmin+ (Fig. 4A, yellow arrow in old). These aged myogenic cells were of skeletal muscle lineage, based on spontaneous generation of multinucleated myotubes (see Fig. 5B) and based on induced differentiation into myotubes in DMEM + 2% horse serum (not shown). Importantly, the myogenic differentiation of hESCs failed in the aged co-cultures (Fig. 4A, white arrow in old). Furthermore, colonies of hESCs in co-cultures with aged myofibers typically differentiated into cells with fibroblast morphology, which lacked Oct4 expression (not shown). Spontaneous production of desmin+ myogenic cells in control hESC cultures without myofibers, or with young/old mouse sera was less than 0.1% (not shown).Fig. 5In vitro co-culture with hESCs enhanced myogenesis of mouse cells. (A) 1 × 105 hESCs or control hMSCs were co-cultured with 5 × 106 primary mouse myoblasts. hESCs expressing Oct4 (immunodetection shown in red) dramatically enhanced myotube formation of co-cultured mouse myoblasts (immunodetection of eMyHC is shown in green), as compared to co-cultures between mouse myoblasts and human mesenchymal stem cells (Mb + hMSCs) or myoblasts alone (Mb alone). Experiments were carried out in myoblast differentiation medium. Hoechst (blue) labels nuclei throughout this figure. (B) 1 × 105 hESCs or control hMSCs were co-cultured with young or old myofiber-associated satellite cells, as described in Experimental procedures. Co-culture with hESCs (myofiber + hESC), but not hMSCs (myofiber + hMSC) or control medium (DMEM/FBS), greatly enhanced the myogenic potential of both young and old myofiber-associated satellite cells, based on immunodetection of percentage of desmin+ de novo generated myoblasts and multinucleated myotubes. These experiments were carried out in GM. Shown are myogenic responses of mouse cells only, judged by lack of immunoreactivity to human-specific/hESC-specific antigens, such as NuMA and Oct4; and presence of mouse-specific immunoreactivity, e.g., M-cadherin (not shown). Both young and old myofiber associated satellite cells exhibited considerable myogenic improvement over control conditions. n = 3.In concert with the conservation of inhibitory affects of aged systemic niche, the negative influence of local muscle niche was also found to be conserved in its inhibition of hESC and ASC regenerative responses. Specifically, the myogenic capacity (generation of desmin+ myoblasts) was inhibited in young satellite cells co-cultured in a transwell system with aged myofibers (Fig. 4B). In addition, hESC and ASC proliferation (BrdU incorporation) was also inhibited by aged differentiated muscle (Fig. 4A,C). These data suggest that not only systemic but also local organ niches would inhibit key stem cell properties, e.g., myogenic capacity and the rate of proliferation in the aged organism. The conserved inhibitory influences of the differentiated muscle niche on hESC and ASC regenerative responses are summarized in Table 2.Table 2Conservation of stem cell aging in the local organ nicheRate of proliferation ESC/ASC (percentage of BrdU)Myogenic differentiation ESC/ASC (percentage of desmin)Young myofiber60.2 ± 2.5, 40.5 ± 2.67.4 ± 0.9, 47.6 ± 5.0Old myofiber30.1 ± 4.3, 21.5 ± 4.11.3 ± 0.7, 19.7 ± 4.7Quantified results from Fig. 4 are summarized and presented as mean percentages from experimental replicates ± SE. Rate of proliferation (BrdU) and myogenic differentiation (desmin) are shown for both ESCs and ASCs, in the presence of young vs. old differentiated muscle environments (young myofiber or old myofiber). hESCs indirectly enhance and rejuvenate the regeneration of skeletal muscleWhile hESC properties were inhibited by aged differentiated muscle, the myogenic potential of aged satellite cells seemed to be enhanced by co-cultures with hESCs (Fig. 4A). Therefore, we further explored the enhancing and rejuvenating effects of hESCs on myogenic potential in vitro and in vivo, using human mesenchymal stem cells (hMSCs) as a negative control. First, we examined the effects of hESCs on myotube generation by co-culture with primary myoblasts freshly derived from activated-by-injury satellite cells (Conboy et al., 2003). As shown in Fig. 5A (Mb + hESC), primary myoblasts underwent very rapid and robust nascent myotube formation, when co-cultured with hESCs for 48 h in myoblast differentiation medium. Namely, remarkably large fused myotubes containing approximately 50–70 nuclei formed around hESCs colonies (Fig. 5A). In contrast, when co-cultured with hMSCs, myotube formation was no greater than in myoblast cultures alone (Fig. 5A, Mb + hMSC and Mb alone). Encouraged by these data, we analyzed the myogenic potential of young and old satellite cells co-cultured with hESCs for 48 h. As shown in Fig. 5B, hESCs conferred a much-enhanced myogenic capacity on both young and, importantly, old myofiber-associated satellite cells (rapid formation of desmin+ myogenic cells, many of which formed de novo multinucleated myotubes). Control co-cultures of these satellite cells with hMSCs displayed no enhanced myogenicity. In summary, while the myogenic potential (production of desmin+ fusion-competent cells) was more pronounced in young vs. old myofiber-associated satellite cells under all experimental conditions, a finding that is consistent with previous data (Conboy et al., 2003), a clear increase in myogenic potential of old satellite cells was noted in co-cultures with hESCs, as compared to control cultures devoid of hESCs (Fig. 4A,B).Interestingly, in addition to the rejuvenating effects of direct co-cultures shown in Fig. 5, soluble factors present in hESC-conditioned culture supernatants were also able to enhance myogenesis of aged satellite cells (Supplementary Fig. S5). Thus, in agreement with the notion that an established embryonic microniche antagonizes the inhibitory effects of the aged environment on stem cell responses (Fig. 3), the hESC-produced factors enhanced myogenic capacity of even old mouse satellite cells.Establishing that hESC-produced factors enhance adult myogenesis and rejuvenate the regenerative capacity of even aged satellite cells in vitro prompted us to examine whether the regeneration of old injured muscle will be improved by hESC transplantation in vivo. Additionally, based on the data shown above, we speculated that even if the host's repair capacity is improved, hESCs themselves will not be efficiently maintained or expanded in the context of old systemic and local organ environments, and will not directly contribute to the repair of aged skeletal muscle. To test these hypotheses, we injected 5 × 105 hESCs or control hMSCs into the tibialis anterior (TA) and gastrocnemius muscles of young and old mice at 24 h after cardiotoxin-induced injury, when activation/proliferation of endogenous satellite cells normally begins (Conboy et al., 2003, 2005; Wagers & Conboy, 2005). To avoid immune response against hESC antigens, mice were immunosuppressed using FK506 (Ito & Tanaka, 1997; Dumont, 2000). Muscle was isolated 5 days post-injury, when nascent differentiated myofibers normally replace the damaged tissue (Conboy et al., 2003), and 10 µm cryosections were analyzed for the success in tissue repair using hematoxylin and eosin (H&E) histochemistry and eMyHC immunodetection. H&E analysis reveals newly formed myofibers, based on their smaller size and centrally located nuclei. Additionally, de novo myofibers in the damaged area appear positive for eMyHC, while undamaged myofibers remain negative. As shown in Fig. 6A and quantified in 6B, injection of hESCs significantly enhanced regeneration of skeletal muscle. Remarkably, this positive embryonic effect was especially pronounced in old tissue.Fig. 6Skeletal muscle regeneration following hESC transplantation is a balance between the inhibitory influence of aged niches and the rejuvenating effects of hESCs. Young and old tibialis anterior and gastrocnemius muscles were injured by cardiotoxin injection. hESCs or hMSCs were transplanted at the site of injury and were analyzed by cryosectioning at Day 5 after injury (as described in Experimental procedures). (A) Newly regenerated myofibers were detected using eMyHC-specific antibody (green) and staining with H&E. In H&E staining, newly regenerated areas contain smaller, immature myofibers with centrally located nuclei. Uninjured myofibers are much larger, by comparison, with peripherally restricted nuclei. Poorly regenerated areas lack new myofibers and contain areas of fibrosis and inflammation. eMyHC immunodetection is specific for regenerating areas of muscle only. Both assays showed dramatic enhancement of muscle regeneration in ‘old + hESC’ vs. ‘old + hMSC’. Regeneration improvement was also seen in young + hESC, as compared to young + hMSC. (B) Quantification of muscle regeneration was performed by analyzing the density of newly formed myofibers per mm2 of injury site, which is the volume that typically covers the whole injured area. Multiple, 10 µm H&E sections were examined through the entire volume of injury in multiple, independently injured muscles. n = 20; * indicates P < 0.001 (‘old + hMSC’ compared to young + hMSC and ‘old + hMSC’ compared to ‘old + hESC’. (C) H&E and immunofluoresence staining for Oct4, and a human-specific antibody to NuMA, revealed the failure of hESCs to expand or persist in old, but the presence of hESCs in young muscle at 5 days post-transplantation. Hoechst (blue) labels nuclei.Importantly, such enhanced and rejuvenated muscle repair stems from an indirect induction, as hESCs themselves (or control hMSCs) did not physically contribute to the mouse myofibers, as judged by near absence (less than 0.1%) of human-specific NuMA+ nuclei in de novo desmin+ myofibers, analyzed through multiple injury sites. An example of one regenerated myofiber from young muscle injected with hESCs, with NuMA+ nucleus in a field of NuMA−/desmin+ mouse myofibers, is shown in Supplementary Fig. S6. No such NuMA+/desmin+ myofibers were detected in aged regenerated muscle (not shown).In agreement with the in vitro data, establishing that aged systemic and local niches inhibit hESC proliferation and Oct4 expression (Figs 2 and 4 and Supplementary Fig. S2), hESCs failed to expand or even persist in old muscle, as judged by the absence of NuMA+/Oct4+ hESC-derived cells in the aged tissue. In contrast, colonies of Numa+/Oct4+ hESC-derived cells that did not undergo myogenic differentiation were easily detected in young regenerating muscle (Fig. 6C). This finding validates several technical aspects of these experiments, and confirms the contrasting effects of young and old systemic and local organ niches on hESC self-renewal.These data further confirm and extrapolate our findings and demonstrate that when exposed to both aged systemic and local organ niches, hESCs fail to persist and do not contribute to tissue repair directly. At the same time, these embryonic cells indirectly but significantly improve the repair of aged injured muscle in vivo.
The data presented here establish for the first time that both the local environment of old differentiated organ, e.g., skeletal muscle and the systemic milieu dramatically affect the regenerative potential of both hESCs and mouse post-natal myogenic progenitor cells. Not only are the factors promoting myogenic differentiation and proliferation of hESCs likely to become depleted with age, but the aged systemic and local organ niches are likely to contain dominant inhibitors of ASC and hESC regenerative potential (Figs 1, 2, and 4, summarized in Tables 1 and 2). Importantly, the similar inhibitory effects of old mouse serum and old myofibers on satellite cell (Figs 1 and 4C) and hESC (Figs 2 and 4A) proliferation and regenerative capacity suggest the conservation of elements in age-specific extrinsic regulatory mechanisms between evolutionarily distinct species and stem cell types. Additionally, a similarity in the inhibitory properties between systemic and local organ niches is also of interest and may indicate that molecules produced by old tissues have circulatory/endocrine activity; and/or that age-specific systemic inhibitory components become deposited in the old tissues. Humans display broad genetic polymorphisms and behavioral variations, which makes the identification of age-specific molecular changes complicated. In contrast, laboratory mice are genetically and environmentally controlled. Establishing that age-specific signals, regulating stem cell responses, are evolutionarily conserved and soluble enables the formation of rational approaches for the identification and characterization of the inhibitors involved, and for revealing the precise timing of their first appearance in serum and differentiated tissues with advancing age. Significantly, these experiments have also revealed that not only are hESCs able to protect themselves against the negative influences of aged mouse sera (Fig. 3), but these cells also produce factors that dramatically enhance the myogenic capacity of primary myoblasts and young and old satellite cells (Fig. 5), and also significantly improve repair of young and old injured muscle in vivo (Fig. 6). Identification of these embryonic factors would allow us to potentially enrich the arsenal of therapeutic tools for combating age-specific degenerative disorders. The interactions between hESCs and heterochronic differentiated niches, initially identified in vitro, have been confirmed by in vivo experiments. Namely, while the regenerative capacity, or presence, of hESCs is greatly restricted in aged, as compared to young skeletal muscle (where transplanted cells experience both old systemic and local environments), embryonic cells indirectly enhance and rejuvenate muscle repair when introduced at the time of muscle stem cell activation in the host, e.g., at Day 1 after the injury (Fig. 6). It remains to be determined whether the percentage of hESCs direct contribution to desmin+ myofibers in young muscle will be increased by transplanting these cells at a different time-point after muscle injury, e.g., at Days 3–5 (as in co-cultures with myofibers pre-injured for 3 days, Fig. 4A). In any case, the virtual lack of hESC and hMSC direct contribution to the newly regenerated skeletal muscle, when small numbers of these cells were injected into injured tissue, is completely consistent with the body of previous data demonstrating that myofiber-associated satellite cells conduct rapid and robust muscle repair and greatly outnumber injected human cells (Collins et al., 2005; Wagers & Conboy, 2005); that compared to muscle-specific satellite cells, the myogenic differentiation of hESCs in vitro remains very small (Fig. 5, Table 2), and that control hMSCs are not normally myogenic unless these cells overexpress exogenous constitutively active domain of Notch (Dezawa et al., 2005). Intriguingly, the failure of hESCs to strive in old skeletal muscle might represent a therapeutically desirable outcome. For example, while in young tissue hESC derivatives putatively would go on to produce teratomas, it is unlikely that teratoma formation would occur after hESC transplantation into aged skeletal muscle. Thus, the indirect beneficial effects of hESCs on tissue repair are unlikely to be compromised by the oncogenic properties of these embryonic cells in the context of old skeletal muscle. Comprehensively, the results of this work increase our understanding of aging as a process, reveal evolutionary conserved age-specific interactions between stem cells and their differentiated niches, and suggest novel therapeutic approaches for improving the regenerative responses of endogenous or transplanted stem cells in old individuals.
Animal strainsYoung (2–3 months), C57-BL/6 male mice were obtained from pathogen-free breeding colonies at Jackson Laboratories (Bar Harbor, ME, USA). Aged 22–24 months C57-BL/6 male mice were obtained from the National Institute on Aging (NIH). Animals were maintained in the North-West Animal Facility of the University of California, Berkeley, CA, USA, and handled in accordance with the Administrative Panel on Laboratory Animal Care at UC Berkeley. Muscle injury and isolationMyofiber cultures, in which satellite cells were activated by in vivo injury, were set up as previously described (Conboy & Rando, 2002; Conboy et al., 2005). Briefly, mice were injured by direct injection with 5 ng cardiotoxin (CTX-1) (Sigma, St Louis, MO, USA) into the tibialis anterior and gastrocnemius muscles using a 28-gauge needle. After 1–5 days post-injection, injured or uninjured muscle tissue was dissected out. Once isolated, whole muscle was prepared for cryosectioning (see below) or myofiber fragments were obtained from hind limb muscles by enzymatic digestion (see below), trituration, and multiple sedimentation and washing procedures. Additionally, blood was collected from mice for the isolation of sera. Briefly, blood cells were coagulated at 37 °C for 15’ and then were centrifuged repeatedly at 5900 g, 4 °C in a microfuge for 3’ to isolate sera. Mixtures of young and old sera were made 1 : 1. For example, in 5%+ 5% conditions, 50 µL of young and 50 µL old serum were added to 900 µL of culture medium (Opti-MEM or MCM, see co-culture procedures below). Myofiber explant culturesExplant and primary cell cultures were generated from C57-BL/6 mice, as previously described (Conboy & Rando, 2002; Conboy et al., 2003). Dissected gastrocnemius and tibialis anterior muscles underwent enzymatic digestion at 37 °C in DMEM (Invitrogen)/Pen-Strep (Invitrogen)/0.2% Collagenase Type IIA (Sigma) solution. Isolated fibers were resuspended in GM (Ham's F10 nutrient mixture (Mediatech, Inc., Herndon, VA, USA), 20% FBS (Mediatech), 5 ng mL−1 bFGF (Chemicon, Temecula, CA, USA) and 1% Pen-Strep, and cultured on ECM-coated (BD Biosciences, San Jose, CA, USA) plates (diluted 1 : 500 in PBS). Cultures of primary myoblasts were derived from isolated fibers, through repeated passaging, and were maintained in GM. Myoblast differentiation medium [DMEM, supplemented with 2% horse serum (Mediatech)] was used to promote rapid formation of myotubes from cultured myoblasts (Morgan & Partridge, 2003). Human embryonic and mesenchymal stem cell cultureThe federally approved hESC line, H7 (NIH no. WA07, obtained from WiCell Research Institue, Madison, WI, USA), was used in accordance with the UC Berkeley and UC San Francisco Committee on Human Research guidelines, and in accordance with NIH guidelines. To propagate hESCs, routine culturing and maintenance was performed using standard in vitro conditions for both feeder-dependent and feeder-free cultures (Geron Corporation, 2002). Briefly, hESCs grown on MEFs were cultured in standard hESC medium [Knockout™ DMEM, 20% KSR, 1% NEAA, 1 mm l-glutamine (Invitrogen), 0.1 mmβ-mercaptoethanol (Sigma)] and were supplemented with 4 ng mL−1 hbFGF (Invitrogen). Feeder-free hESC cultures were maintained in MEF-conditioned hESC medium (MCM), 4 ng mL−1 hbFGF. Differentiation medium for hESCs (DMEM/FBS) was made by replacing KSR with 20% FBS (Hyclone, Logan, UH, USA). hMSCs were maintained in mesenchymal stem cell GM, MSC-GM™ and were cultured according to supplier recommendations (Cambrex Walkersville, MD, USA). hESCs and hMSCs were typically seeded onto chambered slides coated with a 3% GFR Matrigel™ (BD Biosciences) substrate in PBS. Cells were typically incubated for 48 h at 37 °C, 5% CO2, under the various experimental conditions employed, then were fixed with 70% EtOH/PBS at 4 °C. hESCs and hMSCs were analyzed 24–48 h after experimental treatments, during which no apoptosis-related differences in cell numbers were observed. Heterochronic co-culture systemsHeterochronic systemic cultures were established by culturing myofiber explants (in GM) or hESCs (in MCM) in the presence of young, old or young + old sera for 48 h (Figs 1 and 2 and Supplementary Figs S1–3). In such cultures, hESCs were passaged immediately prior to sera exposure. In contrast, preculturing of hESCs for 24 h in MCM, prior to replacing MCM with MCM + 10% old mouse sera was done for embryonic microniche experiments (Fig. 3). For heterochronic local organ niche cultures, hESCs were co-cultured directly with myofiber explants for 48 h in GM, or were cultured in the presence of supernatants derived from cultured myofiber explants for 48 h (Figs 4A and 5). Specifically, 1 × 105 hESCs or control hMSCs were co-cultured with identical volume, e.g., 100 µL, of young or old myofiber fragments with their associated satellite cells (Fig. 5). In experiments shown in Supplementary Fig. S5, culture-conditioned supernatant produced by hESCs grown in MCM was used as a medium in which 1 × 105 of myofiber-associated young or old satellite cells were cultured for 48 h. In direct co-cultures, mouse vs. human cells were distinguished by immunodetection with human-specific/hESC-specific and mouse-specific antibodies (Supplementary Fig. S4 and see below). To prepare muscle supernatants, explants were cultured for 24 h in GM and cellular debris was removed from conditioned media by multiple rounds of centrifugation. The absence of cells was confirmed by microscopic examination. To mimic the local organ niche for satellite cell assays (Fig. 4B), 1.0 µm transwell (Corning, NY, USA) co-cultures of uninjured explants with activated satellite cells were established. Activated-by-injury (24 h post-injury) satellite cells were seeded onto ECM-coated 12-well plates in Opti-MEM (Invitrogen) and 5% FBS. Transwells were placed over satellite cells and contained isolated myofiber explants from uninjured young or old muscle (i.e., resting muscle). Satellite cells were cultured for 72–96 h in the presence of myofiber explants and were fixed for immunodetection, as described above. Cell transplantationhESCs were grown on MEFs and expanded in 6-well plates. Cells were treated with 1 mg mL−1 Collagenase Type IV (Invitrogen) for 5–10 min, were washed and then incubated with 0.5 mg mL−1 Dispase (Invitrogen) to lift only human cell colonies. Isolated hESCs were washed several times and resuspended in 100 µL hESC medium. Similarly, hMSCs were expanded in 6-well plates, lifted with Trypsin/EDTA (Invitrogen), washed and resuspended in 100 µL hESC medium. Approximately 5 × 105 hESCs or hMSCs were injected into 24 h post-injured gastrocnemius and tibialis anterior muscles of young and old mice, using a 21-gauge needle. Immunosuppression of animals was achieved by intraperitoneal injection of 1 mg kg−1 FK506 (Sigma) at 48 h prior to cell transplantation, and on each day following transplantation. Immunodetection and histological analysisTo assay the affects of heterochronic local and systemic environments on stem cell regenerative potential, hESC, hMSC, and myofiber-derived precursor cell cultures were fixed with 70% EtOH/PBS at 4 °C, and were analyzed by indirect immunofluorescence. Combinations of antibodies were used to co-stain cultures and histosections, in order to determine the percentages of cells that proliferated or differentiated and to distinguish hESCs from mouse cells. Antibodies to the myogenic transcription factors, Myf5/Pax7, the intermediate filament protein, desmin, and the marker of newly formed myotubes, eMyHC, were used to reveal commitment to myogenic differentiation. Cell commitment to this differentiation program was assessed by the efficiency of myotube formation, estimated by the number of nuclei per myotube. Ki67, a cell cycle related nuclear protein consistently absent in quiescent cells, was used as a marker for proliferation. Whereas Ki67 appears in all active phases of the cell cycle, BrdU staining allowed exclusive detection of cells in S-phase, thereby enabling accurate quantification of DNA synthesis. In select cultures, 10 µm BrdU was added for 2 h prior to fixation. BrdU-specific immunostaining required nuclear permeabilization with treatment of 4N HCl. hESCs were distinguished from mouse cells by using a species-specific antibody to the cell-surface marker M-cadherin for murine and the nuclear marker NuMA for human cells. Antibodies to Oct4 were used as a marker of hESC self-renewal/pluripotency. Following permeabilization in PBS, +1% FBS, +0.25% Triton X-100, cells were incubated with primary antibodies (concentration determined as per manufacturer's recommendations) for 1 h at room temperature in PBS, +1% FBS, washed several times, and then incubated with fluorophore-conjugated, species-specific secondary antibodies (diluted 1 : 500 in PBS + 1% FBS) for 1 h at room temperature. For histological analysis, dissected muscle was treated in a 25% sucrose/PBS solution, frozen in OCT compound (Tissue Tek) and cryosectioned at 10 µm. Immunostaining was performed in the manner described above, or H&E staining of cryosections was performed. Nuclei were visualized by Hoechst staining for all immunostains. Samples were analyzed at room temperature by using a Zeiss Axioscope 40 fluorescent microscope, and imaged with an Axiocan MRc camera and AxioVision software. All images depict identical microscope fields at ×20 magnification, unless otherwise noted. ReagentsAntibodies to Oct4 (ab18976), BrdU (BU1/75 (ICR1), and Ki67 (ab15580) were purchased from Abcam (Cambridge, MA, USA). Antibody to M-cadherin (clone 12G4) was acquired from Upstate Biotechnology (Lake Placid, NY, USA), and NuMA antibody (Catalog number NA09L) from EMD Biosciences (San Diego, CA, USA). Antibody to developmental eMyHC (clone RNMy2/9D2) was acquired from Vector Laboratories (Burlingame, CA, USA). Myf5 (GTX77876) and Pax7 (GTX77888) antibodies were obtained from GeneTex (San Antonio, TX, USA). Desmin antibodies (clone DE-U-10 and Catalog number D8281), BrdU labeling reagent and FK506 (Catalog number F4679) were obtained from Sigma. Fluorophore-conjugated secondary antibodies (Alexa Fluor) were obtained from Molecular Probes (Eugene, OR, USA). Statistical analysesA minimum of three replicates were undertaken for each experimental condition. Quantified data are presented as means ± SE. Significance testing was performed using one-way analysis of variance (anova) to compare data from different experimental groups. P values of < 0.05 were considered as statistically significant.
Mapping sites within the genome that are hypersensitive to digestion with DNaseI is an important method for identifying DNA elements that regulate transcription. The standard approach to locating these DNaseI-hypersensitive sites (DHSs) has been to use Southern blotting techniques, although we, and others, have recently published alternative methods using a range of technologies including high-throughput sequencing and genomic array tiling paths. In this article, we describe a novel protocol to use real-time PCR to map DHS. Advantages of the technique reported here include the small cell numbers required for each analysis, rapid, relatively low-cost experiments with minimal need for specialist equipment. Presented examples include comparative DHS mapping of known TAL1/SCL regulatory elements between human embryonic stem cells and K562 cells.
Mapping the location of DNaseI-hypersensitive sites (DHSs) remains central to developing our understanding of transcriptional regulation. Elements with a range of transcriptional regulatory functions have been identified initially as DHSs. These include transcriptional enhancers (1,2) and repressors (3,4) as well as chromatin insulators and barrier elements (5,6). A number of techniques have been published recently that permit the mapping of DHSs without the need for Southern blotting (7–13). These include high-throughput sequencing of cloned DNA libraries derived from DNaseI-digested chromatin (8,9), and a number of different approaches that use genomic tiling path arrays to map DHSs (7,11,13). While these approaches have the advantage of covering large genomic regions with a limited number of experiments, they are inherently costly and less applicable to the rapid DHS mapping of specific genomic sites in a range of cell types. An alternative approach that does permit the targeted semi-quantitative DHS mapping of specific loci has used quantitative real-time PCR to map sites from digested DNA (10). This technique depends on the quantification of relative loss of PCR signal observed when PCR primers amplify across regions of digested DNA compared with amplification of undigested DNA. This approach has been reported as showing good sensitivity, but a limitation is the large number of PCR reactions required to quantify the calculated loss of signal with significant certainty. In this article, we present an alternative method to identify DHS using real-time PCR by adapting the protocol we have used to map DHS using genomic tiling path arrays (11). We have previously demonstrated the high sensitivity and specificity of the basic protocol with regard to identifying DHS across large genomic regions (11). Here, we detail the laboratory protocol that permits the rapid comparative mapping of known and candidate DHS between different cell types using real-time PCR. As examples, we include comparative DHS mapping of regulatory elements located across the extended TAL1 (T-cell acute lymphocytic leukaemia-1, also known as SCL (stem cell leukaemia)) locus in human embryonic stem (hES) cells and the leukaemia cell line K562.
Generating a library of DHSsThe basic protocol is outlined in Figure 1. Nuclei are extracted from living cells, then digested on ice for 1 h with a range of DNaseI concentrations, as detailed in the ‘Materials and methods’ sections. Following RNaseA and proteinaseK treatment, the DNA is extracted and run on a 1% agarose gel to check for the size of digested DNA. The gel in Figure 1A shows the DNA from mouse thymocyte nuclei digested with 0, 40 and 120 units of DNaseI. Maximal enrichment at DHS is usually observed with samples that are not over-digested. In the experiment shown, maximal enrichment at a known control DHS was seen with 40 units DNaseI (real-time PCR quantification shown in Figure 2B). The DNA is then blunt-ended using T4 polymerase (Figure 1B) and ligated with an asymmetric double-stranded linker. After extraction, DNA is amplified using a biotinylated linker-specific primer and 35 thermal polymerase cycles, as detailed in ‘Materials and methods’ section. As the linker will ligate to both ends of digested DNA, the amplification will represent a mix of primer extension and PCR, depending on the length of DNA amplified. As has been previously reasoned (13), one double strand of DNA in a region of DNaseI hypersensitivity is more likely to be digested twice within a short distance than non-hypersensitive DNA. This will lead to the preferential amplification of DNA from regions of DHS. The amplified DNA is then extracted using para-magnetic streptavidin beads, which provides a DNA library representative of whole-genome DHS. Agarose gel electrophoresis of the DNA recovered from the beads confirms that the vast majority of these products are between 300 and 500 base pairs in length (Figure 1B). Real-time PCR quantification of DNA within the DHS libraryFigure 2 documents the quantification of specific DNA sites from different mouse-cell-derived DNA libraries. Figure 2A shows the quantification of material from two primer sets, which are within (primer pair A) and 3′ (primer pair B) to the mouse Stil promoter. The left-hand panels of Figure 2A show the Sybr-green real-time fluorescence profiles using serial 5-fold dilutions of quantified mouse genomic DNA standards with primers A and B. A calculated standard curve then permits the quantification of DNA from this sequence in library samples. The right-hand panels of Figure 2A show the quantification of samples from 0 and 40 units DNaseI-treated material using primers A and B. While there is no difference in amplification between the samples using primer pair B, with primer pair A, the 40 units DNaseI sample amplifies ∼5.5 PCR cycles before the 0 units sample. This equates to greater than 40-fold enrichment at the Stil promoter compared with no observed enrichment downstream of the promoter. Figure 2B shows the quantifiable differences in enrichment at the porphobilinogen deaminase (Hmbs) promoter between primary mouse thymocytes and the mouse T-cell line BW5147. This representative primary mouse cell experiment was performed with the DNA shown in Figure 1A, which confirms that with these cells, maximal enrichment is observed from DNA that is not over-digested. The comparison of the thymocytes with the BW5147 cells illustrates another common finding that, in our experience of primary cell experiments, maximal enrichment is often obtained using lower amounts of DNaseI compared with cell lines. Comparison of real-time PCR DHS with Southern blottingAs the protocol generates template by primer extending away from a DNaseI-digestion site, as well as PCR amplification between DNaseI-digestion sites, there is the potential for the genomic size of the real-time DHS to be larger than sites revealed by Southern blotting. There is also potential for signal to be lost at the most ‘open’ stretch of DNA, due to over-digestion with DNaseI. These issues are addressed in Figure 3. The upper panel shows a Southern blot of the human STIL (SCL/TAL1 interrupting locus) promoter in K562 cells. The BglII restricted fragment is probed from the 3′ end, as shown in the upper panel. This reveals a central DHS ∼500 bp wide, with a suggestion of weaker hypersensitivity for a few hundred base pairs 5′ and 3′ to the central region. The lower panel shows real-time PCR data from K562 material using 10 primer sets, each generating an amplicon ∼120 bp long. This permits the quantification of enrichments over 1200 bp, centred around the STIL transcription start site. The lower panel represents the mean ± SD enrichment from three independent biological replicates from K562 cells. The black bar denotes the location of the ‘core’ hypersensitive site as defined by Southern blotting. There is good correlation between the location of the DHS between the two techniques. The 5′ extension of enrichment seen in the lower panel appears to reflect the weaker 5′ extension seen in the Southern blot in the upper panel. A dip in enrichment is observed in the lower panel over the previously mapped transcription-factor-binding sites (14), which may represent over-digestion of the most accessible core region of the DHS. Quantification of DHS across the TAL1 locus in hES and K562 cellsAs examples of relative enrichments at different regulatory elements, we present a comparison of enrichments across the extended TAL1 locus from human embryonic stem (hES) cells and K562 cells in Figure 4B. Relative quantification of mRNA expression using real-time PCR shows that both of these cell types express STIL, but only K562 cells express TAL1 (Figure 4A). TAL1 expression is critical to the establishment of haematopoiesis in a developing embryo (15). Post embryogenesis, TAL1 expression is maintained in the non-lymphoid haematopoietic system, although in addition, expression is observed in a range of non-haematopoietic tissues, including endothelium and brain (16–18). Over the past 10 years, we and others have dissected the regulatory elements that direct the expression of TAL1 to different tissues (11,19–31). These include the 3′ haematopoietic stem cell enhancer (+19) (24,30), the 5′ endothelial-haematopoietic enhancer (−4) (31), the 3′ erythroid enhancer (+40 in mouse, +50 in human) (28), and a number of neuronal elements (25,26). The location of the STIL promoter has been previously mapped (14), although no other STIL regulatory elements have yet been identified. Figure 4B shows the mean ± SD ratio (DNaseI-treated enrichment/DNaseI-untreated enrichment) of quantitative enrichments from two independent hES cell biological replicates and two independent K562 biological replicates, using primer sets from different locations as indicated in the figure. Primers indicated in red correspond to known regulatory elements. The hES cell cultures were maintained as detailed in methods. Both cell types show significant enrichment at the STIL promoter. Four control regions were selected: −16 kb, upstream of TAL1; +5 and +22 kb, within the TAL1 locus; +70 kb, downstream of TAL1. These sequences were chosen as control regions as they show limited homology between a number of species (26) and were considered highly unlikely to represent regulatory elements. All four regions showed no enrichments between DNaseI-treated and -untreated samples in either the hES cells or K562 cells. The most striking differences between hES cells and K562 cells were found at the TAL1 regulatory elements. With K562 cells, there is prominent enrichment at the TAL1 promoter 1a, and the −4, +19 and +50 enhancers. These cells express high levels of TAL1 transcripts, and were originally derived from a patient with blastic transformation of chronic myeloid leukaemia. K562 cells are relatively undifferentiated, although they have a partial erythroid phenotype. This potentially explains the marked enrichment seen at the +19 stem cell enhancer and the +50 erythroid enhancer. This is similar to data obtained from primary mouse erythroid cells (day 14.5 foetal liver), which show the highest enrichments at the mouse +40 enhancer (homologous to the human +50) (data not shown). In contrast, the hES cells show markedly reduced enrichment at the TAL1 regulatory elements when compared with K562 cells. Although there is enrichment at the +50 enhancer, there is minimal enrichment at the −4, promoter 1A and +19 elements. Reduced/absent accessibility of regulatory elements to DNaseI digestion is consistent with the lack of expression of TAL1 in hES cells (Figure 4A). As ES cells differentiate to form embryoid bodies, expression of TAL1 is rapidly switched on, appearing from day 3 of mouse ES cell differentiation (32). The relative accessibility of the +50 enhancer may reflect a poised chromatin state in hES cells, which permits a rapid response to the changing transcription factor environment that accompanies differentiation.
The development of techniques that permit the rapid comparative mapping of DHS between different cell types will greatly facilitate the study of transcriptional regulation in both normal and diseased cells. Recently published high-throughput techniques that map DHS sites using high-throughput sequencing (8,9) and genomic array tiling paths (7,11,13) have clear advantages of scale over more targeted approaches. Major disadvantages of these approaches include cost and lack of focus. This makes them less suitable for many laboratories that want to assess the chromatin accessibility of a number of defined or presumed regulatory elements in a range of cell types. A real-time-PCR-based approach to DHS mapping has, therefore, a number of potential advantages for researchers interested in specific regulatory questions at defined loci. Real-time PCR is relatively inexpensive compared with the large-scale techniques, and permits a rapid, focused DHS analysis of defined regions of DNA from multiple cell types. It also provides flexibility, as any genomic region can be analysed from the DNA library derived from the DNaseI-treated samples by designing further real-time PCR primer sets. We have previously shown that our basic technique for amplifying a library of DHS generates a template representative of known DHS with excellent sensitivity and specificity (11). Although the experiments presented in this paper were each performed using 5 million cells/digestion condition, we have obtained reproducible data using 5-fold fewer cells as starting material. We feel our technique can deliver acceptable specificity and sensitivity for DHS mapping with small numbers of cells, and will therefore be of use to those researchers working with limited numbers of primary cells. An alternative approach using real-time PCR to define DHS has been previously published (10). The two approaches differ in that Dorscher et al. quantify the DHS through the loss of PCR signal obtained from DHS when DNA is digested, whereas our approach uses real-time PCR to quantify a gain of signal observed from DHS. Dorscher et al. report excellent sensitivity using their approach to map DHS. However, the technique depends on large numbers of comparative quantitative real-time PCR reactions across a region in both digested and undigested material, in order to quantify the loss of enrichment. One advantage of our technique published here is that data can be obtained using far fewer quantitative PCR reactions. The technique is highly reproducible, with relatively little variation in quantifiable enrichments observed between different biological replicates. Moreover, we demonstrate tissue specificity, with variable enrichment at known regulatory elements between different cell types. The technique published here permits the rapid comparative analysis of DHS between different cell types from relatively small numbers of cells. It will have potential use for researchers across a broad spectrum of biology for the study of transcriptional regulation in both healthy and diseased tissues.
Cell cultureCell lines were maintained in culture as previously described (11). Care was taken to ensure maximum viability of cells when taken for experiments. The primary thymocytes used for Figure 2 were prepared by gentle physical disassociation of a whole thymus gland into PBS supplemented with 2% FCS. Cells were filtered to ensure that a single-cell suspension was taken forward for nuclei isolation. Human embryonic stem cells (hES) were grown in chemically defined media in the presence of Activin and FGF2, as detailed previously (33). In these conditions, hES cells remain homogenously undifferentiated. Nuclei preparationUp to 3 × 107 cells were washed in ice-cold PBS and resuspended in 2 ml of ice-cold cell lysis buffer [300 mM sucrose, 10 mM Tris pH 7.4, 15 mM NaCl, 5 mM MgCl, 0.1 mM EGTA, 60 mM KCl, 0.2% NP-40, 0.5 mM DTT, 0.5 μM spermidine, 1× protease inhibitor (complete, Roche)]. After 5 min, the lysed cells were spun at 500 g for 5 min at 4°C with brakes off. After careful removal of supernatant, the nuclei were gently resuspended in 200 μl of ice-cold reaction buffer (20 μl 10× DNaseI buffer, 4 μl glycerol, 176 μl water) using pipette tips with cut off ends. The nuclei were spun again at 500 g for 5 min at 4°C and, following supernatant removal, were resuspended in 30 μl of reaction buffer per DNaseI condition. For example, if 2 × 107 cells were taken to look at four different conditions (e.g. 0, 20, 60, 120 units DNaseI), the nuclei were resuspended in 120 μl of reaction buffer. Separate 30 μl aliquots were then taken and gently mixed with 70 μl of DNaseI mix (see Table 1) on ice. This made a final digestion volume of 100 μl for each sample, which was left to incubate for 1 h on ice in the cold room. After 1 h, 700 μl of nuclei lysis buffer (100 mM tris HCL pH 8, 5 mM EDTA pH 8, 200 mM NaCl, 0.2% SDS) was added to each sample with 50 μg proteinase K. Following gentle mixing with inversion, the lysed samples were incubated at 55°C for 1 h. RNaseA (10 μg (Ambion)) was then added to each sample and further incubated at 37°C for 30 min. DNA was then extracted using standard phenol–chloroform techniques. Care was taken to use cut-off tips and very gentle pipetting to reduce non-specific DNA sheering. Following precipitation, DNA was resuspended in 200 μl of 0.1 TE and quantified using spectophotometry. Samples (1 μg) were analysed using gel-electrophoresis, as shown in Figure 1A. DNA library preparationFollowing quantification, 7.5 μg of DNA was taken for each sample condition. This was blunt-ended using T4 polymerase (10 μl 10× buffer, 0.5 μl 25 mM dNTP, 3 μl BSA, 1 μl T4 polymerase (3 U/μl NE Biolabs), 85.5 μl of water/DNA in 0.1 TE). The samples were mixed gently on ice, then put at 12°C for 16 min. The reaction was stopped with excess EDTA (4 μl 0.5 M) followed by 75°C for 10 min. The DNA was further extracted using phenol–chloroform and precipitated with ethanol. Glycogen carrier (5 μg) was used at this stage, and the pellet resuspended in 80 μl water.The blunt-ended DNA was then ligated to the LP21–25 linker. A stock of linker was prepared by mixing 80 μl LP21 (100 μM: GAATTCAGATCTCCCGGGTCA) with 80 μl LP25 (100 μM: GCGGTGACCCGGGAGATCTG AATTC) and 240 μl water. The mix was then placed on a 95°C hot block and the power supply turned off. When the block had cooled to room temperature, the LP21–25 linker was aliquoted and frozen. The 80 μl DNA sample was split into two samples for ligation (5 μl 10× buffer, 3 μl LP21–25 linker, 3 μl ligase (NE biolabs 400 U/μl and 39 μl DNA). This was mixed well with a pipette and left at 16°C overnight.Following ligation, the DNA was precipitated with ethanol, and resuspended in 42 μl water. The sample was amplified using Vent exo-polymerase and a biotinylated LP25 primer. The mix (5 μl ThermoPol 10× buffer, 0.5 μl B-LP25 (200 μM), 2 μl Vent exo-(NE Biolabs 2 U/μl), 1 μl dNTP (25 mM) and 41.5 μl DNA) was amplified with the following thermal cycle: (95°C 3 min, 95°C 30 s, 61°C 30 s, 72°C 30 s) × 35 cycles.Following amplification, the biotinylated products were extracted using Dynal streptavidin beads. For each sample, 30 μl of beads (Dyabeads M-270; Dynal biotech) were washed twice in 2× binding buffer (10 mM Tris HCL pH7.5, 1 mM EDTA, 2 M NaCl) using a magnet, and then resuspended in 50 μl of 2× binding buffer/sample. Each post amplification sample (50 μl) was then mixed with 50 μl of resuspended beads and incubated at room temperature on a rotator for 1 h. After binding, the samples were washed twice with 2× binding buffer, then once with 1× TE. After the last wash, each sample was resuspended in 30 μl 0.1 TE. The paired samples that were split before ligation were then pooled, making a 60 μl final aliquot. This was heated at 95°C for 10 min to free the DNA from the beads, and then stored at 4°C. Real-time PCR quantificationWe have used Stratagene Brilliant SYBR Green QPCR Master Mix as our standard kit for quantification of genomic controls and samples. We have used a range of primer sets. The HMBS primers are: human—Forward; ATGCTGCCTATTTCAAGGTTGT, Reverse; GAATT GGAACATTGCGACAGT, and mouse—Forward; CGGAGTCATGTCCGGTAAC, Reverse; CGACCAA TAGACGACGAGAA. Primers from the TAL1 locus (human and mouse) are available on request.Amplification conditions and PCR mixes were as recommended by Stratagene. Genomic standard curves were calculated for each primer using serial 5-fold dilutions from 50 ng genomic DNA/PCR reaction. For each sample PCR, a 45 μl stock was made using 1.3 μl of sample DNA + 43.7 μl water. This stock was used as 5 μl per real-time PCR reaction, and following amplification, quantified relative to the genomic DNA standard curve, for each primer set. The quantifications shown in Figure 3 represent the absolute quantification of DNA at each primer set for the 5 μl PCR sample.The mRNA expression levels of TAL1 and STIL in Figure 4A were normalised relative to β-actin and plotted relative to mRNA expression from normal human donor CD34 + cells, as described previously (11).
Human embryonic stem cells (hESCs) and neural progenitor (NP) cells are excellent models for recapitulating early neuronal development in vitro, and are key to establishing strategies for the treatment of degenerative disorders. While much effort had been undertaken to analyze transcriptional and epigenetic differences during the transition of hESC to NP, very little work has been performed to understand post-transcriptional changes during neuronal differentiation. Alternative RNA splicing (AS), a major form of post-transcriptional gene regulation, is important in mammalian development and neuronal function. Human ESC, hESC-derived NP, and human central nervous system stem cells were compared using Affymetrix exon arrays. We introduced an outlier detection approach, REAP (Regression-based Exon Array Protocol), to identify 1,737 internal exons that are predicted to undergo AS in NP compared to hESC. Experimental validation of REAP-predicted AS events indicated a threshold-dependent sensitivity ranging from 56% to 69%, at a specificity of 77% to 96%. REAP predictions significantly overlapped sets of alternative events identified using expressed sequence tags and evolutionarily conserved AS events. Our results also reveal that focusing on differentially expressed genes between hESC and NP will overlook 14% of potential AS genes. In addition, we found that REAP predictions are enriched in genes encoding serine/threonine kinase and helicase activities. An example is a REAP-predicted alternative exon in the SLK (serine/threonine kinase 2) gene that is differentially included in hESC, but skipped in NP as well as in other differentiated tissues. Lastly, comparative sequence analysis revealed conserved intronic cis-regulatory elements such as the FOX1/2 binding site GCAUG as being proximal to candidate AS exons, suggesting that FOX1/2 may participate in the regulation of AS in NP and hESC. In summary, a new methodology for exon array analysis was introduced, leading to new insights into the complexity of AS in human embryonic stem cells and their transition to neural stem cells.Author SummaryDeriving neural progenitors (NP) from human embryonic stem cells (hESC) is the first step in creating homogeneous populations of cells that will differentiate into myriad neuronal subtypes necessary to form a human brain. During alternative RNA splicing (AS), noncoding sequences (introns) in a pre-mRNA are differentially removed in different cell types and tissues, and the remaining sequences (exons) are joined to form multiple forms of mature RNA, playing an important role in cellular diversity. The authors utilized Affymetrix exon arrays with probes targeting hundreds of thousands of exons to study AS comparing human ES to NP. To accomplish this, a novel computational method, REAP (Regression-based Exon Array Protocol), is introduced to analyze the exon array data. The authors showed that REAP candidates are consistent with other types of methods for discovering alternative exons. In addition, REAP candidate alternative exons are enriched in genes encoding serine/theronine kinases and helicase activities. An example is the alternative exon in the SLK (serine/threonine kinase 2) gene that is included in hESC, but excluded in NP as well as in other differentiated tissues. Finally, by comparing genomic sequences across multiple mammals, the authors identified dozens of conserved candidate binding sites that were enriched proximal to REAP candidate exons.
The human central nervous system is composed of thousands of neuronal subtypes originating from neural stem cells (NSCs) that migrate from the developing neural tube. Such neuronal complexity is generated by a vast repertoire of molecular, genetic, and epigenetic mechanisms, such as the active retrotransposition of transposable elements [1], alternative promoter usage, alternative RNA splicing (AS), alternative polyadenylation, RNA editing, post-translational modifications, and epigenetic modulation [2]. Understanding the processes that generate neuronal diversity is key to gaining insights into neuronal development and paving new avenues for biomedical research. Human embryonic stem cells (hESCs) are pluripotent cells that propagate perpetually in culture as undifferentiated cells and can be induced to differentiate into a multitude of cell types both in vitro and in vivo [3]. As hESCs can theoretically generate all cell types that make up an organism, they serve as an important model for understanding early human embryonic development. In addition, the hESCs are a nearly infinite source for generating specialized cells such as neurons and glia for potential therapeutic purposes [4,5]. In recent years, methods have been introduced to induce hESCs to differentiate into neural progenitors (NPs) [6,7] and neuronal and glial subtypes [8–12]. The therapeutic interest in understanding the molecular basis of pluripotency and differentiation has led to many studies comparing transcriptional profiles in different hESC lines and the study of expression changes during the differentiation of hESCs to various lineages [13–17]. NSCs and progenitor cells (NPs) are present throughout development and persist into adulthood [18–20]. They are critical for both basic research and developing approaches to treat neurological disorders, such as Parkinson disease and amyotrophic lateral sclerosis (ALS), and stroke or head injuries [21,22]. NSCs and NPCs can be isolated from human fetal brain tissue [23–26], as well as from several regions of the adult human brain, such as the cortex, hippocampus, and the subventricular zone (SVZ) of the lateral ventricles [26–35]. Several studies have explored expression patterns of NPCs. For example, Wright et al. identified “expressed” and “not expressed” genes in NPCs isolated from the human embryonic cortex [24]; Cai et al. used the massively parallel signature sequencing profiling (MPSS) technique to analyze expression of fetal NPCs in comparison to hESCs and astrocyte precursors [27]; Maisel et al. used Affymetrix Gene Chip arrays to compare adult and fetal NPCs propagated in neurospheres [35]. However, as with hESCs, the focus thus far has been primarily on transcriptional differences, which ignores differential RNA processing such as AS, polyadenylation, degradation, or promoter usage. AS is frequently used to regulate gene expression and to generate tissue-specific mRNA and protein isoforms [36–39]. Recent studies using splicing-sensitive microarrays suggested that up to 75% of human genes undergo AS, where multiple isoforms are derived from the same genetic loci [40]. This functional complexity underscores the challenge and importance of elucidating AS regulation. AS appears to play a dominant role in regulating neuronal gene expression and function [41,42]. Examples of splicing regulators that are enriched and function specifically in neuronal cells include the brain-specific splicing factor Nova [43,44] and neural-specific polypyrimidine tract binding protein (nPTB), which antagonizes its paralogous PTB to regulate exon exclusion in neuronal cells [45–47]. Finally, an early report estimating that 15% of point mutations disrupt splicing underscores the importance of splicing in human disease [48]. Indeed, the disruption of specific AS events has been implicated in several human genetic diseases, such as frontotemporal dementia and parkinsonism, Frasier syndrome, and atypical cystic fibrosis [49]. While insights into the regulation of AS have come predominantly from the molecular dissection of individual genes [36,49], it is becoming clear that molecular rules can be identified from large-scale studies of both constitutive splicing and AS [40]. Most systematic global analyses on AS have focused on comparisons across differentiated human tissues [50–52]. Only one study, utilizing expressed sequence tag (EST) collections from stem cells, has attempted to find AS differences between embryonic and hematopoietic stem cells [53]. However, utilizing ESTs to identify AS has intrinsic problems, as ESTs tend to be biased for the 3′ ends of genes, and full coverage of the genome by ESTs is severely limited by sequencing costs. The commercial availability of Affymetrix exon arrays provides an alternative approach to interrogate the expression of every known and predicted exon in the human genome. The Affymetrix GeneChip Human Exon 1.0 ST array contains ∼5.4 million features used to interrogate ∼1 million exon clusters (collections of overlapping) of known and predicted exons with more than 1.4 million probesets, with an average of four probes per exon. Our goal was to identify and characterize AS events that distinguish pluripotent hESCs from multipotent NPs, paving the way for future candidate gene approaches to study the impact of AS in hESCs and NPs. However, as different hESC lines were established under different culture conditions from embryos with unique genetic backgrounds, we expected that hESCs and their derived NPs might have distinct epigenetic and molecular signatures [54]. As both common and cell-line specific alternatively spliced exons are likely to be important in regenerative research, in our study two separate hESC lines were used, with independent protocols for differentiating the hESCs into NPs positive for Sox1, an early neuroectodermal marker. As an endogenously occurring population of NPs, human central nervous system stem cells grown as neurospheres (hCNS-SCns) were utilized as a natural benchmark for derived NPs. We developed an approach called REAP (Regression-based Exon Array Protocol), which is based on robust regression that analyzed signal estimates from Affymetrix exon array data to identify AS exons. Experimental validation revealed alternative exons that distinguish hESCs from NPs; some of them also distinguish hESCs from a variety of differentiated tissues. A comparison of REAP-predicted alternative events with independent methods, such as using publicly available transcripts (ESTs and mRNAs) and computational predictions based on genomic sequence information alone [55], showed a strong concordance of REAP-identified AS exons with AS events identified from these orthogonal methods. Finally, using analysis of the sequences flanking REAP-identified alternative exons, we were able to discover known and novel cis-regulatory elements that potentially regulate these AS events.
Derivation of Neural Progenitors from Embryonic Stem CellsNPs were independently derived from two hESC lines, and RNA extracted from the cell lines was processed and hybridized onto Affymetrix Human 1.0 ST exon arrays. Immunohistochemical and reverse-transcriptase polymerase chain reaction (RT-PCR) analyses demonstrated that the hESCs expressed pluripotent marker genes, and the derived NPs expressed multipotent and neurogenic markers similar to hCNS-SCns. Undifferentiated Cythera (Cyt-ES) and HUES6 (HUES6-ES) hESC lines were maintained in culture as previously described [12,23,56]. Utilizing specific antibodies, we observed that undifferentiated Cyt-ES and HUES6-ES cells were positive for the pluripotent markers Oct4, SSEA-4, and Tra-1–80 (unpublished data). NPs were derived from the hESC cell lines using protocols optimized for each line (see Materials and Methods). Greater than 90% of derived NP cells (Cyt-NP from Cyt-ES and HUES6-NP from HUES6-ES) were positive for Sox1, an early neuroectodermal marker, and Nestin (Figure 1A), and negative for Oct4 (unpublished data). As a natural benchmark for the derived NPs, we utilized hCNS-SCns, which were previously isolated from fresh human fetal brain tissues using antibodies to cell-surface markers and fluorescence-activated cell sorting [12,23]. The hCNS-SCns form neurospheres in culture which are greater than 90% Nestin and Sox1 positive, and differentiate into both neurons and glial cells in vitro [12,23]. Immunohistochemical analysis confirmed that hCNS-SCns were negative for Oct4 (unpublished data) and positive for Sox1 and Nestin (Figure 1A).Figure 1Molecular Characterization of Human Embryonic Stem Cell Lines and Neuronal Progenitors(A) Immunohistochemical analysis of markers in NPs derived from the hESC lines (Cyt-NP from Cyt-ES; and HUES6-NP from HUES6-ES) and in hCNS-SCns. Cyt-NP, HUES6-NP, and hCNS-SCns cells were Nestin and Sox1 positive. Nuclei stained positive for Dapi. White horizontal bar indicated 15 μm.(B) Gene-level signal estimates of marker genes (GAPDH, Oct4, Nanog, Nestin, Notch1, DNER, and Sox1) from Affymetrix exon array analysis. Vertical bars indicated average log2 normalized signal estimates, and error bars represented standard deviations from three independent replicate experiments per cell type.(C) RT-PCR of marker genes (GAPDH, Oct4, Nanog, Nestin, Notch1, DNER, and Sox1).Here, known molecular markers were subjected to RT-PCR measurements, which were compared to gene-level signal estimates generated from the exon array data. Total RNA was extracted, and labeled cDNA targets were generated from three independent preparations of each cell type, namely Cyt-ES, HUES6-ES, Cyt-NP, HUES6-NP, and hCNS-SCns. To facilitate downstream analyses, instead of utilizing the meta-gene sets available from the manufacturers, we generated our own gene models by clustering alignments of ESTs and mRNAs to annotated known genes from the University of California Santa Cruz (UCSC) Genome Browser Database. After hybridization, scanning, and extraction of signal estimates for each probeset on the exon arrays, gene-level estimates were computed based on our gene models using available normalization and signal estimation software from Affymetrix. For every gene, a t-statistic and corresponding p-value were computed representing the relative enrichment of the expression of the gene in hESC versus NP, such as in Cyt-ES versus Cyt-NP. After correcting for multiple hypothesis testing using the Benjamini-Hochberg method, a p-value cutoff of 0.01 was used to identify enriched genes. Close inspection of all pairs of hESC-NP comparisons revealed a generally significant overlap from 31% to 85% of the smaller of two compared sets of enriched genes (see Figure S1). Thus for the purpose of identifying overall pluripotent and neural lineage-specific genes, the collective set of NPs (Cyt-NP, HUES6-NP, and hCNS-SCns) was compared to the collective set of hESCs (Cyt-ES and HUES6-ES).Oct4 and Nanog, which are important in maintaining the pluripotent state of embryonic stem cells (ESCs), were highly expressed in hESCs but were significantly lower in NPs (Figure 1B). RT-PCR of Oct4 and Nanog mRNA levels accurately reflected the signal estimates from the array (Figure 1C). Interestingly, Nestin was not significantly higher in NPs as compared to the hESC from the gene-level estimates (p-value 0.065), which was further confirmed by RT-PCR (Figure 1C). Notch was recently identified to be important in promoting the neural lineage entry in mouse ESCs [57] and was shown to regulate stem cell proliferation in somatic mouse and hESC [58]. Gene-level signal estimates suggested that Notch was significantly higher in hCNS-SCns relative to hESCs, but levels of Notch were not significantly different in the derived NPs compared to hESCs. Delta/Notch-like EGF-related receptor (DNER), a neuron-specific transmembrane protein, was recently shown to bind to Notch at cell–cell contacts and activates Notch signaling in vitro [59]. RT-PCR validation of DNER confirmed array-derived signal estimates, indicating an enrichment of DNER in NPs relative to hESCs (Figure 1C). Finally, Sox1, a HMG-box protein related to SRY, was shown to be one of the earliest transcription factors expressed in cells committed to the neural fate [60]. Here the gene-level estimates indicated that Sox1 was expressed significantly higher in NPs relative to hESCs (p-value 0.00013, Figure 1B), a finding that was confirmed by RT-PCR (Figure 1C).From these examples, we concluded that RT-PCR validation correlated well with gene-level estimates from the exon array. In addition, the derived NPs had decreased levels of pluripotent markers Oct4 and Nanog but had levels of Sox1 that were comparable to hCNS-SCns. This finding confirmed that the derived NPs were committed to a neural fate and validated the use of hCNS-SCns as a benchmark for NPs.Next we asked whether the highest enriched genes in hESCs relative to NPs reflected our existing knowledge in the literature. Using the above-mentioned groupings of hESCs (Cyt-ES, HUES6-ES) and NPs (Cyt-NP, HUES6-NP, and hCNS-SCns), 2,945 genes were enriched in hESCs relative to NPs; and 552 genes were enriched in the NPs relative to hESCs, at a p-value significance cutoff of 0.01 (correcting for multiple hypothesis testing using the Benjamini-Hochberg method). The 15 most enriched genes in hESCs included genes such as teratocarcinoma-derived growth factor 1 (TDGF1/cripto; p-value < 10−12), zinc finger protein 42 (Zfp42/Rex1; p-value < 10−12), Oct4 (p-value < 10−12), Nanog (p-value < 10−10), lin-28 homolog (p-value < 10−10), cadherin 1 preprotein (p-value < 10−10), claudin 6 (p-value < 10−9), ephrin receptor EphA1 (p-value < 10−9), and erbB3 (p-value < 10−9). TDGF1/cripto was first shown to stimulate DNA synthesis and cell proliferation of both undifferentiated and differentiated embryonic carcinoma cells [61] and was later shown to be important for cardiomyocyte formation from mouse ESC [62]. Oct4, reviewed in [63], and Nanog [64] are crucial for the pluripotency of hESCs. Recently, knockdown of Zfp42/Rex-1 in mouse ESC caused the cells to differentiate [65]. Our gene-level exon array analysis confirmed that the hESCs and NPs were molecularly distinct.To reveal global functional differences between the enriched genes in hESCs or NPs, the enriched genes were subjected to a Gene Ontology (GO, http://www.geneontology.org) analysis as described previously [55]. Enriched genes in hESCs were more likely to be in molecular function categories, such as “RNA binding” (p-value < 10−12), “structural constituent of ribosome” (p-value < 10−51), “exonuclease activity” (p-value < 10−6), “cytochrome-c oxidase activity” (p-value < 10−5), and “ATP binding” (p-value < 10−6), and in biological processes involved with “tRNA processing” (p-value < 10−6) and “protein biosynthesis” (p-value < 10−48), consistent with our knowledge of hESCs as a rapidly proliferating population of cells (Figure 2A). Similar analysis of the enriched genes in NPs revealed an overrepresentation in molecular functional categories, such as “calcium ion binding” (p-value < 10−8) and “structural molecule activity” (p-value < 10−5), and in biological processes involved with “neurogenesis” (p-value < 10−38), “cell adhesion” (p-value < 10−13), “cell motility” (p-value < 10−4), “development” (p-value < 10−6), “neuropeptide signaling pathway” (p-value < 10−4), and “endocytosis” (p-value < 10−4) (Figure 2B). Considering that these were the only categories that were significantly enriched out of more than 18,000 GO terms, and that randomly selected sets of similar numbers of genes did not reveal statistical differences in GO categories, our results confirmed that the global molecular profiles derived from exon array analysis were consistent with known differences between hESCs and NPs.Figure 2Gene Ontology AnalysisDifferential gene expression of hESCs (Cyt-ES and HUES6-ES) and NPs (Cyt-NP, HUES6-NP, and hCNS-SCns) was computed from gene-level signal estimates. Statistical significance for differential gene expression was determined by using t-statistics with Benjamini-Hochberg correction for false discovery rate (p < 0.01). Gene Ontology “molecular function,” “cellular component,” and “biological process” categories, which differed significantly (p < 0.05) in the representation between significantly enriched genes (black bars) and all other genes (white bars), were shown. Statistical significance for GO analysis was assessed by using χ2 statistics with Bonferroni correction for multiple hypothesis testing. GO categories are ordered from top to bottom in order of decreasingly significant bias toward enriched genes.(A) GO analysis of enriched genes in hESCs.(B) GO analysis of enriched genes in NPs.To summarize, firstly immunohistochemical and RT-PCR evidence validated that the cells exhibited expected characteristics; secondly, stage-specific marker gene differences by RT-PCR were reflected accurately by gene-level estimates from the exon arrays; thirdly, the hESC-enriched genes were coherent with known genes that controlled pluripotency and self-renewal; and lastly, the global functional profiles exemplified expected biological differences between hESC and NP cells. Description of the Regression-Based Exon Array ProtocolConvinced that the signal estimates from the exon arrays reflected expected molecular and biological differences between hESCs and NPs, we sought to identify AS events. We compared Cyt-ES to hCNS-SCns to illustrate our approach. First we normalized the data and generated signal estimates with Robust Multichip Analysis (RMA) and estimated the probability that each probeset was detected above background (DABG) using publicly available Affymetrix Power Tools (APT). We analyzed probesets that (i) comprised three or more individual probes; (ii) were localized within the exons of our gene models with evidence from at least three sources (mRNA, EST, or full-length cDNA); and (iii) were detected above background in at least one of the cell lines. In total, 17,430 gene models were represented by probesets that satisfied these criteria.Next we asked whether the probeset expression within each gene model was positively correlated for any two cell lines. To do this we calculated the Pearson correlation coefficient between the vectors of median signal estimates across replicates in Cyt-ES versus hCNS-SCns. The vast majority of genes (>80%) was found to have probeset-level Pearson correlation coefficients of greater than 0.8 (Figure 3A). Next we randomly permuted the association between the median signal estimates and the probesets for each gene in hESCs (or hCNS-SCns) and observed that the distribution of Pearson correlation coefficients for the permuted sets was centered at zero, as expected (Figure 3A). This indicated that the signal estimates for probesets between hESCs and hCNS-SCns were highly correlated and suggested that a scatter plot of probeset signal estimates between hESCs and hCNS-SCns would reveal a linear relationship for the majority of genes. We hypothesized that a linear regression to determine if some probesets behaved unexpectedly in one cell type compared to the other might be a reasonable approach to identify AS exons.Figure 3Description of the REAP Algorithm Comparing Exon Array Signal Estimates from hCNS-SCns and Cyt-ES(A) Histogram of Pearson correlation coefficients computed from median signal estimates for probesets between Cyt-ES versus hCNS-SCns for genes (blue bars). Genes were required to have more than five probesets localized within the exons in the gene. Red bars represented Pearson correlation coefficients computed from exons with shuffled signal estimates.(B) Each probeset contained probeset-level estimates from three replicates each, (a, b, c) in Cyt-ES and (d, e, f) in hCNS-SCns. The five points summarizing the log2 probeset-level estimates are indicated by black filled circles.(C) Each probeset was summarized by five points. Scatter plots of signal estimates for probesets that were present in at least one cell type (Cyt-ES or hCNS-SCns) for the EHBP1 gene. Probesets were considered present if the DABG p-value was <0.05 for all three replicates in the cell type. A regression line derived from robust linear regression with MM estimation is indicated. Points above the line represent probesets within exons that were enriched in Cyt-ES and points below represent exons that were enriched in hCNS-SCns. Points close to the regression line are not significantly different in Cyt-ES versus hCNS-SCns. Boxed points represented the five-point summary of a probeset that was significantly enriched in Cyt-ES but was skipped in hCNS-SCns.(D) Histogram of studentized residuals for points from the scatter plot in (C) in EHBP1.(E) The histogram of studentized residuals for all points for all analyzed probesets (100 bins).(F) The scatter plot of studentized residuals generated from comparing Cyt-ES versus hCNS-SCns and hCNS-SCns versus Cyt-ES of 5,000 randomly chosen probesets.Here, a possible representation of the data was explored. If we had N replicates in one condition and M replicates in the other, we could consider N*M points if we analyzed every possible pairing. For instance, three replicate signal estimates for every probeset per cell line, such as signal estimates a, b, and c in hESCs and d, e, and f in hCNS-SCns, would translate to pairing every signal (d,a), (d,b), (d,c) … (f,a), (f,b), (f,c) for linear regression (Figure 3B). Instead, pairing the signal estimates of all replicates in one condition to the median of the other would only require N + M − 1 points and would capture the variation of the signal estimates of each probeset. For example, we considered (d,b), (e,a), (e,b), (e,c), and (f,b) points where b and d were the median intensities for the replicates in Cyt-ES and hCNS-SCns, respectively (Figure 3B). A scatter plot of all probesets of the EHBP1 (EH domain binding protein, RefSeq identifier NM_015252) is shown in Figure 3C in the format described. Each probeset was represented by 5 points of log-transformed (base 2) values; and each point on the scatter plot reflected the extent of inclusion of an exon in hESCs and in hCNS-SCns (Figure 3C).A classical linear regression model could be proposed to fit the response variable yij, the log2 expression of probeset i in cell-type j (for example, j is Cyt-ESC) to explanatory variables xik, and the log2 expression of probeset i in cell type k (for example, k is hCNS-SCns). However, classical linear regression by least-squares estimation is biased because the least squares predictions are strongly influenced by the outliers, leading to completely incorrect regression line estimates, masking of the outliers, and incorrect predictions of outliers. Therefore, we applied M-estimation robust regression to estimate the line, which is less sensitive to outliers. Fitting was performed using an iterated, re-weighted least squares analysis. Our assumption was that most of the points were “correct,” i.e., that most of the exons were constitutively spliced. Thus, robust regression would find the line that was least dependent on outliers, which would be potential AS exons. This assumption was substantiated by our observation that, using publicly available ESTs and mRNAs, a minority of human exons (7%) have evidence for exon-skipping, the most common form of AS. Using robust regression, the regression line for Cyt-ESC versus hCNS-SCns in the EHBP1 gene is illustrated in Figure 3C. The boxed points belonged to a probeset that was enriched in hESCs but depleted in hCNS-SCns, which was suspected to be due to AS. The difference between the actual and regression-based predicted value, normalized by the estimate of its standard deviation, is called the studentized residuals. Studentized residuals were computed for all probeset pairs in EHBP1, and the histogram depicting their distribution is illustrated in Figure 3D. As expected, the mean of the distribution was close to zero, and the distribution was approximated by a t-distribution with n-p-1 degrees of freedom, where n was the number of points on the scatter plot, and the number of parameters p was 2. The boxed points had studentized residuals of 1.829, 3.104, 2.634, 3.012, and 2.125 with p-values of 0.034, 0.00119, 0.00477, 0.00158, and 0.01780, respectively, computed based on the t-distribution (Figure 3C). At a stringent p-value cutoff of 0.01, four of the five studentized residuals were designated as significant “outliers,” indicating that the probeset was “unusual.” RT-PCR confirmed that the exon, represented by the probeset, was indeed differentially included in hESCs and skipped in hCNS-SCns (Figure 7B). Applying this approach to all gene models revealed that, as expected, the majority of studentized residuals are centered at zero (Figure 3E). Thus far in the example, our analysis was based on regression of hESCs (y-axis) versus hCNS-SCns (x-axis) (Figure 3B–3D). However, robust regression as described was not symmetrical, i.e., parameter estimation of y as a function of x was not the same as that of x as a function of y. The negative slope revealed that probesets enriched in hESCs versus hCNS-SCns (positive valued), were expectedly depleted when hCNS-SCns was compared to hESCs (negative valued; Figure 3F). As our method for predicting candidate alternative exons was based on identification of outliers using robust regression, we named the method REAP. Identification and Removal of False PositivesIn the process of experimentally validating our predictions, we encountered three main sources of false positives (FP) from robust regression. First, we identified genes with probeset signal estimates that were poorly correlated and were not amenable to our method. As an example, the median probeset signal estimates in hESCs and hCNS-SCns of the FIP1L1 gene (gene identifiers BC011543, AL136910) had a Pearson correlation coefficient of 0.38, and the distribution of points was not amenable to robust regression (Figure 4A). To avoid inappropriate application of REAP and generating false predictions, we empirically determined that a gene had to have a Pearson correlation coefficient cutoff of 0.6 before being amenable to REAP analysis. Next, we managed two additional sources of FPs, namely “high-leverage” and “high-influence” points, which we were able to identify by computing the following metrics. For every point, we computed (i) the studentized residual (as described above), (ii) the influence, and (iii) the leverage (see Materials and Methods for more details). Leverage assessed how far away a value of the independent variable was from the mean value; the farther away the observation the more leverage it had. The influence of a point was related to its covariance ratio: a covariance ratio larger (or smaller) than 1 implied that the point was closer (or farther) than was typical to the regression line, so removing it would hurt (or help) the accuracy of the line and would increase (or decrease) the error term variance. Influence was computed as the absolute difference between the covariance ratio and unity. To illustrate further, a point was classified as an “outlier” if it had a large studentized residual (p < 0.01) and low leverage (boxed point “a”); as a “high-leverage” point if it had a low studentized residual and high leverage (boxed point “b”); and as a “high-influence” point if it had a high studentized residual, high leverage, and high influence (boxed point “c”; Figure 4B). Points that resembled boxed point “a” were designated as potential AS events. For example, four of the five boxed points in Figure 3C were “outliers,” and RT-PCR validation indicated that the exon represented by the probeset was indeed skipped in hCNS-SCns (EHBP1, Figure 7B). Points that were “high-leverage,” such as the five points in the CLCN2 gene, were experimentally verified to be a FP (Figure 4C; unpublished data). Points that were “high-influence,” such as the four of five boxed points in the ABCA3 gene were also experimentally verified to be a FP (Figure 4D; unpublished data). In conclusion, in order to reduce the FP rate, all points were evaluated according to the metrics described, and points that were significant “outliers” were considered putative AS events.Figure 4Sources of False Positives(A) Scatter plot of points for the FIP1L1 gene and the line representing the robust regression estimate.(B) Boxed point “a” represents a significant “outlier” (with a significantly different studentized residual and low leverage). Boxed point “b” represents a “high leverage” point (low studentized residual and a high leverage). Boxed point “c” represents a “high influence” point (high studentized residual, high leverage, and high influence).(C) Scatter plot of points for the CLCN2 gene. Boxed points represent “high leverage” points.(D) Scatter plot of points for the ABCA3 gene. Boxed points represent “high influence” points. Global Identification and Characterization of REAP[+] ExonsREAP was applied to identify AS events in NPs compared to hESCs: Cyt-NP versus Cyt-ES; HUES6-NP versus HUES6-ES; hCNS-SCns versus Cyt-ES, and hCNS-SCns versus HUES6-ES. After removing potential FPs, 11,348 genes containing 158,657 probesets were scored by REAP.As described above, for each pair of cell lines compared, each probset was represented by five points, where each point was defined a significant outlier if it had a high residual (p < 0.01), low influence, and high leverage. Points per probeset should be correlated; in other words, if one point was a significant outlier, the other points were expected to be outliers as well. To ensure that this was the case, we counted the number of probesets with N significant outliers, where N was varied from 0 to 5. Next, the identity of the probesets and points derived from them were exchanged with other probesets, keeping constant the total number of points that were considered significant outliers. At N = 0, we observed approximately equal numbers of probesets in the actual versus shuffled controls. In contrast, we observed that there were 1.5 times more probesets with N = 2 significant outliers relative to shuffled controls; 12–31 times more probesets with N = 3; and 17–612 times more probesets that had N = 4 significant outliers (Figure 5A; see Table S1). For example, in hCNS-SCns compared to Cyt-ES, approximately 0.39% (490 of 124,604) of probesets had three significant outliers and 0.25% (308 probesets) had four significant outliers, relative to 0.02% and 0% of shuffled controls, respectively.Figure 5Correlation between “Outliers”(A) The number of probesets with N significant “outliers” was determined for hCNS-SCns versus Cyt-ES, hCNS-SCns versus HUES6-ES, Cyt-NPs versus Cyt-ES, and HUES6-NPs versus HUES6-ES (N = 0, 1, 2, 3, 4, 5). For comparison, points to probeset relationships were randomly permuted, retaining the same number of “outliers.” Vertical bars represent the ratio between the number of actual points and the randomly permutated sets.(B) Similar to (A), except points were counted as “outliers” only if they were “outliers” in both hCNS-SCns versus Cyt-ES and hCNS-SCns versus HUES6-ES (combined hCNS-SCns versus hESC; blue bars); in both HUES6-NP versus HUES6-ES and Cyt-NP versus Cyt-ES (combined derived NP versus hESC; red bars); and in all four comparisons (combined NP versus hESC; yellow bar).Next we asked whether the overlap between related comparisons was higher than expected. Comparing the significant probesets between hCNS-SCns versus Cyt-ES and hCNS-SCns versus HUES6-ES revealed 672 significant probesets (N ≥ 2), whereas if we shuffled the associations between probeset identity and significant outliers, only four significant probesets (N ≥ 2) were identified—a 168-fold enrichment (Figure 5B, Table S1). A total of 236 significant probesets overlapped when we compared the derived NPs to hESCs (Cyt-NP versus Cyt-ES and HUES6-NP versus HUES6-ES), relative to seven significant probesets (34-fold enrichment).At a cutoff of two significant outliers, 1,737 probesets contained in internal exons were defined as positive REAP predictions (hereafter called REAP[+]) exons—candidate AS events that distinguished NP from hESC. Surprisingly, we observed that the majority of REAP[+] exons were specific to the pair of hESC and NP that was compared, likely reflecting differences in genetic origins and/or culturing and differentiation conditions of the cell lines: 614 REAP[+] events were unique to hCNS-SCns versus HUE6-ES; 220 were unique to hCNS-SCns versus Cyt-ES; 439 were unique to HUES6-NP versus HUES6-ES; and 250 were unique to Cyt-NP versus Cyt-ES. The shared events between pairs of comparisons made up a minority of the total number identified: 102 REAP[+] events were found to be in common between hCNS-SCns versus Cyt-ES and hCNS-SCns versus HUES6-ES; 48 between hCNS-SCns versus HUES6-ES and HUES6-NP versus HUES6-ES; and only 17 between hCNS-SCns versus Cyt-ES and Cyt-NP versus Cyt-ES (Table S2). Comparison of REAP to EST-Based Method and ACEScanTraditionally, AS exons were discovered by using EST alignments to genomic loci, and also more recently by computational algorithms that used sequence information extracted from multiple genomes. Here, we compared REAP predictions to both approaches. In the first comparison, publicly available ESTs and mRNA transcripts were aligned to the human genome sequence. 13,934 exons with evidence for exon-skipping and/or inclusion (EST-SE for EST-verified skipped exons) were generated, comprising ∼7% of all internal exons. First we analyzed Cyt-ES versus hCNS-SCns. If we required that none of the points per probeset (exon) was significant, 6% (4,402 of 71,731) of exons (after probeset mapping) had evidence for EST-SE (Figure 6A). Shuffling the mapping between these probesets and exons resulted in 8% (5,777 of 71,731) of exons with evidence for EST-SE (Figure 6A). These percentages were not significantly different from the 7% of exons with EST evidence for AS observed from using all exons. By raising the requirement that probesets had to contain at least one significant point to five significant points, the percentage of EST-SE increased dramatically from 11% (531 of 4,898 exons) to 26% (33 of 126). In comparison, the shuffled probesets at the same requirements remained at ∼8%, rising slightly to 11% at five points, due to small sample sizes. Similar trends were observed with hCNS-SCns versus HUES6-ES and the derived NPs versus hESCs (Figure 6A). Therefore, we concluded that REAP[+] exons were enriched for AS events independently identified by a transcript-based approach.Figure 6Comparison of REAP Predictions for hCNS-SCns versus Cyt-hES, hCNS-SCns versus HUES6-ES, Cyt-NP versus Cyt-ES, and HUES6-NPs versus HUES6-ES with Alternative Exons Identified by an EST-Based Method and ACEScan(A) Black-filled squares represented the fraction of exons containing probesets with N significant points that had EST evidence for exon inclusion or exclusion (N = 0, 1, 2, 3, 4 and 5). White-filled triangles represented similarly computed fractions with permuted probeset to exon mappings.(B) Black-filled squares represented the fraction of exons containing probesets with N significant points that had ACEScan positive scores, indicative of evolutionarily conserved alternative exons. White-filled triangles represented similarly computed fractions with permuted probeset to exon mappings.Next, we compared REAP predictions to a computational approach of identifying exons with AS conserved in human and mouse, ACEScan [55]. ACEScan receives as input orthologous human–mouse exon pairs and flanking intronic regions and computes sequence features and integrates the features into a machine-learning algorithm to assign a real-valued score to the exon. A positive score indicated a higher likelihood of being AS in both human and mouse. ACEScan was updated in the following ways. Firstly, instead of relying on orthology information by Ensembl, and then aligning flanking introns in “orthologous” exons, conserved exonic and intronic regions in human and mouse from genome-wide multiple alignments were extracted. Secondly, whereas in our previous analysis exons from the longest transcript in Ensembl were utilized, now we collapsed all the transcripts available at the UCSC genome browser and analyzed all exons in the entire gene loci. ACEScan was utilized to assign ACEScan scores to all ∼162,000 internal exons in our genes. Exons annotated as first or last exons in Refseq mRNAs were excluded from our analysis, resulting in 4,487 positive-scoring exons, 2-fold more exons than originally published.Here we repeated our analysis with exons with positive ACEScan scores (ACE[+]) instead of EST-SEs. If we required that none of the points per probeset (exon) was significant, 2% (1,645 of 71,731) of exons (after probeset mapping) were ACE[+] (Figure 6B). Shuffling the mapping between these probesets and exons resulted in 3% (2,044 of 71,731) of exons being ACE[+] (Figure 6B). These percentages were not significantly different from the 2.7% observed from all exons (4,487 of the 162,000 exons that were scored by ACEScan). By raising the requirement that probesets had to contain five significant points, the percentage of ACE[+] exons increased from 4% to 11%. However, the sample sizes were small. In comparison, the shuffled probesets at the same requirements remained at ∼4%. Similar overall trends were observed with hCNS-SCns versus HUES6-ES and the derived NPs versus hESCs (Figure 6B). In total, 7.5% (131 of 1,737) of REAP [+] exons were designated as ACEScan[+] compared to 2.4% (2,328 of 97,437) of REAP[−] exons. This result suggested that a small but significantly enriched fraction of AS events in hESCs versus NPs was likely to be evolutionarily conserved in human and mouse. In conclusion, our results suggested that REAP predictions were congruent with predictions from two independent, orthogonal methods. Experimental Validation of Alternative ExonsThe sensitivity and specificity of REAP in the identification of REAP[+] exons was tested by RT-PCR. To validate REAP[+] alternative exons, RT-PCR primers were designed in the flanking exons to amplify both isoforms. To be a positively validated candidate, the PCR products on a gel had to satisfy all of the following criteria: (i) at least one isoform with the expected size must be visible in each cell type; (ii) the relative abundance of the two isoforms must be altered between two cell types and the direction of change have to be consistent with the REAP studentized residuals: in our study positive residuals implied inclusion in hESCs and skipping in NPs, and negative residuals implied inclusion in NP and skipping in hESCs; and (iii) the results were replicable in at least two experiments.For simplicity of design, we tested candidates predicted from Cyt-ES versus hCNS-SCns. Fifteen REAP[+] exons with at least two significant outliers (out of five) were randomly chosen as predicted alternative events and thirty-five exons with less than two significant outliers were randomly chosen as constitutive events (Table S3). Nine of the fifteen exons (60%) were validated as AS events by our criteria. The sensitivity and specificity of the algorithm at the cutoff of two is 69% and 77%. Increasing the cutoff to three increased the specificity to 85%, with a slight decrease in sensitivity to 67% (Figure 7A). The patterns of AS in hESCs were similar in both Cyt-ES and HUES6-ES for all AS events validated, but the NPs (Cyt-NP, HUES6-NP, and hCNS-SCns) had more varied AS. The pattern of AS in the REAP[+] exons in the SLK (serine/threonine kinase 2) and POT1 (protection of telomeres 1) genes showed remarkable agreement within derived NPs and hCNS-SCns (Figure 7B). The AS exon in SLK was observed to be included in hESCs and completely excluded in NPs; the AS exon in the POT1 gene was included more in hESCs and a smaller isoform persisted in NPs. The AS patterns of the other verified REAP[+] exons were consistently similar in hESCs but were more varied in the NP. Interestingly, the patterns of AS in the derived NPs (Cyt-NP and HUES6-NP) were not always identical to those of hCNS-SCns. For example, the AS exon in the EHBP1 (EH domain binding protein 1) gene was included in hESCs but skipped in hCNS-SCns, and both isoforms were present in the derived NPs (Figure 7B). As another example, the AS exon in the SORBS1 (sorbin and SH3 domain containing 1) gene was skipped in hESCs and included in hCNS-SCns, but exhibited an intermediate pattern in the derived NPs. However, in some cases, the AS patterns in the derived NPs were different from both hESCs and hCNS-SCns (such as in the AS exon in UNC84A, SIRT1, and MLLT10).Figure 7RT-PCR Validation of REAP-Predicted Alternative Exons(A) Probesets (exons) were considered REAP[+] candidates if they contained at least N = 2 (white bars), 3 (gray bars), or 4 (black bars) significant outliers. True positive (TP), true negative (TN), false positive (FP), and false negative (FN) rates were calculated from RT-PCR-validated REAP[+] exons at the different cutoffs (N = 2, 3, 4).(B) Nine RT-PCR validated REAP[+] AS events in hESCs (Cyt-ES and HUES6-ES), derived NPs (Cyt-NP and HUES6-NP), and hCNS-SCns. Arrows indicate the larger (exon-included) isoforms and smaller (exon-skipped) isoforms.(C) RT-PCR of REAP[+] alternative exons from EHBP1, SLK, and RAI14 across a panel of human tissues. Arrows indicate the larger (exon-included) isoforms and smaller (exon-skipped) isoforms.First, given three independent samples each from two conditions, we concluded that REAP was able to identify AS events with high specificity but with moderate sensitivity. Second, AS events in hESCs were more similar, whereas the AS events in derived NPs were consistent with or intermediate to the benchmark hCNS-SCns, likely reflecting differences in the cell lines and/or differentiation protocols. In addition, we tested the AS patterns of REAP[+] exons from EHBP1, SLK, and RAI14 in a panel of differentiated human tissues (Figure 7C). The REAP[+] alternative exon in the RAI14 (retinoic acid induced 14) gene was observed to have the same AS pattern in NPs as in frontal and temporal cortex and in several other, non-brain adult tissues, such as heart and spleen. The AS pattern of the REAP[+] exon in the SLK gene in NPs was similar to most differentiated tissues; however, the relatively strong inclusion of the exon in hESCs was unique. Even in esophagus, kidney, liver, and prostate, both isoforms were present. The relative ratio of the exon-included to exon-skipped isoforms in SLK likely represents an ESC-specific AS signature. The alternative exon in the EHBP1 gene was unusual. The exon was included in hESCs but also in frontal cortex and temporal cortex, a finding that was unexpected given the exclusion of the exon in hCNS-SCns (Figure 7C). The AS pattern in hCNS-SCns may represent a transient, early neuronal molecular change. Functional and Expression Characteristics of REAP[+] GenesIn total, 1,500 genes were identified that contained 1,737 REAP[+] exons, 68% of which lacked prior transcript (EST/cDNA) evidence for AS. To determine whether genes that contained REAP[+] exons, which we refer to as REAP[+] genes, are biased toward particular biological activities, REAP[+] genes were compared to a set of REAP analyzed genes not found to have REAP[+] exons (REAP[−] genes). A Gene Ontology analysis revealed that REAP[+] genes are enriched for GO molecular function categories “ATP binding,” “helicase activity,” “protein serine/theronine kinase activity,” “small GTPase regulatory/interacting protein activity,” and “thyroid hormone receptor binding” (Table 1). In terms of GO biological process categories, REAP[+] genes were more frequently involved in “ubiquitin cycle.” Similar results were obtained when we compared REAP[+] genes to all human genes that did not contain REAP[+] exons (Table 1) [55].Table 1Significantly Enriched Gene Ontology Terms in REAP[+] Genes (Cutoff of Two Significant “Outliers” per Probeset)Next we asked if REAP[+] genes are differentially expressed in hESCs compared to NPs and vice versa. For this analysis, the t-statistics computed above measuring the enrichment of a gene in hESCs relative to NPs was utilized for only REAP-analyzed genes. At a defined absolute-valued cutoff, genes were divided into three categories: “enriched in hESCs,” “enriched in NP,” or “unchanged” (Figure 8A). Increasing the t-statistic cutoff from one to five, the fraction of REAP[+] genes relative to REAP-analyzed genes remained constant in the “unchanged” categories (Figure 8B). However, the fraction of REAP[+] exons decreased significantly in “enriched in hESCs” and “enriched in NPs” categories. If we increased the cutoffs on genes that were randomly assigned as REAP[+] and REAP[−], controlling for the same number of genes in each category, we observed that the fraction of REAP[+] exons remained unchanged for all three categories (Figure 8C). To illustrate, at a cutoff of five, 10% (29 of 267) of enriched NP genes were REAP[+] genes and 8.8% (102 of 1,162) of enriched hESC genes were REAP[+], significantly different (p < 0.000005) from the random control where ∼14% of enriched NP and enriched hESC genes were REAP[+]. At a cutoff of five, 14% (1,368 of 9,636) of genes that were expressed at similar levels between hESCs and NPs were REAP[+]. Our results suggested that a strategy of focusing on differentially expressed genes would miss at least 14% of transcriptionally unchanged genes that may nevertheless have functional AS differences between hESCs and NPs.Figure 8Analysis of REAP[+] Genes Relative to Transcriptional Differences(A) Histogam of t-statistics computed from gene-level signal estimates measuring the enrichment of genes in hESC and in NP. Genes on the right of the vertical line at 5 were designated enriched in hESC and genes on the left of the vertical line at −5 were designated enriched in NP; genes in between −5 and 5 were designated as “unchanged” or expressed similarly in hESC and NP.(B) Vertical bars representing the percentage of REAP[+] genes out of all genes in the different classifications (dashed bar: “enriched in hESC”; black filled bar: “unchanged”; white filled bar: “enriched in NP”), at different cutoffs of 1 to 5.(C) Set of genes where REAP[+] designation was randomly chosen. Similar representation as in (B). Conserved Intronic Splicing Regulatory Elements Proximal to REAP[+] hESC and NP ExonsMany, if not most, alternative exons undergo cell type–specific regulation by the binding of trans-factors to splicing regulatory cis-elements located proximal to or within the exons. As many tissue-specific splicing cis-regulatory elements were localized in intronic regions of AS exons, we focused on the identification of intronic splicing regulatory elements (ISREs) proximal to REAP[+] exons. In addition, we wanted to identify both common and cell type–specific ISREs. Three sets of exons were generated: (i) REAP[+] exons that were predicted to be included in NPs and skipped in hESCs (REAP[+]NP); (ii) REAP[+] exons that were predicted to be included in hESCs and skipped in NPs (REAP[+]hESC); and (iii) all REAP[−] exons. Regions of 400 base pairs flanking the exons were targeted for search. Initially, 5-mers that were significantly enriched between the upstream and downstream intronic regions of REAP[+]NP and REAP[+]ES relative to REAP[−] exons were enumerated. We were not able to identify 5-mers that were statistically significantly different.Next, we focused on splicing signals that were conserved across mammalian genomes as a way of enhancing the signal of detecting functional splicing regulatory sequences [66]. Exons that were orthologous across human, dog, rat, and mouse were obtained and the flanking intronic regions were aligned (400 bases upstream and downstream separately; Figure 9A). We enumerated k-mers that were perfectly conserved across all four genomes in the upstream (and downstream) intronic regions. Each conserved k-mer was attributed a χ score representing its enrichment in a set of exons relative to another set of exons. The higher the score, the more frequent the conserved k-mer was in the first set relative to the second set. As a negative control, the associations between REAP scores and exons were shuffled. The enrichment scores for all downstream intronic 5-mers for shuffled REAP[+]NP versus set REAP[−] exons (x-axis), and for shuffled REAP[+]ES exons versus REAP[−] exons (y-axis) were displayed (Figure 9B). At a χ cutoff of three, which corresponded to a p-value of 0.0015, the majority of 5-mers were not significantly enriched in either shuffled set. Confident that no association of k-mers with shuffled REAP exons were found; we repeated the analyses for upstream and downstream intronic 5-mers for the original unshuffled sets. We identified 68 conserved 5-mers enriched upstream of REAP[+]NP exons; and 34 5-mers enriched upstream of REAP[+]ES exons (Figure 9C; Table S4). Of the 5-mers that were significantly enriched upstream of REAP[+]NP exons, we identified a U-rich motif (UUUUU), a GU-rich motif (GUGUG), and a CU-rich motif (CCUCU, CUCUC, UCUCU, GCUCU). It is known that the heterogeneous ribonucleoprotein C (hnRNP C) binding site obtained by SELEX is five “U”s [67]. GU-rich sequences in flanking intronic regions were shown to bind to splicing factor ETR-3 to regulate AS [68]. CU-rich sequences were shown to bind the splicing factor PTB [69]. Of the 5-mers enriched upstream of REAP[+]ES exons, we observed CUAAC, which resembled the splicing branch-signal. Of the six 5-mers that were enriched upstream of both REAP[+]NP and REAP[+]ES exons, we identified GCAUG, which was previously shown to be an intronic splicing cis-element for the mammalian fibronectin and calcitonin/CGRP genes [70–72]. More recently, both mammalian Fox1 and 2 have been demonstrated to regulate alternatively spliced exons via UGCAUG binding sites in neighboring introns in neuronal cell cultures [73].Figure 9Conserved Intronic cis-Elements Enriched Proximal to REAP[+] Alternative Exons(A) Schematic describing the enumeration of intronic elements across 400 bases of flanking mammalian introns (human, dog, rat, and mouse). Red and green horizontal bars represent conserved intronic elements and nonconserved elements, respectively. Internal exons were divided into REAP[+]NP, REAP[+]ES, and REAP[−] exons. The χ statistic was computed to represent the enrichment of conserved elements in intronic regions flanking REAP[+]NP versus REAP[−] exons (x-axis), and REAP[+]ES versus REAP[−] exons (y-axis). The sign represented the direction of change, i.e., positive if enriched in introns flanking REAP[+] versus REAP[−] exon. Each conserved 5-mer was associated with two numbers: the enrichment in introns proximal to REAP[+]NP versus REAP[−] exons (x-axis), and REAP[+]ES versus REAP[−] exons (y-axis).(B) Downstream intronic regions, where the association between REAP[+] designation and the exons was shuffled.(C) Upstream intronic regions. Circled 5-mers in the upper right quadrant represent conserved 5-mers enriched in the upstream intronic regions of REAP[+]NP and REAP[+]ES exons.(D) Downstream intronic regions. Circled 5-mers in the upper right quadrant represent conserved 5-mers enriched in the downstream intronic regions of REAP[+]NP and REAP[+]ES exons.Eighteen conserved 5-mers were significantly enriched in the downstream introns of REAP[+]ES exons; and 76 5-mers were enriched downstream of REAP[+]NP exons (Table S4, Figure 9D). We identified a motif CUCAU resembling the Nova binding site YCAY [74], and a G-rich motif (AGGGG, GGGGA, GGGGC, GGGGG, GGGGU) enriched in the introns downstream of REAP[+]ES exons. G-rich motifs had previously been shown to be part of a bipartite signal that silences AS exons [75]. Of the five 5-mers that were enriched downstream of both REAP[+]NP and REAP[+]ES exons, GCAUG and a U-rich motif (UUUUU) were identified. We concluded that potential ISREs were enriched proximal to a subset of REAP[+] exons; in particular, the Fox1/2 binding site GCUAG may play a regulatory role in controlling AS events in hESCs and NPs.
The ability of ESCs to generate all three embryonic germ layers has raised the exciting possibility that hESCs may become an unlimited source of cells for transplantation therapies involving organs or tissues such as the liver, pancreas, blood, and nervous system, and become tools to explore the molecular mechanisms of human development. Despite such interests, relatively little is understood about the molecular mechanisms defining their pluripotency and the molecular changes important for hESCs to differentiate into specific cell types. To understand these events, protocols are still being developed to differentiate ESCs into a variety of lineages. Of particular biomedical interest is in the capacity of hESCs to be differentiated into a self-renewing population of NPs that can be then further coaxed into a variety of neuronal subtypes, such as dopaminergic neurons that are important in the treatment of Parkinson disease or cholinergic neurons for ALS (amyotrophic lateral sclerosis). While many microarray studies have explored molecular differences between hESCs and derived NPs, most, if not all, have focused on transcriptional changes. These studies have largely ignored intermediate RNA processing events prior to and during translation. In recent years, AS has gained momentum as being important in development, apoptosis, and cancer. REAP, a regression-based method for analyzing exon array data was introduced, and was applied to discover AS events in hESCs, their derived NPs, and in hCNS-SCns. REAP was based on the assumptions that most exons in the gene of interest and in the genome are constitutively spliced and that outliers in a linear pairwise comparison of the signal estimates for probesets in a gene could be detected using a robust regression-based approach. REAP predictions were found to correlate well with transcript-based methods for identifying alternative exons, which interestingly suggested that current databases of transcript information, albeit not specifically enriched for hESC or NPs, in aggregate are nevertheless predictive of AS events in hESC and NP. In addition, REAP[+] exons were also enriched for ACEScan-predicted evolutionarily conserved exons [55]. As ACEScan utilized a different set of information from REAP, the agreement between both algorithms served to further validate the predicted alternative exons. Additional studies in mouse ESCs and neural derivatives will be necessary to determine if these AS events are indeed preserved in these analogous and orthologous cell types. Our finding that only a minority of AS events was common between various hESC to NP comparisons is intriguing. A possible explanation is that the cell lines were not only genetically different, but were also exposed to different isolation and culture conditions. In addition, the different differentiation protocols established as optimal for generating Nestin and Sox1 positive neural precursors may lead to vastly different molecular changes. It is likely that post-transcriptional changes such as AS may be more variable despite the cells being at acknowledged “end-points” defined by a limited set of immunohistochemical markers. Our results are consistent with a recent study that showed that while two well-established hESC lines differentiate into functional neurons, the two lines exhibited distinct differentiation potentials, suggesting that some preprogramming had occurred [76]. In particular, microRNA profiling revealed significant expression differences between the two hESC lines, suggesting that microRNAs, known post-transcriptional regulators, may sway the differentiation properties of the cell lines [76]. We postulated that AS events may serve also to bias the differentiation spectrum of the cells, an important avenue for future work. Experimental validation of REAP[+] exons suggested a high specificity at the expense of relatively moderate sensitivity. We believe that the high FP rates may arise from cross-hybridization effects that remained unaccounted for. However, our specificity of 77% at the cutoff of two significant outliers per probeset allowed us to estimate that at least 1,336 of 1,737 REAP[+] exons were true AS events that changed during neuronal differentiation of hESC cells, and/or were different between endogeneous NPs and hESC. On average, 7% of all human exons have been estimated by transcript data to undergo AS; thus REAP's validation rate of 60% at the cutoff of two is 73-fold (60/7) higher than expected. In addition, we validated nine novel AS events that distinguish hESCs and NPs. Consistent with our computational results, we observed that the AS patterns in hCNS-SCns were not always similar to those of the derived NPs. It was important to point out that while transcriptional expression of these genes did not distinguish these cells from one another, in several instances the REAP-predicted AS event was able to separate derived NPs and hCNS-SCns. A notable exception was the alternative exon in the SLK gene, encoding a serine/threonine kinase protein, which was commonly included in both hESCs, i.e., the exon-excluded isoform was not present in hESCs compared to NPs, as well as in a variety of differentiated tissues. Closer inspection of the REAP[+]-validated AS exon in the SLK gene revealed strong conservation in the intronic region flanking the exon, a hallmark feature of evolutionarily conserved AS exons [55,77,78]. A study analyzing the expression patterns of the SLK gene suggested a potential functional role during embryonic development and in the adult central nervous system [79]; however, to our knowledge, our identification of the SLK alternative exon is the first report of a hESC-biased AS pattern during neuronal differentiation and across a myriad of differentiated tissues. In agreement, GO analysis suggested that genes containing REAP[+] exons were enriched in serine/threonine kinase activity, of which SLK is a family member. Future work will be required to study the impact of AS in these genes in hESCs and NPs. We predict it is unlikely that the alternative exon in the SLK gene is the only case common across hESC and different from differentiated tissues, but further studies will be necessary to identify other hESC-specific exons. REAP[+] exons were underrepresented in genes that were differentially transcriptionally regulated in hESCs and NPs. Our results act as a reminder that focusing only on genes that are differentially expressed will overlook RNA processing events that may be biologically relevant to the system of interest. Finally, we identified potential cis-regulatory intronic elements conserved and enriched proximal to the REAP[+] exons. In particular, the FOX1/2 binding site, GCUAG, was conserved and enriched in the flanking introns of a subset of REAP[+] exons. Further studies will be required to explore the importance of FOX1 family members in early neuronal differentiation. In conclusion, our introduction of REAP and its application to identifying AS events has revealed new and unanticipated insights into hESC biology and their transition to NP cells. Collectively, these exons represent a set of molecular changes that are likely to be important for studying human neural differentiation with applications in neuronal regenerative medicine.
Maintenance and differentiation of hESCs and hCNS-SCns.hESC line Cy203 (Cythera) was cultured as previously described [12]. To differentiate into neuroepithelial precursor cells, colonies were manually isolated from mouse embryonic fibroblasts (MEFs) and cut in small pieces. These pieces were transferred to a T75 flask with hESCs differentiation media (same hESC medium but 10% KSR and no FGF-2). Medium was changed the next day by transferring the floating hESC aggregates to a new flask. After culturing for a week, the hESC cell aggregates formed mature embroid bodies (EBs; ∼10 um round clusters with dark centers). EBs were plated on a coated 10-cm dish in hESC differentiation media. The next day, the medium was changed to DMEM/F12 supplemented with ITS and fibronectin. Medium was changed every other day for a week or until the cells formed rosette-like columnar structures that were isolated manually. These structures were then transferred to coated dishes in neural induction medium (DMEM/F12 supplemented with N2 and FGF-2) for a week. Elongated single cells were separated from leftover aggregates using non-enzymatic dissociation. After one to two passages, the cells formed a monolayer of homogeneous NPs (negative for Sox1 immunostaining). Upon confluence, cells will form neurospheres that can also be isolated from the neuroepithelial precursor cells (positive for Sox1 immunostaining). At any of these two stages, pan-neuronal differentiation can be achieved after three to four weeks. hESC line HUES6 was cultured on MEF feeders as previously described (http://www.mcb.harvard.edu/melton/hues/) or on GFR matrigel coated plates. Cells grown on matrigel were grown in MEF-conditioned medium and FGF-2 was used at 20 ng/mL instead of 10 ng/mL for cells grown on MEFs. To differentiate neuroepithelial precursors, colonies were removed by treatment with collagenase IV (Sigma) and washed three times in growth media. The pieces of colonies were resuspended in HUES growth media without FGF2 in an uncoated bacterial Petri dish to form EBs. After one week, EBs were plated on polyornathine/laminin coated plates in DMEM/F12 supplemented with N2 and FGF2. Rosette structures were manually collected and enzymatically dissociated with TryPLE (Invitrogen), plated on polyornathine/laminin coated plates, and grown in DMEM/F12 supplemented with N2 and B27-RA and 20 ng/mL FGF-2. Cells could be grown as a monolayer for up to at least ten passages. Cells were Sox1 and nestin positive and readily differentiated into neurons upon withdrawal of FGF-2. Human central nervous system stem cell line FBR1664 (StemCells) which is referred to as hCNS-SCns in the main text was cultured as previously described [23]. The cells were cultured in medium consisting of Ex Vivo 15 (BioWhittaker) medium with N2 supplement (GIBCO), FGF2 (20 ng/mL), epidermal growth factor (20 ng/mL), lymphocyte inhibitory factor (10 ng/mL), 0.2 mg/ml heparin, and 60 ug/mL N-acetylcysteine. Cultures were fed weekly and passaged at ∼two to three weeks using collagenases (Roche). The following antibodies and corresponding dilutions were utilized for the immunohistochemical analysis of marker genes in Cyt-ES and HUES6-ES: Sox2 (Chemicon, 1:500), Oct4 (Santa Cruz, 1:500), Sox1 (Chemicon, 1:500), Nestin (Pharmingen, 1:250); hCNS-SCns: Sox2 (Chemicon, 1:200), Nestin (Chemicon, 1:200). RNA preparation and array hybridization.Total RNA from cells was processed as follows. Cells were lysed in 1 mL of RNA-bee (Teltest). The RNA was isolated by chloroform extraction of the aqueous phase, followed by isopropanol precipitation as per the manufacturer's instructions. The precipitated RNA was washed in 75% ethanol and eluted with DEPC-treated water. Five ug of RNA was treated with RQ1 DNAase (Promega) according to the manufacturer's instructions. One ug of total RNA for each sample was processed using the Affymetrix GeneChip Whole Transcript Sense Target Labeling Assay (Affymetrix). Ribosomal RNA was reduced with the RiboMinus Kit (Invitrogen). Target material was prepared using commercially available Affymetrix GeneChip WT cDNA Synthesis Kit, WT cDNA Amplification Kit, and WT Terminal Labeling Kit (Affymetrix) as per manufacturer's instructions. Hybridization cocktails containing ∼5 ug of fragmented and labeled DNA target were prepared and applied to GeneChip Human Exon 1.0 ST arrays. Hybridization was performed for 16 hours using the Fluidics 450 station. Arrays were scanned using the Affymetrix 3000 7G scanner and GeneChip Operating Software version 1.4 to produce .CEL intensity files. Detection of AS by RT-PCR.cDNAs were generated from total RNA with Superscript III reverse transcriptase (Invitrogen). PCR reactions were performed with primer pairs designed for AS targets (annealing at 58 °C and amplification for 30 or 35 cycles). PCR products were resolved on either 1.5% or 3% agarose gel in TBE. The Ethidium Bromide-stained gels were scanned with Typhoon 8600 scanner (Molecular Dynamics) for quantification. The number of true positives (TP; false negatives, FN) was computed as the number of REAP[+] (REAP[−]) exons that were validated by RT-PCR as AS. The number of true negatives (TN; or FPs) was computed as the number of REAP[−] (REAP[+]) exons that were validated by RT-PCR as constitutively spliced. The true (false) positive rate was computed as TP (FP) divided by the total number of REAP[+] exons in the experimentally validated set. The true (false) negative rate was computed as the TN (FN) divided by the total number of REAP[−] exons in the experimentally validated set. Sensitivity was computed as TP/(TP+FN) and specificity was computed as TN/(FP+TN). Sequence databases.Genome sequences of human (hg17), dog (canFam1), rat (rn3), and mouse (mm5) were obtained from UCSC, as were the whole-genome MULTIZ alignments [80]. The lists of known human genes (knownGene containing 43,401 entries) and known isoforms (knownIsoforms containing 43,286 entries in 21,397 unique isoform clusters) with annotated exon alignments to human hg17 genomic sequence were processed as follows. Known genes that were mapped to different isoform clusters were discarded. All mRNAs aligned to hg17 that were greater than 300 bases long were clustered together with the known isoforms. Genes containing less than three exons were removed from further consideration. A total of 2.7 million spliced ESTs were mapped onto the 17,478 high-quality genes to infer AS. Exons with canonical splice signals (GT-AG, AT-AC, GC-AG) were retained, resulting in a total of 213,736 exons. Of these, 197,262 (92% of all exons) were constitutive exons, 13,934 exons (7%) had evidence of exon-skipping, 1,615 (1%) exons were mutually exclusive alternative events, 5,930 (3%) exons had alternative 3′ splice sites, 5,181 (2%) exons had alternative 5′ splice sites, and 175 (<1%) exons overlapped another exon, but did not fall into the above classifications. A total of 324,139 probesets from the Affymetrix Human Exon 1.0 ST array were mapped to 208,422 human exons, representing 17,431 genes. These probesets were used to derive gene and exon-level signal estimates from the CEL files. The four-way mammalian (four-mammal) whole-genome alignment (hg17, canFam1, mm5, rn3) was extracted from the eight-way vertebrate MULTIZ alignments (hg17, panTrol1, mm5, rn3, canFam1, galGal2, fr1, danRer1) obtained from the UCSC Genome Browser. Four-way mammal alignments were extracted for all internal exons, and 400 bases of flanking intronic sequence, resulting in a total of 161,731 conserved internal exons. A total of 145,613 (90% of total) conserved internal exons were constitutive exons, 13,653 exons (8%) had evidence of exon-skipping, 1,576 exons were mutually exclusive alternative events, 5,818 exons had alternative 3′ splice sites, 5,046 exons had alternative 5′ splice sites, and 168 exons overlapped another exon. Exon array analysis.The Affymetrix Power Tools (APT) suite of programs was obtained from http://www.affymetrix.com/support/developer/powertools/index.affx. Exon (probeset) and gene-level signal estimates were derived from the CEL files by RMA–sketch normalization as a method in the apt-probeset-summarize program. To determine if the signal intensity for a given probeset is above the expected level of background noise, we utilized the DABG (detection above background) quantification method available in the apt-probeset-summarize program as part of Affymetrix Power Tools (APT). Briefly, DABG compared the signal for each probe to a background distribution of signals from anti-genomic probes with the same GC content. The DABG algorithm generated a p-value representing the probability that the signal intensity of a given probe was part of the background distribution. We considered a probeset with a DABG p-value lower than 0.05 as detected above background. The statistic thCNS-SCns,ESC = (μhCNS-SCns − μESC) / sqrt (((nhCNS-SCns − 1)σ2hCNS-SCns + (nESC − 1)σ2ESC)(nhCNS-SCns + nESC)) / ((nhCNS-SCnsnESC) (nhCNS-SCns + nESC − 2))), where nhCNS-SCns and nESC were the number of replicates, μhCNS-SCns and μESC were the mean, and σ2hCNS-SCns and σ2ESC were the variances of the expression values for the two datasets used to represent the differential enrichment of a gene using gene-level estimates in hCNS-SCns relative to hESCs. Multiple hypothesis testing was corrected by controlling for the false discovery rate (Benjamini-Hochberg). AS detection by REAP.The log2 signal estimate xij for probeset i in cell-type j had to satisfy two conditions, otherwise the probeset was discarded: (i) 2 < xij < 10,000 for all conditions/cell-types j; and (ii) DABG p-value < 0.05 for all replicates in at least one condition/cell-type j. A gene had to have five probesets that satisfied the two conditions above in order to be considered for robust regression analysis. After generating the points (as described in the Results section), we utilized the robust regression method rlm in R-package “MASS” (version 6.1–2) with M-estimation and a maximum iteration setting of 30 to estimate the linear function yi = αxi + β. For each probeset, we computed the error term ei,, which was the difference between the actual value yi and the estimated value ξi, from the estimated function ξi = Axi + B, where A and B were estimates of α and β. The error term variance was estimated by se2 = Σei2/(n − p), which was used to estimate the variance of the predicted value, sξi2 = se2(n−1 + (xi − μx)2 / sx2(n − 1)). Here, n referred to the number of points (generated for each gene), and p referred to the number of independent variables (p = 2 in our method); and μx = Σxi2/n; sx2 = n−1 Σ(xi − μx)2. Following Belsley et al. [81], we defined the leverage hi of the ith point as hi = n−1 + (xi − μx)2 / sx2(n − 1). Here we considered a point to have high leverage if hi > 3p/n. Next, we calculated the covariance ratio, covi = (si2/sr2)p/(1 − hi), which is the ratio of the determinant of the covariance matrix after deleting the ith observation to the determinant of the covariance matrix with the entire sample. We considered a point to have high influence if \|covi − 1\| > 3p/n. Lastly, we computed the studentized residuals, rstudenti = ei / (s(i)2 (1 − hi)0.5), where s(i)2 = (n-p)se2 / (n-p-1) – ei2 / (n-p-1)(1 − hi), the error term variance after deleting the ith point. As rstudenti was distributed as Student's t-distribution with n-p-1 degrees of freedom, each rstudenti value was associated with a p-value. We considered a point to be an “outlier” if p < 0.01. Identification of motifs.The enrichment score of a sequence element of length k (k-mer) in one set of sequences (set 1) versus another set of sequences (set 2) was represented by the nonparametric χ2 statistic with Yates correction, computed from the two by two contingency table, T (T11: number of occurrences of the element in set 1; T12: number of occurrences of all other elements of similar length in set 1; T21: number of occurrences of element in set 2; T22: number of occurrences of all other elements of similar length in set 2. All elements had to be greater than 5. To correct for multiple hypothesis testing, p-values were multiplied by the total number of comparisons.
BackgroundMuch of our current knowledge of the molecular expression profile of human embryonic stem cells (hESCs) is based on transcriptional approaches. These analyses are only partly predictive of protein expression however, and do not shed light on post-translational regulation, leaving a large gap in our knowledge of the biology of pluripotent stem cells.ResultsHere we describe the use of two large-scale western blot assays to identify over 600 proteins expressed in undifferentiated hESCs, and highlight over 40 examples of multiple gel mobility variants, which are suspected protein isoforms and/or post-translational modifications. Twenty-two phosphorylation events in cell signaling molecules, as well as potential new markers of undifferentiated hESCs were also identified. We confirmed the expression of a subset of the identified proteins by immunofluorescence and correlated the expression of transcript and protein for key molecules in active signaling pathways in hESCs. These analyses also indicated that hESCs exhibit several features of polarized epithelia, including expression of tight junction proteins.ConclusionOur approach complements proteomic and transcriptional analysis to provide unique information on human pluripotent stem cells, and is a framework for the continued analyses of self-renewal.
Human embryonic stem cells (hESCs) are pluripotent cells isolated from the inner cell mass of the blastocyst [1]. They can be maintained for prolonged periods in culture and differentiate to representatives of the three germ layers as well as trophoblasts and germ cells. This differentiation potential may be used to model certain aspects of human embryogenesis, including the development and differentiation of pluripotent and other stem cell types during the processes of gastrulation, neurogenesis and organogenesis. Thus, hESCs provide a unique and powerful system to study otherwise intractable aspects of human development. Furthermore, these approaches have the potential to provide differentiated cell types for cell replacement therapies of degenerative disorders such as Parkinson's disease and Type I diabetes [2,3]. Before these cell therapy applications are developed, an understanding of the molecular and cellular mechanisms that drive self-renewal and differentiation is required. Fundamental to this understanding is the elucidation of the transcriptome and proteome of hESCs, using approaches that lay a framework for functional analyses of the unique properties of these cells. Large-scale gene expression analyses such as microarray, massive parallel signature sequencing (MPSS), expressed sequenced tag (EST) enumeration, and serial analysis of gene expression (SAGE) have been used to compare multiple hESC lines [4-7]; hESCs to germ cell tumors [8]; or to differentiated derivatives in embryoid bodies [9-11] or neural populations [12]. These approaches have highlighted an expanded set of transcripts that mark the pluripotent state [4,13,14], cross-species commonalities in the molecular profile of ESCs [6,12,15], prominent receptors expressed by hESCs [8] and pathways that may play a role in the regulation of pluripotency [16,17]. Nevertheless, cataloguing the cellular transcriptome is only predictive of protein expression and typically does not shed light on post-transcriptional regulation. For example, while tens of thousands of transcripts can be followed simultaneously with SAGE, microarrays and MPSS, these methods do not routinely detect differences in transcript splice variants, or polyadenylation status. These differences may have profound effects on translation, as well as the isoform and function of the protein produced. Finally, numerous post-translational modifications are known to regulate protein function, including enzymatic cleavage, covalent coupling to other molecules, glycosylation, phosphorylation and ubiquitination. These issues all highlight potential shortfalls in our understanding of the hESC proteome. Several practical approaches for proteomic analyses are currently available, the most established of which is the 2-dimensional (2D) separation of proteins by polyacrylamide gel electrophoresis (PAGE). HPLC-tandem mass spectrometry (HPLC-MS/MS) based technology is rapidly evolving and has recently been used to detect protein expression in multiple cell types. An alternate approach is the recent large-scale adaptation of standard western blotting [18]. In this procedure, a large well is used to separate the sample by PAGE and lanes are created on the membrane containing immobilized protein with the use of a manifold. Compatible combinations of primary antibodies are predetermined, with the criterion of being able to identify proteins that do not co-migrate. Different combinations of primary antibodies are added to each well, with appropriate dilutions of each primary antibody so that expressed proteins are detected in a single condition. The scalability of the system depends on defining suitable combinations of primary antibodies, with up to 1000 antibodies in 200 lanes being used in the largest screens thus far. Detection software is used to identify proteins based on their expected and observed gel mobility. Unlike 2D PAGE and HPLC-MS/MS, large-scale western blotting only identifies proteins for which antibodies are already available. While this is not an appropriate screen for identifying uncharacterized proteins, it greatly simplifies the verification and functional analyses of proteins that are detected. In addition, this approach is highly flexible, and if desired can be focused to particular sets of proteins or protein function, such as cell signaling molecules. Importantly, the foundation of this approach is the large amount of data on individual antibodies, which are already available and characterized in the literature. More recently, two research groups have conducted proteomic analyses of hESCs using MS [19-22]. In the present study, we used two large-scale western blot systems to examine the expression of > 1000 proteins in hESCs and detected > 600 proteins that were grouped into 18 functional classes. In addition, we identified 42 examples of multiple bands for a single protein, likely to be protein isoforms and/or post-translational modifications, and 22 phosphorylation events in cell signaling molecules. We correlated the expression of members of key active pathways in our transcriptional and proteomic databases and confirmed the validity of this approach. Using these approaches we identified new markers for undifferentiated hESCs and highlighted unrecognized epithelial characteristics of hESCs. Our data confirm the importance of proteomic analyses in complementing transcriptional profiling and provide a framework for continued analyses of the molecular and cellular biology of pluirpotent hESCs.
PowerBlot analysis of hESCsWe first employed a large-scale western blot screen, the PowerBlot system, to profile protein expression in undifferentiated hESCs. This system used 934 antibodies toward proteins representing 22 diverse classes of function, such as transcription factors, the MAP kinase (MAPK) pathway, and apoptosis, among others. To expand a large-scale culture of BG01 cells for this assay, a collagenase- and trypsin- based passaging method was used [23]. While these conditions have been associated with the accumulation of trisomies of chromosomes 12, 17 and X [24], the ease of use of these cultures and similarity in gene expression and differentiation potential to karyotypically normal BG01 hESCs [11,24,25] make them suitable for such large scale applications. For the PowerBlot screen, whole cell lysate from BG01 hESCs was separated on five 4–15% gradient gels. Each blot contained size markers and 39 lanes. Each lane was screened with 1–8 antibodies in combinations that had been predetermined to enable accurate identification of well-separated proteins (Fig. 1A–E). The gels and blots were performed in duplicate and expressed proteins were identified by their predicted size and verified by visual inspection.Figure 1PowerBlot analysis of undifferentiated BG01 hESCs. This large-scale western blot consisted of five gels run in duplicate and probed with 934 antibodies. (A-E) One set of blots is shown at a contrast that highlights most bands. (F) A representative lane (gel C, lane 24) aligned with protein markers used for band identification. (G) Scatter-plot of the normalized average intensity (i.u.) values for each protein indicating a linear relationship between duplicate blots. Datasets for this analysis are in Additional Tables 1 and 2.A total of 545 antibodies detected bands of appropriate size, which could be compressed to 529 proteins with unique SwissProt identification numbers (Fig. 1A–E and Additional File 1). An enlargement of a representative lane (lane 24 of Blot C) alongside protein markers is shown in Fig. 1F. Thirteen proteins including AKT, caveolin1 and ERK1 were detected in multiple lanes using the same or different antibodies. Information on the antibody catalogue number and dilution, band intensity for each repeat and the averaged value, description of protein function, and Entrez gene and SwissProt database identification numbers is shown in Additional File 1. Three hundred and eighty three antibodies did not detect bands in this screen, indicating lack of expression, or possibly technical issues with detection under standard conditions (Additional File 1).The size of the detected proteins ranged from 15 kD (GS15) to 280 kD (ABP-280). The average intensity of the detected proteins ranged from 195 to 117926 normalized intensity units (i.u.), with an average of 5367 i.u. The proteins with the highest band intensity were the B2 Bradykinin Receptor (117926 i.u.), Karyopherin α (80698 i.u.), and BiP (74922 i.u.), whilst the proteins with the lowest intensity that could be verified by visual inspection were Inhibitor 2 (247 i.u.), Caspase 8 (201 i.u.), and OXA1Hs (195 i.u.). Finally, the consistency of this assay was demonstrated by plotting the normalized average intensity values for each protein, which revealed a linear relationship between the duplicate samples (Fig. 1G). Kinexus analysis of hESCsA more focused screen was used to profile expression of protein kinases, phosphatases and phosphorylated sites in cell signaling molecules in hESCs. The Kinexus assays contained 140 antibodies to these related classes of proteins and phospho-sites. Karyotypically normal BG03 hESCs grown on a fibronectin matrix in MEF-CM [26] were used for this analysis, and whole cell lysate was separated on four 12.5% gels for western blotting. Eighty five immunoreactive bands were identified, representing 38 protein kinases and 16 phosphatases, their isoforms, and 22 phosphorylated sites in signaling molecules (Fig. 2A–D, Additional File 1). Sixty-four antibodies did not detect their corresponding antigen (Additional File 1).Figure 2Kinexus blots of undifferentiated BG03 cells. Four blots were used to probe BG03 lysate with (A, B) 76 antibodies for protein kinases, (C) 27 antibodies for phosphatases and (D) 37 antibodies for phosphoylated sites in cell signaling molecules. Identified bands are indicated (). Datasets for this analysis are in Additional Tables 1 and 2. Functional classification of proteins expressed in hESCsThe PowerBlot and Kinexus assays identified a diverse range of proteins expressed in hESCs. To further annotate these data, the detected proteins were ordered into 18 subgroups based on protein function (Additional File 2). For example, 16 factors with known or implied roles in the regulation of self-renewal or pluripotency of mESCs or hESCs, such as Oct4 [27], STAT3 [28], members of the FGF [29], PI3 kinase [30], Src [31] or MAPK pathways [32], and phosphorylated isoforms of GSK3, STAT3 and p38 MAPK, were grouped under "Pluripotency" (Fig. 3A and Additional File 2). Another functional group (Cell surface) consisted of 20 transmembrane or cell surface proteins (Additional File 2). This included several receptors for peptides and growth factors, such as neurotensin receptor 3, the B2 bradykinin, endothelin 1, and thrombin receptors, and the glial derived neurotrophic factor receptor α (Fig. 3B). These molecules may be useful as targets for cell sorting experiments, and expression of these receptors could identify bioactive peptides or growth factors that may influence hESC self-renewal or differentiation.Figure 3Functional classification and mobility variants of proteins detected in hESCs. (A) Proteins with known or suggested roles in self-renewal are shown, including Oct4, STAT3, Smad2/3 and FGF2 (Additional Table 2, "Pluripotency"). Isoforms of FGF2, and phospho-GSK3 are indicated (). (B) Cell surface proteins are shown, including Connexin 43, E-Cad and GDNFRα (Additional Table 2, "Cell Surface"). Other functional classes of proteins are indicated in Additional Table 2. (C) A total of 42 proteins, including FGF2, HSP70 and ERK1, were found to have multiple bands in either the PowerBlot or Kinexus blots. These bands migrated closely but were sufficiently separated from other detected proteins. Bands predicted to be isoforms of the indicated protein are highlighted in some panels ().Other functional classification of the proteins detected by the PowerBlot screen included: transcription factors (71 proteins), nucleus and nuclear transport (144), cytoskeleton (75), cell adhesion (45), MAP kinase pathway (24), protein kinase A (13), protein kinase C (20), tyrosine kinases (15), adaptors and tyrosine kinase substrates (51), protein phosphatases (17), GTPases and regulators (42), calcium signaling (23), cell cycle (87), apoptosis (61), membrane research (62), and other functions (51) (Additional File 1). Some proteins were included in multiple functional categories due to overlapping properties, such as AIM-1, which was included in the cell cycle as well as in the nucleus/nuclear transport categories. The Kinexus expression data was organized separately into cell signaling-related functional groups (Additional File 1). In addition, 35 proteins were detected by both the PowerBlot and Kinexus systems (Table 1).Table 1Proteins detected by both PowerBlot and Kinexus systemsProtein nameSwiss NrProtein nameSwiss NrBMXP51813MEK2P36506CaM Kinase KinaseQ64572MKP2Q62767Casein Kinase I epsilonP49674p38 alpha/SAPK2aQ16539Casein Kinase II alpha/CK2aP19139PaxillinP49024Cdk1/Cdc2P06493PKA CP17612Cdk5Q00535PKC betaP05771Cdk7P50613PKC deltaQ05655DAP KinaseP53355PP2A Catalytic alphaP05323DAP3P51398PP5/PPTP53042ERK1Q63538PTP1BP18031ERK2P27703PTP1C/SHP1P29350FAKQ00944PTP1D/SHP2Q06124GSK-3 betaP18266RbP13405I kappa B alphaP25963RskQ15418IKK betaO14920Stat1A46159JAK1P23458Stat3P52631JNK1P45983VHRP51452MEK1Q02750 Detection of protein isoforms or post-translational variantsUnlike many cDNA-based gene expression assays, western blotting has the capacity to detect multiple protein isoforms due to translation of different mRNA splice variants, as well as post-translational modifications such as enzymatic cleavage, glycosylation, or phosphorylation. Examination of the blots described here identified 42 examples of multiple banding for a single target antigen (Fig. 3C). These candidates exhibited closely migrating multiple bands, which were close to their predicted size but were sufficiently separated from other proteins. For example, four closely migrating bands were observed for FGF2 (Fig. 3C, top panel), which may represent known glycosylation variants of this growth factor [33]. Other known examples of post-translational modifications included those of HSP70, IKKgamma and ERK1. Verification of protein expression by immunocytochemistryThe PowerBlot and Kinexus assays identified proteins based on their expected and observed molecular weight, using combinations of antibodies that had been predetermined to detect proteins of sufficiently different sizes. Proteins known to be expressed by hESCs and also identified by these assays, included Oct4, E-CAD, Connexin 43 and Hsp70. To verify expression using a complementary approach, we performed immunoflurorescent staining for 10 proteins not previously reported to be expressed in hESCs by immunocytochemistry, using karyotypically normal BG01 cultures (Fig. 4A–K). These included ABP-280, a homodimeric actin-binding protein often associated with membrane glycoproteins; CtBP1 and CtBP2, two C terminal binding proteins that are a class of transcription corepressors; GS-28, a golgi protein; HDJ-2, a member of the DnaJ-related Hsp40 (heat shock protein 40) subfamily; L-Caldesmon, a cytoplasmic actin-binding protein; Rabaptin, a GTP-binding protein; phosphorylated-p130 Cas, a docking protein with an amino-terminal SH3 domain that may function as a molecular switch that regulates CAS (Crk-associated substrate) tyrosine phosphorylation; Ras-GAP and phosphorylated Ras-GAP (p-Y460), a protein that down-regulates the signal transducer p21ras; and ShcC, a protein with an N-terminal phosphotyrosine-binding domain. These proteins were all expressed by hESCs, with the expected subcellular localization (Fig. 4A–K). Oct4 was used as a positive control (Fig. 4L). These results suggested that most of the bands in the PowerBlot and Kinexus assays were likely to be correctly identified.Figure 4Verification of protein expression using immunocytochemistry. (A-K) Ten proteins that were detected in undifferentiated hESCs by western blotting were also detected by immunofluorescence of BG01 cells grown in MEF-CM. Ras-GAP (pY460) is a phosphorylated form of Ras-GAP. The same antibodies were used in this analysis as in the PowerBlot assay, except phospho-p130 Cas (Tyr165). (L) Oct4 was used as a positive control. (M-R) Oct4, TNIK and p130 Cas as markers of undifferentiated hESCs. BG01 cultures were partially differentiated by exposure to 10% fetal bovine serum for 3 days. (M) Oct4 was expressed uniformly in undifferentiated cells, (P) but was downregulated in morphologically differentiated areas after 3 days in serum (arrowhead). (N) TNIK expression was localized to the cytoplasm, and (N, Q) expression appeared to be restricted to morphologically undifferentiated cells (arrowhead). (O) p130 Cas was detected in a membrane/peripheral-cytoplasmic pattern in undifferentiated cells, (R) but this distribution was substantially altered in differentiating cells with a flattened morphology, which exhibited a general cytoplasmic, or perinuclear profile. Scale bar for A-L: (A, L) 200 μm; (C, D, F, H, I, J, K) 100 μm; (B, E, G) 50 μm. Scale bar for M-R: (M, N, P, Q): 100 μm ; (O, R): 50 μm.Preliminary analyses also indicated that expression of some of these proteins was downregulated in differentiated cells, including p130 Cas and the Traf2- and Nck-interacting kinase (TNIK). TNIK is known to be involved in the inhibition of cell spreading via disruption of F-actin [34,35]. Immunofluorescence was used to examine the expression of TNIK and p130 Cas during early differentiation of hESCs. BG01 cultures were partially differentiated by growth in serum containing media for 3 days. This condition generated heterogeneous populations containing Oct4+ cells with characteristic hESC morphology and less tightly packed, and morphologically differentiated areas, lacking expression of Oct4 (Fig 4M, P). TNIK was expressed highly in undifferentiated hESCs, and in the undifferentiated areas at day 3, but was downregulated in areas undergoing morphological differentiation (Fig 4N, Q). This may indicate that TNIK is active in hESCs and degraded rapidly upon differentiation. p130 Cas was detected in a membrane/peripheral-cytoplasmic pattern in hESCs (Fig 4O). The distribution of p130Cas was substantially altered in differentiating cells with a flattened morphology, exhibiting a general cytoplasmic, or perinuclear profile (Fig 4R). This could indicate an alteration in the function of p130 Cas as pluripotent cells differentiate. These analyses suggested that the change in expression or distribution of these proteins could be used as markers for undifferentiated hESCs. Comparison of proteomic and transcriptional profiles of hESCsWe have previously employed the Illumina Bead Array system for the large-scale profiling of gene expression in hESCs using 24,000 transcript probes [11]. To compare proteomic and transcriptional analyses of hESCs, the levels of > 600 proteins detected using large scale blotting were correlated with the levels of transcripts detected with the Illumina platform (Additional File 3). In general, a close match between the expression level of transcript and protein was observed: transcripts for nearly all the detected proteins were also identified in the Illumina analysis, and most proteins expressed at high levels also exhibited high mRNA levels.We reasoned that a focused comparison of specific signaling pathways using a combination of proteomic and transcriptional data was likely to be much more informative than a global interrogation of hESCs. Several major signal pathways that have been suggested to be involved in self-renewal were examined to test this approach. These included the FGF, TGFβ, GSK3β/Wnt/β-catenin and Jak/Stat pathways [17,29,36-39], as well as the more recently suggested MAPK/ERK and Gap junction pathways [32,40]. Correlating transcriptional and proteomic data provided direct confirmation that these pathways were present and likely functional in hESCs (Table 2). For example, FGF2 protein was expressed highly in hESCs and expression of key members of the TGFβ, Wnt, Jak/Stat and Gap junction pathways, namely Stat1, SMADs, GSK3β, β-catenin and Connexin 43, were detected in both transcriptional and proteomic databases.Table 2Signal pathways that may be active in hESCsNameProteinmRNATGF βStat1++++++PAI-1/SERPINE1+++-Smad2/3++++Jun+++Smad4/DPC4+++Endoglin+-WntCtBP2+++++++PP2A Catalytic alpha/PPP2CA+++++++EBP50/SLC9A3R1++++++beta-Catenin/Ctnnb1++++Cyclin D3/CCND3++++GSK-3 beta++++Jun+++Casein Kinase II alpha/CSNK2A1++++Jak-StatStat1++++++Crk++++Stat3/2+++++Stat6+++++PTP1B+++++JAK1++-Glucocorticoid R/NR3C1++-Thrombin Receptor/PAR1/F2R+++SHPS-1/PTPNS1++++MCM5+++++Smad2/3++++Tyk2++++Jun+++Bcl-x/BCL2L1++++Smad4/DPC4+++Stat5A++GPCRB2 Bradykinin Receptor/BDKRB2++++-Neurotensin Receptor 3/SORT1+++-Endopeptidase 3.4.24.16/NLN++++IP3R-3++++SHC++++Gap JunctionCdk1/Cdc2++++++GRB2++++++MEK1/MAP2K1++++++PKA C++-PKA RI alpha++-PKC alpha++-C-Raf/RAF1++++ZO-1/TJP1+++++Connexin-43/GJA1++++IGFPKC iota++++++MEK1/MAP2K1++++++Rsk/RPS6KA1+++++GRB2++++++MEK2/MAP2K2++++++PI3Kinase/PIK3R1+++++pan ERK/MAPK1+++++Crk++++eIF-4E+++++ShcC+++-PAI-1/SERPINE1+++-C-Raf++++SHC+++++PKC beta/PRKCB1++++NCK++++PKB alpha/Akt+++GSK-3 beta++++Ercc-1++++Fatty Acid Synthase/FASN+++++Jun+++RAFT1/FRAP++++PTP1D/SHP2/PTPN11++++SCAMP1++++Bcl-x/BCL2L1++++p70s6k/RPS6KB1+-PI3-Kinase p170/PIK3C2A++PTP1B/PTPN1+++Dok1/p62dok+++PI3-Kinase p110 alpha/PIK3CA+-ERBBEphA4/Sek++++-ShcC/SHC3+++-c-erb-B2/ERBB2++++C-Raf/RAF1++++SHC/SHC1+++++GDNFI kappa B epsilon/NFKBIE++++++GRB2++++++MEK2++++++NCK+++++C-Raf++++Ras-GAP/RASA1++++SHC+++++GDNFR-alpha/Gfra1++-Jun+++IKK beta++++pan-JNK/SAPK1/MAPK10+++NBS1/ARTN++Dok1/p62dok+++Tight JunctionPTEN++++++PP2A Catalytic alpha+++++++PKC iota++++++Sec8/SEC8L1+++++beta-Catenin/CTNNB1++++CDC42+++++AF6/MLLT4+++++PKC alpha++-Yes++++Rho/ARHA+++++ZO-1/TJP1+++++CASK+++Symplekin/SYMPK++-Ras/NRAS++++Casein Kinase II alpha/CSNK2A1++++VAP33/VAPA+++alpha-Catenin/Ctnna1+++MAPKpan ERK++++++MEK1++++++Rsk++++++ERK2++++++MEK2/Map2k2++++++MST3/STK25++++++ERK1+++++CDC42+++++C-Raf++++p38 alpha/SAPK2a++-G3BP+++++TFII-I/GTF2IRD1++++MST1/STK4++++MKP2/Dusp4++++Ras++++Phospho-p38MAPK (T180/Y182)+++pan-JNK/SAPK1+++Inhibitor2/PPP1R2++++ABP-280++++++14-3-3 epsilon/YWHAE++++MAPKAPK-5+++TAO1+PBK+++MKK3b/Map2k3++Protein expression level: > 10,000: ++++; 5,000–10,000: +++; 1,000–5,000:++; 100–1,000: + mRNA gene expression level: > 5,000:++++; 1,000–5,000: +++; 100–1,000: ++; 30–100: +*: not included in the gene expression arrayThis independent confirmation of known networks led us to examine other pathways that showed a similar correlation but have not been identified as key regulators of either self-renewal or differentiation, or suggest unappreciated characteristics of hESCs. Four signaling pathways (IGF, ERBB2, GPCR, and GDNF) and the tight junction complex were highlighted by this analysis (Table 2), and expression of key proteins in these pathways was confirmed. A detailed study demonstrating the importance of the IGF and ERBB2 pathways in hESC self-renewal has been performed and enabled the development of a defined medium for hESC maintenance (TCS and AJR, submitted). Tight junctions are apical cell-cell junctions found in epithelia that establish a barrier to the extracellular environment and a border for apical-basolateral polarity. While hESCs grow in colonies that are highly reminiscent of epithelia, and have been shown to be coupled by gap junctions [40], the formation of tight junction complexes has not been described. hESCs expressed the ZO1 and occludin tight junction proteins along cell borders as expected in polarized epithelia. The distribution of ZO1 expression changed dramatically as hESCs proliferated in culture. When tight junction complexes were disrupted by disaggreagation to single cells, only a subset of cells showed ZO1 staining 4 days after plating (Fig. 5). Continued proliferation to a confluent monolayer on day 7 was accompanied by widespread expression of ZO1, suggesting the formation of a general tight junction barrier. These cultures were undifferentiated and retained uniform expression of Oct4 protein (not shown). ERBB2 and 3 are members of the epidermal growth factor (EGF)-receptor family, which regulate epithelial proliferation via EGF-family ligands. ERBB2 and 3 transcripts are expressed by hESCs [8], are known to function as a heterodimer [41], and transmit a strong proliferative signal for hESCs by Heregulin 1β (an EGF-family ligand) (TCS and AJR, submitted). Immunofluorescence revealed general cell surface expression of ERBB2 on hESCs. Conversely, ERBB3 was highly localized to a concentrated area, and observed in cells that also expressed ZO1. Epithelial cells are known to localize ERBB receptors to the basolateral side of tight junctions, which serves to functionally separate receptors from ligands [42,43]. This is a basic epithelial wound healing mechanism, whereby disruption of the tight junction barrier by injury immediately exposes receptors to extracelluar ligands [43]. These staining patterns are also suggestive of basolateral sorting of ERBB3 in hESCs. The pathways and complexes identified by these analyses lay a framework for future functional analyses of signaling networks in hESCs.Figure 5Tight junction proteins and ERBB2/3 expression in hESCs. BG01 hESCs were disaggregated to single cells using accutase [52] and cultured in defined conditions. (A) ZO1 expression four and (B) seven days after plating, indicating progressive tight junction formation. (C) Occludin expression 5 days after plating. (D) General cell surface expression of ERBB2, in the same field of view as (A). (E) Localized expression of ERBB3, in the same field of view as (B). (F) Higher magnification of ERBB3 localization in ZO1 expressing BG01 cells, 5 days after plating. Nuclei were stained with DAPI.
Attempts to harness the potential of hESCs for models of human embryogenesis and cell therapy applications will be greatly enhanced by a detailed understanding of their molecular characteristics. This includes definition of the transcripts, splice variants, and protein isoforms expressed by these cells. Post-translational modifications such as phosphorylation and glycosylation, and the receptors and signaling pathways active in the pluripotent state, or during early differentiation, also need to be determined. This should also be complemented by an understanding of epigenetic characteristics of pluripotency, including methylation, imprinting and chromatin conformation. Such a comprehensive definition of the molecular state of hESCs will enable more accurate prediction and testing of the conditions used for growth and differentiation of hESCs, by precise genetic modification or application of specific growth factor cocktails and reagents. For example, a scalable, fully defined and GMP-certified culture system will need to be developed for the eventual development of hESC-based cellular therapies. Progress has been made in defining growth factor conditions that support self-renewal [44-46], and hESC lines have been isolated in the absence of mouse embryonic fibroblasts and in animal protein free culture conditions [47,48]. A more refined understanding of the biology of hESCs has contributed the development of a defined medium utilizing ligands for IGF1R and ERBB2/3 receptors to promote in self-renewal (TCS and AJR, submitted). We and others have performed transcriptional analyses of hESCs, using cDNA and oligonucleotide microarrays, SAGE, MPSS and EST enumeration. These techniques have enabled the collation and comparison of transcriptional profiles from multiple hESC lines and their differentiated derivatives and have highlighted an expanded set of hESC specific markers and signaling pathways that may regulate self-renewal or differentiation. Using pathway analysis we were also able to identify key pathways that are active in ESCs (reviewed in [16]). While these efforts have been highly valuable in defining the transcriptional profile of undifferentiated hESCs, they are only predictive of translation and do not shed light on post-translational events in this unique cell type. These processes may also be highly regulated, which could contribute significantly to the overall conversion of genetic information to actual protein function. We report here a proteomic analysis of pluripotent hESCs by using two large-scale western blotting systems and highlight post-translational events in undifferentiated hESCs. The expression of 545 bands was detected, potentially representing 529 proteins, or their migratory isoforms. In addition, one hundred and forty phospho-specific antibodies were used to identify 85 different phosphorylated sites, on 76 proteins in these cells. The detected proteins were annotated into functional classes representing diverse cellular processes. For example, multiple proteins were detected that have been suggested to regulate the pluirpotent state in mouse ESCs or hESCs. Defining the interplay of these multiple signaling pathways will be critical in understanding the self-renewal versus differentiation decisions of hESCs. Therefore, our data provide a powerful framework for the functional analysis of specific proteins, protein classes, or molecular pathways. In particular, the availability of antibodies for candidate proteins is a major benefit of this approach compared to 2D-gel or HPLC-MS/MS based proteomics. Although these western blotting approaches are currently more limited in scope than most large-scale cDNA based assays, detecting up to 1000 proteins compared to tens of thousands of transcripts, they have the potential to highlight translational events and post-translational modifications. By comparison, SAGE and MPSS are limited to detecting short sequence "tags" adjacent to the poly-A tail of transcripts, and may not distinguish splice variants with the same 3' exon. We detected 42 proteins with multiple closely migrating bands (Fig. 3C), suggestive of closely related isoforms or post-translational modifications such as phosphorylation. These focused proteomic approaches are therefore likely to be highly complimentary to transcriptional analyses in investigating the functional expression of the genome in hESCs and during cellular differentiation. One potential issue with this approach is that multiple antibodies are included in each lane, which could possibly lead to misidentification of bands. To demonstrate that identified proteins were expressed in hESCs, the same antibodies used in the PowerBlot assay were used to confirm expression of 10 representative proteins by immunofluorescence (Fig. 4). Furthermore, 13 proteins were detected with multiple different antibodies, and 35 proteins (Table 1) were detected in both the PowerBlot and Kinexus assays. This provided internal, or independent, confirmation of expression of these proteins. Other studies have also demonstrated the expression of several of the proteins we detected in hESCs. These include Oct4, a key marker of the pluripotent state, Connexin 43 and GSK3β, confirming the reliability of large-scale western blotting. Finally, several proteins detected by our assays were also detected in hESCs by MS approaches including Karyopherin α [19]. Additionally, the PowerBlot assay was performed in duplicate, and was shown to be highly reproducible. This suggested that this approach should be informative when comparing hESCs to their differentiated derivatives. Two candidate proteins, TNIK and p130 Cas, were downregulated, or exhibited altered localization upon spontaneous differentiation of hESCs, respectively. This indicated that they were novel markers of undifferentiated cells and molecules that could be functionally involved with self-renewal. It is impossible in an initial manuscript to analyze and rigorously test all the predictions that could be made from comparing transcriptional and proteomic data sets. However, we did examine key features to illustrate the power of this methodology. Potential new markers for hESCs were identified, the expression and activation of proteins in key self-renewal pathways were confirmed, and a diverse range of proteins were detected and expression correlated with transcriptional analyses. In addition, we highlighted several candidate signaling pathways that may be relevant to self-renewal. Examination of tight junction protein expression indicated that undifferentiated hESCs could form polarized epithelia, which has also been recently suggested by ultrastructural analyses [49]. Discrete localization of ERBB3 may also suggest basolateral separation of this receptor from soluble ligand. These analyses highlight that predictions from a combination of transcriptional and proteomic approaches will serve to focus the investigation of hESCs in the future.
In summary, we generated a focused proteome of hESCs using large-scale western blotting and sorted the detected proteins according to function and signaling pathways. This characterization provides important basic information on expressed proteins, their isoforms and post-translational modifications, and tools for the continued investigation of the underlying molecular characteristics of hESCs. Importantly, we provide a list of tools, in the form of commercially available antibodies, which can be used to interrogate the function of these molecules in self-renewal or differentiation.
Culture of human embryonic stem cellsFor the PowerBlot analysis, enzymatically passaged BG01 hESCs were grown as described previously [23]. These conditions were necessary to scale up the culture to generate the milligram amounts of protein lysate required for this analysis. These conditions maintain cell populations that express the appropriate markers of pluripotency and can differentiate to representatives of all three germ layers, but may lead to eventual accumulation of trisomies for chromosomes 12, 17 or X [26]. For the Kinexus assays, BG03 hESCs were maintained in MEF-conditioned medium (MEF-CM) without the accumulation of karyotypic abnormalities as described previously [14,26].hESCs were also maintained in a defined medium as indicated. These conditions are described in detail elsewhere (TCS, AJR, submitted). Briefly, the media consisted of DMEM/F12 (Invitrogen), 2% fatty acid-free Cohn's fraction V BSA (Serologicals), 1× nonessential amino acids, 50 U/ml penicillin/streptomycin, 50 μg/ml ascorbic acid, 10 μg/ml bovine transferrin, 0.1 mM β-mecaptoethanol (all from Invitrogen), 1× Trace Elements A, B & C (Mediatech), 10 ng/ml hergulin1β (Peprotech), 10 ng/ml activinA (R&D Systems), 200 ng/ml LR3-IGF1 (JRH Biosciences), and 8 ng/ml FGF2 (R&D Systems). Cultures were passaged using Collagenase IV and plated on growth factor depleted Matrigel (BD Biosciences) diluted 1:200. These cultures were karyotypically normal.To partially differentiate hESC cultures for immunostaining analysis, karyotypically normal BG01 cells were plated on matrigel and grown for three days in DMEM/F12 containing 10% fetal calf serum (HyClone), 1× nonessential amino acids, 20 mM L-glutamine, 50 U/ml penicillin/streptomycin, and 0.1 mM β-mecaptoethanol. PowerBlot assaysBG01 hESC lysate was prepared in 10 mM Tris-HCl pH 7.4, 1 mM sodium orthovanadate and 1% SDS, and the PowerBlot assays were performed by BD Biosciences (BD Biosciences). Briefly, 200 μg of protein lysate was loaded in a single, gel-wide well, on a SDS-4–15% gradient polyacrylamide gel. The full PowerBlot screen consisted of five gels, which were blotted and probed with 934 antibodies, and was performed in duplicate with the same cell lysate. The gel dimensions were 130 × 100 × 0.5 mm, and proteins were separated at 150 volts for 1.5 hours, and transferred to an Immobilon-P membrane (Millipore). The membranes were blocked and clamped in a manifold that created 40 lanes across each membrane. A mix of 1 to 8 mouse monoclonal primary antibodies was added to each lane, in dilutions and combinations that had been predetermined to enable accurate identification of well-separated proteins. The predicted sizes of detectable proteins in the blots ranged from 10–540 kD, and the dilutions of the primary antibodies ranged from 1:250 to 1:15,000.The blots were removed from the manifolds, washed and incubated with goat anti-mouse secondary antibody conjugated to the Alexa680 fluorophore (Molecular Probes). The membranes were scanned using the Odyssey Imaging System (LI-COR). Molecular weight standards were generated by adding a cocktail of antibodies to P190 (190 kD), Adaptin beta (106 kD), STAT-3 (92 kD), PTP1D (72 kD), Mek-2 (46 kD), RACK-1 (36 kD), GRB-2 (24 kD) and Rap2 (21 kD) to lane 40 of gels A-D. Molecular standards for gel E were generated by adding a cocktail of antibodies to Exportin-1/CRM1 (112 kD), MCM (83 kD), Nucleoporin p62 (62 kD), α-tubulin (55 kD), Actin (42 kD), KNP-1/HES1 (28 kD) and NTF2 (15 kD) to lane 16, and antibodies to p190 (190 kD), Hip1R (120 kD), Transportin (101 kD), Calreticulin (60 kD), Arp3 (50 kD), eIF-6 (27 kD) and Rap2 (21 kD) to lane 17.Bands were detected and raw signal intensity captured automatically using the PDQuest software (Bio-Rad). To normalize the signal intensities, the total raw quantity of each band was divided by the average intensity value of the molecular standards in that image and the normalized values for the duplicate samples were averaged and expressed as normalized intensity units (i.u.). These values represent the relative signal intensity observed for each identified protein band, rather than relative expression levels of different proteins, due to differences in the efficiencies of antibody binding and dilution of the primary antibodies used. Proteins were identified based on the similarity of expected and observed band migration profiles and bands that could not be identified were excluded from the analysis. All identified proteins were verified by visual inspection, and proteins exhibiting a low signal intensity, with an averaged signal of < 1000 i.u., were verified by visual inspection using contrast enhancement in Adobe Photoshop. Bands with > 800 i.u. could typically be observed without additional image enhancement. Microsoft Excel files were generated that contained information on: gel number, lane number, antibody catalogue number (BD Biosciences), protein name, expected size, observed size, repeat 1 i.u. value, repeat 2 i.u. value, averaged i.u. value, antibody dilution, outline of protein function, Entrez gene and SwissProt identification numbers. These tables were used to list expressed proteins (Additional File 1). Kinexus assaysPreparation of the BG03 cell lysate and western blotting was performed according to published protocols [50]. Briefly, cell lysate was prepared in 20 mM MOPS pH 7.0, 2 mM EGTA, 5 mM EDTA, 30 mM sodium fluoride, 40 mM β-glycerolphosphate pH 7.2, 20 mM sodium pyrophosphate, 1 mM sodium orthovanadate, 1 mM PMSF, 3 mM benzamidine, 5 μM pepstatin, 10 μM leupeptin, 0.5% nonidet P-40, with the final pH adjusted to 7.2. The Kinexus assays for protein kinases (KPKS-1.2A and B [76 antibodies]), phosphatases (KPPS-1.2 [27 antibodies]) and phosporylated sites in cell signaling molecules (KPSS-3.1 [37 antibodies]) were performed by Kinexus. The Bio-Rad Mini-PROTEAN 3 electrophoresis system was used to separate proteins by SDS-PAGE. For each assay, 250 μg of cell lysate was loaded in a single well spanning the width of the stacking gel, then separated through a 12.5% SDS-Polyacrylamide gel and transferred to a PVDF membrane. A 20-lane manifold was placed over the membrane and a different mixture of up to 3 primary antibodies was added to each well. The combinations of primary antibodies had been predetermined to detect well-separated proteins, avoiding crossreaction to different proteins that co-migrate. The primary antibodies were rabbit and goat polyclonal, and mouse monoclonal antibodies, diluted 1:1000. After incubation with the primary antibodies, the membranes were removed from the manifolds, washed and incubated with a mix of the appropriate secondary antibodies. The secondary antibodies were donkey anti-rabbit (at 1:5000), sheep anti-mouse (at 1:10,000) and bovine anti-goat (at 1:10,000), all conjugated with horse radish peroxidase. The membranes were washed and immunoreactive bands detected by enhanced chemiluminescence (Amersham-Pharmacia) using a FluorS Max Multi-imager (Bio-Rad). Prestained size markers (201.5, 156.8, 106, 79.7, 48.4, 37.8, 23.3, and 18.2 kD) and predetermined human-specific protein migration profiles were used to accurately identify proteins using the Kinexus immuno-reactivity identification system (IRIS) software. Detected proteins were verified by visual inspection. ImmunocytochemistryImmunocytochemistry and staining procedures were as described previously [51]. Briefly, cells were fixed with 4% paraformaldehyde for half an hour, blocked in blocking buffer (5% goat serum, 1% BSA, 0.1% Triton X-100) for 1 hour followed by incubation with the primary antibody at 4°C overnight. Appropriately coupled secondary antibodies (Molecular Probes) were used for single and double labeling. All secondary antibodies were tested for cross reactivity and non-specific immunoreactivity. The following antibodies were used: ABP-280 (1:250, BD Biosciences 610798), CtBP1 (1:1000, BD Biosciences 612042), CtBP2 (1:1000, BD Biosciences 612044), GS-28 (1:2000, BD Biosciences 611184), HDJ-2 (1:100, BD Biosciences 611872), L-Caldesmon (1:2000, BD Biosciences 610660), Rabaptin-5 (1:500, BD Biosciences 611080), phospho-p130 Cas (Tyr165) (1:50, Cell Signaling Technology 4015), phospho-Ras-GAP (pY460) (1:250, BD Biosciences 612736), Ras-GAP (1:250, BD Biosciences 610043), Shc-C (1:1000, BD Biosciences 610642), Oct-4 (Santa Cruz biotechnology, 1:200 SC-8628), TNIK (1:100, BD Biosciences, 612250), p130 Cas (1:100, BD Biosciences, 610272), ERBB2 (1:100, Lab Vision, 9G6.10), ERBB3 (1:100, R&D Systems, MAB348), ZO1 (1:100, Invitrogen, 61–7300), or Occludin (1:100, Invitrogen, 71–1500). Hoechst (Invitrogen) or DAPI (Sigma) were used to identify nuclei, and Triton X-100 was omitted when staining for extracellular antigens (ZO1, occludin, ERBB2/3). Images were captured on an Olympus or Nikon fluorescence microscope. lllumina data and comparison to proteomic databaseExpression levels of proteins detected by the PowerBlot assay were compared to our previous published database of multiple hESC lines examined using the Illumina bead array platform (Liu et al., 2006). Averaged transcript expression signals from the BG01, BG02 and BG03 cell lines were converted to a +/- format, based on the following criteria: A mean transcript detection level of > 5,000 was designated as ++++; 1,000–5,000 as +++; 100–1,000 as ++; 30–100 as +; and signals < 30 was represented as -. In parallel, the protein expression levels were converted to a +/- format based on these criteria: i.u. > 10,000 as ++++; 5,000–10,000 as +++; 1,000–5,000 as ++; 100–1,000 as +. In addition, genes were categorized into the same functional/signaling pathways as per the western blot database.
BackgroundHuman embryonic stem cells (hESCs) offer a virtually unlimited source of neural cells for structural repair in neurological disorders, such as stroke. Neural cells can be derived from hESCs either by direct enrichment, or by isolating specific growth factor-responsive and expandable populations of human neural stem cells (hNSCs). Studies have indicated that the direct enrichment method generates a heterogeneous population of cells that may contain residual undifferentiated stem cells that could lead to tumor formation in vivo.Methods/Principal FindingsWe isolated an expandable and homogenous population of hNSCs (named SD56) from hESCs using a defined media supplemented with epidermal growth factor (EGF), basic fibroblast growth factor (bFGF) and leukemia inhibitory growth factor (LIF). These hNSCs grew as an adherent monolayer culture. They were fully neuralized and uniformly expressed molecular features of NSCs, including nestin, vimentin and radial glial markers. These hNSCs did not express the pluripotency markers Oct4 or Nanog, nor did they express markers for the mesoderm or endoderm lineages. The self-renewal property of the hNSCs was characterized by a predominant symmetrical mode of cell division. The SD56 hNSCs differentiated into neurons, astrocytes and oligodendrocytes throughout multiple passages in vitro, as well as after transplantation. Together, these criteria confirm the definitive NSC identity of the SD56 cell line. Importantly, they exhibited no chromosome abnormalities and did not form tumors after implantation into rat ischemic brains and into naïve nude rat brains and flanks. Furthermore, hNSCs isolated under these conditions migrated toward the ischemia-injured adult brain parenchyma and improved the independent use of the stroke-impaired forelimb two months post-transplantation.Conclusions/SignificanceThe SD56 human neural stem cells derived under the reported conditions are stable, do not form tumors in vivo and enable functional recovery after stroke. These properties indicate that this hNSC line may offer a renewable, homogenous source of neural cells that will be valuable for basic and translational research.
To date there have been no effective treatments for improving residual structural and functional deficits resulting from stroke. Current therapeutic approaches, such as the use of thrombolytics, benefit only 1 to 4% of patients [1]. Consequently, the majority of stroke patients experience progression of ischemia associated with debilitating neurological deficits. Recent evidence has suggested that the transplantation of cells derived from cord blood, bone marrow or brain tissue (fetal and adult) enhances sensorimotor function in experimental models of stroke [2], [3]. However, the normal human-derived somatic stem cells used in these studies have a limited capacity to differentiate into the diverse neural cell types optimal for structural and physiological tissue repair and are not amenable for large-scale cell production. Unlike other sources of stem cells, hESC lines possess a nearly unlimited self-renewal capacity and the developmental potential to differentiate into virtually any cell type of the organism. As such, they constitute an ideal source of cells for regenerative medicine. The successful derivation of hESC lines from the inner cell mass of preimplantation embryos and their long-term maintenance in vitro over multiple passages has been demonstrated [4] and standardized. Differentiation and enrichment processes that direct hESCs towards a neural lineage have also been achieved. To promote neuralization, ESCs were cultured in a defined media supplemented with morphogens or growth factors [5], [6], [7] or cultured under conditions that promote “rosettes”, structures morphologically similar to the developing neural tube [8], [9]. This neuralization process has proven invaluable in understanding the specification of hESC-derived neural tissue [10], [11], [12]. However, the enriched neural progeny derived displayed overgrowth and limited migration after grafting into normal newborn mice [13] and lesioned adult rat striatum [12], [14], [15], [16]. The inner cores of these grafts contained tumorigenic precursor cells (reviewed in [17]). These findings suggest that neural cells generated by acute exposure to growth factors and/or morphogens may still be heterogeneous and potentially tumorigenic. We report an alternative method for the isolation and the perpetuation of a multipotent hNSC line from the hESCs with a primitive mode of self-renewal. We also demonstrate their long-term expansion, non-tumorigenic properties and functional engraftability in an experimental model of stroke.
1. In vitro isolation, growth and differentiation of hESC-derived hNSCsThe hESCs were maintained and expanded on mouse feeder layer in media supplemented with bFGF (Figure 1A). After cell dissociation, a portion of the hESCs was cultured in serum free medium containing EGF, bFGF and LIF. These factors are known to stimulate the proliferation of human fetal-derived NSCs [18], [19]. After 3 days in vitro (DIV), there was selective survival and growth of cells that aggregated in clusters or spheres (Figure 1B). These primary spheres were harvested and replated in fresh media. During the following week, the spheres attached to the flask and a fibroblast-like cell population began to migrate out (Figure 1C). Secondary spheres (2° spheres) were generated from these cultures and lifted off by the end of the week leaving a hollow in the middle of the attached cells (Figure 1D). The floating 2° spheres were collected and replated in fresh growth medium for 2 weeks. The cultures were then passaged by collagenase cell dissociation every 7 DIV for an additional 4 passages (Figure S1). At the 5th and 6th passages, spheres were dissociated into a single-cell suspension using trypsin-EDTA. At this stage there was a change in the hNSCs' adherent properties, and the cells began to grow as a monolayer with multiple foci of cells throughout the culture (Figure 1F). The adherent hNSC culture stained uniformly for nestin (Figure 1K), vimentin (Figure 1L) and with the radial glial marker 3CB2 (Figure 1M) indicating their homogeneity and NSC identity. Under these culture conditions, it is noteworthy that we did not observe the formation of rosettes which has been previously reported to occur under certain conditions during neuralization of hESCs [8], [20], [21]. RT-PCR analysis confirmed that these hNSCs did not express the pluripotency transcripts Oct-4 and Nanog (Figure 1I). Furthermore, the hNSCs did not express transcripts for brachyury and foxa2, marker genes for mesoderm and endoderm respectively (negative result, data not shown).10.1371/journal.pone.0001644.g001Figure 1Isolation and purification of hNSCs from the hESCs.The hESCs were grown on a mouse feeder layer (A). Primary neurospheres (B) were isolated and replated to eliminate other non-neural cells. The selectively harvested secondary neurospheres (arrow in C), left behind hollow cores in the surface area (star in D) where they attached earlier. They were perpetuated for an additional 5 passages (E). These 2° spheres were then passaged using a single cell dissociation protocol (F). Arrow in F shows an example of a focus of proliferating cells. (G, H) The hNSCs were passaged every 5–7 days, as described in the Methods section. Starting from an initial population of 1 million cells, the cumulative cell number was calculated at each passage as the fold of increase×the total cell number and plotted as logarithm with base 2 in function of time (G). The cell perpetuation (G) and population doubling (H) analysis demonstrated the continuous and stable growth of the hNSCs. (I) RT-PCR analysis showing the down regulation of the pluripotency transcripts Oct4 and Nanog in secondary neurospheres and in expanded hNSCs at passage 8 (P8). (J) Cytogenetic evaluation of the SD56 hNSCs line at passage 12 by standard G-banding was performed. Twenty metaphase cells were analyzed and showed a normal female chromosome complement (46,XX). Isolated and expanded hNSCs expressed the neural precursor cell markers nestin (K), Vimentin (L) and the radial glial cell marker 3CB2 (M) in virtually all the progeny. (N-P) Clonal self-renewal ability of the isolated hNSCs through symmetrical cell division. Single (N), two-cell stage (O) and four-cell stage (P) of a hNSC proliferating over a 3-day culture period. Note the symmetrical segregation of BrdU and nestin in the progeny. Bars: (A, B, C, D) 200 µm; (E, F) 100 µm; (K–M) 20 µm; (N–P) 10 µm.To ascertain self-renewal ability under clonal conditions, a single cell suspension was plated at clonal density (1–2 cell/10 µl). To determine if the hNSCs divide symmetrically, we pulsed cultures with the thymidine analog, bromodeoxyuridine (BrdU), after plating and looked for the expression of nestin in the progeny. Cultures were fixed after 1, 2 or 3 DIV (Figure 1N–P). After 2 days, plated single cells first underwent a symmetric cell division and gave rise to daughter cells that were both positive for BrdU and nestin. The clone of cells continued to grow over the 3 DIV and all the progeny expressed nestin. BrdU labeling demonstrated that it was rare for only one daughter cell to inherit the BrdU and thus had undergone asymmetric segregation of the chromatids (Figure S2). G-band karyotyping of these hNSCs confirmed the normal, non-transformed nature of cells after 12 passages (Figure 1J). We named the derived hNSCs SD56 (intermittently referred to as SD56 hNSCs or hNSCs).Under these defined growth conditions, the hNSCs showed stable growth with a 2.7±0.2 fold increase every 5 to 7 days (Figure 1G). The population doubling at each passage averaged at 1.4±0.1 (Figure 1H). The viability of hNSCs at each passage was consistent at the approximate value of 98%. The projected cumulative cell numbers demonstrated that trillions of cells could be generated over a period of 5 months (Figure 1G). We expanded the isolated hNSCs lines for over 20 passages with a stable phenotype. An initial cell bank of 75 vials containing 2 to 5 million cells each was generated and cryopreserved.Upon removal of the mitogenic factors and plating on a coverslip pre-coated with poly-L-ornithine (PLO) substrate, the hNSCs spontaneously differentiated into neurons, astrocytes and oligodendrocytes, a property that is consistent with normal multipotent hNSCs (Figure 2). After 2 DIV, hNSCs expressed transcripts for the neural-specific genes nestin, Notch1 and neural cell adhesion molecule (N-CAM) (Figure 2A) and for the lineage specific markers β-tubulin class III, medium-size neurofilament (NF-M) and microtubule-associated protein 2 (MAP-2) for neurons, GFAP for astrocytes and myelin basic protein (MBP) for oligodendrocytes (Figure 2A). Transcripts for the GABAergic cell marker glutamic acid decarboxylase (GAD) were expressed, but transcripts for the tyrosine hydroxylase (TH), a marker for dopaminergic neurons, were undetectable. Immunocytochemical analysis (Figure 2B–F) of 10 day-old cultures demonstrated that the proportion of nestin+ cells was 36.6±2.7%, 62.5±2.8% expressed the neuronal marker TuJ1, 1.9±0.3% expressed the astrocytic marker GFAP and 7.1±0.4% were oligodendrocytes and expressed galactocerebrocide (GC) (Figure 2F). A subset (9.8±1.6%) of the GFAP+ astrocytes co-expressed nestin.10.1371/journal.pone.0001644.g002Figure 2hESC-derived hNSCs spontaneously differentiated into the 3 principal central nervous system cell types.Dissociated hNSCs were washed free of growth factors and plated on poly-L-onithine coated glass coverslips. Differentiated cultures were either harvested after 2 DIV for total RNA extraction and RT-PCR analysis or fixed after 10 DIV and processed for indirect immunocytochemistry. (A) Differentiated hNSCs expressed the neural-specific transcripts nestin and Notch1 as well as transcripts: for neurons (β-tubulin class III, MAP-2, NCAM and medium-size neurofilament, NF-M), for astrocytes (GFAP) and for oligodendrocytes (MBP). The hNSCs expressed transcripts for GAD, but not for TH. B, C & D, morphology of NSC-derived progeny differentiated into GFAP+ astrocytes (B), GC-expressing oligodendrocytes (C) and TuJ1+ neurons (D), DAPI (blue) show life cell nuclei. (E) Photo showing cultures double-immunostained for TuJ1 (green) and nestin (red) (DAPI, blue). (F) Quantitative analysis of immunostained 10 day-old cultures for the 3 neural cell types. Results are mean±s.e.m. of three independent experiments, each performed in duplicate. Bars: (c) 20 µm; (d, e) 10 µm. 2. The isolated hNSCs are normal and do not form tumors in normal nude ratsThe self-renewal and pluripotent abilities of the hESCs are also associated with tumorigenic properties. Therefore, the first critical step toward developing therapeutic hNSCs is to verify that they are non-tumorigenic. The monolayer culture of SD56 hNSCs clearly demonstrated contact inhibition of growth, a normal karyotype and did not express the pluripotency transcripts Oct-4 and Nanog. Removal of mitogenic factors and continued culture on plastic resulted in cell senescence that is characteristic of non-transformed cells. To determine whether SD56 hNSCs form tumors in vivo, we transplanted them at high density (see Methods) into the forebrain and subcutaneously into the flank of nude rats. The animals were kept for a two-month post-transplant survival period. To label mitotically active cells in vivo during S-phase, the rats were injected IP with BrdU (50 mg/kg) every 8 hours during the last 24 hours before euthanasia. The transit amplifying endogenous precursors located in the subventricular zone (SVZ) were labeled (Figure S3); however, we were unable to detect grafted SD56 hNSCs co-localizing the human-specific nuclear marker hNuc and BrdU (Figure S3). No surviving SD56 hNSCs were detected in the flank of the transplanted animals suggesting that the grafted cells are not tumorigenic. 3. Transplanted cells survived, migrated toward and engrafted into the stroke-damaged host tissueTo investigate the survival and functional engraftment in an injury environment, hNSCs (4×105) were transplanted into the ischemic boundary zone in the rat striatum one week after the middle cerebral artery occlusion (MCAO) was performed. Animals were euthanized two months later and the brains processed for histo-pathology and immunocytochemistry. Grafted SD56 hNSCs, identified with hNuc, demonstrated a 37.0±15.8% survival rate and a remarkable dispersion toward the stroke-damaged tissue with no sign of overgrowth or tumorigenesis. The majority of grafted cells (61.2±4.7%) migrated at least 200 µm away from the injection site and penetrated an average distance of 806.4±49.3 µm into the stroke-damaged tissue (Figure 3A–C). Immunostaining with the blood vessel marker, GluT1, revealed dilated vessels in the infarcted striatum and a close association between vessels and the grafted hNSCs (Figure 3B, 3C). The grafted cells rarely expressed the proliferation marker Ki67 (Figure 3D), 29.8±4.4% expressed nestin (Figure 3E), 6.5±0.9% expressed doublecortin (DCX) and 60.8±8.1% were TuJ1+ (Figure 3F, G). Grafted cells rarely co-expressed the astroglial marker GFAP (Figure 3H) or differentiated into CNPase-expressing oligodendrocytes (Figure 3I). Immunostaining for GAD demonstrated that 25.1±2.3% of grafted hNSCs differentiated into GABAergic neurons while less than 2% were positive for glutamate (Figure 3J, K).10.1371/journal.pone.0001644.g003Figure 3Dispersion, engraftment and differentiation of the hNSCs in stroke-lesioned animals.(A) Schematic drawing of a frontal section through the striatum illustrating the dispersion of grafted hNSCs in the focal ischemia-lesioned parenchyma (shaded area). (B, C) Photos show frontal sections through the graft in the striatum immunostained with the human specific antibodies: anti-hNuc (green in B & C) and anti-GluT1 (red, B & C) showing blood vessels and dispersed hNSCs in the graft zone. C: higher magnification of the inset in B. (D–I) Photos taken from frontal sections through the graft in the striatum double immunoprocessed for cell proliferation and neural lineage markers. (D) Note the endogenous Ki67+ cells (red cells, arrow) in the stroke damaged area and the hNuc+ (green)/Ki67- grafted hNSCs (arrowheads). (E) Examples of grafted SD56 hNSCs showing co-expression of hNuc (green) and nestin (red). (F) Confocal 3D reconstructed orthogonal images of the hNuc+(green)/DCX+(red) NSCs (arrowheads) viewed in the x-z plan on the top and y-z plan on the right. (G) Examples show the majority of grafted NSC progeny co-expressing hNuc (red) and the neuronal marker TuJ1 (green). Grafted NSCs rarely differentiate into GFAP+ astrocytes (H). In I, rare example of grafted NSC progeny becoming an oligodendrocyte identified by the expression of CNPase (green). Grafted NSCs expressed the GABAergic marker GAD65/67 (J) and rarely expressed glutamate (K). (Abbreviations: Cx: cortex, Str: striatum). Bars: (B, C) 100 µm; (D, F) 20 µm; (E, G–K) 10 µm. 4. Transplanted cells improve sensorimotor function of the stroke-disabled forelimbWe asked whether transplanted SD56 hNSCs could enhance the recovery of sensorimotor function that is compromised in the stroke-injured rats. We used the cylinder test to measure the sensorimotor asymmetry in forelimb use during spontaneous exploration [22]. To establish the baseline of the stroke-induced sensorimotor deficit, spontaneous behavior of rats in a transparent cylinder was videotaped one week after stroke (pre-transplant, Figure 4). Tests were then conducted 4 and 8 weeks after vehicle and SD56 hNSCs transplantation. Stable asymmetry in forelimb use was observed 7 days post-stroke (pre-transplant, Figure 4). Ischemic rats used their impaired forelimbs (contralateral to lesion) during lateral exploration less than they did before stroke. Transplantation of SD56 hNSCs significantly enhanced the independent use of the impaired contralateral forelimb 4 weeks post transplantation (P<0.05 vs pre-transplant). Eight weeks after transplantation the improvement in the use of the impaired forelimb was stable and significant when compared to the pre-transplant group and significantly improved in comparison to vehicle treated group at 8 weeks (Figure 4). In the vehicle treated group, the independent use of the contralateral forelimb remained impaired 4 and 8 weeks post-injection. In an independent study and using the same MCAO rat animal model, we found that transplantation of dermal fibroblasts did not improve the stroke-induced motor deficits (unpublished data).10.1371/journal.pone.0001644.g004Figure 4Transplantation of NSCs improves sensorimotor function of the stroke-disabled forelimb.Forelimb use during spontaneous lateral exploration was measured in the cylinder test (see Method and Results sections for details). Groups of vehicle injected (n = 7) and hNSCs (n = 10) transplanted are represented. The animals were tested as described in Method section. Note the significant increase in the independent use of the impaired contralateral forelimb at 4 and 8 weeks post transplantation (P<0.05 vs pre-transplant group). The contralateral forelimb remained impaired in the vehicle treated group at 4 and 8 weeks post-injection. Bars represent percentages±s.e.m. of steps taken by the ipsilateral, contralateral and both forelimbs simultaneously. *P<0.05 vs pre-transplant group; #P<0.05 vs vehicle groups.
Our results indicate that a self-renewable and homogenous population of hNSCs, SD56, was derived from hESCs using defined media supplemented with a specific combination of growth factors. The SD56 hNSCs grew as an adherent monolayer culture, uniformly expressed molecular features of hNSCs including nestin, vimentin and the radial glial marker 3CB2, and did not express the pluripotency markers Oct4 or Nanog. The self-renewal property of the hNSCs was characterized by a predominant symmetrical mode of cell division. They exhibited no chromosomal abnormalities and demonstrated non-tumorigenic properties after implantation into ischemic brains and into naïve nude rat brains and flanks. Furthermore, the transplanted SD56 hNSCs migrated toward the stroke-damaged adult brain parenchyma, engrafted and improved the independent use of the stroke-impaired forelimb. Maintenance of stem cells requires symmetrical and asymmetrical cell divisions to both expand and to give rise to specialized progeny of a specific tissue (reviewed in [23]). In vivo, a complex cellular micro-environment or niche ensures the self-maintenance property of NSCs [24], [25], [26], [27]. In vitro, defined growth factors and extracellular matrices support stem cell self-renewal [28], [29]. The embryonic stem cells can propagate in a predominantly proliferative symmetrical mode, leading to homogeneous cell cultures growing relatively quickly with minimal cell differentiation [30], [31], [32], [33], [34]. Tissue specific stem cells, however, self-renew in a predominant asymmetric mode to maintain them selves and compensate for the loss of differentiated cells due to disease or injury. Thus, NSCs isolated from developing or adult brain grow as a mixture of undifferentiated and differentiated cells due the predominant asymmetrical mode of cell division [35], [36], [37], [38], [39], [40], [41]. A recent study has reported that a murine ESC-derived NSC line (LC1) is propagated as an adherent homogenous culture with a dominant mode of symmetrical self-renewal [21]. A combination of EGF and FGF2 was sufficient to propagate these NSCs as an adherent monolayer. However, the SD56 hNSC line described here required the combination of EGF, bFGF and LIF for self-maintenance. Although there are morphological and molecular similarities between our hNSCs and the NSCs previously described [21], the methods of isolation and growth are different. In addition to the different combination of growth factors used, the hNSC line we have isolated did not go through the rosette-structure stage. The in vitro analysis of BrdU incorporation and nestin expression indicated that our hNSCs divide predominantly symmetrically. This type of growth pattern is characteristic of primitive normal stem cells undergoing mostly symmetric cell division to increase the stem cell pool at the early stage of development or during tissue regeneration after injury [23]. RT-PCR and immunocytochemistry analysis demonstrated that these undifferentiated SD56 cells did not express any pluripotency, endodermal or mesodermal markers. Furthermore, the SD56 hNSCs described here exhibited the multipotential characteristic to differentiate into neurons, astrocytes and oligodendrocytes both in vitro and upon transplantation. Together these findings suggest that the hNSC line we isolated are appropriately programmed and share similar characteristics with the definitive NSCs of the developing brain. The SD56 hNSCs demonstrated a remarkable ability to migrate toward the stroke-damaged parenchyma of the adult rat brain. This directed migration by the majority of the grafted cells could be due to their uniform cellular composition, which results in an equal response to the host microenvironment. In previous studies, subpopulations of transplanted hESCs that were enriched in neural cells migrated in host microenvironments conducive to cell migration, such as the developing brain or in structures such as the rostral migratory stream [13], [20]. In the adult lesioned brain, the grafted hESC-derived neural cells proliferated and formed rosettes [14], teratomas [12], [15] or a cellular mass that induced a gliotic host response whereby local astrocytes demarcated the grafts [16]. Enriched neural cultures derived from mouse [42] and monkey ESCs [43] have produced behavioral improvements when transplanted into animal models of stroke and brain injury. However, in these cases, the transplanted non-human ESCs also formed a mass with signs of overgrowth in the core, as well as deformations [44], [45], [46]. ESCs plated at low density acquire neural identity within few hours after plating [47]. Interestingly, nearly all viable cells expressed nestin, the early neural fate marker Sox1, and the pluripotency marker Oct4. Together, these studies are seminal and suggest that complete neuralization may not be achieved through certain enrichment processes, consequently the neural cells could revert to a pluripotent stage [17]. The dispersion of the grafted hNSCs within host parenchyma may allow for more graft-host interactions that could stabilize differentiation, inhibit growth and prevent gliotic host response. In the present study, SD56 hNSC-transplanted animals demonstrated a stable improvement in the sensorimotor function when evaluated for spontaneous exploratory activity in the cylinder test that detects long-lasting deficits in forelimb use in the experimental models of stroke [22]. The transplantation of hNSCs significantly enhanced the independent use of the impaired contralateral forelimb 8 weeks post transplantation. This is the first report demonstrating that the transplantation of hNSCs derived from hESCs can improve neurologic behavior after experimental stroke. Together, these findings are encouraging and suggest that these cells are promising for future development. In addition to their therapeutic application, the hNSCs isolated under the reported conditions offer a means to interrogate host environments and unravel mechanistic features of self-renewal, non-tumorigenicity and functional engraftability in animal models of neurological disorders.
hESC and NSC CulturesThe hESC line H9 (WiCell Research Institute) was propagated every 5 to 7 days on irradiated mouse embryonic fibroblasts. The cell culture media consisted of a 1∶1 mixture of Dulbecco's modified Eagle's medium (DMEM) and F12 nutrient, 20% serum replacement (Invitrogen), 0.1 mM β-mercaptoethanol, 2 µg/ml heparin and 4 ng/ml bFGF (R&D Systems). To generate the NSCs, dissociated hESCs were cultured in a chemically defined medium composed of DMEM/F12 (1∶1) including glucose (0.6%), glutamine (2 mM), sodium bicarbonate (3 mM), and HEPES buffer (5 mM) [all from Sigma except glutamine (Invitrogen)]. A defined hormone mix and salt mixture (Sigma), including insulin (25 mg/ml), transferrin (100 mg/ml), progesterone (20 nM), putrescine (60 mM), and selenium chloride (30 nM) was used in place of serum. The medium was supplemented with EGF (20 ng/ml), bFGF (10 ng/ml) and LIF (10 ng/ml). Dissociated hNSCs were plated at a density of 100,000 cell/ml in Corning T75 (Invitrogen) culture flasks in the defined media together with the growth factors. After 5–7 DIV, the adherent culture was incubated in 0.025%trypsin/0.01% EDTA (w/v) for 1 min followed by the addition of trypsin inhibitor (Invitrogen) then gently triturated to achieve single cell suspension. The cells were then washed twice with fresh medium and reseeded in fresh growth factor-containing media at 100,000 cells/ml. This procedure was performed for 21 passages and the fold of increase and population doubling were calculated at each passage. For clonal analysis, single spheres or confluent hNSC cultures were single cell dissociated and serially diluted to yield 1–2 cell/10 µl. A 10-µl-cell suspension was then added to each of 96 or 24 well plates containing 200–300 µl of growth media. Wells containing one viable cell were marked the next day and re-scored 5 to 7 days later for cell proliferation. The differentiation of the hNSCs was performed as previously described [48]. Dissociated hNSCs were plated at a density of 106 cells/ml in control (media/hormone mix) medium devoid of any growth factors and supplemented with 1% fetal bovine serum (FBS) on poly-L-ornithine-coated (15 mg/ml; Sigma) glass coverslips in 24-well Nunclon culture dishes with 0.5 ml/well. After 2, 7–15 DIV cultures were fixed and processed for immunocytochemistry or used for RT-PCR analysis. Karyotype analysisLong-term cultures of hNSCs were incubated at 37°C and harvested for metaphase chromosomes when the cultures were 75% confluent. Metaphase chromosomes were obtained by standard chromosome harvest methods by exposure to Colcemid at 0.1 µg/ml for 1 hour at 37°C, a 2-minute exposure to trypsin/EDTA, hypotonized with 0.057 M KCl and fixed with 3∶1 methanol:acetic acid. Slide preparations were made by dropping the fixed cell pellet onto cold, wet slides and air-dried. After incubating the slides at 90°C for 30 minutes, chromosomes were trypsin banded and then Wright/Giemsa stained for G-banding analysis. Twenty metaphase cells were completely analyzed and a normal female chromosome complement was found (46,XX). Tumorigenicity in nude ratsAll animal experiments were conducted according to the National Institute of Health (NIH) guidelines and approved by the IACUC. Normal adult NIH-Nude rats (n = 5, 8 week-old, Taconic, Germantown, New York, United States) were used to test the tumorigenic potential of the SD56 hNSCs. Undifferentiated hNSCs from passage 9 were single cell dissociated using trypsin-EDTA and suspended at the concentration of 125,000 cell/µl in preparation for cell transplantation. Two µl of the cell suspension were stereotaxically transplanted into 4 sites within the striatum at the following coordinates: AP: +1.0 mm, ML: +3.2 mm, DV: −5.0; AP: +0.5 mm, ML: +3.0 mm, DV: −5.0; AP: −0.5 mm, ML: +3.0 mm, DV: −5.0; AP: −1.0 mm, ML: +3.5 mm, DV: −5.0 mm with the incisor bar set at 3.4 mm. The injection rate was 1 µl/min, and the cannula was left in place for an additional 5 min before retraction. For the flank tumor assay, 2×106 cells (125,000 cell/µl) were injected subcutaneously to the side of the adult nude rats. To label mitotically active cells in vivo during S-phase, the rats were injected IP with the BrdU (50 mg/kg, Sigma) every 8 hours during the last 24 hours before euthanasia. After 2-month survival time, rats were euthanized and a postmortem examination for tumor formation was performed. Induction of Focal Ischemia and Cell TransplantationAll animal experimentations were conducted according to the National Institute of Health (NIH) guidelines and approved by the IACUC. Sprague Dawley adult male rats (n = 17, 275g–310g, Charles River Laboratories, Wilmington, Massachusetts, United States) were subjected to one and a half hour suture occlusion of the middle cerebral artery (MCAO), as previously described [49] and immunosuppressed 2 days before cell transplantation and daily thereafter for one week with i.p. injections of cyclosporine A (20 mg/ml, Sandimmune, Novartis Pharmaceuticals). Thereafter oral cyclosporine was used at 210 µg/ml in drinking water until euthanasia. Undifferentiated SD56 hNSCs from passages between P9 and P13 were single cell dissociated using trypsin-EDTA in preparation for cell transplantation. One week after the stroke lesion, 2 µl of the hNSCs, at a concentration of 50,000 cell/µl, were stereotaxically transplanted into 4 sites within the lesioned striatum (n = 10) at the following coordinates: AP: +1.0 mm, ML: +3.2 mm, DV: −5.0; AP: +0.5 mm, ML: +3.0 mm, DV: −5.0; AP: −0.5 mm, ML: +3.0 mm, DV: −5.0; AP: −1.0 mm, ML: +3.5 mm, DV: −5.0 mm with the incisor bar set at 3.4 mm. The injection rate was 1 µl/min, and the cannula was left in place for an additional 5 min before retraction. As a control group, we used rats subjected to ischemia and injected with the vehicle (n = 7). All animals underwent baseline motor behavioral assessment before and after the ischemic lesion, and 4 & 8 weeks after cell transplantation. The animals were killed after 2-month survival time by transcardial perfusion with phosphate buffered saline (PBS) followed by 4% paraformaldehyde. The brains were cryoprotected in an increasing gradient of 10, 20 and 30% sucrose solution and cryostat sectioned at 40 µm and processed for immunocytochemistry. ImmunocytochemistryCultures were fixed with 4% paraformaldehyde for 15 min. Both cells and brain sections were rinsed in PBS for 3×5 min then incubated for 2 hrs (cultures) or overnight (brain sections) with the appropriate primary antibodies for multiple labeling. Secondary antibodies raised in the appropriate hosts and conjugated to FITC, RITC, AMCA, CY3 or CY5 chromogenes (Jackson ImmunoResearch) were used. Cells and sections were counterstained with the nuclear marker 4′,6-diamidine-2′-phenylindole dihydrochloride (DAPI). Positive and negative controls were included in each run. Immunostained sections were coverslipped using fluorsave (Calbiochem) as the mounting medium. The following antibodies were used: Anti-human Nuclei (hNuc, monoclonal 1∶100, Chemicon), Anti-TuJ1 (monoclonal 1∶100, Covance; Polyclonal 1∶200, Aves Labs); anti-GAD65/67 (polyclonal 1∶1000, Chemicon); Anti-glial fibrillary acidic protein (GFAP, monoclonal 1∶1000, Chemicon; polyclonal 1∶200, Aves Labs); Anti-galactocerebrocide (GC, monoclonal 1∶250, Chemicon); Anti-CNPase (polyclonal 1∶200, Aves Labs); Anti-Glucose Transporter type 1 (Glut-1 polyclonal, 1∶500, Chemicon); Anti-Nestin (polyclonal 1∶1000, Chemicon); Anti-vimentin (monoclonal 1∶500, Calbiochem); Anti-3CB2 (monoclonal 1∶500, Developmental Studies Hybridoma Bank); Anti-doublecortin (DCX, polyclonal 1∶250, SantaCruz Biotechnology); Anti-Ki67 (polyclonal 1∶250, SantaCruz Biotechnology). Fluorescence was detected, analyzed and photographed with a Zeiss LSM550 laser scanning confocal photomicroscope. For each animal, quantitative estimates of the total number of grafted cells were stereologically determined using the optical fractionator procedure [50]. A computer-assisted image analysis system was performed using Stereo Investigator software (MicroBrightField, Inc.). The rostral and caudal limits of the reference volume were determined by first and last frontal sections containing grafted cells. The striatum and cortex were accurately outlined at low magnification (2.5× objective). The optical fractionator probe was selected to perform systematic sampling of the immunoreactive cell population distributed within the serial sections to estimate the population number in the volume of tissue. The counting frame of the optical fractionator was defined at 50×50 µm squares and the systematic sampling was performed by translating a grid with 200×200 µm squares onto the sections of interest using the Stereo Investigator software. The sample sites were systematically and automatically generated by the computer and examined using a 60× objective of a Nikon Eclipse TE 300 microscope. The counting frame displayed inclusion and exclusion lines and only immunoreactive cell bodies falling within the counting frame with no contact with the exclusion lines were counted. The cell dispersion was measured by counting the number of cells within 200 µm distance from the graft site. The number and distance in µm of cells dispersed beyond 200 µm was also measured. An average of 2,000 cells was counted per animal. Double labeling was determined using the confocal laser scanning microscope by random sampling of 100 or more cells per marker for each animal, scoring first for hNuc+, followed by DAPI+ nuclei and then the marker of choice. The double labeling was always confirmed in x-z and y-z cross-sections produced by the orthogonal projections of z-series. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) analysisTotal RNA was extracted from cultured cells using RNAeasy kit (Quiagen). Aliquots (1 µg) of total RNA from the cells were reverse transcribed in the presence of 50 mM Tris-HCl, pH 8.3, 75 mM KCl, 3 mM MgCl2, 10 mM DTT, 0.5 µM dNTPs, and 0.5 µg oligo-dT(12–18) with 200 U Superscript RNase H-Reverse Transcriptase (Invitrogen). PCR amplification was performed using standard procedure with Taq Polymerase. Aliquots of cDNA equivalent to 50 ng of total RNA were amplified in 25 µl reactions containing 10 mM Tris-HCl, pH 8.3, 50 mM KCl, 1.5 mM MgCl2 , 50 pmol of each primer, 400 µM dNTPs, and 0.5 U AmpliTaq DNA polymerase (Perkin-Elmer). PCR was performed using the following thermal profile: 4 min at 94°C; 1 min at 94°C, 1 min at 60°C, 1.5 min at 72°C, for 30–40 cycles; 7 min at 72°C, and finally a soak at 4°C overnight. The following day, 10 µl aliquots of the amplified products were run on a 2% agarose Tris–acetate gel containing 0.5 mg/ml ethidium bromide. The products were visualized through a UV transilluminator, captured in a digital format using Quantify One Gel Analysis software (Bio-Rad Laboratories) on a Macintosh G4 computer.The PCR primers specific to each transcript were as follows: GFAP, forward (F), 5′-TCATCGCTCAGGAGGTCCTT–3′ Reverse (R), 5′-CTG TTGCCAGAGATGGAGGTT–3′; MAP2 (F) 5′-GAAGACTCGCATCCGAATGG–3′, (R) 5′-CGCAGGATAGGAGGAAGAGACT–3′; MBP (F) 5′-TTAGCTGAATTC GCGTGTGG–3′, (R) 5′-GAGGAAGTGAATGAGCCGGTTA-3′ were deigned using the Primer Designer software, Version 2.0 (Scientific and Educational Software) [48]. 18S, β-tubulin class III, N-CAM, Nestin, NF-M, Notch-1 primers [51]. Oct4, Nanog primers [11]. FOXa2 (HNF3B), Brachyury primers [52]. Behavioral testsThe cylinder test was used to assess the spontaneous forelimb use during lateral exploration movement [22]. Rats were placed in a transparent acrylic cylinder (20 cm diameter) for 5 minutes. The cylinder encourages use of the forelimbs for vertical exploration. A mirror was placed behind the cylinder so that the forelimbs could be viewed at all times. Testing sessions were videotaped and forelimb use was scored by a blinded operator. Movements scored were the independent use of the left or right forelimb or simultaneous use of both the left and right forelimb to contact the wall of the cylinder during a full rear, to initiate a weight-shifting movement, or to regain center of gravity while moving laterally in a vertical posture along the wall. Animals were tested for their baselines after stroke and 4 and 8 weeks after cell transplantation. Statistical analysisOutcome measurement for each experiment was reported as mean±SEM. All data were analyzed using SPSS 11 for Mac OS X (SPSS Inc.). Significance of inter-group differences was performed by applying Student's t-test where appropriate. The One-Way ANOVA analysis was used to compare group differences for the forelimb use as the dependant variable and groups as the single independent factor variable. Differences between the groups were determined using Bonferroni's post hoc test. A P-value of less than 0.05 was considered to be statistically significant.

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