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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.
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17967047
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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.
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17967047
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Alternative RNA splicing (AS), a major form of post-transcriptional gene regulation, is important in mammalian development and neuronal function.
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17967047
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Human ESC, hESC-derived NP, and human central nervous system stem cells were compared using Affymetrix exon arrays.
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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.
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17967047
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Experimental validation of REAP-predicted AS events indicated a threshold-dependent sensitivity ranging from 56% to 69%, at a specificity of 77% to 96%.
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17967047
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REAP predictions significantly overlapped sets of alternative events identified using expressed sequence tags and evolutionarily conserved AS events.
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17967047
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Our results also reveal that focusing on differentially expressed genes between hESC and NP will overlook 14% of potential AS genes.
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17967047
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In addition, we found that REAP predictions are enriched in genes encoding serine/threonine kinase and helicase activities.
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[] |
17967047
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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.
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17967047
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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.
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17967047
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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.
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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.
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17967047
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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.
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17967047
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The authors utilized Affymetrix exon arrays with probes targeting hundreds of thousands of exons to study AS comparing human ES to NP.
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To accomplish this, a novel computational method, REAP (Regression-based Exon Array Protocol), is introduced to analyze the exon array data.
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[] |
17967047
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The authors showed that REAP candidates are consistent with other types of methods for discovering alternative exons.
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[] |
17967047
|
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In addition, REAP candidate alternative exons are enriched in genes encoding serine/theronine kinases and helicase activities.
|
[] |
17967047
|
[
{
"tags": [
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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.
|
[] |
17967047
|
[
{
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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.
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17967047
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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.
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17967047
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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].
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17967047
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Understanding the processes that generate neuronal diversity is key to gaining insights into neuronal development and paving new avenues for biomedical research.
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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].
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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.
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In addition, the hESCs are a nearly infinite source for generating specialized cells such as neurons and glia for potential therapeutic purposes [4,5].
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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].
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17967047
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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].
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17967047
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NSCs and progenitor cells (NPs) are present throughout development and persist into adulthood [18–20].
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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].
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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].
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Several studies have explored expression patterns of NPCs.
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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].
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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.
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17967047
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AS is frequently used to regulate gene expression and to generate tissue-specific mRNA and protein isoforms [36–39].
|
[] |
17967047
|
[
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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].
|
[] |
17967047
|
[
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This functional complexity underscores the challenge and importance of elucidating AS regulation.
|
[] |
17967047
|
[
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AS appears to play a dominant role in regulating neuronal gene expression and function [41,42].
|
[] |
17967047
|
[
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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].
|
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17967047
|
[
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Finally, an early report estimating that 15% of point mutations disrupt splicing underscores the importance of splicing in human disease [48].
|
[] |
17967047
|
[
{
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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].
|
[] |
17967047
|
[
{
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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].
|
[] |
17967047
|
[
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Most systematic global analyses on AS have focused on comparisons across differentiated human tissues [50–52].
|
[] |
17967047
|
[
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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].
|
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] |
17967047
|
[
{
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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.
|
[] |
17967047
|
[
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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.
|
[] |
17967047
|
[
{
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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.
|
[] |
17967047
|
[
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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.
|
[
{
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{
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] |
17967047
|
[
{
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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].
|
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}
] |
17967047
|
[
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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.
|
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] |
17967047
|
[
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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.
|
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] |
17967047
|
[
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}
] |
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.
|
[] |
17967047
|
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Experimental validation revealed alternative exons that distinguish hESCs from NPs; some of them also distinguish hESCs from a variety of differentiated tissues.
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17967047
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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.
|
[] |
17967047
|
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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.
|
[] |
17967047
|
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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.
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17967047
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[
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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.
|
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17967047
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[
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Undifferentiated Cythera (Cyt-ES) and HUES6 (HUES6-ES) hESC lines were maintained in culture as previously described [12,23,56].
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17967047
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[
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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).
|
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17967047
|
[
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NPs were derived from the hESC cell lines using protocols optimized for each line (see Materials and Methods).
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17967047
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[
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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).
|
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17967047
|
[
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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].
|
[
{
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] |
17967047
|
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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].
|
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17967047
|
[
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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.
|
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17967047
|
[
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Cyt-NP, HUES6-NP, and hCNS-SCns cells were Nestin and Sox1 positive.
|
[
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{
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17967047
|
[
{
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}
] |
Nuclei stained positive for Dapi.
|
[] |
17967047
|
[
{
"tags": [
"O",
"O",
"O",
"O",
"O",
"O"
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"Nuclei",
"stained",
"positive",
"for",
"Dapi",
"."
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}
] |
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.
|
[] |
17967047
|
[
{
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}
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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.
|
[] |
17967047
|
[
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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.
|
[
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17967047
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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.
|
[] |
17967047
|
[
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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.
|
[] |
17967047
|
[
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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.
|
[
{
"end": 173,
"label": "CellLine",
"start": 167
}
] |
17967047
|
[
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After correcting for multiple hypothesis testing using the Benjamini-Hochberg method, a p-value cutoff of 0.01 was used to identify enriched genes.
|
[] |
17967047
|
[
{
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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).
|
[] |
17967047
|
[
{
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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).
|
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{
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{
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}
] |
17967047
|
[
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RT-PCR of Oct4 and Nanog mRNA levels accurately reflected the signal estimates from the array (Figure 1C).
|
[] |
17967047
|
[
{
"tags": [
"O",
"O",
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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).
|
[
{
"end": 57,
"label": "CellType",
"start": 54
}
] |
17967047
|
[
{
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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].
|
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{
"end": 138,
"label": "CellType",
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{
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},
{
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"label": "CellType",
"start": 93
},
{
"end": 77,
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"start": 63
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] |
17967047
|
[
{
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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.
|
[
{
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{
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] |
17967047
|
[
{
"tags": [
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"O",
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}
] |
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].
|
[] |
17967047
|
[
{
"tags": [
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RT-PCR validation of DNER confirmed array-derived signal estimates, indicating an enrichment of DNER in NPs relative to hESCs (Figure 1C).
|
[
{
"end": 125,
"label": "CellType",
"start": 120
},
{
"end": 107,
"label": "CellType",
"start": 104
}
] |
17967047
|
[
{
"tags": [
"O",
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"O",
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"O",
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}
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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].
|
[
{
"end": 149,
"label": "Tissue",
"start": 143
}
] |
17967047
|
[
{
"tags": [
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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.
|
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17967047
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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.
|
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] |
17967047
|
[
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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.
|
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17967047
|
[
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Next we asked whether the highest enriched genes in hESCs relative to NPs reflected our existing knowledge in the literature.
|
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{
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17967047
|
[
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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).
|
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17967047
|
[
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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).
|
[
{
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17967047
|
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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].
|
[
{
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17967047
|
[
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Oct4, reviewed in [63], and Nanog [64] are crucial for the pluripotency of hESCs.
|
[
{
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] |
17967047
|
[
{
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Recently, knockdown of Zfp42/Rex-1 in mouse ESC caused the cells to differentiate [65].
|
[] |
17967047
|
[
{
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Our gene-level exon array analysis confirmed that the hESCs and NPs were molecularly distinct.
|
[
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"label": "CellType",
"start": 54
},
{
"end": 67,
"label": "CellType",
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}
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17967047
|
[
{
"tags": [
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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].
|
[
{
"end": 75,
"label": "CellType",
"start": 70
},
{
"end": 82,
"label": "CellType",
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}
] |
17967047
|
[
{
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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).
|
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{
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{
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17967047
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}
] |
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).
|
[
{
"end": 45,
"label": "CellType",
"start": 42
}
] |
17967047
|
[
{
"tags": [
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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.
|
[
{
"end": 382,
"label": "CellType",
"start": 379
},
{
"end": 374,
"label": "CellType",
"start": 369
}
] |
17967047
|
[
{
"tags": [
"O",
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] |
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.
|
[
{
"end": 85,
"label": "CellLine",
"start": 80
},
{
"end": 88,
"label": "CellLine",
"start": 80
},
{
"end": 67,
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"start": 62
},
{
"end": 97,
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{
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},
{
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"label": "CellLine",
"start": 69
},
{
"end": 128,
"label": "CellType",
"start": 121
},
{
"end": 112,
"label": "CellLine",
"start": 107
}
] |
17967047
|
[
{
"tags": [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-CellType",
"O",
"B-CellLine",
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"B-CellLine",
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"O",
"B-CellType",
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"O",
"O",
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"tokens": [
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"."
]
}
] |
Statistical significance for differential gene expression was determined by using t-statistics with Benjamini-Hochberg correction for false discovery rate (p < 0.01).
|
[] |
17967047
|
[
{
"tags": [
"O",
"O",
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")",
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]
}
] |
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.
|
[] |
17967047
|
[
{
"tags": [
"O",
"O",
"O",
"O",
"O",
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]
}
] |
Statistical significance for GO analysis was assessed by using χ2 statistics with Bonferroni correction for multiple hypothesis testing.
|
[] |
17967047
|
[
{
"tags": [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
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"testing",
"."
]
}
] |
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