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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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CellFinder
|
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.
|
[] |
CellFinder
|
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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CellFinder
|
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.
|
[] |
CellFinder
|
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.
|
[] |
CellFinder
|
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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CellFinder
|
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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CellFinder
|
Undifferentiated Cythera (Cyt-ES) and HUES6 (HUES6-ES) hESC lines were maintained in culture as previously described [12,23,56].
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CellFinder
|
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that undifferentiated Cyt-ES and HUES6-ES cells were positive for the pluripotent markers Oct4, SSEA-4, and Tra-1–80 (unpublished data).
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CellFinder
|
NPs were derived from the hESC cell lines using protocols optimized for each line (see Materials and Methods).
|
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] |
CellFinder
|
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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] |
CellFinder
|
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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] |
CellFinder
|
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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CellFinder
|
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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CellFinder
|
Cyt-NP, HUES6-NP, and hCNS-SCns cells were Nestin and Sox1 positive.
|
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CellFinder
|
Nuclei stained positive for Dapi.
|
[] |
CellFinder
|
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.
|
[] |
CellFinder
|
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.
|
[] |
CellFinder
|
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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CellFinder
|
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.
|
[] |
CellFinder
|
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.
|
[] |
CellFinder
|
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.
|
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CellFinder
|
After correcting for multiple hypothesis testing using the Benjamini-Hochberg method, a p-value cutoff of 0.01 was used to identify enriched genes.
|
[] |
CellFinder
|
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).
|
[] |
CellFinder
|
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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] |
CellFinder
|
RT-PCR of Oct4 and Nanog mRNA levels accurately reflected the signal estimates from the array (Figure 1C).
|
[] |
CellFinder
|
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).
|
[
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] |
CellFinder
|
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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CellFinder
|
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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] |
CellFinder
|
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].
|
[] |
CellFinder
|
RT-PCR validation of DNER confirmed array-derived signal estimates, indicating an enrichment of DNER in NPs relative to hESCs (Figure 1C).
|
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CellFinder
|
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].
|
[
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CellFinder
|
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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CellFinder
|
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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CellFinder
|
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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CellFinder
|
Next we asked whether the highest enriched genes in hESCs relative to NPs reflected our existing knowledge in the literature.
|
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CellFinder
|
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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CellFinder
|
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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CellFinder
|
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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] |
CellFinder
|
Oct4, reviewed in [63], and Nanog [64] are crucial for the pluripotency of hESCs.
|
[
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] |
CellFinder
|
Recently, knockdown of Zfp42/Rex-1 in mouse ESC caused the cells to differentiate [65].
|
[] |
CellFinder
|
Our gene-level exon array analysis confirmed that the hESCs and NPs were molecularly distinct.
|
[
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CellFinder
|
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].
|
[
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CellFinder
|
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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CellFinder
|
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).
|
[
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CellFinder
|
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.
|
[
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CellFinder
|
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.
|
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{
"end": 75,
"label": "CellLine",
"start": 69,
"text": null
},
{
"end": 128,
"label": "CellType",
"start": 121,
"text": null
},
{
"end": 112,
"label": "CellLine",
"start": 107,
"text": null
}
] |
CellFinder
|
Statistical significance for differential gene expression was determined by using t-statistics with Benjamini-Hochberg correction for false discovery rate (p < 0.01).
|
[] |
CellFinder
|
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.
|
[] |
CellFinder
|
Statistical significance for GO analysis was assessed by using χ2 statistics with Bonferroni correction for multiple hypothesis testing.
|
[] |
CellFinder
|
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.
|
[
{
"end": 192,
"label": "CellType",
"start": 189,
"text": null
},
{
"end": 151,
"label": "CellType",
"start": 146,
"text": null
}
] |
CellFinder
|
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.
|
[] |
CellFinder
|
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.
|
[
{
"end": 189,
"label": "CellType",
"start": 186,
"text": null
},
{
"end": 181,
"label": "CellType",
"start": 176,
"text": null
}
] |
CellFinder
|
We compared Cyt-ES to hCNS-SCns to illustrate our approach.
|
[
{
"end": 29,
"label": "CellType",
"start": 22,
"text": null
},
{
"end": 18,
"label": "CellLine",
"start": 12,
"text": null
},
{
"end": 31,
"label": "CellType",
"start": 22,
"text": null
}
] |
CellFinder
|
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).
|
[] |
CellFinder
|
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.
|
[] |
CellFinder
|
In total, 17,430 gene models were represented by probesets that satisfied these criteria.
|
[] |
CellFinder
|
Next we asked whether the probeset expression within each gene model was positively correlated for any two cell lines.
|
[] |
CellFinder
|
To do this we calculated the Pearson correlation coefficient between the vectors of median signal estimates across replicates in Cyt-ES versus hCNS-SCns.
|
[
{
"end": 150,
"label": "CellType",
"start": 143,
"text": null
},
{
"end": 152,
"label": "CellType",
"start": 143,
"text": null
},
{
"end": 135,
"label": "CellLine",
"start": 129,
"text": null
}
] |
CellFinder
|
The vast majority of genes (>80%) was found to have probeset-level Pearson correlation coefficients of greater than 0.8 (Figure 3A).
|
[] |
CellFinder
|
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).
|
[
{
"end": 130,
"label": "CellType",
"start": 123,
"text": null
},
{
"end": 132,
"label": "CellType",
"start": 123,
"text": null
},
{
"end": 118,
"label": "CellType",
"start": 113,
"text": null
}
] |
CellFinder
|
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.
|
[
{
"end": 182,
"label": "CellType",
"start": 177,
"text": null
},
{
"end": 82,
"label": "CellType",
"start": 73,
"text": null
},
{
"end": 196,
"label": "CellType",
"start": 187,
"text": null
},
{
"end": 68,
"label": "CellType",
"start": 63,
"text": null
},
{
"end": 194,
"label": "CellType",
"start": 187,
"text": null
},
{
"end": 80,
"label": "CellType",
"start": 73,
"text": null
}
] |
CellFinder
|
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.
|
[] |
CellFinder
|
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).
|
[
{
"end": 221,
"label": "CellLine",
"start": 215,
"text": null
},
{
"end": 238,
"label": "CellType",
"start": 229,
"text": null
},
{
"end": 105,
"label": "CellLine",
"start": 99,
"text": null
},
{
"end": 94,
"label": "CellType",
"start": 85,
"text": null
},
{
"end": 236,
"label": "CellType",
"start": 229,
"text": null
},
{
"end": 92,
"label": "CellType",
"start": 85,
"text": null
}
] |
CellFinder
|
Genes were required to have more than five probesets localized within the exons in the gene.
|
[] |
CellFinder
|
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.
|
[
{
"end": 230,
"label": "CellType",
"start": 223,
"text": null
},
{
"end": 205,
"label": "CellLine",
"start": 199,
"text": null
},
{
"end": 232,
"label": "CellType",
"start": 223,
"text": null
}
] |
CellFinder
|
The five points summarizing the log2 probeset-level estimates are indicated by black filled circles.(C) Each probeset was summarized by five points.
|
[] |
CellFinder
|
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.
|
[
{
"end": 99,
"label": "CellLine",
"start": 93,
"text": null
},
{
"end": 112,
"label": "CellType",
"start": 103,
"text": null
},
{
"end": 110,
"label": "CellType",
"start": 103,
"text": null
}
] |
CellFinder
|
Probesets were considered present if the DABG p-value was <0.05 for all three replicates in the cell type.
|
[] |
CellFinder
|
A regression line derived from robust linear regression with MM estimation is indicated.
|
[] |
CellFinder
|
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.
|
[
{
"end": 83,
"label": "CellLine",
"start": 77,
"text": null
},
{
"end": 148,
"label": "CellType",
"start": 139,
"text": null
},
{
"end": 146,
"label": "CellType",
"start": 139,
"text": null
}
] |
CellFinder
|
Points close to the regression line are not significantly different in Cyt-ES versus hCNS-SCns.
|
[
{
"end": 94,
"label": "CellType",
"start": 85,
"text": null
},
{
"end": 77,
"label": "CellLine",
"start": 71,
"text": null
},
{
"end": 92,
"label": "CellType",
"start": 85,
"text": null
}
] |
CellFinder
|
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.
|
[
{
"end": 439,
"label": "CellLine",
"start": 433,
"text": null
},
{
"end": 103,
"label": "CellLine",
"start": 97,
"text": null
},
{
"end": 394,
"label": "CellLine",
"start": 388,
"text": null
},
{
"end": 132,
"label": "CellType",
"start": 123,
"text": null
},
{
"end": 423,
"label": "CellType",
"start": 416,
"text": null
},
{
"end": 409,
"label": "CellType",
"start": 402,
"text": null
},
{
"end": 130,
"label": "CellType",
"start": 123,
"text": null
},
{
"end": 425,
"label": "CellType",
"start": 416,
"text": null
},
{
"end": 411,
"label": "CellType",
"start": 402,
"text": null
}
] |
CellFinder
|
Here, a possible representation of the data was explored.
|
[] |
CellFinder
|
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.
|
[] |
CellFinder
|
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).
|
[
{
"end": 155,
"label": "CellType",
"start": 146,
"text": null
},
{
"end": 126,
"label": "CellType",
"start": 121,
"text": null
},
{
"end": 153,
"label": "CellType",
"start": 146,
"text": null
}
] |
CellFinder
|
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.
|
[] |
CellFinder
|
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).
|
[
{
"end": 142,
"label": "CellLine",
"start": 136,
"text": null
},
{
"end": 156,
"label": "CellType",
"start": 147,
"text": null
},
{
"end": 154,
"label": "CellType",
"start": 147,
"text": null
}
] |
CellFinder
|
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.
|
[] |
CellFinder
|
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).
|
[
{
"end": 470,
"label": "CellType",
"start": 463,
"text": null
},
{
"end": 183,
"label": "CellType",
"start": 174,
"text": null
},
{
"end": 181,
"label": "CellType",
"start": 174,
"text": null
},
{
"end": 166,
"label": "CellType",
"start": 161,
"text": null
},
{
"end": 472,
"label": "CellType",
"start": 463,
"text": null
},
{
"end": 359,
"label": "CellLine",
"start": 352,
"text": null
}
] |
CellFinder
|
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.
|
[] |
CellFinder
|
Therefore, we applied M-estimation robust regression to estimate the line, which is less sensitive to outliers.
|
[] |
CellFinder
|
Fitting was performed using an iterated, re-weighted least squares analysis.
|
[] |
CellFinder
|
Our assumption was that most of the points were “correct,” i.e., that most of the exons were constitutively spliced.
|
[] |
CellFinder
|
Thus, robust regression would find the line that was least dependent on outliers, which would be potential AS exons.
|
[] |
CellFinder
|
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.
|
[] |
CellFinder
|
Using robust regression, the regression line for Cyt-ESC versus hCNS-SCns in the EHBP1 gene is illustrated in Figure 3C.
|
[
{
"end": 71,
"label": "CellType",
"start": 64,
"text": null
},
{
"end": 56,
"label": "CellLine",
"start": 49,
"text": null
},
{
"end": 73,
"label": "CellType",
"start": 64,
"text": null
}
] |
CellFinder
|
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.
|
[
{
"end": 90,
"label": "CellType",
"start": 83,
"text": null
},
{
"end": 92,
"label": "CellType",
"start": 83,
"text": null
},
{
"end": 66,
"label": "CellType",
"start": 61,
"text": null
}
] |
CellFinder
|
The difference between the actual and regression-based predicted value, normalized by the estimate of its standard deviation, is called the studentized residuals.
|
[] |
CellFinder
|
Studentized residuals were computed for all probeset pairs in EHBP1, and the histogram depicting their distribution is illustrated in Figure 3D.
|
[] |
CellFinder
|
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.
|
[] |
CellFinder
|
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).
|
[] |
CellFinder
|
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.”
|
[] |
CellFinder
|
RT-PCR confirmed that the exon, represented by the probeset, was indeed differentially included in hESCs and skipped in hCNS-SCns (Figure 7B).
|
[
{
"end": 127,
"label": "CellType",
"start": 120,
"text": null
},
{
"end": 104,
"label": "CellType",
"start": 99,
"text": null
},
{
"end": 129,
"label": "CellType",
"start": 120,
"text": null
}
] |
CellFinder
|
Applying this approach to all gene models revealed that, as expected, the majority of studentized residuals are centered at zero (Figure 3E).
|
[] |
CellFinder
|
Thus far in the example, our analysis was based on regression of hESCs (y-axis) versus hCNS-SCns (x-axis) (Figure 3B–3D).
|
[
{
"end": 96,
"label": "CellType",
"start": 87,
"text": null
},
{
"end": 70,
"label": "CellType",
"start": 65,
"text": null
},
{
"end": 94,
"label": "CellType",
"start": 87,
"text": null
}
] |
CellFinder
|
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).
|
[
{
"end": 222,
"label": "CellType",
"start": 217,
"text": null
},
{
"end": 320,
"label": "CellType",
"start": 315,
"text": null
},
{
"end": 239,
"label": "CellType",
"start": 230,
"text": null
},
{
"end": 298,
"label": "CellType",
"start": 289,
"text": null
},
{
"end": 237,
"label": "CellType",
"start": 230,
"text": null
},
{
"end": 296,
"label": "CellType",
"start": 289,
"text": null
}
] |
CellFinder
|
As our method for predicting candidate alternative exons was based on identification of outliers using robust regression, we named the method REAP.
|
[] |
CellFinder
|
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.
|
[] |
CellFinder
|
First, we identified genes with probeset signal estimates that were poorly correlated and were not amenable to our method.
|
[] |
CellFinder
|
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