Identification of stage-specific gene expression signatures in response to retinoic acid during the neural differentiation of mouse embryonic stem cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE37602 | GEO | 2012/04/27
SECONDARY ACCESSION(S): PRJNA162391
REPOSITORIES: GEO
Similar Datasets
Project description:We analyzed gene expression profiling by an bioinformatic approach to identify the stage-specific gene sets associated with neural differentiation of mESCs. We exposed mESCs (B6G-2 cells) to titrated doses of retinoic acid (RA) for 4 days during embryoid body formation and then performed morphological analysis on day of differentiation (DoD) 8 and 29, or genomic microarray analysis on DoD 0, 2, 8 and 29. Microarray data were analyzed by clustering methods such as self-organizing map and principal component analysis. A gene network analysis for selected gene sets was then performed using Bayesian statistics to identify stage-specific gene expression signatures in response to RA. 9 total samples: 1 vehcle, 8 Retinoic acid and two or three doses of each developmental stage(Day) [Vehicle Control (0.1% dimethyl sulfoxide), estradiol (1nM and 10nM), dehydrotestosterone (1nM and 10nM) bisphenol A (1pM and 100pM), triiodothyronine (1nM and 10nM), dexamethasone (1nM and 10nM), 2,3,7,8-tetrachlorodibenzo-p-dioxin (1nM and 10nM), 4(OH)-2’3,3’4’-5’-pentachlo biphenyls (1nM and 10nM), thalidomide (100nM and 0.01mM), bis(2-ethylhexyl)phthalate (0.001mM and 0.1mM), permethrin (100nM and 0.01mM), cyclopamine (100nM and 0.01mM), methoprene acid (0.001mM and 0.1mM)]; 1 Timepoint [48 hours].
2012-04-26 | E-GEOD-37602 | biostudies-arrayexpress
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Project description:Decidualization is critical for the embryonic implantation and successful pregnancy. ATRA can suppress in-vitro decidualization of human endometrial stromal cells (hESCs) induced by MPA and estrogen treatment. However, the mechanism by which RA suppressed estrogen and progesterone induced decidualization of mESCs is not clear. We used microarrays to investigate the mechanism by which all-trans RA (ATRA) regulates the decidualization of endometrial stroma cells (mESCs). mESCs were isolated at day 4 of pseudopregnancy and cultured with administration of E2 and P4 in the presence or absence of ATRA for 72h.
2013-11-05 | E-GEOD-52033 | biostudies-arrayexpress
Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10. Synovial tisssue from healthy joints, OA joints, and RA joints.
2014-02-21 | E-GEOD-55235 | biostudies-arrayexpress
Project description:An expert-pathologist-reviewed epithelial ovarian cancer reference library (n = 50) used to assign the histopathology of epithelial ovarian cell lines using compositional statistics and random gene-sets
2016-11-08 | GSE73551 | GEO
Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.
2014-03-05 | GSE55457 | GEO
Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.
2014-03-05 | GSE55584 | GEO
Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.
2014-02-21 | GSE55235 | GEO
Project description:Decidualization is critical for the embryonic implantation and successful pregnancy. ATRA can suppress in-vitro decidualization of human endometrial stromal cells (hESCs) induced by MPA and estrogen treatment. However, the mechanism by which RA suppressed estrogen and progesterone induced decidualization of mESCs is not clear. We used microarrays to investigate the mechanism by which all-trans RA (ATRA) regulates the decidualization of endometrial stroma cells (mESCs).
2013-11-05 | GSE52033 | GEO
Project description:At a given time, rheumatoid arthritis (RA) is a heterogeneous disease and its underlying molecular mechanisms is still poorly understood. Because previous microarrays studies have only focused on late RA stages, we aimed to compare the biological and molecular profiles in early and long-standing (LS) RA. Synovial biopsies obtained by arthroscopy were performed in 4 early untreated RA patients, 4 LS treated RA patients and 7 control patients. Extracted mRNA were linearly amplified and used for large-scale gene expression profiling by cDNA arrays. By gene ontology analysis, the different gene combinations obtained by comparison of early, LS RA and normal synovium were used to identify the biological processes, molecular functions and pathways involved at each stage. Three combinations of 719, 116 and 52 transcripts dissociated respectively early from LS RA patients, early or LS RA from normal synovium. We identified several gene clusters and distinct molecular signatures specifically related to early or LS RA suggesting the involvement of different pathological mechanisms at each stage. Early and LS RA have distinct molecular signatures with the participation of different biological processes during the course of the disease. These results suggest that a better knowledge of the main biological processes involved at a given RA stage might help the rheumatologist to choose the more appropriate treatment.
2008-10-03 | GSE13026 | GEO