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Mus musculus
17 Downloadable Samples
Description
Various substances have been reported to enhance the cardiac differentiation of embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). Ascorbic Acid had a cardiogenic effect in mESC CGR8 cell line. Transcriptome of AA-treated CGR8 ESCs did not reveal any significant changes in gene expression as compared to untreated cells.
Publication Title
Ascorbic Acid-Induced Cardiac Differentiation of Murine Pluripotent Stem Cells: Transcriptional Profiling and Effect of a Small Molecule Synergist of Wnt/β-Catenin Signaling Pathway.
Alternate Accession IDs
Sample Metadata Fields
Specimen part, Cell line
View Samples- Mus musculus
- 1 Downloadable Sample
Description
The two vertebrate Gsk-3 isoforms, Gsk-3a and Gsk-3b, are encoded by distinct genetic loci and exhibit mostly redundant function in murine embryonic stem cells (ESCs). Here we report that deletion of both Gsk-3a and Gsk-3b in mouse ESCs results in misregulated expression of imprinted genes and hypomethylation of corresponding imprinted loci. Treatment of wild-type ESCs with small molecule inhibitors of Gsk-3 phenocopies the DNA hypomethylation of imprinted loci observed in Gsk-3 null ESCs. We provide evidence that DNA hypomethylation in Gsk-3 null ESCs is due to a reduction in the levels of the de novo DNA methyltransferase, Dnmt3a2.
Publication Title
Phosphatidylinositol 3-kinase (PI3K) signaling via glycogen synthase kinase-3 (Gsk-3) regulates DNA methylation of imprinted loci.
Alternate Accession IDs
Sample Metadata Fields
Specimen part
View Samples- Mus musculus, Rattus norvegicus, Homo sapiens
- 4 Downloadable Samples
Description
The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Alternate Accession IDs
Sample Metadata Fields
Sex, Age, Specimen part, Race, Compound
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GSE24061- Mus musculus
- 4 Downloadable Samples
Description
The Hamner data set (endpoint A) was provided by The Hamner Institutes for Health Sciences (Research Triangle Park, NC, USA). The study objective was to apply microarray gene expression data from the lung of female B6C3F1 mice exposed to a 13-week treatment of chemicals to predict increased lung tumor incidence in the 2-year rodent cancer bioassays of the National Toxicology Program. If successful, the results may form the basis of a more efficient and economical approach for evaluating the carcinogenic activity of chemicals. Microarray analysis was performed using Affymetrix Mouse Genome 430 2.0 arrays on three to four mice per treatment group, and a total of 70 mice were analyzed and used as the MAQC-II's training set (GEO Series GSE6116). Additional data from another set of 88 mice were collected later and provided as the MAQC-II's external validation set (this Series). The training dataset had already been deposited in GEO by its provider and its accession number is GSE6116.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Alternate Accession IDs
Sample Metadata Fields
Specimen part, Compound
View Samples- Homo sapiens
- 6 Downloadable Samples
Illumina NovaSeq 6000
Description
Analyzing the kenetics of alveolar macrophage turnover after human lung transplantation and identifying protein and transcriptional differences between donor and recipient-derived alveolar macrophages Overall design: Bulk RNA sequencing performed from FACS sorted donor and recipient-derived alveolar macrophages derived from the bronchoalveolar lavage of lung transplant recipients, defined as CD45+, Live, lineage negative, CD64+CD206+ cells.
Publication Title
Rate of recipient-derived alveolar macrophage development and major histocompatibility complex cross-decoration after lung transplantation in humans.
Alternate Accession IDs
Sample Metadata Fields
Specimen part, Subject
View Samples- Mus musculus, Homo sapiens
- 22 Downloadable Samples
Description
Genomic technologies have unmasked molecularly distinct subgroups among tumors of the same histological type; but understanding the biologic basis of these subgroups has proved difficult since their defining alterations are often numerous, and the cellular origins of most cancers remain unknown. We sought to decipher complex genomic data sets by matching the genetic alterations contained within these, with candidate cells of origin, to generate accurate disease models. Using an integrated genomic analysis we first identified subgroups of human ependymoma: a form of neural tumor that arises throughout the central nervous system (CNS). Validated alterations included amplifications and homozygous deletions of genes not yet implicated in ependymoma. Matching the transcriptomes of human ependymoma subgroups to those of distinct types of mouse radial glia (RG)neural stem cells (NSCs) that we identified previously to be a candidate cell of origin of ependymoma - allowed us to select RG types most likely to represent cells of origin of disease subgroups. The transcriptome of human cerebral ependymomas that amplify EPHB2 and delete INK4A/ARF matched most closely that of embryonic cerebral Ink4a/Arf-/- RG: remarkably, activation of EphB2 signaling in this RG type, but not others, generated highly penetrant ependymomas that modeled accurately the histology and transcriptome of one human cerebral tumor subgroup (subgroup D). Further comparative genomic analysis revealed selective alterations in the copy number and expression of genes that regulate neural differentiation, particularly synaptogenesis, in both mouse and human subgroup D ependymomas; pinpointing this pathway as a previously unknown target of ependymoma tumorigenesis. Our data demonstrate the power of comparative genomics to sift complex genetic data sets to identify key molecular alterations in cancer subgroups.
Publication Title
Cross-species genomics matches driver mutations and cell compartments to model ependymoma.
Alternate Accession IDs
Sample Metadata Fields
Sex, Age, Specimen part, Disease, Disease stage
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