Project description:Generation and extensive characterization of a gender-balanced, racially/ethnically diverse library of iPSCs from forty clinically healthy human individuals who range in age from 22-61. Specifically, here we provide mRNAseq data of duplicate samples of one clone from each of these forty iPSC lines.
Project description:Baseline transcriptomic signatures of cardiomyocytes differentiated from hiPSC lines generated from clinically well-characterized, diverse healthy human individuals. We provide mRNAseq data of various replicate samples of cardiomyocytes differentiated from 6 hiPSC lines.
Project description:Single cell transcriptomic signatures of cardiomyocytes differentiated from hiPSC lines generated from clinically well-characterized, diverse healthy human individuals. We provide single cell mRNAseq data of cardiomyocytes differentiated from 4 hiPSC lines.
Project description:Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is an integral part of the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. A key aim of DToxS is to generate both proteomic and transcriptomic signatures that cab predict adverse effects, especially cardiotoxicity, of drugs approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments (308 cell line/drug combinations + 64 HeLa control lysates + 9 auxiliary treatment samples) have been conducted at the Center for Advanced Proteomics Research at Rutgers-New Jersey Medical School. The integrated proteomic and transcriptomic signatures have been used for computational network analysis to identify cellular signatures of cardiotoxicity that may predict drug-induced toxicity and possible mitigation of such toxicities by mixing different drugs. Both raw and processed proteomics data have been carefully controlled for quality and have been made publicly available via the PRoteomics IDEntifications (PRIDE) database. As such, this broad drug-stimulated proteomic dataset is valuable for the prediction drug toxicities and their mitigation.
Project description:Using an ex-vivo testicular culture from rats, we carried out transcriptomic experiments to identify the pathway of toxicity ellicited by the fungicides carbendazim, iprodione alone or in combination. we used commercial Agilent Microarray GE 4x44K Rat (V3) Gene Expression Microarray (G2514F) AMADID : 028282
Project description:Using an ex-vivo testicular culture from rats, we carried out transcriptomic experiments to identify the pathway of toxicity ellicited by the fungicides carbendazim, iprodione alone or in combination. we used commercial Agilent Microarray GE 4x44K Rat (V3) Gene Expression Microarray (G2514F) AMADID : 028282 Cultures were performed with and without fungicides. When required, CBZ, IPR or mixture was added beginning from day 2, at 50 nM or 500 nM for CBZ and at 50 nM or 500 nM for IPR; 50 nM each or 500 nM each for the mixture. For microarray experiments, three different pools of seminiferous tubules were exposed to two concentrations of CBZ, IPR or their mixture (50 nM and 500 nM) or to complete medium with vehicle (control cells) for 7, 14, and 21 days. Each condition of exposure to fungicides was compared with control cells at the same time point.
Project description:Tumor cell lines and drug-resistant counterparts. These data support the publication Gyorffy et al, Oncogene 2005 (July), Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. We contrasted the expression profiles of 13 different human tumor cell lines of gastric (EPG85-257), pancreatic (EPP85-181), colon (HT29) and breast (MCF7 and MDA-MB-231) origin and their counterparts resistant to the topoisomerase inhibitors daunorubicin, doxorubicin or mitoxantrone. We interrogated cDNA arrays with 43 000 cDNA clones ( approximately 30 000 unique genes) to study the expression pattern of these cell lines. A cell type comparison design experiment design type compares cells of different type for example different cell lines. Using regression correlation