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Integrated multi-omics analysis identification of genetic and epigenetic features that predict human pluripotent stem cell-derived progenitor differentiation to cardiomyocytes [ATAC-Seq]

GSE254294 Homo sapiens Genome binding/occupancy profiling by high throughput sequencing 4 samples Submitted 2024/09/05 Platform GPL18573
Summary
Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are advancing cardiovascular development and disease modeling, drug testing, and regenerative therapies. However, hPSC-CM production is hindered by significant variability in the differentiation process. Establishment of early quality markers to monitor lineage progression and predict terminal differentiation outcomes would address this robustness and reproducibility roadblock in hPSC-CM production. We performed an integrated transcriptomic and epigenomic analysis to assess how attributes of the cardiac progenitor cell (CPC) affect CM differentiation outcome. Our analysis identified predictive markers of CPCs that give rise to high purity CM batches, including TTN, TRIM55, DUSP5, DUSP6, GIPR, RHOBTB3, CRIP2, SLC7A11, MAB21L2, and CALD1. We also gained insight into mechanisms of batch failure and dominant non-CM cell types generated in failed batches. This study demonstrates how integrated multi-omic analysis of progenitor cells can identify quality attributes of that progenitor and predict differentiation outcomes, thereby improving differentiation protocols and increasing process robustness.
Published in
Integrated multi-omics analysis identifies features that predict human pluripotent stem cell-derived progenitor differentiation to cardiomyocytes
Simmons AD, Baumann C, Zhang X et al. · Journal of molecular and cellular cardiology 2024 · PMID 39222876 · doi:10.1016/j.yjmcc.2024.08.007
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Direct links to NCBI, no account and no request form: the whole study as GSE254294_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 4 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1069656 and SRA study SRP486128. Searching any of these in the dataset finder brings you back here.

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