AI Article Synopsis
- - Human induced pluripotent stem cells (iPSCs) offer significant research potential, but current testing methods for pluripotency are inconsistent and rely on unclear markers.
- - The study employs long-read nanopore transcriptome sequencing to identify 172 genes related to specific cell states, validating 12 as unique markers for pluripotent and differentiated fates, like endoderm and mesoderm.
- - A new machine learning-based scoring system called "hiPSCore" is developed to effectively classify cell states and predict their potential in forming specialized cells and organoids, improving iPSC assessment by minimizing time and resource use.
Article Abstract
Human induced pluripotent stem cells (iPSCs) have great potential in research, but pluripotency testing faces challenges due to non-standardized methods and ambiguous markers. Here, we use long-read nanopore transcriptome sequencing to discover 172 genes linked to cell states not covered by current guidelines. We validate 12 genes by qPCR as unique markers for specific cell fates: pluripotency (CNMD, NANOG, SPP1), endoderm (CER1, EOMES, GATA6), mesoderm (APLNR, HAND1, HOXB7), and ectoderm (HES5, PAMR1, PAX6). Using these genes, we develop a machine learning-based scoring system, "hiPSCore", trained on 15 iPSC lines and validated on 10 more. hiPSCore accurately classifies pluripotent and differentiated cells and predicts their potential to become specialized 2D cells and 3D organoids. Our re-evaluation of cell fate marker genes identifies key targets for future studies on cell fate assessment. hiPSCore improves iPSC testing by reducing time, subjectivity, and resource use, thus enhancing iPSC quality for scientific and medical applications.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11447164 | PMC |
| http://dx.doi.org/10.1038/s41467-024-52922-1 | DOI Listing |
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