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.