A velocity-informed framework for resolving functional stratification in rare human stem cells

Single-cell transcriptomics has transformed our understanding of stem cell biology; however, the analysis of extremely rare and quiescent populations remains limited by the low cell numbers and functional inaccessibility. Here, we present a velocity-informed framework to resolve transcriptional stratification in ultra-rare stem cells, using very small embryonic-like stem cells (VSELs) from human umbilical cord blood as a proof-of-concept model. By integrating single-cell RNA sequencing with dynamical RNA velocity analysis, we identify two kinetically distinct VSEL subsets: a CD34⁺ fraction characterized by deep quiescence, and a CD133⁺ fraction exhibiting transcriptional priming toward lineage commitment. Pathway-level analyses further reveal state-specific programs of stress response, metabolic regulation, and developmental gene networks. Importantly, our approach highlights how kinetic modeling can uncover functional continua even in populations not amenable to conventional assays. This framework is broadly applicable to rare or controversial stem cell types, providing a generalizable strategy to dissect cellular heterogeneity and state transitions where direct functional validation is constrained.