Authors
Alisa B Chankhunthod, Rhys Sorenson-Graff, Juliane Mercoli, Takashi Ishida, Stacey Dozono, Rachel Wellington, Youyi Fong, Brandon Hadland, Helong Gary Zhao
Published in
bioRxiv : the preprint server for biology. Oct 07, 2026. Epub Oct 07, 2026.
Abstract
Achieving near-single-cell precision in the prospective identification of long-term engrafting hematopoietic stem cells (LT-HSCs) remains challenging, likely because current static, point-in-time single-cell phenotyping technologies do not capture the dynamic nature of cell fate decisions. Here, we overcome this barrier by longitudinally tracking individual hematopoietic stem and progenitor cells (HSPCs) in ex vivo niche cultures using live imaging, coupled with downstream immunophenotyping, single-cell transcriptomics, and transplant assays. Deep learning analysis of live imaging data achieved 98.5% average precision in predicting serially-engrafting LT-HSC (serial-LT-HSC) identity from each cell's behaviorome (the totality of observed behaviors), outperforming currently available technologies. Serial-LT-HSCs exhibited lower motility among HSPC populations, which was associated with reduced Activin signaling activity and elevated expression of cell adhesion genes. Correspondingly, antagonizing Activin signaling by Inhibin-A treatment in ex vivo niche cultures suppressed HSPC motility while increasing the frequency of post-expansion serial-LT-HSCs, revealing a novel role for Activin signaling in HSPC behavior and fate determination. Collectively, this study establishes a broadly applicable framework that connects behavioromic analysis and single-cell transcriptomics to enable near-single-cell precision in predicting stem cell fate and uncover molecular mechanisms governing behavior-fate relationships.
PMID:
42926802
Bibliographic data and abstract were imported from PubMed on 25 Oct 2026.
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