bioRxivCell BiologyPreprint
Proliferation history and transcription factor levels drive direct conversion
N. B. Wang, B. A. Lende-Dorn, H. O. Adewumi, A. M. Beitz, P. Han, T. M. O'Shea, K. E. Galloway
Abstract
The sparse and stochastic nature of reprogramming has obscured our understanding of how transcription factors drive cells to new identities. To overcome this limit, we developed a compact, portable reprogramming system that increases direct conversion of fibroblasts to motor neurons by two orders of magnitude. We show that subpopulations with different reprogramming potentials are distinguishable by proliferation history. By controlling for proliferation history and titrating each transcription factor, we find that conversion correlates with levels of the pioneer transcription factor Ngn2, whereas conversion shows a biphasic response to Lhx3. Increasing the proliferation rate of adult human fibroblasts generates morphologically mature, induced motor neurons at high rates. Using compact, optimized, polycistronic cassettes, we generate motor neurons that graft with the murine central nervous system, demonstrating the potential for in vivo therapies. One Sentence SummaryUsing a systems and synthetic biology approach to study the molecular determinants of reprogramming, we find that proliferation history and transcription factor levels drive cell fate in direct conversion to motor neurons. HighlightsO_LIminimal high-efficiency cocktail allows systematic interrogation of the reprogramming process C_LIO_LIhistory provides a principal axis to distinguish transcription factors influence C_LIO_LIof the transcription factor cocktail impacts reprogramming efficiency and dynamics C_LIO_LIof individual transcription factors differentially influence the rate of reprogramming C_LIO_LIearly hyperproliferation increases direct conversion of adult human fibroblasts C_LIO_LIptimal cocktail allows neurotrophic factor-free reprogramming and in vivo grafting C_LI
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: clustering
- approach: deep learning
- approach: statistical model
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