Cell SystemsGenomicsPeer-reviewed journal article
Aligning single-cell developmental and reprogramming trajectories identifies molecular determinants of reprogramming outcome
Davide CacchiarelliiD, Xiaojie QiuiD, Sanjay R SrivatsaniD, Anna ManfrediiD, Michael J. ZilleriD, Eliah G. OverbeyiD, Antonio GrimaldiiD, Jonna GrimsbyiD, Prapti PokhareliD, Kenneth J. LivakiD, Shuqiang LiiD, Alexander MeissneriD, Tarjei Sigurd MikkelseniD, John L. RinniD, Cole TrapnelliD
Abstract
Cellular reprogramming through manipulation of defined factors holds great promise for large-scale production of cell types needed for use in therapy, as well as for expanding our understanding of the general principles of gene regulation. MYOD-mediated myogenic reprogramming, which converts many cell types into contractile myotubes, remains one of the best characterized model system for direct conversion by defined factors. However, why MYOD can efficiently convert some cell types into myotubes but not others remains poorly understood. Here, we analyze MYOD-mediated reprogramming of human fibroblasts at pseudotemporal resolution using single-cell RNA-Seq. Successfully reprogrammed cells navigate a trajectory with two branches that correspond to two barriers to reprogramming, with cells that select incorrect branches terminating at aberrant or incomplete reprogramming outcomes. Differential analysis of the major branch points alongside alignment of the successful reprogramming path to a primary myoblast trajectory revealed Insulin and BMP signaling as crucial molecular determinants of an individual cells reprogramming outcome, that when appropriately modulated, increased efficiency more than five-fold. Our single-cell analysis reveals that MYOD is sufficient to reprogram cells only when the extracellular milieu is favorable, supporting MYOD with upstream signaling pathways that drive normal myogenesis in development.
- organism: human
- assay: scRNA-seq
- research focus: clustering
- research focus: trajectory inference
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 76
- FWCI
- 4.00
- Field percentile
- top 4%
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