Deep learning the cis-regulatory code of chromatin dynamics during cellular reprogramming

The concentration and stoichiometry of transcription factors (TFs) determine cellular identity and can be manipulated to drive cell state transitions. Understanding how changes in TF concentration regulate chromatin state and expression across cell state transitions remains a challenge. We investigated this relationship by profiling chromatin accessibility and gene expression at single-cell resolution across a densely sampled time course of reprogramming human fibroblasts to induced pluripotent stem cells via ectopic expression of OCT4, SOX2, KLF4, and MYC (OSKM). Using deep learning sequence models of base-resolution chromatin accessibility profiles across cell states, we deciphered predictive transcription factor (TF) motif syntax in regulatory elements, inferred affinity- and concentration-dependent dynamics of TF footprints, linked peaks to putative target genes, and elucidated rewiring of cis-regulatory networks. Our models reveal that early in reprogramming, OSK, at supraphysiological concentrations, rapidly open transient regulatory elements by occupying non-canonical low-affinity binding sites. As OSK concentration falls, the accessibility of these transient elements decays as a function of motif affinity. We find that these OSK-dependent transient elements sequester the somatic TF AP-1. This redistribution is strongly associated with the silencing of fibroblast-specific genes within individual nuclei. Together, our integrated single-cell resource and models reveal insights into the cis-regulatory code of reprogramming at unprecedented resolution. We establish a quantitative, predictive framework that links TF stoichiometry, motif syntax, and somatic silencing to provide new perspectives on the control of cell identity by TFs during fate transitions.