Epigenetic landscapes explain partially reprogrammed cells and identify key reprogramming genes

TL;DR

This paper introduces a novel framework combining genomic data with spin-glass physics to model epigenetic landscapes, explaining cell fate decisions, reprogramming processes, and identifying key genes for cell fate transitions.

Contribution

It presents an explicit construction of epigenetic landscapes integrating data and physics, predicting hybrid cell states and identifying reprogramming factors.

Findings

Partially reprogrammed cells are hybrids co-expressing multiple fate genes.

Model reproduces known reprogramming protocols.

Identifies candidate transcription factors for new cell fates.

Abstract

A common metaphor for describing development is a rugged "epigenetic landscape" where cell fates are represented as attracting valleys resulting from a complex regulatory network. Here, we introduce a framework for explicitly constructing epigenetic landscapes that combines genomic data with techniques from spin-glass physics. Each cell fate is a dynamic attractor, yet cells can change fate in response to external signals. Our model suggests that partially reprogrammed cells are a natural consequence of high-dimensional landscapes, and predicts that partially reprogrammed cells should be hybrids that co-express genes from multiple cell fates. We verify this prediction by reanalyzing existing datasets. Our model reproduces known reprogramming protocols and identifies candidate transcription factors for reprogramming to novel cell fates, suggesting epigenetic landscapes are a powerful paradigm for understanding cellular identity.