Single cell RNA-seq identifies the origins of heterogeneity in efficient cell transdifferentiation and reprogramming
- Center for Genomic Regulation, Spain
- Harvard University, United States
- Centro Nacional de Análisis Genómico, Spain
- Centro Nacional d'Anàlisi Genòmica, Spain
Abstract
Forced transcription factor expression can transdifferentiate somatic cells into other specialized cell types or reprogram them into induced pluripotent stem cells (iPSCs) with variable efficiency. To better understand the heterogeneity of these processes, we used single-cell RNA sequencing to follow the transdifferentation of murine pre-B cells into macrophages as well as their reprogramming into iPSCs. Even in these highly efficient systems, there was substantial variation in the speed and path of fate conversion. We predicted and validated that these differences are inversely coupled and arise in the starting cell population, with Mychigh large pre-BII cells transdifferentiating slowly but reprogramming efficiently and Myclow small pre-BII cells transdifferentiating rapidly but failing to reprogram. Strikingly, differences in Myc activity predict the efficiency of reprogramming across a wide range of somatic cell types. These results illustrate how single cell expression and computational analyses can identify the origins of heterogeneity in cell fate conversion processes.
Data availability
Single cell gene expression data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) under accession number GSE112004
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Single cell expression analysis uncouples transdifferentiation and reprogrammingNCBI Gene Expression Omnibus, GSE112004.
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Murine bone marrow B cell precursorsNCBI Gene Expression Omnibus, GSE13.
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Immunological Genome Project data Phase 1NCBI Gene Expression Omnibus, GSE15907.
Article and author information
Author details
Marcos Plana-Carmona
Funding
H2020 European Research Council
- Thomas Graf
H2020 European Research Council
- Ben Lehner
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: The protocol was approved by the Committee on the Ethics of Animal Experiments of the Generalitat de Catalunya (Permit Number: JMC-071001P3). All surgery was performed under sodium pentobarbital anesthesia, and every effort was made to minimize suffering.
Copyright
© 2019, Francesconi et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Single cell RNA-seq identifies the origins of heterogeneity in efficient cell transdifferentiation and reprogramming
eLife 8:e41627.
https://doi.org/10.7554/eLife.41627
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Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.
