Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments
- New York University, United States
Abstract
Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.
Data availability
Sequencing data has been deposited in GEO: GSE125162Figures 2-7 (& Supplemental Figures 2-7) are generated from a single R markdown document. The scripts and all data necessary to do this analysis are provided as Supplemental Data 1. The raw output (knit HTML file) is provided as Supplemental Data 6.Interactive versions of several figures are available have been made available with the Shiny library in R: http://shiny.bio.nyu.edu/cj59/YeastSingleCell2019/The Inferelator package is available on GitHub and through python package managers (i.e. pip) under an open source license (BSD).
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Single-cell RNA-seq reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stressNCBI Gene Expression Omnibus, GSE102475.
Article and author information
Author details
Christopher A Jackson
Funding
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK103358)
- Richard Bonneau
National Institute of General Medical Sciences (R01GM107466)
- David Gresham
National Science Foundation (MCB1818234)
- David Gresham
Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD096770)
- Richard Bonneau
National Science Foundation (IOS1546218)
- Richard Bonneau
National Cancer Institute (R01CA229235)
- Richard Bonneau
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Naama Barkai, Weizmann Institute of Science, Israel
Version history
- Received: August 21, 2019
- Accepted: January 10, 2020
- Accepted Manuscript published: January 27, 2020 (version 1)
- Version of Record published: February 6, 2020 (version 2)
- Version of Record updated: February 27, 2020 (version 3)
Copyright
© 2020, Jackson 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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Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments
eLife 9:e51254.
https://doi.org/10.7554/eLife.51254
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