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.
Article and author information
Author details
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
Publication 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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