Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

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).

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Christopher A Jackson

    Center For Genomics and Systems Biology, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8769-2710
  2. Dayanne M Castro

    Department of Biology, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Giuseppe-Antonio Saldi

    Department of Biology, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Richard Bonneau

    Center For Genomics and Systems Biology, New York University, New York, United States
    For correspondence
    bonneau@nyu.edu
    Competing interests
    The authors declare that no competing interests exist.
  5. David Gresham

    Center For Genomics and Systems Biology, New York University, New York, United States
    For correspondence
    dgresham@nyu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4028-0364

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

  1. Naama Barkai, Weizmann Institute of Science, Israel

Version history

  1. Received: August 21, 2019
  2. Accepted: January 10, 2020
  3. Accepted Manuscript published: January 27, 2020 (version 1)
  4. Version of Record published: February 6, 2020 (version 2)
  5. 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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  1. Christopher A Jackson
  2. Dayanne M Castro
  3. Giuseppe-Antonio Saldi
  4. Richard Bonneau
  5. David Gresham
(2020)
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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https://doi.org/10.7554/eLife.51254

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