1. Computational and Systems Biology
Download icon

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

Research Article
  • Cited 14
  • Views 11,307
  • Annotations
Cite this article as: eLife 2020;9:e51254 doi: 10.7554/eLife.51254

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

  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

Publication 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.

Metrics

  • 11,307
    Page views
  • 1,140
    Downloads
  • 14
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Chromosomes and Gene Expression
    2. Computational and Systems Biology
    Michael Roland Wolff et al.
    Research Article

    Chromatin dynamics are mediated by remodeling enzymes and play crucial roles in gene regulation, as established in a paradigmatic model, the S. cerevisiae PHO5 promoter. However, effective nucleosome dynamics, i.e. trajectories of promoter nucleosome configurations, remain elusive. Here, we infer such dynamics from the integration of published single-molecule data capturing multi-nucleosome configurations for repressed to fully active PHO5 promoter states with other existing histone turnover and new chromatin accessibility data. We devised and systematically investigated a new class of 'regulated on-off-slide' models simulating global and local nucleosome (dis)assembly and sliding. Only seven of 68145 models agreed well with all data. All seven models involve sliding and the known central role of the N-2 nucleosome, but regulate promoter state transitions by modulating just one assembly rather than disassembly process. This is consistent with but challenges common interpretations of previous observations at the PHO5 promoter and suggests chromatin opening by binding competition.

    1. Cell Biology
    2. Computational and Systems Biology
    Taraneh Zarin et al.
    Research Advance Updated

    In previous work, we showed that intrinsically disordered regions (IDRs) of proteins contain sequence-distributed molecular features that are conserved over evolution, despite little sequence similarity that can be detected in alignments (Zarin et al., 2019). Here, we aim to use these molecular features to predict specific biological functions for individual IDRs and identify the molecular features within them that are associated with these functions. We find that the predictable functions are diverse. Examining the associated molecular features, we note some that are consistent with previous reports and identify others that were previously unknown. We experimentally confirm that elevated isoelectric point and hydrophobicity, features that are positively associated with mitochondrial localization, are necessary for mitochondrial targeting function. Remarkably, increasing isoelectric point in a synthetic IDR restores weak mitochondrial targeting. We believe feature analysis represents a new systematic approach to understand how biological functions of IDRs are specified by their protein sequences.