Systematic morphological profiling of human gene and allele function via Cell Painting

  1. Mohammad Hossein Rohban
  2. Shantanu Singh
  3. Xiaoyun Wu
  4. Julia B Berthet
  5. Mark-Anthony Bray
  6. Yashaswi Shrestha
  7. Xaralabos Varelas
  8. Jesse S Boehm
  9. Anne E Carpenter  Is a corresponding author
  1. Broad Institute, United States
  2. Boston University School of Medicine, United States
  3. Novartis Institutes for BioMedical Research, United States

Abstract

We hypothesized that human genes and disease-associated alleles might be systematically functionally annotated using morphological profiling of cDNA constructs, via a microscopy-based Cell Painting assay. Indeed, 50% of the 220 tested genes yielded detectable morphological profiles, which grouped into biologically meaningful gene clusters consistent with known functional annotation (e.g., the RAS-RAF-MEK-ERK cascade). We used novel subpopulation-based visualization methods to interpret the morphological changes for specific clusters. This unbiased morphologic map of gene function revealed TRAF2/c-REL negative regulation of YAP1/WWTR1-responsive pathways. We confirmed this discovery of functional connectivity between the NF-κB pathway and Hippo pathway effectors at the transcriptional level, thereby expanding knowledge of these two signaling pathways that critically regulate tumor initiation and progression. We make the images and raw data publicly available, providing an initial morphological map of major biological pathways for future study.

Data availability

The following data sets were generated

Article and author information

Author details

  1. Mohammad Hossein Rohban

    Broad Institute, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6589-850X
  2. Shantanu Singh

    Broad Institute, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Xiaoyun Wu

    Broad Institute, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Julia B Berthet

    Department of Biochemistry, Boston University School of Medicine, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Mark-Anthony Bray

    Novartis Institutes for BioMedical Research, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Yashaswi Shrestha

    Broad Institute, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Xaralabos Varelas

    Department of Biochemistry, Boston University School of Medicine, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2882-4541
  8. Jesse S Boehm

    Broad Institute, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Anne E Carpenter

    Broad Institute, Cambridge, United States
    For correspondence
    anne@broadinstitute.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1555-8261

Funding

National Science Foundation (NSF CAREER DBI 1148823)

  • Anne E Carpenter

Broad Institute

  • Anne E Carpenter

Carlos Slim Foundation

  • Anne E Carpenter

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Jeffrey Settleman, Calico Life Sciences, United States

Version history

  1. Received: December 7, 2016
  2. Accepted: March 14, 2017
  3. Accepted Manuscript published: March 18, 2017 (version 1)
  4. Version of Record published: April 10, 2017 (version 2)

Copyright

© 2017, Rohban 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

  • 10,939
    views
  • 1,723
    downloads
  • 120
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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)

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

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

  1. Mohammad Hossein Rohban
  2. Shantanu Singh
  3. Xiaoyun Wu
  4. Julia B Berthet
  5. Mark-Anthony Bray
  6. Yashaswi Shrestha
  7. Xaralabos Varelas
  8. Jesse S Boehm
  9. Anne E Carpenter
(2017)
Systematic morphological profiling of human gene and allele function via Cell Painting
eLife 6:e24060.
https://doi.org/10.7554/eLife.24060

Share this article

https://doi.org/10.7554/eLife.24060

Further reading

    1. Computational and Systems Biology
    David Geller-McGrath, Kishori M Konwar ... Jason E McDermott
    Tools and Resources

    The reconstruction of complete microbial metabolic pathways using ‘omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Kenya Hitomi, Yoichiro Ishii, Bei-Wen Ying
    Research Article

    As the genome encodes the information crucial for cell growth, a sizeable genomic deficiency often causes a significant decrease in growth fitness. Whether and how the decreased growth fitness caused by genome reduction could be compensated by evolution was investigated here. Experimental evolution with an Escherichia coli strain carrying a reduced genome was conducted in multiple lineages for approximately 1000 generations. The growth rate, which largely declined due to genome reduction, was considerably recovered, associated with the improved carrying capacity. Genome mutations accumulated during evolution were significantly varied across the evolutionary lineages and were randomly localized on the reduced genome. Transcriptome reorganization showed a common evolutionary direction and conserved the chromosomal periodicity, regardless of highly diversified gene categories, regulons, and pathways enriched in the differentially expressed genes. Genome mutations and transcriptome reorganization caused by evolution, which were found to be dissimilar to those caused by genome reduction, must have followed divergent mechanisms in individual evolutionary lineages. Gene network reconstruction successfully identified three gene modules functionally differentiated, which were responsible for the evolutionary changes of the reduced genome in growth fitness, genome mutation, and gene expression, respectively. The diversity in evolutionary approaches improved the growth fitness associated with the homeostatic transcriptome architecture as if the evolutionary compensation for genome reduction was like all roads leading to Rome.