Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Shuffle-Seq: En masse combinatorial encoding for n-way genetic interaction screens

View ORCID ProfileAtray Dixit, Olena Kuksenko, David Feldman, Aviv Regev
doi: https://doi.org/10.1101/861443
Atray Dixit
1Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 0212, USA
2Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, USA
3Coral Genomics, San Francisco, CA 94107, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Atray Dixit
Olena Kuksenko
1Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 0212, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
David Feldman
1Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 0212, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aviv Regev
1Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 0212, USA
4Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02140, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: aregev@broadinstitute.org
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Genetic interactions, defined as the non-additive phenotypic impact of combinations of genes, are a hallmark of the mapping from genotype to phenotype. However, genetic interactions remain challenging to systematically test given the massive number of possible combinations. In particular, while large-scale screening efforts in yeast have quantified pairwise interactions that affect cell viability, or synthetic lethality, between all pairs of genes as well as for a limited number of three-way interactions, it has previously been intractable to perform the large screens needed to comprehensively assess interactions in a mammalian genome. Here, we develop Shuffle-Seq, a scalable method to assay genetic interactions. Shuffle-Seq leverages the co-inheritance of genetically encoded barcodes in dividing cells and can scale in proportion to sequencing throughput. We demonstrate the technical validity of Shuffle-Seq and apply it to screening for mechanisms underlying drug resistance in a melanoma model. Shuffle-Seq should allow screens of hundreds of millions of combinatorial perturbations and facilitate the understanding of genetic dependencies and drug sensitivities.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted December 02, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Shuffle-Seq: En masse combinatorial encoding for n-way genetic interaction screens
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Shuffle-Seq: En masse combinatorial encoding for n-way genetic interaction screens
Atray Dixit, Olena Kuksenko, David Feldman, Aviv Regev
bioRxiv 861443; doi: https://doi.org/10.1101/861443
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Shuffle-Seq: En masse combinatorial encoding for n-way genetic interaction screens
Atray Dixit, Olena Kuksenko, David Feldman, Aviv Regev
bioRxiv 861443; doi: https://doi.org/10.1101/861443

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4672)
  • Biochemistry (10334)
  • Bioengineering (7655)
  • Bioinformatics (26281)
  • Biophysics (13497)
  • Cancer Biology (10663)
  • Cell Biology (15392)
  • Clinical Trials (138)
  • Developmental Biology (8485)
  • Ecology (12802)
  • Epidemiology (2067)
  • Evolutionary Biology (16818)
  • Genetics (11380)
  • Genomics (15454)
  • Immunology (10592)
  • Microbiology (25159)
  • Molecular Biology (10196)
  • Neuroscience (54373)
  • Paleontology (399)
  • Pathology (1663)
  • Pharmacology and Toxicology (2889)
  • Physiology (4332)
  • Plant Biology (9223)
  • Scientific Communication and Education (1585)
  • Synthetic Biology (2553)
  • Systems Biology (6769)
  • Zoology (1459)