Exploring Quantitative Yeast Phenomics with Single-Cell Analysis of DNA Damage Foci

Erin B. Styles, Karen J. Founk, Lee A. Zamparo, Tina L. Sing, Dogus Altintas, Cyril Ribeyre, Virginie Ribaud, Jacques Rougemont, David Mayhew, Michael Costanzo, Matej Usaj, Adrian J. Verster, Elizabeth N. Koch, Daniele Novarina, Marco Graf, Brian Luke, Marco Muzi-Falconi, Chad L. Myers, Robi David Mitra, David ShoreGrant W. Brown, Zhaolei Zhang, Charles Boone, Brenda J. Andrews

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


A significant challenge of functional genomics is to develop methods for genome-scale acquisition and analysis of cell biological data. Here, we present an integrated method that combines genome-wide genetic perturbation of Saccharomyces cerevisiae with high-content screening to facilitate the genetic description of sub-cellular structures and compartment morphology. As proof of principle, we used a Rad52-GFP marker to examine DNA damage foci in ∼20 million single cells from ∼5,000 different mutant backgrounds in the context of selected genetic or chemical perturbations. Phenotypes were classified using a machine learning-based automated image analysis pipeline. 345 mutants were identified that had elevated numbers of DNA damage foci, almost half of which were identified only in sensitized backgrounds. Subsequent analysis of Vid22, a protein implicated in the DNA damage response, revealed that it acts together with the Sgs1 helicase at sites of DNA damage and preferentially binds G-quadruplex regions of the genome. This approach is extensible to numerous other cell biological markers and experimental systems.

Original languageEnglish (US)
Pages (from-to)264-277.e10
JournalCell Systems
Issue number3
StatePublished - Sep 28 2016

Bibliographical note

Funding Information:
We thank Helena Friesen, Michael Cox, Yolanda Chong, Elena Kuzmin, Edith Cheng, Zhen-Yuan Lin, and Anne-Claude Gingras for technical assistance and advice. We thank Harsha Garadi Suresh for comments on the manuscript. This work was supported primarily by grant MOP-97939 and Foundation grants FDN-143264 and FDN-143265 from the Canadian Institutes for Health Research (to B.A. and C.B.) and by a grant from the National Institutes of Health (R01HG005853, to C.B., B.A., and C.L.M.), Contributions from the G.W.B. lab were supported by Impact grant 702310 from the Canadian Cancer Society Research Institute and NSERC Discovery Grant RGPIN 326897-12. E.B.S. was supported by a scholarship from the Canadian Institute of Health Research. Infrastructure for high-content imaging was acquired with funds from the Canadian Foundation for Innovation and the Ministry of Research and Innovation (Ontario; grant 21745, to B.J.A. and C.B.). B.J.A. and C.B. are senior fellows and co-director (C.B.) of the Genetic Networks program of the Canadian Institute for Advanced Research.

Publisher Copyright:
© 2016 Elsevier Inc.


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