Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins
Alex X Lu,
Yolanda T Chong,
Ian Shen Hsu,
Bob Strome,
Louis-Francois Handfield,
Oren Kraus,
Brenda J Andrews,
Alan M Moses
Affiliations
Alex X Lu
Department of Computer Science, University of Toronto, Toronto, Canada
Yolanda T Chong
Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
Ian Shen Hsu
Department of Cell and Systems Biology, University of Toronto, Toronto, Canada
Bob Strome
Department of Cell and Systems Biology, University of Toronto, Toronto, Canada
Louis-Francois Handfield
Department of Computer Science, University of Toronto, Toronto, Canada
Oren Kraus
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
Brenda J Andrews
Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada
Department of Computer Science, University of Toronto, Toronto, Canada; Department of Cell and Systems Biology, University of Toronto, Toronto, Canada; Center for Analysis of Genome Evolution and Function, University of Toronto, Toronto, Canada
The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization changes independently for each perturbation. To distinguish changes that are targeted responses to the specific perturbation or more generalized programs, we developed a scalable approach to visualize the localization behavior of proteins across multiple experiments as a quantitative pattern. By applying this approach to 24 experimental screens consisting of nearly 400,000 images, we differentiated specific responses from more generalized ones, discovered nuance in the localization behavior of stress-responsive proteins, and formed hypotheses by clustering proteins that have similar patterns. Previous approaches aim to capture all localization changes for a single screen as accurately as possible, whereas our work aims to integrate large amounts of imaging data to find unexpected new cell biology.
