Image-based spatiotemporal causality inference for protein signaling networks

Xiongtao Ruan, Christoph Wuelfing, Robert Murphy

Research output: Contribution to journalArticle (Academic Journal)

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Abstract

Motivation: Efforts to model how signaling and regulatory networks work in cells have largely either not considered spatial organization or have used compartmental models with minimal spatial resolution. Fluorescence microscopy provides the ability to monitor the spatiotemporal distribution of many molecules during signaling events, but as of yet no methods have been described for large scale image analysis to learn a complex protein regulatory network. Here we present and evaluate methods for identifying how changes in concentration in one cell region influence concentration of other proteins in other regions.
Results: Using 3D confocal microscope movies of GFP-tagged T cells undergoing costimulation, we learned models containing putative causal relationships among 12 proteins involved in T cell signaling. The models included both relationships consistent with current knowledge and novel predictions deserving further exploration. Further, when these models were applied to the initial frames of movies of T cells that had been only partially stimulated, they predicted the localization of proteins at later times with statistically significant accuracy. The methods, consisting of spatiotemporal alignment, automated region identification, and causal inference, are anticipated to be applicable to a number of biological systems.
Original languageEnglish
JournalBioinformatics
Volume33
Issue number14
Early online date12 Jul 2017
DOIs
Publication statusPublished - 12 Jul 2017

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