Abstract
The evaluation of protein localization changes in cells under diverse chemical and genetic perturbations is now possible due to the increasing quantity of screens that systematically image thousands of proteins in an organism. Integrating information from different screens provides valuable contextual information about the protein function. For example, proteins that change localization in response to many different stressful environmental perturbations may have different roles than those that only change in response to a few. We developed, to our knowledge, the first protocol that permits the quantitative comparison and clustering of protein localization changes across multiple screens. Our analysis allows for the exploratory analysis of proteins according to their pattern of localization changes across many different perturbations, potentially discovering new roles by association.
Keywords: Proteomics, Image analysis, Cell biology, Computational biology, Unsupervised machine learning, Protein localization, Cluster analysis
Background
Automated high-throughput microscopy technologies can now generate image datasets showing the expression and localization of the majority of the proteome in yeast cells (Mattiazzi Usaj et al., 2016). A key aim of these datasets is to identify and compare proteins that change localization in a chemical or genetic perturbation compared to an untreated wild-type baseline. Previous work has generally focused on identifying all localization changes for a single screen as accurately as possible (Tkach et al., 2012; Chong et al., 2015; Kraus et al., 2017), but has not provided a way to systematically compare these changes. In Lu et al. (2018), we showed that the pattern in which proteins change localization can be inferred by integrating information from microscopy images for each protein under different perturbations. By grouping together proteins with similar patterns of change across different perturbations, we better understand protein function. Here, we describe our protocol for extracting measurements about protein localization from images, comparing the differences between screens, and integrating data from different screens for cluster analysis. Our method is unsupervised and automatically infers proteomic changes from data (Lu and Moses, 2016), allowing it to scale to new datasets with no retraining of parameters.
Equipment
Hardware:
Software
Procedure
Data analysis
We refer to Lu et al. (2018) for details and procedures for the specific downstream analyses that we conducted on the clusters obtained from this data. As a high-throughput method, there are points in the protocol in which false positives and negatives may arise; we refer users to the discussion section of Lu et al. (2018) for an overview of these, as well as best practices on how to interpret our output.
Acknowledgments
This work was funded by the National Science and Engineering Research Council, Canada Research Chairs, Canada Foundation for Innovation, Canadian Institutes of Health Research, and Canadian Institute for Advanced Research.
Competing interests
The authors declare no competing interests.
References
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