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PLoS Comput Biol. 2015 Dec 1;11(12):e1004614. doi: 10.1371/journal.pcbi.1004614. eCollection 2015 Dec.

Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules.

Johnson GR1,2, Li J2,3, Shariff A1,2, Rohde GK1,2,3,4, Murphy RF1,2,3,5,6.

Author information

1
Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
2
Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
3
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
4
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
5
Departments of Biological Sciences and Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
6
Faculty of Biology and Freiburg Institute for Advanced Studies, Albert Ludwig University of Freiburg, Freiburg, Germany.

Abstract

Characterizing the spatial distribution of proteins directly from microscopy images is a difficult problem with numerous applications in cell biology (e.g. identifying motor-related proteins) and clinical research (e.g. identification of cancer biomarkers). Here we describe the design of a system that provides automated analysis of punctate protein patterns in microscope images, including quantification of their relationships to microtubules. We constructed the system using confocal immunofluorescence microscopy images from the Human Protein Atlas project for 11 punctate proteins in three cultured cell lines. These proteins have previously been characterized as being primarily located in punctate structures, but their images had all been annotated by visual examination as being simply "vesicular". We were able to show that these patterns could be distinguished from each other with high accuracy, and we were able to assign to one of these subclasses hundreds of proteins whose subcellular localization had not previously been well defined. In addition to providing these novel annotations, we built a generative approach to modeling of punctate distributions that captures the essential characteristics of the distinct patterns. Such models are expected to be valuable for representing and summarizing each pattern and for constructing systems biology simulations of cell behaviors.

PMID:
26624011
PMCID:
PMC4704559
DOI:
10.1371/journal.pcbi.1004614
[Indexed for MEDLINE]
Free PMC Article

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