Interpretation of protein subcellular location patterns in 3D images across cell types and resolutions

Authors:
Xiang Chen;Robert F. Murphy
Affiliations:
Center for Bioimage Informatics and Departments of Biological Sciences and Machine Learning, Carnegie Mellon University, Pittsburgh, PA;Center for Bioimage Informatics and Departments of Biological Sciences and Machine Learning, Carnegie Mellon University, Pittsburgh, PA and Department of Biomedical Engineering, Carnegie Mellon Un ...
Venue:
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Year:
2007

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Abstract

Detailed knowledge of the subcellular location of all proteins and how they change under various conditions is essential for systems biology efforts to recreate the behavior of cells and organisms. Systematic study of sub-cellular patterns requires automated methods to determine the location pattern for each protein and how it relates to others. Our group has designed sets of numerical features that characterize the location patterns in high-resolution fluorescence microscope images, has shown that these can be used to distinguish patterns better than visual examination, and has used them to automatically group proteins by their patterns. In the current study, we sought to extend our approaches to images obtained from different cell types, microscopy techniques and resolutions. The results indicate that 1) transformation of subcellular location features can be performed so that similar patterns from different cell types are grouped by automated clustering; and 2) there are several basic location patterns whose recognition is insensitive to image resolution over a wide range. The results suggest strategies to be used for collecting and analyzing images from different cell types and with different resolutions.