Automated interpretation of protein subcellular location patterns: implications for early cancer detection and assessment.
Fluorescence microscopy is a powerful tool for analyzing the subcellular distributions of proteins, but that power has not been fully utilized because most analysis of those distributions has been done by visual examination. This limitation can be overcome using automated pattern recognition methods widely used in other fields. This article summarizes work demonstrating that automated systems can recognize the patterns of major organelles in both two- and three-dimensional images of cultured cells, and that these systems can distinguish similar patterns better than visual examination. The basis for these systems are sets of Subcellular Location Features that capture the essence of subcellular patterns without being sensitive to the extensive variation that occurs in the size, shape, and orientation of cells in microscope images. These features can also be used to make sensitive, statistical comparisons of the distribution of a protein between two conditions, such as in the presence and absence of a drug. The possible use of automated pattern analysis methods for improving detection of abnormal cells in cancerous or precancerous tissues is also discussed.
Murphy
Robert F
RF
Department of Biological Sciences, and Center for Automated Learning and Discovery, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA. murphy@cmu.edu
D001331:Automation; D002478:Cells, Cultured; D006367:HeLa Cells; D006801:Humans; D015388:Organelles / Q000737:chemistry*; D011506:Proteins / Q000032:analysis*
D011506:Proteins