A major source of information for identifying subcellular location on a proteome-wide basis will be imaging of tagged proteins in living cells using fluorescence microscopy. We have previously developed automated systems to interpret images from such experiments and demonstrated that they can perform as well or better than visual inspection. Recent work demonstrates that these methods can be applied to large collections of images from sources as diverse as yeast expressing GFP-tagged proteins and human tissues imaged by immunocytochemistry. A distinct but related task is learning what location patterns exist. We have demonstrated clustering of mouse proteins into subcellular location families that share a statistically indistinguishable pattern. To communicate each pattern, we have developed approaches to learning generative models of subcellular patterns. Integration of high-throughput microscopy and automated model building with cell modeling systems will permit accurate, well-structured information on subcellular location to be incorporated into systems biology efforts.
['Robert F. Murphy']
Automated proteome-wide determination of subcellular location using high throughput microscopy