Automated Quantitative Live Cell Fluorescence Microscopy
- 1Department of Developmental Biology, Stanford University School of Medicine, Stanford, California 94305
- 2Division of Biological Sciences, University of California San Diego, La Jolla, California 92093-0349
- Correspondence: mike.fero{at}stanford.edu
Abstract
Advances in microscopy automation and image analysis have given biologists the tools to attempt large scale systems-level experiments on biological systems using microscope image readout. Fluorescence microscopy has become a standard tool for assaying gene function in RNAi knockdown screens and protein localization studies in eukaryotic systems. Similar high throughput studies can be attempted in prokaryotes, though the difficulties surrounding work at the diffraction limit pose challenges, and targeting essential genes in a high throughput way can be difficult. Here we will discuss efforts to make live-cell fluorescent microscopy based experiments using genetically encoded fluorescent reporters an automated, high throughput, and quantitative endeavor amenable to systems-level experiments in bacteria. We emphasize a quantitative data reduction approach, using simulation to help develop biologically relevant cell measurements that completely characterize the cell image. We give an example of how this type of data can be directly exploited by statistical learning algorithms to discover functional pathways.
Footnotes
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Editors: Lucy Shapiro and Richard Losick
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Additional Perspectives on Cell Biology of Bacteria available at www.cshperspectives.org
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