![]() ![]() The first two of these tasks tend to be application- and lab-specific, while the latter two lend themselves well to the development of shared tools for all those faced with complex flow cytometry analyses. All of these components are related and, done well, serve to reinforce each other. (1) acquisition of high-quality data, (2) tools for data organization, annotation, and query, (3) tools for data manipulation, and (4) techniques and statistical methods for data analysis. There are a number of challenges associated with the analysis of these large, complex flow cytometry data sets. Powerful analysis tools are needed to properly explore and analyze data sets in which each sample has many stimuli, cell subpopulations, and phosphoprotein measurements. This adds another layer of complexity to flow cytometry data sets. There is also a growing appreciation that it is important to assess cells not only in their quiescent state, but also in response to various stimuli. Recent advances in instrumentation such as 4 and 5 color laser systems and the availability of reagents and protocols for assessing internal proteins and their phosphorylation state are serving to make flow cytometry a very important tool for understanding disease processes in human biology. Flow cytometers measure individual cells, and thus are capable of revealing subtleties of biology that other technologies cannot detect. Flow cytometry is a high-information content platform that is increasingly becoming a high-throughput platform as well. ![]()
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