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BD FACS™ CAP (CAP = combinatorial antibody profile) is a screening tool for rapid characterization of human cell surface protein expression profiles using semi-automated high-throughput flow cytometry. The current configuration consists of 229 directly conjugated antibodies arrayed in a 96-well plate as three-color cocktails, which enables the characterization of each of the 229 individual surface markers. Each individual cell type of interest is analyzed on the 96-well screening plates and the data are acquired on a flow cytometer equipped with a high-throughput sampler. The expression level of each marker for each cell type is then calculated using semiautomated custom flow cytometry software. The process of characterizing these surface markers in a highly efficient manner using BD FACS™ CAP is enabled by automated liquid handling for staining, automated flow cytometry for data acquisition, and standardized algorithms for automated data analysis.

Flow cytometry (FCM) is an analytical tool widely used for cancer and HIV/AIDS research, and treatment, stem cell manipulation and detecting microorganisms in environmental samples. Current data standards do not capture the full scope of FCM experiments and there is a demand for software tools that can assist in the exploration and analysis of large FCM datasets. We are implementing a standardized approach to capturing, analyzing, and disseminating FCM data that will facilitate both more complex analyses and analysis of datasets that could not previously be efficiently studied. Initial work has focused on developing a community-based guideline for recording and reporting the details of FCM experiments. Open source software tools that implement this standard are being created, with an emphasis on facilitating reproducible and extensible data analyses. As well, tools for electronic collaboration will assist the integrated access and comprehension of experiments to empower users to collaborate on FCM analyses. This coordinated, joint development of bioinformatics standards and software tools for FCM data analysis has the potential to greatly facilitate both basic and clinical research--impacting a notably diverse range of medical and environmental research areas.