B cells are central players in multiple autoimmune rheumatic diseases as a result of the imbalance between pathogenic and protective B cell functions, which are presumably mediated by distinct B cell subsets. While definitive functional studies are harder to perform with human B cells than in mice, the availability of many well-defined surface and intracellular markers has set the stage for informative human studies. The Flow Cytometry Core in the Lowance Center has been undertaking a comprehensive B cell phenotyping approach using polychromatic flow cytometry (PFC) to properly adjudicate functional significance and pathogenic relevance to separate B cell populations. With this approach, we are able to not only recognize the disturbances of B cell homeostasis in a number of human conditions (including autoimmunity, transplantation, infection or vaccination) but also identify several novel B cell subsets that are dysregulated in diseases. These novel B cell subsets in turn become the subjects of hypothesis-driven studies into their development and regulation.
Increasingly complex high-dimensional PFC data create new challenges for data mining and interpretation. Just as challenging is the difficulty in the level of standardization required for large data sets and multicenter studies typical of large clinical trials. We have collaborated with experts in computational biology to develop clustering algorithms (FLOCK, SPADE) that can identify discrete cell populations based on simultaneous assessment of multiple parameters, and hold significant promise for the automated data analysis of PFC data. Furthermore, we have applied a global B cell profiling approach, in which all of the B cell subset data are considered simultaneously to obtain a system-wide view of B cell populations, rather than scrutinizing individual subset in isolation. In this manner, patient-specific B cell fingerprints are generated that can be directly compared to the profile of other patients. Such an approach could potentially identify markers of patient segmentation and disease progression and have important implications for the use of B cell profiling in personalized medicine.