Development and application of data standards for flow cytometry

Flow cytometry is a method of identifying and sorting cells and their components by staining with a fluorescent dye and detecting the resulting fluorescence (usually by laser beam illumination). Flow cytometry is widely used in health research (e.g. for stem cell identification and vaccine development), and in the diagnosis, monitoring and treatment of a variety of diseases, including cancers and HIV/AIDS.

Recent advances in high-throughput flow cytometry allows for the analysis of thousands of samples per day, creating detailed descriptions about millions of individual cells. Managing and analyzing this volume of data is a challenge that Dr. Ryan Brinkman is addressing through the development of data standards, algorithms, and bioinformatics tools. Dr. Brinkman is also applying these methodologies to the analysis of several large clinical flow cytometry datasets in an effort to identify biomarkers for lymphoma, neonatal auto-immunity, and graft versus host disease.