Development of a pipeline for the analysis of flow cytometry data

Flow cytometry (FCM) is a method of sorting and measuring types of cells by fluorescent labelling of markers on the surface of the cells. It plays a critical role in basic research and clinical therapy in the areas of cancer, HIV and stem cell manipulation. For example, it can be used to diagnose some types of cancer, based on which labelled antibodies bind to a particular cell’s surface. It is widely recognized that one of the main stumbling blocks for FCM analysis is in data processing and interpretation, which heavily relies on manual processes to identify particular cell populations and to find correlations between these cell populations and their clinical diagnosis and outcome (e.g. survival). Manual analysis of FCM data is a process that is highly tedious, time-consuming (to the level of impracticality for some datasets), subjective and based on intuition rather than standardized statistical inference. Dr. Ali Bashashati has developed a “pipeline” for automatic analysis of FCM data – a computational platform that can identify cell populations, find biomarkers that correlate with clinical outcomes, and label the samples as normal or diseased. Preliminary evaluations of this pipeline have shown accuracy levels of more than 90 per cent in identifying some sub-types of lymphoma. Moreover, a biomarker that contributes to a more aggressive behaviour of a specific sub-type of lymphoma has been discovered. Bashashati is now testing and refining the platform to improve its analytical power and applicability to a range of FCM data, testing its performance across a number of ongoing FCM studies in BC. Ultimately, he hopes to provide an accurate, powerful computational platform to increase the efficiency of using FCM for research and clinical purposes.