Automated analysis of high-throughput flow cytometry data from hematopoietic stem cell experiments

Flow cytometry (FCM) is a technology that allows the rapid quantification of physical and chemical properties of up to millions of cells in a sample. It is a technology commonly used in drug discovery, health research, medical diagnosis and treatment, and vaccine development. Recent technological advancements in optics and reagents allow the quantification of up to 21 parameters per cell and advancements in robotics allow the use of FCM as a high-throughput technology. Lagging in the development of FCM technologies is the data analysis component. Conventional analysis of FCM data is labour intensive, subjective, hard to reproduce, error prone and not standardized. Indeed, the traditional analysis represents one of the main bottlenecks for the future adoption of recent technological advancements in biomedical research and the clinical environment. Here, an analysis framework developed for the automated analysis of FCM data derived from hematopoietic stem cell (HSC) transplant experiments using data generated in the Terry Fox Laboratory is presented. The data analysis pipeline developed aims to simplify approaches to analyze such data and generated automated tools for accurate analysis and quality control. The tool presented achieves equivalent results when compared to the traditional analysis, but avoids the traditional need for continuous user interaction. Incorporated into the analysis pipeline, is a model to predict the repopulation outcome from the HSC transplant experiments. Because HSC purification strategies are typically below 50%, more than half of the mice transplanted with a single cell will not be repopulated. The repopulation prediction model showed a performance of correctly identifying 81% of the mice that did not showed a positive engraftment, while keeping the incorrect misclassification of positive engraftments below 5%.Science, Faculty ofGraduat