Abstract
Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation, making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined.
Highlights
Flow cytometry (FCM) is widely used in health research and treatment for a variety of tasks, such as providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, in the diagnosis and monitoring of leukemia and lymphoma patients, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases [1,2,3,4,5,6,7,8]
In FCM, intact cells and their constituent components are tagged with fluorescently conjugated monoclonal antibodies and/or stained with fluorescent reagents and analyzed individually by a flow cytometer
The need for completely automated analysis of FCM data is becoming more evident with the advances in highthroughput FCM technology
Summary
Flow cytometry (FCM) is widely used in health research and treatment for a variety of tasks, such as providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, in the diagnosis and monitoring of leukemia and lymphoma patients, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases [1,2,3,4,5,6,7,8]. Due to recent hardware advances it is possible to analyze thousands of samples per day This has dramatically increased the efficiency and use of this technique and allowed the adoption of FCM to high-throughput settings. A flow cytometer is capable of measuring up to 20 cell characteristics, for up to millions of individual cells per sample aliquot [26, 32]. This technology can be used to examine many cellular parameters on live or fixed cells, including surface, cytoplasmic, and nuclear proteins, DNA, RNA, reactive-oxygen species, intracellular pH, and calcium flux. Measurement of the expression of cellular-activation markers, intracellular cytokines, immunological signaling, and cytoplasmic and nuclear cell cycle and transcription factors can be readily performed [9, 11, 12, 27, 28, 33,34,35]
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