Contemporary Challenges in Clinical Flow Cytometry: Small Samples, Big Data, Little Time.

Abstract

Immunophenotypic analysis of cell populations by flow cytometry has an established role in primary diagnosis and disease monitoring of many hematologic diseases. A persistent problem in evaluation of specimens is suboptimal cell counts and low cell viability, which results in an undesirable rate of analysis failure. In addition, the increased amount of data generated in flow cytometry challenges existing data analysis and reporting paradigms. We describe current and emerging technological improvements in cell analysis that allow the clinical laboratory to perform multiparameter analysis of specimens, including those with low cell counts and other quality issues. These technologies include conventional multicolor flow cytometry and new high-dimensional technologies, such as spectral flow cytometry and mass cytometry that enable detection of over 40 antigens simultaneously. The advantages and disadvantages of each approach are discussed. We also describe new innovations in flow cytometry data analysis, including artificial intelligence-aided techniques. Improvements in analytical technology, in tandem with innovations in data analysis, data storage, and reporting mechanisms, help to optimize the quality of clinical flow cytometry. These improvements are essential because of the expanding role of flow cytometry in patient care.