Abstract
This article highlights several sources of artifact that interfere with optimal analysis of flow cytometric data. Such problems are compounded when flow cytometry is performed on mechanically and enzymatically disaggregated solid tissues or on cultured cells, where subcellular debris, apoptotic or necrotic cells, and highly autofluorescent cells may comprise a substantial proportion of acquired events. We provide real-world examples of tissues that pose specific analytical challenges (bone marrow, breast cancer, lung cancer and adipose tissue) and suggest approaches to improve data analysis. These include the use of a sequential or hierarchical gating process, which envisions analysis as consisting of three parts: (1) removal of artifact; (2) defining classifying populations; and (3) measuring outcomes on the classifying populations. Tools for removal of artifact include use of the time parameter to detect and remove fluidic perturbations, use of doublet discrimination to avoid analysis of cell clusters, measurement of DNA content to remove subcellular debris and late apoptotic cells, Boolean gating to recognize and remove auto-fluorescent events, and the use of a dump gate (markers known to be negative on the population of interest, but expressed on interfering cells). Implementation of these strategies, as appropriate, extends the usefulness of flow cytometry to a wider range of applications.