In flow cytometry, how do you know the data you are looking at is real, and not due to artifacts caused by nonspecific staining, air bubbles or cell clumps, inaccurate compensation or equipment failures? Robust, specific and accurate flow cytometry data relies on users running regular quality control checks on their flow cytometers, as well as developing assays and staining panels that include controls that assure the data is reliable.
Flow cytometry assays are well suited for quantifying numerous cell types in parallel, especially now that modern flow cytometers can detect upwards of 18+ colors. In any given flow cytometry assay, the reportable results (or ‘reportables’) are the data that describe the different cell subsets. As an example, reportables can be the percentage represented by a particular B cell subset out of total lymphocytes. But reportables must be calculated from cells that fall into target staining ranges (usually set during panel development or validation) for the antibodies used to define the discrete populations. Larger, more complex staining panels can result in significantly more reportables when you account for all the subpopulations that are positive or negative for different cell surface markers.
These complex panels affirm the importance of validated flow cytometry assays that include staining panels that have antibodies which have been checked individually for their staining specificity. Compensation and FMO (fluorescence minus one) controls assure that the combination of antibodies in a given staining panel can be used together to accurately differentiate cell types. Assay validation assures that data on any individual marker can be analyzed at a later time in combination with other markers when a previously unknown reportable may be defined, therefore making your data “evergreen”.
Taking the time to validate assays and define reportables is essential to using flow cytometry for preclinical and clinical studies. Consider working with experts in assay validation and reportables to assure that your data is trustworthy and reliable.