Abstract
Current flow cytometry (FCM) reagents and instrumentation allow for the measurement of an unprecedented number of parameters for any given cell within a homogenous or heterogeneous population. While this provides a great deal of power for hypothesis testing, it also generates a vast amount of data, which is typically analyzed manually through a processing called “gating.” For large experiments, such as high-content screens, in which many parameters are measured, the time required for manual analysis as well as the technical variability inherent to manual gating can increase dramatically, even becoming prohibitive depending on the clinical or research goal. In the following article, we aim to provide the reader an overview of automated FCM analysis as well as an example of the implementation of FLOw Clustering without K, a tool that we consider accessible to researchers of all levels of computational expertise. In most cases, computational assistance methods are more reproducible and much faster than manual gating, and for some, also allow for the discovery of cellular populations that might not be expected or evident to the researcher. We urge any researcher who is planning or has previously performed large FCM experiments to consider implementing computational assistance into their analysis pipeline.
Highlights
Recent advances in flow cytometry (FCM) have provided researchers in the fields of cellular and clinical immunology an incredible amount of leverage toward testing new hypotheses
Flow cytometry analysis software can be broadly grouped into two areas: those driven by graphical-user interfaces (GUIs), which can be controlled by mouse and simple keyboard commands, and command-line driven modules, which require at least some computer programing language expertise
This brief comparison demonstrates the power of using FLOw Clustering without K (FLOCK), namely the ability to identify cellular populations that were not specified in the manual gating strategy, the reduced time expenditure, and the high level of comparability to the results determined by manual analysis
Summary
Recent advances in flow cytometry (FCM) have provided researchers in the fields of cellular and clinical immunology an incredible amount of leverage toward testing new hypotheses. These include improvements to reagents and instrumentation employed in traditional fluorescence-based FCM, allowing for the measurement of up to 20 parameters for any given cell [1], as well as the introduction of mass cytometry [CyTOF, reviewed in Ref. We urge any researcher who has conducted or is considering conducting a large-scale FCM study to evaluate these tools as well as many of the other exquisite techniques that are freely available to the scientific community
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