An Introduction to Automated Flow Cytometry Gating Tools and Their Implementation

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.

Flow cytometric analysis of circulating endothelial cells and endothelial progenitors for clinical purposes in oncology: A critical evaluation

“…The authors suggested steps that must be followed to reduce the sources of error in FCM results. The importance of standardizing an appropriate gating strategy and multiple data analysis methods are highlighted in detail in one previous study (56).…”

Automated and simplified identification of normal and abnormal plasma cells in Multiple Myeloma by flow cytometry

“…It would be interesting to compare our automated software with other computational methods (45,46). The use of antibody pools allows the identification of PC as efficiently as the reference method by using the same number of markers (CD19, CD27, CD56, CD117, and CD200), while decreasing the number of fluorophores.…”

“…Indeed, results obtained with manual gating rely on a subjective analysis and this can generate inter-and intra-analyst differences. It would be interesting to compare our automated software with other computational methods (45,46). Compared to the standard recommended 8-color panel using two tubes from the Euroflow consortium (12), the antibody pools of the smart panel could decrease the number of fluorophores and lasers needed with the advantage of the automated PC detection and analysis and similar specificity and sensitivity.…”

Analysis of S1P Receptor Expression by Uterine Immune Cells Using Standardized Multi-parametric Flow Cytometry

“…Appropriate adjustment of biexponential scaling is important to clearly and completely visualize all populations, particularly for intracellular cytokine staining. For more complex examination of S1P signalling, computational assistance analysis could be an alternative only after critical assessment and verification [16,17].…”