Automated flow cytometric analysis across large numbers of samples and cell types

- The novel FlowGM® flow cytometry workflow targets large numbers of samples.
- Largely automated analysis with minimal operator guidance
- Quantification of 24 cell types across 115 Milieu Intérieur samples and 4 panels
- Embedding of results in FCS files permits inspection and validation in FlowJo.
- Validated performance is on par with, or exceeding quality of manual gating.

Multi-parametric flow cytometry is a key technology for characterization of immune cell phenotypes. However, robust high-dimensional post-analytic strategies for automated data analysis in large numbers of donors are still lacking. Here, we report a computational pipeline, called FlowGM, which minimizes operator input, is insensitive to compensation settings, and can be adapted to different analytic panels. A Gaussian Mixture Model (GMM)-based approach was utilized for initial clustering, with the number of clusters determined using Bayesian Information Criterion. Meta-clustering in a reference donor permitted automated identification of 24 cell types across four panels. Cluster labels were integrated into FCS files, thus permitting comparisons to manual gating. Cell numbers and coefficient of variation (CV) were similar between FlowGM and conventional gating for lymphocyte populations, but notably FlowGM provided improved discrimination of “hard-to-gate” monocyte and dendritic cell (DC) subsets. FlowGM thus provides rapid high-dimensional analysis of cell phenotypes and is amenable to cohort studies.