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Abstract
Background Technological advances in flow cytometry allow for analysis of larger and more diverse panels of markers simultaneously, enabling a deeper understanding of complex biological processes. The number of marker combinations and cell population interactions can pose challenges using traditional manual gating approaches. Furthermore, manual gating is time-consuming and subject to technician expertise, introducing variability. Automated computational gating methods can mitigate this variability and allow for more efficient and objective analysis of these complex high-dimensional flow cytometry data.
Methods We developed an automated computational gating pipeline, {staRgate} (available soon on GitHub.com). This pipeline leverages existing R packages and incorporates a novel univariable density gating approach that determines the gating threshold based on the first and second derivatives of the kernel density estimate of the marker intensity distribution. It also integrates a customized gating template specifically designed to identify various T-cell subpopulations based on lineage, differentiation, and functional characteristics (figure 1).1 2 This method is control-free, and accounts for different distribution shapes: unimodal distributions with left or right tails, multimodal, or mixed distributions. We implemented {staRgate} on a 29-marker flow cytometry panel with data from two separate clinical trials involving peripheral blood mononuclear cells samples from patients both before and during treatment with immune checkpoint inhibitors. To evaluate the performance of {staRgate} compared to manual gating, we calculated Lin’s concordance correlation coefficient (CCC) based on percentage of positive cells for each subpopulation.
Results The median (interquartile range, IQR) CCC for the first trial gated on 266 subpopulations (n=141 samples) was 0.97 (0.95–0.98). 97% of the compared subpopulations had mean differences within +/-10% and standard deviations (SD) of the subpopulation differences ranged from 0.5–18%. Figure 2 shows a sample-level comparison (CCC=0.99). The second trial consisted of 298 samples, each gated on 97 subpopulations, and the median (IQR) CCC was 0.93 (0.89–0.95). 90% of the compared subpopulations had mean differences within +/-10% and SD of subpopulation differences was 0.8–21%.
Conclusions High concordance correlations between manual and automated gating, confirmed in two separate trials, indicate that the proposed method successfully replicates the manual gating strategy. The observed variations in differences are within previously reported inter-site variability in gating of T-cell subpopulations.3 Integration of this pipeline in immune monitoring allows for more efficient and timely processing of high-dimensional flow cytometry samples, increasing the potential for guiding clinical treatment decisions.
References
Monaco G, Chen H, Poidinger M, Chen J, de Magalhães JP, Larbi A. flowAI: automatic and interactive anomaly discerning tools for flow cytometry data. Bioinformatics. 2016 Aug 15;32(16):2473–80.
Finak G, Frelinger J, Jiang W, Newell EW, Ramey J, Davis MM, Kalams SA, De Rosa SC, Gottardo R. OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. PLoS Comput Biol. 2014 Aug 28;10(8):e1003806
Finak G, Langweiler M, Jaimes M, Malek M, Taghiyar J, Korin Y, Raddassi K, Devine L, Obermoser G, Pekalski ML, Pontikos N. Standardizing flow cytometry immunophenotyping analysis from the human immunophenotyping consortium. Sci Rep. 2016 Feb 10;6(1):1–1.
Workflow of staRgate
Sample-level concordance: comparison of manual and automated gating results for 266 cell subpopulations where point size indicates parent population size, and 45-degree line indicates equality and dotted lines indicate +/-10% difference. The points fall close to the equality line and most fall within the +/-10% difference, indicating high concordance (concordance correlation = 0.99).
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