Abstract
Abstract Background/Aims Treating rheumatoid arthritis (RA) continues to rely heavily on a trial-and-error approach. The potential of deep immunophenotyping of peripheral blood remains to be proven in tailoring treatment to an individual’s biological variability. Here, we aim to compare the analysis of single-cell data using an unsupervised automated clustering approach with conventional biaxial gating to identify T cell imbalances RA. Methods Unstimulated and stimulated peripheral blood mononuclear cells from 10 RA and 10 healthy controls (HC) were immunophenotyped by mass cytometry (CyTOF) using a T-cell panel. Firstly, a conventional biaxial gating strategy and standard definitions of Th cell subsets were applied to compare subset frequencies between cases and controls. Secondly, publicly-available unsupervised analytical workflows [1] were used to automatically define cell clusters hypothesis-free. Their differential abundance between RA and HC was assessed with Mixed-effects modelling of Associations of Single Cells (MASC)[2]. Results Applying unsupervised automated clustering algorithms clearly identifies unusual marker combinations and a Th1/Th17 : Th2/Treg imbalance in RA in this small sample size, while conventional analytical techniques fail to do so. Cell clusters, including a classical pro-inflammatory Th1-like (IL-2+ T-bet+) subset and a novel IL-17+ T-bet+ subset were significantly enriched in RA compared to HC (odds ratio 4.9, P = 6.6 x 10-4; odds ratio 5.5, P = 1.1 x 10-2). In contrast, a FoxP3+ IL-2+ HLA-DR+ cluster was amongst those reduced in RA (odds ratio 0.2, P = 2.0 x 10-7). Conclusion Automated clustering techniques are more powerful than conventional analytical methods when analysing high-dimensional single-cell data. A hypothesis-free approach allows identification of novel cell clusters with unexpected marker combinations, such as T-bet and IL-17, which might otherwise be overlooked with biaxial gating.
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