Automated clustering reveals CD4

Background

Despite the report of an imbalance between CD4

Objectives

To capture previously reported Th imbalance in RA with deep immunophenotyping techniques; to compare hypothesis-free unsupervised automated clustering with hypothesis-driven conventional biaxial gating and explore if Th cell heterogeneity accounts for conflicting association results.

Methods

Unstimulated and stimulated peripheral blood mononuclear cells from 10 patients with RA and 10 controls were immunophenotyped with a 37-marker panel by mass cytometry (chemokine receptors, intra-cellular cytokines, intra-nuclear transcription factors). First, conventional biaxial gating and standard definitions of Th cell subsets were applied to compare subset frequencies between cases and controls. Second, unsupervised clustering was performed with FlowSOM and analysed using mixed-effects modelling of Associations of Single Cells (MASC).

Results

Conventional analytical techniques fail to identify classical Th subset imbalance, while unsupervised automated clustering, by allowing for unusual marker combinations, identified an imbalance between pro- and anti-inflammatory subsets. For example, a pro-inflammatory Th1-like (IL-2

Conclusion

Taking an unbiased approach to large dataset analysis using automated clustering algorithms captures non-canonical CD4

Informations de copyright

Copyright © 2023 Mulhearn, Marshall, Sutcliffe, Hannes, Fonseka, Hussell, Raychaudhuri, Barton and Viatte.

Déclaration de conflit d'intérêts

Authors CF and SH are currently employed by the companies eGenesis and BioNTech, respectively. They were not employed by these companies during the conception of the study or the generation of the experimental data. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.