Mass cytometry identifies imbalance of multiple immune-cell subsets associated with biologics treatment in ankylosing spondylitis

Objective

This study aims to comprehensively investigate immune-cell landscapes in ankylosing spondylitis (AS) patients and explore longitudinal immunophenotyping changes induced by biological agents.

Methods

We employed mass cytometry with 35 cellular markers to analyze blood samples from 34 AS patients and 13 healthy controls (HC). Eleven AS patients were re-evaluated 1 month (4 patients) and 3 months (7 patients) after treatment with biological agents. Flow Self-Organizing Maps (FlowSOM) clustering was performed to identify specific cellular metaclusters. We compared cellular abundances across distinct subgroups and validated subset differences using gating strategies in flow cytometry scatter plots, visualized with FlowJo software. The proportions of differential subsets were then used for intercellular and clinical correlation analysis, as well as for constructing diagnostic models based on the random forest algorithm.

Results

In AS patients, we identified and validated nine different immune-cell subsets compared to HC. Three subsets increased: helper T-cell 17 (Th17), mucosa-associated invariant T-cell (MAIT), and classical monocytes (CM). Six subsets decreased: effector memory T-cell (TEM), naïve B cells, transitional B cells, IL10+ memory B cells, non-classical monocytes (NCM), and neutrophils. Treatments with biological agents could rectify cellular abnormalities, particularly the imbalance of CM/NCM. Furthermore, these subsets may serve as biomarkers for assessing disease activity and constructing effective diagnostic models for AS.

Conclusion

These findings provide novel insights into the specific patterns of immune cell in AS, facilitating the further development of novel biomarkers and potential therapeutic targets for AS patients.