1287-P: Deep Immune Phenotyping of Type 1 Diabetes by Machine Learning

Breakdown of self-tolerance is an important common mechanism in autoimmunity. We use machine learning (ML) to identify common patterns and dissimilarities between type 1 diabetes (T1D) , rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) based on immune phenotyping. PBMCs were isolated from patients with T1D, RA, SLE, and controls. A FACS approach was applied, and a traditional analysis was compared to a ML method implemented on R and based on self-organizing maps (Fig) . Our pipeline includes unsupervised pre-gating, normalization, FlowSOM clustering, and a statistical model (GLMM) , to check for significant differential abundances of cell populations among the autoimmune conditions. After applying our automated workflow to one T cell panel we could identify 14 new cell clusters present in all the samples. The GLMM test revealed a cluster with a significant difference (p=0.035) and a trending one (p=0.059) on the abundance across the different diseases. In particular, CD4^pos T cells expressing high IL-7 receptor (CD127) levels and median amounts of CD15s but low CD25, CD161 and FoxP3 are increased in T1D whereas CD4⁺CD25⁺⁺CD15s⁺FoxP3^lowCD161^lowCD45RA^- cells are increased in SLE.

In conclusion, our ML workflow identifies a new subtype of T cells significantly increased in T1D. This unsupervised analysis approach for large datasets enables the discovery of new biomarkers complementing traditional workflows.

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