Document resource
Objectives Systemic autoimmune diseases (SADs) are heterogeneous and difficult to classify using clinical criteria alone. This study aims to evaluate high-content immunophenotyping as a biomarker-driven approach to reclassify patients into biologically distinct subgroups and characterize their immunopathological signatures across multiple molecular layers.Methods Peripheral blood samples from 101 patients diagnosed with 7 different SADs (systemic lupus erythematosus (SLE), Sjögren’s disease (SjD), systemic sclerosis (SSC), mixed connective tissue disease (MCTD), undifferentiated connective tissue disease (UCTD), rheumatoid arthritis (RA), primary anti-phospholipid syndrome (PAPS)), and 22 healthy controls (CTR) were analyzed using a 36-plex mass cytometry panel. Immune cell phenotypes were compared across clinical diagnoses and further reclassified using Monte Carlo reference-based consensus clustering. Multi-omics profiling, including immune-related proteomics and transcriptomics, was used to characterize clusters. In addition, a drug repurposing analysis was performed to identify candidate therapeutics specific to each molecular subgroup.Results Differential analysis by diagnosis did not reveal any disease-specific patterns in cellular composition or phenotype. Instead, the results highlighted immunological similarities between clinically distinct groups, for example, MCTD, SLE, and SjD on one side, and RA, SSc, and UCTD on the other. Therefore, individual patients were analyzed and classified into three phenotypically distinct clusters (C1–C3), each comprising a mix of diagnostic entities but sharing common immunophenotypic features. The defining characteristics of these clusters were largely driven by granulocyte phenotypes and CD38 expression in lymphoid populations. C1 was associated with increased expression of apoptosis-related molecules, IFN signature, complement activation, and autoantibody production. C2 was characterized by IL-13, IL-4, eotaxin, and TGF-beta signature, together with pathways related to megakaryocyte and platelet function, suggesting a Th2-associated and pro-fibrotic environment. C3 represented the transitional phenotype between C1 and C2. Drug repurposing analysis revealed distinct therapeutic profiles for clusters C1 and C2, while C3 shared overlapping candidate molecules.Conclusions Our findings demonstrate that high-dimensional immunophenotyping, combined with multi-modal data, can uncover clinically relevant patient subgroups independent of the specific SAD diagnoses. This approach holds promise as a stratification tool in precision rheumatology, potentially guiding targeted therapeutic interventions based on immune profiles.Funding IMI, PRECISESADS (GA#115565), 3TR (GA#831434), EFPIA.