Integration of T cell repertoire, CyTOF, genotyping and symptomatology data reveals subphenotypic variability in COVID-19 patients.

COVID-19 manifests a broad spectrum of clinical outcomes, from asymptomatic cases to severe disease. While several biomarkers have been proposed, comprehensive immunological analyses integrating mass cytometry (CyTOF) and T-cell receptor sequencing (TCRseq) data remain limited. In this study, we applied the Latent Class Model based on the Bayesian Information Criterion (LCM-BIC) algorithm to integrate immunophenotyping, including monocyte-macrophage counts from CyTOF, T-cell receptor repertorie data via TCRseq, SNPs data from ACE2 (rs2285666), MX1 (rs469390), and TMPRSS2 (rs2070788), and symptomatology data from 61 Spanish COVID-19 patients (33 mild, 28 severe). We identified three novel and distinct patient clusters with significant differences in TCR diversity, monocyte subpopulations, and V allele usage and disease outcome. Cluster 1 was predominantly enriched in severe cases, characterized by unique immunological features. Deep learning analysis of TCR amino acid sequences further distinguished Cluster 1 from the others, identifying SARS-CoV-2-specific TCR sequences associated with disease severity. In addition, analysis of residue sensitivity of cluster 1 SARS-CoV-2-specific TCR sequences further identified conserved aminoacids located in key central positions of the complementarity-determining region 3. This study highlights the value of integrating immunophenotyping and genetic profiling to identify novel immunological markers and patterns, aiding in the stratification and management of COVID-19 patients based on their immune profiles and genetic background.