Multiomic analyses uncover immunological signatures in kidney transplantation

Abstract

Identifying biomarkers in kidney transplant patients is essential for early detection of rejection, personalized treatment and improved overall outcomes. It improves our ability to monitor the health of the transplanted organ and tailor interventions to the specific needs of each patient. Here we compiled a multicenter, multiomic dataset of the kidney transplant landscape. Using multi-omics factor analysis (MOFA), we sought to uncover sources of biological variability in patients’ blood, urine and allograft at the epigenetic and transcriptomic levels. MOFA reveals multicellular immune signatures characterized by distinct monocyte, natural killer and T cell substates explaining a large proportion of inter-patient variance. We also identified specific factors that reflect allograft rejection, complement activation or induction treatment. Factor 1 mainly explained the molecular variations in patients’ circulation and discriminated antibody-mediated rejection from T-cell mediated rejection. Factor 2 captured some of the molecular variation occurring within the allograft and associated with complement/monocytes crosstalk. Factor 4 captured the impact of ATG induction. These data provide proof-of-concept of MOFA’s ability to reveal multicellular immune profiles in kidney transplantation, opening up new directions for mechanistic, biomarker and therapeutic studies.