Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus - Li, Yixi; Ma, Chiyu; Liao, Shengyou; Qi, Suwen; Meng, Shuhui; Cai, Wanxia; Dai, Weier; Cao, Rui; Dong, Xiangnan; Krämer, Bernhard K.; Yun, Chen; Hocher, Berthold; et al. (Frontiers in Immunology, 2022)
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with no cure. Effective diagnosis and precise assessing disease exacerbation remain major challenges. We performed peripheral blood mononuclear cells (PBMC) proteomics of a discovery cohort, including active SLE and inactive SLE patients, rheumatoid arthritis (RA) patients and healthy controls (HC). Screening of the PBMC proteome identified 1023, 168, and 124 proteins significantly changed between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE. We performed a machine learning pipeline, contributing to the identification of two biomarker combinations that could accurately distinguish SLE patients from controls or discriminate between active and inactive SLE, respectively. The two biomarker combinations were further validated via use of enzyme-linked immunosorbent assays (ELISAs) in another cohort, with results that were in line with the discovery cohort results. Among them, six-protein combination (OAS3, IFIT3, MX1, STAT1, STAT2, and TOMM40) exhibited good performance for SLE disease diagnosis by validated in an independent cohort, with AUC of 0.723 and 0.815 in distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1 and STAT2) showed robust performance for assessing disease exacerbation (AUC=0.990). Next, single cell RNA sequence (scRNAseq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated potential immune cellular sources of each of these PBMC biomarkers. Hence, unbiased proteomic quantifications and experimental validation of PBMC samples from two cohorts of SLE patients identified biomarker combinations for diagnosis and activity monitoring. Further, the immune cell subtypes origin of biomarkers was obtained using PBMC scRNAseq. These findings presented valuable PBMC biomarkers associated with SLE, and might reveal potential therapeutic targets.