Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus

Abstract

IntroductionSystemic lupus erythematosus (SLE) is a chronic autoimmune disease for which there is no cure. Effective diagnosis and precise assessment of disease exacerbation remains a major challenge.MethodsWe performed peripheral blood mononuclear cell (PBMC) proteomics of a discovery cohort, including patients with active SLE and inactive SLE, patients with rheumatoid arthritis (RA), and healthy controls (HC). Then, we performed a machine learning pipeline to identify biomarker combinations. The biomarker combinations were further validated using enzyme-linked immunosorbent assays (ELISAs) in another cohort. Single-cell RNA sequencing (scRNA-seq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated the potential immune cellular sources of each of these PBMC biomarkers.ResultsScreening of the PBMC proteome identified 1023, 168, and 124 proteins that were significantly different between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE, respectively. The machine learning pipeline identified two biomarker combinations that accurately distinguished patients with SLE from controls and discriminated between active and inactive SLE. The validated results of ELISAs for two biomarker combinations were in line with the discovery cohort results. Among them, the six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) exhibited good performance for SLE disease diagnosis, with AUC of 0.723 and 0.815 for distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1, and STAT2) showed a robust performance in assessing disease exacerbation (AUC=0.990). Further, the potential immune cellular sources of nine PBMC biomarkers, which had the consistent changes with the proteomics data, were elucidated by PBMC scRNAseq.DiscussionUnbiased proteomic quantification and experimental validation of PBMC samples from two cohorts of patients with SLE were identified as biomarker combinations for diagnosis and activity monitoring. Furthermore, the immune cell subtype origin of the biomarkers in the transcript expression level was determined using PBMC scRNAseq. These findings present valuable PBMC biomarkers associated with SLE and may reveal potential therapeutic targets.

Elucidating the Role of Immune Cell Dysregulation in Atypical Hemolytic Uremic Syndrome: A Comprehensive Single-Cell Sequencing Analysis

“…Elevated mitochondria-related gene expression suggests cell metabolism's role in aHUS clinical course, warranting further investigation. Notably, these gene expression patterns were not observed in other autoimmune diseases like systemic lupus erythematosus [20][21][22][23] or immunoglobulin G4-related disease [24], highlighting the unique immune cell profile in aHUS.…”

Mitochondrial-related hub genes in dermatomyositis: muscle and skin datasets-based identification and in vivo validation

“…Silencing of CMPK2 in macrophages results in augmented ROS and perturbation of mitochondrial structure, leading to the upregulation of pro-inflammatory genes IL1β, TNFα, and IL8 ( Arumugam et al, 2022 ). Studies have reported a significantly increase of CMPK2 expression in active SLE compared to healthy individuals and inactive SLE patients, making it a promising biomarker for diagnosis and evaluation of SLE activity ( Xu et al, 2008 ; Li et al, 2022 ). Leucine aminopeptidase 3 (LAP3), encoded by the LAP3 gene, exhibits dual subcellular localization in both the cytosol and mitochondria ( Sickmann et al, 2003 ).…”

Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records

“…ML models have been extensively explored for diagnosing SLE, defining clinical phenotypes, determining outcomes, and informing therapeutic decisions [ 23 ]. Previous studies utilizing ML to facilitate SLE diagnosis have employed diverse input data, including EHRs, genetic biomarkers, proteomics, lipidomes, or a combination of these data types [ 23 , 24 , 27 – 32 ]. This study is the first to incorporate genome-wide SNPs, PRS, and EHRs in an ML analysis.…”

“…This study is the first to incorporate genome-wide SNPs, PRS, and EHRs in an ML analysis. Moreover, ML algorithms for diagnostic purposes in previous studies included RF, LASSO, SVM, LR, XGB, and Partial Least Square [ 23 , 24 , 27 – 32 ]. This study is the first to attempt to compare the diagnostic accuracy among six ML models.…”