Table_2_Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus.xlsx

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.

系统性红斑狼疮（SLE）是一种尚无治愈方法的慢性自身免疫性疾病。有效诊断和精确评估病情恶化仍然是重大挑战。研究方法：我们对一组包括活动性SLE、非活动性SLE、类风湿性关节炎（RA）患者和健康对照者（HC）的发现队列进行了外周血单个核细胞（PBMC）蛋白质组学分析。随后，我们通过机器学习流程来识别生物标志物组合。这些生物标志物组合随后通过另一队列中的酶联免疫吸附测定（ELISAs）进行进一步验证。来自活动性SLE、非活动性SLE和HC PBMC样本的单细胞RNA测序（scRNA-seq）数据进一步阐明了这些PBMC生物标志物的潜在免疫细胞来源。研究结果：PBMC蛋白质组学筛选发现，在SLE与HC、SLE与RA、活动性SLE与非活动性SLE之间，分别有1023、168和124种蛋白质存在显著差异。机器学习流程识别出两种生物标志物组合，这些组合能够准确区分SLE患者与对照者，并区分活动性SLE与非活动性SLE。两种生物标志物组合的ELISAs验证结果与发现队列结果一致。其中，六蛋白组合（IFIT3、MX1、TOMM40、STAT1、STAT2和OAS3）在SLE疾病诊断中表现出良好的性能，区分SLE与HC和RA的AUC分别为0.723和0.815。九蛋白组合（PHACTR2、GOT2、L选择素、CMC4、MAP2K1、CMPK2、ECPAS、SRA1和STAT2）在评估疾病恶化方面表现出稳健的性能（AUC=0.990）。此外，通过PBMC scRNAseq阐明了与蛋白质组学数据一致变化的九个PBMC生物标志物的潜在免疫细胞来源。讨论：对两个SLE患者队列的PBMC样本进行无偏蛋白质组定量和实验验证，确定了用于诊断和活动监测的生物标志物组合。此外，利用PBMC scRNAseq确定了转录表达水平上生物标志物的免疫细胞亚型起源。这些发现提供了与SLE相关的有价值PBMC生物标志物，可能揭示潜在的治疗靶点。