Table_2_Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes.xlsx

Heterogeneity and limited comprehension of chronic autoimmune disease pathophysiology cause accurate diagnosis a challenging process. With the increasing resources of single-cell sequencing data, a reasonable way could be found to address this issue. In our study, with the use of large-scale public single-cell RNA sequencing (scRNA-seq) data, analysis of dataset integration (3.1 × 105 PBMCs from fifteen SLE patients and eight healthy donors) and cellular cross talking (3.8 × 105 PBMCs from twenty-eight SLE patients and eight healthy donors) were performed to identify the most crucial information characterizing SLE. Our findings revealed that the interactions among the PBMC subpopulations of SLE patients may be weakened under the inflammatory microenvironment, which could result in abnormal emergences or variations in signaling patterns within PBMCs. In particular, the alterations of B cells and monocytes may be the most significant findings. Utilizing this powerful information, an efficient mathematical model of unbiased random forest machine learning was established to distinguish SLE patients from healthy donors via not only scRNA-seq data but also bulk RNA-seq data. Surprisingly, our mathematical model could also accurately identify patients with rheumatoid arthritis and multiple sclerosis, not just SLE, via bulk RNA-seq data (derived from 688 samples). Since the variations in PBMCs should predate the clinical manifestations of these diseases, our machine learning model may be feasible to develop into an efficient tool for accurate diagnosis of chronic autoimmune diseases.

慢性自身免疫性疾病的病理异质性与现有认知局限，使得精准诊断成为极具挑战的工作。随着单细胞测序数据资源的持续丰富，我们有望找到解决该难题的合理路径。本研究依托大规模公开单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）数据，针对数据集整合（纳入15名系统性红斑狼疮（Systemic Lupus Erythematosus, SLE）患者与8名健康志愿者的3.1×10^5个外周血单个核细胞（Peripheral Blood Mononuclear Cell, PBMC））与细胞互作分析（纳入28名SLE患者与8名健康志愿者的3.8×10^5个PBMC）展开，以筛选出表征SLE的核心关键信息。研究结果显示，在炎症微环境中，SLE患者PBMC亚群间的互作可能出现减弱，进而导致PBMC内信号通路模式产生异常激活或变异。尤为重要的是，B细胞与单核细胞的异常变化或为本研究最具价值的发现。基于上述关键信息，本研究构建了一款高效的无偏随机森林机器学习模型，该模型不仅可通过scRNA-seq数据，还可利用批量RNA测序（bulk RNA-seq）数据区分SLE患者与健康志愿者。令人意外的是，依托来自688个样本的bulk RNA-seq数据，该模型还可精准识别类风湿关节炎（Rheumatoid Arthritis, RA）与多发性硬化（Multiple Sclerosis, MS）患者，而非仅局限于SLE。鉴于PBMC的异常变化往往先于此类疾病的临床症状出现，本研究构建的机器学习模型有望开发为一款可用于慢性自身免疫性疾病精准诊断的高效工具。