Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes.
Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes.
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准确的机器学习模型利用 B 细胞和单核细胞的信息诊断慢性自身免疫性疾病
DOI：
10.3389/fimmu.2022.870531
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发表时间：
2022
影响因子：
7.3
通讯作者：
中科院分区：
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异质性和对慢性自身免疫性疾病病理生理的有限理解导致准确诊断是一个具有挑战性的过程。随着单细胞测序数据资源的增加，可以找到一种合理的方法来解决这一问题。在我们的研究中，利用大规模公开的单细胞RNA测序（scRNA-seq）数据，对数据集整合（来自15名SLE患者和8名健康供者的3.1 × 105个pbmc）和细胞串音（来自28名SLE患者和8名健康供者的3.8 × 105个pbmc）进行分析，以确定表征SLE的最关键信息。我们的研究结果表明，SLE患者PBMC亚群之间的相互作用可能在炎症微环境下被削弱，这可能导致PBMC内信号模式的异常出现或变化。特别是，B细胞和单核细胞的改变可能是最重要的发现。利用这些强大的信息，建立了一个有效的无偏随机森林机器学习数学模型，不仅通过scRNA-seq数据，而且通过大量RNA-seq数据来区分SLE患者和健康供体。令人惊讶的是，我们的数学模型还可以通过大量RNA-seq数据（来自688个样本）准确识别类风湿关节炎和多发性硬化症患者，而不仅仅是SLE患者。由于pbmc的变化应该早于这些疾病的临床表现，因此我们的机器学习模型可能会发展成为一种准确诊断慢性自身免疫性疾病的有效工具。
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.