Analyses of single-cell and bulk RNA sequencing combined with machine learning reveal the expression patterns of disrupted mitophagy in schizophrenia

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-09-18 DOI:10.3389/fpsyt.2024.1429437
Wei Yang, Kun Lian, Jing Ye, Yuqi Cheng, Xiufeng Xu
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Abstract

BackgroundMitochondrial dysfunction is an important factor in the pathogenesis of schizophrenia. However, the relationship between mitophagy and schizophrenia remains to be elucidated.MethodsSingle-cell RNA sequencing datasets of peripheral blood and brain organoids from SCZ patients and healthy controls were retrieved. Mitophagy-related genes that were differentially expressed between the two groups were screened. The diagnostic model based on key mitophagy genes was constructed using two machine learning methods, and the relationship between mitophagy and immune cells was analyzed. Single-cell RNA sequencing data of brain organoids was used to calculate the mitophagy score (Mitoscore).ResultsWe found 7 key mitophagy genes to construct a diagnostic model. The mitophagy genes were related to the infiltration of neutrophils, activated dendritic cells, resting NK cells, regulatory T cells, resting memory T cells, and CD8 T cells. In addition, we identified 12 cell clusters based on the Mitoscore, and the most abundant neurons were further divided into three subgroups. Results at the single-cell level showed that Mitohigh_Neuron established a novel interaction with endothelial cells via SPP1 signaling pathway, suggesting their distinct roles in SCZ pathogenesis.ConclusionWe identified a mitophagy signature for schizophrenia that provides new insights into disease pathogenesis and new possibilities for its diagnosis and treatment.
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单细胞和大容量 RNA 测序分析与机器学习相结合,揭示了精神分裂症患者有丝分裂紊乱的表达模式
背景线粒体功能障碍是精神分裂症发病机制中的一个重要因素。方法检索SCZ患者和健康对照组的外周血和脑器官组织的单细胞RNA测序数据集。方法检索了 SCZ 患者和健康对照组的外周血和脑器质性组织的单细胞 RNA 测序数据集,筛选了两组间差异表达的有丝分裂相关基因。利用两种机器学习方法构建了基于关键有丝分裂基因的诊断模型,并分析了有丝分裂与免疫细胞之间的关系。结果 我们发现了7个关键有丝分裂基因,从而构建了一个诊断模型。有丝分裂基因与中性粒细胞、活化树突状细胞、静息NK细胞、调节性T细胞、静息记忆T细胞和CD8 T细胞的浸润有关。此外,我们还根据Mitoscore确定了12个细胞群,并将最丰富的神经元进一步分为三个亚群。单细胞水平的研究结果表明,Mitohigh_Neuron通过SPP1信号通路与内皮细胞建立了新的相互作用,这表明它们在SCZ发病机制中扮演着不同的角色。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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