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外围免疫模式可对ALS发病和进展进行强有力的跨平台预测

Luvna Dhawka, Baggio A Evangelista, Omeed K Arooji

bioRxiv : the preprint server for biology

|September 5, 2025

概括

肌缩侧面硬化 (ALS) 是一种免疫协调失调的疾病, 识别免疫细胞网络中断的新生物标志物可以帮助分层患者并指导ALS治疗.

科学领域:

神经免疫学
系统免疫学
生物标志物的发现

背景情况:

肌缩侧面硬化 (ALS) 在患者的进展率上表现出显著的异质性.
缺乏预后生物标志物阻碍了临床决策和治疗ALS的患者分层.

研究的目的:

识别与ALS进展相关的免疫细胞网络干扰.
开发用于预测ALS疾病状态和进展率的生物标志物.

主要方法:

从ALS患者和健康对照人群中对220万个免疫细胞进行质量细胞测量 (CyTOF).
对免疫细胞类型的相关性模式和网络组织的分析.
开发用于疾病分层的机器学习模型.

主要成果:

免疫细胞的协调模式, 而不是单个细胞的频率, 区分ALS的进展.
在对照组中观察到从B细胞/基细胞中心转变为中性细胞/T细胞主导的模式.
使用免疫细胞相互作用的机器学习模型优于使用细胞频率进行疾病分层的模型.

结论:

这种疾病的特征在于免疫系统的协调能力下降,
中心/效应细胞 CD4+ T 细胞相互作用和血细胞树突细胞/调节性 T 细胞的比率是关键的区分因素.
研究结果表明,ALS和其他神经退行性疾病的潜在生物标志物和细胞类型特异性治疗方法.

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