在自闭症谱系障碍中识别与免疫相关的分子生物标志物,使用数据独立获取蛋白质学和机器学习
Jun He1, Qingqing Hu2, Sifeng Wang1
1College of Life Sciences, Hunan Normal University; Changsha Maternal and Child Health Hospital, Affiliated to Hunan Normal University.
Journal of visualized experiments : JoVE
|October 13, 2025
概括
研究人员开发了一种可复制的方法,使用数据独立获取 (DIA) 质谱和机器学习 (ML) 来找到自闭症谱系障碍 (ASD) 生物标志物. 这种方法以高精度识别了八种免疫蛋白,为新的诊断工具铺平了道路.
科学领域:
- 生物化学 生物化学
- 蛋白质组学是指蛋白质组学.
- 计算生物学 计算生物学
背景情况:
- 自闭症谱系障碍 (ASD) 诊断依赖于行为观察,缺乏客观的生物标志物.
- 血清蛋白质组分析为识别ASD可靠生物标志物提供了潜在的潜力.
- 之前的蛋白质组研究面临着可复制性和识别低丰度蛋白质的挑战.
研究的目的:
- 建立一个可复制的协议,用于识别ASD的血清蛋白生物标志物.
- 为了利用数据独立获取 (DIA) 质谱来进行全面的蛋白质组分析.
- 应用机器学习 (ML) 进行可靠的生物标志物面板选择和诊断模型开发.
主要方法:
- 分析了99名患有自闭症儿童和70名对照儿童的血清样本.
- 高丰度蛋白质被耗尽,随后进行标准化制备和分离.
- 数据独立采集 (DIA) 质谱法用于高分辨率蛋白质组分析.
- 机器学习算法用于差异蛋白质表达分析和模型构建.
主要成果:
- 八种与免疫相关的蛋白质被确定为ASD生物标志物发展的强有力的候选者.
- 在交叉验证中,一个物流回归模型实现了95.27%的准确性,Kappa值为0.9025,AUC值为1.000.
- 该DIA-MS和ML方法在生物标志物发现中表现出高的可重复性和稳定性.
结论:
- 基于DIA的蛋白质组学与ML相结合,为ASD生物标志物发现提供了一个强大的框架.
- 鉴定的免疫蛋白小组显示出开发ASD客观诊断工具的巨大潜力.
- 这种方法可以适应在其他复杂的神经和精神疾病中发现生物标志物.
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