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在缺血性中风中绘制中性粒细胞命运和功能:用于转化见解的单细胞路线图

Junbo Zhang¹, Zhuohui Chen², Yanyi Peng¹

¹Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.

Biochemical and biophysical research communications

|September 18, 2025

概括

研究人员确定了与中风相关的NETs基因 (SN基因),并开发了一种机器学习模型,用于在超急性阶段诊断缺血性中风 (IS). 这种在体外和体内验证的模型显示了早期IS检测和治疗策略的前景.

科学领域:

神经科学是一个神经科学.
免疫学免疫学免疫学
生物医学工程生物医学工程

背景情况:

缺血性中风 (IS) 是导致死亡和残疾的主要原因,需要改进诊断和预后工具.
中性细胞外细胞陷 (NETs) 参与IS的发病,但它们的确切作用和与其他免疫成分的相互作用需要进一步调查.

研究的目的:

研究NET在IS中的作用,并确定关键的基因和涉及的途径.
利用机器学习开发一种用于早期检测IS的诊断模型.
通过体外和体外实验验验证已识别的生物标志物和诊断模型.

主要方法:

利用单个样本基因组丰富分析和差异表达分析来识别与中风相关的NETs基因 (SN基因).
开发了一种基于机器学习的诊断模型,其中包含了SN基因.
构建了中性粒细胞转换的单细胞地图,并进行了体外 (OGD) 和体内 (tMCAO模型) 验证.

主要成果:

在IS患者中,NETs显示出显著的差异表达,与中性粒细胞和巨细胞活性相关.
一个使用两个SN基因 (F12和PLXDC2) 的诊断模型在超急性IS检测中实现了AUC>0.98.
在体外和体内验证证证实了F12和PLXDC2对IS的预测价值.

结论:

确定了关键基因,免疫细胞,途径和参与IS中NET形成的ceRNA网络.
开发了一种强大的诊断模型,用于高精度的超急性IS检测.
这些发现表明,有可能改善IS的早期诊断和治疗策略.

关键词:

诊断模型是一个诊断模型.基因签名基因签名缺血性中风是因为缺血性中风.中性粒细胞外细胞陷这是一个PPI PPI.在WGCNA中,WGCNA是WGCNA.ceRNARNA是什么意思

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