使用机器学习的拉曼光谱法对调控性T细胞激活标记物的无标签估计
Aria Azari-Pour1,2,3, Ali Chamkalani4,5, Shreyas Rangan4,5
1Center for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK. aa2479@cam.ac.uk.
Scientific reports
|November 4, 2025
概括
这项研究引入了一种非侵入性的拉曼光谱法,用于监测调控性T细胞激活. 这种技术可以准确地估计细胞激活标记物,帮助临床制造过程.
科学领域:
- 免疫学 免疫学 免疫学
- 生物技术是生物技术.
- 频谱学是一种光谱学.
背景情况:
- 调控性T细胞 (Tregs) 是重要的免疫细胞,具有重要的临床兴趣.
- 监控Treg激活对于制造过程中的过程控制至关重要,但目前的方法是侵入性的和昂贵的.
- 迫切需要一种非侵入性的在线方法来监测Treg激活.
研究的目的:
- 开发和验证一种用于监测调控性T细胞激活状态的非侵入性方法.
- 利用拉曼光谱和数据分析来准确估计Treg激活标记.
- 为了在临床制造环境中实现实时监控.
主要方法:
- 采用拉曼光谱,一种无标签的光学技术,用于数据采集.
- 开发了一个[公式:参见文本]规范化的最小平方模型,使用来自六个Treg捐赠者的光谱数据.
- 使用来自两个独立的外部Treg捐赠者的光谱数据验证了该模型.
主要成果:
- 在量化估计Treg激活标记物方面取得了高准确性.
- 在未见的数据上成功验证了模型的预测能力.
- 展示了一个强大的程序,用于有效的Treg激活状态估计.
结论:
- 一种基于拉曼光谱的非侵入性方法可以准确监测Treg激活.
- 这种方法可以与用于细胞制造的线上探针集成.
- 开发的方法为Treg过程监控提供了一个无标签的,高效的替代方案.
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