用拉曼光谱检测调节性T细胞的非侵入性检测
N Pavillon1, E L Lim2, A Tanaka2,3
1Biophotonics Laboratory, Immunology Frontier Research Center (IFReC), Osaka University, Yamadaoka 3-1, Suita, Osaka, 565-0871, Japan. n-pavillon@ifrec.osaka-u.ac.jp.
Scientific reports
|June 18, 2024
概括
研究人员开发了一种非侵入性的拉曼光谱法,用于识别调节性T细胞 (Tregs). 这种技术与机器学习相结合,可以准确检测活的,不变的Tregs,克服了治疗应用传统方法的局限性.
科学领域:
- 免疫学 免疫学 免疫学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 调节性T细胞 (Tregs) 对免疫自我耐受性至关重要,并具有治疗潜力.
- 目前的Treg鉴定依赖于侵入性细胞内标记物或不太特定的表面标记物,损害细胞活力和纯度.
- 现有的方法在准确分离活体,未经改变的Tregs用于研究和临床使用方面面临挑战.
研究的目的:
- 开发和验证一种非侵入性方法来识别活的,未经改变的调节性T细胞 (Tregs).
- 克服Treg净化和分析中侵入性标记物和替代表面标记物的局限性.
- 建立一种可靠的Treg检测方法,适用于小鼠和人类细胞.
主要方法:
- 采用拉曼光谱法用于光学检测活的,不变的Tregs.
- 集成机器学习,特别是调整后勤回归,用于数据分析和Treg识别.
- 在Foxp3记者和小鼠细胞上验证了该方法,随后在人类外周血液T细胞上验证了该方法.
- 纳入策略来管理样品纯度并确保模型的稳定性.
主要成果:
- 在识别Tregs方面取得了超过80%的准确性,与标准分类纯度相当.
- 证明了可靠地检测Tregs的能力在独立的捐赠者不包括在模型培训集.
- 成功地将非侵入性方法应用于小鼠和人类T细胞样本.
结论:
- 拉曼光谱与机器学习相结合,提供了一种可行的非侵入性方法来识别活的,不变的Tregs.
- 这种方法超越了传统的Treg鉴定技术的局限性,使得细胞更纯净地分离出来.
- 在独立捐赠者中证明的可靠性代表了Tregs.在实际治疗应用方面取得的重大进展.
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