通过自发刺激的拉曼空间定位自动检测亚细胞规模的代谢动力学
Xing Chen1,2,3, Mingbo Chi1,2,3, Siqi Wang1,3
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China.
Analytical chemistry
|February 13, 2026
概括
一个新的双模态拉曼显微镜平台通过结合刺激拉曼散射 (SRS) 和自发拉曼模式来增强细胞分析. 这提高了分子映射和细胞识别准确度,在代谢研究中达到98%以上.
科学领域:
- 生物医学光学 生物医学光学
- 化学成像技术 化学成像技术
- 细胞生物学 细胞生物学
背景情况:
- 由于对比度低,无标签成像在定位亚细胞结构方面面临挑战.
- 自发拉曼显微镜的精度受到细胞异质性和定位精度的限制.
研究的目的:
- 开发一种新的双模式,无标签的3D自动细胞分析平台,集成自发和刺激拉曼散射 (SRS).
- 提高细胞内有机体的形态特征,化学绘图和分子成分分析.
- 通过拉曼光谱和机器学习来提高细胞识别的准确性.
主要方法:
- 集成SRS 3D快速成像以获取分子空间分布 (在1分钟内).
- 自动化,亚微米定位的自发拉曼光谱,用于高分辨率的光谱采集.
- 开发一种用于测量自发拉曼检测中轴定位一致性的新方法.
- 机器学习的应用用于基于拉曼光谱数据的细胞识别.
主要成果:
- SRS成像提供了快速的3D分子映射.
- 自动自发拉曼光谱学使有机体的详细分子成分分析成为可能.
- 增强的轴定位精度提高了细胞歧视模型的性能,达到98%以上.
- 分析显示细胞系 (不和酸盐) 和临床样本 (酸盐,棕酸盐,白血病患者中较高胆固醇) 中的脂质滴芯组成不同.
结论:
- 双模式平台为无标签的细胞分析提供了更高的精度和可重复性.
- 改进的定位精度大大提高了基于机器学习的细胞识别.
- 拉曼光谱揭示了正常和患病细胞之间的脂质滴状成分的显著代谢差异.
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