通过基于机器学习的同声定位和扩散分析,对胰岛素类型的多样细胞内贩运
Sara Vogt Bleshøy1,2,3,4, Jacob Kæstel-Hansen1,2,3, Annette Juma Nielsen1,2
1Department of Chemistry & Nanoscience Center, University of Copenhagen, Copenhagen, Denmark.
iScience
|February 3, 2026
概括
研究人员在活细胞中追踪胰岛素类似物,与内源性胰岛素相比,揭示了明显的细胞内贩运和溶酶体同位分化的差异. 这种机器学习方法增强了对糖尿病治疗中的胰岛素模拟生物学的理解.
科学领域:
- 细胞生物学 细胞生物学
- 分子内分泌学分子内分泌学
- 生物物理学的生物物理.
背景情况:
- 胰岛素信号传递对代谢平衡至关重要;其失调会导致糖尿病.
- 目前研究胰岛素类型的方法缺乏时间动态和生理相关性.
- 区分胰岛素类似物与内源性胰岛素的细胞内贩运是具有挑战性的.
研究的目的:
- 在活细胞中直接比较复合人胰岛素 (HI) 和胰岛素亚斯巴特 (IAsp) 的细胞内贩运.
- 开发和应用一种新的机器学习平台,用于分析单粒子动态和局部化.
- 为了研究胰岛素类似物细胞路径的微妙差异.
主要方法:
- 在ATTO655标记HI (HI655) 和IAsp (IAsp655) 的活细胞成像.
- 应用定位指纹,用于时间解析定位分析的机器学习框架.
- 利用深度学习辅助的单粒子扩散分析 (DeepSPT) 进行定量跟踪.
主要成果:
- 在活细胞内的IAsp655和HI655的扩散行为中观察到显著的差异.
- 在两种胰岛素形式之间发现了清晰的溶酶体同位化模式.
- 开发的平台成功地捕获了时间贩运动态,与静态方法不同.
结论:
- 这项研究揭示了胰岛素阿斯巴特和内源性人体胰岛素之间细胞内贩运的显著差异.
- 新的机器学习方法提供了一个强大的工具,用于剖析复杂的细胞过程和识别微妙的生物变异.
- 这项研究促进了对细胞水平上胰岛素模拟行为的理解,这与糖尿病治疗优化有关.
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