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几何深度学习和多实例学习用于3D细胞形状分析

Matt De Vries¹, Lucas G Dent², Nathan Curry³

¹Department of Cancer Biology, Institute of Cancer Research, London, UK; Department of Physics, Imperial College London, London, UK; Sentinal4D, London, UK.

|March 20, 2025

概括

使用MorphoMIL的深度学习分析3D细胞形状,以揭示药物效应和细胞状态. 这种计算管道准确地预测细胞反应,并揭示药物发现的微妙形态变化.

科学领域:

计算生物学是一种计算生物学.
细胞生物学细胞生物学
生物物理学的生物物理.

背景情况:

细胞3D形态反映了细胞的状态和功能.
了解形态 - 现象型关系对于生物学见解至关重要.

研究的目的:

开发一个深度学习管道 (MorphoMIL) 用于3D细胞和核形状的概况.
分析形态特征,了解各种干扰下的细胞状态.
应用MorphoMIL用于药物发现和剖析表型异质性.

主要方法:

利用几何深度学习和基于注意力的多实例学习.
处理的3D点云数据用于形态签名提取.
将管道应用于超过95,000个带有化学和遗传干扰的黑色素瘤细胞.

主要成果:

莫尔福米尔准确地预测了药物干扰和细胞状态.
鉴定了与扰乱相关的微妙形态变化.
揭示了与信号活动和细胞状态异质性相关的关键形状.

结论:

MorphoMIL提供可扩展,高通量形态分析.
该框架为细胞生物学和药物反应提供了可解释的见解.
在药物发现数据集中展示了卓越的性能和概括性.

关键词:

三维细胞形状的3D细胞形状细胞形态细胞形态学细胞简介细胞简介细胞骨架细胞骨架几何深度学习的几何深度学习照明表显微镜显微镜多个实例的学习学习.

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