De Vries Matt, Dent Lucas G, Curry Nathan, Rowe-Brown Leo, Bousgouni Vicky, Fourkioti Olga, Naidoo Reed, Sparks Hugh, Tyson Adam, Dunsby Chris, Bakal Chris
Department of Cancer Biology, Institute of Cancer Research, London, UK; Department of Physics, Imperial College London, London, UK; Sentinal4D, London, UK.
Department of Cancer Biology, Institute of Cancer Research, London, UK.
Cell Syst. 2025 Mar 19;16(3):101229. doi: 10.1016/j.cels.2025.101229.
The three-dimensional (3D) morphology of cells emerges from complex cellular and environmental interactions, serving as an indicator of cell state and function. In this study, we used deep learning to discover morphology representations and understand cell states. This study introduced MorphoMIL, a computational pipeline combining geometric deep learning and attention-based multiple-instance learning to profile 3D cell and nuclear shapes. We used 3D point-cloud input and captured morphological signatures at single-cell and population levels, accounting for phenotypic heterogeneity. We applied these methods to over 95,000 melanoma cells treated with clinically relevant and cytoskeleton-modulating chemical and genetic perturbations. The pipeline accurately predicted drug perturbations and cell states. Our framework revealed subtle morphological changes associated with perturbations, key shapes correlating with signaling activity, and interpretable insights into cell-state heterogeneity. MorphoMIL demonstrated superior performance and generalized across diverse datasets, paving the way for scalable, high-throughput morphological profiling in drug discovery. A record of this paper's transparent peer review process is included in the supplemental information.
细胞的三维（3D）形态源自复杂的细胞与环境相互作用，是细胞状态和功能的一个指标。在本研究中，我们使用深度学习来发现形态学表征并理解细胞状态。本研究引入了MorphoMIL，这是一种结合几何深度学习和基于注意力的多实例学习的计算流程，用于剖析3D细胞和细胞核形状。我们使用3D点云输入，并在单细胞和群体水平上捕捉形态学特征，同时考虑到表型异质性。我们将这些方法应用于超过95,000个经临床相关的、调节细胞骨架的化学和基因扰动处理的黑色素瘤细胞。该流程准确地预测了药物扰动和细胞状态。我们的框架揭示了与扰动相关的细微形态变化、与信号传导活性相关的关键形状，以及对细胞状态异质性的可解释见解。MorphoMIL表现出卓越的性能，并能在不同数据集上进行推广，为药物发现中可扩展的高通量形态学分析铺平了道路。本文透明的同行评审过程记录包含在补充信息中。
