艾伦细胞科学研究所Matheus P. Viana小组揭示了基于点云的三维多片胞内结构的可解释表示学习。相关论文于2025年7月3日发表于国际顶尖学术期刊《自然—方法学》杂志上。

在这里，研究团队介绍了一个适合形态的表示学习框架，主题是三维旋转不变自编码器和点云。这个框架的主题是学习独立于方向、紧凑和可解释的复杂形状的表示。研究团队将其框架应用于具有点状形态（例如，DNA复制焦点）和多态形态（例如，核仁）的细胞内结构。研究小组通过在效率、生成能力和表示表现力指标上执行多指标基准测试，探索了与基于图像的自编码器相比，该框架在性能上的权衡。课题组发现所提出的框架包含了多片结构的底层形态，可以促进对每个结构的子基元的无监督发现。该团队展示了这种方法如何也可以应用于表型分析主题的核仁图像数据集后的药物扰动。

据介绍，理解亚细胞组织的一个关键挑战是以客观、机械和可推广的方式量化具有复杂多片形态的细胞内结构的可解释测量。

附：英文原文

Title: Interpretable representation learning for 3D multi-piece intracellular structures using point clouds

Author: Vasan, Ritvik, Ferrante, Alexandra J., Borensztejn, Antoine, Frick, Christopher L., Garrison, Philip, Gaudreault, Nathalie, Mogre, Saurabh S., Mohammed, Fatwir S., Morris, Benjamin, Pires, Guilherme G., Saelid, Daniel, Rafelski, Susanne M., Theriot, Julie A., Viana, Matheus P.

Issue&Volume: 2025-07-03

Abstract: A key challenge in understanding subcellular organization is quantifying interpretable measurements of intracellular structures with complex multi-piece morphologies in an objective, robust and generalizable manner. Here we introduce a morphology-appropriate representation learning framework that uses three-dimensional rotation-invariant autoencoders and point clouds. This framework is used to learn representations of complex shapes that are independent of orientation, compact and interpretable. We apply our framework to intracellular structures with punctate morphologies (for example, DNA replication foci) and polymorphic morphologies (for example, nucleoli). We explore the trade-offs in the performance of this framework compared to image-based autoencoders by performing multi-metric benchmarking across efficiency, generative capability and representation expressivity metrics. We find that the proposed framework, which embraces the underlying morphology of multi-piece structures, can facilitate the unsupervised discovery of subclusters for each structure. We show how this approach can also be applied to phenotypic profiling using a dataset of nucleolar images following drug perturbations.

DOI: 10.1038/s41592-025-02729-9

Source: https://www.nature.com/articles/s41592-025-02729-9