这篇文章详细介绍了研究人员如何构建人类亚细胞结构的全球地图，他们将其命名为多尺度整合细胞可视化门户和绘图工具包（Multiscale Integrated Cell Visualization Portal and Mapping Toolkit）。研究人员通过结合亲和纯化-质谱（AP-MS）和免疫荧光（IF）图像等多模式数据，利用自监督机器学习模型，在 U2OS 骨肉瘤细胞中分析了 5,100 多种蛋白质，最终解析出 275 种分子组件。这份细胞地图不仅能够揭示新的蛋白质功能并指导 3D 结构建模（例如使用 AlphaFold），还可用于分析细胞类型特异性和多重定位现象，并在解读儿科癌症基因组突变方面表现出重要应用价值。此外，该研究还使用整体细胞尺寸排阻色谱-质谱（SEC-MS）对这些组件进行了系统验证，并利用大型语言模型（LLMs）辅助了组件的命名和功能注释。References: Schaffer L V, Hu M, Qian G, et al. Multimodal cell maps as a foundation for structural and functional genomics[J]. Nature, 2025: 1-10.
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