近日，洛桑联邦理工学院Charlotte Bunne及其研究组发现了虚拟组织基础模型跨尺度解析空间蛋白质组学。该项研究成果发表在2026年8月5日出版的《自然》上。

在这里，课题组提出了虚拟组织（VirTues），这是一个通用的空间蛋白质组学基础模型，可以直接从多重成像数据中学习标记感知的蛋白质、细胞、生态位和组织的多尺度表示。从单一的预训练主干，VirTues支持标记重建、细胞分割和分型、生态位注释、空间生物标记发现和患者分层，包括跨异质面板和数据集的零采样注释。在三阴性乳腺癌中，VirTues衍生的生物标志物预测抗PD-L1化疗免疫治疗反应，并在独立队列中对无病生存进行分层，优于来自相同数据集和当前临床分层方案的最新生物标志物。

研究人员表示，空间蛋白质组学技术已经改变了人们对癌症复杂组织结构的理解，但也给计算分析带来了独特的挑战。每项研究的主题都是不同的标记组和方案，大多数方法都是针对单个队列定制的，这限制了知识的转移和生物标记的发现。

附：英文原文

Title: The Virtual Tissues foundation model resolves spatial proteomics across scales

Author: Wenckstern, Johann, Jain, Eeshaan, von Querfurth, Benedikt, Cheng, Yexiang, Vasilev, Kiril, Pariset, Matteo, Cheng, Phil F., Liakopoulos, Petros, Michielin, Olivier, Wicki, Andreas, Gut, Gabriele, Bunne, Charlotte

Issue&Volume: 2026-08-05

Abstract: Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis1. Each study uses a different marker panel and protocol, and most methods are tailored to single cohorts, which limits knowledge transfer and robust biomarker discovery. Here we present Virtual Tissues (VirTues), a general-purpose foundation model for spatial proteomics that learns marker-aware, multi-scale representations of proteins, cells, niches and tissues directly from multiplex imaging data. From a single pretrained backbone, VirTues supports marker reconstruction, cell segmentation and typing, niche annotation, spatial biomarker discovery and patient stratification, including zero-shot annotation across heterogeneous panels and datasets. In triple-negative breast cancer, VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response2 and stratify disease-free survival in an independent cohort3, outperforming state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes.

DOI: 10.1038/s41586-026-10884-y

Source: https://www.nature.com/articles/s41586-026-10884-y