用基础模型融合影像与基因数据，精准解析肿瘤微环境细胞特征。
CellSymphony: Deciphering the molecular and phenotypic orchestration of cells with single-cell pathomics
- 通过基础模型提取影像与基因数据的单细胞级嵌入表示
- 在三种癌症中准确标注细胞类型并发现独特微环境区域
- 适合研究肿瘤异质性与空间生物学的科研人员
Xenium 是一种新型空间转录组学平台，可在亚细胞分辨率下对复杂肿瘤组织进行分析。尽管组织病理图像包含丰富的形态信息，但如何从中提取稳健的细胞级特征，并与空间转录组数据整合仍是关键挑战。我们提出 CellSymphony，一个灵活的多模态框架，利用 Xenium 转录组数据和组织病理图像在真正单细胞分辨率下的基础模型嵌入表示。通过学习融合空间基因表达与形态上下文的联合表征，CellSymphony 实现了细胞类型的高精度注释，并在三种癌症类型中揭示了不同的微环境龛区。该工作展示了基础模型与多模态融合在解码复杂组织生态系统中细胞生理与表型协同机制方面的潜力。
原文摘要 · Abstract (English)
Xenium, a new spatial transcriptomics platform, enables subcellular-resolution profiling of complex tumor tissues. Despite the rich morphological information in histology images, extracting robust cell-level features and integrating them with spatial transcriptomics data remains a critical challenge. We introduce CellSymphony, a flexible multimodal framework that leverages foundation model-derived embeddings from both Xenium transcriptomic profiles and histology images at true single-cell resolution. By learning joint representations that fuse spatial gene expression with morphological context, CellSymphony achieves accurate cell type annotation and uncovers distinct microenvironmental niches across three cancer types. This work highlights the potential of foundation models and multimodal fusion for deciphering the physiological and phenotypic orchestration of cells within complex tissue ecosystems.
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