Spatially resolved gene expression profiling provides insight into tissue organization and cell–cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.

空间解析的基因表达谱提供了对组织组织和细胞间串扰的深入了解；然而，基于测序的空间转录组学（ST）缺乏单细胞分辨率。目前的 ST 分析方法需要单细胞 RNA 测序数据作为严格解释细胞状态的参考，大多数不使用相关的组织学图像，并且无法推断多个组织之间的共享邻域。在这里，我们介绍 Starfysh，这是一个使用深度生成模型的计算工具箱，该模型结合了原型分析和任何已知的细胞类型标记，可以在没有单细胞参考的情况下表征已知或新的组织特异性细胞状态。 Starfysh 使用组织学图像改进了复杂组织中空间动力学的表征，并能够将生态位作为跨组织的空间中心进行比较。对原发性雌激素受体（ER）阳性乳腺癌、三阴性乳腺癌（TNBC）和化生性乳腺癌（MBC）组织进行综合分析，确定了具有患者和疾病特异性细胞类型组成的空间中心，并揭示了代谢重编程塑造侵袭性 MBC 中的免疫抑制中枢。