Recent in situ multiplexed profiling techniques provide insight into microenvironment formation, maintenance, and transformation through a lens of localized cellular phenotype distribution. In this article, we introduce a method for recovering signatures of microenvironments from such data. We use topic models to identify characteristic cell types overrepresented in neighborhoods that serve as proxies for microenvironment. Furthermore, by assuming spatial coherence among neighboring microenvironments our model limits the number of parameters that need to be learned and permits data-driven decisions about the size of cellular neighborhoods. We apply this method to uncover anatomically known structures in mouse spleen—identifying distinct population of spleen B cells that are defined by their characteristic neighborhoods. Next, we apply the method to a dataset of triple-negative breast cancer tumors from 41 patients to study the structure of tumor-immune boundary. We uncover previously reported tumor-immune microenvironment near the tumor-immune boundary enriched for immune cells with high Indoleamine-pyrrole 2,3-dioxygenase (IDO) and Programmed death-ligand 1 (PD-L1) and a novel, immunosuppressed, microenvironment-enriched for cells expressing CD45 and FoxP3.

中文翻译：

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使用空间 LDA 对多路复用图像进行建模揭示了新的组织微环境。

最近的原位多重分析技术通过局部细胞表型分布的镜头提供了对微环境形成、维护和转化的洞察。在本文中，我们介绍了一种从此类数据中恢复微环境签名的方法。我们使用主题模型来识别作为微环境代理的社区中过度代表的特征细胞类型。此外，通过假设相邻微环境之间的空间一致性，我们的模型限制了需要学习的参数数量，并允许关于细胞邻域大小的数据驱动决策。我们应用这种方法来揭示小鼠脾脏中解剖学上已知的结构——识别由其特征邻域定义的不同脾脏 B 细胞群。下一个，我们将该方法应用于来自 41 名患者的三阴性乳腺癌肿瘤数据集，以研究肿瘤免疫边界的结构。我们发现先前报道的肿瘤免疫边界附近的肿瘤免疫微环境富含具有高吲哚胺-吡咯 2,3-双加氧酶 (IDO) 和程序性死亡配体 1 (PD-L1) 的免疫细胞，以及一种新型的免疫抑制微环境-富含表达 CD45 和 FoxP3 的细胞。