Single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic studies by providing unprecedented cellular and molecular throughputs, but spatial information of individual cells is lost during tissue dissociation. While imaging-based technologies such as in situ sequencing show great promise, technical difficulties currently limit their wide usage. Here we hypothesize that cellular spatial organization is inherently encoded by cell identity and can be reconstructed, at least in part, by ligand-receptor interactions, and we present CSOmap, a computational tool to infer cellular interaction de novo from scRNA-seq. We show that CSOmap can successfully recapitulate the spatial organization of multiple organs of human and mouse including tumor microenvironments for multiple cancers in pseudo-space, and reveal molecular determinants of cellular interactions. Further, CSOmap readily simulates perturbation of genes or cell types to gain novel biological insights, especially into how immune cells interact in the tumor microenvironment. CSOmap can be a widely applicable tool to interrogate cellular organizations based on scRNA-seq data for various tissues in diverse systems.

单细胞 RNA 测序 (scRNA-seq) 通过提供前所未有的细胞和分子通量，彻底改变了转录组学研究，但单个细胞的空间信息在组织解离过程中丢失。虽然原位测序等基于成像的技术显示出巨大的前景，但目前的技术困难限制了它们的广泛使用。在这里，我们假设细胞空间组织本质上是由细胞身份编码的，并且可以至少部分地通过配体-受体相互作用进行重建，并且我们提出了 CSOmap，这是一种从 scRNA-seq 从头推断细胞相互作用的计算工具。我们表明，CSOmap 可以成功地概括人和小鼠多个器官的空间组织，包括伪空间中多种癌症的肿瘤微环境，并揭示细胞相互作用的分子决定因素。此外，CSOmap 很容易模拟基因或细胞类型的扰动以获得新的生物学见解，尤其是免疫细胞如何在肿瘤微环境中相互作用。CSOmap 可以是一种广泛适用的工具，可根据不同系统中各种组织的 scRNA-seq 数据来询问细胞组织。