Single-cell RNA sequencing (scRNA-seq) enables the systematic identification of cell populations in a tissue, but characterizing their spatial organization remains challenging. We combine a microarray-based spatial transcriptomics method that reveals spatial patterns of gene expression using an array of spots, each capturing the transcriptomes of multiple adjacent cells, with scRNA-Seq generated from the same sample. To annotate the precise cellular composition of distinct tissue regions, we introduce a method for multimodal intersection analysis. Applying multimodal intersection analysis to primary pancreatic tumors, we find that subpopulations of ductal cells, macrophages, dendritic cells and cancer cells have spatially restricted enrichments, as well as distinct coenrichments with other cell types. Furthermore, we identify colocalization of inflammatory fibroblasts and cancer cells expressing a stress-response gene module. Our approach for mapping the architecture of scRNA-seq-defined subpopulations can be applied to reveal the interactions inherent to complex tissues.

单细胞 RNA 测序 (scRNA-seq) 可以系统地识别组织中的细胞群，但表征它们的空间组织仍然具有挑战性。我们结合了一种基于微阵列的空间转录组学方法，该方法使用一系列点揭示基因表达的空间模式，每个点捕获多个相邻细胞的转录组，以及从同一样本生成的 scRNA-Seq。为了注释不同组织区域的精确细胞组成，我们引入了一种多模态交叉分析方法。将多模式交叉分析应用于原发性胰腺肿瘤，我们发现导管细胞、巨噬细胞、树突状细胞和癌细胞的亚群具有空间限制的富集，以及与其他细胞类型的明显共富集。此外，我们确定了炎症成纤维细胞和表达应激反应基因模块的癌细胞的共定位。我们绘制 scRNA-seq 定义的亚群结构的方法可用于揭示复杂组织固有的相互作用。