Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94143, USA.
Diabetes Center, University of California, San Francisco, San Francisco, CA 94143, USA.
Sci Adv. 2021 Apr 21;7(17). doi: 10.1126/sciadv.abg4755. Print 2021 Apr.
Single-cell RNA sequencing (scRNA-seq) of tissues has revealed remarkable heterogeneity of cell types and states but does not provide information on the spatial organization of cells. To better understand how individual cells function within an anatomical space, we developed XYZeq, a workflow that encodes spatial metadata into scRNA-seq libraries. We used XYZeq to profile mouse tumor models to capture spatially barcoded transcriptomes from tens of thousands of cells. Analyses of these data revealed the spatial distribution of distinct cell types and a cell migration-associated transcriptomic program in tumor-associated mesenchymal stem cells (MSCs). Furthermore, we identify localized expression of tumor suppressor genes by MSCs that vary with proximity to the tumor core. We demonstrate that XYZeq can be used to map the transcriptome and spatial localization of individual cells in situ to reveal how cell composition and cell states can be affected by location within complex pathological tissue.
单细胞 RNA 测序 (scRNA-seq) 对组织进行分析，揭示了细胞类型和状态的显著异质性，但无法提供细胞空间组织的信息。为了更好地理解单个细胞在解剖空间中的功能，我们开发了 XYZeq 工作流程，该流程将空间元数据编码到 scRNA-seq 文库中。我们使用 XYZeq 对小鼠肿瘤模型进行了分析，从数万细胞中捕获了具有空间条码的转录组。对这些数据的分析揭示了不同细胞类型的空间分布，以及肿瘤相关间充质干细胞 (MSC) 中与细胞迁移相关的转录组程序。此外，我们还发现了 MSC 中肿瘤抑制基因的局部表达，其表达程度随与肿瘤核心的距离而变化。我们证明，XYZeq 可用于对原位单个细胞的转录组和空间定位进行作图，从而揭示细胞组成和细胞状态如何受到复杂病理组织中位置的影响。
