Department of Genetics, UT MD Anderson Cancer Center, Houston, TX, USA.
Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Nat Biotechnol. 2022 Aug;40(8):1190-1199. doi: 10.1038/s41587-022-01233-1. Epub 2022 Mar 21.
Single-cell RNA sequencing methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics assays can profile spatial regions in tissue sections, but do not have single-cell resolution. Here, we developed a computational method called CellTrek that combines these two datasets to achieve single-cell spatial mapping through coembedding and metric learning approaches. We benchmarked CellTrek using simulation and in situ hybridization datasets, which demonstrated its accuracy and robustness. We then applied CellTrek to existing mouse brain and kidney datasets and showed that CellTrek can detect topological patterns of different cell types and cell states. We performed single-cell RNA sequencing and spatial transcriptomics experiments on two ductal carcinoma in situ tissues and applied CellTrek to identify tumor subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data show that CellTrek can accurately map single cells in diverse tissue types to resolve their spatial organization.
单细胞 RNA 测序方法可以对单细胞的转录组进行分析，但不能保留空间信息。相反，空间转录组学检测可以对组织切片中的空间区域进行分析，但不具有单细胞分辨率。在这里，我们开发了一种名为 CellTrek 的计算方法，该方法结合了这两个数据集，通过共嵌入和度量学习方法实现了单细胞空间映射。我们使用模拟和原位杂交数据集对 CellTrek 进行了基准测试，结果表明其具有准确性和鲁棒性。然后，我们将 CellTrek 应用于现有的小鼠大脑和肾脏数据集，并表明它可以检测不同细胞类型和细胞状态的拓扑模式。我们对两个原位导管癌组织进行了单细胞 RNA 测序和空间转录组学实验，并应用 CellTrek 来识别仅限于不同导管的肿瘤亚克隆，以及与肿瘤区域相邻的特定 T 细胞状态。我们的数据表明，CellTrek 可以准确地将不同组织类型的单细胞映射到它们的空间组织中。
