Abstract 2129: Spatial charting of single cell transcriptomes in tissues

Abstract

Abstract Single cell RNA sequencing (scRNA-seq) methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics (ST) assays can profile spatial regions in tissue sections but do not have single cell genomic resolution. To address this issue, computational approaches (e.g., cell2location, RCTD) have been designed to deconvolute ST spots into proportions of different cell types. However, the spatial deconvolution method has the following limitations, 1) it can only infer cell type proportions of each spot and cannot reach higher granular "cell states" mapping; 2) it only predict categorical labels and cannot infer continuous cell information (e.g., lineage trajectories, gene signatures, continuous phenotypes) at a spatial resolution. Here, we developed a computational approach called CellTrek that combines these two datasets to achieve single cell spatial mapping. Using a machine learning-based metric learning approach, CellTrek learned a cell-spot graph and then transfer spatial coordinates to cells. The CellTrek toolkit also provides two downstream analysis modules, including SColoc for spatial colocalization analysis and SCoexp for spatial co-expression analysis. We benchmarked CellTrek using a simulation and two in situ datasets. We then applied CellTrek to reconstruct cellular spatial structures in existing datasets from normal mouse brain and kidney tissues. We also performed scRNA-seq and ST experiments on two ductal carcinoma in situ (DCIS) tissues and applied CellTrek to identify subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data shows that CellTrek can accurately map single cells in diverse tissue types to resolve their spatial organization into cellular neighborhoods and tissue structures. This method provides a new paradigm that is distinct from ST deconvolution, enabling a more flexible and direct investigation of single cell data with spatial topography. Citation Format: Runmin Wei, Siyuan He, Shanshan Bai, Emi Sei, Min Hu, Alastair Thompson, Ken Chen, Savitri Krishnamurthy, Nicholas Navin. Spatial charting of single cell transcriptomes in tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2129.