Estimation Of Cell Lineage And Inter-Cell Interactions From Spatial Transcriptomics Data.

Abstract The spatial transcriptomics (ST) technology has enabled geographical profiling of tumor gene expression. However, each ST spot may detect mixture signals from diverse immune or malignant cells of unknown lineages, and local tissue densities may vary significantly across regions. Therefore, the decomposition of ST cell lineages remains a challenge that cannot be resolved by previous decomposition methods for fixed cell types in bulk tumors. We developed the Spatial Cell Estimator (SpaCE) to infer the cell identities and intercellular interactions for tumor ST data. SpaCE first estimates the cancer cell clonal abundance through modeling the copy number segmental variations across ST spots. Then, we utilized a constrained regression to determine the hierarchy of immune cell lineages and local tissue densities. The prediction accuracy was validated through both simulation and double-blinded pathology annotations on hematoxylin and eosin images. Based on reliable cell lineage inference, SpaCE can further reveal how intercellular interactions affect the pathway and gene activities in distinct regions to modulate the cancer progression. Citation Format: Beibei Ru, Jinlin Huang, Yu Zhang, Kenneth Aldape, Peng Jiang. Estimation of cell lineage and inter-cell interactions from spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2795.