Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics

• Main Authors: Manu P. Kumar, Jinyan Du, Georgia Lagoudas, Yang Jiao, Andrew Sawyer, Daryl C. Drummond, Douglas A. Lauffenburger, Andreas Raue
• Format: Article
• Language: English
• Published: Elsevier 2018-11-01
• Series: Cell Reports
• Online Access: http://www.sciencedirect.com/science/article/pii/S221112471831636X

Summary: Tumor ecosystems are composed of multiple cell types that communicate by ligand-receptor interactions. Targeting ligand-receptor interactions (for instance, with immune checkpoint inhibitors) can provide significant benefits for patients. However, our knowledge of which interactions occur in a tumor and how these interactions affect outcome is still limited. We present an approach to characterize communication by ligand-receptor interactions across all cell types in a microenvironment using single-cell RNA sequencing. We apply this approach to identify and compare the ligand-receptor interactions present in six syngeneic mouse tumor models. To identify interactions potentially associated with outcome, we regress interactions against phenotypic measurements of tumor growth rate. In addition, we quantify ligand-receptor interactions between T cell subsets and their relation to immune infiltration using a publicly available human melanoma dataset. Overall, this approach provides a tool for studying cell-cell interactions, their variability across tumors, and their relationship to outcome. : Tumors are composed of cancer cells and many non-malignant cell types, such as immune and stromal cells. To better understand how all cell types in a tumor cooperate to facilitate malignant growth, Kumar et al. studied communication between cells via ligand and receptor interactions using single-cell data and computational modeling. Keywords: computational analysis, single-cell RNA sequencing, cell-cell communication, ligand-receptor interaction, tumor microenvironment, syngeneic mouse models, cancer patient samples