Abstract
Signal transduction is the primary mechanism for cell-cell communication and scRNA-seq technology holds great promise for studying this communication at high levels of resolution. Signaling pathways are highly dynamic and cross-talk among them is prevalent. Due to these two features, simply examining expression levels of ligand and receptor genes cannot reliably capture the overall activities of signaling pathways and the interactions among them.
We describe CytoTalk for de novo construction of cell type–specific signaling networks using single-cell transcriptomic data. Using an integrated intracellular and intercellular gene network as the input, CytoTalk identifies candidate pathways using the prize-collecting Steiner forest algorithm. Using high-throughput spatial transcriptomic data and single-cell RNA sequencing data with receptor gene perturbation, we demonstrate that CytoTalk has substantial improvement over existing algorithms. To better understand plasticity of signaling networks across tissues and developmental stages, we perform a comparative analysis of signaling networks between macrophages and endothelial cells across human adult and fetal tissues. Our analysis reveals an overall increased plasticity of signaling networks across adult tissues and specific network nodes that contribute to increased plasticity. CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.
Bio
Dr. Hu’s research focuses on the development of genomic and computational methods to model the cooperation patterns between genes and cells using bulk and single-cell omics data. He has applied their developed algorithm, OptiCon (Nature Communications, 2019), to nominate candidate targets of combination therapy for three adult cancers and one pediatric cancer, which have been validated in vitro and in vivo. In ongoing projects, Dr. Hu is using their proposed single-cell analysis algorithm, CytoTalk (Science Advances, 2021), to characterize heterogeneous tumor-immune cell communication patterns from a pan-cancer perspective.