CytoTalk: De novo construction of signal transduction networks using single-cell transcriptome data


主講人:胡宇軒  西安電子科技大學副教授




主講人介紹:胡宇軒,西安電子科技大學華山菁英副教授,西安市重點實驗室計算生物信息學研究所教師。主要從事單細胞轉錄組及空間轉錄組計算分析,細胞間通信網絡建模,基因調控網絡模式挖掘及其在組合治療應用方面的研究。成果發表在計算生物學領域著名期刊《Nature Communications》等。博士期間在賓夕法尼亞大學/費城兒童醫院進行國家公派聯合培養,從事耐藥性相關計算建模等研究。

內容介紹:Single-cell technology has opened the door for studying signal transduction in a complex tissue at unprecedented resolution. However, there is a lack of analytical methods for de novo construction of signal transduction pathways using single-cell omics data. Here we present CytoTalk, a computational method for de novo constructing cell type-specific signal transduction networks using single-cell RNA-Seq data. CytoTalk first constructs intracellular and intercellular gene-gene interaction networks using an information-theoretic measure between two cell types. Candidate signal transduction pathways in the integrated network are identified using the prize-collecting Steiner forest algorithm. We applied CytoTalk to single-cell RNA-Seq data sets on mouse visual cortex and olfactory bulb and evaluated predictions using high-throughput spatial transcriptomics data generated from the same tissues. Compared to published methods, genes in our inferred signaling pathways have significantly higher spatial expression correlation only in cells that are spatially closer to each other, suggesting improved accuracy of CytoTalk. Furthermore, using single-cell RNA-Seq data with receptor gene perturbation, we found that predicted pathways are enriched for differentially expressed genes between the receptor knockout and wild type cells, further validating the accuracy of CytoTalk. In summary, CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.