PT  - JOURNAL ARTICLE
AU  - Jingyu Cheng
AU  - Lulu Yan
AU  - Qing Nie
AU  - Xiaoqiang Sun
TI  - Modeling spatial intercellular communication and multilayer signaling regulations using stMLnet
AID  - 10.1101/2022.06.27.497696
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.06.27.497696
4099  - http://biorxiv.org/content/early/2022/06/29/2022.06.27.497696.short
4100  - http://biorxiv.org/content/early/2022/06/29/2022.06.27.497696.full
AB  - Multicellular organisms require intercellular and intracellular signaling to coordinately regulate different cell functions. The technological advance of spatial transcriptomics (ST) lets us leverage spatial information to better elucidate cell signaling and functioning. Here, we present stMLnet, a method that infers spatial intercellular communication and multilayer signaling regulations from ST data by quantifying distance-weighted ligand–receptor (LR) signaling activity based on diffusion and mass action models and mapping it to intracellular targets. We demonstrated the applicability of stMLnet on a breast cancer ST dataset and benchmarked its performance using multiple cell line perturbation datasets, synthetic data, and LR-target correlations stratified by cellular distance. We then applied stMLnet to an ST dataset of SARS-CoV-2-infected lung tissue, revealing positive feedback circuits between alveolar epithelial cells, macrophages, and monocytes in a COVID-19 microenvironment. Furthermore, we applied stMLnet to analyze glioma-macrophage interactions for deciphering intercellular and intracellular signaling mechanisms underlying immunotherapy resistance in gliomas. Our proposed method provides an effective tool for predicting LR-target regulations between interacting cells, which can advance the mechanistic and functional understanding of cell–cell communication.