Modeling and inference of spatial intercellular communications and multilayer signaling regulations using stMLnet

Abstract

Abstract Multicellular organisms require intercellular and intracellular signaling to coordinately regulate different cell functions. Although many methods of cell-cell communication (CCC) inference have been developed, they seldom account for both the intracellular signaling responses and global spatial information. The recent advancement of spatial transcriptomics (ST) provides unprecedented opportunities to better decipher CCC signaling and functioning. In this paper, we propose an ST -based m ultilayer net work method, stMLnet, for inferring spatial intercellular communication and multilayer signaling regulations by quantifying distance-weighted ligand–receptor signaling activity based on diffusion and mass action models and mapping it to intracellular targets. We benchmark stMLnet with existing methods using simulation data and 8 real datasets of cell type-specific perturbations. Furthermore, we demonstrate the applicability of stMLnet on six ST datasets acquired with four different technologies (e.g., seqFISH+, Slide-seq v2, MERFIS and Visium), showing its effectiveness and reliability on ST data with varying spatial resolutions and gene coverages. Finally, stMLnet identifies positive feedback circuits between alveolar epithelial cells, macrophages, and monocytes via multilayer signaling pathways within a COVID-19 microenvironment. Our proposed method provides an effective tool for predicting multilayer signaling regulations between interacting cells, which can advance the mechanistic and functional understanding of spatial CCCs.