Multi-scale network inference framework using single cell transcriptomic data

Cancer, characterized by uncontrolled cell proliferation, tumor invasion and aberrant signaling, involves complex intercellular and intracellular interactions within tumors. To address this complexity, we developed a framework to integrate single cell transcriptomic data with interactomes and model tumors as multi-scale networks. Our approach integrates reconstructed gene regulatory networks (GRN), signaling networks and receptor-ligand interactions to model intracellular and intercellular communication. We applied this framework to a publicly available single-cell transcriptomic data from LUAD patient tumors and constructed a multi-scale network containing GRNs and signaling networks specific to the cell subtypes and cell-cell interactions across subtypes within the primary tumor. Our analysis identified differentially expressed genes between normal and cancer cells, and between primary tumors and brain metastases, highlighting the critical role of receptor-ligand crosstalk in oncogenic signaling. Our approach revealed the role of several receptor-ligand crosstalk between different cell subtypes that can mediate oncogenic signaling. Additionally, our results suggest that the abnormal expression of transcription factors in cancer cells crucially influences oncogenesis and tumor suppression. Our framework is easily adaptable to various single cell transcriptomic data as well as other omic data types. Overall, our framework uses multi-scale network inference from single-cell transcriptomics to enhance our understanding of intracellular pathways and cell-type interactions, integrating multiple biological network layers.