Network-Based Bioinformatics Reveal Microenvironment-Driven Cell-to-Cell Communication in the Progression of Multiple Myeloma.

Single-cell RNA sequencing (scRNAseq) captures unique profiles of individual cells and uncovers cell-to-cell communication (CCC) through ligand-receptor (LR) interactions. Moreover, it reveals signalling mechanisms underlying cellular heterogeneity and complexity in downstream responses in healthy and disease states. In this work, we developed a composite computational pipeline to track CCC patterns in the tumour microenvironment (TME) during Multiple Myeloma (MM) progression as a case study. Three publicly available scRNAseq datasets were analysed using basic single-cell analytics and stage-specific CCC networks were reconstructed with CellChat, in a microenvironment-specific approach. Basic network analytics (CytoHubba) were performed to identify key cell nodes based on network topology metrics; differential network rewiring (DyNet) was performed to calculate rewired nodes. Follow-up analyses were conducted with NicheNet to investigate downstream responses and target genes influenced by CCC. Our network analyses highlighted dendritic cells (DCs), plasmacytoid DCs (pDCs), hematopoietic stem cells (HSCs), red pulp macrophages (RPMs), natural killer (NK) cells, and T and B cells as important cell nodes. Moreover, in neutrophils, the HLA-DRA-JUN-FOS was shown to play a key role in the progression of monoclonal gammopathies of uncertain significance (MGUS) to active MM by supporting cancer hallmarks and MM pathophysiology. To conclude, our work suggests an explanatory-computational pipeline that incorporates well-known frameworks in a hypothesis-driven scope, which leads to results relevant to the pathophysiology of MM.