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

Area of Science:

• Immunology
• Computational Biology
• Oncology

Background:

• Single-cell RNA sequencing (scRNAseq) enables detailed analysis of cellular heterogeneity and cell-to-cell communication (CCC).
• Understanding CCC is crucial for deciphering complex biological processes in both health and disease states, particularly in the tumor microenvironment (TME).

Purpose of the Study:

• To develop and apply a computational pipeline for tracking CCC patterns during Multiple Myeloma (MM) progression.
• To identify key cellular players and signaling pathways involved in MM pathophysiology using scRNAseq data.

Main Methods:

• Analysis of three public scRNAseq datasets using standard single-cell analytics.
• Reconstruction of stage-specific CCC networks with CellChat and microenvironment-specific analysis.
• Network analysis using CytoHubba and differential network rewiring with DyNet.
• Investigation of downstream responses and target genes using NicheNet.

Main Results:

• Identification of dendritic cells (DCs), plasmacytoid DCs (pDCs), hematopoietic stem cells (HSCs), red pulp macrophages (RPMs), natural killer (NK) cells, and T and B cells as critical cell nodes.
• Discovery of the HLA-DRA-JUN-FOS pathway in neutrophils as a key driver in the progression from monoclonal gammopathies of uncertain significance (MGUS) to active MM.
• Elucidation of the role of this pathway in supporting cancer hallmarks and MM pathophysiology.

Conclusions:

• The developed computational pipeline provides a robust framework for analyzing CCC in the TME.
• The findings offer valuable insights into the molecular mechanisms driving MM progression.
• This approach can be applied to other complex diseases for hypothesis-driven research.