Advancing Extracellular Vesicle Research: A Review of Systems Biology and Multiomics Perspectives

Abstract

Extracellular vesicles (EVs) are membrane-bound vesicles secreted by various cell types into the extracellular space and play a role in intercellular communication. Their molecular cargo varies depending on the cell of origin and its functional state. As a result, EVs serve as representatives of their parent cells and reservoirs of disease biomarkers. Their presence in diverse bodily fluids has fueled interest in their potential for biomarker discovery and signaling research. Advances in mass spectrometry, high-throughput sequencing, and bioinformatics have expanded the molecular characterization of EVs, while emerging tools, including artificial intelligence (AI), image-based systems biology, and curated EV repositories, are driving exploration of disease-associated molecular signatures. Omics technologies generate extensive, multidimensional datasets that can be analyzed using bioinformatics techniques in conjunction with traditional statistical methods. Systems-based approaches, such as network analysis, computer modeling, and AI, are particularly effective for interpreting these complex datasets. However, their application in EV studies requires a solid understanding of EV-specific biological principles and analytical tools to ensure accuracy. By leveraging these analytical strategies, systems biology aims to unravel the intricate organization of biological processes, providing insights into how EVs interact within cells and organisms, and how they can be utilized to advance disease diagnostics, monitor disease progression, and develop novel therapeutic strategies. This review aims to elucidate the state-of-the-art in EV research, integrating multiomics, modeling, and disease-specific insights. EV-specific data repositories and the future of EVs in systems biology will also be highlighted.