Multi-Omics Data Integration in Extracellular Vesicle Biology-Utopia or Future Reality?

• Published in: International journal of molecular sciences Vol. 21; no. 22; p. 8550
• Main Authors: Chitoiu, Leona, Dobranici, Alexandra, Gherghiceanu, Mihaela, Dinescu, Sorina, Costache, Marieta
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13-11-2020; MDPI
• Subjects: Animals; Biological properties; Biology; Biomarkers; Biomolecules; Biosynthesis; Brain cancer; Cell Communication; Cell-Derived Microparticles - metabolism; Communication; Crosstalk; data heterogeneity; Data integration; Deoxyribonucleic acid; Disease; DNA; Extracellular vesicles; Extracellular Vesicles - metabolism; Fluids; Genomes; Genomics; Heterogeneity; Humans; Lipids; Lung cancer; Metabolomics; Metastasis; Mitochondrial DNA; multi-omics; Physiology; Proteomics; Review; Statistical methods; Systems Biology; Transcriptomics; systems biology; multi-omics; extracellular vesicles; data integration; data heterogeneity
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms21228550

Extracellular vesicles (EVs) are membranous structures derived from the endosomal system or generated by plasma membrane shedding. Due to their composition of DNA, RNA, proteins, and lipids, EVs have garnered a lot of attention as an essential mechanism of cell-to-cell communication, with various implications in physiological and pathological processes. EVs are not only a highly heterogeneous population by means of size and biogenesis, but they are also a source of diverse, functionally rich biomolecules. Recent advances in high-throughput processing of biological samples have facilitated the development of databases comprised of characteristic genomic, transcriptomic, proteomic, metabolomic, and lipidomic profiles for EV cargo. Despite the in-depth approach used to map functional molecules in EV-mediated cellular cross-talk, few integrative methods have been applied to analyze the molecular interplay in these targeted delivery systems. New perspectives arise from the field of systems biology, where accounting for heterogeneity may lead to finding patterns in an apparently random pool of data. In this review, we map the biological and methodological causes of heterogeneity in EV multi-omics data and present current applications or possible statistical methods for integrating such data while keeping track of the current bottlenecks in the field.