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Open AccessReview

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

1
Ultrastructural Pathology and Bioimaging Laboratory, ‘Victor Babeș’ National Institute of Pathology, Bucharest 050096, Romania
2
Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest 050095, Romania
3
Department of Cellular, Molecular Biology and Histology, ‘Carol Davila’ University of Medicine and Pharmacy, Bucharest 050474, Romania
4
Research Institute of the University of Bucharest, University of Bucharest, Bucharest 050663, Romania
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(22), 8550; https://doi.org/10.3390/ijms21228550
Received: 15 October 2020 / Revised: 10 November 2020 / Accepted: 11 November 2020 / Published: 13 November 2020
(This article belongs to the Special Issue Functional Genomics in Health and Disease)
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. View Full-Text
Keywords: extracellular vesicles; multi-omics; data heterogeneity; data integration; systems biology extracellular vesicles; multi-omics; data heterogeneity; data integration; systems biology
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MDPI and ACS Style

Chitoiu, L.; Dobranici, A.; Gherghiceanu, M.; Dinescu, S.; Costache, M. Multi-Omics Data Integration in Extracellular Vesicle Biology—Utopia or Future Reality? Int. J. Mol. Sci. 2020, 21, 8550. https://doi.org/10.3390/ijms21228550

AMA Style

Chitoiu L, Dobranici A, Gherghiceanu M, Dinescu S, Costache M. Multi-Omics Data Integration in Extracellular Vesicle Biology—Utopia or Future Reality? International Journal of Molecular Sciences. 2020; 21(22):8550. https://doi.org/10.3390/ijms21228550

Chicago/Turabian Style

Chitoiu, Leona; Dobranici, Alexandra; Gherghiceanu, Mihaela; Dinescu, Sorina; Costache, Marieta. 2020. "Multi-Omics Data Integration in Extracellular Vesicle Biology—Utopia or Future Reality?" Int. J. Mol. Sci. 21, no. 22: 8550. https://doi.org/10.3390/ijms21228550

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