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

Leona Chitoiu; Alexandra Dobranici; Mihaela Gherghiceanu; Sorina Dinescu; Marieta Costache

doi:10.3390/ijms21228550

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

International Journal of Molecular Sciences ◽

10.3390/ijms21228550 ◽

2020 ◽

Vol 21 (22) ◽

pp. 8550

Author(s):

Leona Chitoiu ◽

Alexandra Dobranici ◽

Mihaela Gherghiceanu ◽

Sorina Dinescu ◽

Marieta Costache

Keyword(s):

Plasma Membrane ◽

Targeted Delivery ◽

Cell Communication ◽

Extracellular Vesicle ◽

Heterogeneous Population ◽

Omics Data ◽

Membrane Shedding ◽

Endosomal System ◽

Targeted Delivery Systems ◽

Omics Data Integration

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.

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Lysosomal Exocytosis, Exosome Release and Secretory Autophagy: The Autophagic- and Endo-Lysosomal Systems Go Extracellular

International Journal of Molecular Sciences ◽

10.3390/ijms21072576 ◽

2020 ◽

Vol 21 (7) ◽

pp. 2576 ◽

Cited By ~ 15

Author(s):

Sandra Buratta ◽

Brunella Tancini ◽

Krizia Sagini ◽

Federica Delo ◽

Elisabetta Chiaradia ◽

...

Keyword(s):

Plasma Membrane ◽

Molecular Mechanisms ◽

Current Knowledge ◽

Cell Communication ◽

Lysosomal Storage ◽

Cellular Processes ◽

Age Related ◽

Extracellular Release ◽

A Cell ◽

Endosomal System

Beyond the consolidated role in degrading and recycling cellular waste, the autophagic- and endo-lysosomal systems play a crucial role in extracellular release pathways. Lysosomal exocytosis is a process leading to the secretion of lysosomal content upon lysosome fusion with plasma membrane and is an important mechanism of cellular clearance, necessary to maintain cell fitness. Exosomes are a class of extracellular vesicles originating from the inward budding of the membrane of late endosomes, which may not fuse with lysosomes but be released extracellularly upon exocytosis. In addition to garbage disposal tools, they are now considered a cell-to-cell communication mechanism. Autophagy is a cellular process leading to sequestration of cytosolic cargoes for their degradation within lysosomes. However, the autophagic machinery is also involved in unconventional protein secretion and autophagy-dependent secretion, which are fundamental mechanisms for toxic protein disposal, immune signalling and pathogen surveillance. These cellular processes underline the crosstalk between the autophagic and the endosomal system and indicate an intersection between degradative and secretory functions. Further, they suggest that the molecular mechanisms underlying fusion, either with lysosomes or plasma membrane, are key determinants to maintain cell homeostasis upon stressing stimuli. When they fail, the accumulation of undigested substrates leads to pathological consequences, as indicated by the involvement of autophagic and lysosomal alteration in human diseases, namely lysosomal storage disorders, age-related neurodegenerative diseases and cancer. In this paper, we reviewed the current knowledge on the functional role of extracellular release pathways involving lysosomes and the autophagic- and endo-lysosomal systems, evaluating their implication in health and disease.

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Cytochalasin-B-Inducible Nanovesicle Mimics of Natural Extracellular Vesicles That Are Capable of Nucleic Acid Transfer

Micromachines ◽

10.3390/mi10110750 ◽

2019 ◽

Vol 10 (11) ◽

pp. 750 ◽

Cited By ~ 1

Author(s):

Oshchepkova ◽

Neumestova ◽

Matveeva ◽

Artemyeva ◽

Morozova ◽

...

Keyword(s):

Plasma Membrane ◽

Nucleic Acid ◽

Extracellular Vesicles ◽

Cytochalasin B ◽

Cell Communication ◽

Extracellular Vesicle ◽

Hek293 Cells ◽

Nucleic Acid Delivery ◽

Loading Capacity ◽

Oligonucleotide Delivery

Extracellular vesicles provide cell-to-cell communication and have great potential for use as therapeutic carriers. This study was aimed at the development of an extracellular vesicle-based system for nucleic acid delivery. Three types of nanovesicles were assayed as oligonucleotide carriers: mesenchymal stem cell-derived extracellular vesicles and mimics prepared either by cell treatment with cytochalasin B or by vesicle generation from plasma membrane. Nanovesicles were loaded with a DNA oligonucleotide by freezing/thawing, sonication, or permeabilization with saponin. Oligonucleotide delivery was assayed using HEK293 cells. Extracellular vesicles and mimics were characterized by a similar oligonucleotide loading level but different efficiency of oligonucleotide delivery. Cytochalasin-B-inducible nanovesicles exhibited the highest level of oligonucleotide accumulation in HEK293 cells and a loading capacity of 0.44 ± 0.05 pmol/µg. The loaded oligonucleotide was mostly protected from nuclease action.

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0311 - Effect of ammonia on the dynamics of anaerobic digestion microbiome: omics data integration in a time-course context

10.26226/morressier.5b5199beb1b87b000ecee94b ◽

2018 ◽

Author(s):

Olivier Chapleur

Keyword(s):

Anaerobic Digestion ◽

Data Integration ◽

Time Course ◽

Omics Data ◽

Omics Data Integration

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Faculty Opinions recommendation of Segregation of open Major Histocompatibility Class I conformers at the plasma membrane and during endosomal trafficking reveals conformation-based sorting in the endosomal system.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.10803959.11710057 ◽

2011 ◽

Author(s):

Joyce Solheim ◽

Laura Simone

Keyword(s):

Plasma Membrane ◽

Class I ◽

Endosomal Trafficking ◽

Major Histocompatibility ◽

Major Histocompatibility Class ◽

Endosomal System ◽

Major Histocompatibility Class I

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Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

Molecular Omics ◽

10.1039/d1mo00117e ◽

2021 ◽

Author(s):

Kevin Chappell ◽

Kanishka Manna ◽

Charity L. Washam ◽

Stefan Graw ◽

Duah Alkam ◽

...

Keyword(s):

Breast Cancer ◽

Data Integration ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Biological Pathways ◽

Omics Data ◽

Insight Into ◽

Omics Data Integration

Multi-omics data integration of triple negative breast cancer (TNBC) provides insight into biological pathways.

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CSF extracellular vesicles and risk of disease activity after a first demyelinating event

Multiple Sclerosis Journal ◽

10.1177/1352458520987542 ◽

2021 ◽

pp. 135245852098754

Author(s):

Gloria Dalla Costa ◽

Tommaso Croese ◽

Marco Pisa ◽

Annamaria Finardi ◽

Lorena Fabbella ◽

...

Keyword(s):

Disease Activity ◽

Extracellular Vesicles ◽

Prognostic Markers ◽

Cell Communication ◽

Extracellular Vesicle ◽

Intervention Strategies ◽

Targeted Intervention ◽

Improve Risk Stratification ◽

Risk Of Disease ◽

In Cis

Background: Extracellular vesicles (EVs), a recently described mechanism of cell communication, are released from activated microglial cells and macrophages and are a candidate biomarker in diseases characterized by chronic inflammatory process such as multiple sclerosis (MS). Methods: We explored cerebrospinal fluid extracellular vesicle (CSF EV) of myeloid origin (MEVs), cytokine and chemokine levels in patients with clinically isolated syndrome (CIS). Results: We found that CSF MEVs were significantly higher in CIS patients than in controls and were inversely correlated to CSF CCL2 levels. MEVs level were significantly associated with an shorter time to evidence of disease activity (hazard ratio: 1.01, 95% confidence interval: 1.00–1.02, p < 0.01) independently from other known prognostic markers. Conclusion: After a first demyelinating event, CSF EVs may improve risk stratification of these patients and allow more targeted intervention strategies.

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MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies

Scientific Reports ◽

10.1038/s41598-021-81200-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mario Zanfardino ◽

Rossana Castaldo ◽

Katia Pane ◽

Ornella Affinito ◽

Marco Aiello ◽

...

Keyword(s):

User Interface ◽

Data Integration ◽

Graphical User Interface ◽

Data Science ◽

Heterogeneous Data ◽

Biological Information ◽

Omics Data ◽

Correlation Clustering ◽

Downstream Analysis ◽

Omics Data Integration

AbstractAnalysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.

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