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Exosomes and Microvesicles

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Cover of 'Exosomes and Microvesicles'

Table of Contents

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    Book Overview
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    Chapter 1 Methods to Analyze EVs.
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    Chapter 2 Tunable Resistive Pulse Sensing for the Characterization of Extracellular Vesicles.
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    Chapter 3 Immuno-characterization of Exosomes Using Nanoparticle Tracking Analysis.
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    Chapter 4 Imaging and Quantification of Extracellular Vesicles by Transmission Electron Microscopy.
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    Chapter 5 Quantitative Analysis of Exosomal miRNA via qPCR and Digital PCR.
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    Chapter 6 Small RNA Library Construction for Exosomal RNA from Biological Samples for the Ion Torrent PGM™ and Ion S5™ System.
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    Chapter 7 A Protocol for Isolation and Proteomic Characterization of Distinct Extracellular Vesicle Subtypes by Sequential Centrifugal Ultrafiltration.
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    Chapter 8 Multiplexed Phenotyping of Small Extracellular Vesicles Using Protein Microarray (EV Array).
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    Chapter 9 Purification and Analysis of Exosomes Released by Mature Cortical Neurons Following Synaptic Activation.
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    Chapter 10 A Method for Isolation of Extracellular Vesicles and Characterization of Exosomes from Brain Extracellular Space.
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    Chapter 11 Isolation of Exosomes and Microvesicles from Cell Culture Systems to Study Prion Transmission.
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    Chapter 12 Isolation of Platelet-Derived Extracellular Vesicles.
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    Chapter 13 Bioinformatics Tools for Extracellular Vesicles Research.
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    Chapter 14 Preparation and Isolation of siRNA-Loaded Extracellular Vesicles.
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    Chapter 15 Interaction of Extracellular Vesicles with Endothelial Cells Under Physiological Flow Conditions.
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    Chapter 16 Flow Cytometric Analysis of Extracellular Vesicles.
Attention for Chapter 13: Bioinformatics Tools for Extracellular Vesicles Research.
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Chapter title
Bioinformatics Tools for Extracellular Vesicles Research.
Chapter number 13
Book title
Exosomes and Microvesicles
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6728-5_13
Pubmed ID
Book ISBNs
978-1-4939-6726-1, 978-1-4939-6728-5
Authors

Shivakumar Keerthikumar, Lahiru Gangoda, Yong Song Gho, Suresh Mathivanan, Keerthikumar, Shivakumar, Gangoda, Lahiru, Gho, Yong Song, Mathivanan, Suresh

Editors

Andrew F Hill

Abstract

Extracellular vesicles (EVs) are a class of membranous vesicles that are released by multiple cell types into the extracellular environment. This unique class of extracellular organelles which play pivotal role in intercellular communication are conserved across prokaryotes and eukaryotes. Depending upon the cell origin and the functional state, the molecular cargo including proteins, lipids, and RNA within the EVs are modulated. Owing to this, EVs are considered as a subrepertoire of the host cell and are rich reservoirs of disease biomarkers. In addition, the availability of EVs in multiple bodily fluids including blood has created significant interest in biomarker and signaling research. With the advancement in high-throughput techniques, multiple EV studies have embarked on profiling the molecular cargo. To benefit the scientific community, existing free Web-based resources including ExoCarta, EVpedia, and Vesiclepedia catalog multiple datasets. These resources aid in elucidating molecular mechanism and pathophysiology underlying different disease conditions from which EVs are isolated. Here, the existing bioinformatics tools to perform integrated analysis to identify key functional components in the EV datasets are discussed.

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 35%
Researcher 7 21%
Student > Doctoral Student 3 9%
Student > Master 3 9%
Student > Bachelor 3 9%
Other 4 12%
Unknown 2 6%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 35%
Agricultural and Biological Sciences 9 26%
Medicine and Dentistry 4 12%
Immunology and Microbiology 2 6%
Computer Science 1 3%
Other 2 6%
Unknown 4 12%