EV characterization is challenging due to the broad size ranges and variation in cargo the molecules exhibit. 

The first step after isolation is generally quantification, which is often estimated indirectly through quantification of marker proteins. A number of markers have been identified and used to confirm the presence of EVs in preparations including Rab GTPase, SNAREs, and tetraspannins.(1) However, a major drawback of indirectly quantifying EVs is that their composition can depend strongly on cell type and health. 

Mass spectrometry can also be used to identify protein cargo in EVs, and determine whether the sample may contain protein aggregates or other contaminants.(2, 3) Finally, there are a number of commercially available kits for EV quantification, however, the most robust methods appear to be Flow Cytometry, Electron Microscopy (EM) and Nanoparticle Tracking Analysis (NTA)
In addition to quantification, accurate determination of EV size is also a challenging but critical endeavor. 

characterization of extracellular vesicle

Analytical Ultracentrifugation (AUC) is a powerful, first principle technique that can be used to gain insights into size, heterogeneity, and shape. In fact, populations of molecules with broad size ranges like EVs are becoming increasingly accessible through the use of advanced AUC methods.  However, NTA and Dynamic Light Scattering (DLS) are perhaps the most widely used methods for direct sizing of EVs. Electron microscopy can be used to visualize vesicle morphology and size, and when used in combination with immuno-labeling, can elucidate specific features of EVs, such as surface proteins.(4) Finally, advanced Flow Cytometry techniques can also be used to detect, characterize, and sort functional EVs with extreme precision.

Other techniques that have been used to assess quality and quantity of EV preparations include(4)

  • Flow cytometry
  • Atomic force microscopy
  • Optical single particle tracking
  • Tunable resistive pulse sensing

In most cases, the ultimate goal of EV isolation is not simply to collect EVs, but to interrogate their cargoes as a means of understanding this mysterious form of intercellular communication.

To identify the molecular composition of EVs, researchers have conventionally used SDS-PAGE followed by immunoblotting, as well as proteomics approaches. However, contamination is a major consideration when evaluating individual EV components. In fact, Jeppesen et. al. used high-resolution iodixanol density gradient ultracentrifugation to determine that 10 of the top 25 most commonly identified EVs were actually the result of contamination. 

A plethora of molecules including DNA,(4) RNA,(6, 7) and protein(8, 9) have been identified in EVs and have been used to analyze the impact of the small messengers. Some of these analyses, like RNA-seq, require carefully and reproducibly assembled reactions, which can be accomplished with liquid handling automation, while others, like ELISAs, require plate preparation, incubation, and plate reading capabilities, all of which can be programmed into a seamless workflow with smart, flexible automation.

Despite the vast uncertainty surrounding EVs, the EV community has done an excellent job of providing resources to the field and making information widely available, including: 

  • Vesiclepedia, a community compendium for extracellular vesicles that is freely accessible online. Here, viewers can access user-deposited datasets and information to help guide experiments. 
  • ExoCarta, a tool to help researchers specifically identify/characterize exosomal cargoes. The database contains proteins, RNA sequences, and lipids that have been identified in specific exosomal preparations. 

Standardization of EV isolation and characterization is an essential step for reliable and reproducible results from assays and other downstream applications, including clinical and therapeutic applications. 

The myriad sources of EV collection, as well as the variety of isolation techniques, has presented many challenges to standardizing protocols and obtaining consistent and reproducible results.(4)



  1. Raposo G, Stoorvogel W. Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol. 2013;200(4):373-83.
  2. Abramowicz A, Widlak P, Pietrowska M. Proteomic analysis of exosomal cargo: the challenge of high purity vesicle isolation. Mol Biosyst. 2016;12(5):1407-19.
  3. Keerthikumar S, Gangoda L, Liem M, Fonseka P, Atukorala I, Ozcitti C, et al. Proteogenomic analysis reveals exosomes are more oncogenic than ectosomes. Oncotarget. 2015;6(17):15375-96.
  4. Witwer KW, Buzas EI, Bemis LT, Bora A, Lasser C, Lotvall J, et al. Standardization of sample collection, isolation and analysis methods in extracellular vesicle research. J Extracell Vesicles. 2013;2.
  5. Lazaro-Ibanez E, Sanz-Garcia A, Visakorpi T, Escobedo-Lucea C, Siljander P, Ayuso-Sacido A, et al. Different gDNA content in the subpopulations of prostate cancer extracellular vesicles: apoptotic bodies, microvesicles, and exosomes. Prostate. 2014;74(14):1379-90.
  6. San Lucas FA, Allenson K, Bernard V, Castillo J, Kim DU, Ellis K, et al. Minimally invasive genomic and transcriptomic profiling of visceral cancers by next-generation sequencing of circulating exosomes. Ann Oncol. 2016;27(4):635-41.
  7. Stevanato L, Thanabalasundaram L, Vysokov N, Sinden JD. Investigation of Content, Stoichiometry and Transfer of miRNA from Human Neural Stem Cell Line Derived Exosomes. PLoS One. 2016;11(1):e0146353.
  8. Lasser C, O'Neil SE, Shelke GV, Sihlbom C, Hansson SF, Gho YS, et al. Exosomes in the nose induce immune cell trafficking and harbour an altered protein cargo in chronic airway inflammation. J Transl Med. 2016;14(1):181.
  9. Winston CN, Goetzl EJ, Akers JC, Carter BS, Rockenstein EM, Galasko D, et al. Prediction of conversion from mild cognitive impairment to dementia with neuronally derived blood exosome protein profile. Alzheimers Dement (Amst). 2016;3:63-72.