Abstract
Cells release small, phospholipid membrane-enclosed particles, collectively referred to as extracellular vesicles (EVs), into their surroundings to enable intercellular communication. EVs have numerous functions in physiological and pathophysiological processes and show considerable promise for diagnostic and therapeutic applications. Technologies have rapidly evolved over the past two decades, providing a powerful, versatile toolset for preparing and characterizing EVs to facilitate research and translational efforts. However, considering the plethora of methods available, it is challenging to understand what makes one method more suited for a given experiment than another. The heterogeneity of EVs as well as the diversity in composition of their surroundings further add to this challenge. This Primer provides guidance for EV analysis across ecosystems, including accessible body- and environment-derived sources. We summarize the multi-step process of EV preparation, cover the guiding principles and considerations when performing and interpreting EV experiments, and reflect on the limitations and challenges in the fields of fundamental biology, biomarker development and therapeutic strategies.
Introduction
Extracellular vesicles (EVs) are micro- or nanostructures released by all eukaryotes and prokaryotes, from where they diffuse to their surroundings and interact with target cells to exert a plethora of functions1. EVs are delimited by a membrane reflecting the originating cell’s plasma membrane structure: a lipid bilayer exposing extracellular domains of proteins and sugars2. During their biogenesis, EVs are actively or passively packed with a diverse range of molecules, including lipids, proteins and nucleic acids3,4. In addition, depending on their surroundings, EV membranes are decorated with a diffuse and patchy biomolecular corona that may affect their destination and function5–8 (Fig. 1). As carriers of cellular information into accessible surroundings, EVs have tremendous potential as novel diagnostic tools that could monitor health status across diverse ecosystems9,10. Another exciting prospect is the exploitation of EVs for the treatment of ecosystem disturbances as therapeutic targets, novel drug delivery agents or standalone therapeutics11.
Fig. 1 |. Extracellular vesicles.
a, Extracellular vesicles (EVs) are membrane-delimited particles composed of a lipid bilayer, membrane and intraluminal proteins (including proteins specific to tissues and diseases), lipids, nucleic acids, glycans and metabolites, and are surrounded by a biomolecular corona. The bulk of EVs consists of a heterogeneous population of membrane vesicles of various origins. EVs are released by eukaryotic (blue) and prokaryotic (yellow) cells, both in a healthy or diseased state. b, EV subpopulations are discriminated on the basis of their biogenesis in eukaryotic cells (as exosomes, ectosomes and apoptotic bodies) and prokaryotic cells (as outer membrane vesicles (OMVs), outer inner membrane vesicles (OIMVs), explosive outer membrane vesicles (EOMVs) and cytoplasmic membrane vesicles (CMVs)). The structure (including size, density, surface charge and refractive index) and composition (including proteins, lipids, nucleic acids, metabolites and glycans) of EVs vary in different EV subpopulations and can be examined using different preparation and characterization methods. Common characteristics are indicated by shared colour shading. ARRDC1, arrestin-domain-containing protein 1; EpCAM, epithelial cell adhesion molecule; HER2, human epidermal growth factor receptor 2; LTA, lipoteichoic acid; LPS, lipopolysaccharide; MHC, major histocompatibility complex; miRNA, microRNA; OmpA/F, outer membrane protein A/F; PSMA, prostate-specific membrane antigen; TCA, tricarboxylic acid.
EV analysis requires a versatile toolset to cope with diversity at the level of cell type (eukaryotic or prokaryotic cells), cell state (diseased or engineered) and EVs themselves (biogenesis, structure and composition). The toolset implemented to study EVs varies according to the source and the specific aims of the study, and this contributes to the overall diversity of methodological specifications and limitations11,12 (Fig. 1). Eukaryotic cells secrete EVs through plasma-membrane budding, producing ectosomes, or through fusion of multi-vesicular endosomes with the plasma membrane, producing exosomes. During controlled cell death, eukaryotic cells fragment into apoptotic bodies1,2. Gram-negative bacteria release EVs by outward budding of the outer membrane, giving rise to outer membrane vesicles (OMVs) or upon explosive cell lysis, resulting in outer inner membrane vesicles (OIMVs) or explosive outer membrane vesicles (EOMVs). Gram-positive bacteria release EVs as cytoplasmic membrane vesicles (CMVs), during bubbling cell death13,14. Archaea share mechanisms of EV generation through plasma membrane budding with eukaryotes and bacteria15. Although EVs differ in their actual subcellular formation route, within or even across each domain of life their structure and/or composition partially overlap, rendering it difficult to depict the biogenesis pathway of EVs once released from the cells. Consequently, the term ‘extracellular vesicles’ was proposed in 2011 to describe the heterogeneous population of membrane-delimited particles released by cells16 and later adopted in the minimal information guidelines proposed by the International Society for Extracellular Vesicles (ISEV)17,18. The generic term ‘extracellular vesicles’ can be annotated with operational terms to define EV subpopulations based upon origin (for example, cell type or state-defined), structure (for example, biophysical properties such as size or density range) and composition (for example, biochemical properties such as surface antigens)19–21 (Fig. 1).
Once formed and released from their originating cell, EVs diffuse into their surroundings (tissues, body fluids, plant fluids, biofilms and ocean seawater). In practice, these surroundings are sampled as sources for EV analysis, and such sources vary in their volume, viscosity and overall particle composition. Diverse cell types in a variety of states contribute EVs. In the body, blood transports EVs from diverse cell types and states, such as eukaryotic EVs from blood cells and tissues, as well as bacterial EVs upon increased permeability of mucosal barriers6,19,22; faeces contains EVs released by bacteria and epithelial cells of the gastrointestinal tract, but also EVs from other microbiota that so far remain largely unexplored19,20,22. Food-derived EVs (for example, fruit-, vegetable- or milk-derived EVs) may further affect the body ecosystem23,24. Cells do not only release EVs in their surroundings, but also soluble components (for example, cytokines and chemokines or quorum-sensing peptides) and other biological nanoparticles (for example, exomeres and supermeres)25–27. In addition, EV sources also contain micro- or nanoparticles of diverse origins that often vastly outnumber EVs (for example, lipoprotein particles in blood)6,28 or may include abundant other EVs that outnumber the EV subpopulations of interest16.
So far, EV analysis usually requires a first set of methods to prepare EVs from their source and a second set of methods to characterize EVs. The range of available protocols for preparing and characterizing EVs requires an informed selection of the most appropriate toolset for the defined study objectives. Indeed, disparate methods prepare EVs with diverse specificity and efficiency, values that define the inclusion or exclusion of EV subpopulations and other materials, and this results in varying biochemical and functional information6,29–33 (Box 1). Similarly, different characterization methods deliver variable biochemical information on intraluminal and membrane-associated molecules and inconsistent biophysical information such as EV size estimations and particle concentrations34,35. For example, some biophysical characterization methods have a limited sensitivity and therefore cannot measure smaller-sized EV subpopulations, leading to an underestimation of EV numbers. Others have a limited resolution and fail to distinguish EVs from other materials, resulting in an overestimation of particle concentrations12. Furthermore, a limited sensitivity and/or resolution will influence size characteristics.
Box 1. Selecting preparation and characterization methods.
Making an educated decision about the appropriate preparation and characterization methods for a particular experiment requires an understanding of the performance metrics.
Performance metrics for extracellular vesicle (EV) preparation
Performance metrics for EV preparation include efficiency, to what extent a method is able to recover EVs present in the source, and specificity, to what extent the method is able to selectively separate EVs and eliminate other materials. Typically, higher efficiency comes at a cost of co-separation with other materials, and therefore a lower specificity, which can confound downstream analysis. Efficiency can be determined by spiking a defined amount of trackable reference EVs and measuring their recovery. Specificity can be determined by evaluating both EV-enriched and depleted markers and/or microscopy-based visualization. Furthermore, the capacity of the method to preserve EV integrity (the conformation, membrane structure and extrafacial surface) and prevent aggregation needs to be considered when selecting the most appropriate EV preparation methodology for a particular application.
Performance metrics for EV characterization
Performance metrics for EV characterization include resolution, the ability to distinguish EVs from other materials or EV subpopulations from one another, and sensitivity, the lowest EV size that can be detected. EV characterization methods with a limit of detection not fully covering the EV size distribution will affect the determination of EV concentration and size.
Performance metrics for integrated EV preparation and characterization workflow
Performance metrics for integrated EV preparation and characterization workflow include high-throughput capacity, turn-around time, hands-on time, running cost, ease of use, sample volume, reproducibility/repeatability and scalability. These performance metrics are fundamental to the move from bench to application.
The interdisciplinary interest in the applications of EVs from a variety of sources gives rise to the major challenge of ensuring adequate use of the most optimal toolset for EV analysis. This Primer provides a comprehensive overview of the versatile toolset available for EV analysis, with guidance to manage the multitude of challenges. The Primer outlines various EV preparation and characterization methods that are often used and describes how to assess the quality of EV samples and retrieve biological and functional information. The EV research field is evolving fast, so we provide overall guiding principles to facilitate the integration of new preparation and characterization methods. Key applications of EVs ranging from fundamental biology towards diagnostics and therapeutics are described and detailed with relevant methodological considerations. Data reproducibility and deposition are reviewed in the context of the field’s rigour and standardization efforts. Finally, we consider the future outlook, indicating the challenges awaiting academic and industrial partners before the potential of EV-based diagnostics and therapeutics can become reality.
Experimentation
Disentangling the structure, composition and function of EVs present in diverse sources is challenging. Initiation of this endeavour requires an understanding of the composition of the sources and the principles of the methods used to prepare EVs or EV subpopulations from the sources. Here we detail the current understanding of other materials, such as extracellular particles and molecules, residing in commonly explored sources and summarize the diverse methodological principles frequently used to prepare EVs for further characterization (Fig. 2). Typically, the experimentation phase comprises the following decision cascade: select and explore the composition of the source, design and implement the protocol to prepare bulk EVs and/or subpopulations thereof, and store and use aliquots of EV preparations for complementary characterization, including quality control and further downstream application (Fig. 2a).
Fig. 2 |. Extracellular vesicle preparation.
a, Steps to consider when setting up an appropriate extracellular vesicle (EV) preparation workflow. b, Size and density ranges of EVs and other materials. The size and density range for EVs are indicated. Non-EV particles represent protein complexes, protein aggregates, exomeres and supermeres. c, Size, density and surface properties such as charge and antigens are used to enrich (one step) or separate (at least two consecutive steps) EVs from other materials or to fractionate EV subpopulations. cfDNA, cell-free DNA; cfRNA, cell-free RNA; HDL, high-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; LPS, lipopolysaccharide; LTA, lipoteichoic acid; VLDL, very-low-density lipoprotein.
Source
Eukaryotic and/or prokaryotic EVs have been prepared from a variety of sources sampled from the (human) body ecosystem, including body fluids, tissue or cell-conditioned media, and the environmental ecosystem, such as plants and ocean seawater6,19,36–39. Here, we summarize different types of other material such as extracellular particles and molecules residing in these sources (Fig. 2b) and pinpoint key pre-analytical parameters (in Table 1) that require transparent reporting while referring the reader to respective position papers in the field for extended guidance. Although we do not particularly focus on all the sources studied in this Primer, similar conclusions can be drawn for and extrapolated to other sources, which may guide and stimulate the future development of source-specific guidelines.
Table 1|.
Biofluid-specific other materials and pre-analytical variables
| Biofluid source | Biofluid-specific other materials | Pre-analytical variables | |
|---|---|---|---|
| The (human) body ecosystem | Blood6,20,53,60,71,186–188 | Lipoprotein particles (HDL, LDL, IDL, VLDL and chylomicron) Cell-free nucleic acids (cfRNA, cfDNA) Soluble proteins and protein complexes (albumin and immunoglobulins) |
Patient characteristics: age, biological sex, level of physical activity, pregnancy Physiological state: prandial state, current illness, exercise before blood collection Choice of blood collection tube and the collection of serum or blood plasma Sample collection details: venipuncture procedure, blood-processing interval, transport of blood tube Centrifugation protocol: time, g-force, temperature |
| Urine29,43,44,180 | Soluble proteins and protein complexes (uromodulin and albumin) Cell-free nucleic acids (cfRNA, cfDNA) |
Patient characteristics: age, biological sex, level of physical activity Physiological state: fluid intake, time of collection, diet, exercise before urine collection, medication Sample collection details: digital rectal examination, volume, void Centrifugation protocol: time, g-force |
|
| Tissues and cells46,50 | Soluble proteins and protein complexes (Ago2) Intracellular components, artefactual nanoparticles and debris Components present in FBS (albumin and EVs) |
Tissue collection details: volume/mass, time until processing Tissue processing parameters: mechanical dissection (cutting or crushing), enzymatic treatment Culture conditions: media formulations, EV depletion of FBS, two-versus three-dimensional culture conditions, duration of incubation Cell information: cell type, viability, confluency, cell number, passage, activation state |
|
| Other biofluids | Soluble proteins and protein complexes Biofluid-specific other materials |
Patient characteristics: age, biological sex Physiological state: current illness, timepoint of collection Sample collection details: consider key parameters specific for the biofluid of interest Sample processing details: centrifugation conditions |
|
| The environmental ecosystem | Plant38,118 | Intracellular components, artefactual nanoparticles and debris Soluble proteins and protein complexes |
Plant characteristics: plant species, age, time of day Growing conditions: light, humidity, temperature Plant fluid extraction details: infiltration parameters, infiltration buffer Sample processing details: centrifugation conditions |
| Aquatic ecosystem39,56 | Phages, viruses and inorganic colloids Soluble proteins and protein complexes |
Sample collection details: timepoint, volume, sample location, sampling depth Sample processing details: centrifugation conditions |
|
| Other environmental ecosystems | Soluble proteins and protein complexes Biofluid-specific other materials |
Sample collection details: consider key parameters specific for the biofluid of interest Sample processing details: centrifugation conditions |
|
| Microbiota189,190 | Soluble proteins and protein complexes (free LPS) Other bacterial components (flagella, pili and fimbria) Contamination by broken membranes and cytoplasmic proteins |
Bacterial cell information: strain, population density, growth phase Growth conditions: medium type, medium supplements, volume, temperature, shaking speed and growth period |
This List is not exhaustive: other pre-analytical variables that influence extracellular vesicle (EV) preparations could be included. cfDNA, cell-free DNA; cfRNA, cell-free RNA; FBS, fetal bovine serum; HDL, high-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; LPS, lipopolysaccharide; VLDL, very-low-density lipoprotein.
The body ecosystem.
Blood is a minimally invasive liquid biopsy that contains blood cells (erythrocytes and lymphocytes), platelets, EVs and other extracellular particles including lipoprotein particles, cell-free nucleic acids and protein aggregates as well as highly abundant small- and large-molecular-weight proteins such as albumin and immunoglobulins6,20,28,40. The majority of EVs originate from blood cells and platelets but other cells and tissues (such as adipocytes, endothelial cells and cells from the central nervous system, as well as muscle, lung and liver tissues) contribute EVs to blood plasma6,41. Under defined (patho)physiological conditions, the balance in the origins of blood EVs shifts9,27,42. In addition to EVs from the host, EVs from interacting microbiota can also be directly released into or diffuse into the blood19.
Urine is a non-invasive liquid biopsy containing EVs, cell-free nucleic acids, protein aggregates as well as highly abundant small- and large-molecular-weight proteins such as albumin and uromodulin, soluble components like organic and inorganic molecules, epithelial and blood cells, bacteria and viruses36,43,44. The majority of EVs in urine samples originate from cells of the kidneys, bladder and urogenital tract, and their resident bacteria29,43, although EVs from other tissues in the body ecosystem have been identified in urine43. Specific EV subpopulations from the urogenital tract can be enriched by particular interventions, such as digital rectal examination, which increases the quantity of EVs originating from prostatic luminal epithelium cells, or by the (patho)physiological status of the host29,45.
Culture media conditioned in vitro by cell lines, primary cell cultures or tissues have been sampled and analysed for EVs. EVs can be directly extracted from tissues or upon time-restricted ex vivo cultivation of the tissue27,29,46–49. Media sampled from tissues contain EVs, soluble proteins and other materials released by the different cells that compose the tissue, but also intracellular vesicles or artefactual particles released from cells broken during tissue harvesting or processing27. During cell cultivation, EVs and other materials are released, including soluble proteins and protein complexes31. In addition, cells are regularly cultivated in culture medium supplemented with fetal bovine serum (FBS), containing albumin and EVs50, which are often depleted by ultracentrifugation or ultrafiltration50,51. Alternatively, cells are incubated in serum-free conditions which, depending on the cell type, induce stress52.
Other sources from the body ecosystem include faeces, milk, saliva, cerebrospinal fluid, bronchoalveolar lavage, semen, tears, ascites, amniotic fluid, pleural effusions, ocular effluent and aqueous humour, nasal secretions, synovial fluid and bile53,54. These sources are explored for eukaryotic and prokaryotic EVs in a variety of mammals, such as humans, mice and dogs. These research fields are under development with guidelines in progress by the ISEV Rigor and Standardization subcommittee for a selection of sources. However, like the sources highlighted in this Primer, considerations of other materials such as extracellular particles and soluble proteins, pre-processing steps before EV preparation, key parameters — age, biological sex and diet — affecting EV concentration and content, and transparent reporting thereof are crucial and should be identified prior to EV analysis.
The environmental ecosystem.
EVs have been identified in the apoplast of plant leaves, roots, grains and seeds55. Besides EVs, apoplastic fluid contains antioxidants, proteins, peptides and hormones, but also intracellular components and artificial nanoparticles generated by rupture of tissues and cells during the collection process38. Bacterial EVs released by diverse taxa of marine microbiota have been identified in ocean seawater39,56. EV concentrations in open-ocean seawater samples vary with sampling depth and location and environmental conditions39,57. Other materials present in aquatic environments include phages, viruses and inorganic colloids. Overall, and also in environmental ecosystems, other materials such as non-EV extracellular particles and soluble proteins, collection and pre-processing steps, and parameters affecting EV concentration and cargo composition need to be evaluated and transparently reported.
EV preparation
The EV preparation protocol is not a one-size-fits-all solution and is dependent on the characteristics of the source (sample volume and viscosity, relative abundance of other materials and so on), EV subpopulations of interest and the study aim. In this section, we pinpoint the typical decision cascade required to establish an EV preparation protocol. This decision cascade can be guided by recommendations available by means of position papers, guidelines, research manuscripts with similar study aims or by exploring identical sources and evaluation of performance metrics (Box 1 and Fig. 3a,b).
Fig. 3 |. Extracellular vesicle preparation methodology.
a, Overview of different extracellular vesicle (EV) preparation methods and their performance metrics. The turn-around time of the precipitation and immunoprecipitation methods depends on whether an overnight incubation step is included or not. The running cost of the precipitation method depends on whether a homemade or a commercial solution is used. The running cost and whether specialized equipment is necessary for size-exclusion chromatography (SEC) depends on whether a pressure-based pump system is used, or whether the SEC column is eluted by gravity. b, Different EV preparation methods lead to different specificity and efficiency. Overall, as we move from concentration methods to enrichment methods and then to separation workflows, specificity increases but efficiency decreases. c, Size-based filtration or volume-exclusion-based precipitation methods allow the liquid to be concentrated, facilitating storage and implementation of enrichment methods. Size-based filtration or volume-exclusion-based precipitation methods allow concentration of the source, facilitating storage and implementation of enrichment methods. The theoretical separation between EVs and other materials is visualized for clarity here and might not be as clearcut when performed on different sources. AF4, asymmetric-flow field flow fractionation; AIEX, anion-exchange chromatography; DC, density cushion; DG, density gradient; DUC, differential ultracentrifugation; IP, immunoprecipitation; P, precipitation; TFF, tangential flow filtration; UF, ultrafiltration.
The most implemented preparation protocol is not inherently the best fit for a particular study; a gold standard for EV preparation does not exist and may never become available, considering the interdisciplinary interest in EVs. Consequently, quality control experiments to assess performance metrics during set-up of the preparation protocol are inevitable (Box 1). EV preparation protocols must consider the separation of EVs from other materials as well as the fractionation of EV subpopulations, and typically comprise a combination of methods that allow us to concentrate, enrich and/or separate EVs or fractionate EV subpopulations based upon their biophysical and/or biochemical characteristics, such as surface charge, size and density and/or surface antigens, respectively (Fig. 3c). Each protocol step usually results in some loss of EVs or EV subpopulations. The selection of protocol steps depends on the volume and complexity of the source, other materials residing in the source and EVs or subpopulations of interest. Generally, the more complex the source, the more steps are required. The required specificity of a preparation for EVs or EV subpopulations and the efficiency of the protocol depends on the intended end use of the EVs and influences the set-up of the preparation protocol. Other performance metrics such as turn-around time, running cost and ease of use further define the selection of the most suitable EV preparation protocol (Box 1 and Fig. 3a). Considering that each EV preparation protocol results in a differential efficiency and specificity, protocol parameters should be transparently reported.
Pre-processing.
Live and dead cells, cell debris and — if smaller EVs are of interest — larger particles are removed from the source by sequential low-speed centrifugation or microfiltration, using filters with a pore diameter of 0.8 μm, 0.45 μm or 0.22 μm (refs. 6,37,58). The selection of centrifugation speed, pore diameter or filter type can result in the enrichment or depletion of defined EV subpopulations and other source materials. The use of centrifugation, pressure or vacuum to advance the sample through the filter can create vesicle-like artefacts or lead to deformation and disruption of EVs. The pre-processing is preferentially performed prior to storage of the source to avoid bursting of cells59. Pre-processed sources can be stored at −80 °C but repeated freeze–thaw cycles should be avoided43,60.
Concentration.
The overall aim of concentration is to reduce the volume of the source while retaining EVs and other materials, which facilitates storage and enables further enrichment steps. Typically, concentration of the source retrieves EVs with high efficiency and low specificity (Box 1 and Fig. 3a,b).
Ultrafiltration uses filter membranes with a molecular weight cut-off value (for example, 10 kDa, 100 kDa or 300 kDa) to reduce the volume of the source, resulting in the depletion of smaller-sized materials (like soluble proteins) while increasing EV concentrations. Depending on the composition of the source, membrane pores are prone to blockage, which can be avoided by tangential flow filtration, creating a biofluid flow tangentially across the membrane surface61. Pore size and filter type affect efficiency, and ultrafiltration may induce aggregation or affect integrity58.
Alternatively, hydrophilic polymer-based or protamine-induced charge-based precipitation and acetate-based salting-out are implemented to concentrate the source62. Precipitation enables sedimentation of EVs and other materials by low-speed centrifugation upon mixing the source with hydrophilic polymer, protamine or acetate. Remnants of precipitation solutions in the preparation may affect EV characteristics and thus downstream analysis, but may be depleted by additional enrichment or separation steps.
Enrichment.
The overall aim of enrichment is to retain EVs or particular EV subpopulations while reducing the amount of other materials. EVs are enriched from their source (pre-processed and concentrated or not depending on the volume) by exploiting their size (30–1,000 nm), density (1.08–1.21 g ml−1), surface charge (−10 mV to −50 mV) or surface antigens (such as tetraspanins CD9, CD63 and CD81 for mammalian EVs) (Figs. 1 and 2). This step results in enrichment of particular EV subpopulations with the selected biophysical or biochemical characteristics, while other materials and EV subpopulations that do not overlap in size, density, surface charge and/or surface antigen are depleted. Typically, enrichment of pre-processed and/or concentrated sources results in lower efficiency and higher specificity (Box 1 and Fig. 3a,b).
Size-exclusion chromatography (SEC) separates EVs on the basis of their hydrodynamic size from other materials over a column filled with a stationary phase of a porous gel filtration polymer (Fig. 3c). SEC columns can be gravity-eluted or connected to a liquid chromatography system, orchestrating the flow rates through the column63,64. Asymmetrical flow field-flow fractionation (AF4) uses two perpendicular flows within a channel with a solid wall on top and a membrane on the bottom to enrich particles according to their hydrodynamic size65,66. The crossflow presses all particles towards the bottom of the channel, while particle diffusion (depending on the hydrodynamic size) determines their distance from the bottom wall. A longitudinal flow perpendicular to the crossflow displaces different-sized particles with differential velocity following the parabolic flow profile67,68 (Fig. 3c). Differential ultracentrifugation (DUC) uses successive centrifugation steps at increasing g-force/time to enrich EVs of interest based on size or mass37,69 (Fig. 3c). Overall, size-based enrichment using SEC, AF4 or DUC partially depletes EV preparations from soluble proteins (for example, albumin), high-density (6–13 nm) and low-density (20–25 nm) lipoprotein particles in blood plasma6,63, and uromodulin complexes in urine29,70. EVs are not separated from similar-sized materials, such as Apo-B-positive very-low-density lipoprotein particles (30–90 nm) and chylomicrons (90–1,000 nm) in blood plasma6,40. The efficiency and specificity of size-based methods depends on the protocol set-up. SEC performance depends on the column height, the ratio of source volume to column bed volume, the pore size of the stationary phase and the quality of the column stacking71. For AF4 analysis, optimization of multiple parameters such as injection volume, channel height, type of membrane, focusing time, detector flow and crossflow profile is required67,68. A variety of DUC protocols are reported in the literature11, applying different centrifugation speeds, durations and rotors, experimental settings that variably affect efficiency and specificity72,73. Since not all materials have the same distance from the bottom of the centrifuge tube, not all EVs will pellet, whereas other materials will co-pellet, affecting efficiency and specificity, respectively27,42. Depending on the source, size-based enrichment methods may affect EV integrity (for example, the extrafacial surface and aggregation), as has been shown for SEC or DUC7,74. EV-enriched fractions obtained by SEC and AF4 are diluted, which may warrant a concentration step for downstream applications (with potential impact on efficiency)58.
In density gradient ultracentrifugation, the EV-enriched preparation is layered on top (top-down density gradient ultracentrifugation, enriching EVs based upon mass and density) or placed at the bottom (bottom-up density gradient ultracentrifugation, enriching EVs based upon density) of a density gradient, depending on the composition of the source6,20,29,59,62. Upon centrifugation, particles migrate towards their buoyant density. g-force and time define whether particles reach their buoyant density (Fig. 3c). EV-rich density gradient fractions are reduced in particles with different densities such as soluble proteins (1.35 g ml−1), cell-free RNA (1.62 g ml−1), cell-free DNA (1.70 g ml−1), and some lipoprotein particles such as low-density lipoprotein (1.019–1.063 g ml−1) and very-low-density lipoprotein (0.950–1.006 g ml−1) particles, and chylomicrons (<0.950 g ml−1) in blood plasma, while particles with a similar density, such as high-density lipoprotein particles (1.063–1.21 g ml−1) remain present. The efficiency and specificity of density gradient centrifugation depend on centrifugation speed and duration, type of rotor, type of density medium, and configuration of the density gradient. Removal of the gradient medium (for example, by DUC or SEC) is necessary for particular downstream applications20,71. In density cushion ultracentrifugation a layer of density medium is overlaid with a large volume of source, allowing for a higher input compared with density gradient centrifugation40,75.
Besides size or mass density, EVs can also be enriched on the basis of their surface characteristics (Fig. 3c). Anion-exchange chromatography or bead capture separate negatively charged EVs from positively charged materials76. Subsequent modulation of pH or the ionic strength of the buffer releases EVs from the resin or beads for downstream applications77. Alternatively, cation-exchange chromatography depletes positively charged other materials from the EVs78, and electrophoresis-based approaches, integrated or not in microfluidic devices, enrich EVs from other materials based upon their electrophoretic mobility79. Affinity capture enriches EV subpopulations positive for the designated surface antigen while further depleting other materials and EV subpopulations21,80–83. Antibodies or aptamers specifically targeting surface-exposed molecules, such as tetraspanins (CD9, CD63 and CD81), MHC-class molecules or cell-type-specific molecules for mammalian EVs and specific cell-wall constituents for prokaryotic EVs, are immobilized on magnetic beads, filters, affinity columns, monolithic silica microtips or chips. Elution of the captured EVs while maintaining EV integrity is not straightforward, limiting its applicability to selected downstream applications.
Separation.
The overall aim of separation is to retain EVs or particular EV subpopulations with selected biophysical or biochemical characteristics while further depleting other materials to minute amounts. EVs are separated by submitting the source (pre-processed and concentrated, or not, depending on the volume) to at least two consecutive enrichment steps exploiting EV size, density or surface characteristics42 (Fig. 2c). Typically, separation workflows result in low efficiency and high specificity (Box 1 and Fig. 3a,b). The efficiency and specificity of separation is defined by the selection and the sequential order of enrichment steps. Sequential implementation of enrichment steps that exploit the same EV characteristic results in increased specificity and reduced efficiency compared with a single enrichment step. For example, a second SEC (size-based enrichment) loading EV-enriched fractions prepared from blood plasma by a first SEC (size-based enrichment) will further deplete EV samples from other materials (like soluble proteins and high-density and low-density lipoprotein particles) and EV subpopulations that are smaller in size, while retaining similar-sized materials (like very-low-density lipoprotein particles and chylomicrons)71 (Figs. 2 and 3c). Sequential implementation of enrichment steps that exploit various EV characteristics results in increased specificity and reduced efficiency compared with a single enrichment step and compared with sequential implementation of enrichment steps that exploit the same EV characteristic. For example, density gradient centrifugation (density-based enrichment) of EV-enriched fractions obtained by SEC (size-based enrichment) from blood plasma will further deplete similar-sized EV subpopulations and other materials (like very-low-density lipoprotein particles and chylomicrons) that differ in density6,20,84.
Fractionation.
The overall aim of fractionation is to prepare EV subpopulations that differ in biochemical or biophysical characteristics (size, density or surface antigens) (Fig. 2c and Fig. 3c). Bulk EVs are separated based on their hydrodynamic size by SEC85,86 or AF4 (refs. 25,87–89). DUC fractionates apoptotic bodies (2,000 × g–10,000 × g), larger EVs (10,000 × g–20,000 × g) and smaller EVs (≥100,000 × g)21,37,69. Bottom-up density gradient centrifugation fractionates lower-density (1.103–1.137 g ml−1) and higher-density (1.144–1.192 g ml−1) EV subpopulations21,85,90–92. Particular set-ups of top-down density gradient centrifugation, known as velocity gradients, use smaller incremental percentages of density medium within a narrower density range, and have been used to fractionate EVs from retroviruses and virus-like particles42,93–95. Eukaryotic EVs (1.083–1.111 g ml−1) and bacterial EVs (1.133–1.201 g ml−1) can be separated from each other by bottom-up or top-down density gradient centrifugation depending on the source19,20. Immunoaffinity capture fractionates EVs positive or negative for specific surface antigens21,81, EVs secreted at the basolateral or apical side of polarized cells96, and EVs from different cell types in biofluids80,83. Considering that fractionation is based upon enrichment steps that are used to deplete EV preparation from other materials, enrichment or separation prior to fractionation results in increased specificity although lower efficiency.
Storage
Discussion of the most optimal storage conditions, including temperature, time, type of vial, EV concentrations, freezing steps and thawing steps is still ongoing. Given the discordant results between research groups, optimal storage conditions may vary between sources, efficiency and specificity of preparation protocols and envisioned downstream applications. Ideally EV preparations are used fresh but in practice this is not always realistic. In general, concentrated EV preparations are stored for short periods (<48 h) at 4 °C and for longer time intervals at −80 °C (ref. 97). Since freeze–thaw cycles and increased storage time result in decreased EV concentrations and increased particle size (probably as a consequence of EV fusion or aggregation), EV preparations are aliquoted according to their intended use98. To improve stability, different storage buffers, cryo-preservation and lyophilization have been suggested99. However, their suitability may be restricted to selected sources, preparation protocols or downstream applications and warrants careful evaluation20,42,100.
Results
Basic characterization of EV preparations reveals what is present — presumably the desired EVs, but possibly a variety of other materials — and what is not present. A 100% pure preparation of EVs or EV subpopulations is unrealistic, and other materials may even have an important functional role along with EVs8. Quality assessment through basic characterization allows us to determine whether the preparation is fit for the desired purpose and supports proper interpretation and conclusions in downstream analysis (Fig. 4a). For example, the therapeutic effect or side effects of EV preparations may depend on the presence of other materials. On the other hand, comprehensive downstream profiling of EV preparations may depend on the near-complete depletion of those other materials. Several caveats challenge accurate measurements of EV preparations, including their heterogeneity in size and lower refractive index, the variable extent of other materials, as well as the dynamic measurement range of the available characterization methods. The use of orthogonal characterization methods is recommended to obtain a fine-grained biophysical and biochemical view of the presence or depletion of EVs, EV subpopulations and other materials in EV preparations. The selection of methods for basic characterization can be guided by recommendations available by means of position papers, guidelines, research manuscripts with similar study aims and performance metrics (Box 1 and Figs. 4 and 5). Following basic characterization, EV preparations are ready for downstream applications that assess cargo composition using deep omics or function using in vitro, ex vivo or in vivo models. Here, we review approaches for basic characterization (Fig. 4).
Fig. 4 |. Extracellular vesicle characterization.
a, Different steps and tools to consider when setting up an appropriate extracellular vesicle (EV) characterization workflow. b, Biophysical characterization of EV preparations reveals size, concentration, surface charge, morphology and other mechanical aspects in a single particle (using nanoparticle tracking analysis (NTA), modified flow cytometry (FC), (tunable) resistive pulse sensing ((T)RPS), transmission electron microscopy (TEM), cryo-electron microscopy (cryo-EM) and atomic force microscopy (AFM)) or bulk (using dynamic light scattering (DLS), static light scattering (SLS) and multi-angle light scattering (MALS)) mode. Using antibody-labelling strategies, biophysical characterization can be combined with biochemical characterization (for example, fluorescent NTA, single-particle interferometric reflectance imaging sensors (SP-IRIS), immune-modified FC, immunogold-labelled EM and super-resolution microscopy). Biochemical characterization methods that do not deliver biophysical information in a single particle (like Raman spectroscopy) or bulk mode (like immunoblotting, enzyme-linked immunosorbent assay (ELISA), omics, bead-based FC and biomolecular concentration measurements) allow for the detection of membrane-anchored as well as intraluminal content. Output information and assay duration are key aspects when selecting the most appropriate EV characterization workflow. Commonly assessed EV-enriched markers include CD9, CD63, CD81, Alix, Flotillin 1 and Syntenin (using super-resolution microscopy or immunoblotting, for example). Uromodulin is evaluated as an EV-depleted marker in EV preparations from urine (using immunoblotting, for example). EV-TRACK, transparent reporting and centralizing knowledge in extracellular vesicle research; MISEV, Minimal Information for Studies of EVs.
Fig. 5 |. Extracellular vesicle characterization methodology.
Overview of various extracellular vesicle (EV) characterization methods and their performance metrics. The sample amount for immunoblotting analysis can vary depending on methodological sensitivity, for example between normal western blot versus capillary western blot. The sample amount required for biomolecular concentration measurements varies depending on the abundance of measured biomolecule, whether protein, nucleic acids or others. AFM, atomic force microscopy; cryo-EM, cryo-electron microscopy; DLS, dynamic light scattering; ELISA, enzyme-linked immunosorbent assay; FC, flow cytometry; immuno-EM, immuno-electron microscopy; MALS, multi-angle light scattering; NTA, nanoparticle tracking analysis; RI, refractive index; SLS, static light scattering; SP-IRIS, single-particle interferometric reflectance imaging sensors; TEM, transmission electron microscopy; (T)RPS, (tunable) resistive pulse sensing.
Biophysical characterization
Biophysical characterization of EV preparations concerns the measurement of size, concentration, surface charge, refractive index, morphology and mechanical aspects like stiffness. Biophysical characterization methods can be categorized as optical or non-optical methods71,101.
Optical methods exploit the light-scattering properties of particles in solution to determine their concentration or infer their radius. This can be done particle by particle using nanoparticle tracking analysis (NTA) instruments by tracking the motion of the particle in suspension indicative for its size, single-particle interferometric reflectance imaging sensors (SP-IRIS) using sensing surfaces to enhance contrast, or flow cytometers and similar flow instruments designed specifically for smaller particles by illuminating EVs one by one and collecting forward- and side-scattered light. EVs can be analysed in bulk with dynamic light scattering (DLS), which uses temporal fluctuations to obtain the hydrodynamic radius, static light scattering (SLS), which uses one detector to analyse the radius of gyration, or multi-angle light scattering (MALS) instruments, which use the angular dependency to obtain the radius of gyration. NTA also allows optional definition of surface charge by measuring the electrophoretic mobility of particles in an electric field. SP-IRIS and flow cytometers are compatible with EV- or EV-subpopulation-specific fluorescent antibody labelling strategies, which improve size and concentration measurements and allow for complementary molecular phenotyping. Technically, NTA is also compatible with fluorescent antibody labelling strategies, but low detection sensitivity and fluorophore bleaching have limited its use for molecular phenotyping. Overall, optical methods perform fast and semi-accurate quantification but cannot detect smaller-sized particles lower in refractive index or distinguish EVs or EV subpopulations from other materials unless fluorescent antibody labelling strategies or total lipid labelling strategies are used. Non-optical methods exploit the size, morphology and mechanical characteristics of EVs. Resistive pulse sensing (RPS) estimates the size of a particle based on its ability to occlude a nanopore and thus alter an electrical signal while also measuring concentration and refractive index. Both transmission electron microscopy and cryo-electron microscopy use electrons to visualize EV morphology and other materials. Atomic force microscopy (AFM) studies EV surface topography and stiffness/elasticity. Biophysical characterization can be confounded by sample preparation steps for transmission electron microscopy (fixation and dehydration affect size and morphology) and AFM (surface adherence affects mechanical properties). Immunogold labelling (used for electron microscopy) or antibody-based capture (used for AFM) also support molecular phenotyping. Each instrument used for biophysical characterization has a unique sensitivity and resolution which is not always sharply defined102. Most optical methods and some non-optical methods are unable to detect or accurately size EVs at the upper and lower end of the size range. For example, flow cytometry, NTA and RPS instruments have a decreased resolution for smaller particles. In addition, each instrument requires specific settings which may affect EV analysis and require transparent reporting103. Furthermore, characterization methods differ in turn-around time, running cost, ease-of-use and output information which need to be considered for selecting orthogonal characterization methods (Fig. 5).
Size measurements reported as size distributions, besides the mean or mode, allow us to demonstrate the presence of particles within the expected size range of EVs (30–1,000 nm, Fig. 1). Size measurements should take into account technological limitations — for example, the minimum EV size detection range of NTA is 70–90 nm for biological particles — and proper instrument calibration before EV measurements104. Most beads used to calibrate instruments possess higher refractive indexes than EVs do, which may lead to improper instrument calibration and result in an overestimation of EV size, because smaller EVs are not detected. Similarly, particle concentration measurements must take into account instrument sensitivity and resolution, because a reduced capacity to detect smaller particles may result in underestimated particle concentrations. Considering that size and concentration measurements are instrument-dependent, results and instrument settings should be transparently reported and must not be directly compared105. For biophysical characterization methods with lower resolution, concentration measurements are reported as particles per millilitre, as for example SLS, NTA, (tunable) RPS ((T)RPS) and MALS (Fig. 4b and Fig. 5). These measurements are often performed to quantitatively assess the efficiency of the preparation protocol (Box 1). Zeta potential measurements indicate the surface charge of EVs, which range from −10 mV to −50 mV in phosphate buffered saline, or in dilutions thereof (Fig. 1). EV zeta potential measurements provide information about colloidal stability and the interaction of EVs with their surroundings, including the tendency of EVs to aggregate. The refractive index of EVs can be determined by most optical methods and is estimated to be between 1.37 and 1.39, or >1.40 for some EVs larger than 200 nm (refs. 102,106) (Fig. 1). Electron microscopy and AFM contribute morphological, topographical and mechanical information (Fig. 4b and Fig. 5). These analyses are usually performed to qualitatively assess the specificity of the preparation protocol (Box 1). We note that EVs with various morphologies have been described in different biofluids107–110.
Biochemical characterization
Biochemical characterization complements biophysical characterization, allowing selective phenotyping of bulk EVs, EV subpopulations and other materials. Classically, biochemical characterization focuses on specific EV-associated molecules (enriched markers). However, to obtain a general view of the total particle diversity in the EV preparation, source-specific non-EV-associated molecules (depleted markers) must be assessed. Basic biomolecular concentration measurements (using protein, nucleic acid or lipid quantification assays) can guide protocol development and guide decisions for downstream analysis such as input normalization.
Enriched and depleted markers analysed in EV research often involve proteins, and are frequently evaluated using antibody-based methods. The use of fluorescent or gold-tagged antibodies — often recognizing membrane-anchored proteins but upon permeabilization also intraluminal proteins — supports the evaluation of enriched markers simultaneously with biophysical characteristics by single-particle analysis, including fluorescent-triggered NTA, immune-modified flow cytometry, SP-IRIS, immunogold electron microscopy, and super-resolution microscopy (Fig. 4b and Fig. 5). Bulk methods such as immunoblotting, enzyme-linked immunosorbent assay (ELISA), deep omics and bead-based flow cytometry do not deliver biophysical information or information on EV size subpopulations, but allow for the straightforward detection of membrane-anchored and intraluminal proteins and depleted markers (Fig. 4b and Fig. 5). Enriched and depleted markers are typically analysed to assess the specificity of the EV preparation protocol (Box 1), can be evaluated for multiple steps or fractions and thus can guide the set-up of an EV preparation protocol.
EV-enriched proteins include transmembrane- or membrane-anchored proteins from the lipid bilayer, which may enable their origin to be determined, and detection of intraluminal proteins ensures that EV preparations are not empty membrane fragments but contain cytoplasmic cargo (Fig. 1). Mammalian EVs can carry general EV surface proteins (such as the tetraspanins CD9, CD63 and CD81, the major histocompatibility complex (MHC) class I, and integrins17,18), cell-type-specific proteins (such as epithelial cell adhesion molecule (EpCAM) for epithelial cells, CD61 for platelets and CD45 for immune cells)80, tissue-specific proteins (such as A33 for intestinal tissue)111 or disease-specific proteins (such as human epidermal growth factor receptor 2 (HER2) for breast cancer and prostate-specific membrane antigen (PSMA) for prostate cancer)29,112. Intraluminal proteins of mammalian EVs include proteins of the endosomal sorting complex required for transport (ESCRT) (such as Tsg101 and Alix)113, sorting-related and trafficking-related proteins (such as Rab GTPases and syntenin)114,115 and proteins involved in the secretion of specific EV subtypes (such as arrestin-domain-containing protein 1 (ARRDC1) in ARRDC1-mediated microvesicles)116. Plant-derived EVs carry tetraspanin TET8, which shows structural similarity to mammalian tetraspanin CD63 (refs. 38,117), as well as the trafficking-related proteins PEN1, PEN3 and PATL1 (ref. 118), while the surface of Gram-negative bacteria-derived EVs is marked by the presence of outer membrane protein A (OmpA)13,20.
Compared with EV-enriched proteins, the selection of EV-depleted proteins is less straightforward because EVs carry a source-dependent biomolecular corona5–7. To this day, why, how, where and when this biomolecular corona is formed remains unknown. One must therefore consider the possibility that what was previously considered to be an EV-depleted marker (a protein, but also other molecules like nucleic acids) might serve a functional purpose in combination with EVs or be an integral part of the EV’s extrafacial surface8. Therefore, control experiments including RNase, DNase and protease treatments with or without the inclusion of detergent treatment need to be considered17,119. In addition, the selection of EV-depleted markers may be further guided by novel insights in EV biogenesis pathways. For example, in cell cultures, EV-depleted proteins may originate from intracellular organelles that, if present, might indicate a contribution of controlled cell death. However, recent data suggest the involvement of additional endomembranes (such as endoplasmic reticulum, nuclear envelopes and mitochondria) in EV biogenesis and function120–122. Overall, the evaluation of selected presumed EV-depleted markers, such as source-specific abundant proteins or protein aggregates (for example, albumin in blood plasma or cell culture and uromodulin in urine), during multiple steps and over multiple fractions obtained during the EV preparation protocol is straightforward and provides information about their selective enrichment or depletion in EV-enriched fractions. The integration of EV-depleted markers permits for a more stringent interpretation of EV preparations and can guide decisions for downstream analysis but also increase our understanding of what an EV truly consists of. Finally, depleted markers may also include EV-enriched markers of prokaryotic or eukaryotic origin to assess levels of cross-contamination20.
Although less routinely used in the EV field, other EV-enriched or EV-depleted biomolecules can be considered. The surface of Gram-positive-bacteria-derived EVs is marked by the presence of lipoteichoic acid (LTA), while that of Gram-negative-bacteria-derived EVs is marked by lipopolysaccharide (LPS). The presence of high-density lipoproteins in EV preparations can be assessed by high-density-lipoprotein-associated microRNAs (miRNAs; such as has-miR-451), and the presence of platelet-derived EVs can be assessed by platelet-associated miRNAs (such as has-miR-25)6. Differences in lipid composition, glycosylation profile and nucleic acid abundance between EVs and other materials have been identified and could be further explored to obtain EV-enrichment and -depletion profiles25.
Applications
The number of studies using EVs for the diagnosis and treatment of various pathophysiological conditions has substantially increased. These studies exploit the complex cargo composition of EVs to create a multicomponent diagnostic window for the detection and monitoring of pathophysiological conditions or use EVs as therapeutic vehicles to deliver functional cargo to diseased cells. In this section we cover how insights in fundamental biology fuel EV-based diagnostic and therapeutic applications and discuss how the priority of performance metrics associated with EV experimentation (Box 1) varies from bench (fundamental biology) to application (diagnostics and therapeutics) (Fig. 6).
Fig. 6 |. Extracellular vesicle applications.
Insights into structure and composition of extracellular vesicles (EVs) fuel diagnostic applications, whereas insights into uptake, release and function fuel the therapeutic applications of EVs. When moving EV research from bench (fundamental biology) to application (diagnostics and therapeutics), different performance metrics need to be considered.
Fundamental biology
Cargo composition can provide information on EV biogenesis and function and is often explored using deep omics approaches on EV preparations obtained from sources under various conditions. Depending on the preparation protocol used, these experiments provide information on the average cargo composition of bulk EVs or EV subpopulations (Fig. 6). EVs display antigenic surface proteins from their source cell, but also contain surface proteins to target cell(s) or tissues, factilitating enrichment of EVs from a particular source or with a particular fate48. Spectrometry- and spectroscopy-based methods (LC-MS/MS or Raman spectroscopy), nucleic acid sequencing, lipidomics, metabolomics, glycomics and metallomics can be used to understand molecular differences in EV preparations. Nucleic acids can be surface-bound or contained within EVs, and analysed for diverse aspects including mutations, methylations, RNA types and concentration alterations123. Extracting these molecules from EV preparations adds a level of complexity, however. Furthermore, molecular input normalization might prove challenging owing to the low abundance of some molecules in EVs, like nucleic acids124. In these instances, data normalization may be supported by biophysical or biochemical characteristics (for example, particle or biomolecular concentrations) or source specifics (for example, sample volume or cell number). Deep omics may generate large datasets that require bioinformatic tools to enable data interpretation125–127. For some omics platforms, minimum information guidelines have been created to assist study design and reporting128–130. In addition to gaining insights into cargo composition, deep omics analysis of EV preparations supports the assessment of performance metrics like specificity and repeatability/reproducibility, providing guidance for further optimizations of the preparation protocol, which may help in translation towards diagnostic or therapeutic applications (Box 1). Insights into EV structure such as size, density, surface charge and refractive index result from basic characterization. Comparison of EV-enriched versus non-EV-enriched fractions or different EV subpopulations reveals whether the composition or structure is associated with one or a wider variety of carriers6,48,84. The optimal integrated workflow to study EV cargo composition should be specific, preserve integrity and have high reproducibility/repeatability (Fig. 6).
EV preparations can also be implemented to study their cellular uptake mechanisms and functions. Depending on the preparation protocol used, these experiments provide information on the average uptake and function of bulk EVs or EV subpopulations. EV uptake and function has primarily been studied in monolayers (two-dimensional), which do not necessarily reflect EV biology in vivo. Recent advances in live and super-resolution microscopy, combined with novel EV labelling strategies (including genetic labelling and luminal or lipid dye labelling131–133), allow for EV tracking in living organisms in vivo or three-dimensional models in vitro131. Overall, awareness of potential artefacts is fundamental to avoiding overinterpretation when attributing functions to bulk EVs or EV subpopulations. For example, some labelling dyes are very long-lived, can transfer to other materials besides EVs and can form particles that are difficult to distinguish from EVs, meaning that detection of the label inside the cell does not necessarily mean that EVs or their cargo have been internalized. Labelling strategies may modify biophysical or biochemical characteristics of EVs, potentially altering their function134,135. The attribution of functions to bulk EVs or EV subpopulations can be supported using side-by-side comparisons of EVs versus other materials that are depleted from EVs by separation or EV subpopulations by fractionation. Other fundamental aspects to EV functional assays include the identification of effective dose ranges and dosing strategies, for example by EV characteristics (such as particle or protein concentration) or source specifics (such as cell number or source volume)136–138. When studying biogenesis or release mechanisms, bulk EV preparations are less suitable, given that EV subpopulations based on biogenesis (such as exosomes and ectosomes) share some of their biochemical and biophysical characteristics. Considering that EV biogenesis relies on mechanisms shared by various intracellular trafficking processes, it remains challenging to study EV preparations after blocking EV release from cells via perturbation or interference with different levels of specificity. The optimal integrated workflow to study EV uptake and function should be specific, preserve integrity and be highly repeatable and reproducible.
Diagnostics and therapeutics
The analysis of EVs as biomarkers with which to detect and monitor disturbances in diverse ecosystems has gained widespread interest, especially since EVs are easily accessible in diverse sources, offer perspectives for multi-analyte biomarker measurements, are present in high concentrations and are relatively stable9,12. Novel biomarker identification is often gained through deep omics of EV preparations obtained under physiological versus pathophysiological conditions6,27,29,93,139–141 (Fig. 6). Protein content can be analysed for enriched or depleted proteins, while nucleic acids can be analysed for diverse aspects including mutations, methylations, types (for example, miRNA, tRNA, mRNA) and changes in concentration. Furthermore, lipid content, metabolite content, glycan profiles and metal content may differ between physiological and pathophysiological conditions. Other EV read-outs that may be altered in pathophysiological conditions include biomolecular concentrations (protein and RNA) and/or particle concentrations42,136. Increased insights in fundamental biology including EV biogenesis and cargo sorting will be instrumental to the fine-tuning and widespread implementation of EV-based diagnostics. Although mutated mRNAs in EVs are now clinically used as biomarkers for the prognostic assessment of prostate cancer142,143, multiple methodological challenges delay the overall translation of EV-associated biomarkers into diagnostic applications. Biomarker discovery at the bench frequently builds upon a combination of protocol steps that prepare EVs from their source with high specificity while retaining integrity but are prone to reduced efficiency and higher source-volume requirements to ensure sensitivity/resolution, and are time-consuming and not cost-efficient; whereas EV-based diagnostic applications require high-throughput and short turn-around and hands-on-time.
EVs have emerged as a key cell-free strategy for treating a range of pathophysiological conditions, with a rapid transition from promising in vitro studies and in vivo animal models towards the first clinical trials. EVs have multiple desirable features as therapeutic agents, including high physiochemical stability, scalability, tunability, low immunogenicity, the intrinsic capacity to transfer biological information between cells and across biological barriers, and the presence of surface proteins that direct (for example) cell-selective fusion. Native EV-based therapies exploit the natural biological function of EVs prepared from in vitro cultured cells or biofluids (Fig. 6). Multiple preclinical trials have independently revealed that EVs from diverse cells and biofluid sources carry inherent therapeutic potential144–148. EVs can be loaded with therapeutic molecules like nucleic acids or proteins either exogenously or endogenously, the former by incorporating cargo in or on prepared EVs and the latter by incorporating the cargo during EV biogenesis (Fig. 6). Exogenous loading of EVs can be achieved by electroporation, co-incubation or sonication. Endogenous loading is performed by manipulating the cell that is producing the EVs. Either passive loading, by overexpressing the therapeutic cargo with or without the addition of domains that promote membrane localization (for example, myristoylation), or active loading, by using a genetic fusion between the cargo and a protein that localizes into EVs, is performed149,150. Viral packaging strategies can also be exploited for loading of nucleic acids or proteins into EVs as many viruses hijack EV secretion pathways42,151. Although native EV surface molecules may be sufficient to direct cell-selective uptake, engineering of the EV surface composition may aid or enhance efficient targeting152. Alternatively, targeting of therapeutic EVs to specific sites can be achieved by conjugating nanobody fragments or RNA aptamers to the EV surface153,154. Physical methods can be used to generate biological nanovesicles from cells, such as by consecutive extrusion of cells through filters, and artificial lipid vesicles can be engineered to incorporate EV features or components155. Biomimetic functionalization using EV membranes to confer stealth and targeting properties to nanoparticles is a unique drug delivery approach that requires more study to increase EV membrane extrusion efficiency156 (Fig. 6). There are, however, multiple methodological challenges that have restricted the widespread clinical and pharmaceutical adoption of EV-based therapeutics. EV preparation techniques that preserve integrity while allowing scalability and high repeatability/reproducibility must be selected. The development of methods to upscale manufacturing to good manufacturing practice standards with high efficiency and repeatability/reproducibility is important, as is, for example, the use of bioreactor-based culture system or the use of engineered bacterial EVs and plant EVs instead of mammalian EVs149,157. Evaluation of the optimal route of administration, potency, efficacy and dose is crucial. Increased insights in fundamental biology including EV biogenesis and cargo composition will be instrumental to the fine-tuning and widespread implementation of EV-based therapeutics158.
So far, only a few clinical studies have been performed with therapeutic EVs. Primarily, mesenchymal stem cell (MSC)-derived EVs have been used to treat inflammatory processes such as cytokine storms. During the COVID-19 pandemic, multiple studies were testing the efficacy of intravenous treatment with GMP-manufactured EVs produced from MSCs159. These trials reported appropriate safety, but were not conclusive regarding efficacy, as no placebo arm was employed. One clinical trial also tested EVs overexpressing CD24, a molecule known to react with siglec10, given by inhalation. According to the clinical trial, the patients’ disease severity diminished rapidly, and a majority could leave the hospital only a few days after treatment160, presumably as a result of attenuated excessive inflammation, possibly via induction of neutrophil apoptosis. However, this study also lacked a placebo arm. To date, the only placebo-controlled study performed with therapeutic MSC-derived EVs is a wound-healing study performed adjacent to laser surgery for acne scarring161. To establish efficacy of any EV-based therapeutic drug candidate, diligent clinical trials will need to be performed, including appropriate placebo control arms and a double-blind study design.
Reproducibility and data deposition
The diversity in the composition of sources and heterogeneity in EV subpopulations requires branched development of complementary preparation and characterization methods to advance EV research and application. Validated controls and adequate reporting of experimental parameters are required when different methods are implemented, to ensure that data can be interpreted and reproduced (Fig. 4a). Furthermore, methodological comparison, cross-laboratory studies, reference materials and transparency in reporting experimental parameters will be indispensable. The EV research community has launched multiple initiatives to cope with some of these challenges (Table 2).
Table 2 |.
Guidelines for standardized and reproducible extracellular vesicle research and data deposition
| Repository | Description | Curated | Updated | |
|---|---|---|---|---|
| Reproducibility and standardization | MISEV17,18 | Minimal Information for Studies of EVs (MISEV): a position statement of the International Society for Extracellular Vesicles | NA | Yes |
| EV-TRACK11,170,191 | Transparent Reporting And Centralizing Knowledge in EV research | Yes | Yes | |
| EV data deposition | Vesiclepedia164,171 | A compendium of RNA, proteins, lipids and metabolites in EVs | Yes | Yes |
| Exocarta165,192,193 | A web-based compendium of exosomal cargo | Yes | No | |
| EVpedia166,194,195 | A community web resource for prokaryotic and eukaryotic EV research | Yes | No | |
| ExoRBase167,196 | An atlas of mRNA, lncRNA and circRNA in extracellular vesicles from human biofluids | Yes | Yes | |
| EVmiRNA168 | A database of miRNA profiling in EVs | Yes | Yes | |
| EV-ADD169 | Database for EV-associated DNA in human liquid biopsy samples | Yes | Yes | |
| Cargo-specific data deposition | PRIDE172,197–201 | The proteomics identification database | Yes | Yes |
| The Global Proteome Machine Database (GPMDB)173 | Open-source system for analysing, validating, and storing protein identification data | No | Yes | |
| MassIVE174 | Mass spectrometry interactive virtual environment | No | Yes | |
| PeptideAtlas202,203 | A multi-organism, publicly accessible compendium of peptides identified in a large set of tandem mass spectrometry proteomics experiments | No | Yes | |
| exRNA204 | The data repository of the Extracellular RNA Communication Consortium (ERCC) | No | Yes | |
| Gene Expression Omnibus (GEO)177 | A database repository of high-throughput gene expression data and hybridization arrays, chips and microarrays | No | Yes | |
| Excerpt178 | A comprehensive analytic platform for extracellular RNA profiling | No | Yes | |
| Method-specific data deposition | MIFlowCyt205 MIFlowCyt-EV163 |
A framework for standardized reporting of (EV) flow cytometry experiments | NA | Yes |
| MIQE guidelines130,206,207 | Minimum information for publication of quantitative real-time PCR experiments | NA | Yes |
circRNA, circular RNA; EV, extracellular vesicle; lncRNA, long non-coding RNA; miRNA, microRNA; NA, not applicable.
MISEV guidelines
To promote rigour and reproducibility in EV research, ISEV oversees an ongoing initiative known as the Minimal Information for Studies of EVs (MISEV). The first MISEV position statement appeared in 2014, a product of the ISEV Board of Directors, followed by a community-wide consensus effort published in 2018 (MISEV2018)17,18. MISEV2018 provides advice on six major topics: nomenclature, source materials and pre-processing variables, separation, characterization, functional studies and reporting requirements. The general principles of the MISEV guidelines apply to EVs produced by all organisms and all cells present in all EV sources. The MISEV guidelines are regularly updated, including descriptions of progress and advice from numerous topic-specific task forces that the ISEV Rigor and Standardization Subcommittee governs162. In addition to MISEV guidelines, method-specific guidelines are of course applicable, such as the MIQE guidelines when performing quantitative real-time PCR experiments130 or MIFlowCyt-EV when performing flow cytometry experiments163.
EV-TRACK knowledgebase
To cope with the diversity of available methods, researchers need to be aware of experimental parameters and transparently report them so that others can understand the experiment and reproduce the results12. In 2017 the freely accessible crowdsourcing EV-TRACK knowledgebase was released; it centralizes EV biology and methodology to encourage authors, reviewers, editors and funders to put experimental guidelines, like the MISEV guidelines, into practice11. To achieve this, EV-TRACK consists of seven enabling features. Researchers are encouraged to upload experimental parameters related to EV experiments pre-publication, including sample type, pre-analytical variables, preparation protocol and characterization method. EV experiments are assigned an EV-METRIC based on the reported experimental parameters. This metric informs researchers whether sufficient information has been provided to interpret and reproduce the experiment. By means of the EV-METRIC, EV-TRACK familiarizes researchers with relevant experimental parameters. Each annotated experiment receives a unique identifier, the EV-TRACK ID. Annotated experiments become publicly accessible after curation by the EV-TRACK administrators and publication of manuscript. EV-TRACK also systematically catalogues preparation protocols, characterization methods and biochemical/biophysical characteristics of EVs, all of which can be easily queried. In addition, method-specific frameworks for standardized reporting have been established and integrated into EV-TRACK, such as MIFlowCyt-EV for standardized reporting of flow cytometry experiments163.
(Meta)data sharing and deposition
Multiple web-based compendia have been developed with the aim of cataloguing the molecular composition of EVs. This is fundamental since datasets generated in EV research — like protein, nucleic acid and lipid datasets — remain isolated because of the lack of a data-sharing system. Vesiclepedia, ExoCarta and EVpedia, and more recently ExoRbase, EVmiRNA and EV-ADD, are all web-based compendia of proteins, nucleic acids, lipids and metabolites identified in EVs164–169. These databases are driven by community annotation and present EV cargo composition retrieved using a variety of preparation protocols. Dependent upon the specificity and efficiency of the preparation protocol, this cargo thus associates with differential likelihood to EVs, EV subpopulations or other materials. One of the main challenges of these databases, developed to drive biological knowledge discovery, is how to ensure access to unbiased experimental information to interpret the EV content. For the 2019 update of Vesiclepedia, this was achieved by providing the users the EV-METRIC linked to the EV-TRACK entry for the reported studies, a first step towards integrating EV-TRACK knowledge in EV-related databases170,171. Unfortunately, not all compendia are still available or regularly updated and curated, warranting caution when implementing these databases for data comparison or meta-analysis. Central repositories for depositing and sharing omics data include PRIDE, GPMDB, MassIVE and PeptideAtlas for proteomics data, and exRNA, GEO and ExceRpt for transcriptomics data172–178. In addition to molecular cargo composition, raw images and videos generated using characterization methods are generally not reported in publications and remain an untapped potential for data re-analysis. Thus, a centralized data repository system combined with knowledge of experimental parameters (EV-TRACK annotation) would maximize the utility of the collected data and further advance biological knowledge discovery.
Reference materials
Reference materials are instrumental for developing, improving and assessing the performance of EV methods, and for quantitative and objective data interpretation10. Developing EV reference materials is challenging considering the biophysical and biochemical characteristics as well as the heterogeneity of EVs. Ideally, EV reference materials are of biological origin, have similar physical and biochemical characteristics to sample EVs, and are distinguishable from sample EVs when used for spike-in purposes179. Biological EV reference materials developed to date include cells disrupted through ultrasonication, sample EVs separated from cell culture supernatant or bodily fluids, and retroviruses engineered with a fusion protein of the viral envelope glycoprotein and green fluorescent protein (GFP). From liposome to retrovirus, lipid dyes can be used to track the reference material from sample EV differentially. Considering that lipid dyes are prone to photobleaching and micelle formation, EVs containing fluorescently tagged proteins have further advanced the field. For example, recombinant EVs containing GFP-tagged HIV-1 Gag protein are highly fluorescent, differentially trackable, share biophysical and biochemical characteristics with sample EVs and can be combined with a variety of preparation and characterization methods42,179.
Limitations and optimizations
A versatile toolset for preparing and characterizing EV preparations is required to enable their analysis in diverse sources for a range of applications. Consequently, understanding the limitations of each method is crucial to selecting and optimizing the most appropriate toolset for a particular experiment and is fundamental throughout the chain of pre-analytical, analytical and post-analytical steps (Fig. 7 and Table 1).
Fig. 7 |. Limitations, optimizations and challenges for extracellular vesicle research.
The limitations and optimizations in EV preparation are identical for different extracellular vesicle (EV) sources. Pre-analytical variables such as sample collection and transport have a large impact on the final EV sample. Multiple analytical aspects associated with EV preparation and characterization methods need to be considered. Furthermore, post-analytical storage conditions such as storage buffer and temperature affect the outcome of downstream applications. The EV research field is furthermore facing multiple challenges. Knowledge of the biology of EVs needs to be expanded and there is a need for powerful and versatile multiparametric in situ analysis. Furthermore, the EV research field should keep investing in transparency, rigour and standardization, as well as regulatory affairs and interlaboratory studies to direct EV research towards widespread clinical application. Finally, diligent clinical trials are needed to bring EV research from bench to application.
Pre-analytical
An extensive constellation of pre-analytical variables, including donor-specific variables, source collection, stabilization, transport, processing and storage, may affect EV integrity and recovery10,180 (Table 1). It is crucial to plan source collection and experimental procedures to maximize the number of known, reportable parameters, and then to report as many pre-analytical variables as are known. Reference materials that are commutable but differentially trackable from sample EVs are fundamental to identifying and optimizing pre-analytical variables.
Analytical
Multiple analytical aspects require consideration when preparing EVs from biofluid sources and drawing conclusions from EV experiments.
First, a limitation of current methods is their inability to completely separate EVs from other materials and fractionate different EV subpopulations. Indeed, a preparation protocol that achieves both high specificity and high efficiency is currently not available. Therefore, researchers should consider the specificity of the selected protocol (as in, concentration, enrichment or separation; Fig. 3) and interpret whether the analysed feature can be assigned to EVs or is more likely to be attributable to other materials, or a combination thereof. For example, concentration methods typically result in high efficiency but low specificity31,181. Enrichment methods have an increased ability to separate EV subpopulations and a selection of other materials but with moderate efficiency and specificity. Separation workflows using an orthogonal combination of methods that exploit the same or different features of EVs further increase the specificity, but the multiple-step process may lower the efficiency6,40,48,62,84. We note that efficiency refers to EV efficiency and not total particle or biomolecular concentration efficiency. Biological reference materials that share biophysical and biochemical characteristics with sample EVs allow evaluation of performance metrics such as efficiency and specificity.
Second, the potential impact of the preparation protocol on EV function and composition is not fully understood. EVs released by the source cell into the surroundings acquire a biomolecular corona. Distinguishing this biomolecular corona from other materials in the biofluid source is challenging, especially considering that the extracellularly acquired biomolecular corona is typically composed of other materials (including proteins and nucleic acids) that are likely to contaminate EV preparations. In addition, particular preparation steps such as polymer-based precipitation or immune affinity capture may alter the behaviour of EVs in downstream applications.
Third, we are still unable to accurately measure all EVs in any given sample because concentration, enrichment or separation protocols may deplete EV subpopulations; and characterization methods encounter challenges associated with the size and low biomolecular content of EVs. Bulk EV analysis methods assessing the biomolecular characteristics of EVs (such as using western blot, ELISA and bead-based flow cytometry) have the advantage that EVs of all sizes can be analysed, but the acquired data represent an average of the total EV population. Although single EV analysis has the potential to cope with this issue, these methods encounter physics-based limitations, such as the inability to measure smaller EVs. In contrast to small dense particles like solid gold, EVs (<70 nm) do not scatter enough light to be tracked and analysed by NTA104. The minimum detectable EV size affects concentration measurements and size information such as size distribution profiles104. Consequently, different characterization methods will result in different absolute EV concentrations34. Furthermore, the presence or absence of proteins on EVs, measured using fluorescent labels, can only be determined in those EVs that are within the detection range of the instrument. To be able to standardize and compare results among the different characterization methods, reference materials and transparency in reporting are fundamental102,163.
Post-analytical
Protocols should be open and transparent, so that results can be adequately interpreted and proper conclusions can be drawn as the technology advances12. Post-analytical storage conditions of EV preparations, such as buffer and temperature, may affect EV integrity and recovery and the outcome of downstream functional assays, diagnostic tests or therapeutic applications99,182–185.
Recommendations
In conclusion, we recommend evaluating EV preparations carefully for both EV and non-EV characteristics to understand the specificity and efficiency of the preparation protocol, to use complementary/orthogonal methods to characterize EV preparations given that different methods have different limitations, and to include the procedural controls and reference materials to assess the impact of pre-analytical, analytical and post-analytical variables and transparently report these variables.
Outlook
Concerted efforts towards rigour/standardization, methodological development and increased understanding of fundamental biology have resulted in a tremendous leap forward during the past few years, culminating in the exploration of EVs in diverse ecosystems and pioneering steps towards their diagnostic and therapeutic translation. Working with EVs presents several challenges and requires further methodological developments to enable their widespread application across ecosystems.
The cornerstone of diverse applications, including the study of fundamental biology, biomarker discovery and therapeutic uses, is the preparation of EVs from their source. We now know that the set-up of the preparation protocol defines specificity and efficiency. Basic characterization, complying with the field’s minimal information guidelines, allows us to assess these performance metrics and supports researchers in estimating the likelihood that the observed function or identified biomarker is associated with EVs. Nevertheless, much remains to be explored in terms of the impact of the preparation protocol on the attribution of functions or biomarkers to bulk EVs and/or EV subpopulations. Methodological advances enabling the preparation of bulk EVs or EV subpopulations with high specificity and high efficiency while retaining integrity, including the extrafacial surface, will undoubtedly contribute insights into their biological function, information that may instruct biomarker or therapeutic translation. Indeed, the preparation protocol required to enable EV-associated biomarker discovery or evaluate EV function probably differs from the diagnostic test or biopharmaceutical production processes for routine use, which demand reproducibility, high-throughput and cost-effective performance, and compatibility with automation, scale-up and low or high sample input volumes.
The characterization of EV preparations is challenged by their nanometre size and complex but also low-abundance cargo. Bulk characterization methods report the average biophysical or biochemical information of the total EV population. By contrast, recent developments in single EV characterization methods enable multiparametric analysis, opening up the possibility to study EV subpopulations in situ, without a priori preparation steps directly in the source, and to establish a more fine-grained view of their function and biophysical/biochemical composition. These insights enable the identification of the native state of EVs in the source: information that is fundamental to the development of protocols for preparing EVs while retaining their integrity and biomolecular corona. With the development of even more powerful and versatile high-throughput imaging and omics technologies, innovative ways to capture single EVs and the integration of artificial intelligence into bio-informatic tools, mapping sources at the single-EV level may become routine within the next decade, further enhancing biomarker and therapeutic translation.
Multiple preparation protocols and characterization methods are used to advance fundamental biology, biomarker discovery and therapeutic development. The diverse implementation of preparation and characterization methods across sources has challenged the EV research field. However, methodological heterogeneity has successfully resulted in the development of methodological advances, guidance by position papers and MISEV guidelines, and tools to enable transparency, rigour and standardization. These advances have contributed to the identification of novel (patho)physiological functions, the reporting of other extracellular particles, and the application of EVs as biomarkers and therapeutics. Therefore, methodological heterogeneity should be embraced, provided that the basic principles of research transparency, rigour and reproducibility are maintained. A high level of cooperation between researchers, clinicians and competent authorities, together with the validation of EV biomarkers and functions in interlaboratory studies, will guide regulation and lead to the widespread application of EVs.
Acknowledgements
This work was carried out with the financial support of the Fund for Scientific Research (FWO), Kom Op Tegen Kanker (KOTK) and a European Research Council (ERC) Consolidator Grant (number 101045156). C.T., K.W.W., C.L. and A.H. gratefully acknowledge interactions with participants of the ISEV workshop “Open, reproducible and standardized EV research” (Ghent, Belgium, 2019) and/or the EMBL/EMBO course “Extracellular vesicles: from biology to biomedical applications”, which contributed to the conceptualization of this Primer.
Glossary
- Asymmetrical flow field-flow fractionation
Separation method using two perpendicular flows within a channel consisting of an impermeable and a semipermeable plate to separate particles based on size
- Density cushion ultracentrifugation
Enrichment method in which a single layer of a defined density is established at the bottom of the tube, with a larger source volume loaded on top, to enrich or deplete materials displaying higher versus lower density than the cushion
- Density gradient ultracentrifugation
Separation method in which extracellular vesicles are separated from the source based on their intrinsic buoyancy values using a continuous or discontinuous gradient constructed with media of various densities
- Ectosomes
Subtype of extracellular vesicles secreted by cells through direct plasma membrane budding
- Efficiency
The extent to which a method can recover extracellular vesicles present in the source
- Exosomes
Subtype of extracellular vesicles formed within the endosomal compartment and released upon fusion of these multi-vesicular endosomes with the plasma membrane
- Integrity
The extent to which the conformation, membrane structure and extrafacial surface of the extracellular vesicles are preserved and aggregation is prevented
- Specificity
The extent to which a method can selectively separate extracellular vesicles and eliminate other materials
- Zeta potential
Physical property present on the surface of extracellular vesicles, often used as an indicator of surface charge and colloidal stability, represented as the effective net charge (in units of millivolts, mV)
Footnotes
Competing interests
A.H., C.T., A.F.H., and K.W.W. are inventors on patents and/or patent applications related to extracellular vesicle products. A.F.H. has consulted for Vivazome, Beagle Biotechnology and FujiFilm Cellular Dynamics in the field of EVs. C.L. and J.L. are inventors on multiple EV-associated patents for putative clinical utilization. J.L. owns equity in Codiak BioSciences Inc. and Exocure Biosciences Inc. and consults in the field of EVs through Vesiclebio AB. C.L. owns equity in Exocure Bioscience Inc. J.L. has previously consulted for Clara Biotech INC, Nanoview Biosciences INC, and is currently consulting for ExoCoBio INC. Ionis Pharmaceuticals, Yuvan Research, and AgriSciX have sponsored research in the laboratory of K.W.W. K.W.W. is or has been an advisory board member of ShiftBio, Exopharm, NovaDip and ReNeuron; K.W.W. holds stock options with NeuroDex; and consults privately as Kenneth Witwer Consulting. L.L., C.P. and L.M.-J. declare no competing interests.
References
- 1.Buzas EI The roles of extracellular vesicles in the immune system. Nat. Rev. Immunol. 10.1038/s41577-022-00763-8 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.van Niel G, D’Angelo G & Raposo G Shedding light on the cell biology of extracellular vesicles. Nat. Rev. Mol. Cell Biol. 19, 213–228 (2018). [DOI] [PubMed] [Google Scholar]
- 3.Villarroya-Beltri C et al. Sumoylated hnRNPA2B1 controls the sorting of miRNAs into exosomes through binding to specific motifs. Nat. Commun. 4, 2980 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Ferreira JV et al. LAMP2A regulates the loading of proteins into exosomes. Sci. Adv. 8, eabm1140 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Tóth EÁ et al. Formation of a protein corona on the surface of extracellular vesicles in blood plasma. J. Extracell. Vesicles 10, e12140 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Vergauwen G et al. Robust sequential biophysical fractionation of blood plasma to study variations in the biomolecular landscape of systemically circulating extracellular vesicles across clinical conditions. J. Extracell. Vesicles 10, e12122 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wolf M et al. A functional corona around extracellular vesicles enhances angiogenesis, skin regeneration and immunomodulation. J. Extracell. Vesicles 11, e12207 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Buzas EI Opportunities and challenges in studying the extracellular vesicle corona. Nat. Cell Biol. 24, 1322–1325 (2022). [DOI] [PubMed] [Google Scholar]
- 9.Hendrix A The nature of blood(y) extracellular vesicles. Nat. Rev. Mol. Cell Biol. 22, 243–243 (2021). [DOI] [PubMed] [Google Scholar]
- 10.Geeurickx E & Hendrix A Targets, pitfalls and reference materials for liquid biopsy tests in cancer diagnostics. Mol. Asp. Med. 72, 100828 (2020). [DOI] [PubMed] [Google Scholar]
- 11.Van Deun J et al. EV-TRACK: transparent reporting and centralizing knowledge in extracellular vesicle research. Nat. Methods 14, 228–232 (2017). [DOI] [PubMed] [Google Scholar]
- 12.De Wever O & Hendrix A A supporting ecosystem to mature extracellular vesicles into clinical application. EMBO J. 38, e101412 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Toyofuku M, Nomura N & Eberl L Types and origins of bacterial membrane vesicles. Nat. Rev. Microbiol. 17, 13–24 (2019). [DOI] [PubMed] [Google Scholar]
- 14.Toyofuku M, Schild S, Kaparakis-Liaskos M & Eberl L Composition and functions of bacterial membrane vesicles. Nat. Rev. Microbiol. 10.1038/s41579-023-00875-5 (2023). [DOI] [PubMed] [Google Scholar]
- 15.Brown L, Wolf JM, Prados-Rosales R & Casadevall A Through the wall: extracellular vesicles in Gram-positive bacteria, mycobacteria and fungi. Nat. Rev. Microbiol. 13, 620–630 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.György B et al. Membrane vesicles, current state-of-the-art: emerging role of extracellular vesicles. Cell. Mol. Life Sci. 68, 2667–2688 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Théry C et al. Minimal Information for Studies of Extracellular Vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J. Extracell. Vesicles 7, 1535750 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Lötvall J et al. Minimal experimental requirements for definition of extracellular vesicles and their functions: a position statement from the International Society for Extracellular Vesicles. J. Extracell. Vesicles 3, 26913 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Tulkens J et al. Increased levels of systemic LPS-positive bacterial extracellular vesicles in patients with intestinal barrier dysfunction. Gut 69, 191–193 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Tulkens J, De Wever O & Hendrix A Analyzing bacterial extracellular vesicles in human body fluids by orthogonal biophysical separation and biochemical characterization. Nat. Protoc. 15, 40–67 (2020). [DOI] [PubMed] [Google Scholar]
- 21.Kowal J et al. Proteomic comparison defines novel markers to characterize heterogeneous populations of extracellular vesicle subtypes. Proc. Natl Acad. Sci. USA 113, E968–E977 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Hendrix A & De Wever O Systemically circulating bacterial extracellular vesicles: origin, fate, and function. Trends Microbiol. 30, 213–216 (2022). [DOI] [PubMed] [Google Scholar]
- 23.Samuel M et al. Oral administration of bovine milk-derived extracellular vesicles induces senescence in the primary tumor but accelerates cancer metastasis. Nat. Commun. 12, 3950 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Nemati M et al. Plant-derived extracellular vesicles: a novel nanomedicine approach with advantages and challenges. Cell Commun. Signal. 20, 69 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Zhang H et al. Identification of distinct nanoparticles and subsets of extracellular vesicles by asymmetric flow field-flow fractionation. Nat. Cell Biol. 20, 332–343 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Zhang Q et al. Supermeres are functional extracellular nanoparticles replete with disease biomarkers and therapeutic targets. Nat. Cell Biol. 23, 1240–1254 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Hoshino A et al. Extracellular vesicle and particle biomarkers define multiple human cancers. Cell 182, 1044–1061.e18 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Simonsen JB What are we looking at? Extracellular vesicles, lipoproteins, or both? Circ. Res. 121, 920–922 (2017). [DOI] [PubMed] [Google Scholar]
- 29.Dhondt B et al. Unravelling the proteomic landscape of extracellular vesicles in prostate cancer by density-based fractionation of urine. J. Extracell. Vesicles 9, 1736935 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Cocozza F, Grisard E, Martin-Jaular L, Mathieu M & Théry C SnapShot: extracellular vesicles. Cell 182, 262–262.e1 (2020). [DOI] [PubMed] [Google Scholar]
- 31.Van Deun J et al. The impact of disparate isolation methods for extracellular vesicles on downstream RNA profiling. J. Extracell. Vesicles 3, 24858 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Willms E, Cabañas C, Mäger I, Wood MJA & Vader P Extracellular vesicle heterogeneity: subpopulations, isolation techniques, and diverse functions in cancer progression. Front. Immunol. 9, 738 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Roux Q et al. Depletion of soluble cytokines unlocks the immunomodulatory bioactivity of extracellular vesicles. J. Extracell. Vesicles 10.1002/jev2.12339 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Maas SLN et al. Possibilities and limitations of current technologies for quantification of biological extracellular vesicles and synthetic mimics. J. Control. Release 200, 87–96 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Arab T et al. Characterization of extracellular vesicles and synthetic nanoparticles with four orthogonal single-particle analysis platforms. J. Extracell. Vesicles 10, e12079 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Grange C & Bussolati B Extracellular vesicles in kidney disease. Nat. Rev. Nephrol. 18, 499–513 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Théry C, Amigorena S, Raposo G & Clayton A Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr. Protoc. Cell Biol. 10.1002/0471143030.cb0322s30 (2006). [DOI] [PubMed] [Google Scholar]
- 38.Pinedo M, de la Canal L & de Marcos Lousa C A call for rigor and standardization in plant extracellular vesicle research. J. Extracell. Vesicles 10, e12048 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Biller SJ et al. Bacterial vesicles in marine ecosystems. Science 343, 183–186 (2014). [DOI] [PubMed] [Google Scholar]
- 40.Karimi N et al. Detailed analysis of the plasma extracellular vesicle proteome after separation from lipoproteins. Cell. Mol. Life Sci. 75, 2873–2886 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Li Y et al. EV-origin: enumerating the tissue-cellular origin of circulating extracellular vesicles using exLR profile. Comput. Struct. Biotechnol. J. 18, 2851–2859 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Geeurickx E et al. The generation and use of recombinant extracellular vesicles as biological reference material. Nat. Commun. 10, 3288 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Erdbrügger U et al. Urinary extracellular vesicles: a position paper by the Urine Task Force of the International Society for Extracellular Vesicles. J. Extracell. Vesicles 10, e12093 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Merchant ML, Rood IM, Deegens JKJ & Klein JB Isolation and characterization of urinary extracellular vesicles: implications for biomarker discovery. Nat. Rev. Nephrol. 13, 731–749 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Linxweiler J & Junker K Extracellular vesicles in urological malignancies: an update. Nat. Rev. Urol. 17, 11–27 (2020). [DOI] [PubMed] [Google Scholar]
- 46.Crescitelli R, Lässer C & Lötvall J Isolation and characterization of extracellular vesicle subpopulations from tissues. Nat. Protoc. 16, 1548–1580 (2021). [DOI] [PubMed] [Google Scholar]
- 47.Vella LJ et al. A rigorous method to enrich for exosomes from brain tissue. J. Extracell. Vesicles 6, 1348885 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Jeppesen DK et al. Reassessment of exosome composition. Cell 177, 428–445.e18 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Crescitelli R et al. Subpopulations of extracellular vesicles from human metastatic melanoma tissue identified by quantitative proteomics after optimized isolation. J. Extracell. Vesicles 9, 1722433 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Urzì O, Bagge RO & Crescitelli R The dark side of foetal bovine serum in extracellular vesicle studies. J. Extracell. Vesicles 11, e12271 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Lehrich BM, Liang Y & Fiandaca MS Foetal bovine serum influence on in vitro extracellular vesicle analyses. J. Extracell. Vesicles 10, e12061 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Li J et al. Serum-free culture alters the quantity and protein composition of neuroblastoma-derived extracellular vesicles. J. Extracell. Vesicles 4, 26883 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Witwer KW et al. Standardization of sample collection, isolation and analysis methods in extracellular vesicle research. J. Extracell. Vesicles 2, 20360 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Yáñez-Mó M et al. Biological properties of extracellular vesicles and their physiological functions. J. Extracell. Vesicles 4, 27066 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.De Palma M et al. Plant roots release small extracellular vesicles with antifungal activity. Plants 9, E1777 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Schatz D & Vardi A Extracellular vesicles — new players in cell–cell communication in aquatic environments. Curr. Opin. Microbiol. 43, 148–154 (2018). [DOI] [PubMed] [Google Scholar]
- 57.Biller SJ et al. Environmental and taxonomic drivers of bacterial extracellular vesicle production in marine ecosystems. Appl. Environ. Microbiol. 10.1128/aem.00594-23 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Vergauwen G et al. Confounding factors of ultrafiltration and protein analysis in extracellular vesicle research. Sci. Rep. 7, 2704 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Zonneveld MI et al. Recovery of extracellular vesicles from human breast milk is influenced by sample collection and vesicle isolation procedures. J. Extracell. Vesicles 10.3402/jev.v3.24215 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Lacroix R et al. Impact of pre-analytical parameters on the measurement of circulating microparticles: towards standardization of protocol. J. Thromb. Haemost. 10, 437–446 (2012). [DOI] [PubMed] [Google Scholar]
- 61.Visan KS et al. Comparative analysis of tangential flow filtration and ultracentrifugation, both combined with subsequent size exclusion chromatography, for the isolation of small extracellular vesicles. J. Extracell. Vesicles 11, 12266 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Zhang X, Borg EGF, Liaci AM, Vos HR & Stoorvogel W A novel three step protocol to isolate extracellular vesicles from plasma or cell culture medium with both high yield and purity. J. Extracell. Vesicles 9, 1791450 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Böing AN et al. Single-step isolation of extracellular vesicles by size-exclusion chromatography. J. Extracell. Vesicles 3, 23430 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Welton JL, Webber JP, Botos L-A, Jones M & Clayton A Ready-made chromatography columns for extracellular vesicle isolation from plasma. J. Extracell. Vesicles 4, 27269 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Giddings JC, Yang FJ & Myers MN Flow-field-flow fractionation: a versatile new separation method. Science 193, 1244–1245 (1976). [DOI] [PubMed] [Google Scholar]
- 66.Wahlund KG & Giddings JC Properties of an asymmetrical flow field-flow fractionation channel having one permeable wall. Anal. Chem. 59, 1332–1339 (1987). [DOI] [PubMed] [Google Scholar]
- 67.Sitar S et al. Size characterization and quantification of exosomes by asymmetrical-flow field-flow fractionation. Anal. Chem. 87, 9225–9233 (2015). [DOI] [PubMed] [Google Scholar]
- 68.Zhang H & Lyden D Asymmetric-flow field-flow fractionation technology for exomere and small extracellular vesicle separation and characterization. Nat. Protoc. 14, 1027–1053 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Raposo G et al. B lymphocytes secrete antigen-presenting vesicles. J. Exp. Med. 183, 1161–1172 (1996). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Lozano-Ramos I et al. Size-exclusion chromatography-based enrichment of extracellular vesicles from urine samples. J. Extracell. Vesicles 4, 27369 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Coumans Frank AW et al. Methodological guidelines to study extracellular vesicles. Circ. Res. 120, 1632–1648 (2017). [DOI] [PubMed] [Google Scholar]
- 72.Jeppesen DK et al. Comparative analysis of discrete exosome fractions obtained by differential centrifugation. J. Extracell. Vesicles 3, 25011 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Cvjetkovic A, Lötvall J & Lässer C The influence of rotor type and centrifugation time on the yield and purity of extracellular vesicles. J. Extracell. Vesicles 10.3402/jev.v3.23111 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Linares R, Tan S, Gounou C, Arraud N & Brisson AR High-speed centrifugation induces aggregation of extracellular vesicles. J. Extracell. Vesicles 4, 29509 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Gupta S et al. An improvised one-step sucrose cushion ultracentrifugation method for exosome isolation from culture supernatants of mesenchymal stem cells. Stem Cell Res. Ther. 9, 180 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Seo N et al. Distinguishing functional exosomes and other extracellular vesicles as a nucleic acid cargo by the anion-exchange method. J. Extracell. Vesicles 11, e12205 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Midekessa G et al. Zeta potential of extracellular vesicles: toward understanding the attributes that determine colloidal stability. ACS Omega 5, 16701–16710 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Van Deun J et al. Integrated dual-mode chromatography to enrich extracellular vesicles from plasma. Adv. Biosyst. 4, e1900310 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.Lewis JM et al. Integrated analysis of exosomal protein biomarkers on alternating current electrokinetic chips enables rapid detection of pancreatic cancer in patient blood. ACS Nano 12, 3311–3320 (2018). [DOI] [PubMed] [Google Scholar]
- 80.Multia E, Tear CJY, Palviainen M, Siljander P & Riekkola M-L Fast isolation of highly specific population of platelet-derived extracellular vesicles from blood plasma by affinity monolithic column, immobilized with anti-human CD61 antibody. Anal. Chim. Acta 1091, 160–168 (2019). [DOI] [PubMed] [Google Scholar]
- 81.Mathieu M et al. Specificities of exosome versus small ectosome secretion revealed by live intracellular tracking of CD63 and CD9. Nat. Commun. 12, 4389 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Clayton A et al. Analysis of antigen presenting cell derived exosomes, based on immuno-magnetic isolation and flow cytometry. J. Immunol. Methods 247, 163–174 (2001). [DOI] [PubMed] [Google Scholar]
- 83.Sharma P et al. Immunoaffinity-based isolation of melanoma cell-derived exosomes from plasma of patients with melanoma. J. Extracell. Vesicles 7, 1435138 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Onódi Z et al. Isolation of high-purity extracellular vesicles by the combination of iodixanol density gradient ultracentrifugation and bind-elute chromatography from blood plasma. Front. Physiol. 9, 1479 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Willms E et al. Cells release subpopulations of exosomes with distinct molecular and biological properties. Sci. Rep. 6, 22519 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86.Guan S et al. Size-dependent sub-proteome analysis of urinary exosomes. Anal. Bioanal. Chem. 411, 4141–4149 (2019). [DOI] [PubMed] [Google Scholar]
- 87.Ashby J et al. Distribution profiling of circulating microRNAs in serum. Anal. Chem. 86, 9343–9349 (2014). [DOI] [PubMed] [Google Scholar]
- 88.Kim YB, Yang JS, Lee GB & Moon MH Evaluation of exosome separation from human serum by frit-inlet asymmetrical flow field-flow fractionation and multiangle light scattering. Anal. Chim. Acta 1124, 137–145 (2020). [DOI] [PubMed] [Google Scholar]
- 89.Wu B et al. Separation and characterization of extracellular vesicles from human plasma by asymmetrical flow field-flow fractionation. Anal. Chim. Acta 1127, 234–245 (2020). [DOI] [PubMed] [Google Scholar]
- 90.R, C. et al. Subpopulations of extracellular vesicles from human metastatic melanoma tissue identified by quantitative proteomics after optimized isolation. J. Extracell. Vesicles 9, 1722433 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Lázaro-Ibáñez E et al. DNA analysis of low- and high-density fractions defines heterogeneous subpopulations of small extracellular vesicles based on their DNA cargo and topology. J. Extracell. Vesicles 8, 1656993 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.Lässer C et al. Two distinct extracellular RNA signatures released by a single cell type identified by microarray and next-generation sequencing. RNA Biol. 14, 58–72 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 93.Martin-Jaular L et al. Unbiased proteomic profiling of host cell extracellular vesicle composition and dynamics upon HIV-1 infection. EMBO J. 40, e105492 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 94.Cantin R, Diou J, Bélanger D, Tremblay AM & Gilbert C Discrimination between exosomes and HIV-1: purification of both vesicles from cell-free supernatants. J. Immunol. Methods 338, 21–30 (2008). [DOI] [PubMed] [Google Scholar]
- 95.Liao Z et al. Acetylcholinesterase is not a generic marker of extracellular vesicles. J. Extracell. Vesicles 8, 1628592 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 96.Tauro BJ et al. Two distinct populations of exosomes are released from LIM1863 colon carcinoma cell-derived organoids. Mol. Cell. Proteom. 12, 587–598 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 97.Barreiro K et al. Urinary extracellular vesicles: assessment of pre-analytical variables and development of a quality control with focus on transcriptomic biomarker research. J. Extracell. Vesicles 10, e12158 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 98.Gelibter S et al. The impact of storage on extracellular vesicles: a systematic study. J. Extracell. Vesicles 11, e12162 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99.Görgens A et al. Identification of storage conditions stabilizing extracellular vesicles preparations. J. Extracell. Vesicles 11, e12238 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 100.Trenkenschuh E et al. Enhancing the stabilization potential of lyophilization for extracellular vesicles. Adv. Healthc. Mater. 11, e2100538 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Rupert DLM, Claudio V, Lässer C & Bally M Methods for the physical characterization and quantification of extracellular vesicles in biological samples. Biochim. Biophys. Acta Gen. Subj. 1861, 3164–3179 (2017). [DOI] [PubMed] [Google Scholar]
- 102.Welsh JA et al. Towards defining reference materials for measuring extracellular vesicle refractive index, epitope abundance, size and concentration. J. Extracell. Vesicles 9, 1816641 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 103.Welsh JA et al. MPAPASS software enables stitched multiplex, multidimensional EV repertoire analysis and a standard framework for reporting bead-based assays. Cell Rep. Methods 2, 100136 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 104.van der Pol E et al. Particle size distribution of exosomes and microvesicles determined by transmission electron microscopy, flow cytometry, nanoparticle tracking analysis, and resistive pulse sensing. J. Thromb. Haemost. 12, 1182–1192 (2014). [DOI] [PubMed] [Google Scholar]
- 105.Vogel R et al. Measuring particle concentration of multimodal synthetic reference materials and extracellular vesicles with orthogonal techniques: who is up to the challenge? J. Extracell. Vesicles 10, e12052 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 106.Gardiner C et al. Measurement of refractive index by nanoparticle tracking analysis reveals heterogeneity in extracellular vesicles. J. Extracell. Vesicles 10.3402/jev.v3.25361 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 107.Yuana Y et al. Cryo-electron microscopy of extracellular vesicles in fresh plasma. J. Extracell. Vesicles 2, 21494 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 108.Höög JL & Lötvall J Diversity of extracellular vesicles in human ejaculates revealed by cryo-electron microscopy. J. Extracell. Vesicles 4, 28680 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 109.Poliakov A, Spilman M, Dokland T, Amling CL & Mobley JA Structural heterogeneity and protein composition of exosome-like vesicles (prostasomes) in human semen. Prostate 69, 159–167 (2009). [DOI] [PubMed] [Google Scholar]
- 110.Broad K et al. Unraveling multilayered extracellular vesicles: speculation on cause. J. Extracell. Vesicles 12, e12309 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 111.Mathivanan S et al. Proteomics analysis of A33 immunoaffinity-purified exosomes released from the human colon tumor cell line LIM1215 reveals a tissue-specific protein signature. Mol. Cell. Proteom. 9, 197–208 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 112.Nanou A, Zeune LL, Bidard F-C, Pierga J-Y & Terstappen LWMM HER2 expression on tumor-derived extracellular vesicles and circulating tumor cells in metastatic breast cancer. Breast Cancer Res. 22, 86 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 113.Vietri M, Radulovic M & Stenmark H The many functions of ESCRTs. Nat. Rev. Mol. Cell Biol. 21, 25–42 (2020). [DOI] [PubMed] [Google Scholar]
- 114.Wei D et al. RAB31 marks and controls an ESCRT-independent exosome pathway. Cell Res. 31, 157–177 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 115.Baietti MF et al. Syndecan-syntenin-ALIX regulates the biogenesis of exosomes. Nat. Cell Biol. 14, 677–685 (2012). [DOI] [PubMed] [Google Scholar]
- 116.Nabhan JF, Hu R, Oh RS, Cohen SN & Lu Q Formation and release of arrestin domain-containing protein 1-mediated microvesicles (ARMMs) at plasma membrane by recruitment of TSG101 protein. Proc. Natl Acad. Sci. USA 109, 4146–4151 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 117.Cai Q et al. Plants send small RNAs in extracellular vesicles to fungal pathogen to silence virulence genes. Science 360, 1126–1129 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 118.Rutter BD & Innes RW Growing pains: addressing the pitfalls of plant extracellular vesicle research. New Phytol. 228, 1505–1510 (2020). [DOI] [PubMed] [Google Scholar]
- 119.Driedonks TAP et al. Y-RNA subtype ratios in plasma extracellular vesicles are cell type-specific and are candidate biomarkers for inflammatory diseases. J. Extracell. Vesicles 9, 1764213 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 120.Zhou X et al. MitoEVs: a new player in multiple disease pathology and treatment. J. Extracell. Vesicles 12, e12320 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 121.Barman B et al. VAP-A and its binding partner CERT drive biogenesis of RNA-containing extracellular vesicles at ER membrane contact sites. Dev. Cell 57, 974–994.e8 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 122.Arya SB, Chen S, Jordan-Javed F & Parent CA Ceramide-rich microdomains facilitate nuclear envelope budding for non-conventional exosome formation. Nat. Cell Biol. 24, 1019–1028 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 123.O’Brien K, Breyne K, Ughetto S, Laurent LC & Breakefield XO RNA delivery by extracellular vesicles in mammalian cells and its applications. Nat. Rev. Mol. Cell Biol. 21, 585–606 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 124.Hermann S et al. Transcriptomic profiling of cell-free and vesicular microRNAs from matched arterial and venous sera. J. Extracell. Vesicles 8, 1670935 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 125.Love MI, Huber W & Anders S Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 126.McDermaid A, Monier B, Zhao J, Liu B & Ma Q Interpretation of differential gene expression results of RNA-seq data: review and integration. Brief. Bioinform 20, 2044–2054 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 127.Tyanova S & Cox J Perseus: a bioinformatics platform for integrative analysis of proteomics data in cancer research. Methods Mol. Biol. 1711, 133–148 (2018). [DOI] [PubMed] [Google Scholar]
- 128.Taylor CF et al. The minimum information about a proteomics experiment (MIAPE). Nat. Biotechnol. 25, 887–893 (2007). [DOI] [PubMed] [Google Scholar]
- 129.Li S et al. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat. Biotechnol. 32, 888–895 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 130.Bustin SA et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009). [DOI] [PubMed] [Google Scholar]
- 131.Verweij FJ et al. The power of imaging to understand extracellular vesicle biology in vivo. Nat. Methods 10.1038/s41592-021-01206-3 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 132.Zomer A et al. In vivo imaging reveals extracellular vesicle-mediated phenocopying of metastatic behavior. Cell 161, 1046–1057 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 133.Wu AY-T et al. Multiresolution imaging using bioluminescence resonance energy transfer identifies distinct biodistribution profiles of extracellular vesicles and exomeres with redirected tropism. Adv. Sci. 7, 2001467 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 134.Hoshino A et al. Tumour exosome integrins determine organotropic metastasis. Nature 527, 329–335 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 135.Magoling BJA et al. Membrane protein modification modulates big and small extracellular vesicle biodistribution and tumorigenic potential in breast cancers in vivo. Adv. Mater. 35, e2208966 (2023). [DOI] [PubMed] [Google Scholar]
- 136.Peinado H et al. Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat. Med. 18, 883–891 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 137.Kang M, Jordan V, Blenkiron C & Chamley LW Biodistribution of extracellular vesicles following administration into animals: a systematic review. J. Extracell. Vesicles 10, e12085 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 138.Gupta D, Zickler AM & El Andaloussi S Dosing extracellular vesicles. Adv. Drug Deliv. Rev. 178, 113961 (2021). [DOI] [PubMed] [Google Scholar]
- 139.Dhondt B, Lumen N, De Wever O & Hendrix A Preparation of multi-omics grade extracellular vesicles by density-based fractionation of urine. STAR Protoc. 1, 100073 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 140.Lam SM et al. A multi-omics investigation of the composition and function of extracellular vesicles along the temporal trajectory of COVID-19. Nat. Metab. 3, 909–922 (2021). [DOI] [PubMed] [Google Scholar]
- 141.Lischnig A, Bergqvist M, Ochiya T & Lässer C Quantitative proteomics identifies proteins enriched in large and small extracellular vesicles. Mol. Cell. Proteom. 21, 100273 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 142.McKiernan J et al. A novel urine exosome gene expression assay to predict high-grade prostate cancer at initial biopsy. JAMA Oncol. 2, 882–889 (2016). [DOI] [PubMed] [Google Scholar]
- 143.McKiernan J et al. A prospective adaptive utility trial to validate performance of a novel urine exosome gene expression assay to predict high-grade prostate cancer in patients with prostate-specific antigen 2–10 ng/ml at initial biopsy. Eur. Urol. 74, 731–738 (2018). [DOI] [PubMed] [Google Scholar]
- 144.Kordelas L et al. MSC-derived exosomes: a novel tool to treat therapy-refractory graft-versus-host disease. Leukemia 28, 970–973 (2014). [DOI] [PubMed] [Google Scholar]
- 145.Nassar W et al. Umbilical cord mesenchymal stem cells derived extracellular vesicles can safely ameliorate the progression of chronic kidney diseases. Biomater. Res. 20, 21 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 146.Guo S-C et al. Exosomes derived from platelet-rich plasma promote the re-epithelization of chronic cutaneous wounds via activation of YAP in a diabetic rat model. Theranostics 7, 81–96 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 147.Dai S et al. Phase I clinical trial of autologous ascites-derived exosomes combined with GM-CSF for colorectal cancer. Mol. Ther. J. Am. Soc. Gene Ther. 16, 782–790 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 148.Petousis-Harris H et al. Effectiveness of a group B outer membrane vesicle meningococcal vaccine against gonorrhoea in New Zealand: a retrospective case-control study. Lancet. 390, 1603–1610 (2017). [DOI] [PubMed] [Google Scholar]
- 149.Dooley K et al. A versatile platform for generating engineered extracellular vesicles with defined therapeutic properties. Mol. Ther. J. Am. Soc. Gene Ther. 29, 1729–1743 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 150.Wang Q et al. ARMMs as a versatile platform for intracellular delivery of macromolecules. Nat. Commun. 9, 960 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 151.Sedlik C et al. Different immunogenicity but similar antitumor efficacy of two DNA vaccines coding for an antigen secreted in different membrane vesicle-associated forms. J. Extracell. Vesicles 3, 24646 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 152.Alvarez-Erviti L et al. Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes. Nat. Biotechnol. 29, 341–345 (2011). [DOI] [PubMed] [Google Scholar]
- 153.Meyer C et al. Pseudotyping exosomes for enhanced protein delivery in mammalian cells. Int. J. Nanomed. 12, 3153–3170 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 154.Pi F et al. Nanoparticle orientation to control RNA loading and ligand display on extracellular vesicles for cancer regression. Nat. Nanotechnol. 13, 82–89 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 155.Witwer KW & Wolfram J Extracellular vesicles versus synthetic nanoparticles for drug delivery. Nat. Rev. Mater. 6, 103–106 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 156.Van Deun J et al. Feasibility of mechanical extrusion to coat nanoparticles with extracellular vesicle membranes. Cells 9, 1797 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 157.Gupta D, Wiklander OPB, Wood MJA & El-Andaloussi S Biodistribution of therapeutic extracellular vesicles. Extracell. Vesicles Circ. Nucleic Acids 4, 170–190 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 158.Driedonks T et al. Pharmacokinetics and biodistribution of extracellular vesicles administered intravenously and intranasally to Macaca nemestrina. J. Extracell. Biol. 1, e59 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 159.Mattoli S & Schmidt M Investigational use of mesenchymal stem/stromal cells and their secretome as add-on therapy in severe respiratory virus infections: challenges and perspectives. Adv. Ther. 10.1007/s12325-023-02507-z (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 160.Shapira S et al. A novel platform for attenuating immune hyperactivity using EXO-CD24 in COVID-19 and beyond. EMBO Mol. Med. 14, e15997 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 161.Kwon HH et al. Combination treatment with human adipose tissue stem cell-derived exosomes and fractional CO2 laser for acne scars: a 12-week prospective, double-blind, randomized, split-face study. Acta Derm. Venereol. 100, adv00310 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 162.Witwer KW et al. Updating MISEV: evolving the minimal requirements for studies of extracellular vesicles. J. Extracell. Vesicles 10, e12182 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 163.Welsh JA et al. MIFlowCyt-EV: a framework for standardized reporting of extracellular vesicle flow cytometry experiments. J. Extracell. Vesicles 9, 1713526 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 164.Kalra H et al. Vesiclepedia: a compendium for extracellular vesicles with continuous community annotation. PLoS Biol. 10, e1001450 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 165.Keerthikumar S et al. ExoCarta: a web-based compendium of exosomal cargo. J. Mol. Biol. 428, 688–692 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 166.Kim D-K, Lee J, Simpson RJ, Lötvall J & Gho YS EVpedia: a community web resource for prokaryotic and eukaryotic extracellular vesicles research. Semin. Cell Dev. Biol. 40, 4–7 (2015). [DOI] [PubMed] [Google Scholar]
- 167.Lai H et al. exoRBase 2.0: an atlas of mRNA, lncRNA and circRNA in extracellular vesicles from human biofluids. Nucleic Acids Res. 50, D118–D128 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 168.Liu T et al. EVmiRNA: a database of miRNA profiling in extracellular vesicles. Nucleic Acids Res. 47, D89–D93 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 169.Tsering T et al. EV-ADD, a database for EV-associated DNA in human liquid biopsy samples. J. Extracell. Vesicles 11, e12270 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 170.Roux Q, Van Deun J, Dedeyne S & Hendrix A The EV-TRACK summary add-on: integration of experimental information in databases to ensure comprehensive interpretation of biological knowledge on extracellular vesicles. J. Extracell. Vesicles 9, 1699367 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 171.Pathan M et al. Vesiclepedia 2019: a compendium of RNA, proteins, lipids and metabolites in extracellular vesicles. Nucleic Acids Res. 47, D516–D519 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 172.Perez-Riverol Y et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 173.Craig R, Cortens JP & Beavis RC Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 3, 1234–1242 (2004). [DOI] [PubMed] [Google Scholar]
- 174.Choi M et al. MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets. Nat. Methods 17, 981–984 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 175.Deutsch EW, Lam H & Aebersold R PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429–434 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 176.Das S et al. The extracellular RNA communication consortium: establishing foundational knowledge and technologies for extracellular RNA research. Cell 177, 231–242 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 177.Edgar R, Domrachev M & Lash AE Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 178.Rozowsky J et al. exceRpt: a comprehensive analytic platform for extracellular RNA profiling. Cell Syst. 8, 352–357.e3 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 179.Geeurickx E et al. Recombinant extracellular vesicles as biological reference material for method development, data normalization and assessment of (pre-)analytical variables. Nat. Protoc. 16, 603–633 (2021). [DOI] [PubMed] [Google Scholar]
- 180.van Royen ME et al. The quick reference card ‘Storage of urinary EVs’ — a practical guideline tool for research and clinical laboratories. J. Extracell. Vesicles 12, e12286 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 181.Veerman RE et al. Molecular evaluation of five different isolation methods for extracellular vesicles reveals different clinical applicability and subcellular origin. J. Extracell. Vesicles 10, e12128 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 182.Maroto R et al. Effects of storage temperature on airway exosome integrity for diagnostic and functional analyses. J. Extracell. Vesicles 6, 1359478 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 183.Bebesi T et al. Storage conditions determine the characteristics of red blood cell derived extracellular vesicles. Sci. Rep. 12, 977 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 184.Yuan F, Li Y-M & Wang Z Preserving extracellular vesicles for biomedical applications: consideration of storage stability before and after isolation. Drug Deliv. 28, 1501–1509 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 185.van de Wakker SI et al. Influence of short term storage conditions, concentration methods and excipients on extracellular vesicle recovery and function. Eur. J. Pharm. Biopharm. 170, 59–69 (2022). [DOI] [PubMed] [Google Scholar]
- 186.Clayton A et al. Considerations towards a roadmap for collection, handling and storage of blood extracellular vesicles. J. Extracell. Vesicles 8, 1647027 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 187.Karimi N, Dalirfardouei R, Dias T, Lötvall J & Lässer C Tetraspanins distinguish separate extracellular vesicle subpopulations in human serum and plasma — contributions of platelet extracellular vesicles in plasma samples. J. Extracell. Vesicles 11, e12213 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 188.Bettin B et al. Removal of platelets from blood plasma to improve the quality of extracellular vesicle research. J. Thromb. Haemost. 20, 2679–2685 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 189.Dauros Singorenko P et al. Isolation of membrane vesicles from prokaryotes: a technical and biological comparison reveals heterogeneity. J. Extracell. Vesicles 6, 1324731 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 190.Abdel-Rahman MA et al. Efficient homofermentative l-(+)-lactic acid production from xylose by a novel lactic acid bacterium, Enterococcus mundtii QU 25. Appl. Environ. Microbiol. 77, 1892–1895 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 191.Van Deun J & Hendrix A, EV-TRACK consortium. Is your article EV-TRACKed? J. Extracell. Vesicles 6, 1379835 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 192.Mathivanan S & Simpson RJ ExoCarta: a compendium of exosomal proteins and RNA. Proteomics 9, 4997–5000 (2009). [DOI] [PubMed] [Google Scholar]
- 193.Mathivanan S, Fahner CJ, Reid GE & Simpson RJ ExoCarta 2012: database of exosomal proteins, RNA and lipids. Nucleic Acids Res. 40, D1241–D1244 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 194.Kim D-K et al. EVpedia: an integrated database of high-throughput data for systemic analyses of extracellular vesicles. J. Extracell. Vesicles 2, 20384 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 195.Kim D-K et al. EVpedia: a community web portal for extracellular vesicles research. Bioinformatics 31, 933–939 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 196.Li S et al. exoRBase: a database of circRNA, lncRNA and mRNA in human blood exosomes. Nucleic Acids Res. 46, D106–D112 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 197.Martens L et al. PRIDE: the proteomics identifications database. Proteomics 5, 3537–3545 (2005). [DOI] [PubMed] [Google Scholar]
- 198.Jones P et al. PRIDE: new developments and new datasets. Nucleic Acids Res. 36, D878–D883 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 199.Vizcaíno JA et al. The proteomics identifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063–D1069 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 200.Vizcaíno JA et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44, D447–D456 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 201.Perez-Riverol Y et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 202.Deutsch EW et al. Human plasma PeptideAtlas. Proteomics 5, 3497–3500 (2005). [DOI] [PubMed] [Google Scholar]
- 203.Desiere F et al. The PeptideAtlas project. Nucleic Acids Res. 34, D655–D658 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 204.Murillo OD et al. exRNA Atlas analysis reveals distinct extracellular RNA cargo types and their carriers present across human biofluids. Cell 177, 463–477.e15 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 205.Lee JA et al. MIFlowCyt: the minimum information about a flow cytometry experiment. Cytometry A 73, 926–930 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 206.Huggett JF et al. The digital MIQE guidelines: minimum information for publication of quantitative digital PCR experiments. Clin. Chem. 59, 892–902 (2013). [DOI] [PubMed] [Google Scholar]
- 207.dMIQE Group & Huggett JF. The digital MIQE guidelines update: minimum information for publication of quantitative digital PCR experiments for 2020. Clin. Chem. 66, 1012–1029 (2020). [DOI] [PubMed] [Google Scholar]