Article Figures & Data
Figures
- Fig. 1
Schematic representation of subpopulations of extracellular vesicles and their biogenesis. Exosomes are derived from different multivesicular bodies populations and from amphisomes (Klumperman and Raposo, 2014; Jeppesen et al., 2019). Ectosomes are generated by blebbing of the plasma membrane (El Andaloussi et al., 2013). Apoptotic bodies are a subpopulation of ectosomes that are formed by blebbing and fragmentation during apoptosis (Martin et al., 1995). Exopheres are large ectosomes that contain dysfunctional mitochondria (Nicolás-Ávila et al., 2020). Large oncosomes are large ectosomes purposely released by tumor cells (Minciacchi et al., 2015). Intracellular membrane–derived ectosomes are derived from the endoplasmic reticulum and squeeze through pores in the plasma membrane (Sun et al., 2021). Arrestin-domain-containing protein 1 (ARRDC1)-mediated microvesicles (ARMMs) are a distinct type of small ectosomes (Nabhan et al., 2012). Midbody remnants are vesicles formed between cells during cell division (Rai et al., 2021). Migrasomes are formed at the end of long retraction fibers and contain smaller vesicles. When the fibers break during migration, the smaller vesicles are released (Ma et al., 2015). Protrusion-derived ectosomes are released during movement of filopodia and lamellipodia (Hu et al., 2019).
- Fig. 2
Schematic representation of different EV separation techniques. Separation of EV subtypes based on size differences: (A) ultracentrifugation, (B) density gradient centrifugation, (C) size exclusion chromatography, (D) asymmetric-flow field-flow fractionation, (E) ultrafiltration, and (F) microfluidic separation. Separation of EV subtypes based on differential marker expression: (G) affinity separation. Separation of EV subtypes based on charge differences: (H) anion exchange chromatography.
- Fig. 3
Technical challenges for isolation and functional comparison of EV subtypes. There are several technical challenges regarding functional EV heterogeneity studies. (A) Although most EV subtype separation methods are based on differences in size, the correlation between EV size and biogenesis is largely absent. (B) Specific markers to capture unique EV subpopulations with affinity-based methods are unknown. (C) As EV subtypes may differ in size and content, it is challenging to perform comparative quantification studies. (D) Additional separation steps necessary to purify EV subpopulations have a negative impact on EV recovery. (E) Nonvesicular coisolates, including lipoproteins, histones, proteasomes and other large protein complexes, may contaminate specific EV subtypes to different degrees.
- Fig. 4
Translational challenges regarding EV heterogeneity. (A) The mechanisms through which EVs exert their regenerative functions are not well understood. (B) It is currently unknown if EV subtypes function in an additive, synergistic, or counteractive manner. (C) Batch-to-batch variability in the production of different EV subtypes due to clonal drift or environmental factors impacts EV efficacy. (D) It is highly challenging to trace the biogenetic origin of EVs after their secretion.
- Fig. 5
Future perspectives for the use of EV heterogeneity studies to assist the development of EV therapeutics. (A) Selection of functional active EV subtypes will most likely lead to increased potency of EV treatments. (B) Stimulation of the production of functionally active EV subpopulations with external stimuli will increase the potency of EV preparations. (C) Selection of the most suitable EV subpopulation for cargo loading will lead to a more tailored, synergistic approach.
Tables
Population Subpopulation Size Suggested Marker or Cargo Reference Exosomes 30–150 nm Syntenin, LAMP1/2, ALIX, TSG101, CD63 (Andreu and Yáñez-Mó, 2014; Greening et al., 2017; Mathieu et al., 2021) Ectosomes 50–10,000 nm Annexin A1/2, basigin, SLC3A2 (Jeppesen et al., 2019; Mathieu et al., 2021) Apoptotic bodies 50–2000 nm ICAM-3, phosphatidylserine, histones, mitochondrial content (Fadok et al., 1992; Martínez and Freyssinet, 2001; Taylor et al., 2008; Torr et al., 2012; Atkin-Smith et al., 2015) Exopheres ∼3.5 μm Phosphatidylserine, LC3, mitochondrial content (Melentijevic et al., 2017; Nicolás-Ávila et al., 2020) Large oncosomes 1–10 μm Cytokeratin 18, caveolin-1, ARF6, GAPDH, HSPA5 (Di Vizio et al., 2012; Minciacchi et al., 2015) Intracellular membrane-derived ectosomes 50–120 nm Phosphatidylserine, cytokines (Sun et al., 2021) ARMMs ∼50 nm TSG101, VSP4 (Nabhan et al., 2012) Midbody remnants 200–600 nm Microtubules (Rai et al., 2021) Migrasomes 50–100 nm TSPAN4, TSPAN7, cholesterol, integrins (Huang et al., 2019; Jiang et al., 2019) Protrusion-derived ectosomes ∼30 nm Cholesterol, HSP90, cytoskeletal proteins, prominin-1 (Hu et al., 2019; Nishimura et al., 2021; Hurbain et al., 2022; D’Angelo et al., 2023) Extracellular particles (biogenesis unknown) Exomeres HSP90, argonaute, amyloid precursor proteins (Zhang et al., 2018; Zhang et al., 2019) Supermeres TGF-βI, HSPA13, enolase 2 (Zhang et al., 2021) Separation Type Time Purity Scalability Recovery Suitability for Functional Heterogeneity Studies Ultracentrifugation Size/density — + +/− + + Density gradient centrifugation Density/size — ++ – — + Size exclusion chromatography Size +/− + +/− +/− + Asymmetric-flow field-flow fractionation Size, charge +/− + +/− ++ ++ Ultrafiltration Size + +/− + + + Microfluidic techniques Size + + – + – Affinity-based methods Specific markers +/− ++ — — – Anion exchange chromatography Charge +/− + +/− + + +, positive trait; −, negative trait.
Therapeutic Target EV Source Non-EV Removal Fractionsa EV Marker Enrichment Reference Kidney
In vitro: murine tubular epithelial cells
In vivo: acute kidney injury mouse modelBone marrow MSCs 3000 × g tEV-100K
lEV-10K
sEV-100KlEV: CD29, CD44, CD73, CD105
sEV: CD63, CD9, CD29, CD44, CD73, CD105(Bruno et al., 2017) Kidney
In vitro: human umbilical vein endothelial cells
In vivo: acute kidney injury mouse modelEndothelial colony-forming cells 2500 × g lEV-20K
sEV-100KlEV: Caveolin-1
sEV: TSG101, CD63(Burger et al., 2015) Kidney
In vitro: murine tubular epithelial cellsBone marrow MSCs 1500 × g HD-EV
MD-EV
LD-EVHD-EV: CD29, annexin A2
MD-EV: CD63, CD81, CD29, integrin α5, annexin A2, HLA-1
LD-EV: none(Collino et al., 2017) Immune cells
In vitro: T- and B- cells
In vivo: Collagen-induced arthritis mouse modelBone marrow MSCs 300 + 2500 × g
Filter 0.22 μmtEV-100K
lEV-18K
sEV-100KlEV: Sca-1, CD44, CD29
sEV: CD9 and CD81, HSP70, TSG101, ALIX(Cosenza et al., 2018) Cartilage
In vitro: murine chondrocytes
In vivo: Collagen-induced arthritis mouse modelMurine bone marrow MSCs 300 + 2500 × g
Filter 0.22 μmlEV-18K
sEV-100KlEV: CD29, CD44 and Sca-1
sEV: CD9, CD81(Cosenza et al., 2017) Neural tissue
In vitro: cortical neuron culture and dorsal root ganglia culturesMenstrual MSCs 200 + 2000 × g tEV-100K
lEV-10K
sEV-100KlEV: CD73, CD90, CD105, integrin α1, CD63, HLA-1
sEV: CD63, TSG101, HSP70, HSP90(Lopez-Verrilli et al., 2016) Neural tissue
In vitro: dopaminergic neuron (suspension-like) cultureSHEDs cultured in a bioreactor 300 + 2000 × g lEV-20K
sEV-100K(Jarmalavičiūtė et al., 2015) Pancreas
In vivo: diabetes mouse modelPathfinder cells 1000 × g lEV-16K
sEV-120KlEV: Integrin-β1, CD40
sEV: CD9, CD63, CD81, TSG101, Rab5B, integrin β1(McGuinness et al., 2016) Endothelium
In vitro: human aortic endothelial cellsPlasma samples of chronic coronary syndrome patients and healthy controls Filter 1 μm
Filter 0.2 μmlEV-16K
sEV-100KlEV: TSG101
sEV: TSG101, CD63, CD81(Kränkel et al., 2020) aEV fractions showing significant regenerative effects are marked in bold, whereas damaging effects are marked in italic and underlined. Total EV (tEV).
