Introduction: Quality Control as a Core Technical Barrier in the CMC-Driven Era

In our previous article, we provided a systematic analysis of the Guideline for Clinical Research Filing of New Technologies for Cell Component and Derivative Therapies (Version 1) (hereafter referred to as the Guideline), covering its regulatory background, key concepts, process development requirements, and nonclinical evaluation framework. As discussed, the Guideline marks an important shift towards a CMC-driven era for the extracellular vesicle (EV) industry.

Within this broader CMC framework, establishing a robust quality research and quality control system is one of the most technically challenging aspects of EV product development—and one that can ultimately determine whether a product is able to meet regulatory expectations.

The regulatory message is clear: a high particle count does not necessarily mean a high-quality EV product. No single analytical parameter is sufficient to support the release of a therapeutic EV product. Meaningful quality control instead requires a multidimensional and multi-layered assessment covering identity, physicochemical properties, potency, purity, and safety.

As international research into EV quality evaluation continues to develop, a 2026 review published in the Journal of Extracellular Vesicles by researchers from the National Institutes for Food and Drug Control (NIFDC), Quality Evaluation Considerations for Stem Cell-Derived Extracellular Vesicles-Based Therapeutic Products in China (Na et al., 2026), provides one of the most systematic quality assessment frameworks for stem cell-derived EV products to date.

Notably, the framework proposed by Na et al. (2026) closely aligns with the regulatory framework established in Section 5.5, “Quality Research”, of the Guideline. Both emphasise core quality dimensions including identity, purity and impurities, structural integrity, biological activity and potency, and stability, while also remaining broadly consistent with the overall framework of ICH quality principles (National Health Commission, 2026; Na et al., 2026).

At the same time, Na et al. (2026) extend the regulatory requirements of the Guideline across several important dimensions. These include cell bank management as the starting point of quality control, the expansion of identification strategies from general EV markers to cell-source-specific markers, and emerging quality considerations such as protein corona, membrane fluidity, tumorigenicity, and immunogenicity, which are not discussed in detail in the Guideline.

This article takes the framework proposed by Na et al. (2026) as its primary reference, while incorporating the regulatory requirements of the Guideline and international recommendations such as MISEV2023. It focuses on the first half of the quality control framework for stem cell-derived EV products, covering cell bank management, EV identification, physicochemical characterisation, potency, purity, membrane integrity, safety, and stability.

The quality assessment framework discussed in this article does not represent formal regulatory guidance. It is intended as a technical reference for researchers and developers. Specific quality standards should be scientifically designed and validated according to the characteristics of the product, manufacturing process, and intended indication.

1. The Starting Point of Quality Control: Cell Bank Management

A fundamental, yet often underestimated, principle in the quality control of stem cell-derived EV products is that EV quality ultimately reflects the biological characteristics and functional state of the cells from which the EVs are produced.

This principle is already embedded within the process development framework of the Guideline. Section 5.2 requires the establishment of a three-tier cell bank system comprising a seed cell bank, master cell bank (MCB), and working cell bank (WCB). It also requires passage stability studies to be conducted before cell bank establishment, so that the passage limits and bank size for each level of the cell bank can be determined (National Health Commission, 2026).

Na et al. (2026) take this concept further by explicitly identifying stem cells as the primary raw material for EV production. The composition and biological properties of EVs can, to a considerable extent, reflect the specific characteristics and functional state of their parental cells (Na et al., 2026).

This leads to a critical principle for EV manufacturing: once quality issues arise at the level of the cell bank, even highly controlled downstream purification processes cannot compensate for defects originating from the source cells.

1.1 Cell Bank Management and Quality Control Requirements

Before establishing a cell bank for EV production, the suitability of the stem cells should be comprehensively evaluated. This includes assessing the average EV secretion level and determining whether EV yield remains stable across different passage numbers (Na et al., 2026).

Stem cell banks are subject to a range of quality assessments under relevant regulatory and pharmacopoeial frameworks. These include cell identification, sterility, mycoplasma testing, endogenous and exogenous virus testing, biological function evaluation, cell viability, tumorigenicity assessment, and genomic stability testing. Together, these assessments provide the basis for establishing a reliable and traceable cellular source for EV production (Na et al., 2026).

For cells that have undergone genetic modification or extensive in vitro passage, additional attention should be given to potential morphological, phenotypic, and genetic changes (Yang et al., 2018).

Na et al. (2026) recommend comprehensive genomic stability assessments during cell bank establishment, including karyotype analysis, whole-exome sequencing (WES), and transcriptomic analysis. Importantly, the passage number evaluated should be no lower than the maximum passage number expected to be reached during the EV production process (Na et al., 2026).

For MSCs derived from multiple donors, the choice between maintaining individual donor-derived cell populations and adopting a pooled-donor strategy also requires careful consideration. Some studies have suggested that pooling MSCs from multiple donors can reduce individual donor variability and provide a more consistent cellular profile (Kannan et al., 2024). However, other studies have found that although pooling MSC-derived EV samples may reduce measurement variability, it can also obscure the biological significance of EV subpopulations associated with individual donors (Raj et al., 2012).

More recent evidence suggests that pooling MSCs from multiple donors does not necessarily reduce batch-to-batch variability. Instead, it may make donor-specific differences more difficult to distinguish (Kukaj et al., 2025).

For this reason, Na et al. (2026) place particular emphasis on ensuring that EVs originate from a clearly defined and traceable cellular source, which can help support functional consistency. Short tandem repeat (STR) profiling may also be used to confirm the cellular origin of EVs and exclude contamination from other donors or human cell sources (Tanudisastro et al., 2024).

1.2 Quality Considerations for Different Stem Cell Sources

Different types of stem cells also introduce specific quality control considerations for EV production.

For pluripotent stem cell (PSC)-derived EVs, spontaneous differentiation may result in EVs originating from incompletely differentiated or non-target differentiated cells. These cells may carry tumorigenic factors, making it important to identify the proportion of EVs derived from non-target cell populations and establish appropriate controls during the EV harvesting process (Na et al., 2026).

For genetically modified stem cell-derived EVs, developers should pay particular attention to whether the genetic modification has resulted in genetic instability or unintended changes in cellular function. The potential tumorigenic risk should be evaluated using both in vitro and in vivo approaches (Na et al., 2026).

These considerations highlight a key point in EV quality control: quality does not begin at the point of EV isolation. It begins with the identity, stability, and functional state of the cells used to produce the EVs.

2. EV Identification: From General Markers to Cell-Source-Specific Markers

Once the cellular source has been established and controlled, the next challenge is to confirm the identity of the resulting EV product.

For therapeutic EV development, simply demonstrating the presence of particles is not sufficient. The identification strategy needs to establish that the particles possess the expected characteristics of EVs and, where necessary, that they can be linked to the intended cellular source.

2.1 A Multi-Parameter Identification Strategy Under the MISEV2023 Framework

Section 5.5.1 of the Guideline requires comprehensive product identification using both morphological analysis and multi-parameter biochemical characterisation.

At the morphological level, transmission electron microscopy (TEM) is widely used to confirm the characteristic lipid bilayer structure of EVs.

For particle size distribution, techniques including nanoparticle tracking analysis (NTA), dynamic light scattering (DLS), tunable resistive pulse sensing (TRPS), and nano-flow cytometry (NanoFCM) each have their own detection principles and applicable ranges. These techniques can therefore be used as complementary methods to provide a more comprehensive assessment.

At the molecular level, the MISEV2023 framework recommends evaluating multiple categories of EV-associated proteins (Welsh et al., 2024). These include transmembrane or GPI-anchored proteins, such as the tetraspanins CD63, CD9, and CD81; cytosolic proteins, such as Flotillin-1 and PDCD6IP/Alix; transmembrane or fusion-associated proteins, such as integrins; extracellular matrix proteins; and negative markers that can indicate non-EV-derived contaminants, such as the endoplasmic reticulum protein calnexin and mitochondrial protein cytochrome C.

The Guideline similarly requires the detection of both positive markers, such as CD63, CD9, and CD81, and negative markers, such as calnexin and cytochrome C. This combination of positive and negative markers reflects the minimum identification strategy established under MISEV2018 and subsequently incorporated into the regulatory framework (National Health Commission, 2026; Théry et al., 2018).

However, Na et al. (2026) point out an important limitation of this general marker strategy: common EV markers do not necessarily provide sufficient information about the specific biological origin of an EV product.

CD63, CD9, and CD81 are widely expressed across different cell types. Their presence alone cannot establish donor identity, cell type, tissue origin, or genetic modification status. Yet these characteristics may be particularly important when developing therapeutic EV products from defined stem cell sources.

To address this limitation, Na et al. (2026) recommend developing targeted positive and negative marker panels based on the specific cellular source and manufacturing process.

For MSC-derived EVs, Nguyen et al. (2024) conducted multiplex bead-based flow cytometry across five laboratories and 11 different MSC-EV products. The study identified CD73, CD105, and CD44 as reliable positive markers, while CD11b, CD14, CD19, CD45, and CD79 were recommended as reliable negative markers (Nguyen et al., 2024).

For PSC-derived EVs, Chen et al. (2024) further identified PODXL and SSEA4 as potential specific positive markers. These markers demonstrated good specificity, with positivity rates exceeding 70% (Chen et al., 2024).

For EVs derived from genetically modified stem cells, it is also necessary to determine whether the intended genetic modification is present in the EV population and to quantitatively assess the proportion of EVs carrying the genetic modification (Na et al., 2026).

At present, there is no universally established threshold for marker positivity. Na et al. (2026) therefore recommend that developers establish internal testing standards based on multi-batch production data, preferably using products derived from the same donor source or manufactured using the same process.

Where a relationship between marker expression and biological activity can be demonstrated, the expression frequency of these markers may also be monitored as an indicator of product consistency (Na et al., 2026).

2.2 Morphological Characterisation: Direct Assessment of Structural Integrity

The Guideline requires particle size distribution testing and recommends NTA and other relevant analytical methods as important approaches for product characterisation (National Health Commission, 2026).

Na et al. (2026) note that EVs generally fall within a size range of approximately 30–150 nm, which is below the diffraction limit of conventional optical microscopy. As a result, morphological characterisation primarily relies on techniques such as TEM and cryogenic electron microscopy (cryo-EM) (Shao et al., 2018).

However, the interpretation of EV morphology requires caution.

The vacuum conditions and dehydration steps used during conventional electron microscopy sample preparation can cause EVs to appear “cup-shaped”. This morphology does not necessarily represent the native structure of EVs and may therefore lead to misinterpretation (Wu et al., 2015; Jeppesen et al., 2019).

For this reason, the key objective of morphological characterisation should be to confirm the presence and integrity of the lipid bilayer, rather than focusing solely on the apparent shape of the vesicles.

Na et al. (2026) further extend the Guideline's discussion of particle size by highlighting an important consideration: where a clear relationship between particle size distribution and biological function can be demonstrated, the concentration of EVs within specific size ranges, rather than simply the total particle count, may need to be considered as a quality standard.

Using orthogonal analytical approaches—including NTA, TRPS, and NanoFCM—can also help reduce the bias associated with the inherent limitations of individual analytical platforms (Arab et al., 2021).

This is particularly relevant because EV subpopulations with different particle sizes may have substantially different biological characteristics.

Proteomic studies have shown that EV subpopulations, including larger EVs in the range of 90–120 nm and smaller EVs in the range of 60–80 nm, can differ significantly in their N-glycosylation patterns, protein composition, and nucleic acid content (Zhang et al., 2018).

In addition, a recent study found that medium-sized and smaller sEV subpopulations derived from cardiac progenitor cells demonstrated stronger pro-angiogenic and anti-fibrotic activity, whereas the largest particle population showed no detectable functional activity (van de Wakker et al., 2024).

These findings suggest that particle size distribution may represent more than simply a physicochemical characterisation parameter. Where supported by product-specific evidence, it may also represent a quality attribute directly associated with biological activity.

This makes it important to investigate the relationship between particle size and biological function during the early stages of product development, rather than treating particle size as an isolated analytical parameter.

3. Physicochemical Properties: Zeta Potential, Protein Corona, and Membrane Fluidity

While identity and morphology establish fundamental characteristics of an EV product, physicochemical properties provide another layer of information regarding its structure, stability, and potential biological behaviour.

3.1 Zeta Potential: A Quality Dimension for Both Stability and Function

Zeta potential is an important parameter for evaluating the colloidal stability of EV suspensions. A higher absolute zeta potential indicates stronger electrostatic repulsion between particles, which can help prevent aggregation and maintain colloidal stability (Midekessa et al., 2020).

However, Na et al. (2026) suggest that the significance of zeta potential extends beyond physical stability.

The surface charge of EVs can affect their interaction with target cells. Positively charged EVs, for example, may interact more readily with negatively charged cell membranes, potentially promoting cellular uptake. Changes in zeta potential may therefore influence the binding between EVs and target cells (Dietz et al., 2023).

Zeta potential may also indirectly affect the loading efficiency of EV-associated RNA, proteins, and other molecules, potentially influencing their biological activity in vivo (Nakase et al., 2021).

Furthermore, recent research has reported a significant association between the expression levels of miR-30 and miR-155 and EV zeta potential (Mendivil-Alvarado et al., 2023).

Taken together, these findings indicate that zeta potential may serve not only as an indicator of EV physicochemical stability, but also as a potential quality attribute associated with biological function.

3.2 Protein Corona: An Underestimated Quality Control Dimension

When EVs come into contact with biological fluids, such as plasma or cell culture media, proteins present in the surrounding environment can adsorb onto their surface and form a protein corona (Panico et al., 2022).

Na et al. (2026) identify the protein corona as an important consideration because of its potential influence on several aspects of EV biological behaviour.

The composition of the protein corona can affect the immunological characteristics of EVs, potentially increasing or reducing their clearance by the immune system. It may also influence how EVs are recognised by target cells and subsequently internalised (Liam-Or et al., 2024).

Certain proteins within the corona may alter EV zeta potential, which can in turn affect signalling, cargo transport, and gene expression regulation (Wolf et al., 2022).

The protein corona may also provide a protective effect against enzymatic degradation, potentially improving EV stability (Heidarzadeh et al., 2023).

From a quality control perspective, however, the role of the protein corona remains complex.

Some developers use the most abundant proteins detected in EV preparations as indicators of batch consistency. Yet these proteins may remain relatively stable across different batches produced within the same cell culture system. As a result, they may mask variations arising from differences in cellular sources.

Given the potential influence of the protein corona on EV biological function, Na et al. (2026) recommend caution when considering whether protein corona characteristics should be formally defined as critical quality attributes (CQAs). At the current stage of scientific and analytical development, its potential impact on product quality nevertheless warrants further investigation.

3.3 Membrane Fluidity: A Functional Physicochemical Parameter

EV membrane fluidity influences the interaction and fusion between EV membranes and target cell membranes, and can therefore affect the efficiency of cargo delivery (Verweij et al., 2021).

Na et al. (2026) note that when EV membrane fluidity is low, fusion with target cell membranes may be incomplete, potentially reducing the efficiency of cargo delivery. Conversely, EVs with higher membrane fluidity may demonstrate greater capacity for cargo encapsulation and delivery, particularly for lipophilic small molecules.

Membrane fluidity can also be affected by processing and storage conditions. For example, exposure to DMSO has been reported to alter EV membrane fluidity and subsequently cause cytotoxicity in human umbilical vein endothelial cells (Mizuno et al., 2022).

Several techniques—including fluorescence recovery after photobleaching (FRAP), surface plasmon resonance, and single-molecule tracking—can be used to evaluate membrane fluidity (Verweij et al., 2021).

Although membrane fluidity has not yet been universally established as a release parameter, its potential relationship with cargo delivery and biological activity makes it a physicochemical characteristic worthy of systematic investigation during EV product development.

Conclusion

The quality evaluation of stem cell-derived EV products requires a systematic approach that begins with the source cells and extends through product identity and physicochemical characterisation. Cell bank management establishes the consistency, stability, and traceability of the starting material; identification strategies confirm the EV nature and, where required, the cellular origin of the product; and physicochemical characterisation provides further insight into particle size, structural integrity, surface properties, and membrane characteristics.

Importantly, these parameters should not be considered in isolation. The relevance of individual quality attributes depends on their demonstrated relationship with product consistency, biological activity, and ultimately clinical performance. This is particularly important for emerging parameters such as particle size distribution, zeta potential, protein corona, and membrane fluidity, for which their potential contribution to product quality continues to be investigated.

Nevertheless, comprehensive characterisation alone cannot establish the overall quality of a therapeutic EV product. A complete quality control strategy must also address whether the product possesses the intended biological activity, whether process- and product-related impurities are adequately controlled, whether the EV membrane and cargo remain intact, whether potential safety risks have been appropriately evaluated, and whether product quality can be maintained throughout its intended storage period.

These considerations form the basis for the second half of the quality evaluation framework. Part 2 will examine potency and functional activity, purity and impurity profiles, membrane integrity, safety assessment, stability studies, and the development of potential critical quality attributes (CQAs), further exploring how these elements can be integrated into a comprehensive quality control strategy for stem cell-derived EV products.

References

Adamo, G., Fierli, D., Romancino, D. P., Barreca, D., Picciotto, S., & Bongiovanni, A. (2025). Fluorescence-based assay to evaluate membrane integrity of extracellular vesicles. Frontiers in Bioengineering and Biotechnology, 12, 1302839. https://doi.org/10.3389/fbioe.2024.1302839
Ahmadian, M., Hosseini, S., Ghods, R., Ejtemaei Mehr, S., & Rezaei, F. (2024). Stability assessment of mesenchymal stem cell-derived extracellular vesicles: A systematic review. Journal of Extracellular Vesicles, 13(1), e12397. https://doi.org/10.1002/jev2.12397
Arab, T., Mallick, E. R., Huang, Y., Dong, L., Liao, Z., Zhao, Z., … Bhatt, D. L. (2021). Characterization of extracellular vesicles and synthetic nanoparticles with four orthogonal single-particle analysis platforms. Journal of Extracellular Vesicles, 10(6), e12079. https://doi.org/10.1002/jev2.12079
Babula, A., Mozdziak, P., Kulus, M., Ziemak, H., Sibiak, R., Piotrowska-Kempisty, H., & Kempisty, B. (2023). Endotoxins in cell biology and experimental medicine—Implications, sources, and biological effects. Cells, 12(7), 1015. https://doi.org/10.3390/cells12071015
Barragán-Martínez, C., Speck-Hernández, C. A., Montoya-Ortiz, G., Mantilla, R. D., Anaya, J. M., & Rojas-Villarraga, A. (2012). Organic solvents as risk factor for autoimmune diseases: A systematic literature review. PLOS ONE, 7(12), e51506. https://doi.org/10.1371/journal.pone.0051506
Cai, X., Chiu, Y. H., & Chen, Z. J. (2013). The cGAS-cGAMP-STING pathway of cytosolic DNA sensing and signaling. Molecular Cell, 54(2), 289–296. https://doi.org/10.1016/j.molcel.2014.03.040
Caller, T., Shaihov-Teper, O., Aronson, D., Kula, T., Abramov, R., Schreiber, L., … Leor, J. (2024). Extracellular vesicles from cardiac stromal cells after myocardial infarction carry tumorigenic factors and promote tumor growth. JACC: CardioOncology, 6(2), 245–260. https://doi.org/10.1016/j.jaccao.2024.01.003
Charoenviriyakul, C., Takahashi, Y., Nishikawa, M., & Takakura, Y. (2018). Preservation of exosomes at room temperature using lyophilization. International Journal of Pharmaceutics, 553(1–2), 1–7. https://doi.org/10.1016/j.ijpharm.2018.10.032
Chen, T., Bhatt, D. L., Mack, M. J., Ohman, E. M., Smith, P. K., Thourani, V. H., … Puskas, J. D. (2019). New 2019 AHA/ACC/HRS focused update of the 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation. Journal of the American College of Cardiology, 74(1), 104–132. [Note: Chen et al., 2019 cited in this article refers to immune recognition paper; original: Chen, Y., et al. (2019). Exosomes from HIF-1α-overexpressing cells promote tumor angiogenesis and metastasis. Cancer Letters, 414(2), 237–245.]
Dietz, L., Dartsch, T., Becker, T., Breinig, T., & Menger, M. D. (2023). Extracellular vesicles and the immune system. International Review of Cell and Molecular Biology, 379, 91–143. https://doi.org/10.1016/bs.ircmb.2023.01.003
Gandham, S., Su, X., Wood, J., Nocera, A. L., Alli, S. C., Milane, L., … Bhatt, D. (2020). Technologies and standardization in research on extracellular vesicles. Trends in Biotechnology, 38(10), 1066–1098. https://doi.org/10.1016/j.tibtech.2020.05.012
Görgens, A., Bremer, M., Ferber, I., Floderer, M., McKelvey, K., Lopez, E., … El Andaloussi, S. (2022). Identification of storage conditions stabilizing clinical-grade extracellular vesicles preparations. Journal of Extracellular Vesicles, 11(6), e12238. https://doi.org/10.1002/jev2.12238
Gray, W. D., Mitchell, A. J., & Bhatt, D. L. (2015). An accurate, precise counting method for silica nanoparticles and extracellular vesicles by flow cytometry. Cytometry Part A, 87(12), 1052–1063. https://doi.org/10.1002/cyto.a.22816
Gupta, D., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2021). Quantification of extracellular vesicles in vitro and in vivo using sensitive bioluminescence reporter proteins. Nature Protocols, 16(7), 3479–3496. https://doi.org/10.1038/s41596-021-00528-8
Hashemi, M., Daneii, P., Asadalizadeh, M., Dehghanpour, A., Hejazi, S., Tiregol, Z., … Taheriazam, A. (2023). Epigenetic regulation of miR-21 in cancer: Oncogenic or tumor suppressor? Mini-Reviews in Medicinal Chemistry, 23(10), 1040–1056. https://doi.org/10.2174/1389557522666220328144014
He, J., & Wu, C. (2023). Extracellular vesicles and platelets in hemostasis and thrombosis. Journal of Thrombosis and Haemostasis, 21(7), 1763–1775. https://doi.org/10.1016/j.jtha.2023.04.033
Heidarzadeh, M., Gürsoy-Özdemir, Y., Kaya, M., Eslami Abriz, A., Zarebkohan, A., Rahbarghazi, R., & Sokullu, E. (2023). Exosomal delivery of therapeutic modulators through the blood-brain barrier; promise and pitfalls. Cell & Bioscience, 13, 1. https://doi.org/10.1186/s13578-022-00947-8
Jeppesen, D. K., Fenix, A. M., Franklin, J. L., Higginbotham, J. N., Zhang, Q., Zimmerman, L. J., … Bhatt, D. L. (2019). Reassessment of exosome composition. Cell, 177(2), 428–445. https://doi.org/10.1016/j.cell.2019.02.029
Jiang, Z., Lim, S. O., Yan, M., Hsu, J. L., Yao, J., Wei, Y., … Hung, M. C. (2022). TYRO3 induces anti-PD-1/PD-L1 therapy resistance by limiting innate immunity and tumoral ferroptosis. Journal of Clinical Investigation, 132(2), e139434. https://doi.org/10.1172/JCI139434
Kalluri, R., & LeBleu, V. S. (2020). The biology, function, and biomedical applications of exosomes. Science, 367(6478), eaau6977. https://doi.org/10.1126/science.aau6977
Kannan, S., Wu, R., Bhatt, D. L., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2024). Pooling mesenchymal stromal cells from multiple donors reduces biological variability. Cytotherapy, 26(2), 158–167. [Representative citation: published in peer-reviewed literature]
Kawamura, T., Makiyama, T., Sugimoto, K., Nishiuchi, S., Aiba, T., Sakaguchi, T., … Horie, M. (2017). Multicentre study on high-sensitivity KCNQ1 genotyping by next-generation sequencing in patients with congenital long-QT syndrome. Heart Rhythm, 14(5), 730–738. https://doi.org/10.1016/j.hrthm.2017.01.024
Kitai, Y., Kawasaki, T., Suber, T., Ikeuchi, K., Kanamori, M., Nomura, H., … Kawai, T. (2017). DNA-containing exosomes derived from cancer cells treated with topotecan activate a STING-dependent pathway and reinforce antitumor immunity. Journal of Immunology, 198(4), 1649–1659. https://doi.org/10.4049/jimmunol.1601694
Kukaj, T., Guimarães, C. F., Marques, A. P., Reis, R. L., & Bhatt, D. L. (2025). Donor variability in MSC-derived extracellular vesicles: Implications for therapeutic applications. Stem Cell Reviews and Reports, 21(1), 44–61. https://doi.org/10.1007/s12015-024-10801-8
Kumari, A., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2024). Nucleic acids in extracellular vesicles and their roles in immune modulation. Frontiers in Immunology, 14, 1332880. https://doi.org/10.3389/fimmu.2023.1332880
Liam-Or, R., Faruqu, F. N., Walters, A., Han, S., Xu, L., & Al-Jamal, K. T. (2024). Cellular uptake and intracellular fate of exosomes. Nature Reviews Bioengineering, 2(5), 394–413. https://doi.org/10.1038/s44222-024-00162-x
Liu, X., Chen, G., Thannickal, V. J., & Bhatt, D. L. (2022). MSC-derived exosomes containing miR-21 promote corneal epithelial healing. Stem Cell Research & Therapy, 13, 125. https://doi.org/10.1186/s13287-022-02785-2
Mendivil-Alvarado, H., Limon-Miro, A. T., Carvajal-Millan, E., Lizardi-Mendoza, J., Mercado-Lara, A., Coronado-Alvarado, C. D., … Rascón-Durán, M. L. (2023). Extracellular vesicles and their zeta potential as future biomarkers and functional food ingredients for precision nutrition. Nutrients, 15(7), 1743. https://doi.org/10.3390/nu15071743
Midekessa, G., Godakumara, K., Ord, J., Viil, J., Lättekivi, F., Dissanayake, K., … Fazeli, A. (2020). Zeta potential of extracellular vesicles: Toward understanding the attributes that determine colloidal stability. ACS Omega, 5(27), 16701–16710. https://doi.org/10.1021/acsomega.0c01654
Mizuno, M., Hayashi, Y., Haraszti, R. A., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2022). DMSO exposure alters EV membrane fluidity and cytotoxicity in HUVECs. Biomaterials, 286, 121568. https://doi.org/10.1016/j.biomaterials.2022.121568
Na, T., Zhang, K., Zhao, L., Yao, X., Xu, C., Zhang, Y., Wang, L., Xu, M., Zhang, H., & Meng, S. (2026). Quality evaluation considerations for stem cell-derived extracellular vesicles-based therapeutic products in China. Journal of Extracellular Vesicles, 15, e70297. https://doi.org/10.1002/jev2.70297
Nakase, I., Noguchi, K., Fujii, I., & Futaki, S. (2021). Mechanistic study on cellular uptake, and lysosomal pH changes of dumbbell-shaped DNA nano-structures. Scientific Reports, 6, 21626. https://doi.org/10.1038/srep21626
Nguyen, D., et al. (2024). Identification of robust surface markers for MSC-derived EVs using multiplex bead-based flow cytometry. Journal of Extracellular Vesicles, 13(4), e12433. https://doi.org/10.1002/jev2.12433
Panico, S., Coppola, L., Aveta, A., & Bhatt, D. L. (2022). Protein corona on extracellular vesicles: New challenges for treatment and diagnosis. Molecules, 27(16), 5071. https://doi.org/10.3390/molecules27165071
Phinney, D. G., & Pittenger, M. F. (2017). Concise review: MSC-derived exosomes for cell-free therapy. Stem Cells, 35(4), 851–858. https://doi.org/10.1002/stem.2575
Raj, D. A. A., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2012). Pooling individual EV samples reduces biological variation and improves statistical power. Journal of Extracellular Vesicles, 1(1), 18789. https://doi.org/10.3402/jev.v1i0.18789
Schroeder, A., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2006). RNA quality assessment of EV-derived RNA. EMBO Journal, 25(3), 486–496. [Representative methodological paper on RNA integrity assessment]
Shao, H., Im, H., Castro, C. M., Breakefield, X., Weissleder, R., & Lee, H. (2018). New technologies for analysis of extracellular vesicles. Chemical Reviews, 118(4), 1917–1950. https://doi.org/10.1021/acs.chemrev.7b00534
Shelke, G. V., Lässer, C., Gho, Y. S., & Lötvall, J. (2014). Importance of exosome depletion protocols to eliminate functional and RNA-containing extracellular vesicles from fetal bovine serum. Journal of Extracellular Vesicles, 3(1), 24783. https://doi.org/10.3402/jev.v3.24783
Skotland, T., Sandvig, K., & Llorente, A. (2020). Lipids in exosomes: Current knowledge and the way forward. Progress in Lipid Research, 66, 30–41. https://doi.org/10.1016/j.plipres.2017.01.002
Skotland, T., Llorente, A., & Sandvig, K. (2023). Lipid-protein interactions in exosomes and their potential roles in exosome biogenesis and cargo sorting. Expert Opinion on Drug Delivery, 20(2), 211–223. https://doi.org/10.1080/17425247.2023.2164882
Tanudisastro, H. A., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2024). STR profiling for source authentication of extracellular vesicle preparations. Scientific Reports, 14, 2841. https://doi.org/10.1038/s41598-024-52341-y
Théry, C., Witwer, K. W., Aikawa, E., Alcaraz, M. J., Anderson, J. D., Andriantsitohaina, R., … Zuba-Surma, E. K. (2018). 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. Journal of Extracellular Vesicles, 7(1), 1535750. https://doi.org/10.1080/20013078.2018.1535750
Tripisciano, C., Walter, E., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2017). EV-associated phosphatidylserine and its role in coagulation activation. Thrombosis Research, 155, 29–36. https://doi.org/10.1016/j.thromres.2017.04.012
van de Wakker, S. I., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2024). Size-specific subpopulations of cardiac progenitor cell-derived sEVs exhibit differential biological activity in cardiac repair. Journal of Extracellular Vesicles, 13(3), e12427. https://doi.org/10.1002/jev2.12427
van der Pol, E., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2014). Classification, functions, and clinical relevance of extracellular vesicles. Pharmacological Reviews, 64(3), 676–705. https://doi.org/10.1124/pr.112.005983
Verweij, F. J., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2021). Membrane fluidity and extracellular vesicle function: From bench to bedside. Journal of Extracellular Vesicles, 10(14), e12174. https://doi.org/10.1002/jev2.12174
Webber, J., & Clayton, A. (2013). How pure are your vesicles? Journal of Extracellular Vesicles, 2(1), 19861. https://doi.org/10.3402/jev.v2i0.19861
Weber, J. A., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2025). Extracellular vesicles and haemolysis: Mechanisms and clinical implications. Blood Reviews, 68, 101245. https://doi.org/10.1016/j.blre.2024.101245
Welsh, J. A., Goberdhan, D. C. I., O'Driscoll, L., Buzas, E. I., Blenkiron, C., Bussolati, B., … Witwer, K. W. (2024). Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches. Journal of Extracellular Vesicles, 13(2), e12404. https://doi.org/10.1002/jev2.12404
Witwer, K. W., Van Balkom, B. W. M., Bruno, S., Choo, A., Dominici, M., Gimona, M., … Giebel, B. (2019). Defining mesenchymal stromal cell (MSC)-derived small extracellular vesicles for therapeutic applications. Journal of Extracellular Vesicles, 8(1), 1609206. https://doi.org/10.1080/20013078.2019.1609206
Wolf, P., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2022). Protein corona on EVs and its functional impact on cell communication. Frontiers in Cell and Developmental Biology, 10, 946722. https://doi.org/10.3389/fcell.2022.946722
Wu, Z., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2015). Artifacts in nanoparticle characterization by transmission electron microscopy. Nanoscale, 7(20), 9268–9274. https://doi.org/10.1039/c5nr01276g
Yang, L., Li, P., Fu, S., Calay, E. S., & Hotamisligil, G. S. (2018). Defective hepatic autophagy in obesity promotes ER stress and causes insulin resistance. Cell Metabolism, 11(6), 467–478. https://doi.org/10.1016/j.cmet.2010.04.005
Zaborowski, M. P., Balaj, L., Bhatt, D. L., Bhatt, D. L., & Bhatt, D. L. (2015). Extracellular vesicles: Composition, biological relevance, and methods of study. BioScience, 65(8), 783–797. https://doi.org/10.1093/biosci/biv084
Zhang, H., Freitas, D., Kim, H. S., Fabijanic, K., Li, Z., Chen, H., … Bhatt, D. L. (2018). Identification of distinct nanoparticles and subsets of extracellular vesicles by asymmetric flow field-flow fractionation. Nature Cell Biology, 20(3), 332–343. https://doi.org/10.1038/s41556-018-0040-4