Extracellular vesicles (EVs) are defined as “particles that are released from cells, are delimited by a lipid bilayer, and cannot replicate on their own” (Welsh et al., 2024). EVs were first identified in 1946 in blood plasma by Chargaff and his colleague West; at the time, the authors described them as a ‘particulate fraction’ that sedimented at a speed of 31,000 g (Couch et al., 2021). The term ‘extracellular vesicles’ was first used in a scientific publication in 1971 (Witwer and Théry, 2019). Since then, EV-based research has garnered significant attention from scientific researchers in recent years. However, these efforts were hampering the advancement of the field by the presence of significant discrepancies among the different studies in various crucial facets, such as the nomenclature, isolation process, characterization, and enumeration methods of EV subsets. This led to the establishment of the International Society of Extracellular Vesicles (ISEVs) in 2011, with their first guideline published in 2014 (Lötvall et al., 2014), with two consequent amendments published in 2018 and 2023 (Théry et al., 2018; Welsh et al., 2024).

EVs are commonly categorized into three subtypes depending on their size (from smallest to largest): small-sized EVs (sEVs) or exosomes, and large-sized EVs (lEVs), which include microvesicles (MVs), large ectosomes, and apoptotic bodies (Welsh et al., 2024). This nomenclature is of an operational nature as it is based on a physical characteristic such as size. sEVs have a size of <200 nm, while lEVs have a size of >200 nm. Each subtype is released by a different mechanism; sEVs are released through the endosomal pathway, while lEVs are released by direct budding from the plasma membrane, and both can contain versatile cargo from the cell’s cytoplasm (Agouni et al., 2014; El-Gamal et al., 2019; Zaborowski et al., 2015). Despite the recommendations of the ISEVs, research publications continue to use outdated and inaccurate terminology. Methods for isolating and characterizing EVs are crucial for comprehending their biological roles and possible applications. Common techniques for isolating EVs include differential ultracentrifugation (dUC), which separates EVs by density; precipitation methods that use specific reagents to aggregate EVs; and size-exclusion chromatography, which sorts EVs based on size. More recent methods, such as microfluidic devices and immunoaffinity capture, enhance specificity and yield. Once isolated, characterization typically involves techniques like nanoparticle tracking analysis (NTA) to measure size and concentration, flow cytometry (FCM) for surface marker identification, and electron microscopy (EM) for morphological analysis. Additionally, proteomic and lipidomic studies provide important insights into the molecular composition of EVs, revealing potential biomarkers and functional characteristics (Agouni et al., 2014; El-Gamal et al., 2019).

Diabetes mellitus has become a widespread and serious health issue, significantly increasing in prevalence over recent decades. It is associated with various complications, including macrovascular conditions like coronary heart disease and stroke, as well as microvascular conditions such as diabetic nephropathy (DN) (Tomic et al., 2022). DN is one of the most prominent and long-term diabetic complications. It is the primary cause of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) worldwide, accounting for half of all cases. DN is typically defined by the presence of chronic kidney disease in a person with diabetes, characterized by persistently elevated levels of albumin in the urine (albumin-to-creatinine ratio ≥30 mg/g) and/or reduced kidney function (eGFR <60 mL/min/1.73 m²) for at least 3 months (Hoogeveen, 2022). Pathologically, the primary characteristics of DN consist of an expansion of the glomerular mesangial matrix, thickening of the basement membrane, glomerular sclerosis, and inflammation and fibrosis in the tubulointerstitial area. While hyperglycemia-induced vascular dysfunction serves as the main trigger for DN, its progression involves various pathological mechanisms, such as inflammation, oxidative stress, autophagy, damage to podocytes, and renal fibrosis (Jin and Zhang, 2024).

EVs have been studied extensively in DN and continue to be a prominent research focus in the field (Tao et al., 2020). Diabetes-induced EVs harness several signaling pathways that contribute to the deterioration of kidney function; for example, encapsulating transforming growth factor beta (TGF-β), mediating its downstream signaling cascade on different kidney cell types (Lu et al., 2020), and mediating endothelial dysfunction through ER-induced cellular stress (Safiedeen et al., 2017; Osman et al., 2020). Research has pinpointed specific EV cargo that indicate renal injury and inflammation, suggesting they could serve as biomarkers for early diagnosis and monitoring disease progression. Some of these discovered biomarkers include microRNA (miR)-21 (Ding et al., 2021), and alpha1-antitrypsin (Ning et al., 2020). Despite the eminent scientific publications on the topic, there remains much to be explored. Overall, the exploration of EVs in DN provides important insights into the disease mechanisms and possible intervention strategies. Several systematic reviews have examined EVs in the context of DN, with an exclusive focus on studies involving human subjects. However, to our knowledge, this is the first systematic review that summarizes and discusses preclinical studies investigating the large-sized EV subset, MVs, in DN.

Benchmarked against MISEV2023, only Burger et al. and Zhang et al. delivered strong orthogonal identification (FCM with lineage markers plus EM; Burger et al. also used NTA), whereas several others relied on bulk protein or activity assays for quantification (e.g., BCA/Bradford/ELISA) with limited calibration or detection-limit reporting. Impurity/co-isolate assessment was generally not described. Dose/exposure reporting was clear mainly in Munkonda et al.‘s work (10 μg/mL; 30 min–72 h) and Ravindran et al.‘s (10 μg/mL; 24 h); Zhong et al. reported an amount (30 µg) without concentration, and others lacked dosing detail. EV-depleted media were used or generated in three studies - Zhong et al., Munkonda et al., and Ravindran et al., but not consistently elsewhere. Functional controls (EV-depleted supernatant, detergent-disrupted vesicles, dose–response) were rarely specified. Terminology across studies was pragmatic (MVs/MPs) with no unsupported biogenesis claims. Collectively, these gaps affect interpretation by (i) increasing the risk that observed effects reflect co-isolated material rather than solely MVs; (ii) misclassifying dose when using protein mass or activity units, limiting cross-study comparability and obscuring true dose–response; (iii) allowing serum-derived EV background when EV-depleted media are absent, inflating baseline signals; and (iv) weakening causal inference and specificity in the absence of negative controls and calibrated detection limits. As a result, findings should be weighed as directional rather than definitive, with biomarker claims (e.g., early urinary MVs) and mechanistic links (e.g., ROS, profibrotic signaling) considered plausible but contingent on studies that standardize particle-resolved dosing, impurity controls, and functional assay rigor.

Integrating evidence across models is essential to move from signals to mechanisms. In vivo readouts indicate when and where renal injury emerges, while in vitro systems resolve the cell-type pathways that generate those signals. Accordingly, we align common endpoints (MV abundance, viability, ROS/NO, inflammatory and morphological readouts) and harmonize key methodological descriptors (EV-depleted media, dosing metrics, exposure windows) to map convergent mechanisms and separate true biology from workflow-driven variability.

In in vivo studies (including urinary readouts), MVs consistently point to very early glomerular injury; for example, urinary podocyte MVs rise before albuminuria is detectable, and platelet-derived MVs can be found within glomerular structures. These whole-organ signals pair well with in vitro studies that pinpoint cell-type mechanisms: platelet MVs injure glomerular endothelium; podocyte MVs activate p38 MAPK/CD36 in proximal tubules to promote profibrotic signaling; MSC-derived MVs deliver miR-451a that targets p15/p19; and high glucose drives podocyte MV biogenesis through NOX4/ROS. Figure 2 summarizes the key findings from the seven studies and their link to DN. Some of the variability across studies likely comes from methodological differences rather than biology alone—e.g., uneven use of EV-depleted media, different ways of defining dose (protein mass vs. particle counts), and exposure times that range from minutes to days. Tightening these factors (standardized serum depletion, particle-resolved dosing with calibration, and harmonized exposure windows) would make experimental outcomes more comparable across labs and clarify which MV-driven pathways are truly linked to DN progression.

This systematic review identified several key preclinical studies that demonstrate the involvement of MVs in renal injury and inflammation associated with DN. Preclinical systematic reviews and meta-analyses are valuable for uncovering factors that might unintentionally obstruct the translation from bench to bedside, as well as for highlighting variability in the existing literature (Ineichen et al., 2024). In the current review, we have examined and compared various aspects of the selected studies. Firstly, we classified the seven studies according to the experimental model used, in vitro or in vivo. This led to the identification of the origin/type of MVs explored, with platelet-derived and urinary MVs being the primary sources in in vivo studies, while for in vitro studies, the only sample type to be collected is the conditioned cell culture medium. Other vital aspects of EV-based studies are the methods of choice for isolation, characterization, and quantification. Besides affecting the yield and purity of EVs isolated, the isolation method can impede their biological function by altering their integrity (De Sousa et al., 2023; Akbar et al., 2022). The results showed that dC is the most common technique used for the isolation of MVs. This observation aligns with existing literature, which indicates that this particular technique is the predominant approach for isolating various subpopulations of EVs across the field (Akbar et al., 2022). The centrifugation speeds used by the selected studies were similar, and this is crucial as the relative centrifugal force (RCF) can affect the purity and yield of MVs; methodologies of included studies stated a range of 10,000 to 20,000 × g to efficiently pellet MVs. Furthermore, the duration of centrifugation plays a critical role; longer centrifugation times can enhance separation efficiency but may also risk damaging the MVs if not carefully optimized (Konoshenko et al., 2018). It is important to note that dC is different than dUC; ultracentrifugation is a method that uses extremely high centrifugation speeds, approximately 150,000 × g, which is mostly used for small EVs, i.e., exosomes, while dC is a process of consequent rounds of centrifugations with incremental RCF, interrupted by washing steps, to remove cell debris first, and then pellet particles such as MVs (Nigro et al., 2021). MVs, being larger than exosomes, can be isolated using lower centrifugal forces and speeds. Despite the precise description of the centrifugation settings in most of the studies, notably, only a few described the characterization and quantification methods of choice for the isolated particles. Generally, FCM and NTA are some of the most common techniques employed in the included studies and other studies as well for all subsets of EVs (Bağcı et al., 2022). Annexin V, which was used as a marker in most of the included studies, is a widely used marker for characterizing EVs in FCM and Western blotting; it binds to phosphatidylserine, a lipid exposed on EV surfaces during apoptosis. When fluorescently labeled, Annexin V enables quantification and identification of EVs, particularly those from apoptotic cells, providing insights into their origin and function. Other markers were used mostly for identifying a specific type of MVs based on their cell of origin, e.g., podocyte-derived MVs. The EV research community continues to grapple with the challenge of achieving reliable EV quantification, as no standardized method has yet been universally accepted or established. In general, the most widely used methods in recent literature to quantify the number of EVs in a given sample volume and assess their size distribution are NTA and microsphere-based FCM (Hartjes et al., 2019). Another less rigorous method used by research laboratories is colorimetric-based protein quantification assays, which were used by three studies identified in our systematic review. While these assays are widely employed methods for assessing protein concentration, they may exhibit limitations in specificity and sensitivity necessary for precise quantification of EVs. This is primarily due to the complex mixture of proteins and other biomolecules that may be present in the sample, which these assays may not adequately differentiate between and EV-specific proteins. Consequently, this lack of specificity could result in inaccurate estimations of EV levels. Therefore, it is generally viewed as a less reliable method compared to more stringent techniques like NTA or FCM. In terms of EV quantification, one study found that compared to an FCM-based method, there was a significantly lower level of detected proteins, i.e., MVs, in the colorimetric protein assay (Vergauwen et al., 2017).

The seven studies examined employed a diverse array of experimental techniques to investigate various endpoints, highlighting the complex nature of investigating MVs in DN. A significant subset of investigations focused on quantitative alterations in MV levels after renal injury induction, providing foundational data on MV dynamics as potential biomarkers of nephropathic progression. Conversely, a triad of studies explored the in vitro effects of diabetogenic MVs on cellular models, elucidating potential mechanisms of MV-mediated cellular dysfunction. These investigations emphasized the production of ROS and NO, key indicators of oxidative and nitrosative stress. However, the limited evaluation of these biomarkers indicates a deficiency in comprehensive oxidative stress profiling. While most included studies assessed inflammatory markers such as TNF-α and CXCL7, their absence in one study suggests methodological inconsistencies across the research landscape. Additionally, the examination of renal cell morphological alterations in select studies underscores the significance of structural parameters in the characterization of DN. The inconsistent findings are largely due to the variations in isolation methods, dose estimation, media preparation, and instrument calibration, which may introduce contaminants or measurement errors. Hence, aligning future studies with MISEV2023 guidelines will help minimize these discrepancies and improve reproducibility.

While MISEV has been essential for rigor, fully implementing the breadth of its recommendations can be rough for many labs and sometimes nearly impossible to execute in full in basic-science settings, given that the 2023 update spans from basic to advanced approaches and even acknowledges that not all conceivable controls can be run simultaneously in any given system. This feasibility gap shows up in field audits: the EV-TRACK analysis of 1,742 EV experiments reported widespread heterogeneity and under-reporting, with <6% of experiments scoring above 50% on its EV-METRIC checklist (Van Deun et al., 2017).

The collective findings from these studies underscore the critical need for methodological standardization in future MV research within the context of DN. This standardization is essential for enhancing inter-study comparability, improving reproducibility, enabling comprehensive biomarker profiling, integrating morphological and molecular data, ensuring temporal consistency, and establishing uniform quantification methods. The diverse experimental approaches employed reflect both the multifaceted nature of MV research in DN and areas requiring more consistent exploration. Future investigations should address these gaps by establishing consensus on essential biomarkers and cellular parameters, developing standardized protocols for MV isolation and analysis, implementing multi-omics approaches, utilizing advanced imaging techniques consistently, and incorporating in vivo models that better recapitulate human DN pathophysiology. By addressing these aspects, the field can progress towards a more unified understanding of MV pathophysiology in DN, potentially uncovering novel diagnostic markers and therapeutic targets. This concerted effort towards standardization and comprehensive analysis will be crucial in advancing our understanding of MVs’ role in DN and translating this knowledge into clinical applications, ultimately improving the management of this complex disease.

This systematic review has several notable limitations that should be considered. Firstly, the selection criteria may have introduced bias by excluding studies published in languages other than English, which could have led to the omission of relevant findings. Secondly, given the lack of an established quality assessment tool for preclinical non-interventional studies, evaluating the risk of bias was not feasible, which allowed for the possible impact of methodological flaws on the reliability of the conclusions. Moreover, the substantial heterogeneity in the nature and origin of MVs, combined with the wide variability in models used to generate or isolate EVs and the treatment methods involving MVs, constrained our ability to conduct a meta-analysis that could have offered clearer insights. These limitations emphasize the need for caution in interpreting the results and suggest that future research should strive to include a wider range of studies and employ standardized definitions and methodologies for EV isolation and characterization, whether in DN or any other disease model.