Circulating extracellular vesicles (EVs) are increasingly recognized as important mediators of intercellular communication and promising sources of minimally invasive biomarkers. Yet, defining what constitutes an EV in complex biofluids such as human plasma remains a persistent challenge. Conventional markers often struggle to distinguish vesicles from abundant co-isolated components, including lipoproteins, protein aggregates, and other nanoparticles.
In a recent Nature Cell Biology study, a team at the Baker Heart and Diabetes Institute, Australia, and collaborating universities addressed this challenge by combining high-sensitivity mass spectrometry, lipidomics, and single-vesicle flow cytometry with machine-learning-based data integration. Through extensive biochemical, biophysical, and multi-omics characterization, the work establishes a molecular reference framework for human circulating EVs.
We reached out to David Greening, Head of Molecular Proteomics at the Baker Heart and Diabetes Institute, and lead author of the paper, to learn more about the team’s motivations and challenges – and how integrated multi-omics could improve reproducibility, classification, and translational potential in EV research.
What initially motivated your team to rethink the molecular definition of human circulating extracellular vesicles?
Extracellular vesicles are the master communicators of multicellular life – key in orchestrating the complex signalling networks that define physiology. Over the last several decades, extracellular vesicle trafficking has emerged as an important mechanism of cell communication. As new concepts have linked EVs to many physiological and pathological processes – and as their presence in blood plasma has become clear – the field of EV research has represented a new paradigm in exploring and translating EV-based therapies and their use as diagnostic, prognostic, and predictive biomarkers.
Nevertheless, important questions remained – principally, how to reliably resolve EVs from the complexity of plasma. Conventional EV markers are too inconsistent to reliably separate vesicles from other plasma components, including abundant proteins, aggregates, and lipoproteins. Furthermore, few studies have performed high-quality EV isolation across large, diverse groups while simultaneously analyzing and integrating both protein and lipid data. Using our strategy, these signatures were validated across multiple independent cohorts and EV subtypes, providing not only a technical solution but also a conceptual framework for the field.
EVs are also emerging as clinically relevant tools, with the potential for minimally invasive sampling from blood or other bodily fluids. Their complex molecular cargo reflects their cells and tissues of origin, and advances in detection sensitivity are increasingly enabling that cargo to be identified and monitored.
In this study, we employed technologies including high-sensitivity mass spectrometry for protein and lipid identification and single-vesicle flow cytometry, alongside multiple machine-learning frameworks for comprehensive analysis, integration, and interrogation. We also used density fractionation enrichment and lipid-based affinity capture, combined with extensive biophysical and biochemical characterization.
Could you briefly explain how your multi-omics workflow was designed?
Our discovery includes a conserved set of 182 proteins and 52 lipids intrinsic to circulating EVs, as well as a panel of 29 proteins and 114 lipids that are non-EV features in plasma. Together, these serve as biological markers for EV research in human samples.
In the study, we used high-resolution density gradient separation (DGS) to isolate a major EV subtype, known as small EVs, from human plasma. We verified the enrichment strategy and EV identity using a range of biochemical and biophysical characterization methods, ensuring clear separation of EVs from non-EV particles in plasma. We then generated detailed proteome and lipidome maps, defining EV hallmark features and, in parallel, identifying markers that distinguish non-EV particles. These markers – ADAM10 and PS(36:1) in particular – enable precise differentiation between EV and non-EV particles using machine learning.
Our findings highlight how unbiased multi-omics profiling can identify novel, biologically relevant molecular features in human circulating EVs. By offering biologically grounded criteria for defining EV identity, the work addresses the longstanding challenge of EV purity in plasma isolations. Rather than relying on depletion of known contaminants or inconsistent operational definitions, we integrated multi-omics profiling with machine learning to identify conserved markers with robust classification and translational potential.
Did any of the findings particularly surprise you?
What surprised us most was the exceptional diversity of plasma itself – and of circulating EVs from different cellular origins. While single-vesicle resolution would offer even deeper insight, bulk EV analysis can still uncover biologically meaningful and source-representative features which contribute to systemic intercellular communication. Regarding source attribution, protein signatures associated with diverse cell types were represented in plasma EVs including endothelial cells, fibroblasts, hepatocytes, cardiomyocytes, kidney cells and haematopoietic cells (such as platelets).
This freely-accessible molecular reference map – termed EVMap – provides a platform to resolve and understand what is an EV from circulation, including the network of proteins displayed on their membrane surface, and key information on their likely cell, tissue, and organ origins.
As a new diagnostic strategy, circulating EVs also offer the ability to decode their molecular “language” and uncover new patterns that could identify people with early signs of coronary heart disease – years before symptoms appear. The knowledge established today lays the groundwork for the targeting and detection strategies of tomorrow. By understanding the EV surfaceome (the surface “barcode” displayed), we can begin to engineer “designer vesicles” that mimic the body’s own communication system, and enable real-time, dynamic therapeutic monitoring.
It revealed that hallmark features such as ADAM10 and PS(36:1) are selectively present on only a subset of EVs, underscoring the heterogeneity of the EV population. This selective distribution highlights the need to better understand how cargo is loaded during biogenesis, and what functional distinctions exist among EV subtypes. Future single-vesicle analyses that combine nanoscale ‘-omics’ with advanced statistical and AI methods will be crucial for resolving the full spectrum of EV diversity.
How do you see this changing the way we study extracellular vesicles and their roles in human health and disease?
By offering biologically grounded criteria for defining EV identity, this study addresses the longstanding issue of the purity of EVs isolated from plasma – more than lists and universal EV subset – but a benchmark molecular criteria in their definition.
With the EVMap, we have a high-resolution blueprint of proteins and lipids in human circulating extracellular vesicles (EVs) – creating a framework for practical foundation across basic, translational and clinical applications. As the field advances, these hallmark features will enhance the specificity and reproducibility of EV-related studies, enabling the development of scalable diagnostic platforms and targeted therapeutic strategies.
This refined, highly selective multi-molecular marker set is compatible with targeted, scalable assays such as enzyme-linked immunosorbent assay or targeted MS, making it highly practical for clinical translation. Furthermore, the ranked feature list generated by our machine learning framework provides a valuable resource for the research community, enabling prioritization of alternative markers based on available reagents or disease-specific applications.
Looking ahead, what are the next steps for this work?
The EVMap opens new frontiers of exploration across a range of fields; from bioengineering technology, to biomarker discovery and early disease detection, clinical management, and population health. By decoding these EV messages, we’re beginning to truly read the body’s own health reports. Through diverse collaborations, our team is now expanding this work to integrate EV multi-omics data from larger cohorts, to evolve into liquid biopsy tools with real clinical impact.
Challenges and active areas of research remain, including advances in capture and detection technologies, improving reproducibility, and the continued development of standardization and robust assessment workflows. Another major focus is deciphering EV heterogeneity – how diverse EV subtypes relate to disease drivers, and how these insights can be translated clinically.
The immediate next steps include focused investigations on molecular stability across diverse disease states, to establish the use of these core EV features as a reliable foundation for human EV studies in large population-based cohorts. This will enable consistent characterization and cross-study comparability moving forward.
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