The study of exosome quantification has grown exponentially in recent years. These extracellular vesicles have proven to be valuable tools as disease biomarkers and potential vehicles in cell and gene therapies. However, as research advances, a recurring challenge emerges: how to ensure the standardization and validation of assays for accurate quantification and characterization. Lack of consistency between laboratories can limit result comparability and the clinical translation of discoveries.
In this context, advanced analytical methods have become essential. Tools such as NanoFCM, vesicle flow cytometry, ExoView, and NTA enhanced with machine learning algorithms allow the study of exosomes with unprecedented precision, though challenges remain.
NanoFCM represents a qualitative leap compared to traditional flow cytometry. This approach enables the detection of individual particles, precise measurement of their size and concentration, and evaluation of surface markers such as the tetraspanins CD9, CD63, and CD81. Its high resolution allows identification of exosome subpopulations that were previously overlooked. However, to achieve comparable results across laboratories, it is essential to establish robust calibration standards.
Meanwhile, vesicle flow cytometry adapts the principles of flow cytometry to nanoscale vesicles. This technique offers the advantage of multiparametric analysis, allowing simultaneous characterization of purity markers and cell-origin markers. Using standardized size and fluorescence controls is crucial to reduce variability between experiments.
Another increasingly relevant technique is ExoView, which combines the capture of exosomes on antibody-functionalized surfaces with high-resolution imaging. This method not only allows exosome quantification but also explores their molecular composition and subpopulations, integrating information on size, concentration, and surface markers in a single assay.
Finally, Nanoparticle Tracking Analysis (NTA), traditionally used to measure particle concentration and size, has been enhanced with machine learning (ML) algorithms. This innovation allows more precise discrimination between true particles and contaminants, corrects measurement artifacts, and harmonizes results across laboratories, significantly improving assay reproducibility.
Despite technological advances, interlaboratory variability remains a frequent problem. Differences in sample preparation, instrument calibration, and data analysis can lead to inconsistent results. To address this, experts recommend implementing standardized isolation and storage protocols, using internal controls and reference standards, and ensuring uniform staff training. Additionally, ML-based analysis tools help normalize results and reduce biases.
A critical aspect of exosome characterization is assessing purity. Traditionally, tetraspanins (CD9, CD63, and CD81) are used as universal exosome markers. However, these proteins do not identify the cellular source of the vesicle. Therefore, many laboratories complement these assays with origin-specific markers, providing a more complete and reliable view of the sample composition. Combining both approaches is key to achieving robust and reproducible results.
Exosome quantification and characterization is a complex process that requires combining advanced technologies, standardized protocols, and rigorous analysis. Using NanoFCM, vesicle flow cytometry, ExoView, and ML-enhanced NTA, along with purity assays that integrate tetraspanins and origin-specific markers, constitutes the best strategy for ensuring reliable and reproducible results. Only in this way can we advance toward clinical translation and fully harness the potential of these vesicles in diagnostics and innovative therapies.