Exosome Characterization Platform for Early Detection of Breast Cancer

Project Information

EXCEED

Grant agreement ID: 101066038

DOI

10.3030/101066038

Project closed

EC signature date 23 May 2022

Start date 1 April 2023

End date 31 March 2026

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 268 803,36

Coordinated by

KOC UNIVERSITY
Türkiye

Periodic Reporting for period 2 - EXCEED (Exosome Characterization Platform for Early Detection of Breast Cancer)

Reporting period: 2025-04-01 to 2026-03-31

Summary of the context and overall objectives of the project

Extracellular vesicles (EVs) are nanoscale particles naturally released by cells into biological fluids such as blood, urine, and saliva. Because EVs carry proteins, nucleic acids, lipids, and other molecular information originating from their parent cells, they have emerged as promising biomarkers for non-invasive disease detection and monitoring. However, existing technologies for EV analysis often require complex sample preparation, fluorescent labeling, expensive instrumentation, or extensive laboratory infrastructure, limiting their broader adoption in research and clinical settings.

The EXCEED project (“Exosome Characterization Platform for Early Detection of Breast Cancer”) aimed to develop innovative label-free technologies for the detection, sizing, and characterization of EVs and other nanoscale biological particles. The project was supported through the Marie Skłodowska-Curie Actions (MSCA) Global Fellowship programme and conducted through collaboration between Stanford University (United States) and Koç University (Türkiye). The fellowship combined expertise in optical engineering, interferometric microscopy, Raman spectroscopy, artificial intelligence, computational imaging, and translational medicine to create new tools for liquid biopsy applications.

The central objective of the project was to develop a multimodal platform capable of characterizing individual EVs without the need for fluorescent labels or amplification methods. To achieve this goal, the project integrated interferometric microscopy for highly sensitive particle detection and sizing with Raman spectroscopy for molecular characterization. Advanced computational approaches, including Bayesian inference and machine learning, were also developed to improve data analysis and enable quantitative characterization of nanoscale particles. Together, these technologies provide complementary physical and molecular information from individual EVs, creating new opportunities for liquid biopsy and biomarker discovery.

Although initially focused on breast cancer, the technologies developed during the project proved broadly applicable and were successfully extended to additional biomedical applications of other extracellular vesicle-based translational studies. The project also established new international collaborations and follow-on research activities involving larger clinical cohorts and future diagnostic applications.

By advancing label-free characterization of extracellular vesicles and nanoscale biomarkers, EXCEED contributes to ongoing efforts in personalized medicine, early disease detection, and non-invasive diagnostics. The project supports European priorities in health innovation, digital technologies, and translational research, while creating opportunities for future scientific, clinical, and commercial impact. In the long term, the technologies developed through EXCEED may contribute to more accessible, affordable, and information-rich diagnostic tools for a wide range of diseases.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the reporting period, the EXCEED project made substantial progress toward its scientific and technological objectives, culminating in the development and validation of a novel multimodal platform for the label-free characterization of extracellular vesicles (EVs). A key achievement was the design and implementation of a widefield, common-path interferometric microscope capable of detecting and sizing individual nanoparticles, including exosomes as small as ~30 nm. The system integrated a piezo-actuated axial scanning module and optimized substrates to enhance optical contrast for low-refractive-index particles. Two critical computational tools were introduced to advance this effort: a depth scanning correlation (DSC) algorithm for enhanced particle detection and background suppression, and a Bayesian sizing algorithm utilizing Markov Chain Monte Carlo (MCMC) fitting to provide particle diameter estimates with associated uncertainty. Validation of the platform using scanning electron microscopy (SEM) and nanoparticle tracking analysis (NTA) confirmed the system’s sensitivity and accuracy. Building upon the interferometric imaging foundation, a scanned Raman spectroscopy module was developed and precisely integrated into the platform. A smart acquisition mechanism was implemented, enabling Raman spectral acquisition guided by interferometric particle localization. To overcome the challenges of background noise and reproducibility inherent to Surface-Enhanced Raman Spectroscopy (SERS), the previously developed DSC algorithm was adapted to spectral data. By analyzing Raman spectra, this method enabled reliable isolation of particle-specific molecular signals, significantly improving signal-to-noise ratio and spectral reproducibility. These enhancements allowed the platform to capture meaningful molecular information from EVs across various substrate type.The platform was then applied to the characterization of exosomes isolated from cancer cell lines. Exosomes were isolated using a microfluidic platform and immobilized on engineered substrates for analysis. The integrated interferometric-Raman system successfully performed label-free sizing and preliminary molecular profiling of cancer-derived EVs, revealing spectral features that distinguished them from controls. These findings suggest the platform’s strong potential for EV subtyping based on molecular signatures. Further validation across different cell types demonstrated the system’s robustness and applicability in diverse biological contexts.

Collectively, these accomplishments represent a significant advancement in the field of label-free EV analysis. The EXCEED platform integrates sensitive interferometric imaging with molecularly specific Raman profiling, supported by advanced data analysis algorithms, and lays a strong foundation for future diagnostic and translational studies.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The EXCEED project has introduced a technically advanced platform that integrates widefield interferometric imaging with scanned Raman spectroscopy for label-free, single-particle analysis of extracellular vesicles (EVs). Unlike conventional methods that rely on bulk measurements or labeling, the EXCEED system enables high-resolution sizing and preliminary molecular profiling of individual exosomes in the 30–150 nm range. The integration of Bayesian sizing algorithms and a depth-scanning correlation (DSC) method has improved background suppression and enhanced measurement reproducibility across different substrates.These developments represent a meaningful step forward in multimodal, label-free EV characterization. The ability to combine physical and chemical information at the single-particle level may help address current limitations in vesicle-based diagnostics. Initial experiments with cell-line–derived exosomes have shown the platform’s potential to detect molecular differences between vesicle populations, laying the foundation for more targeted applications in future phases. Further work is needed to validate the platform with clinical samples and benchmark it against existing technologies.