Periodic Reporting for period 1 - EXCEED (Exosome Characterization Platform for Early Detection of Breast Cancer)
Período documentado: 2023-04-01 hasta 2025-03-31
Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early detection is critical for improving patient outcomes, yet current diagnostic approaches often rely on imaging or biopsy-based methods that can be invasive, costly, or limited in sensitivity. Recent research has shown that extracellular vesicles (EVs)—nanoscale particles secreted by all cells, including cancer cells—carry valuable molecular information that reflects the physiological and pathological state of their origin. These vesicles have emerged as promising biomarkers for cancer diagnostics and monitoring.
The EXCEED project—“Exosome Characterization Platform for Early Detection of Breast Cancer”—aims to harness the potential of EVs for the development of a next-generation diagnostic platform capable of sensitive, label-free, and rapid characterization of individual EVs using a multimodal optical approach. The project is funded under the prestigious Marie Skłodowska-Curie Actions (MSCA) Global Fellowship scheme and is carried out in collaboration between leading institutions in the United States (Stanford University) and Europe (Koç University).The key scientific objective of EXCEED is to develop and validate a novel imaging system that integrates interferometric and Raman spectroscopy techniques for profiling EVs without the need for complex labeling or amplification steps. This multimodal approach allows researchers to visualize and classify EVs based on their size, structure, and molecular signatures, opening up new possibilities for non-invasive cancer detection at an early stage. The project’s broader goal is to lay the groundwork for a point-of-care diagnostic system that could one day be used in clinical settings, offering a fast and affordable alternative to conventional diagnostics. Such a system could significantly impact cancer screening and monitoring, especially in resource-limited environments, contributing to global health equity.
By addressing urgent clinical needs and advancing the state-of-the-art in EV detection, EXCEED supports the European Commission’s strategic priorities on health innovation, personalized medicine, and responsible research and innovation. Its outcomes are expected to contribute to long-term impacts on early cancer detection, improved patient care, and the translation of academic research into societal benefit.
The EXCEED project—“Exosome Characterization Platform for Early Detection of Breast Cancer”—aims to harness the potential of EVs for the development of a next-generation diagnostic platform capable of sensitive, label-free, and rapid characterization of individual EVs using a multimodal optical approach. The project is funded under the prestigious Marie Skłodowska-Curie Actions (MSCA) Global Fellowship scheme and is carried out in collaboration between leading institutions in the United States (Stanford University) and Europe (Koç University).The key scientific objective of EXCEED is to develop and validate a novel imaging system that integrates interferometric and Raman spectroscopy techniques for profiling EVs without the need for complex labeling or amplification steps. This multimodal approach allows researchers to visualize and classify EVs based on their size, structure, and molecular signatures, opening up new possibilities for non-invasive cancer detection at an early stage. The project’s broader goal is to lay the groundwork for a point-of-care diagnostic system that could one day be used in clinical settings, offering a fast and affordable alternative to conventional diagnostics. Such a system could significantly impact cancer screening and monitoring, especially in resource-limited environments, contributing to global health equity.
By addressing urgent clinical needs and advancing the state-of-the-art in EV detection, EXCEED supports the European Commission’s strategic priorities on health innovation, personalized medicine, and responsible research and innovation. Its outcomes are expected to contribute to long-term impacts on early cancer detection, improved patient care, and the translation of academic research into societal benefit.
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.
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.
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.