PHOtonic integrated OCT-enhanced flow cytometry for canceR and cardiovascular diagnostics enabled by Extracellular VEsicles discRimination

Imaging tools such as the Computed Tomography (CT) and the Magnetic Resonance Imaging (MRI) can offer diagnosis of the cancer and the cardiovascular disease (CVD), but not insight into the molecular mechanisms that promote their occurrence, progression and possible resistance to treatment. Information about these mechanisms is present however in the blood.

Extracellular vesicles (EVs) are secreted into the blood, and can inform us about the state of their cells of origin, and by extension, about the presence and progression of diseases. Unfortunately, their detection is still imperfect due to the ultra-small size (50-200 nm) of most of them.

PHOREVER will develop a disruptive multi-sensing platform that will enable for the first time the reliable detection of EVs with size down to 80 nm, the detection of EVs with specific biomarkers (proteins) on their surface, and the calculation of the corresponding EV concentrations in the blood.

Its operation will be based on 3 sensing modalities: Flow-cytometry (FCM) with 4 wavelengths (405, 488, 633 and 785 nm) as the main modality for EV detection and size classification, dual-channel swept-source optical coherence tomography (SS-OCT) with 2.5 µm resolution for imaging of the sensing area and noise reduction of the FCM measurements, and fluorescence sensing at 488 nm for biomarker detection after staining.

The key components will be the 2 photonic integrated circuits in TriPleX and the 3 microfluidic chips, which will be integrated as a compact point-of-care device. The medical impact can be ground-breaking.

The first use case will be related to pancreatic cancer with focus on progression monitoring, metastasis risk assessment, and treatment efficacy evaluation.

The second use case will be related to stroke with focus on its fast and precise diagnosis for time-to-treatment reduction.

In either case, data analysis empowered by artificial intelligence will correlate the measurement data to disease specific medical information.