Identification of extracellular vesicles from their Raman spectra via self-supervised learning

Extracellular vesicles (EVs) released from cells attract interest for their possible role in health and diseases. The detection and characterization of EVs is challenging due to the lack of specialized methodologies. Raman spectroscopy, however, has been suggested as a novel approach for biochemical analysis of EVs. To extract information from the spectra, a novel deep learning architecture is explored as a versatile variant of autoencoders. The proposed architecture considers the frequency range separately from the intensity of the spectra. This enables the model to adapt to the frequency range, rather than requiring that all spectra be pre-processed to the same frequency range as it was trained on. It is demonstrated that the proposed architecture accepts Raman spectra of EVs and lipoproteins from 13 biological sources and from two laboratories. High reconstruction accuracy is maintained despite large variances in frequency range and noise level. It is also shown that the architecture is able to cluster the biological nanoparticles by their Raman spectra and differentiate them by their origin without pre-processing of the spectra or supervision during learning. The model performs label-free differentiation, including separating EVs from activated vs. non-activated blood platelets and EVs/lipoproteins from prostate cancer patients versus non-cancer controls. The differentiation is evaluated by creating a neural network classifier that observes the features extracted by the model to classify the spectra according to their sample origin. The classification reveals a test sensitivity of 92.2% and selectivity of 92.3% over 769 measurements from two labs that have different measurement configurations.</p

EV trapping: Raman characterization of single tumor-derived extracellular vesicles

The search for cancer biomarkers of easy access and with diagnostic and prognostic value has led to a growing interest in very small particles that are released not only by healthy cells but also by cancer cells. These membrane bound particles, known as extracellular vesicles (EVs), may be present in body fluids of cancer patients, such as in the blood. The idea of detecting and distinguishing these tumor-derived extracellular vesicles (tdEVs) from other small particles in body fluids has motivated us to explore and develop technology that can distinguish single tdEVs from other particles. The aim of this thesis is to detect and characterize biological nanoparticles in blood, specifically tdEVs, at the single particle level. Hence, this thesis explores various methods that enable, in a novel way, the detection and chemical characterization of individual particles and the discrimination of tdEVs from other EVs and non-EV particles, such as lipoprotein particles, in a label-free manner. One method explored is the correlation of scanning electron microscopy (SEM) and Raman spectroscopy that enables the acquisition of high resolution SEM images and the spatial correlation with chemical information as obtained from Raman micro-spectroscopic imaging. Another method is the development of optical trapping and synchronized Rayleigh and Raman scattering (OT-sRRs) for the detection and characterization of single biological nanoparticles, such as tdEVs, directly in suspension and in a label-free manner. This thesis describes the implementation of various novel methods to study biological nanoparticles in blood, from cancer cells to tdEVs and from model nanoparticles to nanoparticles in the plasma of cancer patients. These developments open an avenue not only to exploit the potential of tdEVs as cancer biomarkers, but also to study other particles in body fluids and, with that, the general nanoparticle profile, which may be affected under pathological conditions such as cancer

Immunocapturing of extracellular vesicles on stainless steel for multi-modal individual characterization with correlative light, electron and probe microscopy

Here, we report a robust platform for multi-modal analysis of immuno-captured individual extracellular vesicles (EVs). Stainless steel substrates were surface-modified to covalently immobilize specific antibodies targeting proteins found on EVs. Using PDMS microchannels, EVs were selectively captured on the substrates. Next, individual EVs were retraced and correlatively characterized here using SEM, AFM and Raman Spectroscopy.</p

Immunocapturing of extracellular vesicles on stainless steel for multi-modal individual characterization with correlative light, electron and probe microscopy

Here, we report a robust platform for multi-modal analysis of immuno-captured individual extracellular vesicles (EVs). Stainless steel substrates were surface-modified to covalently immobilize specific antibodies targeting proteins found on EVs. Using PDMS microchannels, EVs were selectively captured on the substrates. Next, individual EVs were retraced and correlatively characterized here using SEM, AFM and Raman Spectroscopy.</p

Multi-modal analysis of tumor-derived extracellular vesicles immunocaptured from plasma

Extracellular vesicles have emerged in recent years as highly promising for understanding cell communication, drug delivery, and medical applications. Specifically, tumor-derived extracellular vesicles (tdEVs) have demonstrated excellent prognostic value in cancer diagnostics compared to imaging approaches. Despite the growing body of expertise regarding EVs, great challenges remain, notably in their handling and characterization. In complex media, other particles with similar characteristics may occlude measurements. Here, a platform is presented for the immunocapturing of tdEVs for identifying their origin followed by further multi-modal analysis by Raman spectroscopy, confocal microscopy and atomic force microscopy (AFM)