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.

Physical association of low density lipoprotein particles and extracellular vesicles unveiled by single particle analysis.

Extracellular vesicles (EVs) in blood plasma are recognized as potential biomarkers for disease. Although blood plasma is easily obtainable, analysis of EVs at the single particle level is still challenging due to the biological complexity of this body fluid. Besides EVs, plasma contains different types of lipoproteins particles (LPPs), that outnumber EVs by orders of magnitude and which partially overlap in biophysical properties such as size, density and molecular makeup. Consequently, during EV isolation LPPs are often co-isolated. Furthermore, physical EV-LPP complexes have been observed in purified EV preparations. Since co-isolation or association of LPPs can impact EV-based analysis and biomarker profiling, we investigated the presence and formation of EV-LPP complexes in biological samples by using label-free atomic force microscopy, cryo-electron tomography and synchronous Rayleigh and Raman scattering analysis of optically trapped particles and fluorescence-based high sensitivity single particle flow cytometry. Furthermore, we evaluated the impact on flow cytometric analysis in the presence of LPPs using in vitro spike-in experiments of purified tumour cell line-derived EVs in different classes of purified human LPPs. Based on orthogonal single-particle analysis techniques we demonstrate that EV-LPP complexes can form under physiological conditions. Furthermore, we show that in fluorescence-based flow cytometric EV analysis staining of LPPs, as well as EV-LPP associations, can influence quantitative and qualitative EV analysis. Lastly, we demonstrate that the colloidal matrix of the biofluid in which EVs reside impacts their buoyant density, size and/or refractive index (RI), which may have consequences for down-stream EV analysis and EV biomarker profiling.

Clinical applications and challenges in the field of extracellular vesicles.

Body fluids contain cell-derived particles called extracellular vesicles (EVs). EVs are released by cells and are present in all body fluids (i. e. liquid biopsies). EVs contribute to physiology and pathology and offer a plethora of potential clinical applications, ranging from biomarkers to therapeutic applications. In this manuscript we provide an overview of this new and rapidly growing research field, along with its challenges and opportunities.

Extracellular Vesicles: A New Source of Biomarkers in Pediatric Solid Tumors? A Systematic Review.

Virtually every cell in the body releases extracellular vesicles (EVs), the contents of which can provide a "fingerprint" of their cellular origin. EVs are present in all bodily fluids and can be obtained using minimally invasive techniques. Thus, EVs can provide a promising source of diagnostic, prognostic, and predictive biomarkers, particularly in the context of cancer. Despite advances using EVs as biomarkers in adult cancers, little is known regarding their use in pediatric cancers. In this review, we provide an overview of published clinical and in vitro studies in order to assess the potential of using EV-derived biomarkers in pediatric solid tumors. We performed a systematic literature search, which yielded studies regarding desmoplastic small round cell tumor, hepatoblastoma, neuroblastoma, osteosarcoma, and rhabdomyosarcoma. We then determined the extent to which the in vivo findings are supported by in vitro data, and vice versa. We also critically evaluated the clinical studies using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) system, and we evaluated the purification and characterization of EVs in both the in vivo and in vitro studies in accordance with MISEV guidelines, yielding EV-TRACK and PedEV scores. We found that several studies identified similar miRNAs in overlapping and distinct tumor entities, indicating the potential for EV-derived biomarkers. However, most studies regarding EV-based biomarkers in pediatric solid tumors lack a standardized system of reporting their EV purification and characterization methods, as well as validation in an independent cohort, which are needed in order to bring EV-based biomarkers to the clinic.

Organosilicon uptake by biological membranes.

Organosilicon compounds are ubiquitous in everyday use. Application of some of these compounds in food, cosmetics and pharmaceuticals is widespread on the assumption that these materials are not systemically absorbed. Here the interactions of various organosilicon compounds (simeticone, hexamethyldisilazane and polydimethylsiloxane) with cell membranes and models thereof were characterized with a range of analytical techniques, demonstrating that these compounds were retained in or on the cell membrane. The increasing application of organosilicon compounds as replacement of other plastics calls for a better awareness and understanding of these interactions. Moreover, with many developments in biotechnology relying on organosilicon materials, it becomes important to scrutinize the potential effect that silicone leaching may have on biological systems.

Label-free identification and chemical characterisation of single extracellular vesicles and lipoproteins by synchronous Rayleigh and Raman scattering.

Extracellular vesicles (EVs) present in blood originate from cells of different origins such as red blood cells (RBCs), platelets and leukocytes. In patients with cancer, a small portion of EVs originate from tumour cells and their load is associated with poor clinical outcome. Identification of these tumour-derived extracellular vesicles (tdEVs) is difficult as they are outnumbered by EVs of different tissue of origin as well a large number of lipoproteins (LPs) that are in the same size range. In order to detect tdEVs from the abundant presence of other particles, single-particle techniques are necessary. Here, synchronous Rayleigh and Raman scattering is used for that purpose. This combination of light scattering techniques identifies optically trapped single particles based on Rayleigh scattering and distinguishes differences in chemical composition of particle populations based on Raman scattering. Here, we show that tdEVs can be distinguished from RBC EVs and LPs in a label-free manner and directly in suspension.

Synchronized Rayleigh and Raman scattering for the characterization of single optically trapped extracellular vesicles.

Extracellular Vesicles (EVs) can be used as biomarkers in diseases like cancer, as their lineage of origin and molecular composition depend on the presence of cancer cells. Recognition of tumor-derived EVs (tdEVs) from other particles and EVs in body fluids requires characterization of single EVs to exploit their biomarker potential. We present here a new method based on synchronized Rayleigh and Raman light scattering from a single laser beam, which optically traps single EVs. Rapidly measured sequences of the Rayleigh scattering amplitude show precisely when an individual EV is trapped and the synchronously acquired Raman spectrum labels every time interval with chemical information. Raman spectra of many single EVs can thus be acquired with great fidelity in an automated manner by blocking the laser beam at regular time intervals. This new method enables single EV characterization from fluids at the single particle level.

Immuno-capture of extracellular vesicles for individual multi-modal characterization using AFM, SEM and Raman spectroscopy.

Tumor-derived extracellular vesicles (tdEVs) are promising blood biomarkers for cancer disease management. However, blood is a highly complex fluid that contains multiple objects in the same size range as tdEVs (30 nm-1 µm), which obscures an unimpeded analysis of tdEVs. Here, we report a multi-modal analysis platform for the specific capture of tdEVs on antibody-functionalized stainless steel substrates, followed by their analysis using SEM, Raman spectroscopy and AFM, at the single EV level in terms of size and size distribution, and chemical fingerprint. After covalent attachment of anti-EpCAM (epithelial cell adhesion molecule) antibodies on stainless steel substrates, EV samples derived from a prostate cancer cell line (LnCAP) were flushed into a microfluidic device assembled with this stainless steel substrate for capture. To track the captured objects between the different analytical instruments and subsequent correlative analysis, navigation markers were fabricated onto the substrate from a cyanoacrylate glue. Specific capture of tdEVs on the antibody-functionalized surface was demonstrated using SEM, AFM and Raman imaging, with excellent correlation between the data acquired by the individual techniques. The particle distribution was visualized with SEM. Furthermore, a characteristic lipid-protein band at 2850-2950 cm-1 was observed with Raman spectroscopy, and with AFM the size distribution and surface density of the captured EVs was assessed. Finally, correlation of SEM and Raman images enabled discrimination of tdEVs from cyanoacrylate glue particles, highlighting the capability of this multi-modal analysis platform for distinguishing tdEVs from contamination. The trans-instrumental compatibility of the stainless steel substrate and the possibility to spatially correlate the images of the different modalities with the help of the navigation markers open new avenues to a wide spectrum of combinations of different analytical and imaging techniques for the study of more complex EV samples.