Label-Free Prostate Cancer Detection by Characterization of Extracellular Vesicles Using Raman Spectroscopy

Identification of fluoroquinolone-resistant Mycobacterium tuberculosis through high-level data fusion of Raman and laser-induced breakdown spectroscopy

Accurate and rapid diagnosis of drug susceptibility of Mycobacterium tuberculosis is crucial for the successful treatment of tuberculosis, a persistent global public health threat. To shorten diagnosis times and enhance accuracy, this study introduces a fusion model combining laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. This model offers a rapid and accurate method for diagnosing drug-resistance. LIBS and Raman spectroscopy provide complementary information, enabling accurate identification of drug resistance in tuberculosis. Although individual use of LIBS or Raman spectroscopy achieved approximately 90% accuracy in identifying drug resistance, the fusion model significantly improved identification accuracy to 98.3%. Given the fast measurement capabilities of both techniques, this fusion approach is expected to markedly decrease the time required for diagnosis.

Advances in exosome plasmonic sensing: Device integration strategies and AI-aided diagnosis

Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, due to their label-free, real-time, and high-sensitivity features. Their advantages in multiplex immunoassays of minimal liquid samples establish the leading position in various diagnostic studies. This review delineates the application principles of plasmonic sensing technologies, highlighting the importance of exosomes-based spectrum and image signals in disease diagnostics. It also introduces advancements in miniaturizing plasmonic biosensing platforms of exosomes, which can facilitate point-of-care testing for future healthcare. Nowadays, inspired by the surge of artificial intelligence (AI) for science and technology, more and more AI algorithms are being adopted to process the exosome spectrum and image data from plasmonic detection. Using representative algorithms of machine learning has become a mainstream trend in plasmonic biosensing research for exosome liquid biopsy. Typically, these algorithms process complex exosome datasets efficiently and establish powerful predictive models for precise diagnosis. This review further discusses critical strategies of AI algorithm selection in exosome-based diagnosis. Particularly, we categorize the AI algorithms into the interpretable and uninterpretable groups for exosome plasmonic detection applications. The interpretable AI enhances the transparency and reliability of diagnosis by elucidating the decision-making process, while the uninterpretable AI provides high diagnostic accuracy with robust data processing by a "black-box" working mode. We believe that AI will continue to promote significant progress of exosome plasmonic detection and mobile healthcare in the near future.

Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review

In recent decades, research on Extracellular Vesicles (EVs) has gained prominence in the life sciences due to their critical roles in both health and disease states, offering promising applications in disease diagnosis, drug delivery, and therapy. However, their inherent heterogeneity and complex origins pose significant challenges to their preparation, analysis, and subsequent clinical application. This review is structured to provide an overview of the biogenesis, composition, and various sources of EVs, thereby laying the groundwork for a detailed discussion of contemporary techniques for their preparation and analysis. Particular focus is given to state-of-the-art technologies that employ both microfluidic and non-microfluidic platforms for EV processing. Furthermore, this discourse extends into innovative approaches that incorporate artificial intelligence and cutting-edge electrochemical sensors, with a particular emphasis on single EV analysis. This review proposes current challenges and outlines prospective avenues for future research. The objective is to motivate researchers to innovate and expand methods for the preparation and analysis of EVs, fully unlocking their biomedical potential.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

Extracellular vesicles from activated platelets possess a phospholipid-rich biomolecular profile and enhance prothrombinase activity

BACKGROUND

Extracellular vesicles (EVs), in particular those derived from activated platelets, are associated with a risk of future venous thromboembolism.

OBJECTIVES

To study the biomolecular profile and function characteristics of EVs from control (unstimulated) and activated platelets.

METHODS

Biomolecular profiling of single or very few (1-4) platelet-EVs (control/stimulated) was performed by Raman tweezers microspectroscopy. The effects of such EVs on the coagulation system were comprehensively studied.

RESULTS

Raman tweezers microspectroscopy of platelet-EVs followed by biomolecular component analysis revealed for the first time 3 subsets of EVs: (i) protein rich, (ii) protein/lipid rich, and (iii) lipid rich. EVs from control platelets presented a heterogeneous biomolecular profile, with protein-rich EVs being the main subset (58.7% ± 3.5%). Notably, the protein-rich subset may contain a minor contribution from other extracellular particles, including protein aggregates. In contrast, EVs from activated platelets were more homogeneous, dominated by the protein/lipid-rich subset (>85%), and enriched in phospholipids. Functionally, EVs from activated platelets increased thrombin generation by 52.4% and shortened plasma coagulation time by 34.6% ± 10.0% compared with 18.6% ± 13.9% mediated by EVs from control platelets (P = .015). The increased procoagulant activity was predominantly mediated by phosphatidylserine. Detailed investigation showed that EVs from activated platelets increased the activity of the prothrombinase complex (factor Va:FXa:FII) by more than 6-fold.

CONCLUSION

Our study reports a novel quantitative biomolecular characterization of platelet-EVs possessing a homogenous and phospholipid-enriched profile in response to platelet activation. Such characteristics are accompanied with an increased phosphatidylserine-dependent procoagulant activity. Further investigation of a possible role of platelet-EVs in the pathogenesis of venous thromboembolism is warranted.

Novel loading protocol combines highly efficient encapsulation of exogenous therapeutic toxin with preservation of extracellular vesicles properties, uptake and cargo activity

Extracellular vesicles (EVs) have mostly been investigated as carriers of biological therapeutics such as proteins and RNA. Nevertheless, small-molecule drugs of natural or synthetic origin have also been loaded into EVs, resulting in an improvement of their therapeutic properties. A few methods have been employed for EV cargo loading, but poor yield and drastic modifications of vesicles remain unsolved challenges. We tested a different strategy based on temporary pH alteration through incubation of EVs with alkaline sodium carbonate, which resulted in conspicuous exogenous molecule incorporation. In-depth characterization showed that vesicle size, morphology, composition, and uptake were not affected. Our method was more efficient than gold-standard electroporation, particularly for a potential therapeutic toxin: the plant Ribosome Inactivating Protein saporin. The encapsulated saporin resulted protected from degradation, and was efficiently conveyed to receiving cancer cells and triggered cell death. EV-delivered saporin was more cytotoxic compared to the free toxin. This approach allows both the structural preservation of vesicle properties and the transfer of protected cargo in the context of drug delivery.

Toward the nanoscale chemical and physical probing of milk-derived extracellular vesicles using Raman and tip-enhanced Raman spectroscopy

Tip-enhanced Raman spectroscopy (TERS) is an advanced technique to perform local chemical analysis of the surface of a sample through the improvement of the sensitivity and the spatial resolution of Raman spectroscopy by plasmonic enhancement of the electromagnetic signal in correspondence with the nanometer-sized tip of an atomic force microscope (AFM). In this work, TERS is demonstrated to represent an innovative and powerful approach for studying extracellular vesicles, in particular bovine milk-derived extracellular vesicles (mEVs), which are nanostructures with considerable potential in drug delivery and therapeutic applications. Raman spectroscopy has been used to analyze mEVs at the micrometric and sub-micrometric scales to obtain a detailed Raman spectrum in order to identify the 'signature' of mEVs in terms of their characteristic molecular vibrations and, therefore, their chemical compositions. With the ability to improve lateral resolution, TERS has been used to study individual mEVs, demonstrating the possibility of investigating a single mEV selected on the surface of the sample and, moreover, analyzing specific locations on the selected mEV with nanometer lateral resolution. TERS potentially allows one to reveal local differences in the composition of mEVs providing new insights into their structure. Also, thanks to the intrinsic properties of TERS to acquire the signal from only the first few nanometers of the surface, chemical investigation of the lipid membrane in correspondence with the various locations of the selected mEV could be performed by analyzing the peaks of the Raman shift in the relevant range of the spectrum (2800-3000 cm-1). Despite being limited to mEVs, this work demonstrates the potential of TERS in the analysis of extracellular vesicles.

Plasmonic dielectric antennas for hybrid optical nanotweezing and optothermoelectric manipulation of single nanosized extracellular vesicles

This paper showcases an experimental demonstration of near-field optical trapping and dynamic manipulation of an individual extracellular vesicle. This is accomplished through the utilization of a plasmonic dielectric nanoantenna designed to support an optical anapole state-a non-radiating optical state resulting from the destructive interference between electric and toroidal dipoles in the far-field, leading to robust near-field enhancement. To further enhance the field intensity associated with the optical anapole state, a plasmonic mirror is incorporated, thereby boosting trapping capabilities. In addition to demonstrating near-field optical trapping, the study achieves dynamic manipulation of extracellular vesicles by harnessing the thermoelectric effect. This effect is induced in the presence of an ionic surfactant, cetyltrimethylammonium chloride (CTAC), combined with plasmonic heating. Furthermore, the thermoelectric effect improves trapping stability by introducing a wide and deep trapping potential. In summary, our hybrid plasmonic-dielectric trapping platform offers a versatile approach for actively transporting, stably trapping, and dynamically manipulating individual extracellular vesicles.

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.

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly.

Acoustofluidic lysis of cancer cells and Raman spectrum profiling

The lysis of cancer cells inside a sessile droplet was performed using traveling surface acoustic waves (SAWs) without any chemical reagents. Raman spectrum profiling was then carried out to explore detailed cell-derived data. The Rayleigh waves formed by an interdigital transducer were made to propagate along the surface of an LiNbO3 substrate. Polystyrene microparticles (PSMPs) were used to establish mechanical cell lysis effectively, and gold nanoparticles (AuNPs) were added to enhance the Raman signals from the lysed cells by SAWs. The lysis efficiency was evaluated according to the size and concentration of the PSMPs in experiments where the frequency was varied. Lysis occurred mainly by mechanical collision using PSMPs in a high-frequency domain, and the lysis efficiency was improved by increasing the application time and the energy density of the SAWs. Raman signals from the lysed cells were greatly enhanced by nanogaps formed by the AuNPs, which were evenly distributed irrespective of the SAWs through the frequency-independent behavior of the AuNPs. Finally, detailed Raman spectra of MDA-MB-231, malignant breast cancer cells, were acquired, and various organic matter-derived peaks were observed. The 95% confidence region for cells subjected to lysis was more widely distributed than that of cells not subjected to lysis. The proposed SAW platform is expected to facilitate the detection of small quantities and to be applied in biomedical applications.

Demonstrating low Raman background in UV-written SiO2 waveguides

Raman spectroscopy can give a chemical 'fingerprint' from both inorganic and organic samples, and has become a viable method of measuring the chemical composition of single biological particles. In parallel, integration of waveguides and microfluidics allows for the creation of miniaturized optical sensors in lab-on-a-chip devices. The prospect of combining integrated optics and Raman spectroscopy for Raman-on-chip offers new opportunities for optical sensing. A major limitation for this is the Raman background of the waveguide. This background is very low for optical fibers but remains a challenge for planar waveguides. In this work, we demonstrate that UV-written SiO2 waveguides, designed to mimic the performance of optical fibers, offer a significantly lower background than competing waveguide materials such as Si3N4. The Raman scattering in the waveguides is measured in absolute units and compared to that of optical fibers and Si3N4 waveguides. A limited study of the sensitivity of the Raman scattering to changes in pump wavelength and in waveguide design is also conducted. It is revealed that UV-written SiO2 waveguides offer a Raman background lower than -107.4 dB relative to a 785 nm pump and -106.5 dB relative to a 660 nm pump. Furthermore, the UV-written SiO2 waveguide demonstrates a 15 dB lower Raman background than a Si3N4 waveguide and is only 8.7 - 10.3 dB higher than optical fibers. Comparison with a polystyrene bead (in free space, diameter 7 µm) reveal an achievable peak SNR of 10.4 dB, showing the potential of UV-SiO2 as a platform for a Raman-on-chip device capable of measuring single particles.

High resolution imaging and analysis of extracellular vesicles using mass spectral imaging and machine learning

Extracellular vesicles (EVs) are potentially useful biomarkers for disease detection and monitoring. Development of a label-free technique for imaging and distinguishing small volumes of EVs from different cell types and cell states would be of great value. Here, we have designed a method to explore the chemical changes in EVs associated with neuroinflammation using Time-of-Flight Secondary Ion Mass spectrometry (ToF-SIMS) and machine learning (ML). Mass spectral imaging was able to identify and differentiate EVs released by microglia following lipopolysaccharide (LPS) stimulation compared to a control group. This process requires a much smaller sample size (1 µL) than other molecular analysis methods (up to 50 µL). Conspicuously, we saw a reduction in free cysteine thiols (a marker of cellular oxidative stress associated with neuroinflammation) in EVs from microglial cells treated with LPS, consistent with the reduced cellular free thiol levels measured experimentally. This validates the synergistic combination of ToF-SIMS and ML as a sensitive and valuable technique for collecting and analysing molecular data from EVs at high resolution.

Feasibility of Raman spectroscopic identification of gall bladder cancer using extracellular vesicles extracted from bile

Extracellular vesicles (EVs), which are heterogeneous membrane-based vesicles with bilayer cell membrane structures, could be versatile biomarkers for the identification of diverse diseases including cancers. With this potential, this study has attempted the Raman spectroscopic identification of gall bladder (GB) cancer by directly measuring the EV solution extracted from human bile without further sample drying. For this purpose, bile samples were obtained from four normal individuals and 21 GB polyp, eight hepatocellular carcinoma (HCC), and five GB cancer patients, and EVs were extracted from each of the bile samples. The Raman peak shapes of the EVs extracted from the GB cancer samples, especially the relative intensities of peaks in the 1560-1340 cm-1 range, were dissimilar to those of the samples from the normal, GB polyp, and HCC groups. The intensity ratios of peaks at 1537 and 1453 cm-1 and at 1395 and 1359 cm-1 of the GB cancer samples were lower and higher, respectively, than those of the samples of the remaining three groups. The differences of peak intensity ratios were statistically significant based on the Mann-Whitney U test. DNA/RNA bases, amino acids, and bile salts contributed to the spectra of EVs, and their relative abundances seemed to vary according to the occurrence of GB cancer. The varied metabolite compositions and/or structures of EVs were successfully demonstrated by the dissimilar peak intensity ratios in the Raman spectra, thereby enabling the discrimination of GB cancer.

Scalable trapping of single nanosized extracellular vesicles using plasmonics

Heterogeneous nanoscale extracellular vesicles (EVs) are of significant interest for disease detection, monitoring, and therapeutics. However, trapping these nano-sized EVs using optical tweezers has been challenging due to their small size. Plasmon-enhanced optical trapping offers a solution. Nevertheless, existing plasmonic tweezers have limited throughput and can take tens of minutes for trapping for low particle concentrations. Here, we present an innovative approach called geometry-induced electrohydrodynamic tweezers (GET) that overcomes these limitations. GET generates multiple electrohydrodynamic potentials, allowing parallel transport and trapping of single EVs within seconds. By integrating nanoscale plasmonic cavities at the center of each GET trap, single EVs can be placed near plasmonic cavities, enabling instant plasmon-enhanced optical trapping upon laser illumination without detrimental heating effects. These non-invasive scalable hybrid nanotweezers open new horizons for high-throughput tether-free plasmon-enhanced single EV trapping and spectroscopy. Other potential areas of impact include nanoplastics characterization, and scalable hybrid integration for quantum photonics.

3D plasmonic coral nanoarchitecture paper for label-free human urine sensing and deep learning-assisted cancer screening

Practical human biofluid sensing requires a sensor device to differentiate patients from the normal group with high sensitivity and specificity. Label-free molecular identification from human biofluids allows direct classification of abnormal samples, providing insights for disease diagnosis and finding of new biomarkers. Here, we introduce a label-free surface-enhanced Raman scattering sensor based on a three-dimensional plasmonic coral nanoarchitecture (3D-PCN), which has strong electromagnetic field enhancement through multiple hot spots. The 3D-PCN was synthesized on a paper substrate via direct one-step gold reduction, forming a coral-like nanoarchitecture with high absorption property for biofluids. This was fabricated as a urine test strip and then integrated with a handheld Raman system to develop an on-site urine diagnostic platform. The developed platform successfully classified the human prostate and pancreatic cancer urines in a label-free method supported by two types of deep learning networks, with high clinical sensitivity and specificity. Our technology has the potential to be utilized not only for urinary cancer diagnosis but also for various human biofluid sensing systems as a future point-of-care testing platform.

Distance-controlled surface-enhanced Raman spectroscopy of nanoparticles

Biological particles, e.g., viruses, lipid particles, and extracellular vesicles, are attracting significant research interest due to their role in biological processes and potential in practical applications, such as vaccines, diagnostics, and therapies. Their surface and interior contain many different molecules including lipids, nucleic acids, proteins, and carbohydrates. In this Letter, we show how distance-controlled surface-enhanced Raman spectroscopy (SERS) is a promising method to extract essential information from the spatial origin of the signal. This is a highly important parameter in the analysis of these biological particles. The principle of the method is demonstrated by using polystyrene (PS) beads as a biological particle model conjugated with gold nanospheres (AuNSs) functioning as distance-controlled SERS probes via biotin-streptavidin binding. By tuning the size of AuNSs, the Raman signal from the PS beads can be weakened while the signal from the biotin-streptavidin complex is enhanced.

Self-supervised learning for inter-laboratory variation minimization in surface-enhanced Raman scattering spectroscopy

Surface-enhanced Raman scattering (SERS) spectroscopy is still considered poorly reproducible despite its numerous advantages and is not a sufficiently robust analytical technique for routine implementation outside of academia. In this article, we present a self-supervised deep learning-based information fusion technique to minimize the variance in the SERS measurements of multiple laboratories for the same target analyte. In particular, a variation minimization model, coined the minimum-variance network (MVNet), is designed. Moreover, a linear regression model is trained using the output of the proposed MVNet. The proposed model showed improved performance in predicting the concentration of the unseen target analyte. The linear regression model trained on the output of the proposed model was evaluated by several well-known metrics, such as root mean square error of prediction (RMSEP), BIAS, standard error of prediction (SEP), and coefficient of determination (R²). The leave-one-lab-out cross-validation (LOLABO-CV) results indicate that the MVNet also minimizes the variance of completely unseen laboratory datasets while improving the reproducibility and linear fit of the regression model. The Python implementation of MVNet and the code for the analysis can be found on the GitHub page https://github.com/psychemistz/MVNet.

Metasurface supporting quasi-BIC for optical trapping and Raman-spectroscopy of biological nanoparticles

Optical trapping combined with Raman spectroscopy have opened new possibilities for analyzing biological nanoparticles. Conventional optical tweezers have proven successful for trapping of a single or a few particles. However, the method is slow and cannot be used for the smallest particles. Thus, it is not adapted to analyze a large number of nanoparticles, which is necessary to get statistically valid data. Here, we propose quasi-bound states in the continuum (quasi-BICs) in a silicon nitride (Si₃N₄) metasurface to trap smaller particles and many simultaneously. The quasi-BIC metasurface contains multiple zones with high field-enhancement ('hotspots') at a wavelength of 785 nm, where a single nanoparticle can be trapped at each hotspot. We numerically investigate the optical trapping of a type of biological nanoparticles, namely extracellular vesicles (EVs), and study how their presence influences the resonance behavior of the quasi-BIC. It is found that perturbation theory and a semi-analytical expression give good estimates for the resonance wavelength and minimum of the potential well, as a function of the particle radius. This wavelength is slightly shifted relative to the resonance of the metasurface without trapped particles. The simulations show that the Q-factor can be increased by using a thin metasurface. The thickness of the layer and the asymmetry of the unit cell can thus be used to get a high Q-factor. Our findings show the tight fabrication tolerances necessary to make the metasurface. If these can be overcome, the proposed metasurface can be used for a lab-on-a-chip for mass-analysis of biological nanoparticles.

Dean-Flow-Coupled Elasto-Inertial Focusing Accelerates Exosome Purification to Facilitate Single Vesicle Profiling

Exosomes are recognized as noteworthy biomarkers playing unprecedented roles in intercellular communication and disease diagnosis and treatment. It is a prerequisite to obtain high-purity exosomes for the comprehension of exosome biochemistry and further illustration of their functionality/mechanisms. However, the isolation of nanoscale exosomes from endogenous proteins is particularly challenging for small-volume biological samples. Herein, a Dean-flow-coupled elasto-inertial microfluidic chip (DEIC) was developed. It consists of a spiral microchannel with dimensional confined concave structures and facilitates elasto-inertial separation of exosomes with lower protein contaminants from cell culture medium and human serum. The presence of 0.15% (w/v) poly-(oxyethylene) controls the elastic lift force acting on suspended nanoscale particles and makes it feasible for field-free purification of integrity exosomes with a 70.6% recovery and a 91.4% removal rate for proteins. As a proof of concept, the technique demonstrated the individual-vesicle-level biomarker (EpCAM and PD-L1) profiling in combination with simultaneous aptamer-mediated analysis to disclose the sensibility for immune response. Overall, DEIC enables the collection of high-purity exosomes and exhibits potential in integration with downstream analyses of exosomes.

Isolation and characterization of extracellular vesicles and future directions in diagnosis and therapy

Extracellular vesicles (EVs) are a unique and heterogeneous class of lipid bilayer nanoparticles secreted by most cells. EVs are regarded as important mediators of intercellular communication in both prokaryotic and eukaryotic cells due to their ability to transfer proteins, lipids and nucleic acids to recipient cells. In addition to their physiological role, EVs are recognized as modulators in pathological processes such as cancer, infectious diseases, and neurodegenerative disorders, providing new potential targets for diagnosis and therapeutic intervention. For a complete understanding of EVs as a universal cellular biological system and its translational applications, optimal techniques for their isolation and characterization are required. Here, we review recent progress in those techniques, from isolation methods to characterization techniques. With interest in therapeutic applications of EVs growing, we address fundamental points of EV-related cell biology, such as cellular uptake mechanisms and their biodistribution in tissues as well as challenges to their application as drug carriers or biomarkers for less invasive diagnosis or as immunogens. This article is categorized under: Diagnostic Tools > Biosensing Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease.

On an Affordable Approach towards the Diagnosis and Care for Prostate Cancer Patients Using Urine, FTIR and Prediction Machines

Prostate cancer is a widespread form of cancer that affects patients globally and is challenging to diagnose, especially in its early stages. The common means of diagnosing cancer involve mostly invasive methods, such as the use of patient’s blood as well as digital biopsies, which are relatively expensive and require a considerable amount of expertise. Studies have shown that various cancer biomarkers can be present in urine samples from patients who have prostate cancers; this paper aimed to leverage this information and investigate this further by using urine samples from a group of patients alongside FTIR analysis for the prediction of prostate cancer. This investigation was carried out using three sets of data where all spectra were preprocessed with the linear series decomposition learner (LSDL) and post-processed using signal processing methods alongside a contrast across nine machine-learning models, the results of which showcased that the proposed modeling approach carries potential to be used for clinical prediction of prostate cancer. This would allow for a much more affordable and high-throughput means for active prediction and associated care for patients with prostate cancer. Further investigations on the prediction of cancer stage (i.e., early or late stage) were carried out, where high prediction accuracy was obtained across the various metrics that were investigated, further showing the promise and capability of urine sample analysis alongside the proposed and presented modeling approaches.

Shining a Light on Cancer - Photonics in Microfluidic Tumor Modelling and Biosensing

Microfluidic platforms represent a powerful approach to miniaturizing important characteristics of cancers, improving in vitro testing by increasing physiological relevance. Different tools can manipulate cells and materials at the microscale, but few offer the efficiency and versatility of light and optical technologies. Moreover, light-driven technologies englobe a broad toolbox for quantifying critical biological phenomena. Herein, we review the role of photonics in microfluidic 3D cancer modeling and biosensing from three major perspectives. First, we look at optical-driven technologies that allow biomaterials and living cells to be manipulated with micro-sized precision and the opportunities to advance 3D microfluidic models by engineering cancer microenvironments' hallmarks, such as their architecture, cellular complexity, and vascularization. Second, we delve into the growing field of optofluidics, exploring how optical tools can directly interface microfluidic chips, enabling the extraction of relevant biological data, from single fluorescent signals to the complete 3D imaging of diseased cells within microchannels. Third, we review advances in optical cancer biosensing, focusing on how light-matter interactions can detect biomarkers, rare circulating tumor cells, and cell-derived structures such as exosomes. We overview photonic technologies' current challenges and caveats in microfluidic 3D cancer models, outlining future research avenues that may catapult the field. This article is protected by copyright. All rights reserved.

Recent advances in optical label-free characterization of extracellular vesicles

Extracellular vesicles (EVs) are complex biological nanoparticles endogenously secreted by all eukaryotic cells. EVs carry a specific molecular cargo of proteins, lipids, and nucleic acids derived from cells of origin and play a significant role in the physiology and pathology of cells, organs, and organisms. Upon release, they may be found in different body fluids that can be easily accessed via noninvasive methodologies. Due to the unique information encoded in their molecular cargo, they may reflect the state of the parent cell and therefore EVs are recognized as a rich source of biomarkers for early diagnostics involving liquid biopsy. However, body fluids contain a mixture of EVs released by different types of healthy and diseased cells, making the detection of the EVs of interest very challenging. Recent research efforts have been focused on the detection and characterization of diagnostically relevant subpopulations of EVs, with emphasis on label-free methods that simplify sample preparation and are free of interfering signals. Therefore, in this paper, we review the recent progress of the label-free optical methods employed for the detection, counting, and morphological and chemical characterization of EVs. We will first briefly discuss the biology and functions of EVs, and then introduce different optical label-free techniques for rapid, precise, and nondestructive characterization of EVs such as nanoparticle tracking analysis, dynamic light scattering, atomic force microscopy, surface plasmon resonance spectroscopy, Raman spectroscopy, and SERS spectroscopy. In the end, we will discuss their applications in the detection of neurodegenerative diseases and cancer and provide an outlook on the future impact and challenges of these technologies to the field of liquid biopsy via EVs.

Label-free discrimination of tumorigenesis stages using in vitro prostate cancer bone metastasis model by Raman imaging

Metastatic prostate cancer colonizes the bone to pave the way for bone metastasis, leading to skeletal complications associated with poor prognosis and morbidity. This study demonstrates the feasibility of Raman imaging to differentiate between cancer cells at different stages of tumorigenesis using a nanoclay-based three-dimensional (3D) bone mimetic in vitro model that mimics prostate cancer bone metastasis. A comprehensive study comparing the classification of as received prostate cancer cells in a two-dimensional (2D) model and cancer cells in a 3D bone mimetic environment was performed over various time intervals using principal component analysis (PCA). Our results showed distinctive spectral differences in Raman imaging between prostate cancer cells and the cells cultured in 3D bone mimetic scaffolds, particularly at 1002, 1261, 1444, and 1654 cm-1, which primarily contain proteins and lipids signals. Raman maps capture sub-cellular responses with the progression of tumor cells into metastasis. Raman feature extraction via cluster analysis allows for the identification of specific cellular constituents in the images. For the first time, this work demonstrates a promising potential of Raman imaging, PCA, and cluster analysis to discriminate between cancer cells at different stages of metastatic tumorigenesis.

Raman spectroscopy combined with comprehensive gas chromatography for label-free characterization of plasma-derived extracellular vesicle subpopulations

Raman spectroscopy together with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GCxGC-TOFMS) was employed to characterize exomere- (<50 nm) and exosome-sized (50-80 nm) EVs isolated from human plasma by the novel on-line immunoaffinity chromatography - asymmetric flow field-flow fractionation method. CD9⁺, CD63⁺, and CD81⁺ EVs were selected to represent general EV subpopulations secreted into plasma, while CD61+EVs represented the specific EV subset derived from platelets. Raman spectroscopy could distinguish EVs from non-EV particles, including apolipoprotein B-100-containing lipoproteins, signifying its potential in EV purity assessment. Moreover, platelet-derived (CD61⁺) EVs of both exomere and exosome sizes were discriminated from other EV subpopulations due to different biochemical compositions. Further investigations demonstrated composition differences between exomere- and exosome-sized EVs, confirming the applicability of Raman spectroscopy in distinguishing EVs, not only from different origins but also sizes. In addition, fatty acids that act as building blocks for lipids and membranes in EVs were studied by GCxGC-TOF-MS. The results achieved highlighted differences in EV fatty acid compositions in both esterified (membrane lipids) and non-esterified (free fatty acids) fractions, indicating possible differences in membrane structures, biological functions, and roles in cell-to-cell communications of EV subpopulations.

Overview of extracellular vesicle characterization techniques and introduction to combined reflectance and fluorescence confocal microscopy to distinguish extracellular vesicle subpopulations

Extracellular vesicles (EVs) are nanoparticles (30 to 1000 nm in diameter) surrounded by a lipid-bilayer which carry bioactive molecules between local and distal cells and participate in intercellular communication. Because of their small size and heterogenous nature they are challenging to characterize. Here, we discuss commonly used techniques that have been employed to yield information about EV size, concentration, mechanical properties, and protein content. These include dynamic light scattering, nanoparticle tracking analysis, flow cytometry, transmission electron microscopy, atomic force microscopy, western blotting, and optical methods including super-resolution microscopy. We also introduce an innovative technique for EV characterization which involves immobilizing EVs on a microscope slide before staining them with antibodies targeting EV proteins, then using the reflectance mode on a confocal microscope to locate the EV plane. By then switching to the microscope's fluorescence mode, immunostained EVs bearing specific proteins can be identified and the heterogeneity of an EV preparation can be determined. This approach does not require specialist equipment beyond the confocal microscopes that are available in many cell biology laboratories, and because of this, it could become a complementary approach alongside the aforementioned techniques to identify molecular heterogeneity in an EV preparation before subsequent analysis requiring specialist apparatus.

Raman Spectroscopy in Prostate Cancer: Techniques, Applications and Advancements

Optical techniques are widely used tools in the visualisation of biological species within complex matrices, including biopsies, tissue resections and biofluids. Raman spectroscopy is an emerging analytical approach that probes the molecular signature of endogenous cellular biomolecules under biocompatible conditions with high spatial resolution. Applications of Raman spectroscopy in prostate cancer include biopsy analysis, assessment of surgical margins and monitoring of treatment efficacy. The advent of advanced Raman imaging techniques, such as stimulated Raman scattering, is creating opportunities for real-time in situ evaluation of prostate cancer. This review provides a focus on the recent preclinical and clinical achievements in implementing Raman-based techniques, highlighting remaining challenges for clinical applications. The research and clinical results achieved through in vivo and ex vivo Raman spectroscopy illustrate areas where these evolving technologies can be best translated into clinical practice.

Coincident Fluorescence-Burst Analysis of the Loading Yields of Exosome-Mimetic Nanovesicles with Fluorescently-Labeled Cargo Molecules

The possible targeting functionality and low immunogenicity of exosomes and exosome-like nanovesicles make them promising as drug-delivery carriers. To tap into this potential, accurate non-destructive methods to load them and characterize their contents are of utmost importance. However, the small size, polydispersity, and aggregation of nanovesicles in solution make quantitative characterizations of their loading particularly challenging. Here, an ad-hoc methodology is developed based on burst analysis of dual-color confocal fluorescence microscopy experiments, suited for quantitative characterizations of exosome-like nanovesicles and of their fluorescently-labeled loading. It is applied to study exosome-mimetic nanovesicles derived from animal extracellular-vesicles and human red blood cell detergent-resistant membranes, loaded with fluorescently-tagged dUTP cargo molecules. For both classes of nanovesicles, successful loading is proved and by dual-color coincident fluorescence burst analysis, size statistics and loading yields are retrieved and quantified. The procedure affords single-vesicle characterizations well-suited for the investigation of a variety of cargo molecules and biological nanovesicle combinations besides the proof-of-principle demonstrations of this study. The results highlight a powerful characterization tool essential for optimizing the loading process and for advanced engineering of biomimetic nanovesicles for therapeutic drug delivery.

Infrared and Raman spectroscopy for purity assessment of extracellular vesicles

Extracellular vesicles (EVs) are a complex and heterogeneous population of nanoparticles involved in cell-to-cell communication. Recently, numerous studies have indicated the potential of EVs as therapeutic agents, drug carriers and diagnostic tools. However, the results of these studies are often difficult to evaluate, since different characterization methods are used to assess the purity, physical and biochemical characteristics of the EV samples. In this study, we compared four methods for the EV sample characterization and purity assessment: i) the particle-to-protein ratio based on particle analyses with nanoparticle tracking and protein concentration by bicinchoninic acid assay, ii) Western Blot analysis for specific EV biomarkers, iii) two spectroscopic lipid-to-protein ratios by either the attenuated total reflection Fourier transform infrared (ATR-FTIR) or Raman spectroscopy. The results confirm the value of Raman and ATR-FTIR spectroscopy as robust, fast and operator independent tools that require only a few microliters of EV sample. We propose that the spectroscopic lipid-to-protein (Li/Pr) ratios are reliable parameters for the purity assessment of EV preparations. Moreover, apart from determining protein concentrations, we show that ATR-FTIR spectroscopy can also be used for indirect measurements of EV concentrations. Nevertheless, the Li/Pr ratios do not represent full characterization of the EV preparations. For a complete characterization of selected EV preparations, we recommend also additional use of particle size distribution and EV biomarker analysis.

Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing

The mortality of ovarian cancer is closely related to its poor rate of early detection. In the search of an efficient diagnosis method, Raman spectroscopy of blood features as a promising technique allowing simple, rapid, minimally-invasive and cost-effective detection of cancers, in particular ovarian cancer. Although Raman spectroscopy has been demonstrated to be effective to detect ovarian cancers with respect to normal controls, a binary classification remains idealized with respect to the real clinical practice. This work considered a population of 95 woman patients initially suspected of an ovarian cancer and finally fixed with a cancer or a cyst. Additionally, 79 normal controls completed the ensemble of samples. Such sample collection proposed us a study case where a ternary classification should be realized with Raman spectroscopy of the collected blood samples coupled with suitable spectroscopic data treatment algorithms. In the medical as well as data points of view, the appearance of the cyst case considerably reduces the distances among the different populations and makes their distinction much more difficult, since the intermediate cyst case can share the specific features of the both cancer and normal cases. After a proper spectrum pretreatment, we first demonstrated the evidence of different behaviors among the Raman spectra of the 3 types of samples. Such difference was further visualized in a high dimensional space, where the data points of the cancer and the normal cases are separately clustered, whereas the data of the cyst case were scattered into the areas respectively occupied by the cancer and normal cases. We finally developed and tested an ensemble of models for a ternary classification with 2 consequent steps of binary classifications, based on machine learning algorithms, allowing identification with sensitivity and specificity of 81.0% and 97.3% for cancer samples, 63.6% and 91.5% for cyst samples, 100% and 90.6% for normal samples.

Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy

Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements often cause problems that produce varying results. It raises concerns about the reproducibility of the results. Consequently, the manual classification of the SERS spectrum requires carefully controlled experimentation that further hinders the large-scale adaptation. Recent advances in machine learning offer decent opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are missing. Towards this end, we provide the SERS spectral benchmark dataset of lead(II) nitride (Pb(NO3)2) for a heavy metal ion detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. The proposed model can successfully identify the Pb(NO3)2 molecule from SERS measurements of independent test experiments. In particular, the proposed model shows an 84.6% balanced accuracy for the cross-batch testing task.

Untargeted urine metabolite profiling by mass spectrometry aided by multivariate statistical analysis to predict prostate cancer treatment outcome

One of the key barriers to the prostate cancer is monitor treatment response. Here we described a conceptually new ‘MS-statistical analysis-metabolome’ strategy for discovery of metabolic features.

Raman spectroscopy of urinary extracellular vesicles to stratify patients with chronic kidney disease in type 2 diabetes

In this study, we verified the hypothesis that Raman signature of urinary extracellular vesicles (UEVs) can be used to stratify patients with diabetes at various stages of chronic kidney disease (CKD). Patients with type 2 diabetes diagnosed with different stages of CKD and healthy subjects were enrolled in the study. UEVs were isolated using low-vacuum filtration followed by ultracentrifugation. Correlation analysis, multiple linear regression and principal component analysis were used to find differences between spectral fingerprints of UEVs derived from both groups of patients. Electron microscopy and nanoparticle tracking analysis were applied to characterize the size and morphology of UEVs. We observed significant correlations between selected Raman bands measured for UEVs and clinical parameters. We found significant differences in the area under the specific bands originating mainly from proteins and lipids between the study groups. Based on the tryptophan and amide III bands, we were able to predict the estimated glomerular filtration rate (eGFR). Principal component analysis, partial least squares regression (PLSR) and correlation analysis of the UEV Raman spectra supported the results obtained from the direct analysis of Raman spectra. Our analysis revealed that PLSR and a regression model including tryptophan and amide III bands allows to estimate the value of eGFR.

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network

Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.

Single Particle Automated Raman Trapping Analysis of Breast Cancer Cell-Derived Extracellular Vesicles as Cancer Biomarkers

Extracellular vesicles (EVs) secreted by cancer cells provide an important insight into cancer biology and could be leveraged to enhance diagnostics and disease monitoring. This paper details a high-throughput label-free extracellular vesicle analysis approach to study fundamental EV biology, toward diagnosis and monitoring of cancer in a minimally invasive manner and with the elimination of interpreter bias. We present the next generation of our single particle automated Raman trapping analysis─SPARTA─system through the development of a dedicated standalone device optimized for single particle analysis of EVs. Our visualization approach, dubbed dimensional reduction analysis (DRA), presents a convenient and comprehensive method of comparing multiple EV spectra. We demonstrate that the dedicated SPARTA system can differentiate between cancer and noncancer EVs with a high degree of sensitivity and specificity (&gt;95% for both). We further show that the predictive ability of our approach is consistent across multiple EV isolations from the same cell types. Detailed modeling reveals accurate classification between EVs derived from various closely related breast cancer subtypes, further supporting the utility of our SPARTA-based approach for detailed EV profiling.

Progress in Nanomaterials-Based Optical and Electrochemical Methods for the Assays of Exosomes

Exosomes with diameters of 30-150 nm are small membrane-bound vesicles secreted by a variety of cells. They play an important role in many biological processes, such as tumor-related immune response and intercellular signal transduction. Exosomes have been considered as emerging and noninvasive biomarkers for cancer diagnosis. Recently, a large number of optical and electrochemical biosensors have been proposed for sensitive detection of exosomes. To meet the increasing demands for ultrasensitive detection, nanomaterials have been integrated with various techniques as powerful components. Because of their intrinsic merits of biological compatibility, excellent physicochemical features and unique catalytic ability, nanomaterials have significantly improved the analytical performances of exosome biosensors. In this review, we summarized the recent progress in nanomaterials-based biosensors for the detection of cancer-derived exosomes, including fluorescence, colorimetry, surface plasmon resonance spectroscopy, surface enhanced Raman scattering spectroscopy, electrochemistry, electrochemiluminescence and so on.

Surface enhanced Raman scattering of extracellular vesicles for cancer diagnostics despite isolation dependent lipoprotein contamination

Given the emerging diagnostic utility of extracellular vesicles (EVs), it is important to account for non-EV contaminants. Lipoprotein present in EV-enriched isolates may inflate particle counts and decrease sensitivity to biomarkers of interest, skewing chemical analyses and perpetuating downstream issues in labeling or functional analysis. Using label free surface enhanced Raman scattering (SERS), we confirm that three common EV isolation methods (differential ultracentrifugation, density gradient ultracentrifugation, and size exclusion chromatography) yield variable lipoprotein content. We demonstrate that a dual-isolation method is necessary to isolate EVs from the major classes of lipoprotein. However, combining SERS analysis with machine learning assisted classification, we show that the disease state is the main driver of distinction between EV samples, and largely unaffected by choice of isolation. Ultimately, this study describes a convenient SERS assay to retain accurate diagnostic information from clinical samples by overcoming differences in lipoprotein contamination according to isolation method.

Tracking of the biochemical changes upon pleomorphic adenoma progression using vibrational microspectroscopy

Head and neck tumors can be very challenging to treat because of the risk of problems or complications after surgery. Therefore, prompt and accurate diagnosis is extremely important to drive appropriate treatment decisions, which may reduce the chance of recurrence. This paper presents the original research exploring the feasibility of Fourier transform infrared (FT-IR) and Raman spectroscopy (RS) methods to investigate biochemical alterations upon the development of the pleomorphic adenoma. Principal component analysis (PCA) was used for a detailed assessment of the observed changes and to determine the spectroscopic basis for salivary gland neoplastic pathogenesis. It is implied that within the healthy margin, as opposed to the tumoral tissue, there are parts that differ significantly in lipid content. This observation shed new light on the crucial role of lipids in tissue physiology and tumorigenesis. Thus, a novel approach that eliminates the influence of lipids on the elucidation of biochemical changes is proposed. The performed analysis suggests that the highly heterogeneous healthy margin contains more unsaturated triacylglycerols, while the tumoral section is rich in proteins. The difference in protein content was also observed for these two tissue types, i.e. the healthy tissue possesses more proteins in the anti-parallel β-sheet conformation, whereas the tumoral tissue is dominated by proteins rich in unordered random coils. Furthermore, the pathogenic tissue shows a higher content of carbohydrates and reveals noticeable differences in nucleic acid content. Finally, FT-IR and Raman spectroscopy methods were proposed as very promising methods in the discrimination of tumoral and healthy tissues of the salivary gland.

Identification of amyloid beta in small extracellular vesicles via Raman spectroscopy

One of the hallmarks of Alzheimer's disease (AD) pathogenesis is believed to be the production and deposition of amyloid-beta (Aβ) peptide into extracellular plaques. Existing research indicates that extracellular vesicles (EVs) can carry Aβ associated with AD. However, characterization of the EVs-associated Aβ and its conformational variants has yet to be realized. Raman spectroscopy is a label-free and non-destructive method that is able to assess the biochemical composition of EVs. This study reports for the first time the Raman spectroscopic fingerprint of the Aβ present in the molecular cargo of small extracellular vesicles (sEVs). Raman spectra were measured from sEVs isolated from Alzheimer's disease cell culture model, where secretion of Aβ is regulated by tetracycline promoter, and from midbrain organoids. The averaged spectra of each sEV group showed considerable variation as a reflection of the biochemical content of sEVs. Spectral analysis identified more intense Raman peaks at 1650 cm^-1 and 2930 cm^-1 attributable to the Aβ peptide incorporated in sEVs produced by the Alzheimer's cell culture model. Subsequent analysis of the spectra by principal component analysis differentiated the sEVs of the Alzheimer's disease cell culture model from the control groups of sEVs. Moreover, the results indicate that Aβ associated with secreted sEVs has a α-helical secondary structure and the size of a monomer or small oligomer. Furthermore, by analyzing the lipid content of sEVs we identified altered fatty acid chain lengths in sEVs that carry Aβ that may affect the fluidity of the EV membrane. Overall, our findings provide evidence supporting the use of Raman spectroscopy for the identification and characterization of sEVs associated with potential biomarkers of neurological disorders such as toxic proteins.

Using single-vesicle technologies to unravel the heterogeneity of extracellular vesicles

Extracellular vesicles (EVs) are heterogeneous lipid containers with a complex molecular cargo comprising several populations with unique roles in biological processes. These vesicles are closely associated with specific physiological features, which makes them invaluable in the detection and monitoring of various diseases. EVs play a key role in pathophysiological processes by actively triggering genetic or metabolic responses. However, the heterogeneity of their structure and composition hinders their application in medical diagnosis and therapies. This diversity makes it difficult to establish their exact physiological roles, and the functions and composition of different EV (sub)populations. Ensemble averaging approaches currently employed for EV characterization, such as western blotting or 'omics' technologies, tend to obscure rather than reveal these heterogeneities. Recent developments in single-vesicle analysis have made it possible to overcome these limitations and have facilitated the development of practical clinical applications. In this review, we discuss the benefits and challenges inherent to the current methods for the analysis of single vesicles and review the contribution of these approaches to the understanding of EV biology. We describe the contributions of these recent technological advances to the characterization and phenotyping of EVs, examination of the role of EVs in cell-to-cell communication pathways and the identification and validation of EVs as disease biomarkers. Finally, we discuss the potential of innovative single-vesicle imaging and analysis methodologies using microfluidic devices, which promise to deliver rapid and effective basic and practical applications for minimally invasive prognosis systems.

Surface-Enhanced Raman Spectroscopy to Characterize Different Fractions of Extracellular Vesicles from Control and Prostate Cancer Patients

Extracellular vesicles (EVs) are membrane-enclosed structures ranging in size from about 60 to 800 nm that are released by the cells into the extracellular space; they have attracted interest as easily available biomarkers for cancer diagnostics. In this study, EVs from plasma of control and prostate cancer patients were fractionated by differential centrifugation at 5000× g, 12,000× g and 120,000× g. The remaining supernatants were purified by ultrafiltration to produce EV-depleted free-circulating (fc) fractions. Spontaneous Raman and surface-enhanced Raman spectroscopy (SERS) at 785 nm excitation using silver nanoparticles (AgNPs) were employed as label-free techniques to collect fingerprint spectra and identify the fractions that best discriminate between control and cancer patients. SERS spectra from 10 µL droplets showed an enhanced Raman signature of EV-enriched fractions that were much more intense for cancer patients than controls. The Raman spectra of dehydrated pellets of EV-enriched fractions without AgNPs were dominated by spectral contributions of proteins and showed variations in S-S stretch, tryptophan and protein secondary structure bands between control and cancer fractions. We conclude that the AgNPs-mediated SERS effect strongly enhances Raman bands in EV-enriched fractions, and the fractions, EV12 and EV120 provide the best separation of cancer and control patients by Raman and SERS spectra.

Surface-Enhanced Raman Scattering (SERS) Spectroscopy for Sensing and Characterization of Exosomes in Cancer Diagnosis

Simple Summary

The distinct molecular and biological properties of exosomes, together with their abundance and stability, make them an ideal target in liquid biopsies for early diagnosis and disease monitoring. On the other hand, in recent years, nanomaterial-based optical biosensors have been extensively investigated as novel, rapid and sensitive tools for exosome detection and discrimination. The scope of this review is to summarize and coherently discussed the diverse applications, challenges and limitations of nanosensors based on surface-enhanced Raman spectroscopy (SERS) as the optosensing technique.

Abstract

Exosomes are emerging as one of the most intriguing cancer biomarkers in modern oncology for early cancer diagnosis, prognosis and treatment monitoring. Concurrently, several nanoplasmonic methods have been applied and developed to tackle the challenging task of enabling the rapid, sensitive, affordable analysis of exosomes. In this review, we specifically focus our attention on the application of plasmonic devices exploiting surface-enhanced Raman spectroscopy (SERS) as the optosensing technique for the structural interrogation and characterization of the heterogeneous nature of exosomes. We summarized the current state-of-art of this field while illustrating the main strategic approaches and discuss their advantages and limitations.

The Emerging World of Membrane Vesicles: Functional Relevance, Theranostic Avenues and Tools for Investigating Membrane Function

Lipids are essential components of cell membranes and govern various membrane functions. Lipid organization within membrane plane dictates recruitment of specific proteins and lipids into distinct nanoclusters that initiate cellular signaling while modulating protein and lipid functions. In addition, one of the most versatile function of lipids is the formation of diverse lipid membrane vesicles for regulating various cellular processes including intracellular trafficking of molecular cargo. In this review, we focus on the various kinds of membrane vesicles in eukaryotes and bacteria, their biogenesis, and their multifaceted functional roles in cellular communication, host-pathogen interactions and biotechnological applications. We elaborate on how their distinct lipid composition of membrane vesicles compared to parent cells enables early and non-invasive diagnosis of cancer and tuberculosis, while inspiring vaccine development and drug delivery platforms. Finally, we discuss the use of membrane vesicles as excellent tools for investigating membrane lateral organization and protein sorting, which is otherwise challenging but extremely crucial for normal cellular functioning. We present current limitations in this field and how the same could be addressed to propel a fundamental and technology-oriented future for extracellular membrane vesicles.

Single-vesicle imaging and co-localization analysis for tetraspanin profiling of individual extracellular vesicles

Extracellular vesicles (EVs) are secreted nano-sized vesicles that contain cellular proteins, lipids, and nucleic acids. Although EVs are expected to be biologically diverse, current analyses cannot adequately characterize this diversity because most are ensemble methods that inevitably average out information from diverse EVs. Here we describe a single vesicle analysis, which directly visualizes marker expressions of individual EVs using a total internal-reflection microscopy and analyzes their co-localization to investigate EV subpopulations. The single-vesicle imaging and co-localization analysis successfully illustrated the diversity of EVs and revealed distinct patterns of tetraspanin expressions. Application of the analysis demonstrated similarities and dissimilarities between the EV fractions that had been acquired from different conventional EV isolation methods. The analysis method developed in this study will provide a new and reliable tool for investigating characteristics of single EVs, and the findings of the analysis might increase understanding of the characteristics of EVs.

Mode-splitting in a microring resonator for self-referenced biosensing

Self-referenced biosensing based on mode-splitting on a microring resonator is experimentally demonstrated. A Bragg grating integrated on the surface of the ring provides coupling between the clockwise and counterclockwise travelling modes of the pristine ring resonator lifting their degeneracy. The amount of mode-splitting is directly related to the reflectivity of the grating and it is only affected by structurally modifying the grating. Environmental perturbations to the surroundings of the gratings, such as temperature and bulk refractive index variations, have a minor effect on the amount of mode-splitting. This principle allows the realization of a self-referenced sensing scheme based on the detection of variations of the mode-splitting induced by structural changes to the grating. In this work, a polymethyl methacrylate (PMMA) Bragg grating is integrated onto a ring resonator in Al₂O₃. It is shown both theoretically and experimentally that the amount of splitting of a resonance varies minimally under temperature or bulk refractive index perturbations. However, the structural change of attaching a layer of biomolecules inside the grating does affect its reflectivity and the amount of mode splitting present. This result represents the first proof-of-concept demonstration of an integrated mode-splitting biosensor insensitive to temperature and refractive index variations of the liquid matrix where the molecules to be detected are embedded. The reported results pave the road towards the realization of truly self-referenced biosensors.