Hybrid Nanoplasmonic Porous Biomaterial Scaffold for Liquid Biopsy Diagnostics Using Extracellular Vesicles

Label-free single-vesicle based surface enhanced Raman spectroscopy: A robust approach for investigating the biomolecular composition of small extracellular vesicles

Small extracellular vesicles (sEVs) are cell-released vesicles ranging from 30-150nm in size. They have garnered increasing attention because of their potential for both the diagnosis and treatment of disease. The diversity of sEVs derives from their biological composition and cargo content. Currently, the isolation of sEV subpopulations is primarily based on bio-physical and affinity-based approaches. Since a standardized definition for sEV subpopulations is yet to be fully established, it is important to further investigate the correlation between the biomolecular composition of sEVs and their physical properties. In this study, we employed a platform combining single-vesicle surface-enhanced Raman spectroscopy (SERS) and machine learning to examine individual sEVs isolated by size-exclusion chromatography (SEC). The biomolecular composition of each vesicle examined was reflected by its corresponding SERS spectral features (biomolecular "fingerprints"), with their roots in the composition of their collective Raman-active bonds. Origins of the SERS spectral features were validated through a comparative analysis between SERS and mass spectrometry (MS). SERS fingerprinting of individual vesicles was effective in overcoming the challenges posed by EV population averaging, allowing for the possibility of analyzing the variations in biomolecular composition between the vesicles of similar and/or different sizes. Using this approach, we uncovered that each of the size-based fractions of sEVs contained particles with predominantly similar SERS spectral features. Indeed, more than 84% of the vesicles residing within a particular group were clearly distinguishable from that of the other EV sub-populations, despite some spectral variations within each sub-population. Our results suggest the possibility that size-based EV fractionation methods produce samples where similarly eluted sEVs are correlated with their respective biochemical contents, as reflected by their SERS spectra. Our findings therefore highlight the possibility that the biogenesis and respective biological functionalities of the various sEV fractions may be inherently different.

Exosomes in diagnostic and therapeutic applications of ovarian cancer

Ovarian cancer accounts for more deaths than any other female reproductive tract cancer. The major reasons for the high mortality rates include delayed diagnoses and drug resistance. Hence, improved diagnostic and therapeutic options for ovarian cancer are a pressing need. Extracellular vesicles (EVs), that include exosomes provide hope in both diagnostic and therapeutic aspects. They are natural lipid nanovesicles secreted by all cell types and carry molecules that reflect the status of the parent cell. This facilitates their potential use as biomarkers for an early diagnosis. Additionally, EVs can be loaded with exogenous cargo, and have features such as high stability and favorable pharmacokinetic properties. This makes them ideal for tumor-targeted delivery of biological moieties. The International Society of Extracellular Vesicles (ISEV) based on the Minimal Information for Studies on Extracellular Vesicles (MISEV) recommends the usage of the term "small extracellular vesicles (sEVs)" that includes exosomes for particles that are 30-200 nm in size. However, majority of the studies reported in the literature and relevant to this review have used the term "exosomes". Therefore, this review will use the term "exosomes" interchangeably with sEVs for consistency with the literature and avoid confusion to the readers. This review, initially summarizes the different isolation and detection techniques developed to study ovarian cancer-derived exosomes and the potential use of these exosomes as biomarkers for the early diagnosis of this devastating disease. It addresses the role of exosome contents in the pathogenesis of ovarian cancer, discusses strategies to limit exosome-mediated ovarian cancer progression, and provides options to use exosomes for tumor-targeted therapy in ovarian cancer. Finally, it states future research directions and recommends essential research needed to successfully transition exosomes from the laboratory to the gynecologic-oncology clinic.

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.

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.

Controlling the Nucleation and Growth of Salt from Bodily Fluid for Enhanced Biosensing Applications

Surface-enhanced Raman spectroscopy (SERS) represents a transformative tool in medical diagnostics, particularly for the early detection of key biomarkers such as small extracellular vesicles (sEVs). Its unparalleled sensitivity and compatibility with intricate biological samples make it an ideal candidate for revolutionizing noninvasive diagnostic methods. However, a significant challenge that mars its efficacy is the throughput limitation, primarily anchored in the prerequisite of hotspot and sEV colocalization within a minuscule range. This paper delves deep into this issue, introducing a never-attempted-before approach which harnesses the principles of crystallization-nucleation and growth. By synergistically coupling lasers with plasmonic resonances, we navigate the challenges associated with the analyte droplet drying method and the notorious coffee ring effect. Our method, rooted in a profound understanding of crystallization's materials science, exhibits the potential to significantly increase the areal density of accessible plasmonic hotspots and efficiently guide exosomes to defined regions. In doing so, we not only overcome the throughput challenge but also promise a paradigm shift in the arena of minimally invasive biosensing, ushering in advanced diagnostic capabilities for life-threatening diseases.

A Multi-Fluorophore Staining Scheme for Identification and Quantification of Vomocytosis

Vomocytosis is a process by which fungal pathogens, for instance, Cryptococcus neoformans (CN), escape from the digestive phagolysosome of phagocytic cells after ingestion. Interestingly, this expulsion leaves both the pathogen and phagocyte unharmed, and is believed to be an important mechanism by which CNs disseminate throughout infected hosts. This phenomenon was discovered in 2006, and research to date has relied almost entirely on quantification via manual counting of vomocytosis events in time-lapse microscopy videos. This archaic method has the significant disadvantages of requiring excessive labor in manual analysis, limited throughput capabilities, and low accuracy due to subjectivity. Here, we present an alternative method to measure vomocytosis rates using a multi-fluorophore reporter system comprised of two in situ staining steps during infection and a flow cytometry readout. This approach overcomes the limitations of conventional time lapse microscopy methods, with key advantages of high throughput capability, simple procedural steps, and accurate objective readouts. This study rigorously characterizes this vomocytosis reporter system in CN-infected MΦ and DC cultures via fluorescence microscopy, confocal microscopy, and flow cytometry. Here, this fluorescent tool is used to observe differences in expulsion rates after phagosome-modifying drug treatments and additionally utilized to distinguish differences in biochemical compositions among fluorescence-activated cell sorted fungal populations via Raman spectroscopy. Furthermore, this reporter scheme is demonstrated to be adaptable for use in measuring potential biomaterial particle expulsion events. Ultimately, the fluorescent reporter system presented here provides a universal tool for vomocytosis rate measurement of phagocytosed material. This facile approach opens the door to previously unfeasible types of vomocytosis-related studies such as high throughput treatment mechanistic screening and downstream characterization of expelled material.

Strategies and Progress of Raman Technologies for Cellular Uptake Analysis of the Drug Delivery Systems

Nanoparticle (NP)-based drug delivery systems have the potential to significantly enhance the pharmacological and therapeutic properties of drugs. These systems enhance the bioavailability and biocompatibility of pharmaceutical agents via enabling targeted delivery to specific tissues or organs. However, the efficacy and safety of these systems are largely dependent on the cellular uptake and intracellular transport of NPs. Thus, it is crucial to monitor the intracellular behavior of NPs within a single cell. Yet, it is challenging due to the complexity and size of the cell. Recently, the development of the Raman instrumentation offers a versatile tool to allow noninvasive cellular measurements. The primary objective of this review is to highlight the most recent advancements in Raman techniques (spontaneous Raman scattering, bioorthogonal Raman scattering, coherence Raman scattering, and surface-enhanced Raman scattering) when it comes to assessing the internalization of NP-based drug delivery systems and their subsequent movement within cells.

Label-free approaches for extracellular vesicle detection

Extracellular vesicles (EVs) represent pivotal mediators in cell-to-cell communication. They are lipid-membranous carriers of several biomolecules, which can be produced by almost all cells. In the current Era of precision medicine, EVs gained growing attention thanks to their potential in both biomarker discovery and nanotherapeutics applications. However, current technical limitations in isolating and/or detecting EVs restrain their standard use in clinics. This review explores all the state-of-the-art analytical technologies which are currently overcoming these issues. On one end, several innovative optical-, electrical-, and spectroscopy-based detection methods represent advantageous label-free methodologies for faster EV detection. On the other end, microfluidics-based lab-on-a-chip tools support EV purification from low-concentrated samples. Altogether, these technologies will strengthen the routine application of EVs in clinics.

A review of recent progress in the application of Raman spectroscopy and SERS detection of microplastics and derivatives

The global environmental concern surrounding microplastic (MP) pollution has raised alarms due to its potential health risks to animals, plants, and humans. Because of the complex structure and composition of microplastics (MPs), the detection methods are limited, resulting in restricted detection accuracy. Surface enhancement of Raman spectroscopy (SERS), a spectral technique, offers several advantages, such as high resolution and low detection limit. It has the potential to be extensively employed for sensitive detection and high-resolution imaging of microplastics. We have summarized the research conducted in recent years on the detection of microplastics using Raman and SERS. Here, we have reviewed qualitative and quantitative analyses of microplastics and their derivatives, as well as the latest progress, challenges, and potential applications.

SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis

Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection.

Nanostructured-Based Optical Readouts Interfaced with Machine Learning for Identification of Extracellular Vesicles

Extracellular vesicles (EVs) are shed from cancer cells into body fluids, enclosing molecular information about the underlying disease with the potential for being the target cancer biomarker in emerging diagnosis approaches such as liquid biopsy. Still, the study of EVs presents major challenges due to their heterogeneity, complexity, and scarcity. Recently, liquid biopsy platforms have allowed the study of tumor-derived materials, holding great promise for early-stage diagnosis and monitoring of cancer when interfaced with novel adaptations of optical readouts and advanced machine learning analysis. Here, recent advances in labeled and label-free optical techniques such as fluorescence, plasmonic, and chromogenic-based systems interfaced with nanostructured sensors like nanoparticles, nanoholes, and nanowires, and diverse machine learning analyses are reviewed. The adaptability of the different optical methods discussed is compared and insights are provided into prospective avenues for the translation of the technological approaches for cancer diagnosis. It is discussed that the inherent augmented properties of nanostructures enhance the sensitivity of the detection of EVs. It is concluded by reviewing recent integrations of nanostructured-based optical readouts with diverse machine learning models as novel analysis ventures that can potentially increase the capability of the methods to the point of translation into diagnostic applications.

Recent developments in biosensing methods for extracellular vesicle protein characterization

Research into extracellular vesicles (EVs) has grown significantly over the last few decades with EVs being widely regarded as a source of biomarkers for human health and disease with massive clinical potential. Secreted by every cell type in the body, EVs report on the internal cellular conditions across all tissue types. Their presence in readily accessible biofluids makes the potential of EV biosensing highly attractive as a noninvasive diagnostic platform via liquid biopsies. However, their small size (50-250 nm), inherent heterogeneity, and the complexity of the native biofluids introduce challenges for effective characterization, thus, limiting their clinical utility. This has led to a surge in the development of various novel EV biosensing techniques, with capabilities beyond those of conventional methods that have been directly transferred from cell biology. In this review, key detection principles used for EV biosensing are summarized, with a focus on some of the most recent and fundamental developments in the field over the last 5 years. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > In Vitro Nanoparticle-Based Sensing.

Recent progress in surface-enhanced Raman spectroscopy-based biosensors for the detection of extracellular vesicles

Extracellular vesicles (EVs) are a type of mediator that enables intercellular communication. Moreover, EVs carry critical molecular information from parental cells, making them ideal biomarkers for clinical screening and diagnosis. Currently, several sensing technologies have been established to sensitively detect EVs. Among them, surface-enhanced Raman spectroscopy (SERS) has become a powerful analytical tool with high sensitivity and low detection limits. In this review, we first cover the biological characteristics of EVs and the principle of SERS amplification. Then, we describe the recent progress in SERS technology applied to detect EVs, including direct label-free methods and indirect labeling strategies, in which substrate fabrication and nanoprobe assembly were emphasized. Furthermore, SERS technology could also be used to characterize or monitor the behavior of programmable EVs. Finally, we discuss the prospects and issues to be addressed for the development of SERS technology for EV analysis.

Artificial Intelligent Label-Free SERS Profiling of Serum Exosomes for Breast Cancer Diagnosis and Postoperative Assessment

Breast cancer subtypes have important implications of treatment responses and clinical outcomes. Exosomes have been considered as promising biomarkers for liquid biopsies, but the utility of exosomes for accurate diagnosis of distinct breast cancer subtypes is a grand challenge due to the difficulty in uncovering the subtle compositional difference in complex clinical settings. Herein, we report an artificial intelligent surface-enhanced Raman spectroscopy (SERS) strategy for label-free spectroscopic analysis of serum exosomes, allowing for accurate diagnosis of breast cancer and assessment of surgical outcomes. Our deep learning algorithm trained with SERS spectra of cancer cell-derived exosomes is demonstrated with a 100% prediction accuracy for human patients with different breast cancer subtypes who do not undergo surgery using SERS spectra of serum exosomes. Furthermore, when combined with similarity analysis by principal component analysis, our approach is able to evaluate the surgical outcomes of breast cancer of distinct molecular subtypes.

Gold Nanopyramid Arrays for Non-Invasive Surface-Enhanced Raman Spectroscopy-Based Gastric Cancer Detection via sEVs

Gastric cancer (GC) is one of the most common and lethal types of cancer affecting over one million people, leading to 768,793 deaths globally in 2020 alone. The key for improving the survival rate lies in reliable screening and early diagnosis. Existing techniques including barium-meal gastric photofluorography and upper endoscopy can be costly and time-consuming and are thus impractical for population screening. We look instead for small extracellular vesicles (sEVs, currently also referred as exosomes) sized ⌀ 30-150 nm as a candidate. sEVs have attracted a significantly higher level of attention during the past decade or two because of their potentials in disease diagnoses and therapeutics. Here, we report that the composition information of the collective Raman-active bonds inside sEVs of human donors obtained by surface-enhanced Raman spectroscopy (SERS) holds the potential for non-invasive GC detection. SERS was triggered by the substrate of gold nanopyramid arrays we developed previously. A machine learning-based spectral feature analysis algorithm was developed for objectively distinguishing the cancer-derived sEVs from those of the non-cancer sub-population. sEVs from the tissue, blood, and saliva of GC patients and non-GC participants were collected (n = 15 each) and analyzed. The algorithm prediction accuracies were reportedly 90, 85, and 72%. "Leave-a-pair-of-samples out" validation was further performed to test the clinical potential. The area under the curve of each receiver operating characteristic curve was 0.96, 0.91, and 0.65 in tissue, blood, and saliva, respectively. In addition, by comparing the SERS fingerprints of individual vesicles, we provided a possible way of tracing the biogenesis pathways of patient-specific sEVs from tissue to blood to saliva. The methodology involved in this study is expected to be amenable for non-invasive detection of diseases other than GC.

Extracellular vesicle contents as non-invasive biomarkers in ovarian malignancies

Ovarian cancer most commonly presents at an advanced stage where survival is approximately 30% compared with >80% if diagnosed and treated before disease spreads. Diagnostic capabilities have progressed from surgical staging via laparotomy to image-guided biopsies and immunohistochemistry staining, along with advances in technology and medicine. Despite improvements in diagnostic capabilities, population-level screening for ovarian cancer is not recommended. Extracellular vesicles (EVs) are 40–150 nm structures formed when the cellular lipid bilayer invaginates. These structures function in cell signaling, immune responses, cancer progression, and establishing the tumor microenvironment. EVs are found in nearly every bodily fluid, including serum, plasma, ascites, urine, and effusion fluid, and contain molecular cargo from their cell of origin. This cargo can be analyzed to yield information about a possible malignancy. In this review we describe how the cargo of EVs has been studied as biomarkers in ovarian cancer. We bring together studies analyzing evidence for various cargos as ovarian cancer biomarkers. Then, we describe the role of EVs in modulation of the tumor microenvironment. This review also summarizes the therapeutic and translational potential of EVs for their optimal utilization as non-invasive biomarkers for novel treatments against cancer.

Extracellular Vesicles (EVs) Are Copurified with Feline Calicivirus, yet EV-Enriched Fractions Remain Infectious

Feline calicivirus (FCV) is a major cause of upper respiratory disease in cats and is often used as a model for human norovirus, making it of great veterinary and human medical importance. However, questions remain regarding the route of entry of FCV in vivo. Increasing work has shown that extracellular vesicles (EVs) can be active in viral infectivity, yet there is no work examining the role of EVs in FCV infection. Here, we begin to address this knowledge gap by characterizing EVs produced by a feline mammary epithelial cell line (FMEC). We have confirmed that EVs are produced by infected and mock-infected FMECs and that both virions and EVs are coisolated with standard methods of virus purification. We also show that they can be enriched differentially by continuous iodixanol density gradient. EVs were enriched at a density of 1.10 g/mL confirmed by tetraspanin expression, size profile, and transmission electron microscopy (TEM). Maximum enrichment of FCV at a density of 1.18 g/mL was confirmed by titration, quantitative reverse transcriptase PCR (q-RT PCR), and TEM. However, infectious virus was recovered from nearly all samples. When used to infect in vitro epithelium, both EV-rich and virus-rich fractions had the same levels of infectiousness as determined by percentage of wells infected or titer achieved postinfection. These findings highlight the importance of coisolates during viral purification, showing that EVs may represent a parallel route of entry that has previously been overlooked. Additional experiments are necessary to explore the role of EVs in FCV infection. IMPORTANCE Feline calicivirus (FCV) is a common cause of upper respiratory infection in cats. Both healthy and infected cells produce small particles called extracellular vesicles (EVs), which are nanoparticles that act as messengers between cells and can be hijacked during viral infection. Historically, the role of EVs in viral infection has been overlooked, and subsequently no group has studied the role of EVs in FCV infection. We hypothesized that EVs may play a role in FCV infection. Here, we show that EVs are copurified with FCV when collecting virus. To study their individual effects, we successfully enrich for viral particles and EVs separately by taking advantage of their different densities. Our initial studies show that EV-enriched versus virus-enriched fractions are equally able to infect cells in culture. These findings highlight the need to both consider the purity of virus after purification and to further study EVs' role in natural FCV infection.

Classification of Preeclamptic Placental Extracellular Vesicles Using Femtosecond Laser Fabricated Nanoplasmonic Sensors

Placental extracellular vesicles (EVs) play an essential role in pregnancy by protecting and transporting diverse biomolecules that aid in fetomaternal communication. However, in preeclampsia, they have also been implicated in contributing to disease progression. Despite their potential clinical value, current technologies cannot provide a rapid and effective means of differentiating between healthy and diseased placental EVs. To address this, a fabrication process called laser-induced nanostructuring of SERS-active thin films (LINST) was developed to produce scalable nanoplasmonic substrates that provide exceptional Raman signal enhancement and allow the biochemical fingerprinting of EVs. After validating the performance of LINST substrates with chemical standards, placental EVs from tissue explant cultures were characterized, demonstrating that preeclamptic and normotensive placental EVs have classifiably distinct Raman spectra following the application of advanced machine learning algorithms. Given the abundance of placental EVs in maternal circulation, these findings encourage immediate exploration of surface-enhanced Raman spectroscopy (SERS) of EVs as a promising method for preeclampsia liquid biopsies, while this novel fabrication process will provide a versatile and scalable substrate for many other SERS applications.

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.

Gold Nanoparticles Based Optical Biosensors for Cancer Biomarker Proteins: A Review of the Current Practices

Cancer prognosis depends on the early detection of the disease. Gold nanoparticles (AuNPs) have attracted much importance in biomedical research due to their distinctive optical properties. The AuNPs are easy to fabricate, biocompatible, surface controlled, stable, and have surface plasmonic properties. The AuNPs based optical biosensors can intensely improve the sensitivity, specificity, resolution, penetration depth, contrast, and speed of these devices. The key optical features of the AuNPs based biosensors include localized surface plasmon resonance (LSPR), SERS, and luminescence. AuNPs based biomarkers have the potential to sense the protein biomarkers at a low detection level. In this review, the fabrication techniques of the AuNPs have been reviewed. The optical biosensors based on LSPR, SERS, and luminescence are also evaluated. The application of these biosensors for cancer protein detection is discussed. Distinct examples of cancer research that have a substantial impact on both scientific and clinical research are presented.

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

Utilising extracellular vesicles for early cancer diagnostics: benefits, challenges and recommendations for the future

To increase cancer patient survival and wellbeing, diagnostic assays need to be able to detect cases earlier, be applied more frequently, and preferably before symptoms develop. The expansion of blood biopsy technologies such as detection of circulating tumour cells and cell-free DNA has shown clinical promise for this. Extracellular vesicles released into the blood from tumour cells may offer a snapshot of the whole of the tumour. They represent a stable and multifaceted complex of a number of different types of molecules including DNA, RNA and protein. These represent biomarker targets that can be collected and analysed from blood samples, offering great potential for early diagnosis. In this review we discuss the benefits and challenges of the use of extracellular vesicles in this context and provide recommendations on where this developing field should focus their efforts to bring future success.

Positively-charged plasmonic nanostructures for SERS sensing applications

Surface-enhanced Raman (SERS) spectroscopy has been establishing itself as an ultrasensitive analytical technique with a cross-disciplinary range of applications, which scientific growth is triggered by the continuous improvement in the design of advanced plasmonic materials with enhanced multifunctional abilities and tailorable surface chemistry. In this regard, conventional synthetic procedures yield negatively-charged plasmonic materials which can hamper the adhesion of negatively-charged species. To tackle this issue, metallic surfaces have been modified via diverse procedures with a broad array of surface ligands to impart positive charges. Cationic amines have been preferred because of their ability to retain a positive zeta potential even at alkaline pH as well as due to their wide accessibility in terms of structural features and cost. In this review, we will describe and discuss the different approaches for generating positively-charged plasmonic platforms and their applications in SERS sensing.

Integration of ligands equipped with quaternary amines on plasmonic surfaces generates positively-charged nanomaterials suitable for electrostatically binding negatively-charged species paving the way for their application in SERS sensing.

Characterization of ovarian cancer-derived extracellular vesicles by surface-enhanced Raman spectroscopy

Ovarian cancer is the most lethal gynecological malignancy, owing to the fact that most cases are diagnosed at a late stage. To improve prognosis and reduce mortality, we must develop methods for the early diagnosis of ovarian cancer. A step towards early and non-invasive cancer diagnosis is through the utilization of extracellular vesicles (EVs), which are nanoscale, membrane-bound vesicles that contain proteins and genetic material reflective of their parent cell. Thus, EVs secreted by cancer cells can be thought of as cancer biomarkers. In this paper, we present gold nanohole arrays for the capture of ovarian cancer (OvCa)-derived EVs and their characterization by surface-enhanced Raman spectroscopy (SERS). For the first time, we have characterized EVs isolated from two established OvCa cell lines (OV-90, OVCAR3), two primary OvCa cell lines (EOC6, EOC18), and one human immortalized ovarian surface epithelial cell line (hIOSE) by SERS. We subsequently determined their main compositional differences by principal component analysis and were able to discriminate the groups by a logistic regression-based machine learning method with ∼99% accuracy, sensitivity, and specificity. The results presented here are a great step towards quick, facile, and non-invasive cancer diagnosis.

Ultrasensitive Exosome Detection by Modularized SERS Labeling for Postoperative Recurrence Surveillance

Exosome-based liquid biopsy holds great potential in monitoring tumor progression. Current exosome detection biosensors rely on signal amplification strategies to improve sensitivity; however, these strategies pay little attention to manipulating the number of signal reporters, limiting the rational optimization of the biosensors. Here, we have developed a modularized surface-enhanced Raman spectroscopy (SERS) labeling strategy, where each Raman reporter is coupled with lysine as a signal-lysine module, and thus the number of Raman reporters can be precisely controlled by the modularized solid-phase peptide synthesis. Using this strategy, we screened out an optimum Raman biosensor for ultrasensitive exosome detection, with the limit of detection of 2.4 particles per microliter. This biosensor enables a successful detection of the tumor with an average diameter of approximately 3.55 mm, and thus enables successful surveillance of the postoperative tumor recurrence in mice models and distinguishing cancer patients from healthy subjects. Our work provides a de novo strategy to precisely amplify signals toward a myriad of biosensor-related medical applications.

Nanostructure-based surface-enhanced Raman scattering for diagnosis of cancer

Cancer is a malignant disease that seriously affects human health and life. Early diagnosis and timely treatment can significantly improve the survival rate of cancer patients. Surface-enhanced Raman scattering (SERS) is an optical technology that can detect and image samples at the single-molecule level. It has the advantages of rapidity, high specificity, high sensitivity and no damage to the sample. The performance of SERS is highly dependent on the properties, size and morphology of the SERS substrate. Preparation of SERS substrates with good reproducibility and chemical stability is a key factor in realizing the wide application of SERS technology in cancer diagnosis. In this review we provide a detailed presentation of the latest research on SERS in cancer diagnosis and the detection of cancer biomarkers, mainly focusing on nanotechnological approaches in cancer diagnosis by using SERS. We also consider the future development of nanostructure-based SERS in cancer diagnosis.

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.

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.

Biophotonics for diagnostic detection of extracellular vesicles

Extracellular Vesicles (EVs) are versatile carriers for biomarkers involved in the pathogenesis of multiple human disorders. Despite the increasing scientific and commercial interest in EV application in diagnostics, traditional biomolecular techniques usually require consistent sample amount, rely on operator-dependent and time- consuming procedures and cannot cope with the nano-size range of EVs, limiting both sensitivity and reproducibility of results. The application of biophotonics, i.e. light-based methods, for the diagnostic detection of EVs has brought to the development of innovative platforms with excellent sensitivity. In this review, we propose an overview of the most promising and emerging technologies used in the field of EV-related biomarker discovery. When tested on clinical samples, the reported biophotonic approaches in most cases have managed to discriminate between nanovesicles and contaminants, achieved much higher resolution compared to traditional procedures, and reached moderate to excellent diagnostic accuracy, thus demonstrating great potentialities for their clinical translation.

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.

Recent advances in surface-enhanced Raman scattering-based microdevices for point-of-care diagnosis of viruses and bacteria

This minireview reports the recent advances in surface-enhanced Raman scattering (SERS)-based assay devices for the diagnosis of infectious diseases. SERS-based detection methods have shown promise in overcoming the low sensitivity and multiplex detection problems inherent to fluorescence detection. Therefore, it is interesting to investigate the current status, challenges, and applications associated with SERS-based microdevices for the point-of-care (POC) diagnosis of infectious diseases. The majority of this review highlights three different types of microdevices, namely microfluidic channels, lateral flow assay strips, and three-dimensional nanostructured substrates. Furthermore, the integration of portable Raman spectrophotometry with microdevices provides an ideal platform for the diagnosis of various infectious diseases in the field. Integrated SERS-based assay systems also enable measurements in minimal sample volumes and at low analyte concentrations of viral or bacterial samples. A significant number of studies using the SERS-based assay system have been performed recently to realize POC diagnostics, especially under resource-limited conditions. This portable SERS sensor is expected to be a next-generation POC assay system that could overcome the limitations of current fluorescence-based assay systems. This minireview summarizes recent advances in the development of SERS-based microdevices for the diagnosis of infectious diseases. Lastly, challenges to overcome and future perspectives are discussed.