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

Deep autoencoder as an interpretable tool for Raman spectroscopy investigation of chemical and extracellular vesicle mixtures

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool that provides valuable insight into the molecular contents of chemical and biological samples. However, interpreting Raman spectra from complex or dynamic datasets remains challenging, particularly for highly heterogeneous biological samples like extracellular vesicles (EVs). To overcome this, we developed a tunable and interpretable deep autoencoder for the analysis of several challenging Raman spectroscopy applications, including synthetic datasets, chemical mixtures, a chemical milling reaction, and mixtures of EVs. We compared the results with classical methods (PCA and UMAP) to demonstrate the superior performance of the proposed technique. Our method can handle small datasets, provide a high degree of generalization such that it can fill unknown gaps within spectral datasets, and even quantify relative ratios of cell line-derived EVs to fetal bovine serum-derived EVs within mixtures. This simple yet robust approach will greatly improve the analysis capabilities for many other Raman spectroscopy applications.

Plasma-derived Extracellular Vesicles (EVs) as Biomarkers of Sepsis in Burn Patients via Label-free Raman Spectroscopy

Sepsis following burn trauma is a global complication with high mortality, with ~60% of burn patient deaths resulting from infectious complications. Sepsis diagnosis is complicated by confounding clinical manifestations of the burn injury, and current biomarkers markers lack the sensitivity and specificity required for prompt treatment. Circulating extracellular vesicles (EVs) from patient liquid biopsy as biomarkers of sepsis due to their release by pathogens from bacterial biofilms and roles in subsequent immune response. This study applies Raman spectroscopy to patient plasma derived EVs for rapid, sensitive, and specific detection of sepsis in burn patients, achieving 97.5% sensitivity and 90.0% specificity. Furthermore, spectral differences between septic and non-septic burn patient EVs could be traced to specific glycoconjugates of bacterial strains associated with sepsis morbidity. This work illustrates the potential application of EVs as biomarkers in clinical burn trauma care, and establishes Raman analysis as a fast, label-free method to specifically identify features of bacterial EVs relevant to infection amongst the host background.

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.

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.

Cancer diagnosis using label-free SERS-based exosome analysis

Exosomes, carrying distinctive biomolecules reflective of their parent cell's status and origin, show promise as liquid biopsy biomarkers for cancer diagnosis. However, their clinical translation remains challenging due to their relatively low concentration in body fluids. Surface-Enhanced Raman spectroscopy (SERS) has recently gained significant attention as a label-free and sensitive technique for exosome analysis. This review explores label-free SERS for exosome detection, covering exosome isolation and characterization methods, advancements in SERS substrates, and fingerprint analysis techniques using machine learning. Furthermore, we emphasize the challenges and offer insights into the future prospects of SERS-based exosome analysis to enhance cancer diagnosis.

Towards rapid colorimetric detection of extracellular vesicles using optofluidics-enhanced color-changing optical metasurface

Efficient transportation and delivery of analytes to the surface of optical sensors are crucial for overcoming limitations in diffusion-limited transport and analyte sensing. In this study, we propose a novel approach that combines metasurface optics with optofluidics-enabled active transport of extracellular vesicles (EVs). By leveraging this combination, we show that we can rapidly capture EVs and detect their adsorption through a color change generated by a specially designed optical metasurface that produces structural colors. Our results demonstrate that the integration of optofluidics and metasurface optics enables spectrometer-less and label-free colorimetric read-out for EV concentrations as low as 107 EVs/ml, achieved within a short incubation time of two minutes.

Rigorous Comparison of Extracellular Vesicle Processing to Enhance Downstream Analysis for Glioblastoma Characterization

Extracellular vesicles (EVs) are highly sought after as a source of biomarkers for disease detection and monitoring. Tumor EV isolation, processing, and evaluation from biofluids is convoluted by EV heterogeneity and biological contaminants and is limited by technical processing efficacy. This study rigorously compares common bulk EV isolation workflows (size exclusion chromatography, SEC; membrane affinity, MA) alongside downstream RNA extraction protocols to investigate molecular analyte recovery. EV integrity and recovery is evaluated using a variety of technologies to quantify total intact EVs, total and surface proteins, and RNA purity and recovery. A comprehensive evaluation of each analyte is performed, with a specific emphasis on maintaining user (n = 2), biological (n = 3), and technical replicates (n≥3) under in vitro conditions. Subsequent study of tumor EV spike-in into healthy donor plasma samples is performed to further validate biofluid-derived EV purity and isolation for clinical application. Results show that EV surface integrity is considerably preserved in eluates from SEC-derived EVs, but RNA recovery and purity, as well as bulk protein isolation, is significantly improved in MA-isolated EVs. This study concludes that EV isolation and RNA extraction pipelines govern recovered analyte integrity, necessitating careful selection of processing modality to enhance recovery of the analyte of interest.

Recommendations for reproducibility of cerebrospinal fluid extracellular vesicle studies

Cerebrospinal fluid (CSF) is a clear, transparent fluid derived from blood plasma that protects the brain and spinal cord against mechanical shock, provides buoyancy, clears metabolic waste and transports extracellular components to remote sites in the brain. Given its contact with the brain and the spinal cord, CSF is the most informative biofluid for studies of the central nervous system (CNS). In addition to other components, CSF contains extracellular vesicles (EVs) that carry bioactive cargoes (e.g., lipids, nucleic acids, proteins), and that can have biological functions within and beyond the CNS. Thus, CSF EVs likely serve as both mediators of and contributors to communication in the CNS. Accordingly, their potential as biomarkers for CNS diseases has stimulated much excitement for and attention to CSF EV research. However, studies on CSF EVs present unique challenges relative to EV studies in other biofluids, including the invasive nature of CSF collection, limited CSF volumes and the low numbers of EVs in CSF as compared to plasma. Here, the objectives of the International Society for Extracellular Vesicles CSF Task Force are to promote the reproducibility of CSF EV studies by providing current reporting and best practices, and recommendations and reporting guidelines, for CSF EV studies. To accomplish this, we created and distributed a world-wide survey to ISEV members to assess methods considered 'best practices' for CSF EVs, then performed a detailed literature review for CSF EV publications that was used to curate methods and resources. Based on responses to the survey and curated information from publications, the CSF Task Force herein provides recommendations and reporting guidelines to promote the reproducibility of CSF EV studies in seven domains: (i) CSF Collection, Processing, and Storage; (ii) CSF EV Separation/Concentration; (iii) CSF EV Size and Number Measurements; (iv) CSF EV Protein Studies; (v) CSF EV RNA Studies; (vi) CSF EV Omics Studies and (vii) CSF EV Functional Studies.

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.

Engineering a tunable micropattern-array assay to sort single extracellular vesicles and particles to detect RNA and protein in situ

The molecular heterogeneity of extracellular vesicles (EVs) and the co-isolation of physically similar particles, such as lipoproteins (LPs), confounds and limits the sensitivity of EV bulk biomarker characterization. Herein, we present a single-EV and particle (siEVP) protein and RNA assay (siEVP PRA) to simultaneously detect mRNAs, miRNAs, and proteins in subpopulations of EVs and LPs. The siEVP PRA immobilizes and sorts particles via positive immunoselection onto micropatterns and focuses biomolecular signals in situ. By detecting EVPs at a single-particle resolution, the siEVP PRA outperformed the sensitivities of bulk-analysis benchmark assays for RNA and protein. To assess the specificity of RNA detection in complex biofluids, EVs from various glioma cell lines were processed with small RNA sequencing, whereby two mRNAs and two miRNAs associated with glioblastoma multiforme (GBM) were chosen for cross-validation. Despite the presence of single-EV-LP co-isolates in serum, the siEVP PRA detected GBM-associated vesicular RNA profiles in GBM patient siEVPs. The siEVP PRA effectively examines intravesicular, intervesicular, and interparticle heterogeneity with diagnostic promise.

Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures

Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

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.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

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.

SERS-Based Biosensors Combined with Machine Learning for Medical Application

Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.

Advances in Purification, Modification, and Application of Extracellular Vesicles for Novel Clinical Treatments

Extracellular vesicles (EV) are membrane vesicles surrounded by a lipid bilayer membrane and include microvesicles, apoptotic bodies, exosomes, and exomeres. Exosome-encapsulated microRNAs (miRNAs) released from cancer cells are involved in the proliferation and metastasis of tumor cells via angiogenesis. On the other hand, mesenchymal stem cell (MSC) therapy, which is being employed in regenerative medicine owing to the ability of MSCs to differentiate into various cells, is due to humoral factors, including messenger RNA (mRNA), miRNAs, proteins, and lipids, which are encapsulated in exosomes derived from transplanted cells. New treatments that advocate cell-free therapy using MSC-derived exosomes will significantly improve clinical practice. Therefore, using highly purified exosomes that perform their original functions is desirable. In this review, we summarized advances in the purification, modification, and application of EVs as novel strategies to treat some diseases.

Emerging SERS biosensors for the analysis of cells and extracellular vesicles

Cells and their derived extracellular vesicles (EVs) or exosomes contain unique molecular signatures that could be used as biomarkers for the detection of severe diseases such as cancer, as well as monitoring the treatment response. Revealing these molecular signatures requires developing non-invasive ultrasensitive tools to enable single molecule/cell-level detection using a small volume of sample with low signal-to-noise ratio background and multiplex capability. Surface-enhanced Raman scattering (SERS) can address the current limitations in studying cells and EVs through two main mechanisms: plasmon-enhanced electric field (the so-called electromagnetic mechanism (EM)), and chemical mechanism (CM). In this review, we first highlight these two SERS mechanisms and then discuss the nanomaterials that have been used to develop SERS biosensors based on each of the aforementioned mechanisms as well as the combination of these two mechanisms in order to take advantage of the synergic effect between electromagnetic enhancement and chemical enhancement. Then, we review the recent advances in designing label-aided and label-free SERS biosensors in both colloidal and planar systems to investigate the surface biomarkers on cancer cells and their derived EVs. Finally, we discuss perspectives of emerging SERS biosensors in future biomedical applications. We believe this review article will thus appeal to researchers in the field of nanobiotechnology including material sciences, biosensors, and biomedical fields.

Magnetofluoro-Immunosensing Platform Based on Binary Nanoparticle-Decorated Graphene for Detection of Cancer Cell-Derived Exosomes

Multi-functionalized carbon nanomaterials have attracted interest owing to their excellent synergic properties, such as plasmon resonance energy transfer and surface-enhanced Raman scattering. Particularly, nanoparticle (NP)-decorated graphene (GRP) has been applied in various fields. In this study, silver NP (AgNP)- and magnetic iron oxide NP (IONP)-decorated GRP were prepared and utilized as biosensing platforms. In this case, AgNPs and GRP exhibit plasmonic properties, whereas IONPs exhibit magnetic properties; therefore, this hybrid nanomaterial could function as a magnetoplasmonic substrate for the magnetofluoro-immunosensing (MFI) system. Conversely, exosomes were recently considered high-potential biomarkers for the diagnosis of diseases. However, exosome diagnostic use requires complex isolation and purification methods. Nevertheless, we successfully detected a prostate-cancer-cell-derived exosome (PC-exosome) from non-purified exosomes in a culture media sample using Ag/IO-GRP and dye-tetraspanin antibodies (Ab). First, the anti-prostate-specific antigen was immobilized on the Ag/IO-GRP and it could isolate the PC-exosome from the sample via an external magnetic force. Dye-tetraspanin Ab was added to the sample to induce the sandwich structure. Based on the number of exosomes, the fluorescence intensity from the dye varied and the system exhibited highly sensitive and selective performance. Consequently, these hybrid materials exhibited excellent potential for biosensing platforms.

A cryostat-based frozen section method to increase the yield of extracellular vesicles extracted from different tissues

Extracellular vesicles (EVs) are small vesicles mediating intercellular communications that have been widely used in disease diagnosis. Extracting EVs from tissues is of great importance, but current approaches are finite and the EV yield is limited. Here, the authors introduced a new method to increase EV yield based on frozen sectioning. With a standardized, semiautomated tissue-slicing procedure in a cryostat, the authors successfully isolated EVs from hearts, kidneys and stomachs. The morphology, size distribution and purity of those isolated EVs were evaluated. Additionally, compared with the traditional scalpel section method, they confirmed the higher yield of tissue-derived EVs with the cryostat-based method. The authors believe that the new method they developed would largely facilitate the research and clinical application of EVs.

Selective isolation of extracellular vesicles from minimally processed human plasma as a translational strategy for liquid biopsies

Background

Intercellular communication is mediated by extracellular vesicles (EVs), as they enclose selectively packaged biomolecules that can be horizontally transferred from donor to recipient cells. Because all cells constantly generate and recycle EVs, they provide accurate timed snapshots of individual pathophysiological status. Since blood plasma circulates through the whole body, it is often the biofluid of choice for biomarker detection in EVs. Blood collection is easy and minimally invasive, yet reproducible procedures to obtain pure EV samples from circulating biofluids are still lacking. Here, we addressed central aspects of EV immunoaffinity isolation from simple and complex matrices, such as plasma.

Methods

Cell-generated EV spike-in models were isolated and purified by size-exclusion chromatography, stained with cellular dyes and characterized by nano flow cytometry. Fluorescently-labelled spike-in EVs emerged as reliable, high-throughput and easily measurable readouts, which were employed to optimize our EV immunoprecipitation strategy and evaluate its performance. Plasma-derived EVs were captured and detected using this straightforward protocol, sequentially combining isolation and staining of specific surface markers, such as CD9 or CD41. Multiplexed digital transcript detection data was generated using the Nanostring nCounter platform and evaluated through a dedicated bioinformatics pipeline.

Results

Beads with covalently-conjugated antibodies on their surface outperformed streptavidin-conjugated beads, coated with biotinylated antibodies, in EV immunoprecipitation. Fluorescent EV spike recovery evidenced that target EV subpopulations can be efficiently retrieved from plasma, and that their enrichment is dependent not only on complex matrix composition, but also on the EV surface phenotype. Finally, mRNA profiling experiments proved that distinct EV subpopulations can be captured by directly targeting different surface markers. Furthermore, EVs isolated with anti-CD61 beads enclosed mRNA expression patterns that might be associated to early-stage lung cancer, in contrast with EVs captured through CD9, CD63 or CD81. The differential clinical value carried within each distinct EV subset highlights the advantages of selective isolation.

Conclusions

This EV isolation protocol facilitated the extraction of clinically useful information from plasma. Compatible with common downstream analytics, it is a readily implementable research tool, tailored to provide a truly translational solution in routine clinical workflows, fostering the inclusion of EVs in novel liquid biopsy settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40364-022-00404-1.

Use of Cysteamine and Glutaraldehyde Chemicals for Robust Functionalization of Substrates with Protein Biomarkers—An Overview on the Construction of Biosensors with Different Transductions

Currently, several biosensors are reported to confirm the absence/presence of an abnormal level of specific human biomarkers in research laboratories. Unfortunately, public marketing and/or pharmacy accessibility are not yet possible for many bodily fluid biomarkers. The questions are numerous, starting from the preparation of the substrates, the wet/dry form of recognizing the (bio)ligands, the exposure time, and the choice of the running buffers. In this context, for the first time, the present overview summarizes the pre-functionalization of standard and nanostructured solid/flexible supports with cysteamine (Cys) and glutaraldehyde (GA) chemicals for robust protein immobilization and detection of biomarkers in body fluids (serum, saliva, and urine) using three transductions: piezoelectrical, electrochemical, and optical, respectively. Thus, the reader can easily access and compare step-by-step conjugate protocols published over the past 10 years. In conclusion, Cys/GA chemistry seems widely used for electrochemical sensing applications with different types of recorded signals, either current, potential, or impedance. On the other hand, piezoelectric detection via quartz crystal microbalance (QCM) and optical detection by surface plasmon resonance (LSPR)/surface-enhanced Raman spectroscopy (SERS) are ultrasensitive platforms and very good candidates for the miniaturization of medical devices in the near future.

The Promise of Circulating Tumor DNA in Head and Neck Cancer

As the seventh most common cancer globally, head and neck cancers (HNC) exert considerable disease burden, with an estimated 277,597 deaths worldwide in 2020 alone. Traditional risk factors for HNC include tobacco, alcohol, and betel nut; more recently, human papillomavirus has emerged as a distinct driver of disease. Currently, limitations of cancer screening and surveillance methods often lead to identifying HNC in more advanced stages, with associated poor outcomes. Liquid biopsies, in particular circulating tumor DNA (ctDNA), offer the potential for enhancing screening, early diagnosis, and surveillance in HNC patients, with potential improvements in HNC patient outcomes. In this review, we examine current methodologies for detecting ctDNA and highlight current research illustrating viral and non-viral ctDNA biomarker utilities in HNC screening, diagnosis, treatment response, and prognosis. We also summarize current challenges and future directions for ctDNA testing in HNC patients.

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.

Biosensing Extracellular Vesicle Subpopulations in Neurodegenerative Disease Conditions

Extracellular vesicles (EVs) are secreted nanoparticles that are involved in intercellular communication and that modulate a wide range of biological processes in normal and disease conditions. However, EVs are highly heterogeneous in terms of origin in the cell, size, and density. As a result, complex protocols are required to identify and characterize specific EV subpopulations, limiting biomedical applications, notably in diagnostics. Here, we show that combining quartz crystal microbalance with dissipation (QCM-D) and nanoplasmonic sensing (NPS) provides a facile method to track the viscoelastic properties of small EVs. We applied this multisensing strategy to analyze small EVs isolated by differential ultracentrifugation from knock-in mouse striatal cells expressing either a mutated allele or wild-type allele of huntingtin (Htt), the Huntington's disease gene. Our results validate the sensing strategy coupling QCM-D and NPS and suggest that the mass and viscoelastic dissipation of EVs can serve as potent biomarkers for sensing the intercellular changes associated with the neurodegenerative condition.

Surface enhanced Raman scattering for probing cellular biochemistry

Surface enhanced Raman scattering (SERS) from biomolecules in living cells enables the sensitive, but also very selective, probing of their biochemical composition. This minireview discusses the developments of SERS probing in cells over the past years from the proof-of-principle to observe a biochemical status to the characterization of molecule-nanostructure and molecule-molecule interactions and cellular processes that involve a wide variety of biomolecules and cellular compartments. Progress in applying SERS as a bioanalytical tool in living cells, to gain a better understanding of cellular physiology and to harness the selectivity of SERS, has been achieved by a combination of live cell SERS with several different approaches. They range from organelle targeting, spectroscopy of relevant molecular models, and the optimization of plasmonic nanostructures to the application of machine learning and help us to unify the information from defined biomolecules and from the cell as an extremely complex system.