Surface-Enhanced Raman Spectroscopy as a Tool for Distinguishing Extracellular Vesicles under Autophagic Conditions: A Marker for Disease Diagnostics

An All-in-One Nanohole Array for Size-Exclusive Trapping and High-Throughput Digital Counting of Single Extracellular Vesicles for Non-Invasive Cancer Screening

The analysis of single small extracellular vesicles (sEVs) could distinguish the heterogeneity of sEVs thus better extract tumor-related signatures. Current protocols for the analysis of single sEV rely mainly on the advanced techniques and require lengthy isolation procedures, limiting applications in clinical diagnosis. Herein, we developed a one-step procedure for rapid isolation of single sEVs from urine, along with an analytical pipeline for the diagnosis of early bladder cancer (BCa). Single sEVs are isolated by an EV-imprinted gold nanohole (EI-AuNH) array that selectively traps individual sEVs and spatially enhances their Raman spectra. After the invalid spectral data from incomplete or absent sEVs was eliminated using Smart-Filter, a convolutional neural network model identifies the origin of the spectra and generates a digital count matrix for each patient. By integrating the digital count data of both tumor-associated and normal sEVs, our model achieves an accuracy of 97.37% in early diagnosis of BCa. Feature extraction using explainable AI identified nine BCa-related signatures, with noticeable reduction on cholesterol and lipids in BCa-associated sEVs. These signatures could further distinguish BCa from other cancers. Overall, the present non-invasive and highly accurate diagnosis platform may revolutionize clinical disease diagnostics through simplified single sEV isolation and advanced modeling.

Raman spectroscopy in extracellular vesicles analysis: Techniques, applications and advancements

Raman spectroscopy provides a robust approach for detailed analysis of the chemical and molecular profiles of extracellular vesicles (EVs). Recent advancements in Raman techniques have significantly enhanced the sensitivity and accuracy of EV characterization, enabling precise detection and profiling of molecular components within EV samples. This review introduces and compares various Raman-based techniques for EV characterization. These include Raman spectroscopy (RS), which provides fundamental molecular information; Raman trapping analysis (RTA), which combines optical trapping with Raman scattering for the manipulation and analysis of individual EVs; surface-enhanced Raman spectroscopy (SERS), which enhances the Raman signal through the use of metallic nanostructures, significantly improving sensitivity; and microfluidic SERS, which integrates SERS with microfluidic platforms to allow high-throughput, label-free analysis of EVs in biological fluids. In addition to comparing various Raman techniques, this review provides a comprehensive analysis that includes comparisons of machine learning methods, EV isolation techniques, and characterization strategies. By integrating these approaches, the review presents a holistic perspective on Raman-based EV analysis, covering profiling, purity, heterogeneity and size analysis as well as imaging. The combined assessment of Raman technologies with advanced computational and experimental methodologies supports the development of more robust diagnostic and therapeutic applications involving EVs.

Raman spectroscopy of large extracellular vesicles derived from human microvascular endothelial cells to detect benzo[a]pyrene exposure

Extracellular vesicles (EVs) have shown great potential as biomarkers since they reflect the physio-pathological status of the producing cell. In the context of cytotoxicity, it has been found that exposing cells to toxicants leads to changes in protein expression and the cargo of the EVs they produce. Here, we studied large extracellular vesicles (lEVs) derived from human microvascular endothelial cells (HMEC-1) to detect the modifications induced by cell exposure to benzo[a]pyrene (B[a]P). We used a custom CaF2-based biochip which allowed hyphenated techniques of investigation: surface plasmon resonance imaging (SPRi) to monitor the adsorption of objects, atomic force microscopy (AFM) to characterise EVs' size and morphology, and Raman spectroscopy to detect molecular modifications. Results obtained on EVs by Raman microscopy and tip-enhanced Raman spectroscopy (TERS) showed significant differences induced by B[a]P in the high wavenumber region of Raman spectra (2800 to 3000 cm-1), corresponding mainly to lipid modifications. Two types of spectra were detected in the control sample. A support vector machine (SVM) model was trained on the pre-processed spectral data to differentiate between EVs from cells exposed or not to B[a]P at the spectrum level; this model could achieve a sensitivity of 88% and a specificity of 99.5%. Thus, this experimental setup facilitated the distinction between EVs originating from two cell culture conditions and enabled the discrimination of EV subsets within one cell culture condition.

Effective enrichment of trace exosomes for the label-free SERS detection via low-cost thermophoretic profiling

Exosome-based liquid biopsies possess great potential in monitoring cancer development However, current exosome detection biosensors require large exosome volumes, showing the weak detection sensitivity. Besides, these methods pay little attention to in situ analysis of exosomes, hence limiting the provision of more accurate clinically-relevant information. Herein, we develop an innovative label-free biosensor combining the low-cost thermophoretic enrichment method with the surface-enhanced Raman spectroscopy (SERS) detection. Based on the thermophoretic enrichment strategy, exosomes and gold nanoparticles can be enriched together into a small area with a scale of 500 μm within 10 min. The Raman signals of various exosomes derived from normal, cancerous cell lines and human serum are dynamically monitored in situ, with the limit of detection of 102-103 particles per microliter, presenting higher sensitivity compared with the similar label-free SERS detection. The spectral data set of different exosomes is applied to train for multivariate classification of cell types and to estimate how the normal exosome data resemble cancer cell exosome. The reliable classification and identification of different exosomes can be realized. The current biosensor is convenient, low-cost and requires small exosome volumes (∼3 μL), and if validated in larger cohorts may contribute to the tumor prediction and diagnosis.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

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.

Detection of live SARS-CoV-2 virus and its variants by specially designed SERS-active substrates and spectroscopic analyses

A method using label-free surface enhanced Raman spectroscopy (SERS) based on substrate design is provided for an early detection and differentiation of spike glycoprotein mutation sites in live SARS-CoV-2 variants. Two SERS-active substrates, Au nanocavities (Au NCs) and Au NPs on porous ZrO₂ (Au NPs/pZrO₂), were used to identify specific peaks of A.3, Alpha, and Delta variants at different concentrations and demonstrated the ability to provide their SERS spectra with detection limits of 0.1–1.0% (or 10⁴⁻⁵ copies/mL). Variant identification can be achieved by cross-examining reference spectra and analyzing the substrate-analyte relationship between the suitability of the analyte upon the hotspot(s) formed at high concentrations and the effective detection distance at low concentrations. Mutation sites on the S1 chain of the spike glycoprotein for each variant may be related and distinguishable. This method does not require sample preprocessing and therefore allows for fast screening, which is of high value for more comprehensive and specific studies to distinguish upcoming variants.

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.

Optical photothermal infrared spectroscopy can differentiate equine osteoarthritic plasma extracellular vesicles from healthy controls

Equine osteoarthritis is a chronic degenerative disease of the articular joint, characterised by cartilage degradation resulting in pain and reduced mobility and thus is a prominent equine welfare concern. Diagnosis is usually at a late stage through clinical examination and radiographic imaging, whilst treatment is symptomatic not curative. Extracellular vesicles are nanoparticles that are involved in intercellular communication. The objective of this study was to investigate the feasibility of Raman and Optical Photothermal Infrared Spectroscopies to detect osteoarthritis using plasma-derived extracellular vesicles, specifically differentiating extracellular vesicles in diseased and healthy controls within the parameters of the techniques used. Plasma samples were derived from thoroughbred racehorses. A total of 14 samples were selected (control; n = 6 and diseased; n = 8). Extracellular vesicles were isolated using differential ultracentrifugation and characterised using nanoparticle tracking analysis, transmission electron microscopy, and human tetraspanin chips. Samples were then analysed using combined Raman and Optical Photothermal Infrared Spectroscopies. Infrared spectra were collected between 950-1800 cm-1. Raman spectra had bands between the wavelengths of 900-1800 cm-1 analysed. Spectral data for both Raman and Optical Photothermal Infrared Spectroscopy were used to generate clustering via principal components analysis and classification models were generated using partial least squared discriminant analysis in order to characterize the techniques' ability to distinguish diseased samples. Optical Photothermal Infrared Spectroscopy could differentiate osteoarthritic extracellular vesicles from healthy with good classification (93.4% correct classification rate) whereas Raman displayed poor classification (correct classification rate = -64.3%). Inspection of the infrared spectra indicated that plasma-derived extracellular vesicles from osteoarthritic horses contained increased signal for proteins, lipids and nucleic acids. For the first time we demonstrated the ability to use optical photothermal infrared spectroscopy combined with Raman spectroscopy to interrogate extracellular vesicles and osteoarthritis-related samples. Optical Photothermal Infrared Spectroscopy was superior to Raman in this study, and could distinguish osteoarthritis samples, suggestive of its potential use diagnostically to identify osteoarthritis in equine patients. This study demonstrates the potential of Raman and Optical Photothermal Infrared Spectroscopy to be used as a future diagnostic tool in clinical practice, with the capacity to detect changes in extracellular vesicles from clinically derived samples.

Tracking of Extracellular Vesicles' Biodistribution: New Methods and Approaches

Extracellular vesicles (EVs) are nanosized lipid bilayer vesicles that are released by almost all cell types. They range in diameter from 30 nm to several micrometres and have the ability to carry biologically active molecules such as proteins, lipids, RNA, and DNA. EVs are natural vectors and play an important role in many physiological and pathological processes. The amount and composition of EVs in human biological fluids serve as biomarkers and are used for diagnosing diseases and monitoring the effectiveness of treatment. EVs are promising for use as therapeutic agents and as natural vectors for drug delivery. However, the successful use of EVs in clinical practice requires an understanding of their biodistribution in an organism. Numerous studies conducted so far on the biodistribution of EVs show that, after intravenous administration, EVs are mostly localized in organs rich in blood vessels and organs associated with the reticuloendothelial system, such as the liver, lungs, spleen, and kidneys. In order to improve resolution, new dyes and labels are being developed and detection methods are being optimized. In this work, we review all available modern methods and approaches used to assess the biodistribution of EVs, as well as discuss their advantages and limitations.

Differential of cholangiocarcinoma disease using Raman spectroscopy combined with multivariate analysis

Cholangiocarcinoma (CCA) is a type of cancer, which 5-year survival is lower than 20 %, and which is detected mostly in advanced stage of the disease. Unfortunately, there are no diagnostic tools, which could show changes in the body indicating the development of the disease. Therefore, in this study, we investigate Raman spectroscopy as a promising analytical tool in medical diagnostics and as a method, which would allow to distinguish between healthy patients and patients suffering from cholangiocarcinoma. The obtained Raman spectra showed, that lower intensities of peaks corresponding to amino acids and proteins, as well as higher intensities of peaks originating from lipids vibrations were observed in healthy individuals in comparison with cancer patients. Moreover, Partial Last Square (PLS), Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA) of Raman spectra indicate that the ranges between 800 cm^-1 and 1800 cm^-1, 3477 cm^-1 -3322 cm^-1 and 1394 cm^-1 -1297 cm^-1 allow to distinguish cancer patients from healthy ones. The obtained results showed, that Raman spectroscopy is a good candidate, to become in future one of the diagnostic tools of Cholangiocarcinoma.

Cell-Based Ambient Venturi Autosampling and Matrix-Assisted Laser Desorption Ionization Mass Spectrometric Imaging of Secretory Products

A cell-based ambient Venturi autosampling device was established for the monitoring of dynamic cell secretions in response to chemical stimulations in real time with temporal resolution on the order of a second. Detection of secretory products of cells and screening of bioactive compounds are primarily performed on an ambient autosampling probe and matrix-assisted laser desorption ionization (MALDI) mass spectrometry. It takes advantage of the Venturi effect in which the fluid flowing through an inlet capillary tube is automatically fed into a parallel array of multiple outlet capillaries. Cells are incubated inside the inlet capillary tube that is connected with either a syringe pump or liquid chromatography (LC) for the transfer of single compounds or mixtures, respectively. Secretory products were continuously pushed into the outlet capillaries and then spotted into a compressed thin film of the matrix material 9-aminoacridine for MALDI mass spectrometric imaging. In physiological pH, without the use of high voltages and without the use of chemical derivatizations, this platform can be applied to the direct assay of neurotransmitters or other secretory products released from cells in response to the stimulation of individual compounds or LC-separated eluates of natural mixtures. It provides a new way to identify bioactive compounds with a detection limit down to 0.04 fmol/pixel.

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.

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.

Raman Fingerprint of Extracellular Vesicles and Conditioned Media for the Reproducibility Assessment of Cell-Free Therapeutics

Extracellular Vesicles (EVs) and Conditioned Medium (CM) are promising cell-free approaches to repair damaged and diseased tissues for regenerative rehabilitation purposes. They both entail several advantages, mostly in terms of safety and handling, compared to the cell-based treatment. Despite the growing interest in both EVs and CM preparations, in the light of a clinical translation, a number of aspects still need to be addressed mainly because of limits in the reproducibility and reliability of the proposed protocols. Raman spectroscopy (RS) is a non-destructive vibrational investigation method that provides detailed information about the biochemical composition of a sample, with reported ability in bulk characterization of clusters of EVs from different cell types. In the present brief report, we acquired and compared the Raman spectra of the two most promising cell-free therapeutics, i.e., EVs and CM, derived from two cytotypes with a history in the field of regenerative medicine, adipose-derived mesenchymal stem/stromal cells (ASCs) and dermal fibroblasts (DFs). Our results show how RS can verify the reproducibility not only of EV isolation, but also of the whole CM, thus accounting for both the soluble and the vesicular components of cell secretion. RS can provide hints for the identification of the soluble factors that synergistically cooperate with EVs in the regenerative effect of CM. Still, we believe that the application of RS in the pipeline of cell-free products preparation for therapeutic purposes could help in accelerating translation to clinics and regulatory approval.

Ultrasensitive Characterization of the Prion Protein by Surface-Enhanced Raman Scattering: Selective Enhancement via Electrostatic Tethering of the Intrinsically Disordered Domain with Functionalized Silver Nanoparticles

Surface-enhanced Raman scattering (SERS) circumvents the inherent insensitivity of Raman spectroscopy and offers a powerful tool for the ultrasensitive detection and characterization of biomolecules at low concentrations. Here we show that SERS via electrostatic tethering between surface-modified negatively charged silver nanoparticles and highly positively charged intrinsically disordered N-terminal domain of the prion protein allows highly sensitive and reproducible protein detection and characterization at as low as hundreds of nanomolar protein concentrations. These measurements preferentially illuminate a selective part of the protein due to a sharp dependence of the near-field intensity on the distance between the nanoparticle surface and the protein. We also demonstrate that by shortening the length of the disordered tail it is possible to achieve a domain-selective Raman enhancement to study the C-terminal globular domain. Our tether-length-dependent SERS methodology will serve as a potent, noninvasive, and label-free strategy to detect and characterize a wide range of proteins possessing disordered segments.

Diagnosis of intrahepatic cholangiocarcinoma by Raman spectroscopy provides high efficiency: A protocol for systematic review and meta-analysis

BACKGROUND

We aim to evaluate the efficiency of Raman spectroscopy (RS) in diagnosing suspected patients with intrahepatic cholangiocarcinoma (ICC), manifested by diagnostic sensitivity, specificity, and accuracy.

METHODS

We will research widely the articles concerning the use of RS in ICC through authenticated database including PubMed/Medline, EMBASE, Web of Science, Ovid, Web of Knowledge, Cochrane Library, and CNKI between January 2012 and November 2020, retrieving at least 1500 spectra with strict criteria. This study will be carried out in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We are going to summarize the test performance using random effects models.

RESULTS

Based on the pooled sensitivity, specificity, and diagnostic accuracy, we intend to provide the relative diagnostic efficiency in ICC through RS.

CONCLUSION

Through this systematic review and meta-analysis, we intend to provide the pooled sensitivity, specificity and diagnostic accuracy of RS in the diagnosis of suspected ICC. Other parameters like positive likelihood ratios (LR), negative LR, diagnostic odds ratio (DOR), and area under curve (AUC) of the summary receiver operating characteristics (SROC) curve will also be calculated and related figures will be drawn to help illustrate the efficacy of RS in the diagnosis of ICC.