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

APPROACH: Sensitive Detection of Exosomal Biomarkers by Aptamer-Mediated Proximity Ligation Assay and Time-Resolved Förster Resonance Energy Transfer

Exosomal biomarker detection holds great importance in the field of in vitro diagnostics, offering a non-invasive and highly sensitive approach for early disease detection and personalized treatment. Here, we proposed an "APPROACH" strategy, combining aptamer-mediated proximity ligation assay (PLA) with rolling circle amplification (RCA) and time-resolved Förster resonance energy transfer (TR-FRET) for the sensitive and semi-homogenous detection of exosomal biomarkers. PLA probes consisted of a cholesterol-conjugated oligonucleotide, which anchored to the membrane of an exosome, and a specific aptamer oligonucleotide that recognized a target protein of the exosome; the proximal binding of pairs of PLA probes to the same exosome positioned the oligonucleotides in the vicinity of each other, guiding the hybridization and ligation of two subsequently added backbone and connector oligonucleotides to form a circular DNA molecule. Circular DNA formed from PLA underwent rolling circle amplification (RCA) for signal amplification, and the resulting RCA products were subsequently quantified by TR-FRET. The limits of detection provided by APPROACH for the exosomal biomarkers CD63, PD-L1, and HER2 were 0.46 ng∙μL-1, 0.77 ng∙μL-1, and 1.1 ng∙μL-1, respectively, demonstrating excellent analytical performance with high sensitivity and quantification accuracy. Furthermore, the strategy afforded sensitive detection of exosomal CD63 with a LOD of 1.56 ng∙μL-1 in complex biological matrices, which underscored its anti-interference capability and potential for in vitro detection. The proposed strategy demonstrates wide-ranging applicability in quantifying diverse exosomal biomarkers while exhibiting robust analytical characteristics, including high sensitivity and accuracy.

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

Development and application of an optimised Bayesian shrinkage prior for spectroscopic biomedical diagnostics

BACKGROUND AND OBJECTIVE

Classification of vibrational spectra is often challenging for biological substances containing similar molecular bonds, interfering with spectral outputs. To address this, various approaches are widely studied. However, whilst providing powerful estimations, these techniques are computationally extensive and frequently overfit the data. Shrinkage priors, which favour models with relatively few predictor variables, are often applied in Bayesian penalisation techniques to avoid overfitting.

METHODS

Using the logit-normal continuous analogue of the spike-and-slab (LN-CASS) as the shrinkage prior and modelling, we have established classification for accurate analysis, with the established system found to be faster than conventional least absolute shrinkage and selection operator, horseshoe or spike-and-slab. These were examined versus coefficient data based on a linear regression model and vibrational spectra produced via density functional theory calculations. Then applied to Raman spectra from saliva to classify the sample sex.

RESULTS

Subsequently applied to the acquired spectra from saliva, the evaluated models exhibited high accuracy (AUC>90 %) even when number of parameters was higher than the number of observations. Analyses of spectra for all Bayesian models yielded high-classification accuracy upon cross-validation. Further, for saliva sensing, LN-CASS was found to be the only classifier with 100 %-accuracy in predicting the output based on a leave-one-out cross validation.

CONCLUSIONS

With potential applications in aiding diagnosis from small spectroscopic datasets and are compatible with a range of spectroscopic data formats. As seen with the classification of IR and Raman spectra. These results are highly promising for emerging developments of spectroscopic platforms for biomedical diagnostic sensing systems.

'When is a hotspot a good nanospot' - review of analytical and hotspot-dominated surface enhanced Raman spectroscopy nanoplatforms

Substrate development in surface-enhanced Raman spectroscopy (SERS) continues to attract research interest. In order to determine performance metrics, researchers in foundational SERS studies use a variety of experimental means to characterize the nature of substrates. However, often this process would appear to be performed indiscriminately without consideration for the physical scale of the enhancement phenomena. Herein, we differentiate between SERS substrates whose primary enhancing structures are on the hundreds of nanometer scale (analytical SERS nanosubstrates) and those whose main mechanism derives from nanometric-sized gaps (hot-spot dominated SERS substrates), assessing the utility of various characterization methods for each substrate class. In this context, characterization approaches in white-light spectroscopy, electron beam methods, and scanning probe spectroscopies are reviewed. Tip-enhanced Raman spectroscopy, wavelength-scanned SERS studies, and the impact of surface hydrophobicity are also discussed. Conclusions are thus drawn on the applicability of each characterization technique regarding amenability for SERS experiments that have features at different length scales. For instance, while white light spectroscopy can provide an indication of the plasmon resonances associated with 10 s-100 s nm-scale structures, it may not reveal information about finer surface texturing on the true nm-scale, critical for SERS' sensitivity, and in need of investigation via scanning probe techniques.

Label-free screening of common urinary system tumors from blood plasma based on surface-enhanced Raman spectroscopy

BACKGROUND

The incidence of common urinary system tumors has been rising rapidly in recent years, and most urinary system-derived tumors lack specific biomarkers.

OBJECTIVES

To explore the efficacy of surface-enhanced Raman spectroscopy (SERS) of blood plasma in screening three common urinary system tumors, including bladder cancer (BC), prostate cancer (PCa), and renal cell carcinoma (RCC).

METHODS

SERS plasma spectra from 125 plasma samples, including 25 PCa, 38 RCC, 24 BC patients, and 38 normal volunteers, were collected. All candidates had no other comorbidities. The Diagnosis was based on the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and the effectiveness of the diagnostic algorithms was verified using the Receiver Operating Characteristic Curve (ROC).

RESULTS

There are significant differences in SERS signals between PCa, BC, RCC, and normal plasma, especially at 639, 889, 1010, 1136, and 1205 cm-1. The PCA-LDA results show that high sensitivity (100 %), specificity (100 %), and accuracy (100 %) could be achieved for screening the PCa, RCC, BC group vs. the normal group, the PCa group vs. the BC and RCC group, respectively. The diagnostic sensitivity, specificity, and accuracy for the BC group vs. the RCC group are 79.2 %, 71.1 %, and 75.15 %, respectively. The integrated area under the ROC curve (AUC) is 1.0, 1.0, and 1.0 for the PCa, RCC, and BC group vs. the normal group, respectively. The AUC of the PCa group vs. the BC group and RCC group and the BC group vs. the RCC group are 1.0, 1.0, and 0.842, respectively.

CONCLUSIONS

Label-free plasma-SERS technology with PCA-LDA analysis could be a useful screening method for detecting urinary system tumors (PCa, RCC, and BC) in this exploratory study.

Detection of micro-plasma-induced exosomes secretion in a fibroblast-melanoma co-culture model

BACKGROUND

Melanoma is a highly aggressive tumor and a significant cause of skin cancer-related death. Timely diagnosis and treatment require identification of specific biomarkers in exosomes secreted by melanoma cells. In this study, label-free surface-enhanced Raman spectroscopy (SERS) method with size-matched selectivity was used to detect membrane proteins in exosomes released from a stimulated environment of fibroblasts (L929) co-cultured with melanoma cells (B16-F10). To promote normal secretion of exosomes, micro-plasma treatment was used to gently induce the co-cultured cells and slightly increase the stress level around the cells for subsequent detection using the SERS method.

RESULTS AND DISCUSSION

Firstly, changes in reactive oxygen species/reactive nitrogen species (ROS/RNS) concentrations in the cellular microenvironment and the viability and proliferation of healthy cells are assessed. Results showed that micro-plasma treatment increased extracellular ROS/RNS levels while modestly reducing cell proliferation without significantly affecting cell survival. Secondly, the particle size of secreted exosomes isolated from the culture medium of L929, B16-F10, and co-cultured cells with different micro-plasma treatment time did not increase significantly under single-cell conditions at short treatment time but might be changed under co-culture condition or longer treatment time. Third, for SERS signals related to membrane protein biomarkers, exosome markers CD9, CD63, and CD81 can be assigned to significant Raman shifts in the range of 943-1030 and 1304-1561 cm-1, while the characteristics SERS peaks of L929 and B16-F10 cells are most likely located at 1394/1404, 1271 and 1592 cm-1 respectively.

SIGNIFICANCE AND NOVELTY

Therefore, this micro-plasma-induced co-culture model provides a promising preclinical approach to understand the diagnostic potential of exosomes secreted by cutaneous melanoma/fibroblasts. Furthermore, the label-free SERS method with size-matched selectivity provides a novel approach to screen biomarkers in exosomes secreted by melanoma cells, aiming to reduce the use of labeling reagents and the processing time traditionally required.

Plasmonic Nanoparticle-Enhanced Optical Techniques for Cancer Biomarker Sensing

This review summarizes recent advances in leveraging localized surface plasmon resonance (LSPR) nanotechnology for sensitive cancer biomarker detection. LSPR arising from noble metal nanoparticles under light excitation enables the enhancement of various optical techniques, including surface-enhanced Raman spectroscopy (SERS), dark-field microscopy (DFM), photothermal imaging, and photoacoustic imaging. Nanoparticle engineering strategies are discussed to optimize LSPR for maximum signal amplification. SERS utilizes electromagnetic enhancement from plasmonic nanostructures to boost inherently weak Raman signals, enabling single-molecule sensitivity for detecting proteins, nucleic acids, and exosomes. DFM visualizes LSPR nanoparticles based on scattered light color, allowing for the ultrasensitive detection of cancer cells, microRNAs, and proteins. Photothermal imaging employs LSPR nanoparticles as contrast agents that convert light to heat, producing thermal images that highlight cancerous tissues. Photoacoustic imaging detects ultrasonic waves generated by LSPR nanoparticle photothermal expansion for deep-tissue imaging. The multiplexing capabilities of LSPR techniques and integration with microfluidics and point-of-care devices are reviewed. Remaining challenges, such as toxicity, standardization, and clinical sample analysis, are examined. Overall, LSPR nanotechnology shows tremendous potential for advancing cancer screening, diagnosis, and treatment monitoring through the integration of nanoparticle engineering, optical techniques, and microscale device platforms.

Nanoscopic and Functional Characterization of Keratinocyte-Originating Exosomes in the Wound Fluid of Non-Diabetic and Diabetic Chronic Wound Patients

Exosomes, a class of extracellular vesicles of endocytic origin, play a critical role in paracrine signaling for successful cell-cell crosstalk in vivo. However, limitations in our current understanding of these circulating nanoparticles hinder efficient isolation, characterization, and downstream functional analysis of cell-specific exosomes. In this work, we sought to develop a method to isolate and characterize keratinocyte-originated exosomes (hExo κ ) from human chronic wound fluid. Furthermore, we studied the significance of hExo κ in diabetic wounds. LC-MS-MS detection of KRT14 in hExo κ and subsequent validation by Vesiclepedia and Exocarta databases identified surface KRT14 as a reliable marker of hExo κ . dSTORM nanoimaging identified KRT14+ extracellular vesicles (EV κ ) in human chronic wound fluid, 23% of which were of exosomal origin. An immunomagnetic two-step separation method using KRT14 and tetraspanin antibodies successfully isolated hExo κ from the heterogeneous pool of EV in chronic wound fluid of 15 non-diabetic and 22 diabetic patients. Isolated hExo κ (Ø75-150nm) were characterized per EV-track guidelines. dSTORM images, analyzed using online CODI followed by independent validation using Nanometrix, revealed hExo κ Ø as 80-145nm. The abundance of hExo κ was low in diabetic wound fluids and negatively correlated with patient HbA1c levels. The hExo κ isolated from diabetic wound fluid showed a low abundance of small bp RNA (<200 bp). Raman spectroscopy underscored differences in surface lipids between non-diabetic and diabetic hExo κ Uptake of hExo κ by monocyte-derived macrophages (MDM) was low for diabetics versus non-diabetics. Unlike hExo κ from non-diabetics, the addition of diabetic hExo κ to MDM polarized with LPS and INFγ resulted in sustained expression of iNOS and pro-inflammatory chemokines known to recruit macrophage (mϕ) This work provides maiden insight into the structure, composition, and function of hExo κ from chronic wound fluid thus providing a foundation for the study of exosomal malfunction under conditions of diabetic complications such as wound chronicity.

New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.

Early cancer detection by SERS spectroscopy and machine learning

A new approach for early detection of multiple cancers is presented by integrating SERS spectroscopy of serum molecular fingerprints and machine learning.

Emerging integrated SERS-microfluidic devices for analysis of cancer-derived small extracellular vesicles

Cancer-derived small extracellular vesicles (sEVs) are specific subgroups of lipid bilayer vesicles secreted from cancer cells to the extracellular environment. They carry distinct biomolecules (e.g., proteins, lipids and nucleic acids) from their parent cancer cells. Therefore, the analysis of cancer-derived sEVs can provide valuable information for cancer diagnosis. However, the use of cancer-derived sEVs in clinics is still limited due to their small size, low amounts in circulating fluids, and heterogeneous molecular features, making their isolation and analysis challenging. Recently, microfluidic technology has gained great attention for its ability to isolate sEVs in minimal volume. In addition, microfluidics allows the isolation and detection of sEVs to be integrated into a single device, offering new opportunities for clinical application. Among various detection techniques, surface-enhanced Raman scattering (SERS) has emerged as a promising candidate for integrating with microfluidic devices due to its ultra-sensitivity, stability, rapid readout, and multiplexing capability. In this tutorial review, we start with the design of microfluidics devices for isolation of sEVs and introduce the key factors to be considered for the design, and then discuss the integration of SERS and microfluidic devices by providing descriptive examples of the currently developed platforms. Lastly, we discuss the current limitations and provide our insights for utilising integrated SERS-microfluidics to isolate and analyse cancer-derived sEVs in clinical settings.

Electrochemical aptasensor based on the engineered core-shell MOF nanostructures for the detection of tumor antigens

It is essential to develop ultrasensitive biosensors for cancer detection and treatment monitoring. In the development of sensing platforms, metal-organic frameworks (MOFs) have received considerable attention as potential porous crystalline nanostructures. Core-shell MOF nanoparticles (NPs) have shown different diversities, complexities, and biological functionalities, as well as significant electrochemical (EC) properties and potential bio-affinity to aptamers. As a result, the developed core-shell MOF-based aptasensors serve as highly sensitive platforms for sensing cancer biomarkers with an extremely low limit of detection (LOD). This paper aimed to provide an overview of different strategies for improving selectivity, sensitivity, and signal strength of MOF nanostructures. Then, aptamers and aptamers-modified core-shell MOFs were reviewed to address their functionalization and application in biosensing platforms. Additionally, the application of core-shell MOF-assisted EC aptasensors for detection of several tumor antigens such as prostate-specific antigen (PSA), carbohydrate antigen 15-3 (CA15-3), carcinoembryonic antigen (CEA), human epidermal growth factor receptor-2 (HER2), cancer antigen 125 (CA-125), cytokeratin 19 fragment (CYFRA21-1), and other tumor markers were discussed. In conclusion, the present article reviews the advancement of potential biosensing platforms toward the detection of specific cancer biomarkers through the development of core-shell MOFs-based EC aptasensors.

High resolution compact spectrometer system based on scattering and spectral reconstruction

In this Letter, we present a compact scattering spectrometer system based on fluorosilicate glass ceramics. By the algorithmic spectral calibration and reconstruction, we achieve wavelength detection with a resolution of 0.1 nm. Numerous nanocrystals embedded in the glass host in the glass ceramics result in a significant natural multilayer scattering medium, which can provide a 60% scattering efficiency for incident light while increasing the optical path of incident light transmitting in the medium. The glass ceramics scattering medium with a rather compact physical size is integrated with a low-cost camera to compose an optical spectral system, which has potential application in lab-on-a-chip optical spectroscopy.

A review of sensors for classification and subtype discrimination of cancer: Insights into circulating tumor cells and tumor-derived extracellular vesicles

Liquid biopsy can reflect the state of tumors in vivo non-invasively, thus providing a strong basis for the early diagnosis, individualized treatment monitoring and prognosis of tumors. Circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tdEVs) contain information-rich components, such as nucleic acids and proteins, and they are essential markers for liquid biopsies. Their capture and analysis are of great importance for the study of disease occurrence and development and, consequently, have been the subject of many reviews. However, both CTCs and tdEVs carry the biological characteristics of their original tissue, and few reviews have focused on their function in the staging and classification of cancer. In this review, we focus on state-of-the-art sensors based on the simultaneous detection of multiple biomarkers within CTCs and tdEVs, with clinical applications centered on cancer classification and subtyping. We also provide a thorough discussion of the current challenges and prospects for novel sensors with the ultimate goal of cancer classification and staging. It is hoped that these most advanced technologies will bring new insights into the clinical practice of cancer screening and diagnosis.

Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Status quo of Extracellular Vesicle isolation and detection methods for clinical utility

Extracellular vesicles (EVs) are nano-sized particles that hold tremendous potential in the clinical space, as their biomolecular profiles hold a key to non-invasive liquid biopsy for cancer diagnosis and prognosis. EVs are present in most bodily fluids, hence are easily obtainable from patients, advantageous to that of traditional, invasive tissue biopsies and imaging techniques. However, there are certain constraints that hinder clinical use of EVs. The translation of EV biomarkers from "bench-to-bedside" is encumbered by the methods of EV isolation and subsequent biomarker detection currently implemented in laboratories. Although current isolation and detection methods are effective, they lack practicality, with their requirement for high bodily fluid volumes, low equipment availability, slow turnaround times and high costs. The high demand for techniques that overcome these limitations has resulted in significant advancements in nanotechnological devices. These devices are designed to integrate EV isolation and biomarker detection into a one-step method of direct EV detection from bodily fluids. This provides promise for the acceleration of EVs into current clinical standards. This review highlights the importance of EVs as cancer biomarkers, the methodological obstacles currently faced in clinical studies and how novel nanodevices could advance clinical translation.

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.

Cascaded Deep Convolutional Neural Networks as Improved Methods of Preprocessing Raman Spectroscopy Data

Machine learning has had a significant impact on the value of spectroscopic characterization tools, particularly in biomedical applications, due to its ability to detect latent patterns within complex spectral data. However, it often requires extensive data preprocessing, including baseline correction and denoising, which can lead to an unintentional bias during classification. To address this, we developed two deep learning methods capable of fully preprocessing raw Raman spectroscopy data without any human input. First, cascaded deep convolutional neural networks (CNN) based on either ResNet or U-Net architectures were trained on randomly generated spectra with augmented defects. Then, they were tested using simulated Raman spectra, surface-enhanced Raman spectroscopy (SERS) imaging of chemical species, low resolution Raman spectra of human bladder cancer tissue, and finally, classification of SERS spectra from human placental extracellular vesicles (EVs). Both approaches resulted in faster training and complete spectral preprocessing in a single step, with more speed, defect tolerance, and classification accuracy compared to conventional methods. These findings indicate that cascaded CNN preprocessing is ideal for biomedical Raman spectroscopy applications in which large numbers of heterogeneous spectra with diverse defects need to be automatically, rapidly, and reproducibly preprocessed.

Facile Microwave Assisted Synthesis of Silver Nanostars for Ultrasensitive Detection of Biological Analytes by SERS

We report a very simple, rapid and reproducible method for the fabrication of anisotropic silver nanostars (AgNS) that can be successfully used as highly efficient SERS substrates for different bioanalytes, even in the case of a near-infra-red (NIR) excitation laser. The nanostars have been synthesized using the chemical reduction of Ag+ ions by trisodium citrate. This is the first research reporting the synthesis of AgNS using only trisodium citrate as a reducing and stabilizing agent. The key elements of this original synthesis procedure are rapid hydrothermal synthesis of silver nanostars followed by a cooling down procedure by immersion in a water bath. The synthesis was performed in a sealed bottom flask homogenously heated and brought to a boil in a microwave oven. After 60 s, the colloidal solution was cooled down to room temperature by immersion in a water bath at 35 °C. The as-synthesized AgNS were washed by centrifugation and used for SERS analysis of test molecules (methylene blue) as well as biological analytes: pharmaceutical compounds with various Raman cross sections (doxorubicin, atenolol & metoprolol), cell lysates and amino acids (methionine & cysteine). UV-Vis absorption spectroscopy, (Scanning) Transmission Electron Microscopy ((S)TEM) and Atomic Force Microscopy (AFM) have been employed for investigating nanostars' physical properties.

Design and fabrication of bimetallic plasmonic colloids through cold nanowelding

The integration of Au and Ag into nanoalloys has emerged as an intriguing strategy to further tailor and boost the plasmonic properties of optical substrates. Conventional approaches for fabricating these materials via chemical reductions of metal salts in solution suffer from some limitations, such as the possibility of retaining the original morphology of the monometallic substrate. Spontaneous nanowelding at room temperature has emerged as an alternative route to tailor Au/Ag nanomaterials. Herein, we perform a thorough study on the cold-welding process of silver nanoparticles onto gold substrates to gain a better understanding of the role of different variables in enabling the formation of well-defined bimetallic structures that retain the original gold substrate morphology. To this end, we systematically varied the size of silver nanoparticles, dimensions and geometries of gold substrates, solvent polarity and structural nature of the polymeric coating. A wide range of optical and microscopy techniques have been used to provide a complementary and detailed description of the nanowelding process. We believe this extensive study will provide valuable insights into the optimal design and engineering of bimetallic plasmonic Ag/Au structures for application in nanodevices.

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.

Exosome detection via surface-enhanced Raman spectroscopy for cancer diagnosis

As nanoscale extracellular vesicles, exosomes are secreted by various cell types, and they are widely distributed in multiple biological fluids. Studies have shown that tumor-derived exosomes can carry a variety of primary tumor-specific molecules, which may represent a novel tool for the early detection of cancer. However, the clinical translation of exosomes remains a challenge due to the requirement of large quantities of samples when enriching the cancer-related exosomes in biological fluids, the insufficiency of traditional techniques for exosome subpopulations, and the complex exosome isolation of the current commercially available exosome phenotype profiling approaches. The evolving surface-enhanced Raman scattering (SERS) technology, with properties of unique optoelectronics, easy functionalization, and the particular interaction between light and nanoscale metallic materials, can achieve sensitive detection of exosomes without large quantities of samples and multiplexed phenotype profiling, providing a new mode of real-time and noninvasive analysis for cancer patients. In the present review, we mainly discussed exosome detection based on SERS, especially SERS immunoassay. The basic structure and function of exosomes were firstly introduced. Then, recent studies using the SERS technique for cancer detection were critically reviewed, which mainly included various SERS substrates, biological modification of SERS substrates, SERS-based exosome detection, and the combination of SERS and other technologies for cancer diagnosis. This review systematically discussed the essential aspects, limitations, and considerations of applying SERS technology in the detection and analysis of cancer-derived exosomes, which could provide a valuable reference for the early diagnosis of cancer through SERS technology. STATEMENT OF SIGNIFICANCE: Surface-enhanced Raman scattering (SERS) has been applied to exosomes detection to obtain better diagnostic results. In past three years, several reviews have been published in exosome detection, which were narrowly focus on methods of exosome detection. Selection and surface functionalization of the substrate and the combination detection with different methods based on SERS will provide new strategies for the detection of exosomes. This review will focus on the above aspects. This emerging detection method is constantly evolving and contributing to the early discovery of diseases in the future.

Plasmonic Azobenzene Chemoreporter for Surface-Enhanced Raman Scattering Detection of Biothiols

Low molecular weight thiols (biothiols) are highly active compounds extensively involved in human physiology. Their abnormal levels have been associated with multiple diseases. In recent years, major efforts have been devoted to developing new nanosensing methods for the low cost and fast quantification of this class of analytes in minimally pre-treated samples. Herein, we present a novel strategy for engineering a highly efficient surface-enhanced Raman scattering (SERS) spectroscopy platform for the dynamic sensing of biothiols. Colloidally stable silver nanoparticles clusters equipped with a specifically designed azobenzene derivative (AzoProbe) were generated as highly SERS active substrates. In the presence of small biothiols (e.g., glutathione, GSH), breakage of the AzoProbe diazo bond causes drastic spectral changes that can be quantitatively correlated with the biothiol content with a limit of detection of ca. 5 nM for GSH. An identical response was observed for other low molecular weight thiols, while larger macromolecules with free thiol groups (e.g., bovine serum albumin) do not produce distinguishable spectral alterations. This indicates the suitability of the SERS sensing platform for the selective quantification of small biothiols.

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.

Advances in the Application of Exosomes Identification Using Surface-Enhanced Raman Spectroscopy for the Early Detection of Cancers

Exosomes are small nanoscale vesicles with a double-layered lipid membrane structure secreted by cells, and almost all types of cells can secrete exosomes. Exosomes carry a variety of biologically active contents such as nucleic acids and proteins, and play an important role not only in intercellular information exchange and signal transduction, but also in various pathophysiological processes in the human body. Surface-enhanced Raman Spectroscopy (SERS) uses light to interact with nanostructured materials such as gold and silver to produce a strong surface plasmon resonance effect, which can significantly enhance the Raman signal of molecules adsorbed on the surface of nanostructures to obtain a rich fingerprint of the sample itself or Raman probe molecules with ultra-sensitivity. The unique advantages of SERS, such as non-invasive and high sensitivity, good selectivity, fast analysis speed, and low water interference, make it a promising technology for life science and clinical testing applications. In this paper, we briefly introduce exosomes and the current main detection methods. We also describe the basic principles of SERS and the progress of the application of unlabeled and labeled SERS in exosome detection. This paper also summarizes the value of SERS-based exosome assays for early tumor diagnosis.

The role of the metabolite cargo of extracellular vesicles in tumor progression

Metabolomic reprogramming in tumor and stroma cells is a hallmark of cancer but understanding its effects on the metabolite composition and function of tumor-derived extracellular vesicles (EVs) is still in its infancy. EVs are membrane-bound sacs with a complex molecular composition secreted by all living cells. They are key mediators of intercellular communication both in normal and pathological conditions and play a crucial role in tumor development. Although lipids are major components of EVs, most of the EV cargo studies have targeted proteins and nucleic acids. The potential of the EV metabolome as a source for biomarker discovery has gained recognition recently, but knowledge on the biological activity of tumor EV metabolites still remains limited. Therefore, we aimed (i) to compile the list of metabolites identified in tumor EVs isolated from either clinical specimens or in vitro samples and (ii) describe their role in tumor progression through literature search and pathway analysis.

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.

Current and Future Advancements of Raman Spectroscopy Techniques in Cancer Nanomedicine

Raman scattering is one of the most used spectroscopy and imaging techniques in cancer nanomedicine due to its high spatial resolution, high chemical specificity, and multiplexity modalities. The flexibility of Raman techniques has led, in the past few years, to the rapid development of Raman spectroscopy and imaging for nanodiagnostics, nanotherapy, and nanotheranostics. This review focuses on the applications of spontaneous Raman spectroscopy and bioimaging to cancer nanotheranostics and their coupling to a variety of diagnostic/therapy methods to create nanoparticle-free theranostic systems for cancer diagnostics and therapy. Recent implementations of confocal Raman spectroscopy that led to the development of platforms for monitoring the therapeutic effects of anticancer drugs in vitro and in vivo are also reviewed. Another Raman technique that is largely employed in cancer nanomedicine, due to its ability to enhance the Raman signal, is surface-enhanced Raman spectroscopy (SERS). This review also explores the applications of the different types of SERS, such as SERRS and SORS, to cancer diagnosis through SERS nanoprobes and the detection of small-size biomarkers, such as exosomes. SERS cancer immunotherapy and immuno-SERS (iSERS) microscopy are reviewed.

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

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

Targets and Tools: Nucleic Acids for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS) merges nanotechnology with conventional Raman spectroscopy to produce an ultrasensitive and highly specific analytical tool that has been exploited as the optical signal read-out in a variety of advanced applications. In this feature article, we delineate the main features of the intertwined relationship between SERS and nucleic acids (NAs). In particular, we report representative examples of the implementation of SERS in biosensing platforms for NA detection, the integration of DNA as the biorecognition element onto plasmonic materials for SERS analysis of different classes of analytes (from metal ions to microorgniasms) and, finally, the use of structural DNA nanotechnology for the precise engineering of SERS-active nanomaterials.

Paper-based plasmonic substrates as surface-enhanced Raman scattering spectroscopy platforms for cell culture applications

The engineering of advanced materials capable of mimicking the cellular micro-environment while providing cells with physicochemical cues is central for cell culture applications. In this regard, paper meets key requirements in terms of biocompatibility, hydrophilicity, porosity, mechanical strength, ease of physicochemical modifications, cost, and ease of large-scale production, to be used as a scaffold material for biomedical applications. Most notably, paper has demonstrated the potential to become an attractive alternative to conventional biomaterials for creating two-dimensional (2D) and three-dimensional (3D) biomimetic cell culture models that mimic the features of in vivo tissue environments for improving our understanding of cell behavior (e.g. growth, cell migration, proliferation, differentiation and tumor metastasis) in their natural state. On the other hand, integration of plasmonic nanomaterials (e.g. gold nanoparticles) within the fibrous structure of paper opens the possibility to generate multifunctional scaffolds equipped with biosensing tools for monitoring different cell cues through physicochemical signals. Among different plasmonic based detection techniques, surface-enhanced Raman scattering (SERS) spectroscopy emerged as a highly specific and sensitive optical tool for its extraordinary sensitivity and the ability for multidimensional and accurate molecular identification. Thus, paper-based plasmonic substrates in combination with SERS optical detection represent a powerful future platform for monitoring cell cues during cell culture processes. To this end, in this review, we will describe the different methods for fabricating hybrid paper-plasmonic nanoparticle substrates and their use in combination with SERS spectroscopy for biosensing and, more specifically, in cell culture applications.