Protein Quantification and Imaging by Surface-Enhanced Raman Spectroscopy and Similarity Analysis

Sensitive detection and labelling of proteins with a molecular rotor-based fluorogenic probe

Reported herein is a sensitive fluorogenic reagent based on an environment-sensitive molecular rotor for detecting and labelling proteins. The rapid binding of the probe to protein amino residues results in a significant enhancement in fluorescence emission, while its reversible interaction with free amino acids remains non-fluorescent. We demonstrate the utility of this reagent for wash-free and real-time imaging of membrane proteins in living cells and for rapidly determining the minimal inhibitory concentration (MIC) of antibiotics against bacteria.

Innovative Applications and Perspectives of Surface-Enhanced Raman Spectroscopy Technology in Biomedicine

Surface-enhanced Raman spectroscopy (SERS) has become a revolutionary technique in the biomedical field, providing unparalleled sensitivity for the detection and characterization of biological samples. In this review, recent SERS innovations are comprehensively discussed, including advanced substrate materials, different SERS detection strategies, and multimodal approaches that combine SERS with other biotechnologies. Among them, the role of SERS in the accurate diagnosis of tumors is highlighted, which has promoted accurate molecular analysis and real-time monitoring of treatment effects. In addition, the growing potential of SERS in the treatment of chronic diseases such as cardiovascular disease, diabetes, and neurodegenerative diseases is discussed. Moreover, the integration with microfluidic chip systems for precise single-cell analysis is presented. To give a forward-looking view, the key challenges faced by SERS technology are also proposed, and possible solutions to overcome these obstacles are provided.

Raman Spectral Feature Enhancement Framework for Complex Multiclassification Tasks

Raman spectroscopy enables label-free clinical diagnosis in a single step. However, identifying an individual carrying a specific disease from people with a multi-disease background is challenging. To address this, we developed a Raman spectral implicit feature augmentation with a Raman Intersection, Union, and Subtraction augmentation strategy (RIUS). RIUS expands the data set without requiring additional labeled data by leveraging set operations at the feature level, significantly enhancing model performance across various applications. On a challenging 30-class bacterial classification task, RIUS demonstrated a substantial improvement, increasing the accuracy of ResNet by 2.1% and that of SE-ResNet by 1.4%, achieving accuracies of 85.7% and 87.1%, respectively, on the Bacteria-ID-4 Data set, where RIUS improved ResNet and SE-ResNet accuracies by 13.6% and 14.5%, respectively, with only ten samples per category. When the sample size was reduced, accuracy gains increased to 31.7% and 38.3%, demonstrating the method's robustness across different sample volumes. Compared to basic augmentation, our method exhibited superior performance across various sample volumes and demonstrated exceptional adaptability to different levels of complexity. RIUS exhibited superior performance, particularly in complex settings. Moreover, cluster analysis validated the effectiveness of the implicit feature augmentation module and the consistency between theoretical design and experimental results. We further validated our approach using clinical serum samples from 70 breast cancer patients and 70 controls, achieving an AUC of 0.94 and a sensitivity of 92.9%. Our approach enhances the potential for precisely identifying diseases in complex settings and offers plug-and-play enhancement for existing classification models.

Liquid Biopsy-Based Detection and Response Prediction for Depression

Proactively predicting antidepressant treatment response before medication failures is crucial, as it reduces unsuccessful attempts and facilitates the development of personalized therapeutic strategies, ultimately enhancing treatment efficacy. The current decision-making process, which heavily depends on subjective indicators, underscores the need for an objective, indicator-based approach. This study developed a method for detecting depression and predicting treatment response through deep learning-based spectroscopic analysis of extracellular vesicles (EVs) from plasma. EVs were isolated from the plasma of both nondepressed and depressed groups, followed by Raman signal acquisition, which was used for AI algorithm development. The algorithm successfully distinguished depression patients from healthy individuals and those with panic disorder, achieving an AUC accuracy of 0.95. This demonstrates the model's capability to selectively diagnose depression within a nondepressed group, including those with other mental health disorders. Furthermore, the algorithm identified depression-diagnosed patients likely to respond to antidepressants, classifying responders and nonresponders with an AUC accuracy of 0.91. To establish a diagnostic foundation, the algorithm applied explainable AI (XAI), enabling personalized medicine for companion diagnostics and highlighting its potential for the development of liquid biopsy-based mental disorder diagnosis.

Multi-Biomarker Profiling for Precision Diagnosis of Lung Cancer

Multi-biomarker analysis can enhance the accuracy of the single-biomarker analysis by reducing the errors caused by genetic and environmental differences. For this reason, multi-biomarker analysis shows higher accuracy in early and precision diagnosis. However, conventional analysis methods have limitations for multi-biomarker analysis because of their long pre-processing times, inconsistent results, and large sample requirements. To solve these, a fast and accurate precision diagnostic method is introduced for lung cancer by multi-biomarker profiling using a single drop of blood. For this, surface-enhanced Raman spectroscopic immunoassay (SERSIA) is employed for the accurate, quick, and reliable quantification of biomarkers. Then, it is checked the statistical relation of the multi-biomarkers to differentiate between healthy controls and lung cancer patients. This approach has proven effective; with 20 µL of blood serum, lung cancer is diagnosed with 92% accuracy. It also accurately identifies the type and stage of cancer with 87% and 85%, respectively. These results show the importance of multi-biomarker analysis in overcoming the challenges posed by single-biomarker diagnostics. Furthermore, it markedly improves multi-biomarker-based analysis methods, illustrating its important impact on clinical diagnostics.

Integrative Metabolome and Proteome Analysis of Cerebrospinal Fluid in Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Recent studies have highlighted the significant role of cerebrospinal fluid (CSF) in reflecting pathophysiological PD brain conditions by analyzing the components of CSF. Based on the published literature, we created a single network with altered metabolites in the CSF of patients with PD. We analyzed biological functions related to the transmembrane of mitochondria, respiration of mitochondria, neurodegeneration, and PD using a bioinformatics tool. As the proteome reflects phenotypes, we collected proteome data based on published papers, and the biological function of the single network showed similarities with that of the metabolomic network. Then, we analyzed the single network of integrated metabolome and proteome. In silico predictions based on the single network with integrated metabolomics and proteomics showed that neurodegeneration and PD were predicted to be activated. In contrast, mitochondrial transmembrane activity and respiration were predicted to be suppressed in the CSF of patients with PD. This review underscores the importance of integrated omics analyses in deciphering PD's complex biochemical networks underlying neurodegeneration.

Self-Referenced Au Nanoparticles-Coated Glass Wafers for In Situ SERS Monitoring of Cell Secretion

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for discrimination of bimolecules in complex systems. However, its practical applications face challenges such as complicated manufacturing procedures and limited scalability of SERS substrates, as well as poor reproducibility during detection which compromises the reliability of SERS-based analysis. In this study, we developed a convenient method for simultaneous fabrication of massive SERS substrates with an internal standard to eliminate the substrate-to-substrate differences. We first synthesized Au@CN@Au nanoparticles (NPs) which contain embedded internal standard molecules with a single characteristic peak in the Raman-silent region, and then deposited the NPs on 6 mm glass wafers in a 96-well plate simply by centrifugation for 3 min. The one-time obtained 96 SERS substrates have excellent intrasubstrate uniformity and intersubstrate repeatability for SERS detection by using the internal standard (relative standard deviation = 10.47%), and were able to detect both charged and neutral molecules (crystal violet and triphenylphosphine) at a concentration of 10-9 M. Importantly, cells can be directly cultured on glass wafers in the 96-well plate, enabling real time monitoring of the secretes and metabolism change in response to external stimulation. We found that the release of nucleic acids, amino acids and lipids by MDA-MB-231 cells significantly increased under hypoxic conditions. Overall, our approach enables fast and large-scale production of Au@CN@Au NPs-coated glass wafers as SERS substrates, which are homogeneous and highly sensitive for monitoring trace changes of biomolecules.

DNA origami-templated gold nanorod dimer nanoantennas: enabling addressable optical hotspots for single cancer biomarker SERS detection

The convergence of DNA origami and surface-enhanced Raman spectroscopy (SERS) has opened a new avenue in bioanalytical sciences, particularly in the detection of single-molecule proteins. This breakthrough has enabled the development of advanced sensor technologies for diagnostics. DNA origami offers a highly controllable framework for the precise positioning of nanostructures, resulting in superior SERS signal amplification. In our investigation, we have successfully designed and synthesized DNA origami-based gold nanorod monomer and dimer assemblies. Moreover, we have evaluated the potential of dimer assemblies for label-free detection of a single biomolecule, namely epidermal growth factor receptor (EGFR), a crucial biomarker in cancer research. Our findings have revealed that the significant Raman amplification generated by DNA origami-assembled gold nanorod dimer nanoantennas facilitates the label-free identification of Raman peaks of single proteins, which is a prime aim in biomedical diagnostics. The present work represents a significant advancement in leveraging plasmonic nanoantennas to realize single protein SERS for the detection of various cancer biomarkers with single-molecule sensitivity.

Simultaneous Protein Colorful Imaging via Raman Signal Classification

Protein imaging aids diagnosis and drug development by revealing protein-drug interactions or protein levels. However, the challenges of imaging multiple proteins, reduced sensitivity, and high reliance on specific protein properties such as Raman peaks or refractive index hinder the understanding. Here, we introduce multiprotein colorful imaging through Raman signal classification. Our method utilized machine learning-assisted classification of Raman signals, which are the distinctive features of label-free proteins. As a result, three types of proteins could be imaged simultaneously. In addition, we could quantify individual proteins from a mixture of multiple proteins over a wide detection range (10 fg/mL-1 μg/mL). These results showed a 1000-fold improvement in sensitivity and a 30-fold increase in the upper limit of detection compared to existing methods. These advances will enhance our understanding of biology and facilitate the development of disease diagnoses and treatments.

Metal-Organic Framework-Based Surface-Enhanced Raman Scattering Sensing Platform for Trace Malondialdehyde Detection in Tears

Disease biomarkers in tears are crucial for clinical diagnosis and health monitoring. However, the limited volume of tear samples, low concentration of tear biomarkers, and complex tear composition present challenges for precise testing. We introduce a spot-on testing platform of metal-organic framework (MOF)-based surface-enhanced Raman scattering (SERS) capillary column, which is capable of target molecules selective separation and enrichment for tear biomarkers in situ detection. It consists of Au nanostars for effective SERS signal and a porous MOF shell for separating impurities through molecular sieving effect. This platform allows for simultaneous collection and detection of tear, capturing the disease biomarker malondialdehyde in tears with a 9.38 × 10-9 mol/L limit of detection. Moreover, we designed a hand-held device based on this tubular SERS sensor, successfully diagnosing patients with dry eye disease. This functional capillary column enables noninvasive and rapid diagnosis of biomarkers in biofluids, providing potential for disease diagnosis and healthcare monitoring.

Nanoplasmonic Detection of EGFR Mutations Based on Extracellular Vesicle-Derived EGFR-Drug Interaction

Analysis of membrane proteins from extracellular vesicles (EVs) has emerged as an important strategy for molecular cancer diagnosis. The epidermal growth factor receptor (EGFR) is one of the most well-known oncogenic membrane proteins, particularly in non-small cell lung cancer (NSCLC), where targeted therapies using tyrosine kinase inhibitors (TKIs) are often addressed based on EGFR mutation status. Consequently, several studies aimed at analyzing oncogenic membrane proteins have been proposed for cancer diagnosis. However, conventional protein analysis still faces limitations due to the requirement for large sample quantities and extensive post-labeling processes. Here, we develop a nanoplasmonic detection method for EGFR mutations in the diagnosis of NSCLC based on interactions between EGFR loaded in EVs and TKI. Gefitinib is selected as a model TKI due to its strong signals in the surface-enhanced Raman spectroscopy (SERS) and mutation-dependent binding affinity to EGFR. We demonstrate an SERS signal attributed to gefitinib at a higher value in the EGFR exon 19 deletion, both in cells and EVs, compared to wild-type and exon 19 deletion/T790M variants. Furthermore, we observe a significantly higher gefitinib SERS signal in EGFR obtained from exon 19 deletion NSCLC patient plasma-derived EVs compared with those from wild-type and exon 19 deletion/T790M EVs. Since our approach utilizes an analysis of the SERS signal generated by the interaction between oncogenic membrane proteins within EVs and targeted drugs, its diagnostic applicability could potentially extend to other liquid biopsy methods based on EVs.

Alveoli-Like Multifunctional Scaffolds for Optical and Electrochemical In Situ Monitoring of Cellular Responses from Type II Pneumocytes

While breathing, alveoli are exposed to external irritants, which contribute to the pathogenesis of lung disease. Therefore, in situ monitoring of alveolar responses to stimuli of toxicants under in vivo environments is important to understand lung disease. For this purpose, 3D cell cultures are recently employed for examining cellular responses of pulmonary systems exposed to irritants; however, most of them have used ex situ assays requiring cell lysis and fluorescent labeling. Here, an alveoli-like multifunctional scaffold is demonstrated for optical and electrochemical monitoring of cellular responses of pneumocytes. Porous foam with dimensions like the alveoli structure is used as a backbone for the scaffold, wherein electroactive metal-organic framework crystals, optically active gold nanoparticles, and biocompatible hyaluronic acid are integrated. The fabricated multifunctional scaffold allows for label-free detection and real-time monitoring of oxidative stress released in pneumocytes under toxic-conditions via redox-active amperometry and nanospectroscopy. Moreover, cellular behavior can be statistically classified based on fingerprint Raman signals collected from the cells on the scaffold. The developed scaffold is expected to serve as a promising platform to investigate cellular responses and disease pathogenesis, owing to its versatility in monitoring electrical and optical signals from cells in situ in the 3D microenvironments.

Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning

Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutations rapidly. However, it has low diagnostic accuracy since there are more normal proteins than mutated proteins in body fluids. To increase the diagnostic accuracy, we analyzed plasma exosomes using nanoplasmonic spectra and deep learning. Exosomes, a promising biomarker, are abundant in plasma and stably carry intact proteins originating from mother cells. However, the mutated exosomal proteins cannot be detected sensitively because of the subtle changes in their structure. Therefore, we obtained Raman spectra that provide molecular information about structural changes in mutated proteins. To extract the unique features of the protein from complex Raman spectra, we developed a deep-learning classification algorithm with two deep-learning models. Consequently, controls with wild-type proteins and patients with mutated proteins were classified with high accuracy. As a proof of concept, we discriminated the lung cancer patients with mutations in the epidermal growth factor receptor (EGFR), L858R, E19del, L858R + T790M, and E19del + T790M, from controls with an accuracy of 0.93. Moreover, the protein mutation status of the patients with primary (E19del, L858R) and secondary (+T790M) mutations was clearly monitored. Overall, our technique is expected to be applied as a novel method for companion diagnostic and treatment monitoring.

Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers

Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases.

Dual Biomimetic Recognition-Driven Plasmonic Nanogap-Enhanced Raman Scattering for Ultrasensitive Protein Fingerprinting and Quantitation

Protein assays with fingerprints and high sensitivity are essential for biomedical research and applications. However, the prevailing methods mainly rely on indirect or labeled immunoassays, failing to provide fingerprint information. Herein, we report a dual biomimetic recognition-driven plasmonic nanogap-enhanced Raman scattering (DBR-PNERS) strategy for ultrasensitive protein fingerprinting and quantitation. A pair of molecularly imprinted nanoantennas were rationally engineered for specifically trapping a target protein into well-defined plasmonic nanogaps through dual-terminal recognition for ultrahigh Raman signal amplification. Meanwhile, a Raman-active small molecule was embedded into the nanoantenna as an internal standard to provide a ratiometric assay for robust quantitation. DBR-PNERS exhibited several significant merits over existing approaches, including fingerprinting, ultrahigh sensitivity, quantitation robustness, speed, sample consumption, and so on. Therefore, it can be a promising tool for a protein assay and holds a great perspective in important applications.

Controllable Nanoparticle Aggregation through a Superhydrophobic Laser-Induced Graphene Dynamic System for Surface-Enhanced Raman Scattering Detection

Surface-enhanced Raman scattering (SERS) is widely used for low-concentration molecular detection; however, challenges related to detection uniformity and repeatability are bottlenecks for practical application, especially as regards ultrasensitive detection. Here, through the coupling of bionics and fluid mechanics, a lotus-leaf effect and rose-petal effect (LLE-RPE)-integrated superhydrophobic chip is facilely developed using laser-induced graphene (LIG) fabricated on a polyimide film. Dense and uniform aggregation of gold nanoparticles (AuNPs) in droplets is realized through a constant contact angle (CCA) evaporation mode in the dynamic enrichment process, facilitating reliable ultrasensitive detection. The detection chip consists of two components: an LLE zone containing an ethanol-treated LIG superhydrophobic surface with a low-adhesive property, which functions as an AuNP-controllable aggregation zone, and an RPE zone containing an as-fabricated LIG superhydrophobic surface with water-solution pinning ability, which functions as a droplet solvent evaporation and a AuNP blending zone. AuNPs realize uniform aggregation during rolling on the LLE zone, and then get immobilized on the RPE zone to complete evaporation of the solvent, followed by Raman detection. Here, based on dense and uniform AuNP aggregation, the detection system achieves high-efficiency (242 s/18 μL) and ultralow-concentration (10^-17 M) detection of a target analyte (rhodamine 6G). The proposed system constitutes a simple approach toward high-performance detection for chemical analysis, environmental monitoring, biological analysis, and medical diagnosis.

Label-Free Physical Techniques and Methodologies for Proteins Detection in Microfluidic Biosensor Structures

Proteins in biological fluids (blood, urine, cerebrospinal fluid) are important biomarkers of various pathological conditions. Protein biomarkers detection and quantification have been proven to be an indispensable diagnostic tool in clinical practice. There is a growing tendency towards using portable diagnostic biosensor devices for point-of-care (POC) analysis based on microfluidic technology as an alternative to conventional laboratory protein assays. In contrast to universally accepted analytical methods involving protein labeling, label-free approaches often allow the development of biosensors with minimal requirements for sample preparation by omitting expensive labelling reagents. The aim of the present work is to review the variety of physical label-free techniques of protein detection and characterization which are suitable for application in micro-fluidic structures and analyze the technological and material aspects of label-free biosensors that implement these methods. The most widely used optical and impedance spectroscopy techniques: absorption, fluorescence, surface plasmon resonance, Raman scattering, and interferometry, as well as new trends in photonics are reviewed. The challenges of materials selection, surfaces tailoring in microfluidic structures, and enhancement of the sensitivity and miniaturization of biosensor systems are discussed. The review provides an overview for current advances and future trends in microfluidics integrated technologies for label-free protein biomarkers detection and discusses existing challenges and a way towards novel solutions.

Label-free determination of liver cancer stages using surface-enhanced Raman scattering coupled with preferential adsorption of hydroxyapatite microspheres

Here, we explored a label-free albumin targeted analysis method by utilizing hydroxyapatite (HAp) to adsorb-release serum albumin, in conjunction with surface-enhanced Raman scattering (SERS) for screening liver cancer (LC) at different tumor (T) stages. Excitingly, albumin can be preferentially adsorbed by HAp as compared with other serum proteins. Moreover, we developed a novel strategy using a high concentration of PO₄^3- solution as the albumin-release agent. This method overcomes the shortcomings of the traditional purification technology of serum albumin, which requires acid to release protein, and ensures that the structure and properties of albumin are not damaged. The SERS spectra of serum albumin obtained from three sample groups were analyzed to verify the feasibility of this new method: healthy volunteers (n = 35), LC patients with T1 stage (n = 25) and LC patients with T2-T4 stage (n = 23). Furthermore, principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to classify the early T (T1) stage LC vs. normal group and advanced T (T2-T4) stage LC vs. normal group, yielding high diagnostic accuracies of 90.00% and 96.55%, respectively, which showed a 10% improvement in diagnostic accuracy for the early stage detection of cancer as compared with previous studies. The results of this exploratory work demonstrated that HAp-adsorbed-released serum albumin combined with SERS analysis has great potential for label-free, noninvasive and sensitive detection of different T stages of liver cancer.

Potential of Raman spectroscopic techniques to study proteins

Proteins are large, complex molecules responsible for various biological processes. However, protein misfolding may lead to various life-threatening diseases. Therefore, it is vital to understand the shape and structure of proteins. Despite numerous techniques, a mechanistic understanding of the protein folding process is still unclear. Therefore, new techniques are continually being explored. In the present article, we have discussed the importance of Raman spectroscopy, Raman Optical Activity (ROA) and various other advancements in Raman spectroscopy to understand protein structure and conformational changes based on the review of our earlier work and recent literature. A Raman spectrum of a protein provides unique signatures for various secondary structures like helices, beta-sheets, turns, random structures, etc., and various amino acid residues such as tyrosine, tryptophan, and phenylalanine. We have shown how Raman spectra can differentiate between bovine serum albumin (BSA) and lysozyme protein based on their difference in sequence and structure (primary, secondary and tertiary). Although it is challenging to elucidate the structure of a protein using a Raman spectrum alone, Raman spectra can be used to differentiate small changes in conformations of proteins such as BSA during melting. Various new advancements in technique and data analyses in Raman spectroscopic studies of proteins have been discussed. The last part of the review focuses on the importance of the ROA spectrum to understand additional features about proteins. The ROA spectrum is rich in information about the protein backbone due to its rigidity compared to its side chains. Furthermore, the ROA spectra of lysozyme and BSA have been presented to show how ROA provides extra information about the solvent properties of proteins.

Gold Nanoparticles in Cancer Theranostics

Conventional cancer treatments, such as surgical resection, radiotherapy, and chemotherapy, have achieved significant progress in cancer therapy. Nevertheless, some limitations (such as toxic side effects) are still existing for conventional therapies, which motivate efforts toward developing novel theranostic avenues. Owning many merits such as easy surface modification, unique optical properties, and high biocompatibility, gold nanoparticles (AuNPs and GNPs) have been engineered to serve as targeted delivery vehicles, molecular probes, sensors, and so on. Their small size and surface characteristics enable them to extravasate and access the tumor microenvironment (TME), which is a promising solution to realize highly effective treatments. Moreover, stimuli-responsive properties (respond to hypoxia and acidic pH) of nanoparticles to TME enable GNPs’ unrivaled control for effective transport of therapeutic cargos. In this review article, we primarily introduce the basic properties of GNPs, further discuss the recent progress in gold nanoparticles for cancer theranostics, with an additional concern about TME stimuli-responsive studies.

Recent progress of surface-enhanced Raman spectroscopy for subcellular compartment analysis

Organelles are involved in many cell life activities, and their metabolic or functional disorders are closely related to apoptosis, neurodegenerative diseases, cardiovascular diseases, and the development and metastasis of cancers. The explorations of subcellular structures, microenvironments, and their abnormal conditions are conducive to a deeper understanding of many pathological mechanisms, which are expected to achieve the early diagnosis and the effective therapy of diseases. Organelles are also the targeted locations of drugs, and they play significant roles in many targeting therapeutic strategies. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical tool that can provide the molecular fingerprint information of subcellular compartments and the real-time cellular dynamics in a non-invasive and non-destructive way. This review aims to summarize the recent advances of SERS studies on subcellular compartments, including five parts. The introductions of SERS and subcellular compartments are given. SERS is promising in subcellular compartment studies due to its molecular specificity and high sensitivity, and both of which highly match the high demands of cellular/subcellular investigations. Intracellular SERS is mainly cataloged as the labeling and label-free methods. For subcellular targeted detections and therapies, how to internalize plasmonic nanoparticles or nanostructure in the target locations is a key point. The subcellular compartment SERS detections, SERS measurements of isolated organelles, investigations of therapeutic mechanisms from subcellular compartments and microenvironments, and integration of SERS diagnosis and treatment are sequentially presented. A perspective view of the subcellular SERS studies is discussed from six aspects. This review provides a comprehensive overview of SERS applications in subcellular compartment researches, which will be a useful reference for designing the SERS-involved therapeutic systems.

Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies

Extracellular vesicles (EVs) have been widely investigated as promising biomarkers for the liquid biopsy of diseases, owing to their countless roles in biological systems. Furthermore, with the notable progress of exosome research, the use of label-free surface-enhanced Raman spectroscopy (SERS) to identify and distinguish disease-related EVs has emerged. Even in the absence of specific markers for disease-related EVs, label-free SERS enables the identification of unique patterns of disease-related EVs through their molecular fingerprints. In this review, we describe label-free SERS approaches for disease-related EV pattern identification in terms of substrate design and signal analysis strategies. We first describe the general characteristics of EVs and their SERS signals. We then present recent works on applied plasmonic nanostructures to sensitively detect EVs and notable methods to interpret complex spectral data. This review also discusses current challenges and future prospects of label-free SERS-based disease-related EV pattern identification.