Surface-enhanced Raman scattering in cancer detection and imaging

Current research status of Raman spectroscopy in glioma detection

Glioma is the most common primary tumor of the nervous system. Conventional diagnostic methods for glioma often involve time-consuming or reliance on externally introduced materials. Consequently, there is an urgent need for rapid and reliable diagnostic techniques. Raman spectroscopy has emerged as a promising tool, offering rapid, accurate, and label-free analysis with high sensitivity and specificity in biomedical applications. In this review, the fundamental principles of Raman spectroscopy have been introduced, and then the progress of applying Raman spectroscopy in biomedical studies has been summarized, including the identification and typing of glioma. The challenges encountered in the clinical application of Raman spectroscopy for glioma have been discussed, and the prospects have also been envisioned.

Utilization of microdroplets as optical lenses for surface-enhanced Raman spectroscopy (SERS) enhancement on localized silver nanoparticle-decorated porous silicon substrates

Surface-enhanced Raman spectroscopy (SERS) is a widely used analytical technique known for its high sensitivity and broad applicability. Despite its potential, SERS faces challenges related to detection sensitivity and reproducibility. This study proposes an innovative method to enhance SERS performance by employing water microdroplets as optical lenses on localized silver nanoparticle-decorated porous silicon (LocAg-PS) substrates. The hydrophobic nature of the LocAg-PS substrate not only ensures precise positioning of the microdroplet lenses on the silver nanoparticle grafted pad (AgNP pad) but also forms a plano-convex-like microdroplet lens for the focusing of the excitation laser and the collection of scattered light. Experimental results demonstrate that using microdroplet lenses enhances the SERS signal intensity and reproducibility, providing a rapid and cost-effective solution for advanced SERS analysis.

Fragment-Fusion Transformer: Deep Learning-Based Discretization Method for Continuous Single-Cell Raman Spectral Analysis

Raman spectroscopy has become an important single-cell analysis tool for monitoring biochemical changes at the cellular level. However, Raman spectral data, typically presented as continuous data with high-dimensional characteristics, is distinct from discrete sequences, which limits the application of deep learning-based algorithms in data analysis due to the lack of discretization. Herein, a model called fragment-fusion transformer is proposed, which integrates the discrete fragmentation of continuous spectra based on their intrinsic characteristics with the extraction of intrafragment features and the fusion of interfragment features. The model integrates the intrinsic feature-based fragmentation of spectra with transformer, constructing the fragment transformer block for feature extraction within fragments. Interfragment information is combined through the pyramid design structure to improve the model's receptive field and fully exploit the spectral properties. During the pyramidal fusion process, the information gain of the final extracted features in the spectrum has been enhanced by a factor of 9.24 compared to the feature extraction stage within the fragment, and the information entropy has been enhanced by a factor of 13. The fragment-fusion transformer achieved a spectral recognition accuracy of 94.5%, which is 4% higher compared to the method without fragmentation and fusion processes on the test set of cell Raman spectroscopy identification experiments. In comparison to common spectral classification models such as KNN, SVM, logistic regression, and CNN, fragment-fusion transformer has achieved 4.4% higher accuracy than the best-performing CNN model. Fragment-fusion transformer method has the potential to serve as a general framework for discretization in the field of continuous spectral data analysis and as a research tool for analyzing the intrinsic information within spectra.

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

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

Multiplexed electrochemical nucleic acid sensor based on visible light-mediated metal-free thiol-yne click reaction for simultaneous detection of different nucleic acid targets

Simultaneous detection of multiple tumor biomarkers with a simple and low-cost assay is crucial for early cancer detection and diagnosis. Herein, we presented a low-cost and simple assay for multiplexed detection of tumor biomarkers using a spatially separated electrodes strategy. The sensor is fabricated based on a metal-free thiol-yne click reaction, which is mediated by visible light, on commercially available indium tin oxide (ITO) electrodes. Four biomarkers, including p53 DNA, Brca2 DNA, K-ras DNA, and MicroRNA-204 RNA, were used as model analytes, and the corresponding oligonucleotide probes were modified on the desired electrode units sequentially with 530 nm irradiation light in the presence of photosensitizer Eosin Y. By this visible light-mediated coupling reaction, oligonucleotide probe densities of up to 9.2 ± 0.7 × 1010 molecules/cm2 were readily obtained on the ITO electrode surface. The proposed multiplexed E-NA sensor could detect four different nucleic acid targets concurrently without crosstalk among adjacent electrodes and was also successfully applied for detecting targets in a 20% fetal calf serum sample. The detection limits for p53 DNA, Brca2 DNA, K-ras DNA, and MicroRNA-204 RNA were 0.72 nM, 0.97 nM, 2.15 nM, and 1.73 nM, respectively. The developed approach not only has a great potential for developing cost-effective biosensors on affordable substrates for nucleic acid target detection, but also be easily extended to detect other targets by modifying the specific oligonucleotide probes anchored on the electrode.

A review of clinical use of surface-enhanced Raman scattering-based biosensing for glioma

Glioma is the most common malignant tumor of the nervous system in recent centuries, and the incidence rate of glioma is increasing year by year. Its invasive growth and malignant biological behaviors make it one of the most challenging malignant tumors. Maximizing the resection range (EOR) while minimizing the impact on normal brain tissue is crucial for patient prognosis. Changes in metabolites produced by tumor cells and their microenvironments might be important indicators. As a powerful spectroscopic technique, surface-enhanced Raman scattering (SERS) has many advantages, including ultra-high sensitivity, high specificity, and non-invasive features, which allow SERS technology to be widely applied in biomedicine, especially in the differential diagnosis of malignant tumor tissues. This review first introduced the clinical use of responsive SERS probes. Next, the sensing mechanisms of microenvironment-responsive SERS probes were summarized. Finally, the biomedical applications of these responsive SERS probes were listed in four sections, detecting tumor boundaries due to the changes of pH-responsive SERS probes, SERS probes to guide tumor resection, SERS for liquid biopsy to achieve early diagnosis of tumors, and the application of free-label SERS technology to detect fresh glioma specimens. Finally, the challenges and prospects of responsive SERS detections were summarized for clinical use.

Mesoporous Silica-Layered Gold Nanorod Core@Silver Shell Nanostructures for Intracellular SERS Imaging and Phototherapy

Precision diagnosis-guided efficient treatment is crucial to extending the lives of cancer patients. The integration of surface-enhanced Raman scattering (SERS) imaging and phototherapy into a single nanoplatform has been considered a more accurate diagnosis and treatment strategy for cancer nanotheranostics. Herein, we constructed a new type of mesoporous silica-layered gold nanorod core@silver shell nanostructures loaded with methylene blue (GNR@Ag@mSiO2-MB) as a multifunctional nanotheranostic agent for intracellular SERS imaging and phototherapy. The synthesized GNR@Ag@mSiO2-MB nanostructures possessed a uniform core-shell structure, strong near-infrared (NIR) absorbance, photothermal conversion efficiency (65%), dye loading ability, SERS signal, and Raman stability under phototherapy conditions. Under single 785 nm NIR laser irradiation, the intracellular GNR@Ag@mSiO2-MB nanostructures were dramatically decreased to <9%, which showed excellent photothermal and photodynamic effects toward cancer cell killing, indicating that the combination of photothermal therapy (PTT) and photodynamic therapy (PDT) of the GNR@Ag@mSiO2-MB nanostructures could greatly enhance the therapeutic efficacy of cancer cell death. GNR@Ag@mSiO2-MB nanostructures demonstrated a strong Raman signal at 450 and 502 cm-1, corresponding to the δ(C-N-C) mode, suggesting that the Raman bands of GNR@Ag@mSiO2-MB nanostructures were more efficient to detect CT-26 cell SERS imaging with high specificity. Our results indicate that GNR@Ag@mSiO2-MB nanostructures offer an excellent multifunctional nanotheranostic platform for SERS imaging and synergistic anticancer phototherapy in the future.

Novel SERS biosensor for rapid detection of breast cancer based on Ag2O-Ag-PSi nanochips

Ag2O-Ag-PSi (porous silicon) surface-enhanced Raman scattering (SERS) chip was successfully synthesized by electrochemical corrosion, in situ reduction and heat treatment technology. The influence of different heat treatment temperature on SERS performance of the chip is studied. The results show that the chip treated at 300 °C has the best SERS performance. The chip was composed of Ag2O-Ag nano core shell with a diameter of 40-60 nm and porous silicon substrate. Then, the optimized chip was used to perform SERS test on serum samples from 30 healthy volunteers and 30 early breast cancer patients, and the baseline was corrected by LabSpec6 software. Finally, the data were analyzed by principal component analysis combined with t-distributed Stochastic Neighbor Embedding (PCA-t-SNE). The results showed that the accuracy of the improved substrate combined with multivariate statistical method was 98%. The shelf life of the chips exceeded six months due to the presence of the Ag2O shell. This study provides a basis for developing a low-cost rapid and sensitive early screening technology for breast cancer.

Current advances in detecting genetic and epigenetic biomarkers of colorectal cancer

Colorectal carcinoma (CRC) is the third most common cancer in terms of diagnosis and the second in terms of mortality. Recent studies have shown that various proteins, extracellular vesicles (i.e., exosomes), specific genetic variants, gene transcripts, cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and altered epigenetic patterns, can be used to detect, and assess the prognosis of CRC. Over the last decade, a plethora of conventional methodologies (e.g., polymerase chain reaction [PCR], direct sequencing, enzyme-linked immunosorbent assay [ELISA], microarray, in situ hybridization) as well as advanced analytical methodologies (e.g., microfluidics, electrochemical biosensors, surface-enhanced Raman spectroscopy [SERS]) have been developed for analyzing genetic and epigenetic biomarkers using both optical and non-optical tools. Despite these methodologies, no gold standard detection method has yet been implemented that can analyze CRC with high specificity and sensitivity in an inexpensive, simple, and time-efficient manner. Moreover, until now, no study has critically reviewed the advantages and limitations of these methodologies. Here, an overview of the most used genetic and epigenetic biomarkers for CRC and their detection methods are discussed. Furthermore, a summary of the major biological, technical, and clinical challenges and advantages/limitations of existing techniques is also presented.

Function and clinical application of exosome-how to improve tumor immunotherapy?

In recent years, immunotherapy has been increasingly used in clinical practice to treat tumors. However, immunotherapy's efficacy varies between tumor types and patient populations, and long-term drug resistance often occurs during treatment. Therefore, it is essential to explore the molecular mechanisms of immunotherapy to improve its efficacy. In this review, we focus on the significance of tumor-derived exosomes in the clinical treatment of tumors and how modifying these exosomes may enhance immune effectiveness. Specifically, we discuss exosome components, such as RNA, lipids, and proteins, and the role of membrane molecules on exosome surfaces. Additionally, we highlight the importance of engineered exosomes for tumor immunotherapy. Our goal is to propose new strategies to improve the efficacy of tumor immunotherapy.

Prospects of Microfluidic Technology in Nucleic Acid Detection Approaches

Conventional diagnostic techniques are based on the utilization of analyte sampling, sensing and signaling on separate platforms for detection purposes, which must be integrated to a single step procedure in point of care (POC) testing devices. Due to the expeditious nature of microfluidic platforms, the trend has been shifted toward the implementation of these systems for the detection of analytes in biochemical, clinical and food technology. Microfluidic systems molded with substances such as polymers or glass offer the specific and sensitive detection of infectious and noninfectious diseases by providing innumerable benefits, including less cost, good biological affinity, strong capillary action and simple process of fabrication. In the case of nanosensors for nucleic acid detection, some challenges need to be addressed, such as cellular lysis, isolation and amplification of nucleic acid before its detection. To avoid the utilization of laborious steps for executing these processes, advances have been deployed in this perspective for on-chip sample preparation, amplification and detection by the introduction of an emerging field of modular microfluidics that has multiple advantages over integrated microfluidics. This review emphasizes the significance of microfluidic technology for the nucleic acid detection of infectious and non-infectious diseases. The implementation of isothermal amplification in conjunction with the lateral flow assay greatly increases the binding efficiency of nanoparticles and biomolecules and improves the limit of detection and sensitivity. Most importantly, the deployment of paper-based material made of cellulose reduces the overall cost. Microfluidic technology in nucleic acid testing has been discussed by explicating its applications in different fields. Next-generation diagnostic methods can be improved by using CRISPR/Cas technology in microfluidic systems. This review concludes with the comparison and future prospects of various microfluidic systems, detection methods and plasma separation techniques used in microfluidic devices.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

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.

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

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

Non-Invasive and Minute-Frequency 3D Tomographic Imaging Enabling Long-Term Spatiotemporal Observation of Single Cell Fate

Non-invasive and rapid imaging technique at subcellular resolution is significantly important for multiple biological applications such as cell fate study. Label-free refractive-index (RI)-based 3D tomographic imaging constitutes an excellent candidate for 3D imaging of cellular structures, but its full potential in long-term spatiotemporal cell fate observation is locked due to the lack of an efficient integrated system. Here, a long-term 3D RI imaging system incorporating a cutting-edge white light diffraction phase microscopy module with spatiotemporal stability, and an acoustofluidic device to roll and culture single cells in a customized live cell culture chamber is reported. Using this system, 3D RI imaging experiments are conducted for 250 cells and demonstrate efficient cell identification with high accuracy. Importantly, long-term and frequency-on-demand 3D RI imaging of K562 and MCF-7 cancer cells reveal different characteristics during normal cell growth, drug-induced cell apoptosis, and necrosis of drug-treated cells. Overall, it is believed that the proposed 3D tomographic imaging technique opens up a new avenue for visualizing intracellular structures and will find many applications such as disease diagnosis and nanomedicine.

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.

Advances in the intraoperative delineation of malignant glioma margin

Surgery plays a critical role in the treatment of malignant glioma. However, due to the infiltrative growth and brain shift, it is difficult for neurosurgeons to distinguish malignant glioma margins with the naked eye and with preoperative examinations. Therefore, several technologies were developed to determine precise tumor margins intraoperatively. Here, we introduced four intraoperative technologies to delineate malignant glioma margin, namely, magnetic resonance imaging, fluorescence-guided surgery, Raman histology, and mass spectrometry. By tracing their detecting principles and developments, we reviewed their advantages and disadvantages respectively and imagined future trends.

Al2O3@Ag composite structure as SERS substrate for sensitive detection of sodium thiocyanate

Sodium thiocyanate (NaSCN) can be added to fresh milk to enhance the sterilization ability of the lactoperoxidase system (LP system) in milk, extending shelf life. However, excessive intake of NaSCN can be harmful to human health because it can prevent absorption of iodine leading to disease. Also NaSCN can be used as a marker to distinguish smokers from non-smokers. In this work, we successfully synthesized meatball-like Al₂O₃@Ag composite structures as surface-enhanced Raman scattering (SERS) substrates using a simple wet chemical method adapted to conventional laboratory conditions. The substrate exhibited strong SERS enhancement for NaSCN. Under the optimal experiment conditions, we obtained a detection limit of 0.28 μg L^-1 and a quantification limit of 1 μg L^-1, R² = 0.992. Based on the analysis of the intensity of SERS characteristic peak, the substrate had good reproducibility and uniformity. In summary, the Al₂O₃@Ag composite structure achieved sensitive SERS detection of NaSCN. Combining the facile and low-cost methods, we believe that the SERS detection method developed in this work can be used as a potential candidate for biosensing applications in the future.

Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review

Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nanostructures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates.

Grain-Boundary-Induced Ultrasensitive Molecular Detection of Graphene Film

Graphene has been considered a promising platform for molecular detection due to the graphene-enhanced Raman scattering (GERS) effect. However, the GERS performance of pristine graphene is limited by a low chemically active surface and insufficient density of states (DOS). Although diverse defects have been introduced, it remains a great challenge to improve the enhancement performance. Here, we show that graphene grain boundaries (GBs) possess stronger adsorption capacity and more abundant DOS. Thus, GERS performance increases with the atomic percentage of GBs, which makes nanocrystalline graphene (NG) film a superior GERS substrate. For R6G as a probe molecule, a low detection limit of 3 × 10^-10 M was achieved. Utilizing the high chemical activity of GBs, we also fabricated NG film decorated with Au particles using a one-step quenching strategy, and this hybrid film exhibits an extremely low limit of detection down to 5 × 10^-11 M, outperforming all the reported graphene-based systems.

Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection

Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.

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.

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

Miniaturized Chemical Tags for Optical Imaging

Recent advances in optical bioimaging have prompted the need for minimal chemical reporters that can retain the molecular recognition properties and activity profiles of biomolecules. As a result, several methodologies to reduce the size of fluorescent and Raman labels to a few atoms (e.g., single aryl fluorophores, Raman-active triple bonds and isotopes) and embed them into building blocks (e.g., amino acids, nucleobases, sugars) to construct native-like supramolecular structures have been described. The integration of small optical reporters into biomolecules has also led to smart molecular entities that were previously inaccessible in an expedite manner. In this article, we review recent chemical approaches to synthesize miniaturized optical tags as well as some of their multiple applications in biological imaging.

Advanced screening and tailoring strategies of pesticide aptamer for constructing biosensor

The rapid development of aptamers has helped address the challenges presented by the wide existed pesticides contaminations. Screening of aptamers with excellent performance is a prerequisite for successfully constructing biosensors, while further tailoring of aptamers with enhanced activity greatly improved the assay performance. Firstly, this paper reviewed the advanced screening strategies for pesticides aptamers, including immobilization screening that preserves the native structures of targets, non-immobilized screening based on nanomaterials, capillary electrophoresis-systematic evolution of ligands by exponential enrichment (CE-SELEX), virtual screening in silico, high-throughput selection, and rational secondary library generation methods, which contributed significantly to improve the success rate of screening, reduce the screening time, and ensure aptamer binding affinity. Secondly, the precise tailoring strategies for pesticides aptamers were modularly elaborated, containing deletion, splitting, elongation, and fusion, which provided various advantages like cost-efficiency, enhanced binding affinity, and new derived functional motifs. Thirdly, the developed aptamer-based biosensors (aptasensors) for pesticide detection were systematically reviewed according to the different signal output modes. Finally, the challenges and future perspectives of pesticide detection are discussed comprehensively.

High-Throughput Surface-Enhanced Raman Scattering for Screening Chemical Sensor Candidates Enabled by Bipolar Electrochemistry

A variety of hydrothermal or electrochemical methods have been explored to prepare noble metal nanostructures as surface-enhanced Raman scattering (SERS) substrates. However, most of those metallic nanoarrays are structurally homogeneous, which makes it laborious to select the high-performance substrates for particular Raman sensing purposes. Here, a high-throughput SERS imaging strategy is demonstrated for the first time for screening chemical sensors with sub-nanomolar sensitivities. Bipolar electrochemistry was applied to generate Au or Au-Ag gradient nanoarrays with diverse chemical compositions, morphologies, and particle dimensions ranging from several nanometers to micrometers. The selected "hot-spots" on the Au-Ag alloy nanoarray exhibited a 660-fold enhancement in SERS intensity compared to those on the pure Au gradient nanoarray. The SERS screening of 4-aminothiophenol, 4-nitrothiophenol, and 4-mercaptobenzoic acid was carried out that provided a limit of detection (LOD) between 1 and 5 pM. The distinctive LODs among three thiophenolic Raman probes are ascribed to the differences in the affinity of the probe to the alloy, orientation of the metal-ligand monolayer, or plasmonic environment of the nanoarray surface. As a continuous, rapid, and cost-effective manner to fabricate transitional nanostructures and screen out SERS responsive sites, this method not only facilitates controllable synthesis of noble metal nanoarrays but has the potential to provide an alternative tool for ultrasensitive chemical sensing on a wide range of bimetallic substrates.

Ultrafast Detection of SARS-CoV-2 Spike Protein (S) and Receptor-Binding Domain (RBD) in Saliva Using Surface-Enhanced Raman Spectroscopy

Controlling contagious diseases necessitates using diagnostic techniques that can detect infection in the early stages. Although different diagnostic tools exist, there are still challenges related to accuracy, rapidity, cost-effectiveness, and ease of use. Surface-enhanced Raman spectroscopy (SERS) is a rapid, simple, less expensive, and accurate method. We continue our previous work published on SERS detection of the SARS-CoV-2 receptor-binding domain (RBD) in water. In this work, we replace water with saliva to detect SARS-CoV-2 proteins at very low concentrations and during a very short time. We prepared a very low concentration of 10−9 M SARS-CoV-2 spike protein (S) and SARS-CoV-2 receptor-binding domain (RBD) in saliva to mimic a real case scenario. Then, we drop them on a SERS substrate. Using modified SERS measurements on the control and the sample containing the biomolecules, confirmed the sensitivity of the target identification. This technique provides different diagnostic solutions that are fast, simple, non-destructive and ultrasensitive. Simulation of the real-world of silicon wire covered with silver and gold, were performed using an effective and accurate tool, COMSOL Multiphysics software, for the enhancement properties study.

PLGA-Gold Nanocomposite: Preparation and Biomedical Applications

A composite system consisting of both organic and inorganic nanoparticles is an approach to prepare a new material exhibiting “the best of both worlds”. In this review, we highlight the recent advances in the preparation and applications of poly(lactic-co-glycolic acid)-gold nanoparticles (PLGA-GNP). With its current clinically use, PLGA-based nanocarriers have promising pharmaceutical applications and can “extract and utilize” the fascinating optical and photothermal properties of encapsulated GNP. The resulting “golden polymeric nanocarrier” can be tracked, analyzed, and visualized using the encapsulated gold nanoprobes which facilitate a better understanding of the hosting nanocarrier’s pharmacokinetics and biological fate. In addition, the “golden polymeric nanocarrier” can reveal superior nanotherapeutics that combine both the photothermal effect of the encapsulated gold nanoparticles and co-loaded chemotherapeutics. To help stimulate more research on the development of nanomaterials with hybrid and exceptional properties, functionalities, and applications, this review provides recent examples with a focus on the available chemistries and the rationale behind encapsulating GNP into PLGA nanocarriers that has the potential to be translated into innovative, clinically applicable nanomedicine.

An Electrochemical and Raman Scattering Dual Detection Biosensor for Rapid Screening and Biomolecular Profiling of Cancer Biomarkers

Detecting circulating biomarkers sensitively and quantitatively is paramount for cancer screening, diagnosis, and treatment selection. Particularly, screening of a panel of circulating protein biomarkers followed by mapping of individual biomarkers could assist better diagnosis and understanding of the cancer progression mechanisms. Herein, we present a miniaturized biosensing platform with dual readout schemes (electrochemical and Surface enhanced Raman scattering (SERS)) for rapid cancer screening and specific biomarker expressional profiling to support cancer management. Our approach utilizes a controlled nanomixing phenomena under alternative current electrohydrodynamic condition to improve the isolation of cancer-associated circulating proteins (i.e., Epidermal growth factor receptor (EGFR), BRAF, Programmed death-ligand 1 (PD-L1)) with antibody functionalized sensor surface for rapid and efficient isolation of the targets and subsequent labelling with SERS nanotags. The method employs Differential Pulse Voltammetry (DPV) for rapidly screening for the presence of the circulating proteins on biosensor surface irrespective of their type. Upon positive DPV detection, SERS is applied for sensitive read-out of individual biomarkers biomarker levels. In a proof-of-concept study, we demonstrate the dual detection biosensor for analysing circulating BRAF, EGFR and PDL-1 proteins and successfully screened both ensemble and individual biomarker expressional levels as low as 10 pg (1 ng/mL). Our findings clearly indicate the potential of the proposed method for cancer biomarker analysis which may drive the translation of this dual sensing concept in clinical settings.

Surface-enhanced Raman probes based on gold nanomaterials for in vivo diagnosis and imaging

Surface-enhanced Raman scattering (SERS) has received considerable attention from researchers due to its high molecular specificity, high sensitivity, non-invasive and multiplexing. Recently, various metal substrates have been exploited for SERS analysis and imaging. Among them, gold nanomaterials are important SERS substrates with outstanding surface plasmon resonance effects, structural adjustability and good biocompatibility, making them widely used in biomedical diagnosis and clinical fields. In this minireview, we discuss the latest progress about the application of gold-based nanomaterials as SERS probes in biomedical research, primarily for in vivo disease diagnosis and imaging. This review mainly includes the basic shapes and morphologies of gold based SERS probes, such as gold nanoparticles (AuNPs), gold nanorods (AuNRs), gold nanostars (AuNSs), as well as other gold nanostructures. Finally, a brief outlook for the future development of SERS technique in the context of efficient diagnostics and therapy guidance is provided. We hope that this minireview will facilitate the design and future development of Surface-enhanced Raman probes based on gold nanomaterials.

Bio-inspired Superwettable Surface for the Detection of Cancer Biomarker: A Mini Review

Inspired by nature, superwettable material-based biosensors have aroused wide interests due to their potential in cancer biomarker detection. This mini review mainly summarized the superwettable materials as novel biosensing substrates for the development of evaporation-induced enrichment-based signal amplification and visual biosensing method. Biosensing applications based on the superhydrophobic surfaces, superwettable micropatterned surfaces, and slippery lubricant-infused porous surfaces for various cancer biomarker detections were described in detail. Finally, an insight of remaining challenges and perspectives of superwettable biosensor is proposed.

Emerging Technologies for the Detection of Cancer Micrometastasis

The most efficient way to treat tumors is through surgery. However, many cancer patients have a poor prognosis even when they undergo radical excision at an early stage. Micrometastasis is one of the most critical factors that induced this situation. Undetected micrometastasis can lead to the failure of initial treatment. Therefore, preoperative and intraoperative detection of micrometastasis could have a significant clinical influence on the prognosis and optimal therapy for cancer patients. Additionally, to achieve this goal, researchers have aimed to create more effective detection technologies. Herein, we classify the currently reported micrometastasis detection technologies, introduce some representative samples for each technology, including the limitations, and provide future directions to overcome the limitations.

Rapid and non-invasive discrimination of acute leukemia bone marrow supernatants by Raman spectroscopy and multivariate statistical analysis

A simple and non-invasive detection method for acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) was established by systematically investigating the characteristics of bone marrow supernatants from 61 AML patients, 22 ALL patients, and 5 volunteers without hematological tumors by Raman spectroscopy and orthogonal partial least squares discriminant analysis (OPLS-DA). The control group could be well distinguished from the AML and ALL groups by Raman peaks of 859, 1031, 1437, 1443, 1446, 1579, and 1603 cm^-1 and from the AML subtypes groups (AML-M2, AML-M3, AML-M4, and AML-M5) by the Raman peaks of 859, 1221, 1230, 1437, 1443, and 1603 cm^-1, indicating high sensitivity and specificity of the method. Potentially important variables of acute leukemia (AL) prognosis, such as cholesterol, high-density lipoprotein, low-density lipoprotein, adenosine deaminase, and hemoglobin, could be effectively identified by Raman peaks of 1437, 1443, and 1579 cm^-1. Therefore, Raman spectroscopy can be considered as a new non-invasive clinical tool for the detection of different types of AL and can be used to correlate biochemical parameters of AL patients with the classification and prognosis of AL.

Cancer Cell-Specific Enhanced Raman Imaging and Photothermal Therapeutic Effect Based on Reversibly pH-Responsive Gold Nanoparticles

Stimuli-responsive nanoparticles are favorable for improving the selective delivery and rational vocation that easily avoids the undesirable barriers or side effects, leading to a further improved therapeutic efficiency. Furthermore, multifunctional nanomaterials have been extensively developed as attractive candidates for theranostic reagents for cancer treatment. In this article, we developed reversibly pH-responsive gold nanoparticles (AuNPs) with an enhanced Raman scattering signal as well as an efficient photothermal effect and demonstrated their applications as a theranostic reagent for cancer treatment. Surfaces of these AuNPs were modified with mixed layers of Cy3-modified single-stranded DNA (ssDNA-Cy3) for Raman probing and a negative charge supply and cytochrome C (Cyt C) for pH-responsive charge inversion. This combination of pH-responsive ligands and Raman probes played an important role in inducing the assembly or disassembly of AuNPs corresponding to the neighboring pH, accompanied by an additional highly distinguished Raman signal intensity. An operative reversible response of the AuNPs to pH is endowed with the characteristic behavior of AuNPs with the cancerous cell's acidic microenvironment of low pH. The responsive aggregation of AuNPs in a lower pH medium provides highly amplified signals attributed to well-formed hot spots between the particle surfaces that deliver better Raman scattering signals. The acidic pH-responsive aggregation of the particles also provided efficient photothermal treatments using a long-wavelength laser light with the benefit of deeper penetration for cancer cells. In vitro experiments employing cancer cells and control normal cells well-demonstrated the specificity of the particles to cancer cells in terms of highly enhanced Raman imaging and therapeutic efficiency.

Improving the stability of plasmonic magnesium nanoparticles in aqueous media

This work describes two different core-shell architectures based on Mg nanoparticles (NPs) synthesised in order to improve Mg's stability in aqueous solutions. The shell thickness in Mg-polydopamine NPs can be modulated from 5 to >50 nm by ending the polymerization at different times; the resulting structures stabilize the metallic, plasmonic core in water for well over an hour. Mg-silica NPs with shells ranging from 5 to 30 nm can also be prepared via a modified Stöber procedure and they retain optical properties in 5% water-in-isopropanol solutions. These new architectures allow Mg nanoplasmonics to be investigated as an alternative to Ag and Au in a broader range of experimental conditions for a rich variety of applications.

Construction of Optimal SERS Hotspots Based on Capturing the Spike Receptor-Binding Domain (RBD) of SARS-CoV-2 for Highly Sensitive and Specific Detection by a Fish Model

It is highly challenging to construct the best SERS hotspots for the detection of proteins by surface-enhanced Raman spectroscopy (SERS). Using its own characteristics to construct hotspots can achieve the effect of sensitivity and specificity. In this study, we built a fishing mode device to detect the receptor-binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at low concentrations in different detection environments and obtained a sensitive SERS signal response. Based on the spatial resolution of proteins and their protein-specific recognition functions, SERS hotspots were constructed using aptamers and small molecules that can specifically bind to RBD and cooperate with Au nanoparticles (NPs) to detect RBD in the environment using SERS signals of beacon molecules. Therefore, two kinds of AuNPs modified with aptamers and small molecules were used in the fishing mode device, which can specifically recognize and bind RBD to form a stable hotspot to achieve high sensitivity and specificity for RBD detection. The fishing mode device can detect the presence of RBD at concentrations as low as 0.625 ng/mL and can produce a good SERS signal response within 15 min. Meanwhile, we can detect an RBD of 0.625 ng/mL in the mixed solution with various proteins, and the concentration of RBD in the complex environment of urine and blood can be as low as 1.25 ng/mL. This provides a research basis for SERS in practical applications for protein detection work.

A review of surface-enhanced Raman spectroscopy in pathological processes

With the continuous growth of the human population and new challenges in the quality of life, it is more important than ever to diagnose diseases and pathologies with high accuracy, sensitivity and in different scenarios from medical implants to the operation room. Although conventional methods of diagnosis revolutionized healthcare, alternative analytical methods are making their way out of academic labs into clinics. In this regard, surface-enhanced Raman spectroscopy (SERS) developed immensely with its capability to achieve single-molecule sensitivity and high-specificity in the last two decades, and now it is well on its way to join the arsenal of physicians. This review discusses how SERS is becoming an essential tool for the clinical investigation of pathologies including inflammation, infections, necrosis/apoptosis, hypoxia, and tumors. We critically discuss the strategies reported so far in nanoparticle assembly, functionalization, non-metallic substrates, colloidal solutions and how these techniques improve SERS characteristics during pathology diagnoses like sensitivity, selectivity, and detection limit. Moreover, it is crucial to introduce the most recent developments and future perspectives of SERS as a biomedical analytical method. We finally discuss the challenges that remain as bottlenecks for a routine SERS implementation in the medical room from in vitro to in vivo applications. The review showcases the adaptability and versatility of SERS to resolve pathological processes by covering various experimental and analytical methods and the specific spectral features and analysis results achieved by these methods.

From Raman to SESORRS: moving deeper into cancer detection and treatment monitoring

Raman spectroscopy is a non-invasive technique that allows specific chemical information to be obtained from various types of sample. The detailed molecular information that is present in Raman spectra permits monitoring of biochemical changes that occur in diseases, such as cancer, and can be used for the early detection and diagnosis of the disease, for monitoring treatment, and to distinguish between cancerous and non-cancerous biological samples. Several techniques have been developed to enhance the capabilities of Raman spectroscopy by improving detection sensitivity, reducing imaging times and increasing the potential applicability for in vivo analysis. The different Raman techniques each have their own advantages that can accommodate the alternative detection formats, allowing the techniques to be applied in several ways for the detection and diagnosis of cancer. This feature article discusses the various forms of Raman spectroscopy, how they have been applied for cancer detection, and the adaptation of the techniques towards their use for in vivo cancer detection and in clinical diagnostics. Despite the advances in Raman spectroscopy, the clinical application of the technique is still limited and certain challenges must be overcome to enable clinical translation. We provide an outlook on the future of the techniques in this area and what we believe is required to allow the potential of Raman spectroscopy to be achieved for clinical cancer diagnostics.

Multiplexed Liquid Biopsy and Tumor Imaging Using Surface-Enhanced Raman Scattering

The recent improvements in diagnosis enabled by advances in liquid biopsy and oncological imaging significantly better cancer care. Both these complementary approaches, which are used for early tumor detection, characterization, and monitoring, can benefit from applying techniques based on surface-enhanced Raman scattering (SERS). With a detection sensitivity at the single-molecule level, SERS spectroscopy is widely used in cell and molecular biology, and its capability for the in vitro detection of several types of cancer biomarkers is well established. In the last few years, several intriguing SERS applications have emerged, including in vivo imaging for tumor targeting and the monitoring of drug release. In this paper, selected recent developments and trends in SERS applications in the field of liquid biopsy and tumor imaging are critically reviewed, with a special emphasis on results that demonstrate the clinical utility of SERS.

Nanoparticle surface-enhanced Raman spectroscopy as a noninvasive, label-free tool to monitor hematological malignancy

Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.

Ultra-fast and onsite interrogation of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in waters via surface enhanced Raman scattering (SERS)

The outbreak of coronavirus infectious disease-2019 (COVID-19) pneumonia challenges the rapid interrogation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in human and environmental samples. In this study, we developed an assay using surface enhanced Raman scattering (SERS) coupled with multivariate analysis to detect SARS-CoV-2 in an ultra-fast manner without any pretreatment (e.g., RNA extraction). Using silver-nanorod SERS array functionalized with cellular receptor angiotensin-converting enzyme 2 (ACE2), we obtained strong SERS signals of ACE2 at 1032, 1051, 1089, 1189, 1447 and 1527 cm^-1. The recognition and binding of receptor binding domain (RBD) of SARS-CoV-2 spike protein on SERS assay significantly quenched the spectral intensities of most peaks and exhibited a shift from 1189 to 1182 cm^-1. On-site tests on 23 water samples with a portable Raman spectrometer proved its accuracy and easy-operation for spot detection of SARS-CoV-2 to evaluate disinfection performance, explore viral survival in environmental media, assess viral decay in wastewater treatment plant and track SARS-CoV-2 in pipe network. Our findings raise a state-of-the-art spectroscopic tool to screen and interrogate viruses with RBD for human cell entry, proving its feasibility and potential as an ultra-fast detection tool for wastewater-based epidemiology.

SARS-CoV-2 Receptor Binding Domain as a Stable-Potential Target for SARS-CoV-2 Detection by Surface-Enhanced Raman Spectroscopy

In this work, we report a new approach for detecting SARS-CoV-2 RBD protein (RBD) using the surface-enhanced Raman spectroscopy (SERS) technique. The optical enhancement was obtained thanks to the preparation of nanostructured Ag/Au substrates. Fabricated Au/Ag nanostructures were used in the SERS experiment for RBD protein detection. SERS substrates show higher capabilities and sensitivity to detect RBD protein in a short time (3 s) and with very low power. We were able to push the detection limit of proteins to a single protein detection level of 1 pM. The latter is equivalent to 1 fM as a detection limit of viruses. Additionally, we have shown that the SERS technique was useful to figure out the presence of RBD protein on antibody functionalized substrates. In this case, the SERS detection was based on protein-antibody recognition, which led to shifts in the Raman peaks and allowed signal discrimination between RBD and other targets such as Bovine serum albumin (BSA) protein. A perfect agreement between a 3D simulated model based on finite element method and experiment was reported confirming the SERS frequency shift potential for trace proteins detection. Our results could open the way to develop a new prototype based on SERS sensitivity and selectivity for rapid detection at a very low concentration of virus and even at a single protein level.

Multiplex Electrochemiluminescence Polarization Assay Based on the Surface Plasmon Coupling Effect of Au NPs and Ag@Au NPs

A novel multiplex electrochemiluminescence (ECL) polarization assay was developed to detect breast cancer-related genes BRCA1 and BRCA2 simultaneously based on the polarization characteristics of surface plasmon-coupled electrochemiluminescence (SPC-ECL). In this work, boron nitride quantum dots (BN QDs) were used as ECL emitters, and gold nanoparticles (Au NPs) and gold-coated silver nanoparticles (Ag@Au NPs) were employed as surface plasmon materials. The surface plasmon coupling resonance of different metal NPs not only enhanced the ECL intensity but also converted the isotropic emission into directional emission. This study revealed the relation between the structure of metal nanomaterials and SPC-ECL, and a high polarization-resolved sensing system was designed to detect multitarget DNA from 100 aM to 1 nM simultaneously. Polarization-based multiple ECL analysis has broad prospects in related cancer diagnosis and treatment evaluation.