Refractive-Index-Insensitive Nanolaminated SERS Substrates for Label-Free Raman Profiling and Classification of Living Cancer Cells

Pixelated High-Q Metasurfaces for in Situ Biospectroscopy and Artificial Intelligence-Enabled Classification of Lipid Membrane Photoswitching Dynamics

Nanophotonic devices excel at confining light into intense hot spots of electromagnetic near fields, creating exceptional opportunities for light-matter coupling and surface-enhanced sensing. Recently, all-dielectric metasurfaces with ultrasharp resonances enabled by photonic bound states in the continuum (BICs) have unlocked additional functionalities for surface-enhanced biospectroscopy by precisely targeting and reading out the molecular absorption signatures of diverse molecular systems. However, BIC-driven molecular spectroscopy has so far focused on end point measurements in dry conditions, neglecting the crucial interaction dynamics of biological systems. Here, we combine the advantages of pixelated all-dielectric metasurfaces with deep learning-enabled feature extraction and prediction to realize an integrated optofluidic platform for time-resolved in situ biospectroscopy. Our approach harnesses high-Q metasurfaces specifically designed for operation in a lossy aqueous environment together with advanced spectral sampling techniques to temporally resolve the dynamic behavior of photoswitchable lipid membranes. Enabled by a software convolutional neural network, we further demonstrate the real-time classification of the characteristic cis and trans membrane conformations with 98% accuracy. Our synergistic sensing platform incorporating metasurfaces, optofluidics, and deep learning reveals exciting possibilities for studying multimolecular biological systems, ranging from the behavior of transmembrane proteins to the dynamic processes associated with cellular communication.

Perspective on Tailored Nanostructure-Dominated SPP Effects for SERS

Localized surface plasmon resonance (LSPR) excited by an incident light can normally produce strong surface-enhanced Raman scattering (SERS) at the nanogaps among plasmonic nano-objects (so-called hot spots), which is extensively explored. In contrast, surface plasmon polaritons (SPPs) that can be generated by an incident beam via particular structures with a conservation of wave vectors can excite SERS effects as well. SPPs actually play an indispensable role in high-performance SERS devices but receive much less attention. In this perspective, SPPs and their couplings with LSPR for SERS excitations with differing effectiveness through particular plasmonic/dielectric structures/configurations, along with relevant fabrication approaches, are profoundly reviewed and commented on from a unique perspective from in situ to ex situ excitations of SERS enabled by spatiotemporally separated multiple processes of SPPs. Quantitative design of particular configurations/architectures enabling highly efficient and effective multiple processes of SPPs is particularly emphasized as one giant leap toward ultimate full quantitative design of intrinsically high-performance SERS chips and very critical for their batch manufacturability and applications as well. The viewpoints and prospects about innovative SERS devices based on tailored structure-dominated SPPs effects and their coupling with LSPR are presented and discussed.

Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning-Assisted Multiplex SERS Fingerprinting

Accurate and rapid differentiation and authentication of agricultural products based on their origin and quality are crucial to ensuring food safety and quality control. However, similar chemical compositions and complex matrices often hinder precise identification, particularly for adulterated samples. Herein, we propose a novel method combining multiplex surface-enhanced Raman scattering (SERS) fingerprinting with a one-dimensional convolutional neural network (1D-CNN), which enables the effective differentiation of the category, origin, and grade of agricultural products. This strategy leverages three different SERS-active nanoparticles as multiplex sensors, each tailored to selectively amplify the signals of preferentially adsorbed chemicals within the sample. By strategically combining SERS spectra from different NPs, a 'SERS super-fingerprint' is constructed, offering a more comprehensive representation of the characteristic information on agricultural products. Subsequently, utilizing a custom-designed 1D-CNN model for feature extraction from the 'super-fingerprint' significantly enhances the predictive accuracy for agricultural products. This strategy successfully identified various agricultural products and simulated adulterated samples with exceptional accuracy, reaching 97.7% and 94.8%, respectively. Notably, the entire identification process, encompassing sample preparation, SERS measurement, and deep learning analysis, takes only 35 min. This development of deep learning-assisted multiplex SERS fingerprinting establishes a rapid and reliable method for the identification and authentication of agricultural products.

SERS-Active Printable Hydrogel for 3D Cell Culture and Imaging

Hydrogel-based three-dimensional (3D) cell culture systems mimic the salient elements of extracellular matrices and promote native cell function. However, high-resolution 3D cell imaging that can provide biological information about multiple features of individual cells is yet to be realized. In this context, we incorporated plasmonic gold nanoparticles (AuNPs) into an alginate/gelatin hydrogel to produce surface-enhanced Raman spectroscopy (SERS)-active hydrogel inks for the 3D printing and culturing of Vero cells. Dense incorporation of AuNPs enables production of a printed 3D grid structure with 3D SERS performance, but with no measurable adverse effects on cell growth. Label-free SERS spectra were collected within a hydrogel, and a random forest binary classifier was developed to discriminate Vero cell signals from the hydrogel background with an accuracy of 87.5%. The results suggest that SERS signals from cellular components, such as proteins, lipids, and carbohydrates, account for this discrimination. We demonstrate visualization of cell shape, location, and density by combining predicted binary maps with peak feature intensity maps in 2D and 3D. SERS images with a resolution of ≈3 μm match well with the microscopy images and show clear increases in intensity with incubation time. We suggest that 3D SERS cell imaging is a promising means to examine the effect of external cell stimuli on cellular behavior for diagnostic purposes.

Glioma Single-Cell Biomechanical Analysis by Cyclic Conical Constricted Microfluidics

Determining the grade of glioma is a critical step in choosing patients' treatment plans in clinical practices. The pathological diagnosis of patient's glioma samples requires extensive staining and imaging procedures, which are expensive and time-consuming. Current advanced uniform-width-constriction-channel-based microfluidics have proven to be effective in distinguishing cancer cells from normal tissues, such as breast cancer, ovarian cancer, prostate cancer, etc. However, the uniform-width-constriction channels can result in low yields on glioma cells with irregular morphologies and high heterogeneity. In this research, we presented an innovative cyclic conical constricted (CCC) microfluidic device to better differentiate glioma cells from normal glial cells. Compared with the widely used uniform-width-constriction microchannels, the new CCC configuration forces single cells to deform gradually and obtains the biophysical attributes from each deformation. The human-derived glioma cell lines U-87 and U-251, as well as the human-derived normal glial astrocyte cell line HA-1800 were selected as the proof of concept. The results showed that CCC channels can effectively obtain the biomechanical characteristics of different 12-25 μm glial cell lines. The patient glioma samples with WHO grades II, III, and IV were tested by CCC channels and compared between Elastic Net (ENet) and Lasso analysis. The results demonstrated that CCC channels and the ENet can successfully select critical biomechanical parameters to differentiate the grades of single-glioma cells. This CCC device can be potentially further applied to the extensive family of brain tumors at the single-cell level.

Recent Advances in Metaphotonic Biosensors

Metaphotonic devices, which enable light manipulation at a subwavelength scale and enhance light-matter interactions, have been emerging as a critical pillar in biosensing. Researchers have been attracted to metaphotonic biosensors, as they solve the limitations of the existing bioanalytical techniques, including the sensitivity, selectivity, and detection limit. Here, we briefly introduce types of metasurfaces utilized in various metaphotonic biomolecular sensing domains such as refractometry, surface-enhanced fluorescence, vibrational spectroscopy, and chiral sensing. Further, we list the prevalent working mechanisms of those metaphotonic bio-detection schemes. Furthermore, we summarize the recent progress in chip integration for metaphotonic biosensing to enable innovative point-of-care devices in healthcare. Finally, we discuss the impediments in metaphotonic biosensing, such as its cost effectiveness and treatment for intricate biospecimens, and present a prospect for potential directions for materializing these device strategies, significantly influencing clinical diagnostics in health and safety.

Industrial wastewater source tracing: The initiative of SERS spectral signature aided by a one-dimensional convolutional neural network

The spectral fingerprint is a significant concept in nontarget screening of environmental samples to direct identification efforts to relevant and important features. Surface-enhanced Raman scattering (SERS) has long been recognized as an optical method that can provide fingerprint-like chemical information at the single-molecule level. Here, the advanced one-dimensional convolutional neural network (1D-CNN) approach was applied to accurately identify the SERS spectral signature of industrial wastewaters for source tracing. A total of 66,000 SERS spectra were acquired from wastewaters of 22 factories across 10 industrial categories at three excitation wavelengths after data augmentation. The dataset was used to train a 1D-CNN model consisting of three convolutional layers to achieve adequate feature extraction of SERS spectra. As a proof-of-concept, multimixed wastewater samples were used to simulate practical pollution scenarios and evaluate the application potential of the model. The SERS-1D-CNN platform can identify the amount and factory information of wastewaters in multimixed samples, which achieves a recognition accuracy rate of 97.33%. The results suggest that even in a complex and unknown water environment, the 1D-CNN model can accurately identify industrial wastewaters in precollected datasets, exhibiting excellent potential in pollution source tracing.

In Situ Spatiotemporal SERS Measurements and Multivariate Analysis of Virally Infected Bacterial Biofilms Using Nanolaminated Plasmonic Crystals

In situ spatiotemporal biochemical characterization of the activity of living multicellular biofilms under external stimuli remains a significant challenge. Surface-enhanced Raman spectroscopy (SERS), combining the molecular fingerprint specificity of vibrational spectroscopy with the hotspot sensitivity of plasmonic nanostructures, has emerged as a promising noninvasive bioanalysis technique for living systems. However, most SERS devices do not allow reliable long-term spatiotemporal SERS measurements of multicellular systems because of challenges in producing spatially uniform and mechanically stable SERS hotspot arrays to interface with large cellular networks. Furthermore, very few studies have been conducted for multivariable analysis of spatiotemporal SERS datasets to extract spatially and temporally correlated biological information from multicellular systems. Here, we demonstrate in situ label-free spatiotemporal SERS measurements and multivariate analysis of Pseudomonas syringae biofilms during development and upon infection by bacteriophage virus Phi6 by employing nanolaminate plasmonic crystal SERS devices to interface mechanically stable, uniform, and spatially dense hotspot arrays with the P. syringae biofilms. We exploited unsupervised multivariate machine learning methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), to resolve the spatiotemporal evolution and Phi6 dose-dependent changes of major Raman peaks originating from biochemical components in P. syringae biofilms, including cellular components, extracellular polymeric substances (EPS), metabolite molecules, and cell lysate-enriched extracellular media. We then employed supervised multivariate analysis using linear discriminant analysis (LDA) for the multiclass classification of Phi6 dose-dependent biofilm responses, demonstrating the potential for viral infection diagnosis. We envision extending the in situ spatiotemporal SERS method to monitor dynamic, heterogeneous interactions between viruses and bacterial networks for applications such as phage-based anti-biofilm therapy development and continuous pathogenic virus detection.

Au/SiNCA-based SERS analysis coupled with machine learning for the early-stage diagnosis of cisplatin-induced liver injury

Cisplatin has been widely applied in the clinical treatment of various cancers, whereas liver injury induced by its hepatotoxicity is still a severe issue. Reliable identification of early-stage cisplatin-induced liver injury (CILI) can improve clinical care and help to streamline drug development. Traditional methods, however, cannot achieve enough information at the subcellular level due to the requirement of the labeling process and low sensitivity. To overcome these, we designed an Au-coated Si nanocone array (Au/SiNCA) to fabricate the microporous chip as the surface-enhanced Raman scattering (SERS) analysis platform for the early diagnosis of CILI. A CILI rat model was established, and the exosome spectra were obtained. The principal component analysis (PCA)-representation coefficient-based k-nearest centroid neighbor (RCKNCN) classification algorithm was proposed as the multivariate analysis method to build the diagnosis and staging model. The PCA-RCKNCN model has been validated to achieve a satisfactory result, with accuracy and AUC of over 97.5%, and sensitivity and specificity of over 95%, indicating that SERS combined with the PCA-RCKNCN analysis platform can be a promising tool for clinical applications.

Electroporation-Assisted Surface-Enhanced Raman Detection for Long-Term, Label-Free, and Noninvasive Molecular Profiling of Live Single Cells

Molecule characterization of live single cells is greatly important in disease diagnoses and personalized treatments. Conventional molecule detection methods, such as mass spectrography, gene sequencing, or immunofluorescence, are usually destructive or labeled and unable to monitor the dynamic change of live cellular molecules. Herein, we propose an electroporation-assisted surface-enhanced Raman scattering (EP-SERS) method using a microchip to implement label-free, noninvasive, and continuous detections of the molecules of live single cells. The microchip containing microelectrodes with nanostructured EP-SERS probes has a multifunction of cell positioning, electroporation, and SERS detection. The EP-SERS method capably detects both the intracellular and extracellular molecules of live single cells without losing cell viability so as to enable long-term monitoring of the molecular pathological process in situ. We detect the molecules of single cells for two breast cancer cell lines with different malignancies (MCF-7 and MDA-MB-231), one liver cancer cell line (Huh-7), and one normal cell line (293T) using the EP-SERS method and classify these cell types to achieve high accuracies of 91.4-98.3% using their SERS spectra. Furthermore, 24 h continuous monitoring of the heterogeneous molecular responses of different cancer cell lines under doxorubicin treatment is successfully implemented using the EP-SERS method. This work provides a long-term, label-free, and biocompatible approach to simultaneously detect intracellular and extracellular molecules of live single cells on a chip, which would facilitate research and applications of cancer diagnoses and personalized treatments.

Application of SERS in In-Vitro Biomedical Detection

Surface-enhanced Raman scattering (SERS), as a rapid and nondestructive biological detection method, holds great promise for clinical on spot and early diagnosis. In order to address the challenging demands of on spot detection of biomedical samples, a variety of strategies has been developed. These strategies include substrate structural and component engineering, data processing techniques, as well as combination with other analytical methods. This report summarizes the recent SERS developments for biomedical detection, and their promising applications in cancer detection, virus or bacterial infection detection, miscarriage spotting, neurological disease screening et al. The first part discusses the frequently used SERS substrate component and structures, the second part reports on the detection strategies for nucleic acids, proteins, bacteria, and virus, the third part summarizes their promising applications in clinical detection in a variety of illnesses, and the forth part reports on recent development of SERS in combination with other analytical techniques. The special merits, challenges, and perspectives are discussed in both introduction and conclusion sections.

Combined Morphological and Spectroscopic Diagnostic of HER2 Expression in Breast Cancer Tissues Based on Label-Free Surface-Enhanced Raman Scattering

Breast cancer is the most commonly diagnosed cancer type worldwide. Overexpression of human epidermal growth factor receptor 2 (HER2) is an important subtype of breast cancer and results in an increased risk of recurrence and metastasis in patients. At present, immunohistochemistry (IHC) is used to detect the expression of HER2 in breast cancer tissues as the golden standard. However, IHC has some shortcomings, such as large subjective impact, long time consumption, expensive reagents, etc. In this paper, a combined morphological and spectroscopic diagnostic method based on label-free surface-enhanced Raman scattering (SERS) for HER2 expression in breast cancer is proposed. It can not only quantitively detect HER2 expression in breast cancer tissues by spectroscopic measurements but also give morphological images reflecting the distribution of HER2 in tissues. The results show that the consistency between this method and IHC is 95% and achieves the annotation of tumor regions on tissue sections. This method is time-consuming, quantifiable, intuitive, scalable, and easy to understand. Combined with deep learning approaches, it is expected to promote the development of clinical detection and diagnosis technology for breast cancer and other cancers.

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.

Silver-Decorated Silicon Nanostructures: Fabrication and Characterization of Nanoscale Terraces as an Efficient SERS-Active Substrate

Rich and highly dense surface-enhanced Raman (SERS) hotspots available in the SERS-active platform are highly anticipated in SERS measurements. In this work, conventional silicon wafer was treated to have wide exposure to terraces available within the silicon nanostructures (Si-NSs). High-resolution field emission scanning electron microscopic (FESEM) investigations confirmed that the terraces were several microns wide and spread over different steps. These terraces were further decorated with silver nanoparticles (Ag-NPs) of different shapes and sizes to achieve SERS-active hotspots. Based on more than 150 events, a histogram of the size distribution of Ag-NPs indicated a relatively narrow size distribution, 29.64 ± 4.66 nm. The coverage density was estimated to be ~4 × 10¹⁰ cm^-2. The SERS-activity of Ag-NPs -decorated Si-NSs was found to be enhanced with reference to those obtained in pristine Si-NSs. Finite difference time domain models were developed to support experimental observations in view of electromagnetic (EM) near-field distributions. Three archetype models; (i) dimer of same constituent Ag-NPs, (ii) dimer of different constituent Ag-NPs, and (iii) linear trimer of different constituent Ag-NPs were developed. EM near-field distributions were extracted at different incident polarizations. Si-NSs are well-known to facilitate light confinement, and such confinement can be cascaded within different Ag-NPs-decorated terraces of Si-NSs.

Ultrasensitive multiplex SERS immunoassay based on porous Au-Ag alloy nanoparticle-amplified Raman signal probe and encoded photonic crystal beads

An ultrasensitive multiplex surface-enhanced Raman scattering (SERS) immunoassay was developed using porous Au-Ag alloy nanoparticles (p-AuAg NPs) as Raman signal amplification probe coupling with encoded photonic crystal microsphere. p-AuAg NPs were synthesized and modified with the second antibody (Ab₂) and Raman tag (mercaptobenzoic acid, MBA) to prepare a Raman signal-amplified probe. The high porosity of the p-AuAg NPs enables significant coupling of the localized surface plasmon resonance and thus abundant inherent hotspots for Raman signal enhancement. 3D-ordered silver nanoparticles-coated silica photonic crystal beads (Ag/SPCBs) were prepared as encoded SERS substrate for multiplex detection using their reflection peaks. The signal-amplified probe was used for multiplex detection of tumor markers carcinoembryonic antigen (CEA) and alpha fetoprotein (AFP). The wide linear ranges of 10^-7-10³ ng/mL for CEA and 10^-4-10³ ng/mL for AFP with detection limits of 1.22 × 10^-8 ng/mL and 2.47 × 10^-5 ng/mL for CEA and AFP at a signal-to-noise ratio of 3 were obtained. The proposed multiplex SERS immunoassay method displays ultrahigh sensitivity, wide linear range, and excellent specificity, which can be successfully applied to measure clinical serum samples with satisfactory results. The research provides a novel SERS signal enhancement strategy for the multiplex bioassay.

SERS Monitored Kinetic Process of Gaseous Thiophenol Compound in Plasmonic MOF Nanoparticles

Benefiting from the electromagnetic enhancement of noble metal nanoparticles (NPs) and the capture ability of organic frameworks, plasmonic metal-organic framework (MOF) structures have greatly promoted the development of gas detection by surface-enhanced Raman spectroscopy (SERS). In those detections, the kinetic process of gaseous molecules in plasmonic-MOF structures has a great influence on SERS spectra, which is still lacking intensive investigation in previous reports. In this work, the kinetic processes of gaseous thiophenol compounds (TPC) in the plasmonic Zeolitic Imidazolate Framework (Ag@ZIF) core-shell NPs are studied by SERS spectra. The experimental data demonstrate that the SERS intensities of gaseous TPC could be enhanced once more in an H₂ mixed gas environment with different functional groups of TPC. Further results reveal that the two-step enhancement of SERS intensities is not only related to the thicknesses of the MOF shell but also affected by the ambient mixed gas. To understand this novel phenomenon, the binding energy between the gaseous molecule and ZIF is calculated based on first-principles computation. In combination with the plasmonic properties of the Ag core, a molecular collision model is introduced here to show the distribution of gaseous TPC molecules in ZIF, which could be responsible for this interesting two-step enhancement of SERS intensities. Furthermore, the H₂ assisted kinetic process of gaseous p-aminothiophenol (PATP) is also analyzed by the classical pseudo-first-order kinetic model, which is consistent with our experimental SERS data. Our work not only reveals the novel phenomenon of plasmonic-MOF structures to improve the gas detection by SERS spectra but also enriches the understanding of the microcosmic process of gaseous molecules in the mixed gas environment to optimize MOF structures for gas capture and storage.

Label-Free Sensing with Metal Nanostructure-Based Surface-Enhanced Raman Spectroscopy for Cancer Diagnosis

Surface-Enhanced Raman Spectroscopy (SERS) is a powerful analytical technique for the detection of small analytes with great potential for medical diagnostic applications. Its high sensitivity and excellent molecular specificity, which stems from the unique fingerprint of molecular species, have been applied toward the detection of different types of cancer. The noninvasive and rapid detection offered by SERS highlights its applicability for point-of-care (PoC) deployment for cancer diagnosis, screening, and staging, as well as for predicting tumor recurrence and treatment monitoring. This review provides an overview of the progress in label-free (direct) SERS-based chemical detection for cancer diagnosis with the main focus on the advances in the design and preparation of SERS substrates on the basis of metal nanoparticle structures formed via bottom-up strategies. It begins by introducing a synopsis of the working principles of SERS, including key chemometric approaches for spectroscopic data analysis. Then it introduces the advances of label-free sensing with SERS in cancer diagnosis using biofluids (blood, urine, saliva, sweat) and breath as the detection media. In the end, an outlook of the advances and challenges in cancer diagnosis via SERS is provided.

Label-Free Plasmon-Enhanced Spectroscopic HER2 Detection for Dynamic Therapeutic Surveillance of Breast Cancer

The expression of human epidermal growth factor receptor-2 (HER2) has important implications for pathogenesis, progression, and therapeutic efficacy of breast cancer. The detection of its variation during the treatment is crucial for therapeutic decision-making but remains a grand challenge, especially at the cellular level. Here, we develop a machine learning-driven surface-enhanced Raman spectroscopy (SERS)-integrated strategy for label-free detection of cellular HER2. Specifically, our method allows the extraction of cell-rich spectral signatures utilized for identification and classification of cancer cells with distinct HER2 expression with a high accuracy of 99.6%. By combining label-free SERS detection and machine learning-driven chemometric analysis, we are able to perform longitudinal monitoring of therapeutic efficacy at the cellular level during the treatment of HER2+ breast cancer, which aids in the subsequent decision-making and management. This work provides a promising technique capable of performing dynamic label-free spectroscopic detection for therapeutic surveillance of diseases.

Microporous Multiresonant Plasmonic Meshes by Hierarchical Micro-Nanoimprinting for Bio-Interfaced SERS Imaging and Nonlinear Nano-Optics

Microporous mesh plasmonic devices have the potential to combine the biocompatibility of microporous polymeric meshes with the capabilities of plasmonic nanostructures to enhance nanoscale light-matter interactions for bio-interfaced optical sensing and actuation. However, scalable integration of dense and uniformly structured plasmonic hotspot arrays with microporous polymeric meshes remains challenging due to the processing incompatibility of conventional nanofabrication methods with flexible microporous substrates. Here, scalable nanofabrication of microporous multiresonant plasmonic meshes (MMPMs) is achieved via a hierarchical micro-/nanoimprint lithography approach using dissolvable polymeric templates. It is demonstrated that MMPMs can serve as broadband nonlinear nanoplasmonic devices to generate second-harmonic generation, third-harmonic generation, and upconversion photoluminescence signals with multiresonant plasmonic enhancement under fs pulse excitation. Moreover, MMPMs are employed and explored as bio-interfaced surface-enhanced Raman spectroscopy mesh sensors to enable in situ spatiotemporal molecular profiling of bacterial biofilm activity. Microporous mesh plasmonic devices open exciting avenues for bio-interfaced optical sensing and actuation applications, such as inflammation-free epidermal sensors in conformal contact with skin, combined tissue-engineering and biosensing scaffolds for in vitro 3D cell culture models, and minimally invasive implantable probes for long-term disease diagnostics and therapeutics.

Current methods and emerging approaches for detection of programmed death ligand 1

Various tumor cells overexpress programmed death ligand 1 (PD-L1), a main immune checkpoint protein (ICP) embedded in the tumor cells membrane, to evade immune recognition through the interaction between PD-L1 and its receptor programmed death 1 (PD-1) which is from T-cells for maintaining immune tolerance. So inhibitors targeting the PD-1 or PD-L1 can block the PD-1/PD-L1 signaling pathway to restore the recognition activity of the immune system to tumor cells, which also have been utilized as a novel approach to improve the clinical therapeutic effect for cancer patients. Since not all cancer patients can respond to these inhibitors effectively, previous diagnosis of PD-L1 is significant to target the right treatments for cancer patients. This review pays attention to the PD-L1 detection and recent progress in the measurement of PD-L1 concentration, including various detection methods based on optical sensors as well as electrochemical assays. Apart from above those, we also focus on the prospects of PD-L1 detection in precision medicine.

Emergence of Responsive Surface-Enhanced Raman Scattering Probes for Imaging Tumor-Associated Metabolites

As a core hallmark of cancer, metabolic reprogramming alters the metabolic networks of cancer cells to meet their insatiable appetite for energy and nutrient. Tumor-associated metabolites, the products of metabolic reprogramming, are valuable in evaluating tumor occurrence and progress timely and accurately because their concentration variations usually happen earlier than the aberrances demonstrated in tissue structure and function. As an optical spectroscopic technique, surface-enhanced Raman scattering (SERS) offers advantages in imaging tumor-associated metabolites, including ultrahigh sensitivity, high specificity, multiplexing capacity, and uncompromised signal intensity. This review first highlights recent advances in the development of stimuli-responsive SERS probes. Then the mechanisms leading to the responsive SERS signal triggered by tumor metabolites are summarized. Furthermore, biomedical applications of these responsive SERS probes, such as the image-guided tumor surgery and liquid biopsy examination for tumor molecular typing, are summarized. Finally, the challenges and prospects of the responsive SERS probes for clinical translation are also discussed.

Wafer-Scale and Cost-Effective Manufacturing of Controllable Nanogap Arrays for Highly Sensitive SERS Sensing

The metallic nanogap has been proved as an efficient architecture for surface-enhanced Raman scattering (SERS) applications. Although a lot of nanogap fabrication methods have been proposed in the last few decades, the economical and high-yield manufacturing of sub-10 nm gaps remains a challenge. Here, we present a convenient and cost-effective fabrication method for wafer-scale patterning of metallic nanogaps, which simply combines photolithographic metal patterning, swelling-induced nanocracking, and superimposition metal sputtering without requiring expensive nanofabrication equipment. By controlling the swelling time and metal deposition thickness, the gap size can be precisely defined, down to the sub-10 nm scale. Furthermore, we demonstrate that the fabricated nanogap array can be used as an excellent SERS substrate for molecule measurements and shows a high Raman enhancement factor of ∼10⁸ and a high sensitivity for the detection of rhodamine 6G (R6G) molecules, even down to 10^-14 M, indicating an extraordinary capability for single-molecule detection. Due to its high controllability and wafer-scale fabrication capability, this nanogap fabrication method offers a promising route for highly sensitive and economical SERS detections.

Differentiating toxic and nontoxic congeneric harmful algae using the non-polar metabolome

Recognition and rejection of chemically defended prey is critical to maximizing fitness for predators. Paralytic shellfish toxins (PSTs) which strongly inhibit voltage-gated sodium channels in diverse animal taxa are produced by several species of the bloom-forming algal genus Alexandrium where they appear to function as chemical defenses against grazing copepods. Despite PSTs being produced and localized within phytoplankton cells, some copepods distinguish toxic from non-toxic prey, selectively ingesting less toxic cells, in ways that suggest cell surface recognition perhaps associated with non-polar metabolites. In this study LC/MS and NMR-based metabolomics revealed that the non-polar metabolomes of two toxic species (Alexandrium catenella and Alexandrium pacificum) vary considerably from their non-toxic congener Alexandrium tamarense despite all three being very closely related. Toxic and non-toxic Alexandrium spp. were distinguished from each other by metabolites belonging to seven lipid classes. Of these, 17 specific metabolites were significantly more abundant in both toxic A. catenella and A. pacificum compared to non-toxic A. tamarense suggesting that just a small portion of the observed metabolic variability is associated with toxicity. Future experiments aimed at deciphering chemoreception mechanisms of copepod perception of Alexandrium toxicity should consider these metabolites, and the broader lipid classes phosphatidylcholines and sterols, as potential candidate cues.

Complex cellular environments imaged by SERS nanoprobes using sugars as an all-in-one vector

Raman spectroscopy coupled with confocal microscopy offers an alternative bioimaging technique overcoming limitations associated with sensitivity, tissue penetration and image resolution. Allied to the surface-enhanced Raman scattering (SERS) properties of gold nanoparticles (AuNP), we designed SERS nanoprobes with enhanced properties and straightforward application as bio-labelling agents for gliomas. The ensuing nanoprobes coated with simple sugar units (galactose or glucose) allowed assessing information about their intracellular localization (vesicular structures), with impressive sensitivity towards complex environments and proved the ability to overcome biological auto-fluorescence and high penetration in tissues. We validate the use of sugars as an all-in-one vector (Raman reporter, conferring high stability, biocompatibility and affinity to glioma cells) as imaging agents using an impressive technique.

A digital SERS sensing platform using 3D nanolaminate plasmonic crystals coupled with Au nanoparticles for accurate quantitative detection of dopamine

We report a digital surface-enhanced Raman spectroscopy (SERS) sensing platform using the arrays of 3D nanolaminate plasmonic crystals (NLPC) coupled with Au nanoparticles and digital (on/off) SERS signal analysis for the accurate quantitative detection of dopamine (DA) at ultralow concentrations. 3D NLPC SERS substrates were fabricated to support the optically dense arrays of vertically-stacked multi-nanogap hotspots and combined with Raman tag-conjugated Au nanoparticles for NLPC-based dual-recognition structures. We demonstrate that the 3D NLPC-based dual-recognition structures including Au nanoparticle-induced additional hotspots can enable more effective SERS enhancement through the molecular recognition of DA. For the accurate quantification of DA at ultralow concentrations, we conducted digital SERS analysis to reduce stochastic signal variation due to various microscopic effects, including molecular orientation/position variation and the spatial distribution of nanoparticle-coupled hotspots. The digital SERS analysis allowed the SERS mapping results from the DA-specific dual-recognition structures to be converted into binary "On/Off" states; the number of "On" events was directly correlated with low-abundance DA molecules down to 1 pM. Therefore, the digital SERS platform using the 3D NLPC-based dual-recognition structures coupled with Au nanoparticles and digital SERS signal analysis can be used not only for the ultrasensitive, accurate, and quantitative determination of DA, but also for the practical and rapid analysis of various molecules on nanostructured surfaces.

SERS and MALDI-TOF MS based plasma exosome profiling for rapid detection of osteosarcoma

Osteosarcoma is the most frequent primary bone cancer, particularly among children and adolescents. The early diagnosis of osteosarcoma is significant for timely clinical treatment to reduce the mortality of patients. Exosomes play a significant role in intercellular communication and serve as promising biomarkers in liquid biopsy for the diagnosis and monitoring of tumors. Herein, we report the utility of surface-enhanced Raman scattering (SERS) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for rapid identification of osteosarcoma. We firstly profiled the intrinsic SERS signals and MALDI-TOF mass fingerprints of different subgroups of extracellular vesicles (EVs) and the corresponding cells, demonstrating that the SERS signals and MALDI-TOF mass spectra of exosomes from different types of cells were more discriminative compared to those of large and medium EVs and the cells themselves. Then, we characterized plasma-derived exosomes of 15 osteosarcoma patients and 15 healthy volunteers using SERS and MALDI-TOF MS, revealing distinctive biochemical differences in the spectra. We further utilized a data fusion approach to combine the two types of spectroscopic techniques, differentiating osteosarcoma patients from healthy controls with higher precision than either technique. The results reveal that the non-invasive liquid biopsy method using SERS and MALDI-TOF MS fingerprinting of exosomes has great potential for rapid diagnosis of osteosarcoma.

Nanostructure-based surface-enhanced Raman scattering for diagnosis of cancer

Cancer is a malignant disease that seriously affects human health and life. Early diagnosis and timely treatment can significantly improve the survival rate of cancer patients. Surface-enhanced Raman scattering (SERS) is an optical technology that can detect and image samples at the single-molecule level. It has the advantages of rapidity, high specificity, high sensitivity and no damage to the sample. The performance of SERS is highly dependent on the properties, size and morphology of the SERS substrate. Preparation of SERS substrates with good reproducibility and chemical stability is a key factor in realizing the wide application of SERS technology in cancer diagnosis. In this review we provide a detailed presentation of the latest research on SERS in cancer diagnosis and the detection of cancer biomarkers, mainly focusing on nanotechnological approaches in cancer diagnosis by using SERS. We also consider the future development of nanostructure-based SERS in cancer diagnosis.

Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis

In recent years, bioanalytical surface-enhanced Raman spectroscopy (SERS) has blossomed into a fast-growing research area. Owing to its high sensitivity and outstanding multiplexing ability, SERS is an effective analytical technique that has excellent potential in bioanalysis and diagnosis, as demonstrated by its increasing applications in vivo. SERS allows the rapid detection of molecular species based on direct and indirect strategies. Because it benefits from the tunable surface properties of nanostructures, it finds a broad range of applications with clinical relevance, such as biological sensing, drug delivery and live cell imaging assays. Of particular interest are early-stage-cancer detection and the fast detection of pathogens. Here, we present a comprehensive survey of SERS-based assays, from basic considerations to bioanalytical applications. Our main focus is on SERS-based pathogen detection methods as point-of-care solutions for early bacterial infection detection and chronic disease diagnosis. Additionally, various promising in vivo applications of SERS are surveyed. Furthermore, we provide a brief outlook of recent endeavours and we discuss future prospects and limitations for SERS, as a reliable approach for rapid and sensitive bioanalysis and diagnosis.

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.

Surface-Enhanced Raman Spectroscopy of Bacterial Metabolites for Bacterial Growth Monitoring and Diagnosis of Viral Infection

Bacterial metabolites are intermediate products of bacterial metabolism and their production reflects metabolic activity. Herein, we report the use of surface-enhanced Raman spectroscopy (SERS) for detection of both volatile and nonvolatile metabolites and the application of this approach for bacterial growth quantification and diagnosis of viral infection. The time-dependent SERS signal of the volatile metabolite dimethyl disulfide in the headspace above bacteria growing on an agar plate was detected and quantified. In addition, SERS signals arising from the plate reflected nutrient consumption and production of nonvolatile metabolites. The measurement of metabolite accumulation can be used for bacterial quantification. In the presence of bacteriophage virus, bacterial metabolism is suppressed, and the relative decrease in SERS intensity reflects the initial virus concentration. Using multivariate analysis, we detect viral infection with a prediction accuracy of 93%. Our SERS-based approach for metabolite production monitoring provides new insights toward viral infection diagnosis.

Concurrent Imaging of Surface-Enhanced Raman and Mie Scattering from Built-in Nanogap Plasmonic Particles

We report a bimodal imaging method that can spatially resolve and concurrently correlate SERS and background-free Mie scattering signals. By examining two types of nanoparticle assemblies with different types of plasmonic junctions, namely raspberry-like metamolecules (raspberry-MMs) containing intraparticle nanogaps and groups of Au nanocubes forming interparticle gaps, we were able to rapidly screen SERS-active particles among the entire population of nanoparticles. Ratiometric analysis of SERS/Mie scattering revealed distinct behaviors for these intra- and interparticle nanogaps. In particular, raspberry-MMs showed a high fraction of SERS-active particles with the SERS intensity essentially insensitive to the nanoparticle aggregation state and a predictable environmental dependence. In comparison, nanocube clusters exhibited highly heterogeneous SERS/Mie scattering ratios and unpredictable intensity fluctuations but higher maximum SERS intensity. This dual-imaging approach constitutes an in situ visualization tool that enables simultaneous and stoichiometric analysis of dual signals consisting of elastic and inelastic scattering, which can significantly improve the reliability of SERS measurements.

Three-dimensional SERS sensor based on the sandwiched G@AgNPs@G/PDMS film

Three-dimensional (3D) SERS substrate with the denser "hotspots" is synthesized by the constriction of PDMS film decorated with sandwiched graphene@AgNPs@graphene (G@AgNPs@G) nanostructure. Graphene layers above and below the AgNPs are used to absorb molecules onto the "hotspots", and prevent the oxidation of AgNPs in our design. PDMS films can be easily shrunk for 3D structures, causing advantages in enhancement ability and light-matter interaction. Benefiting from the above advantages, a detection limit of 10^-14 M (CV) and enhancement factor (EF) of 3.9 × 10⁹ were obtained in our experiment. Theoretical analyses (FDTD) were also used to study the enhancement mechanism. For practical purposes, in-situ detection of MG molecules on the fish surface and the label-free detection of DNA base of adenine (A) and cytosine (C) were also studied. The high enhancement factor, great sensitivity, reliability, and stability of substrate reasonably proved that it can be used as an excellent SERS substrate for biomolecular detection.

Tracking endocytosis and intracellular distribution of spherical nucleic acids with correlative single-cell imaging

A comprehensive understanding of interactions between nanoparticles (NPs) and biological components is critical to the clinical application of NPs and nanomedicine. Here we provide a step-by-step correlative imaging approach to investigate plasmonic NPs of different aggregation states at the single-cell level. Traceable spherical nucleic acids (SNAs) are fabricated by decorating 50-nm spherical gold NPs with fluorophore-labeled DNA, serving as dually emissive (fluorescent and plasmonic) NPs. The in situ correlative imaging with dark-field microscopy (DFM) and fluorescence microscopy (FM) reveals intracellular distribution of SNAs, whereas DFM combined with scanning electron microscopy (SEM) allows semi-quantification of SNA clustering states in solution. The imaging data are analyzed by ImageJ and a colorimetry-based algorithm written in Python. The clustering states of SNAs in a single cell can be efficiently distinguished within 20 s. This method can be readily installed to monitor real-time endocytosis and cellular distribution of plasmonic NPs of different aggregation states and to quantitatively image targets of interest (e.g., specific DNA, messenger RNA, peptides or proteins) in living cells. The entire procedure can be completed in 3-5 d and requires standard DFM, FM and SEM imaging and data analysis skills and equipment.

A disposable paper-based hydrophobic substrate for highly sensitive surface-enhanced Raman scattering detection

Detection of target analytes with high sensitivity and reproducibility remains a challenge for surface-enhanced Raman scattering (SERS) due to the lack of cost-effective and highly sensitive substrates. In this study, a hydrophobic SERS substrate capable of concentrating nanoparticles and analytes was prepared by spin-coating lubricating liquid onto commercial paper. The condensation effect of the paper-based hydrophobic substrate induced aggregation of gold nanoparticles (Au NPs) to generate ''hot spots'' for SERS and to drive analytes to the hot-spot areas for more sensitive detection. The obtained SERS signal intensity was 5-fold higher than that obtained using common paper, and a detection limit (LOD) of 4.3 × 10^-10 M for rhodamine 6G (R6G) was achieved. Randomly selected points on the substrate and different batches of substrates all exhibited high reproducibility, and the relative standard deviation (RSD) at 1362 cm^-1 is approximately 11%. A further application of the hydrophobic substrate was demonstrated by the detection of cytochrome C within a linear detection range of 3.90 × 10^-8 M-1.25 × 10^-6 M. In addition, the prepared substrate can obtain identifiable SERS spectra of cancer cells and non-cancer cells because a large number of AuNP or Au NPs clusters can adhere to cells, resulting in the construction of a 3D hotspot matrix. The disposable hydrophobic paper substrate eliminates the problem of solution diffusion, and also provides an effective platform for biomolecular screening detection.

Plasmonic Electronic Raman Scattering as Internal Standard for Spatial and Temporal Calibration in Quantitative Surface-Enhanced Raman Spectroscopy

Ultrasensitive surface-enhanced Raman spectroscopy (SERS) still faces difficulties in quantitative analysis because of its susceptibility to local optical field variations at plasmonic hotspots in metallo-dielectric nanostructures. Current SERS calibration approaches using Raman tags have inherent limitations due to spatial occupation competition with analyte molecules, spectral interference with analyte Raman peaks, and photodegradation. Herein, we report that plasmon-enhanced electronic Raman scattering (ERS) signals from metal can serve as an internal standard for spatial and temporal calibration of molecular Raman scattering (MRS) signals from analyte molecules at the same hotspots, enabling rigorous quantitative SERS analysis. We observe a linear dependence between ERS and MRS signal intensities upon spatial and temporal variations of excitation optical fields, manifesting the |E|4 enhancements for both ERS and MRS processes at the same hotspots in agreement with our theoretical prediction. Furthermore, we find that the ERS calibration's performance limit can result from orientation variations of analyte molecules at hotspots.

Sub-10 nm Nanolaminated Al2O3/HfO2 Coatings for Long-Term Stability of Cu Plasmonic Nanodisks in Physiological Environments

By supporting localized plasmon modes, metal-based plasmonic nanostructures can confine optical fields at deep-subwavelength scale in various applications, such as biological and chemical sensing, nanoscale light emission, and solar energy harvesting. While Cu is a low-cost complementary metal oxide semiconductor (CMOS) compatible material, its poor chemical stability limits the use of Cu plasmonic nanodevices in corrosive biochemical aqueous environments. In this paper, we demonstrate that sub-10 nm Al₂O₃/HfO₂ nanolaminated coatings can significantly extend the lifetime of Cu nanodisk arrays from ∼5 h to ∼180 days in the physiological environment of 1× phosphate-buffered saline (PBS) at 37 °C. Cu nanodisk arrays are fabricated using freestanding Au nanohole array films as the physical vapor deposition masks and sub-10 nm nanolaminated coatings composed of alternating Al₂O₃ and HfO₂ nanolayers are grown on Cu nanodisk arrays by atomic layer deposition (ALD). Time-dependent optical extinction measurements of Cu nanodisk arrays are conducted in 1× solutions at 37 °C to investigate the anticorrosion performance for different pure and nanolaminated ALD coatings. We observe a linear relationship between the lifetime of Cu nanodisk arrays in 1× PBS at 37 °C and the nanolaminated coating thickness, and ∼1.3 nm nanolaminated coatings of ∼10 ALD cycles can extend the lifetime of Cu plasmonics up to ∼20 days. Furthermore, we find that the anticorrosion performance of Al₂O₃/HfO₂ nanolaminated ALD coatings strongly depends on the processing and the geometric parameters, such as the annealing temperature and the nanolaminated backbone unit size.

Optimizing the SERS Performance of 3D Substrates through Tunable 3D Plasmonic Coupling toward Label-Free Liver Cancer Cell Classification

Three-dimensional (3D) plasmonic nanostructures are emerging as excellent surface-enhanced Raman spectroscopy (SERS) substrates for chemical and biomedical applications. However, the correlation of 3D (including both in-plane and out-of-plane) plasmonic coupling with the SERS properties to deepen the understanding of 3D SERS substrates remains a challenge. Here, we perform correlation studies of 3D plasmonic coupling and SERS properties of the 3D hierarchical SERS substrates by tuning the multiscale structural elements. The effects of zero-dimensional (0D; the size of the building blocks), one-dimensional (1D; the thickness of the 3D substrates), and two-dimensional (2D; the composition of individual monolayers) structural elements on 3D plasmonic coupling are studied by performing UV-vis-near-infrared (NIR) spectroscopy and measuring SERS performance. It shows that both the extinction spectra and SERS enhancement are tuned at the 3D structural level. It is demonstrated that the plasmonic resonance wavelength (PRW) stemming from the 3D plasmonic coupling correlates with the SERS averaged surface enhancement factor (ASEF) and is improved by more than tenfold at the optimum 3D nanostructure. The optimized substrate is used to quantitatively analyze two small biological molecules. Moreover, as a proof-of-concept study, the substrate is first applied to differentiate between living liver normal and cancer cells with a high prediction accuracy through the spectral features of the cell membranes and the metabolites secreted outside the cells. We expect that the tuning of plasmonic coupling at the 3D level can open up new routes to design high-performance SERS substrates for wide applications.

Scalable nanolaminated SERS multiwell cell culture assay

This paper presents a new cell culture platform enabling label-free surface-enhanced Raman spectroscopy (SERS) analysis of biological samples. The platform integrates a multilayered metal-insulator-metal nanolaminated SERS substrate and polydimethylsiloxane (PDMS) multiwells for the simultaneous analysis of cultured cells. Multiple cell lines, including breast normal and cancer cells and prostate cancer cells, were used to validate the applicability of this unique platform. The cell lines were cultured in different wells. The Raman spectra of over 100 cells from each cell line were collected and analyzed after 12 h of introducing the cells to the assay. The unique Raman spectra of each cell line yielded biomarkers for identifying cancerous and normal cells. A kernel-based machine learning algorithm was used to extract the high-dimensional variables from the Raman spectra. Specifically, the nonnegative garrote on a kernel machine classifier is a hybrid approach with a mixed nonparametric model that considers the nonlinear relationships between the higher-dimension variables. The breast cancer cell lines and normal breast epithelial cells were distinguished with an accuracy close to 90%. The prediction rate between breast cancer cells and prostate cancer cells reached 94%. Four blind test groups were used to evaluate the prediction power of the SERS spectra. The peak intensities at the selected Raman shifts of the testing groups were selected and compared with the training groups used in the machine learning algorithm. The blind testing groups were correctly predicted 100% of the time, demonstrating the applicability of the multiwell SERS array for analyzing cell populations for cancer research.