An excitation wavelength-optimized, stable SERS biosensing nanoplatform for analyzing adenoviral and AstraZeneca COVID-19 vaccination efficacy status using tear samples of vaccinated individuals

Current Trends in In Vitro Diagnostics Using Surface-Enhanced Raman Scattering in Translational Biomedical Research

Immunoassays using surface-enhanced Raman scattering (SERS) are prosperous in disease diagnosis due to their excellent multiplexing ability, high sensitivity, and large dynamic range. Given the recent advancements in SERS immunoassays, this work provides a comprehensive overview, from fundamental principles to practical applications. An mRNA sensor utilizing Raman spectroscopy is a detection method that leverages the unique vibrational characteristics of mRNA molecules to identify and quantify their presence in a sample, often achieved through a technique called SERS, where specially designed nanoparticles amplify the Raman signal, allowing for the highly sensitive detection of even small amounts of mRNA. This review analyzes SERS assays used to detect RNA biomarkers, which show promise in cancer diagnostics and are being actively studied clinically. To selectively detect a specific mRNA sequence, a probe molecule (e.g., a DNA oligonucleotide complementary to the target mRNA) is attached to the SERS substrate, allowing the target mRNA to hybridize and generate a detectable Raman signal upon binding. Thus, the discussion includes proposals to enhance SERS immunoassay performance, along with future challenges and perspectives, offering concise, valid guidelines for platform selection based on application.

Recent Plasmonic Gold- and Silver-Assisted Raman Spectra for Advanced SARS-CoV-2 Detection

COVID-19 has become one of the deadliest epidemics in the past years. In efforts to combat the deadly disease besides vaccines, drug therapies, and facemasks, significant focus has been on designing specific methods for the sensitive and accurate detection of SARS-CoV-2. Of these, surface-enhanced Raman scattering (SERS) is an attractive analytical tool for the identification of SARS-CoV-2. SERS is the phenomenon of enhancement of Raman intensity signals from molecular analytes anchored onto the surfaces of roughened plasmonic nanomaterials. This work gives an updated summary of plasmonic gold nanomaterials (AuNMs) and silver nanomaterials (AgNMs)-based SERS technologies to identify SARS-CoV-2. Due to extreme "hot spots" promoting higher electromagnetic fields on their surfaces, different shapes of AuNMs and AgNMs combined with Raman probes have been reviewed for enhancing Raman signals of probe molecules for quantifying the virus. It also reviews progress made recently in the design of certain specific Raman probe molecules capable of imparting characteristic SERS response/tags for SARS-CoV-2 detection.

Cerebrospinal fluid-induced stable and reproducible SERS sensing for various meningitis discrimination assisted with machine learning

Cerebrospinal fluid (CSF)-based pathogen or biochemical testing is the standard approach for clinical diagnosis of various meningitis. However, misdiagnosis and missed diagnosis always occur due to the shortages of unusual clinical manifestations and time-consuming shortcomings, low sensitivity, and poor specificity. Here, for the first time, we propose a simple and reliable CSF-induced SERS platform assisted with machine learning (ML) for the diagnosis and identification of various meningitis. Stable and reproducible SERS spectra are obtained within 30 s by simply mixing the colloidal silver nanoparticles (Ag NPs) with CSF sample, and the relative standard deviation of signal intensity is achieved as low as 2.1%. In contrast to conventional salt agglomeration agent-induced irreversible aggregation for achieving Raman enhancement, a homogeneous and dispersed colloidal solution is observed within 1 h for the mixture of Ag NPs/CSF (containing 110-140 mM chloride), contributing to excellent SERS stability and reproducibility. In addition, the interaction processes and potential enhancement mechanisms of different Ag colloids-based SERS detection induced by CSF sample or conventional NaCl agglomeration agents are studied in detail through in-situ UV-vis absorption spectra, SERS analysis, SEM and optical imaging. Finally, an ML-assisted meningitis classification model is established based on the spectral feature fusion of characteristic peaks and baseline. By using an optimized KNN algorithm, the classification accuracy of autoimmune encephalitis, novel cryptococcal meningitis, viral meningitis, or tuberculous meningitis could be reached 99%, while an accuracy value of 68.74% is achieved for baseline-corrected spectral data. The CSF-induced SERS detection has the potential to provide a new type of liquid biopsy approach in the fields of diagnosis and early detection of various cerebral ailments.

Molybdenum Disulfide-Assisted Spontaneous Formation of Multistacked Gold Nanoparticles for Deep Learning-Integrated Surface-Enhanced Raman Scattering

Several fabrication methods have been developed for label-free detection in various fields. However, fabricating high-density and highly ordered nanoscale architectures by using soluble processes remains a challenge. Herein, we report a biosensing platform that integrates deep learning with surface-enhanced Raman scattering (SERS), featuring large-area, close-packed three-dimensional (3D) architectures of molybdenum disulfide (MoS2)-assisted gold nanoparticles (AuNPs) for the on-site screening of coronavirus disease (COVID-19) using human tears. Some AuNPs are spontaneously synthesized without a reducing agent because the electrons induced on the semiconductor surface reduce gold ions when the Fermi level of MoS2 and the gold electrolyte reach equilibrium. With the addition of polyvinylpyrrolidone, a two-dimensional large-area MoS2 layer assisted in the formation of close-packed 3D multistacked AuNP structures, resembling electroless plating. This platform, with a convolutional neural network-based deep learning model, achieved outstanding SERS performance at subterascale levels despite the microlevel irradiation power and millisecond-level acquisition time and accurately assessed susceptibility to COVID-19. These results suggest that our platform has the potential for rapid, low-damage, and high-throughput label-free detection of exceedingly low analyte concentrations.

Raman spectroscopy assisted tear analysis: A label free, optical approach for noninvasive disease diagnostics

In recent times, tear fluid analysis has garnered considerable attention in the field of biomarker-based diagnostics due to its noninvasive sample collection method. Tears encompass a reservoir of biomarkers that assist in diagnosing not only ocular disorders but also a diverse list of systemic diseases. This highlights the necessity for sensitive and dependable screening methods to employ tear fluid as a potential noninvasive diagnostic specimen in clinical environments. Considerable research has been conducted to investigate the potential of Raman spectroscopy-based investigations for tear analysis in various diagnostic applications. Raman Spectroscopy (RS) is a highly sensitive and label free spectroscopic technique which aids in investigating the molecular structure of samples by evaluating the vibrational frequencies of molecular bonds. Due to the distinct chemical compositions of different samples, it is possible to obtain a sample-specific spectral fingerprint. The distinctive spectral fingerprints obtained from Raman spectroscopy enable researchers to identify specific compounds or functional groups present in a sample, aiding in diverse biomedical applications. Its sensitivity to changes in molecular structure or environment provides invaluable insights into subtle alterations associated with various diseases. Thus, Raman Spectroscopy has the potential to assist in diagnosis and treatment as well as prognostic evaluation. Raman spectroscopy possesses several advantages, such as the non-destructive examination of samples, remarkable sensitivity to structural variations, minimal prerequisites for sample preparation, negligible interference from water, and the aptness for real-time investigation of tear samples. The purpose of this review is to highlight the potential of Raman spectroscopic technique in facilitating the clinical diagnosis of various ophthalmic and systemic disorders through non-invasive tear analysis. Additionally, the review delves into the advancements made in Raman spectroscopy with regards to paper-based sensing substrates and tear analysis methods integrated into contact lenses. Furthermore, the review also addresses the obstacles and future possibilities associated with implementing Raman spectroscopy as a routine diagnostic tool based on tear analysis in clinical settings.

One-Stop Plasmonic Nanocube-Excited SERS Immunoassay Platform of Multiple Cardiac Biomarkers for Rapid Screening and Progressive Tracing of Acute Myocardial Infarction

Rapid and precise acute myocardial infarction (AMI) diagnosis is essential for preventing patient death. In addition, the complementary roles of creatine kinase muscle brain (CK-MB) and cardiac troponin I (cTnI) cardiac biomarkers in the early and late stages of AMI demand their simultaneous detection, which is difficult to implement using conventional fluorescence and electrochemical technologies. Here, a nanotechnology-based one-stop immuno-surface-enhanced Raman scattering (SERS) detection platform is reported for multiple cardiac indicators for the rapid screening and progressive tracing of AMI events. Optimal SERS is achieved using optical property-based, excitation wavelength-optimized, and high-yield anisotropic plasmonic gold nanocubes. Optimal immunoassay reaction efficiencies are achieved by increasing immobilized antibodies. Multiple simultaneous detection strategies are implemented by incorporating two different Raman reports with narrow wavenumbers corresponding to two indicators and by establishing a computational SERS mapping process to accurately detect their concentrations, irrespective of multiple enzymes in the human serum. The SERS platform precisely estimated AMI onset and progressive timing in human serum and made rapid AMI identification feasible using a portable Raman spectrometer. This integrated platform is hypothesized to significantly contribute to emergency medicine and forensic science by providing timely treatment and observation.

Colloidal SERS measurement of enrofloxacin with petaloid nanostructure clusters formed by terminal deoxynucleotidyl transferase catalyzed cytosine-constituted ssDNA

We developed petal-like plasmonic nanoparticle (PLNP) clusters-based colloidal SERS method for enrofloxacin (EnFX) detection. PLNPs were synthesized by the regulation of single-stranded DNA composed of homo-cytosine deoxynucleotides (hC) catalyzed by terminal deoxynucleotidyl transferase. SERS hot spots were created via the agglomeration process of PLNPs by adding an inorganic salt potassium iodide solution, in which EnFX molecules were attached to the negatively charged PLNPs surface by electrostatic interactions. This approach enabled direct in situ detection of antibiotic residues, achieving a limit of detection (LOD) of 1.15 μg/kg for EnFX. The spiked recoveries of the SERS method were approximately 92.7% to 107.2% and the RSDs ranged from 1.05% to 7.8%, indicating that the method can be applied to actual sample detection. This colloidal SERS measurement platform would be very promising in various applications, especially in real-time and on-site food safety screening owing to its rapidness, simplicity, and sensitivity.

Post-Vaccination Detection of SARS-CoV-2 Antibody Response with Magnetic Nanoparticle-Based Electrochemical Biosensor System

Here, we report magnetic nanoparticle-based biosensor platforms for the rapid detection of SARS-CoV-2 antibody responses in human serum. The use of the proposed system enabled the detection of anti-SARS-CoV-2 spike (S) and nucleocapsid (N) proteins at a concentration of ng/mL in both buffer and real serum samples. In particular, the protocol, which is considered an indicator of innate immunity after vaccination or post-infection, could be useful for the evaluation of antibody response. We included a total of 48 volunteers who either had COVID-19 but were not vaccinated or who had COVID-19 and were vaccinated with CoronoVac or Biontech. Briefly, in this study, which was planned as a cohort, serum samples were examined 3, 6, and 12 months from the time the volunteers' showed symptoms of COVID-19 with respect to antibody response in the proposed system. Anti-S Ab and anti-N Ab were detected with a limit of detection of 0.98 and 0.89 ng/mL, respectively. These data were confirmed with the corresponding commercial an electrochemiluminescence immunoassay (ECLIA) assays. Compared with ECLIA, more stable data were obtained, especially for samples collected over 6 months. After this period, a drop in the antibody responses was observed. Our findings showed that it could be a useful platform for exploring the dynamics of the immune response, and the proposed system has translational use potential for the clinic. In conclusion, the MNP-based biosensor platform proposed in this study, together with its counterparts in previous studies, is a candidate for determining natural immunity and post-vaccination antibody response, as well as reducing the workload of medical personnel and paving the way for screening studies on vaccine efficacy.

Ag NC and Ag NP/PorC Film-Based Surface-Enhanced Raman Spectroscopy-Type Immunoassay for Ultrasensitive Prostate-Specific Antigen Detection

Surface-enhanced Raman scattering (SERS) is a spectral detection technology with high sensitivity and detectivity and can be used to detect the fingerprint information of the molecules with ultralow concentration. Herein, a kind of immunostructure constructed by Ag nanoparticle/porous carbon (Ag NP/PorC) films as the immunosubstrate and Ag NCs as the immunoprobes was presented for ultralow level prostate-specific antigen (PSA) detection. Experimentally, the Ag NP/PorC film was first prepared with a facile method by carbonizing the gelatin-AgNO₃ film in air, and Ag NCs were synthesized by the hydrothermal method. Then, the Ag NP/PorC film was modified by PSA antibodies as the substrate, while Ag NCs were decorated by R6G and PSA antibodies for probes. The sandwiched SERS detection embodiment was constructed by the immunoreaction between the PSA and PSA antibody predecorated on the substrate and probes. Our results show that the proposed SERS-type immunoassay is highly sensitive and selective to a wide range of PSA concentrations from 10^-5 to 10^-12 g/mL. Thereafter, it was also implemented to detect the PSA level in human serum, and the results successfully reproduce the PSA levels as those measured by the chemiluminescence method with a recovery rate above 90%. All in all, this SERS-type immunoassay provides a promising method for the early diagnosis of prostate cancer.

Raman spectroscopy for viral diagnostics

Raman spectroscopy offers the potential for fingerprinting biological molecules at ultra-low concentration and therefore has potential for the detection of viruses. Here we review various Raman techniques employed for the investigation of viruses. Different Raman techniques are discussed including conventional Raman spectroscopy, surface-enhanced Raman spectroscopy, Raman tweezer, tip-enhanced Raman Spectroscopy, and coherent anti-Stokes Raman scattering. Surface-enhanced Raman scattering can play an essential role in viral detection by multiplexing nanotechnology, microfluidics, and machine learning for ensuring spectral reproducibility and efficient workflow in sample processing and detection. The application of these techniques to diagnose the SARS-CoV-2 virus is also reviewed.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-023-01059-4.

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.

Recent advances of Au@Ag core-shell SERS-based biosensors

The methodological advancements in surface-enhanced Raman scattering (SERS) technique with nanoscale materials based on noble metals, Au, Ag, and their bimetallic alloy Au-Ag, has enabled the highly efficient sensing of chemical and biological molecules at very low concentration values. By employing the innovative various type of Au, Ag nanoparticles and especially, high efficiency Au@Ag alloy nanomaterials as substrate in SERS based biosensors have revolutionized the detection of biological components including; proteins, antigens antibodies complex, circulating tumor cells, DNA, and RNA (miRNA), etc. This review is about SERS-based Au/Ag bimetallic biosensors and their Raman enhanced activity by focusing on different factors related to them. The emphasis of this research is to describe the recent developments in this field and conceptual advancements behind them. Furthermore, in this article we apex the understanding of impact by variation in basic features like effects of size, shape varying lengths, thickness of core-shell and their influence of large-scale magnitude and morphology. Moreover, the detailed information about recent biological applications based on these core-shell noble metals, importantly detection of receptor binding domain (RBD) protein of COVID-19 is provided.

Sensitive and reproducible detection of SARS-CoV-2 using SERS-based microdroplet sensor

Surface-enhanced Raman scattering (SERS)-based assays have been recently developed to overcome the low detection sensitivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SERS-based assays using magnetic beads in microtubes slightly improved the limit of detection (LoD) for SARS-CoV-2. However, the sensitivity and reproducibility of the method are still insufficient for reliable SARS-CoV-2 detection. In this study, we developed a SERS-based microdroplet sensor to dramatically improve the LoD and reproducibility of SARS-CoV-2 detection. Raman signals were measured for SERS nanotags in 140 droplets passing through a laser focal volume fixed at the center of the channel for 15 s. A comparison of the Raman signals of SERS nanotags measured in a microtube with those measured for multiple droplets in the microfluidic channel revealed that the LoD and coefficient of variation significantly improved from 36 to 0.22 PFU/mL and 21.2% to 1.79%, respectively. This improvement resulted from the ensemble average effects because the signals were measured for SERS nanotags in multiple droplets. Moreover, the total assay time decreased from 30 to 10 min. A clinical test was performed on patient samples to evaluate the clinical efficacy of the SERS-based microdroplet sensor. The assay results agreed well with those measured by the reverse transcription-polymerase chain reaction (RT-PCR) method. The proposed SERS-based microdroplet sensor is expected to be used as a new point-of-care diagnostic platform for quick and accurate detection of SARS-CoV-2 in the field.

Progress in Biosensors for the Point-of-Care Diagnosis of COVID-19

Coronavirus disease 2019 (COVID-19) is a highly virulent infection that has caused a pandemic since 2019. Early diagnosis of the disease has been recognized as one of the important approaches to minimize the pathological impact and spread of infection. Point-of-care tests proved to be substantial analytical tools, and especially lateral flow immunoassays (lateral flow tests) serve the purpose. In the last few years, biosensors have gained popularity. These are simple but highly sensitive and accurate analytical devices composed from a selective molecule such as an antibody or antigen and a sensor platform. Biosensors would be an advanced alternative to current point-of-care tests for COVID-19 diagnosis and standard laboratory methods as well. Recent discoveries related to point-of-care diagnostic tests for COVID-19, the development of biosensors for specific antibodies and specific virus parts or their genetic information are reviewed.