Label-free Surface-enhanced Raman Scattering Research Articles

Combinatorial Effect of Tricomponent Dual-Rim Nanoring Building Blocks: Label-Free SERS Detection of Biomolecules.

Detecting weakly adsorbing molecules via label-free surface-enhanced Raman scattering (SERS) has presented a significant challenge. To address this issue, we propose a novel approach for creating tricomponent SERS substrates using dual-rim nanorings (DRNs) made of Au, Ag, and CuO, each possessing distinct functionalities. Our method involves depositing different metals on Pt nanoring skeletons to obtain each nanoring with varying surface compositions while maintaining a similar size and shape. Next, the mixture of these nanorings is transferred into a monolayer assembly with homogeneous intermixing on a solid substrate. The surface of the CuO DRNs has dangling bonds (Cu2+) that facilitate the strong adsorption of carboxylates through the formation of chelating bonds, while the combination of Au and Ag DRNs significantly enhances the SERS signal intensity through a strong coupling effect. Notably, the tricomponent assemblies enable the successful SERS-based analysis of biomolecules such as amino acids, proteins, nucleobases, and nucleotides.

Label-Free Surface-Enhanced Raman Scattering Detection of Fire Blight Pathogen Using a Pathogen-Specific Bacteriophage.

Fire blight is one of the most devastating plant diseases, causing severe social and economic problems. Herein, we report a novel method based on label-free surface-enhanced Raman scattering (SERS) combined with an Erwinia amylovora-specific bacteriophage that allows detecting efficiently fire blight bacteria E. amylovora for the first time. To achieve the highest SERS signals for E. amylovora, we synthesized and compared plasmonic nanoparticles (PNPs) with different sizes, i.e., bimetallic gold core-silver shell nanoparticles (Au@AgNPs) and monometallic gold nanoparticles (AuNPs) and utilized the coffee-ring effect for the self-assembly of PNPs and enrichment of fire blight bacteria. Furthermore, we investigated the changes in the SERS spectra of E. amylovora after incubation with an E. amylovora-specific bacteriophage, and we found considerable differences in the SERS signals as a function of the bacteriophage incubation time. The results indicate that our bacteriophage-based label-free SERS analysis can specifically detect E. amylovora without the need for peak assignment on the SERS spectra but simply by monitoring the changes in the SERS signals over time. Therefore, our facile method holds great potential for the label-free detection of pathogenic bacteria and the investigation of viral-bacterial interactions.

Synergistic enhancement of ultrahigh SERS activity via Cu2O@Ag Core-Shell structure for accurate label-free identification of breast tumor subtypes

Conventional methodology for accurate identification of breast tumor subtypes is complex and time-consuming. Label-free SERS (surface-enhanced Raman scattering) enables rapid detection of biomolecules and provides intrinsic fingerprint information of analytes. Exploring reliable SERS bioprobes with high sensitivity and repeatability is essential for efficient typing of cancer cells. In this study, a novel core-shell noble metal-semiconductor Cu2O@Ag heterostructure with uniform morphology and outstanding SERS activity for label-free detection has been proposed. The Cu2O@Ag SERS substrates exhibit exceptional sensitivity to detect 4 nitrophenthiol (4NTP) at extreme low concentration (10−15 M), and the SERS spectra demonstrate excellent selectivity and repeatability (RSD ≈ 8.84%). The enhancement mechanism is attributed to the synergistic enhancement of photoinduced charge transfer and boosted surface plasmon resonance effect, confirmed by reduced fluorescence lifetime of methylene blue on SERS substrates and enhanced electromagnetic field intensity via finite-difference time-domain computational simulation. The ultrahigh sensitivity and good signal stability endow Cu2O@Ag SERS bioprobe potential for accurate detection of four breast cancer cells subtypes. The rich fingerprint information from SERS spectra is processed using linear differentiation analysis, revealing a high accuracy of 93.3% for subtypes classification. These results demonstrate an excellent SERS bioprobe with high sensitivity and repeatability for precision diagnosis has been developed.

ISERS: from nanotag design to protein assays and ex vivo imaging.

Proteins are an eminently important class of ubiquitous biomacromolecules with diverse biological functions, and numerous techniques for their detection, quantification, and localisation have been developed. Many of these methods exploit the selectivity arising from molecular recognition of proteins/antigens by immunoglobulins. The combination of surface-enhanced Raman scattering (SERS) with such "immuno"-techniques to immuno-SERS (iSERS) is the central topic of this review, which is focused on colloidal SERS nanotags, i.e., molecularly functionalised noble metal nanoparticles conjugated to antibodies, for their use in protein assays and ex vivo imaging. After contrasting the fundamental differences between label-free SERS and iSERS, including a balanced description of the advantages and drawbacks of the latter, we describe the usual workflow of iSERS experiments. Milestones in the development of the iSERS technology are summarised from a historical perspective. By highlighting selected examples from the literature, we illustrate the conceptual progress that has been achieved in the fields of iSERS-based protein assays and ex vivo imaging. Finally, we attempt to predict what is necessary to fully exploit the transformative potential of the iSERS technology by stimulating the transition from research in academic labs into applications for the benefit of our society.

Label-free SERS for rapid identification of interleukin 6 based on intrinsic SERS fingerprint of antibody‑gold nanoparticles conjugate

A label-free surface-enhanced Raman scattering (SERS) was designed for sensitive detection of interleukin-6 (IL-6). The sensing element composed of anti-IL-6 antibodies adsorbed on the surface of spherical gold nanoparticles (AuNPs) as SERS-active surface. The principle of detection was probing antibody conformational changes using its intrinsic SERS fingerprint after binding to IL-6. Comparison of SERS spectra of antibody before and after binding to IL-6 showed that secondary structure of antibody does not change upon binding to IL-6. Vibrational information from disulfide bonds ν(SS) in antibody structure indicated some changes of geometry around SS bridges as a consequence of the immunocomplex formation. Transmission electron microscopy (TEM) and UV–Vis spectroscopy were used to confirm AuNPs conjugation with antibody as well as IL-6 binding to antibody on the surface of AuNPs. The SERS-based immunoassay showed a wide linear range (2.0–1000 pg mL−1) and a high sensitivity with a limit of detection (LOD) as low as 0.91 pg mL−1 (0.04 pM) without using any extrinsic Raman label. UV–Vis spectroscopy was employed as a conventional method for IL-6 detection based on observation of any change in the position of localized surface plasmon resonance (LSPR) band of AuNPs-antibody conjugates with LOD of 10 ng mL−1.

Rapid classification of SARS-CoV-2 variant strains using machine learning-based label-free SERS strategy

The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have directly impacted its biological functions, leading to enhanced transmission and triggering an immune escape effect. Therefore, prompt identification of these mutations is crucial for formulating targeted treatment plans and implementing precise prevention and control measures. In this study, the label-free surface-enhanced Raman scattering (SERS) technology combined with machine learning (ML) algorithms provide a potential solution for accurate identification of SARS-CoV-2 variants. We establish a SERS spectral database of SARS-CoV-2 variants and demonstrate that a diagnostic classifier using a logistic regression (LR) algorithm can provide accurate results within 10 min. Our classifier achieves 100% accuracy for Beta (B.1.351/501Y.V2), Delta (B.1.617), Wuhan (COVID-19) and Omicron (BA.1) variants. In addition, our method achieves 100% accuracy in blind tests of positive and negative human nasal swabs based on the LR model. This method enables detection and classification of variants in complex biological samples. Therefore, ML-based SERS technology is expected to accurately discriminate various SARS-CoV-2 variants and may be used for rapid diagnosis and therapeutic decision-making.

Label-Free Surface-Enhanced Raman Scattering Bioanalysis Based on Au@Carbon Dot Nanoprobes.

Surface-enhanced Raman scattering (SERS) technology has attracted more and more attention in the biomedical field due to its ability to provide molecular fingerprint information of biological samples, as well as its potential in single-cell analysis. This work aims to establish a simple strategy for label-free SERS bioanalysis based on Au@carbon dot nanoprobes (Au@CDs). Here, polyphenol-derived CDs are utilized as the reductant to rapidly synthesize core-shell Au@CD nanostructures, which allows powerful SERS performance even when the concentration of methylene blue (MB) is as low as 10-9 M, due to the cooperative Raman enhancement mechanism. For bioanalysis, Au@CDs can serve as a unique SERS nanosensor to identify the cellular components of biosamples (e.g., cancer cells and bacteria). The molecular fingerprints from different species can be further distinguished after combination with the principal component analysis. In addition, Au@CDs also enable label-free SERS imaging to analyze intracellular composition profiles. This strategy offers a feasible, label-free SERS bioanalysis, opening up a new prospect for nanodiagnosis.

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.

Label-Free SERS Analysis of Serum Using Ag NPs/Cellulose Nanocrystal/Graphene Oxide Nanocomposite Film Substrate in Screening Colon Cancer.

Label-free surface-enhanced Raman scattering (SERS) analysis shows tremendous potential for the early diagnosis and screening of colon cancer, owing to the advantage of being noninvasive and sensitive. As a clinical diagnostic tool, however, the reproducibility of analytical methods is a priority. Herein, we successfully fabricated Ag NPs/cellulose nanocrystals/graphene oxide (Ag NPs/CNC/GO) nanocomposite film as a uniform SERS active substrate for label-free SERS analysis of clinical serum. The Ag NPs/CNC/GO suspensions by self-assembling GO into CNC solution through in-situ reduction method. Furthermore, we spin-coated the prepared suspensions on the bacterial cellulose membrane (BCM) to form Ag NPs/CNC/GO nanocomposite film. The nanofilm showed excellent sensitivity (LOD = 30 nM) and uniformity (RSD = 14.2%) for Nile Blue A detection. With a proof-of-concept demonstration for the label-free analysis of serum, the nanofilm combined with the principal component analysis-linear discriminant analysis (PCA-LDA) model can be effectively employed for colon cancer screening. The results showed that our model had an overall prediction accuracy of 84.1% for colon cancer (n = 28) and the normal (n = 28), and the specificity and sensitivity were 89.3% and 71.4%, respectively. This study indicated that label-free serum SERS analysis based on Ag NPs/CNC/GO nanocomposite film combined with machine learning holds promise for the early diagnosis of colon cancer.

A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence.

Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.

Deep Learning-Based Label-Free Surface-Enhanced Raman Scattering Screening and Recognition of Small-Molecule Binding Sites in Proteins.

Identification of small-molecule binding sites in proteins is of great significance in analysis of protein function and drug design. Modified sites can be recognized via proteolytic cleavage followed by liquid chromatography-mass spectrometry (LC-MS); however, this has always been impeded by the complexity of peptide mixtures and the elaborate synthetic design for tags. Here, we demonstrate a novel technique for identifying protein binding sites using a deep learning-based label-free surface-enhanced Raman scattering (SERS) screening (DLSS) strategy. In DLSS, the deep learning model that was trained with large SERS signals could detect signal features of small molecules with high accuracy (>99%). Without any secondary tag, the small molecules are directly complexed with proteins. After proteolysis and LC, SERS signals of all LC fractions are collected and input into the model, whereby the fractions containing the small-molecule-modified peptides can be recognized by the model and sent to MS/MS to identify the binding site(s). By using an automated DLSS system, we successfully identified the modification sites of fomepizole in alcohol dehydrogenase, which is coordinated with zinc along with three peptides. We also showed that the DLSS strategy works for identification of amino-acid residues that covalently bond with ibrutinib in Bruton tyrosine kinase. These results suggest that the DLSS strategy, which provides high molecular recognition capability to LC-MS analysis, has potential in drug discovery, proteomics, and metabolomics.

The key role of ergothioneine in label-free surface-enhanced Raman scattering spectra of biofluids: a retrospective re-assessment of the literature.

Label-free surface-enhanced Raman scattering (SERS) has recently gained attention in the field of liquid biopsy as a rapid and relatively inexpensive technique that could significantly ease clinical diagnosis and prognosis by investigating a biofluid sample with a laser. Indeed, SERS spectra provide information about a set of metabolites present in the analysed biofluid, thereby offering biochemical insight into specific health conditions. Ergothioneine plays a key role since it is one of the few metabolites in biofluids that are detectable by label-free SERS. In the past decade, many studies characterizing biofluids or other biological samples have unknowingly linked this amino acid with crucial metabolic processes, including inflammation, in a plethora of diseases. However, since the SERS spectrum of ergothioneine has been reported only recently, most past studies inadvertently assigned what are now recognized as the spectral features of this compound to other molecules. The purpose of the present review is to summarize and re-evaluate these studies in the light of the recent SERS characterization of ergothioneine so as to better recognize the role of ergothioneine in many clinical conditions.

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

We introduce a label-free surface-enhanced Raman scattering (SERS) biosensing platform equipped with metallic nanostructures that can identify the efficacy of Oxford-AstraZeneca (AZD1222) vaccine in vaccinated individuals using non-invasive tear samples. We confirmed the hypothesis that the tears of people who receive the AZD1222 vaccine may be similar to those of adenovirus epidemic keratoconjunctivitis patients since the Oxford-AstraZeneca vaccine is derived from a replication-deficient ChAdOx1 vector of chimpanzee adenovirus. Additionally, we confirmed the potential of the three markers for estimating the vaccination status via analyzing the signals emanating from antibodies or immunoglobulin G by-product using our label-free, SERS biosensing technique with a high reproducibility (<3% relative standard deviation), femtomole-scale limit of detection (1 × 10−14 M), and high SERS response of >108. Therefore, our label-free SERS biosensing nanoplatforms with long-term storage and robust stability will enable rapid and robust monitoring of the vaccine presence in vaccinated individuals.

Ag Nanorods for Label-Free Surface-Enhanced Raman Scattering Analysis of Cancer Cells from Cell Lysates

Ag Nanorods for Label-Free Surface-Enhanced Raman Scattering Analysis of Cancer Cells from Cell Lysates

Label-free Surface Enhanced Raman Scattering (SERS) on Centrifugal Silver Plasmonic Paper (CSPP): A Novel Methodology for Unprocessed Biofluids Sampling and Analysis.

Label-free SERS is a powerful bio-analytical technique in which molecular fingerprinting is combined with localized surface plasmons (LSPs) on metal surfaces to achieve high sensitivity. Silver and gold colloids are among the most common nanostructured substrates used in SERS, but since protein-rich samples such as serum or plasma can hinder the SERS effect due to protein–substrate interactions, they often require a deproteinization step. Moreover, SERS methods based on metal colloids often suffer from a poor reproducibility. Here, we propose a paper-based SERS sampling method in which unprocessed human serum samples are first soaked on paper strips (0.4 × 2 cm2), and then mixed with colloidal silver nanoparticles by centrifugation to obtain a Centrifugal Silver Plasmonic Paper (CSPP). The CSPP methodology has the potential to become a promising tool in bioanalytical SERS applications: it uses common colloidal substrates but without the need for sample deproteinization, while having a good reproducibility both in terms of overall spectral shape (r > 0.96) and absolute intensity (RSD < 10%). Moreover, this methodology allows SERS analysis more than one month after serum collection on the paper strip, facilitating storage and handling of clinical samples (including shipping from clinical sites to labs).

Investigation of factors that impact the label-free surface-enhanced Raman scattering (SERS) for the detection and discrimination of Salmonella Enteritidis

A simple and rapid label-free surface-enhanced Raman scattering (SERS) method involving silver nanoparticles, was developed to detect and discriminate Salmonella Enteritidis (S. Enteritidis) not only at a specie (Escherichia coli O157: H7; E. coli O157: H7), or a serovar level (Salmonella Gaminara; S. Gaminara) but also between the viable and non-viable state of bacteria. Parameters that influenced the bacterial spectra were optimized, such as particle size of silver nanosphere and type of silver nanosphere's coating agent. The signal intensity of the spectra of bacteria was dependent on the particle size of the silver nanospheres. Whereas the patterns of different bacterial spectra varied based upon the silver nanosphere's coating agent. In this study, citrate-silver nanosphere (C-AgNPs) successfully discriminated the spectral data of S. Enteritidis from those of E. coli O157: H7, S. Gaminara, and non-viable S. Enteritidis when analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA) of full-spectrum contribution. The method allowed the detection of S. Enteritidis to the concentration of 4 log CFU/mL. These results indicated the efficacy of the developed label-free SERS technique for the rapid detection and discrimination of S. Enteritidis from other closely related bacteria.

Ergothioneine, a dietary amino acid with a high relevance for the interpretation of label-free surface enhanced Raman scattering (SERS) spectra of many biological samples.

Intense SERS spectra of the natural amino acid ergothioneine (ERG) are obtained on different substrates upon 785nm excitation. A characteristic spectral pattern with a distinctive intense band at 480-486cm-1 is conserved when substrates of different type and characteristics are used. On the basis of available literature, we propose ERG is adsorbed on the metal surface in its thiolate form via the sulphur and heterocyclic nitrogen. The same spectral pattern is obtained in SERS spectra of filtered erythrocytes lysates, confirming the presence of ERG in those cells. The occurrence of ERG bands in label-free SERS spectra of serum and plasma reported in literature by different authors is discussed, highlighting the importance of this amino acid for the interpretation of SERS spectra of these biofluids.

Direct Detection of Bacteria Using Positively Charged Ag/Au Bimetallic Nanoparticles: A Label-free Surface-Enhanced Raman Scattering Study Coupled with Multivariate Analysis

Rapid detection and discrimination of pathogenic bacteria for food safety, environmental pollution, medical diagnoses, and chemical and biological threats remains a considerable challenge. In the p...

Surface-enhanced Raman scattering-active photonic crystal fiber probe: Towards next generation liquid biopsy sensor with ultra high sensitivity.

The tremendous enhancement factors that surface-enhanced Raman scattering (SERS) possesses coupled with the flexibility of photonic crystal fibers (PCFs) pave the way to a new generation of ultrasensitive biosensors. Thanks to the unique structure of PCFs, which allows direct incorporation of an analyte into the axially aligned air channels, interaction between the analyte and excitation light could be increased many folds leading to flexible, reliable and sensitive probes that can be used in preclinical or clinical biosensing. SERS-active PCF probes provide unique opportunity to develop an opto-fluidic liquid biopsy needle sensor that enables one-step integrated sample collection and testing for disease diagnosis. Specificity being a key parameter to biosensors, the PCF inside the biopsy needle could be functionalized with targeting moieties to detect specific biomarkers. In this review article, we present some of the most promising recent biosensors based on PCFs including hollow-core PCFs, suspended-core PCFs and side-channel PCFs. We provide a wide range of applications of such platform using Raman spectroscopy, label free SERS or labeled SERS detection and analyze some of the main challenges to be addressed for translating it to a clinically viable next generation sensitive biopsy needle sensing probe.

Characterization and identification of lung cancer cells from blood cells with label-free surface-enhanced Raman scattering

Lung cancer has become one of the types of malignant tumors that seriously threaten human life worldwide. It is of great significance to detect lung cancer cells from peripheral blood. In this paper, silver film surface-enhanced Raman scattering (SERS) is used to study SERS spectra of blood cells, various types of lung cancer cell lines and clinically isolated lung cancer cell samples. The results show that the nucleic acid characteristic peaks at 730 cm−1 and 1330 cm−1 are almost absent in red blood cells, and have a small enhancement in white blood cells. There is different enhancement in lung cancer cells; among them there is great enhancement of nucleic acid characteristic peaks in small cell lung cancer (SCLC) cell lines and their clinical cell samples, and smaller enhancement compared to SCLC in non-small cell lung cancer cell lines, such as squamous cell carcinoma, adenocarcinoma and so on. The SERS spectra of blood cells, SCLC cell lines and their clinical cell samples can be completely distinguished by the intensity ratio of the characteristic peak at 730 cm−1 to the subtracted background peak at 900 cm−1. This study lays a solid foundation for the identification of circulating tumor cells (CTCs) of SCLC by label-free SERS, and may be applied to the label-free SERS detection of CTCs.