Establishment of a reliable scheme for obtaining highly stable SERS signal of biological serum

Photonic-plasmonic resonator for SERS biodetection

To improve the laser utilization efficiency and avoid photodamage in surface-enhanced Raman spectroscopy (SERS), it is imperative to introduce photon technology into the field of SERS detection. A major challenge is the inefficient interaction between the substrate and the incident wavelength, resulting in limited Raman enhancement at a relatively low level. Here, we sputtered plasmonic Au nanoparticles (NPs) onto photonic TiOx nanocavities, creating a novel hybrid photonic-plasmonic resonator that achieves a large degree of optical manipulation and long-term localization. By facilely controlling the size of Au NPs, the resonance wavelength of plasmonic Au NPs can be matched with the photonic nanocavity to maximize the light trapping intensity, which leads to a synergistic enhancement of SERS via the electromagnetic and chemical mechanisms, resulting in a SERS enhancement up to 1.75 × 109 under non-resonant excitation. In particular, the substrate can achieve strong absorption and localization for long wavelengths, thus enabling a large SERS enhancement with a small light intensity, which can effectively avoid the photodamage that may occur in Raman testing. The substrate can detect various biomolecules, including biomarkers in serum, thus realizing the differentiation of different cancers. This study provides a powerful and sensitive platform for SERS, facilitating bioanalysis and disease diagnosis in complex systems.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages

BACKGROUND

Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer.

RESULTS

Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set.

SIGNIFICANCE

The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

Flower-like Ag-decked non-stoichiometric Bi2O3-x/rGO hybrid nanocomposite SERS substrates for an effective detection of Rhodamine 6G dye molecules

In early years, SERS-active substrates were generally noble metals. However, their practical applications were limited due to their poor biocompatibility, low uniformity and high cost. Recently, the utilization of semiconductor SERS-active substrates has greatly expanded the applications of SERS in many fields. However, metal-free SERS-active substrates have a low enhancement factor (EF), which can be overcome by adjusting their oxygen deficiency or through the effective preparation of non-stoichiometric semiconducting oxide materials. This is the key strategy and may work as an efficient and simple way to achieve high sensitivity and obtain an enhancement factor (G-factor) comparable to that of noble metals. Here, we report the preparation of flower-like rGO-Bi2O3/Bi2O2.75 and rGO-Ag-Bi2O3/Bi2O2.75 hybrid thin film nanocomposites using a liquid/liquid interface method (LLI) for the first time. In addition to the synergic effect of different enhancement mechanisms, the 3-D flower-like morphology of the substrate shows more favourable properties to improve the G-factor due to the existence of more hotspots. The rGO-Ag-Bi2O3/Bi2O2.75 hybrid thin-film nanocomposites show an EF of 1.8 × 109 with a detection ability of up to 1 nM towards Rhodamine 6G (R6G), which is highly toxic to humans and the aquatic environment.

Python-assisted detection and photothermal inactivation of Salmonella typhimurium and Staphylococcus aureus on a background-free SERS chip

In this study, a background-free surface-enhanced Raman scattering (SERS) chip with a sandwich configuration was fabricated to enable reliable detection and photothermal inactivation of multiple bacteria. The SERS chip consists of a graphene-coated, phenylboronic-modified plasmonic gold substrate (pAu/G/PBA), and two aptamer-functionalized core (gold)-shell (Prussian blue/Poly-L-lysine and 4-mercaptobenzonitrile/polydopamine) SERS tags (Au@PB@PLL@Apt and Au@MB@PDA@Apt). The detection signals rely on the characteristic and nonoverlapping Raman bands of the SERS tags within the Raman-silent region (1800-2800 cm-1), where no background signals from the sample matrix are observed, leading to improved detection sensitivity and accuracy. Considering the relatively large size of bacteria (e.g., micron level), a rapid Raman mapping technique was chosen over conventional point-scan methods to achieve more reliable quantitative analysis of bacteria. This technique involves collecting and analyzing intensity signals of SERS tags from all the scattering points with an average ensemble effect, which is facilitated by the use of Python. As a proof-of-concept, model bacterium of Salmonella typhimurium and Staphylococcus aureus were successfully detected using the SERS chip with a dynamic range of 10-107 CFU/mL. Additionally, the SERS chip demonstrated successful detection of these bacteria in whole blood samples. Moreover, the photothermal effect of pAu/G led to efficient bacteria elimination, achieving approximately 100% eradication. This study integrated a background-free SERS chip with a Python-assisted rapid Raman mapping technique, resulting in a reliable, rapid and accurate method for detecting and eliminating multiple bacteria, which may provide a promising alternative for multiple screening of bacteria in real samples.

Research Progress of SERS Sensors Based on Hydrogen Peroxide and Related Substances

Hydrogen peroxide (H2O2) has an important role in living organisms, and its detection is of great importance in medical, chemical, and food safety applications. This review provides a comparison of different types of Surface-enhanced Raman scattering (SERS) sensors for H2O2 and related substances with respect to their detection limits, which are of interest due to high sensitivity compared to conventional sensors. According to the latest research report, this review focuses on the sensing mechanism of different sensors and summarizes the linear range, detection limits, and cellular applications of new SERS sensors, and discusses the limitations in vivo and future prospects of SERS technology for the detection of H2O2.

An auxiliary diagnostic technology and clinical efficacy evaluation in knee osteoarthritis based on serum surface-enhanced Raman spectroscopy

Knee osteoarthritis (KOA), a progressive joint disease, is a leading source of chronic pain and disability, and its diagnosis mainly depends on medical imaging findings and clinical symptoms. This study aimed to explore an auxiliary diagnostic technology and clinical efficacy evaluation in KOA based on surface-enhanced Raman scattering (SERS). Three sequential experiments were performed: 1) preliminary observation of the therapeutic effects of icariin (ICA); 2) using serum SERS spectra obtained from rat models belonging to sham group, KOA group and icariin treatment group, respectively, to analyze the KOA-related expression profiles; 3) employing partial least squares (PLS) and support vector machines (SVM) algorithms to establish KOA diagnosis model. Pathological changes verified the efficacy of icariin in KOA. Raman peak assignment combined with spectral difference analysis reflected the biochemical changes associated with KOA, including amino acid, carbohydrates and collagen. ICA intervention significantly reversed these changes, although full recovery could not be achieved. Based on PLS-SVM approach, the sensitivity, specificity and accuracy of 100%, 98.33% and 98.89%, respectively, were obtained for screening KOA. This work proves that SERS has great potential to be used as an auxiliary diagnostic technology for KOA, and is also helpful for the exploration of novel KOA treatment agent.

Colorimetric and visual sensing of ferrous ion by Fenton reaction-stimulated etching of triangular gold nanoplates

In this study, a method for ultrasensitive sensing of Fe²⁺ based on Fenton reaction mediated etching of triangular gold nanoplates (Au NPLs) was developed. In this assay, the etching of Au NPLs by H₂O₂ was accelerated in the presence of Fe²⁺ due to the generation of superoxide free radical (O₂^·-) via Fenton reaction. With the concentration of Fe²⁺ increased, the shape of Au NPLs changed from triangular to sphere with the blue shifted localized surface plasmon resonance, accompanying a series of consecutive color changes from blue, bluish purple, purple, reddish purple and finally to pink. The rich color variations enable rapid visual quantitative determination of Fe²⁺ within 10 min. A good linear relationship between the peak shifts and the concentration of Fe²⁺ was obtained in the range of 0.035 to 1.5 μM (R² = 0.996). Favorable sensitivity and selectivity in the presence of other tested metal ions were achieved in the proposed colorimetric assay. The detection limits (3ơ/k) for Fe²⁺ was 26 nM by UV-vis spectroscopy, and the clearly discernible concentration of Fe²⁺ was as low as 0.07 μM by naked eyes. The recoveries of fortified samples in pond water and serum samples ranged from 96% to 106% with interday relative standard deviations <3.6% in all cases, demonstrating the applicability of the assay for measuring Fe²⁺ in real samples.

Machine learning-assisted internal standard calibration label-free SERS strategy for colon cancer detection

Liquid biopsies have significance for early colon cancer screening and improving patient survival. Recently, several researchers have applied surface-enhanced Raman spectroscopy (SERS) for the label-free and non-invasive detection of serum. Most of these studies performed the assay using a mixture of noble metal nanoparticles (NMNPs) with serum. However, SERS analysis of serum remains a challenge in terms of reproducibility and stability, as NMNPs tend to aggregate when mixed with serum, resulting in a non-uniform distribution of hot spots. Here, we report on the non-invasive identification of colon cancer (CC) using an internal standard (IS)-calibrated label-free serum SERS assay in combination with machine learning. Serum SERS spectra of 50 CC patients and 50 health volunteers have been obtained using silver nanoparticle (Ag NP) colloid and mercaptopropionic acid-modified Ag NPs (Ag NPs-MPA) as the SERS substrates. Decision tree (DT), random forest (RF), and principal component and linear discriminant analysis (PCA-LDA) algorithms were utilized to establish the diagnosis model for SERS spectra data classifying. The results show that the RF model provides a high diagnostic accuracy compared to PCA-LDA. Following calibration with IS molecules, high diagnostic accuracy of over 90% and 100% specificity can be achieved with DT, RF, and PCA-LDA algorithms to differentiate between cancer and normal groups. The results from this exploratory work demonstrate that serum SERS detection combined with multivariate statistical methods and IS calibration has great potential for the non-invasive and label-free detection of CC.

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.

Trends in Application of SERS Substrates beyond Ag and Au, and Their Role in Bioanalysis

This article compares the applications of traditional gold and silver-based SERS substrates and less conventional (Pd/Pt, Cu, Al, Si-based) SERS substrates, focusing on sensing, biosensing, and clinical analysis. In recent decades plethora of new biosensing and clinical SERS applications have fueled the search for more cost-effective, scalable, and stable substrates since traditional gold and silver-based substrates are quite expensive, prone to corrosion, contamination and non-specific binding, particularly by S-containing compounds. Following that, we briefly described our experimental experience with Si and Al-based SERS substrates and systematically analyzed the literature on SERS on substrate materials such as Pd/Pt, Cu, Al, and Si. We tabulated and discussed figures of merit such as enhancement factor (EF) and limit of detection (LOD) from analytical applications of these substrates. The results of the comparison showed that Pd/Pt substrates are not practical due to their high cost; Cu-based substrates are less stable and produce lower signal enhancement. Si and Al-based substrates showed promising results, particularly in combination with gold and silver nanostructures since they could produce comparable EFs and LODs as conventional substrates. In addition, their stability and relatively low cost make them viable alternatives for gold and silver-based substrates. Finally, this review highlighted and compared the clinical performance of non-traditional SERS substrates and traditional gold and silver SERS substrates. We discovered that if we take the average sensitivity, specificity, and accuracy of clinical SERS assays reported in the literature, those parameters, particularly accuracy (93-94%), are similar for SERS bioassays on AgNP@Al, Si-based, Au-based, and Ag-based substrates. We hope that this review will encourage research into SERS biosensing on aluminum, silicon, and some other substrates. These Al and Si based substrates may respond efficiently to the major challenges to the SERS practical application. For instance, they may be not only less expensive, e.g., Al foil, but also in some cases more selective and sometimes more reproducible, when compared to gold-only or silver-only based SERS substrates. Overall, it may result in a greater diversity of applicable SERS substrates, allowing for better optimization and selection of the SERS substrate for a specific sensing/biosensing or clinical application.

Differentiation and prognostic stratification of acute myeloid leukemia by serum-based spectroscopy coupling with metabolic fingerprints

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by complex molecular and cytogenetic abnormalities. New approaches to predict the prognosis of AML have increasingly attracted attention. There were 98 non-M3 AML cases and 48 healthy controls were enrolled in the current work. Clinically routine assays for cytogenetic and molecular genetic analyses were performed on the bone marrow samples of patients with AML. Meanwhile, metabolic profiling of these AML subjects was also performed on the serum samples by combining Ag nanoparticle-based surface-enhanced Raman spectroscopy (SERS) with proton nuclear magnetic resonance (NMR) spectroscopy. Although most of the routine biochemical test showed no significant differences between the M0-M2 and M5 groups, the metabolic profiles were significantly different either between AML subtypes or between prognostic risk subgroups. Specific SERS bands were screened to serve as potential markers for AML subtypes. The results demonstrated that the classification models for M0-M2 and M5 shared two bands (i.e., 1328 and 741 cm^-1 ), all came from nucleic acid signals. Furthermore, Metabolic profiles provided various differential metabolites responsible for different AML subtypes, and we found altered pathways mainly included energy metabolism like glycolysis, pyruvate metabolism, and metabolisms of nucleic acid bases as well as specific amino acid metabolisms. It is concluded that integration of SERS and NMR provides the rational and could be reliable to reveal AML differentiation, and meanwhile lay the basis for experimental and clinical practice to monitor disease progression and prognostic evaluation.

Development and Validation of a Raman Spectroscopic Classification Model for Cervical Intraepithelial Neoplasia (CIN)

Simple Summary

There is an unmet clinical need for new methods to aid clinicians in the early detection of cervical cancer and precancer. Spectroscopic methods such as Raman spectroscopy can provide a rapid, label-free and nondestructive measurement of the biochemical fingerprint of cells, tissues and biofluids. This study aims to demonstrate the clinical utility of Raman spectroscopy for the identification of cervical precancer. Raman spectra were recorded from cervical smear samples (n = 662) and a classifier was developed based on histology. A classification accuracy of 91.3% was achieved in an independent blinded test set (n = 69), demonstrating the potential clinical utility of Raman spectroscopy.

Abstract

The mortality associated with cervical cancer can be reduced if detected at the precancer stage, but current methods are limited in terms of subjectivity, cost and time. Optical spectroscopic methods such as Raman spectroscopy can provide a rapid, label-free and nondestructive measurement of the biochemical fingerprint of a cell, tissue or biofluid. Previous studies have shown the potential of Raman spectroscopy for cervical cancer diagnosis, but most were pilot studies with small sample sizes. The aim of this study is to show the clinical utility of Raman spectroscopy for identifying cervical precancer in a large sample set with validation in an independent test set. Liquid-based cervical cytology samples (n = 662) (326 negative, 200 cervical intraepithelial neoplasia (CIN)1 and 136 CIN2+) were obtained as a training set. Raman spectra were recorded from single-cell nuclei and subjected to a partial least squares discriminant analysis (PLSDA). In addition, the PLSDA classification model was validated using a blinded independent test set (n = 69). A classification accuracy of 91.3% was achieved with only six of the blinded samples misclassified. This study showed the potential clinical utility of Raman spectroscopy with a good classification of negative, CIN1 and CIN2+ achieved in an independent test set.

Highly sensitive SERS substrates with multi-hot spots for on-site detection of pesticide residues

Pesticide residues will be a huge threat to food security and ecological environment; therefore, there is an urgent need to achieve rapid and on-site detection of pesticide residues. Herein, a plasmonic substrate with multiple "hot spots" was fabricated by transferring three-dimensional (3D) Au nanoparticles (NPs) onto the polydimethylsiloxane (PDMS) membrane for highly sensitive surface-enhanced Raman scattering (SERS) detection of pesticide residues. In combination with 3D-FDTD simulations, high SERS enhancement (EF = 1.2 × 10⁸) and high detection sensitivity (LOD = 6.3 × 10^-10 M) were achieved, mainly due to the enhanced electromagnetic fields around the "hot spots". Additionally, the PDMS-based SERS substrate held good transparency and flexibility, enabling conformal contact with non-planar surfaces and allowing the laser to penetrate the back of the analytes. Combined with a portable Raman spectrometer, the substrates holds great potential for rapid, high-sensitive, and on-site detection of contaminants in food, especially for the analyte on the nonplanar surfaces.

Recent Advances and Applications in Paper-Based Devices for Point-of-Care Testing

Point-of-care testing (POCT), as a portable and user-friendly technology, can obtain accurate test results immediately at the sampling point. Nowadays, microfluidic paper-based analysis devices (μPads) have attracted the eye of the public and accelerated the development of POCT. A variety of detection methods are combined with μPads to realize precise, rapid and sensitive POCT. This article mainly introduced the development of electrochemistry and optical detection methods on μPads for POCT and their applications on disease analysis, environmental monitoring and food control in the past 5 years. Finally, the challenges and future development prospects of μPads for POCT were discussed.

Plasmonic Gold Nanoisland Film for Bacterial Theranostics

Plasmonic nanomaterials have been intensively explored for applications in biomedical detection and therapy for human sustainability. Herein, plasmonic gold nanoisland (NI) film (AuNIF) was fabricated onto a glass substrate by a facile seed-mediated growth approach. The structure of the tortuous gold NIs of the AuNIF was demonstrated by scanning electron microscopy and energy-dispersive X-ray spectroscopy. Based on the ultraviolet-visible spectrum, the AuNIF revealed plasmonic absorption with maximum intensity at 624 nm. With the change to the surface topography created by the NIs, the capture efficiency of Escherichia coli (E. coli) by the AuNIF was significantly increased compared to that of the glass substrate. The AuNIF was applied as a surface-enhanced Raman scattering (SERS) substrate to enhance the Raman signal of E. coli. Moreover, the plasmonic AuNIF exhibited a superior photothermal effect under irradiation with simulated AM1.5 sunlight. For photothermal therapy, the AuNIF also displayed outstanding efficiency in the photothermal killing of E. coli. Using a combination of SERS detection and photothermal therapy, the AuNIF could be a promising platform for bacterial theranostics.

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).