Toward the nanoscale chemical and physical probing of milk-derived extracellular vesicles using Raman and tip-enhanced Raman spectroscopy.

Tip-enhanced Raman spectroscopy (TERS) is an advanced technique to perform local chemical analysis of the surface of a sample through the improvement of the sensitivity and the spatial resolution of Raman spectroscopy by plasmonic enhancement of the electromagnetic signal in correspondence with the nanometer-sized tip of an atomic force microscope (AFM). In this work, TERS is demonstrated to represent an innovative and powerful approach for studying extracellular vesicles, in particular bovine milk-derived extracellular vesicles (mEVs), which are nanostructures with considerable potential in drug delivery and therapeutic applications. Raman spectroscopy has been used to analyze mEVs at the micrometric and sub-micrometric scales to obtain a detailed Raman spectrum in order to identify the 'signature' of mEVs in terms of their characteristic molecular vibrations and, therefore, their chemical compositions. With the ability to improve lateral resolution, TERS has been used to study individual mEVs, demonstrating the possibility of investigating a single mEV selected on the surface of the sample and, moreover, analyzing specific locations on the selected mEV with nanometer lateral resolution. TERS potentially allows one to reveal local differences in the composition of mEVs providing new insights into their structure. Also, thanks to the intrinsic properties of TERS to acquire the signal from only the first few nanometers of the surface, chemical investigation of the lipid membrane in correspondence with the various locations of the selected mEV could be performed by analyzing the peaks of the Raman shift in the relevant range of the spectrum (2800-3000 cm-1). Despite being limited to mEVs, this work demonstrates the potential of TERS in the analysis of extracellular vesicles.

Honeycomb-Inspired Surface-Enhanced Raman Scattering Microarray for Large-Area Automated Testing of Urease in Saliva Samples.

Surface-enhanced Raman scattering (SERS) technology, as an important analytical tool, has been widely applied in the field of chemical and biomedical sensing. Automated testing is often combined with biochemical analysis technologies to shorten the detection time and minimize human error. The present SERS substrates for sample detection are time-consuming and subject to high human error, which are not conducive to the combination of SERS and automated testing. Here, a novel honeycomb-inspired SERS microarray is designed for large-area automated testing of urease in saliva samples to shorten the detection time and minimize human error. The honeycomb-inspired SERS microarray is decorated with hexagonal microwells and a homogeneous distribution of silver nanostars. Compared with the other four common SERS substrates, the optimal honeycomb-inspired SERS microarray exhibits the best SERS performance. The RSD of 100 SERS spectra continuously collected from saliva samples is 6.56%, and the time of one detection is reduced from 5 min to 10 s. There is a noteworthy linear relationship with a R2 of 0.982 between SERS intensity and urease concentration, indicating the quantitative detection capability of the urease activity in saliva samples. The honeycomb-inspired SERS microarray, combined with automated testing, provides a new way in which SERS technology can be widely used in biomedical applications.

Optical Nanobiosensor Based on Surface-Enhanced Raman Spectroscopy and Catalytic Hairpin Assembly for Early-Stage Lung Cancer Detection via Blood Circular RNA.

Lung cancer has become the leading cause of cancer-related deaths globally. However, early detection of lung cancer remains challenging, resulting in poor outcomes for the patients. Herein, we developed an optical biosensor integrating surface-enhanced Raman spectroscopy (SERS) with a catalyzed hairpin assembly (CHA) to detect circular RNA (circRNA) associated with tumor formation and progression (circSATB2). The signals of the Raman reporter were considerably enhanced by generating abundant SERS "hot spots" with a core-shell nanoprobe and 2D SERS substrate with calibration capabilities. This approach enabled the sensitive (limit of detection: 0.766 fM) and reliable quantitative detection of the target circRNA. Further, we used the developed biosensor to detect the circRNA in human serum samples, revealing that patients with lung cancer had higher circRNA concentrations than healthy subjects. Moreover, we characterized the unique circRNA concentration profiles of the early stages (IA and IB) and subtypes (IA1, IA2, and IA3) of lung cancer. These results demonstrate the potential of the proposed optical sensing nanoplatform as a liquid biopsy and prognostic tool for the early screening of lung cancer.

Digital colloid-enhanced Raman spectroscopy by single-molecule counting.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1-5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine-reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Controllable Fabrication of Sub-10 nm Graphene Nanopores via Helium Ion Microscopy and DNA Detection.

Solid-state nanopores have become a prominent tool in the field of single-molecule detection. Conventional solid-state nanopores are thick, which affects the spatial resolution of the detection results. Graphene is the thinnest 2D material and has the highest spatial detection resolution. In this study, a graphene membrane chip was fabricated by combining a MEMS process with a 2D material wet transfer process. Raman spectroscopy was used to assess the quality of graphene after the transfer. The mechanism behind the influence of the processing dose and residence time of the helium ion beam on the processed pore size was investigated. Subsequently, graphene nanopores with diameters less than 10 nm were fabricated via helium ion microscopy. DNA was detected using a 5.8 nm graphene nanopore chip, and the appearance of double-peak signals on the surface of 20 mer DNA was successfully detected. These results serve as a valuable reference for nanopore fabrication using 2D material for DNA analysis.

Procalcitonin Detection Using Immunomagnetic Beads-Mediated Surface-Enhanced Raman Spectroscopy.

The early detection of procalcitonin (PCT) is crucial for diagnosing bacterial infections due to its high sensitivity and specificity. While colloidal gold colorimetric and immune-chemiluminescence methods are commonly employed in clinical detection, the former lacks sensitivity, and the latter faces challenges with a brief luminescence process and an elevated background. Here, we introduce a novel approach for the quantitative analysis of PCT using surface-enhanced Raman spectroscopy (SERS), leveraging the enhanced properties of metal nanoparticles. Simultaneously, we employed a magnetic nanoparticle coating and surface biofunctionalization modification to immobilize PCT-trapping antibodies, creating the required immune substrates. The resulting magnetic nanoparticles and antibody complexes, acting as carriers and recognition units, exhibited superparamagnetism and the specific recognition of biomarkers. Then, this complex efficiently underwent magnetic separation with an applied magnetic field, streamlining the cumbersome steps of traditional ELISA and significantly reducing the detection time. In conclusion, the exploration of immunomagnetic bead detection technology based on surface-enhanced Raman spectroscopy holds crucial practical significance for the sensitive detection of PCT.

Raman Flow Cytometry and Its Biomedical Applications.

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Recent Advances in Lateral Flow Assays for Viral Protein Detection with Nanomaterial-Based Optical Sensors.

Controlling the progression of contagious diseases is crucial for public health management, emphasizing the importance of early viral infection diagnosis. In response, lateral flow assays (LFAs) have been successfully utilized in point-of-care (POC) testing, emerging as a viable alternative to more traditional diagnostic methods. Recent advancements in virus detection have primarily leveraged methods such as reverse transcription-polymerase chain reaction (RT-PCR), reverse transcription-loop-mediated isothermal amplification (RT-LAMP), and the enzyme-linked immunosorbent assay (ELISA). Despite their proven effectiveness, these conventional techniques are often expensive, require specialized expertise, and consume a significant amount of time. In contrast, LFAs utilize nanomaterial-based optical sensing technologies, including colorimetric, fluorescence, and surface-enhanced Raman scattering (SERS), offering quick, straightforward analyses with minimal training and infrastructure requirements for detecting viral proteins in biological samples. This review describes the composition and mechanism of and recent advancements in LFAs for viral protein detection, categorizing them into colorimetric, fluorescent, and SERS-based techniques. Despite significant progress, developing a simple, stable, highly sensitive, and selective LFA system remains a formidable challenge. Nevertheless, an advanced LFA system promises not only to enhance clinical diagnostics but also to extend its utility to environmental monitoring and beyond, demonstrating its potential to revolutionize both healthcare and environmental safety.

SERS-Based Microneedle Biosensor for In Situ and Sensitive Detection of Tyrosinase.

Tyrosinase (TYR) emerges as a key enzyme that exerts a regulatory influence on the synthesis of melanin, thereby assuming the role of a critical biomarker for the detection of melanoma. Detecting the authentic concentration of TYR in the skin remains a primary challenge. Distinguished from ex vivo detection methods, this study introduces a novel sensor platform that integrates a microneedle (MN) biosensor with surface-enhanced Raman spectroscopy (SERS) technology for the in situ detection of TYR in human skin. The platform utilized dopamine (DA)-functionalized gold nanoparticles (Au NPs) as the capturing substrate and 4-mercaptophenylboronic acid (4-MPBA)-modified silver nanoparticles (Ag NPs) acting as the SERS probe. Here, the Au NPs were functionalized with mercaptosuccinic acid (MSA) for DA capture. In the presence of TYR, DA immobilized on the MN is preferentially oxidized to dopamine quinone (DQ), a process that results in a decreased density of SERS probes on the platform. TYR concentration was detected through variations in the signal intensity emitted by the phenylboronic acid. The detection system was able to evaluate TYR concentrations within a linear range of 0.05 U/mL to 200 U/mL and showed robust anti-interference capabilities. The proposed platform, integrating MN-based in situ sensing, SERS technology, and TYR responsiveness, holds significant importance for diagnosing cutaneous melanoma.

Identification and quantification of pigments in plant leaves using thin layer chromatography-Raman spectroscopy (TLC-Raman).

Carotenoids are yellow, orange, and red pigments commonly found in plants. In leaves, these molecules are essential for photosynthesis, but they also play a major role in plant growth and development. Efficiently monitoring concentrations of specific carotenoids in plant tissues could help to explain plant responses to environmental stressors, infection and disease, fertilization, and other conditions. Previously, Raman methods have been used to demonstrate a correlation between plant fitness and the carotenoid content of leaves. Due to solvatochromatic effects and structural similarities within the carotenoid family, current Raman spectroscopy techniques struggle to assign signals to specific carotenoids with certainty, complicating the determination of amounts of individual carotenoids present in a sample. In this work, we use thin layer chromatography-Raman spectroscopy, or TLC-Raman, to identify and quantify carotenoids extracted from tomato leaves. These quick and accurate methods could be applied to study the relationship between pigment content and a number of factors affecting plant health.

Light-Initiated Imprinted Membrane-Based Biomimetic SERS Sensor toward Selective Detection of Trace MC-LR.

Microcystin-LR (MC-LR) is a severe threat to human and animal health; thus, monitoring it in the environment is essential, especially in water quality protections. Herein, in this work, we synthesize PVDF/CNT/Ag molecular imprinted membranes (PCA-MIMs) via an innovative combination of surface-enhanced Raman spectroscopy (SERS) detection, membrane separation, and molecular-imprinted technique toward the analysis of MC-LR in water. In particular, a light-initiated imprint is employed to protect the chemical structure of the MC-LR molecules. Furthermore, in order to ensure the detection sensitivity, the SERS substrates are combined with the membrane via the assistance of magnetism. The effect of synthesis conditions on the SERS sensitivity was investigated in detail. It is demonstrated from the characteristic results that the PCA-MIMs present high sensitivity to the MC-LR molecules with excellent selectivity against the interfere molecules. Results clearly show that the as-prepared PCA-MIMs hold great potential applications to detect trace MC-LR for the protection of water quality.

Combined Mutual Learning Net for Raman Spectral Microbial Strain Identification.

Infectious diseases pose a significant threat to global health, yet traditional microbiological identification methods suffer from drawbacks, such as high costs and long processing times. Raman spectroscopy, a label-free and noninvasive technique, provides rich chemical information and has tremendous potential in fast microbial diagnoses. Here, we propose a novel Combined Mutual Learning Net that precisely identifies microbial subspecies. It demonstrated an average identification accuracy of 87.96% in an open-access data set with thirty microbial strains, representing a 5.76% improvement. 50% of the microbial subspecies accuracies were elevated by 1% to 46%, especially for E. coli 2 improved from 31% to 77%. Furthermore, it achieved a remarkable subspecies accuracy of 92.4% in the custom-built fiber-optical tweezers Raman spectroscopy system, which collects Raman spectra at a single-cell level. This advancement demonstrates the effectiveness of this method in microbial subspecies identification, offering a promising solution for microbiology diagnosis.

Real-Time Quantitative Evaluation of a Drug during Liposome Preparation Using a Probe-Type Raman Spectrometer.

During the manufacturing process of liposome formulations, it is considered difficult to evaluate their physicochemical properties and biological profiles due to the complexity of their structure and manufacturing process. Conventional quality evaluation is labor-intensive and time-consuming; therefore, there was a need to introduce a method that could perform in-line, real-time evaluation during the manufacturing process. In this study, Raman spectroscopy was used to monitor in real time the encapsulation of drugs into liposomes and the drug release, which are particularly important quality evaluation items. Furthermore, Raman spectroscopy combined with partial least-squares (PLS) analysis was used for quantitative drug evaluation to assess consistency with results from UV-visible spectrophotometry (UV), a common quantification method. The prepared various ciprofloxacin (CPFX) liposomes were placed in cellulose tubes, and a probe-type Raman spectrophotometer was used to monitor drug encapsulation, the removal of unencapsulated drug, and drug release characteristics in real time using a dialysis method. In the Raman spectra of the liposomes prepared by remote loading, the intensities of the CPFX-derived peaks increased upon drug encapsulation and showed a slight decrease upon removal of the unencapsulated drug. Furthermore, the peak intensity decreased more gradually during the drug release. In all Raman monitoring experiments, the discrepancy between quantified values of CPFX concentration in liposomes, as measured by Raman spectroscopy combined with partial least-squares (PLS) analysis, and those obtained through ultraviolet (UV) spectrophotometry was within 6.7%. The results revealed that the quantitative evaluation of drugs using a combination of Raman spectroscopy and PLS analysis was as accurate as the evaluation using UV spectrophotometry, which was used for comparison. These results indicate the promising potential of Raman spectroscopy as an innovative method for the quality evaluation of liposomal formulations.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Simple Matrices.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for highly sensitive qualitative and quantitative analyses of trace targets. However, sensitive SERS detection can only be facilitated with a suitable sample pretreatment in fields related to trace amounts for food safety and clinical diagnosis. Currently, the sample pretreatment for SERS detection is normally borrowed and improved from the ones in the lab, which yields a high recovery but is tedious and time-consuming. Rapid detection of trace targets in a complex environment is still a considerable issue for SERS detection. Herein, we proposed a liquid-liquid extraction method coupled with a back-extraction method for sample pretreatment based on the pH-sensitive reversible phase transition of the weak organic acids and bases, where the lowest detectable concentrations were identical before and after the pretreatment process. The sensitive (µg L-1 level) and rapid (within 5 min) SERS detection of either koumine, a weak base, or celastrol, a weak acid, was demonstrated in different drinking water samples and beverages. Furthermore, target generality was demonstrated for a variety of weak acids and bases (2 < pKa < 12), and the hydrophilicity/hydrophobicity of the target determines the pretreatment efficiency. Therefore, the LLE-BE coupled SERS was developed as an easy, rapid, and low-cost tool for the trace detection of the two types of targets in simple matrices, which paved the way toward trace targets in complex matrices.

Sandwich-like CuNPs@AgNPs@PSB SERS substrates for sensitive detection of R6G and Forchlorfenuron.

The development of a highly sensitive, synthetically simple and economical SERS substrate is technically very important. A fast, economical, sensitive and reproducible CuNPs@AgNPs@ Porous silicon Bragg reflector (PSB) SERS substrate was prepared by electrochemical etching and in situ reduction method. The developed CuNPs@AgNPs@PSB has a large specific surface area and abundant "hot spot" region, which makes the SERS performance excellent. Meanwhile, the successful synthesis of CuNPs@AgNPs can not only modulate the plasmon resonance properties of nanoparticles, but also effectively prolong the time stability of Cu nanoparticles. The basic performance of the substrate was evaluated using rhodamine 6G (R6G). (Detection limit reached 10-15 M, R2 = 0.9882, RSD = 5.3 %) The detection limit of Forchlorfenuron was 10 µg/L. The standard curve with a regression coefficient of 0.979 was established in the low concentration range of 10 µg/L -100 µg/L. This indicates that the prepared substrates can accomplish the detection of pesticide residues in the low concentration range. The prepared high-performance and high-sensitivity SERS substrate have a very promising application in detection technology.

Detection of lung cancer through SERS analysis of serum.

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Raman spectroscopic analysis for osteoporosis identification in humans with hip fractures.

Osteoporosis is a significant health concern. While multiple techniques have been utilized to diagnose this condition, certain limitations still persist. Raman spectroscopy has shown promise in predicting bone strength in animal models, but its application to humans requires further investigation. In this study, we present an in vitro approach for predicting osteoporosis in 10 patients with hip fractures using Raman spectroscopy. Raman spectra were acquired from exposed femoral heads collected during surgery. Employing a leave-one-out cross-validated linear discriminant analysis (LOOCV-LDA), we achieved accurate classification (90 %) between osteoporotic and osteopenia groups. Additionally, a LOOCV partial least squares regression (PLSR) analysis based on the complete Raman spectra demonstrated a significant prediction (r2 = 0.84, p < 0.05) of bone mineral density as measured by dual X-ray absorptiometry (DXA). To the best of our knowledge, this study represents the first successful demonstration of Raman spectroscopy correlating with osteoporotic status in humans.

Analysis of intra-specific variations in the venom of individual snakes based on Raman spectroscopy.

The knowledge of variations in the composition of venoms from different snakes is important from both theoretical and practical points of view, in particular, at developing and selecting an antivenom. Many studies on this topic are conducted with pooled venoms, while the existence and significance of variations in the composition of venoms between individual snakes of the same species are emphasized by many authors. It is important to study both inter- and intra-specific, including intra-population, venom variations, because intra-specific variations in the venom composition may affect the effectiveness of antivenoms as strongly as inter-specific. In this work, based on venom Raman spectroscopy with principal component analysis, we assessed the variations in venoms of individual snakes of the Vipera nikolskii species from two populations and compared these intra-specific variations with inter-specific variations (with regard to the other related species). We demonstrated intra-specific (inter- and intra-population) differences in venom compositions which are smaller than inter-specific variations. We also assessed the compositions of V. nikolskii venoms from two populations to explain inter-population differences. The method used is rapid and requires virtually no preparation of samples, used in extremely small quantities, allowing the venoms of individual snakes to be analyzed. In addition, the method is informative and capable of detecting fairly subtle differences in the composition of venoms.

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.