SERS liquid biopsy: An emerging tool for medical diagnosis

Portable point-of-care surface enhanced Raman scattering spectroscopy for the quantification of glutathione in whole blood microsamples

Glutathione (GSH) is a non-protein tripeptide thiol that plays a prominent role in oxidative stress defense. GSH concentration is particularly critical in the neonatal period, especially for premature newborns that face increased susceptibility to oxidative stress. Monitoring GSH levels provides valuable insights into newborn health, helping to tailor care to their specific needs. The aim of this study was the development of a sensor specifically targeted for its use in neonatology, enabling GSH determination in only 2 μL of whole blood. The newly developed sensing system simplifies sample processing, addressing a critical need in clinical applications. Unlike current methods that demand fast pre-processing of relatively large sample volumes, expensive equipment, and skilled personnel, the developed approach streamlines the analytical process. By using 2 μL of whole blood, a single syringe filter for sample treatment, a deuterated internal standard (IS) for signal normalization, and Surface Enhanced Raman Scattering (SERS) spectroscopy with a silver colloid substrate for GSH detection, the set-up's characteristics are compatible with point-of-care applications. The analytical procedure was validated and applied to diverse populations including healthy adults (N = 63) and newborns (N = 35), yielding GSH concentration values ranging from 0.6 to 1.8 and 0.8-2.1 mM, respectively. This new optical sensor offers a quick and cost-effective solution to support the assessment of GSH levels in newborns that can greatly benefit not only neonatal care, but also the study of adult populations for health monitoring.

Diagnosis of neuropsychiatric systemic lupus erythematosus by label-free serum microsphere-coupled SERS fingerprints with machine learning

Surface-enhanced Raman spectroscopy (SERS) is a powerful optical technique for non-invasive and label-free bioanalysis of liquid biopsy, facilitating to diagnosis of potential diseases. Neuropsychiatric systemic lupus erythematosus (NPSLE) is one of the subgroups of systemic lupus erythematosus (SLE) with serious manifestations for a high mortality rate. Unfortunately, lack of well-established gold standards results in the clinical diagnosis of NPSLE being a challenge so far. Here we develop a novel Raman fingerprinting machine learning (ML-) assisted diagnostic method. The microsphere-coupled SERS (McSERS) substrates are employed to acquire Raman spectra for analysis via convolutional neural network (CNN). The McSERS substrates demonstrate better performance to distinguish the Raman spectra from serums between SLE and NPSLE, attributed to the boosted signal-to-noise ratio of Raman intensities due to the multiple optical regulation in microspheres and AuNPs. Eight statistically-significant (p-value <0.05) Raman shifts are identified, for the first time, as the characteristic spectral markers. The classification model established by CNN algorithm demonstrates 95.0% in accuracy, 95.9% in sensitivity, and 93.5% in specificity for NPSLE diagnosis. The present work paves a new way achieving clinical label-free serum diagnosis of rheumatic diseases by enhanced Raman fingerprints with machine learning.

Direct comparison of different protocols to obtain surface enhanced Raman spectra of human serum

Label-free Surface Enhanced Raman Spectroscopy (SERS) is a rapid technique that has been extensively applied in clinical diagnosis and biomedicine for the analysis of biofluids. The purpose of this approach relies on the ability to detect specific "metabolic fingerprints" of complex biological samples, but the full potential of this technique in diagnostics is yet to be exploited, mainly because of the lack of common analytical protocols for sample preparation and analysis. Variation of experimental parameters, such as substrate type, laser wavelength and sample processing can greatly influence spectral patterns, making results from different research groups difficult to compare. This study aims at making a step toward a standardization of the protocols in the analysis of human serum samples with Ag nanoparticles, by directly comparing the SERS spectra obtained from five different methods in which parameters like laser power, nanoparticle concentration, incubation/deproteinization steps and type of substrate used vary. Two protocols are the most used in the literature, and the other three are "in-house" protocols proposed by our group; all of them are employed to analyze the same human serum sample. The experimental results show that all protocols yield spectra that share the same overall spectral pattern, conveying the same biochemical information, but they significantly differ in terms of overall spectral intensity, repeatability, and preparation steps of the sample. A Principal Component Analysis (PCA) was performed revealing that protocol 3 and protocol 1 have the least variability in the dataset, while protocol 2 and 4 are the least repeatable.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

Novel non-invasive method for urine mapping: Deep-learning-enabled SERS spectroscopy for the rapid differential detection of kidney allograft injury

The kidney allograft has been under continuous attack from diverse injuries since the very beginning of organ procurement, leading to a gradual decline in function, chronic fibrosis, and allograft loss. It is vital to routinely and precisely monitor the risk of injuries after renal transplantation, which is difficult to achieve because the traditional laboratory tests lack sensitivity and specificity, and graft biopsies are invasive with the risk of many complications and time-consuming. Herein, a novel method for the diagnosis of graft injury is demonstrated, using deep learning-assisted surface-enhanced Raman spectroscopy (SERS) of the urine analysis. Specifically, we developed a hybrid SERS substrate composed of gold and silver with high sensitivity to the urine composition under test, eliminating the need for labels, which makes measurements easy to perform and meanwhile results in extremely abundant and complex Raman vibrational bands. Deep learning algorithms were then developed to improve the interpretation of the SERS spectral fingerprints. The deep learning model was trained with SERS signals of urine samples of recipients with different injury types including delayed graft function (DGF), calcineurin-inhibitor toxicity (CNIT), T cell-mediated rejection (TCMR), antibody-mediated rejection (AMR), and BK virus nephropathy (BKVN), which explored the features of these types and achieved the injury differentiation with an overall accuracy of 93.03%. The results highlight the potential of combining label-free SERS spectroscopy with deep learning as a method for liquid biopsy of kidney allograft injuries, which can provide great potential to diagnose and evaluate allograft injuries, and thus extend the life of kidney allografts.

Quantitative detecting low concentration polystyrene nanoplastics in aquatic environments via an Ag/Nb2CTx (MXene) SERS substrate

In this study, the plasmonic Ag nanoparticles (Ag NPs) were uniformly anchored on the high conductivity Nb2CTx (MXene) nanosheets to construct an Ag/Nb2CTx substrate for surface-enhanced Raman spectroscopy (SERS) detection of polystyrene (PS) nanoplastics. The KI addition (0.15 mol/L), the volume ratio between substrate colloid and nanoplastic suspension (2:1), and the mass ratio of Nb2CTx in substrate (14%) on SERS performance were optimized. The EM hot spots of Ag/Nb2CTx are significantly enlarged and enhanced, elucidated by FDFD simulation. Then, the linear relationship between the PS nanoplastics concentration with three different sizes (50, 300, and 500 nm) and the SERS intensity was obtained (R2 > 0.976), wherein, the detection limit was as low as 10-4 mg/mL for PS nanoplastic. Owing to the fingerprint feature, the Ag/Nb2CTx-14% substrate successfully discerns the mixtures from two-component nanoplastics. Meanwhile, it exhibits excellent stability of PS nanoplastics on different detection sites. The recovery rates of PS nanoplastics with different sizes in lake water ranged from 94.74% to 107.29%, with the relative standard deviation (RSD) ranging from 2.88% to 8.30%. Based on this method, the expanded polystyrene (EPS) decomposition behavior was evaluated, and the PS concentrations in four water environments were analyzed. This work will pave the way for the accurate quantitative detection of low concentration of nanoplastics in aquatic environments.

SERS detection platform based on a nucleic acid aptamer-functionalized Au nano-dodecahedron array for efficient simultaneous testing of colorectal cancer-associated microRNAs

A surface-enhanced Raman scattering (SERS) detection platform was constructed based on Au nano-dodecahedrons (AuNDs) functionalized with nucleic acid aptamer-specific binding and self-assembly techniques. SERS labels were prepared by modifying Raman signaling molecules and complementary aptamer chains and were bound on the aptamer-functionalized AuNDs array. Using this protocol, the limits of detection (LODs) of miR-21 and miR-18a in the serum were 6.8 pM and 7.6 pM, respectively, and the detection time was 5 min. Additionally, miR-21 and miR-18a were detected in the serum of a mouse model of colorectal cancer. The results of this protocol were consistent with quantitative real-time polymerase chain reaction (qRT-PCR). This method provides an efficient and rapid method for the simultaneous testing of miRNAs, which has great potential clinical value for the early detection of colorectal cancer (CRC).

Recent developments and applications of surface enhanced Raman scattering spectroscopy in safety detection of fruits and vegetables

This article reviewed the latest research progress of Surface-enhanced Raman Spectroscopy (SERS) in the security detection of fruits and vegetables in recent years, especially in three aspects: pesticide residues, microbial toxin contamination and harmful microorganism infection. The binding mechanism and application potential of SERS detection materials (including universal type and special type) and carrier materials (namely rigid and flexible materials) were discussed. Finally, the application prospect of SERS in fruit and vegetable safety detection was explored, and the problems to be solved and development trends were put forward. The poor stability and reproducibility of SERS substrates make it difficult for practical applications. It is necessary to continuously optimize SERS substrates and develop small and portable Raman spectroscopy analyzers. In the future, SERS technology is expected to play an important role in human health, food safety and economy.

Self-Organized SERS Substrates with Efficient Analyte Enrichment in the Hot Spots

One of the requirements of an efficient surface-enhanced Raman spectroscopy (SERS) substrate is a developed surface morphology with a high density of "hot spots", nm-scale spacings between plasmonic nanoparticles. Of particular interest are plasmonic architectures that could enable self-localization (enrichment) of the analyte in the hot spots. We report a straightforward method of fabrication of efficient SERS substrates that comply with these requirements. The basis of the substrate is a large-area film of tightly packed SiO2 spheres formed by their quick self-assembling upon drop casting from the solution. Thermally evaporated thin Ag layer is converted by quick thermal annealing into nanoparticles (NPs) self-assembled in the trenches between the silica spheres, i.e., in the places where the analyte molecules get localized upon deposition from solution and drying. Therefore, the obtained substrate morphology enables an efficient enrichment of the analyte in the hot spots formed by the densely arranged plasmonic NPs. The high efficiency of the developed SERS substrates is demonstrated by the detection of Rhodamine 6G down to 10-13 mol/L with an enhancement factor of ∼108, as well as the detection of low concentrations of various nonresonant analytes, both small dye molecules and large biomolecules. The developed approach to SERS substrates is very straightforward for implementation and can be further extended to using gold or other plasmonic NPs.

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.

Recent Progresses in Optical Biosensors for Interleukin 6 Detection

Interleukin 6 (IL-6) is pleiotropic cytokine with pathological pro-inflammatory effects in various acute, chronic and infectious diseases. It is involved in a variety of biological processes including immune regulation, hematopoiesis, tissue repair, inflammation, oncogenesis, metabolic control, and sleep. Due to its important role as a biomarker of many types of diseases, its detection in small amounts and with high selectivity is of particular importance in medical and biological fields. Laboratory methods including enzyme-linked immunoassays (ELISAs) and chemiluminescent immunoassays (CLIAs) are the most common conventional methods for IL-6 detection. However, these techniques suffer from the complexity of the method, the expensiveness, and the time-consuming process of obtaining the results. In recent years, too many attempts have been conducted to provide simple, rapid, economical, and user-friendly analytical approaches to monitor IL-6. In this regard, biosensors are considered desirable tools for IL-6 detection because of their special features such as high sensitivity, rapid detection time, ease of use, and ease of miniaturization. In this review, current progresses in different types of optical biosensors as the most favorable types of biosensors for the detection of IL-6 are discussed, evaluated, and compared.

Challenges and opportunities for SERS in the infrared: materials and methods

In the wake of a global, heightened interest towards biomarker and disease detection prompted by the SARS-CoV-2 pandemic, surface enhanced Raman spectroscopy (SERS) positions itself again at the forefront of biosensing innovation. But is it ready to move from the laboratory to the clinic? This review presents the challenges associated with the application of SERS to the biomedical field, and thus, to the use of excitation sources in the near infrared, where biological windows allow for cell and through-tissue measurements. Two main tackling strategies will be discussed: (1) acting on the design of the enhancing substrate, which includes manipulation of nanoparticle shape, material, and supramolecular architecture, and (2) acting on the spectral collection set-up. A final perspective highlights the upcoming scientific and technological bets that need to be won in order for SERS to stably transition from benchtop to bedside.

Surface-Enhanced Raman Analysis of Uric Acid and Hypoxanthine Analysis in Fractionated Bodily Fluids

In recent years, the disease burden of hyperuricemia has been increasing, especially in high-income countries and the economically developing world with a Western lifestyle. Abnormal levels of uric acid and hypoxanthine are associated with many diseases, and therefore, to demonstrate improved methods of uric acid and hypoxanthine detection, three different bodily fluids were analysed using surface-enhanced Raman spectroscopy (SERS) and high-performance liquid chromatography (HPLC). Gold nanostar suspensions were mixed with series dilutions of uric acid and hypoxanthine, 3 kDa centrifugally filtered human blood serum, urine and saliva. The results show that gold nanostars enable the quantitative detection of the concentration of uric acid and hypoxanthine in the range 5-50 μg/mL and 50-250 ng/mL, respectively. The peak areas of HPLC and maximum peak intensity of SERS have strongly correlated, notably with the peaks of uric acid and hypoxanthine at 1000 and 640 cm^-1, respectively. The r² is 0.975 and 0.959 for uric acid and hypoxanthine, respectively. Each of the three body fluids has a number of spectral features in common with uric acid and hypoxanthine. The large overlap of the spectral bands of the SERS of uric acid against three body fluids at spectra peaks were at 442, 712, 802, 1000, 1086, 1206, 1343, 1436 and 1560 cm^-1. The features at 560, 640, 803, 1206, 1290 and 1620 cm^-1 from hypoxanthine were common to serum, saliva and urine. There is no statistical difference between HPLC and SERS for determination of the concentration of uric acid and hypoxanthine (p > 0.05). For clinical applications, 3 kDa centrifugal filtration followed by SERS can be used for uric acid and hypoxanthine screening is, which can be used to reveal the subtle abnormalities enhancing the great potential of vibrational spectroscopy as an analytical tool. Our work supports the hypnosis that it is possible to obtain the specific concentration of uric acid and hypoxanthine by comparing the SER signals of serum, saliva and urine. In the future, the analysis of other biofluids can be employed to detect biomarkers for the diagnosis of systemic pathologies.

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.

Interface-Dependent Selectivity in Plasmon-Driven Chemical Reactions

Plasmonic nanoparticles can drive chemical reactions powered by sunlight. These processes involve the excitation of surface plasmon resonances (SPR) and the subsequent charge transfer to adsorbed molecular orbitals. Nonetheless, controlling the flow of energy and charge from SPR to adsorbed molecules is still difficult to predict or tune. Here, we show the crucial role of halide ions in modifying the energy landscape of a plasmon-driven chemical reaction by carefully engineering the nanoparticle-molecule interface. By doing so, the selectivity of plasmon-driven chemical reactions can be controlled, either enhancing or inhibiting the metal-molecule charge and energy transfer or by regulating the vibrational pumping rate. These results provide an elegant method for controlling the energy flow from plasmonic nanoparticles to adsorbed molecules, in situ, and selectively targeting chemical bonds by changing the chemical nature of the metal-molecule interface.

Molecularly imprinted 3D SERS sensor with inorganic frameworks for specific and recyclable SERS sensing application

Poor selectivity and reusability of Au/Ag nanostructures are the main challenges for surface-enhanced Raman spectroscopy (SERS) in real sample detection. Herein, a novel specific and reusable three-dimensional (3D) SERS sensor with dual functions of selective trapping and photocatalytic degradation was designed. Firstly, Au-Ag bimetallic nanoparticles decorated silicon nanowires array (SiNWs-AuAg) were prepared as 3D SERS substrate. Then, silicon-based inorganic-framework molecularly imprinted TiO₂ (TiO₂@SiMIP) was synthesized and immobilized on SiNWs-AuAg by using rhodamine 6G (R6G) as template molecule. Owing to the excellent SERS performance of SiNWs-AuAg and the specific affinity of TiO₂@SiMIP to template molecule, the prepared SERS sensor enables sensitive and selective detection of R6G in food samples with a limit of detection (LOD) of 0.27 nM. In addition, due to the photocatalysis of TiO₂ and the stability of silicon-based inorganic framework, the residual templates in TiO₂@SiMIP can be completely removed by UV irradiation, and the imprinted cavity of regenerated sensors still maintained good selectivity after regeneration by UV irradiation.

A novel cascade signal amplification strategy integrating CRISPR/Cas13a and branched hybridization chain reaction for ultra-sensitive and specific SERS detection of disease-related nucleic acids

The molecular diagnosis of disease by high-sensitively and specifically detecting extremely trace amounts of nucleic acid biomarkers in biological samples is still a great challenge, and the powerful sensing strategy has become an urgent need for basic researches and clinical applications. Herein, a novel one-pot cascade signal amplification strategy (Cas13a-bHCR) integrating CRISPR/Cas13a system (Cas13a) and branched hybridization chain reaction (bHCR) was proposed for ultra-highly sensitive and specific SERS assay of disease-related nucleic acids on SERS-active silver nanorods sensing chips. The Cas13a-bHCR based SERS assay of gastric cancer-related miRNA-106a (miR-106a) can be achieved within 60 min and output significantly enhanced SERS signal due to the multiple signal amplification, which possesses a good linear calibration curve from 10 aM to 1 nM with the limit of detection (LOD) low to 8.55 aM for detecting gastric cancer-related miR-106a in human serum. The Cas13a-bHCR based SERS sensing also shows good specificity, uniformity, repeatability and reliability, and has good practicability for detection of miR-106a in clinical samples, which can provide a potential powerful tool for SERS detection of disease-related nucleic acids and promise brighter prospects in the field of clinical diagnosis of early disease.

Sensitive SERS detection of oral squamous cell carcinoma-related miRNAs in saliva via a gold nanohexagon array coupled with hybridization chain reaction amplification

In this work, a highly specific and sensitive method for the detection of dual miRNAs was successfully developed by a hybridization chain reaction (HCR) amplification coupled with surface-enhanced Raman scattering (SERS) on Au-Ag hollow nanoparticles (Au-Ag HNPs) and a gold nanohexagon (AuNH) array. Two Raman reporter-labelled and hairpin DNA-modified Au-Ag HNPs acted as SERS probes (Au-Ag HNPs@4-MBA@HP_1-1, Au-Ag HNPs@4-MBA@HP_2-1, Au-Ag HNPs@DTNB@HP_1-2, and Au-Ag HNPs@DTNB@HP_2-2), and the hairpin DNA-modified AuNH array acted as the capture substrate. The HCR process could be triggered by the presence of target miRNAs, and long DNA hybridization chains on the substrate were formed by self-assembly rapidly, causing significant signal enhancement. Using the mentioned strategy, a low detection limit (LOD) of 6.51 aM for miR-31 and 6.52 aM for miR-21 in human saliva were obtained, showing the biosensor's remarkable sensitivity. The proposed biosensor also displays a significant specificity in detecting target miRNAs by introducing different interfering factors. This method has been successfully applied to detect and identify miR-21 and miR-31 in saliva from oral squamous cell carcinoma (OSCC) patients and healthy subjects. The results were consistent with those of the traditional test method in detecting target miRNAs, which confirmed the good accuracy of our method. Hence, the new assay method has great potential to be a valuable platform for detecting miRNAs in the early diagnosis of OSCC.

Recent Progress in Biosensors for Detection of Tumor Biomarkers

Cancer is a leading cause of death worldwide, with an increasing mortality rate over the past years. The early detection of cancer contributes to early diagnosis and subsequent treatment. How to detect early cancer has become one of the hot research directions of cancer. Tumor biomarkers, biochemical parameters for reflecting cancer occurrence and progression have caused much attention in cancer early detection. Due to high sensitivity, convenience and low cost, biosensors have been largely developed to detect tumor biomarkers. This review describes the application of various biosensors in detecting tumor markers. Firstly, several typical tumor makers, such as neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), prostate-specific antigen (PSA), squamous cell carcinoma antigen (SCCA), carbohydrate, antigen19-9 (CA19-9) and tumor suppressor p53 (TP53), which may be helpful for early cancer detection in the clinic, are briefly described. Then, various biosensors, mainly focusing on electrochemical biosensors, optical biosensors, photoelectrochemical biosensors, piezoelectric biosensors and aptamer sensors, are discussed. Specifically, the operation principles of biosensors, nanomaterials used in biosensors and the application of biosensors in tumor marker detection have been comprehensively reviewed and provided. Lastly, the challenges and prospects for developing effective biosensors for early cancer diagnosis are discussed.

Intelligent and thermo-responsive Au-pluronic® F127 nanocapsules for Raman-enhancing detection of biomolecules

Thermo-responsive Raman-enhanced nanocapsules were successfully fabricated by Pluronic® F127 (F127) decorated with gold nanoparticles (AuNPs) for surface-enhanced Raman scattering (SERS) detection of biomolecules. F127 nanocapsules changes from hydrophilicity (swelling) to hydrophobicity (de-swelling) when the temperature increases from 15 °C to 37 °C, owing to the lower critical solution temperature (LCST) of F127 is about 26.5 °C. The size of nanocapsules would be enormous shrinking from 160 nm to 20 nm, resulting in a significant decrease in the distance between AuNPs to enhance hot spot effect, which increases the sensitivity of SERS detection. Based on the thermo-sensitive behavior, the ratio of AuNPs and F127 would be manipulated to find the optimal SERS enhancement effect. SERS nanocapsules can rapidly detect biomolecules (adenine and R6G) with limit of detection (LOD) lower than 10^-6 M. In addition, the relatively difficult to detect clinical samples, carboxyl-terminal parathyroid hormone fragments (C-PTH), can also be measured by the thermo-responsive SERS nanocapsules developed in this work. It is expected the biomolecules can be adsorbed at low temperature (15 °C), as well as collected and concentrated at high temperature (37 °C) for SERS detection, to increase the sensitivity and stability of SERS detection.

Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection

Simple Summary

Blood is considered a rich reservoir of biomarkers for disease diagnosis. Surface-enhanced Raman scattering (SERS) is known for its high sensitivity and has been successfully employed to differentiate blood samples from cancer patients versus healthy individuals. Different from previous reports, this study aims at investigating the reliability of the observed results by varying several parameters influencing the observed spectra. Thus, blood taken from 30 healthy individuals as the control group, 30 patients with different types of cancers, and 15 patients with various types of chronic diseases were used in the study. The results revealed that spectral differences in the cancer group was directly related to the presence of cancer-related biomarkers. Although data were obtained from only small group of patients, the recorded sensitivity and specificity values clearly show the power of the technique to detect cancer.

Abstract

Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.

Highly Sensitive, Robust, and Recyclable TiO2/AgNP Substrate for SERS Detection

Label-free biosensors provide an important platform for detecting chemical and biological substances without needing extra labeling agents. Unlike surface-based techniques such as surface plasmon resonance (SPR), interference, and ellipsometry, surface-enhanced Raman spectroscopy (SERS) possesses the advantage of monitoring analytes both on surfaces and in solutions. Increasing the SERS enhancement is crucial to preparing high-quality substrates without quickly losing their stability, sensitivity, and repeatability. However, fabrication methods based on wet chemistry, nanoimprint lithography, spark discharge, and laser ablation have drawbacks of waste of time, complicated processes, or nonreproducibility in surface topography. This study reports the preparation of recyclable TiO₂/Ag nanoparticle (AgNP) substrates by using simple arc ion plating and direct-current (dc) magnetron sputtering technologies. The deposited anatase-phased TiO₂ ensured the photocatalytic degradation of analytes. By measuring the Raman spectra of rhodamine 6G (R6G) in titrated concentrations, a limit of detection (LOD) of 10⁻⁸ M and a SERS enhancement factor (EF) of 1.01 × 10⁹ were attained. Self-cleaning was performed via UV irradiation, and recyclability was achieved after at least five cycles of detection and degradation. The proposed TiO₂/AgNP substrates have the potential to serve as eco-friendly SERS enhancers for label-free detection of various chemical and biological substances.

Dual-functional SERRS and fluorescent aptamer sensor for abscisic acid detection via charged gold nanorods

Abscisic acid (ABA) is a plant hormone, which plays an important role in plant growth, crop cultivation and modern agricultural engineering management. Accordingly, the detection of ABA content combined with new techniques and methods has become a more and more popular problem in the field of agricultural engineering. In this work, a SERRS and fluorescence dual-function sensor based on the fluorescence quenching and Raman enhancement properties of gold nanorods (AuNRs) was developed, and applied to the detection of plant hormone ABA. The dual-function reporter molecule Rhodamine isothiocyanate (RBITC) and complementary DNA (cDNA) were modified on AuNRs (AuNRs@RBITC@cDNA) as signal probes and aptamer modified magnetic nanoparticles (Fe3O4MNPs@Apt) as capture probes. Through the specific recognition of ABA aptamer and its complementary chains, an dual-function aptamer sensor based on SERRS and fluorescence was constructed. When ABA molecules were present in the detection system, the signal probes were detached from the capture probes due to the preferential binding between aptamer and ABA molecules. SERS signal of the reporter molecules appeared in the supernatant after magnetic separation, and it increased with the increase of ABA concentration. If the etching agent that can etch AuNRs was added to the supernatant, the AuNRs was etching disappeared, then the signal molecules fall off from the AuNRs, and the fluorescence signal intensity would recovered. The intensity of fluorescence signal also increased with the increase of ABA concentration. Thus, the quantitative relationship between ABA concentration and SERRS intensity and fluorescence intensity of signal molecules was established. The linear range of SERRS detection was 100 fM–0.1 nM, the detection limit was 38 fM; The linear range of fluorescence detection was 1 pM–100 nM, the detection limit is 0.33 p.m. The constructed dual-effect sensor was used in the recovery laboratory of real ABA samples, the recovery rate was up to 85–108%.

Plasmonic colloidal Au nanoparticles in DMSO: a facile synthesis and characterisation

We report a new pathway for the synthesis of plasmonic gold nanoparticles (Au NPs) in a bio-compatible medium. A modified room temperature approach based on the standard Turkevich synthesis, using sodium citrate as a reducing and stabilizing agent, results in a highly stable colloidal suspension of Au NPs in dimethyl sulfoxide (DMSO). The mean NP size of about 15 nm with a fairly low size distribution is revealed by scanning electron microscopy. The stability test through UV-vis absorption spectroscopy indicates no sign of aggregation for months. The Au NPs are also characterized by X-ray photoelectron, Raman scattering, and FTIR spectroscopies. The stabilisation mechanism of the Au NPs in DMSO is concluded to be similar to that of NPs synthesized in water. The Au NPs obtained in this work are applicable as SERS substrates, as proved by common analytes. In terms of bio-applications, they do not possess such side-effects as pronounced antibacterial activity, based on the tests performed on non-pathogenic Gram-positive or Gram-negative bacteria.

Design and Synthesis of Novel Raman Reporters for Bioorthogonal SERS Nanoprobes Engineering

Surface-enhanced Raman spectroscopy (SERS) exploiting Raman reporter-labeled nanoparticles (RR@NPs) represents a powerful tool for the improvement of optical bio-assays due to RRs’ narrow peaks, SERS high sensitivity, and potential for multiplexing. In the present work, starting from low-cost and highly available raw materials such as cysteamine and substituted benzoic acids, novel bioorthogonal RRs, characterized by strong signal (103 counts with FWHM < 15 cm−1) in the biological Raman-silent region (>2000 cm−1), RRs are synthesized by implementing a versatile, modular, and straightforward method with high yields and requiring three steps lasting 18 h, thus overcoming the limitations of current reported procedures. The resulting RRs’ chemical structure has SH-pendant groups exploited for covalent conjugation to high anisotropic gold-NPs. RR@NPs constructs work as SERS nanoprobes demonstrating high colloidal stability while retaining NPs’ physical and vibrational properties, with a limit of detection down to 60 pM. RR@NPs constructs expose carboxylic moieties for further self-assembling of biomolecules (such as antibodies), conferring tagging capabilities to the SERS nanoprobes even in heterogeneous samples, as demonstrated with in vitro experiments by transmembrane proteins tagging in cell cultures. Finally, thanks to their non-overlapping spectra, we envision and preliminary prove the possibility of exploiting RR@NPs constructs simultaneously, aiming at improving current SERS-based multiplexing bioassays.

SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine?

SERS analysis of biofluids, coupled with classification algorithms, has recently emerged as a candidate for point-of-care medical diagnosis. Nonetheless, despite the impressive results reported in the literature, there are still gaps in our knowledge of the biochemical information provided by the SERS analysis of biofluids. Therefore, by a critical assignment of the SERS bands, our work aims to provide a systematic analysis of the molecular information that can be achieved from the SERS analysis of serum and urine obtained from breast cancer patients and controls. Further, we compared the relative performance of five different machine learning algorithms for breast cancer and control samples classification based on the serum and urine SERS datasets, and found comparable classification accuracies in the range of 61-89%. This result is not surprising since both biofluids show striking similarities in their SERS spectra providing similar metabolic information, related to purine metabolites. Lastly, by carefully comparing the two datasets (i.e., serum and urine) we show that it is possible to link the misclassified samples to specific metabolic imbalances, such as carotenoid levels, or variations in the creatinine concentration.

Targeting non-coding RNAs to overcome cancer therapy resistance

It is now well known that non-coding RNAs (ncRNAs), rather than protein-coding transcripts, are the preponderant RNA transcripts. NcRNAs, particularly microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are widely appreciated as pervasive regulators of multiple cancer hallmarks such as proliferation, apoptosis, invasion, metastasis, and genomic instability. Despite recent discoveries in cancer therapy, resistance to chemotherapy, radiotherapy, targeted therapy, and immunotherapy continue to be a major setback. Recent studies have shown that ncRNAs also play a major role in resistance to different cancer therapies by rewiring essential signaling pathways. In this review, we present the intricate mechanisms through which dysregulated ncRNAs control resistance to the four major types of cancer therapies. We will focus on the current clinical implications of ncRNAs as biomarkers to predict treatment response (intrinsic resistance) and to detect resistance to therapy after the start of treatment (acquired resistance). Furthermore, we will present the potential of targeting ncRNA to overcome cancer treatment resistance, and we will discuss the challenges of ncRNA-targeted therapy—especially the development of delivery systems.

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

BACKGROUND

Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine.

METHODS

Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types.

RESULTS

Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy.

CONCLUSION

miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

Spectroscopic Study of Phytosynthesized Ag Nanoparticles and Their Activity as SERS Substrate

The affordable and scalable synthesis of noble metal nanoparticles that are biocompatible without additional functionalization steps has been a growing field of research, stimulated by numerous prospective applications of these NPs. In the case of phytosynthesized or biogenic noble metal NPs, the mechanism of NP stabilization by biomolecules contained in each particular plant extract or living organism determines the possible applications of these NPs. In this work, we investigated Ag NPs synthesized in water with plant extracts of common toothwort (Lathraea squamaria) and two species of pepper (Capsicum annuum and Capsicum chinense). From FTIR and XPS, we drew conclusions about the composition of the functional groups and molecules that stabilize NPs in each extract, such as polysaccharide compounds (pectins, cellulose, glycosides and phenolic acids). Distinct characteristic IR features of amide I and amide II proteins were observed, which are common in plant extracts, while features of amide III were not distinctly observed in our extracts. A Raman spectroscopy study revealed weak own-SERS activity of the biomolecules of the extract and high efficiency of the NPs in the enhancement of “external” analytes, such as dyes and antibodies. This is the first report of the efficient SERS application of phytosynthesized Ag NPs.

Toward a SERS Diagnostic Tool for Discrimination between Cancerous and Normal Bladder Tissues via Analysis of the Extracellular Fluid

Vibrational spectroscopy provides the possibility for sensitive and precise detection of chemical changes in biomolecules due to development of cancers. In this work, label-free near-infrared surface enhanced Raman spectroscopy (SERS) was applied for the differentiation between cancerous and normal human bladder tissues via analysis of the extracellular fluid of the tissue. Specific cancer-related SERS marker bands were identified by using a 1064 nm excitation wavelength. The prominent spectral marker band was found to be located near 1052 cm-1 and was assigned to the C-C, C-O, and C-N stretching vibrations of lactic acid and/or cysteine molecules. The correct identification of 80% of samples is achieved with even limited data set and could be further improved. The further development of such a detection method could be implemented in clinical practice for the aid of surgeons in determining of boundaries of malignant tumors during the surgery.

Human metabolite detection by surface-enhanced Raman spectroscopy

Metabolites are important biomarkers in human body fluids, conveying direct information of cellular activities and physical conditions. Metabolite detection has long been a research hotspot in the field of biology and medicine. Surface-enhanced Raman spectroscopy (SERS), based on the molecular “fingerprint” of Raman spectrum and the enormous signal enhancement (down to a single-molecule level) by plasmonic nanomaterials, has proven to be a novel and powerful tool for metabolite detection. SERS provides favorable properties such as ultra-sensitive, label-free, rapid, specific, and non-destructive detection processes. In this review, we summarized the progress in recent 10 years on SERS-based sensing of endogenous metabolites at the cellular level, in tissues, and in biofluids, as well as drug metabolites in biofluids. We made detailed discussions on the challenges and optimization methods of SERS technique in metabolite detection. The combination of SERS with modern biomedical technology were also anticipated.