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Rapid detection of uremic toxins is crucial due to their severe health risks, including oxidative stress, inflammation, neurotoxicity, cardiovascular complications, and progression of chronic kidney disease. Surface-enhanced Raman spectroscopy (SERS) may provide sensitive, fast, and clinical-grade real-time monitoring of these toxins, enabling effective management with timely dialysis treatments. This study introduces a 3D-printed microchamber that integrates the fabrication of plasmonic metal nanoparticles for the in-flow detection of biological toxins and pharmaceutical drugs using SERS, making it ideal for on-site diagnostics in clinical settings. The microchamber supports quantitative and highly reproducible detection with liquid volumes under 100 μL, which is crucial for trace-level biomarker detection and minimizing cross-contamination. It employs a tunable solvent exchange method for the in situ synthesis of silver nanoparticles (AgNPs) on flexible PDMS or rigid Si wafer substrates, avoiding costly nanofabrication techniques. Ultralow detection limits were achieved for two model compounds and three pharmaceutical drugs: 10 -11 M for rhodamine 6G, 10 -7 M for adenine, and 10 -6 M for the pharmaceutical drugs. A total of 13 biological toxins, including three neurotransmitters, one neuromodulator, five amino acids, two polyamines, and two urea cycle metabolites, were detected with quantitative limits ranging from 10 -3 to 10 -6 M, all below permissible levels and aligning with physiological conditions. SERS detection within microchambers facilitates rapid on-site analysis, proving ideal for personalized health monitoring, point-of-care diagnostics, and environmental pollution assessment.

The microbiome represents the natural presence of microorganisms, and exploring, understanding, and leveraging its functions will bring about significant breakthroughs in life sciences and applications. Raman-activated cell sorting (RACS) enables the correlation of phenotype and genotype at the single-cell level, offering a solution to the bottleneck in microbial community functional analysis caused by challenges in cultivating diverse microorganisms. However, current labor-intensive manual procedures fall short in catering to the demands of single-cell functional analysis in microbial communities. To address this issue, we developed an artificial intelligence-assisted Raman-activated cell sorting system (AI-RACS) that integrates precise single-cell positioning, automated data collection, optical tweezers capture, and single-cell printing to elevate microbial single-cell RACS from manual to automated, validating the efficacy of the system by isolating aluminum-tolerant microbes from acidic soil microbiota. Leveraging the AI-RACS framework, we sorted 13 strains from red soil samples under near-in situ conditions, with all demonstrating strong aluminum tolerance. AI-RACS efficiently segregates microbial cells from intricate environmental samples, investigating their functional attributes and presenting a novel tool for microbial research and applications.

This study reports development of a microfluidic device for highly sensitive and selective detection of a β-amyloid peptide (Aβ 1-42 ) in simulated cerebrospinal fluid, using surface-enhanced Raman spectroscopy (SERS). The device ensemble comprises a purine ligand (Pu) and its interaction with silver nanoparticles (AgNPs) to generate SERS hotspots. The low surface energy of the synthesized Pu ligand and high surface energy of AgNPs are utilized for the functionalization and formation of a Pu-AgNP SERS substrate. We have integrated a novel polydimethylsiloxane (PDMS) microfluidic device with Pu-AgNPs using a combination of photo- and soft lithography fabrication, sealed by thermal cross-linking with another layer of PDMS, to produce an effective screening platform for Aβ 1-42 . The SERS spectrum from the microfluidic device affords almost noise-free measurements, with excellent limit-of-detection values.

In this study, we developed ratiometric surface-enhanced Raman scattering (SERS) biosensors using Ag-Au alloy nanoflowers as SERS substrates, molecules having amide bonds and alkyne groups (Tag A) as Raman reporters, and sodium thiocyanate as an internal standard molecule (Tag B) for the sensitive detection of human carboxylesterase-1 (hCE1) in HepG-2 cells. The correlation between HepG-2 cell damage and hCE1 activity levels was investigated. Both Tag A's alkyne group and Tag B's cyanide group produced characteristic SERS signals in the Raman-silent region ( I 2000 cm -1 and I 2115 cm -1 , respectively). The hydrolysis of the amide bond in Tag A via hCE1 and the shedding of the alkyne group led to a reduction in the SERS signal intensity observed at I 2000 cm -1 . Conversely, the SERS signal intensity of Tag B at I 2115 cm -1 exhibited a consistent pattern. As the activity level of hCE1 and the ratiometric peak intensity ( I 2000 cm -1 / I 2115 cm -1 ) correlated negatively, hCE1 could be quantitatively detected within the range of 10 -2 to 2 × 10 2 ng·mL -1 , with a detection limit of 7.3 pg·mL -1 . The ratiometric SERS probe strategy, in which a ratio response is employed, permits sensitive and reproducible SERS detection by facilitating intrinsic calibration to rectify signal fluctuations resulting from temporal and spatial variations in the detection conditions. Concurrently, the implementation of Raman-silent region reporter molecules mitigates the interference from endogenous biomolecules in SERS measurements and offers a novel approach for achieving highly sensitive and interference-free detection of intracellular hCE1.

Dysregulation of receptor tyrosine kinases (RTKs) has been shown to correlate with cancer cell proliferation and drug resistance. Thus, monitoring the activity of RTKs at a chemical level could provide new biomedical insights and methods to assess the drug efficacy. However, direct monitoring of kinase activity is challenging and most commonly relies on in vitro techniques such as Western blotting and ELISAs. Herein, we report the development of a gold nanoparticle-based surface-enhanced Raman scattering (SERS) sensor, which allows the real-time monitoring of tyrosine phosphorylation of a reporter peptide (Axltide) by the Axl enzyme. We demonstrate that our sensor shows strong signal localization, and we are able to detect tyrosine phosphorylation of the reporter peptide through chemical phosphorylation and enzymatically with similar peak changes to those observed in the spontaneous Raman spectra. Through monitoring the SERS spectrum, we can observe changes in phosphorylation in real time.

Sensitive and accurate detection of thiamethoxam in tea is significant to ensuring consumer health. In this study, surface-enhanced Raman scattering (SERS) tags were prepared by using a polyphenol-mediated coating engineering strategy. This approach involved the self-assembly of tannic acid (TA) and self-polymerization of benzene-1,4-dithiol (BDT) on the surface of gold nanoparticles, resulting in the formation of Au@pBDT-TA. The SERS tags possess high Raman signals and antibody adsorption properties, avoiding the complex decoration or label steps of SERS reporters. We discovered that Au@pBDT-TA, composed of gold nanoparticles at 40 nm and a pBDT-TA thickness of 10 nm, was optimal for the development of the SERS lateral flow immunoassay (LFIA). In comparison to the gold nanoparticle-based LFIA, the SERS-LFIA demonstrated 4-fold and 720-fold enhancements in visual and quantitative limits of detection in buffer solution. The SERS-LFIA demonstrated quantitative limits of detection of 0.06 and 0.1 ng/g for black and green tea, respectively, with a broader linear range spanning over 2 orders of magnitude and a short detection time of 20 min. The proposed SERS-LFIA not only offers a sensitive and reliable method for monitoring thiamethoxam in tea but also possesses a versatile potential that can be easily adapted for the trace detection of various other targets.

The use of nanozymes has become a promising auxiliary approach to "turn on" surface-enhanced Raman scattering (SERS) signals for the label-free detection of disease markers. Nevertheless, there are still major challenges to develop bifunctional nanomaterials with both excellent enzyme-like activity and high SERS performance. To this end, a novel Z-scheme MoO 3- x /CuS heterojunction was first constructed as a powerful "two-in-one" substrate, which can not only catalyze leucomalachite green (LMG) to SERS-active malachite green (MG) but also serve as an efficient substrate to amplify the SERS signal of catalysate. Due to the strong interfacial coupling effect between the MoO 3- x and CuS nanomaterial, which promoted the separation and transport of carriers in the heterojunction, the MoO 3- x /CuS heterojunction showed higher peroxidase-like activity compared to individual components and the previously reported heterojunction nanozymes. Inspired by these results, a sandwich-type SERS immunoassay for the detection of the cerebral infarction biomarker S100 calcium-binding protein (S100B) was proposed based on the output signal of MG at 1620 cm -1 . Furthermore, introducing the antifouling material chitosan on the surface of the MoO 3- x /CuS heterojunction can effectively resist nonspecific protein adsorption and significantly improve the detection accuracy of the immunoassay. Therefore, the SERS immunoassay based on the MoO 3- x /CuS heterojunction realized highly sensitive and selective detection of S100B in the concentration range of 0.001 to 100 ng/mL, with a low limit of detection of 0.47 pg/mL. The developed method has been successfully used for the accurate detection of S100B in clinical serum. The results showed that the level of S100B in the serum of cerebral infarction patients can be distinguished from those of healthy individuals and intracranial tumor patient controls. In addition, the acquired values of S100B in the serum of cerebral infarction patients based this strategy were well consistent with the results of electrochemiluminescence (ECL) detection with a relative error of less than ±7.3. It is expected that this work may open up a paradigm for improving detection sensitivity and accuracy for the early diagnosis and treatment monitoring of cerebral infarction in the clinic.

Conventional solid-based SERS substrates often face challenges with inconsistent sample distribution, while liquid-based SERS substrates are prone to aggregation and precipitation, resulting in irreproducible signals in both cases. In this study, we tackled this dilemma by designing and synthesizing raspberry-like plasmonic nanoaggregates that exhibit a high density of hotspots and are colloidally stable at the same time. In particular, the nanoaggregates consist of a core made of functionalized polystyrene (PS) microspheres, which act as a template for rapid self-assembly of Au@Ag core-shell nanoparticles to form raspberry-like hierarchical nanoaggregates within 5 min of mixing. The optimized nanoaggregates can be used as reproducible and stable SERS substrates for a range of wastewater pollutants (e.g., rhodamine 6G (R6G) and malachite green (MG)) and nucleobases (e.g., adenine and uracil), with the detection limits as low as 1 × 10 -10 , 1 × 10 -16 , 3 × 10 -8 , and 3 × 10 -7 M, respectively. Additionally, the trace detection of adenine in clinical urine samples has been successfully demonstrated. Our modular assembly approach opens up new possibilities in SERS substrate design and advanced trace-chemical detection technologies.

Biopharmaceutical resins are pivotal inert matrices used across industry and academia, playing crucial roles in a myriad of applications. For biopharmaceutical process research and development applications, a deep understanding of the physical and chemical properties of the resin itself is frequently required, including for drug purification, drug delivery, and immobilized biocatalysis. Nevertheless, the prevailing methodologies currently employed for elucidating these important aspects of biopharmaceutical resins are often lacking, frequently require significant sample alteration, are destructive or ionizing in nature, and may not adequately provide representative information. In this work, we propose the use of unsupervised machine learning technologies, in the form of both non-negative matrix factorization (NMF) and k -means segmentation, in conjugation with Raman hyperspectral imaging to rapidly elucidate the molecular and spatial properties of biopharmaceutical resins. Leveraging our proposed technology, we offer a new approach to comprehensively understanding important resin-based systems for application across biopharmaceuticals and beyond. Specifically, focusing herein on a representative resin widely utilized across the industry (i.e., Immobead 150P), our findings showcase the ability of our machine learning-based technology to molecularly identify and spatially resolve all chemical species present. Further, we offer a comprehensive evaluation of optimal excitation for hyperspectral imaging data collection, demonstrating results across 532, 638, and 785 nm excitation. In all cases, our proposed technology deconvoluted, both spatially and spectrally, resin and glass substrates via NMF. After NMF deconvolution, image segmentation was also successfully accomplished in all data sets via k -means clustering. To the best of our knowledge, this is the first report utilizing the combination of two unsupervised machine learning methodologies, combining NMF and k -means, for the rapid deconvolution and segmentation of biopharmaceutical resins. As such, we offer a powerful new data-rich experimentation tool for application across multidisciplinary fields for a deeper understanding of resins.

Exploring the structural basis of membrane proteins is significant for a deeper understanding of protein functions. In situ analysis of membrane proteins and their dynamics, however, still challenges conventional techniques. Here we report the first attempt to immobilize membrane protein complexes on surface-enhanced Raman scattering (SERS)-active supports, titanium dioxide-coated silver (Ag@TiO 2 ) nanoparticles. Biocompatible immobilization of microsomal monooxygenase complexes is achieved through lipid fission and fusion. SERS activity of the Ag@TiO 2 nanoparticles enables in situ monitoring of protein-protein electron transfer and enzyme catalysis in real time. Through SERS fingerprints of the monooxygenase redox centers, the correlations between these protein-ligand interactions and reactive oxygen species generation are revealed, providing novel insights into the molecular mechanisms underlying monooxygenase-mediated apoptotic regulation. This study offers a novel strategy to explore structure-function relationships of membrane protein complexes and has the potential to advance the development of novel reactive oxygen species-inducing drugs for cancer therapy.

Surface-enhanced Raman spectroscopy (SERS) has been demonstrated as an effective method for elucidating secondary structural characteristics of DNA. However, the inherent complexity of the DNA conformation and the lack of SERS samples pose challenges for identifying numerous secondary structures. To address these issues, a synergistic method integrating machine learning with SERS was proposed so as to analyze the SERS spectra of 54 well-defined conformational oligonucleotides, namely, G-quadruplex (G4), i-motif (iM), double-strand (DS), and single-strand (SS) configurations. Principal component analysis (PCA) effectively segregated the oligonucleotides into three distinct conformational groups (G4s, iMs, and others). Furthermore, linear discriminant analysis (LDA), K-nearest neighbor (KNN), and support vector machine (SVM) approaches were utilized to improve the typing accuracy of 54 trained sequences. This enabled the correct classification of the structures of five untrained sequences, as well as the identification of the predominant conformations including G4, iM, and DS formed by two complementary G-rich and C-rich sequences in acidic and neutral pH conditions. The results of this study demonstrated the potential of the proposed methodology for rapid screening and prediction of secondary DNA conformations.

Liquid droplets, formed by intracellular liquid-liquid phase separation (LLPS), are called membraneless organelles. They provide transient enzymatic reaction fields for maintaining cellular homeostasis, although they might transform into aggregates, leading to neurodegenerative diseases. To understand the nature of intracellular droplets, it is crucial to quantify the liquid droplets inside a living cell as well as to elucidate the underlying biological mechanism. In this study, we performed near-infrared fluorescence and Raman imaging to quantify chemical components inside stress granules (SGs) formed by LLPS in living cells. The Raman images reveal that the nucleic acid concentration inside the SGs was more than 20% higher than the surrounding cytoplasm, whereas the lipid concentration was lower. Quantitative Raman intensity analysis using a water Raman band as an internal standard enables in situ concentration determination of nucleic acids in the SGs and other organelles. The intensity of the biomolecular C-H bands relative to the water band indicates that the crowding environment inside the SGs depends on the stress type; under oxidative stress, the inside of the SGs was nearly identical to the outside, whereas it was sparser in hyperosmotic stressed cells, suggesting that the high concentrations of nucleic acids play a pivotal role in maintaining the environments inside the SGs. These results demonstrate that intracellular droplets are not always highly condensed.

Cell membranes are primarily composed of lipids, membrane proteins, and carbohydrates, and the related studies of membrane components and structures at different stages of disease development, especially membrane proteins, are of great significance. Here, we investigate the chemical signature profiles of cell membranes as biomarkers for cancer cells via label-free surface-enhanced Raman scattering (SERS). A magnetic plasmonic nanoprobe was proposed by decorating magnetic beads with silver nanoparticles, allowing self-driven cell membrane-targeted accumulation within 5 min. SERS profiles of three types of breast cells were achieved under the plasmon enhancement effect of these nanoprobes. Membrane fingerprint spectra from breast cell lines were further classified with the convolutional neural network model, which perfectly distinguished between two breast cancer subtypes. We further tested various clinical samples using this method and obtained fingerprint spectra from primary cells and frozen slices. This study presents a practical, user-friendly approach for label-free and in situ analysis of cell membranes, which can work for early tumor screening and treatment assessment for tumors reliant on the Molecular profiles of cell membranes. Additionally, this method can be applied universally to explore cell membrane components of other cells, thus assisting Human Cell Atlas.

Sports doping remains a significant challenge in competitive sports. Given that urine analysis is the standard for detecting doping, developing rapid, sensitive, accurate, and high-throughput methods for stimulant detection in urine is crucial. Surface-enhanced Raman scattering (SERS) tag-based immunoassays have emerged as powerful analytical tools known for their high sensitivity and specificity, holding particular promise for stimulant detection in urine samples. However, both the Raman signals of typical SERS tags and sample matrices are within the Raman fingerprint region (<1800 cm -1 ), which could lead to spectrum overlap, potentially reducing detection accuracy and sensitivity. By recognizing this, we designed a competitive immunoassay that integrates two types of zero-background SERS tags and magnetic separation. These innovative SERS tags exhibit distinctive Raman peaks within the Raman-silent region (1800-2800 cm -1 ), effectively mitigating potential spectrum overlap with background sample signals. Moreover, magnetic separation not only enhances operational simplicity but also improves the system's anti-interference capability. Using clenbuterol (CL) and higenamine (HM) as model targets, the SERS-based competitive immunoassay demonstrated sensitive detection of individual CL or HM standards, with limits of detection (LODs) of 0.87 and 0.71 pg/mL, respectively. In multiplex mode, CL and HM can be simultaneously detected with LODs of 1.0 and 0.81 pg/mL, respectively. Furthermore, the recovery rates in urine samples ranged from 83 to 116% (relative standard deviation, RSD ≤ 6.4%) for CL and from 82 to 103% (RSD ≤ 5.1%) for HM, further confirming the reliability of the SERS-based immunoassay for practical applications.

Uric acid is typically measured through blood tests, which can be inconvenient and uncomfortable for patients. Herein, we propose a wearable surface-enhanced Raman scattering (SERS) chip, incorporating a hydrogel membrane with integrated plasmonic trimers, for noninvasive monitoring of uric acid in sweat. The plasmonic trimers feature sub 5 nm nanogaps, generating strong electromagnetic fields to boost the Raman signal of surrounding molecules. Simultaneously, the hydrogel membrane pumps sweat through these gaps, efficiently capturing sweat biomarkers for SERS detection. The chip can achieve saturation adsorption of sweat within 5 min, eliminating variations in individual sweat production rates. Dynamic SERS tracking of uric acid and lactic acid levels during anaerobic exercise reveals a temporary suppression of uric acid metabolism, likely due to metabolic competition with lactic acid. Furthermore, long-term monitoring correlates well with blood test results, confirming that regular exercise helps reduce serum uric acid levels and supporting its potential in managing hyperuricemia.

Nanoplastics (NPs) are emerging pollutants that undergo inevitable aging in the environment, raising concerns about human exposure and health hazards. Research on the cytotoxicity of various polymer types of NPs, aged nanoplastics (aNPs), and their interactions with proteins (aNPs-protein corona) is still nascent. Traditional cytotoxicity detection methods often rely on end point assays with restricted temporal resolution and analysis of single or multiple biomarkers. Here, we propose a novel approach integrating the 3D dynamic SERS strategy (DSS) with machine learning to rapidly analyze the cell fate and death modes induced by NPs, aNPs, and aNPs-protein corona complexes at the molecular level. PS, PVC, PMMA, and PC products from the water environment were used to prepare the corresponding NPs, and the impact of UV irradiation on their physicochemical properties was examined. DSS systematically maps the molecular changes in the cellular secretome caused by these NPs. Machine learning effectively extracts information from complex spectra, differentiating between biological samples. Results show prolonged UV exposure increases cell sensitivity to ferroptosis and cytotoxicity in various aNPs, while the protein corona on aNPs significantly mitigates toxicity associated with surface oxygen-containing functional groups, resulting in a reduced similarity to ferroptosis signatures. 3D DSS with machine learning technique analyzes the overall metabolite profile at the molecular level rather than individual biomarkers. This study is the first attempt to compare the biotoxicity of diverse polymer NPs, aNPs, and aNPs-protein coronas at cellular and molecular levels in human hepatocytes, enhancing our understanding of the complex biological impacts of NPs.

Hepatic fibrosis is a common chronic liver disease, and its severe progression can culminate in cirrhosis and hepatocellular carcinoma (HCC). Precise diagnosis and staging of hepatic fibrosis are essential to prevent liver cirrhosis and HCC. Simultaneous detection of multiplex collagen biomarkers within liver tissue is crucial for staging hepatic fibrosis. We herein for the first time constructed multiplex collagen fingerprinting for the staging of hepatic fibrosis using high-precision fluorescence-guided surface-enhanced Raman scattering (SERS) imaging. SERS/fluorescent probes, collectively referred to as SF, comprising silver nanoparticles (Ag NPs), Raman reporters, and FAM-labeled collagen targeting peptides. These probes exhibit exceptional aqueous dispersion and stability, attributed to the increased number of Asp residues in CTP. Meanwhile, SF probes, namely SF-I, SF-IV, and SF-D have demonstrated specific targeting of type I, type IV, and denatured collagen, respectively, within hepatic fibrotic tissues. The results from fluorescence-guided SERS imaging underscore the method's capacity for typing, localization, and quantification of collagen, thus providing novel insights into collagen's role in the development of hepatic fibrosis. The collagen fingerprinting strategy offers a potent toolkit for the multifaceted profiling of collagen superfamilies, holding significant implications for the precise staging of hepatic fibrosis.

Rapid quantitative testing of illegal drugs is urgently needed for precisely cracking down on drug crimes. Herein, an optimized paper-based surface-enhanced Raman spectroscopy (SERS) platform with patterned printing of plasmonic nanoparticles was constructed for the on-site quick testing of illegal drugs in urine. The filter paper was first coated with a layer of positive-charged chitosan, so as to reduce its roughness by filling the holes of the cellulose matrix and enhance the adhesion of negative-charged silver ink. Subsequently, hydrophobic modification was performed based on the binary silylation reaction, which could obviously improve the sensitivity of the paper-based SERS substrate by concentrating the amount of analyte. Meanwhile, SERS-active silver ink was fabricated and further printed on the surface of the above modified paper with custom-designed pattern (3 × 6). The performance of this SERS platform was assessed by using crystal violet (CV) as a model tag, and the obtained results proved it possesses excellent sensitivity and reproducibility, in which the relative standard deviation (RSD) dropped remarkably. More importantly, as a proof of concept, rapid detection of standard methylamphetamine (MAMP), one of the most widely abused drugs, was achieved with a limit of detection (LOD) of 1.43 ppb using a portable Raman spectrometer. And it also had a good capability in human urine sample detection, with a correlation index ( R 2 ) up to 0.9927. This optimized paper-based SERS platform was easily manufactured, cheap, and portable, providing a new strategy for the on-site detection of illicit drugs.

We report on a surface-enhanced Raman scattering (SERS) platform for the detection of five beta-blockers (β-blockers): atenolol, esmolol, labetalol, sotalol, and propranolol. Key to this platform was a two-dimensional substrate formed by self-assembling large Ag@SiO 2 nanoparticles (Ag@SiO 2 NPs) on a silicon wafer. The close arrangement of these large nanoparticles on the surface generated a strong and uniform electromagnetic field, which enhanced SERS signal intensity for the detection of small amounts of the target molecules. The intensities of characteristic peaks of the five β-blocker drugs increased linearly with the increase of their concentrations in the range of 10 -5 to 10 -8 mol/L. The detection limits were 10 -10 mol/L for propranolol, 10 -9 mol/L for atenolol, labetalol, and sotalol, and 10 -8 mol/L for esmolol. Determination of these five β-blocker drugs added to human urine samples, using a portable Raman spectroscopy instrument, showed quantitative recovery (93-101%). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) of SERS spectral data improved the differentiation among these five β-blockers. This study highlights the potential of the developed SERS platform for rapid, on-site detection of illicit drugs and for antidoping screening.

Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, underscoring an urgent need for strategies that enable early detection and phenotypic classification. Here, we conducted a label-free surface-enhanced Raman spectroscopic (SERS) analysis of serum exosomes from 643 participants to elucidate the biochemical deregulation associated with LC progression and the unique phenotypes of different LC subtypes. Iodide-modified silver nanofilms were prepared to rapidly acquire SERS spectra with a high signal-to-noise ratio using 0.5 μL of patient exosomes. We performed interpretable and automated machine learning (ML) analysis of differential SERS features of serum exosomes to build LC diagnostic models, which achieved accuracies of 100% and 81% for stage I lung adenocarcinoma and its preneoplasia, respectively. In addition, the ML-derived exosomal SERS models effectively recognized different LC subtypes and disease stages to guide precision treatment. Our findings demonstrate that spectral fingerprinting of circulating exosomes holds promise for decoding the clinical status of LC, thus aiding in improving the clinical management of patients.

In this study, we investigate the impact of deuteration on the formation of phase-separated domains in supported lipid monolayers using hyperspectral Tip-Enhanced Raman Spectroscopy (TERS) imaging. The intricate organization of biological membranes plays a crucial role in cellular functions. Various factors that influence domain formation have been identified in previous studies such as lipid tail length and cholesterol concentration. Deuterium labeling of lipids has proven useful for probing cellular structures and dynamics, but its impact on lipid phase separation remains underexplored. By examining 1:1 mixed monolayers of dipalmitoylphosphatidylcholine (DPPC) and deuterated DPPC on Au(111) surfaces, we reveal partial segregation of domains rich in deuterated and nondeuterated lipids. This study addresses a gap in knowledge by examining the impact of deuteration on lipid tail behavior, offering new insights into how even subtle structural modifications can influence phase behavior. Furthermore, it demonstrates that TERS can be a powerful, nondestructive, and label-free nanoanalytical tool for analyzing lipid membranes and advance the field of membrane biophysics.

Total-internal-reflection tip-enhanced Raman spectroscopy (TIR-TERS) imaging of amyloid-β (Aβ 1-42 -L34T) fibrils is performed with nanoscale spatial resolution in water, using TERS tips fabricated by bipolar electrodeposition. Ideal experimental parameters are corroborated by both theoretical simulations and TIR-TERS measurements. TIR-TERS imaging reveals the predominant parallel β-sheet secondary structure of Aβ 1-42 -L34T fibrils as well as the nanoscale spatial distribution of tyrosine, histidine, and phenylalanine aromatic amino acids. Their proportion in TERS spectra can be qualitatively explained by the combined effect of their localization in the Aβ 1-42 -L34T fibril structure and their molecular orientation with respect to the excitation laser light polarization. Conclusions drawn from the TERS experiments in water corroborate and significantly enrich our previous study in ambient air, thus confirming that hydration has only a marginal impact on the structure of such amyloid fibrils. This first TIR-TERS study in liquid opens fascinating perspectives for future applications in biology.

Conventional assays such as immunohistochemistry (IHC) and in situ hybridization (ISH) used in clinical procedures for quantification of the human epidermal growth factor receptor-2 (HER2) status in breast cancer have many limitations. In the current study, we have used HER2 expression in a broad range of breast cancer phenotypes to explore the potential utility of a novel immunodetection technique using Raman spectroscopy and Raman imaging combined with artificial intelligence models. The correlations between the Raman method and conventional HER2 testing methodologies (IHC and ISH) have been tested. Raman measurements showed a strong linear correlation ( p = 0.05, R 2 =0,9816) with IHC analysis in the studied breast cell lines: MCF-10A, MCF-7, MDA-MB-231, HTB-30 (SK-BR-3), and AU-565 represent normal, nontumorigenic epithelial cells, triple-positive breast carcinoma, and triple-negative breast cancer cell lines. Analytic testing of Raman spectroscopy and Raman imaging demonstrated that this method may offer advantages over the currently used diagnostic methodologies.

It is a big challenge to monitor pathogens in food with high selectivity. In this study, we reported an ultrasensitive method for Escherichia coli O157:H7 detection based on immunomagnetic separation and labeled surface-enhanced Raman scattering (SERS). The bacterium was identified by heterogeneous recognition elements, monoclonal antibody (mAb), and aptamer. E. coli O157:H7 was separated and enriched by magnetic nanoparticles modified by mAb, and then a plasmonic nanostructure functionalized by aptamers with embedded Raman tags and interior gaps was utilized for further discrimination and detection. The selectivity was enhanced by two binding sites. The higher Raman enhancement was obtained by strong local electromagnetic field oscillation in the gap and the firm embedment of 4-mercaptopyridine (4-Mpy). Optimum experiments created that SERS signals of 4-Mpy at 1010 cm -1 had a good linearity with E. coli O157:H7 at a large range of 10 to 10 7 CFU/mL with a limit of detection of 2 CFU/mL. This method has great potential for on-site food pathogenic bacterial detection.

The rapid and sensitive detection of amino acids is important not only for fundamental studies but also for the establishment of a healthy society. However, conventional detection methods have been hampered by the difficulties of low sensitivity, long sampling and detection times, and expensive operation and instruments. Here, we report the plasma engineering of bioresource-derived graphene quantum dots (GQDs) as surface-enhanced Raman scattering (SERS)-active materials for the rapid and sensitive detection of amino acids. Surface-functionalized GQDs with tuned structures and band gaps were synthesized from earth-abundant bioresources by using reactive microplasmas under ambient conditions. Detailed microscopy and spectroscopy studies indicate that the SERS properties of the synthesized GQDs can be tuned by controlling the band gaps of synthesized GQDs. The plasma-synthesized metal-free GQDs with surface functionalities showed improved SERS properties for rapid amino acid detection with low detection limits of 10 -5 M for tyrosine and phenylalanine. Theoretical calculations suggest that charge transfer between GQDs and amino acids can enhance the SERS response of the GQDs. Our work provides insights into the controlled engineering of SERS-active nanographene-based materials using the plasma-enhanced method.

In this study, we propose a novel label-free detection strategy based on surface-enhanced Raman spectroscopy (SERS) for detecting deoxynivalenol (DON) and nivalenol (NIV), analyzing the interactions between these fungal toxins and Ag/Au substrates using density functional theory (DFT). The DFT calculation results indicate that the oxygen atoms in DON and NIV molecules exhibit high electronegativity, suggesting that they can serve as active sites in interaction with the metal surfaces in the SERS effect. By constructing DON/NIV-Ag 6 /Au 6 complex models and evaluating their binding energies, we demonstrate the formation of stable complexes, showing characteristic frequency shifts, broadening, and selective enhancement in theoretical Raman spectra. The results indicate that the structural differences between DON and NIV lead to significant variations in the characteristic frequencies of their theoretical SERS spectra, providing a reproducible and interference-resistant method for the label-free detection of these fungal toxins. This work enhances the understanding of molecular-substrate interactions in SERS effects and provides a theoretical basis for the application of label-free SERS sensing strategies based on characteristic frequency shifts.

Raman spectroscopy of muscle provides a molecular fingerprint to identify the disease. Previous work has demonstrated effectiveness in differentiating between two groups of equal sizes (e.g., healthy vs disease) but imbalanced multiclass scenarios are more common in medicine. We performed in vivo Raman spectroscopy in a total of 151 mice across four different histopathologies (healthy, acute myopathy, chronic myopathy, neurogenic), with variable numbers in each (class "imbalance"). Using hierarchical modeling and synthetic data generation, we demonstrate high sensitivity (94%) for detection of healthy muscle and high specificity (≥97%) for disease. Further, we demonstrate the potential for unique biomarker development by demonstrating variations in the protein structure across different pathologies. The findings demonstrate the potential of Raman spectroscopy to provide accurate disease identification and unique molecular insights.

This study represents a highly sensitive and selective approach to protein screening using surface-enhanced Raman scattering (SERS) facilitated by octahedral Au nanotrenches (OANTs). OANTs are a novel class of nanoparticles characterized by narrow, trench-like excavations indented into the eight facets of a Au octahedron. This unique configuration maximizes electromagnetic near-field focusing as the gap distance decreases to ∼1 nm. Owing to geometrical characteristics of the OANTs, near-field focusing can be maximized through the confinement and reflectance of light trapped within the trenches. We used Ni ions and molecular linkers to confer selective binding affinity for His-tagged proteins on the surfaces of the OANTs for SERS-based protein screening. Remarkably, SERS-based protein screening with the surface-modified OANTs yielded outstanding screening capabilities: 100% sensitivity and 100% selectivity in distinguishing His-tagged human serum albumin (HSA) from native HSA. This highlights the significantly enhanced protein screening capabilities achieved through the synergistic combination of SERS and the OANTs.

In the clinical environment, the identification of phylogenetic closely related genera and species like Clostridium and Bacillus spp. is challenging. Both genera contain representatives of pathogenic and nonpathogenic species that need to be distinguished for a proper diagnostic read-out. Therefore, reliable and accurate detection methods must be employed for the correct identification of these genera and species. Despite their high pathogenicity, clostridial infections and food contaminations present significant challenges due to their unique cultivation conditions and developmental needs. Therefore, in many diagnostic protocols, the toxins are used for microbiological documentation. However, the applied laboratory methods suffer in accuracy, sometimes require large bacterial loads to provide reliable results, and cannot differentiate pathogenic from nonpathogenic strains. Here, Raman spectroscopy was employed to create an extensive Raman database consisting of pathogenic and nonpathogenic Bacillus and Clostridium species. These genera, as well as representatives of Paraclostridium and Clostridioides were specifically selected for their phylogenetic relation, cultivation conditions, and metabolic activity. A chemometric evaluation of the Raman spectra of single vegetative cells revealed a high discriminating power at the genus level. However, bacilli are considerably easier to classify at the species level than clostridia. The discrimination between the genera and species was based on their phylogeny and not their aerobic and anaerobic cultivation conditions. These encouraging results demonstrated that Raman spectroscopy coupled with chemometrics is a robust and helpful method for differentiating Clostridium species from Bacillus , Clostridioides , and Paraclostridium species. This approach has the potential to be a valuable tool in clinical diagnostics.

Artificial intelligence combined with Raman spectroscopy for disease diagnosis is on the rise. However, these methods require a large amount of annotated spectral data for modeling to achieve high diagnostic accuracy. Annotating labels consumes significant medical resources and time. To reduce dependence on labeled medical data resources, we propose a method called Multisource Unsupervised Raman Spectroscopy Domain Adaptation Model with Reconstructed Target Domains (MURDA). It transfers knowledge learned from source domain data sets of different diseases to an unlabeled target domain data set. Compared to knowledge transfer from a single source domain, knowledge from multiple disease source domains provides more generalized knowledge. Considering the diversity of autoimmune diseases and the limited sample size, we apply MURDA to assist in the medical diagnosis of autoimmune diseases. Additionally, we propose a Double-Branch Multiscale Convolutional Self-Attention (DMCS) feature extractor that is more suitable for spectral data feature extraction. On three sets of serum Raman spectroscopy data sets for autoimmune diseases, the multisource domain adaptation diagnostic accuracy of MURDA was superior to traditional single source and multisource models, with accuracy rates of 73.6%, 83.4%, and 82.9%, respectively. Compared with pure source tasks without domain adaptation, it improved by 15.1%, 36%, and 21.6%, respectively. This demonstrates the effectiveness of Raman spectroscopy combined with MURDA in diagnosing autoimmune diseases. We investigated the important decision dependency peaks in migration tasks, providing assistance for future research on artificial intelligence combined with Raman spectroscopy for diagnosing autoimmune diseases. Furthermore, to validate the effectiveness and generalization performance of MURDA, we conducted experiments on the publicly available RRUFF data set, exploring the application of multisource unsupervised domain adaptation in more Raman spectroscopy scenarios.

I., Background: Human induced pluripotent stem cell (hiPSC) derived cardiomyocytes (CMs) have been utilized in drug toxicity evaluation, drug discovery, and treating heart failure patients, showing substantial effects. Ensuring the quality, purity, and maturation of hiPSC-CMs during large-scale production is crucial. There is a growing demand for a novel method to characterize cell molecular profiles without labels and without causing damage. II., Methods: In this study, we employed label-free Raman microscopy to evaluate hiPSC-derived CMs. The study involved the characterization of cell molecular profiles without labels and without causing damage. The correlation between Raman spectroscopy of specific components, such as cytochrome c and myoglobin, and CM purity and maturation following hiPSC differentiation was investigated. Additionally, the validation of this correlation was performed by assessing mixtures of commercially available CMs (iCell cardiomyocytes2) and fibroblasts at various ratios as well as hiPSC-derived CMs with different efficiencies. Furthermore, CMs were matured using rapid pacing of traveling waves, and the Raman profiles of matured CMs were compared to those of immature ones. III., Results: Raman spectroscopy indicated that the cytochrome c and myoglobin showed correlation with the purity and maturation of CMs following differentiation of hiPSCs. This correlation was validated through experiments involving different CM-fibroblast mixtures and hiPSC-derived CMs with varying efficiencies. Moreover, matured CMs exhibited markedly different Raman profiles compared to immature ones, indicating the potential of Raman imaging as a tool for assessing CM maturation. IV., Conclusions: We discovered that Raman spectroscopy of certain components, such as cytochrome c and myoglobin, correlates with the CM purity and maturation following hiPSC differentiation. The findings of this study highlight the potential of label-free Raman microscopy as a nondestructive, high-content, and time-efficient method for quality control of hiPSC-derived CMs. This approach could significantly contribute to ensuring the quality and maturity of hiPSC-CMs for various applications in drug discovery and regenerative medicine.

In the realm of disease diagnostics, particularly for conditions such as proteinuria and hemoglobinuria, the quest for a method that combines accurate, label-free detection of protein compositions and their conformational changes remains a formidable challenge. In this study, we introduce an innovative Ag/Au plasmonic hybrid coupling nanoarray (Ag/Au PHCN) architecture marked by sub-10 nm interparticle gaps. These nanoarrays, leveraging plasmonic hybrid coupling and synergistic enhancement mechanisms, create a plethora of uniform surface-enhanced Raman spectroscopy (SERS) hotspots. The Ag/Au PHCN substrates demonstrated unparalleled sensitivity in the unmarked detection of hemoglobin (HGB), bovine serum albumin (BSA), and cytochrome C (Cyt.C) in bodily fluids, incorporating the advantages of high sensitivity, high reproducibility, durability, recyclability, and biocompatibility. Notably, the detection limits for BSA and HGB are unprecedented at 0.5 and 5 ng/mL, respectively. This achievement sets a new benchmark for label-free protein detection using two-dimensional nanostructures. Crucially, the Ag/Au PHCNs possess the novel capability to discern protein conformational changes post denaturation, underscoring their potential in probing protein functionalities. Most importantly, these nanoarrays can differentiate between normal and proteinuria-affected urine samples and monitor protein content variations over time, heralding a new era in clinical diagnostics with particular relevance to proteinuria and hemoglobinuria detection.

Radiotherapy is one of the most common approaches for cancer treatment, especially in the case of peripheral nervous system tumors. As it requires exposure to high doses of ionizing radiation, it is important to look for substances that support efficient reduction of the tumor volume with simultaneous prevention of the surrounding noncancerous cells. Cannabidiol (CBD), which exhibits both anticancer and neuroprotective properties, was applied as a potential modulator of radiological response; however, its influence on cells undergoing irradiation remains elusive. Here, we have applied high-resolution optical spectroscopy techniques to capture biomolecules associated with CBD shielding of normal and damaging cancerous cells upon X-ray exposure. Conventional Raman (RS) and Fourier transformed infrared (FT-IR) spectroscopies provided semiquantitative information mainly about changes in the concentration of total lipids, DNA, cholesteryl esters, and phospholipids in cells. A through assessment of the single cells by atomic force microscopy coupled with infrared spectroscopy (AFM-IR) allowed us to determine not only the alterations in DNA content but also in its conformation due to cell treatment. Pronounced nanoscale changes in cholesteryl ester metabolites, associated with CBD treatment and radiation, were also observed. AFM-IR chemoselective maps of the single cells indicate the modified distribution of cholesteryl esters with 40 nm spatial resolution. Based on the obtained results, we propose a label-free and fast analytical method engaging optical spectroscopy to assess the mechanism of normal and cancerous cell susceptibility to ionizing radiation when pretreated with CBD.

This paper proposes a novel multicomponent gas-sensing optical fiber probe system. It utilizes a precisely engineered Platinum-coated capillary fabricated via Atomic Layer Deposition (ALD) technology as the core for enhanced Raman spectroscopy, marking the first application of ALD in creating such a structure for gas Raman sensing. The noble metal capillary gas Raman probe demonstrates a low detection limit of 55 ppm for CO 2 with a 30 s exposure time and good repeatability in multicomponent gas sensing. The capillary exhibits excellent stability, environmental resistance, and a large core diameter, enabling a rapid gas exchange rate and making it suitable for practical applications.

Exosomes, nanosized extracellular vesicles containing biomolecular cargo, are increasingly recognized as promising noninvasive biomarkers for cancer diagnosis, particularly for their role in carrying tumor-specific molecular information. Traditional methods for exosome detection face challenges such as complexity, time consumption, and the need for sophisticated equipment. This study addresses these challenges by introducing a novel droplet microfluidic platform integrated with a surface-enhanced Raman spectroscopy (SERS)-based aptasensor for the rapid and sensitive detection of HER2-positive exosomes from breast cancer cells. Our approach utilized an on-chip salt-induced gold nanoparticles (GNPs) aggregation process in the presence of HER2 aptamers and HER2-positive exosomes, enhancing the hot spot-based SERS signal amplification. This platform achieved a limit of detection of 4.5 log 10 particles/mL with a sample-to-result time of 5 min per sample. Moreover, this platform has been successfully applied for HER2 status testing in clinical samples to distinguish HER2-positive breast cancer patients from HER2-negative breast cancer patients. High sensitivity, specificity, and the potential for high-throughput screening of specific tumor exosomes make this SERS-based droplet system a potential liquid biopsy technology for early cancer diagnosis.

Prostate cancer remains a major health concern, with prostate-specific antigen (PSA) being a key biomarker for its detection and monitoring. However, PSA levels often fall into a "gray zone", where PSA levels are not clearly indicative of cancer, thus complicating early diagnosis and treatment decisions. Glycosylation profiles, which often differ between healthy and diseased cells, have emerged as potential biomarkers to enhance the specificity and sensitivity of cancer diagnosis in these ambiguous cases. We propose the integration of two complementary techniques, namely quartz-crystal microbalance with dissipation (QCM-D) and surface-enhanced Raman scattering (SERS) to study PSA glycan profiles. QCM-D offers real-time operation, PSA mass quantification, and label-free detection with high sensitivity, as well as enhanced specificity and reduced cross-reactivity when using nucleic acid aptamers as capture ligands. Complementary SERS sensing enables the determination of the glycosylation pattern on PSA, at low concentrations and without the drawbacks of photobleaching, thereby facilitating multiplexed glycosylation pattern analysis. This integrated setup could retrieve a data set comprising analyte concentrations and associated glycan profiles in relevant biological samples, which may eventually improve early disease detection and monitoring. Prostate-specific antigen (PSA), a glycoprotein secreted by prostate epithelial cells, serves as our proof-of-concept analyte. Our platform allows multiplex targeting of PSA multiplex glycosylation profiles of PSA at "gray zone" concentrations for prostate cancer diagnosis. We additionally show the use of SERS for glycan analysis in PSA secreted from prostate cancer cell lines after androgen-based treatment. Differences in PSA glycan profiles from resistant cell lines after androgen-based treatment may eventually improve cancer treatment.

Small-molecule biomarkers are ubiquitous in biological fluids with pathological implications, but major challenges persist in their quantitative analysis directly in complex clinical samples. Herein, a molecular-sieving label-free surface-enhanced Raman spectroscopy (SERS) biosensor is reported for selective quantitative analysis of trace small-molecule trimetazidine (TMZ) in clinical samples. Our biosensor is fabricated by decorating a superhydrophobic monolayer of microporous metal-organic frameworks (MOF) shell-coated Au nanostar nanoparticles on a silicon substrate. The design strategy principally combines the hydrophobic surface-enabled physical confinement and preconcentration, MOF-assisted molecular enrichment and sieving of small molecules, and sensitive SERS detection. Our biosensor utilizes such a "molecular confinement-and-sieving" strategy to achieve a five orders-of-magnitude dynamic detection range and a limit of detection of ≈0.5 nM for TMZ detection in either urine or whole blood. We further demonstrate the applicability of our biosensing platform for longitudinal label-free SERS detection of the TMZ level directly in clinical samples in a mouse model.

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) is an ultrahigh-resolution spectroscopic method for directly obtaining the complex vibrational mode information on individual molecules. SM-SERS offers a wide range of submolecular information on the hidden heterogeneity in its functional groups and varying structures, dynamics of conformational changes, binding and reaction kinetics, and interactions with the neighboring molecule and environment. Despite the richness in information on individual molecules and potential of SM-SERS in various detection targets, including large and complex biomolecules, several issues and practical considerations remain to be addressed, such as the requirement of long integration time, challenges in forming reliable and controllable interfaces between nanostructures and biomolecules, difficulty in determining hotspot size and shape, and most importantly, insufficient signal reproducibility and stability. Moreover, utilizing and interpreting SERS spectra is challenging, mainly because of the complexity and dynamic nature of molecular fingerprint Raman spectra, and this leads to fragmentary analysis and incomplete understanding of the spectra. In this Perspective, we discuss the current challenges and future opportunities of SM-SERS in views of system approaches by integrating molecules of interest, Raman dyes, plasmonic nanostructures, and artificial intelligence, particularly for detecting and analyzing biomolecules to realize the validation and expansion of information space in SM-SERS.

Since the discovery of penicillin, a vast array of microbial antibiotics has been identified and applied in the medical field. Globally, the search for drug candidates via microbial screening is ongoing. Traditional screening methods, however, are time-consuming and require labor-intensive sample processing, significantly reducing throughput. This research introduces a Raman spectroscopy-based screening system tailored to the in situ analysis of microbial colonies on solid culture media. Employing multivariate curve resolution-alternating least-squares (MCR-ALS) for spectral decomposition, our approach reveals the production of secondary metabolites at the single colony level. We enhanced the microbial culture method, enabling direct, high signal-to-noise (S/N) ratio Raman spectroscopic measurements of colonies of Escherichia coli and actinomycetes species. Through semisupervised MCR analysis using the known spectra of actinorhodin and undecylprodigiosin as references, we accurately assessed the production of these compounds by Streptomyces coelicolor A3(2). Furthermore, we herein successfully detected the production of amphotericin B by Streptomyces nodosus , even in the absence of prior spectral information. This demonstrates the potential of our technique in the discovery of secondary metabolites. In addition to enabling the detection of the above-mentioned compounds, this analysis revealed the heterogeneity of the spatial distribution of their production in each colony. Our technique makes a significant contribution to the advancement of microbial screening, offering a rapid, efficient alternative to conventional methods and opening avenues for secondary metabolites discovery.

Traditional diagnostic methods, such as blood tests, are invasive and time-consuming, while sweat biomarkers offer a rapid physiological assessment. Surface-enhanced Raman spectroscopy (SERS) has garnered significant attention in sweat analysis because of its high sensitivity, label-free nature, and nondestructive properties. However, challenges related to substrate reproducibility and interference from the biological matrix persist with SERS. This study developed a novel ratio-based 3D hydrogel SERS chip, providing a noninvasive solution for real-time monitoring of pH and glucose levels in sweat. Encapsulating the probe molecule (4-MBN) in nanoscale gaps to form gold nanoflower-like nanotags with internal standards and integrating them into an agarose hydrogel to create a 3D flexible SERS substrate significantly enhances the reproducibility and stability of sweat analysis. Gap-Au nanopetals modified with probe molecules are uniformly dispersed throughout the porous hydrogel structure, facilitating the effective detection of the pH and glucose in sweat. The 3D hydrogel SERS chip demonstrates a strong linear relationship in pH and glucose detection, with a pH response range of 5.5-8.0 and a glucose detection range of 0.01-5 mM, with R 2 values of 0.9973 and 0.9923, respectively. In actual sweat samples, the maximum error in pH detection accuracy is only 1.13%, with a lower glucose detection limit of 0.25 mM. This study suggests that the ratio-based 3D hydrogel SERS chip provides convenient, reliable, and rapid detection capabilities with substantial application potential for analyzing body fluid pH and glucose.

Phenylketonuria (PKU) is one of the most common genetic metabolic diseases, especially among newborns. Traditional clinical examination of newborn blood samples for PKU is invasive, laborious, and limited to hospitals and healthcare facilities. We reported herein a SERS-based sensor array with three thiophenolic nanoreceptors built on a patterned nanorod vertical array for rapid and inexpensive detection of characteristic volatile biomarkers indicative of PKU in the urine and accurate classification of newborn baby patients all performed on a hand-held SERS spectrophotometer. The well-ordered array was generated from the volatility-driven assembly of gold nanorods (AuNRs) into an upright and closely packed hexagonal configuration. The uniformly distributed nanowells between AuNRs offered an intense and aspect-ratio-dependent plasmonic field for the molecular enhancement of SERS outputs. The SERS-based detector was integrated into a test chip for regular monitoring of volatile phenylketone bodies in the spiked solution or patients' urine within 5 min, allowing the quantification of a wide variety of normal or abnormal metabolites at their physiologically relevant concentration range. The detection limits for common biomarkers of PKU, including phenylpyruvic acid, 4-hydroxyphenylacetic acid, and phenylacetic acid, were at a few μM and well below the diagnostic thresholds. Moreover, the volatile headspace mixtures from a given urine sample could be fingerprinted by the sensor array and discriminated using machine-learning algorithms. Ultimately, the discrimination of baby patients among 26 cases of mild and classic PKU phenotypes and 17 cases of healthy volunteers could be realized with an overall accuracy of 97%. This hand-held SERS platform plays a pivotal role in advancing healthcare applications in quick screening of neonatal PKU through a facile urinary vapor test.

The architecture of cell culture, two-dimensional (2D) versus three-dimensional (3D), significantly impacts various cellular factors, including cell-cell interactions, nutrient and oxygen gradients, metabolic activity, and gene expression profiles. This can result in different cellular responses during cancer drug treatment, with 3D-cultured cells often exhibiting higher resistance to chemotherapeutic drugs. While various genetic and proteomic analyses have been employed to investigate the underlying mechanisms of this increased resistance, complementary techniques that provide experimental evidence of spatial molecular profiling data are limited. Stimulated Raman scattering (SRS) microscopy has demonstrated its capability to measure both intracellular drug uptake and growth inhibition. In this work, we applied three-band (C-D, C-H, and fingerprint regions) SRS imaging to 2D and 3D cell cultures and performed a comparative analysis of drug uptake and response with the goal of understanding whether the difference in drug uptake explains the drug resistance in 3D culture compared to 2D. Our investigations revealed that despite similar intracellular drug levels in 2D and 3D A549 cells during lapatinib treatment, the growth of 3D spheroids was less impacted, supporting an enhanced drug tolerance in the 3D microenvironment. We further elucidated drug penetration patterns and the resulting heterogeneous cellular responses across different spheroid layers. Additionally, we investigated the role of the extracellular matrix in modulating drug delivery and cell response and discovered that limited drug penetration in 3D could also contribute to lower drug response. Our study provides valuable insights into the intricate mechanisms of increased drug resistance in 3D tumor models during cancer drug treatments.

Monitoring neurochemicals and imaging the molecular content of brain tissues in vitro , ex vivo , and in vivo is essential for enhancing our understanding of neurochemistry and the causes of brain disorders. This review explores the potential applications of surface-enhanced Raman scattering (SERS) nanosensors in neurosciences, where their adoption could lead to significant progress in the field. These applications encompass detecting neurotransmitters or brain disorders biomarkers in biofluids with SERS nanosensors, and imaging normal and pathological brain tissues with SERS labeling. Specific studies highlighting in vitro, ex vivo , and in vivo analysis of brain disorders using fit-for-purpose SERS nanosensors will be detailed, with an emphasis on the ability of SERS to detect clinically pertinent levels of neurochemicals. Recent advancements in designing SERS-active nanomaterials, improving experimentation in biofluids, and increasing the usage of machine learning for interpreting SERS spectra will also be discussed. Furthermore, we will address the tagging of tissues presenting pathologies with nanoparticles for SERS imaging, a burgeoning domain of neuroscience that has been demonstrated to be effective in guiding tumor removal during brain surgery. The review also explores future research applications for SERS nanosensors in neuroscience, including monitoring neurochemistry in vivo with greater penetration using surface-enhanced spatially offset Raman scattering (SESORS), near-infrared lasers, and 2-photon techniques. The article concludes by discussing the potential of SERS for investigating the effectiveness of therapies for brain disorders and for integrating conventional neurochemistry techniques with SERS sensing.

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for discrimination of bimolecules in complex systems. However, its practical applications face challenges such as complicated manufacturing procedures and limited scalability of SERS substrates, as well as poor reproducibility during detection which compromises the reliability of SERS-based analysis. In this study, we developed a convenient method for simultaneous fabrication of massive SERS substrates with an internal standard to eliminate the substrate-to-substrate differences. We first synthesized Au@CN@Au nanoparticles (NPs) which contain embedded internal standard molecules with a single characteristic peak in the Raman-silent region, and then deposited the NPs on 6 mm glass wafers in a 96-well plate simply by centrifugation for 3 min. The one-time obtained 96 SERS substrates have excellent intrasubstrate uniformity and intersubstrate repeatability for SERS detection by using the internal standard (relative standard deviation = 10.47%), and were able to detect both charged and neutral molecules (crystal violet and triphenylphosphine) at a concentration of 10 -9 M. Importantly, cells can be directly cultured on glass wafers in the 96-well plate, enabling real time monitoring of the secretes and metabolism change in response to external stimulation. We found that the release of nucleic acids, amino acids and lipids by MDA-MB-231 cells significantly increased under hypoxic conditions. Overall, our approach enables fast and large-scale production of Au@CN@Au NPs-coated glass wafers as SERS substrates, which are homogeneous and highly sensitive for monitoring trace changes of biomolecules.

In the intricate landscape of the tumor microenvironment, both cancer and stromal cells undergo rapid metabolic adaptations to support their growth. Given the relevant role of the metabolic secretome in fueling tumor progression, its unique metabolic characteristics have gained prominence as potential biomarkers and therapeutic targets. As a result, rapid and accurate tools have been developed to track metabolic changes in the tumor microenvironment with high sensitivity and resolution. Surface-enhanced Raman scattering (SERS) is a highly sensitive analytical technique and has been proven efficient toward the detection of metabolites in biological media. However, profiling secreted metabolites in complex cellular environments such as those in tumor-stroma 3D in vitro models remains challenging. To address this limitation, we employed a SERS-based strategy to investigate the metabolic secretome of pancreatic tumor models within 3D cultures. We aimed to monitor the immunosuppressive potential of stratified pancreatic cancer-stroma spheroids as compared to 3D cultures of either pancreatic cancer cells or cancer-associated fibroblasts, focusing on the metabolic conversion of tryptophan into kynurenine by the IDO-1 enzyme. We additionally sought to elucidate the dynamics of tryptophan consumption in correlation with the size, temporal evolution, and composition of the spheroids, as well as assessing the effects of different drugs targeting the IDO-1 machinery. As a result, we confirm that SERS can be a valuable tool toward the optimization of cancer spheroids, in connection with their tryptophan metabolizing capacity, potentially allowing high-throughput spheroid analysis.

Rapid identification of drug mechanisms is vital to the development and effective use of chemotherapeutics. Herein, we develop a multichannel surface-enhanced Raman scattering (SERS) sensor array and apply deep learning approaches to realize the rapid identification of the mechanisms of various chemotherapeutic drugs. By implementing a series of self-assembled monolayers (SAMs) with varied molecular characteristics to promote heterogeneous physicochemical interactions at the interfaces, the sensor can generate diversified SERS signatures for directly high-dimensionality fingerprinting drug-induced molecular changes in cells. We further train the convolutional neural network model on the multidimensional SAM-modulated SERS data set and achieve a discriminatory accuracy toward 99%. We expect that such a platform will contribute to expanding the toolbox for drug screening and characterization and facilitate the drug development process.

Surface-enhanced Raman scattering (SERS) represents a promising avenue for DNA detection as it offers intrinsic chemical insights with high sensitivity compared to conventional methods. However, label-free and quantitative detection of unmodified DNA by SERS remains a major challenge in DNA analysis. To overcome this challenge, we propose a positively charged plasmonic nanosurface for DNA capture and quantitative analysis. Highly sensitive and uniform SERS enhancement was realized by a three-dimensional plasmonic array supporting well-designed hybrid plasmonic modes. Subsequently, the plasmonic array was modified with an electrostatically functionalized PDDA (poly(diene-dimethylammonium-chloride)) self-assembled monolayer in a single step. The effectiveness of the resulting PDDA-SERS substrate was demonstrated by the label-free and quantitative detection of base content and base mutation in hybridized DNA. The PDDA-SERS substrate provides a robust platform for SERS analysis not only of DNA but also of other electronegative analytes.

Plasmonic Au-Ag nanostars are excellent surface-enhanced Raman scattering (SERS) probes due to bimetallic coupling and the tip effect. However, the existing preparation methods of AuAg nanostars cannot achieve controlled growth of the Ag layer on the branches of nanostars and so cannot display their SERS to the maximum extent, thus limiting its sensitivity in biosensing. Herein, a novel strategy "PEI (polyethylenimine)-guided Ag deposition method" is proposed for synthesizing AuAg core-shell nanostars (AuAg@Ag NS) with a tunable distribution of the Ag layer from the core to the tip, which offers an avenue for investigating the correlation between SERS efficiency and the extent of spread of the Ag layer. It is found that AuAg@Ag NS with a Ag layer coated the whole branch has the strongest SERS performance because the coupling between the tips and Ag layer is maximized. Meanwhile, as a completely closed core-shell structure, AuAg@Ag NS can confine and anchor 4-ATP inside the Ag layer to avoid an unstable SERS signal. By connecting the aptamer, a reliable internal standard nanoprobe with a SERS enhancement factor (EF) up to 1.86 × 10 8 is prepared. Okada acid is detected through competitive adsorption of this SERS probes, and the detection limit is 36.6 pM. The results gain fundamental insights into tailoring the nanoparticle morphologies and preparation of internal standard nanoprobes and also provide a promising avenue for marine toxin detection in food safety.

Raman spectroscopy has become an important single-cell analysis tool for monitoring biochemical changes at the cellular level. However, Raman spectral data, typically presented as continuous data with high-dimensional characteristics, is distinct from discrete sequences, which limits the application of deep learning-based algorithms in data analysis due to the lack of discretization. Herein, a model called fragment-fusion transformer is proposed, which integrates the discrete fragmentation of continuous spectra based on their intrinsic characteristics with the extraction of intrafragment features and the fusion of interfragment features. The model integrates the intrinsic feature-based fragmentation of spectra with transformer, constructing the fragment transformer block for feature extraction within fragments. Interfragment information is combined through the pyramid design structure to improve the model's receptive field and fully exploit the spectral properties. During the pyramidal fusion process, the information gain of the final extracted features in the spectrum has been enhanced by a factor of 9.24 compared to the feature extraction stage within the fragment, and the information entropy has been enhanced by a factor of 13. The fragment-fusion transformer achieved a spectral recognition accuracy of 94.5%, which is 4% higher compared to the method without fragmentation and fusion processes on the test set of cell Raman spectroscopy identification experiments. In comparison to common spectral classification models such as KNN, SVM, logistic regression, and CNN, fragment-fusion transformer has achieved 4.4% higher accuracy than the best-performing CNN model. Fragment-fusion transformer method has the potential to serve as a general framework for discretization in the field of continuous spectral data analysis and as a research tool for analyzing the intrinsic information within spectra.

Surface-enhanced Raman scattering (SERS) offers a promising, cost-effective alternative for the rapid, sensitive, and quantitative analysis of potential biomarkers in exhaled gases, which is crucial for early disease diagnosis. However, a major challenge in SERS is the effective detection of gaseous analytes, primarily due to difficulties in enriching and capturing them within the substrate's "hotspot" regions. This study introduces an advanced gas sensor combining mesoporous gold (MesoAu) and metal-organic frameworks (MOFs), exhibiting high sensitivity and rapid detection capabilities. The MesoAu provides abundant active sites and interconnected mesopores, facilitating the diffusion of analytes for detection. A ZIF-8 shell enveloping MesoAu further enriches target molecules, significantly enhancing sensitivity. A proof-of-concept experiment demonstrated a detection limit of 0.32 ppb for gaseous benzaldehyde, indicating promising prospects for the rapid diagnosis of early stage lung cancer. This research also pioneers a novel approach for constructing hierarchical plasmonic nanostructures with immense potential in gas sensing.