Machine learning-based exosome profiling of multi-receptor SERS sensors for differentiating adenocarcinoma in situ from early-stage invasive adenocarcinoma.

Exosomes, extracellular vesicles released by cells, hold potential as diagnostic markers for the early detection of lung cancer. Despite their clinical promise, current technologies lack rapid and effective means to discriminate between exosomes derived from adenocarcinoma in situ (AIS) and early-stage invasive adenocarcinoma (IAC). This challenge arises from the intrinsic structural heterogeneity of exosomes, necessitating the development of advanced methodologies for precise differentiation. Here, we demonstrate a novel approach for plasma exosome detection utilizing multi-receptor surface-enhanced Raman spectroscopy (SERS) technology to differentiate between AIS and early-stage IAC. To accomplish this, we synthesized a stable and uniform two-dimensional SERS substrate (BC/Au NPs film) by fabricating gold nanoparticles onto bacterial cellulose. We then enhanced its capabilities by introducing multi-receptor SERS functionality via modifying the substrate with both low-specificity and physicochemical-selective molecules. Furthermore, by strategically combining all capturer-exosome SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of the exosome. Combining these features with partial least squares regression-discriminant analysis (PLS-DA) modeling significantly improved discriminatory accuracy, achieving 90% sensitivity and 95% specificity in distinguishing AIS from early-stage IAC. Our developed SERS sensor provides an effective method for early detection of lung cancer, thereby paving a new way for innovative advancements in diagnosing lung cancer.

Two-dimensional substrate assisted SERS immunosensor for accurate detection of carcinoembryonic antigen.

Accurate and sensitive detection of carcinoembryonic antigen (CEA) is essential for the detection of various diseases in healthcare and the medical field. Currently, due to the high false negative rate of CEA assay in clinical setting and its use as a common indicator for early cancer screening, a novel CEA detection method with high sensitivity, increased specificity and the lower cost has become a clinical challenge. Here, a facile sandwich type immunosensor based on surface-enhanced Raman scattering (SERS) was presented including 4-mercaptobenzonitrile (4MBN) labeled gold core-silver shell nanoparticles (Au@4MBN@Ag NPs) as SERS tag and 4-mercaptophenylboronic acid (4-MPBA) functionalized two-dimensional (2D) silver nanoparticle film (Ag FM) as SERS capture substrate for CEA detection. A linearity of 10-9-10-14M was observed with high sensitivity and excellent selectivity for the detection of CEA. Additionally, the spiking experiment yielded 105.33-127.00% recovery with variation coefficients below 10% demonstrating high assay accuracy and precision. The immunosensor we proposed here is a promising approach to quantify CEA in liquid biopsy samples with high sensitivity, which could be further developed for early cancer screening.

Machine learning-assisted global DNA methylation fingerprint analysis for differentiating early-stage lung cancer from benign lung diseases.

DNA methylation plays a critical role in the development of human tumors. However, routine characterization of DNA methylation can be time-consuming and labor-intensive. We herein describe a sensitive, simple surface-enhanced Raman spectroscopy (SERS) approach for identifying the DNA methylation pattern in early-stage lung cancer (LC) patients. By comparing SERS spectra of methylated DNA bases or sequences with their counterparts, we identified a reliable spectral marker of cytosine methylation. To move toward clinical applications, we applied our SERS strategy to detect the methylation patterns of genomic DNA (gDNA) extracted from cell line models as well as formalin-fixed paraffin-embedded tissues of early-stage LC and benign lung diseases (BLD) patients. In a clinical cohort of 106 individuals, our results showed distinct methylation patterns in gDNA between early-stage LC (n = 65) and BLD patients (n = 41), suggesting cancer-induced DNA methylation alterations. Combined with partial least square discriminant analysis, early-stage LC and BLD patients were differentiated with an area under the curve (AUC) value of 0.85. We believe that the SERS profiling of DNA methylation alterations, together with machine learning could potentially offer a promising new route toward the early detection of LC.

Two-dimensional glass/p-ATP/Ag NPs as multifunctional SERS substrates for label-free quantification of uric acid in sweat.

Abnormal uric acid (UA) content in body fluids can fully reflect the status of metabolism and immunity in the body. We have developed a simple, efficient and label-free surface enhanced Raman scattering (SERS) method for UA detection. Briefly, p-aminothiophenol (p-ATP) was used as the internal standard molecule and linking molecule to prepare a glass/p-ATP/Ag NPs SERS substrate. The Raman characteristic peak of p-ATP at 1076 cm-1 can be used as an internal standard molecule to correct the signal fluctuation of UA detection. The results show that the SERS method owns a linear response with a ranging from 5 × 10-6 to 10-3 M of UA characteristic peak of both 693 cm-1 and 493 cm-1 with a determination coefficient (R2) of 0.9878 and 0.9649, respectively. Additionally, the SERS sensor has been further used for the analysis of UA in sweat and good recoveries were obtained for the sensing of sweat. We believe that the developed SERS substrate has potential for applications in healthcare monitoring.

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

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

Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.

BACKGROUND/PURPOSE OF THE STUDY: There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH. METHODS: Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module. RESULTS: Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70-0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48-0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51-0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups. CONCLUSIONS: Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers ( http://www.pan-chess.cn/calculator/RAMAN_score ).

Ratiometric SERS quantitative analysis of tyrosinase activity based on gold-gold hybrid nanoparticles with Prussian blue as an internal standard.

Tyrosinase (TYR) is a polyphenol oxidase that regulates melanin biosynthesis. Abnormal levels of TYR have been confirmed closely associated with melanoma cancer and other diseases, making the establishment of highly sensitive and accurate quantitative detection of TYR is thus essential for fundamental research and clinical applications. Herein, we proposed a new strategy that combines surface-enhanced Raman scattering (SERS) with Dopamine (DA) and Prussian blue (PB) functionalized gold-gold hybrid nanoparticles for TYR detection. DA is oxidized to dopaquinone with the presence of TYR, leading to the consumption of DA in the reaction solution and the corresponding decrease in DA characteristic peak intensity at 1480 cm-1. Our SERS quantitative assay of TYR with "on-off" sensing strategy yields an excellent limit of detection (LOD) of 3 × 10-3 U mL-1 in a linear range of 10-3 to 100 U mL-1. In particular, the intensity ratio of 1480 cm-1 to 2121 cm-1 allows us to achieve remarkably reliable quantitative detection of TYR, with the 2121 cm-1 peak intensity of the standard internal (IS) molecule PB being used to correct SERS signal fluctuations. Furthermore, our proposed assay has been successfully demonstrated to quantify TYR spiked in human serum samples, with excellent percentage recovery of 90.0-110.6 %. The potential of our ratiometric SERS strategy for the performance evaluation of TYR inhibitors has also been verified. Our work is therefore expected to provide a new route for accurate and reliable monitoring of TYR activity in the complex biological environment.

Early detection of lung cancer via biointerference-free, target microRNA-triggered core-satellite nanocomposites.

MicroRNAs (miRNAs) are emerging as essential liquid biopsy markers for early cancer detection. Currently, the clinical applications of miRNAs are lagging behind due to their high sequence similarity and rarity. Herein, we propose biointerference-free, target-triggered core-satellite nanocomposites for ultrasensitive surface-enhanced Raman spectroscopy (SERS) detection of lung cancer-related miRNA-21. Through the hybridization-based recognition effect, we observe an enormous SERS signal enhancement caused by miRNA-21-triggered assembly of core-satellite nanocomposites. This enables the sensitive detection of miRNA-21 down to the 0.1 fM level in a linear range of 10 fM to 1 nM. The use of a biointerference-free reporter further allows quantitative and direct detection of miRNA-21 from complex plasma samples, without RNA pre-extraction. As a proof of principle, we measure the level of plasma miRNA-21 in 20 lung cancer patients and 10 healthy participants. Significantly higher levels of miRNA-21 are determined in lung cancer patients than in healthy participants, with clear lower expression in stage I (n = 10) than in stage III-IV (n = 10) lung cancer patients. We, therefore, believe that this proposed strategy will have high clinical potential for sensitive quantification of miRNA markers in liquid biopsy samples and act as a complementary method for the early detection of lung cancer.

Facilely self-assembled and dual-molecule calibration aptasensor based on SERS for ultra-sensitive detection of tetrodotoxin in pufferfish.

Tetrodotoxin (TTX) is one of the most lethal neurotoxins, so the reliable quantitative analysis of TTX is crucial for food and environmental safety monitoring. Herein, a novel dual-molecule calibration aptasensor was developed for detection of TTX based on Surface-enhanced Raman scattering (SERS). The adaptive surface has high affinity recognition sites for the target of interest, which ensures the high specificity and stability of the aptasensor. In addition, the uniquely labeled signal molecules located in the Raman silent region (1800-2400 cm-1) can avoid the interference of other exogenous biological signal molecules. Meanwhile, in quantitative analysis, the SERS signal generated by the reporter is calibrate in real time using the second-order peak of silicon molecule (Si). The detection linear range of the aptasensor was 0.0319 ng/mL-319.27 ng/mL, with a limit of detection (LOD) of 0.024 ng/mL and the excellent uniformity (RSD = 8.8%) for TTX detection. As a promising and versatile detection candidate, the ultra-sensitive quantitative detection aptasensor of TTX had important practical significance, which can offer more favorable persuasion for TTX analysis in real seafood samples.

Reusable 3D silver superposed silica SERS substrate based on the Griess reaction for the ratiometric detection of nitrite.

When nitrite is ingested and absorbed by the body, it can be converted into highly toxic nitrosamines (carcinogens, teratogens, and mutagens), posing health risks to the general population. Therefore, it calls for establishing a method for determination of nitrite. In this paper, the glass-SiO2-Ag surface-enhanced Raman scattering (SERS) substrate with a large number of "hot spots" were prepared by two kinds of silane coupling agents. The SERS substrate had high sensitivity and repeatability. Silicon dioxide supported the silver nanoparticles (Ag NPs), which increased surface roughness of the substrate, generated a great quantity of hot spots and enhanced the SERS signal. In the SERS spectrum, the intensity ratio of the two characteristic peaks (1287 cm-1 and 1076 cm-1) had a good linear correlation with the logarithm of the concentration of nitrite, R2 = 0.9652. The recoveries of 50 µM and 100 µM nitrite in three kinds of foods, three kinds of cosmetics and tap water were 90.9-105.3%.

Label-free Raman spectroscopy: A potential tool for early diagnosis of diabetic keratopathy.

Diabetes has become a major public health problem worldwide, and the incidence of diabetes has been increasing progressively. Diabetes is prone to cause various complications, among which diabetic keratopathy (DK) emphasizes the significant impact on the cornea. The current diagnosis of DK lacks biochemical markers that can be used for early and non-invasive screening and detection. In contrast, in this study, Raman spectroscopy, which demonstrates non-destructive, label-free features, especially the unique advantage of providing molecular fingerprint information for target substances, were utilized to interrogate the intrinsic information of the corneal tissues from normal and diabetic mouse models, respectively. Visually, the Raman spectral response derived from the biochemical components and biochemical differences between the two groups were compared. Moreover, multivariate analysis methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were carried out for advanced statistical analysis. PCA yields a diagnostic results of 57.4% sensitivity, 89.2% specificity, 74.8% accuracy between the diabetic group and control group; Moreover, PLS-DA was employed to enhance the diagnostic ability, showing 76.1% sensitivity, 86.1% specificity, and 87.6% accuracy between the diabetic group and control group. Our proof-of-concept results show the potential of Raman spectroscopy-based techniques to help explore the underlying pathogenesis of DK disease and thus be further expanded for potential applications in the early screening of diabetic diseases.

Interference-free SERS tags for ultrasensitive quantitative detection of tyrosinase in human serum based on magnetic bead separation.

Tyrosinase (TYR) expression and activity determine the rate and yield of melanin production. Studies have shown that TYR is a potential biomarker for melanoma and highly sensitive detection of TYR benefits early diagnosis of melanoma-related diseases. In this study, we developed a method that combines surface-enhanced Raman scattering (SERS) and sandwich-type immunity for sensitive detection of TYR, in which 4-mercaptobenzonitrile (4 MB) embedded between the Au core and Au shell (Au4MB @ Au) core-shell structure was employed as a SERS probe for quantitative detection of TYR while the magnetic bead serves as a capture substrate. Our results demonstrated that under magnetic separation, the specific SERS signal obtained is highly correlated with TYR concentrations. Furthermore, the combination of magnetic beads and Au4MB @ Au core-shell structure significantly improved the sensitivity of the sensing platform, resulting in detection limits of 0.45 ng mL-1. More importantly, the detection and analysis of TYR concentration in human serum samples showed good accuracy and an excellent recovery rate. Accuracy of the system was investigated from % recovery of spiked TYR standard solutions and found to be in the range of 90-104%, which further verified the feasibility and reliability of our method applied in a complex environment. We anticipate this SERS-based immunoassay method to be applied to TYR detection in the clinical setting and to be extended to other promising related fields.

Highly sensitive detection of tryptophan (Trp) in serum based on diazo-reaction coupling with Surface-Enhanced Raman Scattering and colorimetric assay.

Tryptophan plays an important role in the metabolic pathway and tryptophan metabolism is abnormal in tumor tissues. Therefore, Trp and its metabolites in body fluids can be used as tumor markers for cancer monitoring. However, the traditional tryptophan detection method has many shortcomings, such as low sensitivity, time-consuming and so on, which is difficult to meet the clinical needs. Herein, a rapidly, sensitive and reliable method for indirectly detecting Tryptophan (Trp) in serum was proposed by combining diazo-coupling reaction mechanism with surface-enhanced Raman scattering (SERS). The limit of detection (LOD) of Trp can be as low as 20 nM, which is much lower than existing detection methods. In addition, because of obvious change in color in diazo-coupling reaction, a colorimetric detection was applied for convenient determination of Trp in the concentration ranged from 3 × 10-5 - 10-3 M. Furthermore, with the support of good results by SERS assay, the presented method was successfully carried out for the determination of Trp in serum that came from healthy people and colorectal cancer patients. Comparing the difference of Trp content in serum between the two groups, it was found that tryptophan metabolism disorder exists in colorectal cancer patients, which suggested that the accurate detection of tryptophan content may provide important reference for the pathogenesis, prediction and prevention of colorectal cancer.

Silver nanocube coupling with a nanoporous silver film for dual-molecule recognition based ultrasensitive SERS detection of dopamine.

Dopamine (DA) is one of the catecholamine neurotransmitters used for the treatment of neural disorders. In this study, a novel sensor based on surface-enhanced Raman scattering (SERS) with dual molecule-recognition for ultrasensitive detection of DA was presented, with a limit of detection (LOD) of 40 fM, without any pretreatment of clinical samples. To realize the sensitive and selective detection of DA in complex samples, the nanoporous silver film (AgNF) surfaces were functionalized with mercaptopropionic acid (MPA) to accurately capture DA, while silver nanocubes (AgNCs) were modified with 4-mercaptobenzene boronic acid (4-MPBA) as a Raman reporter for the quantitative detection of DA. The nanogaps between AgNCs and the AgNF led to the generation of an abundance of hot spots for the SERS signal and thus effectively improved the sensitivity of DA detection. Measurements of DA concentrations in clinical body fluids such as human serum and urine samples are also demonstrated, showing excellent performance for DA detection in a complex environment. Our results demonstrate the promising potential for the ultrasensitive detection of DA for the potential diagnosis of DA-related diseases.

Interference-free and high precision biosensor based on surface enhanced Raman spectroscopy integrated with surface molecularly imprinted polymer technology for tumor biomarker detection in human blood.

The reliable quantitative analysis of tumor biomarkers in circulating blood is crucial for cancer early screening, therapy monitoring and prognostic prediction. Herein, a novel biosensor combing surface-enhanced Raman spectroscopy (SERS) and surface molecularly imprinted polymer (SMIP) technology was developed for quantitative detection of carcinoembryonic antigen (CEA) that is closely related to several common cancers. Owing to the use of SMIP, recognition sites with high affinity to the target of interest can be well imprinted on the surface of SERS substrate, leading to a more stable and specific capture ability. In addition, two layers of core-shell nanoparticles were integrated to this SERS substrate to form highly efficient electromagnetic enhancement for SERS measurement via the generation of lots of "hot spot". Besides, a unique Raman reporter (CC) with silent Raman signals at 2024 cm-1 was capsulated in the nanoparticles to avoid the optical noises originating from endogenous molecules at fingerprint region (300-1800 cm-1). Meanwhile, we employed an internal standard molecular (CN) to real time correct the fluctuating signals of Raman reporter when performing the quantitative analysis. Due to these features, a limit of detection (LOD) of 0.064 pg mL-1 with the detection range of 0.1 pg mL-1 - 10 µg mL-1 can be achieved by this assay. Excitingly, this technology even showed wonderful performances for CEA detection in real blood from cancer patients, demonstrating great potential for biomarker-based cancer screening.

Facile Ag-Film Based Surface Enhanced Raman Spectroscopy Using DNA Molecular Switch for Ultra-Sensitive Mercury Ions Detection.

Heavy metal pollution has long been the focus of attention because of its serious threat to human health and the environment. Surface enhanced Raman spectroscopy (SERS) has shown great potential for metal detection owing to many advantages, including, requiring fewer samples, its minimal damage to specimen, and its high sensitivity. In this work, we proposed a simple and distinctive method, based on SERS, using facile silver film (Ag-film) combined with a DNA molecular switch, which allowed for the highly specific detection of heavy metal mercury ions (Hg2+). When in the presence of Hg2+ ions, the signals from Raman probes attach to single-stranded DNA, which will be dramatically enhanced due to the specific structural change of DNA strands-resulting from the interaction between Hg2+ ions and DNA bases. This SERS sensor could achieve an ultralow limit of detection (1.35 × 10-15 M) for Hg2+ detection. In addition, we applied this SERS sensor to detect Hg2+ in real blood samples. The results suggested that this SERS platform could be a promising alternative tool for Hg2+ detection in clinical, environmental, and food inspection.

Diazotization-Coupling Reaction-Based Determination of Tyrosine in Urine Using Ag Nanocubes by Surface-Enhanced Raman Spectroscopy.

A novel, simple, and highly sensitive method was developed to detect the concentration of tyrosine-derived azo dye indirectly using silver nanocubes (AgNCs) as a substrate on a super-hydrophobic silver film by surface-enhanced Raman spectroscopy (SERS). Diazotization-coupling reaction occurred between diazonium ions and the phenolic tyrosine, resulting in three new typical peaks in the SERS spectrum of the azo dye that was formed on the AgNCs, indicating strong SERS activity. Subsequently, the limit of detection of this approach was as low as 10-12 M for tyrosine. Moreover, the SERS intensities of the three typical SERS signals of the analyte were linearly correlated with the logarithm of concentration of the Tyrosine. The proposed method shows great potential for tyrosine detection in the urine samples of normal humans.