Gastric cancer detection based on blood plasma surface-enhanced Raman spectroscopy excited by polarized laser light

A pathway for detection of gastric cancer biomarkers via using a layer-by-layer coated D-shaped grinding long-period fiber grating sensor

Gastric cancer significantly contributes to global cancer mortality, often leading to inoperable stages and high recurrence rates post-surgery. Elevated levels of G-17 and G-gly have been identified as potential risk factors, particularly in patients with duodenal ulcers. This study introduces an innovative D-shaped grinding long-period fiber grating sensor (D-LLPFGs) designed for non-invasive detection of the gastrin G-17 antigen, employing a layer-by-layer chemical self-assembly to bond G-17 antibodies onto the fiber surface through hydrogen bonding. The D-LLPFGs sensor demonstrated significant spectral shifts within 1 min of antigen-antibody interaction, highlighting its rapid detection capability. At an optimized antibody concentration of 4 μg/ml, antigen testing across different concentrations (10, 12.5, 20, 50 μg/ml) showed peak changes at 12.5 μg/ml antigen, with a 1.186 nm shift and 0.503 dB loss. The sensor exhibited a wavelength sensitivity of 0.095 nm/μg/ml, indicating its high sensitivity and potential in gastric cancer classification, diagnosis, and treatment. This research concludes that the D-shaped fiber sensor is an effective and sensitive tool for detecting G-17 antigen levels, presenting a significant advancement in non-invasive gastric cancer diagnosis. Its quick response time and high sensitivity highlight its potential for broad biomedical applications, offering a new avenue for early cancer detection and improving patient prognosis. The success of this study opens the door to further exploration and implementation of fiber optic sensors in clinical settings, marking a significant step forward in the fight against gastric cancer.

Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review

In recent decades, research on Extracellular Vesicles (EVs) has gained prominence in the life sciences due to their critical roles in both health and disease states, offering promising applications in disease diagnosis, drug delivery, and therapy. However, their inherent heterogeneity and complex origins pose significant challenges to their preparation, analysis, and subsequent clinical application. This review is structured to provide an overview of the biogenesis, composition, and various sources of EVs, thereby laying the groundwork for a detailed discussion of contemporary techniques for their preparation and analysis. Particular focus is given to state-of-the-art technologies that employ both microfluidic and non-microfluidic platforms for EV processing. Furthermore, this discourse extends into innovative approaches that incorporate artificial intelligence and cutting-edge electrochemical sensors, with a particular emphasis on single EV analysis. This review proposes current challenges and outlines prospective avenues for future research. The objective is to motivate researchers to innovate and expand methods for the preparation and analysis of EVs, fully unlocking their biomedical potential.

Mapping the intellectual structure and emerging trends for the application of nanomaterials in gastric cancer: A bibliometric study

BACKGROUND

Recent reviews have outlined the main nanomaterials used in relation to gastrointestinal tumors and described the basic properties of these materials. However, the research hotspots and trends in the application of nanomaterials in gastric cancer (GC) remain obscure.

AIM

To demonstrate the knowledge structure and evolutionary trends of research into the application of nanomaterials in GC.

METHODS

Publications related to the application of nanomaterials in GC were retrieved from the Web of Science Core Collection for this systematic review and bibliometric study. VOSviewer and CiteSpace were used for bibliometric and visualization analyses.

RESULTS

From 2000 to 2022, the application of nanomaterials in GC developed rapidly. The keyword co-occurrence analysis showed that the related research topics were divided into three clusters: (1) The application of nanomaterials in GC treatment; (2) The application and toxicity of nanomaterials in GC diagnosis; and (3) The effects of nanomaterials on the biological behavior of GC cells. Complexes, silver nanoparticles, and green synthesis are the latest high-frequency keywords that represent promising future research directions.

CONCLUSION

The application of nanomaterials in GC diagnosis and treatment and the mechanisms of their effects on GC cells have been major themes in this field over the past 23 years.

Nasopharyngeal carcinoma cell screening based on the electroporation-SERS spectroscopy

Nasopharyngeal carcinoma (NPC) is a malignant tumor in head and neck. Early diagnosis can effectively improve the survival rate of patients. Nasopharyngeal exfoliative cytology, as a convenient and noninvasive auxiliary diagnostic method, is suitable for the population screening of NPC, but its diagnostic sensitivity is low. In this study, an electroporation-based SERS technique was proposed to detect and screen the clinical nasopharyngeal exfoliated cell samples. Firstly, nasopharyngeal swabs was used to collected the nasopharyngeal exfoliated cell samples from NPC patients (n = 54) and healthy volunteers (n = 60). Then, gold nanoparticles, as the Raman scattering enhancing substrates, were rapidly introduced into cells by electroporation technique for surface-enhanced Raman scattering (SERS) detection. Finally, SERS spectra combined with principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to diagnose and distinguish NPC cell samples. Raman peak assignments combined with spectral differences reflected the biochemical changes associated with NPC, including nucleic acid, amino acid and carbohydrates. Based on the PCA-LDA approach, the sensitivity, specificity and accuracy of 98.15 %, 96.67 % and 97.37 %, respectively, were achieved for screening NPC. This study offers valuable assistance for noninvasive NPC auxiliary diagnosis, and has grate potential in expanding the application of the SERS technique in clinical cell sample testing.

An outlier removal method based on PCA-DBSCAN for blood-SERS data analysis

Surface-enhanced Raman spectroscopy (SERS) has shown promising potential in cancer screening. In practical applications, Raman spectra are often affected by deviations from the spectrometer, changes in measurement environments, and anomalies in spectrum characteristic peak intensities due to improper sample storage. Previous research has overlooked the presence of outliers in categorical data, leading to significant impacts on model learning outcomes. In this study, we propose a novel method, called Principal Component Analysis and Density Based Spatial Clustering of Applications with Noise (PCA-DBSCAN) to effectively remove outliers. This method employs dimensionality reduction and spectral data clustering to identify and remove outliers. The PCA-DBSCAN method introduces adjustable parameters (Eps and MinPts) to control the clustering effect. The effectiveness of the proposed PCA-DBSCAN method is verified through modeling on outlier-removed datasets. Further refinement of the machine learning model and PCA-DBSCAN parameters resulted in the best cancer screening model, achieving 97.41% macro-average recall and 97.74% macro-average F1-score. This paper introduces a new outlier removal method that significantly improves the performance of the SERS cancer screening model. Moreover, the proposed method serves as inspiration for outlier detection in other fields, such as biomedical research, environmental monitoring, manufacturing, quality control, and hazard prediction.

Dual-band chirality-selective absorbing by plasmonic metasurfaces with breaking mirror and rotational symmetry

In this work, we proposed a state-of-the-art metasurface model that breaks the mirror symmetry and rotation symmetry of the structure. It consists of two-layer rotating gold split rings, and has the capability of chirality-selective absorption for circularly polarized light (CPL) in two bands. The absorption peaks for left- and right- circularly polarized (LCP&RCP) light appeared at 989 nm and 1404 nm, respectively, with the maximum absorptivity of 98.5% and 96.3%, respectively. By changing the rotation angle of the two-layer gold split rings, it could also be designed as a single-band chiral metasurface absorber, which only absorbed RCP light but not LCP light, and the absorptivity of RCP light could be up to 97.4%. Furthermore, we found our designed absorbers had the characteristics of great circular dichroism (CD) and symmetric absorption. The physical mechanism of the selective absorption of CPL by the absorbers may be explained by the current vector analysis. In addition, the absorption peak could be tuned with the changing of the geometrical parameters of the structure. The proposed chirality-selective metasurface absorbers could be used in CD spectral detection, optical communication, optical filtering, and other fields.

Detection of nasopharyngeal cancer cells using the laser tweezer Raman spectroscopy technology

Nasopharyngeal cancer (NPC), which arises from the nasopharyngeal epithelial lining, is one of the common malignant otorhinolaryngological tumors in China. Due to its insidious anatomical location and highly invasive and metastatic features, it is challenging to detect NPC at early stages. In this work, a rapid laser tweezer Raman spectroscopic (LTRS) system was built and used to trap and characterize single NPC cells. Using LTRS, high-quality Raman signals of the normal nasopharyngeal cell line (NP69) and NPC cells could be successfully obtained. By analysing the Raman peaks, some unique changes were found in components, such as DNA, amide I and amide III, in NPC cells compared with normal cells. In addition, we also used a multivariate statistical algorithm to establish a diagnostic model for identifying NPC cells with an accuracy of 90.0%. These results demonstrate that LTRS in combination with the multivariate statistical analysis is a convenient and high-efficiency cell identification technology, providing a novel and rapid methodology for NPC detection at the single cell level.

Solid-State Au Nanocone Arrays Substrate for Reliable SERS Profiling of Serum for Disease Diagnosis

Surface-enhanced Raman scattering (SERS) is a widely used rapid and noninvasive method for detecting biological substances in serum samples and is commonly employed in disease screening and diagnosis. Solid-state nanoarray SERS substrates used in serum detection may cause spectral instability due to imperfections in the detection method. For the purpose of identifying optimal detection conditions, various dilution levels of the serum were tested in this study. The study found that a complete and stable serum SERS spectrum can be obtained when the serum is diluted by a factor of 50. The study reports the successful preparation of an Au nanocone array (Au NCA) plasmonic substrate with a uniform, controllable microstructure and high activity, achieved through a combination of PS colloidal sphere template-assisted reactive ion etching (RIE) process and magnetron sputtering deposition technology. Based on this substrate, a standard detection scheme was developed to obtain highly stable and repeatable serum SERS spectra. The study verified the reliability of the optimized serum detection scheme by comparing the SERS spectra of serum samples from healthy individuals and gastric cancer patients, and confirmed the potential benefits of the scheme for disease screening and diagnosis.

An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker

BACKGROUND AND OBJECTIVE

Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca.

METHODS

Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELİSA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study.

RESULTS

In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm^-1 was observed and, the Raman intensity of amide III, II, I, and CH₂ proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm^-1, as well as between 2700 and 3000 cm^-1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm^-1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm.

CONCLUSIONS

The obtained results suggest, that Raman shifts at 1302 and 1306 cm^-1 could be spectroscopic markers of gastric cancer.

Surface-enhanced Raman spectroscopy for the characterization of different anatomical subtypes of oral cavity cancer

BACKGROUND

The prognosis for oral cancer patients is still very poor worldwide. Early detection and treatment therapy remain the key issue to be addressed for improved patient survival. The characteristic Raman spectral features associated with the biochemical changes in the blood serum samples can be used for the diagnosis of diseases, particularly for oral cancer. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for non-invasive and early detection of oral cancer by analyzing molecular changes in body fluids.

OBJECTIVES

To detect oral cavity anatomical subsites (buccal mucosa, cheek, hard palate, lips, mandible, maxilla, tongue and tonsillar region) cancers by using blood serum samples, SERS with principal component analysis is used.

MATERIAL AND METHOD

SERS is employed with silver nanoparticles for the analysis and detection of oral cancer serum samples by comparing with healthy serum samples. SERS spectra are recorded by Raman instrument and preprocessed using the statistical tool. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) are used to discriminate between oral cancer serum samples and control serum samples.

RESULTS

Some major SERS peaks are observed at 1136 cm^-1 (Phospholipids) and 1006 cm^-1 (Phenylalanine) remain higher in intensities for oral cancer spectra as compared to healthy spectra. The peak at 1241 cm^-1 (amide III) is observed only in oral cancer serum samples while absent in healthy serum samples. Higher protein and DNA contents were detected in SERS mean spectra of oral cancer. Moreover, PCA is used to identify the biochemical differences in the form of SERS features which is used to differentiate between oral cancer and healthy blood serum samples, while PLS-DA is used to build differentiation model of oral cancer serum samples and healthy control serum samples. PLS-DA provides successful differentiation with 94% specificity and 95.5% sensitivity.

CONCLUSIONS

SERS can be used for the diagnosis of oral cancer and to identify metabolic changes that occur during disease development.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Combining Raman spectroscopy and machine learning to assist early diagnosis of gastric cancer

Gastric cancers, with gastric adenocarcinoma (GAC) as the most common histological type, cause quite a few of deaths. In order to improve the survival rate after GAC treatment, it is important to develop a method for early detection and therapy support of GAC. Raman spectroscopy is a potential tool for probing cancer cell due to its real-time and non-destructive measurements without any additional reagents. In this study, we use Raman spectroscopy to examine GAC samples, and distinguish cancerous gastric mucosa from normal gastric mucosa. Average Raman spectra of two groups show differences at 750 cm^-1, 1004 cm^-1, 1449 cm^-1, 1089-1128 cm^-1, 1311-1367 cm^-1 and 1585-1665 cm^-1, These peaks were assigned to cytochrome c, phenylalanine, phospholipid, collagen, lipid, and unsaturated fatty acid respectively. Furthermore, we build a SENet-LSTM model to realize the automatic classification of cancerous gastric mucosa and normal gastric mucosa, with all preprocessed Raman spectra in the range of 400-1800 cm^-1 as input. An accuracy 96.20% was achieved. Besides, by using masking method, we found the Raman spectral features which determine the classification and explore the explainability of the classification model. The results are consistent with the conclusions obtained from the average spectrum. All results indicate it is potential for pre-cancerous screening to combine Raman spectroscopy and machine learning.

Rapid Identification of Benign Gallbladder Diseases Using Serum Surface-Enhanced Raman Spectroscopy Combined with Multivariate Statistical Analysis

In this study, we looked at the viability of utilizing serum to differentiate between gallbladder (GB) stones and GB polyps using Surface-enhanced Raman spectroscopy (SERS), which has the potential to be a quick and accurate means of diagnosing benign GB diseases. Rapid and label-free SERS was used to conduct the tests on 148 serum samples, which included those from 51 patients with GB stones, 25 patients with GB polyps and 72 healthy persons. We used an Ag colloid as a Raman spectrum enhancement substrate. In addition, we employed orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component linear discriminant analysis (PCA-LDA) to compare and diagnose the serum SERS spectra of GB stones and GB polyps. The diagnostic results showed that the sensitivity, specificity, and area under curve (AUC) values of the GB stones and GB polyps based on OPLS-DA algorithm reached 90.2%, 97.2%, 0.995 and 92.0%, 100%, 0.995, respectively. This study demonstrated an accurate and rapid means of combining serum SERS spectra with OPLS-DA to identify GB stones and GB polyps.

Noninvasive cholangitis and cholangiocarcinoma screening based on serum Raman spectroscopy and support vector machine

The feasibility of serum Raman spectroscopy for rapid screening of cholangitis and cholangiocarcinoma (CCA) was explored Raman spectra were collected from 49 patients with cholangitis, 38 patients with CCA, and 55 healthy volunteers. Normalized mean Raman spectra and spectral attributions reveal disease-specific biomolecular differences. Support vector machine (SVM) was used to establish the two-way (cholangitis vs healthy, CCA vs healthy etc.) and 3-way (cholangitis vs CCA vs healthy) classification model, and leave-one-out cross-validation (LOOCV) was used to verify these models' performance. Based on the support vector machine algorithm, serum Raman spectroscopy could identify cholangitis and CCA. Its diagnostic sensitivity, and specificity were 89.80%, 94.55%, and 89.50%, 98.18%, respectively. This study demonstrates that label-free serum Raman spectroscopy analysis technique combined with SVM diagnostic algorithm has great potential for noninvasive cholangitis and CCA screening.

Diagnosis of urogenital cancer combining deep learning algorithms and surface-enhanced Raman spectroscopy based on small extracellular vesicles

PURPOSE

To identify and compare the capacities of serum and serum-derived small extracellular vesicles (EV) in diagnosis of common urogenital cancer combining Surface-enhanced Raman spectroscopy (SERS) and Convolutional Neural Networks (CNN).

MATERIALS AND METHODS

We collected serum samples from 32 patients with prostate cancer (PCa), 33 patients with renal cell cancer (RCC) and 30 patients with bladder cancer (BCa) as well as 35 healthy control (HC), which were thereafter used to enrich extracellular vesicles by ultracentrifuge. Label-free SERS was utilized to collect Raman spectra from serum and matched EV samples. We constructed CNN models to process SERS data for classification of malignant patients and healthy controls (HCs).

RESULTS

We collected 650 and 1206 spectra from serum and serum-derived EV, respectively. CNN models of EV spectra revealed high testing accuracies of 79.3%, 78.7% and 74.2% in diagnosis of PCa, RCC and BCa, respectively. In comparison, serum SERS-based CNN model had testing accuracies of 73.0%, 71.1%, 69.2% in PCa, RCC and BCa, respectively. Moreover, CNN models based on EV SERS data show significantly higher diagnostic capacities than matched serum CNN models with the area under curve (AUC) of 0.80, 0.88 and 0.74 in diagnosis of PCa, RCC and BCa, respectively.

CONCLUSION

Deep learning-based SERS analysis of EV has great potentials in diagnosis of urologic cancer outperforming serum SERS analysis, providing a novel tool in cancer screening.

Fused Raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics

As a rapid, label-free, non-destructive analytical measurement requiring little to no sample preparation, Raman spectroscopy shows great promise for liquid biopsy cancer detection and diagnosis. We carried out Raman analysis and mass spectrometry of plasma and saliva from more than 50 subjects in a cohort of head and neck cancer patients and benign controls (e.g., patients with benign oral masses). Unsupervised data models were built to assess diagnostic performance. Raman spectra collected from either biofluid provided moderate performance to discriminate cancer samples. However, by fusing together the Raman spectra of plasma and saliva for each patient, subsequent analytical models delivered an impressive sensitivity, specificity, and accuracy of 96.3%, 85.7%, and 91.7%, respectively. We further confirmed that the metabolites driving the differences in Raman spectra for our models are among the same ones that drive mass spectrometry models, unifying the two techniques and validating the underlying ability of Raman to assess metabolite composition. This study bolsters the relevance of Raman to provide additive value by probing the unique chemical compositions across biofluid sources. Ultimately, we show that a simple data augmentation routine of fusing plasma and saliva spectra provided significantly higher clinical value than either biofluid alone, pushing forward the potential of clinical translation of Raman spectroscopy for liquid biopsy cancer diagnostics.

Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma

Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.

Development of a SERS based cancer diagnosis approach employing cryosectioned thyroid tissue samples on PDMS

An efficient SERS based novel analytical approach named Cryosectioned-PDMS was developed systematically and evaluated applying on 64 thyroid biopsy samples. To utilize thyroid biopsy samples, a 20-μl volume of h-AgNPs suspension was dropped on a 5-μm thick cryosectioned biopsy specimen placed on the PDMS coated glass slide. The SERS spectra from a 10 × 10 points array acquired by mapping 22.5 μm × 22.5 μm sized area from suspended dried droplets placed on the tissue surface. The probability of correctly predicted performance for diagnosis of malignant, benign and healthy tissues was resulted in the accuracy of 100 % for the spectral bands at 667, 724, 920, 960, 1052, 1096, 1315 and 1457 cm^-1 using PCA-fed LDA machine learning. The Cryosectioned-PDMS biophotonic approach with PCA-LDA predictive model demonstrated that the vibrational signatures can accurately recognize the fingerprint of cancer pathology from a healthy one with a simple and fast sample preparation methodology.

Subtype discrimination of acute myeloid leukemia based on plasma SERS technique

Acute myeloid leukemia (AML) is a common hematologic malignancy. To this day, diagnose of AML and its genetic mutation still rely on invasive and time-consuming methods. In this study, 222 plasma samples were collected to discuss the performance of surface-enhanced Raman spectroscopy (SERS) to discriminate AML subtype acute promyelocytic leukemia and acute monocytic leukemia based on plasma. The Ag nanoparticles-based SERS technique was used to explore the biochemical differences among different AML subtypes. With the help of powerful supervised and unsupervised algorithms, the performance using the whole spectra and band intensities was confirmed to identify different subtypes of AML. The results demonstrated the intensities of several bands and band-intensity ratios were significantly different between groups, thus related to the discrimination of several AML subtypes and control. Combining indexes of band-intensity ratios, the result of multi-indexes ROC has excellent performance in differentiating AML patient with healthy control. Our work demonstrated the great potential of SERS technique as a rapid and micro detection method in clinical laboratory field, it's a new and powerful tool for analyzing human blood plasma.

Surface-Enhanced Raman Spectroscopy of Pretreated Plasma Samples Predicts Disease Recurrence in Muscle-Invasive Bladder Cancer Patients Undergoing Neoadjuvant Chemotherapy and Radical Cystectomy

Objective

To explore the value of surface-enhanced Raman spectroscopy analysis of pretreated plasma samples in prediction of bladder cancer (BCa) recurrence after neoadjuvant chemotherapy (NAC) and radical cystectomy (RC).

Patients and Methods

SERS was used to analyze plasma samples collected before biopsy and treatment in BCa patients undergoing NAC and RC. The value of clinicopathological parameters and distinctive SERS peaks in the prediction of disease recurrence were analyzed in Cox regression proportional hazard analysis and Log rank test. Principal component analysis and linear discriminant analysis (PCA-LDA) were employed to process spectral data and construct diagnostic algorithms.

Results

A total of 88 patients with 440 plasma SERS spectra were collected. The SRES spectra from recurrent patients were compared with patients who remained recurrence free. The SERS demonstrated higher levels of circulating free nucleic acid components in recurrent population, which is represented by significantly higher intensities at SERS peaks of 725 cm⁻¹, 1328 cm⁻¹ and 1455 cm⁻¹. The SERS also detected significantly lower levels of tryptophan shown as lower significantly intensities at the 1558 cm⁻¹, which is proved to be an independent predictor of BCa recurrence. The addition of SERS peaks of 1558 cm⁻¹ to classic clinicopathological predictors including pathological tumor stage, lymph node metastasis and pathological downstaging can significantly enhance the power of the predictive model from 0.66 to 0.76 in the area under curve (AUC) of receiver operating characteristic (ROC) curves. Meanwhile, the PCA-LDA diagnostic model based on SERS spectra reveals a high accuracy of 85.2% in prediction of disease recurrence and the AUC of 0.92 in the ROC curve. When validated in the leave-one-out cross-validation method, the accuracy of the model remained 84.1%.

Conclusion

We show that SERS analysis of plasma before NAC treatment can accurately classify patients with different risks of disease recurrence after surgery and improve the power of clinicopathological predictive models, thus refining clinical decision-making.

Raman Spectroscopy: A Novel Technology for Gastric Cancer Diagnosis

Gastric cancer is usually diagnosed at late stage and has a high mortality rate, whereas early detection of gastric cancer could bring a better prognosis. Conventional gastric cancer diagnostic methods suffer from long diagnostic times, severe trauma, and a high rate of misdiagnosis and rely heavily on doctors’ subjective experience. Raman spectroscopy is a label-free molecular vibrational spectroscopy technique that identifies the molecular fingerprint of various samples based on the inelastic scattering of monochromatic light. Because of its advantages of non-destructive, rapid, and accurate detection, Raman spectroscopy has been widely studied for benign and malignant tumor differentiation, tumor subtype classification, and section pathology diagnosis. This paper reviews the applications of Raman spectroscopy for the in vivo and in vitro diagnosis of gastric cancer, methodology related to the spectroscopy data analysis, and presents the limitations of the technique.

Label-free surface enhanced Raman spectroscopy analysis of blood serum via coffee ring effect for accurate diagnosis of cancers

Surface-enhanced Raman spectroscopy (SERS) is considered as an ultrasensitive, non-invasive as well as rapid detection technology for cancer diagnosis. In this study, we developed a novel blood serum analysis strategy using coffee ring effect-assisted label-free SERS for different types of cancer screening. Additionally, the pretreated Ag nanoparticles (Ag NPs) were mixed with the serum from liver cancer patients (n = 40), prostate cancer patients (n = 32) and healthy volunteers (n = 30) for SERS measurement. The droplets of Ag NPs-serum mixture formed the coffee ring on the peripheral after air-drying, and thus extremely enhancing Raman signal and ensuring the stability and reliability of SERS detection. Partial least square (PLS) and support vector machine (SVM) algorithms were utilized to establish the diagnosis model for SERS spectra data classifying, yielding the high diagnostic accuracy of 98.04% for normal group and two types of cancers simultaneously distinguishing. More importantly, for the unknown testing set, an ideal diagnostic accuracy of 100% could be achieved by PLS-SVM algorithm for differentiating cancers from the normal group. The results from this exploratory work demonstrate that serum SERS detection combined with PLS-SVM diagnostic algorithm and coffee ring effect has great potential for the noninvasive and label-free detection of cancer.

Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers

This review surveys Infrared, Raman/SERS and Brillouin spectroscopies for medical diagnostics and detection of biomarkers in biofluids, that include urine, blood, saliva and other biofluids. These optical sensing techniques are non-contact, noninvasive and relatively rapid, accurate, label-free and affordable. However, those techniques still have to overcome some challenges to be widely adopted in routine clinical diagnostics. This review summarizes and provides insights on recent advancements in research within the field of vibrational spectroscopy for medical diagnostics and its use in detection of many health conditions such as kidney injury, cancers, cardiovascular and infectious diseases. The six comprehensive tables in the review and four tables in supplementary information summarize a few dozen experimental papers in terms of such analytical parameters as limit of detection, range, diagnostic sensitivity and specificity, and other figures of merits. Critical comparison between SERS and FTIR methods of analysis reveals that on average the reported sensitivity for biomarkers in biofluids for SERS vs FTIR is about 103 to 105 times higher, since LOD SERS are lower than LOD FTIR by about this factor. High sensitivity gives SERS an edge in detection of many biomarkers present in biofluids at low concentration (nM and sub nM), which can be particularly advantageous for example in early diagnostics of cancer or viral infections.HighlightsRaman, Infrared spectroscopies use low volume of biofluidic samples, little sample preparation, fast time of analysis and relatively inexpensive instrumentation.Applications of SERS may be a bit more complicated than applications of FTIR (e.g., limited shelf life for nanoparticles and substrates, etc.), but this can be generously compensated by much higher (by several order of magnitude) sensitivity in comparison to FTIR.High sensitivity makes SERS a noninvasive analytical method of choice for detection, quantification and diagnostics of many health conditions, metabolites, and drugs, particularly in diagnostics of cancer, including diagnostics of its early stages.FTIR, particularly ATR-FTIR can be a method of choice for efficient sensing of many biomarkers, present in urine, blood and other biofluids at sufficiently high concentrations (mM and even a few µM)Brillouin scattering spectroscopy detecting visco-elastic properties of probed liquid medium, may also find application in clinical analysis of some biofluids, such as cerebrospinal fluid and urine.

Non-invasive discrimination of multiple myeloma using label-free serum surface-enhanced Raman scattering spectroscopy in combination with multivariate analysis

We report non-invasive discrimination of multiple myeloma (MM) using label-free serum surface-enhanced Raman scattering (SERS) spectroscopy in combination with multivariate analysis. Colloidal silver nano-particles (AgNPs) were used as the SERS substrate. High quality serum SERS spectra were obtained from 53 MM patients and 44 healthy controls (HCs). The SERS spectral differences demonstrated variation of relative concentrations of biomolecules in the serum of MM patients in comparison to HCs. Multivariate analysis methods, including principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM), were used to build discrimination models for MM. Leave-one-out cross-validation (LOOCV) was used to evaluate the performances of the models, in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUC). Using the SVM model, the accuracy for discrimination of MM was achieved as 78.4%, and the corresponding sensitivity, specificity, and AUC values were 0.830, 0.727, and 0.840, respectively. The results show that the serum SERS in combination with multivariate analysis could be a fast, non-invasive, and cost-effective technique for discrimination of MM.

Label-free diagnosis of breast cancer based on serum protein purification assisted surface-enhanced Raman spectroscopy

Serum protein is generally used to assess the severity of disease, as well as cancer progression and prognosis. Herein, a simple and rapid serum proteins analysis method combined with surface-enhanced Raman spectroscopy (SERS) technology was applied for breast cancer detection. The cellulose acetate membrane (CA) was employed to extract human serum proteins from 30 breast cancer patients and 45 healthy volunteers and then extracted proteins were mixed with silver nanoparticles for SERS measurement. Additionally, we also mainly assessed the use of different ratios of proteins-silver nanoparticles (Ag NPs) mixture to generate maximum SERS signal for clinical samples detection. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-support vector machines (PLS-SVM) were used to analyze the obtained serum protein SERS spectra and establish the diagnostic model. The results demonstrate that the PLS-SVM model provides superior performance in the classification of breast cancer diagnosis compared with PCA-LDA. This exploratory work demonstrates that the label-free SERS analysis technique combined with CA membrane purified serum proteins has great potential for breast cancer diagnosis.

Optimized Classification of Suspended Particles in Seawater by Dense Sampling of Polarized Light Pulses

Suspended particles affect the state and vitality of the marine ecosystem. In situ probing and accurately classifying the suspended particles in seawater have an important impact on ecological research and environmental monitoring. Individual measurement of the optical polarization parameters scattered by the suspended particles has been proven to be a powerful tool to classify the particulate compositions in seawater. In previous works, the temporal polarized light pulses are sampled and averaged to evaluate the polarization parameters. In this paper, a method based on dense sampling of polarized light pulses is proposed and the experimental setup is built. The experimental results show that the dense sampling method optimizes the classification and increases the average accuracy by at least 16% than the average method. We demonstrate the feasibility of dense sampling method by classifying the multiple types of particles in mixed suspensions and show its excellent generalization ability by multi-classification of the particles. Additional analysis indicates that the dense sampling method basically takes advantage of the high-quality polarization parameters to optimize the classification performance. The above results suggest that the proposed dense sampling method has the potential to probe the suspended particles in seawater in red-tide early warning, as well as sediment and microplastics monitoring.

Application of Gold Nanoparticle-Based Materials in Cancer Therapy and Diagnostics

Several metal nanoparticles have been developed for medical application. While all have their benefits, gold nanoparticles (AuNPs) are ideal in cancer therapy and diagnosis as they are chemically inert and minimally toxic. Several studies have shown the potential of AuNPs in the therapeutic field, as photosensitizing agents in sonochemical and photothermal therapy and as drug delivery, as well as in diagnostics and theranostics. Although there is a significant number of reviews on the application of AuNPs in cancer medicine, there is no comprehensive review on their application both in therapy and diagnostics. Therefore, considering the high number of studies on AuNPs’ applications, this review summarizes data on the application of AuNPs in cancer therapy and diagnostics. In addition, we looked at the influence of AuNPs’ shape and size on their biological properties. We also present the potential use of hybrid materials based on AuNPs in sonochemical and photothermal therapy and the possibility of their use in diagnostics. Despite their potential, the use of AuNPs and derivatives in cancer medicine still has some limitations. In this review, we provide an overview of the biological, physicochemical, and legal constraints on using AuNPs in cancer medicine.

Surface enhanced Raman scattering of extracellular vesicles for cancer diagnostics despite isolation dependent lipoprotein contamination

Given the emerging diagnostic utility of extracellular vesicles (EVs), it is important to account for non-EV contaminants. Lipoprotein present in EV-enriched isolates may inflate particle counts and decrease sensitivity to biomarkers of interest, skewing chemical analyses and perpetuating downstream issues in labeling or functional analysis. Using label free surface enhanced Raman scattering (SERS), we confirm that three common EV isolation methods (differential ultracentrifugation, density gradient ultracentrifugation, and size exclusion chromatography) yield variable lipoprotein content. We demonstrate that a dual-isolation method is necessary to isolate EVs from the major classes of lipoprotein. However, combining SERS analysis with machine learning assisted classification, we show that the disease state is the main driver of distinction between EV samples, and largely unaffected by choice of isolation. Ultimately, this study describes a convenient SERS assay to retain accurate diagnostic information from clinical samples by overcoming differences in lipoprotein contamination according to isolation method.

Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) spectra of salivary glands carcinoma, tumor and healthy tissues and their homogenates analyzed by chemometry: Towards development of the novel tool for clinical diagnosis

In this study, two approaches to salivary glands studies are presented: Raman imaging (RI) of tissue cross-section and surface-enhanced Raman spectroscopy (SERS) of tissue homogenates prepared according to elaborated protocol. Collected and analyzed data demonstrate the significant potential of SERS combined with multivariate analysis for distinguishing carcinoma or tumor from the normal salivary gland tissues as a rapid, label-free tool in cancer detection in oncological diagnostics. Raman imaging allows a detailed analysis of the cell wall's chemical composition; thus, the compound's distribution can be semi-quantitatively analyzed, while SERS of tissue homogenates allow for detailed analysis of all moieties forming these tissues. In this sense, SERS is more sensitive and reliable to study any changes in the area of infected tissues. Principal component analysis (PCA), as an unsupervised pattern recognition method, was used to identify the differences in the SERS salivary glands homogenates. The partial least squares-discriminant analysis (PLS-DA), the supervised pattern classification technique, was also used to strengthen further the computed model based on the latent variables in the SERS spectra. Moreover, the chemometric quantification of obtained data was analyzed using principal component regression (PCR) multivariate calibration. The presented data prove that the PCA algorithm allows for 91% in seven following components and the determination between healthy and tumor salivary gland homogenates. The PCR and PLS-DA methods predict 90% and 95% of the variance between the studied groups (in 6 components and 4 factors, respectively). Moreover, according to calculated RMSEC (RMSEP), R2C (R2P) values and correlation accuracy (based on the ROC curve), the PLS-DA model fits better for the studied data. Thus, SERS methods combined with PLS-DA analysis can be used to differentiate healthy, neoplastic, and mixed tissues as a competitive tool in relation to the commonly used method of histopathological staining of tumor tissue.

Single cell detection using intracellularly-grown-Au-nanoparticle based surface-enhanced Raman scattering spectroscopy for nasopharyngeal cell line classification

The aim of this study was to evaluate the feasibility of applying intracellularly-grown-Au-nanoparticle (IGAuNP)-based surface-enhanced Raman scattering (SERS) technology to classify two types of nasopharyngeal cancer (NPC) cell lines (CNE2 and CNE1). The IGAuNP technology provides excellent delivery efficiency of Au NPs to the cytoplasm and nucleus, thus leading to an extraordinary enhancement of the Raman signals of cells. Compared with normal Raman scattering (NRS) spectra of cells, IGAuNP-based SERS spectra not only have a high signal-to-noise ratio, but also can detect more characteristic Raman peaks, which can be used to explore more differences when comparing the biochemical components of different nasopharyngeal carcinoma cell lines. Based on the linear discriminant analysis (LDA) and support vector machine (SVM) analysis of SERS spectral data, an exciting result with a diagnostic sensitivity of 100%, specificity of 100%, and accuracy of 100%, could be achieved to differentiate CNE2 and CNE1 cells, which is better than the result obtained by NRS spectroscopy. This exploratory study indicated that the SERS technology based on IGAuNPs in conjunction with multivariate statistical analysis methods has great potential in the identification of nasopharyngeal carcinoma cell lines.

Investigations of Dianhydro-D-glucitol adsorbed on AuNPs surface: In silico and in vitro approach based on anticancer activity studies against A549 lung cancer cell lines

The adsorption behavior of a lung cancer agent, Dianhydro-D-glucitol (DIS) on gold nanoparticles (AuNPs) was studied using surface-enhanced Raman scattering techniques. The stabilized geometry, inter- and intra-molecular hydrogen bond, and harmonic vibrational wavenumbers of DIS and DIS adsorbed on AuNPs (DISA) surface have been investigated with the help of the density functional theory (DFT) method. The stability of the molecules arising from stereoelectronic interactions, leading to its bioactivity, has been confirmed using natural bond orbital (NBO) analysis, which was further substantiated by the narrow HOMO LUMO energy gap obtained for DISA, from frontier molecular orbital analysis as well as electronic spectral analysis. The molecular electrostatic potential analysis along with local and global reactivity descriptors predicts the reactive site of the molecules. Molecular docking study was performed to obtain information about protein-ligand reactions for DIS and DISA, with different cancer proteins. This study enlightens the potential of SERS agents for targeted drug delivery and photothermal. The in vitro cytotoxic effects of DPH and DPHA molecules on lung cancer cell lines were analyzed using the MTT assay and the SERS method.

Facile PEG-based isolation and classification of cancer extracellular vesicles and particles with label-free surface-enhanced Raman scattering and pattern recognition algorithm

Extracellular vesicles and particles (EVPs), which contain the same surface proteins as their mother cells, are promising biomarkers for cancer liquid biopsy. However, most of the isolation methods of EVPs are time-consuming and complicated, and hence, sensitive detection and classification methods are required for EVPs. Here, we report a facile polyethylene glycol (PEG)-based method for isolating and classifying EVPs with label-free surface-enhanced Raman scattering (SERS) and pattern recognition algorithm. There are only three steps in the PEG-based isolation method, and it does not require ultracentrifugation, which makes it a low-cost and easy-to-use method. Three types of common male cancer cell lines, namely leukemia (THP-1), prostate cancer (DU-145), and colorectal cancer (COLO-205), and one healthy male blood sample, were utilized to isolate EVPs. To collect the SERS spectra of EVPs, a novel planar nanomaterial, namely amino molybdenum oxide (AMO) nanoflakes, was applied, with the enhancement factor being obtained as 3.2 × 10². Based on the principal component analysis and support vector machine (PCA-SVM) algorithm, cancer and normal EVPs were classified with 97.4% accuracy. However, among the cancer EVPs, the accuracy, precision, and sensitivity were found to be 90.0%, 90.9%, and 83.3% for THP-1; 86.7%, 80.0%, and 92.3% for DU-145; 96.7%, 83.3%, and 100% for COLO-205, respectively. Thus, this work will improve the isolation, detection, and classification of EVPs and promote the development of cancer liquid biopsies.

Raman-based metabonomics unravels metabolic changes related to a first-line tenofovir-based treatment in a small cohort of South African HIV-infected patients

In addition to immunological disorders, human immunodeficiency virus (HIV) also causes metabolic abnormalities. Though successful in viral suppression and immune restoration, continued use of antiretroviral therapy (ART) has also been linked to the development of several metabolic ailments. Currently, the only clinical markers used to manage and monitor the development of HIV-induced metabolic disorders, disease progression as well as observing individual's response to antiviral treatment are CD4 count, viral loads and several other single variable colometric assays. Despite the common use of these clinical markers, these markers remain unreliable and limited in the ability to monitor the development of metabolic disorders as well as monitor treatment response. Given these limitations, it is imperative to discover and develop reliable biological markers for overall HIV disease management. Here, Raman spectroscopy was used to profile metabolic changes in the plasma of 22 HIV⁺ receiving a first-line tenofovir-based combination antiretroviral therapy compared to their 8 HIV⁺ ART^- and 10 HIV^- counterparts. Multivariate statistical analysis was performed in order to classify the samples into their respective groups and to identify significantly altered metabolites between the control and experimental groups. Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) discriminant analysis identified significant differences (p < 0.05) in 9 different metabolites. Alterations were identified in spectral regions associated with glucose (1124 cm^-1), lipids/phospholipids (1116 cm^-1, 1098 cm^-1, 1077 cm^-1), proteins (1120 cm^-1), nucleic acids (1081 cm^-1) and phenylalanine (1103 cm^-1). Pathway analysis also revealed 3 significantly altered pathways. This study presented the reproducible nature of Raman spectroscopy in distinguishing between HIV-infected (treated and untreated) and uninfected blood plasma and allowed for the detection and identification of treatment induced metabolite changes. The results obtained in the study may, therefore, give insights into understanding the metabolic effect of HIV therapy.

Raman spectroscopy-based biomarker screening by studying the fingerprint characteristics of chronic lymphocytic leukemia and diffuse large B-cell lymphoma

Raman spectroscopy (RS) can provide fingerprint-type information on biochemical molecules. RS-based blood plasma analysis of solid tumors has been reported in recent years; however, there are no studies on the use of this analysis for detecting blood diseases. We studied the features of blood plasma in patients with diffuse large B-cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL) by RS with the aim of developing a simple blood test for noninvasive DLBCL and CLL detection. We analyzed blood plasma from 33 DLBCL patients, 39 CLL patients and 30 healthy volunteers. Orthogonal partial least squares discriminant analysis (OPLS-DA) could build two clusters with almost no overlap between DLBCL/CLL and the controls. We used the prediction set to test the model built by OPLS-DA. For the CLL model, the sensitivity was 92.86%, and the specificity was 100%, whereas for the DLBCL model, the sensitivity was 80% and the specificity was 92.31%. We found Raman bands specific to both DLBCL and CLL patients in comparison with the healthy volunteers. Most importantly, we found that the combination of the 1445 cm^-1 and 1655 cm^-1 Raman shifts could discriminate DLBCL from CLL and even the other solid tumors reported to date. Further analysis of the assignments of 1655 cm^-1 also gave us a clue to find potential important variables hemoglobin and serum albumin related with the CLL prognosis. Our exploratory study primarily demonstrated the great potential of developing RS blood plasma analysis as a novel clinical tool for the noninvasive detection of DLBCL and CLL.

Surface-enhanced Raman scattering analysis of serum albumin via adsorption-exfoliation on hydroxyapatite nanoparticles for noninvasive cancers screening

Combining serum albumin via adsorption-exfoliation on hydroxyapatite particles (HAp) with surface-enhanced Raman scattering (SERS), we developed a novel quantitative analysis of albumin method from blood serum for cancers screening applications. The quantitatively analysis obtained by our HAp method had a good linear relationship from 1 to 10 g/dL, and the lower limit of detection was less than the albumin prognostic factor for disease (3.5 g/dL). Serum albumin was adsorbed and exfoliated by HAp from serum samples of liver cancer patients, breast cancer patients and healthy volunteers and mixed with silver colloids to perform SERS spectral analysis. Based on the PLS-SVM algorithm, the diagnostic accuracies of liver cancer patients and breast cancer patients were 100% and 96.68%, respectively. Moreover, this algorithm successfully predicted the unidentified subjects with a diagnostic accuracy of 93.75%. This exploratory work demonstrated that HAp-adsorbed-exfoliated serum proteins combined with SERS spectroscopy has great potential for cancer screening.

Surface-enhanced Raman spectroscopy of preoperative serum samples predicts Gleason grade group upgrade in biopsy Gleason grade group 1 prostate cancer

PURPOSE

To predict Gleason grade group (GG) upgrade in biopsy Gleason grade group 1 (GG1) prostate cancer (CaP) patients using surface-enhanced Raman spectroscopy (SERS).

MATERIALS AND METHODS

Preoperative serum samples of patients with biopsy GG1 and subsequent radical prostatectomy were analyzed using SERS. The role of clinical variables and distinctive SERS spectra in the prediction of GG upgrade were evaluated. Principal component analysis and linear discriminant analysis (PCA-LDA) were used to manage spectral data and develop diagnostic algorithms.

RESULTS

A total of 342 preoperative serum SERS spectra from 114 patients were obtained. SERS detected a higher level of circulating free nucleic acid bases and a lower level of lipids in patients with GG upgrade to GG3 and higher, presenting as SERS spectral peaks of 728 cm^-1 and 1,655 cm^-1, respectively. Both spectral peaks were independent predictors of GG upgrade and their addition to clinical predictors of PSA and positive core percent significantly improved predictive power of the logistic regression model with area under curve improved from 0.65 to 0.80 (P = 0.0045). Meanwhile, PCA-LDA diagnostic model based on serum SERS spectra showed a high accuracy of 91.2% in predicted groups and remained stable with a sensitivity, specificity, and accuracy of 65%, 97.3%, 86.0%, respectively when validated by leave-one-out cross-validation method.

CONCLUSIONS

By analyzing preoperative serum samples, SERS combined with PCA-LDA model could be a promising tool for prediction of Gleason GG upgrade in biopsy GG1 CaP and assist in treatment decision-making in clinical practice.

Differentiation between stages of non-alcoholic fatty liver diseases using surface-enhanced Raman spectroscopy

Non-alcoholic fatty liver disease (NAFLD) is a chronic disorder progressing from an initial benign accumulation of fat (NAFL) towards steatohepatitis (NASH), a degenerative form that can lead to liver cirrhosis and cancer. The development of non-invasive, rapid and accurate method to diagnose NASH is of high clinical relevance. Surface-enhanced Raman spectroscopy (SERS) of plasma was tested as a method to distinguish NAFL from NASH. SERS spectra from plasma of female patients diagnosed with NAFL (n = 32) and NASH (n = 35) were obtained in few seconds, using a portable Raman spectrometer. The sample consisted of 5 μL of biofluid deposited on paper coated with Ag nanoparticles. The spectra show consistent differences between the NAFL and NASH patients, with the uric acid/hypoxanthine band area ratio statistically different (p-value <0.001) between the two groups. The average figures of merit for a diagnostic test based on these ratios, as derived from a repeated 4-fold cross-validation of a logistic regression model, are all between 0.73 and 0.79, with an average area under the curve of 0.81. We conclude that SERS may be a reliable and rapid method to discriminate NAFLD from NASH.

Human blood test based on surface-enhanced Raman spectroscopy technology using different excitation light for nasopharyngeal cancer detection

Nasopharyngeal carcinoma (NPC), a kind of squamous cell carcinoma, occurs in the top and the side wall of nasopharyngeal, which harms human health and life. In this study, a novel blood test (SERS) was carried out for 30 NPC patients and 30 normal ones. Using multi-variate statistical analysis for spectral data, the diagnostic sensitivities of 89.3% (50/56) and 85.7% (48/56) can be achieved for 633 and 785 nm exciting wavelength, respectively. Also corresponding specificities are 71.4% (41/56) and 78.6% (44/56), respectively. These results demonstrated that the two kinds of excitation wavelength all have the feasibility of obtaining high-quality SERS spectra to differentiate cancer from normal samples. Furthermore, the performance of the SERS test with 785 nm wavelength excitation is nearly equal to the SERS experimental effect under 633 nm wavelength excitation for NPC detection.

Centroid-position-based autofocusing technique for Raman spectroscopy

In Raman spectroscopy, it is crucial to focus the laser on the sample in order to guarantee the intensity and repeatability of the characteristic peaks, which is known as autofocus. In this paper, we propose a novel low-cost scheme based on the subtle placement of the laser source and the image sensor. We confirm the feasibility of monitoring the focus status through the centroid position of the laser spot's image (CPSI) in theory. Both the simulation and experimental results illustrate that the distance-ordinate function is similar in shape to the logarithm, which not only helps to shorten the autofocus time but also achieves the sub-decimeter measuring range and micrometer resolution near the focal point. Meanwhile, we discuss in detail how to obtain the desired performance by adjusting the extrinsic camera parameters and the way to overcome the disturbance of the noise, ambient light and non-normal incidence. An autofocus-free handheld Raman spectrograph utilizes this method to autofocus the alcohol in the centrifuge tube successfully and the spectral reproducibility is improved. Our results may pave the way to a novel autofocus approach for Raman mapping in vivo.

Analysis of Human Colon by Raman Spectroscopy and Imaging-Elucidation of Biochemical Changes in Carcinogenesis

Noninvasive Raman imaging of non-fixed and unstained human colon tissues based on vibrational properties of noncancerous and cancerous samples can effectively enable the differentiation between noncancerous and tumor tissues. This work aimed to evaluate the biochemical characteristics of colon cancer and the clinical merits of multivariate Raman image and spectroscopy analysis. Tissue samples were collected during routine surgery. The non-fixed, fresh samples were used to prepare micrometer sections from the tumor mass and the tissue from the safety margins outside of the tumor mass. Adjacent sections were used for typical histological analysis. We have found that the chemical composition identified by Raman spectroscopy of the cancerous and the noncancerous colon samples is sufficiently different to distinguish pathologically changed tissue from noncancerous tissue. We present a detailed analysis of Raman spectra for the human noncancerous and cancerous colon tissue. The multivariate analysis of the intensities of lipids/proteins/carotenoids Raman peaks shows that these classes of compounds can statistically divide analyzed samples into noncancerous and pathological groups, reaffirming that Raman imaging is a powerful technique for the histochemical analysis of human tissues. Raman biomarkers based on ratios for lipids/proteins/carotenoids content were found to be the most useful biomarkers in spectroscopic diagnostics.

Rapid SERS-based recognition of cell secretome on the folic acid-functionalized gold gratings

Nowadays, functionalization of the plasmon-supported nanostructured surface is considered as a powerful tool for tumour cell recognition. In this study, the SERS on a surface plasmon polariton-supported gold grating functionalized with folic acid was used to demonstrate an unpretentious recognition of melanoma-associated fibroblasts. Using cultivation media conditioned by different cells, we were able to detect reproducible differences in the secretome of melanoma-associated and normal control fibroblasts. The homogeneous distribution of plasmon energy along the grating surface was proved to provide excellent SERS signal reproducibility, while, to increase the affinity of (bio)molecules to SERS substrate, folic acid molecules were covalently grafted to the gold gratings. As proof of concept, fibroblasts were cultured in vitro, and culture media from the normal and tumour-associated lines were collected and analysed with our proposed SERS substrates. Identifying individual peaks of the Raman spectra as well as comparing their relative intensities, we showed that the proposed functional SERS platform can recognise the melanoma-associated cells without the need for further statistical spectral evaluation directly. We also demonstrated that the SERS chip created provided a stable SERS signal over a period of 90 days without loss of sensitivity. Graphical abstract.

A Review on Surface-Enhanced Raman Scattering

Surface-enhanced Raman scattering (SERS) has become a powerful tool in chemical, material and life sciences, owing to its intrinsic features (i.e., fingerprint recognition capabilities and high sensitivity) and to the technological advancements that have lowered the cost of the instruments and improved their sensitivity and user-friendliness. We provide an overview of the most significant aspects of SERS. First, the phenomena at the basis of the SERS amplification are described. Then, the measurement of the enhancement and the key factors that determine it (the materials, the hot spots, and the analyte-surface distance) are discussed. A section is dedicated to the analysis of the relevant factors for the choice of the excitation wavelength in a SERS experiment. Several types of substrates and fabrication methods are illustrated, along with some examples of the coupling of SERS with separation and capturing techniques. Finally, a representative selection of applications in the biomedical field, with direct and indirect protocols, is provided. We intentionally avoided using a highly technical language and, whenever possible, intuitive explanations of the involved phenomena are provided, in order to make this review suitable to scientists with different degrees of specialization in this field.

A novel urine analysis technique combining affinity chromatography with Au nanoparticle based surface enhanced Raman spectroscopy for potential applications in non-invasive cancer screening

Modified nucleoside in urine samples is one of the most common biomarkers for cancer screening. Therefore, we developed a novel detection method for modified nucleoside detection in human urine. In this work, the modified nucleoside from real cancer patient's urine samples was first separated and purified using the affinity chromatography (AC) technology relying on its specific adsorption capacity. Then, surface-enhanced Raman spectroscopy (SERS) technology with the capability of single molecular detection was used to sensitively characterize the biomolecular features of modified nucleoside. A total of 141 high-quality SERS spectra of urinary modified nucleoside can be obtained from 50 gastric cancer patients and 43 breast cancer patients, as well as 48 healthy volunteers. Using principal component analysis combined with linear discriminant analysis (PCA-LDA), the diagnostic sensitivities for identifying gastric cancer vs normal, breast cancer vs normal, gastric cancer vs breast cancer were 84.0%, 76.7% and 82.0%, respectively, and the corresponding diagnostic specificities for each combination were 95.8%, 87.5% and 90.7%, respectively. These results show that this novel method based on urinary modified nucleoside detection combining AC and SERS technologies holds promising potential for developing a specific, non-invasive and label-free tool for cancer screening.

Assessment of treatment efficacy using surface-enhanced Raman spectroscopy analysis of urine in rats with kidney transplantation or kidney disease

BACKGROUND

Individuals who have kidney disease or kidney transplants need routine assessment of their kidney damage and function, which are largely measured based on histological examination of kidney biopsies, blood test, and urinalysis. These methods are practically difficult or inconvenient, and expensive. The objective of this study was to develop a model to estimate the kidney damage and function by surface-enhanced Raman spectroscopy (SERS).

METHODS

Urine samples were collected from two previous studies: renal allograft recipient Lewis rats receiving anti-TGF-β antibody or control antibody treatment and obese diabetic ZSF1 rats with kidney disease fed with whole grape powder-containing chow or control chow. Silver nanoparticle-based SERS spectra of urine were measured. SERS spectra were analyzed using principal component analysis (PCA) combined with linear discriminant analysis (LDA) and partial least squires (PLS) analysis.

RESULTS

PCA/LDA separated anti-TGF-β antibody-treated group from control group with 90% sensitivity and 70% specificity in kidney transplants, and grape-fed group from controls with 72.7% sensitivity and 60% specificity in diabetic kidneys. The receiver operating characteristic curves showed that the integration area under the curve was 0.850 ± 0.095 (p = 0.008) in kidney transplant groups and 0.800 ± 0.097 (p = 0.02) in diabetic kidney groups. PLS predicted the biochemical parameters of kidney function using the SERS spectra, resulting in R² = 0.8246 (p < 0.001,urine protein), R² = 0.8438 (p < 0.001, urine creatinine), R² = 0.9265 (p < 0.001, urea), R² = 0.8719 (p < 0.001, serum creatinine), and R² = 0.6014 (p < 0.001, urine protein to creatinine ratio).

CONCLUSION

Urine SERS spectral analysis suggesting that it may become a convenient method for rapid assessment of renal impairment.

Surface-enhanced Raman spectroscopy before radical prostatectomy predicts biochemical recurrence better than CAPRA-S

Objective

The objective of this study was to evaluate the performance of surface-enhanced Raman spectroscopy (SERS) in the prediction of early biochemical recurrence after radical prostatectomy (RP).

Patients and methods

We synthesized monodisperse gold nanoparticles as SERS-enhanced substrates and analyzed preoperative plasma samples of patients who underwent RP. The roles of clinical risk model (Cancer of the Prostate Risk Assessment [CAPRA] score) and distinctive SERS spectra on prediction of early biochemical recurrence were evaluated. The principal component analysis and linear discriminant analysis (PCA-LDA) were used to manage the spectral data and develop diagnostic algorithm.

Results

A total of 306 preoperative plasma Raman spectra from 102 patients were collected. SERS spectrum from those who developed early biochemical recurrence were compared to those who remained biochemical recurrence-free. The SERS detected more abundant circulating free nucleic acid bases in biochemical recurrence population, presenting significant stronger intensities at SERS spectral bands 725 and 1,328 cm⁻¹. The addition of Raman spectral peak 1,328 cm⁻¹ to CAPRA postsurgical (CAPRA-S) score significantly improved the predictive power of logistic regression model compared to simple CAPRA score (P<0.001). Meanwhile, the leave-one-out cross-validation method was used to validate the PCA-LDA model and revealed the sensitivity, specificity, and accuracy of 65.8%, 87.5%, and 79.4%, respectively. The receiver operating characteristic (ROC) curve was used to evaluate the performance of different models. Area under the ROC curve of the CAPRA-S score model alone was 0.77, however, when combined with Raman spectral peak 1,328 cm⁻¹, it improved to 0.81.

Conclusion

Our primary results suggested that SERS could be a meaningful technique for prediction of early biochemical recurrence in prostate cancer.

In-line whole blood fractionation for Raman analysis of blood plasma

Raman studies of dynamically expanded cell-free plasma domains in microfluidic blood flow.

Recent Progress on Liquid Biopsy Analysis using Surface-Enhanced Raman Spectroscopy

Traditional tissue biopsy is limited in understanding heterogeneity and dynamic evolution of tumors. Instead, analyzing circulating cancer markers in various body fluids, commonly referred to as "liquid biopsy", has recently attracted remarkable interest for their great potential to be applied in non-invasive early cancer screening, tumor progression monitoring and therapy response assessment. Among the various approaches developed for liquid biopsy analysis, surface-enhanced Raman spectroscopy (SERS) has emerged as one of the most powerful techniques based on its high sensitivity, specificity, tremendous spectral multiplexing capacity for simultaneous target detection, as well as its unique capability for obtaining intrinsic fingerprint spectra of biomolecules. In this review, we will first briefly explain the mechanism of SERS, and then introduce recently reported SERS-based techniques for detection of circulating cancer markers including circulating tumor cells, exosomes, circulating tumor DNAs, microRNAs and cancer-related proteins. Cancer diagnosis based on SERS analysis of bulk body fluids will also be included. In the end, we will summarize the "state of the art" technologies of SERS-based platforms and discuss the challenges of translating them into clinical settings.

Label-Free Detection of Human Serum Using Surface-Enhanced Raman Spectroscopy Based on Highly Branched Gold Nanoparticle Substrates for Discrimination of Non-Small Cell Lung Cancer

Surface-enhanced Raman spectroscopy (SERS) is a good candidate for the development of fast and easy-to-use diagnostic tools, possibly used on serum in screening tests. In this study, a potential label-free serum test based on SERS spectroscopy was developed to analyze human serum for the diagnosis of the non-small cell lung cancer (NSCLC). We firstly synthesized novel highly branched gold nanoparticles (HGNPs) at high yield through a one-step reduction of HAuCl4 with dopamine hydrochloride at 60°C. Then, HGNP substrates with good reproducibility, uniformity, and high SERS effect were fabricated by the electrostatically assisted (3-aminopropyl) triethoxysilane-(APTES-) functionalized silicon wafer surface-sedimentary self-assembly method. Using as-prepared HGNP substrates as a high-performance sensing platform, SERS spectral data of serum obtained from healthy subjects, lung adenocarcinoma patients, lung squamous carcinoma patients, and large cell lung cancer patients were collected. The difference spectra among different types of NSCLC were compared, and analysis result revealed their intrinsic difference in types and contents of nucleic acids, proteins, carbohydrates, amino acids, and lipids. SERS spectra were analyzed by principal component analysis (PCA), which was able to distinguish different types of NSCLC. Considering its time efficiency, being label-free, and sensitivity, SERS based on HGNP substrates is very promising for mass screening NSCLC and plays an important role in the detection and prevention of other diseases.