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

We have recently applied surface-enhanced Raman spectroscopy (SERS) for blood plasma analysis for non-invasive nasopharyngeal cancer detection and obtained good preliminary results. The aim of this study was to develop a more robust SERS spectroscopy based blood plasma analysis method for non-invasive gastric cancer detection. The effect of different laser polarizations (non-polarized, linear-polarized, right-handed circularly polarized, and left-handed circularly polarized) on blood plasma SERS spectroscopy was explored for the first time. Silver nanoparticles as the SERS-substrate were directly mixed with blood plasma to enhance the Raman scattering of various biomolecular constituents. High quality SERS spectra were obtained using a fiber optic probe and a dispersive type near infrared Raman system. Blood plasma samples from gastric cancer patients (n=32) and healthy subjects (n=33) were analyzed. The diagnostic performance for differentiating gastric cancer plasma from normal plasma was evaluated. Principal component analysis combined with linear discriminant analysis (LDA) of the obtained spectral data was used to develop diagnostic algorithms. Classification results obtained from cross-validation of the LDA model based on the four spectral data sets of different laser polarizations demonstrated different diagnostic sensitivities and specificities: 71.9% and 72.7% for non-polarized laser excitation, 75% and 87.9% for linear-polarized laser excitation, 81.3% and 78.8% for right-handed circularly polarized laser excitation, 100% and 97% for left-handed circularly polarized laser excitation. The results from this exploratory study demonstrated that plasma SERS spectroscopy with left-handed circularly polarized laser excitation has great promise of becoming a clinically useful diagnostic tool for non-invasive gastric cancer detection.     

Combining near-infrared-excited autofluorescence and Raman spectroscopy improves in vivo diagnosis of gastric cancer

This study aims to evaluate the diagnostic utility of the combined near-infrared (NIR) autofluorescence (AF) and Raman spectroscopy for improving in vivo detection of gastric cancer at clinical gastroscopy. A rapid Raman endoscopic technique was employed for in vivo spectroscopic measurements of normal (n=1098) and cancer (n=140) gastric tissues from 81 gastric patients. The composite NIR AF and Raman spectra in the range of 800-1800 cm(-1) were analyzed using principal component analysis (PCA) and linear discriminant (LDA) to extract diagnostic information associated with distinctive spectroscopic processes of gastric malignancies. High quality in vivo composite NIR AF and Raman spectra can routinely be acquired from the gastric within 0.5s. The integrated intensity over the range of 800-1800 cm(-1) established the diagnostic implications (p=1.6E-14) of the change of NIR AF intensity associated with neoplastic transformation. PCA-LDA diagnostic modeling on the in vivo tissue NIR AF and Raman spectra acquired yielded a diagnostic accuracy of 92.2% (sensitivity of 97.9% and specificity of 91.5%) for identifying gastric cancer from normal tissue. The integration area under the receiver operating characteristic (ROC) curve using the combined NIR AF and Raman spectroscopy was 0.985, which is superior to either the Raman spectroscopy or NIR AF spectroscopy alone. This work demonstrates that the complementary Raman and NIR AF spectroscopy techniques can be integrated together for improving the in vivo diagnosis and detection of gastric cancer at endoscopy.     

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.      

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.     

Therapeutic prognosis of prostate cancer using surface-enhanced Raman scattering of patient urine and multivariate statistical analysis

Surface-enhanced Raman scattering (SERS) is highly sensitive and label-free analytical technique based on Raman spectroscopy aided by field-multiplying plasmonic nanostructures. We report the use of SERS measurements of patient urine in conjunction with biostatistical algorithms to assess the treatment response of prostate cancer (PCa) in 12 recurrent (Re) and 63 nonrecurrent (NRe) patient cohorts. Multiple Raman spectra are collected from each urine sample using monodisperse silver nanoparticles (AgNPs) for Raman signal enhancement. Genetic algorithms-partial least squares-linear discriminant analysis (GA-PLS-LDA) was employed to analyze the Raman spectra. Comprehensive GA-PLS-LDA analyses of these Raman spectral features (p = 3.50 × 10⁻¹⁶ ) yield an accuracy of 86.6%, sensitivity of 86.0%, and specificity 87.1% in differentiating the Re and NRe cohorts. Our study suggests that SERS combined with multivariate GA-PLS-LDA algorithm can potentially be used to detect and monitor the risk of PCa relapse and to aid with decision-making for optimal intermediate secondary therapy to recurred patients.     

Label‐free diagnosis for colorectal cancer through coffee ring‐assisted surface‐enhanced Raman spectroscopy on blood serum

Surface‐enhanced Raman spectroscopy (SERS) is garnering considerable attention for the swift diagnosis of pathogens and abnormal biological status, that is, cancers. In this work, a simple, fast and inexpensive optical sensing platform is developed by the design of SERS sampling and data analysis. The pretreatment of spectral measurement employed gold nanoparticle colloid mixing with the serum from patients with colorectal cancer (CRC). The droplet of particle‐serum mixture formed coffee‐ring‐like region at the rim, providing strong and stable SERS profiles. The obtained spectra from cancer patients and healthy volunteers were analyzed by unsupervised principal component analysis (PCA) and supervised machine learning model, such as support‐vector machine (SVM), respectively. The results demonstrate that the SVM model provides the superior performance in the classification of CRC diagnosis compared with PCA. In addition, the values of carcinoembryonic antigen from the blood samples were compiled with the corresponding SERS spectra for SVM calculation, yielding improved prediction results.     

Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection

We herein report a novel, reliable and inexpensive method for detecting esophageal cancer using blood plasma resonance Raman spectroscopy combined with multivariate analysis methods. The blood plasma samples were divided into late stage cancer group (n = 164), early stage cancer group (n = 35) and normal group (n = 135) based on clinical pathological diagnosis. Using a specially designed quartz capillary tube as sample holder, we obtained higher quality resonance Raman spectra of blood plasma than existing method. The study demonstrated that the carotenoids levels in blood plasma were reduced in esophageal cancer patients. The area under the receiver operating characteristic curve (and 95% confidence interval) calculated by wavenumber selection and principal component analysis combined with linear discriminant analysis (PC-LDA) algorithm were 0.894 (0.858-0.929), 0.901 (0.841-0.960) and 0.871 (0.799-0.942) for differentiating late cancer from normal, late cancer from early cancer, and early cancer from normal respectively. The contribution from the two carotenoids wavenumber regions of 1155 and 1515 cm⁻¹ were more than 84.2%. The results show that the plasma carotenoids could be a potential biomarker for screening esophageal cancer using resonance Raman spectroscopy combined with wavenumber selection and PC-LDA algorithms.      

Optical diagnosis of laryngeal cancer using high wavenumber Raman spectroscopy

We report the implementation of the transnasal image-guided high wavenumber (HW) Raman spectroscopy to differentiate tumor from normal laryngeal tissue at endoscopy. A rapid-acquisition Raman spectroscopy system coupled with a miniaturized fiber-optic Raman probe was utilized to realize real-time HW Raman (2800-3020 cm(-1)) measurements in the larynx. A total of 94 HW Raman spectra (22 normal sites, 72 tumor sites) were acquired from 39 patients who underwent laryngoscopic screening. Significant differences in Raman intensities of prominent Raman bands at 2845, 2880 and 2920 cm(-1) (CH(2) stretching of lipids), and 2940 cm(-1) (CH(3) stretching of proteins) were observed between normal and cancer laryngeal tissue. The diagnostic algorithms based on principal components analysis (PCA) and linear discriminant analysis (LDA) together with the leave-one subject-out, cross-validation method on HW Raman spectra yielded a diagnostic sensitivity of 90.3% (65/72) and specificity of 90.9% (20/22) for laryngeal cancer identification. This study demonstrates that HW Raman spectroscopy has the potential for the noninvasive, real-time diagnosis and detection of laryngeal cancer at the molecular level.     

血浆HSP60 和TSGF联合检测甲状腺癌的早期诊断价值

目的:究血浆热休克蛋白-60(HSP60)与肿瘤特异性生长因子(TSGF)联合检测对甲状腺癌的早期诊断价值。方法:于2011年1月~2015年11月期间选取254例甲状腺癌患者(定为甲状腺癌组)、156例结节性甲状腺肿患者(结节性甲状腺肿组)和202例健康者(健康组)为研究对象,采用ELISA法检测三组血浆HSP60、TSGF水平,分析其与甲状腺癌病理因素的关系及诊断的敏感度和特异度。结果:甲状腺癌组的血浆HSP60、TSGF水平均显著高于结节性甲状腺肿组以及健康组,且结节性甲状腺肿组高于健康组,差异均具有统计学意义(P0.05)。甲状腺癌患者血浆HSP60、TSGF水平与年龄、性别、临床分期均无相关性(P0.05)。血浆HSP60、TSGF单独诊断甲状腺癌的敏感度和特异度分别为68.75%、81.82%和83.33%、71.88%,两者联合诊断的敏感度和特异度则分别达到了88.89%和86.72%。结论:在甲状腺癌的早期诊断中,采用血浆HSP60和TSGF联合检测的方式可靠性较高,值得推广应用。     

Chiroptical spectroscopy and metabolomics for blood‐based sensing of pancreatic cancer

To enable the early diagnosis of pancreatic cancer, the search for and definition of reliable biomarkers remain a subject of great interest, with the specificity and sensitivity of the currently used biomarkers being below the required values. We tested a novel diagnostic approach for pancreatic cancer based on the specific molecular signature of blood plasma components. To acquire more detailed structural information, structure‐sensitive chiroptical methods (electronic circular dichroism and Raman optical activity) were supplemented by conventional Raman and infrared spectroscopies. The obtained spectra were subsequently processed by linear discriminant analysis yielding high values of specificity and sensitivity. In addition, to monitor not only large biomolecules as potential biomarkers but also those of low molecular weight, we conducted an analysis of blood plasma samples by using metabolomics. The achieved results suggest a panel of promising biomarkers for a reliable detection of pancreatic cancer.     

Back Cover: A novel urine analysis technique combining affinity chromatography with Au nanoparticle based surface enhanced Raman spectroscopy for potential applications in non‐invasive cancer screening (J. Biophotonics 4/2019)

A novel urine analysis technique combining affinity chromatography with Au nanoparticle‐based SERS spectroscopy for potential applications in noninvasive gastric cancer and breast cancer screening. Both the gastric cancer and the breast cancer group can be discriminated from the normal group using SERS spectroscopy combined multivariate diagnostic algorithm, leading to high diagnostic accuracy. These results demonstrate that the urine analysis method has great potential for cancer detection in liquid biopsies. Further details can be found in the article by Xueliang Lin, Lingna Wang, Huijing Lin, et al. ( e201800327 ).

     

Identification of blood species based on surface-enhanced Raman scattering spectroscopy and convolutional neural network

The identification of blood species is of great significance in many aspects such as forensic science, wildlife protection, and customs security and quarantine. Conventional Raman spectroscopy combined with chemometrics is an established method for identification of blood species. However, the Raman spectrum of trace amount of blood could hardly be obtained due to the very small cross-section of Raman scattering. In order to overcome this limitation, surface-enhanced Raman scattering (SERS) was adopted to analyze trace amount of blood. The 785 nm laser was selected as the optimal laser to acquire the SERS spectra, and the blood SERS spectra of 19 species were measured. The convolutional neural network (CNN) was used to distinguish the blood of 19 species including human. The recognition accuracy of the blood species was obtained with 98.79%. Our study provides an effective and reliable method for identification and classification of trace amount of blood.     

Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction

The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high‐dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA‐SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm^?1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.     

Surface-Enhanced Raman Spectroscopy of the Endothelial Cell Membrane

We applied surface-enhanced Raman spectroscopy (SERS) to cationic gold-labeled endothelial cells to derive SERS-enhanced spectra of the bimolecular makeup of the plasma membrane. A two-step protocol with cationic charged gold nanoparticles followed by silver-intensification to generate silver nanoparticles on the cell surface was employed. This protocol of post-labelling silver-intensification facilitates the collection of SERS-enhanced spectra from the cell membrane without contribution from conjugated antibodies or other molecules. This approach generated a 100-fold SERS-enhancement of the spectral signal. The SERS spectra exhibited many vibrational peaks that can be assigned to components of the cell membrane. We were able to carry out spectral mapping using some of the enhanced wavenumbers. Significantly, the spectral maps suggest the distribution of some membrane components are was not evenly distributed over the cells plasma membrane. These results provide some possible evidence for the existence of lipid rafts in the plasma membrane and show that SERS has great potential for the study and characterization of cell surfaces.     

Antibody-based SERS diagnostics of fhit protein without label

Surface-enhanced Raman scattering (SERS) is a particularly promising technique that has the potential to perform highly selective and sensitive in situ measurements of antibody-antigen reactions. This work describes the use of silver (Ag) colloids for immunoassay-based SERS detection of the fragile histidine triad (Fhit) protein. Alterations in Fhit protein expression have been associated with several human cancers, and, thus, the detection of Fhit protein is important because it can potentially be used as a cancer diagnostic biomarker, for both cancer detection and therapy.     

Single cell analysis using surface enhanced Raman scattering (SERS) tags

Fluorescence is a mainstay of bioanalytical methods, offering sensitive and quantitative reporting, often in multiplexed or multiparameter assays. Perhaps the best example of the latter is flow cytometry, where instruments equipped with multiple lasers and detectors allow measurement of 15 or more different fluorophores simultaneously, but increases beyond this number are limited by the relatively broad emission spectra. Surface enhanced Raman scattering (SERS) from metal nanoparticles can produce signal intensities that rival fluorescence, but with narrower spectral features that allow a greater degree of multiplexing. We are developing nanoparticle SERS tags as well as Raman flow cytometers for multiparameter single cell analysis of suspension or adherent cells. SERS tags are based on plasmonically active nanoparticles (gold nanorods) whose plasmon resonance can be tuned to give optimal SERS signals at a desired excitation wavelength. Raman resonant compounds are adsorbed on the nanoparticles to confer a unique spectral fingerprint on each SERS tag, which are then encapsulated in a polymer coating for conjugation to antibodies or other targeting molecules. Raman flow cytometry employs a high resolution spectral flow cytometer capable of measuring the complete SERS spectra, as well as conventional flow cytometry measurements, from thousands of individual cells per minute. Automated spectral unmixing algorithms extract the contributions of each SERS tag from each cell to generate high content, multiparameter single cell population data. SERS-based cytometry is a powerful complement to conventional fluorescence-based cytometry. The narrow spectral features of the SERS signal enables more distinct probes to be measured in a smaller region of the optical spectrum with a single laser and detector, allowing for higher levels of multiplexing and multiparameter analysis.     

Surface enhanced Raman scattering for narcotic detection and applications to chemical biology

Raman spectroscopy is rapidly finding favour for applications in the life science because of the ease with which it can be used to extract significant data from tissue and cells. However, the Raman effect is an inherently weak effect, which hinders the analysis of low concentration analytes. Raman sensitivity can be improved via the surface enhanced Raman scattering (SERS) effect. In SERS, Raman spectra are dramatically amplified when a molecule is adsorbed onto nano-roughened noble metal surfaces such as silver and gold. The degree of enhancement enables single-molecule detection, which offers the potential for the unambiguous identification of analytes at concentrations that are useful in both a forensic and a chemical biology context. Here we discuss some of the practical applications of SERS to both low-level narcotic detection, and how this can be applied to chemical biology.     

Molecular conformation changes along the malignancy revealed by optical nanosensors

An interdisciplinary approach employing functionalized nanoparticles and ultrasensitive spectroscopic techniques is reported here to track the molecular changes in early stage of malignancy. Melanoma tissue tracking at molecular level using both labelled and unlabelled silver and gold nanoparticles has been achieved using surface enhanced Raman scattering (SERS) technique. We used skin tissue from ex vivo mice with induced melanoma. Raman and SERS molecular characterization of melanoma tissue is proposed here for the first time. Optical nanosensors based on Ag and Au nanoparticles with chemisorbed cresyl violet molecular species as labels revealed sensitive capability to tissues tagging and local molecular characterization. Sensitive information originating from surrounding native biological molecules is provided by the tissue SERS spectra obtained either with visible or NIR laser line. Labelled nanoparticles introduced systematic differences in tissue response compared with unlabelled ones, suggesting that the label functional groups tag specific tissue components revealed by proteins or nucleic acids bands. Vibrational data collected from tissue are presented in conjunction with the immunohistochemical analysis. The results obtained here open perspectives in applied plasmonic nanoparticles and SERS for the early cancer diagnostic based on the appropriate spectral databank.     

Quantitative Surface-Enhanced Raman for Gene Expression Estimation

We demonstrate for the first time, to our knowledge, a unique gene expression assay by surface-enhanced Raman scattering (SERS) using nonfluorescent Raman labels to quantify gene expression at the resolution of alternative splicing using RNA extracted from cancer cells without any amplification steps. Our approach capitalizes on the inherent plasmon-phonon mode of SERS substrates as a self-referencing standard for the detection and quantification of genetic materials. A strategy integrating S1 nuclease digestion with SERS detection was developed to quantify the expression levels of splice junction Δ(9,10), a segment of the breast cancer susceptibility gene 1 (BRCA1) from MCF-7 and MDA-MB-231 cells. Quantification results were cross-validated using two Raman tags and qualitatively confirmed by RT-PCR. Our methodology based on SERS technology provides reliable gene expression data with high sensitivity, bypassing the intricacies involved in fabricating a consistent SERS substrate.     

Structural analysis of biological aliphatic compounds using surface-enhanced Fourier transform Raman spectroscopy

The surface-enhanced Raman scattering (SERS) technique for Fourier transform Raman spectrometry is employed to reveal the chemical structure of biological aliphatic compounds consisting of folded, long aliphatic chains. The structural analysis is performed via the measurements of the accordion-vibration modes generated in the ordered, long aliphatic chain. The SERS spectra after subtraction of a background spectrum give segment lengths that are almost perfectly consistent with the chemical structures studied by mass spectrometry. The agreement of the SERS results with those of mass spectrometry suggests the positions of kinks in the long hydrocarbon chain. The combination technique of SERS and mass spectrometry is useful to discuss the structure of folded, long biological lipids.