Label-free surface-enhanced Raman spectroscopy for detection of colorectal cancer and precursor lesions using blood plasma

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

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

Diatomite-Based, Flexible SERS Immunosensor Platform for Rapid, Specific, and Sensitive Detection of Circulating Cancer-Specific Protein Biomarkers in Serum Using Raman Probes

Cancer is one of the most actively researched diseases having a high mortality rate when not detected at an early stage. Thus, rapid, simultaneous, and sensitive quantification of cancer biomarkers plays an important role in early diagnosis, with patient impact to disability adjusted life years. Herein, a diatomite-based SERS flexible platform for the rapid and sensitive detection of circulating cancer-specific protein biomarkers in serum is presented. In this approach, diatomite/AgNPs strips with maximum SERS activity prepared using the layer-by-layer (LbL) technique were modified with specific antibodies, and specific antigens (HER2, CA15-3, PSA, and MUC4) were captured and detected. By using Raman probes specific to the captured antigens in serum, a SERS limit of detection (LOD) of 0.1 ng/mL was measured (calculated LOD < 0.1 ng/mL). This value is lower than the cutoff amount of cancer antigens in the person's blood. The specificity for the antigens of each antibody was calculated to be higher than 95%. As a result, an immunosensor for rapid detection of cancer biomarkers in serum with good specificity, high sensitivity, good reproducibility, and low cost has been demonstrated. Overall, we show that the prepared diatomite-based SERS substrate with a high surface-to-volume ratio is a useable platform for immunoassay tests.

A Review on Emerging Techniques for Diagnosis of Colorectal Cancer

Common detection methods in practice for diagnosing colorectal cancer (CRC) are painful and invasive leading to less participation of individuals for CRC diagnosis. Whereas, improved or enhanced imaging systems and other minimally invasive techniques with shorter detection times deliver greater detail and less discomfort in individuals. Thus, this review is a summary of the diagnostic tests, ranging from the simple potential use in developing a flexible CRC treatment to the patient's potential benefits in receiving less invasive procedures and the advanced treatments that might provide a better assessment for the diagnosis of CRC and reduce the mortality related to CRC.

Rapid label-free detection of cholangiocarcinoma from human serum using Raman spectroscopy

Cholangiocarcinoma (CCA) is highly prevalent in the northeastern region of Thailand. Current diagnostic methods for CCA are often expensive, time-consuming, and require medical professionals. Thus, there is a need for a simple and low-cost CCA screening method. This work developed a rapid label-free technique by Raman spectroscopy combined with the multivariate statistical methods of principal component analysis and linear discriminant analysis (PCA-LDA), aiming to analyze and classify between CCA (n = 30) and healthy (n = 30) serum specimens. The model's classification performance was validated using k-fold cross validation (k = 5). Serum levels of cholesterol (548, 700 cm-1), tryptophan (878 cm-1), and amide III (1248,1265 cm-1) were found to be statistically significantly higher in the CCA patients, whereas serum beta-carotene (1158, 1524 cm-1) levels were significantly lower. The peak heights of these identified Raman marker bands were input into an LDA model, achieving a cross-validated diagnostic sensitivity and specificity of 71.33% and 90.00% in distinguishing the CCA from healthy specimens. The PCA-LDA technique provided a higher cross-validated sensitivity and specificity of 86.67% and 96.67%. To conclude, this work demonstrated the feasibility of using Raman spectroscopy combined with PCA-LDA as a helpful tool for cholangiocarcinoma serum-based screening.

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.

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.

Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing

The mortality of ovarian cancer is closely related to its poor rate of early detection. In the search of an efficient diagnosis method, Raman spectroscopy of blood features as a promising technique allowing simple, rapid, minimally-invasive and cost-effective detection of cancers, in particular ovarian cancer. Although Raman spectroscopy has been demonstrated to be effective to detect ovarian cancers with respect to normal controls, a binary classification remains idealized with respect to the real clinical practice. This work considered a population of 95 woman patients initially suspected of an ovarian cancer and finally fixed with a cancer or a cyst. Additionally, 79 normal controls completed the ensemble of samples. Such sample collection proposed us a study case where a ternary classification should be realized with Raman spectroscopy of the collected blood samples coupled with suitable spectroscopic data treatment algorithms. In the medical as well as data points of view, the appearance of the cyst case considerably reduces the distances among the different populations and makes their distinction much more difficult, since the intermediate cyst case can share the specific features of the both cancer and normal cases. After a proper spectrum pretreatment, we first demonstrated the evidence of different behaviors among the Raman spectra of the 3 types of samples. Such difference was further visualized in a high dimensional space, where the data points of the cancer and the normal cases are separately clustered, whereas the data of the cyst case were scattered into the areas respectively occupied by the cancer and normal cases. We finally developed and tested an ensemble of models for a ternary classification with 2 consequent steps of binary classifications, based on machine learning algorithms, allowing identification with sensitivity and specificity of 81.0% and 97.3% for cancer samples, 63.6% and 91.5% for cyst samples, 100% and 90.6% for normal samples.

Non-invasive diagnosis of colorectal cancer by Raman spectroscopy: Recent developments in liquid biopsy and endoscopy approaches

Colorectal cancer (CRC) is the third most common cancer diagnosed globally and is also one of the leading causes of cancer deaths in both men and women. The progression of CRC is slow and is often contained in colon but the risk increases with age. Based on the high certainty that the net benefit of screening in an age group is substantial, screening for CRC is recommended beginning at the age of 50. Currently, most of the incidence is concentrated in developed countries but the rate is increasing rapidly in developing geographies. Detecting CRC at an early stage is critical to reduce morbidity and mortality. Colonoscopy is the most preferred screening method but not very widely implemented due to practical considerations such as cost involved, lack of personnel and facility. To address these concerns, Raman spectroscopy (RS) has been suggested as a viable alternative due to its potential as a rapid non-invasive diagnostic tool. Recently, several studies have been reported but many variations of RS applications in CRC exists and are not well understood by non-specialists. This review focuses particularly on developments of Raman based liquid biopsy and endoscopic studies in order to throw light on each of their significance and limitations. Necessary developments in the future to translate RS into a clinical tool for screening and diagnosis of CRC are also briefly presented.

Rapid and label-free urine test based on surface-enhanced Raman spectroscopy for the non-invasive detection of colorectal cancer at different stages

The concept of being able to urinate in a cup and screen for colorectal cancer (CRC) is fascinating to the public at large. Here, a simple and label-free urine test based on surface-enhanced Raman spectroscopy (SERS) was employed for CRC detection. Significant spectral differences among normal, stages I-II, and stages III-IV CRC urines were observed. Using discriminant function analysis, the diagnostic sensitivities of 95.8%, 80.9%, and 84.3% for classification of normal, stages I-II, and stages III-IV CRC were achieved in training model, indicating the great promise of urine SERS as a rapid, convenient and noninvasive method for CRC staging detection.

Highly accurate colorectal cancer prediction model based on Raman spectroscopy using patient serum

BACKGROUND

Colorectal cancer (CRC) is an important disease worldwide, accounting for the second highest number of cancer-related deaths and the third highest number of new cancer cases. The blood test is a simple and minimally invasive diagnostic test. However, there is currently no blood test that can accurately diagnose CRC.

AIM

To develop a comprehensive, spontaneous, minimally invasive, label-free, blood-based CRC screening technique based on Raman spectroscopy.

METHODS

We used Raman spectra recorded using 184 serum samples obtained from patients undergoing colonoscopies. Patients with malignant tumor histories as well as those with cancers in organs other than the large intestine were excluded. Consequently, the specific diseases of 184 patients were CRC (12), rectal neuroendocrine tumor (2), colorectal adenoma (68), colorectal hyperplastic polyp (18), and others (84). We used the 1064-nm wavelength laser for excitation. The power of the laser was set to 200 mW.

RESULTS

Use of the recorded Raman spectra as training data allowed the construction of a boosted tree CRC prediction model based on machine learning. Therefore, the generalized R² values for CRC, adenomas, hyperplastic polyps, and neuroendocrine tumors were 0.9982, 0.9630, 0.9962, and 0.9986, respectively.

CONCLUSION

For machine learning using Raman spectral data, a highly accurate CRC prediction model with a high R² value was constructed. We are currently planning studies to demonstrate the accuracy of this model with a large amount of additional data.

Promoting identification of amyotrophic lateral sclerosis based on label-free plasma spectroscopy

Objective

Amyotrophic lateral sclerosis (ALS) is an adult‐onset fatal neurodegenerative disease which lacks identified biological markers. A label‐free plasma surface‐enhanced Raman spectroscopy (SERS) method was developed to explore a simple and noninvasive test for ALS.

Methods

ALS patients were enrolled serially and plasma samples were collected at the time of diagnosis prior to the start of ALS treatment. SERS spectra were recorded using a Renishaw micro‐Raman system.

Results

To exclude the interference by varying disease severity, we enrolled three groups of ALS patients, including ALS‐1 (n = 60; ALSFRS‐R ≥ 42 and time interval ≤ 12 months), ALS‐2 (n = 61; ALSFRS‐R < 42 and time interval ≤ 12 months), and ALS‐3 (n = 61; ALSFRS‐R ≥ 38 and time interval> 12 months). The SERS spectra were analyzed using principal component analysis (PCA), which showed that ALS‐1, ALS‐2, ALS‐3, and control groups were separated significantly. Then, decision tree (DT) models and receiver operating characteristic curves were employed and identified that bands at 722 and 739 cm⁻¹, and ratios of 635–722 cm⁻¹ and 635–739 cm⁻¹ were able to distinguish ALS from controls significantly. Finally, we highlighted six metabolism pathways correlated with ALS, including phenylalanine‐tyrosine‐tryptophan biosynthesis, aminoacyl‐tRNA biosynthesis, phenylalanine metabolism, pantothenate and CoA biosynthesis, porphyrin and chlorophyll metabolism, and pyrimidine metabolism.

Interpretation:

Plasma SERS could be a promising tool for the detection of ALS. The bands at 722 and 739 cm⁻¹, and the ratios of 635–722 cm⁻¹ and 635–739 cm⁻¹ could serve as potential indicator for ALS.

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.

Diagnostic spectro-cytology revealing differential recognition of cervical cancer lesions by label-free surface enhanced Raman fingerprints and chemometrics

Herein we have stepped-up on a strategic spectroscopic modality by utilizing label free ultrasensitive surface enhanced Raman scattering (SERS) technique to generate a differential spectral fingerprint for the prediction of normal (NRML), high-grade intraepithelial lesion (HSIL) and cervical squamous cell carcinoma (CSCC) from exfoliated cell samples of cervix. Three different approaches i.e. single-cell, cell-pellet and extracted DNA from oncology clinic as confirmed by Pap test and HPV PCR were employed. Gold nanoparticles as the SERS substrate favored the increment of Raman intensity exhibited signature identity for Amide III/Nucleobases and carotenoid/glycogen respectively for establishing the empirical discrimination. Moreover, all the spectral invention was subjected to chemometrics including Support Vector Machine (SVM) which furnished an average diagnostic accuracy of 94%, 74% and 92% of the three grades. Combined SERS read-out and machine learning technique in field trial promises its potential to reduce the incidence in low resource countries.

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.

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.

SERS-based differential diagnosis between multiple solid malignancies: breast, colorectal, lung, ovarian and oral cancer

Purpose

Surface-enhanced Raman scattering (SERS) spectroscopy on serum and other biofluids for cancer diagnosis represents an emerging field, which has shown promising preliminary results in several types of malignancies. The purpose of this study was to demonstrate that SERS spectroscopy on serum can be employed for the differential diagnosis between five of the leading malignancies, ie, breast, colorectal, lung, ovarian and oral cancer.

Patients and methods

Serum samples were acquired from healthy volunteers (n=39) and from patients diagnosed with breast (n=42), colorectal (n=109), lung (n=33), oral (n=17), and ovarian cancer (n=13), comprising n=253 samples in total. SERS spectra were acquired using a 532 nm laser line as excitation source, while the SERS substrates were represented by Ag nanoparticles synthesized by reduction with hydroxylamine. The classification accuracy yielded by SERS was assessed by principal component analysis–linear discriminant analysis (PCA-LDA).

Results

The sensitivity and specificity in discriminating between cancer patients and controls was 98% and 91%, respectively. Cancer samples were correctly assigned to their corresponding cancer types with an accuracy of 88% for oral cancer, 86% for colorectal cancer, 80% for ovarian cancer, 76% for breast cancer and 59% for lung cancer.

Conclusion

SERS on serum represents a promising strategy of diagnosing cancer which can discriminate between cancer patients and controls, as well as between cancer types such as breast, colorectal, lung ovarian and oral cancer.

Prognostic analysis of amyotrophic lateral sclerosis based on clinical features and plasma surface-enhanced Raman spectroscopy

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a wide range of survival times. We aimed to explore prognostic factors related to short survival based on clinical features and plasma metabolic signatures using surface-enhanced Raman spectroscopy (SERS). One hundred and thirty-eight sporadic ALS cases were enrolled serially, including 62 for the short-duration group (≤3 years) and 76 for the long-duration group (>3 years). Multivariate analysis showed that an older age of onset (>60 years; odds ratio [OR] = 3.98, 95% CI: 1.09-14.53), lower body mass index (BMI) (<18.5; OR = 6.80, 95% CI: 1.36-33.92), and lower ALSFRS-R score (<35; OR = 6.03, 95% CI: 1.42-25.63) were associated with higher odds of tracheotomy or death, while a higher uric acid (UA) level showed a protective effect (>356.36 μmol/L; OR = 0.19, 95% CI: 0.05-0.73). SERS analysis showed significant differences between the two groups, and pathway analysis highlighted five main metabolic pathways, including metabolisms of glutathione, pyrimidine, phenylalanine, galactose, and phenylalanine-tyrosine-tryptophan biosynthesis. In conclusion, age of onset, BMI, ALSFRS-R score and UA, together with dysregulation of glucose, amino acid, nucleic acid, and antioxidant metabolism contributed to disease progression, and are therefore potential therapeutic targets for ALS.

Diagnosis accuracy of Raman spectroscopy in colorectal cancer: A PRISMA-compliant systematic review and meta-analysis

BACKGROUND

The clinical significance of Raman spectroscopy (RS) in colorectal cancer (CRC) patients still remains underestimated. We performed this meta-analysis to elucidate the diagnostic value in CRC patients.

METHODS

We systematically searched electronic databases for published articles. Fixed effect model and random effect model were used to calculate the pooled sensitivity, specificity, diagnostic accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and positive posttest probability (PPP) of CRC. Meta-regression and subgroup analysis were conducted to assess potential source of heterogeneity. We also used Egger linear regression tests to assess risk of publication bias.

RESULTS

Thirteen studies had been included (679 patients: 186 with premalignant lesions and 493 with malignant lesions). The pooled sensitivity, specificity, diagnostic accuracy, PLR, NLR, DOR and PPP for CRC screening using RS were 0.94 (0.92-0.96), 0.94 (0.88-0.97), 0.96 (0.94-0.98), 16.44 (7.80-34.63), 0.062 (0.043-0.090), 263.65 (99.03-701.96) and 86%, respectively.

CONCLUSION

RS is a potentially useful tool for future CRC screening. It also offers potentially early detection for CRC patients.

Plasmonics for Biosensing

Techniques based on plasmonic resonance can provide label-free, signal enhanced, and real-time sensing means for bioparticles and bioprocesses at the molecular level. With the development in nanofabrication and material science, plasmonics based on synthesized nanoparticles and manufactured nano-patterns in thin films have been prosperously explored. In this short review, resonance modes, materials, and hybrid functions by simultaneously using electrical conductivity for plasmonic biosensing techniques are exclusively reviewed for designs containing nanovoids in thin films. This type of plasmonic biosensors provide prominent potential to achieve integrated lab-on-a-chip which is capable of transporting and detecting minute of multiple bio-analytes with extremely high sensitivity, selectivity, multi-channel and dynamic monitoring for the next generation of point-of-care devices.

Weighted spectral reconstruction method for discrimination of bacterial species with low signal-to-noise ratio Raman measurements

Raman spectroscopy is a label-free and non-destructive spectroscopic technique that has been explored for bacterial identification. However, noise often interferes with the interesting Raman peaks because the Raman signal is inherently weak, especially for bacterial samples. Although this problem can be solved by increasing the exposure time or the power of the excitation laser, a longer acquisition time is required or the risk of sample damage is increased. In contrast, short exposure time and low laser power often lead to inadequate acquisition of Raman scattering, in which the Raman spectra with low signal-to-noise ratio (SNR) is difficult to be further analyzed. In order to quickly and accurately characterize biological samples by using low SNR Raman measurements, a weighted spectral reconstruction based method was developed and tested on Raman spectra with low SNR from 20 bacterial samples of two species. Principal component analysis followed by support vector machine was applied on the reference Raman spectra and the spectra recovered from the low SNR Raman measurements by the proposed method, the traditional spectral reconstruction method, and four other commonly used de-noising methods for the discrimination of bacterial species. The results showed that a classification accuracy of 90% was achieved based on our method, which was comparable to that of the reference Raman spectra and showed significant advantages over other spectral recovery methods. Therefore, the weighted spectral reconstruction method can preserve the most biochemical information for the bacterial species' identification while removing the noise from the low SNR Raman spectra, in which the advantages of lesser sample damage and shorter acquisition time would promote wider biomedical applications of Raman spectroscopy.

Raman spectra recovered from low SNR Raman measurements by weighted spectral reconstruction method show excellent preservation of information about bacterial identification.

Combining SERS analysis of serum with PSA levels for improving the detection of prostate cancer

Aim: Previous studies regarding surface-enhanced Raman scattering (SERS) of serum have shown promising initial results in discriminating prostate cancer, a strategy which could complement standard tests such as the prostate-specific antigen (PSA). Materials & methods: SERS spectra of serum samples were combined with serum PSA levels to improve the discrimination accuracy between prostate cancer and nonmalignant pathologies in a cohort of 54 patients using principal component analysis-linear discriminant analysis (PCA-LDA). Results & discussion: Combining SERS spectra with serum PSA levels in a single PCA-LDA model could discriminate between the two groups with an overall accuracy of 94%, yielding better results than either method alone. Conclusion: These results highlight that combining SERS-based cancer screening with serum PSA levels represents a promising strategy for improving the accuracy of prostate cancer diagnosis.

Exploring the margins of SERS in practical domain: An emerging diagnostic modality for modern biomedical applications

Excellent multiplexing capability, molecular specificity, high sensitivity and the potential of resolving complex molecular level biological compositions augmented the diagnostic modality of surface-enhanced Raman scattering (SERS) in biology and medicine. While maintaining all the merits of classical Raman spectroscopy, SERS provides a more sensitive and selective detection and quantification platform. Non-invasive, chemically specific and spatially resolved analysis facilitates the exploration of SERS-based nano probes in diagnostic and theranostic applications with improved clinical outcomes compared to the currently available so called state-of-art technologies. Adequate knowledge on the mechanism and properties of SERS based nano probes are inevitable in utilizing the full potential of this modality for biomedical applications. The safety and efficiency of metal nanoparticles and Raman reporters have to be critically evaluated for the successful translation of SERS in to clinics. In this context, the present review attempts to give a comprehensive overview about the selected medical, biomedical and allied applications of SERS while highlighting recent and relevant outcomes ranging from simple detection platforms to complicated clinical applications.

Application of surface-enhanced Raman spectroscopy in diagnosis and staging of gastric cancer

AIM

To investigate the value of surface-enhanced Raman spectroscopy (SERS) in the diagnosis of gastric cancer (GC) as well as its feasibility in distinguishing GC of different TNM stages.

METHODS

InVia laser confocal microscope-Raman spectrometer was used to examine the sera of patients with pathologically confirmed GC (63 cases), those with gastric precursor lesions (45 cases), and healthy volunteers (50 cases). One-way ANOVA, Student's t test, principal component analysis (PCA), and linear discriminant analysis (LDA) were used to process and analyze the Raman spectral data, and the receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficiency.

RESULTS

The average SERS spectra of sera differed significantly among GC patients, patients with precancerous lesions, and healthy volunteers. The intensity of Raman spectra located at 725, 1099, 1133, and 1589/cm was significantly stronger in GC patients than in normal controls, while the intensity of Raman spectra at 1004, 1328, 1446, and 1657/cm was significantly stronger in normal persons. A strong enhancement in the intensity of the peak at approximately 815/cm was observed in the spectra of the serum of GC patients. At the Raman shift of 1133, 1446, and 1589/cm, Raman intensity for serum samples was significantly stronger in GC patients with TNM stage Ⅲ/Ⅳ disease than in those with stage Ⅰ/Ⅱ disease, while the Raman intensity at the Raman shift of 1004/cm was significantly stronger in patients with stage Ⅰ/Ⅱ disease. The sensitivity, specificity, and accuracy of SERS combined with multivariate PCA-LDA in diagnosing GC were 96.8% (61/63), 78% (39/50), and 88.5% (100/113), respectively, and the area under the ROC curve was 0.927. The sensitivity, specificity, and accuracy of SERS combined with multivariate PCA-LDA in distinguishing TNM stage Ⅰ/Ⅱ GC and stage Ⅲ/Ⅳ disease were 97.5% (39/40), 73.9% (17/23), and 88.9% (56/63), respectively, and the area under the ROC curve was 0.857.

CONCLUSION

Detection and analysis of sera based on SERS can effectively identify patients with GC, those with gastric precancerous lesions, and healthy volunteers. Furthermore, it can effectively distinguish GC of different stages. SERS is expected to become a new method for early diagnosis, clinical decision guidance, and prognosis evaluation of GC.

Label-free Detection for a DNA Methylation Assay Using Raman Spectroscopy

Background:

DNA methylation has been suggested as a biomarker for early cancer detection and treatment. Varieties of technologies for detecting DNA methylation have been developed, but they are not sufficiently sensitive for use in diagnostic devices. The aim of this study was to determine the suitability of Raman spectroscopy for label-free detection of methylated DNA.

Methods:

The methylated promoter regions of cancer-related genes cadherin 1 (CDH1) and retinoic acid receptor beta (RARB) served as target DNA sequences. Based on bisulfite conversion, oligonucleotides of methylated or nonmethylated probes and targets were synthesized for the DNA methylation assay. Principal component analysis with linear discriminant analysis (PCA-DA) was used to discriminate the hybridization between probes and targets (methylated probe and methylated target or nonmethylated probe and nonmethylated target) of CDH1 and RARB from nonhybridization between the probe and targets (methylated probe and nonmethylated target or nonmethylated probe and methylated target).

Results:

This study revealed that the CDH1 and RARB oligo sets and their hybridization data could be classified using PCA-DA. The classification results for CDH1 methylated probe + CDH1 methylated target versus CDH1 methylated probe + CDH1 unmethylated target showed sensitivity, specificity, and error rates of 92%, 100%, and 8%, respectively. The classification results for the RARB methylated probe + RARB methylated target versus RARB methylated probe + RARB unmethylated target showed sensitivity, specificity, and error rates of 92%, 93%, and 11%, respectively.

Conclusions:

Label-free detection of DNA methylation could be achieved using Raman spectroscopy with discriminant analysis.

"Optical and Surface Enhanced Raman Scattering properties of Ag modified silicon double nanocone array"

Surface enhanced Raman scattering (SERS) systems with large number of active sites exhibit superior capability in detection of low concentration analytes. In this paper, we present theoretical as well as experimental studies on the optical properties of a unique hybrid nanostructure, Ag NPs decorated silicon double nanocones (Si-DNCs) array, which provide high density of hot spots. The Si-DNC array is fabricated by employing electron beam lithography together with plasma etching process. Multipole analysis of the scattering spectra, based on the multipole expansion theory, confirms that the toroidal dipole moment dominates over other electric and magnetic multipole moments in the Si-DNCs array. This response occurs as a result of generating current densities flowing in opposite directions and consequently generating H-field vortexes inside the nanocones. Moreover, SERS applicability of this type of nanostructure is examined. For this purpose, the Si-DNCs array is decorated with Ag nanoparticles (NPs) by means of electroless deposition method. Simulation results indicate that combination of multiple resonances, including LSPR resonance of Ag NPs, longitudinal standing wave resonance of Ag layer and inter-particle interaction in the gap region, result in a significant SERS enhancement. Our experimental results demonstrate that Si-DNC/Ag NPs array substrate provides excellent reproducibility and ultrahigh sensitivity.

Leukemia cells detection based on electroporation assisted surface-enhanced Raman scattering

In this study, an electroporation-based surface-enhanced Raman scattering (SERS) technique was employed to differentiate the human myeloid leukemia cells from the normal human bone marrow mononuclear cells with the aim to develop a fast and label-free method for leukemia cell screening. The Ag nanoparticles were delivered into living cells by electroporation, and then high quality SERS spectra were successfully obtained from 60 acute promyelocytic leukemia cells (HL60 cell line), 60 chronic myelogenous leukemia cells (K562 cell line) and 60 normal human bone marrow mononuclear cells (BMC). Principal component analysis (PCA) combined with linear discriminant analysis (LDA) differentiated the leukemia cell SERS spectra (HL60 plus K562) from normal cell SERS spectra (BMC) with high sensitivity (98.3%) and specificity (98.3%). Furthermore, partial least squares (PLS) approach was employed to develop a diagnostic model. The model successfully predicted the unidentified subjects with a diagnostic accuracy of 96.7%. This exploratory work demonstrates that the electroporation-based SERS technique combined with PCA-LDA and PLS diagnostic algorithms possesses great promise for cancer cell screening.

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

Gastrointestinal cancer is a leading contributor to cancer-related morbidity and mortality worldwide. Early diagnosis currently plays a key role in the prognosis of patients with gastrointestinal cancer. Despite the advances in endoscopy over the last decades, missing lesions, undersampling and incorrect sampling in biopsies, as well as invasion still result in a poor diagnostic rate of early gastrointestinal cancers. Accordingly, there is a pressing need to develop non-invasive methods for the early detection of gastrointestinal cancers. Biomedical optical spectroscopy, including infrared spectroscopy, Raman spectroscopy, diffuse scattering spectroscopy and autofluorescence, is capable of providing structural and chemical information about biological specimens with the advantages of non-destruction, non-invasion and reagent-free and waste-free analysis and has thus been widely investigated for the diagnosis of oesophageal, gastric and colorectal cancers. This review will introduce the advances of biomedical optical spectroscopy techniques, highlight their applications for the early detection of gastrointestinal cancers and discuss their limitations.

Raman Spectroscopy of Blood and Blood Components

Blood is a bodily fluid that is vital for a number of life functions in animals. To a first approximation, blood is a mildly alkaline aqueous fluid (plasma) in which a large number of free-floating red cells (erythrocytes), white cells (leucocytes), and platelets are suspended. The primary function of blood is to transport oxygen from the lungs to all the cells of the body and move carbon dioxide in the return direction after it is produced by the cells' metabolism. Blood also carries nutrients to the cells and brings waste products to the liver and kidneys. Measured levels of oxygen, nutrients, waste, and electrolytes in blood are often used for clinical assessment of human health. Raman spectroscopy is a non-destructive analytical technique that uses the inelastic scattering of light to provide information on chemical composition, and hence has a potential role in this clinical assessment process. Raman spectroscopic probing of blood components and of whole blood has been on-going for more than four decades and has proven useful in applications ranging from the understanding of hemoglobin oxygenation, to the discrimination of cancerous cells from healthy lymphocytes, and the forensic investigation of crime scenes. In this paper, we review the literature in the field, collate the published Raman spectroscopy studies of erythrocytes, leucocytes, platelets, plasma, and whole blood, and attempt to draw general conclusions on the state of the field.

Gold Nanoparticle Based Platforms for Circulating Cancer Marker Detection

Detection of cancer-related circulating biomarkers in body fluids has become a cutting-edge technology that has the potential to noninvasively screen cancer, diagnose cancer at early stage, monitor tumor progression, and evaluate therapy responses. Traditional molecular and cellular detection methods are either insensitive for early cancer intervention or technically costly and complicated making them impractical for typical clinical settings. Due to their exceptional structural and functional properties that are not available from bulk materials or discrete molecules, nanotechnology is opening new horizons for low cost, rapid, highly sensitive, and highly specific detection of circulating cancer markers. Gold nanoparticles have emerged as a unique nanoplatform for circulating biomarker detection owning to their advantages of easy synthesis, facile surface chemistry, excellent biocompatibility, and remarkable structure and environment sensitive optical properties. In this review, we introduce current gold nanoparticle-based technology platforms for the detection of four major classes of circulating cancer markers - circulating tumor cells, vesicles, nucleic acids, and proteins. The techniques will be summarized in terms of signal detection strategies. Distinctive examples are provided to highlight the state-of-the-art technologies that significantly advance basic and clinical cancer research.

Evaluation of expressed prostatic secretion and serum using surface-enhanced Raman spectroscopy for the noninvasive detection of prostate cancer, a preliminary study

Surface-enhanced Raman spectroscopy (SERS) involving expressed prostatic secretion (EPS) and serum was investigated; the objective was to determine if this approach could distinguish prostate cancer from benign prostatic hyperplasia. A total of 120 SERS spectra for EPS and 96 spectra for serum were gathered from patients within a prospective contemporary biopsy cohort. Significant differences in spectra between prostate cancer and benign prostatic hyperplasia were tentatively assigned to component changes in EPS and serum samples. Principal component analysis and linear discriminate analysis were utilized to evaluate the spectral data for EPS and serum, to build diagnostic algorithms. The leave-one-out cross-validation method was used to validate the diagnostic algorithms; it revealed diagnostic sensitivities of 75% and 60%, specificities of 75% and 76.5%, and accuracies of 75% and 68% for EPS and serum, respectively. The results suggest that EPS and serum SERS analysis could be a potential tool for prostate cancer detection.

Introduction to the bio-optics: design and application

The "Bio-Optics: Design and Application" symposium, held on April 12-15, 2015, in Vancouver, BC, was an important step in a continuous journey to closely connect technological design advancement and biomedical applications. The symposium presented a broad range of innovations in diagnostic devices, endoscopy, optical microscopy, optical coherence tomography, multi-modal imaging, and highlighted specific applications including cancer diagnostics, detection of infectious disease and point of care, as well as microsurgery treatment.