Differentiation of healthy brain tissue and tumors using surface-enhanced Raman scattering

Label-free screening of common urinary system tumors from blood plasma based on surface-enhanced Raman spectroscopy

BACKGROUND

The incidence of common urinary system tumors has been rising rapidly in recent years, and most urinary system-derived tumors lack specific biomarkers.

OBJECTIVES

To explore the efficacy of surface-enhanced Raman spectroscopy (SERS) of blood plasma in screening three common urinary system tumors, including bladder cancer (BC), prostate cancer (PCa), and renal cell carcinoma (RCC).

METHODS

SERS plasma spectra from 125 plasma samples, including 25 PCa, 38 RCC, 24 BC patients, and 38 normal volunteers, were collected. All candidates had no other comorbidities. The Diagnosis was based on the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and the effectiveness of the diagnostic algorithms was verified using the Receiver Operating Characteristic Curve (ROC).

RESULTS

There are significant differences in SERS signals between PCa, BC, RCC, and normal plasma, especially at 639, 889, 1010, 1136, and 1205 cm-1. The PCA-LDA results show that high sensitivity (100 %), specificity (100 %), and accuracy (100 %) could be achieved for screening the PCa, RCC, BC group vs. the normal group, the PCa group vs. the BC and RCC group, respectively. The diagnostic sensitivity, specificity, and accuracy for the BC group vs. the RCC group are 79.2 %, 71.1 %, and 75.15 %, respectively. The integrated area under the ROC curve (AUC) is 1.0, 1.0, and 1.0 for the PCa, RCC, and BC group vs. the normal group, respectively. The AUC of the PCa group vs. the BC group and RCC group and the BC group vs. the RCC group are 1.0, 1.0, and 0.842, respectively.

CONCLUSIONS

Label-free plasma-SERS technology with PCA-LDA analysis could be a useful screening method for detecting urinary system tumors (PCa, RCC, and BC) in this exploratory study.

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.

Toward a SERS Diagnostic Tool for Discrimination between Cancerous and Normal Bladder Tissues via Analysis of the Extracellular Fluid

Vibrational spectroscopy provides the possibility for sensitive and precise detection of chemical changes in biomolecules due to development of cancers. In this work, label-free near-infrared surface enhanced Raman spectroscopy (SERS) was applied for the differentiation between cancerous and normal human bladder tissues via analysis of the extracellular fluid of the tissue. Specific cancer-related SERS marker bands were identified by using a 1064 nm excitation wavelength. The prominent spectral marker band was found to be located near 1052 cm^-1 and was assigned to the C-C, C-O, and C-N stretching vibrations of lactic acid and/or cysteine molecules. The correct identification of 80% of samples is achieved with even limited data set and could be further improved. The further development of such a detection method could be implemented in clinical practice for the aid of surgeons in determining of boundaries of malignant tumors during the surgery.

Discrimination of glioma patient-derived cells from healthy astrocytes by exploiting Raman spectroscopy

Glioblastoma multiforme (GBM) is one of the most common and aggressive brain tumors. It presents a very bad prognosis with a patients' overall survival of 12-15 months; treatment failure is mainly ascribable to tumor recurrence. The development of new tools, that could help the precise detection of the tumor border, is thus an urgent need. During the last decades, different vibrational spectroscopy techniques have been developed to distinguish cancer tissue from heathy tissue; in the present work, we compared GBM cells deriving from four patients with healthy human astrocytes using Raman spectroscopy. We have shown that the region between 1000 and 1300 cm^-1 is enough informative for this discrimination, indeed highlighting that peaks related to DNA/RNA and cytochrome c are increased in cancer cells. Finally, our model has been able to discriminate cancer cells from healthy cells with an average accuracy of 92.5%. We believe that this study might help to further understand which are the essential Raman peaks exploitable in the detection of cancer cells, with important perspectives under a diagnostic point of view.

Investigation of mammalian cells expressing SARS-CoV-2 proteins by surface-enhanced Raman scattering and multivariate analysis

COVID-19 has caused millions of cases and deaths all over the world since late 2019. Rapid detection of the virus is crucial for controlling its spread through a population. COVID-19 is currently detected by nucleic acid-based tests and serological tests. However, these methods have limitations such as the requirement of high-cost reagents, false negative results and being time consuming. Surface-enhanced Raman scattering (SERS), which is a powerful technique that enhances the Raman signals of molecules using plasmonic nanostructures, can overcome these disadvantages. In this study, we developed a virus-infected cell model and analyzed this model by SERS combined with Principal Component Analysis (PCA). HEK293 cells were transfected with plasmids encoding the nucleocapsid (N), membrane (M) and envelope (E) proteins of SARS-CoV-2 via polyethyleneimine (PEI). Non-plasmid transfected HEK293 cells were used as the control group. Cellular uptake was optimized with green fluorescence protein (GFP) plasmids and evaluated by fluorescence microscopy and flow cytometry. The transfection efficiency was found to be around 60%. The expression of M, N, and E proteins was demonstrated by western blotting. The SERS spectra of the total proteins of transfected cells were obtained using a gold nanoparticle-based SERS substrate. Proteins of the transfected cells have peak positions at 646, 680, 713, 768, 780, 953, 1014, 1046, 1213, 1243, 1424, 2102, and 2124 cm^-1. To reveal spectral differences between plasmid transfected cells and non-transfected control cells, PCA was applied to the spectra. The results demonstrated that SERS coupled with PCA might be a favorable and reliable way to develop a rapid, low-cost, and promising technique for the detection of COVID-19.

Discrimination of waterborne pathogens, Cryptosporidium parvum oocysts and bacteria using surface-enhanced Raman spectroscopy coupled with principal component analysis and hierarchical clustering

Waterborne pathogens (parasites, bacteria) are serious threats to human health. Cryptosporidium parvum is one of the protozoan parasites that can contaminate drinking water and lead to diarrhea in animals and humans. Rapid and reliable detection of these kinds of waterborne pathogens is highly essential. Yet, current detection techniques are limited for waterborne pathogens and time-consuming and have some major drawbacks. Therefore, rapid screening methods would play an important role in controlling the outbreaks of these pathogens. Here, we used label-free surface-enhanced Raman Spectroscopy (SERS) combined with multivariate analysis for the detection of C. parvum oocysts along with bacterial contaminants including, Escherichia coli, and Staphylococcus aureus. Silver nanoparticles (AgNPs) are used as SERS substrate and samples were prepared with simply mixed of concentrated AgNPs with microorganisms. Each species presented distinct SERS spectra. Principal component analysis (PCA) and hierarchical clustering were performed to discriminate C. parvum oocysts, E. coli, and S. aureus. PCA was used to visualize the dataset and extract significant spectral features. According to score plots in 3 dimensional PCA space, species formed distinct group. Furthermore, each species formed different clusters in hierarchical clustering. Our study indicates that SERS combined with multivariate analysis techniques can be utilized for the detection of C. parvum oocysts quickly.

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

Broadband SERS Enhancement by DNA Origami Assembled Bimetallic Nanoantennas with Label-Free Single Protein Sensing

Engineering hotspots in surface-enhanced Raman spectroscopy (SERS) through precisely controlled assembly of plasmonic nanostructures capable of expanding intense field enhancement are highly desirable to enhance the potentiality of SERS as a label-free optical tool for single molecule detection. Inspired by DNA origami technique, we constructed plasmonic dimer nanoantennas with a tunable gap decorated with Ag-coated Au nanostars on origami. Herein, we demonstrate the single-molecule SERS enhancements of three dyes with emission in different spectral regions after incorporation of single dye molecules in between two nanostars. The enhancement factors (EFs) achieved in the range of 10⁹-10¹⁰ for all the single dye molecules, under both resonant and nonresonant excitation conditions, would enable enhanced photostability during time-series measurement. We further successfully explored the potential of our designed nanoantennas to accommodate and detect a single thrombin protein molecule after selective placement in the wide nanogap of 10 nm. Our results suggest that such nanoantennas can serve as a broadband SERS enhancer and enable specific detection of target biological molecules with single-molecule sensitivity.

Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques

To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.

X-ray-Based Techniques to Study the Nano-Bio Interface

X-ray-based analytics are routinely applied in many fields, including physics, chemistry, materials science, and engineering. The full potential of such techniques in the life sciences and medicine, however, has not yet been fully exploited. We highlight current and upcoming advances in this direction. We describe different X-ray-based methodologies (including those performed at synchrotron light sources and X-ray free-electron lasers) and their potentials for application to investigate the nano-bio interface. The discussion is predominantly guided by asking how such methods could better help to understand and to improve nanoparticle-based drug delivery, though the concepts also apply to nano-bio interactions in general. We discuss current limitations and how they might be overcome, particularly for future use in vivo.

Glioma biopsies Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples

Identifying tumor cells infiltrating normal-appearing brain tissue is critical to achieve a total glioma resection. Raman spectroscopy (RS) is an optical technique with potential for real-time glioma detection. Most RS reports are based on formalin-fixed or frozen samples, with only a few studies deployed on fresh untreated tissue. We aimed to probe RS on untreated brain biopsies exploring novel Raman bands useful in distinguishing glioma and normal brain tissue. Sixty-three fresh tissue biopsies were analyzed within few minutes after resection. A total of 3450 spectra were collected, with 1377 labelled as Healthy and 2073 as Tumor. Machine learning methods were used to classify spectra compared to the histo-pathological standard. The algorithms extracted information from 60 different Raman peaks identified as the most representative among 135 peaks screened. We were able to distinguish between tumor and healthy brain tissue with accuracy and precision of 83% and 82%, respectively. We identified 19 new Raman shifts with known biological significance. Raman spectroscopy was effective and accurate in discriminating glioma tissue from healthy brain ex-vivo in fresh samples. This study added new spectroscopic data that can contribute to further develop Raman Spectroscopy as an intraoperative tool for in-vivo glioma detection.

Label-free discrimination of glioma brain tumors in different stages by surface enhanced Raman scattering

According to the WHO classification criteria, the most common intracranial tumor gliomas can be divided into four grades based on their symptoms. Among them, Grade Ⅰ and Grade II are low-grade gliomas, and Grade III and Grade IV are high-grade gliomas. Because gliomas have a high lethal rate, they have received widespread attention in the medical field. Based on these circumstances, a rapid and facile surface enhanced Raman scattering (SERS) method using silver nano particle-decorated silver nanorod (AgNPs@AgNR) as substrates were developed for the discrimination of gliomas. Compared with SERS-active silver nanoparticles and silver nanorod substrates, the prepared AgNPs@AgNR substrates showed an outstanding SERS performance with an enhancement factor up to 1.37 × 10⁹. Combined AgNPs@AgNR substrate with principal component analysis (PCA), we achieved rapid discrimination of healthy brain tissue and gliomas at different grades. The spectra obtained from the tissue illustrate prominently spectral differences which can be applied to identify whether it came from a healthy region or from a glioma. The most prominently difference between the SERS spectrum of healthy brain tissue and that of gliomas at different grades is the reduction in quotient of two characteristic peaks at 653 and 724 cm^-1. Furthermore, healthy brain tissue and Grade II gliomas as low grade gliomas as well as Grade III and Grade IV as high-grade gliomas can be clearly distinguished by three-dimensional PCA. Preliminary results indicate that the SERS spectra based on AgNPs@AgNR substrates can be applied for a rapid identification owing to its simple preparation of specimen and high-speed spectral acquirement.

Spatiotemporal dynamic monitoring of fatty acid-receptor interaction on single living cells by multiplexed Raman imaging

Numerous fatty acid receptors have proven to play critical roles in normal physiology. Interactions among these receptor types and their subsequent membrane trafficking has not been fully elucidated, due in part to the lack of efficient tools to track these cellular events. In this study, we fabricated the surface-enhanced Raman scattering (SERS)-based molecular sensors for detection of two putative fatty acid receptors, G protein-coupled receptor 120 (GPR120) and cluster of differentiation 36 (CD36), in a spatiotemporal manner in single cells. These SERS probes allowed multiplex detection of GPR120 and CD36, as well as a peak that represented the cell. This multiplexed sensing system enabled the real-time monitoring of fatty acid-induced receptor activation and dynamic distributions on the cell surface, as well as tracking of the receptors' internalization processes on the addition of fatty acid. Increased SERS signals were seen in engineered HEK293 cells with higher fatty acid concentrations, while decreased responses were found in cell line TBDc1, suggesting that the endocytic process requires innate cellular components. SERS mapping results confirm that GPR120 is the primary receptor and may work synergistically with CD36 in sensing polyunsaturated fatty acids and promoting Ca²⁺ mobilization, further activating the process of fatty acid uptake. The ability to detect receptors' locations and monitor fatty acid-induced receptor redistribution demonstrates the specificity and potential of our multiplexed SERS imaging platform in the study of fatty acid-receptor interactions and might provide functional information for better understanding their roles in fat intake and development of fat-induced obesity.

Advances in Translational Nanotechnology: Challenges and Opportunities

The burgeoning field of nanotechnology aims to create and deploy nanoscale structures, devices, and systems with novel, size-dependent properties and functions. The nanotechnology revolution has sparked radically new technologies and strategies across all scientific disciplines, with nanotechnology now applied to virtually every area of research and development in the US and globally. NanoFlorida was founded to create a forum for scientific exchange, promote networking among nanoscientists, encourage collaborative research efforts across institutions, forge strong industry-academia partnerships in nanoscience, and showcase the contributions of students and trainees in nanotechnology fields. The 2019 NanoFlorida International Conference expanded this vision to emphasize national and international participation, with a focus on advances made in translating nanotechnology. This review highlights notable research in the areas of engineering especially in optics, photonics and plasmonics and electronics; biomedical devices, nano-biotechnology, nanotherapeutics including both experimental nanotherapies and nanovaccines; nano-diagnostics and -theranostics; nano-enabled drug discovery platforms; tissue engineering, bioprinting, and environmental nanotechnology, as well as challenges and directions for future research.

From single cells to complex tissues in applications of surface-enhanced Raman scattering

This tutorial review explores how three of the most common methods for introducing nanoparticles to single cells for surface-enhanced Raman scattering measurements can be adapted for experiments with complex tissues.

Surface Enhanced Raman Spectroscopy for Single Molecule Protein Detection

A two-step process of protein detection at a single molecule level using SERS was developed as a proof-of-concept platform for medical diagnostics. First, a protein molecule was bound to a linker in the bulk solution and then this adduct was chemically reacted with the SERS substrate. Traut’s Reagent (TR) was used to thiolate Bovine serum albumin (BSA) in solution followed by chemical cross linking to a gold surface through a sulfhydryl group. A Glycine-TR adduct was used as a control sample to identify the protein contribution to the SER spectra. Gold SERS substrates were manufactured by electrochemical deposition. Solutions at an ultralow concentration were used for attaching the TR adducts to the SERS substrate. Samples showed the typical behavior of a single molecule SERS including spectral fluctuations, blinking and Raman signal being generated from only selected points on the substrate. The fluctuating SER spectra were examined using Principle Component Analysis. This unsupervised statistics allowed for the selecting of spectral contribution from protein moiety indicating that the method was capable of detecting a single protein molecule. Thus we have demonstrated, that the developed two-step methodology has the potential as a new platform for medical diagnostics.

Full-Scale Label-Free Surface-Enhanced Raman Scattering Analysis of Mouse Brain Using a Black Phosphorus-Based Two-Dimensional Nanoprobe

The brain takes the vital role in human physiological and psychological activities. The precise understanding of the structure of the brain can supply the material basis for the psychological behavior and cognitive ability of human beings. In this study, a fast molecular fingerprint analysis of mouse brain tissue was performed using surface-enhanced Raman scattering (SERS) spectroscopy. A nanohybrid consisting of flake-like black phosphorus (BP) and Au nanoparticles (BP-AuNSs) served as the novel SERS substrate for the spectral analysis of brain tissue. BP-AuNSs exhibited outstanding SERS activity compared to the traditional citrate-stabilized Au nanoparticles, which could be largely ascribed to the plentiful hot spots formed in the BP nanosheet. Rapid, full-scale and label-free SERS imaging of mouse brain tissue was then realized with a scanning speed of 56 ms per pixel. Fine textures and clear contour were observed in the SERS images of brain tissue, which could be well in accordance with the classical histological analysis; however, it could avoid the disadvantages in the processing procedure of tissue section. Additionally, the SERS spectra illustrated plentiful biochemical fingerprint of brain tissue, which indicated the molecular composition of various encephalic regions. The SERS difference spectrum of the left versus right hemisphere revealed the biochemical difference between the two hemispheres, which helped to uncover the psychological and cognitive models of the left and right hemispheres.

Applications of Raman spectroscopy in cancer diagnosis

Novel approaches toward understanding the evolution of disease can lead to the discovery of biomarkers that will enable better management of disease progression and improve prognostic evaluation. Raman spectroscopy is a promising investigative and diagnostic tool that can assist in uncovering the molecular basis of disease and provide objective, quantifiable molecular information for diagnosis and treatment evaluation. This technique probes molecular vibrations/rotations associated with chemical bonds in a sample to obtain information on molecular structure, composition, and intermolecular interactions. Raman scattering occurs when light interacts with a molecular vibration/rotation and a change in polarizability takes place during molecular motion. This results in light being scattered at an optical frequency shifted (up or down) from the incident light. By monitoring the intensity profile of the inelastically scattered light as a function of frequency, the unique spectroscopic fingerprint of a tissue sample is obtained. Since each sample has a unique composition, the spectroscopic profile arising from Raman-active functional groups of nucleic acids, proteins, lipids, and carbohydrates allows for the evaluation, characterization, and discrimination of tissue type. This review provides an overview of the theory of Raman spectroscopy, instrumentation used for measurement, and variation of Raman spectroscopic techniques for clinical applications in cancer, including detection of brain, ovarian, breast, prostate, and pancreatic cancers and circulating tumor cells.

Application of nanoparticles in cancer detection by Raman scattering based techniques

In vitro detection technique Raman spectroscopy (Rs), in one number times another Rs  based expert ways of art and so on, are useful instruments for cancer  discovery. top gave greater value to Raman spectroscopy  sers is a relatively new careful way for in vitro  and in vivo discovery that takes away bad points of simple Raman spectroscopy (Rs). Raman spectroscopy (RS) and in particular, multiple RS-based techniques are useful for cancer detection. Surface enhanced Raman spectroscopy (SERS) is a relatively new method for both in vitro and in vivo detection, which eliminates the drawbacks of simple RS. Using nanoparticles has elevated the sensitivity and specificity of SERS. SERS has the potential to increase sensitivity, specificity and spatial resolution in cancer detection, especially in cooperation with other diagnostic imaging tools such as magnetic resonance imaging (MRI) and PET-scan polyethylene terephthalate. Developing a hand held instrument for detecting cancer or other illnesses may also be feasible by using SERS. Frequently, novel nanoparticles are used in SERS. With a focus on nanoparticle utilization, we review the benefits of RS in cancer detection and related biomarkers. With a focus on nanoparticles utilizations, the benefits of RS in cancer detection and related biomarkers were reviewed. In addition, Raman applications to detect some of prevalent were discussed. Also more investigated cancers such as breast and colorectal cancer, multiple nanostructures and their possible special biomarkers, especially as SERS nano-tag have been reviewed. The main purpose of this article is introducing of most popular nanotechnological approaches in cancer detection by using Raman techniques. Moreover, have been caught up on detection and reviewed some of the most prevalent and also more investigated cancers such as breast, colorectal cancer, multiple intriguing nanostructures, especially as SERS nano-tag, special cancer biomarkers and related approaches. The main purpose of this article is to introduce the most popular nanotechnological approaches in cancer detection by using Raman techniques.

Intraoperative Raman Spectroscopy

Surgical excision of brain tumors provides a means of cytoreduction and diagnosis while minimizing neurologic deficit and improving overall survival. Despite advances in functional and three-dimensional stereotactic navigation and intraoperative MRI, delineating tissue in real time with physiologic confirmation is challenging. Raman spectroscopy has potential to be an important modality in the intraoperative evaluation of tissue during surgical resection. In vitro experimental studies have shown that this technique can be used to differentiate normal brain tissue from tissue with infiltrating cancer cells and dense cancerous masses with high specificity, indicating the feasibility of this method for in vivo application.

Thermodynamic and conformational changes of protein toward interaction with nanoparticles: a spectroscopic overview

Nanoparticles (NPs) in different forms have been widely used in medicine and pharmaceutics for diagnosis and drug delivery.

Raman spectroscopy for cancer diagnosis: how far have we come?

Raman spectroscopy is increasingly investigated for cancer diagnosis. As the potential of the technique is explored and realized, it is slowly making its way into clinics. There are more reports in recent years showing promise that it can help clinicians for cancer diagnosis. However, a number of challenges remain to be overcome, especially in vivo cancer diagnosis. In this article, the recent progress of the technique toward clinical cancer diagnosis is discussed from a critical perspective.

Label-free and direct protein detection on 3D plasmonic nanovoid structures using surface-enhanced Raman scattering

In this paper we use surface-enhanced Raman spectroscopy (SERS) on 3D metallic structures for label-free detection and characterization of proteins of interest at low concentrations. The substrates are prepared via nanopatterning with latex nano/microparticles and Cr and Ag sputtering, yielding stable, tunable, and mechanically flexible plasmonic structures. The nanovoids generate a SERS signal of the proteins of interest that is background free and independent of the protein charge. Concentrations as low as 0.05 μg mL(-1) could be detected for 4 different proteins. The proteins also exhibit significantly different SERS spectra on these substrates, which is an important feature for future label-free direct detection schemes.

Surface-enhanced Raman scattering-based detection of cancerous renal cells

Surface-enhanced Raman scattering (SERS) is used for the differentiation of human kidney adenocarcinoma, human kidney carcinoma, and non-cancerous human kidney embryonic cells. Silver nanoparticles (AgNPs) are used as substrate in the experiments. A volume of colloidal suspension containing AgNPs is added onto the cultured cells on a CaF(2) slide, and the slide is dried at the overturned position. A number of SERS spectra acquired from the three different cell lines are statistically analyzed to differentiate the cells. Principal component analysis (PCA) combined with linear discriminate analysis (LDA) was performed to differentiate the three kidney cell types. The LDA, based on PCA, provided for classification among the three cell lines with 88% sensitivity and 84% specificity. This study demonstrates that SERS can be used to identify renal cancers by combining this new sampling method and LDA algorithms.

Rapid detection of oral cancer using Ag–TiO2 nanostructured surface-enhanced Raman spectroscopic substrates

Developed Ag–TiO₂ based large area SERS substrate that enables spectroscopic detection and classification of oral squamous cell carcinoma with a specificity and sensitivity of 95.83% and 100%, respectively.

Raman spectroscopic investigation of the chemopreventive response of naringenin and its nanoparticles in DMBA-induced oral carcinogenesis

Raman spectroscopy is a vibrational spectroscopic technique that can be used to optically probe the biomolecular changes associated with tumor progression. The aim of the present study is to investigate the biomolecular changes in chemopreventive response of prepared naringenin-loaded nanoparticles (NARNPs) relative to efficacy of free naringenin (NAR) during 7,12-dimethyl benz(a)anthracene (DMBA)-induced oral carcinogenesis by Fourier Transform Raman (FT-Raman) spectroscopy. Oral squamous cell carcinoma (OSCC) was developed in the buccal pouch of golden Syrian hamsters by painting with 0.5% DMBA in liquid paraffin three times a week for 14weeks. Raman spectra differed significantly between the control and tumor tissues, with tumors showing higher percentage signals for nucleic acids, phenylalanine and tryptophan and a lower in the percentage of phospholipids. Moreover, oral administration of free NAR and NARNPs significantly increased phospholipids and decreased the levels of tryptophan, phenylalanine and nucleic acid contents. On a comparative basis, NARNPs was found to have a more potent antitumor effect than free NAR in completely preventing the formation of squamous cell carcinoma and in improving the biochemical status to a normal range in DMBA-induced oral carcinogenesis. The present study further suggest that Raman spectroscopy could be a valuable tool for rapid and sensitive detection of specific biomolecular changes in response to chemopreventive agents.

Surface-enhanced Raman scattering: an emerging label-free detection and identification technique for proteins

The detection and identification of biologically important molecules has critical importance in several fields such as medicine, biotechnology, and pharmacology. Surface-enhanced Raman scattering (SERS) is a powerful emerging vibrational spectroscopic technique that allows not only for the characterization, but also for the identification and detection of biomacromolecules in a very short time. In this review, efforts to utilize SERS for label-free protein detection and identification is summarized after a short introduction of proteins and the technique.

Protein analytical assays for diagnosing, monitoring, and choosing treatment for cancer patients

Cancer treatment is often hindered by inadequate methods for diagnosing the disease or insufficient predictive capacity regarding therapeutic efficacy. Targeted cancer treatments, including Bcr-Abl and EGFR kinase inhibitors, have increased survival for some cancer patients but are ineffective in other patients. In addition, many patients who initially respond to targeted inhibitor therapy develop resistance during the course of treatment. Molecular analysis of cancer cells has emerged as a means to tailor treatment to particular patients. While DNA analysis can provide important diagnostic information, protein analysis is particularly valuable because proteins are more direct mediators of normal and diseased cellular processes. In this review article, we discuss current and emerging protein assays for improving cancer treatment, including trends toward assay miniaturization and measurement of protein activity.

Identification of pediatric brain neoplasms using Raman spectroscopy

PURPOSE

Raman spectroscopy can quickly and accurately diagnose tissue in near real-time. This study evaluated the capacity of Raman spectroscopy to diagnose pediatric brain tumors.

EXPERIMENTAL DESIGN

Samples of untreated pediatric medulloblastoma (4 samples and 4 patients), glioma (i.e. astrocytoma, oligodendroglioma, ependymoma, ganglioglioma and other gliomas; 27 samples and 19 patients), and normal brain samples (33 samples and 5 patients) were collected fresh from the operating room or from our frozen tumor bank. Samples were divided and tested using routine pathology and Raman spectroscopy. Twelve Raman spectra were collected per sample. Support vector machine analysis was used to classify spectra using the pathology diagnosis as the gold standard.

RESULTS

Normal brain (321 spectra), glioma (246 spectra) and medulloblastoma (82 spectra) were identified with 96.9, 96.7 and 93.9% accuracy, respectively, when compared with each other. High-grade ependymomas (41 spectra) were differentiated from low-grade ependymomas (25 spectra) with 100% sensitivity and 96.0% specificity. Normal brain tissue was distinguished from low-grade glioma (118 spectra) with 91.5% sensitivity and 97.8% specificity. For these analyses, the tissue-level classification was determined to be 100% accurate.

CONCLUSION

These results suggest Raman spectroscopy can accurately distinguish pediatric brain neoplasms from normal brain tissue, similar tumor types from each other and high-grade from low-grade tumors.

Clinical SERS: are we there yet?

Surface Enhanced Raman Spectroscopy or SERS has witnessed many successes over the past 3 decades, owing particularly to its simplicity of use as well as its highly-multiplexing capability. This article provides an overview of SERS and its applicability in the field of bio-medicine. We will preview recent developments in SERS substrate designs, and the various sensing technologies that are based on the SERS phenomenon. An overview of the clinical applications of SERS is also included. Finally, we provide an opinion on the future trends of this unique spectroscopic technique.

Bio-imaging, detection and analysis by using nanostructures as SERS substrates

Surface-enhanced Raman scattering (SERS) is a phenomenon that occurs on nanoscale-roughed metallic surface. The magnitude of the Raman scattering signal can be greatly enhanced when the scatterer is placed in the very close vicinity of the surface, which enables this phenomenon to be a highly sensitive analytical technique. SERS inherits the general strongpoint of conventional Raman spectroscopy and overcomes the inherently small cross section problem of a Raman scattering. It is a sensitive and nondestructive spectroscopic method for biological samples, and can be exploited either for the delivery of molecular structural information or for the detection of trace levels of analytes. Therefore, SERS has long been regarded as a powerful tool in biomedical research. Metallic nanostructure plays a key role in all the biomedical applications of SERS because the enhanced Raman signal can only be obtained on the surface of a finely divided substrate. This review focuses on progress made in the use of SERS as an analytical technique in bio-imaging, analysis and detection. Recent progress in the fabrication of SERS active nanostructures is also highlighted.