Characterizing variability in in vivo Raman spectra of different anatomical locations in the upper gastrointestinal tract toward cancer detection

Multi-Wavelength Raman Differentiation of Malignant Skin Neoplasms

Raman microspectroscopy has become an effective method for analyzing the molecular appearance of biomarkers in skin tissue. For the first time, we acquired in vitro Raman spectra of healthy and malignant skin tissues, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), at 532 and 785 nm laser excitation wavelengths in the wavenumber ranges of 900-1800 cm-1 and 2800-3100 cm-1 and analyzed them to find spectral features for differentiation between the three classes of the samples. The intensity ratios of the bands at 1268, 1336, and 1445 cm-1 appeared to be the most reliable criteria for the three-class differentiation at 532 nm excitation, whereas the bands from the higher wavenumber region (2850, 2880, and 2930 cm-1) were a robust measure of the increased protein/lipid ratio in the tumors at both excitation wavelengths. Selecting ratios of the three bands from the merged (532 + 785) dataset made it possible to increase the accuracy to 87% for the three classes and reach the specificities for BCC + SCC equal to 87% and 81% for the sensitivities of 95% and 99%, respectively. Development of multi-wavelength excitation Raman spectroscopic techniques provides a versatile non-invasive tool for research of the processes in malignant skin tumors, as well as other forms of cancer.

Quantitative Raman chemical imaging of intracellular drug-membrane aggregates and small molecule drug precipitates in cytoplasmic organelles

Raman confocal microscopes have been used to visualize the distribution of small molecule drugs within different subcellular compartments. This visualization allows the discovery, characterization, and detailed analysis of the molecular transport phenomena underpinning the Volume of Distribution - a key parameter governing the systemic pharmacokinetics of small molecule drugs. In the specific case of lipophilic small molecules with large Volumes of Distribution, chemical imaging studies using Raman confocal microscopes have revealed how weakly basic, poorly soluble drug molecules can accumulate inside cells by forming stable, supramolecular complexes in association with cytoplasmic membranes or by precipitating out within organelles. To study the self-assembly and function of the resulting intracellular drug inclusions, Raman chemical imaging methods have been developed to measure and map the mass, concentration, and ionization state of drug molecules at a microscopic, subcellular level. Beyond the field of drug delivery, Raman chemical imaging techniques relevant to the study of microscopic drug precipitates and drug-lipid complexes which form inside cells are also being developed by researchers with seemingly unrelated scientific interests. Highlighting advances in data acquisition, calibration methods, and computational data management and analysis tools, this review will cover a decade of technological developments that enable the conversion of spectral signals obtained from Raman confocal microscopes into new discoveries and information about previously unknown, concentrative drug transport pathways driven by soluble-to-insoluble phase transitions occurring within the cytoplasmic organelles of eukaryotic cells.

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

A novel detection technology for early gastric cancer based on Raman spectroscopy

Despite universal endoscopic screening, early detection of gastric cancer is challenging, led researchers to seek for a novel approach in detecting. Raman spectroscopy measurements as a fingerprint of biochemical structure, enable accurate prediction of gastric lesions non-destructively. This study aimed to evaluate the diagnostic power of Raman spectroscopy in early gastric cancer (EGC), and reveal dynamic biomolecular changes in vitro from normal to EGC. To clarify the biochemical alterations in Correa's cascade, Raman spectra of human normal gastric mucosa, intestinal metaplasia, dysplasia, and adenocarcinoma were compared at tissue and cellular levels based on a self-developed data processing program. For effectively identify EGC, Raman spectroscopy was used combined with multiple machine learning methods, including partial least-squares discriminant analysis (PLS-DA), support vector machine (SVM), and convolutional neural network (CNN) with leave-one-out (LOO) cross validation. A total of 450 Raman spectra were investigated in this study. The upregulation of ν_sym(O-P-O) backbone (p < 0.001) was identified as a favorable factor for the diagnosis of EGC, the area under the ROC curve (AUC) was up to 0.918. In addition, higher levels of lactic acid (p < 0.001), lipids (p < 0.001), phenylalanine (p = 0.002), and carotenoids (p < 0.001) were detected in EGC. Multivariate machine learning methods for diagnosis of EGC based on Raman spectroscopy, the sensitivity, specificity, accuracy, and AUC were 91.0%, 100%, 94.8%, and 95.8% for SVM, and 84.8%, 92.0%, 88.8%, and 95.5% for CNN, respectively. Raman spectroscopy can be used as a powerful tool for detecting EGC while elucidating biomolecular dynamics in tumorigenesis. (Chictr.org.cn, ChiCTR2200060720.).

In Vivo Longitudinal Monitoring of Disease Progression in Inflammatory Arthritis Animal Models Using Raman Spectroscopy

Current techniques for monitoring disease progression and testing drug efficacy in animal models of inflammatory arthritis are either destructive, time-consuming, subjective, or require ionizing radiation. To accommodate this, we have developed a non-invasive and label-free optical system based on Raman spectroscopy for monitoring tissue alterations in rodent models of arthritis at the biomolecular level. To test different sampling geometries, the system was designed to collect both transmission and reflection mode spectra. Mice with collagen antibody-induced arthritis and controls were subject to in vivo Raman spectroscopy at the tibiotarsal joint every 3 days for 14 days. Raman-derived measures of bone content correlated well with micro-computed tomography bone mineral densities. This allowed for time-resolved quantitation of bone densities, which indicated gradual bone erosion in mice with arthritis. Inflammatory pannus formation, bone erosion, and bone marrow inflammation were confirmed by histological analysis. In addition, using library-based spectral decomposition, we quantified the progression of bone and soft tissue components. In general, the tissue components followed significantly different tendencies in mice developing arthritis compared to the control group in line with the histological analysis. In total, this demonstrates Raman spectroscopy as a versatile technique for monitoring alterations to both mineralized and soft tissues simultaneously in rodent models of musculoskeletal disorders. Furthermore, the technique presented herein allows for objective repeated within-animal measurements potentially refining and reducing the use of animals in research while improving the development of novel antiarthritic therapeutics.

Impact of preprocessing methods on the Raman spectra of brain tissue

Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification. To allow direct comparisons between measurements preprocessing of the Raman signal is required. There are many recognized methods for preprocessing Raman spectra; however, there is no universal standard. In this paper, six different preprocessing methods were tested on Raman spectra (n > 900) from fresh brain tissue samples (n = 34). The sample cohort included both primary brain tumors, such as adult-type diffuse gliomas and meningiomas, as well as metastases of breast cancer. Each tissue sample was classified according to the CNS WHO 2021 guidelines. The six methods include both direct and iterative polynomial fitting, mathematical morphology, signal derivative, commercial software, and a neural network. Data exploration was performed using principal component analysis, t-distributed stochastic neighbor embedding, and k-means clustering. For each of the six methods, the parameter combination that explained the most variance in the data, i.e., resulting in the highest Gap-statistic, was chosen and compared to the other five methods. Depending on the preprocessing method, the resulting clusters varied in number, size, and associated spectral features. The detected features were associated with hemoglobin, neuroglobin, carotenoid, water, and protoporphyrin, as well as proteins and lipids. However, the spectral features seen in the Raman spectra could not be unambiguously assigned to tissue labels, regardless of preprocessing method. We have illustrated that depending on the chosen preprocessing method, the spectral appearance of Raman features from brain tumor tissue can change. Therefore, we argue both for caution in comparing spectral features from different Raman studies, as well as the importance of transparency of methodology and implementation of the preprocessing. As discussed in this study, Raman spectroscopy for in vivo guidance in neurosurgery requires fast and adaptive preprocessing. On this basis, a pre-trained neural network appears to be a promising approach for the operating room.

In vivo spectroscopy: optical fiber probes for clinical applications

INTRODUCTION

Fiber optic probe based in-vivo spectroscopy techniques are fast and highly objective methods for intraoperative diagnoses and minimally invasive surgical interventions for all procedures where endoscopic observations are carried out for cancers of different types. The Raman spectral features provide molecular fingerprint-type information and can reveal the subjects' pathological state in label-free manner, making endoscopy multiplexed fiber optic probe-based devices with the potential for translation from bench to bedside for routine applications.

AREAS COVERED

This review provides a general overview of different fiber-optic probes for in-vivo measurements with emphasis on Raman spectroscopy for biomedical application. Various aspects such as fiber-optic probe, radiation source, detector, and spectrometer for extracting optimum spectral features have also been discussed.

EXPERT OPINION

: Optical spectroscopy-based fiber probe systems with "Chip-on-Tip" technology, combined with machine learning, can in the near future, become a complimentary diagnostic tool to magnetic resonance imaging (MRI), computed tomography (CT) scan, ultrasound, etc. Hyperspectral imaging and fluorescence-based devices are in the advanced stage of technology readiness level (TRL), and with advances in lasers and miniature spectroscopy systems, probe-based Raman devices are also coming up.

Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy

The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. Rapid and accurate identification of AD biomarkers in the brain is critical to providing key insights into AD and facilitating the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification. Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aβ and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman-machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.

Raman Spectroscopy: A Novel Technology for Gastric Cancer Diagnosis

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

Integrated photodynamic Raman theranostic system for cancer diagnosis, treatment, and post-treatment molecular monitoring

Theranostics, the combination of diagnosis and therapy, has long held promise as a means to achieving personalised precision cancer treatments. However, despite its potential, theranostics has yet to realise significant clinical translation, largely due the complexity and overriding toxicity concerns of existing theranostic nanoparticle strategies. Methods: Here, we present an alternative nanoparticle-free theranostic approach based on simultaneous Raman spectroscopy and photodynamic therapy (PDT) in an integrated clinical platform for cancer theranostics. Results: We detail the compatibility of Raman spectroscopy and PDT for cancer theranostics, whereby Raman spectroscopic diagnosis can be performed on PDT photosensitiser-positive cells and tissues without inadvertent photosensitiser activation/photobleaching or impaired diagnostic capacity. We further demonstrate that our theranostic platform enables in vivo tumour diagnosis, treatment, and post-treatment molecular monitoring in real-time. Conclusion: This system thus achieves effective theranostic performance, providing a promising new avenue towards the clinical realisation of theranostics.

Establishing spectrochemical changes in the natural history of oesophageal adenocarcinoma from tissue Raman mapping analysis

Raman spectroscopy is a fast and sensitive technique able to identify molecular changes in biological specimens. Herein, we report on three cases where Raman microspectroscopy was used to distinguish normal vs. oesophageal adenocarcinoma (OAC) (case 1) and Barrett’s oesophagus vs. OAC (cases 2 and 3) in a non-destructive and highly accurate fashion. Normal and OAC tissues were discriminated using principal component analysis plus linear discriminant analysis (PCA-LDA) with 97% accuracy (94% sensitivity and 100% specificity) (case 1); Barrett’s oesophagus vs. OAC tissues were discriminated with accuracies ranging from 98 to 100% (97–100% sensitivity and 100% specificity). Spectral markers responsible for class differentiation were obtained through the difference-between-mean spectrum for each group and the PCA loadings, where C–O–C skeletal mode in β-glucose (900 cm⁻¹), lipids (967 cm⁻¹), phosphodioxy (1296 cm⁻¹), deoxyribose (1456 cm⁻¹) and collagen (1445, 1665 cm⁻¹) were associated with normal and OAC tissue differences. Phenylalanine (1003 cm⁻¹), proline/collagen (1066, 1445 cm⁻¹), phospholipids (1130 cm⁻¹), CH₂ angular deformation (1295 cm⁻¹), disaccharides (1462 cm⁻¹) and proteins (amide I, 1672/5 cm⁻¹) were associated with Barrett’s oesophagus and OAC tissue differences. These findings show the potential of using Raman microspectroscopy imaging for fast and accurate diagnoses of oesophageal pathologies and establishing subtle molecular changes predisposing to adenocarcinoma in a clinical setting.

Graphical abstract

Simple Defocused Fiber Optic Volume Probe for Subsurface Raman Spectroscopy in Turbid Media

We investigated the ability to perform deep subsurface Raman spectroscopy in turbid media using a simple fiber optic volume probe. Being able to collect Raman signals from regions deep within a biological sample provides the ability to noninvasively study underlying living tissue and tissue engineered constructs with high chemical specificity. Spatially offset Raman spectroscopy has shown great potential for obtaining subsurface Raman signals in biological samples. The applicability of the method for in vivo studies depends on the system complexity and small size probes are often desirable. Most real-time studies on human patients utilizing Raman spectroscopy have been performed with easy-to-handle miniaturized probes. Here we show both experimentally and theoretically that the sampling depth from a simple volume probe can be controlled by changing the probe to sample distance effectively suppressing Raman and fluorescence contributions from shallow sample layers while favoring the collection of signals from deeper layers. Relative spectral intensities as function of probe to sample distance were investigated for layered phantoms of poly(methyl methacrylate) and trans-stilbene and compared with theory. The volume probe was then utilized for the collection of spectra from phantoms mimicking in vivo transcutaneous measurement configurations of bone and engineered scaffold as well as from an ex vivo sample of bone and soft tissue. Together the results show that Raman fiber optic volume probes can be utilized for subsurface Raman spectroscopy in turbid media, providing a simple alternative to spatially offset Raman systems for, e.g., noninvasive monitoring and studying mineralized tissue and implanted scaffolds in vivo.

Recent Advances in Spontaneous Raman Spectroscopic Imaging: Instrumentation and Applications

BACKGROUND

Spectroscopic imaging based on the spontaneous Raman scattering effects can provide unique fingerprint information in relation to the vibration bands of molecules. Due to its advantages of high chemical specificity, non-invasive detection capability, low sensitivity to water, and no special sample pretreatment, Raman Spectroscopic Imaging (RSI) has become an invaluable tool in the field of biomedicine and medicinal chemistry.

METHODS

There are three methods to implement RSI, including point scanning, line scanning and wide-field RSI. Point-scanning can achieve two-and three-dimensional imaging of target samples. High spectral resolution, full spectral range and confocal features render this technique highly attractive. However, point scanning based RSI is a time-consuming process that can take several hours to map a small area. Line scanning RSI is an extension of point scanning method, with an imaging speed being 300-600 times faster. In the wide-field RSI, the laser illuminates the entire region of interest directly and all the images then collected for analysis. In general, it enables more accurate chemical imaging at faster speeds.

RESULTS

This review focuses on the recent advances in RSI, with particular emphasis on the latest developments on instrumentation and the related applications in biomedicine and medicinal chemistry. Finally, we prospect the development trend of RSI as well as its potential to translation from bench to bedside.

CONCLUSION

RSI is a powerful technique that provides unique chemical information, with a great potential in the fields of biomedicine and medicinal chemistry.

Inkjet-printed micro-calibration standards for ultraquantitative Raman spectral cytometry

Herein we report the development of a cytometric analysis platform for measuring the contents of individual cells in absolute (picogram) scales; this study represents the first report of Raman-based quantitation of the absolute mass - or the total amount - of multiple endogenous biomolecules within single-cells. To enable ultraquantitative calibration, we engineered single-cell-sized micro-calibration standards of known composition by inkjet-printer deposition of biomolecular components in microarrays across the surface of silicon chips. We demonstrate clinical feasibility by characterizing the compositional phenotype of human skin fibroblast and porcine alveolar macrophage cell populations in the respective contexts of Niemann-Pick disease and drug-induced phospholipidosis: two types of lipid storage disorders. We envision this microanalytical platform as the foundation for many future biomedical applications, ranging from diagnostic assays to pathological analysis to advanced pharmaco/toxicokinetic research studies.

An Expandable Mechanopharmaceutical Device (3): a Versatile Raman Spectral Cytometry Approach to Study the Drug Cargo Capacity of Individual Macrophages

PURPOSE

To improve cytometric phenotyping abilities and better understand cell populations with high interindividual variability, a novel Raman-based microanalysis was developed to characterize macrophages on the basis of chemical composition, specifically to measure and characterize intracellular drug distribution and phase separation in relation to endogenous cellular biomolecules.

METHODS

The microanalysis was developed for the commercially-available WiTec alpha300R confocal Raman microscope. Alveolar macrophages were isolated and incubated in the presence of pharmaceutical compounds nilotinib, chloroquine, or etravirine. A Raman data processing algorithm was specifically developed to acquire the Raman signals emitted from single-cells and calculate the signal contributions from each of the major molecular components present in cell samples.

RESULTS

Our methodology enabled analysis of the most abundant biochemicals present in typical eukaryotic cells and clearly identified "foamy" lipid-laden macrophages throughout cell populations, indicating feasibility for cellular lipid content analysis in the context of different diseases. Single-cell imaging revealed differences in intracellular distribution behavior for each drug; nilotinib underwent phase separation and self-aggregation while chloroquine and etravirine accumulated primarily via lipid partitioning.

CONCLUSIONS

This methodology establishes a versatile cytometric analysis of drug cargo loading in macrophages requiring small numbers of cells with foreseeable applications in toxicology, disease pathology, and drug discovery.

Raman spectroscopy with a 1064-nm wavelength laser as a potential molecular tool for prostate cancer diagnosis: a pilot study

Raman spectroscopy is widely used to investigate the structure and property of the molecules from their vibrational transitions and may allow for the diagnosis of cancer in a fast, objective, and nondestructive manner. This experimental study aims to propose the use of the 1064-nm wavelength laser in a Raman spectroscopy and to evaluate its discrimination capability in prostate cancer diagnosis. Seventy-four spectra from patients who underwent radical prostatectomy were evaluated. The acquired signals were filtered, normalized, and corrected for possible oscillations in the laser intensity and fluorescence effects. Wilcoxon tests revealed significant differences between the benign and malign samples associated with the deformation vibration characteristic of nucleic acids, proteins, and lipids. A classifier based on support vector machines was able to predict the Gleason scores of the samples with 95% of accuracy, opening a perspective for the use of the 1064-nm excitatory wavelength in prostatic cancer diagnosis.

Raman Spectroscopy and Imaging for Cancer Diagnosis

Raman scattering has long been used to analyze chemical compositions in biological systems. Owing to its high chemical specificity and noninvasive detection capability, Raman scattering has been widely employed in cancer screening, diagnosis, and intraoperative surgical guidance in the past ten years. In order to overcome the weak signal of spontaneous Raman scattering, coherent Raman scattering and surface-enhanced Raman scattering have been developed and recently applied in the field of cancer research. This review focuses on innovative studies of the use of Raman scattering in cancer diagnosis and their potential to transition from bench to bedside.

In-vivo Raman spectroscopy: from basics to applications

For more than two decades, Raman spectroscopy has found widespread use in biological and medical applications. The instrumentation and the statistical evaluation procedures have matured, enabling the lengthy transition from ex-vivo demonstration to in-vivo examinations. This transition goes hand-in-hand with many technological developments and tightly bound requirements for a successful implementation in a clinical environment, which are often difficult to assess for novice scientists in the field. This review outlines the required instrumentation and instrumentation parameters, designs, and developments of fiber optic probes for the in-vivo applications in a clinical setting. It aims at providing an overview of contemporary technology and clinical trials and attempts to identify future developments necessary to bring the emerging technology to the clinical end users. A comprehensive overview of in-vivo applications of fiber optic Raman probes to characterize different tissue and disease types is also given.

Raman biophysical markers in skin cancer diagnosis

Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin's biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed.

Real time near-infrared Raman spectroscopy for the diagnosis of nasopharyngeal cancer

Near-infrared (NIR) Raman spectroscopy has been investigated as a tool to differentiate nasopharyngeal cancer (NPC) from normal nasopharyngeal tissue in an ex-vivo setting. Recently, we have miniaturized the fiber-optic Raman probe to investigate its utility in real time in-vivo surveillance of NPC patients. A posterior probability model using partial linear square (PLS) mathematical technique was constructed to verify the sensitivity and specificity of Raman spectroscopy in diagnosing NPC from post-irradiated and normal tissue using a diagnostic algorithm from three significant latent variables. NIR-Raman signals of 135 sites were measured from 79 patients with either newly diagnosed NPC (N = 12), post irradiated nasopharynx (N = 37) and normal nasopharynx (N = 30). The mean Raman spectra peaks identified differences at several Raman peaks at 853 cm⁻¹, 940 cm⁻¹, 1078 cm⁻¹, 1335 cm⁻¹, 1554 cm⁻¹, 2885 cm⁻¹ and 2940 cm⁻¹ in the three different nasopharyngeal conditions. The sensitivity and specificity of distinguishing Raman signatures among normal nasopharynx versus NPC and post-irradiated nasopharynx versus NPC were 91% and 95%; and 77% and 96% respectively. Real time near-infrared Raman spectroscopy has a high specificity in distinguishing malignant from normal nasopharyngeal tissue in vivo, and may be investigated as a novel non-invasive surveillance tool in patients with nasopharyngeal cancer.

Characterizing biochemical and morphological variations of clinically relevant anatomical locations of oral tissue in vivo with hybrid Raman spectroscopy and optical coherence tomography technique

This study aims to characterize biochemical and morphological variations of the clinically relevant anatomical locations of in vivo oral tissue (ie, alveolar process, lateral tongue and floor of the mouth) by using hybrid Raman spectroscopy (RS) and optical coherence tomography (OCT) technique. A total of 1049 in vivo fingerprint (FP: 800-1800 cm^-1 ) and high wavenumber (HW: 2800-3600 cm^-1 ) Raman spectra were acquired from different oral tissue (alveolar process = 331, lateral tongue = 339 and floor of mouth = 379) of 26 normal subjects in the oral cavity under the OCT imaging guidance. The total Raman dataset were split into 2 parts: 80% for training and 20% for testing. Tissue optical attenuation coefficients of alveolar process, lateral tongue and the floor of the mouth were derived from OCT images, revealing the inter-anatomical morphological differences; while RS uncovers subtle FP/HW Raman spectral differences among different oral tissues that can be attributed to the differences in inter- and intra-cellular proteins, lipids, DNA and water structures and conformations, enlightening biochemical variability of different oral tissues at the molecular level. Partial least squares-discriminant analysis implemented on the training dataset show that the integrated tissue optical attenuation coefficients and FP/HW Raman spectra provide diagnostic sensitivities of 99.6%, 82.3%, 50.2%, and specificities of 97.0%, 75.1%, 92.1%, respectively, which are superior to using either RS (sensitivities of 90.2%, 77.5%, 48.8%, and specificities of 95.8%, 72.1%, 88.8%) or optical attenuation coefficients derived from OCT (sensitivities of 75.0%, 78.2%, 47.2%, and specificities of 96.2%, 67.7%, 85.0%) for the differentiation among alveolar process, lateral tongue and the floor of the mouth. Furthermore, the diagnostic algorithms applied to the independent testing dataset based on hybrid RS-OCT technique gives predictive diagnostic sensitivities of 100%, 76.5%, 51.3%, and specificities of 95.1%, 77.6%, 89.6%, respectively, for the classifications among alveolar process, lateral tongue and the floor of the mouth, which performs much better than either RS or optical attenuation coefficient derived from OCT imaging. This work suggests that inter-anatomical morphological and biochemical variability are significant which should be considered as an important parameter in the interpretation and rendering of hybrid RS-OCT technique for oral tissue diagnosis and characterization.

Nanostructured surfaces for analysis of anticancer drug and cell diagnosis based on electrochemical and SERS tools

Discovering new anticancer drugs and screening their efficacy requires a huge amount of resources and time-consuming processes. The development of fast, sensitive, and nondestructive methods for the in vitro and in vivo detection of anticancer drugs' effects and action mechanisms have been done to reduce the time and resources required to discover new anticancer drugs. For the in vitro and in vivo detection of the efficiency, distribution, and action mechanism of anticancer drugs, the applications of electrochemical techniques such as electrochemical cell chips and optical techniques such as surface-enhanced Raman spectroscopy (SERS) have been developed based on the nanostructured surface. Research focused on electrochemical cell chips and the SERS technique have been reviewed here; electrochemical cell chips based on nanostructured surfaces have been developed for the in vitro detection of cell viability and the evaluation of the effects of anticancer drugs, which showed the high capability to evaluate the cytotoxic effects of several chemicals at low concentrations. SERS technique based on the nanostructured surface have been used as label-free, simple, and nondestructive techniques for the in vitro and in vivo monitoring of the distribution, mechanism, and metabolism of different anticancer drugs at the cellular level. The use of electrochemical cell chips and the SERS technique based on the nanostructured surface should be good tools to detect the effects and action mechanisms of anticancer drugs.

New insights of Raman spectroscopy for oral clinical applications

Oral injuries are currently diagnosed by histopathological analysis of biopsy, which is an invasive procedure and does not give immediate results.

Raman active components of skin cancer

Raman spectroscopy (RS) has shown great potential in noninvasive cancer screening. Statistically based algorithms, such as principal component analysis, are commonly employed to provide tissue classification; however, they are difficult to relate to the chemical and morphological basis of the spectroscopic features and underlying disease. As a result, we propose the first Raman biophysical model applied to in vivo skin cancer screening data. We expand upon previous models by utilizing in situ skin constituents as the building blocks, and validate the model using previous clinical screening data collected from a Raman optical fiber probe. We built an 830nm confocal Raman microscope integrated with a confocal laser-scanning microscope. Raman imaging was performed on skin sections spanning various disease states, and multivariate curve resolution (MCR) analysis was used to resolve the Raman spectra of individual in situ skin constituents. The basis spectra of the most relevant skin constituents were combined linearly to fit in vivo human skin spectra. Our results suggest collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin and water are the most important model components. We make available for download (see supplemental information) a database of Raman spectra for these eight components for others to use as a reference. Our model reveals the biochemical and structural makeup of normal, nonmelanoma and melanoma skin cancers, and precancers and paves the way for future development of this approach to noninvasive skin cancer diagnosis.

Nano-structure ZnO/Cu2O photoelectrochemical and self-powered biosensor for esophageal cancer cell detection

The p-n heterojunction photoelectrochemical biosensor, which comprises a p-type Cu₂O film formed by electrochemical deposition and n-type ZnO nanorods formed by the hydrothermal method, is prone to photoelectrochemical reactions and self-powered. Four types of human esophageal cancer cells (ECCs) were detected by this biosensor without requiring an extra bias voltage. The measured photocurrent values of high invasion capacity cancer cells was consistently 2 times higher than those measured by a slight invasion capacity cancer cells. The response time, which was about 0.5 s, allowed repeated measurement.

Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.

Clinical characterization of in vivo inflammatory bowel disease with Raman spectroscopy

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), affects over 1 million Americans and 2 million Europeans, and the incidence is increasing worldwide. While these diseases require unique medical care, the differentiation between UC and CD lacks a gold standard, and therefore relies on long term follow up, success or failure of existing treatment, and recurrence of the disease. Here, we present colonoscopy-coupled fiber optic probe-based Raman spectroscopy as a minimally-invasive diagnostic tool for IBD of the colon (UC and Crohn's colitis). This pilot in vivo study of subjects with existing IBD diagnoses of UC (n = 8), CD (n = 15), and normal control (n = 8) aimed to characterize spectral signatures of UC and CD. Samples were correlated with tissue pathology markers and endoscopic evaluation. The collected spectra were processed and analyzed using multivariate statistical techniques to identify spectral markers and discriminate IBD and disease classes. Confounding factors including the presence of active inflammation and the particular colon segment measured were investigated and integrated into the devised prediction algorithm, reaching 90% sensitivity and 75% specificity to CD from this in vivo data set. These results represent significant progress towards improved real-time classification for accurate and automated in vivo detection and discrimination of IBD during colonoscopy procedures.

Raman technologies in cancer diagnostics

Despite significant effort, cancer still remains a leading cause of death worldwide. In order to reduce its burden, the development and improvement of noninvasive strategies for early detection and diagnosis of cancer are urgently needed. Raman spectroscopy, an optical technique that relies on inelastic light scattering arising from molecular vibrations, is one such strategy, as it can noninvasively probe cancerous markers using only endogenous contrast. In this review, spontaneous, coherent and surface enhanced Raman spectroscopies and imaging, as well as the fundamental principles governing the successful use of these techniques, are discussed. Methods for spectral data analysis are also highlighted. Utilization of the discussed Raman techniques for the detection and diagnosis of cancer in vitro, ex vivo and in vivo is described. The review concludes with a discussion of the future directions of Raman technologies, with particular emphasis on their clinical translation.

Raman spectroscopy and regenerative medicine: a review

The field of regenerative medicine spans a wide area of the biomedical landscape-from single cell culture in laboratories to human whole-organ transplantation. To ensure that research is transferrable from bench to bedside, it is critical that we are able to assess regenerative processes in cells, tissues, organs and patients at a biochemical level. Regeneration relies on a large number of biological factors, which can be perturbed using conventional bioanalytical techniques. A versatile, non-invasive, non-destructive technique for biochemical analysis would be invaluable for the study of regeneration; and Raman spectroscopy is a potential solution. Raman spectroscopy is an analytical method by which chemical data are obtained through the inelastic scattering of light. Since its discovery in the 1920s, physicists and chemists have used Raman scattering to investigate the chemical composition of a vast range of both liquid and solid materials. However, only in the last two decades has this form of spectroscopy been employed in biomedical research. Particularly relevant to regenerative medicine are recent studies illustrating its ability to characterise and discriminate between healthy and disease states in cells, tissue biopsies and in patients. This review will briefly outline the principles behind Raman spectroscopy and its variants, describe key examples of its applications to biomedicine, and consider areas of regenerative medicine that would benefit from this non-invasive bioanalytical tool.

Raman endoscopy for monitoring the anticancer drug treatment of colorectal tumors in live mice

A miniaturized Raman endoscope (mRE) system was employed to study the effects of anticancer treatment on colorectal tumors in a live murine model.

Development and validation of Raman spectroscopic classification models to discriminate tongue squamous cell carcinoma from non-tumorous tissue

BACKGROUND

Currently, up to 85% of the oral resection specimens have inadequate resection margins, of which the majority is located in the deeper soft tissue layers. The prognosis of patients with oral cavity squamous cell carcinoma (OCSCC) of the tongue is negatively affected by these inadequate surgical resections. Raman spectroscopy, an optical technique, can potentially be used for intra-operative evaluation of resection margins.

OBJECTIVE

To develop in vitro Raman spectroscopy-based tissue classification models that discriminate OCSCC of the tongue from (subepithelial) non-tumorous tissue.

MATERIALS AND METHODS

Tissue classification models were developed using Principal Components Analysis (PCA) followed by (hierarchical) Linear Discriminant Analysis ((h)LDA). The models were based on a training set of 720 histopathologically annotated Raman spectra, obtained from 25 tongue samples (11 OCSCC and 14 normal) of 10 patients, and were validated by means of an independent validation set of 367 spectra, obtained from 19 tongue samples (6 OCSCC and 13 normal) of 11 patients.

RESULTS

A PCA-LDA tissue classification model 'tumor' versus 'non-tumorous tissue' (i.e. surface squamous epithelium, connective tissue, muscle, adipose tissue, gland and nerve) showed an accuracy of 86% (sensitivity: 100%, specificity: 66%). A two-step PCA-hLDA tissue classification model 'tumor' versus 'non-tumorous tissue' showed an accuracy of 91% (sensitivity: 100%, specificity: 78%).

CONCLUSION

An accurate PCA-hLDA Raman spectroscopy-based tissue classification model for discrimination between OCSCC and (especially the subepithelial) non-tumorous tongue tissue was developed and validated. This model with high sensitivity and specificity may prove to be very helpful to detect tumor in the resection margins.

Surgical Guidance via Multiplexed Molecular Imaging of Fresh Tissues Labeled with SERS-Coded Nanoparticles

The imaging of dysregulated cell-surface receptors (or biomarkers) is a potential means of identifying the presence of cancer with high sensitivity and specificity. However, due to heterogeneities in the expression of protein biomarkers in tumors, molecular imaging technologies should ideally be capable of visualizing a multiplexed panel of cancer biomarkers. Recently, surface-enhanced Raman-scattering (SERS) nanoparticles (NPs) have attracted wide interest due to their potential for sensitive and multiplexed biomarker detection. In this review, we focus on the most recent advances in tumor imaging using SERS-coded NPs. A brief introduction of the structure and optical properties of SERS NPs is provided, followed by a detailed discussion of key imaging issues such as the administration of NPs in tissue (topical versus systemic), the optical configuration and imaging approach of Raman imaging systems, spectral demultiplexing methods for quantifying NP concentrations, and the disambiguation of specific vs. nonspecific sources of contrast through ratiometric imaging of targeted and untargeted (control) NP pairs. Finally, future challenges and directions are briefly outlined.

Clinical instrumentation and applications of Raman spectroscopy

Clinical diagnostic devices provide new sources of information that give insight about the state of health which can then be used to manage patient care. These tools can be as simple as an otoscope to better visualize the ear canal or as complex as a wireless capsule endoscope to monitor the gastrointestinal tract. It is with tools such as these that medical practitioners can determine when a patient is healthy and to make an appropriate diagnosis when he/she is not. The goal of diagnostic medicine then is to efficiently determine the presence and cause of disease in order to provide the most appropriate intervention. The earliest form of medical diagnostics relied on the eye - direct visual observation of the interaction of light with the sample. This technique was espoused by Hippocrates in his 5th century BCE work Epidemics, in which the pallor of a patient's skin and the coloring of the bodily fluids could be indicative of health. In the last hundred years, medical diagnosis has moved from relying on visual inspection to relying on numerous technological tools that are based on various types of interaction of the sample with different types of energy - light, ultrasound, radio waves, X-rays etc. Modern advances in science and technology have depended on enhancing technologies for the detection of these interactions for improved visualization of human health. Optical methods have been focused on providing this information in the micron to millimeter scale while ultrasound, X-ray, and radio waves have been key in aiding in the millimeter to centimeter scale. While a few optical technologies have achieved the status of medical instruments, many remain in the research and development phase despite persistent efforts by many researchers in the translation of these methods for clinical care. Of these, Raman spectroscopy has been described as a sensitive method that can provide biochemical information about tissue state while maintaining the capability of delivering this information in real-time, non-invasively, and in an automated manner. This review presents the various instrumentation considerations relevant to the clinical implementation of Raman spectroscopy and reviews a subset of interesting applications that have successfully demonstrated the efficacy of this technique for clinical diagnostics and monitoring in large (n ≥ 50) in vivo human studies.

Dispersion-based stimulated Raman scattering spectroscopy, holography, and optical coherence tomography

Stimulated Raman scattering (SRS) enables fast, high resolution imaging of chemical constituents important to biological structures and functional processes, both in a label-free manner and using exogenous biomarkers. While this technology has shown remarkable potential, it is currently limited to point scanning and can only probe a few Raman bands at a time (most often, only one). In this work we take a fundamentally different approach to detecting the small nonlinear signals based on dispersion effects that accompany the loss/gain processes in SRS. In this proof of concept, we demonstrate that the dispersive measurements are more robust to noise compared to amplitude-based measurements, which then permit spectral or spatial multiplexing (potentially both, simultaneously). Finally, we illustrate how this method may enable different strategies for biochemical imaging using phase microscopy and optical coherence tomography.

Diagnosis of early-stage esophageal cancer by Raman spectroscopy and chemometric techniques

Esophageal cancer is a disease with high mortality. In order to improve the 5 year survival rate after cancer treatment, it is important to develop a method for early detection of the cancer and for therapy support. There is increasing evidence that Raman spectroscopy, in combination with chemometric analysis, is a powerful technique for discriminating pre-cancerous and cancerous biochemical changes. In the present study, we used Raman spectroscopy to examine early-stage (stages 0 and I) esophageal cancer samples ex vivo. Comparison between the Raman spectra of cancerous and normal samples using a t-test showed decreased concentrations of glycogen, collagen, and tryptophan in cancerous tissue. Partial least squares regression (PLSR) analysis and self-organization maps (SOMs) discriminated the datasets of cancerous and normal samples into two groups, but there was a relatively large overlap between them. Linear discriminant analysis (LDA) based on Raman bands found in the t-test was able to predict the tissue types with 81.0% sensitivity and 94.0% specificity.

Raman spectroscopy for medical diagnostics--From in-vitro biofluid assays to in-vivo cancer detection

Raman spectroscopy is an optical technique based on inelastic scattering of light by vibrating molecules and can provide chemical fingerprints of cells, tissues or biofluids. The high chemical specificity, minimal or lack of sample preparation and the ability to use advanced optical technologies in the visible or near-infrared spectral range (lasers, microscopes, fibre-optics) have recently led to an increase in medical diagnostic applications of Raman spectroscopy. The key hypothesis underpinning this field is that molecular changes in cells, tissues or biofluids, that are either the cause or the effect of diseases, can be detected and quantified by Raman spectroscopy. Furthermore, multivariate calibration and classification models based on Raman spectra can be developed on large "training" datasets and used subsequently on samples from new patients to obtain quantitative and objective diagnosis. Historically, spontaneous Raman spectroscopy has been known as a low signal technique requiring relatively long acquisition times. Nevertheless, new strategies have been developed recently to overcome these issues: non-linear optical effects and metallic nanoparticles can be used to enhance the Raman signals, optimised fibre-optic Raman probes can be used for real-time in-vivo single-point measurements, while multimodal integration with other optical techniques can guide the Raman measurements to increase the acquisition speed and spatial accuracy of diagnosis. These recent efforts have advanced Raman spectroscopy to the point where the diagnostic accuracy and speed are compatible with clinical use. This paper reviews the main Raman spectroscopy techniques used in medical diagnostics and provides an overview of various applications.

Endoscopic Raman Spectroscopy for Molecular Fingerprinting of Gastric Cancer: Principle to Implementation

Currently, positive endoscopic biopsy is the standard criterion for gastric cancer diagnosis but is invasive, often inconsistent, and delayed although early detection and early treatment is the most important policy. Raman spectroscopy is a spectroscopic technique based on inelastic scattering of monochromatic light. Raman spectrum represents molecular composition of the interrogated volume providing a direct molecular fingerprint. Several investigations revealed that Raman spectroscopy can differentiate normal, dysplastic, and adenocarcinoma gastric tissue with high sensitivity and specificity. Moreover, this technique can indentify malignant ulcer and showed the capability to analyze the carcinogenesis process. Automated on-line Raman spectral diagnostic system raised possibility to use Raman spectroscopy in clinical field. Raman spectroscopy can be applied in many fields such as guiding a target biopsy, optical biopsy in bleeding prone situation, and delineating the margin of the lesion. With wide field technology, Raman spectroscopy is expected to have specific role in our future clinical field.

Emerging technology: applications of Raman spectroscopy for prostate cancer

There is a need in prostate cancer diagnostics and research for a label-free imaging methodology that is nondestructive, rapid, objective, and uninfluenced by water. Raman spectroscopy provides a molecular signature, which can be scaled from micron-level regions of interest in cells to macroscopic areas of tissue. It can be used for applications ranging from in vivo or in vitro diagnostics to basic science laboratory testing. This work describes the fundamentals of Raman spectroscopy and complementary techniques including surface enhanced Raman scattering, resonance Raman spectroscopy, coherent anti-Stokes Raman spectroscopy, confocal Raman spectroscopy, stimulated Raman scattering, and spatially offset Raman spectroscopy. Clinical applications of Raman spectroscopy to prostate cancer will be discussed, including screening, biopsy, margin assessment, and monitoring of treatment efficacy. Laboratory applications including cell identification, culture monitoring, therapeutics development, and live imaging of cellular processes are discussed. Potential future avenues of research are described, with emphasis on multiplexing Raman spectroscopy with other modalities.

Characterizing variability of in vivo Raman spectroscopic properties of different anatomical sites of normal colorectal tissue towards cancer diagnosis at colonoscopy

This study aims to characterize the in vivo Raman spectroscopic properties of normal colorectal tissues and to assess distinctive biomolecular variations of different anatomical locations in the colorectum for cancer diagnosis. We have developed a novel 785 nm excitation fiber-optic Raman endoscope that can simultaneously acquire in vivo fingerprint (FP) spectra (800-1800 cm(-1)) and high-wavenumber (HW) Raman spectra (2800-3600 cm(-1)) from the subsurface of colorectal tissue. We applied the FP/HW Raman endoscope for in vivo tissue Raman measurements of various normal colorectal anatomical locations (i.e., ascending colon (n = 182), transverse colon (n = 249), descending colon (n = 124), sigmoid (n = 212), and rectum (n = 362)) in 50 subjects. Partial least-squares (PLS)-discriminant analysis (DA) was employed to evaluate the interanatomical variability. The normal colorectal tissue showed a subtle interanatomical variability in molecular constituents (i.e., proteins, lipids, and water content) and could be divided into three major clusterings: (1) ascending colon and transverse colon, (2) descending colon, and (3) sigmoid and rectum. The PLS-DA multiclass algorithms were able to identify different tissue sites with varying sensitivities (SE) and specificities (SP) (ascending colon: SE: 1.10%, SP: 91.02; transverse colon: SE: 14.06%, SP: 78.78; descending colon: SE: 40.32%, SP: 81.99; sigmoid: SE: 19.34%, SP: 87.90; rectum: SE: 71.55%, SP: 77.84). The interanatomical molecular variability was orders of magnitude less than neoplastic tissue transformation. Further PLS-DA modeling on in vivo FP/HW tissue Raman spectra yielded a diagnostic accuracy of 88.8% (sensitivity: 93.9% (93/99); specificity 88.3% (997/1129) for colorectal cancer detection. This work discloses that interanatomical Raman spectral variability of normal colorectal tissue is subtle compared to cancer tissue, and the simultaneous FP/HW Raman endoscopic technique has promising potential for real-time, in vivo diagnosis of colorectal cancer at the molecular level.

Anatomy of noise in quantitative biological Raman spectroscopy

Raman spectroscopy is a fundamental form of molecular spectroscopy that is widely used to investigate structures and properties of molecules using their vibrational transitions. It relies on inelastic scattering of monochromatic laser light irradiating the specimen. After appropriate filtering the scattered light is dispersed onto a detector to determine the shift from the excitation wavelength, which appears in the form of characteristic spectral patterns. The technique can investigate biological samples and provide real-time diagnosis of diseases. However, despite its intrinsic advantages of specificity and minimal perturbation, the Raman scattered light is typically very weak and limits applications of Raman spectroscopy due to measurement (im)precision, driven by inherent noise in the acquired spectra. In this article, we review the principal noise sources that impact quantitative biological Raman spectroscopy. Further, we discuss how such noise effects can be reduced by innovative changes in the constructed Raman system and appropriate signal processing methods.

Raman spectroscopy provides a noninvasive approach for determining biochemical composition of the pregnant cervix in vivo

The molecular changes that occur with cervical remodelling during pregnancy are not completely understood. This study reviews Raman spectroscopy, an optical technique for detecting changes in the pregnant cervix, and reports preliminary studies on cervical remodelling in mice that suggest that the technique provides advantages over other methods.

CONCLUSION

Raman spectroscopy is sensitive to biochemical changes in the pregnant cervix and has high potential as a tool for detecting premature cervical remodelling in pregnant women.

Rapid ratiometric biomarker detection with topically applied SERS nanoparticles

Multiplexed surface-enhanced Raman scattering (SERS) nanoparticles (NPs) offer the potential for rapid molecular phenotyping of tissues, thereby enabling accurate disease detection as well as patient stratification to guide personalized therapies or to monitor treatment outcomes. The clinical success of molecular diagnostics based on SERS NPs would be facilitated by the ability to accurately identify tissue biomarkers under time-constrained staining and detection conditions with a portable device. In vitro, ex vivo and in vivo experiments were performed to optimize the technology and protocols for the rapid detection (0.1-s integration time) of multiple cell-surface biomarkers with a miniature fiber-optic spectral-detection probe following a brief (5 min) topical application of SERS NPs on tissues. Furthermore, we demonstrate that the simultaneous detection and ratiometric quantification of targeted and nontargeted NPs allows for an unambiguous assessment of molecular expression that is insensitive to nonspecific variations in NP concentrations.

Label-free blood plasma test based on surface-enhanced Raman scattering for tumor stages detection in nasopharyngeal cancer

This study aims to evaluate the feasibility of a label-free nanobiosensor based on blood plasma surface-enhanced Raman spectroscopy (SERS) method for exploring variability of different tumor (T) stages in nasopharyngeal cancer (NPC). Au nanoparticles as the SERS-active nanostructures were directly mixed with human blood plasma to enhance the Raman scattering signals. High quality SERS spectra can be acquired from blood plasma samples belong to 60 healthy volunteers, 25 NPC patients with T1 stage and 75 NPC patients with T2-T4 stage. A diagnostic accuracy of 83.5% and 93.3%, respectively, can be achieved for classification between early T (T1) stage cancer and normal; and advanced T (T2-T4) stage cancer and normal blood groups. This exploratory study demonstrates that the nanobiosensor based on SERS technique in conjunction with PCA-LDA has great potential as a clinical complement for different T stages detection in nasopharyngeal cancer.

Raman spectroscopy analysis of the biochemical characteristics of molecules associated with the malignant transformation of gastric mucosa

Objective

The purpose of this study was to comparatively analyze the signature Raman spectra of genomic DNA, nuclei, and tissue of normal gastric mucosa and gastric cancer and to investigate the biochemical transformation of molecules associated with gastric mucosa malignancy.

Method

Genomic DNA, nuclei, and tissue from normal gastric mucosa and gastric cancer were analyzed by Raman spectroscopy.

Results

1) The Raman spectrum of gastric cancer genomic DNA showed that two peaks appeared, one at approximately 1090 cm^-1 with a higher intensity than the peak at 1050 cm^-1 in the spectrum. Characteristic peaks appeared at 950 cm^-1, 1010 cm^-1, and 1100-1600 cm^-1. 2) Using a hematoxylin and eosin (H&E)-stained section, the intensity of the characteristic peak of nucleic acids at 1085 cm^-1 was increased and shifted to 1088 cm^-1 in cancer cells. The relative intensity of the characteristic peaks of nucleoproteins at 755 cm^-1 and 1607 cm^-1 was significantly increased in cancer cells compared with normal cells. 3) Compared with normal tissues, the peak representing PO^2- symmetric stretching vibration shifted from 1088 cm^-1 to 1083 cm^-1 in cancer tissue, and the characteristic peak for collagen at 938 cm^-1 shifted to 944 cm^-1. In addition, an extra characteristic peak indicating C = C stretching vibration appeared at 1379 cm^-1 in the lipid spectrum in cancer tissue.

Conclusions

The position, intensity, and shape of peaks in the Raman spectra of DNA, nuclei, and tissue from gastric cancer were significantly different compared with those of normal cells. These results indicate that the DNA phosphate backbone becomes unstable in cancer cells and might be broken; the relative content of histones is increased and stable; the relative collagen content is reduced, facilitating cancer cell metastasis; and the relative content of unsaturated fatty acids is increased, increasing the mobility of the plasma membrane of cancer cells.

Endoscopic Raman spectroscopy enables objective diagnosis of dysplasia in Barrett's esophagus

BACKGROUND

Early detection and targeted endoscopic resection of Barrett's esophagus-associated high-grade dysplasia (HGD) can prevent progression to invasive esophageal malignancy. Raman spectroscopy, a highly sophisticated analytical technique, has been translated into an endoscopic tool to facilitate rapid, objective diagnosis of dysplasia in the esophagus.

OBJECTIVE

To evaluate the ability of endoscopic Raman spectroscopy (ERS) to objectively detect esophageal HGD and adenocarcinoma.

DESIGN

A total of 798 one-second spectra were measured from 673 ex vivo esophageal tissue samples, collected from patients with Barrett's esophagus by using a novel endoscopic Raman probe. Spectra were correlated with consensus histopathology. Multivariate analysis was used to evaluate the classification accuracy of ERS ex vivo.

SETTING

Probe measurements were conducted in the laboratory. Tissue specimens were collected from the operating theatre and endoscopy unit.

PATIENTS

Tissue from 62 patients was included in the study.

INTERVENTIONS

Endoscopic biopsy/resection or esophagectomy was performed where indicated clinically.

MAIN OUTCOME MEASUREMENT

Diagnostic performance of ERS for detection of HGD and esophageal adenocarcinoma.

RESULTS

ERS demonstrated a sensitivity of 86% and a specificity of 88% for detecting HGD and adenocarcinoma. The ability to grade dysplasia and differentiate intestinal metaplasia from nonintestinal metaplasia columnar-lined esophagus was also demonstrated. Diagnostic classification was based on objective measurement of the biochemical profile of different tissue types. The potential for combination ERS and narrow-band imaging was also demonstrated.

LIMITATIONS

Measurements were taken from ex vivo tissue.

CONCLUSION

ERS enables rapid, accurate, objective diagnosis of superficial esophageal disease (metaplasia, dysplasia, intramucosal cancer) in clinically applicable time scales.

Raman endoscopy for the in situ investigation of advancing colorectal tumors in live model mice

The aim of the present study is to evaluate the capability of a miniaturized Raman endoscope (mRE) system to monitor the advancement of colorectal tumors in model mice as a method that is noninvasive to the tumor itself. Nevertheless, the endoscope is narrow enough to observe the inside of the mouse colon in such a way that is semi-noninvasive to the animal. However, the mRE system allowed the visualization and Raman spectral measurement of any targeted point within the colorectal tumor in model mice under anesthesia, without damaging the tissue (i.e., noninvasively). Continuous monitoring of the same tumor allowed the observation of alterations in its molecular composition and size, along with its advancement. The tumor lesion was discriminated from normal tissues of the control mouse with an accuracy of 86.8%. We succeeded in visualizing and performing Raman spectral observations on a shrinking polyp type tumor. The Raman analysis suggested that it was not cured but supposedly transformed to another tumor type.

Probing lymphoma infiltration in spleen of AKR/J mice chronically exposed to electromagnetic fields for risk assessment--toward noninvasive modeling

Using the AKR/J mouse model, the potential of Raman spectroscopy for monitoring lymphoma in predisposed subjects is demonstrated by discriminating lymphoma infiltration in spleens; the relevance of different excitation profiles is shown. Under green excitation with optimal fluorescence bleaching, stronger DNA bands, intensity variations at amide-III and phenylalanine bands, and the behavior of the 1606/1639 cm(-1) doublet correlate with tumorigenesis. Under red excitation, Raman fingerprints with multivariate models help to discriminate AKR/J-mouse histological subtypes: Lymphoblastic lymphoma (LB) is found significantly distant from both separated lymphocytic lymphoma (LL) and healthy spleen; this agrees with histology since LB has well differentiated large lymphoma cells, while LL, with smaller cells similar to normal lymphocytes, usually cannot be discriminated from normal tissue without histoimmunoassays.