Raman spectroscopy for early detection of laryngeal malignancy: preliminary results

A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a complex cancer influenced by various factors. This study explores the use of single-cell Raman spectroscopy as a potential diagnostic tool for investigating biomolecular changes associated with NPC carcinogenesis.

METHODS

Seven NPC cell lines, one immortalised nasopharyngeal epithelial cell line, six nasopharyngeal mucosa tissues and seven NPC tissue samples were analysed by performing confocal Raman spectroscopic measurements and imaging. The single-cell Raman spectral dataset was used to quantify relevant biomolecules and build machine learning classification models. Metabolomic profiles were investigated using ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS).

RESULTS

By generating a metabolic map of seven NPC cell lines, we identified an interplay of altered metabolic processes involving nucleic acids, amino acids, lipids and sugars. The results from spatially resolved Raman maps and UPLC-MS/MS metabolomics were consistent, revealing an increase of unsaturated fatty acids in cancer cells, particularly in highly metastatic 5-8F and poorly differentiated CNE2 cells. The classification model achieved a nearly perfect classification when identifying NPC and non-NPC cells with an ROC-AUC of 0.99 and a value of 0.97 when identifying 13 tissue samples.

CONCLUSION

This study unveils a complex interplay of metabolic network and highlights the potential roles of unsaturated fatty acids in NPC progression and metastasis. This renders further research to provide deeper insights into NPC pathogenesis, identify new metabolic targets and improve the efficacy of targeted therapies in NPC. Artificial intelligence-aided analysis of single-cell Raman spectra has achieved high accuracies in the classification of both cancer cells and patient tissues, paving the way for a simple, less invasive and accurate diagnostic test.

Stable SERS Detection of Lactobacillus fermentum Using Optical Tweezers in a Microfluidic Environment

Rapid identification of fermented lactic acid bacteria has long been a challenge in the brewing industry. This study combined label-free surface-enhanced Raman scattering (SERS) and optical tweezer technology to construct a test platform within a microfluidic environment. Six kinds of lactic acid bacteria common in industry were tested to prove the stability of the SERS spectra. The results demonstrated that the utilization of optical tweezers to securely hold the bacteria significantly enhanced the stability of the SERS spectra. Furthermore, SVM and XGBoost machine learning algorithms were utilized to analyze the obtained Raman spectra for identification, and the identification accuracies exceeded 95% for all tested lactic acid bacteria. The findings of this study highlight the crucial role of optical tweezers in improving the stability of SERS spectra by capturing bacteria in a microfluidic environment, prove that this technology could be used in the rapid identification of lactic acid bacteria, and show great significance in expanding the applicability of the SERS technique for other bacterial testing purposes.

Raman spectroscopy to discriminate laryngeal squamous cell carcinoma from non-cancerous surrounding tissue

As for many solid cancers, laryngeal cancer is treated surgically, and adequate resection margins are critical for survival. Raman spectroscopy has the capacity to accurately differentiate between cancer and non-cancerous tissue based on their molecular composition, which has been proven in previous work. The aim of this study is to investigate whether Raman spectroscopy can be used to discriminate laryngeal cancer from surrounding non-cancerous tissue. Patients surgically treated for laryngeal cancer were included. Raman mapping experiments were performed ex vivo on resection specimens and correlated to histopathology. Water concentration analysis and CH-stretching region analysis were performed in the high wavenumber range of 2500-4000 cm-1. Thirty-four mapping experiments on 22 resection specimens were used for analysis. Both laryngeal cancer and all non-cancerous tissue structures showed high water concentrations of around 75%. Discriminative information was only found to be present in the CH-stretching region of the Raman spectra of the larynx (discriminative power of 0.87). High wavenumber region Raman spectroscopy can discriminate laryngeal cancer from non-cancerous tissue structures. Contrary to the findings for oral cavity cancer, water concentration is not a discriminating factor for laryngeal cancer.

The Biochemical Characteristics in Experimental Keratomycosis

PURPOSE

To investigate the biochemical characteristics in experimental keratomycosis.

METHODS

Experimental mice were injected with Fusarium solanum solution Controls mice received liposomes containing phosphate-buffered saline (PBS-LIP). Raman spectroscopy was used to analyze the biochemical characteristics. The infiltration of inflammatory cells was analyzed by histopathology. Cytokine mRNA levels were detected by real-time polymerase chain reaction.

RESULTS

Raman Spectroscopy: In the experimental group, collagen, lipids, amide I and III were decreased, amide II, hyper proline amino acids, and arginine were increased, and proline and phenylalanine were significantly increased on day 3. Histopathology: more severe keratomycosis developed in the experimental group than in the control group. Statistically significant mRNA expression of Collagen4\MMP2\MMP9\TIMP1.MMP9 was negatively correlated with the secretion of Collagen4.

CONCLUSIONS

Matrix metalloproteinases are involved in biochemical changes in keratomycosis.

Euclidean distance-based Raman spectroscopy (EDRS) for the prognosis analysis of gastric cancer: A solution to tumor heterogeneity

The prognosis analysis of gastric cancer is critical for selection of treatments and development of advanced therapeutic methods. A prognosis approach that is accurate, fast, convenient, and of low cost for gastric cancers is in high demand. Raman spectroscopy is a label-free and non-destructive technique to provide molecular fingerprints of biological samples, holding promises for cancer prognosis. However, the major challenge of gastric cancer prognosis lies in the widely existing tumor heterogeneity, which leads to unexpected spectral variations within one type of samples. In this work, we have developed the Euclidean distance (ED)-based Raman spectroscopy (EDRS) method for the prognosis analysis of gastric cancer to eliminate the influence of tumor heterogeneity. Raman spectra were first collected on the slices of paraffin-preserved tumor tissues from gastric cancer patients. A standard spectrum to represent the 'worst prognostic tumor cells' was then established. The similarity between each spectrum of tissues and the standard spectrum was assessed by ED, to provide a direct assessment on the prognosis status. We have successfully classified the patients into poor and favorable prognosis groups, either based on the averaged regional ED values (sensitivity of 75 %, specificity of 96.8 %), or based on the minimal ED values at the patient level (sensitivity of 90 %, specificity of 100 %). EDRS was also investigated for survival analysis (AUC = 0.955), much better than the commonly applied post-neoadjuvant therapy (ypTNM) category (AUC = 0.718). Our work highlights EDRS as a rapid, accurate, low-cost and robust tool for heterogeneous cancer-related prognosis assessment and survival prediction, providing new insights for spectroscopic tumor analysis.

Metabolic Reprogramming in Colon Cancer Cells Persistently Infected with Newcastle Disease Virus

Newcastle disease virus (NDV) is an oncolytic agent against various types of mammalian cancers. As with all cancer therapies, the development of cancer resistance, both innate and acquired, is becoming a challenge. In this study, we investigated persistently NDV-infected Caco-2 colon cancer cells, designated as virus-resistant (VR) Caco-2 cells, which were then able to resist NDV-mediated oncolysis. We applied single-cell Raman spectroscopy, combined with deuterium isotope probing (Raman-DIP) techniques, to investigate the metabolic adaptations and dynamics in VR Caco-2 cells. A linear discriminant analysis (LDA) model demonstrated excellent performance in differentiating VR Caco-2 from Caco-2 cells at single-cell level. By comparing the metabolic profiles in a time-resolved manner, the de novo synthesis of proteins and lipids was found upregulated, along with decreased DNA synthesis in VR Caco-2. The results suggest that VR Caco-2 cells might reprogram their metabolism and divert energy from proliferation to protein synthesis and lipidic modulation. The ability to identify and characterise single resistant cells among a population of cancer cells would help develop a deeper understanding of the resistance mechanisms and better tactics for developing effective cancer treatment.

Stain-free Gram staining classification of pathogens via single-cell Raman spectroscopy combined with machine learning

Gram staining (GS) is one of the routine microbiological operations to classify bacteria based on the cell wall structure. Accurate GS classification of pathogens is of great significance since it helps correct administration of antimicrobial treatment. The laborious procedure and low sensitivity results related to conventional GS have resulted in reluctance among clinicians. In this study, we integrate confocal Raman spectroscopy and machine learning techniques to distinguish Gram-negative (GN) or Gram-positive (GP) bacteria. A single-cell Raman database including seven most common clinical pathogens (three GP strains and four GN strains) was constructed. Machine learning algorithms including the support-vector machine (SVM), k-nearest neighbors' algorithm (k-NN), gradient boosting machine (GBM), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) were trained to achieve the binary classification for GS. With such a relatively small database, the SVM model achieved the highest accuracy of 98.1%. The molecular signatures of GN and GP embedded in their Raman fingerprints were identified with hierarchical cluster analysis (HCA). The results indicated that Raman peaks for peptidoglycan and teichoic acid were the most significant factors that contributed to accurate classification. The Raman machine learning approach could greatly enhance the diagnosis of pathogenic infections.

Fabrication of optoplasmonic particles through electroless deposition and the application in SERS-based screening of nodule-involved lung cancer

In this work, a core-satellite optoplasmonic particle containing a silica microsphere covered with gold nanoparticles (AuNPs) was developed through wet chemistry synthesis in aqueous phase. The electroless deposition and galvanic replacement were employed to anchor AuNPs onto silica sphere surface. The escalated as well as expanded electric field enhancement within the dielectric-metallic interface was analyzed through finite difference time domain (FDTD) simulation. The numerical models and the surface-enhancement Raman spectroscopy (SERS) measurements over blood serum both support that the equatorial plane is the preferred collecting plane for improved signal intensity and stability. The nanocomposite emerged lower relative standard deviation (RSD) in repetitive measurement compared to AuNPs. In practice, this hybrid structure was applied for lung cancer diagnosis based on serum SERS spectra analysis of the patients diagnosed with nodules. The prediction with the aid of principal component analysis (PCA) and support-vector machine (SVM) was attempted for the classification of healthy, benign and relatively malignant sample groups. The accuracy of distinguish benign samples from malignant ones reaches over 90%. These advantages make the structure a promising SERS substrate for the early screening of cancer based on the non-invasive biological samples.

In situ identification of environmental microorganisms with Raman spectroscopy

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

The Effects of Interacting With a Paro Robot After a Stressor in Patients With Psoriasis: A Randomised Pilot Study

Objective

Stress can play a role in the onset and exacerbation of psoriasis. Psychological interventions to reduce stress have been shown to improve psychological and psoriasis-related outcomes. This pilot randomised study investigated the feasibility of a brief interaction with a Paro robot to reduce stress and improve skin parameters, after a stressor, in patients with psoriasis.

Methods

Around 25 patients with psoriasis participated in a laboratory stress task, before being randomised to either interact with a Paro robot or sit quietly (control condition) for 30 min. Raman spectroscopy and trans-epidermal water loss were measured at baseline, after the stressor and after the intervention as indexes of acute skin changes. Psychological variables, including self-reported stress and affect, were also measured at the three time-points.

Results

No statistically significant differences between the two conditions were found for any of the outcomes measured. However, effect sizes suggest significance could be possible with a larger sample size. Changes in the psychological and Raman spectroscopy outcomes across the experimental session were found, indicating the feasibility of the procedures.

Conclusion

This pilot study showed that a brief interaction with a Paro robot was a feasible intervention for patients with psoriasis, but future trials should broaden the inclusion criteria to try to increase recruitment rates. Studying people who are highly stressed, depressed or who are stress-responders may increase the power of the intervention to show effects using a longer-term intervention.

From Raman to SESORRS: moving deeper into cancer detection and treatment monitoring

Raman spectroscopy is a non-invasive technique that allows specific chemical information to be obtained from various types of sample. The detailed molecular information that is present in Raman spectra permits monitoring of biochemical changes that occur in diseases, such as cancer, and can be used for the early detection and diagnosis of the disease, for monitoring treatment, and to distinguish between cancerous and non-cancerous biological samples. Several techniques have been developed to enhance the capabilities of Raman spectroscopy by improving detection sensitivity, reducing imaging times and increasing the potential applicability for in vivo analysis. The different Raman techniques each have their own advantages that can accommodate the alternative detection formats, allowing the techniques to be applied in several ways for the detection and diagnosis of cancer. This feature article discusses the various forms of Raman spectroscopy, how they have been applied for cancer detection, and the adaptation of the techniques towards their use for in vivo cancer detection and in clinical diagnostics. Despite the advances in Raman spectroscopy, the clinical application of the technique is still limited and certain challenges must be overcome to enable clinical translation. We provide an outlook on the future of the techniques in this area and what we believe is required to allow the potential of Raman spectroscopy to be achieved for clinical cancer diagnostics.

An overview of the current clinical status of optical imaging in head and neck cancer with a focus on Narrow Band imaging and fluorescence optical imaging

Early and accurate identification of head and neck squamous cell carcinoma (HNSCC) is important to improve treatment outcomes and prognosis. New optical imaging techniques may assist in both the diagnostic process as well as in the operative setting by real-time visualization and delineation of tumor. Narrow Band Imaging (NBI) is an endoscopic technique that uses blue and green light to enhance mucosal and submucosal blood vessels, leading to better detection of (pre)malignant lesions showing aberrant blood vessel patterns. Fluorescence optical imaging makes use of near-infrared fluorescent agents to visualize and delineate HNSCC, resulting in fewer positive surgical margins. Targeted fluorescent agents, such as fluorophores conjugated to antibodies, show the most promising results. The aim of this review is: (1) to provide the clinical head and neck surgeon an overview of the current clinical status of various optical imaging techniques in head and neck cancer; (2) to provide an in-depth review of NBI and fluorescence optical imaging, as these techniques have the highest potential for clinical implementation; and (3) to describe future improvements and developments within the field of these two techniques.

Spectroscopic, density functional theoretical study, molecular docking, and in vitro studies based on anticancer activity studies against A549 lung cancer cell line of diphenylhydantoin adsorbed on AuNPs surface

The optimized geometry, FT-Raman, FT-IR, surface-enhanced Raman scattering, UV-Vis spectra, frontier molecular orbital analysis, molecular electrostatic potential analysis, and local and global reactivity descriptors of diphenylhydantoin (DPH) and diphenylhydantoin@AuNPs (DPHA) molecule have been investigated with the help of density functional theory method (B3LYP/6-31++G [d,p] together with LANL2DZ) and was compared and analyzed with the corresponding experimental data in order to identify their structural and bonding features responsible for their bioactivity. In-silico (molecular docking) biological activity screening of the molecules together with the in-vitro (SERS and MTT assay) analysis confirms the anticancer activity of DPH and DPHA molecules. The results of the structure-activity studies and bioactivity studies signify that the DPHA molecule is more active than the DPH molecule against lung cancer.

LPCANet: Classification of Laryngeal Cancer Histopathological Images Using a CNN with Position Attention and Channel Attention Mechanisms

Laryngeal cancer is one of the most common malignant tumors in otolaryngology, and histopathological image analysis is the gold standard for the diagnosis of laryngeal cancer. However, pathologists have high subjectivity in their diagnoses, which makes it easy to miss diagnoses and misdiagnose. In addition, according to a literature search, there is currently no computer-aided diagnosis (CAD) algorithm that has been applied to the classification of histopathological images of laryngeal cancer. Convolutional neural networks (CNNs) are widely used in various other cancer classification tasks. However, the potential global and channel relationships of images may be ignored, which will affect the feature representation ability. Simultaneously, due to the lack of interpretability, the results are often difficult to accept by pathologists. we propose a laryngeal cancer classification network (LPCANet) based on a CNN and attention mechanisms. First, the original histopathological images are sequentially cropped into patches. Then, the patches are input into the basic ResNet50 to extract the local features. Then, a position attention module and a channel attention module are added in parallel to capture the spatial dependency and the channel dependency, respectively. The two modules produce the fusion feature map to enhance the feature representation and improve network classification performance. Moreover, the fusion feature map is extracted and visually analyzed by the grad-weighted class activation map (Grad_CAM) to provide a certain interpretability for the final results. The three-class classification performance of LPCANet is better than those of the five state-of-the-art classifiers (VGG16, ResNet50, InceptionV3, Xception and DenseNet121) on the two original resolutions (534 * 400 and 1067 * 800). On the 534 * 400 data, LPCANet achieved 73.18% accuracy, 74.04% precision, 73.15% recall, 72.9% F1-score, and 0.8826 AUC. On the 1067 * 800 data, LPCANet achieved 83.15% accuracy, 83.5% precision, 83.1% recall, 83.1% F1-score, and 0.9487 AUC. The results show that LPCANet enhances the feature representation by capturing the global and channel relationships and achieves better classification performance. In addition, the visual analysis of Grad_CAM makes the results interpretable, which makes it easier for the results to be accepted by pathologists and allows the method to become a second tool for auxiliary diagnosis.

Imaging of Oral SCC Cells by Raman Micro-Spectroscopy Technique

We used Raman micro-spectroscopy technique to analyze the molecular changes associated with oral squamous cell carcinoma (SCC) cells in the form of frozen tissue. Previously, Raman micro-spectroscopy technique on human tissue was mainly based on spectral analysis, but we worked on imaging of molecular structure. In this study, we evaluated the distribution of four components at the cell level (about 10 μm) to describe the changes in protein and molecular structures of protein belonging to malignant tissue. We analyzed ten oral SCC samples of five patients without special pretreatments of the use of formaldehyde. We obtained cell level images of the oral SCC cells at various components (peak at 935 cm⁻¹: proline and valine, 1004 cm⁻¹: phenylalanine, 1223 cm⁻¹: nucleic acids, and 1650 cm⁻¹: amide I). These mapping images of SCC cells showed the distribution of nucleic acids in the nuclear areas; meanwhile, proline and valine, phenylalanine, and amide I were detected in the cytoplasm areas of the SCC cells. Furthermore, the peak of amide I in the cancer area shifts to the higher wavenumber side, which indicates the α-helix component may decrease in its relative amounts of protein in the β-sheet or random coil conformation. Imaging of SCC cells with Raman micro-spectroscopy technique indicated that such a new observation of cancer cells is useful for analyzing the detailed distribution of various molecular conformation within SCC cells.

Mesoscopic fluorescence lifetime imaging: Fundamental principles, clinical applications and future directions

Fluorescence lifetime imaging (FLIm) is an optical spectroscopic imaging technique capable of real-time assessments of tissue properties in clinical settings. Label-free FLIm is sensitive to changes in tissue structure and biochemistry resulting from pathological conditions, thus providing optical contrast to identify and monitor the progression of disease. Technical and methodological advances over the last two decades have enabled the development of FLIm instrumentation for real-time, in situ, mesoscopic imaging compatible with standard clinical workflows. Herein, we review the fundamental working principles of mesoscopic FLIm, discuss the technical characteristics of current clinical FLIm instrumentation, highlight the most commonly used analytical methods to interpret fluorescence lifetime data and discuss the recent applications of FLIm in surgical oncology and cardiovascular diagnostics. Finally, we conclude with an outlook on the future directions of clinical FLIm.

Cigarette smoke-induced changes in the murine vocal folds: a Raman spectroscopic observation

Raman spectroscopic methods are being projected as novel tools to study the early invisible molecular level changes in a label-free manner. In the present study, we have used Raman spectroscopy to explore the earliest biochemical changes in murine vocal folds in response to time-bound cigarette smoke exposure. Mice were exposed to cigarette smoke for 2 or 4-weeks through a customized smoke inhalation system. The larynx was collected and initial evaluations using standard methods of analysis such as histopathology and immunofluorescence was performed. Concurrent unstained sections were used for Raman imaging. Two common pathological features of vocal fold disorders including alterations in collagen content and epithelial hypercellularity, or hyperplasia, were observed. The mean spectra, principal component analysis, and Raman mapping also revealed differences in the collagen content and hypercellularity in the smoke exposed tissues. The differences in 2-week exposed tissues were found to be more prominent as compared to 4-week. This was attributed to adaptive responses and the already reported biphasic effects, which suggest that collagen synthesis is significantly reduced at higher cigarette smoke concentrations. Overall findings of the study are supportive of the prospective application of Raman imaging in monitoring changes due to cigarette smoke in the vocal folds.

Current Intraoperative Imaging Techniques to Improve Surgical Resection of Laryngeal Cancer: A Systematic Review

Simple Summary

Laryngeal cancer is a prevalent head and neck malignancy, with poor prognosis and low survival rates for patients with advanced disease. The recurrence rate for advanced laryngeal cancer is between 25 and 50%. In order to improve surgical resection of laryngeal cancer and reduce local recurrence rates, various intraoperative optical imaging techniques have been investigated. In this systematic review we identify these technologies, evaluating the current state and future directions of optical imaging for this indication. Evidently, the investigated imaging modalities are generally unsuitable for deep margin assessment, and, therefore, inadequate to guide resection in advanced laryngeal disease. We discuss two optical imaging techniques that can overcome these limitations and suggest how they can be used to achieve adequate margins in laryngeal cancer at all stages.

Abstract

Laryngeal cancer is a prevalent head and neck malignancy, with poor prognosis and low survival rates for patients with advanced disease. Treatment consists of unimodal therapy through surgery or radiotherapy in early staged tumors, while advanced stage tumors are generally treated with multimodal chemoradiotherapy or (total) laryngectomy followed by radiotherapy. Still, the recurrence rate for advanced laryngeal cancer is between 25 and 50%. In order to improve surgical resection of laryngeal cancer and reduce local recurrence rates, various intraoperative optical imaging techniques have been investigated. In this systematic review, we identify these technologies, evaluating the current state and future directions of optical imaging for this indication. Narrow-band imaging (NBI) and autofluorescence (AF) are established tools for early detection of laryngeal cancer. Nonetheless, their intraoperative utility is limited by an intrinsic inability to image beyond the (sub-)mucosa. Likewise, contact endoscopy (CE) and optical coherence tomography (OCT) are technically cumbersome and only useful for mucosal margin assessment. Research on fluorescence imaging (FLI) for this application is sparse, dealing solely with nonspecific fluorescent agents. Evidently, the imaging modalities that have been investigated thus far are generally unsuitable for deep margin assessment. We discuss two optical imaging techniques that can overcome these limitations and suggest how they can be used to achieve adequate margins in laryngeal cancer at all stages.

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

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

Selective Detection of Nano-Sized Diagnostic Markers Using Au-ZnO Nanorod-Based Surface-Enhanced Raman Spectroscopy (SERS) in Ureteral Obstruction Models

Background

This study investigated the diagnosis of renal diseases using a biochip capable of detecting nano-sized biomarkers. Raman measurements from a kidney injury model were taken, and the feasibility of early diagnosis was assessed.

Materials and Methods

Rat models with mild and severe unilateral ureteral obstructions were created, with the injury to the kidney varying according to the tightness of the stricture. After generating the animal ureteral obstruction models, urine was collected from the kidney and bladder.

Results and Discussion

After confirming the presence of renal injury, urine drops were placed onto a Raman chip whose surface had been enhanced with Au-ZnO nanorods, allowing nano-sized biomarkers that diffused into the nanogaps to be selectively amplified. The Raman signals varied according to the severity of the renal damage, and these differences were statistically confirmed.

Conclusion

These results confirm that ureteral stricture causes kidney injury and that signals in the urine from the release of nano-biomarkers can be monitored using surface-enhanced Raman spectroscopy.

Rapid and label-free identification of different cancer types based on surface-enhanced Raman scattering profiles and multivariate statistical analysis

Rapid detection and classification of cancer cells with label-free and non-destructive methods are helpful for rapid screening of cancer patients in clinical settings. Here, surface-enhanced Raman scattering (SERS) was used for rapid, unlabeled, and non-destructive detection of seven different cell types, including human cancer cells and non-tumorous cells. Au nanoparticles were used as enhanced substrates and directly added to cell surfaces. The single cellular SERS signals could be easily and stably collected in several minutes, and the cells maintained structural integrity over one hour. Different types of cells had unique Raman phenotypes. By applying multivariate statistical analysis to the Raman phenotypes, the cancer cells and non-tumorous cells were accurately identified. The high sensitivity enabled this method to discriminate subtle molecular changes in different cell types, and the accuracy reached 81.2% with principal components analysis and linear discriminant analysis. The technique provided a rapid, unlabeled, and non-destructive method for the detection and identification of various cancer types.

Efficient Capture and Raman Analysis of Circulating Tumor Cells by Nano-Undulated AgNPs-rGO Composite SERS Substrates

The analysis of circulating tumor cells (CTCs) in the peripheral blood of cancer patients is critical in clinical research for further investigation of tumor progression and metastasis. In this study, we present a novel surface-enhanced Raman scattering (SERS) substrate for the efficient capture and characterization of cancer cells using silver nanoparticles-reduced graphene oxide (AgNPs-rGO) composites. A pulsed laser reduction of silver nanowire-graphene oxide (AgNW-GO) mixture films induces hot-spot formations among AgNPs and artificial biointerfaces consisting of rGOs. We also use in situ electric field-assisted fabrication methods to enhance the roughness of the SERS substrate. The AgNW-GO mixture films, well suited for the proposed process due to its inherent electrophoretic motion, is adjusted between indium tin oxide (ITO) transparent electrodes and the nano-undulated surface is generated by applying direct-current (DC) electric fields during the laser process. As a result, MCF7 breast cancer cells are efficiently captured on the AgNPs-rGO substrates, about four times higher than the AgNWs-GO films, and the captured living cells are successfully analyzed by SERS spectroscopy. Our newly designed bifunctional substrate can be applied as an effective system for the capture and characterization of CTCs.

SERS studies on normal epithelial and cancer cells derived from clinical breast cancer specimens

Surface-enhanced Raman scattering (SERS) spectroscopy of single-cell suspensions obtained from fresh specimens of breast cancer tissue and normal breast tissue by mechanical enzymatic digestion was obtained and analysed, which is different from most Raman studies using breast cancer cell lines. Random forest classification was implemented to develop effective diagnostic algorithms for the classification of SERS of different typed cells. We first examined the SERS spectra of the primary breast cancer single cell and normal epithelial single cell obtained by flow sorting cytometry due to their biomarkers of CD326+/CD45-. Comparison analyses on their SERS spectra disclose that the nucleic acid and protein levels of the primary breast cancer single cell are higher than those of the normal epithelial single cell, while the lipids are at a relatively lower level. An important finding is that the cholesterol, palmitic acid, and sphingomyelin in the cancer cell profiles exhibit stronger than those of normal cells, while the glycans are at a relatively lower level. Furthermore, the standard deviation (SD) of the normal epithelial single cell is larger than that of the breast cancer cell, and the SD of the primary breast cancer single cell is more obvious than that of the normal epithelial cells. In addition, the prospective application of an algorithm to the dataset results in an accuracy of 78.2%, a precision of 75.5%, and a recall of 66.7%. The breast cancer diagnostic model laid a solid foundation for judgment of breast-conserving surgical margins and early diagnosis of breast cancer.

Identification of Lesional Tissues and Nonlesional Tissues in Early Gastric Cancer Endoscopic Submucosal Dissection Specimens Using a Fiber Optic Raman System

Aim

To identify lesional and nonlesional tissues from early gastric cancer (EGC) patients by Raman spectroscopy to build a diagnostic model and effectively diagnose EGC.

Method

Specimens were collected by endoscopic submucosal dissection from 13 patients with EGC, and 55 sets of standard Raman spectral data (each integrated 10 times) were obtained using the fiber optic Raman system; there were 33 sets of lesional tissue data, including 18 sets of high-grade intraepithelial neoplasia (HGIN) data and 15 sets of adenocarcinoma data, and 22 sets of nonlesional tissue data. After the preprocessing steps, the average Raman spectrum was obtained.

Results

The nonlesional tissues showed peaks at 891 cm⁻¹, 1103 cm⁻¹, 1417 cm⁻¹, 1206 cm⁻¹, 1234 cm⁻¹, 1479 cm⁻¹, 1560 cm⁻¹, and 1678 cm⁻¹. Compared with the peaks corresponding to nonlesional tissues, the peaks of the lesional tissues shifted by different magnitudes, and a new characteristic peak at 1324 cm⁻¹ was observed. Comparing the peak intensity ratio and the integral energy ratio of the lesional tissues with those of the nonlesional tissues revealed a significant difference between the two groups (independent-samplest-test, P < 0.05). Considering the peak intensity ratio of I1560 cm⁻¹/I1103 cm⁻¹ as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 98.8%, 93.9%, and 91.9%, respectively. Considering the integral energy ratio (noncontinuous frequency band and continuous frequency band) as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 99.2-99.6%, 93.9-97.0%, and 95.5%, respectively.

Conclusions

The integral energy ratio of the Raman spectrum could be considered an effective indicator for the diagnosis of EGC.

Raman spectroscopy for rapid intra-operative margin analysis of surgically excised tumour specimens

Raman spectroscopy, a form of vibrational spectroscopy, has the ability to provide sensitive and specific biochemical analysis of tissue. This review article provides an in-depth analysis of the suitability of different Raman spectroscopy techniques in providing intra-operative margin analysis in a range of solid tumour pathologies. Surgical excision remains the primary treatment of a number of solid organ cancers. Incomplete excision of a tumour and positive margins on histopathological analysis is associated with a worse prognosis, the need for adjuvant therapies with significant side effects and a resulting financial burden. The provision of intra-operative margin analysis of surgically excised tumour specimens would be beneficial for a number of pathologies, as there are no widely adopted and accurate methods of margin analysis, beyond histopathology. The limitations of Raman spectroscopic studies to date are discussed and future work necessary to enable translation to clinical use is identified. We conclude that, although there remain a number of challenges in translating current techniques into a clinically effective tool, studies so far demonstrate that Raman Spectroscopy has the attributes to successfully perform highly accurate intra-operative margin analysis in a clinically relevant environment.

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

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

Optimization of ZnO Nanorod-Based Surface Enhanced Raman Scattering Substrates for Bio-Applications

Nanorods based on ZnO for surface enhanced Raman spectroscopy are promising for the non-invasive and rapid detection of biomarkers and diagnosis of disease. However, optimization of nanorod and coating parameters is essential to their practical application. With the goal of establishing a baseline for early detection in biological applications, gold-coated ZnO nanorods were grown and coated to form porous structures. Prior to gold deposition, the grown nanorods were 30–50 nm in diameter and 500–600 nm in length. Gold coatings were grown on the nanorod structure to a series of thicknesses between 100 and 300 nm. A gold coating of 200 nm was found to optimize the Rhodamine B model analyte signal, while performance for rat urine depended on the biomarkers to be detected. These results establish design guidelines for future use of Au-ZnO nanorods in the study and early diagnosis of inflammatory diseases.

Review of Modern Techniques for the Assessment of Skin Hydration

Skin hydration is a complex process that influences the physical and mechanical properties of skin. Various technologies have emerged over the years to assess this parameter, with the current standard being electrical probe-based instruments. Nevertheless, their inability to provide detailed information has prompted the use of sophisticated spectroscopic and imaging methodologies, which are capable of in-depth skin analysis that includes structural and composition details. Modern imaging and spectroscopic techniques have transformed skin research in the dermatological and cosmetics disciplines, and are now commonly employed in conjunction with traditional methods for comprehensive assessment of both healthy and pathological skin. This article reviews current techniques employed in measuring skin hydration, and gives an account on their principle of operation and applications in skin-related research.

Diagnosis in a Preclinical Model of Bladder Pain Syndrome Using a Au/ZnO Nanorod-based SERS Substrate

To evaluate the feasibility of ZnO nanorod-based surface enhanced Raman scattering (SERS) diagnostics for disease models, particularly for interstitial cystitis/bladder pain syndrome (IC/BPS), ZnO-based SERS sensing chips were developed and applied to an animal disease model. ZnO nanorods were grown to form nano-sized porous structures and coated with gold to facilitate size-selective biomarker detection. Raman spectra were acquired on a surface enhanced Raman substrate from the urine in a rat model of IC/BPS and analyzed using a statistical analysis method called principal component analysis (PCA). The nanorods grown after the ZnO seed deposition were 30 to 50 nm in diameter and 500 to 600 nm in length. A volume of gold corresponding to a thin film thickness of 100 nm was deposited on the grown nanorod structure. Raman spectroscopic signals were measured in the scattered region for nanometer biomarker detection to indicate IC/BPS. The Raman peaks for the control group and IC/BPS group are observed at 641, 683, 723, 873, 1002, 1030, and 1355 cm⁻¹, which corresponded to various bonding types and compounds. The PCA results are plotted in 2D and 3D. The Raman signals and statistical analyses obtained from the nano-sized biomarkers of intractable inflammatory diseases demonstrate the possibility of an early diagnosis.

Raman spectroscopy for medulloblastoma

PURPOSE

The aim of the study is to use Raman spectroscopy to analyze the biochemical composition of medulloblastoma and normal tissues from the safety margin of the CNS and to find specific Raman biomarkers capable of differentiating between tumorous and normal tissues.

METHODS

The tissue samples consisted of medulloblastoma (grade IV) (n = 11). The tissues from the negative margins were used as normal controls. Raman images were generated by a confocal Raman microscope-WITec alpha 300 RSA.

RESULTS

Raman vibrational signatures can predict which tissue has tumorous biochemistry and can identify medulloblastoma. The Raman technique makes use of the fact that tumors contain large amounts of protein and far less lipids (fatty compounds), while healthy tissue is rich in both.

CONCLUSION

The ability of Raman spectroscopy and imaging to detect medulloblastoma tumors fills the niche in diagnostics. These powerful analytical techniques are capable of monitoring tissue morphology and biochemistry. Our results demonstrate that RS can be used to discriminate between normal and medulloblastoma tissues.

Label-free and stable serum analysis based on Ag-NPs/PSi surface-enhanced Raman scattering for noninvasive lung cancer detection

Surface-enhanced Raman scattering (SERS) has a broad application prospect in the field of tumor detection owing to its ultrahigh detective sensitivity. However, SERS analysis of serum remain a challenge in terms of repeatability and stability due to the maldistribution of the silver nanoparticles (Ag-NPs)-serum. With the aim to make up for this shortcoming, we report a new method for obtaining stable serum Raman signals utilizing the ordered arrays of pyramidal silicon (PSi) and Ag-NPs. We prove the practicability of this method by detecting the samples of serum from 50 lung cancer patients and 50 normal healthy people. Principal component analysis (PCA) of the serum SERS spectra shows that the spectral data of the two sample groups can form obvious and completely separated clusters. The receiver operating characteristic curve provides the sensitivity (100%) and specificity (90%) from the PCA-LDA method. This research indicates that a stable and label-free analysis technique of serum SERS based on Ag-NPs/PSi and PCA-LDA is promising for noninvasive lung cancer diagnoses.

Histological coherent Raman imaging: a prognostic review

Histopathology plays a central role in diagnosis of many diseases including solid cancers. Efforts are underway to transform this subjective art to an objective and quantitative science. Coherent Raman imaging (CRI), a label-free imaging modality with sub-cellular spatial resolution and molecule-specific contrast possesses characteristics which could support the qualitative-to-quantitative transition of histopathology. In this work we briefly survey major themes related to modernization of histopathology, review applications of CRI to histopathology and, finally, discuss potential roles for CRI in the transformation of histopathology that is already underway.

Multi-centre Raman spectral mapping of oesophageal cancer tissues: a study to assess system transferability

The potential for Raman spectroscopy to provide early and improved diagnosis on a wide range of tissue and biopsy samples in situ is well documented. The standard histopathology diagnostic methods of reviewing H&E and/or immunohistochemical (IHC) stained tissue sections provides valuable clinical information, but requires both logistics (review, analysis and interpretation by an expert) and costly processing and reagents. Vibrational spectroscopy offers a complimentary diagnostic tool providing specific and multiplexed information relating to molecular structure and composition, but is not yet used to a significant extent in a clinical setting. One of the challenges for clinical implementation is that each Raman spectrometer system will have different characteristics and therefore spectra are not readily compatible between systems. This is essential for clinical implementation where classification models are used to compare measured biochemical or tissue spectra against a library training dataset. In this study, we demonstrate the development and validation of a classification model to discriminate between adenocarcinoma (AC) and non-cancerous intraepithelial metaplasia (IM) oesophageal tissue samples, measured on three different Raman instruments across three different locations. Spectra were corrected using system transfer spectral correction algorithms including wavenumber shift (offset) correction, instrument response correction and baseline removal. The results from this study indicate that the combined correction methods do minimize the instrument and sample quality variations within and between the instrument sites. However, more tissue samples of varying pathology states and greater tissue area coverage (per sample) are needed to properly assess the ability of Raman spectroscopy and system transferability algorithms over multiple instrument sites.

In-vivo optical imaging in head and neck oncology: basic principles, clinical applications and future directions

Head and neck cancers become a severe threat to human's health nowadays and represent the sixth most common cancer worldwide. Surgery remains the first-line choice for head and neck cancer patients. Limited resectable tissue mass and complicated anatomy structures in the head and neck region put the surgeons in a dilemma between the extensive resection and a better quality of life for the patients. Early diagnosis and treatment of the pre-malignancies, as well as real-time in vivo detection of surgical margins during en bloc resection, could be leveraged to minimize the resection of normal tissues. With the understanding of the head and neck oncology, recent advances in optical hardware and reagents have provided unique opportunities for real-time pre-malignancies and cancer imaging in the clinic or operating room. Optical imaging in the head and neck has been reported using autofluorescence imaging, targeted fluorescence imaging, high-resolution microendoscopy, narrow band imaging and the Raman spectroscopy. In this study, we reviewed the basic theories and clinical applications of optical imaging for the diagnosis and treatment in the field of head and neck oncology with the goal of identifying limitations and facilitating future advancements in the field.

Rapid diagnostics of liver steatosis by Raman spectroscopyviafiber optic probe: a pilot study

Raman spectroscopyviafiber optic probes enables assessment of the liver condition and rapid quantification of liver steatosis, thus, this technique has the potential as a diagnostic tool.

Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67  mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

Proof-of-concept Raman spectroscopy study aimed to differentiate thyroid follicular patterned lesions

Inter-observer variability and cancer over-diagnosis are emerging clinical problems, especially for follicular patterned thyroid lesions. This challenge strongly calls for a new clinical tool to reliably identify neoplastic lesions and to improve the efficiency of differentiation between benign and malignant neoplasms, especially considering the increased diagnosis of small carcinomas and the growing number of thyroid nodules. In this study, we employed a Raman spectroscopy (RS) microscope to investigate frozen thyroid tissues from fourteen patients with thyroid nodules. To generate tissue classification models, a supervised statistical analysis of the Raman spectra was performed. The results obtained demonstrate an accuracy of 78% for RS based diagnosis to discriminate between normal parenchyma and follicular patterned thyroid nodules, and 89% accuracy - for very challenging follicular lesions (carcinoma versus adenoma). RS translation into intraoperative diagnosis of frozen sections and in preoperative analysis of biopsies can be very helpful to reduce unnecessary surgery in patients with indeterminate cytological reports.

Micro-Raman spectroscopic study of thyroid tissues

Thyroid carcinomas are the most common endocrine malignancy. Inconclusive results for the analysis of malignancies are an issue in the diagnosis of thyroid carcinomas; 20% of thyroid cancer diagnoses are indeterminate or suspicious, resulting in a surgical procedure without immediate need. The use of Raman spectroscopy may help improve the diagnosis of thyroid carcinoma. In this study, 30 thyroid samples, including normal thyroid, goiter and thyroid cancer, were analyzed by confocal Raman spectroscopy. Principal component analysis (PCA), linear discriminant analysis (LDA) with cross validation and binary logistic regression (BLR) analysis were applied to discriminate among tissues. Significant discrimination was observed, with a consistent rate of concordant pairs of 89.2% for normal thyroid versus cancer, 85.7% for goiter versus cancer and 80.6% for normal thyroid versus goiter using just the amide III region. Raman spectroscopy was thus proven to be an important and fast tool for the diagnosis of thyroid tissues. The spectral region of 1200-1400cm^-1 discriminated normal versus goiter tissues despite the great similarity of these tissues.

Raman spectroscopy can discriminate between normal, dysplastic and cancerous oral mucosa: a tissue-engineering approach

Head and neck cancer (HNC) is the sixth most common malignancy worldwide. Squamous cell carcinoma, the primary cause of HNC, evolves from normal epithelium through dysplasia before invading the connective tissue to form a carcinoma. However, less than 18% of suspicious oral lesions progress to cancer, with diagnosis currently relying on histopathological evaluation, which is invasive and time consuming. A non-invasive, real-time, point-of-care method could overcome these problems and facilitate regular screening. Raman spectroscopy is a non-invasive optical technique with the ability to extract molecular level information to help determine the functional groups present in a tissue and the molecular conformations of tissue constituents. In the present study, Raman spectroscopy was assessed for its ability to discriminate between normal, dysplastic and HNC. Tissue engineered models of normal, dysplastic and HNC were constructed using normal oral keratinocytes, dysplastic and HNC cell lines, and their biochemical content predicted by interpretation of spectral characteristics. Spectral differences were evident in both the fingerprint (600/cm to 1800/cm) and high wave-number compartments (2800/cm to 3400/cm). Visible differences were seen in peaks relating to lipid content (2881/cm), protein structure (amide I, amide III), several amino acids and nucleic acids (600/cm to 1003/cm). Multivariate data analysis algorithms successfully identified subtypes of dysplasia and cancer, suggesting that Raman spectroscopy not only has the potential to differentiate between normal, pre-malignant and cancerous tissue models but could also be sensitive enough to detect subtypes of dysplasia or cancer on the basis of their subcellular differences. Copyright © 2016 John Wiley & Sons, Ltd.

Real-time in vivo diagnosis of laryngeal carcinoma with rapid fiber-optic Raman spectroscopy

We assess the clinical utility of a unique simultaneous fingerprint (FP) (i.e., 800-1800 cm^-1) and high-wavenumber (HW) (i.e., 2800-3600 cm^-1) fiber-optic Raman spectroscopy for in vivo diagnosis of laryngeal cancer at endoscopy. A total of 2124 high-quality in vivo FP/HW Raman spectra (normal = 1321; cancer = 581) were acquired from 101 tissue sites (normal = 71; cancer = 30) of 60 patients (normal = 44; cancer = 16) undergoing routine endoscopic examination. FP/HW Raman spectra differ significantly between normal and cancerous laryngeal tissue that could be attributed to changes of proteins, lipids, nucleic acids, and the bound water content in the larynx. Partial least squares-discriminant analysis and leave-one tissue site-out, cross-validation were employed on the in vivo FP/HW tissue Raman spectra acquired, yielding a diagnostic accuracy of 91.1% (sensitivity: 93.3% (28/30); specificity: 90.1% (64/71)) for laryngeal cancer identification, which is superior to using either FP (accuracy: 86.1%; sensitivity: 86.7% (26/30); specificity: 85.9% (61/71)) or HW (accuracy: 84.2%; sensitivity: 76.7% (23/30); specificity: 87.3% (62/71)) Raman technique alone. Further receiver operating characteristic analysis reconfirms the best performance of the simultaneous FP/HW Raman technique for laryngeal cancer diagnosis. We demonstrate for the first time that the simultaneous FP/HW Raman spectroscopy technique can be used for improving real-time in vivo diagnosis of laryngeal carcinoma during endoscopic examination.

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.

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.

Using Raman spectroscopy to characterize biological materials

Raman spectroscopy can be used to measure the chemical composition of a sample, which can in turn be used to extract biological information. Many materials have characteristic Raman spectra, which means that Raman spectroscopy has proven to be an effective analytical approach in geology, semiconductor, materials and polymer science fields. The application of Raman spectroscopy and microscopy within biology is rapidly increasing because it can provide chemical and compositional information, but it does not typically suffer from interference from water molecules. Analysis does not conventionally require extensive sample preparation; biochemical and structural information can usually be obtained without labeling. In this protocol, we aim to standardize and bring together multiple experimental approaches from key leaders in the field for obtaining Raman spectra using a microspectrometer. As examples of the range of biological samples that can be analyzed, we provide instructions for acquiring Raman spectra, maps and images for fresh plant tissue, formalin-fixed and fresh frozen mammalian tissue, fixed cells and biofluids. We explore a robust approach for sample preparation, instrumentation, acquisition parameters and data processing. By using this approach, we expect that a typical Raman experiment can be performed by a nonspecialist user to generate high-quality data for biological materials analysis.

Exploring the effect of laser excitation wavelength on signal recovery with deep tissue transmission Raman spectroscopy

The aim of this research was to find the optimal Raman excitation wavelength to attain the largest possible sensitivity in deep Raman spectroscopy of breast tissue.