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Neuromuscular disorders are a group of conditions that can result in weakness of skeletal muscles. Examples include fatal diseases such as amyotrophic lateral sclerosis and conditions associated with high morbidity such as myopathies (muscle diseases). Many of these disorders are known to have abnormal protein folding and protein aggregates. Thus, easy to apply methods for the detection of such changes may prove useful diagnostic biomarkers. Raman spectroscopy has shown early promise in the detection of muscle pathology in neuromuscular disorders and is well suited to characterising the conformational profiles relating to protein secondary structure. In this work, we assess if Raman spectroscopy can detect differences in protein structure in muscle in the setting of neuromuscular disease. We utilise in vivo Raman spectroscopy measurements from preclinical models of amyotrophic lateral sclerosis and the myopathy Duchenne muscular dystrophy, together with ex vivo measurements of human muscle samples from individuals with and without myopathy. Using quantitative conformation profiling and matrix factorisation we demonstrate that quantitative 'conformational fingerprinting' can be used to identify changes in protein folding in muscle. Notably, myopathic conditions in both preclinical models and human samples manifested a significant reduction in α-helix structures, with concomitant increases in β-sheet and, to a lesser extent, nonregular configurations. Spectral patterns derived through non-negative matrix factorisation were able to identify myopathy with a high accuracy (79% in mouse, 78% in human tissue). This work demonstrates the potential of conformational fingerprinting as an interpretable biomarker for neuromuscular disorders. [ABSTRACT FROM AUTHOR]

Raman spectroscopy shows promise as a biomarker for complex nerve and muscle (neuromuscular) diseases. To maximise its potential, several challenges remain. These include the sensitivity to different instrument configurations, translation across preclinical/human tissues and the development of multivariate analytics that can derive interpretable spectral outputs for disease identification. Nonnegative matrix factorisation (NMF) can extract features from high‐dimensional data sets and the nonnegative constraint results in physically realistic outputs. In this study, we have undertaken NMF on Raman spectra of muscle obtained from different clinical and preclinical settings. First, we obtained and combined Raman spectra from human patients with mitochondrial disease and healthy volunteers, using both a commercial microscope and in‐house fibre optic probe. NMF was applied across all data, and spectral patterns common to both equipment configurations were identified. Linear discriminant models utilising these patterns were able to accurately classify disease states (accuracy 70.2–84.5%). Next, we applied NMF to spectra obtained from the mdx mouse model of a Duchenne muscular dystrophy and patients with dystrophic muscle conditions. Spectral fingerprints common to mouse/human were obtained and able to accurately identify disease (accuracy 79.5–98.8%). We conclude that NMF can be used to analyse Raman data across different equipment configurations and the preclinical/clinical divide. Thus, the application of NMF decomposition methods could enhance the potential of Raman spectroscopy for the study of fatal neuromuscular diseases. [ABSTRACT FROM AUTHOR]

Introduction/aims: Raman spectroscopy is an emerging technique for the evaluation of muscle disease. In this study we evaluate the ability of in vivo intramuscular Raman spectroscopy to detect the effects of voluntary running in the mdx model of Duchenne muscular dystrophy (DMD). We also compare mdx data with muscle spectra from human DMD patients.Methods: Thirty 90-day-old mdx mice were randomly allocated to an exercised group (48-hour access to a running wheel) and an unexercised group (n = 15 per group). In vivo Raman spectra were collected from both gastrocnemius muscles and histopathological assessment subsequently performed. Raman data were analyzed using principal component analysis-fed linear discriminant analysis (PCA-LDA). Exercised and unexercised mdx muscle spectra were compared with human DMD samples using cosine similarity.Results: Exercised mice ran an average of 6.5 km over 48 hours, which induced a significant increase in muscle necrosis (P = .03). PCA-LDA scores were significantly different between the exercised and unexercised groups (P < .0001) and correlated significantly with distance run (P = .01). Raman spectra from exercised mice more closely resembled human spectra than those from unexercised mice.Discussion: Raman spectroscopy provides a readout of the biochemical alterations in muscle in both the mdx mouse and human DMD muscle. [ABSTRACT FROM AUTHOR]

The diagnosis of muscle disorders ("myopathies") can be challenging and new biomarkers of disease are required to enhance clinical practice and research. Despite advances in areas such as imaging and genomic medicine, muscle biopsy remains an important but time-consuming investigation. Raman spectroscopy is a vibrational spectroscopy application that could provide a rapid analysis of muscle tissue, as it requires no sample preparation and is simple to perform. Here, we investigated the feasibility of using a miniaturised, portable fibre optic Raman system for the rapid identification of muscle disease. Samples were assessed from 27 patients with a final clinico-pathological diagnosis of a myopathy and 17 patients in whom investigations and clinical follow-up excluded myopathy. Multivariate classification techniques achieved accuracies ranging between 71–77%. To explore the potential of Raman spectroscopy to identify different myopathies, patients were subdivided into mitochondrial and non-mitochondrial myopathy groups. Classification accuracies were between 74–89%. Observed spectral changes were related to changes in protein structure. These data indicate fibre optic Raman spectroscopy is a promising technique for the rapid identification of muscle disease that could provide real time diagnostic information. The application of fibre optic Raman technology raises the prospect of in vivo bedside testing for muscle diseases which would significantly streamline the diagnostic pathway of these disorders. [ABSTRACT FROM AUTHOR]

Muscle biopsy remains an important component of the diagnostic repertoire for patients with suspected mitochondrial disease, underpinning specialist histopathological and biochemical analyses. Raman spectroscopy has not yet been applied to mitochondrial disease, and new fibre optic systems, with advantages in terms of cost and portability, could provide a rapid means to identify muscle pathology. In this study, we aimed to explore the potential of two different formats of Raman spectroscopy to identify mitochondrial disease: a miniaturised fibre optic Raman system and a standard commercial Raman microscope. Raman spectra were recorded from muscle samples from healthy volunteers (n = 10) and patients with genetically confirmed mitochondrial disease (n = 15). Multivariate classification algorithms demonstrated a high level of disease classification performance with both the fibre optic probe system and microscope (area under receiver operating characteristic curves 0.80–0.82). Key spectral changes associated with mitochondrial disease concerned the α‐helical configuration of proteins. The results suggest that Raman spectroscopy of muscle is worthy of further investigation as a technique for the rapid identification of mitochondrial disease. [ABSTRACT FROM AUTHOR]

Introduction: Cancer is responsible for an extraordinary burden of disease, affecting 90.5 million people worldwide in 2015. Outcomes for these patients are improved when the disease is diagnosed at an early, or even precancerous, stage. Raman spectroscopy is demonstrating results that show its ability to detect the molecular changes that are diagnostic of precancerous and cancerous tissue. This review highlights the new advances occurring in this domain.Areas covered: PubMed searches were undertaken to identify new research in the utilisation of Raman spectroscopy in cancer diagnostics. The areas in which Raman spectroscopy is showing promise are covered, including improving the accuracy of identifying precancerous changes, using the technology in real time, in vivo modalities, the search for a biomarker to aid potential screening and predicting the response of the cancer to the treatment regimen.Expert commentary: Many of the examples in this review are focused on Barrett’s oesophagus and oesophageal adenocarcinoma as this is my area of expertise and perfectly exemplifies where Raman spectroscopy could be utilised in clinical practise. The authors discuss the areas where they believe current knowledge is lacking and how Raman spectroscopy could answer the dilemmas that are still faced in the management of cancer. [ABSTRACT FROM AUTHOR]

For several decades, a multitude of studies have documented the ability of Raman spectroscopy (RS) to differentiate between tissue types and identify pathological changes to tissues in a range of diseases. Furthermore, spectroscopists have illustrated that the technique is capable of detecting disease‐specific alterations to tissue before morphological changes become apparent to the pathologist. This study draws comparisons between the information that is obtainable using RS alongside immunohistochemistry (IHC), since histological examination is the current GOLD standard for diagnosing a wide range of diseases. Here, Raman spectral maps were generated using formalin‐fixed, paraffin‐embedded colonic tissue sections from healthy patients and spectral signatures from principal components analysis (PCA) were compared with several IHC markers to confirm the validity of their localizations. PCA loadings identified a number of signatures that could be assigned to muscle, DNA and mucin glycoproteins and their distributions were confirmed with antibodies raised against anti‐Desmin, anti‐Ki67 and anti‐MUC2, respectively. The comparison confirms that there is excellent correlation between RS and the IHC markers used, demonstrating that the technique is capable of detecting compositional changes in tissue in a label‐free manner, eliminating the need for antibodies. [ABSTRACT FROM AUTHOR]

Vulval lichen sclerosus (LS) is a common inflammatory condition associated with an increased risk of developing vulval carcinoma. Diagnosis is usually clinical although biopsy is necessary if the diagnosis is uncertain or if there is a failure to respond to adequate initial treatment. Raman spectroscopy has the potential to be applied in vivo for near real time objective non-invasive optical diagnosis, avoiding the need for invasive tissue biopsies. The aim of this study was to evaluate the diagnostic performance of Raman spectroscopy for differentiating LS from other vulval conditions in fresh vulval biopsies. Biopsies were analysed from 27 women with suspected LS in whom the attending gynaecologist could not establish the diagnosis on clinical presentation alone. Spectral variance was explored using principal component analysis and in conjunction with the histological diagnoses was used to develop and test a multivariate linear discriminant classification model. This model was validated with leave one sample out cross validation and the diagnostic performance of the technique assessed in comparison with the pathology gold standard. After cross validation the technique was able to correctly differentiate LS from other inflammatory vulval conditions with a sensitivity of 91% and specificity of 80%. This study demonstrates Raman spectroscopy has potential as a technique for in vivo non-invasive diagnosis of vulval skin conditions. Applied in the clinical setting this technique may reduce the need for invasive tissue biopsy. Further in vivo study is needed to assess the ability of Raman spectroscopy to diagnose other vulval conditions before clinical application. [ABSTRACT FROM AUTHOR]

Raman spectroscopy (RS) is a powerful technique that permits the non-destructive chemical analysis of cells and tissues without the need for expensive and complex sample preparation. To date, samples have been routinely mounted onto calcium fluoride (CaF2) as this material possesses the desired mechanical and optical properties for analysis, but CaF2 is both expensive and brittle and this prevents the technique from being routinely adopted. Furthermore, Raman scattering is a weak phenomenon and CaF2 provides no means of increasing signal. For RS to be widely adopted, particularly in the clinical field, it is crucial that spectroscopists identify an alternative, low-cost substrate capable of providing high spectral signal to noise ratios with good spatial resolution. Results show that these desired properties are attainable when using mirrored stainless steel as a Raman substrate. When compared with CaF2, data show that stainless steel has a low background signal and provides an average signal increase of 1.43 times during tissue analysis and 1.64 times when analyzing cells. This result is attributed to a double-pass of the laser beam through the sample where the photons from the source laser and the forward scattered Raman signal are backreflected and retroreflected from the mirrored steel surface and focused towards collection optics. The spatial resolution on stainless steel is at least comparable to that on CaF2 and it is not compromised by the reflection of the laser. Steel is a fraction of the cost of CaF2 and the reflection and focusing of photons improve signal to noise ratios permitting more rapid mapping. The low cost of steel coupled with its Raman signal increasing properties and robust durability indicates that steel is an ideal substrate for biological and clinical RS as it possesses key advantages over routinely used CaF2. © 2016 The Authors. Journal of Raman Spectroscopy Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Animal models and archived human biobank tissues are useful resources for research in disease development, diagnostics and therapeutics. For the preservation of microscopic anatomical features and to facilitate long-term storage, a majority of tissue samples are denatured by the chemical treatments required for fixation, paraffin embedding and subsequent deparaffinization. These aggressive chemical processes are thought to modify the biochemical composition of the sample and potentially compromise reliable spectroscopic examination useful for the diagnosis or biomarking. As a result, spectroscopy is often conducted on fresh/frozen samples. In this study, we provide an extensive characterization of the biochemical signals remaining in processed samples (formalin fixation and paraffin embedding, FFPE) and especially those originating from the anatomical layers of a healthy rat colon. The application of chemometric analytical methods (unsupervised and supervised) was shown to eliminate the need for tissue staining and easily revealed microscopic features consistent with goblet cells and the dense populations of cells within the mucosa, principally via strong nucleic acid signals. We were also able to identify the collagenous submucosa- and serosa- as well as the muscle-associated signals from the muscular regions and blood vessels. Applying linear regression analysis to the data, we were able to corroborate this initial assignment of cell and tissue types by confirming the biological origin of each layer by reference to a subset of authentic biomolecular standards. Our results demonstrate the potential of using label-free Raman microspectroscopy to obtain superior imaging contrast in FFPE sections when compared directly to conventional haematoxylin and eosin (H&E) staining. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Raman spectroscopy has shown considerable promise as a medical diagnostic tool. The technique is safe for use in vivo and is capable of rapid, objective assessment of neoplastic and inflammatory tissue enabling early diagnosis and targeted treatment. We review potential applications of Raman spectroscopy in the oesophagus, colon and breast where this new technology has the potential to revolutionise patient care. [ABSTRACT FROM AUTHOR]

The application of semi-supervised methodology to improve the classification performance of a Raman spectroscopic probe for the diagnosis of oesophageal cancer is described. It is well known that gold standard histopathology diagnosis can be highly subjective, particularly for diseases which have several stages, such as cancer. A ‘consensus’ pathology decision can be obtained to ensure a robust gold standard by obtaining a diagnosis from several experts and samples are then only included in standard classification models if they have been assigned the same pathology by all experts. This can result in a significant number of samples that are excluded from the analysis as no consensus was reached. In this work semi-supervised methodology was used to extend Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) to incorporate samples without consensus pathology when discriminating between benign and oesophageal cancer specimens measured using a Raman endoscopic probe ex vivo. We demonstrate that a fully semi-supervised approach improved sensitivity and specificity from 73% and 78% (PCA-LDA) to 78% and 84% (semi-supervised) for discriminating between intestinal metaplasia and dysplasia and from 44% and 66% (PCA-LDA) to 63% and 72% (semi-supervised) when discriminating between intestinal metaplasia and low grade dysplasia. [ABSTRACT FROM AUTHOR]

Summary: Light interacts with tissue in a number of ways including, elastic and inelastic scattering, reflection and absorption, leading to fluorescence and phosphorescence. These interactions can be used to measure abnormal changes in tissue. Initial optical biopsy systems have potential to be used as an adjunct to current investigative techniques to improve the targeting of blind biopsy. Future prospects with molecular-specific techniques may enable objective optical detection providing a real-time, highly sensitive and specific measurement of the histological state of the tissue. Raman spectroscopy has the potential to identify markers associated with malignant change and could be used as diagnostic tool for the early detection of precancerous and cancerous lesions in vivo. The clinical requirements for an objective, non-invasive, real-time probe for the accurate and repeatable measurement of pathological state of the tissue are overwhelming. This paper discusses some of the recent advances in the field. [Copyright &y& Elsevier]

Abstract: Raman spectroscopy has previously been identified as a suitable technique for the analysis of biological samples and recent technological advancements have allowed more rapid forms of spectral mapping to be undertaken. This promising approach potentially allows more biochemical information to be obtained than would normally be possible using a standard chemical stain, however specialised techniques are required to analyse the hyperspectral datasets acquired. In this work Self Organising Maps (SOM) are applied in combination with Principal Component Analysis (PCA) to analyse a single human colon polyp sample containing varying histological features. It is demonstrated that using SOM to compress the data is robust to outlying spectral features allowing greater contrast in the image for the identification of subtle features. As a secondary outcome, the SOM method also provides an alternative visualisation approach that can be used to identify regions of histological interest within a sample. [Copyright &y& Elsevier]