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Background: The clinical potential of Raman spectroscopy is well established but has yet to become established in routine oncology workflows. One barrier slowing clinical adoption is a lack of evidence demonstrating that data taken on one spectrometer transfers across to data taken on another spectrometer to provide consistent diagnoses., Methods: We investigated multi-centre transferability using human oesophageal tissue. Raman spectra were taken across three different centres with different spectrometers of the same make and model. By using a common protocol, we aimed to minimise the difference in machine learning performance between centres., Results: 61 oesophageal samples from 51 patients were interrogated by Raman spectroscopy at each centre and classified into one of five pathologies. The overall accuracy and log-loss did not significantly vary when a model trained upon data from any one centre was applied to data taken at the other centres. Computational methods to correct for the data during pre-processing were not needed., Conclusion: We have found that when using the same make and model of spectrometer, together with a common protocol, across different centres it is possible to achieve system transferability without the need for additional computational instrument correction., (© 2024. Crown.)

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.

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., (© 2022 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons Ltd.)

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., (© 2022 The Authors. Muscle & Nerve published by Wiley Periodicals LLC.)

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.

Neuromuscular diseases result in muscle weakness, disability, and, in many instances, death. Preclinical models form the bedrock of research into these disorders, and the development of in vivo and potentially translational biomarkers for the accurate identification of disease is crucial. Spontaneous Raman spectroscopy can provide a rapid, label-free, and highly specific molecular fingerprint of tissue, making it an attractive potential biomarker. In this study, we have developed and tested an in vivo intramuscular fiber optic Raman technique in two mouse models of devastating human neuromuscular diseases, amyotrophic lateral sclerosis, and Duchenne muscular dystrophy (SOD1 G93A and mdx , respectively). The method identified diseased and healthy muscle with high classification accuracies (area under the receiver operating characteristic curves (AUROC): 0.76-0.92). In addition, changes in diseased muscle over time were also identified (AUROCs 0.89-0.97). Key spectral changes related to proteins and the loss of α-helix protein structure. Importantly, in vivo recording did not cause functional motor impairment and only a limited, resolving tissue injury was seen on high-resolution magnetic resonance imaging. Lastly, we demonstrate that ex vivo muscle from human patients with these conditions produced similar spectra to those observed in mice. We conclude that spontaneous Raman spectroscopy of muscle shows promise as a translational research tool.

The aims of this study were to use qualitative volatile fingerprints obtained using a hybrid sensor array system to screen anti-fungals for controlling the important lung infecting fungus, Aspergillus fumigatus, especially in immunocompromised patients. SIFT-MS was also used to try and identify key volatiles produced by A. fumigatus. Initial studies were carried out to identify the ED(50) and ED(90) (effective dose) for inhibiting growth of A. fumigatus using three anti-fungal compounds, benomyl, tebuconazole and fluconazole. Subsequent studies involved inoculation of malt extract agar plates with spores of A. fumigatus (25 and 37°C) over periods of 24-72 h to examine the headspace volatile fingerprints generated from the sample treatments using the hybrid sensor array system to compare controls and ED(50)/ED(90) concentrations. The sensor responses showed discrimination between treatments after 48-h incubation when benomyl and tebuconazole were used against A. fumigatus at 37°C using Principal Components Analysis and Cluster Analysis. SIFT-MS analysis showed that methyl pentadiene, ethanol, isoprene and methanol were key biomarker volatiles produced by A. fumigatus in the presence of anti-fungal compounds. This may also be a good approach for the development of rapid screening of anti-microbial compounds and potentially useful for monitoring the possible build up of resistance to specific drug types. Volatile fingerprints produced by patient samples could also be used to evaluate whether lung infections are caused by bacteria or specific fungi to facilitate early diagnosis and enable the right drug treatment to be prescribed.

Background: Electronic nose (E-nose) technology has been successfully used to diagnose a number of microbial infections. We have investigated the potential use of an E-nose for the diagnosis of ventilator-associated pneumonia (VAP) by detecting micro-organisms in bronchoalveolar lavage (BAL) fluid in a prospective comparative study of E-nose analysis and microbiology., Materials and Methods: BAL samples were collected using a blind technique from 44 patients following a minimum of 72 h mechanical ventilation. Control samples were collected from six patients mechanically ventilated on the intensive care unit (ICU) immediately following elective surgery. Quantitative microbiological culture and E-nose headspace analysis of the BAL samples were undertaken. Multivariate analysis was applied to correlate E-nose response with microbiological growth., Results: E-nose fingerprints correctly classified 77% of the BAL samples, with and without microbiological growth from patients not on antibiotics. Inclusion of patients on antibiotics resulted in 68% correct classification. Seventy per cent of isolates, cultured in the laboratory from the clinical samples, were accurately discriminated into four clinically significant groups., Conclusions: E-nose technology can accurately discriminate between different microbial species in BAL samples from ventilated patients on ICU at risk of developing VAP with accuracy comparable with accepted microbiological techniques., (© 2010 The Authors. European Journal of Clinical Investigation © 2010 Stichting European Society for Clinical Investigation Journal Foundation.)