Details

Autor(en) / Beteiligte

• Alix, James J. P.
• Plesia, Maria
• Schooling, Chlöe N.
• Dudgeon, Alexander P.
• Kendall, Catherine A.
• Kadirkamanathan, Visakan
• McDermott, Christopher J.
• Gorman, Gráinne S.
• Taylor, Robert W.
• Mead, Richard J.
• Shaw, Pamela J.
• Day, John C.

Titel

Non‐negative matrix factorisation of Raman spectra finds common patterns relating to neuromuscular disease across differing equipment configurations, preclinical models and human tissue

Ist Teil von

• Journal of Raman spectroscopy, 2023-03, Vol.54 (3), p.258-268

Ort / Verlag

England: Wiley Subscription Services, Inc

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Wiley Online Library - AutoHoldings Journals

Beschreibungen/Notizen

• 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. Spectra from different settings were pooled and subjected to non‐negative matrix factorisation. The spectral patterns derived were used to identify disease in each paradigm. Classification performance was superior to traditional principal component‐based analysis.