Non-negative matrix factorisation of Raman spectra finds common patterns relating to neuromuscular disease across differing equipment configurations, preclinical models and human tissue
Non-negative matrix factorisation of Raman spectra finds common patterns relating to neuromuscular disease across differing equipment configurations, preclinical models and human tissue
复制标题
拉曼光谱的非负矩阵分解发现了不同设备配置、临床前模型和人体组织中与神经肌肉疾病相关的共同模式
DOI：
10.1002/jrs.6480
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发表时间：
2022
影响因子：
2.5
通讯作者：
Alix J
中科院分区：
文献类型：
--
作者：
Alix J
拉曼光谱显示出作为复杂神经和肌肉（神经肌肉）疾病的生物标志物的前景。为了最大限度地发挥其潜力，仍然存在一些挑战。这些包括对不同仪器配置的敏感性、跨临床前/人体组织的翻译以及可以导出用于疾病识别的可解释光谱输出的多变量分析的开发。非负矩阵分解（NMF）可以从高维数据集中提取特征，并且非负约束导致物理上真实的输出。在这项研究中，我们对从不同临床和临床前环境中获得的肌肉拉曼光谱进行了 NMF。首先，我们使用商用显微镜和内部光纤探针获得并组合了患有线粒体疾病的人类患者和健康志愿者的拉曼光谱。 NMF 应用于所有数据，并确定了两种设备配置共有的光谱模式。利用这些模式的线性判别模型能够准确地对疾病状态进行分类（准确度 70.2–84.5%）。接下来，我们将 NMF 应用于从杜氏肌营养不良症的 themdxmouse 模型和患有营养不良性肌肉疾病的患者获得的光谱。获得了小鼠/人类共有的光谱指纹，并且能够准确识别疾病（准确度 79.5–98.8%）。我们得出的结论是，NMF 可用于分析不同设备配置和临床前/临床划分的拉曼数据。因此，NMF分解方法的应用可以增强拉曼光谱在致命性神经肌肉疾病研究中的潜力。
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 themdxmouse 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.