Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
Neuroscience Institute, University of Sheffield, Western Bank, Sheffield, UK.
Analyst. 2024 Apr 29;149(9):2738-2746. doi: 10.1039/d4an00320a.
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 Raman spectroscopy measurements from preclinical models of amyotrophic lateral sclerosis and the myopathy Duchenne muscular dystrophy, together with 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.
神经肌肉疾病是一组可导致骨骼肌无力的病症。其中包括肌萎缩侧索硬化症等致命疾病,以及肌肉疾病等发病率较高的病症。这些病症中有许多已知存在异常蛋白质折叠和蛋白质聚集体。因此,易于应用的检测这些变化的方法可能成为有用的诊断生物标志物。拉曼光谱在神经肌肉疾病中检测肌肉病理学方面显示出了早期的应用前景,非常适合描述与蛋白质二级结构相关的构象特征。在这项工作中,我们评估了拉曼光谱是否可以在神经肌肉疾病的背景下检测肌肉中蛋白质结构的差异。我们利用肌萎缩侧索硬化症的临床前模型和肌肉疾病杜氏肌营养不良症的拉曼光谱测量结果,以及患有和不患有肌肉疾病的个体的肌肉样本的测量结果。我们使用定量构象分析和矩阵分解,证明了定量的“构象指纹”可用于识别肌肉中蛋白质折叠的变化。值得注意的是,临床前模型和人类样本中的肌肉疾病都表现出α-螺旋结构的显著减少,同时β-折叠结构增加,非规则构象的程度较小。通过非负矩阵分解得到的光谱模式能够以较高的准确度(在小鼠中为 79%,在人体组织中为 78%)识别肌肉疾病。这项工作证明了构象指纹作为神经肌肉疾病的可解释生物标志物的潜力。