ACS Chem Neurosci. 2019 Nov 20;10(11):4593-4611. doi: 10.1021/acschemneuro.9b00451. Epub 2019 Oct 29.
Aggregation is a pathological hallmark of proteinopathies such as Alzheimer's disease and results in the deposition of β-sheet-rich amyloidogenic protein aggregates. Such proteinopathies can be classified by the identity of one or more aggregated proteins, with recent evidence also suggesting that distinct molecular conformers (strains) of the same protein can be observed in different diseases, as well is in subtypes of the same disease. Therefore, methods for the quantification of pathological changes in protein conformation are central to understanding and treating proteinopathies. In this work, the evolution of Raman spectroscopic molecular signatures of three conformationally distinct proteins, bovine serum albumin (α-helical-rich), β2-microglobulin (β-sheet-rich), and tau (natively disordered), was assessed during aggregation into oligomers and fibrils. The morphological evolution was tracked using atomic force microscopy and corresponding conformational changes were assessed by their Raman signatures acquired in both wet and dried conditions. A deconvolution model was developed which allowed us to quantify the conformation of the nonregular protein tau, as well as for the oligomeric and fibrillar species of each of the proteins. Principle component analysis of the fingerprint region allowed further identification of the distinguishing spectral features and unsupervised distinction. While an increase in β-sheet is seen on aggregation, crucially, however, each protein also retains a significant proportion of its native monomeric structure after aggregation. Thus, spectral analysis of each aggregated species, oligomeric, as well as fibrillar, for each protein resulted in a unique and quantitative "conformational fingerprint". This approach allowed us to provide the first differential detection of both oligomers and fibrils of the three different amyloidogenic proteins, including tau, whose aggregates have never before been interrogated using spontaneous Raman spectroscopy. Quantitative "conformational fingerprinting" by Raman spectroscopy thus demonstrates its huge potential and utility in understanding proteinopathic disease mechanisms and for providing strain-specific early diagnostic markers and targets for disease-modifying therapies.
聚集是蛋白质病变如阿尔茨海默病的病理学标志，导致富含β-折叠的淀粉样蛋白聚集物的沉积。这些蛋白质病变可以根据一种或多种聚集蛋白的身份进行分类，最近的证据还表明，同一种蛋白质的不同分子构象（株）可以在不同的疾病中观察到，也可以在同一种疾病的亚型中观察到。因此，定量研究蛋白质构象变化的方法对于理解和治疗蛋白质病变至关重要。在这项工作中，评估了三种构象不同的蛋白质（富含α-螺旋的牛血清白蛋白、富含β-折叠的β2-微球蛋白和天然无规的 tau）在聚集成低聚物和纤维时的拉曼光谱分子特征的演变。使用原子力显微镜跟踪形态演变，并用干湿条件下获得的拉曼特征评估相应的构象变化。开发了一种解卷积模型，该模型允许我们定量评估非规整蛋白 tau 的构象，以及每种蛋白质的低聚物和纤维形式。指纹区的主成分分析进一步确定了区分光谱特征和无监督区分的特征。虽然在聚集时观察到β-折叠的增加，但至关重要的是，每种蛋白质在聚集后仍保留其天然单体结构的很大比例。因此，对每种聚集物种（低聚物和纤维）的光谱分析，对于每种蛋白质，都产生了独特且定量的“构象指纹”。这种方法使我们能够首次对三种不同淀粉样蛋白原纤维，包括 tau 的低聚物和纤维进行差异检测，tau 的聚集物以前从未使用自发拉曼光谱进行过检测。拉曼光谱的定量“构象指纹分析”因此证明了其在理解蛋白质病变机制以及提供针对特定株的早期诊断标志物和疾病修饰治疗靶点方面的巨大潜力和实用性。
