Laboratory of Nanoscale Biology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Laboratory of Molecular and Chemical Biology of Neurodegeneration, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Nat Commun. 2023 Nov 28;14(1):7816. doi: 10.1038/s41467-023-43440-7.
Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntington's disease, which is caused by a genetic mutation in exon 1 of the Huntingtin protein (Httex1). The fluorescent labels commonly used to visualize and monitor the dynamics of protein expression have been shown to alter the biophysical properties of proteins and the final ultrastructure, composition, and toxic properties of the formed aggregates. To overcome this limitation, we present a method for label-free identification of NDD-associated aggregates (LINA). Our approach utilizes deep learning to detect unlabeled and unaltered Httex1 aggregates in living cells from transmitted-light images, without the need for fluorescent labeling. Our models are robust across imaging conditions and on aggregates formed by different constructs of Httex1. LINA enables the dynamic identification of label-free aggregates and measurement of their dry mass and area changes during their growth process, offering high speed, specificity, and simplicity to analyze protein aggregation dynamics and obtain high-fidelity information.
蛋白质错误折叠和聚集在各种神经退行性疾病（NDD）的发病机制中起着核心作用，包括亨廷顿病，它是由亨廷顿蛋白（Httex1）外显子 1 的基因突变引起的。用于可视化和监测蛋白质表达动力学的荧光标记物已被证明会改变蛋白质的生物物理特性以及形成的聚集体的最终超微结构、组成和毒性特性。为了克服这一限制，我们提出了一种用于无标记鉴定与神经退行性疾病相关的聚集体（LINA）的方法。我们的方法利用深度学习从透射光图像中检测未经标记和未经改变的 Httex1 聚集体，而无需荧光标记。我们的模型在不同的成像条件和由 Httex1 的不同构建体形成的聚集体上都具有鲁棒性。LINA 能够动态识别无标记的聚集体，并测量它们在生长过程中的干质量和面积变化，为分析蛋白质聚集动力学提供了高速、特异性和简单性，并获得高保真信息。
