Ibrahim Khalid A, Cathala Camille, Bevilacqua Carlo, Feletti Lely, Prevedel Robert, Lashuel Hilal A, Radenovic Aleksandra
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. 2025 Jul 24;16(1):6699. doi: 10.1038/s41467-025-60912-0.
The process of protein aggregation, central to neurodegenerative diseases like Huntington's, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease progression and cellular toxicity. Brillouin microscopy offers unique advantages for studying biomechanical properties, yet is limited by slow imaging speed, complicating its use for rapid and dynamic processes like protein aggregation. To overcome these limitations, we developed a self-driving microscope that uses deep learning to predict the onset of aggregation from a single fluorescence image of soluble protein, achieving 91% accuracy. The system triggers optimized multimodal imaging when aggregation is imminent, enabling intelligent Brillouin microscopy of this dynamic biomechanical process. Furthermore, we demonstrate that by detecting mature aggregates in real time using brightfield images and a neural network, Brillouin microscopy can be used to study their biomechanical properties without the need for fluorescence labeling, minimizing phototoxicity and preserving sample health. This autonomous microscopy approach advances the study of aggregation kinetics and biomechanics in living cells, offering a powerful tool for investigating the role of protein misfolding and aggregation in neurodegeneration.
蛋白质聚集过程是亨廷顿氏病等神经退行性疾病的核心，由于其性质不可预测且动力学相对较快，因此研究起来颇具挑战性。了解其生物力学对于阐明其在疾病进展和细胞毒性中的作用至关重要。布里渊显微镜在研究生物力学特性方面具有独特优势，但受成像速度慢的限制，使其难以用于蛋白质聚集等快速动态过程。为克服这些限制，我们开发了一种自动驾驶显微镜，它利用深度学习从可溶性蛋白质的单个荧光图像预测聚集的开始，准确率达到91%。当聚集即将发生时，该系统会触发优化的多模态成像，从而实现对这一动态生物力学过程的智能布里渊显微镜观察。此外，我们证明，通过使用明场图像和神经网络实时检测成熟聚集体，布里渊显微镜可用于研究其生物力学特性，而无需荧光标记，从而将光毒性降至最低并保持样品健康。这种自主显微镜方法推动了活细胞中聚集动力学和生物力学的研究，为研究蛋白质错误折叠和聚集在神经退行性变中的作用提供了一个强大工具。
