Wang Ziyang, Ye Jiarong, Zhang Kunyan, Ding Li, Granzier-Nakajima Tomotaroh, Ranasinghe Jeewan C, Xue Yuan, Sharma Shubhang, Biase Isabelle, Terrones Mauricio, Choi Se Hoon, Ran Chongzhao, Tanzi Rudolph E, Huang Sharon X, Zhang Can, Huang Shengxi
Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States.
College of Information Sciences and Technology, The Pennsylvania State University, University Park, Pennsylvania 16802, United States.
ACS Nano. 2022 Apr 26;16(4):6426-6436. doi: 10.1021/acsnano.2c00538. Epub 2022 Mar 25.
The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. Rapid and accurate identification of AD biomarkers in the brain is critical to providing key insights into AD and facilitating the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification. Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aβ and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman-machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.
阿尔茨海默病（AD）是痴呆最常见的病因，其研究在病因理解、发病机制监测以及早期诊断和有效治疗方法开发方面面临挑战。在大脑中快速准确地识别AD生物标志物对于深入了解AD并推动早期诊断方法的发展至关重要。在这项工作中，我们开发了一个平台，通过在AD转基因动物大脑中采用石墨烯辅助拉曼光谱和机器学习解释来快速筛选AD生物标志物。具体而言，我们收集了有AD和无AD的小鼠脑切片的拉曼光谱，并使用机器学习对AD和非AD光谱进行分类。通过使单层石墨烯与脑切片接触，机器学习分类的准确率从77%提高到了98%。此外，使用线性支持向量机（SVM），我们确定了一个光谱特征重要性图，该图揭示了每个拉曼波数在区分AD和非AD光谱中的重要性。基于此光谱特征重要性图，我们确定了包括Aβ和tau蛋白在内的AD生物标志物以及其他潜在生物标志物，如三油酸甘油酯、磷脂酰胆碱和肌动蛋白，这些已被其他生化研究证实。我们具有可解释性的拉曼 - 机器学习集成方法将促进AD的研究，并可扩展到其他组织和生物流体以及用于各种其他疾病。
