Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy

Abstract

As the most common cause of dementia, the study of Alzheimers disease (AD) faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnosis and effective treatment. Rapid and accurate identification of AD biomarkers in the brain is critical to provide key insights into AD and facilitate 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 significantly increased from 77% to 98% in machine learning classification. Further, using linear supporting 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 A{beta} 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 is promising to greatly accelerate the study of AD and can be extended to other tissues, biofluids, and for various other diseases.