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.