Zhang, C. M. | Search bioRxiv

bioRxiv2021

Reporting amyloid beta levels via bioluminescence imaging with amyloid reservoirs inAlzheimer's disease models

Bioluminescence imaging has changed daily practice in preclinical research of cancers and other diseases in the last decades; however, it has been rarely applied in preclinical research of Alzheimers disease (AD). In this report, we demonstrated that bioluminescence imaging could be used to report the levels of amyloid beta (A{beta}) species in vivo. We hypothesized that AkaLumine, a newly discovered substrate for luciferase, could bind to A{beta} aggregates and plaques. We further speculated that the A{beta} species have the reservoir capacity to sequester and release AkaLumine to control the bioluminescence intensity, which could be used to report the levels of A{beta}s. Our hypotheses have been validated via in vitro solution tests, mimic studies with brain tissues and mice, two-photon imaging with AD mice, and in vivo bioluminescence imaging using transgenic AD mice that were virally transduced with aka Luciferase (AkaLuc), a new luciferase that generates bioluminescence in the near infrared window. As expected, compared to the control group, we observed that the A{beta} group showed lower bioluminescence intensity due to AkaLumine sequestering at early time points, while higher intensity due to AkaLumine releasing at later time points. Lastly, we demonstrated that this method could be used to monitor AD progression and therapeutic effectiveness of avagacestat, a well-studied gamma-secretase inhibitor. Importantly, a good correlation (R2 = 0.81) was established between in vivo bioluminescence signals and A{beta} burdens of the tested AD mice. We believe that our approach can be easily implemented into daily imaging experiments and has tremendous potential to change daily practice of preclinical AD research.

neuroscience↗

bioRxiv2021

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

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.

neuroscience↗