Application of Lightsheet Microscopy to Rapidly Detect and Analyze Cerebral Amyloid Angiopathy Burden in a Murine Model of Alzheimer’s Disease

Identification: Fitzgerald, Griffin


Description

Application of Lightsheet Microscopy to Rapidly Detect and Analyze Cerebral Amyloid Angiopathy Burden in a Murine Model of Alzheimer's Disease
 
Griffin J. Fitzgerald1,2, Jeff C. Hanson1,3, David B. Shaw1,2, David L. McKinzie1,2, Emily C. Collins1,4, Ronald B. DeMattos1,2,5, Yaming Wang1,5, Feng Pan1,2*
1Lilly Research Laboratories, Indianapolis IN, 46225; 2In Vivo Pharmacology, Neurodegeneration Discovery; 3Informatics Capabilities, Information Technology
4Imaging Research and Development, Pain Discovery; 5Molecular Pathology, Neurodegeneration Discovery
*Corresponding Author
 
Cerebral Amyloid Angiopathy (CAA) is a pathological hallmark in many patients with Alzheimer's Disease (AD) and vascular dementia (VD). In these patients, CAA is characterized by amyloid deposits that are adjacent to smooth muscle cells within vessel walls of small-to-medium sized arteries in the brain. Assessment of CAA burden in nonclinical models traditionally requires labor-intensive histological methods that often capture only a small percentage of total amyloid coverage. However, recent advances in imaging acquisition and analysis technology may enable a more comprehensive and objective evaluation of CAA: a possibility that is critical for the development of promising therapeutic treatments. We present proof-of-concept data for a novel methodology that relies on lightsheet microscopy and semi-automated analysis algorithms to map and assess CAA deposition in a murine model of AD. The present implementation of this methodology allows for the rapid, multiplexed visualization of CAA deposition and cerebrovascular architecture while providing semi-automated, unbiased quantification of CAA burden. By circumventing many of the inherent challenges of histological CAA detection, visualization, and quantification, this methodology offers the potential to improve evaluation of the efficacy and safety of promising CAA treatment options. Insights enabled by the application of this methodology may expand our current understanding of CAA, VD, and amyloid-related imaging abnormalities (ARIA) that are observed in AD patient sub-populations during clinical trials.

Credits

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