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Adaptable toolbox to characterize Alzheimer's disease pathology in mouse models.

ABSTRACT: Here, we describe a highly adaptable toolbox for characterizing and analyzing molecular and histopathological changes in Alzheimer's disease (AD) mouse models. We detail optimized and streamlined approaches from sample preparation to image analysis to facilitate reproducible analyses. We also describe the extraction and measurement of the soluble Aβ level by sandwich ELISA in the cortex and hippocampus of AD mouse models before and after plaque deposition. Finally, we outline the steps for image quantification and analysis using Imaris and ImageJ. For complete details on the use and execution of this protocol, please refer to Huang et al. (2021).¹.

SUBMITTER: Huang Y

PROVIDER: S-EPMC9722779 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Adaptable toolbox to characterize Alzheimer's disease pathology in mouse models.

Huang Youtong Y Lemke Greg G

STAR protocols 20221202 4

Here, we describe a highly adaptable toolbox for characterizing and analyzing molecular and histopathological changes in Alzheimer's disease (AD) mouse models. We detail optimized and streamlined approaches from sample preparation to image analysis to facilitate reproducible analyses. We also describe the extraction and measurement of the soluble Aβ level by sandwich ELISA in the cortex and hippocampus of AD mouse models before and after plaque deposition. Finally, we outline the steps for image ...[more]

PMID: 36472212

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Project description:Current methods for measuring the chronic rates of cognitive decline and degeneration in Alzheimer's disease rely on the sensitivity of longitudinal neuropsychological batteries and clinical neuroimaging, particularly structural magnetic resonance imaging of brain atrophy, either at a global or regional scale. There is particular interest in approaches predictive of future disease progression and clinical outcomes using a single time point. If successful, such approaches could have great impact on differential diagnosis, therapeutic treatment and clinical trial inclusion. Unfortunately, it has proven quite challenging to accurately predict clinical and degeneration progression rates from baseline data. Specifically, a key limitation of the previously proposed approaches for disease progression based on the brain atrophy measures has been the limited incorporation of the knowledge from disease pathology progression models, which suggest a prion-like spread of disease pathology and hence the neurodegeneration. Here, we present a new metric for disease progression rate in Alzheimer that uses only MRI-derived atrophy data yet is able to infer the underlying rate of pathology transmission. This is enabled by imposing a spread process driven by the brain networks using a Network Diffusion Model. We first fit this model to each patient's longitudinal brain atrophy data defined on a brain network structure to estimate a patient-specific rate of pathology diffusion, called the pathology progression rate. Using machine learning algorithms, we then build a baseline data model and tested this rate metric on data from longitudinal Alzheimer's Disease Neuroimaging Initiative study including 810 subjects. Our measure of disease progression differed significantly across diagnostic groups as well as between groups with different genetic risk factors. Remarkably, hierarchical clustering revealed 3 distinct clusters based on CSF profiles with >90% accuracy. These pathological clusters exhibit progressive atrophy and clinical impairments that correspond to the proposed rate measure. We demonstrate that a subject's degeneration speed can be best predicted from baseline neuroimaging volumetrics and fluid biomarkers for subjects in the middle of their degenerative course, which may be a practical, inexpensive screening tool for future prognostic applications.

Dataset Information

Adaptable toolbox to characterize Alzheimer's disease pathology in mouse models.

Publications

Adaptable toolbox to characterize Alzheimer's disease pathology in mouse models.

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