Time frame: 2020-2021
Origin: HBP Voucher Programme
Workflow optimization for brain-wide spatial analysis to identify regional and cell-type
correlates of resilience to Alzheimer’s in the AD-BXD mouse population
Although high age is the greatest risk factor for developing Alzheimer’s disease (AD), it is increasingly evident that there is also a strong genetic component to AD. However, some individuals are remaining cognitively intact late in life, despite carrying high-risk mutations in APP or PSEN1 genes, suggesting the presence of genetic modifiers that influence the onset and progression of AD. These resilience factors represent novel targets for therapeutic intervention, but at present, few candidate genes have emerged.
We will use the novel analytic brain-wide spatial analysis tools provided by the Human Brain Project Neuroinformatics Platform to address translationally important, unaddressed questions – “What is the nature of resilience? How does this differ in cases of resilience to AD pathology vs. resilience to cognitive symptoms vs. resilience to non-cognitive symptoms (i.e. sensorimotor decline, sleep dysfunction, bodyweight)”? Our aim is to identify regional and cellular composition changes associated with (or even predicting) susceptibility versus resilience to: 1) aging, 2) Alzheimer’s disease amyloid pathology, 3) cognitive symptoms, and 4) non-cognitive symptoms of Alzheimer’s dementia.
The project will optimize the already existing QUINT workflow for high throughput analysis of a large mouse population that exhibits variation in brain size, organization, and degree of neurodegeneration caused by human familial Alzheimer’s mutations. The adaptations for the large-scale analysis of these data will include the curated non-linear registration of 2D images to the reference atlas, and optimized high-throughput segmentation of cells according to cell type with the machine learning tools and the atlas based quantitative analysis tool. The resultant version of the workflow we be tailored for our very large image series, which will allow us to achieve high quality results at each step that will be easily monitored and validated.