Murine March Madness: Characterizing Late-Onset AD Models Using Spatial Whole Transcriptome Analysis
Join us for Murine March Madness – a series of webinars focused on the integration of nCounter and DSP approaches to mouse model research. Learn how the field is advancing with the insights gained by bulk analysis combined with spatial resolution.
Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder affecting nearly 50 million patients worldwide, with no effective treatment currently available. Although the vast majority of cases are late-onset AD (LOAD), current animal models do not fully recapitulate LOAD and thus are not ideal for the development of therapeutics. The Model Organism Development and Evaluation for Late-onset AD consortium (MODEL-AD) is charged with creating, defining, and distributing novel mouse models of LOAD carrying human-relevant risk factors for broad use. MODEL-AD has created a mouse strain carrying a humanized Aβ1-42 in the mouse App gene as well as two of the strongest genetic risk factors for LOAD: a humanized ApoE knock-in mutation and an R47H point mutation of the Trem2 gene (B6.APOEe4/Trem2*R47H/hAβ).
Utilizing a precommercial version of nanoString’s GeoMx® Mouse Whole Transcriptome Atlas (WTA) content, we analyzed the entire mouse transcriptome throughout the cortex and hippocampus of the B6.APOEe4.Trem2*R47H/hAβ mouse strain at multiple time points (12-16 months of age).
Formalin fixed, paraffin embedded mouse brains were prepared and cut to a thickness of 8 microns. Slides were stained to visualize amyloid beta, microglia and nuclei.
Gene expression differences were observed between the B6.APOEe4/Trem2*R47H/ hAβ mice and C57BL6/J controls, specifically near AD-like pathology, as well as between different brain regions.
The WTA platform provides the opportunity to examine the relationship between pathology and gene expression within a tissue section. We have assessed a novel mouse models of LOAD at young and advanced ages using a novel NanoString WTA platform. We identified that mouse models expressing combinations of LOAD risk variants exhibited AD-relevant signatures. These findings provide a means for measuring all gene activity in tissue sections, across brain regions and ages, and to map where disease-related changes in expression are occur. This technology will enable MODEL-AD to understand the underlying biological mechanisms of AD.
For Research Use Only. Not for Use in Diagnostic Procedures.
Gregory Carter, PhD
The Jackson Laboratory
Greg Carter combines genetic, genomic, imaging, and other data resources to understand the causes and progression of Alzheimer’s disease. He is Head of Bioinformatics for the IU/JAX/Pitt Model Organism Development and Evaluation for Late-onset Alzheimer’s Disease (MODEL-AD) and the Emory/Sage/SGC Target Enablement to Accelerate Therapy Development for Alzheimer’s Disease (TREAT-AD) Centers. His lab is focused on developing computational and experimental strategies to optimize the use of model systems in understanding Alzheimer’s disease and related-dementias. Their work involves the creation and study of new animal models to dissect the genetics, genomics, and neuropathology of late-onset Alzheimer’s, and the integration of data across multiple platforms to prioritize targets and hypotheses for novel therapeutics.