Document Type
Article
Publication Date
5-20-2024
Original Citation
Gurdon B,
Yates S,
Csucs G,
Groeneboom N,
Hadad N,
Telpoukhovskaia M,
Ouellette A,
Ouellette T,
O'Connell K,
Singh S,
Murdy T,
Merchant E,
Bjerke I,
Kleven H,
Schlegel U,
Leergaard T,
Puchades M,
Bjaalie J,
Kaczorowski C.
Detecting the effect of genetic diversity on brain composition in an Alzheimer's disease mouse model. Commun Biol. 2024;7(1):605.
Keywords
JMG, Animals, Alzheimer Disease, Disease Models, Animal, Mice, Brain, Genetic Variation, Mice, Transgenic, Amyloid beta-Peptides, Male
JAX Source
Commun Biol. 2024;7(1):605.
ISSN
2399-3642
PMID
38769398
DOI
https://doi.org/10.1038/s42003-024-06242-1
Grant
This study is part of the National Institute on Aging Resilience-AD program and is supported through the NIA parent grant Systems Genetics Analysis of Resilience to Alzheimer’s disease: R01AG057914 and supplements R01AG057914-02S1 and R01AG057914-03S1 awarded to Dr. Catherine Kaczorowski, The Jackson Laboratory.
Abstract
Alzheimer's disease (AD) is broadly characterized by neurodegeneration, pathology accumulation, and cognitive decline. There is considerable variation in the progression of clinical symptoms and pathology in humans, highlighting the importance of genetic diversity in the study of AD. To address this, we analyze cell composition and amyloid-beta deposition of 6- and 14-month-old AD-BXD mouse brains. We utilize the analytical QUINT workflow- a suite of software designed to support atlas-based quantification, which we expand to deliver a highly effective method for registering and quantifying cell and pathology changes in diverse disease models. In applying the expanded QUINT workflow, we quantify near-global age-related increases in microglia, astrocytes, and amyloid-beta, and we identify strain-specific regional variation in neuron load. To understand how individual differences in cell composition affect the interpretation of bulk gene expression in AD, we combine hippocampal immunohistochemistry analyses with bulk RNA-sequencing data. This approach allows us to categorize genes whose expression changes in response to AD in a cell and/or pathology load-dependent manner. Ultimately, our study demonstrates the use of the QUINT workflow to standardize the quantification of immunohistochemistry data in diverse mice, - providing valuable insights into regional variation in cellular load and amyloid deposition in the AD-BXD model.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.