Modeling single nucleus microglia across species identifies immune pathways and therapeutic candidates in Alzheimer’s disease
5 affiliations
- 1Center for Global Health & Diseases, Department of Pathology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- 2Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
- 3Department of Pathology, Stanford University, Stanford, CA, USA
- 4Center for Global Health & Diseases, Department of Pathology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA. [email protected]
- 5The Blood, Heart, Lung, and Immunology Research Center, Case Western Reserve University, University Hospitals of Cleveland, Cleveland, OH, USA. [email protected]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory loss and behavior change. Studies have found that dysregulation of microglial cells is pivotal to AD pathology. These mechanisms have been studied in mouse models to uncover potential therapeutic biomarkers. Despite these findings, there are limitations to the translatable biological information from mice to humans due to differences in physiology, timeline of disease, and the heterogeneity of humans. To address the inter-species discrepancies, we developed a novel implementation of the Translatable Components Regression (TransComp-R) framework, which integrated microglia single-nuclei mouse and human transcriptomics data to identify biological pathways in mice predictive of human AD. We compared model variations with sparse and traditional principal component analysis. We found that both dimensionality reduction techniques encoded similar AD disease biology on mouse principal components with limited differences in technical performance. Several mouse sparse principal components explained high amounts of variance in humans and significantly differentiated human AD from control microglial cells. Additionally, we identified FDA-approved medications that induced gene expression profiles correlated with projections of healthy human microglia on mouse principal components. Such medications included cabergoline, selumetinib, and palbociclib. This computational framework may support uncovering cross-species disease insights and candidate pharmacological solutions from single-cell datasets.
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BibTeX
@article{bergendorf2026modeling,
title = {{Modeling single nucleus microglia across species identifies immune pathways and therapeutic candidates in Alzheimer’s disease}},
author = {Alexander M. Bergendorf and Jee Hyun Park and Brendan K. Ball and Douglas K. Brubaker},
journal = {npj Systems Biology and Applications},
year = {2026},
month = jul,
doi = {10.1038/s41540-026-00775-3},
url = {https://doi.org/10.1038/s41540-026-00775-3},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Modeling single nucleus microglia across species identifies immune pathways and therapeutic candidates in Alzheimer’s disease
AU - Alexander M. Bergendorf
AU - Jee Hyun Park
AU - Brendan K. Ball
AU - Douglas K. Brubaker
PY - 2026
DA - 2026/07/03
JO - npj Systems Biology and Applications
DO - 10.1038/s41540-026-00775-3
UR - https://doi.org/10.1038/s41540-026-00775-3
AB - Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory loss and behavior change. Studies have found that dysregulation of microglial cells is pivotal to AD pathology. These mechanisms have been studied in mouse models to uncover potential therapeutic biomarkers. Despite these findings, there are limitations to the translatable biological information from mice to humans due to differences in physiology, timeline of disease, and the heterogeneity of humans. To address the inter-species discrepancies, we developed a novel implementation of the Translatable Components Regression (TransComp-R) framework, which integrated microglia single-nuclei mouse and human transcriptomics data to identify biological pathways in mice predictive of human AD. We compared model variations with sparse and traditional principal component analysis. We found that both dimensionality reduction techniques encoded similar AD disease biology on mouse principal components with limited differences in technical performance. Several mouse sparse principal components explained high amounts of variance in humans and significantly differentiated human AD from control microglial cells. Additionally, we identified FDA-approved medications that induced gene expression profiles correlated with projections of healthy human microglia on mouse principal components. Such medications included cabergoline, selumetinib, and palbociclib. This computational framework may support uncovering cross-species disease insights and candidate pharmacological solutions from single-cell datasets.
SN - 2056-7189
ER -