Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model
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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.
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Our findings align with the work of Gurdon and colleagues who detected decreased NeuN label in the hippocampus.
“Our findings align with the work of Gurdon and colleagues8 who detected decreased NeuN label in the hippocampus.”
From Magnetic resonance microscopy maps widespread effects of Alzheimer’s disease on brain structures and behavior in mice (2026) · cites this paper as [8]
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Alzheimer's brains show inter-related changes in RNA and lipid metabolism
Barbash, S., Garfinkel, B., Maoz, R., Simchovitz, A., Nadorp, B., Guffanti, A., Bennett, E. R., Nadeau, C., Türk, A., Paul, L., et al. (2017) · Neurobiology of Disease · doi:10.1016/j.nbd.2017.06.008 · Cited by 57 · see citing works
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Merged magnetic resonance and light sheet microscopy of the whole mouse brain
Johnson, G. A., Tian, Y., Ashbrook, D. G., Cofer, G. P., Cook, J. J. H., Gee, J. C., Hall, A., Hornburg, K. J., Kaczorowski, C. C., Qi, Y. P., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2218617120 · Cited by 56 · see citing works
Self-citationShin, J., Park, S., Lee, H., & Kim, Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-81304-6 · Cited by 56 · see citing works
Cho, H., Sharma, A. K., Zhang, Y., Gross, M. L., & Mirica, L. M. (2020) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.0c00114 · Cited by 53 · see citing works
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Excess APP O -glycosylation by GalNAc-T6 decreases Aβ production
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Variability of age at onset in siblings with familial Alzheimer disease
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The Role of APP O-Glycosylation in Alzheimer’s Disease
Akasaka‐Manya, K., & Manya, H. (2020) · Biomolecules · doi:10.3390/biom10111569 · Cited by 44 · see citing works
Tsui, K. C., Roy, J., Chau, S. C., Wong, K.-H., Shi, L., Poon, C. H., Wang, Y., Strekalova, T. V., Aquili, L., Chang, R. C., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.964336 · Cited by 44 · see citing works
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Systems genetics identifies Hp1bp3 as a novel modulator of cognitive aging
Neuner, S. M., Garfinkel, B. P., Wilmott, L. A., Ignatowska-Jankowska, B. M., Citri, A., Orly, J., Lu, L., Overall, R. W., Mulligan, M. K., Kempermann, G., et al. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.06.008 · Cited by 43 · see citing works
Self-citationData integration through brain atlasing: Human Brain Project tools and strategies
Bjerke, I. E., Øvsthus, M., Papp, E. A., Yates, S. C., Silvestri, L., Fiorilli, J., Pennartz, C. M. A., Pavone, F. S., Puchades, M. A., Leergaard, T. B., et al. (2018) · European Psychiatry · doi:10.1016/j.eurpsy.2018.02.004 · Cited by 43 · see citing works
Self-citationYu, Q., & He, Z. (2017) · Scientific Reports · doi:10.1038/s41598-017-04356-7 · Cited by 38 · see citing works
Digital atlases as a framework for data sharing
Boline, J. (2008) · Frontiers in Neuroscience · doi:10.3389/neuro.01.012.2008 · Cited by 35 · see citing works
Neuner, S. M., Heuer, S. E., Zhang, J., Philip, V. M., & Kaczorowski, C. C. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00035 · Cited by 31 · see citing works
Self-citationDonepezil ameliorates Aβ pathology but not tau pathology in 5xFAD mice
Choi, H.-J., Park, J.-H., Jeong, Y. J., Hwang, J.-W., Lee, S., Lee, H., Seol, E., Kim, I.-W., Cha, B., Seo, J., et al. (2022) · Molecular Brain · doi:10.1186/s13041-022-00948-1 · Cited by 28 · see citing works
CDSeqR: fast complete deconvolution for gene expression data from bulk tissues
Kang, K., Huang, C. D., Li, Y., Umbach, D. M., & Li, L. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04186-5 · Cited by 27 · see citing works
Heuer, S. E., Neuner, S. M., Hadad, N., O’Connell, K. M. S., Williams, R. W., Philip, V. M., Gaiteri, C., & Kaczorowski, C. C. (2020) · Learning & Memory · doi:10.1101/lm.051839.120 · Cited by 26 · see citing works
Self-citationJohnson, T. S., Xiang, S., Dong, T., Huang, Z., Cheng, M., Wang, T., Yang, K., Ni, D., Huang, K., & Zhang, J. (2021) · Scientific Reports · doi:10.1038/s41598-020-79740-x · Cited by 26 · see citing works
Chen, Y.-H., Lin, R.-R., Huang, H.-F., Xue, Y., & Tao, Q. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.848180 · Cited by 25 · see citing works
Lauridsen, K., Ly, A., Prévost, E. D., McNulty, C. J., McGovern, D. J., Tay, J. W., Dragavon, J. M., & Root, D. H. (2022) · eNeuro · doi:10.1523/eneuro.0483-21.2022 · Cited by 24 · see citing works
O’Connell, K. M. S., Ouellette, A. R., Neuner, S. M., Dunn, A. R., & Kaczorowski, C. C. (2019) · Genes Brain & Behavior · doi:10.1111/gbb.12603 · Cited by 23 · see citing works
Self-citationNi, H., Feng, Z., Guan, Y., Jia, X., Wu, C., Jiang, T., Zhong, Q., Yuan, J., Ren, M., Li, X., et al. (2020) · Neuroinformatics · doi:10.1007/s12021-020-09483-7 · Cited by 19 · see citing works
Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models
Gurdon, B., & Kaczorowski, C. C. (2021) · Neurobiology of Disease · doi:10.1016/j.nbd.2021.105558 · Cited by 16 · see citing works
Self-citationTakata, N., Sato, N., Komaki, Y., Okano, H., & Tanaka, K. F. (2021) · Scientific Reports · doi:10.1038/s41598-021-85807-0 · Cited by 14 · see citing works
Telpoukhovskaia, M. A., Hadad, N., Gurdon, B., Dai, Y., Ouellette, A. R., Neuner, S. M., Dunn, A. R., Willcox, J. A. L., Wu, Y., Dumitrescu, L. C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.12.487877 · Cited by 14 · see citing works
Self-citationYoo, C.-H., Kim, J., Baek, H.-M., Chang, K.-A., & Choe, B.-Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065073 · Cited by 13 · see citing works
Purger, D. A., McNutt, T., Achanta, P., Quiñones‐Hinojosa, A., Wong, J. W., & Ford, E. (2009) · Physics in Medicine and Biology · doi:10.1088/0031-9155/54/24/005 · Cited by 12 · see citing works
Park, Y., He, L., Dávila-Velderrain, J., Hou, L., Mohammadi, S., Mathys, H., Peng, Z., Bennett, D. A., Tsai, L., & Kellis, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.21.426000 · Cited by 11 · see citing works
Genetic background influences the 5XFAD Alzheimer's disease mouse model brain proteome
Hurst, C. D., Dunn, A. R., Dammer, E. B., Duong, D. M., Shapley, S. M., Seyfried, N. T., Kaczorowski, C. C., & Johnson, E. C. B. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1239116 · Cited by 10 · see citing works
Self-citationTurning strains into strengths for understanding psychiatric disorders
Moore, S. J., Murphy, G. G., & Cazares, V. A. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0772-y · Cited by 9 · see citing works
Torshizi, A. D., Duan, J., & Wang, K. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab056 · Cited by 7 · see citing works
Johnson, G. A., Tian, Y., Cofer, G. P., Cook, J. C., Gee, J. C., Hall, A., Hornburg, K. J., Qi, Y. P., Yeh, F.-C. F., Wang, N., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.10.479607 · Cited by 7 · see citing works
Decosus: An R Framework for Universal Integration of Cell Proportion Estimation Methods
Anene, C. A., Taggart, E., Harwood, C. A., Pennington, D. J., & Wang, J. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.802838 · Cited by 5 · see citing works
Dai, Y., Dunn, A. R., Hadad, N., Zhang, J.-G., Poirion, O., Korgan, A., White, B., Philip, V., Neuner, S., O’Connell, K. M. S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.08.487664 · Cited by 2 · see citing works
Self-citationGurdon, B., Hadad, N., Telpoukhovskaia, M. A., Yates, S. C., Puchades, M. A., Bjaalie, J. G., & Kaczorowski, C. C. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.047613 · Cited by 2 · see citing works
Self-citation(2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2308718120 · Cited by 2 · see citing works
Gurdon, B., Hadad, N., Telpoukhovskaia, M. A., Yates, S. C., Puchades, M. A., Bjaalie, J. G., & Kaczorowski, C. C. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.061853 · Cited by 1 · see citing works
Self-citationGurdon, B., Yates, S. C., Csucs, G., Groeneboom, N. E., Hadad, N., Telpoukhovskaia, M. A., Ouellette, A. R., Ouellette, T. B., O'Connell, K., Singh, S., et al. (2023) · EBRAINS · doi:10.25493/sz0m-ee6 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
1 of the 123 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [90]Reprinted from Harnessing Genetic Complexity to Enhance Translatability of Alzheimer’s Disease Mouse Models: A Path toward Precision Medicine, Vol 101, Sarah M. Neuner, Sarah E. Heuer, Matthew J. Huentelman, Kristen M.S. O’Connell, Catherine C. Kaczorowski, Issue 3, 399-411, Copyright 2019, with permission from Elsevier. Reprinted from The Lancet, Vol. 101, Sarah M. Neuner, Sarah E. Heuer, Matthew J. Huentelman, Kristen M.S. O’Connell, Catherine C. Kaczorowski, Harnessing Genetic Complexity to Enhance Translatability of Alzheimer’s Disease Mouse Models: A Path toward Precision Medicine, 399-411, Copyright 2019, with permission from Elsevier. PMC6886697 30595332
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Kleven, H., Gillespie, T., Zehl, L., Dickscheid, T., Bjaalie, J. G., Martone, M. E., & Leergaard, T. B. (2023) · Scientific Data · doi:10.1038/s41597-023-02389-4 · Cited by 15 · see citing works
Self-citationPuchades, M. A., Yates, S. C., Csucs, G., Carey, H., Balkir, A., Leergaard, T. B., & Bjaalie, J. G. (2025) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2025.1629388 · Cited by 9 · see citing works
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Self-citationBjerke, I. E., Carey, H., Bjaalie, J. G., Leergaard, T. B., & Kim, J. H. (2024) · Neurochemistry International · doi:10.1016/j.neuint.2024.105899 · Cited by 7 · see citing works
Self-citationDeMBA: a developmental atlas for navigating the mouse brain in space and time
Carey, H., Kleven, H., Øvsthus, M., Yates, S. C., Csucs, G., Puchades, M. A., McMullan, S., Bjaalie, J. G., Leergaard, T. B., & Bjerke, I. E. (2025) · Nature Communications · doi:10.1038/s41467-025-63177-9 · Cited by 7 · see citing works
Self-citationBrain-wide connectivity and novelty response of the dorsal endopiriform nucleus in mice
Manjila, S. B., Son, S., Parmaksiz, D., Kline, H. L., Betty, R., Wu, Y.-T., Pi, H.-J., Shin, D., Liwang, J. K., Kronman, F. N., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115827 · Cited by 6 · see citing works
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Self-citationImam, A., Tunde, A. M., Amin, A., Abdulmajeed, W. I., Attai, A. G., Akorede, A. A., Ijomone, O. M., & Ajao, M. S. (2025) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-025-01027-6 · Cited by 2 · see citing works
Widmayer, S. J., Wooldridge, L. K., Swanzey, E., Barter, M. E., Snow, C., Saul, M. C., Meng, Q., Dumont, B. L., Reinholdt, L. G., & Gatti, D. M. (2025) · Mammalian Genome · doi:10.1007/s00335-025-10148-6 · Cited by 2 · see citing works
Tian, Y., Hornburg, K., Austin, W., Cook, J. J., Qi, Y., White, L. E., Killmar, J. T., Kaczorowski, C. C., Ashbrook, D. G., Johnson, G. A., et al. (2026) · Nature Neuroscience · doi:10.1038/s41593-025-02199-4 · Cited by 1 · see citing works
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STAT3 mediated the isoleucine-induced Alzheimer’s disease progression
Liu, Y., Li, Y., Zhou, Y., Hei, Y., Wang, S., Bajpai, A. K., Chang, X., Wang, X., Xu, F., & Yao, C. (2026) · Brain Research · doi:10.1016/j.brainres.2026.150472
The emerging role of astrocytes in spatial cognition, action and Alzheimer’s disease
Gaborit, M., Arabi, A., & Mariotti, L. (2026) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2026.1811370
- Authors
- Gurdon, B., Yates, S. C., Csucs, G., Groeneboom, N. E., Hadad, N., Telpoukhovskaia, M., Ouellette, A., Ouellette, T., O’Connell, K. M. S., Singh, S., Murdy, T. J., Merchant, E., Bjerke, I., Kleven, H., Schlegel, U., Leergaard, T. B., Puchades, M. A., Bjaalie, J. G., & Kaczorowski, C. C.
- Affiliations
- Jackson Laboratory (US)
- University of Oslo (NO)
- Translational Genomics Research Institute (US)
- Tufts University (US)
- University of Michigan (US)
- Corresponding author
- Bjaalie, Jan G.
- Kaczorowski, Catherine C.
- ORCID
- Gurdon, Brianna
- Yates, Sharon C.
- Csucs, Gergely
- Groeneboom, Nicolaas E.
- Hadad, Niran
- Telpoukhovskaia, Maria
- Ouellette, Andrew
- Ouellette, Tionna
- O’Connell, Kristen M. S.
- Singh, Surjeet
- Murdy, Thomas J.
- Bjerke, Ingvild
- Kleven, Heidi
- Schlegel, Ulrike
- Leergaard, Trygve B.
- Puchades, Maja A.
- Bjaalie, Jan G.
- Kaczorowski, Catherine C.
- Venue
- Communications Biology
- Published
- 2024-05-20
- Volume
- 7
- Issue
- 1
- Pages
- 605-605
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC11106287
- OpenAlex
- W4398141685
- Cited by
- 15
- References
- 123
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 3 · 2023: 2
- FWCI
- 3.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion, cc-by)
- Funders
- European Commission (945539)
- Universitetet i Oslo
- National Institute on Aging (R01AG057914, T32AG062409)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, genetic diversity, amyloid-beta deposition, microglia, astrocytes, immunohistochemistry quantification, hippocampus, bulk RNA-seq, age-related neurodegeneration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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