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Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model
Alzheimer’s disease (AD) is characterized by neurodegeneration, pathology accumulation, and progressive cognitive decline. There is significant variation in age at onset and severity of symptoms highlighting the importance of genetic diversity in the study of AD. To address this, we analyzed cell an...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002670/ https://www.ncbi.nlm.nih.gov/pubmed/36909528 http://dx.doi.org/10.1101/2023.02.27.530226 |
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author | Gurdon, Brianna Yates, Sharon C. Csucs, Gergely Groeneboom, Nicolaas E. Hadad, Niran Telpoukhovskaia, Maria Ouellette, Andrew Ouellette, Tionna O’Connell, Kristen Singh, Surjeet Murdy, Tom Merchant, Erin Bjerke, Ingvild Kleven, Heidi Schlegel, Ulrike Leergaard, Trygve B. Puchades, Maja A. Bjaalie, Jan G. Kaczorowski, Catherine C. |
author_facet | Gurdon, Brianna Yates, Sharon C. Csucs, Gergely Groeneboom, Nicolaas E. Hadad, Niran Telpoukhovskaia, Maria Ouellette, Andrew Ouellette, Tionna O’Connell, Kristen Singh, Surjeet Murdy, Tom Merchant, Erin Bjerke, Ingvild Kleven, Heidi Schlegel, Ulrike Leergaard, Trygve B. Puchades, Maja A. Bjaalie, Jan G. Kaczorowski, Catherine C. |
author_sort | Gurdon, Brianna |
collection | PubMed |
description | Alzheimer’s disease (AD) is characterized by neurodegeneration, pathology accumulation, and progressive cognitive decline. There is significant variation in age at onset and severity of symptoms highlighting the importance of genetic diversity in the study of AD. To address this, we analyzed cell and pathology composition of 6- and 14-month-old AD-BXD mouse brains using the semi-automated workflow (QUINT); which we expanded to allow for nonlinear refinement of brain atlas-registration, and quality control assessment of atlas-registration and brain section integrity. Near global age-related increases in microglia, astrocyte, and amyloid-beta accumulation were measured, while regional variation in neuron load existed among strains. Furthermore, hippocampal immunohistochemistry analyses were combined with bulk RNA-sequencing results to demonstrate the relationship between cell composition and gene expression. Overall, the additional functionality of the QUINT workflow delivers a highly effective method for registering and quantifying cell and pathology changes in diverse disease models. |
format | Online Article Text |
id | pubmed-10002670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-100026702023-03-11 Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model Gurdon, Brianna Yates, Sharon C. Csucs, Gergely Groeneboom, Nicolaas E. Hadad, Niran Telpoukhovskaia, Maria Ouellette, Andrew Ouellette, Tionna O’Connell, Kristen Singh, Surjeet Murdy, Tom Merchant, Erin Bjerke, Ingvild Kleven, Heidi Schlegel, Ulrike Leergaard, Trygve B. Puchades, Maja A. Bjaalie, Jan G. Kaczorowski, Catherine C. bioRxiv Article Alzheimer’s disease (AD) is characterized by neurodegeneration, pathology accumulation, and progressive cognitive decline. There is significant variation in age at onset and severity of symptoms highlighting the importance of genetic diversity in the study of AD. To address this, we analyzed cell and pathology composition of 6- and 14-month-old AD-BXD mouse brains using the semi-automated workflow (QUINT); which we expanded to allow for nonlinear refinement of brain atlas-registration, and quality control assessment of atlas-registration and brain section integrity. Near global age-related increases in microglia, astrocyte, and amyloid-beta accumulation were measured, while regional variation in neuron load existed among strains. Furthermore, hippocampal immunohistochemistry analyses were combined with bulk RNA-sequencing results to demonstrate the relationship between cell composition and gene expression. Overall, the additional functionality of the QUINT workflow delivers a highly effective method for registering and quantifying cell and pathology changes in diverse disease models. Cold Spring Harbor Laboratory 2023-02-28 /pmc/articles/PMC10002670/ /pubmed/36909528 http://dx.doi.org/10.1101/2023.02.27.530226 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Gurdon, Brianna Yates, Sharon C. Csucs, Gergely Groeneboom, Nicolaas E. Hadad, Niran Telpoukhovskaia, Maria Ouellette, Andrew Ouellette, Tionna O’Connell, Kristen Singh, Surjeet Murdy, Tom Merchant, Erin Bjerke, Ingvild Kleven, Heidi Schlegel, Ulrike Leergaard, Trygve B. Puchades, Maja A. Bjaalie, Jan G. Kaczorowski, Catherine C. Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model |
title | Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model |
title_full | Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model |
title_fullStr | Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model |
title_full_unstemmed | Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model |
title_short | Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model |
title_sort | detecting the effect of genetic diversity on brain composition in an alzheimer’s disease mouse model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002670/ https://www.ncbi.nlm.nih.gov/pubmed/36909528 http://dx.doi.org/10.1101/2023.02.27.530226 |
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