Evidence mapPaperPMID 38769398Full record

ArticleCommunications biology2024

Detecting the effect of genetic diversity on brain composition in an Alzheimer's disease mouse model.

Brianna Gurdon, Sharon C Yates, Gergely Csucs, Nicolaas E Groeneboom, Niran Hadad, Maria Telpoukhovskaia, Andrew Ouellette, Tionna Ouellette, Kristen M S O'Connell, Surjeet Singh and 9 more

Abstract read
In one paragraph

Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Low-coverage whole-genome sequencing facilitates accurate and cost-effective haplotype reconstruction in complex mouse crosses.Mammalian genome : official journal of the International Mammalian Genome Society · 2025
    Article
  4. Article
  5. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Brianna Gurdon *The Jackson Laboratory, Bar Harbor, ME, USA.ORCID 0000-0003-4209-5434
Sharon C Yates *Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.ORCID 0000-0002-3001-8725
Gergely CsucsNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.ORCID 0000-0002-2093-6274
Nicolaas E GroeneboomNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Niran HadadThe Jackson Laboratory, Bar Harbor, ME, USA.
Maria TelpoukhovskaiaThe Jackson Laboratory, Bar Harbor, ME, USA.ORCID 0000-0002-5237-4042
Andrew OuelletteThe Jackson Laboratory, Bar Harbor, ME, USA.ORCID 0000-0001-6110-7897
Tionna OuelletteThe Jackson Laboratory, Bar Harbor, ME, USA.
Kristen M S O'ConnellThe Jackson Laboratory, Bar Harbor, ME, USA.
Surjeet SinghThe Jackson Laboratory, Bar Harbor, ME, USA.ORCID 0000-0002-4646-2089
Thomas J MurdyThe Jackson Laboratory, Bar Harbor, ME, USA.ORCID 0000-0002-2247-8078
Erin MerchantThe Jackson Laboratory, Bar Harbor, ME, USA.
Ingvild BjerkeNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.ORCID 0000-0002-2926-6836
Heidi KlevenNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.ORCID 0000-0002-3710-321X
Ulrike SchlegelNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.ORCID 0000-0003-4230-3221
Trygve B LeergaardNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.ORCID 0000-0001-5965-8470
Maja A PuchadesNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.ORCID 0000-0002-4536-8197
Jan G BjaalieNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway. j.g.bjaalie@medisin.uio.no.ORCID 0000-0001-7899-906X
Catherine C KaczorowskiThe University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME, USA. kaczoro@med.umich.edu.ORCID 0000-0001-5430-0816

Funding

EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) No. 945539NIA NIH HHS R01 AG057914
6 · The paper itself

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.

Indexed as

Alzheimer DiseaseBrainDisease Models, AnimalGenetic VariationAmyloid beta-PeptidesAnimalsMaleMiceMice, TransgenicAmyloid beta-Peptides

Identifiers

PMID38769398
PMCPMC11106287

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.