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Record W4206007015 · doi:10.1101/2022.01.06.475218

Concomitant AD and DLB pathologies shape subfield microglia responses in the hippocampus

2022· preprint· en· W4206007015 on OpenAlexaff
Sonja Fixemer, Corrado Ameli, Gaël P. Hammer, Luis Salamanca, Oihane Uriarte Huarte, Chantal Schwartz, Jean‐Jacques Gérardy, Naguib Mechawar, Alexander Skupin, Michel Mittelbronn, David Bouvier

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsMcGill University Health CentreMontreal General HospitalMcGill UniversityDouglas Mental Health University Institute
FundersProthena
KeywordsMicrogliaHippocampal formationAtrophyHippocampusDementia with Lewy bodiesNeuroscienceNeuroinflammationPathologyDementiaMedicineDiseaseBiologyInternal medicineInflammation

Abstract

fetched live from OpenAlex

Abstract Hippocampal alteration is at the centre of memory decline in the most common age-related neurodegenerative diseases: Alzheimer’s disease (AD) and Dementia with Lewy Bodies (DLB). However, the subregional deterioration of the hippocampus differs between both diseases with more severe atrophy in the CA1 subfield of the AD patients. How AD and DLB-typical pathologies compose the various local microenvironment of the hippocampus across AD and DLB needs to be further explored to understand this process. Additionally, microglia responses could further impact the atrophy rate. Some studies suggest that microglia react differently according to the underlying neurodegenerative disorder. How microglia are transformed across hippocampal subfields in AD and DLB, and how their changes are associated with disease-typical pathologies remains to be determined. To these purposes, we performed a volumetric analysis of phospho-Tau (P-Tau), Amyloid-ß (Aß), and phospho-α-Synuclein (P-Syn) loads, quantified and classified microglia according to distinct morphological phenotypes using high-resolution confocal 3D microscopy of hippocampal CA1, CA3 and DG/CA4 subfields of late-onset AD (n=10) and DLB (n=8) as well as age-matched control samples (n=11). We found that each of the Tau, Aß and Synuclein pathologies followed a specific subregional distribution, relatively preserved across AD and DLB. P-Tau, Aß and P-Syn burdens were significantly exacerbated in AD, with Tau pathology being particularly severe in the AD CA1. P-Tau and P-Syn burdens were highly correlated across subfields and conditions (R2Spear = 0.79; P < 0.001) and result from a local co-distribution of P-Tau and P-Syn inclusions in neighbouring neurons, with only a low proportion of double-positive cells. In parallel, we assessed the changes of the microglia responses by measuring 16 morphological features of more than 35,000 individual microglial cells and classifying them into seven-distinct morphological clusters. We found microglia features- and clusters-variations subfield- and condition-dependent. Two of the seven morphological clusters, with more amoeboid and less branched forms, were identified as disease-enriched and found to be further increased in AD. Interestingly, some microglial features or clusters were associated with one but more often with a combination of two pathologies in a subfield-dependent manner. In conclusion, our study shows a multimodal association of the hippocampal microglia responses with the co-occurrence, distribution and severity of AD and DLB pathologies. In DLB hippocampi, pathological imprint and microglia responses follow AD trends but with lesser severity. Our study suggests that the increased pathological burdens of P-Tau and P-Syn and associated microglia alterations are involved in a more severe deterioration of the CA1 in AD as compared to DLB.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.252
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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