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Record W7116989818 · doi:10.1002/alz70862_109795

Whole‐brain cell‐cell interaction patterns explain tissue damage in several neurodegenerative conditions

2025· article· en· W7116989818 on OpenAlexaff
Veronika Pak, Joon Hwan Hong, Gleb Bezgin, Mahsa Dadar, Yashar Zeighami, Yasser Iturria Medina

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsMontreal Neurological Institute and HospitalMcGill University
Fundersnot available
KeywordsReceptorNeurodegenerationSignal transductionDiseaseParkinson's disease

Abstract

fetched live from OpenAlex

BACKGROUND: Disrupted interactions among neurons, glial and vascular cell types can lead to inflammation, vascular dysfunction, and neuronal death, highlighting the need to understand how functional interactions between these cells predispose the development of different neurodegenerative conditions. Here we identified cell-cell interactions across the whole human brain that explain atrophy patterns characteristic to 13 neurodegenerative conditions. METHOD: We generated 1,050 whole-brain neuroimaging maps of ligand-receptor interactions specific to neurons, astrocytes, microglia, oligodendrocytes, oligodendrocyte precursor cells, and endothelial cells. These maps were created by inferring literature-curated ligand-receptor interaction pairs from microarray gene expression derived from post-mortem tissues of six healthy human donors, sourced from the Allen Human Brain Atlas (Figure 1a-b). Next, using Partial Least Squares Regression (PLS) analysis, we identified key LR pairs whose patterns of communication explain the spatial distribution of atrophy maps specific to 13 neurodegenerative conditions (Figure 1c). Atrophy maps were previously generated for early- and late-onset Alzheimer's disease (EOAD and LOAD), clinical and pathological subtypes of frontotemporal lobar degeneration (FTLD), Parkinson's disease (PD), dementia with Lewy bodies (DLB), and amyotrophic lateral sclerosis (ALS). Finally, we performed gene enrichment analyses to uncover underlying signaling pathways that explain future atrophy in neurodegeneration. RESULT: The first latent variable (LV1) accounted for 84.21% of the covariance (p < 0.05), with the COL1A1-CD36 interaction and other CD36-associated pairs playing a dominant role in explaining atrophy patterns (Figure 2a). Atrophy patterns common to five FTLD-related disorders contributed the most to LV1, followed by EOAD and LOAD (Figure 2b). Among the top 10% of ligand-receptor pairs, 28 of 107 showed strong bi-directional signaling between astrocytes and neurons, along with prominent contributions from neuron-microglia and neuron-neuron signalling (Figure 2d). These top ligands and receptors were significantly enriched for pathways including Slit/Robo-mediated axon guidance, opioid prodynorphin, enkephalin release, and Alzheimer's disease presenilin pathway (Figure 2e; p < 0.001, FDR-corrected). CONCLUSION: We identified whole-brain ligand-receptor interactions involved in neuron-astrocyte, neuron-microglia, and neuron-neuron signaling pathways that explain the observed atrophy patterns in multiple neurodegenerative conditions. These key ligands and receptors may serve as potential therapeutic targets and advance our understanding of neurodegeneration.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.299
Teacher spread0.269 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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