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Record W7116864200 · doi:10.1002/alz70861_108495

Identification of traumatic brain injury induced molecular signatures using spatial transcriptomics

2025· article· en· W7116864200 on OpenAlexaff
Mehwish Anwer, Aditya Swaro, Brianna N. Bristow, Jianjia Fan, Wai Hang Cheng, Mark S. Cembrowski, Cheryl L. Wellington

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTranscriptomeTraumatic brain injuryIdentification (biology)Central nervous systemNeuroinformaticsGene

Abstract

fetched live from OpenAlex

BACKGROUND: Traumatic brain injury (TBI) results in multifaceted neuropathology including neuroinflammation, neuronal loss, axonal damage and blood brain barrier damage, which can lead to cognitive impairment and dementia including Alzheimer's disease (AD) and related dementias (ADRDs). Detailed analysis of the molecular and cellular changes that occur in injured brain is crucial to understand TBI's contributions to dementia and AD. The Closed Head Impact Model of Engineered Rotational Acceleration (CHIMERA) is a non-surgical model of impact-acceleration injury that mimics the biomechanics and pathophysiology of human TBI. METHODS: Adult male mice received a mild CHIMERA injury (2.1J) and acute neurological deficits were assessed as compared to sham group. At 7 days post-TBI, cardiac blood was collected for biomarker analysis and brain was harvested for 10x genomics Visium spatial transcriptomic and multiplexed fluorescence in situ hybridization (mFISH) analysis. RESULTS: Injured mice showed delayed righting reflex recovery and poor performance on neurological tests. Increased plasma glial fibrillary acidic protein (GFAP) and neurofilament light levels (NfL) were found in TBI compared to sham mice. We identified widespread (6614 DEGs: 82% downregulated, 18% upregulated) as well as regional cell type specific gene dysregulation in several clusters. We cross-referenced our dataset with genes identified in human genome wide association studies in AD and TBI and found conserved genes in all three datasets (Ace, AdamTS4, AdamTS1, Trem2, ApoE, Cacna1a, RbFox1, Clu, Apoc1, Mapt). In the optic tract cluster, we identified that the astrocytic marker Gfap, microglial marker Aif1 and several disease-associated microglia (DAMs) genes including Apoe, Ctsd, Trem2 were upregulated. GFAP immunolabelling confirmed astrogliosis in optic tract and hippocampus. In the neocortical cluster, we found downregulation of Itm2c, a negative regulator of amyloid-beta peptide production, and Scg5, a chaperone protein that prevent aggregation of secreted proteins associated with neurodegeneration, which may contribute to TBI-associated susceptibility to AD. mFISH labelling validated downregulation of these genes in cortex. CONCLUSIONS: Our data-rich spatial transcriptomics approach identified molecular and cellular substrates crucial to TBI pathology and its comorbidities including AD and ADRD. Together, this work will provide spatial molecular maps of diffuse brain injury and novel insights into TBI's contributions to dementia.

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)0.001
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.311
Teacher spread0.276 · 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
Published2025
Admission routes1
Has abstractyes

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