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Record W4406752419 · doi:10.1289/ehp14285

Single-Cell Multi-Omics Profiling of Immune Cells Isolated from Atherosclerotic Plaques in Male ApoE Knockout Mice Exposed to Arsenic

2025· article· en· W4406752419 on OpenAlexafffund
Kiran Makhani, Xiuhui Yang, France Dierick, Nivetha Subramaniam, Natascha Gagnon, Talin Ebrahimian, Stéphanie Lehoux, Hao Wu, Jun Ding, Koren K. Mann

Bibliographic record

VenueEnvironmental Health Perspectives · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsMila - Quebec Artificial Intelligence InstituteMcGill University Health CentreMcGill UniversityJewish General Hospital
FundersNational Key Research and Development Program of ChinaCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéShandong UniversityNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaHeart and Stroke Foundation of Canada
KeywordsApolipoprotein EImmune systemArsenicBiologyOmicsKnockout mouseCellImmunologyComputational biologyMedicineBioinformaticsPathologyChemistryGeneticsGeneDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Millions worldwide are exposed to elevated levels of arsenic that significantly increase their risk of developing atherosclerosis, a pathology primarily driven by immune cells. While the impact of arsenic on immune cell populations in atherosclerotic plaques has been broadly characterized, cellular heterogeneity is a substantial barrier to in-depth examinations of the cellular dynamics for varying immune cell populations. OBJECTIVES: ) mice to elucidate transcriptomic and epigenetic changes in immune cells induced by arsenic exposure. METHODS: arsenic in drinking water or a tap water control, and single-cell multi-omics profiling was performed on atherosclerotic plaque-resident immune cells. Transcriptomic and epigenetic changes in immune cells were analyzed within the same cell to understand the effects of arsenic exposure. RESULTS: Our data revealed that the transcriptional profile of macrophages from arsenic-exposed mice were significantly different from that of control mice and that differences were subtype specific and associated with cell-cell interaction and cell fates. Additionally, our data suggest that differences in arsenic-mediated changes in chromosome accessibility in arsenic-exposed mice were statistically more likely to be due to factors other than random variation compared to their effects on the transcriptome, revealing markers of arsenic exposure and potential targets for intervention. DISCUSSION: These findings in mice provide insights into how arsenic exposure impacts immune cell types in atherosclerosis, highlighting the importance of considering cellular heterogeneity in studying such effects. The identification of subtype-specific differences and potential intervention targets underscores the significance of understanding the molecular mechanisms underlying arsenic-induced atherosclerosis. Further research is warranted to validate these findings and explore therapeutic interventions targeting immune cell dysfunction in arsenic-exposed individuals. https://doi.org/10.1289/EHP14285.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.246
Teacher spread0.233 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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