Single-Cell Multi-Omics Profiling of Immune Cells Isolated from Atherosclerotic Plaques in Male ApoE Knockout Mice Exposed to Arsenic
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".