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Record W4411539447 · doi:10.1161/atv.45.suppl_1.tu0049

Abstract Tu0049: Leveraging a Transgenic Mouse Model to Study Endothelial Cell-Derived Extracellular Vesicles in Atherosclerosis

2025· article· en· W4411539447 on OpenAlexaff
Mandy Kunze Guo, Corey A. Scipione, Leandro Breda, Kamalben Prajapati, Sneha Raju, Steven R. Botts, Majed Abdul-Samad, Sarvatit Patel, Garry Yu, Andrew C. Dudley, Jason E. Fish, Kathryn L. Howe

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAtherosclerosis and Cardiovascular Diseases
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsCell biologyExosomeBiologyCD63MicrovesiclesEndothelial stem cellInflammationMicrovesicleImmunologyChemistryIn vitroBiochemistry

Abstract

fetched live from OpenAlex

Introduction: Atherosclerosis is characterized by the accumulation of lipoproteins and immune cells, leading to unresolved inflammation in the arterial wall. Endothelial cells (EC) form a barrier, and modulate communication, between circulating and resident vascular cells. EC dysfunction, a hallmark of the disease, disrupts healthy communication. Extracellular vesicles (EVs) are key mediators of intercellular communication, carrying bioactive molecules that influence cell function and signal disease states. While our group previously demonstrated crosstalk between endothelial-derived EVs (EC-EVs) with monocytes and smooth muscle cells in vitro , their role in vivo remains unclear. To address this, we developed a murine model to detect and visualize EC-EVs in atherosclerosis. Methods: We generated an EC-EV tracking model on an Ldlr-/- background using an inducible endothelial-specific Cre to activate the expression of an EV-enriched CD63 protein fused with emerald GFP. Mice ( Ldlr -/- Cdh5Cre ERT2+/- CD63-emGFP + ) aged 8-10 weeks, and Cre - control, received tamoxifen to induce GFP + EC-EVs. Mice were fed a standard chow or high-cholesterol diet for 12 weeks to induce atherosclerosis. EVs were isolated from the aorta and heart, quantified, and characterized by nanoparticle tracking analysis (NTA) and western blot. EC-EVs were visualized in aortic sections via confocal microscopy and immunogold-labelled transmission electron microscopy (TEM). Results: EVs, characterized by EV markers (CD63, Alix and CD9) and the absence of Calnexin, were isolated from the aorta and heart of chow-fed mice and quantified by NTA. CD63-emGFP was detected exclusively in Cre + mice. Confocal imaging of aortic root sections showed a GFP + signal in luminal and adventitial ECs of Cre + mice, with no GFP in Cre - control. Immunogold TEM confirmed GFP + EVs in ECs of Cre + mice. Confocal and TEM imaging revealed EC-EV trafficking in atherosclerotic plaque, with EC-EVs (anti-GFP) localizing with ECs (anti-CD31) and within plaques in CD31 negative cells. Co-staining for GFP and CD68 indicated EC-EVs in macrophages, suggesting communication with myeloid cells. TEM confirmed EC-EVs (GFP gold particles) inside plaque macrophages. Conclusion: This is the first in vivo approach to detect EC-EV trafficking within the atherosclerotic vessel wall. Understanding EC communication in atherosclerosis will aid in the development of new clinical biomarkers and novel therapeutic strategies for vascular diseases.

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)0.000
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.003

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 head0.265
Teacher spread0.231 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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