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Record W4410866824 · doi:10.1161/atvbaha.124.322324

Multiomic Landscape of Extracellular Vesicles in Human Carotid Atherosclerotic Plaque Reveals Endothelial Communication Networks

2025· article· en· W4410866824 on OpenAlexaff
Sneha Raju, Mandy E. Turner, Christian Cao, Majed Abdul-Samad, Neil Punwasi, Mark C. Blaser, Rachel M. Cahalane, Steven R. Botts, Kamalben Prajapati, Sarvatit Patel, Ruilin Wu, Dakota Gustafson, Natalie J. Galant, Lindsey K. Fiddes, Melody Chemaly, Ulf Hedin, Ljubica Matic, Michael A. Seidman, Vallijah Subasri, Sasha A. Singh, Elena Aïkawa, Jason E. Fish, Kathryn L. Howe

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsPrincess Margaret Cancer CentreToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsAngiogenesisMicrovesiclesBiologyTranscriptomeCellEndothelial stem cellCell biologymicroRNAProteomicsNeovascularizationCell typeReceptorCarotid endarterectomyCell signalingPathologyCancer researchSignal transductionMedicineGeneticsGene expressionGeneInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Carotid atherosclerosis is orchestrated by cell-cell communication that drives progression along a clinical continuum (asymptomatic to symptomatic). Extracellular vesicles (EVs) are cell-derived nanoparticles representing a new paradigm in cellular communication. Little is known about their biological cargo, cellular origin/destination, and functional roles in human atherosclerotic plaque. METHODS: EVs were enriched via size exclusion chromatography from human carotid endarterectomy samples dissected into paired plaque and marginal zones (symptomatic n=16, asymptomatic n=13). EV-cargos were assessed via whole transcriptome microRNA-sequencing and mass spectrometry-based proteomics. EV multiomics was integrated with bulk and single-cell RNA-sequencing datasets to predict EV cellular origin and ligand-receptor interactions, and multimodal biological network integration of EV-cargo was completed. EV functional impact was assessed with endothelial angiogenesis assays. RESULTS: Carotid plaques contained more EVs than adjacent marginal zones, with differential enrichment for EV-microRNAs and EV-proteins in key atherogenic pathways. EV cellular origin analysis suggested that tissue EV signatures originated from endothelial cells, smooth muscle cells, and immune cells. Integrated tissue vesiculomics and single-cell RNA-sequencing indicated complex EV-vascular cell communication that changed with disease progression and plaque vulnerability (ie, symptomatic disease). Plaques from symptomatic patients, but not asymptomatic patients, were characterized by increased involvement of endothelial pathways and more complex ligand-receptor interactions, relative to their marginal zones. Plaque EVs were predicted to mediate communication with endothelial cells. Pathway enrichment analysis delineated an endothelial signature with roles in angiogenesis and neovascularization, well-known indices of plaque instability. This was validated functionally, wherein human carotid symptomatic plaque EVs induced sprouting angiogenesis in comparison to their matched marginal zones. CONCLUSIONS: Our findings indicate that EVs may drive dynamic changes in plaques through EV-vascular cell communication and effector functions that typify vulnerability to rupture, precipitating symptomatic disease. The discovery of endothelial-directed angiogenic processes mediated by EVs creates new therapeutic avenues for atherosclerosis.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.274
Teacher spread0.259 · 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

Citations8
Published2025
Admission routes1
Has abstractyes

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