Endothelial cells secrete small extracellular vesicles bidirectionally containing distinct cargo to uniquely reprogram vascular cells in the circulation and vessel wall
Bibliographic record
Abstract
Abstract Rationale: Extracellular vesicles (EVs) contain bioactive cargo including microRNAs (miRNAs) and proteins that are released by cells as a form of cell-cell communication. Endothelial cells (ECs) form the innermost lining of all blood vessels and thereby interface with cells in the circulation as well as cells residing in the vascular wall. It is unknown whether ECs have the capacity to release EVs capable of governing recipient cells within two separate compartments, and how this is affected by endothelial activation commonly seen in atheroprone regions. Objective: Given their boundary location, we propose that ECs utilize bidirectional release of distinct EV cargo in quiescent and activated states to communicate with cells within the circulation and blood vessel wall. Methods and Results: EVs were isolated from primary human aortic endothelial cells (ECs) (+/- IL-1β activation), quantified, and analysed by miRNA transcriptomics and proteomics. Compared to quiescent ECs, activated ECs increased EV release, with miRNA and protein cargo that were related to atherosclerosis. RNA sequencing of EV-treated monocytes and smooth muscle cells (SMCs) revealed that EVs from activated ECs altered pathways that were pro-inflammatory and atherogenic. Apical and basolateral EV release was assessed using ECs on transwells. ECs released more EVs apically, which increased with activation. Apical and basolateral EV cargo contained distinct transcriptomes and proteomes that were altered by EC activation. Notably, basolateral EC-EVs displayed greater changes in the EV secretome, with pathways specific to atherosclerosis. In silico analysis determined that compartment-specific cargo released by the apical and basolateral surfaces of ECs can reprogram monocytes and SMCs, respectively. Conclusions: The demonstration that ECs are capable of polarized EV cargo loading and directional EV secretion reveals a novel paradigm for endothelial communication, which may ultimately enhance our ability to design endothelial-based therapeutics for cardiovascular diseases such as atherosclerosis where ECs are persistently activated. Non-standard Abbreviations and Acronyms cryo-EM cryogenic electron microscopy EC endothelial cell EV extracellular vesicle GO gene ontology HAEC human aortic endothelial cell SMC human aortic vascular smooth muscle cell IL-1β interleukin 1 beta KEGG Kyoto encyclopedia of genes and genomes LC-MS label-free liquid-chromatography mass spectrometry MVB multivesicular body miRNA microRNA RNAseq RNA sequencing TEM transmission electron microscopy TIRF total interal reflection fluorescence microscopy miRNA microRNA Graphical abstract: Polarized endothelial extracellular vesicle communication with luminal and abluminal vascular cells Endothelial cell small extracellular vesicle (EC-EV) release from apical (luminal) and basolateral (abluminal) surfaces in quiescence and after endothelial activation. Quiescent EC-EVs are depicted in blue (bright blue=apical, light blue=basolateral), while activated EC-EVs are depicted in red (bright red=apical, light red=basolateral). Luminal monocyte is represented in purple with upregulation of pro-inflammatory transcripts (bright purple) after uptake of activated EC-EVs from the apical surface, compared to uptake of quiescent apical EC-EVs (light purple). Basolateral EC-EVs are taken up by an abluminal resident smooth muscle cell depicted in yellow. Smooth muscle cell uptake of activated basolateral EC-EVs with upregulation of pro-inflammatory/pro-atherogenic transcripts (bright yellow), as compared to uptake of quiescent EC-EVs (light yellow).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".