Single Endothelial Cell mRNA Sequencing Better Captures the Severity of Coronary Artery Disease Than Targeted Total Arterial Markers of Inflammation and Senescence
Notice bibliographique
Résumé
Endothelial dysfunction is the initial step towards atherosclerotic plaque development and coronary artery disease (CAD). Senescent endothelial cells (SnEC) have been linked to the atherosclerotic burden. In particular, the senescent‐associated secretory phenotype protein angiopoietin‐like 2 (ANGPTL2) was shown to be elevated in the plasma of CAD patients and related to the senescent cellular load in human internal mammary artery (IMA) segments discarded during coronary artery bypass grafting (CABG) surgery. We tested the hypothesis that the accumulation of vascular SnEC causes endothelial dysfunction and precedes the appearance of atherosclerotic lesions. Discarded atheroma‐free IMA segments from 12 patients (11 men and 1 woman, 69±3 years) were collected during consecutive elective CABG surgeries. Arterial rings of IMA segments were mounted in a wire myograph to record isometric changes in tension: arteries were pre‐contracted with U46619, a synthetic analogue of PGH 2 , and endothelium‐dependent relaxations to increasing concentrations of acetylcholine (ACh) were recorded. The maximal relaxation (E max ) and the concentration of ACh inducing 50% of relaxation (EC 50 ) were calculated. Afterwards total mRNA was extracted from the IMA segments. Vascular gene expression of ANGPTL2 and p21 (senescence markers), and CD68 and PAI‐1 (inflammatory markers) were assessed by RT‐qPCR. In parallel, single‐cell RNA sequencing was performed in IMA segments from two age‐matched male patients obtained either during elective (stable CAD) or emergency (unstable CAD, with numerous risk factors) procedures, and differential gene expression was specifically analyzed in vascular EC. Endothelium‐dependent relaxations were characterized by E max (56±7 %, [18–100%], n=12) and pD 2 (‐log EC 50 : 6.9±0.1 [5.8–7.3], n=12). Patients were divided into 2 groups according to their E max (< or > to 50%) to define low and high endothelial function; vascular gene expression of the 4 senescence and inflammatory markers were similar between the two groups, demonstrating that global arterial wall senescence and inflammation did not distinguish severity of endothelial dysfunction. We then used single‐cell mRNA sequencing and analysed the unbiased differential gene expression specifically in vascular EC. From the total IMA cellular counts, EC represented ~2%. Nine genes were identified that were differentially overexpressed in EC from unstable CAD patient that have been associated with senolytic drug targets and cardiovascular diseases (Table ), potentially attesting to the severity of endothelial dysfunction and CAD. In conclusion, unlike total arterial wall mRNA quantification, unbiased single‐cell mRNA sequencing identified differentially upregulated endothelial pathways that may contribute to the severity of the CAD by inducing precocious endothelial dysfunction and senescence. Top genes differentially expressed in endothelial cells between a stable and unstable CAD patient. Percentages represent the percent of endothelial cells expressing the gene in each patient dataset. P‐values are Bonferroni corrected. Stable CAD patient Unstable CAD patient p‐value Gene Associated CVD disorder or drug target 0.0% 41.7% 0.00061 TGFB2 Atrial fibrillation 0.0% 41.7% 0.00061 SQLE Statins 2.9% 58.3% 0.00090 RPPH1 Quercetin (senolytic drug) 7.1% 66.7% 0.00339 ARL14EP Systolic blood pressure 25.7% 100.0% 0.00914 RPL17
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».