In vivo single-cell high-dimensional mass cytometry analysis to track the interaction between <i>Klebsiella pneumoniae</i> and myeloid cells
Notice bibliographique
Résumé
ABSTRACT In vivo single-cell approaches have transformed our understanding of the immune populations in tissues. Mass cytometry (CyTOF), that combines the resolution of mass spectrometry with the ability to conduct multiplexed measurements of cell molecules at the single cell resolution, has enabled to resolve the diversity of immune cell subsets, and their heterogeneous functionality. Here we assess the feasibility of taking CyTOF one step further to immuno profile cells while tracking their interaction with bacteria, a method we term Bac-CyTOF. We focus on the pathogen Klebsiella pneumoniae interrogating the pneumonia mouse model. Using Bac-CyTOF, we unveil the atlas of immune cells of mice infected with a K. pneumoniae virulent strain. The atlas is characterized by a decrease in the populations of alveolar macrophages and monocyte-derived macrophages, whereas neutrophils, and inflammatory monocytes are characterized by an increase in the subpopulations expressing markers characteristic of less active cells such as the immune checkpoint PD-L1. These cells are those with associated bacteria. We show that the type VI secretion system (T6SS) contributes to shape the lung immune landscape. The T6SS governs the interaction with monocytes/macrophages by shifting Klebsiella from alveolar macrophages to interstitial macrophages and limiting the infection of inflammatory monocytes. Lack of T6SS results in cells expressing markers of active cells, and a decrease in the subpopulations expressing PD-L1. By infecting with Klebsiella , and Acinetobacter baumannii strains cleared by mice, we uncover that a heightened recruitment of neutrophils, and relative high levels of alveolar macrophages and eosinophils and the recruitment of a characteristic subpopulation of neutrophils are features of infected mice clearing infections. Lastly, we leverage Bac-CyTOF-generated knowledge platform to investigate the role of the DNA sensor STING in Klebsiella infections. sting −/− infected mice present features of mice clearing the infection including the reduced levels of PD-L1. STING absence facilitates Klebsiella clearance. AUTHOR SUMMARY Host-pathogen interactions are vital to our understanding of infectious disease, as well as its treatment and prevention. Mass cytometry and high-dimensional single-cell data analysis have enabled to resolve the tremendous diversity of immune cell subsets, and their heterogeneous functionality. Here we take this technology one step further to immuno profile cells in vivo while tracking their interaction with bacteria, a method we term Bac-CyTOF. Using this technology, we unveil the atlas of lung immune cells following infection with the human pathogen Klebsiella pneumoniae . The atlas is characterized by an increase in the populations expressing markers characteristic of less active cells such as the immune checkpoint PD-L1. The pathogen interacts with these cells. We uncover the contribution of the antimicrobial nanoweapon T6SS to shape the immune landscape, highlighting its potential in host defence. By probing two pathogens effectively cleared by mice, we reveal features of infections successfully cleared by mice. Lastly, we leverage this knowledge platform to investigate the role of the DNA sensor STING in Klebsiella infections. There was no prior knowledge on the role of STING in Klebsiella infection biology. Our findings suggest Klebsiella may utilize STING signalling for its own benefit because absence of STING facilitates Klebsiella clearance.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».