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Record W4389727828 · doi:10.1101/2023.12.14.571618

In vivo single-cell high-dimensional mass cytometry analysis to track the interaction between <i>Klebsiella pneumoniae</i> and myeloid cells

2023· preprint· en· W4389727828 on OpenAlexfundno aff
Ricardo Calderón-González, Amy Dumigan, Joana Sá‐Pessoa, Adrien Kissenpfennig, José A. Bengoechea

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsnot available
FundersMedical Research CouncilBiotechnology and Biological Sciences Research CouncilDirectorate for Biological SciencesQueen's UniversityQueen's University Belfast
KeywordsBiologyImmune systemMass cytometryKlebsiella pneumoniaeMicrobiologyFlow cytometryInnate immune systemMonocyteCell biologyImmunologyPhenotypeEscherichia coli

Abstract

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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.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.227
Teacher spread0.212 · 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
Published2023
Admission routes1
Has abstractyes

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