Asset Details
Do you wish to reserve the book?
Backtracking metabolic dynamics in single cells predicts bacterial replication in human macrophages
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Backtracking metabolic dynamics in single cells predicts bacterial replication in human macrophages
Do you wish to request the book?
Backtracking metabolic dynamics in single cells predicts bacterial replication in human macrophages
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Backtracking metabolic dynamics in single cells predicts bacterial replication in human macrophages
2025
Overview
Accurately tracking dynamic state transitions is crucial for modeling and predicting biological outcomes, as it captures heterogeneity of cellular responses. To build a model to predict bacterial infection in single cells, we have monitored in parallel infection progression and metabolic parameters in thousands of human primary macrophages infected with the intracellular pathogen
Legionella pneumophila
. By combining live-cell imaging with a tool for classifying cells based on infection outcomes, we were able to trace the specific evolution of metabolic parameters linked to distinct outcomes, such as bacterial replication or cell death. Our findings revealed that early changes in mitochondrial membrane potential (Δ
ψ
m) and in the production of mitochondrial Reactive Oxygen Species (mROS) are associated with macrophages that will later support bacterial growth. We used these data to train an explainable machine-learning model and achieved 83% accuracy in predicting
L. pneumophila
replication in single, infected cells before bacterial replication starts. Our results highlight backtracking as a valuable tool to gain new insights in host-pathogen interactions and identify early mitochondrial alterations as key predictive markers of success of bacterial infection.
Computational models can help to explain the dynamics of cellular infection with pathogens. Here the authors use computational models to assess the single cell infection parameters of human macrophage infection with
Legionella pneumophila
and the effects on immunometabolism at a single cell and population level.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ Bacteria
/ Dyes
/ Humanities and Social Sciences
/ Humans
/ Legionella pneumophila - growth & development
/ Legionella pneumophila - physiology
/ Legionnaires' Disease - metabolism
/ Legionnaires' Disease - microbiology
/ Legionnaires' disease bacterium
/ Membrane Potential, Mitochondrial
/ Microbiology and Parasitology
/ Reactive Oxygen Species - metabolism
/ Science
