Modeling heterogeneous signaling dynamics of macrophages reveals principles of information transmission in stimulus responses

X Xiaolu Guo A Adewunmi Adelaja A Apeksha Singh R Roy Wollman A Alexander Hoffmann (Signaling Systems Laboratory, Department of Microbiology, Immunology and Molecular Genetics, and the Institute for Quantitative and Computational Biosciences, University of California)

Abstract

Abstract Macrophages initiate pathogen-appropriate immune responses with the activation dynamics of transcription factor NFκB mediating specificity. Live-cell imaging revealed the stimulus-response specificity of NFκB dynamics among populations of heterogeneous cells. To study stimulus-response specificity beyond what is experimentally accessible, we develop mathematical model simulations that capture the heterogeneity of stimulus-responsive NFκB dynamics and the stimulus-response specificity performance of the population. Complementing experimental data, extended-dose response simulations improved channel capacity estimates. By collapsing parameter distributions, we locate information loss to receptor modules, while the negative-feedback-containing core module shows remarkable signaling fidelity. Further, constructing virtual single-cell networks reveals the stimulus-response specificity of single cells. We find that despite stimulus-response specificity limitations at the population level, the majority of single cells are capable of responding specifically to immune threats, and that the few instances of stimulus-pair confusion are highly uncorrelated. The diversity of blindspots enable small consortia of macrophages to achieve perfect stimulus distinction.

Article Details

Volume / Issue Vol. 16, Issue 1
Published July 01, 2025
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio

ISSN: 2041-1723 Open Access Life Sciences

Authors (5)

X

Xiaolu Guo

A

Adewunmi Adelaja

A

Apeksha Singh

R

Roy Wollman

A

Alexander Hoffmann

Signaling Systems Laboratory, Department of Microbiology, Immunology and Molecular Genetics, and the Institute for Quantitative and Computational Biosciences, University of California