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对巨噬细胞异质信号动力学进行建模揭示了刺激反应中信息传递的原理。

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

作者信息

Guo Xiaolu, Adelaja Adewunmi, Singh Apeksha, Wollman Roy, Hoffmann Alexander

机构信息

Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA, USA.

Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA, USA.

出版信息

Nat Commun. 2025 Jul 1;16(1):5986. doi: 10.1038/s41467-025-60901-3.

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.

摘要

巨噬细胞通过介导特异性的转录因子NFκB的激活动力学启动针对病原体的免疫反应。活细胞成像揭示了异质细胞群体中NFκB动力学的刺激-反应特异性。为了研究超出实验可及范围的刺激-反应特异性,我们开发了数学模型模拟,以捕捉刺激反应性NFκB动力学的异质性以及群体的刺激-反应特异性表现。作为实验数据的补充,扩展剂量反应模拟改进了通道容量估计。通过合并参数分布,我们将信息损失定位到受体模块,而包含负反馈的核心模块显示出显著的信号保真度。此外,构建虚拟单细胞网络揭示了单细胞的刺激-反应特异性。我们发现,尽管在群体水平上存在刺激-反应特异性限制,但大多数单细胞能够对免疫威胁做出特异性反应,并且少数刺激对混淆的情况高度不相关。盲点的多样性使小巨噬细胞联合体能够实现完美的刺激区分。

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