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Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells
Macrophages must respond appropriately to pathogens and other pro-inflammatory stimuli in order to perform their roles in fighting infection. One way in which inflammatory stimuli can vary is in their dynamics—that is, the amplitude and duration of stimulus experienced by the cell. In this study, we...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Biochemistry and Molecular Biology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579967/ https://www.ncbi.nlm.nih.gov/pubmed/37689116 http://dx.doi.org/10.1016/j.jbc.2023.105230 |
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author | Naigles, Beverly Narla, Avaneesh V. Soroczynski, Jan Tsimring, Lev S. Hao, Nan |
author_facet | Naigles, Beverly Narla, Avaneesh V. Soroczynski, Jan Tsimring, Lev S. Hao, Nan |
author_sort | Naigles, Beverly |
collection | PubMed |
description | Macrophages must respond appropriately to pathogens and other pro-inflammatory stimuli in order to perform their roles in fighting infection. One way in which inflammatory stimuli can vary is in their dynamics—that is, the amplitude and duration of stimulus experienced by the cell. In this study, we performed long-term live cell imaging in a microfluidic device to investigate how the pro-inflammatory genes IRF1, CXCL10, and CXCL9 respond to dynamic interferon-gamma (IFNγ) stimulation. We found that IRF1 responds to low concentration or short duration IFNγ stimulation, whereas CXCL10 and CXCL9 require longer or higherconcentration stimulation to be expressed. We also investigated the heterogeneity in the expression of each gene and found that CXCL10 and CXCL9 have substantial cell-to-cell variability. In particular, the expression of CXCL10 appears to be largely stochastic with a subpopulation of nonresponding cells across all the stimulation conditions tested. We developed both deterministic and stochastic models for the expression of each gene. Our modeling analysis revealed that the heterogeneity in CXCL10 can be attributed to a slow chromatin-opening step that is on a similar timescale to that of adaptation of the upstream signal. In this way, CXCL10 expression in individual cells can remain stochastic in response to each pulse of repeated stimulation, which we also validated by experiments. Together, we conclude that pro-inflammatory genes in the same signaling pathway can respond to dynamic IFNγ stimulus with very different response features and that upstream signal adaptation can contribute to shaping heterogeneous gene expression. |
format | Online Article Text |
id | pubmed-10579967 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Society for Biochemistry and Molecular Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-105799672023-10-18 Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells Naigles, Beverly Narla, Avaneesh V. Soroczynski, Jan Tsimring, Lev S. Hao, Nan J Biol Chem Research Article Macrophages must respond appropriately to pathogens and other pro-inflammatory stimuli in order to perform their roles in fighting infection. One way in which inflammatory stimuli can vary is in their dynamics—that is, the amplitude and duration of stimulus experienced by the cell. In this study, we performed long-term live cell imaging in a microfluidic device to investigate how the pro-inflammatory genes IRF1, CXCL10, and CXCL9 respond to dynamic interferon-gamma (IFNγ) stimulation. We found that IRF1 responds to low concentration or short duration IFNγ stimulation, whereas CXCL10 and CXCL9 require longer or higherconcentration stimulation to be expressed. We also investigated the heterogeneity in the expression of each gene and found that CXCL10 and CXCL9 have substantial cell-to-cell variability. In particular, the expression of CXCL10 appears to be largely stochastic with a subpopulation of nonresponding cells across all the stimulation conditions tested. We developed both deterministic and stochastic models for the expression of each gene. Our modeling analysis revealed that the heterogeneity in CXCL10 can be attributed to a slow chromatin-opening step that is on a similar timescale to that of adaptation of the upstream signal. In this way, CXCL10 expression in individual cells can remain stochastic in response to each pulse of repeated stimulation, which we also validated by experiments. Together, we conclude that pro-inflammatory genes in the same signaling pathway can respond to dynamic IFNγ stimulus with very different response features and that upstream signal adaptation can contribute to shaping heterogeneous gene expression. American Society for Biochemistry and Molecular Biology 2023-09-09 /pmc/articles/PMC10579967/ /pubmed/37689116 http://dx.doi.org/10.1016/j.jbc.2023.105230 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Naigles, Beverly Narla, Avaneesh V. Soroczynski, Jan Tsimring, Lev S. Hao, Nan Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells |
title | Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells |
title_full | Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells |
title_fullStr | Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells |
title_full_unstemmed | Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells |
title_short | Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells |
title_sort | quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579967/ https://www.ncbi.nlm.nih.gov/pubmed/37689116 http://dx.doi.org/10.1016/j.jbc.2023.105230 |
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