. 2023 Jan 5;83(1):121-138.e7.

doi: 10.1016/j.molcel.2022.11.017. Epub 2022 Dec 14.

Macrophage inflammatory and regenerative response periodicity is programmed by cell cycle and chromatin state

Bence Daniel¹, Julia A Belk², Stefanie L Meier³, Andy Y Chen⁴, Katalin Sandor⁵, Zsolt Czimmerer⁶, Zsofia Varga⁷, Krisztian Bene⁷, Frank A Buquicchio⁸, Yanyan Qi⁹, Hugo Kitano¹⁰, Joshua R Wheeler¹¹, Deshka S Foster¹², Michael Januszyk¹², Michael T Longaker¹³, Howard Y Chang¹⁴, Ansuman T Satpathy¹⁵

Affiliations

¹ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA. Electronic address: bdaniel8@stanford.edu.
² Department of Pathology, Stanford University, Stanford, CA 94305, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA.
³ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA.
⁴ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA.
⁵ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA.
⁶ Department of Biochemistry and Molecular Biology, University of Debrecen, Debrecen 4032, Hungary; Institute of Genetics, Biological Research Centre, Eötvös Loránd Research Network, Szeged 6726, Hungary.
⁷ Department of Biochemistry and Molecular Biology, University of Debrecen, Debrecen 4032, Hungary.
⁸ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Program in Immunology, Stanford University, Stanford, CA, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA.
⁹ Department of Pathology, Stanford University, Stanford, CA 94305, USA.
¹⁰ Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
¹¹ Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA.
¹² Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University, Stanford, CA 94305, USA; Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA.
¹³ Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University, Stanford, CA 94305, USA; Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
¹⁴ Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: howchang@stanford.edu.
¹⁵ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA; Program in Immunology, Stanford University, Stanford, CA, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA. Electronic address: satpathy@stanford.edu.

PMID: 36521490
PMCID: PMC9831293
DOI: 10.1016/j.molcel.2022.11.017

Macrophage inflammatory and regenerative response periodicity is programmed by cell cycle and chromatin state

Bence Daniel et al. Mol Cell. 2023.

. 2023 Jan 5;83(1):121-138.e7.

doi: 10.1016/j.molcel.2022.11.017. Epub 2022 Dec 14.

Authors

Affiliations

¹ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA. Electronic address: bdaniel8@stanford.edu.
² Department of Pathology, Stanford University, Stanford, CA 94305, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA.
³ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA.
⁴ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA.
⁵ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA.
⁶ Department of Biochemistry and Molecular Biology, University of Debrecen, Debrecen 4032, Hungary; Institute of Genetics, Biological Research Centre, Eötvös Loránd Research Network, Szeged 6726, Hungary.
⁷ Department of Biochemistry and Molecular Biology, University of Debrecen, Debrecen 4032, Hungary.
⁸ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Program in Immunology, Stanford University, Stanford, CA, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA.
⁹ Department of Pathology, Stanford University, Stanford, CA 94305, USA.
¹⁰ Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
¹¹ Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA.
¹² Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University, Stanford, CA 94305, USA; Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA.
¹³ Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University, Stanford, CA 94305, USA; Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
¹⁴ Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: howchang@stanford.edu.
¹⁵ Department of Pathology, Stanford University, Stanford, CA 94305, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA; Program in Immunology, Stanford University, Stanford, CA, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA. Electronic address: satpathy@stanford.edu.

PMID: 36521490
PMCID: PMC9831293
DOI: 10.1016/j.molcel.2022.11.017

Abstract

Cell cycle (CC) facilitates cell division via robust, cyclical gene expression. Protective immunity requires the expansion of pathogen-responsive cell types, but whether CC confers unique gene expression programs that direct the subsequent immunological response remains unclear. Here, we demonstrate that single macrophages (MFs) adopt different plasticity states in CC, which leads to heterogeneous cytokine-induced polarization, priming, and repolarization programs. Specifically, MF plasticity to interferon gamma (IFNG) is substantially reduced during S-G2/M, whereas interleukin 4 (IL-4) induces S-G2/M-biased gene expression, mediated by CC-biased enhancers. Additionally, IL-4 polarization shifts the CC-phase distribution of MFs toward the G2/M phase, providing a subpopulation-specific mechanism for IL-4-induced, dampened IFNG responsiveness. Finally, we demonstrate CC-dependent MF responses in murine and human disease settings in vivo, including Th2-driven airway inflammation and pulmonary fibrosis, where MFs express an S-G2/M-biased tissue remodeling gene program. Therefore, MF inflammatory and regenerative responses are gated by CC in a cyclical, phase-dependent manner.

Keywords: cell cycle; macrophage plasticity; macrophage polarization; single-cell epigenomics.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests J.A.B. is a consultant for Immunai. A.T.S. is a founder of Immunai and Cartography Biosciences and receives research funding from Allogene Therapeutics and Merck Research Laboratories. H.Y.C. is a member of the Advisory Board of Molecular Cell, is a co-founder of Accent Therapeutics, Boundless Bio, and Cartography Biosciences, and is an advisor to 10x Genomics, Arsenal Biosciences, and Spring Discovery.

Figures

**Figure 1.. Heterogeneous cis-regulatory program of MF polarization.**
**(A)** Scheme of the experimental system. **(B)** UMAP projection of scATAC-seq results from polarized MFs. **(C)** UMAP projection of gene score values for *Arg1* and *Cxcl9*. **(D)** Aggregated scATAC-seq signal in the *Arg1* and *Cxcl9* loci (top). Gene score values and mRNA expression of *Arg1* and *Cxcl9* in CTR (red), IFNG (blue) and IL-4 (green) polarized MFs as determined by scATAC-seq and RT-qPCR (bottom). Data are represented as means ± SD. **(E)** UMAP projection of the alternative polarization trajectory (left). Heatmap of gene scores changing over the polarization trajectory. Genes that lose- (Lost - blue), gain early- (Early - green), or late (Late - red) accessibility are marked and a select set is displayed. *Tlr2*, *Arg1* and *Itgax* gene scores are shown over pseudotime. ChromVAR transcription factor motif deviation scores over pseudotime on the Alternative MF polarization trajectory. **(F)** Same as panel E; for the Classical MF polarization trajectory.

**Figure 2.. MF heterogeneity coincides with cell cycle markers.**
**(A)** scATAC UMAP of MF polarization colored by the 6 MF clusters. Percentage-wise distribution of the clusters across conditions (bottom). **(B)** Heatmap representation of marker gene scores in the polarizaed MF clusters. **(C)** UMAP of *Mki67* gene scores. Violin plot of *Mki67* gene scores in the clusters. **(D)** Bar graphs depict bulk mRNA levels of *Retnla* and *Cxcl9* (left). Data are represented as means ± SD. UMAPs and violin plots show gene score values (log₂ normalized counts+1) (scATAC-seq) for the two genes (middle). UMAPs of gene integration scores (gene expression - scRNA-seq), # - normalized. **(E)** Genome browser snapshots of scATAC-seq signal intensities in the 6 clusters for *Retnla* and *Cxcl9*. **(F)** Peak score heatmap of differentially accessible cis-regulatory regions in the clusters. Homer *de novo* motif search results. Number of regions in each cluster and p-values for the enriched motifs are shown.

**Figure 3.. Cell cycle limits the expression of *Egr2* and *Irf8* during polarization.**
**(A)** UMAP of cell cycle scores in the polarized MF populations. **(B)** Differential gene expression analysis of G1 and G2/M predicted cells from the polarized states (scheme). Heatmap of genes exhibiting G1-biased expression in polarized MFs (right). *Egr2* and *Irf8* transcription factors are marked by red asterisks and their bulk expression level is validated by RT-qPCR. **(C)** Scheme of cell cycle sorting. **(D)** mRNA levels of *Pold2* and *Mki67* measured by RT-qPCR in cell cycle phase-sorted MFs. **(E)** Genome browser view of bulk RNA-seq and RNAPII ChIP-seq results in the *Egr2* locus in polarized MFs. mRNA levels of *Egr2* in cell cycle phases. Statistics: two tailed, unpaired t-test at p<0.05 (n=3). Data are represented as means ± SD. EGR transcription factor footprints in the 6 scATAC clusters. **(F)** Same as panel E; for *Irf8*.

**Figure 4.. Cell cycle phase influences MF plasticity to polarization signals.**
**(A)** Volcano plot of the top 50 differentially expressed gene upon M2(IL-4) polarization determined by scRNA-seq. **(B)** Heatmap of cell cycle-phase sensitive, IL-4-induced genes determined by bulk RNA-seq. **(C)** Genome browser snapshots of genes that exhibit phase-biased expression. **(D)** Validation of cell cycle phase-biased gene expression by RT-qPCR. **(E)** Genome browser snapshots depict scATAC-seq and RNAPII ChIP-seq data in the *Mgl2* and *Retnla* loci in polarized MFs. **(F)** Quantification of MGL2 and RENTLA positive MFs in G1 and S-G2/M cell cycle-phases by flow cytometry. **(G)** Scheme of CRISPR perturbation experiments. RT-qPCR measurements of *Mgl2* and *Retnla* eRNAs and mRNA transcripts in the presence of a non-targeting and enhancer-targeting guide RNAs that target the indicated enhancers. Statistics throughout the figure: two tailed, unpaired t-test at p<0.05, n=3. Data are represented as means ± SD.

**Figure 5.. Cell cycle negatively affects the formation of memory in a MF subset at the chromatin level.**
**(A)** Scheme of the priming model. **(B)** scATAC-seq UMAP of M0(CTR), M2(IL-4) and primed M2p(pIL-4) MFs. **(C)** UMAP colored by the 6 clusters identified. Violin plot depicts the gene score values of *Mki67* in the 6 clusters. Cell cycle icons highlight cycling MF clusters. **(D)** Upset plot of the differentially accessible cis-elements in among the indicated conditions, and their overlap, yielding “memory”, “primed” and “transient” chromatin features. Scheme of revealed chromatin behaviors. **(E)** Heatmap of peak scores that exhibit chromatin behaviors from panel D over the indicated pseudotime trajectory. 25 peaks are shown **(F)** UMAPs depict gene score values for the indicated genes that display different chromatin behaviors. Genome browser views depict scATAC-seq signal in the indicated gene loci. **(G)** Violin plot depicts of the distribution of “Memory” peak scores (accessibility) across the clusters. Statistics: Wilcoxon Signed Rank Test between medians, p<0.0001.

**Figure 6.. IL-4 priming and cell cycle limit the repolarization capacity of IFNG in a subset of MFs.**
**(A)** Scheme of repolarization. **(B)** scATAC-seq UMAP of MF conditions. **(C)** UMAP colored by the 8 chromatin state clusters. Cell cycle icons highlight cycling MF clusters. **(D)** Heatmap of cell cycle marker gene scores. UMAP of *Mki67* gene score values. Genome browser snapshot depicts scATAC-seq signal in cell cycle gene loci. **(E)** Gene score heatmap of cluster 2 markers across all clusters. **(F)** Gene score heatmap of the markers of cluster 2 that also show IFNG-induced, G1-phase-biased expression in bulk RNA-seq in Figure S4D. **(G)** Genome browser views depict bulkATAC-, RNAPII ChIP-, and scATAC-seq (8 clusters) signal in the *Cxcl9* and *Cxcl10* loci. **(H)** mRNA levels of *Irf8*, *Cxcl9* and *Cxcl10* in cell cycle from the indicated conditions. Statistics: two tailed, unpaired t-test at p<0.05, n=3. Data are represented as means ± SD. **(I)** Percentage of MFs in the G2/M phase of the cell cycle as determined by flow cytometry. Average of 3 experiments are used to calculate the percentage-wise distribution of cells in G2/M relative to the highest value (pIL-4).

**Figure 7.. Cycling MFs express tissue regeneration genes.**
**(A)** Experimental scheme. DEGs – differentially expressed genes, IPA – Ingenuity Pathway Analysis. Heatmap represents DEGs across cell cycle phases in M0(CTR) MFs. IPA of the DEGs. Top 10 enriched biological functions are shown. Expression of a select set of genes from the first three enriched biological functions are shown as determined by bulk RNA-seq. **(B)** Feature scatter plots of the indicated gene pairs visualizing co-expression in single MFs (Log₂Normalized Expression) with Pearson correlation coefficients. **(C)** scRNA-seq UMAP of human MFs from idiopathic pulmonary fibrosis (IPF) colored by the identified clusters. UMAP colored by the expression level of *MKI67*. **(D)** Violin plot represents the enrichment of a cell cycle gene signature score across clusters. **(E)** Violin plot represents the fraction of proliferating MFs in cluster 4 from control (Healthy) and IPF lungs. **(F)** Violin plot represents the enrichment of a pro-fibrotic gene signature score across MF clusters. Statistics: Wilcoxon Signed Rank Test at p<0.0001. **(G)** Scheme of the *in vivo* experimental system to induce Th2-type inflammation. **(H)** Boxplots depict the mRNA expression levels of the indicated genes. Statistics: two tailed, unpaired t-test at p<0.05, n=4. **(I)** Representative images of bronchoalveolar lavage (BAL) contents. **(J)** Quantification of MF numbers in the BAL fluid (n=4; top). Fraction of alveolar MFs in G1 and S-G2/M cell cycle phases in the indicated conditions. Statistics: two tailed, unpaired t-test at p<0.05, n=12. **(K)** Boxplots depict gene expression in alveolar MFs from cell cycle-phases in PBS or RWE-treated animals. Statistics: one tailed, paired t-test at p<0.05, n=4. All boxplots: box center line, median; limits, upper and lower quartiles; whiskers, 1.5× interquartile range.

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Macrophage inflammatory and regenerative response periodicity is programmed by cell cycle and chromatin state

Affiliations

Macrophage inflammatory and regenerative response periodicity is programmed by cell cycle and chromatin state

Authors

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Abstract

Conflict of interest statement

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