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Single-cell profiling identifies mechanisms of inflammatory heterogeneity in chronic rhinosinusitis

Nature Immunology volume 23, pages 1484–1494 (2022)Cite this article

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Abstract

The heterogeneous cellular microenvironment of human airway chronic inflammatory diseases, including chronic rhinosinusitis (CRS) and asthma, is still poorly understood. Here, we performed single-cell RNA sequencing (scRNA-seq) on the nasal mucosa of healthy individuals and patients with three subtypes of CRS and identified disease-specific cell subsets and molecules that specifically contribute to the pathogenesis of CRS subtypes. As such, ALOX15+ macrophages contributed to the type 2 immunity-driven pathogenesis of one subtype of CRS, eosinophilic CRS with nasal polyps (eCRSwNP), by secreting chemokines that recruited eosinophils, monocytes and T helper 2 (TH2) cells. An inhibitor of ALOX15 reduced the release of proinflammatory chemokines in human macrophages and inhibited the overactivation of type 2 immunity in a mouse model of eosinophilic rhinosinusitis. Our findings advance the understanding of the heterogeneous immune microenvironment and the pathogenesis of CRS subtypes and identify potential therapeutic approaches for the treatment of CRS and potentially other type 2 immunity-mediated diseases.

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Fig. 1: Identification of cell types in human nasal mucosal microenvironment by 10x Genomics scRNA-seq.
Fig. 2: Compositions and functions of epithelial subsets in control and CRS.
Fig. 3: Overactivated type 2 immune response of lymphocytes contributes to the pathogenesis of eCRSwNP.
Fig. 4: ALOX15+ cDC2s and LAMP3+ DCs primed type 2 immune response in eCRSwNP.
Fig. 5: Enriched ALOX15+ macrophages play key roles in type 2 immune response of eCRSwNP.
Fig. 6: ALOX15 blockade treatment alleviates the type 2 immune response in vitro and in vivo.

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Data availability

The accession number for the raw sequencing data from human samples in this paper is GSA (Genome Sequence Archive) HRA000772. GRCh38 human genome was used as the reference genome (https://cf.10xgenomics.com/supp/cell-exp/refdata-gex-GRCh38-2020-A.tar.gz). Source data are provided with this paper.

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Acknowledgements

We thank C. Liu and X. Yang for technical support with the development of murine rhinosinusitis models and Q. Yang and X. Chen from Guidon Pharmaceutics for technical support with the MACS and Luminex assays. We also thank H. Weng for her help refining the wording of this manuscript. Extended Data Fig. 10 was created using BioRender.com. This project was supported by grants from the National Natural Science Foundation of China (82071027 to W.L.; 82101200 to W.W.; 82071791 and 81602503 to J.Z.; and 91542117, 81673010, U20A20374 and 81471574 to W.H.), the Natural Science Foundation of Beijing (7202162 to W.L.), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-005 to J.Z.; 2021-I2M-1-035 to H.C.; and 2021-I2M-1-053 to Y.X.), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Science (2019XK320034 to W.L.) and CAMS Central Public Welfare Scientific Research Institute Basal Research Expenses (2017PT31014 and 2018PT32004 to H.C. and 2018PT31052 to J.Z.).

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  1. These authors contributed equally: Weiqing Wang, Yi Xu, Lun Wang.

Authors and Affiliations

  1. Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China

    Weiqing Wang, Zhenzhen Zhu, Surita Aodeng, Jinbo Han, Lei Wang, Yuxi Lin, Xiaowei Wang, Yang Zha, Zhiqiang Gao & Wei Lv

  2. Department of Immunology, CAMS Key Laboratory of T Cell and Immunotherapy, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, State Key Laboratory of Medical Molecular Biology, Beijing, China

    Yi Xu, Hui Chen, Menghua Cai, Yu Hu, Wei He & Jianmin Zhang

  3. Department of Internal Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China

    Lun Wang

  4. Analytical Biosciences Limited, Beijing, China

    Zhihao Huang

  5. Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China

    Liangrui Zhou

  6. Department of Otolaryngology Head and Neck Surgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China

    Weihong Jiang

  7. Changzhou Xitaihu Institute for Frontier Technology of Cell Therapy, Changzhou, China

    Jianmin Zhang

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Contributions

W.W., J.Z., W.L. and W.H. designed the study, W.L., X.W. and Y.Z. performed surgeries, W.W., Y.X., Lun Wang, Z.Z., S.A., J.H. and Lei Wang collected patient samples and performed single-cell experiments, Y.X., M.C., H.C. and W.W performed in vitro and in vivo experiments. L.Z., W.W. and M.C. performed histologic analyses, W.W., Lun Wang, Y.X., Z.H., W.J. and Y.L. analyzed data; W.W., Lun Wang, Y.X., J.Z., W.L., W.H., H.C., Y.H., W.J. and Z.G. interpreted data; and W.W., Lun Wang and J.Z. wrote the manuscript. All authors read the manuscript, offered feedback and approved it before submission.

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Correspondence to Wei He, Wei Lv or Jianmin Zhang.

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Nature Immunology thanks Katherine Kedzierska and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Ioana Visan was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Functional characterization of the epithelial cells in control and CRS patients.

a, tSNE plots showing expression of marker genes for all 8 major cell types defined in Fig. 1b. b, tSNE plots showing expression of marker genes for epithelial subsets defined in Fig. 2a. c, Heatmaps of PAGA graph displaying the evolving expression pattern of key genes in four developing pathways of epithelial cells in Fig. 2d. d, Heatmap of the enriched GO biological processes terms among the different epithelial clusters (full list of P value adjusted by Benjamini–Hochberg procedure given in Supplementary Table 5). e, Violin plots of the proliferation score of epithelial subsets with plot center and box corresponding to median and IQR respectively. f-h, Violin plots of the expression levels of selected genes in goblet (f), ciliated (g) and glandular (h) subsets among control and CRS subtypes (MAST with RE adjusted by Benjamini–Hochberg procedure; full list of P value given in Supplementary Table 4). This figure supports Figs. 1, 2.

Extended Data Fig. 2 Pathologic fibroblasts contribute to the abnormal tissue remodeling and nasal polyp formation.

a, tSNE plots displaying 7,558 fibroblasts from 21 individuals (5 healthy controls, 5 CRSsNP, 5 neCRSwNP, and 6 eCRSwNP patients) separated into 8 subsets (left) and their corresponding patient group of origin (right). b, tSNE plots showing expression of marker genes for fibroblast subsets defined in panel a. c, Box and whisker plots showing the fraction of fibroblasts originating from 4 groups in each cluster, with plot center, box and whiskers corresponding to median, IQR and 1.5 × IQR, respectively (MASC adjusted by Benjamini–Hochberg procedure; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; Supplementary Table 2). d, Bubble heatmap showing marker genes across 8 fibroblast clusters from panel a (nonparametric two-sided Wilcoxon rank sum test adjusted by Bonferroni method; full list of P value given in Supplementary Table 3). e, Heatmap of the enriched GO biological processes terms among the different fibroblast clusters (full list of P value adjusted by Benjamini–Hochberg procedure given in Supplementary Table 5). All panels depict the same number of cells and individuals described in panel a.

Extended Data Fig. 3 Compositions and pathologic vascular functions of smooth muscle cells and endothelial cells in control and CRS patients.

a, tSNE plots displaying 1,953 smooth muscle cells from 21 individuals (5 healthy controls, 5 CRSsNP, 5 neCRSwNP, and 6 eCRSwNP patients) separated into 4 subsets (left) and their corresponding patient group of origin (right). b, tSNE plots showing expression of marker genes for SMC subsets defined in panel a. c, Box and whisker plots showing the fraction of SMC originating from 4 groups in each cluster, with plot center, box and whiskers corresponding to median, IQR and 1.5 × IQR, respectively (MASC adjusted by Benjamini–Hochberg procedure; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; Supplementary Table 2). d, Violin plots of the expression levels of selected genes in SMCs among control and CRS subtypes (MAST with RE adjusted by Benjamini–Hochberg procedure; full list of P value given in Supplementary Table 4). e, tSNE plots displaying 8,715 endothelial cells from 21 individuals (5 healthy controls, 5 CRSsNP, 5 neCRSwNP, and 6 eCRSwNP patients) separated into 5 subsets (left) and their corresponding patient group of origin (right). f, tSNE plots showing expression of marker genes for endothelial subsets defined in panel e. g, Box and whisker plots showing the fraction of endothelial cells originating from 4 groups in each cluster, with plot center, box and whiskers corresponding to median, IQR and 1.5 × IQR, respectively (MASC adjusted by Benjamini–Hochberg procedure; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; Supplementary Table 2). h, Violin plots of the expression levels of selected genes in endothelial cells among control and CRS subtypes (MAST with RE adjusted by Benjamini–Hochberg procedure; full list of P value given in Supplementary Table 4). i, Violin plot showing the expression levels of PLAT in 8 major cell types of nasal mucosa. All panels depict the same number of cells and individuals described in panel a or e.

Extended Data Fig. 4 Compositions and functions of T and B lymphocytes contributes to the pathogenesis of eCRSwNP.

a, tSNE plots showing expression of marker genes for generalized T cell subsets defined in Fig. 3a. b, Violin plots of the expression levels of selected genes in CD8+ TRM among control and CRS subtypes (MAST with RE adjusted by Benjamini–Hochberg procedure; full list of P value given in Supplementary Table 4; full list of P value given in Supplementary Table 4). c, tSNE plots showing expression of marker genes for B cell subsets defined in Fig. 3e. d, Box and whisker plots showing the fraction of B lymphocytes originating from 4 groups (5 healthy controls, 5 CRSsNP, 5 neCRSwNP, and 6 eCRSwNP patients) in each cluster, with plot center, box and whiskers corresponding to median, IQR and 1.5 × IQR, respectively (MASC adjusted by Benjamini–Hochberg procedure; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; Supplementary Table 2). This figure supports Fig. 3.

Extended Data Fig. 5 Chemokine signaling interactions in nasal immune microenvironment.

Dot plots showing expression of chemokine ligands (left) and receptors (right) in all nasal cells; only significant interactions between chemokine ligands and receptors were connected by lines. Colored lines highlight the previously unknown ligand sources (target cells) with migrating immune cell types expressing cognate receptor.

Extended Data Fig. 6 Distinct compositions and functions of myeloid cells in control and CRS patients.

a, tSNE plots showing expression of marker genes for myeloid subsets defined in Fig. 4a. b, Violin plots of the expression levels of selected genes in mast cell subsets (nonparametric two-sided Wilcoxon rank sum test adjusted by Bonferroni method; full list of P value given in Supplementary Table 3). c, Correlation analysis of the gene expression similarity of DC subsets in CRS and HCC. d, Violin plots of the M1 (top) and M2 (bottom) phenotype score across 10 macrophage clusters with plot center and box corresponding to median and IQR respectively, calculated by the mean expression of corresponding signature genes. e, tSNE plot separately showing the disease-specific distribution of 11 macrophages clusters in four patient groups. The serial number of each cell cluster is the same with Fig. 5a. f, Violin plots of selected gene expression in tissue-resident macrophages with plot center and box corresponding to median and IQR respectively (nonparametric two-sided Wilcoxon rank sum test adjusted by Bonferroni method). g, Violin plots of selected gene expression in all macrophages with plot center and box corresponding to median and IQR respectively (nonparametric two-sided Wilcoxon rank sum test adjusted by Bonferroni method). h, Graphic summary illustrating the cell origins of arachidonic acid metabolic pathway including enzymes, receptors and their alterations in CRS subtypes. EnC, Endothelial cell; EpC, Epithelial cell; Fb, Fibroblast; MC, Mast cell; Mφ, Macrophage; NE, Neutrophil; NK, NK-CX3CR1; PC, Plasma cell; SMC, Smooth Muscle cell. This figure supports Figs. 4, 5.

Extended Data Fig. 7 Mass cytometry analysis of cell compositions in control and CRS patients.

a, tSNE plot displaying clustering of immune and nonimmune cells pooled from 50,000 cellular events in each group and equally divided among each individual (3 healthy controls, 3 CRSsNP patients, 3 neCRSwNP patients, and 5 eCRSwNP patients). b, tSNE plots showing expression of marker genes for cell subsets defined in the panel a. c, tSNE plot displaying clustering of myeloid cells from panel a. d, tSNE plots showing expression of marker genes for myeloid subsets defined in the panel c. e, tSNE plot displaying clustering of T cells and NK cells from panel a. f, tSNE plots showing expression of marker genes for T cell and NK cell subsets defined in the panel e. g, Box and whisker plots showing the proportion of the CX3CR1+ and CD209+ tissue-resident macrophages in all macrophages and the proportion of the CD8+ TRM in all T cells with plot center, box and whiskers corresponding to median, IQR and 1.5 × IQR, respectively. Unpaired two-sided t-tests. *P < 0.05. h, Violin plots showing the expression levels of CD1b in cDC2s among control and CRS subtypes. Unpaired two-sided t-tests. *P < 0.05, **P < 0.01, ****P < 0.0001. All panels depict the same number of individuals described in panel a.

Extended Data Fig. 8 Expression of ALOX15 in human nasal samples.

a, Representative immunofluorescent assay was performed with anti-ALOX15 (green) and anti-CD3 (red) for T cells, anti-CD20 (red) for B cells, anti-CD138 (red) for plasma cells, anti-EMBP (red) for eosinophils, anti-CD11c (red) for DCs, and anti-Tryptase (red) for mast cells (n = 4 for each group). Scale bars, 10 µm. Results are presented as Mean ± SEM in bar plots. Unpaired two-sided t-tests. *P < 0.05, **P < 0.01. b, The Pearson correlation analysis (two-sided) between ALOX15 expression and four clinical parameters evaluating CRS severity, including tissue eosinophil count/HPF, serum total IgE level, SNOT-22 score, and CT Lund-MacKay score. The expression levels of ALOX15 measured by ELISA in surgical nasal samples of an independent cohort of patients with CRSsNP (n = 27), neCRSwNP (n = 28), and eCRSwNP (n = 32). This figure supports Fig. 6.

Extended Data Fig. 9 ALOX15 blockade treatment alleviates the type 2 immune response in vitro and in vivo.

a, Chemokines with insignificant modulation by ALOX15 in IL-4/IL-13-stimulating macrophages co-incubated with or without PD146176 (n = 3 for each group). Unpaired two-sided t-tests. *P < 0.05. b, Representative western blot confirming the knockdown of ALOX15 expression in IL-4/IL-13-stimulating macrophages transfected with ALOX15 siRNA or scramble siRNA (n = 4 for each group). Unpaired two-sided t-tests. **P < 0.01, ****P < 0.0001. c, Chemokines with insignificant modulation by ALOX15 in IL-4/IL-13-stimulating macrophages transfected with ALOX15 siRNA or scramble siRNA (n = 5 for each group). Unpaired two-sided t-tests. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. d, Schematic drawing showing treatment strategy of a papain-induced eosinophilic rhinosinusitis murine model. e, Flow cytometry gating strategy for total immune cells (CD45+), epithelial cells (CD45−EpCAM+), CD4+ T cells (CD45+CD3+CD4+), eosinophils (CD45+Siglec-F+), B cells (CD45+B220+), macrophages (CD45+F4/80+) in murine sinonasal mucosa. f, Representative plots of flow cytometry showing CD45, EPCAM, CD3, CD4, Siglec-F, B220, F4/80 expression in murine sinonasal mucosa under the indicated conditions at Day 16 (n = 4 for each group), related to Fig. 6g. This figure supports Fig. 6.

Source data

Extended Data Fig. 10 Graphic summary of novel insights on the immune microenvironment in eCRSwNP from this study.

The graphic summary illustrates novel insights on different nasal mucosa immune microenvironments between eCRSwNP and healthy controls, and how the type 2 immune response promotes the pathogenesis of nasal polyps in our scRNA-seq study. In eCRSwNP, the hallmarks of pathogenesis included: (1) hyperplasia of basal cells and deficiency of protective cells and molecules, (2) extracellular matrix remodeling dysfunction by fibroblasts, macrophages, vascular endothelial cells, and smooth muscle cells (SMCs), (3) elevated levels of TH2 cells, ILC2s, IL5RA+ plasma cells, and cytotoxic CX3CR1+ CD8+ TEFF cells and NK cells, and deficiency of CD8+ TRM cells, (4) defects in the NK-cDC1 immunologic surveillance axis and overactivation of the ALOX15+ cDC2-TH2 axis, and very importantly, (5) enrichment of ALOX15+ macrophages with a prominent chemotactic effect as a driver of the type 2 immunity by promoting the infiltration of various inflammatory cells.

Supplementary information

Supplementary Information

Supplementary Text 1–8 and associated references.

Reporting Summary

Supplementary Table 1

Supplementary Tables 1–7.

Source data

Source Data Fig. 6

Unprocessed western blots.

Source Data Extended Data Fig. 9

Unprocessed western blots.

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Wang, W., Xu, Y., Wang, L. et al. Single-cell profiling identifies mechanisms of inflammatory heterogeneity in chronic rhinosinusitis. Nat Immunol 23, 1484–1494 (2022). https://doi.org/10.1038/s41590-022-01312-0

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