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Article

ADGRB3-High and POSTN-High Fibroblasts Are Markers of Endotypic Traits in Chronic Rhinosinusitis

by
Hideyuki Takahashi
1,*,
Toshiyuki Matsuyama
1,
Reika Kawabata-Iwakawa
2,
Yohei Morishita
3,
Takayuki Kawamoto
1 and
Kazuaki Chikamatsu
1
1
Department of Otolaryngology-Head and Neck Surgery, Gunma University Graduate School of Medicine, 3-39-22, Showa-machi, Gunma, Maebashi 371-8511, Japan
2
Division of Integrated Oncology Research, Gunma University Initiative for Advanced Research, Gunma University, 3-39-22, Showa-machi, Gunma, Maebashi 371-8511, Japan
3
Laboratory for Analytical Instruments, Education and Research Support Centre, Gunma University Graduate School of Medicine, 3-39-22, Showa-machi, Gunma, Maebashi 371-8511, Japan
*
Author to whom correspondence should be addressed.
Immuno 2024, 4(4), 646-656; https://doi.org/10.3390/immuno4040038
Submission received: 28 October 2024 / Revised: 29 November 2024 / Accepted: 12 December 2024 / Published: 14 December 2024
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Abstract

Background: Chronic rhinosinusitis (CRS) is a disease characterized by persistent sinonasal mucosal inflammation. Fibroblasts play a crucial role in extracellular matrix production and inflammation. We investigated the heterogeneity of fibroblasts in patients with CRS. Methods: Fibroblasts were isolated from nasal polyp tissues. RNA sequencing was then performed. We also analyzed the GSE136825 dataset obtained from the Gene Expression Omnibus database. Alternatively, fibroblasts were stimulated in vitro. Results: Hierarchical clustering of samples indicated ADGRB3-high and POSTN-high fibroblasts. A Gene Set Enrichment Analysis (GSEA) revealed that cytotoxic immune responses were enriched in ADGRB3-high fibroblasts, while cell cycle pathways were enriched in POSTN-high fibroblasts. Similar GSEA results were observed in the GSE136825 dataset. Additionally, type 1 and type 3 inflammation-related genes were highly expressed in ADGRB3-high samples, whereas type 2-related genes were highly expressed in POSTN-high samples. In vitro, ADGRB3 expression increased in fibroblasts stimulated with IFN-γ, while POSTN increased in those stimulated with IL-4 and IL-13. Conclusions: Our study demonstrates that type 1 inflammation induces ADGRB3-high fibroblasts, associated with the cytotoxic immune response, while type 2 inflammation induces POSTN-high fibroblasts, linked to CRS progression via an elevated cell cycle. The further characterization of fibroblasts could provide insights into the stromal networks in the CRS microenvironment.

1. Introduction

Chronic rhinosinusitis (CRS) is a common chronic disease marked by persistent inflammation of the sinonasal mucosa, with symptoms such as nasal congestion, purulent secretions, and a reduced sense of smell. CRS is frequently linked to the development and prognosis of lower airway diseases, including asthma and chronic obstructive pulmonary disease [1]. CRS is classified into two phenotypes based on the presence of nasal polyps: CRS with nasal polyps (CRSwNP) and CRS without nasal polyps (CRSsNP) [2]. Evidence indicates that CRS endotypes are defined by molecular profiles, including type 1, type 2, and type 3 (also known as Th17) inflammation, and can be applied across two phenotypes [3,4]. The type 1 endotype is defined by the transcription factor T-bet and cytokine interferon (IFN)-γ, produced by Th1 cells, cytotoxic T cells, natural killer (NK) cells, and innate lymphoid cell (ILC) 1. The type 2 endotype involves the transcription factor GATA3 and cytokines interleukin (IL)-4, IL-5, and IL-13 produced by Th2 cells, mast cells, ILC2s, and eosinophils. The type 3 endotype is marked by the transcription factor retinoic acid-related orphan receptor C and cytokines IL-17A, IL-17F, and IL-22, produced by Th17 cells and ILC3s [5,6]. Eosinophilic CRS (ECRS) is a subtype dominated by eosinophilic-dominant inflammation and is recognized as a severe subtype of CRS with poor treatment outcomes [7,8,9]. As the clinical value of CRS molecular endotypes remains unclear, further characterization of these endotypes is needed.
Emerging evidence suggests that fibroblasts drive tissue inflammation in various organs, including both the upper and lower airways [10,11]. In nasal polyps, fibroblasts are the most abundant stromal cells and play key roles in extracellular matrix production and inflammation regulation [12]. Owing to their diverse functional states, it is important to understand fibroblast heterogeneity in tissues [13]. A single-cell RNA sequencing (scRNA-seq) analysis of CRSwNP has identified key cell subtypes of fibroblasts in nasal tissues, including ADGRB3+ and POSTN+ fibroblasts [14]. ADGRB3+ fibroblasts are abundant in control nasal mucosa, while POSTN+ fibroblasts are abundant in nasal polyp tissues. However, the characteristics and clinical significance of these subtypes, particularly ADGRB3+ fibroblasts, remain elusive.
In the present study, we isolated fibroblasts from nasal polyp tissues and performed RNA sequencing (RNA-seq) to characterize their subtypes based on the ADGRB3+ and POSTN+ fibroblast-related gene signatures. To validate our findings, we analyzed RNA-seq data from nasal tissues in a public database. Additionally, we stimulated fibroblasts in vitro to elucidate the relationship between the fibroblast subtypes and type 1, type 2, and type 3 inflammation. Since fibroblasts play a vital role in the regulation of inflammation in CRS, our results provide new insights to understand the stromal network in the CRS microenvironment. Further characterization of fibroblasts would lead to new therapeutic approaches for patients with CRS.

2. Materials and Methods

2.1. Sample Collection

Nasal polyp (NP) tissues were obtained from eight patients with CRSwNP who underwent endoscopic sinus surgery at Gunma University Hospital. None of the patients received systemic corticosteroids or biological medications before the surgery. Patients were divided into two groups, ECRS and non-ECRS, based on tissue eosinophil counts in the NP, with a cutoff value of 100 eosinophils/high-power field [7,15]. Clinical variables, including age, sex, complications, percentage of eosinophils in the blood, eosinophil count in NP tissues, and Lund–Mackay score were collected from electronic medical records. The study protocol was approved by the Institutional Review Board of Gunma University (HS2017-259) and conducted in accordance with the latest version of the Declaration of Helsinki. Written informed consent was obtained from all the patients.

2.2. The Establishment of the Fibroblasts from NP Tissues

Fibroblasts were established from NP tissues as previously reported [16]. Briefly, NP tissues were sliced into 1–3-mm3 pieces and plated in a six-well tissue culture plate with Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 2 mM L-glutamine, 100 units/mL penicillin, and 100 µg/mL streptomycin (all reagents were from Thermo Fisher Scientific, Waltham, MA, USA). After culturing the cells for several passages, the fibroblasts were used for subsequent assays. The purity of the established fibroblasts was confirmed by detecting fibroblast activation protein (FAP), CD90, and α-smooth muscle actin (α-SMA) using flow cytometry. Antibodies used for flow cytometry are listed in Table S1. All fibroblasts were obtained after fewer than 10 passages.

2.3. RNA Sequencing (RNA-Seq) of Fibroblasts

Total RNA was isolated from established fibroblasts using the FastGene RNA Premium Kit (Nippon Genetics, Tokyo, Japan) according to the manufacturer’s protocol. RNA quality was confirmed to be high (RNA integrity number > 9.7) using an Agilent RNA6000 Pico Kit (Agilent Technologies, Santa Clara, CA, USA) and Agilent Bioanalyzer (Agilent Technologies). The sequence library was prepared using the KAPA mRNA HyperPrep Kit (Kapa Biosystems Inc., Wilmington, MA, USA) and SeqCap adapter A Kit (Roche Sequencing Solutions, Inc., Pleasanton, CA, USA) from 1 μg of total RNA. The generated libraries and 1% PhiX were subjected to paired-end sequencing, yielding 43 base pair (bp) reads on the NextSeq500 System (Illumina, San Diego, CA, USA) using a NextSeq500 High Output v2.5 Kit (Illumina). The reads were aligned to the University of California, Santa Cruz, reference human genome 19 (hg19) using spliced transcript alignment to reference (STAR) software v2.5.3a (DNASTAR, Madison, WI, USA). Read counting was performed using RSEM (version 1.3.3). A minimum of 31 million reads was obtained for each sample. The log2-transformed value of the mRNA expression level was calculated based on the normalized expression. The mean expression levels were calculated as follows: for POSTN, ROBO1, CCL26, and PAPPA as POSTN scores, and for SFRP2, CXCL2, DCN, PTGDS, and ADGRB3 as ADGRB3 scores based on the top differentially expressed genes (DEGs) in ADGRB3-high fibroblasts and POSTN-high fibroblasts [14].

2.4. Hierarchical Clustering of Patients

Based on the z-scores of the POSTN score and ADGRB3 score, samples were grouped by unsupervised hierarchical clustering using the hclust function in R. Heat maps were constructed using the pheatmap R package.

2.5. The Acquisition of the GSE136825 Dataset from a Publicly Available Database

The GSE136825 dataset, which includes RNA-seq data and clinical information, was obtained from the Gene Expression Omnibus (GEO) database. In total, 103 samples, including 28 inferior turbinate tissues from non-CRS controls (Ctrl), 42 NP tissues from CRSwNP patients (CRSwNP_NP), and 33 paired inferior turbinate tissues from CRSwNP patients (CRSwNP_IT), were analyzed. The log2-transformed mRNA expression level was calculated based on normalized expression. Mean expression levels were calculated for POSTN, ROBO1, CCL26, and PAPPA as POSTN scores; SFRP2, CXCL2, DCN, PTGDS, and ADGRB3 as ADGRB3 scores; IDO1, CXCL9, CXCL10, HLA-DRA, STAT1, and IFNG as type 1 scores; IL4, IL5, and IL13 as type 2 scores; and IL17A, IL17F, and IL22 as type 3 scores [17,18]. Samples were grouped by unsupervised hierarchical clustering based on z-scores of POSTN score and ADGRB3 score.

2.6. Gene Set Enrichment Analysis (GSEA)

GSEA (GSEA v4, Broad Institute) was used to identify the upregulated pathways in ADGRB3-high and POSTN-high samples. Normalized enrichment score, p-value, and false discovery rate (FDR) q-value were calculated for each gene set using the Hallmark and BioCarta pathway databases.

2.7. Deconvolution Analysis

CIBERSORTx (Stanford University) was used to deconvolute predicted cell fractions from the GSE136825 dataset. The infiltration scores of 22 immune cell types were calculated using the LM22 signature matrix [19]. Calculated scores were visualized using the pheatmap R package.

2.8. In Vitro Stimulation of Established Fibroblasts

The established fibroblasts were plated into a 6-well tissue culture-treated plate (1 × 105 cells/well), cultured overnight, and further cultured in the presence of recombinant IFN-γ (10 ng/mL), IL-4 (100 ng/mL), IL-13 (100 ng/mL), IL-17A (100 ng/mL), and IL-22 (100 ng/mL). After 48 h, the cells were harvested and total RNA was extracted using an RNeasy Mini Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. RT-qPCR was performed in triplicate to quantify the expression levels of ADGRB3, POSTN, ACTA2, and FAP using a Power SYBR Green RNA-to-CT 1-Step Kit (Thermo Fisher Scientific) on an Applied Biosystems StepOne system (Thermo Fisher Scientific), as previously described [20]. As glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was expressed equally regardless of the expression of ADGRB3 and POSTN in the RNA-seq analysis, it was used as an internal control (Figure S1C). The relative expression levels of target genes were determined using the 2−ΔΔCt values.

2.9. Statistical Analysis

Data analyses were performed using GraphPad Prism version 8 (GraphPad Software, Inc., La Jolla, CA, USA) and R (version 4.3.1; the R Foundation for Statistical Computing, Vienna, Austria) in combination with R studio (Boston, MA, USA). Student’s t-test was used to compare continuous variables between the two groups. The chi-square test for independence and Fisher’s exact test were used to compare categorical variables. Two-sided p-values < 0.05 were considered statistically significant.

3. Results

3.1. Characteristics of Patients with CRSwNP

Table 1 summarizes the characteristics of the eight patients with CRSwNP. The patients in the ECRS group had a significantly higher percentage of blood eosinophils and a higher rate of olfactory dysfunction. No significant differences were observed in age, sex, eosinophil count in NP, the Lund–Mackay score, the complication rate of asthma, NSAID-exacerbated respiratory disease, or allergic rhinitis between the two groups.

3.2. Characterization of ADGRB3-High and POSTN-High Fibroblasts

We established fibroblasts from NP tissues obtained from four patients with ECRS and those with non-ECRS, respectively. The fibroblasts were positive for CD90, FAP, and α-SMA and negative for CD11b, CD34, CD45, and EPCAM, confirming the purity of the fibroblasts (Figure 1A). RNA-seq revealed the gene expression profiles of established fibroblasts. Hierarchical clustering of samples based on ADGRB3 and POSTN scores revealed three groups: ADGRB3-high fibroblasts, POSTN-high fibroblasts, and others (Figure 1B). ADGRB3-high fibroblasts comprised both ECRS and non-ECRS samples, whereas POSTN-high fibroblasts comprised only ECRS samples. To evaluate the biological functions and related pathways upregulated in ADGRB3-high and POSTN-high fibroblasts, we performed GSEA. In ADGRB3-high fibroblasts, hallmark pathway gene sets representing cytotoxic immune responses, such as TNFA_SIGNALING_VIA_NFKB, INTERFERON_GAMMA_RESPONSE, and INTERFERON_ALPHA_RESPONSE, were enriched (Figure 1C). Gene sets representing the cell cycle, such as E2F_TARGETS, G2M_CHECKPOINT, MYC_TARGET_V1, and MYC_TARGET_V2 were enriched in POSTN-high fibroblasts (Figure 1D). Similar trends were observed in the BioCarta pathway gene sets, including the enrichment of the CYTOKINE_PATHWAY, TNFR2_PATHWAY, and IL6_PATHWAY in ADGRB3-high fibroblasts and the enrichment of the G2_PATHWAY, MCM_PATHWAY, and CELLCYCLE_PATHWAY in POSTN-high fibroblasts (Figure S1A,B).

3.3. Characterization of ADGRB3-High and POSTN-High Tissues

To validate the RNA-seq results of fibroblasts, we analyzed the RNA-seq data of nasal tissues obtained from the GSE136825 dataset. Hierarchical clustering based on the ADGRB3 and POSTN scores indicated three groups: ADGRB3-high samples, POSTN-high samples, and others (Figure 2A). ADGRB3-high samples mainly comprised CRSwNP_IT, whereas POSTN-high samples mostly comprised CRSwNP_NP (Figure 2A; Table 2). The GSEA revealed that hallmark pathway gene sets representing the cytotoxic immune response, such as TNFA_SIGNALING_VIA_NFKB, INTERFERON_GAMMA_RESPONSE and INTERFERON_ALPHA_RESPONSE, were enriched in ADGRB3-high samples (Figure 2B). In the POSTN-high samples, gene sets representing the cell cycle, such as E2F_TARGETS, G2M_CHECKPOINT, MYC_TARGET_V1, and MITOTIC_SPINDLE, were enriched (Figure 2C). Similar trends were observed in the BioCarta pathway gene sets, including the enrichment of CELLCYCLE_PATHWAY, G1_PATHWAY, G2_PATHWAY, and EFP_PATHWAY in the POSTN-high samples (Figure S2B). In the ADGRB3-high samples, several biocarta pathways related to the immune response and inflammation, including the CCR_PATHWAY, VIP_PATHWAY, 41BB_PATHWAY, and LAIR_PATHWAY, were enriched (Figure S2A).
We next investigated the expression of type 1, type 2, and type 3 inflammation-related genes in each group (Figure 2D,E and Figure S2C). The expression of type 1- and type 3-related genes as well as type 1 and type 3 scores were higher in the ADGRB3-high samples, whereas those of type 2-related genes and type 2 scores were higher in the POSTN-high samples.
A deconvolution analysis was performed to compare the infiltration of immune cell types in ADGRB3-high and POSTN-high samples (Figure 2F,G and Figure S2D). Consistent with the results for type 1- and type-2 related genes, the score of M1 macrophages tended to be higher in the ADGRB3-high samples than in the POSTN-high samples, whereas the score of M2 macrophages was higher in the POSTN-high samples than in the ADGRB3-high samples. Additionally, the infiltration of various immune cell types, including B cells, plasma cells, CD4+ memory T cells, natural killer (NK) cells, monocytes, and activated dendritic cells, tended to be elevated in the ADGRB3-high samples, suggesting an upregulated adaptive immune response.

3.4. In Vitro Stimulation of Established Fibroblasts

As we observed a correlation between the ADGRB3 score/POSTN score and type 1, type 2, and type 3 inflammation in the GSE136825 dataset, we performed in vitro stimulation of established fibroblasts with type 1, type 2, and type 3 cytokines (Figure 3). The expression of ADGRB3 was upregulated by stimulation with IFN-γ, whereas that of POSTN was upregulated by stimulation with IL-4, IL-13, IL-17A, and IL-22. We also evaluated the expression of fibroblast markers ACTA2 and FAP. ACTA2 expression was downregulated by stimulation with IFN-γ, while it was upregulated by stimulation with IL-4 and IL-13.

4. Discussion

Fibroblasts are the main cell type in the connective tissue and play a vital role in the production of the extracellular matrix and regulation of inflammation in CRS [10,12]. However, the functional heterogeneity of fibroblasts in CRS remains unclear. The results of the present study demonstrate that type 1 inflammation induces ADGRB3-high fibroblasts and is associated with the cytotoxic immune response, whereas type 2 inflammation induces POSTN-high fibroblasts and is associated with the progression of CRS through an elevated cell cycle.
In the present study, we established fibroblasts from the NP tissues obtained from patients with CRSwNP. A GSEA using RNA-seq data from fibroblasts revealed that the hallmark pathway gene sets representing cytotoxic immune responses were enriched in ADGRB3-high fibroblasts (Figure 1C). ADGRB3, also known as adhesion G protein-coupled receptor B3 (BAI3), belongs to the cell adhesion group of the G protein-coupled receptor (GPCR) and relates to cell adhesion [21,22], suggesting that ADGRB3-high fibroblasts may contribute to the stability of nasal mucosa through their cell adhesion capacity. In the present study, the bulk RNA-seq of whole nasal tissues revealed that ADGRB3-high samples mainly comprised inferior turbinate tissues from patients with CRSwNP (Table 2). In contrast, scRNA-seq of CRSwNP demonstrated that ADGRB3-high fibroblasts were more abundant in the control tissues than in the nasal tissues adjacent to NPs [14]. As the results of the present study were obtained using bulk RNA-seq data, ADGRB-related gene expression may be influenced by the presence of various cell types in tissues, including epithelial, endothelial, and immune cells. Our results suggest that both fibroblasts and other cell types express ADGRB3-related genes in nasal tissues adjacent to NPs, which may contribute to cell adhesion. In addition, GSEA using bulk RNA-seq data revealed the enrichment of hallmark pathway gene sets representing the cytotoxic immune response in ADGRB3-high samples. These results indicate that ADGRB3-related genes contribute not only to cell adhesion but also to cytotoxic immune responses during the development of CRSwNP. Meanwhile, in RNA-seq data, non-ECRS samples comprised both ADGRB3-high and intermediate samples (Figure 1B), suggesting that the relationship between eosinophilic inflammation and ADGRB3 remains elusive. Further characterization of ADGRB3 will provide new insights for a better understanding of CRSwNP.
Hierarchical clustering revealed that all POSTN-high fibroblasts were isolated from patients with ECRS (Figure 1B). In addition, GSEA using RNA-seq data from fibroblasts indicated that gene sets representing the cell cycle were enriched in POSTN-high fibroblasts (Figure 1D). POSTN, also known as periostin, is an extracellular matrix protein that contributes to subepithelial fibrosis and tissue remodeling [23,24]. Elevated POSTN expression in various otolaryngological diseases, including eosinophilic otitis media, allergic rhinitis, and CRS, has been reported [25,26,27,28]. In patients with CRS, the diffuse expression of POSTN in the NP has been reported to be correlated with severe ECRS [29]. Moreover, high serum periostin levels were correlated with the postoperative recurrence of ECRS. However, the source of POSTN in CRS has not yet been identified. The results of the present study demonstrate that POSTN-high fibroblasts may be a source of periostin in CRSwNP and may contribute to the progression of NP through their elevated proliferative capacity, especially in patients with ECRS.
Bulk RNA-seq data from nasal tissues demonstrated that ADGRB3-high tissues showed a higher expression of type 1- and type 3-related genes (Figure 2D,E). Furthermore, in vitro stimulation of fibroblasts with IFN-γ induced the elevated expression of ADGRB3 (Figure 3). In combination with the GSEA results, type 1 inflammation in CRSwNP may facilitate a cytotoxic immune response in the nasal mucosa by altering fibroblasts into an ADRB3-high phenotype. Meanwhile, POSTN-high tissues showed higher type 2-related gene expression and an elevated infiltration of M2 macrophages (Figure 2D–G). A correlation between POSTN expression and type 2 cytokine expression has been demonstrated in CRSwNP [26,29]. In addition, M2 macrophages are increased in CRSwNP, and the number of M2 macrophages is correlated with the levels of type 2 mediators [30]. The results of the present study are consistent with previous findings. Additionally, we performed in vitro stimulation of established fibroblasts and observed elevated expressions of POSTN and ACTA2 in fibroblasts stimulated with IL-4 and IL-13 (Figure 3). Both POSTN and ACTA2 are known as markers for myofibroblast, which is an activated subset of fibroblasts and regulates connective tissue remodeling [31]. Our results suggest that type 2 cytokines induce myofibroblasts and may contribute to NP progression by regulating connective tissue remodeling. Moreover, in bronchial asthma, lung fibroblasts secrete periostin in response to IL-4 and/or IL-13 to facilitate subepithelial fibrosis [32]. Since both the paranasal sinuses and lungs belong to the respiratory tract and share common features, fibroblasts may contribute to tissue fibrosis in CRSwNP by secreting periostin in response to type 2 inflammation.
A deconvolution analysis revealed the elevated infiltration of various immune cells related to adaptive immune responses in ADGRB3-high samples (Figure 2F,G). Notably, memory T cells were highly infiltrated in ADGRB3-high samples. The generation and maintenance of T cell memory in the nasal and paranasal mucosa are crucial for long-term protection against diverse pathogens, such as Staphylococcus aureus [33,34,35,36]. These results suggest that the ADGRB3-related gene signature may be a hallmark of type 1 inflammation, adaptive immune responses, and long-term T-cell memory in the nasal and paranasal mucosa.

5. Conclusions

In the present study, we demonstrated that type 1 inflammation induces ADGRB3-high fibroblasts and is associated with the cytotoxic immune response, whereas type 2 inflammation induces POSTN-high fibroblasts and is associated with CRS progression through elevated cell cycle activity. Further characterization of these fibroblasts enhances our understanding of stromal crosstalk within the CRS microenvironment.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/immuno4040038/s1, Figure S1: Additional data for Figure 1; Figure S2: Additional data for Figure 2. Table S1: Antibodies used for flow cytometry.

Author Contributions

Conceptualization, H.T. and K.C.; Data Curation, T.M. and H.T.; Formal Analysis, H.T., Y.M., and R.K.-I.; Funding Acquisition, K.C.; Investigation, T.M., Y.M., and T.K.; Methodology, H.T. and K.C.; Project Administration, K.C.; Resources, K.C.; Software, H.T. and R.K.-I.; Supervision, K.C.; Validation, T.M. and K.C.; Visualization, H.T.; Writing—Original Draft Preparation, H.T.; Writing—Review and Editing, K.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported in part by a Grant-in-Aid for Scientific Research (Grant No. 20H03834 to K.C.) from the Ministry of Education, Culture, Sports, Science, and Technology, Japan.

Institutional Review Board Statement

The study protocol was approved by the Institutional Review Board of Gunma University (HS2017-259) and conducted in accordance with the latest version of the Declaration of Helsinki.

Informed Consent Statement

Written informed consent was obtained from all subjects involved in this study.

Data Availability Statement

The GSE136825 dataset is available online: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136825 (accessed on 9 July 2022).

Acknowledgments

We thank Saori Fujimoto (Initiative for Advanced Research, Gunma University) for her technical assistance in the NGS analysis. This work was the result of using research equipment shared in the MEXT Project for promoting the public utilization of advanced research infrastructure (a program for supporting the introduction of the new sharing system) Grant Number JPMXS0420600120.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Immuno 04 00038 g001
Figure 1. Characterization of fibroblasts established from nasal polyps. Fibroblasts were established from nasal polyp tissues obtained from patients with CRSwNP. Flow cytometry and RNA sequencing were then performed. (A) Expression of cell type-specific markers in fibroblasts was evaluated using flow cytometry. (B) Heat map showing POSTN and ADGRB3 scores calculated based on normalized expression of signature genes. Hierarchical clustering of samples was performed. (C,D) Gene Set Enrichment Analysis indicating upregulated pathways in ADGRB3-high and POSTN-high fibroblasts. Bubble plots represent normalized enrichment scores (NES) and false discovery rate (FDR) q-values. CRSwNP, chronic rhinosinusitis with nasal polyps; FAP, fibroblast activation protein; αSMA, alpha smooth muscle actin; ECRS, eosinophilic rhinosinusitis.
Figure 1. Characterization of fibroblasts established from nasal polyps. Fibroblasts were established from nasal polyp tissues obtained from patients with CRSwNP. Flow cytometry and RNA sequencing were then performed. (A) Expression of cell type-specific markers in fibroblasts was evaluated using flow cytometry. (B) Heat map showing POSTN and ADGRB3 scores calculated based on normalized expression of signature genes. Hierarchical clustering of samples was performed. (C,D) Gene Set Enrichment Analysis indicating upregulated pathways in ADGRB3-high and POSTN-high fibroblasts. Bubble plots represent normalized enrichment scores (NES) and false discovery rate (FDR) q-values. CRSwNP, chronic rhinosinusitis with nasal polyps; FAP, fibroblast activation protein; αSMA, alpha smooth muscle actin; ECRS, eosinophilic rhinosinusitis.
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Figure 2. Characterization of nasal tissues based on ADGRB3 score and POSTN score. GSE136825 dataset containing RNA sequencing data from 28 control tissues, 33 IT tissues of CRSwNP, and 42 NP tissues of CRSwNP was analyzed. (A) Heat map showing POSTN and ADGRB3 scores calculated based on normalized expression of signature genes. Hierarchical clustering of samples was performed. (B,C) Gene Set Enrichment Analysis indicating upregulated pathways in ADGRB3-high and POSTN-high samples. Bubble plots represent normalized enrichment scores (NES) and false discovery rate (FDR) q-values. (D) Heat map showing expression of type 1-related, type 2-related, and type 3-related genes and calculated scores. (E) Type 1, type 2, and type 3 scores in ADGRB3-high and POSTN-high samples. (F) Heat map showing infiltration scores of immune cell subsets calculated using CIBERSORTx. (G) Immune cell subset scores in ADGRB3-high and POSTN-high samples. CRSwNP, chronic rhinosinusitis with nasal polyps; NP, nasal polyp; IT, inferior turbinate.
Figure 2. Characterization of nasal tissues based on ADGRB3 score and POSTN score. GSE136825 dataset containing RNA sequencing data from 28 control tissues, 33 IT tissues of CRSwNP, and 42 NP tissues of CRSwNP was analyzed. (A) Heat map showing POSTN and ADGRB3 scores calculated based on normalized expression of signature genes. Hierarchical clustering of samples was performed. (B,C) Gene Set Enrichment Analysis indicating upregulated pathways in ADGRB3-high and POSTN-high samples. Bubble plots represent normalized enrichment scores (NES) and false discovery rate (FDR) q-values. (D) Heat map showing expression of type 1-related, type 2-related, and type 3-related genes and calculated scores. (E) Type 1, type 2, and type 3 scores in ADGRB3-high and POSTN-high samples. (F) Heat map showing infiltration scores of immune cell subsets calculated using CIBERSORTx. (G) Immune cell subset scores in ADGRB3-high and POSTN-high samples. CRSwNP, chronic rhinosinusitis with nasal polyps; NP, nasal polyp; IT, inferior turbinate.
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Figure 3. In vitro stimulation of established fibroblasts. Fibroblasts established from nasal polyps were stimulated with type 1, type 2, and type 3 cytokines in vitro. Bar graphs show relative expression of ADGRB3, POSTN, ACTA2, and FAP quantified using RT-qPCR. GAPDH was used as internal control. Asterisks show statistical significance compared to each control. *, p < 0.05; **, p < 0.01; ****, p < 0.0001.
Figure 3. In vitro stimulation of established fibroblasts. Fibroblasts established from nasal polyps were stimulated with type 1, type 2, and type 3 cytokines in vitro. Bar graphs show relative expression of ADGRB3, POSTN, ACTA2, and FAP quantified using RT-qPCR. GAPDH was used as internal control. Asterisks show statistical significance compared to each control. *, p < 0.05; **, p < 0.01; ****, p < 0.0001.
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Table 1. Characteristics of eight patients with chronic rhinosinusitis with nasal polyps.
Table 1. Characteristics of eight patients with chronic rhinosinusitis with nasal polyps.
ECRS (n = 4)Non-ECRS (n = 4)p-Value
Age
Median (range)71 (35–73)71 (51–83)0.61
Sex
Male21>0.99
Female23
Asthma
Negative140.14
Positive30
N-ERD
Negative34>0.99
Positive10
Olfactory dysfunction
Negative040.03
Positive40
Allergic rhinitis
Negative420.43
Positive02
%Eosinophil in PB
Median (range)8.9 (7.9–12.8)3.6 (0.7–6.4)0.01
Eosinophil count in NP
Median (range)183.5 (76–1100)19.5 (13.7–30.3)0.18
Lund–Mackay score
Right: median (range)7 (6–7)7 (0–11)0.84
Left: median (range)7 (4–8)6.5 (5–11)0.65
Abbreviations: NP, nasal polyp; ECRS, eosinophilic chronic rhinosinusitis; N-ERD, NSAID-exacerbated respiratory disease; PB, peripheral blood.
Table 2. Number of samples in each cluster (GSE136825 dataset).
Table 2. Number of samples in each cluster (GSE136825 dataset).
ADGRB3-High Samples (n = 24)POSTN-High Samples (n = 17)Others (n = 62)p-Value
Sample
Ctrl5221<0.0001
CRSwNP_IT16116
CRSwNP_NP31425
Abbreviations: Ctrl, inferior turbinate tissues from non-CRS controls; CRSwNP, chronic rhinosinusitis with nasal polyps; IT, inferior turbinate; NP, nasal polyp.
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Takahashi, H.; Matsuyama, T.; Kawabata-Iwakawa, R.; Morishita, Y.; Kawamoto, T.; Chikamatsu, K. ADGRB3-High and POSTN-High Fibroblasts Are Markers of Endotypic Traits in Chronic Rhinosinusitis. Immuno 2024, 4, 646-656. https://doi.org/10.3390/immuno4040038

AMA Style

Takahashi H, Matsuyama T, Kawabata-Iwakawa R, Morishita Y, Kawamoto T, Chikamatsu K. ADGRB3-High and POSTN-High Fibroblasts Are Markers of Endotypic Traits in Chronic Rhinosinusitis. Immuno. 2024; 4(4):646-656. https://doi.org/10.3390/immuno4040038

Chicago/Turabian Style

Takahashi, Hideyuki, Toshiyuki Matsuyama, Reika Kawabata-Iwakawa, Yohei Morishita, Takayuki Kawamoto, and Kazuaki Chikamatsu. 2024. "ADGRB3-High and POSTN-High Fibroblasts Are Markers of Endotypic Traits in Chronic Rhinosinusitis" Immuno 4, no. 4: 646-656. https://doi.org/10.3390/immuno4040038

APA Style

Takahashi, H., Matsuyama, T., Kawabata-Iwakawa, R., Morishita, Y., Kawamoto, T., & Chikamatsu, K. (2024). ADGRB3-High and POSTN-High Fibroblasts Are Markers of Endotypic Traits in Chronic Rhinosinusitis. Immuno, 4(4), 646-656. https://doi.org/10.3390/immuno4040038

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