Single-cell RNA-seq profiles of intestinal mucosa in IBS-D rats to explore the mechanism of immune barrier regulation in pathogenesis

Single-cell RNA-seq profiles of intestinal mucosa in IBS-D rats to explore the mechanism of immune barrier regulation in pathogenesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Single-cell RNA-seq profiles of intestinal mucosa in IBS-D rats to explore the mechanism of immune barrier regulation in pathogenesis Liyan Ji, Yongquan Huang, Jiahe Zhang, Jiaman Lin, Zefang Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9205401/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Imbalance of intestinal mucosal immune regulation is a key mechanism in diarrhea-predominant irritable bowel syndrome (IBS-D). We aimed to characterize this dysregulation in the intestinal mucosa at single-cell resolution. Intestinal mucosal cells from control and neonatal maternal separation-induced IBS-D rats were isolated and subjected to single-cell RNA sequencing (scRNA-seq). Bioinformatics analyses included clustering, differential expression, trajectory inference, and cell–cell communication assessment. We obtained transcriptomes from 4,572 high-quality cells, identifying epithelial, stromal, immune, and endothelial lineages. In IBS-D rats, we observed a significant remodeling of the immune compartment, marked by increased monocytes, mast cells, and cycling immune cells, and decreased T and B lymphocyte subsets. Epithelial cells exhibited upregulation of Krt7 and Foxa3, and downregulation of Cst6, Elapor1, and Kcnma1. Cell–cell communication analysis revealed enhanced interactions between epithelial and innate immune cells, involving ligands such as TIGIT. Regulon activity highlighted key transcription factors, including Pax5 in B cells and Mafb in monocytes. Single-cell RNA sequencing reveals that immune barrier dysregulation in the intestinal mucosa of rats with IBS-D is associated with altered immune cell composition, dysregulated expression of epithelial function-related genes, and disrupted intercellular communication, providing novel insights into the pathogenesis of IBS-D. Our high-resolution scRNA-seq atlas delineates the cellular and molecular landscape of intestinal mucosal immune barrier dysfunction in IBS-D, pinpointing novel potential therapeutic targets for this disorder. Biological sciences/Cell biology Biological sciences/Computational biology and bioinformatics Health sciences/Gastroenterology Biological sciences/Immunology Irritable bowel syndrome Diarrhea Single-cell RNA sequencing Intestinal Mucosa Immunity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1.Introduction Irritable bowel syndrome (IBS) is a chronic functional gastrointestinal disorder characterized by abdominal pain or discomfort accompanied by changes in defecation habits. 1 According to symptoms, it is divided into four subtypes, among which the diarrhea-predominant type (IBS-D) is the most common. The main feature of IBS-D is long-term recurrent attacks and difficulty in recovery. It accounts for approximately 40.83% of all IBS cases and shows an increasing trend year by year. 2 Despite its high incidence, diagnosis of IBS-D is difficult due to the complexity of pathogenic factors, unclear pathogenesis, lack of reliable biomarkers, and the existence of multiple, overlapping, and non-specific symptoms. At present, treatment options for IBS-D are very limited, and very few new technologies or drugs have been introduced into clinical practice in the past 10 years. The efficacy of currently used drugs is not satisfactory. The condition is prone to recurrence, and some patients cannot maintain a state of clinical remission. 3 Therefore, identifying effective targeted drugs for the treatment of IBS-D is a current research focus. The colonic mucosal barrier controls the absorption of nutrients, electrolytes, and water, and restricts the intake of toxic substances. 4 As the colonic epithelial mucosa is constantly exposed to food and symbiotic flora, maintaining its permeability and integrity is of vital importance. Disruption of the colonic epithelial mucosal barrier leads to various diseases. 5 At the same time, the distal colon is exposed to numerous microorganisms, including bacteria, fungi, and viruses, whose metabolic products can trigger apoptosis of colonic epithelial cells. Therefore, colonic mucosal tissue needs to strictly regulate the absorption of microbial metabolic products, but the regulatory mechanism remains to be studied. Distal colonic fluid absorption is achieved by regulating the selective permeability of colonic monolayer epithelial cells 6 , which is modulated by the colonic mucus layer and tight junction complexes, and plays a vital role in regulating mucosal permeability to ions, nutrients, and water. 7 However, the role of immune cells in the permeability regulation of colonic monolayer epithelial cells remains unclear. In recent years, an increasing number of studies have shown that immune dysregulation constitutes a key pathological component in IBS-D. 8 Transcriptomic changes have been studied at the organ and tissue level, mainly using microarray technology, to identify several genes related to colonic mucosal inflammation and the immune barrier. 9 However, as different cell types affect colonic mucosal barrier function in IBS-D, these bulk approaches may hinder the identification of new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of complex tissues under biological and pathological conditions. 10 In IBS-D research, scRNA-seq enhances our understanding of IBS-D heterogeneity, cell zonation characteristics, and pathological cell population identification. Here, based on a previously successfully established IBS-D rat model, we reclassified intestinal mucosal cell subpopulations in IBS-D using single-cell sequencing, annotated their markers and functions, explored potential key genes and pathways for IBS-D progression, and further investigated the regulatory directions of the cell subpopulations. 2.Methods 2.1.Construction of IBS-D rats and acquisition of intestinal-related cells. All animal experiments were conducted in accordance with the instructions and guidelines of Guangzhou University of Chinese Medicine on the protection of animals used for scientific purposes, and were carried out under the supervision of individual licenses held within the facilities of Guangzhou University of Chinese Medicine. Using the IBS-D rat model that our research group has successfully established and replicated: SD rats were used. From postnatal day 2 to day 14, suckling rats (Experimental Animal Center of Guangzhou University of Chinese Medicine, Approval Number: SYXK-2023-0092, Age: 2–14 days, Gender: Unspecified) were separated from the mother rats for 180 minutes once daily. Then from the 15th to the 27th day, a paraffin-lubricated infusion catheter (with a diameter of 1 mm) was inserted through the anus for 1 to 2 cm, and 0.2 to 0.5 ml of 0.087 mol/L acetic acid was injected. The dosage of acetic acid was 0.2 ml starting from the 15th day, increasing by 0.1 ml every two days until it remained unchanged at 0.5 ml from the 21st day after birth. Acetic acid was administered to the rectum once a day. After the stimulation, the front shoulder, front upper limb, and chest of the rats were immediately wrapped with paper bags to restrict scratching of the head and face with the front upper limb, without limiting their movement. The restraint time was 1 hour. From the 27th day for two weeks, no operation was performed, and normal feeding was conducted. After modeling was completed, the success of the model was assessed through fecal Bristol classification score, abdominal withdrawal reflex (AWR), and in vitro intestinal motility experiments. Samples were collected under terminal anesthesia (4% isoflurane). The distal colonic epithelium was dissociated and used to make frozen sections. Samples were divided into two parts for each rat. 1) Specimens were prepared by HE staining, and the permeability of the colonic mucosa was observed under a high-power optical microscope (×400). 2) Another part was subjected to dual staining with uranyl acetate and lead citrate. The ultrastructure of the cells was observed under a transmission electron microscope and photographed for storage. At the end of the operation, the rats were euthanized by bloodletting. 2.2.Preparation and analysis of scRNA-seq samples. 2.2.1Preparation and sorting of single-cell subsets of intestinal mucosal cells from the distal colon of rats Digestive enzymes were prepared in advance and placed in a 37℃ water bath to maintain the temperature. After anesthetizing the rats, the freed distal colon was cut into small pieces under moist conditions and placed in an Erlenmeyer flask. Each time, 5 ml of digestive enzyme was added to the flask, and the supernatant was stirred with a rotor. Every 15 minutes, the supernatant was transferred to an EP tube containing serum. After digestion, it was passed through a 40 µm cell strainer, then collected and centrifuged. After centrifugation, cells were diluted and sorted by flow cytometry. 2.2.2 Single-cell sequencing An 8-channel microfluidic "double cross" system was applied to combine Gel Beads containing Barcoded RT Primers, the mixture of cells and enzymes, and oil to form GEMs (oil droplets encapsulating the mixture of Gel Beads, cells, and enzymes). GEMs were collected after the reaction (single-cell capture rate ≈ 65%). After GEM formation, cells were lysed, and the gel beads automatically dissolved to release a large number of barcode sequences. Subsequently, mRNA was reverse transcribed to produce cDNA with Barcode and UMI information. Oil droplet fragmentation was performed, followed by PCR amplification using cDNA as template, cDNA fragmentation, addition of sequencing adaptor P5 and sequencing primer R1, and other standard second-generation sequencing library construction processes (xxNext Ultra II RNA Library Prep Kit, New England Biolabs, catalog# E7765) for single-cell transcriptome sequencing (Illumina NovaSeq 6000 sequencing platform, Illumina, Inc.). After sequencing, the transcriptome sequencing expression profiles of each single cell were obtained. Cells were labeled with Barcode, genes were labeled with UMI, and expression levels were recorded. Cell RangerTM was used to analyze mRNA and obtain counts. 2.2.3 Single-cell bioinformatics analysis Based on toolkits such as Seurat (v4.0.1) 11 , Harmony (v0.1.0) 12 , SingleR (v1.10.0) 13 and ArchR (v1.0.2) 14 in R (v4.4.1), t-SNE dimensionality reduction and K-means clustering were performed on cell transcriptome data, cell populations were identified based on GSEA analysis, and enrichment analysis was applied to analyze regulatory signals and phenotypic signals in subpopulation cells. 2.3.Cell differentiation states and trajectory analysis Epithelial subpopulations were used for cell trajectory analysis. Slingshot (v2.12.0) was used to estimate pseudotime prediction using the slingshot function. 15 2.4.Tissue enrichment analysis Tissue enrichment was performed as previously reported. The observed-to-expected ratio was tested by Chi-square using the cell counts. A ratio greater than 1 represented an enriched distribution, whereas less than 1 meant depletion in a given tissue. 2.5.Differential expression analysis and pathway enrichment Differential expression analysis was performed using the FindAllMarkers function of the Seurat package with the parameters as follows: only.pos = F, min.pct = 0.2, logfc.threshold = 0.25, max.cells.per.ident = 250. Differentially expressed genes were subjected to pathway enrichment using the clusterProfiler (v4.12.0) package with default parameters. 16 Gene sets were obtained from the GO dataset for the rno organism. 2.6.Cell-cell communication Ligand-receptor pairs were identified using the gene expression matrix across samples. Cell-cell communication analysis was conducted by the CellChat (v1.5.0) package following CellChat's protocol. 17 2.7.Regulon activity analysis The gene expression matrix was used for the detection of regulon activity in rats. Transcription factor regulons of cell subpopulations were identified by pySCENIC using Mus musculus species. 18 SCENIC grnboost2 was used to quantify the rank of transcription factors. Regulon activity was calculated by the aucell function. 3.Results 3.1.The single-cell transcriptome study design of colon sigmoid in the control group and IBS-D rat To study the transcriptomic changes in IBS, we collected colon sigmoid tissues from neonatal maternal separation-induced IBS-D model rats and normal rats for single-cell RNA sequencing (Fig. 1A). After quality control, 4,572 cells were obtained for further analysis. Intestinal cells were annotated based on human intestinal cell markers {41586_2021_3852_MOESM9_ESM}. Cells were clustered into four major cell subpopulations, further subclassified into 14 cell subpopulations (Fig. 1B,C). The main cell category annotation results in IBS-D We identified the major cell subpopulations based on the expression of known markers of intestinal cells: epithelial cells (Epcam, Krt8, Krt18), stromal cells (Col1a1, Col1a2), immune cells (Cd3e, Cd8a for T cells; Pax5, Ms4a1 for B cells), and endothelial cells ( Pecam1,Cdh5 ). Cells were clustered into four major subpopulations based on expression of these reported markers (Fig. 1D). The cell-type composition of the major subtypes differed between the IBS-D model and control group (Fig. 1E). Among these cell types, immune cells are among the most abundant cell lineages in the colon sigmoid mucosa, consistent with the human colon 19 , as well as findings in plasma of IBS-D patients. 20 3.2.Epithelial cell clustering and expression of epithelial subpopulation cell markers Epithelial cells were selected for further unsupervised clustering, yielding 10 clusters. We used known marker genes to annotate the epithelial cell subpopulations by mining databases and literature on colon tissues. The 10 clusters include seven subsets: Enterocyte, TA cells, Goblet, EEC, Tuft cells, and Paneth cells (Fig. 2A), similar to the reported composition of epithelial cells in human and rat intestines. 21 Two clusters represented goblet cells, identified by co-expressed markers Ffar4 , Sytl2 , Bcas1 , and Itln1 genes (Fig. 2B). Immature goblet cells, derived from ISC cells, were characterized by co-expressed of Itln1 , Wfdc2 , Clca1 , and Agr2 (Fig. 2C). Intestinal Stem Cells (ISCs) were identified by enriched expression of Slc12a2 (Fig. 2D). The Car1 gene, encoding Carbonic Anhydrase 1, was expressed in Enterocytes (Fig. 2E). Ms4a8 was a marker for Enteroendocrine cells (Fig. 2F). Furthermore, a subset of goblet cells and Paneth cell markers Pla2g2a, Try10 , and Guca2a were weakly expressed in rat colon sigmoid tissues (Fig. 2G and Supplementary Fig. 1B). Tuft cells were detected in rat colon sigmoid, represented by co-expression of Sh2d6 , Hck , Pstpip2 , and Plcg2 (Fig. 2G, and Supplementary Fig. 1A). The last type of cells highly expressed Top2a genes, characterized as proliferative and stem-like cells (Fig. 2H). For epithelial cells, we calculated the differentially expressed genes between the control and model group. Krt7 , Foxa3 , Plet1 were upregulated in model compared to control rat (Fig. 2I-2J). In contrast to control, Cst6 , Elapor1 , Kcnma1 genes were downregulated in model (Fig. 2I-2J). By conducting enrichment analysis, we observed that the numbers of immature goblet cells, Tuft cells, and Top2a + epithelial cells were enriched in model group than in the control group (Fig. 2K). 3.3.Stromal cell clustering and expression of stromal cell gene markers Typical stromal marker Col1a1 , Col1a2 , Col6a1 , and Col6a2 were universally expressed in all eight stromal sub-clusters (Fig. 3A-3B). However, stromal cell subpopulations differed between rats and human. No significant expression of fibroblast markers Tagln , Acta2 , Des , Hhip , or Npnt genes were detected in stromal cells. AABR07037995.1 and Aqp1 genes were highly expressed in stromal cluster c0; C3 was featured by Dpp4 and Bmp7 (Fig. 3C). Meanwhile, high expression of C6, Bco1 , and Kcne4 genes were identified in C4 cluster (Fig. 3C). Dpp4 + C3 and Bco1 + Kcne4 + C4 cell subpopulations were enriched in model group (Fig. 3D). 3.4.Immune cell clustering and expression of immune cell gene markers Inflammation plays an important role in IBS-D. 22 Immune cells were further clustered into 14 clusters, including 13 immune cell subtypes (Fig. 4A). Among these immune cells, monocytes, mast cells, and cycling immune cells were significantly increased in the model group compared to the control group (Fig. 4B). In contrast, T cells (CD8 cells, Tregs) and B cells (Plasma cells, Pax5 + B cells) were significantly decreased in the model group compared to the control group (Fig. 4B). B cells typically expressed Ms4a1 and Vpreb3 , and a subtype of B cells was identified as Pax5 -positive (Fig. 4C). The marker Il1rapl1 gene was highly expressed in Mast cells (Fig. 4C). SSr4 was solely expressed in a separate cluster marked as plasma cells (Fig. 4C). Another subset of B cells was characterized by the expression of Mki67 , Top2a , and Cdk1 genes, classified as cycling immune B cells (Fig. 4D). Five clusters were assigned as T cells. Cd family genes Cd3d , Cd3e , Cd3g , and Cd2 were markers for NK and T cells (Fig. 4E-4F). CD8 cells were featured by Cd8a and Cd8b expression (Fig. 4F). The Ctla4 gene was highly expressed in Tregs (Fig. 4G). We identified differentially expressed genes between IBS-D and normal rats in immune cells. Ccr2 , Adam23 , and Il1rl1 genes were highly upregulated in monocytes of the model compared to the control group (Fig. 4K), whereas RT1-CE16 , Cd7 , and Rinl genes were significantly downregulated in monocytes (Fig. 4K). In cycling immune B cells, Sirpal1 , Trio , Igf1r , as well as Rn18s , Hpgds , and Iglc1 genes were significantly dysregulated (Fig. 4L). Functional analysis of DEGs in monocytes revealed that adaptive immune response, type II interferon production, and T cell cytokine production were dysregulated in the model group (Fig. 4M). Three significant pathways, including oxidative phosphorylation, cytoplasmic translation, and DNA topological change signalings, were enriched for DEGs of immune cycling cells (Fig. 4N). The cytoplasmic translation, oxidative phosphorylation, and signal transduction by p53 class mediator pathways were significantly affected in Tregs of the model group (Fig. 4P). 3.5.Intercellular cell communication in IBS rat model To explore intercellular crosstalk in the colon, we performed CellChat to investigate ligand-receptor pairs across cell subtypes. Common signaling pathways, such as APP, LAMININ, and MIF, were observed in control and model groups (Fig. 5A, and supplementary Fig. 1C). Specific immune-related ligand-receptors were exclusively observed in rat IBS models. We observed that TIGIT was a key ligand aberrantly expressed in epithelial subtypes and epithelial-NK/monocyte-macrophage cell interactions (Fig. 5B, and supplementary Fig. 1D), suggesting crosstalk between epithelial cells and immune cells. Flt3 was exclusively highly expressed in mast cells in the IBS model (Fig. 5B, and supplementary Fig. 1D). Lilrb3 was exclusively expressed in monocyte-macrophages in the rat IBS model (Fig. 5B, and supplementary Fig. 1E). The Pax5 transcription factor (TF) was highly expressed in both mast cells and B cells of the IBS model, but not in the control group (Fig. 5C-5D). Mafb was remarkably expressed in monocyte-macrophages of IBS model rats (Fig. 5C-5D). Hes6 and Ehf TFs were the top two regulons in tuft cells of the IBS model, whereas Rfx3 and Nr2f6 were the top regulons in the control group. 4.Discussion This study systematically deciphered the transcriptional landscape of the colonic mucosa in a rat model of IBS-D at single-cell resolution, revealing compositional shifts, transcriptional signatures, and potential regulatory networks among various cellular subpopulations, including epithelial, immune, and stromal cells. Our findings not only reinforce the central role of intestinal mucosal immune barrier dysfunction in IBS-D pathogenesis but also identify specific cellular subsets and molecular targets involved in this process, providing novel insights into the cellular and molecular mechanisms underlying IBS-D. First, our data confirm a significant remodeling of the immune microenvironment in the IBS-D colonic mucosa. Compared to controls, the model group exhibited a marked increase in monocytes, mast cells, and cycling immune cells, alongside a significant decrease in T-cell subsets (including CD8 + T cells and Tregs) and B-cell populations. This aligns with the characterization of IBS as a state of low-grade inflammation. The expansion of monocytes and their heightened expression of genes such as Ccr2 and Il1rl1 suggests the recruitment and activation of innate immune cells as key drivers of mucosal inflammation. 23 Conversely, the reduction in Tregs may impair intestinal immune tolerance and suppressive regulation, contributing to inflammatory imbalance. 24 This indicates the multidimensional nature of immune dysregulation in IBS-D, where different therapeutic strategies may exert effects by acting on distinct immune hubs. Significant transcriptomic alterations were also observed in epithelial cells, the core component of the physical barrier. We found an increased abundance of immature goblet cells and Tuft cells in the model group. Goblet cells, responsible for mucus secretion, may exhibit compromised function in an immature state, affecting the integrity and function of the mucus layer. 25 Tuft cells, as chemosensory cells, can sense luminal contents and regulate type 2 immune responses 26 ; their increase may be linked to neuro-immune cross-talk dysregulation in IBS-D. Differential expression analysis further identified key molecules associated with epithelial function, such as the upregulation of Krt7 and Foxa3 , and the downregulation of Cst6 , Elapor1 , and Kcnma1 . Notably, Kcnma1 encodes the BK calcium-activated potassium channel, involved in epithelial ion transport and secretion. 27 , 28 Its downregulation may directly contribute to water and electrolyte secretion disturbances underlying diarrhea in IBS-D. 29 These genes constitute a potential molecular basis for epithelial barrier dysfunction. Cell-cell communication network analysis uncovered characteristic alterations in intercellular interactions in IBS-D pathology. We observed specific positive co-occurrence patterns, including interactions between epithelial cells and monocytes/macrophages, between endothelial/epithelial cells and NK cells, and involving Tuft cells, in the model group. Additionally, regulon activity analysis identified key transcription factors—such as Pax5 (B cells) 30 , Mafb (monocytes/macrophages), and Hes6 (Tuft) 31 —that may serve as core regulatory switches driving functional state transitions in different cellular subsets. 5. Conclusion In conclusion, this study constructed a high-resolution single-cell transcriptomic atlas of the IBS-D colonic mucosa, systematically delineating the cellular ecological basis of immune barrier dysfunction from multiple perspectives: cellular composition, gene expression, pathway activity, and cellular crosstalk. The specific cellular subpopulations we identified—including dysfunctional epithelial subsets, cycling immune cells, and imbalanced lymphoid/myeloid cells—along with their characteristic molecular markers (e.g., Kcnma1 , Sirpal1 , Ccr2 ), not only deepen the understanding of IBS-D pathogenesis but also provide a direct foundation for developing novel biomarkers and targeted therapeutic strategies. For example, modulators targeting Kcnma1 channel function or immunomodulators aimed at restoring the Treg/pro-inflammatory myeloid cell balance may represent promising future directions for IBS-D treatment. 32 Limitations of this study include the relatively small sample size and the inherent constraints of animal models in fully recapitulating human disease complexity. Future validation in larger human cohorts, integrated with spatial transcriptomics, proteomics, and other multi-omics technologies, will be essential to further elucidate the spatiotemporal dynamics and functional roles of key molecules within the mucosal niche, ultimately advancing toward precision diagnosis and therapy for IBS-D. Declarations Acknowledgements Not applicable. Funding The present study was supported by the National Natural Science Foundation of China (grant no. 81804047 and 82174303), the State Key Laboratory of Traditional Chinese Medicine Syndrome (grant no. SKLKY2004B0007) and Guangdong Provincial Bureau of Traditional Chinese Medicine (grant no. 20254041). Availability of data and materials The data generated in the present study may be requested from the corresponding author. CRediT authorship contribution statement Qiuke Hou, Liyan Ji and Fengbin Liu designed and supervised the project. Yongquan Huang, Jiahe Zhang, Zefang Yang, Xiling Yang and Jiaman Lin conducted the experiments. Qiuke Hou and Liyan Ji wrote the draft manuscript. Yongquan Huang and Qiuke Hou edited the manuscript. All authors have read and approved the final version of the manuscript. Liyan Ji and Qiuke Hou confirm the authenticity of all the raw data. Ethics approval and consent to participate The animal experiments were approved by the Institutional Animal Care and Use Committee of Guangzhou University of Chinese Medicine (No. 2024257). Data availability Raw sequencing data and processed data have been deposited in the Gene Expression Omnibus (GEO) under GSE326856. Patient consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Camilleri, M. Diagnosis and treatment of irritable bowel syndrome: a review. JAMA 325 , 865–877 (2021). Black, C. J. et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9205401","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":628525017,"identity":"0e998b81-22f3-4a3f-9a56-d59f8dd21d0a","order_by":0,"name":"Liyan Ji","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Liyan","middleName":"","lastName":"Ji","suffix":""},{"id":628525018,"identity":"bb0a5cef-c6c3-4bdd-a3c4-5e75fde6268d","order_by":1,"name":"Yongquan Huang","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yongquan","middleName":"","lastName":"Huang","suffix":""},{"id":628525019,"identity":"d1156e26-5ab1-4696-bb34-fc7a7621a0d3","order_by":2,"name":"Jiahe Zhang","email":"","orcid":"","institution":"Baiyun Hospital of The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiahe","middleName":"","lastName":"Zhang","suffix":""},{"id":628525020,"identity":"ac84f283-182e-4d98-92f1-c47d9c0e9477","order_by":3,"name":"Jiaman Lin","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiaman","middleName":"","lastName":"Lin","suffix":""},{"id":628525021,"identity":"1d23cc3c-c951-43cd-9cb9-8404180f29e9","order_by":4,"name":"Zefang Yang","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zefang","middleName":"","lastName":"Yang","suffix":""},{"id":628525022,"identity":"a4cf6c95-ddc5-4168-8347-bead4efe01ff","order_by":5,"name":"Xiling Yang","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiling","middleName":"","lastName":"Yang","suffix":""},{"id":628525023,"identity":"012cba45-8fc9-4cbf-bfa3-4272a4605e10","order_by":6,"name":"Fengbin Liu","email":"","orcid":"","institution":"Baiyun Hospital of The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fengbin","middleName":"","lastName":"Liu","suffix":""},{"id":628525024,"identity":"4389f22e-be14-4ff5-9447-eb9a71b6abcd","order_by":7,"name":"Qiuke Hou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACAxCRAIT8cKEDxGqRbCBJC0iTAVwlIS3m7D2GNx62pckZn1/+TOJnDoMc340Exs8FeLRY9pwxtkhsyzE2u/EgTbJ3G4Ox5I0EZukZ+Bx2I8dMIrGtInHbjQPHpBm3MSRuuJHAxsxDhJb6zTMOtoG01BOrJSfBgL+ZDaQlwYCgljPHii0SzqUZzrjBxmzZu03CcOaZh83SeLUcb95480dZsjx///GHN35us5HnO5588DM+LSAgASETYGzGBgIaYFr4DxBUOApGwSgYBSMUAADX1k390p0CtQAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Qiuke","middleName":"","lastName":"Hou","suffix":""}],"badges":[],"createdAt":"2026-03-24 01:38:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9205401/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9205401/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107839170,"identity":"e1df2f19-9e72-4bf4-89fc-e536d3421adb","added_by":"auto","created_at":"2026-04-26 17:16:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1941498,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9205401/v1/5bec0f38b1af457b7712d3fe.png"},{"id":107839172,"identity":"564084c5-c2ac-4798-b533-84d9e907b7b1","added_by":"auto","created_at":"2026-04-26 17:16:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2191359,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9205401/v1/26ff950d1d7067d2fa639871.png"},{"id":108803790,"identity":"930eb009-d4b0-45ab-ab63-95592dbc3d20","added_by":"auto","created_at":"2026-05-08 15:07:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1104344,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9205401/v1/1e0897fee0f7a36960c2fec6.png"},{"id":107839174,"identity":"9bd402a8-041d-4f92-9f64-8a9a36f1ad2a","added_by":"auto","created_at":"2026-04-26 17:16:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1211610,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9205401/v1/f79a16d944181bf4169068de.png"},{"id":107869815,"identity":"3bdbe71e-6196-4386-8c45-b600d57cbb6b","added_by":"auto","created_at":"2026-04-27 07:38:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":612745,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9205401/v1/61a6494cbd2554b53e6e4d23.png"},{"id":108808960,"identity":"777b8868-50d7-44f0-a822-8278a4f55af0","added_by":"auto","created_at":"2026-05-08 15:48:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6385874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9205401/v1/dd071a2d-2c36-4add-9617-7641497b4c52.pdf"},{"id":107870662,"identity":"30284d98-65c3-4eea-a1a1-7419a0526796","added_by":"auto","created_at":"2026-04-27 07:40:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1736931,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9205401/v1/78780535ccb8a2a163643bde.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell RNA-seq profiles of intestinal mucosa in IBS-D rats to explore the mechanism of immune barrier regulation in pathogenesis","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eIrritable bowel syndrome (IBS) is a chronic functional gastrointestinal disorder characterized by abdominal pain or discomfort accompanied by changes in defecation habits.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e According to symptoms, it is divided into four subtypes, among which the diarrhea-predominant type (IBS-D) is the most common. The main feature of IBS-D is long-term recurrent attacks and difficulty in recovery. It accounts for approximately 40.83% of all IBS cases and shows an increasing trend year by year.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Despite its high incidence, diagnosis of IBS-D is difficult due to the complexity of pathogenic factors, unclear pathogenesis, lack of reliable biomarkers, and the existence of multiple, overlapping, and non-specific symptoms. At present, treatment options for IBS-D are very limited, and very few new technologies or drugs have been introduced into clinical practice in the past 10 years. The efficacy of currently used drugs is not satisfactory. The condition is prone to recurrence, and some patients cannot maintain a state of clinical remission.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Therefore, identifying effective targeted drugs for the treatment of IBS-D is a current research focus.\u003c/p\u003e \u003cp\u003eThe colonic mucosal barrier controls the absorption of nutrients, electrolytes, and water, and restricts the intake of toxic substances.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e As the colonic epithelial mucosa is constantly exposed to food and symbiotic flora, maintaining its permeability and integrity is of vital importance. Disruption of the colonic epithelial mucosal barrier leads to various diseases.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e At the same time, the distal colon is exposed to numerous microorganisms, including bacteria, fungi, and viruses, whose metabolic products can trigger apoptosis of colonic epithelial cells. Therefore, colonic mucosal tissue needs to strictly regulate the absorption of microbial metabolic products, but the regulatory mechanism remains to be studied. Distal colonic fluid absorption is achieved by regulating the selective permeability of colonic monolayer epithelial cells\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, which is modulated by the colonic mucus layer and tight junction complexes, and plays a vital role in regulating mucosal permeability to ions, nutrients, and water.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e However, the role of immune cells in the permeability regulation of colonic monolayer epithelial cells remains unclear.\u003c/p\u003e \u003cp\u003eIn recent years, an increasing number of studies have shown that immune dysregulation constitutes a key pathological component in IBS-D.\u003csup\u003e8\u003c/sup\u003e Transcriptomic changes have been studied at the organ and tissue level, mainly using microarray technology, to identify several genes related to colonic mucosal inflammation and the immune barrier.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e However, as different cell types affect colonic mucosal barrier function in IBS-D, these bulk approaches may hinder the identification of new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of complex tissues under biological and pathological conditions.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e In IBS-D research, scRNA-seq enhances our understanding of IBS-D heterogeneity, cell zonation characteristics, and pathological cell population identification. Here, based on a previously successfully established IBS-D rat model, we reclassified intestinal mucosal cell subpopulations in IBS-D using single-cell sequencing, annotated their markers and functions, explored potential key genes and pathways for IBS-D progression, and further investigated the regulatory directions of the cell subpopulations.\u003c/p\u003e"},{"header":"2.Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.Construction of IBS-D rats and acquisition of intestinal-related cells.\u003c/h2\u003e \u003cp\u003e All animal experiments were conducted in accordance with the instructions and guidelines of Guangzhou University of Chinese Medicine on the protection of animals used for scientific purposes, and were carried out under the supervision of individual licenses held within the facilities of Guangzhou University of Chinese Medicine. Using the IBS-D rat model that our research group has successfully established and replicated: SD rats were used. From postnatal day 2 to day 14, suckling rats (Experimental Animal Center of Guangzhou University of Chinese Medicine, Approval Number: SYXK-2023-0092, Age: 2\u0026ndash;14 days, Gender: Unspecified) were separated from the mother rats for 180 minutes once daily. Then from the 15th to the 27th day, a paraffin-lubricated infusion catheter (with a diameter of 1 mm) was inserted through the anus for 1 to 2 cm, and 0.2 to 0.5 ml of 0.087 mol/L acetic acid was injected. The dosage of acetic acid was 0.2 ml starting from the 15th day, increasing by 0.1 ml every two days until it remained unchanged at 0.5 ml from the 21st day after birth. Acetic acid was administered to the rectum once a day. After the stimulation, the front shoulder, front upper limb, and chest of the rats were immediately wrapped with paper bags to restrict scratching of the head and face with the front upper limb, without limiting their movement. The restraint time was 1 hour. From the 27th day for two weeks, no operation was performed, and normal feeding was conducted. After modeling was completed, the success of the model was assessed through fecal Bristol classification score, abdominal withdrawal reflex (AWR), and in vitro intestinal motility experiments. Samples were collected under terminal anesthesia (4% isoflurane). The distal colonic epithelium was dissociated and used to make frozen sections. Samples were divided into two parts for each rat. 1) Specimens were prepared by HE staining, and the permeability of the colonic mucosa was observed under a high-power optical microscope (\u0026times;400). 2) Another part was subjected to dual staining with uranyl acetate and lead citrate. The ultrastructure of the cells was observed under a transmission electron microscope and photographed for storage. At the end of the operation, the rats were euthanized by bloodletting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2.Preparation and analysis of scRNA-seq samples.\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1Preparation and sorting of single-cell subsets of intestinal mucosal cells from the distal colon of rats\u003c/h2\u003e \u003cp\u003eDigestive enzymes were prepared in advance and placed in a 37℃ water bath to maintain the temperature. After anesthetizing the rats, the freed distal colon was cut into small pieces under moist conditions and placed in an Erlenmeyer flask. Each time, 5 ml of digestive enzyme was added to the flask, and the supernatant was stirred with a rotor. Every 15 minutes, the supernatant was transferred to an EP tube containing serum. After digestion, it was passed through a 40 \u0026micro;m cell strainer, then collected and centrifuged. After centrifugation, cells were diluted and sorted by flow cytometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Single-cell sequencing\u003c/h2\u003e \u003cp\u003eAn 8-channel microfluidic \"double cross\" system was applied to combine Gel Beads containing Barcoded RT Primers, the mixture of cells and enzymes, and oil to form GEMs (oil droplets encapsulating the mixture of Gel Beads, cells, and enzymes). GEMs were collected after the reaction (single-cell capture rate\u0026thinsp;\u0026asymp;\u0026thinsp;65%). After GEM formation, cells were lysed, and the gel beads automatically dissolved to release a large number of barcode sequences. Subsequently, mRNA was reverse transcribed to produce cDNA with Barcode and UMI information. Oil droplet fragmentation was performed, followed by PCR amplification using cDNA as template, cDNA fragmentation, addition of sequencing adaptor P5 and sequencing primer R1, and other standard second-generation sequencing library construction processes (xxNext Ultra II RNA Library Prep Kit, New England Biolabs, catalog# E7765) for single-cell transcriptome sequencing (Illumina NovaSeq 6000 sequencing platform, Illumina, Inc.). After sequencing, the transcriptome sequencing expression profiles of each single cell were obtained. Cells were labeled with Barcode, genes were labeled with UMI, and expression levels were recorded. Cell RangerTM was used to analyze mRNA and obtain counts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Single-cell bioinformatics analysis\u003c/h2\u003e \u003cp\u003eBased on toolkits such as Seurat (v4.0.1) \u003csup\u003e11\u003c/sup\u003e, Harmony (v0.1.0) \u003csup\u003e12\u003c/sup\u003e, SingleR (v1.10.0) \u003csup\u003e13\u003c/sup\u003e and ArchR (v1.0.2)\u003csup\u003e14\u003c/sup\u003e in R (v4.4.1), t-SNE dimensionality reduction and K-means clustering were performed on cell transcriptome data, cell populations were identified based on GSEA analysis, and enrichment analysis was applied to analyze regulatory signals and phenotypic signals in subpopulation cells.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3.Cell differentiation states and trajectory analysis\u003c/h2\u003e \u003cp\u003eEpithelial subpopulations were used for cell trajectory analysis. Slingshot (v2.12.0) was used to estimate pseudotime prediction using the slingshot function.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4.Tissue enrichment analysis\u003c/h2\u003e \u003cp\u003eTissue enrichment was performed as previously reported. The observed-to-expected ratio was tested by Chi-square using the cell counts. A ratio greater than 1 represented an enriched distribution, whereas less than 1 meant depletion in a given tissue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5.Differential expression analysis and pathway enrichment\u003c/h2\u003e \u003cp\u003eDifferential expression analysis was performed using the FindAllMarkers function of the Seurat package with the parameters as follows: only.pos\u0026thinsp;=\u0026thinsp;F, min.pct\u0026thinsp;=\u0026thinsp;0.2, logfc.threshold\u0026thinsp;=\u0026thinsp;0.25, max.cells.per.ident\u0026thinsp;=\u0026thinsp;250. Differentially expressed genes were subjected to pathway enrichment using the clusterProfiler (v4.12.0) package with default parameters.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Gene sets were obtained from the GO dataset for the rno organism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6.Cell-cell communication\u003c/h2\u003e \u003cp\u003eLigand-receptor pairs were identified using the gene expression matrix across samples. Cell-cell communication analysis was conducted by the CellChat (v1.5.0) package following CellChat's protocol.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7.Regulon activity analysis\u003c/h2\u003e \u003cp\u003eThe gene expression matrix was used for the detection of regulon activity in rats. Transcription factor regulons of cell subpopulations were identified by pySCENIC using \u003cem\u003eMus musculus\u003c/em\u003e species.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e SCENIC grnboost2 was used to quantify the rank of transcription factors. Regulon activity was calculated by the aucell function.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1.The single-cell transcriptome study design of colon sigmoid in the control group and IBS-D rat\u003c/h2\u003e \u003cp\u003eTo study the transcriptomic changes in IBS, we collected colon sigmoid tissues from neonatal maternal separation-induced IBS-D model rats and normal rats for single-cell RNA sequencing (Fig.\u0026nbsp;1A). After quality control, 4,572 cells were obtained for further analysis. Intestinal cells were annotated based on human intestinal cell markers {41586_2021_3852_MOESM9_ESM}. Cells were clustered into four major cell subpopulations, further subclassified into 14 cell subpopulations (Fig.\u0026nbsp;1B,C).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe main cell category annotation results in IBS-D\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe identified the major cell subpopulations based on the expression of known markers of intestinal cells: epithelial cells (Epcam, Krt8, Krt18), stromal cells (Col1a1, Col1a2), immune cells (Cd3e, Cd8a for T cells; Pax5, Ms4a1 for B cells), and endothelial cells (\u003cem\u003ePecam1,Cdh5\u003c/em\u003e). Cells were clustered into four major subpopulations based on expression of these reported markers (Fig.\u0026nbsp;1D). The cell-type composition of the major subtypes differed between the IBS-D model and control group (Fig.\u0026nbsp;1E). Among these cell types, immune cells are among the most abundant cell lineages in the colon sigmoid mucosa, consistent with the human colon\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, as well as findings in plasma of IBS-D patients.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2.Epithelial cell clustering and expression of epithelial subpopulation cell markers\u003c/h2\u003e \u003cp\u003eEpithelial cells were selected for further unsupervised clustering, yielding 10 clusters. We used known marker genes to annotate the epithelial cell subpopulations by mining databases and literature on colon tissues. The 10 clusters include seven subsets: Enterocyte, TA cells, Goblet, EEC, Tuft cells, and Paneth cells (Fig.\u0026nbsp;2A), similar to the reported composition of epithelial cells in human and rat intestines.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Two clusters represented goblet cells, identified by co-expressed markers \u003cem\u003eFfar4\u003c/em\u003e, \u003cem\u003eSytl2\u003c/em\u003e, \u003cem\u003eBcas1\u003c/em\u003e, and \u003cem\u003eItln1\u003c/em\u003e genes (Fig.\u0026nbsp;2B). Immature goblet cells, derived from ISC cells, were characterized by co-expressed of \u003cem\u003eItln1\u003c/em\u003e, \u003cem\u003eWfdc2\u003c/em\u003e, \u003cem\u003eClca1\u003c/em\u003e, and \u003cem\u003eAgr2\u003c/em\u003e (Fig.\u0026nbsp;2C). Intestinal Stem Cells (ISCs) were identified by enriched expression of \u003cem\u003eSlc12a2\u003c/em\u003e (Fig.\u0026nbsp;2D). The \u003cem\u003eCar1\u003c/em\u003e gene, encoding Carbonic Anhydrase 1, was expressed in Enterocytes (Fig.\u0026nbsp;2E). Ms4a8 was a marker for Enteroendocrine cells (Fig.\u0026nbsp;2F). Furthermore, a subset of goblet cells and Paneth cell markers \u003cem\u003ePla2g2a, Try10\u003c/em\u003e, and \u003cem\u003eGuca2a\u003c/em\u003e were weakly expressed in rat colon sigmoid tissues (Fig.\u0026nbsp;2G and Supplementary Fig.\u0026nbsp;1B). Tuft cells were detected in rat colon sigmoid, represented by co-expression of \u003cem\u003eSh2d6\u003c/em\u003e, \u003cem\u003eHck\u003c/em\u003e, \u003cem\u003ePstpip2\u003c/em\u003e, and \u003cem\u003ePlcg2\u003c/em\u003e (Fig.\u0026nbsp;2G, and Supplementary Fig.\u0026nbsp;1A). The last type of cells highly expressed \u003cem\u003eTop2a\u003c/em\u003e genes, characterized as proliferative and stem-like cells (Fig.\u0026nbsp;2H).\u003c/p\u003e \u003cp\u003eFor epithelial cells, we calculated the differentially expressed genes between the control and model group. \u003cem\u003eKrt7\u003c/em\u003e, \u003cem\u003eFoxa3\u003c/em\u003e, \u003cem\u003ePlet1\u003c/em\u003e were upregulated in model compared to control rat (Fig.\u0026nbsp;2I-2J). In contrast to control, \u003cem\u003eCst6\u003c/em\u003e, \u003cem\u003eElapor1\u003c/em\u003e, \u003cem\u003eKcnma1\u003c/em\u003e genes were downregulated in model (Fig.\u0026nbsp;2I-2J). By conducting enrichment analysis, we observed that the numbers of immature goblet cells, Tuft cells, and Top2a\u003csup\u003e+\u003c/sup\u003e epithelial cells were enriched in model group than in the control group (Fig.\u0026nbsp;2K).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3.Stromal cell clustering and expression of stromal cell gene markers\u003c/h2\u003e \u003cp\u003eTypical stromal marker \u003cem\u003eCol1a1\u003c/em\u003e, \u003cem\u003eCol1a2\u003c/em\u003e, \u003cem\u003eCol6a1\u003c/em\u003e, and \u003cem\u003eCol6a2\u003c/em\u003e were universally expressed in all eight stromal sub-clusters (Fig.\u0026nbsp;3A-3B). However, stromal cell subpopulations differed between rats and human. No significant expression of fibroblast markers \u003cem\u003eTagln\u003c/em\u003e, \u003cem\u003eActa2\u003c/em\u003e, \u003cem\u003eDes\u003c/em\u003e, \u003cem\u003eHhip\u003c/em\u003e, or \u003cem\u003eNpnt\u003c/em\u003e genes were detected in stromal cells. \u003cem\u003eAABR07037995.1\u003c/em\u003e and \u003cem\u003eAqp1\u003c/em\u003e genes were highly expressed in stromal cluster c0; C3 was featured by \u003cem\u003eDpp4\u003c/em\u003e and \u003cem\u003eBmp7\u003c/em\u003e (Fig.\u0026nbsp;3C). Meanwhile, high expression of C6, \u003cem\u003eBco1\u003c/em\u003e, and \u003cem\u003eKcne4\u003c/em\u003e genes were identified in C4 cluster (Fig.\u0026nbsp;3C). \u003cem\u003eDpp4\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e C3 and \u003cem\u003eBco1\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eKcne4\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e C4 cell subpopulations were enriched in model group (Fig.\u0026nbsp;3D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4.Immune cell clustering and expression of immune cell gene markers\u003c/h2\u003e \u003cp\u003eInflammation plays an important role in IBS-D.\u003csup\u003e22\u003c/sup\u003e Immune cells were further clustered into 14 clusters, including 13 immune cell subtypes (Fig.\u0026nbsp;4A). Among these immune cells, monocytes, mast cells, and cycling immune cells were significantly increased in the model group compared to the control group (Fig.\u0026nbsp;4B). In contrast, T cells (CD8 cells, Tregs) and B cells (Plasma cells, Pax5\u003csup\u003e+\u003c/sup\u003e B cells) were significantly decreased in the model group compared to the control group (Fig.\u0026nbsp;4B).\u003c/p\u003e \u003cp\u003eB cells typically expressed \u003cem\u003eMs4a1\u003c/em\u003e and \u003cem\u003eVpreb3\u003c/em\u003e, and a subtype of B cells was identified as \u003cem\u003ePax5\u003c/em\u003e-positive (Fig.\u0026nbsp;4C). The marker \u003cem\u003eIl1rapl1\u003c/em\u003e gene was highly expressed in Mast cells (Fig.\u0026nbsp;4C). \u003cem\u003eSSr4\u003c/em\u003e was solely expressed in a separate cluster marked as plasma cells (Fig.\u0026nbsp;4C). Another subset of B cells was characterized by the expression of \u003cem\u003eMki67\u003c/em\u003e, \u003cem\u003eTop2a\u003c/em\u003e, and \u003cem\u003eCdk1\u003c/em\u003e genes, classified as cycling immune B cells (Fig.\u0026nbsp;4D). Five clusters were assigned as T cells. Cd family genes \u003cem\u003eCd3d\u003c/em\u003e, \u003cem\u003eCd3e\u003c/em\u003e, \u003cem\u003eCd3g\u003c/em\u003e, and \u003cem\u003eCd2\u003c/em\u003e were markers for NK and T cells (Fig.\u0026nbsp;4E-4F). CD8 cells were featured by \u003cem\u003eCd8a\u003c/em\u003e and \u003cem\u003eCd8b\u003c/em\u003e expression (Fig.\u0026nbsp;4F). The \u003cem\u003eCtla4\u003c/em\u003e gene was highly expressed in Tregs (Fig.\u0026nbsp;4G).\u003c/p\u003e \u003cp\u003eWe identified differentially expressed genes between IBS-D and normal rats in immune cells. \u003cem\u003eCcr2\u003c/em\u003e, \u003cem\u003eAdam23\u003c/em\u003e, and \u003cem\u003eIl1rl1\u003c/em\u003e genes were highly upregulated in monocytes of the model compared to the control group (Fig.\u0026nbsp;4K), whereas \u003cem\u003eRT1-CE16\u003c/em\u003e, \u003cem\u003eCd7\u003c/em\u003e, and \u003cem\u003eRinl\u003c/em\u003e genes were significantly downregulated in monocytes (Fig.\u0026nbsp;4K). In cycling immune B cells, \u003cem\u003eSirpal1\u003c/em\u003e, \u003cem\u003eTrio\u003c/em\u003e, \u003cem\u003eIgf1r\u003c/em\u003e, as well as \u003cem\u003eRn18s\u003c/em\u003e, \u003cem\u003eHpgds\u003c/em\u003e, and \u003cem\u003eIglc1\u003c/em\u003e genes were significantly dysregulated (Fig.\u0026nbsp;4L). Functional analysis of DEGs in monocytes revealed that adaptive immune response, type II interferon production, and T cell cytokine production were dysregulated in the model group (Fig.\u0026nbsp;4M). Three significant pathways, including oxidative phosphorylation, cytoplasmic translation, and DNA topological change signalings, were enriched for DEGs of immune cycling cells (Fig.\u0026nbsp;4N). The cytoplasmic translation, oxidative phosphorylation, and signal transduction by p53 class mediator pathways were significantly affected in Tregs of the model group (Fig.\u0026nbsp;4P).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5.Intercellular cell communication in IBS rat model\u003c/h2\u003e \u003cp\u003eTo explore intercellular crosstalk in the colon, we performed CellChat to investigate ligand-receptor pairs across cell subtypes. Common signaling pathways, such as APP, LAMININ, and MIF, were observed in control and model groups (Fig.\u0026nbsp;5A, and supplementary Fig.\u0026nbsp;1C). Specific immune-related ligand-receptors were exclusively observed in rat IBS models. We observed that TIGIT was a key ligand aberrantly expressed in epithelial subtypes and epithelial-NK/monocyte-macrophage cell interactions (Fig.\u0026nbsp;5B, and supplementary Fig.\u0026nbsp;1D), suggesting crosstalk between epithelial cells and immune cells. \u003cem\u003eFlt3\u003c/em\u003e was exclusively highly expressed in mast cells in the IBS model (Fig.\u0026nbsp;5B, and supplementary Fig.\u0026nbsp;1D). Lilrb3 was exclusively expressed in monocyte-macrophages in the rat IBS model (Fig.\u0026nbsp;5B, and supplementary Fig.\u0026nbsp;1E). The Pax5 transcription factor (TF) was highly expressed in both mast cells and B cells of the IBS model, but not in the control group (Fig.\u0026nbsp;5C-5D). \u003cem\u003eMafb\u003c/em\u003e was remarkably expressed in monocyte-macrophages of IBS model rats (Fig.\u0026nbsp;5C-5D). \u003cem\u003eHes6\u003c/em\u003e and \u003cem\u003eEhf\u003c/em\u003e TFs were the top two regulons in tuft cells of the IBS model, whereas \u003cem\u003eRfx3\u003c/em\u003e and \u003cem\u003eNr2f6\u003c/em\u003e were the top regulons in the control group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.Discussion","content":"\u003cp\u003eThis study systematically deciphered the transcriptional landscape of the colonic mucosa in a rat model of IBS-D at single-cell resolution, revealing compositional shifts, transcriptional signatures, and potential regulatory networks among various cellular subpopulations, including epithelial, immune, and stromal cells. Our findings not only reinforce the central role of intestinal mucosal immune barrier dysfunction in IBS-D pathogenesis but also identify specific cellular subsets and molecular targets involved in this process, providing novel insights into the cellular and molecular mechanisms underlying IBS-D.\u003c/p\u003e \u003cp\u003eFirst, our data confirm a significant remodeling of the immune microenvironment in the IBS-D colonic mucosa. Compared to controls, the model group exhibited a marked increase in monocytes, mast cells, and cycling immune cells, alongside a significant decrease in T-cell subsets (including CD8\u003csup\u003e+\u003c/sup\u003e T cells and Tregs) and B-cell populations. This aligns with the characterization of IBS as a state of low-grade inflammation. The expansion of monocytes and their heightened expression of genes such as \u003cem\u003eCcr2\u003c/em\u003e and \u003cem\u003eIl1rl1\u003c/em\u003e suggests the recruitment and activation of innate immune cells as key drivers of mucosal inflammation.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Conversely, the reduction in Tregs may impair intestinal immune tolerance and suppressive regulation, contributing to inflammatory imbalance.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e This indicates the multidimensional nature of immune dysregulation in IBS-D, where different therapeutic strategies may exert effects by acting on distinct immune hubs.\u003c/p\u003e \u003cp\u003eSignificant transcriptomic alterations were also observed in epithelial cells, the core component of the physical barrier. We found an increased abundance of immature goblet cells and Tuft cells in the model group. Goblet cells, responsible for mucus secretion, may exhibit compromised function in an immature state, affecting the integrity and function of the mucus layer.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Tuft cells, as chemosensory cells, can sense luminal contents and regulate type 2 immune responses\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e; their increase may be linked to neuro-immune cross-talk dysregulation in IBS-D. Differential expression analysis further identified key molecules associated with epithelial function, such as the upregulation of \u003cem\u003eKrt7\u003c/em\u003e and \u003cem\u003eFoxa3\u003c/em\u003e, and the downregulation of \u003cem\u003eCst6\u003c/em\u003e, \u003cem\u003eElapor1\u003c/em\u003e, and \u003cem\u003eKcnma1\u003c/em\u003e. Notably, \u003cem\u003eKcnma1\u003c/em\u003e encodes the BK calcium-activated potassium channel, involved in epithelial ion transport and secretion.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Its downregulation may directly contribute to water and electrolyte secretion disturbances underlying diarrhea in IBS-D.\u003csup\u003e29\u003c/sup\u003e These genes constitute a potential molecular basis for epithelial barrier dysfunction.\u003c/p\u003e \u003cp\u003eCell-cell communication network analysis uncovered characteristic alterations in intercellular interactions in IBS-D pathology. We observed specific positive co-occurrence patterns, including interactions between epithelial cells and monocytes/macrophages, between endothelial/epithelial cells and NK cells, and involving Tuft cells, in the model group. Additionally, regulon activity analysis identified key transcription factors\u0026mdash;such as Pax5 (B cells)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, Mafb (monocytes/macrophages), and Hes6 (Tuft) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u0026mdash;that may serve as core regulatory switches driving functional state transitions in different cellular subsets.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study constructed a high-resolution single-cell transcriptomic atlas of the IBS-D colonic mucosa, systematically delineating the cellular ecological basis of immune barrier dysfunction from multiple perspectives: cellular composition, gene expression, pathway activity, and cellular crosstalk. The specific cellular subpopulations we identified\u0026mdash;including dysfunctional epithelial subsets, cycling immune cells, and imbalanced lymphoid/myeloid cells\u0026mdash;along with their characteristic molecular markers (e.g.,\u003cem\u003eKcnma1\u003c/em\u003e, \u003cem\u003eSirpal1\u003c/em\u003e, \u003cem\u003eCcr2\u003c/em\u003e), not only deepen the understanding of IBS-D pathogenesis but also provide a direct foundation for developing novel biomarkers and targeted therapeutic strategies. For example, modulators targeting Kcnma1 channel function or immunomodulators aimed at restoring the Treg/pro-inflammatory myeloid cell balance may represent promising future directions for IBS-D treatment.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Limitations of this study include the relatively small sample size and the inherent constraints of animal models in fully recapitulating human disease complexity. Future validation in larger human cohorts, integrated with spatial transcriptomics, proteomics, and other multi-omics technologies, will be essential to further elucidate the spatiotemporal dynamics and functional roles of key molecules within the mucosal niche, ultimately advancing toward precision diagnosis and therapy for IBS-D.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the National Natural Science Foundation of China (grant no. 81804047 and 82174303), the State Key Laboratory of Traditional Chinese Medicine Syndrome (grant no. SKLKY2004B0007) and Guangdong Provincial Bureau of Traditional Chinese Medicine (grant no. 20254041).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated in the present study may be requested from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQiuke Hou, Liyan Ji and Fengbin Liu designed and supervised the project. Yongquan Huang, Jiahe Zhang, Zefang Yang, Xiling Yang and Jiaman Lin conducted the experiments. Qiuke Hou and Liyan Ji wrote the draft manuscript. Yongquan Huang and Qiuke Hou edited the manuscript. All authors have read and approved the final version of the manuscript. Liyan Ji and Qiuke Hou confirm the authenticity of all the raw data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal experiments were approved by the Institutional Animal Care and Use Committee of Guangzhou University of Chinese Medicine (No. 2024257).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequencing data and processed data have been deposited in the Gene Expression Omnibus (GEO) under GSE326856.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCamilleri, M. 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Hepatol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 473\u0026ndash;486 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Irritable bowel syndrome, Diarrhea, Single-cell RNA sequencing, Intestinal Mucosa, Immunity","lastPublishedDoi":"10.21203/rs.3.rs-9205401/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9205401/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImbalance of intestinal mucosal immune regulation is a key mechanism in diarrhea-predominant irritable bowel syndrome (IBS-D). We aimed to characterize this dysregulation in the intestinal mucosa at single-cell resolution. Intestinal mucosal cells from control and neonatal maternal separation-induced IBS-D rats were isolated and subjected to single-cell RNA sequencing (scRNA-seq). Bioinformatics analyses included clustering, differential expression, trajectory inference, and cell\u0026ndash;cell communication assessment. We obtained transcriptomes from 4,572 high-quality cells, identifying epithelial, stromal, immune, and endothelial lineages. In IBS-D rats, we observed a significant remodeling of the immune compartment, marked by increased monocytes, mast cells, and cycling immune cells, and decreased T and B lymphocyte subsets. Epithelial cells exhibited upregulation of Krt7 and Foxa3, and downregulation of Cst6, Elapor1, and Kcnma1. Cell\u0026ndash;cell communication analysis revealed enhanced interactions between epithelial and innate immune cells, involving ligands such as TIGIT. Regulon activity highlighted key transcription factors, including Pax5 in B cells and Mafb in monocytes. Single-cell RNA sequencing reveals that immune barrier dysregulation in the intestinal mucosa of rats with IBS-D is associated with altered immune cell composition, dysregulated expression of epithelial function-related genes, and disrupted intercellular communication, providing novel insights into the pathogenesis of IBS-D. Our high-resolution scRNA-seq atlas delineates the cellular and molecular landscape of intestinal mucosal immune barrier dysfunction in IBS-D, pinpointing novel potential therapeutic targets for this disorder.\u003c/p\u003e","manuscriptTitle":"Single-cell RNA-seq profiles of intestinal mucosa in IBS-D rats to explore the mechanism of immune barrier regulation in pathogenesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 17:15:57","doi":"10.21203/rs.3.rs-9205401/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"37644582343283574685220123111713238438","date":"2026-05-11T07:59:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T13:57:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T10:09:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-09T04:55:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-03T07:18:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-03T07:02:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"966b2b41-8cbc-42b8-bfe9-44e67717d8e5","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"37644582343283574685220123111713238438","date":"2026-05-11T07:59:58+00:00","index":94,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66900266,"name":"Biological sciences/Cell biology"},{"id":66900267,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":66900268,"name":"Health sciences/Gastroenterology"},{"id":66900269,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2026-04-26T17:15:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 17:15:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9205401","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9205401","identity":"rs-9205401","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}