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Single-cell meta-analysis of inflammatory bowel disease with scIBD

Nature Computational Science volume 3, pages 522–531 (2023)Cite this article

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Abstract

Understanding the heterogeneous intestinal microenvironment is critical to uncover the pathogenesis of inflammatory bowel disease (IBD). Recent advances in single-cell RNA sequencing (scRNA-seq) have identified certain cell types and genes that could contribute to IBD; however, a comprehensively integrated analysis of these scRNA-seq datasets is not yet available. Here we introduce scIBD, a platform for single-cell meta-analysis of IBD with interactive and visualization features, which combines highly curated single-cell datasets in a uniform workflow, enabling identifying rare or less-characterized cell types in IBD and dissecting the commonalities, as well as the differences between ulcerative colitis and Crohn’s disease. scIBD also incorporates multifunctional information—including regulon activity, GWAS-implicated risk genes and genes targeted by therapeutics—to infer clinically relevant cell-type specificity. Collectively, scIBD is a user-friendly web-based platform for the community to analyze the transcriptome features and gene regulatory networks associated with the pathogenesis and treatment of IBD at single-cell resolution.

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Fig. 1: Single-cell transcriptional atlas and disease-associated cell subtypes in IBD.
Fig. 2: Linking GWAS-risk genes and therapeutic targets to cell subtypes in scIBD.

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Data availability

All scRNA-seq datasets used in this study were obtained from published research articles. Previously published scRNA-seq are available under accession codes GSE114374 (ref. 3), GSE116222 (ref. 4), GSE148837 (ref. 5), GSE134809 (ref. 17), GSE125527 (ref. 22), GSE157477 (ref. 24), GSE121380 (validation cohort by B. Huang et al.)28, GSE158702 (ref. 58) (from the GEO database; https://www.ncbi.nlm.nih.gov/geo), SCP259 (ref. 16) (from the Single Cell Portal database; https://singlecell.broadinstitute.org/), E-MTAB-8901 (ref. 25), E-MTAB-9543 (adult tissues)27, E-MTAB-9536 (fetal tissues)27 (from the EBI array-express database; https://www.ebi.ac.uk/arrayexpress), HRA000072 (discovery cohort by B. Huang et al.28, from the GSA database; https://ngdc.cncb.ac.cn/gsa-human) and SDY176520 (from the Import database; https://www.immport.org). See Supplementary Table 1 for details. The datasets underlying scIBD can be accessed at the scIBD website or through Figshare59. The source data underlying Figs. 1 and 2, Extended Data Fig. 1e,f and Extended Data Figs. 2–8 and 10 are available in Figshare60. Source Data are provided with this paper.

Code availability

The code used to construct the scIBD website is stored in GitHub at https://github.com/niehu-szbl/scIBD_website_code and Figshare61 and running at http://scibd.cn. All other codes are deposited in GitHub at https://github.com/niehu-szbl/scIBD_SourceCode and Figshare62.

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Acknowledgements

We thank the National Natural Science Foundation of China (grant nos. 82122049, 82272803 and 82341011 to L.Z.; grant no. 32100579 to C.Y.), the National Key Research and Development Program of China (grant no. 2022YFA0912600 to L.Z.) and the Guangdong Basic and Applied Basic Research Foundation (2020A1515110857 to C.Y.) for their support. We thank the Shenzhen Bay Laboratory supercomputing center.

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Authors and Affiliations

  1. School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China

    Hu Nie, Peilu Lin, Jiesheng Li & Lei Zhang

  2. Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, China

    Yu Zhang, Yihong Wan, Jiesheng Li, Chengqian Yin & Lei Zhang

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  1. Hu Nie
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Contributions

H.N. and L.Z. provided conceptualization and wrote the original draft. H.N. performed the data search and acquisition. H.N., P.L. and J.L. contributed to bioinformatics analysis and platform development, and wrote the guide. H.N., P.L., Y.Z., Y.W., J.L., C.Y. and L.Z. reviewed and edited the manuscript. C.Y. and L.Z. provided supervision and resources and performed funding acquisition.

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Correspondence to Lei Zhang.

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Nature Computational Science thanks Tamas Korcsmaros and Ali Rahnavard for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Overview of resources, methodologies, and utilities of scIBD.

a, Summary of scRNA-seq datasets used in this study. All single-cell transcriptomic datasets were retrieved from public databases, including SCP, Immport, SRA, GSA, and EBI. Clinical trials, therapeutic drugs, therapeutic targets, and GWAS-risk genes for IBD were also curated in scIBD. b, In-house workflow to pre-process, integrate, and analyze these scRNA-seq datasets uniformly. c, Utilities of scIBD. scIBD provides various interactive visualization and data analysis interface. d, Method to classify T/NK compartment into three sub-compartments: CD4+ T cell, CD8+ T cell, and ILC compartment. e, Expression levels of IGHA and IGHG genes in non-circulating plasma cells. f, Percentage of IgA+, IgG+, IgA-IgG-, and IgA+IgG+ plasma cells among non-circulating plasma cells. An expression cutoff corresponding to log2(CP10K + 1) = 6 accurately discriminated between IgA+ and IgG+ plasma cells.

Source data

Extended Data Fig. 2 Cellular composition and distribution in scIBD across different developmental stages and datasets.

a, Percentage of cells in each major cluster in scIBD. b, Percentage of cells in each major cluster across fetal, pediatric, and adult stages. c,d, The cellular distribution colored by developmental stage (c) and dataset (d). e, Cell composition of CD4+ T cells, CD8+ T cells, myeloid cells, ILCs, B/plasma cells, epithelial cells, and mesenchymal cells in pediatric and adult individuals.

Source data

Extended Data Fig. 3 Batch correction of scRNA-seq data with BBKNN improved the integration of CD8+ T cells and epithelial cells.

a,b UMAP plots showing the distribution of datasets and cell subtypes before and after batch correction by BBKNN in CD8+ T cells (n = 184,220) (a) and epithelial cells (n = 195,735) (b). c, Boxplot showing the Local Inverse Simpson’s Index (LISI) distribution of data presented in a and b. In the boxplot, center lines denote median values; whiskers denote 1.5 x the interquartile range; the upper and lower limits of the box plot indicate the maximum and minimum values, respectively. The P-values were calculated by two-sided Wilcoxon rank-sum test. P-values < 2.2 × 10−16 for all comparisons. **** indicates P-value < 0.0001. d, UMAP plots showing the cell subtypes in each major cluster.

Extended Data Fig. 4 Examination of rare or less characterized cell subtypes in multiple datasets in scIBD.

a–c, Number of LAMP3+ DCs (a), CD8+ Tc17 cells (b) and DUOX2+ epithelial cells (c) in individual datasets. d, Gene expression of LAMP3 and CCR7 in each cell subtype of myeloid cells in the dataset C. S. Smillie et al., Cell, 2019. e, Gene expression of IL17A, IL22, IL26 and RORC in each cell subtype of CD8+ T cells in the dataset C. S. Smillie et al., Cell, 2019. f, Gene expression levels of DUOX2 and DUOXA2 across all cell subtypes of epithelial cells in the dataset C. S. Smillie et al., Cell, 2019. g, Gene expression levels of DUOX2 and DUOXA2 in enterocytes in healthy (n = 3,994), non-inflamed (n = 2,928), and inflamed (n = 2,112) tissues in the dataset C. S. Smillie et al., Cell, 2019. P-values < 2.2 × 10−16 for all comparisons. **** indicates P-value < 0.0001. Two-sided Wilcoxon rank-sum test.

Source data

Extended Data Fig. 5 Preference of disease status for immune cell subtypes.

a,b, Tissue preference (a) and disease status preference (b) of each immune cluster in adult individuals estimated by the Ro/e. ++++, Ro/e > 2; ++, 1 < Ro/e ≤ 2; +, 0.5 ≤ Ro/e ≤ 1; +/−, 0 < Ro/e < 0.5; −, Ro/e = 0; in which Ro/e denotes the ratio of observed to expected cell number. c, Volcano plot showing differentially expressed genes between CD8+ Tc17 cells (n = 5,258) and CD4+ Th17 cells (n = 5,937). Two-sided Wilcoxon rank-sum test; fold change ≥ 1.25. d, Dot plot showing gene expression of transcriptional factors including MAF, NR1H3, ETV5, KLF9 in myeloid cells.

Source data

Extended Data Fig. 6 Preference of disease status for non-immune cell subtypes.

a–c, Disease status preference of each cell subtype in epithelial (a), mesenchymal (b) and endothelial (c) compartments in adult individuals estimated by the Ro/e. ++++, Ro/e > 2; ++, 1 < Ro/e ≤ 2; +, 0.5 ≤ Ro/e ≤ 1; +/−, 0 < Ro/e < 0.5; −, Ro/e = 0; in which Ro/e denotes the ratio of observed to expected cell number. d, Frequencies of disease-associated cell subsets in each major cluster for each sample. Each dot represents a sample. There were n1, n2, and n3 samples present in each major cluster with a cell count greater than 100 in the CD, HC, and UC groups, respectively. For epithelial cells, n2 = 69, n3 = 34; For mesenchymal cells, n1 = 2, n2 = 39, n3 = 29; For endothelial cells, n1 = 2, n2 = 16, n3 = 17. In the box plots, center lines denote median values; whiskers denote 1.5 x the interquartile range; the upper and lower limits of the box plot indicate the maximum and minimum values, respectively. P-values for HC versus UC were 3.76 × 10−6, 0.028 in DUOX2+ epithelial cell and M-like cell, respectively. P-values for HC versus CD, HC versus UC, and CD versus UC were 0.250, 5.07 × 10−3, 0.340 in inflammatory fibroblast; 0.277, 0.041, 0.027 in reticular fibroblast; 0.227, 1.51 × 10−3, 0.461 in immature pericyte; 0.341, 1.97 × 10−5, 0.083 in adult venous EC (SELE + ), 0.280, 2.89 × 10−3, 0.307 in cycling EC, respectively. ns, not significant; * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001; One-sided t-test. e, Dot plots showing the signature genes of the above disease-associated cell subsets.

Source data

Extended Data Fig. 7 Major cluster-specific risk genes identified in scIBD.

a, Heatmap of gene expression levels of major cluster-specific risk genes of IBD. The expression value across each cell was scaled to a range from 0 to 1 with the function “rescale” in package “scales” in R. b, Violin plot of minor cluster-specific risk genes which were up-regulated in CD or UC comparing with health. The P-values were calculated by two-sided Wilcox rank-sum test. The number of cells in each group was displayed below the violin plot. P-values for HC versus UC were 1.71 × 10-4, 9.88 × 10−4, 0.016 in the first three comparisons, respectively. P-values for HC versus CD, HC versus UC, and CD versus UC were 4.43 × 10−16, 1.93 × 10−201, 6.33 × 10−4; 1.07 × 10−3, 0.629, 0.074; 8.30 × 10−22, 5.04 × 10−5, 4.14 × 10−11; 7.70 × 10−8, 1.10 × 10−29, 2.49 × 10−10; 8.16 × 10−7, 4.29 × 10−5, 0.164, respectively. ns, not significant; * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001; Two-sided Wilcox rank-sum test.

Source data

Extended Data Fig. 8 Cell subtype-specific therapeutic target genes identified in scIBD.

a, Heatmap of gene expression of cell subtype-specific therapeutic target genes for IBD. The expression value across each cell subtype was scaled to a range from 0 to 1 with function “rescale” in package “scales” in R. b, Violin plot of cell subtype-specific therapeutic target genes which were up-regulated in UC or CD comparing with health. The number of cells in each group was displayed below the violin plot. P-values for HC versus CD, HC versus UC, and CD versus UC were 0.016, 0.418, 9.86 × 10−4; 4.32 × 10−38, 5.16 × 10−61, 9.20 × 10−5; 1.92 × 10−12, 7.09 × 10−11, 8.23 × 10−7; 0.010, 0.506, 5.37 × 10−3, respectively. P-values for HC versus UC were 0.038, 4.23 × 10−13 for the last two comparisons, respectively. ns, not significant; * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001; Two-sided Wilcox rank-sum test.

Source data

Extended Data Fig. 9 Characterize the features of scIBD.

scIBD scRNA-seq data were gathered from 12 studies. In addition to traditional analysis, regulon inference was also conducted. To facilitate the visualization and exploration of the meta-analysis results, a user-friendly web platform for scIBD was created, allowing users to browse, search, analyze online, and download all relevant metadata and analytical results.

Extended Data Fig. 10 Two examples to demonstrate the generality of this web resource.

a, Dot plot showing the gene expression levels of MHC II molecules across the cell subtypes of epithelial cells. b, Global UMAP visualization of gene expression of MHC II molecules in scIBD. The expression level of MHC II molecules was calculated by the average expression of HLA-DRA and HLA-DRB1. c, Comparison of the gene expression of MHC II molecules across cell subtypes in the epithelial compartment. d, Comparison of gene expression of MHC II molecules in DUOX2+ epithelial cells (left) and enterocytes (right) across disease statuses. e,f, Differential regulons between healthy (n = 63,411 cells) and UC (n = 19,339 cells) (e), and between healthy and CD (n = 1,123 cells) (f) in epithelial compartment. The horizontal dashed line represents the adjusted-P value threshold of 0.01. The vertical dashed lines represent fold change thresholds of +1.5 or –1.5. The P-values were calculated by two-sided Student’s t-test, then adjusted by the ‘Benjamini & Hochberg’ method. g, Comparison of the activity of PITX1 regulon among cell subtypes in epithelial compartment. h, Comparison of the activity of PITX1 regulon across disease statuses in DUOX2+ epithelial cells from adult individuals. i, Comparison of the activity of PITX1 regulon between colon and rectum in DUOX2+ epithelial cells in inflamed tissues from adult UC patients.

Supplementary information

Reporting Summary

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Supplementary Table 1

Summary of scRNA-seq datasets used in this study.

Supplementary Table 2

Differential gene expression analysis of Tc17 cells and Th17 cells.

Supplementary Table 3

Differential gene expression analysis of Tc17 cells in UC and CD.

Supplementary Table 4

Differential gene expression analysis of AREG+ RTMs and APOE+ RTMs.

Supplementary Table 5

The specificity scores of regulons of APOE+ RTMs and AREG+ RTMs.

Supplementary Table 6

Differential regulon activity analysis of epithelial cells in different disease statuses.

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Nie, H., Lin, P., Zhang, Y. et al. Single-cell meta-analysis of inflammatory bowel disease with scIBD. Nat Comput Sci 3, 522–531 (2023). https://doi.org/10.1038/s43588-023-00464-9

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