Skip to main content
SpringerLink
Log in

Identification of antigen-presentation related B cells as a key player in Crohn’s disease using single-cell dissecting, hdWGCNA, and deep learning

  • Research
  • Published:
Clinical and Experimental Medicine Aims and scope Submit manuscript

Abstract

Crohn’s disease (CD) arises from intricate intercellular interactions within the intestinal lamina propria. Our objective was to use single-cell RNA sequencing to investigate CD pathogenesis and explore its clinical significance. We identified a distinct subset of B cells, highly infiltrated in the CD lamina propria, that expressed genes related to antigen presentation. Using high-dimensional weighted gene co-expression network analysis and nine machine learning techniques, we demonstrated that the antigen-presenting CD-specific B cell signature effectively differentiated diseased mucosa from normal mucosa (Independent external testing AUC = 0.963). Additionally, using MCPcounter and non-negative matrix factorization, we established a relationship between the antigen-presenting CD-specific B cell signature and immune cell infiltration and patient heterogeneity. Finally, we developed a gene-immune convolutional neural network deep learning model that accurately diagnosed CD mucosa in diverse cohorts (Independent external testing AUC = 0.963). Our research has revealed a population of B cells with a potential promoting role in CD pathogenesis and represents a fundamental step in the development of future clinical diagnostic tools for the disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Li M, Yang L, Mu C, et al. Gut microbial metabolome in inflammatory bowel disease: from association to therapeutic perspectives. Comput Struct Biotechnol J. 2022;20:2402–14. https://doi.org/10.1016/j.csbj.2022.03.038.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Roda G, Chien Ng S, Kotze PG, et al. Crohn’s disease. Nat Rev Dis Primers. 2020;6(1):22. https://doi.org/10.1038/s41572-020-0156-2.

    Article  PubMed  Google Scholar 

  3. Stenczel ND, Purcarea MR, Tribus LC, Oniga GH. The role of the intestinal ultrasound in Crohn’s disease diagnosis and monitoring. J Med Life. 2021;14(3):310–5. https://doi.org/10.25122/jml-2021-0067.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Singh S, Murad MH, Fumery M, et al. Comparative efficacy and safety of biologic therapies for moderate-to-severe Crohn’s disease: a systematic review and network meta-analysis. Lancet Gastroenterol Hepatol. 2021;6(12):1002–14. https://doi.org/10.1016/S2468-1253(21)00312-5.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Jaeger N, Gamini R, Cella M, et al. Single-cell analyses of Crohn’s disease tissues reveal intestinal intraepithelial T cells heterogeneity and altered subset distributions. Nat Commun. 2021;12(1):1921. https://doi.org/10.1038/s41467-021-22164-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Veauthier B, Hornecker JR. Crohn’s disease: diagnosis and management. Am Fam Physician. 2018;98(11):661–9.

    PubMed  Google Scholar 

  7. Kong L, Pokatayev V, Lefkovith A, et al. The landscape of immune dysregulation in Crohn’s disease revealed through single-cell transcriptomic profiling in the ileum and colon. Immunity. 2023;56(2):444-458 e5. https://doi.org/10.1016/j.immuni.2023.01.002.

    Article  CAS  PubMed  Google Scholar 

  8. Cupi ML, Sarra M, Marafini I, et al. Plasma cells in the mucosa of patients with inflammatory bowel disease produce granzyme B and possess cytotoxic activities. J Immunol. 2014;192(12):6083–91. https://doi.org/10.4049/jimmunol.1302238.

    Article  CAS  PubMed  Google Scholar 

  9. Tenbrock K, Rauen T. T cell dysregulation in SLE. Clin Immunol. 2022;239:109031. https://doi.org/10.1016/j.clim.2022.109031.

    Article  CAS  PubMed  Google Scholar 

  10. Bataille F, Klebl F, Rummele P, et al. Morphological characterisation of Crohn’s disease fistulae. Gut. 2004;53(9):1314–21. https://doi.org/10.1136/gut.2003.038208.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Huang LJ, Mao XT, Li YY, et al. Multiomics analyses reveal a critical role of selenium in controlling T cell differentiation in Crohn’s disease. Immunity. 2021;54(8):1728-1744 e7. https://doi.org/10.1016/j.immuni.2021.07.004.

    Article  CAS  PubMed  Google Scholar 

  12. Martin JC, Chang C, Boschetti G, et al. Single-cell analysis of Crohn’s disease lesions identifies a pathogenic cellular module associated with resistance to anti-TNF therapy. Cell. 2019;178(6):1493-1508.e20. https://doi.org/10.1016/j.cell.2019.08.008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. VanDussen KL, Stojmirović A, Li K, et al. Abnormal small intestinal epithelial microvilli in patients with Crohn’s disease. Gastroenterology. 2018;155(3):815–28. https://doi.org/10.1053/j.gastro.2018.05.028.

    Article  PubMed  Google Scholar 

  14. Vancamelbeke M, Vanuytsel T, Farré R, et al. Genetic and transcriptomic bases of intestinal epithelial barrier dysfunction in inflammatory bowel disease. Inflamm Bowel Dis. 2017;23(10):1718–29. https://doi.org/10.1097/mib.0000000000001246.

    Article  PubMed  Google Scholar 

  15. Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888-1902.e21. https://doi.org/10.1016/j.cell.2019.05.031.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Fu Y, Guo Z, Wang Y, et al. Single-nucleus RNA sequencing reveals the shared mechanisms inducing cognitive impairment between COVID-19 and Alzheimer’s disease. Front Immunol. 2022;13:967356. https://doi.org/10.3389/fimmu.2022.967356.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Waickman AT, Friberg H, Gromowski GD, et al. Temporally integrated single cell RNA sequencing analysis of PBMC from experimental and natural primary human DENV-1 infections. PLoS Pathog. 2021;17(1):e1009240. https://doi.org/10.1371/journal.ppat.1009240.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Morabito S, Miyoshi E, Michael N, et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat Genet. 2021;53(8):1143–55. https://doi.org/10.1038/s41588-021-00894-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Han J, Zhou Y, Zhang C, et al. Intratumoral immune heterogeneity of prostate cancer characterized by typing and hub genes. J Cell Mol Med. 2023;27(1):101–12. https://doi.org/10.1111/jcmm.17641.

    Article  CAS  PubMed  Google Scholar 

  20. Ionkina AA, Balderrama-Gutierrez G, Ibanez KJ, et al. Transcriptome analysis of heterogeneity in mouse model of metastatic breast cancer. Breast Cancer Res BCR. 2021;23(1):93. https://doi.org/10.1186/s13058-021-01468-x.

    Article  CAS  PubMed  Google Scholar 

  21. Mo S, Shen X, Wang Y, et al. Systematic single-cell dissecting reveals heterogeneous oncofetal reprogramming in the tumor microenvironment of gastric cancer. Hum Cell. 2023;36(2):689–701. https://doi.org/10.1007/s13577-023-00856-z.

    Article  CAS  PubMed  Google Scholar 

  22. Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381–6. https://doi.org/10.1038/nbt.2859.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using Cell Chat. Nat Commun. 2021;12(1):1088. https://doi.org/10.1038/s41467-021-21246-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Franz M, Rodriguez H, Lopes C, et al. GeneMANIA update 2018. Nucleic Acids Res. 2018;46(W1):W60-w64. https://doi.org/10.1093/nar/gky311.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 2008;9 Suppl 1(Suppl 1):S4. https://doi.org/10.1186/gb-2008-9-s1-s4.

    Article  CAS  PubMed  Google Scholar 

  26. Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523. https://doi.org/10.1038/s41467-019-09234-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rusk N. Expanded CIBERSORTx. Nat Methods. 2019;16(7):577. https://doi.org/10.1038/s41592-019-0486-8.

    Article  CAS  PubMed  Google Scholar 

  28. Becht E, Giraldo NA, Lacroix L, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17(1):218. https://doi.org/10.1186/s13059-016-1070-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Brunet JP, Tamayo P, Golub TR, Mesirov JP. Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci USA. 2004;101(12):4164–9. https://doi.org/10.1073/pnas.0308531101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Xie Y, Shi H, Han B. Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model. BMC Pediatr. 2023;23(1):90. https://doi.org/10.1186/s12887-023-03896-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhu T, Tao C, Cheng H, Cong H. Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning. Sci Total Environ. 2022;846:157455. https://doi.org/10.1016/j.scitotenv.2022.157455.

    Article  CAS  PubMed  Google Scholar 

  32. Mao B, Ma J, Duan S, Xia Y, Tao Y, Zhang L. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol. 2021;31(7):4576–86. https://doi.org/10.1007/s00330-020-07562-6.

    Article  PubMed  Google Scholar 

  33. Hung J, Goodman A, Ravel D, et al. Keras R-CNN: library for cell detection in biological images using deep neural networks. BMC Bioinform. 2020;21(1):300. https://doi.org/10.1186/s12859-020-03635-x.

    Article  Google Scholar 

  34. Novac OC, Chirodea MC, Novac CM, et al. Analysis of the application efficiency of TensorFlow and PyTorch in convolutional neural network. Sensors (Basel, Switzerland). 2022;22(22):25. https://doi.org/10.3390/s22228872.

    Article  Google Scholar 

  35. Belarif L, Danger R, Kermarrec L, et al. IL-7 receptor influences anti-TNF responsiveness and T cell gut homing in inflammatory bowel disease. J Clin Investig. 2019;129(5):1910–25. https://doi.org/10.1172/jci121668.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Garcia-Carbonell R, Yao SJ, Das S, Guma M. Dysregulation of intestinal epithelial cell RIPK pathways promotes chronic inflammation in the IBD gut. Front Immunol. 2019;10:1094. https://doi.org/10.3389/fimmu.2019.01094.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Dasari BV, McKay D, Gardiner K. Laparoscopic versus Open surgery for small bowel Crohn’s disease. Cochrane Database Syst Rev. 2011. https://doi.org/10.1002/14651858.CD006956.pub2.

    Article  PubMed  Google Scholar 

  38. Caio G, Lungaro L, Caputo F, et al. Nutritional treatment in Crohn’s disease. Nutrients. 2021. https://doi.org/10.3390/nu13051628.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Benevento G, Avellini C, Terrosu G, Geraci M, Lodolo I, Sorrentino D. Diagnosis and assessment of Crohn’s disease: the present and the future. Expert Rev Gastroenterol Hepatol. 2010;4(6):757–66. https://doi.org/10.1586/egh.10.70.

    Article  PubMed  Google Scholar 

  40. Lautenbach E, Berlin JA, Lichtenstein GR. Risk factors for early postoperative recurrence of Crohn’s disease. Gastroenterology. 1998;115(2):259–67. https://doi.org/10.1016/s0016-5085(98)70191-x.

    Article  CAS  PubMed  Google Scholar 

  41. Ricciuto A, Aardoom M, Orlanski-Meyer E, et al. Predicting outcomes in pediatric Crohn’s disease for management optimization: systematic review and consensus statements from the pediatric inflammatory bowel disease-ahead program. Gastroenterology. 2021;160(1):403–36. https://doi.org/10.1053/j.gastro.2020.07.065.

    Article  CAS  PubMed  Google Scholar 

  42. Gao H, He Q, Xu C, et al. The development and validation of anti-paratuberculosis-nocardia polypeptide antibody [Anti-pTNP] for the diagnosis of Crohn’s disease. J Crohns Colitis. 2022;16(7):1110–23. https://doi.org/10.1093/ecco-jcc/jjac008.

    Article  PubMed  Google Scholar 

  43. Dockes J, Varoquaux G, Poline JB. Preventing dataset shift from breaking machine-learning biomarkers. Gigascience. 2021. https://doi.org/10.1093/gigascience/giab055.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Wu Z, Liu D, Deng F. The role of vitamin d in immune system and inflammatory bowel disease. J Inflamm Res. 2022;15:3167–85. https://doi.org/10.2147/JIR.S363840.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Linares R, Gutierrez A, Marquez-Galera A, et al. Transcriptional regulation of chemokine network by biologic monotherapy in ileum of patients with Crohn’s disease. Biomed Pharmacother. 2022;147:53. https://doi.org/10.1016/j.biopha.2022.112653.

    Article  CAS  Google Scholar 

  46. Burge K, Gunasekaran A, Eckert J, Chaaban H. Curcumin and intestinal inflammatory diseases: molecular mechanisms of protection. Int J Mol Sci. 2019. https://doi.org/10.3390/ijms20081912.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kawaguchi T, Mori M, Saito K, et al. Food antigen-induced immune responses in Crohn’s disease patients and experimental colitis mice. J Gastroenterol. 2015;50(4):394–405. https://doi.org/10.1007/s00535-014-0981-8.

    Article  CAS  PubMed  Google Scholar 

  48. Pyzik M, Sand KMK, Hubbard JJ, Andersen JT, Sandlie I, Blumberg RS. The neonatal Fc receptor (FcRn): a misnomer? Front Immunol. 2019;10:1540. https://doi.org/10.3389/fimmu.2019.01540.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wang TT, Ravetch JV. Functional diversification of IgGs through Fc glycosylation. J Clin Investig. 2019;129(9):3492–8. https://doi.org/10.1172/JCI130029.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Hedin CR, McCarthy NE, Louis P, et al. Altered intestinal microbiota and blood T cell phenotype are shared by patients with Crohn’s disease and their unaffected siblings. Gut. 2014;63(10):1578–86. https://doi.org/10.1136/gutjnl-2013-306226.

    Article  CAS  PubMed  Google Scholar 

  51. Biasci D, Lee JC, Noor NM, et al. A blood-based prognostic biomarker in IBD. Gut. 2019;68(8):1386–95. https://doi.org/10.1136/gutjnl-2019-318343.

    Article  CAS  PubMed  Google Scholar 

  52. Taylor KM, Hanscombe KB, Prescott NJ, et al. Genetic and inflammatory biomarkers classify small intestine inflammation in asymptomatic first-degree relatives of patients with Crohn’s disease. Clin Gastroenterol Hepatol. 2020;18(4):908–16. https://doi.org/10.1016/j.cgh.2019.05.061.

    Article  CAS  PubMed  Google Scholar 

  53. D’Haens GR, van Deventer S. 25 years of anti-TNF treatment for inflammatory bowel disease: lessons from the past and a look to the future. Gut. 2021;70(7):1396–405. https://doi.org/10.1136/gutjnl-2019-320022.

    Article  CAS  PubMed  Google Scholar 

  54. Cattoretti G, Angelin-Duclos C, Shaknovich R, Zhou H, Wang D, Alobeid B. PRDM1/Blimp-1 is expressed in human B-lymphocytes committed to the plasma cell lineage. J Pathol. 2005;206(1):76–86. https://doi.org/10.1002/path.1752.

    Article  CAS  PubMed  Google Scholar 

  55. Dotan I, Yeshurun D, Hallak A, et al. [Treatment of Crohn's disease with anti TNF alpha antibodies—the experience in the Tel Aviv Medical Center]. Harefuah. 2001;140(4):289–93, 368.

  56. Smith MA, Wright G, Wu J, et al. Positive regulatory domain I (PRDM1) and IRF8/PU.1 counter-regulate MHC class II transactivator (CIITA) expression during dendritic cell maturation. J Biol Chem. 2011;286(10):7893–904. https://doi.org/10.1074/jbc.M110.165431.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Twa DDW, Mottok A, Savage KJ, Steidl C. The pathobiology of primary testicular diffuse large B-cell lymphoma: Implications for novel therapies. Blood Rev. 2018;32(3):249–55. https://doi.org/10.1016/j.blre.2017.12.001.

    Article  CAS  PubMed  Google Scholar 

  58. Kong F, Ye S, Zhong Z, et al. Single-cell transcriptome analysis of chronic antibody-mediated rejection after renal transplantation. Front Immunol. 2021;12:767618. https://doi.org/10.3389/fimmu.2021.767618.

    Article  CAS  PubMed  Google Scholar 

  59. Arthur SE, Jiang A, Grande BM, et al. Genome-wide discovery of somatic regulatory variants in diffuse large B-cell lymphoma. Nat Commun. 2018;9(1):4001. https://doi.org/10.1038/s41467-018-06354-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Gao P, Liu H, Huang H, et al. The Crohn Disease-associated ATG16L1(T300A) polymorphism regulates inflammatory responses by modulating TLR- and NLR-mediated signaling. Autophagy. 2022;18(11):2561–75. https://doi.org/10.1080/15548627.2022.2039991.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kopylov U, Afif W, Cohen A, et al. Subcutaneous ustekinumab for the treatment of anti-TNF resistant Crohn’s disease–the McGill experience. J Crohns Colitis. 2014;8(11):1516–22. https://doi.org/10.1016/j.crohns.2014.06.005.

    Article  CAS  PubMed  Google Scholar 

  62. Watanabe T, Minaga K, Kamata K, et al. RICK/RIP2 is a NOD2-independent nodal point of gut inflammation. Int Immunol. 2019;31(10):669–83. https://doi.org/10.1093/intimm/dxz045.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Plevy S. A molecular connection hints at how a genetic risk factor drives Crohn’s disease. Nature. 2021;593(7858):201–3. https://doi.org/10.1038/d41586-021-00979-z.

    Article  CAS  PubMed  Google Scholar 

  64. Luo X, Wang X, Huang S, et al. Paeoniflorin ameliorates experimental colitis by inhibiting gram-positive bacteria-dependent MDP-NOD2 pathway. Int Immunopharmacol. 2021;90:107224. https://doi.org/10.1016/j.intimp.2020.107224.

    Article  CAS  PubMed  Google Scholar 

  65. Alnabhani Z, Hugot JP, Montcuquet N, et al. Respective roles of hematopoietic and nonhematopoietic Nod2 on the Gut microbiota and mucosal homeostasis. Inflamm Bowel Dis. 2016;22(4):763–73. https://doi.org/10.1097/MIB.0000000000000749.

    Article  PubMed  Google Scholar 

  66. Rochereau N, Roblin X, Michaud E, et al. NOD2 deficiency increases retrograde transport of secretory IgA complexes in Crohn’s disease. Nat Commun. 2021;12(1):261. https://doi.org/10.1038/s41467-020-20348-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Elding H, Lau W, Swallow DM, Maniatis N. Dissecting the genetics of complex inheritance: linkage disequilibrium mapping provides insight into Crohn disease. Am J Hum Genet. 2011;89(6):798–805. https://doi.org/10.1016/j.ajhg.2011.11.006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful to all the members of the Bioinfo_composer team, which is the foremost bioinformatics platform in China, for their altruistic assistance.

Funding

This work was supported by JSPS KAKENHI Grant Number JP22K20814.

Author information

Author notes

  1. Xin Shen, Shaocong Mo and Xinlei Zeng have contributed equally to the work.

Authors and Affiliations

  1. Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, 200040, China

    Xin Shen, Shaocong Mo & Lingxi Lin

  2. School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, China

    Xinlei Zeng

  3. Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Yulin Wang

  4. Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China

    Meilin Weng

  5. Laboratory of Clinical Examination and Sports Medicine, Department of Clinical Medicine, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8577, Japan

    Takehito Sugasawa

  6. Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China

    Lei Wang

  7. Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China

    Lei Wang

  8. Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, 305-8577, Japan

    Wenchao Gu & Takahito Nakajima

  9. Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, 371-8511, Japan

    Wenchao Gu

Authors
  1. Xin Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaocong Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinlei Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yulin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lingxi Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Meilin Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Takehito Sugasawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wenchao Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Takahito Nakajima
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XS analyzed the data and presented the results, SCM and WCG designed the research. XLZ wrote the introduction and discussion of the manuscript. YLW and LW modified the manuscript to the submission format. LXL and MLW assisted with the literature search. TS and TN provided suggestions on data analysis.

Corresponding authors

Correspondence to Shaocong Mo or Wenchao Gu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, X., Mo, S., Zeng, X. et al. Identification of antigen-presentation related B cells as a key player in Crohn’s disease using single-cell dissecting, hdWGCNA, and deep learning. Clin Exp Med 23, 5255–5267 (2023). https://doi.org/10.1007/s10238-023-01145-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10238-023-01145-7

Keywords

Search

Navigation