Artikel ; Online: Identification of antigen-presentation related B cells as a key player in Crohn's disease using single-cell dissecting, hdWGCNA, and deep learning.
Clinical and experimental medicine
2023 Band 23, Heft 8, Seite(n) 5255–5267
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 ...
| 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. |
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| Mesh-Begriff(e) | Humans ; Crohn Disease/diagnosis ; Crohn Disease/pathology ; Antigen Presentation ; Deep Learning ; Intestinal Mucosa/pathology ; B-Lymphocytes | |||||
| Sprache | Englisch | |||||
| Erscheinungsdatum | 2023-08-08 | |||||
| Erscheinungsland | Italy | |||||
| Dokumenttyp | Journal Article | |||||
| ZDB-ID | 2053018-3 | |||||
| ISSN | 1591-9528 ; 1591-8890 | |||||
| ISSN (online) | 1591-9528 | |||||
| ISSN | 1591-8890 | |||||
| DOI | 10.1007/s10238-023-01145-7 | |||||
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| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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