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Single-cell transcriptomics reveals immune infiltrate in sepsis
Immune cells and immune microenvironment play important in the evolution of sepsis. This study aimed to explore hub genes related to the abundance of immune cell infiltration in sepsis. The GEOquery package is used to download and organize data from the GEO database. A total of 61 differentially exp...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10126435/ https://www.ncbi.nlm.nih.gov/pubmed/37113759 http://dx.doi.org/10.3389/fphar.2023.1133145 |
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author | Tu, Xusheng Huang, He Xu, Shilei Li, Caifei Luo, Shaoning |
author_facet | Tu, Xusheng Huang, He Xu, Shilei Li, Caifei Luo, Shaoning |
author_sort | Tu, Xusheng |
collection | PubMed |
description | Immune cells and immune microenvironment play important in the evolution of sepsis. This study aimed to explore hub genes related to the abundance of immune cell infiltration in sepsis. The GEOquery package is used to download and organize data from the GEO database. A total of 61 differentially expressed genes (DEGs) between sepsis samples and normal samples were obtained through the ‘limma’ package. T cells, natural killer (NK) cells, monocytes, megakaryocytes, dendritic cells (DCs), and B cells formed six distinct clusters on the t-distributed stochastic neighbor embedding (t-SNE) plot generated using the Seurat R package. Gene set enrichment analysis (GSEA) enrichment analysis showed that sepsis samples and normal samples were related to Neutrophil Degranulation, Modulators of Tcr Signaling and T Cell Activation, IL 17 Pathway, T Cell Receptor Signaling Pathway, Ctl Pathway, Immunoregulatory Interactions Between a Lymphoid and A Non-Lymphoid Cell. GO analysis and KEGG analysis of immune-related genes showed that the intersection genes were mainly associated with Immune-related signaling pathways. Seven hub genes (CD28, CD3D, CD2, CD4, IL7R, LCK, and CD3E) were screened using Maximal Clique Centrality, Maximum neighborhood component, and Density of Maximum Neighborhood Component algorithms. The lower expression of the six hub genes (CD28, CD3D, CD4, IL7R, LCK, and CD3E) was observed in sepsis samples. We observed the significant difference of several immune cell between sepsis samples and control samples. Finally, we carried out in vivo animal experiments, including Western blotting, flow cytometry, Elisa, and qPCR assays to detect the concentration and the expression of several immune factors. |
format | Online Article Text |
id | pubmed-10126435 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101264352023-04-26 Single-cell transcriptomics reveals immune infiltrate in sepsis Tu, Xusheng Huang, He Xu, Shilei Li, Caifei Luo, Shaoning Front Pharmacol Pharmacology Immune cells and immune microenvironment play important in the evolution of sepsis. This study aimed to explore hub genes related to the abundance of immune cell infiltration in sepsis. The GEOquery package is used to download and organize data from the GEO database. A total of 61 differentially expressed genes (DEGs) between sepsis samples and normal samples were obtained through the ‘limma’ package. T cells, natural killer (NK) cells, monocytes, megakaryocytes, dendritic cells (DCs), and B cells formed six distinct clusters on the t-distributed stochastic neighbor embedding (t-SNE) plot generated using the Seurat R package. Gene set enrichment analysis (GSEA) enrichment analysis showed that sepsis samples and normal samples were related to Neutrophil Degranulation, Modulators of Tcr Signaling and T Cell Activation, IL 17 Pathway, T Cell Receptor Signaling Pathway, Ctl Pathway, Immunoregulatory Interactions Between a Lymphoid and A Non-Lymphoid Cell. GO analysis and KEGG analysis of immune-related genes showed that the intersection genes were mainly associated with Immune-related signaling pathways. Seven hub genes (CD28, CD3D, CD2, CD4, IL7R, LCK, and CD3E) were screened using Maximal Clique Centrality, Maximum neighborhood component, and Density of Maximum Neighborhood Component algorithms. The lower expression of the six hub genes (CD28, CD3D, CD4, IL7R, LCK, and CD3E) was observed in sepsis samples. We observed the significant difference of several immune cell between sepsis samples and control samples. Finally, we carried out in vivo animal experiments, including Western blotting, flow cytometry, Elisa, and qPCR assays to detect the concentration and the expression of several immune factors. Frontiers Media S.A. 2023-04-11 /pmc/articles/PMC10126435/ /pubmed/37113759 http://dx.doi.org/10.3389/fphar.2023.1133145 Text en Copyright © 2023 Tu, Huang, Xu, Li and Luo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Tu, Xusheng Huang, He Xu, Shilei Li, Caifei Luo, Shaoning Single-cell transcriptomics reveals immune infiltrate in sepsis |
title | Single-cell transcriptomics reveals immune infiltrate in sepsis |
title_full | Single-cell transcriptomics reveals immune infiltrate in sepsis |
title_fullStr | Single-cell transcriptomics reveals immune infiltrate in sepsis |
title_full_unstemmed | Single-cell transcriptomics reveals immune infiltrate in sepsis |
title_short | Single-cell transcriptomics reveals immune infiltrate in sepsis |
title_sort | single-cell transcriptomics reveals immune infiltrate in sepsis |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10126435/ https://www.ncbi.nlm.nih.gov/pubmed/37113759 http://dx.doi.org/10.3389/fphar.2023.1133145 |
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