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      Comprehensive Transcriptomic Analysis for Thymic Epithelial Cells of Aged Mice and Humans

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          Abstract

          Thymic epithelial cells (TECs) play a critical role in thymic development and thymopoiesis. As individuals age, TECs undergo various changes that impact their functions, leading to a reduction in cell numbers and impaired thymic selection. These age-related alterations have been observed in both mice and humans. However, the precise mechanisms underlying age-related TEC dysfunction remain unclear. Furthermore, there is a lack of a comprehensive study that connects mouse and human biological processes in this area. To address this gap, we conducted an extensive transcriptome analysis of young and old TECs in mice, complemented by further analysis of publicly available human TEC single-cell RNA sequencing data. Our analysis revealed alterations in both known and unknown pathways that potentially contribute to age-related TEC dysfunction. Specifically, we observed downregulation of pathways related to cell proliferation, T cell development, metabolism, and cytokine signaling in old age TECs. Conversely, TGF-β, BMP, and Wnt signaling pathways were upregulated, which have been known to be associated with age-related TEC dysfunctions or newly discovered in this study. Importantly, we found that these age-related changes in mouse TECs were consistently present in human TECs as well. This cross-species validation further strengthens the significance of our findings. In conclusion, our comprehensive analysis provides valuable insight into the biological and immunological characteristics of aged TECs in both mice and humans. These findings contribute to a better understanding of thymic involution and age-induced immune dysfunction.

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          Most cited references54

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Journal
                Immune Netw
                Immune Netw
                IN
                Immune Network
                The Korean Association of Immunologists
                1598-2629
                2092-6685
                October 2023
                21 August 2023
                : 23
                : 5
                : e36
                Affiliations
                [1 ]Laboratory of Immune Regulation, Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Korea.
                [2 ]Department of Pathology, Seoul National University College of Medicine, Seoul 03080, Korea.
                Author notes
                Correspondence to Doo Hyun Chung. Laboratory of Immune Regulation, Department of Biomedical Sciences, Department of Pathology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea. doohyun@ 123456snu.ac.kr

                Sangsin Lee and Seung Geun Song contributed equally to this work.

                Author information
                https://orcid.org/0009-0003-3397-7301
                https://orcid.org/0000-0002-1605-2708
                https://orcid.org/0000-0002-9948-8485
                Article
                10.4110/in.2023.23.e36
                10643332
                37970235
                13f5c296-807c-4c5a-9688-acf093dc2557
                Copyright © 2023. The Korean Association of Immunologists

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 July 2023
                : 04 August 2023
                : 07 August 2023
                Funding
                Funded by: National Research Foundation of Korea, CrossRef https://doi.org/10.13039/501100003725;
                Award ID: NRF-2020R1A2C2008312
                Award ID: RS-2023-00217571
                Funded by: Seoul National University Hospital, CrossRef https://doi.org/10.13039/501100004332;
                Award ID: 0320200010
                Categories
                Original Article

                Immunology
                thymic epithelial cell (tec),transcriptome analysis,aging
                Immunology
                thymic epithelial cell (tec), transcriptome analysis, aging

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