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      Single-cell transcriptome profiling highlights the importance of telocyte, kallikrein genes, and alternative splicing in mouse testes aging

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          Abstract

          Advancing healthcare for elderly men requires a deeper understanding of testicular aging processes. In this study, we conducted transcriptomic profiling of 43,323 testicular single cells from young and old mice, shedding light on 1032 telocytes—an underexplored testicular cell type in previous research. Our study unveiled 916 age-related differentially expressed genes (age-DEGs), with telocytes emerging as the cell type harboring the highest count of age-DEGs. Of particular interest, four genes (Klk1b21, Klk1b22, Klk1b24, Klk1b27) from the Kallikrein family, specifically expressed in Leydig cells, displayed down-regulation in aged testes. Moreover, cell-type-level splicing analyses unveiled 1838 age-related alternative splicing (AS) events. While we confirmed the presence of more age-DEGs in somatic cells compared to germ cells, unexpectedly, more age-related AS events were identified in germ cells. Further experimental validation highlighted 4930555F03Rik, a non-coding RNA gene exhibiting significant age-related AS changes. Our study represents the first age-related single-cell transcriptomic investigation of testicular telocytes and Kallikrein genes in Leydig cells, as well as the first delineation of cell-type-level AS dynamics during testicular aging in mice.

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          Most cited references42

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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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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                gangcai@ntu.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                26 June 2024
                26 June 2024
                2024
                : 14
                : 14795
                Affiliations
                Institute of Reproductive Medicine, Medical School, Nantong University, ( https://ror.org/02afcvw97) Qixiu Road 19, Nantong, 226001 China
                Article
                65710
                10.1038/s41598-024-65710-0
                11208613
                38926537
                1960c3d0-38cc-499a-99c7-855b02599733
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 March 2024
                : 24 June 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31900484
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004608, Natural Science Foundation of Jiangsu Province;
                Award ID: BK20190924
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                data mining,senescence,transcriptomics
                Uncategorized
                data mining, senescence, transcriptomics

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