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      The order and logic of CD4 versus CD8 lineage choice and differentiation in mouse thymus

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          Abstract

          CD4 and CD8 mark helper and cytotoxic T cell lineages, respectively, and serve as coreceptors for MHC-restricted TCR recognition. How coreceptor expression is matched with TCR specificity is central to understanding CD4/CD8 lineage choice, but visualising coreceptor gene activity in individual selection intermediates has been technically challenging. It therefore remains unclear whether the sequence of coreceptor gene expression in selection intermediates follows a stereotypic pattern, or is responsive to signaling. Here we use single cell RNA sequencing (scRNA-seq) to classify mouse thymocyte selection intermediates by coreceptor gene expression. In the unperturbed thymus, Cd4 + Cd8a - selection intermediates appear before Cd4 - Cd8a + selection intermediates, but the timing of these subsets is flexible according to the strength of TCR signals. Our data show that selection intermediates discriminate MHC class prior to the loss of coreceptor expression and suggest a model where signal strength informs the timing of coreceptor gene activity and ultimately CD4/CD8 lineage choice.

          Abstract

          Developing T cells commit to either CD4/helper or CD8/cytotoxic lineage in the thymus, but how CD4 and CD8 coreceptors and TCR signaling dictate this selection process is still unclear. Here the authors use single cell RNA sequencing of mouse thymocytes to show that, in selection intermediates, TCR signaling strength informs coreceptor expression timing.

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          Most cited references45

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

              TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.
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                Author and article information

                Contributors
                matthias.merkenschlager@lms.mrc.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                4 January 2021
                4 January 2021
                2021
                : 12
                : 99
                Affiliations
                [1 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, , Imperial College London, ; London, UK
                [2 ]GRID grid.418827.0, ISNI 0000 0004 0620 870X, Laboratory of Adaptive Immunity, , Institute of Molecular Genetics of the Czech Academy of Sciences, ; Prague, Czech Republic
                [3 ]GRID grid.473715.3, CNAG-CRG, Centre for Genomic Regulation (CRG), , The Barcelona Institute of Science and Technology (BIST), ; Barcelona, Spain
                [4 ]GRID grid.189509.c, ISNI 0000000100241216, Department of Immunology, , Duke University Medical Center, ; Durham, NC USA
                [5 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, Present Address: Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, , King’s College London, ; London, UK
                [6 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Present Address: School of Life Sciences and Biotechnology, , Shanghai Jiao Tong University, ; Shanghai, China
                Author information
                http://orcid.org/0000-0001-5017-1252
                http://orcid.org/0000-0003-4472-2067
                http://orcid.org/0000-0002-3679-2236
                http://orcid.org/0000-0002-1114-1509
                http://orcid.org/0000-0002-3276-1889
                http://orcid.org/0000-0003-3010-3644
                http://orcid.org/0000-0002-2735-3311
                http://orcid.org/0000-0003-2889-3288
                Article
                20306
                10.1038/s41467-020-20306-w
                7782583
                33397934
                f37263f6-8ea1-48ad-9da8-8c8a27f86857
                © The Author(s) 2021

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 June 2020
                : 22 November 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100006955, U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (OER);
                Award ID: 1R35GM136284
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000265, RCUK | Medical Research Council (MRC);
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 099276/Z/12/Z
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                haematopoiesis,lymphocyte differentiation,gene regulation in immune cells,signal transduction

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