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      A single-cell transcriptomic atlas tracking the neural basis of division of labour in an ant superorganism

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          Abstract

          Ant colonies with permanent division of labour between castes and highly distinct roles of the sexes have been conceptualized to be superorganisms, but the cellular and molecular mechanisms that mediate caste/sex-specific behavioural specialization have remained obscure. Here we characterized the brain cell repertoire of queens, gynes (virgin queens), workers and males of Monomorium pharaonis by obtaining 206,367 single-nucleus transcriptomes. In contrast to Drosophila, the mushroom body Kenyon cells are abundant in ants and display a high diversity with most subtypes being enriched in worker brains, the evolutionarily derived caste. Male brains are as specialized as worker brains but with opposite trends in cell composition with higher abundances of all optic lobe neuronal subtypes, while the composition of gyne and queen brains remained generalized, reminiscent of solitary ancestors. Role differentiation from virgin gynes to inseminated queens induces abundance changes in roughly 35% of cell types, indicating active neurogenesis and/or programmed cell death during this transition. We also identified insemination-induced cell changes probably associated with the longevity and fecundity of the reproductive caste, including increases of ensheathing glia and a population of dopamine-regulated Dh31-expressing neurons. We conclude that permanent caste differentiation and extreme sex-differentiation induced major changes in the neural circuitry of ants.

          Abstract

          Using single-cell transcriptomics, the authors generate a brain cell atlas for the pharaoh ant including individuals of different sexes and castes and show changes in cell composition underlying division of labour and reproductive specialization.

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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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            Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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                Author and article information

                Contributors
                liuchuanyu@genomics.cn
                guojiezhang@zju.edu.cn
                liuweiwei@mail.kiz.ac.cn
                Journal
                Nat Ecol Evol
                Nat Ecol Evol
                Nature Ecology & Evolution
                Nature Publishing Group UK (London )
                2397-334X
                16 June 2022
                16 June 2022
                2022
                : 6
                : 8
                : 1191-1204
                Affiliations
                [1 ]GRID grid.21155.32, ISNI 0000 0001 2034 1839, BGI-Shenzhen, ; Shenzhen, China
                [2 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, College of Life Sciences, , University of Chinese Academy of Sciences, ; Beijing, China
                [3 ]GRID grid.419010.d, ISNI 0000 0004 1792 7072, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, ; Kunming, China
                [4 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, Kunming College of Life Science, , University of Chinese Academy of Sciences, ; Kunming, China
                [5 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Section for Ecology and Evolution, Department of Biology, , University of Copenhagen, ; Copenhagen, Denmark
                [6 ]GRID grid.510951.9, ISNI 0000 0004 7775 6738, Shenzhen Bay Laboratory, ; Shenzhen, China
                [7 ]Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China
                [8 ]China National GeneBank, BGI-Shenzhen, Shenzhen, China
                [9 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, James D. Watson Institute of Genome Science, ; Hangzhou, China
                [10 ]GRID grid.9227.e, ISNI 0000000119573309, Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, ; Kunming, China
                [11 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Present Address: Evolutionary and Organismal Biology Research Center, School of Medicine, , Zhejiang University, ; Hangzhou, China
                Author information
                http://orcid.org/0000-0002-5993-0312
                http://orcid.org/0000-0001-9820-5546
                http://orcid.org/0000-0003-3417-479X
                http://orcid.org/0000-0002-5828-5542
                http://orcid.org/0000-0002-5338-5173
                http://orcid.org/0000-0003-0132-8166
                http://orcid.org/0000-0002-3598-1609
                http://orcid.org/0000-0003-2258-0897
                http://orcid.org/0000-0001-6860-1521
                http://orcid.org/0000-0001-5082-9114
                Article
                1784
                10.1038/s41559-022-01784-1
                9349048
                35711063
                dfcb784f-1325-40fc-b077-21cc83836d23
                © The Author(s) 2022

                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
                : 5 October 2021
                : 3 May 2022
                Funding
                Funded by: Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831)
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 31970573
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100008398, Villum Fonden (Villum Foundation);
                Award ID: 25900
                Award Recipient :
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                © The Author(s), under exclusive licence to Springer Nature Limited 2022

                social behaviour,social evolution,gene expression profiling

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