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      Developmental signals control chromosome segregation fidelity during pluripotency and neurogenesis by modulating replicative stress

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      1 , 1 , 1 , 2 , 3 , 4 , 5 , 6 , 3 , 4 , 5 , 6 , 7 , 8 , 1 , 2 , 9 , 10 , 11 , 9 , 1 , 12 , 9 , 7 , 11 , 10 , 13 , 12 , 14 , 3 , 4 , 5 , 6 , 1 , 8 , 2 , 1 ,
      Nature Communications
      Nature Publishing Group UK
      Extracellular signalling molecules, Pluripotency, DNA replication, Developmental neurogenesis, Chromosome segregation

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          Abstract

          Human development relies on the correct replication, maintenance and segregation of our genetic blueprints. How these processes are monitored across embryonic lineages, and why genomic mosaicism varies during development remain unknown. Using pluripotent stem cells, we identify that several patterning signals—including WNT, BMP, and FGF—converge into the modulation of DNA replication stress and damage during S-phase, which in turn controls chromosome segregation fidelity in mitosis. We show that the WNT and BMP signals protect from excessive origin firing, DNA damage and chromosome missegregation derived from stalled forks in pluripotency. Cell signalling control of chromosome segregation declines during lineage specification into the three germ layers, but re-emerges in neural progenitors. In particular, we find that the neurogenic factor FGF2 induces DNA replication stress-mediated chromosome missegregation during the onset of neurogenesis, which could provide a rationale for the elevated chromosomal mosaicism of the developing brain. Our results highlight roles for morphogens and cellular identity in genome maintenance that contribute to somatic mosaicism during mammalian development.

          Abstract

          Here the authors show that the patterning signals WNT, BMP, and FGF control chromosome segregation fidelity during early lineage specification and neurogenesis, which could provide a rationale for the spatio-temporal distribution of genomic mosaicism during human development.

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          Most cited references126

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          Spatial reconstruction of single-cell gene expression

          Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
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            Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *

            Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.
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              Global quantification of mammalian gene expression control.

              Gene expression is a multistep process that involves the transcription, translation and turnover of messenger RNAs and proteins. Although it is one of the most fundamental processes of life, the entire cascade has never been quantified on a genome-wide scale. Here we simultaneously measured absolute mRNA and protein abundance and turnover by parallel metabolic pulse labelling for more than 5,000 genes in mammalian cells. Whereas mRNA and protein levels correlated better than previously thought, corresponding half-lives showed no correlation. Using a quantitative model we have obtained the first genome-scale prediction of synthesis rates of mRNAs and proteins. We find that the cellular abundance of proteins is predominantly controlled at the level of translation. Genes with similar combinations of mRNA and protein stability shared functional properties, indicating that half-lives evolved under energetic and dynamic constraints. Quantitative information about all stages of gene expression provides a rich resource and helps to provide a greater understanding of the underlying design principles.
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                Author and article information

                Contributors
                sergio.acebron@cos.uni-heidelberg.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                28 August 2024
                28 August 2024
                2024
                : 15
                : 7404
                Affiliations
                [1 ]Centre for Organismal Studies (COS), Heidelberg University, ( https://ror.org/038t36y30) Heidelberg, Germany
                [2 ]Department of Molecular Oncology, Section for Cellular Oncology, University Medical Center Göttingen (UMG), ( https://ror.org/021ft0n22) Göttingen, Germany
                [3 ]Oncode Institute, ( https://ror.org/01n92vv28) Utrecht, The Netherlands
                [4 ]Hubrecht Institute, ( https://ror.org/023qc4a07) Utrecht, The Netherlands
                [5 ]KNAW (Royal Netherlands Academy of Arts and Sciences), ( https://ror.org/043c0p156) Utrecht, The Netherlands
                [6 ]University Medical Center Utrecht, ( https://ror.org/0575yy874) Utrecht, The Netherlands
                [7 ]Department of Medical Biochemistry and Biophysics, Umeå University, ( https://ror.org/05kb8h459) Umeå, Sweden
                [8 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, Department of Clinical Neurobiology, , University Hospital Heidelberg and German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [9 ]Genomics Core Facility, European Molecular Biology Laboratory (EMBL), ( https://ror.org/03mstc592) Heidelberg, Germany
                [10 ]Institute of Human Genetics, Heidelberg University, ( https://ror.org/038t36y30) Heidelberg, Germany
                [11 ]MRC Laboratory of Molecular Biology, ( https://ror.org/00tw3jy02) Cambridge, UK
                [12 ]Schaller Research Group, German Cancer Research Center (DKFZ), ( https://ror.org/04cdgtt98) Heidelberg, Germany
                [13 ]Nikon Imaging Center at the University of Heidelberg, Bioquant, ( https://ror.org/038t36y30) Heidelberg, Germany
                [14 ]Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), ( https://ror.org/04cdgtt98) Heidelberg, Germany
                Author information
                http://orcid.org/0000-0002-9358-3676
                http://orcid.org/0000-0003-3427-3413
                http://orcid.org/0000-0003-1751-7455
                http://orcid.org/0000-0003-0988-7720
                http://orcid.org/0000-0002-0352-2547
                http://orcid.org/0000-0003-1708-8259
                http://orcid.org/0000-0002-1599-5747
                http://orcid.org/0000-0002-6953-6845
                http://orcid.org/0000-0002-7004-5664
                http://orcid.org/0000-0002-0855-7917
                http://orcid.org/0000-0002-9442-3551
                http://orcid.org/0000-0002-9531-0452
                http://orcid.org/0000-0001-7915-3648
                http://orcid.org/0000-0002-7694-2497
                Article
                51821
                10.1038/s41467-024-51821-9
                11350214
                39191776
                0736f86b-e7f9-46ff-8f54-71f07fc46d52
                © 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
                : 10 November 2023
                : 9 August 2024
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                © Springer Nature Limited 2024

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                extracellular signalling molecules,pluripotency,dna replication,developmental neurogenesis,chromosome segregation

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