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      Contrasting somatic mutation patterns in aging human neurons and oligodendrocytes

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          SUMMARY

          Characterizing somatic mutations in the brain is important for disentangling the complex mechanisms of aging, yet little is known about mutational patterns in different brain cell types. Here, we performed whole-genome sequencing (WGS) of 86 single oligodendrocytes, 20 mixed glia, and 56 single neurons from neurotypical individuals spanning 0.4–104 years of age and identified >92,000 somatic single-nucleotide variants (sSNVs) and small insertions/deletions (indels). Although both cell types accumulate somatic mutations linearly with age, oligodendrocytes accumulated sSNVs 81% faster than neurons and indels 28% slower than neurons. Correlation of mutations with single-nucleus RNA profiles and chromatin accessibility from the same brains revealed that oligodendrocyte mutations are enriched in inactive genomic regions and are distributed across the genome similarly to mutations in brain cancers. In contrast, neuronal mutations are enriched in open, transcriptionally active chromatin. These stark differences suggest an assortment of active mutagenic processes in oligodendrocytes and neurons.

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          In brief

          By employing single-cell whole-genome sequencing and integrating single-nucleus RNA-seq and single-nucleus ATAC-seq from the same individuals, this study uncovers distinct aging-related patterns of somatic mutation in human oligodendrocytes and neurons, which contribute to a deeper understanding of the mechanisms involved in human brain aging.

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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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            lmerTest Package: Tests in Linear Mixed Effects Models

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              Integrative analysis of 111 reference human epigenomes

              The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but a similar reference has lacked for epigenomic studies. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection to-date of human epigenomes for primary cells and tissues. Here, we describe the integrative analysis of 111 reference human epigenomes generated as part of the program, profiled for histone modification patterns, DNA accessibility, DNA methylation, and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically-relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation, and human disease.
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                Author and article information

                Journal
                0413066
                2830
                Cell
                Cell
                Cell
                0092-8674
                1097-4172
                7 April 2024
                11 April 2024
                18 March 2024
                01 May 2024
                : 187
                : 8
                : 1955-1970.e23
                Affiliations
                [1 ]Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Department of Pediatrics, and Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA 02115, USA
                [2 ]Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA 02115, USA
                [3 ]Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
                [4 ]Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
                [5 ]Sorbonne Université, Institut du Cerveau (Paris Brain Institute) ICM, Inserm, CNRS, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
                [6 ]Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
                [7 ]Division of Genetics, Brigham and Women’s Hospital, Boston, MA 02115, USA
                [8 ]These authors contributed equally
                [9 ]Present address: Merck Research Laboratories, Cambridge, MA 02142, USA
                [10 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                J.G., S.B., and L.J.L. conceived the study; J.G., S.B., B.C., Z.Z., and M.B.M. performed nuclear sorting and PTA amplifications; L.J.L. led bioinformatic analyses, helped by C.L.B., H.J., A.V.T., and A.G.; J.G. and S.B. contributed to bioinformatic analysis interpretation; S.B. contributed to snRNA-seq data analyses; M.B.M. and Z.Z. contributed neuronal PTA data; G.M., B.C., K.B., Y.C., M.B.M., and Z.Z. contributed to droplet digital PCR (ddPCR) experiments; C.A.W. and P.J.P. directed the research; J.G. and L.J.L. wrote the manuscript, greatly helped by S.B.

                Article
                NIHMS1982020
                10.1016/j.cell.2024.02.025
                11062076
                38503282
                9a66d7a2-f570-4747-9ba7-cc72eb570bdc

                This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).

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                Cell biology
                Cell biology

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