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      Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers

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          Abstract

          While the majority of cells contain a single nucleus, cell types such as trophoblasts, osteoclasts, and skeletal myofibers require multinucleation. One advantage of multinucleation can be the assignment of distinct functions to different nuclei, but comprehensive interrogation of transcriptional heterogeneity within multinucleated tissues has been challenging due to the presence of a shared cytoplasm. Here, we utilized single-nucleus RNA-sequencing (snRNA-seq) to determine the extent of transcriptional diversity within multinucleated skeletal myofibers. Nuclei from mouse skeletal muscle were profiled across the lifespan, which revealed the presence of distinct myonuclear populations emerging in postnatal development as well as aging muscle. Our datasets also provided a platform for discovery of genes associated with rare specialized regions of the muscle cell, including markers of the myotendinous junction and functionally validated factors expressed at the neuromuscular junction. These findings reveal that myonuclei within syncytial muscle fibers possess distinct transcriptional profiles that regulate muscle biology.

          Abstract

          Mammalian skeletal muscle is composed of multinucleated myofibers, containing hundreds of nuclei that coordinate cellular function. Here, the authors show that single-nucleus RNA-sequencing reveals rare and emergent myonuclear populations, and uncovers dynamic transcriptional heterogeneity in development and 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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            Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.

            Genome-scale studies have revealed extensive, cell type-specific colocalization of transcription factors, but the mechanisms underlying this phenomenon remain poorly understood. Here, we demonstrate in macrophages and B cells that collaborative interactions of the common factor PU.1 with small sets of macrophage- or B cell lineage-determining transcription factors establish cell-specific binding sites that are associated with the majority of promoter-distal H3K4me1-marked genomic regions. PU.1 binding initiates nucleosome remodeling, followed by H3K4 monomethylation at large numbers of genomic regions associated with both broadly and specifically expressed genes. These locations serve as beacons for additional factors, exemplified by liver X receptors, which drive both cell-specific gene expression and signal-dependent responses. Together with analyses of transcription factor binding and H3K4me1 patterns in other cell types, these studies suggest that simple combinations of lineage-determining transcription factors can specify the genomic sites ultimately responsible for both cell identity and cell type-specific responses to diverse signaling inputs. Copyright 2010 Elsevier Inc. All rights reserved.
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              Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

              Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.
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                Author and article information

                Contributors
                douglas.millay@cchmc.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                11 December 2020
                11 December 2020
                2020
                : 11
                : 6374
                Affiliations
                [1 ]GRID grid.239573.9, ISNI 0000 0000 9025 8099, Division of Molecular Cardiovascular Biology, Cincinnati Children’s Hospital Medical Center, ; Cincinnati, OH USA
                [2 ]GRID grid.239573.9, ISNI 0000 0000 9025 8099, Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, ; Cincinnati, OH USA
                [3 ]GRID grid.239573.9, ISNI 0000 0000 9025 8099, Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, ; Cincinnati, OH USA
                [4 ]GRID grid.24827.3b, ISNI 0000 0001 2179 9593, Department of Pediatrics, , University of Cincinnati College of Medicine, ; Cincinnati, OH USA
                Author information
                http://orcid.org/0000-0001-8500-1878
                http://orcid.org/0000-0002-3782-3962
                http://orcid.org/0000-0001-7977-9122
                http://orcid.org/0000-0001-9689-2469
                http://orcid.org/0000-0001-5188-0720
                Article
                20063
                10.1038/s41467-020-20063-w
                7733460
                33311464
                7549987d-d704-4214-b452-2cb5dedcf4dc
                © The Author(s) 2020

                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
                : 12 May 2020
                : 9 November 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000049, U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging);
                Award ID: R01AG059605
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000875, Pew Charitable Trusts;
                Funded by: FundRef https://doi.org/10.13039/100007172, Cincinnati Children’s Hospital Medical Center (Cincinnati Children’s);
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                musculoskeletal development,gene expression
                Uncategorized
                musculoskeletal development, gene expression

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