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      Characterizing Adult Cochlear Supporting Cell Transcriptional Diversity Using Single-Cell RNA-Seq: Validation in the Adult Mouse and Translational Implications for the Adult Human Cochlea

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          Abstract

          Hearing loss is a problem that impacts a significant proportion of the adult population. Cochlear hair cell (HC) loss due to loud noise, chemotherapy and aging is the major underlying cause. A significant proportion of these individuals are dissatisfied with available treatment options which include hearing aids and cochlear implants. An alternative approach to restore hearing would be to regenerate HCs. Such therapy would require a recapitulation of the complex architecture of the organ of Corti, necessitating regeneration of both mature HCs and supporting cells (SCs). Transcriptional profiles of the mature cell types in the cochlea are necessary to can provide a metric for eventual regeneration therapies. To assist in this effort, we sought to provide the first single-cell characterization of the adult cochlear SC transcriptome. We performed single-cell RNA-Seq on FACS-purified adult cochlear SCs from the Lfng EGFP adult mouse, in which SCs express GFP. We demonstrate that adult cochlear SCs are transcriptionally distinct from their perinatal counterparts. We establish cell-type-specific adult cochlear SC transcriptome profiles, and we validate these expression profiles through a combination of both fluorescent immunohistochemistry and in situ hybridization co-localization and quantitative polymerase chain reaction (qPCR) of adult cochlear SCs. Furthermore, we demonstrate the relevance of these profiles to the adult human cochlea through immunofluorescent human temporal bone histopathology. Finally, we demonstrate cell cycle regulator expression in adult SCs and perform pathway analyses to identify potential mechanisms for facilitating mitotic regeneration (cell proliferation, differentiation, and eventually regeneration) in the adult mammalian cochlea. Our findings demonstrate the importance of characterizing mature as opposed to perinatal SCs.

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          Most cited references81

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          RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

          Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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            Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

            Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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              Fast unfolding of communities in large networks

              Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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                Author and article information

                Contributors
                Journal
                Front Mol Neurosci
                Front Mol Neurosci
                Front. Mol. Neurosci.
                Frontiers in Molecular Neuroscience
                Frontiers Media S.A.
                1662-5099
                05 February 2020
                2020
                : 13
                : 13
                Affiliations
                [1] 1Auditory Restoration and Development Program, National Institute on Deafness and Other Communication Disorders, NIH , Bethesda, MD, United States
                [2] 2National Temporal Bone Laboratory at UCLA, UCLA School of Medicine , Los Angeles, CA, United States
                [3] 3Cellular and Molecular Biology of the Inner Ear Laboratory, UCLA School of Medicine , Los Angeles, CA, United States
                [4] 4Biomedical Research Informatics Office, National Institute of Dental and Craniofacial Research, NIH , Bethesda, MD, United States
                [5] 5Genomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, NIH , Bethesda, MD, United States
                [6] 6Laboratory of Cochlear Development, National Institute on Deafness and Other Communication Disorders, NIH , Bethesda, MD, United States
                Author notes

                Edited by: Fabio Mammano, University of Padova, Italy

                Reviewed by: Andy Groves, Baylor College of Medicine, United States; David Z. He, Creighton University, United States; Albert Edge, Harvard Medical School, United States

                *Correspondence: Michael Hoa michael.hoa@ 123456nih.gov
                Article
                10.3389/fnmol.2020.00013
                7012811
                32116546
                8248b0e5-5578-40f5-bd2c-6e964516db3f
                Copyright © 2020 Hoa, Olszewski, Li, Taukulis, Gu, DeTorres, Lopez, Linthicum, Ishiyama, Martin, Morell and Kelley.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 13 August 2019
                : 16 January 2020
                Page count
                Figures: 5, Tables: 1, Equations: 0, References: 82, Pages: 20, Words: 14657
                Funding
                Funded by: National Institute on Deafness and Other Communication Disorders 10.13039/100000055
                Award ID: DC000088, DC000086, DC000059, DC000039, 1U24DC015910-01
                Categories
                Neuroscience
                Original Research

                Neurosciences
                inner ear,supporting cell subtypes,smfish,adult (mesh),cell cycle,facs,cochlea
                Neurosciences
                inner ear, supporting cell subtypes, smfish, adult (mesh), cell cycle, facs, cochlea

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