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      Single-cell RNA-Seq resolves cellular complexity in sensory organs from the neonatal inner ear

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          Abstract

          In the inner ear, cochlear and vestibular sensory epithelia utilize grossly similar cell types to transduce different stimuli: sound and acceleration. Each individual sensory epithelium is composed of highly heterogeneous populations of cells based on physiological and anatomical criteria. However, limited numbers of each cell type have impeded transcriptional characterization. Here we generated transcriptomes for 301 single cells from the utricular and cochlear sensory epithelia of newborn mice to circumvent this challenge. Cluster analysis indicates distinct profiles for each of the major sensory epithelial cell types, as well as less-distinct sub-populations. Asynchrony within utricles allows reconstruction of the temporal progression of cell-type-specific differentiation and suggests possible plasticity among cells at the sensory–nonsensory boundary. Comparisons of cell types from utricles and cochleae demonstrate divergence between auditory and vestibular cells, despite a common origin. These results provide significant insights into the developmental processes that form unique inner ear cell types.

          Abstract

          Heterogeneous sensory epithelia of the inner ear are difficult to study owing to the few cells that can be isolated. Here the authors provide insight into the developmental processes underlying the formation of these cells by single-cell RNA-Seq.

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          Most cited references52

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          A gene expression atlas of the central nervous system based on bacterial artificial chromosomes.

          The mammalian central nervous system (CNS) contains a remarkable array of neural cells, each with a complex pattern of connections that together generate perceptions and higher brain functions. Here we describe a large-scale screen to create an atlas of CNS gene expression at the cellular level, and to provide a library of verified bacterial artificial chromosome (BAC) vectors and transgenic mouse lines that offer experimental access to CNS regions, cell classes and pathways. We illustrate the use of this atlas to derive novel insights into gene function in neural cells, and into principal steps of CNS development. The atlas, library of BAC vectors and BAC transgenic mice generated in this screen provide a rich resource that allows a broad array of investigations not previously available to the neuroscience community.
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            Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

            Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.
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              Accounting for technical noise in single-cell RNA-seq experiments.

              Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Pub. Group
                2041-1723
                15 October 2015
                2015
                : 6
                : 8557
                Affiliations
                [1 ]Laboratory of Cochlear Development, National Institute on Deafness and Other Communication Disorders, National Institutes of Health , Bethesda, Maryland 20892, USA
                [2 ]Genomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, National Institutes of Health , Bethesda, Maryland 20892, USA
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                ncomms9557
                10.1038/ncomms9557
                4634134
                26469390
                057d60ec-dbbf-477d-91e3-5866957b4436
                Copyright © 2015, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 02 March 2015
                : 03 September 2015
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