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      Single cell RNA analysis identifies cellular heterogeneity and adaptive responses of the lung at birth

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          Abstract

          The respiratory system undergoes a diversity of structural, biochemical, and functional changes necessary for adaptation to air breathing at birth. To identify the heterogeneity of pulmonary cell types and dynamic changes in gene expression mediating adaptation to respiration, here we perform single cell RNA analyses of mouse lung on postnatal day 1. Using an iterative cell type identification strategy we unbiasedly identify the heterogeneity of murine pulmonary cell types. We identify distinct populations of epithelial, endothelial, mesenchymal, and immune cells, each containing distinct subpopulations. Furthermore we compare temporal changes in RNA expression patterns before and after birth to identify signaling pathways selectively activated in specific pulmonary cell types, including activation of cell stress and the unfolded protein response during perinatal adaptation of the lung. The present data provide a single cell view of the adaptation to air breathing after birth.

          Abstract

          The respiratory system is transformed in terms of functional change at birth to adapt to breathing air. Here, the authors examine the molecular changes behind the first breath in the mouse by Drop-seq based RNA sequencing, identifying activation of the unfolded protein response as a perinatal adaptation of the lung.

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          Most cited references40

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          STEM: a tool for the analysis of short time series gene expression data

          Background Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at .
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            Endoplasmic reticulum stress: cell life and death decisions.

            C. Xu (2005)
            Disturbances in the normal functions of the ER lead to an evolutionarily conserved cell stress response, the unfolded protein response, which is aimed initially at compensating for damage but can eventually trigger cell death if ER dysfunction is severe or prolonged. The mechanisms by which ER stress leads to cell death remain enigmatic, with multiple potential participants described but little clarity about which specific death effectors dominate in particular cellular contexts. Important roles for ER-initiated cell death pathways have been recognized for several diseases, including hypoxia, ischemia/reperfusion injury, neurodegeneration, heart disease, and diabetes.
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              Comparative Analysis of Single-Cell RNA Sequencing Methods.

              Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
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                Author and article information

                Contributors
                Jeffrey.Whitsett@cchmc.org
                Yan.Xu@cchmc.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 January 2019
                3 January 2019
                2019
                : 10
                : 37
                Affiliations
                [1 ]ISNI 0000 0000 9025 8099, GRID grid.239573.9, Division of Pulmonary Biology, Department of Pediatrics, University of Cincinnati College of Medicine, , Cincinnati Children’s Hospital Medical Center, ; Cincinnati, 45229 OH USA
                [2 ]ISNI 0000 0000 9025 8099, GRID grid.239573.9, Division of Developmental Biology, , Cincinnati Children’s Hospital Medical Center, ; Cincinnati, 45229 OH USA
                [3 ]ISNI 0000 0000 9025 8099, GRID grid.239573.9, Division of Biomedical Informatics, , Cincinnati Children’s Hospital Medical Center, ; Cincinnati, 45229 OH USA
                Author information
                http://orcid.org/0000-0003-0726-0350
                http://orcid.org/0000-0003-2025-027X
                Article
                7770
                10.1038/s41467-018-07770-1
                6318311
                30604742
                b93e6d14-4755-4604-bfb8-10b15996bd4d
                © The Author(s) 2019

                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
                : 22 March 2018
                : 19 November 2018
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