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10.1183/16000617.0060-2020 http://scihub22266oqcxt.onion/10.1183/16000617.0060-2020 32620586!7719403!32620586     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+Respir+Rev 2020 ; 29 (156): ä Nephropedia Template TP {{tp|p=32620586|t=2020. Breathing fresh air into respiratory research with single-cell RNA sequencing |pdf=|usr=}}{{32620586}} gab.com Text Breathing fresh air into respiratory research with single-cell RNA sequencing JO: Eur Respir Rev http://www.kidney.de/pget.php?&tw=1&pmid=32620586 #FOAvip The complex cellular heterogeneity of the lung poses a unique challenge to researchers in the field. While the use of bulk RNA sequencing has become a ubiquitous technology in systems biology, the technique necessarily averages out individual contributions to the overall transcriptional landscape of a tissue. Single-cell RNA sequencing (scRNA-seq) provides a robust, unbiased survey of the transcriptome comparable to bulk RNA sequencing while preserving information on cellular heterogeneity. In just a few years since this technology was developed, scRNA-seq has already been adopted widely in respiratory research and has contributed to impressive advancements such as the discoveries of the pulmonary ionocyte and of a profibrotic macrophage population in pulmonary fibrosis. In this review, we discuss general technical considerations when considering the use of scRNA-seq and examine how leading investigators have applied the technology to gain novel insights into respiratory biology, from development to disease. In addition, we discuss the evolution of single-cell technologies with a focus on spatial and multi-omics approaches that promise to drive continued innovation in respiratory research. Twit Text FOAVip Breathing fresh air into respiratory research with single-cell RNA sequencing ????Eur Respir Rev http://www.kidney.de/pget.php?&tw=1&pmid=32620586 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32620586 #FOAvip English Wikipedia <ref>{{Cite pmid|32620586}}</ref> Breathing fresh air into respiratory research with single-cell RNA sequencing #MMPMID32620586 Alexander MJ; Budinger GRS; Reyfman PA Eur Respir Rev 2020[Jun]; 29 (156): ä PMID32620586show ga The complex cellular heterogeneity of the lung poses a unique challenge to researchers in the field. While the use of bulk RNA sequencing has become a ubiquitous technology in systems biology, the technique necessarily averages out individual contributions to the overall transcriptional landscape of a tissue. Single-cell RNA sequencing (scRNA-seq) provides a robust, unbiased survey of the transcriptome comparable to bulk RNA sequencing while preserving information on cellular heterogeneity. In just a few years since this technology was developed, scRNA-seq has already been adopted widely in respiratory research and has contributed to impressive advancements such as the discoveries of the pulmonary ionocyte and of a profibrotic macrophage population in pulmonary fibrosis. In this review, we discuss general technical considerations when considering the use of scRNA-seq and examine how leading investigators have applied the technology to gain novel insights into respiratory biology, from development to disease. In addition, we discuss the evolution of single-cell technologies with a focus on spatial and multi-omics approaches that promise to drive continued innovation in respiratory research. |Computational Biology/methods[MESH] |Gene Expression Profiling[MESH] |Humans[MESH] |Lung Diseases/*genetics[MESH] |Pulmonary Medicine/*trends[MESH] |Sequence Analysis, RNA/*methods[MESH] |Single-Cell Analysis/*methods[MESH]Breathing fresh air into respiratory research with single-cell RNA seq(epmc).pdf DeepDyve Pubget Overpricing