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10.1126/science.1247651 http://scihub22266oqcxt.onion/10.1126/science.1247651 C4412462!4412462!24531970     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Science 2014 ; 343 (6172): 776-9 Nephropedia Template TP {{tp|p=24531970|t=2014. Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types |pdf=|usr=}}{{24531970}} gab.com Text Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types JO: Science http://www.kidney.de/pget.php?&tw=1&pmid=24531970 #FOAvip In multi-cellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell type characterization of splenic tissues. Modeling single-cell transcriptional states in dendritic cells and additional hematopoietic cell types uncovers rich cell-type heterogeneity and gene-modules activity in steady-state and after pathogen activation. Cellular diversity is thereby approached through inference of variable and dynamic pathway activity rather than a fixed pre-programmed cell-type hierarchy. These data demonstrate single-cell RNA-Seq as an effective tool for comprehensive cellular decomposition of complex tissues. Twit Text FOAVip Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types ????Science http://www.kidney.de/pget.php?&tw=1&pmid=24531970 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24531970 #FOAvip English Wikipedia <ref>{{Cite pmid|24531970}}</ref> Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types #MMPMID24531970 Jaitin DA; Kenigsberg E; Keren-Shaul H; Elefant N; Paul F; Zaretsky I; Mildner A; Cohen N; Jung S; Tanay A; Amit I Science 2014[Feb]; 343 (6172): 776-9 PMID24531970show ga In multi-cellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell type characterization of splenic tissues. Modeling single-cell transcriptional states in dendritic cells and additional hematopoietic cell types uncovers rich cell-type heterogeneity and gene-modules activity in steady-state and after pathogen activation. Cellular diversity is thereby approached through inference of variable and dynamic pathway activity rather than a fixed pre-programmed cell-type hierarchy. These data demonstrate single-cell RNA-Seq as an effective tool for comprehensive cellular decomposition of complex tissues. äMassively parallel single cell RNA-Seq for marker-free decomposition o(epmc).pdf DeepDyve Pubget Overpricing