Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS

Mass spectrometry-based proteomics typically relies on highly sensitive nano-flow liquid chromatography (LC) but this can reduce robustness and reproducibility. Here, the authors show that micro-flow LC enables robust and reproducible high-throughput proteomics experiments at a very moderate loss of...

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Autores principales:	Yangyang Bian, Runsheng Zheng, Florian P. Bayer, Cassandra Wong, Yun-Chien Chang, Chen Meng, Daniel P. Zolg, Maria Reinecke, Jana Zecha, Svenja Wiechmann, Stephanie Heinzlmeir, Johannes Scherr, Bernhard Hemmer, Mike Baynham, Anne-Claude Gingras, Oleksandr Boychenko, Bernhard Kuster
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/51978dfc79c64bbfbbf6af243fbb23c2
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spelling	oai:doaj.org-article:51978dfc79c64bbfbbf6af243fbb23c22021-12-02T17:31:24ZRobust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS10.1038/s41467-019-13973-x2041-1723https://doaj.org/article/51978dfc79c64bbfbbf6af243fbb23c22020-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13973-xhttps://doaj.org/toc/2041-1723Mass spectrometry-based proteomics typically relies on highly sensitive nano-flow liquid chromatography (LC) but this can reduce robustness and reproducibility. Here, the authors show that micro-flow LC enables robust and reproducible high-throughput proteomics experiments at a very moderate loss of sensitivity.Yangyang BianRunsheng ZhengFlorian P. BayerCassandra WongYun-Chien ChangChen MengDaniel P. ZolgMaria ReineckeJana ZechaSvenja WiechmannStephanie HeinzlmeirJohannes ScherrBernhard HemmerMike BaynhamAnne-Claude GingrasOleksandr BoychenkoBernhard KusterNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yangyang Bian Runsheng Zheng Florian P. Bayer Cassandra Wong Yun-Chien Chang Chen Meng Daniel P. Zolg Maria Reinecke Jana Zecha Svenja Wiechmann Stephanie Heinzlmeir Johannes Scherr Bernhard Hemmer Mike Baynham Anne-Claude Gingras Oleksandr Boychenko Bernhard Kuster Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS
description	Mass spectrometry-based proteomics typically relies on highly sensitive nano-flow liquid chromatography (LC) but this can reduce robustness and reproducibility. Here, the authors show that micro-flow LC enables robust and reproducible high-throughput proteomics experiments at a very moderate loss of sensitivity.
format	article
author	Yangyang Bian Runsheng Zheng Florian P. Bayer Cassandra Wong Yun-Chien Chang Chen Meng Daniel P. Zolg Maria Reinecke Jana Zecha Svenja Wiechmann Stephanie Heinzlmeir Johannes Scherr Bernhard Hemmer Mike Baynham Anne-Claude Gingras Oleksandr Boychenko Bernhard Kuster
author_facet	Yangyang Bian Runsheng Zheng Florian P. Bayer Cassandra Wong Yun-Chien Chang Chen Meng Daniel P. Zolg Maria Reinecke Jana Zecha Svenja Wiechmann Stephanie Heinzlmeir Johannes Scherr Bernhard Hemmer Mike Baynham Anne-Claude Gingras Oleksandr Boychenko Bernhard Kuster
author_sort	Yangyang Bian
title	Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS
title_short	Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS
title_full	Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS
title_fullStr	Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS
title_full_unstemmed	Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS
title_sort	robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow lc–ms/ms
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/51978dfc79c64bbfbbf6af243fbb23c2
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Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS

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