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Abstract
Nano-flow liquid chromatography tandem mass spectrometry (nano-flow LC–MS/MS) is the mainstay in proteome research because of its excellent sensitivity but often comes at the expense of robustness. Here we show that micro-flow LC–MS/MS using a 1 × 150 mm column shows excellent reproducibility of chromatographic retention time (<0.3% coefficient of variation, CV) and protein quantification (<7.5% CV) using data from >2000 samples of human cell lines, tissues and body fluids. Deep proteome analysis identifies >9000 proteins and >120,000 peptides in 16 h and sample multiplexing using tandem mass tags increases throughput to 11 proteomes in 16 h. The system identifies >30,000 phosphopeptides in 12 h and protein-protein or protein-drug interaction experiments can be analyzed in 20 min per sample. We show that the same column can be used to analyze >7500 samples without apparent loss of performance. This study demonstrates that micro-flow LC–MS/MS is suitable for a broad range of proteomic applications.
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.
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1 Technical University of Munich, Chair of Proteomics and Bioanalytics, Freising, Germany (GRID:grid.6936.a) (ISNI:0000000123222966) ; The First Affiliated Hospital of Zhengzhou University, Medical Research Center, Zhengzhou, China (GRID:grid.412633.1)
2 Technical University of Munich, Chair of Proteomics and Bioanalytics, Freising, Germany (GRID:grid.6936.a) (ISNI:0000000123222966)
3 Sinai Health System, Lunenfeld-Tanenbaum Research Institute, Toronto, Canada (GRID:grid.492573.e)
4 Technical University of Munich, Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Freising, Germany (GRID:grid.6936.a) (ISNI:0000000123222966)
5 Technical University of Munich, Chair of Proteomics and Bioanalytics, Freising, Germany (GRID:grid.6936.a) (ISNI:0000000123222966) ; German Cancer Consortium (DKTK), partner site Munich, Munich, Germany (GRID:grid.6936.a) ; German Cancer Research Center (DKFZ), Heidelberg, Germany (GRID:grid.7497.d) (ISNI:0000 0004 0492 0584)
6 Technical University of Munich, Centre for Preventive and Sports Medicine, Klinikum Rechts der Isar, Munich, Germany (GRID:grid.6936.a) (ISNI:0000000123222966)
7 Technical University of Munich, Department of Neurology, Klinikum Rechts der Isar, Medical Faculty, Munich, Germany (GRID:grid.6936.a) (ISNI:0000000123222966) ; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (GRID:grid.452617.3)
8 Thermo Fisher Scientific, Runcorn, UK (GRID:grid.421691.9) (ISNI:0000 0004 6046 1861)
9 Thermo Fisher Scientific, Germering, Germany (GRID:grid.424957.9) (ISNI:0000 0004 0624 9165)
10 Technical University of Munich, Chair of Proteomics and Bioanalytics, Freising, Germany (GRID:grid.6936.a) (ISNI:0000000123222966) ; Technical University of Munich, Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Freising, Germany (GRID:grid.6936.a) (ISNI:0000000123222966) ; German Cancer Consortium (DKTK), partner site Munich, Munich, Germany (GRID:grid.6936.a)