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

Author

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  • Yangyang Bian

    (Technical University of Munich
    The First Affiliated Hospital of Zhengzhou University)

  • Runsheng Zheng

    (Technical University of Munich)

  • Florian P. Bayer

    (Technical University of Munich)

  • Cassandra Wong

    (Sinai Health System)

  • Yun-Chien Chang

    (Technical University of Munich)

  • Chen Meng

    (Technical University of Munich)

  • Daniel P. Zolg

    (Technical University of Munich)

  • Maria Reinecke

    (Technical University of Munich
    German Cancer Consortium (DKTK), partner site Munich
    German Cancer Research Center (DKFZ))

  • Jana Zecha

    (Technical University of Munich)

  • Svenja Wiechmann

    (Technical University of Munich
    German Cancer Consortium (DKTK), partner site Munich
    German Cancer Research Center (DKFZ))

  • Stephanie Heinzlmeir

    (Technical University of Munich)

  • Johannes Scherr

    (Technical University of Munich)

  • Bernhard Hemmer

    (Technical University of Munich
    Munich Cluster for Systems Neurology (SyNergy))

  • Mike Baynham

    (Thermo Fisher Scientific)

  • Anne-Claude Gingras

    (Sinai Health System)

  • Oleksandr Boychenko

    (Thermo Fisher Scientific)

  • Bernhard Kuster

    (Technical University of Munich
    Technical University of Munich
    German Cancer Consortium (DKTK), partner site Munich)

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 ( 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.

Suggested Citation

  • 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 Sch, 2020. "Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-13973-x
    DOI: 10.1038/s41467-019-13973-x
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    Cited by:

    1. Maximilian T. Strauss & Isabell Bludau & Wen-Feng Zeng & Eugenia Voytik & Constantin Ammar & Julia P. Schessner & Rajesh Ilango & Michelle Gill & Florian Meier & Sander Willems & Matthias Mann, 2024. "AlphaPept: a modern and open framework for MS-based proteomics," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Patrick Leopold Rüther & Immanuel Mirnes Husic & Pernille Bangsgaard & Kristian Murphy Gregersen & Pernille Pantmann & Milena Carvalho & Ricardo Miguel Godinho & Lukas Friedl & João Cascalheira & Albe, 2022. "SPIN enables high throughput species identification of archaeological bone by proteomics," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Simon Davis & Connor Scott & Janina Oetjen & Philip D. Charles & Benedikt M. Kessler & Olaf Ansorge & Roman Fischer, 2023. "Deep topographic proteomics of a human brain tumour," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Vadim Demichev & Lukasz Szyrwiel & Fengchao Yu & Guo Ci Teo & George Rosenberger & Agathe Niewienda & Daniela Ludwig & Jens Decker & Stephanie Kaspar-Schoenefeld & Kathryn S. Lilley & Michael Mülleder, 2022. "dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts," Nature Communications, Nature, vol. 13(1), pages 1-8, December.

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