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dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts

Author

Listed:
  • Vadim Demichev

    (Charité – Universitätsmedizin Berlin
    The Francis Crick Institute
    University of Cambridge)

  • Lukasz Szyrwiel

    (Charité – Universitätsmedizin Berlin
    The Francis Crick Institute)

  • Fengchao Yu

    (University of Michigan)

  • Guo Ci Teo

    (University of Michigan)

  • George Rosenberger

    (Columbia University)

  • Agathe Niewienda

    (Charité – Universitätsmedizin Berlin)

  • Daniela Ludwig

    (Charité – Universitätsmedizin Berlin)

  • Jens Decker

    (Bruker Daltonics GmbH & Co. KG)

  • Stephanie Kaspar-Schoenefeld

    (Bruker Daltonics GmbH & Co. KG)

  • Kathryn S. Lilley

    (University of Cambridge)

  • Michael Mülleder

    (Charité – Universitätsmedizin Berlin)

  • Alexey I. Nesvizhskii

    (University of Michigan
    University of Michigan)

  • Markus Ralser

    (Charité – Universitätsmedizin Berlin
    The Francis Crick Institute)

Abstract

The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31492-0
    DOI: 10.1038/s41467-022-31492-0
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    References listed on IDEAS

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    1. 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.
    2. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
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    Cited by:

    1. Humberto J. Ferreira & Brian J. Stevenson & HuiSong Pak & Fengchao Yu & Jessica Almeida Oliveira & Florian Huber & Marie Taillandier-Coindard & Justine Michaux & Emma Ricart-Altimiras & Anne I. Kraeme, 2024. "Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    2. Fengchao Yu & Guo Ci Teo & Andy T. Kong & Klemens Fröhlich & Ginny Xiaohe Li & Vadim Demichev & Alexey I. Nesvizhskii, 2023. "Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Karsten Suhre & Guhan Ram Venkataraman & Harendra Guturu & Anna Halama & Nisha Stephan & Gaurav Thareja & Hina Sarwath & Khatereh Motamedchaboki & Margaret K. R. Donovan & Asim Siddiqui & Serafim Batz, 2024. "Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Mingdu Luo & Yandong Yin & Zhiwei Zhou & Haosong Zhang & Xi Chen & Hongmiao Wang & Zheng-Jiang Zhu, 2023. "A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    6. Valdemaras Petrosius & Pedro Aragon-Fernandez & Nil Üresin & Gergo Kovacs & Teeradon Phlairaharn & Benjamin Furtwängler & Jeff Op De Beeck & Sarah L. Skovbakke & Steffen Goletz & Simon Francis Thomsen, 2023. "Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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