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

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, Alexey I. Nesvizhskii () and Markus Ralser
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Vadim Demichev: Charité – Universitätsmedizin Berlin
Lukasz Szyrwiel: Charité – Universitätsmedizin Berlin
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
Markus Ralser: Charité – Universitätsmedizin Berlin

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31492-0

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DOI: 10.1038/s41467-022-31492-0

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