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; Markus Ralser

doi:10.1038/s41467-022-31492-0

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

Nat Commun. 2022 Jul 8;13(1):3944. doi: 10.1038/s41467-022-31492-0.

Authors

Vadim Demichev^#^{1

2

3}, Lukasz Szyrwiel^#^{4

5}, Fengchao Yu⁶, Guo Ci Teo⁶, George Rosenberger⁷, Agathe Niewienda⁴, Daniela Ludwig⁴, Jens Decker⁸, Stephanie Kaspar-Schoenefeld⁸, Kathryn S Lilley⁹, Michael Mülleder¹⁰, Alexey I Nesvizhskii^{11

12}, Markus Ralser^{4

5}

Affiliations

¹ Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. [email protected].
² Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK. [email protected].
³ Department of Biochemistry and Milner Therapeutics Institute, University of Cambridge, Cambridge, UK. [email protected].
⁴ Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.
⁵ Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
⁶ Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
⁷ Department of Systems Biology, Columbia University, New York, NY, USA.
⁸ Bruker Daltonics GmbH & Co. KG, Bremen, Germany.
⁹ Department of Biochemistry and Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
¹⁰ Core Facility High-Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany.
¹¹ Department of Pathology, University of Michigan, Ann Arbor, MI, USA. [email protected].
¹² Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. [email protected].

^# Contributed equally.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Data Analysis
Humans
Mass Spectrometry / methods
Peptides / analysis
Proteome* / analysis
Proteomics* / methods

Substances

Peptides
Proteome

Abstract

Publication types

MeSH terms

Substances

Grants and funding