Literature DB >> 32312845

Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries.

Lindsay K Pino1, Seth C Just2, Michael J MacCoss3, Brian C Searle4.   

Abstract

Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.
© 2020 Pino et al.

Entities:  

Keywords:  DIA; Data evaluation; data independent acquisition; label-free quantification; mass spectrometry; protein identification; quantification

Mesh:

Substances:

Year:  2020        PMID: 32312845      PMCID: PMC7338082          DOI: 10.1074/mcp.P119.001913

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  57 in total

1.  MSPLIT-DIA: sensitive peptide identification for data-independent acquisition.

Authors:  Jian Wang; Monika Tucholska; James D R Knight; Jean-Philippe Lambert; Stephen Tate; Brett Larsen; Anne-Claude Gingras; Nuno Bandeira
Journal:  Nat Methods       Date:  2015-12       Impact factor: 28.547

2.  Development and validation of a spectral library searching method for peptide identification from MS/MS.

Authors:  Henry Lam; Eric W Deutsch; James S Eddes; Jimmy K Eng; Nichole King; Stephen E Stein; Ruedi Aebersold
Journal:  Proteomics       Date:  2007-03       Impact factor: 3.984

3.  Semi-supervised learning for peptide identification from shotgun proteomics datasets.

Authors:  Lukas Käll; Jesse D Canterbury; Jason Weston; William Stafford Noble; Michael J MacCoss
Journal:  Nat Methods       Date:  2007-10-21       Impact factor: 28.547

4.  Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions.

Authors:  M W Senko; S C Beu; F W McLaffertycor
Journal:  J Am Soc Mass Spectrom       Date:  1995-04       Impact factor: 3.109

5.  DIAlignR Provides Precise Retention Time Alignment Across Distant Runs in DIA and Targeted Proteomics.

Authors:  Shubham Gupta; Sara Ahadi; Wenyu Zhou; Hannes Röst
Journal:  Mol Cell Proteomics       Date:  2019-01-31       Impact factor: 5.911

6.  Generation of High-Quality SWATH® Acquisition Data for Label-free Quantitative Proteomics Studies Using TripleTOF® Mass Spectrometers.

Authors:  Birgit Schilling; Bradford W Gibson; Christie L Hunter
Journal:  Methods Mol Biol       Date:  2017

7.  Quantifying the impact of chimera MS/MS spectra on peptide identification in large-scale proteomics studies.

Authors:  Stephane Houel; Robert Abernathy; Kutralanathan Renganathan; Karen Meyer-Arendt; Natalie G Ahn; William M Old
Journal:  J Proteome Res       Date:  2010-08-06       Impact factor: 4.466

8.  PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data.

Authors:  Ying S Ting; Jarrett D Egertson; James G Bollinger; Brian C Searle; Samuel H Payne; William Stafford Noble; Michael J MacCoss
Journal:  Nat Methods       Date:  2017-08-07       Impact factor: 28.547

9.  Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.

Authors:  George Rosenberger; Isabell Bludau; Uwe Schmitt; Moritz Heusel; Christie L Hunter; Yansheng Liu; Michael J MacCoss; Brendan X MacLean; Alexey I Nesvizhskii; Patrick G A Pedrioli; Lukas Reiter; Hannes L Röst; Stephen Tate; Ying S Ting; Ben C Collins; Ruedi Aebersold
Journal:  Nat Methods       Date:  2017-08-21       Impact factor: 28.547

10.  Plug-and-play analysis of the human phosphoproteome by targeted high-resolution mass spectrometry.

Authors:  Robert T Lawrence; Brian C Searle; Ariadna Llovet; Judit Villén
Journal:  Nat Methods       Date:  2016-03-28       Impact factor: 28.547

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  25 in total

1.  Does Data-Independent Acquisition Data Contain Hidden Gems? A Case Study Related to Alzheimer's Disease.

Authors:  Evan E Hubbard; Lilian R Heil; Gennifer E Merrihew; Jasmeer P Chhatwal; Martin R Farlow; Catriona A McLean; Bernardino Ghetti; Kathy L Newell; Matthew P Frosch; Randall J Bateman; Eric B Larson; C Dirk Keene; Richard J Perrin; Thomas J Montine; Michael J MacCoss; Ryan R Julian
Journal:  J Proteome Res       Date:  2021-11-24       Impact factor: 4.466

2.  Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data.

Authors:  Lilian R Heil; William E Fondrie; Christopher D McGann; Alexander J Federation; William S Noble; Michael J MacCoss; Uri Keich
Journal:  J Proteome Res       Date:  2022-05-12       Impact factor: 5.370

Review 3.  Developing mass spectrometry for the quantitative analysis of neuropeptides.

Authors:  Christopher S Sauer; Ashley Phetsanthad; Olga L Riusech; Lingjun Li
Journal:  Expert Rev Proteomics       Date:  2021-08-26       Impact factor: 4.250

4.  Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes.

Authors:  Alicia L Richards; Kuei-Ho Chen; Damien B Wilburn; Erica Stevenson; Benjamin J Polacco; Brian C Searle; Danielle L Swaney
Journal:  J Proteome Res       Date:  2022-03-02       Impact factor: 5.370

Review 5.  Methods for quantification of glycopeptides by liquid separation and mass spectrometry.

Authors:  Haidi Yin; Jianhui Zhu
Journal:  Mass Spectrom Rev       Date:  2022-01-31       Impact factor: 9.011

6.  Highly Multiplex Targeted Proteomics Enabled by Real-Time Chromatographic Alignment.

Authors:  Philip M Remes; Ping Yip; Michael J MacCoss
Journal:  Anal Chem       Date:  2020-08-12       Impact factor: 6.986

7.  Sex-specific effects of in vitro fertilization on adult metabolic outcomes and hepatic transcriptome and proteome in mouse.

Authors:  Laren Narapareddy; Eric A Rhon-Calderon; Lisa A Vrooman; Josue Baeza; Duy K Nguyen; Clementina Mesaros; Yemin Lan; Benjamin A Garcia; Richard M Schultz; Marisa S Bartolomei
Journal:  FASEB J       Date:  2021-04       Impact factor: 5.834

8.  A Sensitive and Controlled Data-Independent Acquisition Method for Proteomic Analysis of Cell Therapies.

Authors:  Camille Lombard-Banek; Kerstin I Pohl; Edward J Kwee; John T Elliott; John E Schiel
Journal:  J Proteome Res       Date:  2022-04-11       Impact factor: 5.370

9.  Multi-omic profiling of histone variant H3.3 lysine 27 methylation reveals a distinct role from canonical H3 in stem cell differentiation.

Authors:  Yekaterina Kori; Peder J Lund; Matteo Trovato; Simone Sidoli; Zuo-Fei Yuan; Kyung-Min Noh; Benjamin A Garcia
Journal:  Mol Omics       Date:  2022-05-11

10.  Transcriptomic analysis of cardiac gene expression across the life course in male and female mice.

Authors:  Aykhan Yusifov; Vikram E Chhatre; Eva K Koplin; Cortney E Wilson; Emily E Schmitt; Kathleen C Woulfe; Danielle R Bruns
Journal:  Physiol Rep       Date:  2021-07
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