Literature DB >> 34319755

Toward Comprehensive Plasma Proteomics by Orthogonal Protease Digestion.

Andrea Fossati1,2,3, Alicia L Richards1,2,3, Kuei-Ho Chen1,2,3, Devan Jaganath4,5,6, Adithya Cattamanchi4,5,6, Joel D Ernst7, Danielle L Swaney1,2,3.   

Abstract

Rapid and consistent protein identification across large clinical cohorts is an important goal for clinical proteomics. With the development of data-independent technologies (DIA/SWATH-MS), it is now possible to analyze hundreds of samples with great reproducibility and quantitative accuracy. However, this technology benefits from empirically derived spectral libraries that define the detectable set of peptides and proteins. Here, we apply a simple and accessible tip-based workflow for the generation of spectral libraries to provide a comprehensive overview on the plasma proteome in individuals with and without active tuberculosis (TB). To boost protein coverage, we utilized nonconventional proteases such as GluC and AspN together with the gold standard trypsin, identifying more than 30,000 peptides mapping to 3309 proteins. Application of this library to quantify plasma proteome differences in TB infection recovered more than 400 proteins in 50 min of MS acquisition, including diagnostic Mycobacterium tuberculosis (Mtb) proteins that have previously been detectable primarily by antibody-based assays and intracellular proteins not previously described to be in plasma.

Entities:  

Keywords:  DIA-MS; clinical proteomics; label-free quantification; proteases

Mesh:

Substances:

Year:  2021        PMID: 34319755      PMCID: PMC8442619          DOI: 10.1021/acs.jproteome.1c00357

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   5.370


  42 in total

Review 1.  Mass spectrometry-based proteomics.

Authors:  Ruedi Aebersold; Matthias Mann
Journal:  Nature       Date:  2003-03-13       Impact factor: 49.962

2.  Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.

Authors:  Joshua E Elias; Steven P Gygi
Journal:  Nat Methods       Date:  2007-03       Impact factor: 28.547

3.  Multi-in-One: Multiple-Proteases, One-Hour-Shot Strategy for Fast and High-Coverage Phosphoproteomic Investigation.

Authors:  Xiaojing Gao; Qingrun Li; Yansheng Liu; Rong Zeng
Journal:  Anal Chem       Date:  2020-06-17       Impact factor: 6.986

4.  Proteomics. Tissue-based map of the human proteome.

Authors:  Mathias Uhlén; Linn Fagerberg; Björn M Hallström; Cecilia Lindskog; Per Oksvold; Adil Mardinoglu; Åsa Sivertsson; Caroline Kampf; Evelina Sjöstedt; Anna Asplund; IngMarie Olsson; Karolina Edlund; Emma Lundberg; Sanjay Navani; Cristina Al-Khalili Szigyarto; Jacob Odeberg; Dijana Djureinovic; Jenny Ottosson Takanen; Sophia Hober; Tove Alm; Per-Henrik Edqvist; Holger Berling; Hanna Tegel; Jan Mulder; Johan Rockberg; Peter Nilsson; Jochen M Schwenk; Marica Hamsten; Kalle von Feilitzen; Mattias Forsberg; Lukas Persson; Fredric Johansson; Martin Zwahlen; Gunnar von Heijne; Jens Nielsen; Fredrik Pontén
Journal:  Science       Date:  2015-01-23       Impact factor: 47.728

5.  System-Wide Profiling of Protein Complexes Via Size Exclusion Chromatography-Mass Spectrometry (SEC-MS).

Authors:  Andrea Fossati; Fabian Frommelt; Federico Uliana; Claudia Martelli; Matej Vizovisek; Ludovic Gillet; Ben Collins; Matthias Gstaiger; Ruedi Aebersold
Journal:  Methods Mol Biol       Date:  2021

6.  Depletion of abundant plasma proteins and limitations of plasma proteomics.

Authors:  Chengjian Tu; Paul A Rudnick; Misti Y Martinez; Kristin L Cheek; Stephen E Stein; Robbert J C Slebos; Daniel C Liebler
Journal:  J Proteome Res       Date:  2010-10-01       Impact factor: 4.466

7.  Value of using multiple proteases for large-scale mass spectrometry-based proteomics.

Authors:  Danielle L Swaney; Craig D Wenger; Joshua J Coon
Journal:  J Proteome Res       Date:  2010-03-05       Impact factor: 4.466

8.  MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics.

Authors:  Andy T Kong; Felipe V Leprevost; Dmitry M Avtonomov; Dattatreya Mellacheruvu; Alexey I Nesvizhskii
Journal:  Nat Methods       Date:  2017-04-10       Impact factor: 28.547

9.  Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance.

Authors:  Roland Bruderer; Jan Muntel; Sebastian Müller; Oliver M Bernhardt; Tejas Gandhi; Ornella Cominetti; Charlotte Macron; Jérôme Carayol; Oliver Rinner; Arne Astrup; Wim H M Saris; Jörg Hager; Armand Valsesia; Loïc Dayon; Lukas Reiter
Journal:  Mol Cell Proteomics       Date:  2019-04-04       Impact factor: 5.911

Review 10.  Array programming with NumPy.

Authors:  Charles R Harris; K Jarrod Millman; Stéfan J van der Walt; Ralf Gommers; Pauli Virtanen; David Cournapeau; Eric Wieser; Julian Taylor; Sebastian Berg; Nathaniel J Smith; Robert Kern; Matti Picus; Stephan Hoyer; Marten H van Kerkwijk; Matthew Brett; Allan Haldane; Jaime Fernández Del Río; Mark Wiebe; Pearu Peterson; Pierre Gérard-Marchant; Kevin Sheppard; Tyler Reddy; Warren Weckesser; Hameer Abbasi; Christoph Gohlke; Travis E Oliphant
Journal:  Nature       Date:  2020-09-16       Impact factor: 49.962

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  1 in total

1.  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

  1 in total

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