Literature DB >> 24211767

Quantitative accuracy in mass spectrometry based proteomics of complex samples: the impact of labeling and precursor interference.

AnnSofi Sandberg1, Rui M M Branca1, Janne Lehtiö1, Jenny Forshed2.   

Abstract

Knowing the limit of quantification is important to accurately judge the results from proteomics studies. In order to investigate isobaric labels in combination with peptide pre-fractionation by high resolution isoelectric focusing in terms of limit of detection, quantitative accuracy and how to improve it, we used a human cell lysate spiked with 57 protein standards providing reference points across a wide concentration range. Specifically, the impact of precursor mixing (isolation interference and reporter ion interference) on quantitative accuracy was investigated by co-analyzing iTRAQ (8-plex) and TMT (6-plex) labeled peptides. A label-free analysis was also performed. Peptides, labeled or label-free, were analyzed by LC-MS/MS (Orbitrap Velos). We identified 3386 proteins by the label-free approach, 4466 with iTRAQ and 5961 with TMT. A linear range of quantification down to 1fmol was indicated for both isobaric and label-free analysis workflows, with an upper limit exceeding 60fmol. Our results indicate that 6-plex TMT is more sensitive than 8-plex iTRAQ. For isobaric labels, quantitative accuracy was affected by precursor mixing. Based on our evaluation on precursor mixing and accuracy of isobaric label quantification, we propose a cut off of <30% isolation interference for peptide spectrum matches (PSMs) used in the quantification. BIOLOGICAL SIGNIFICANCE: Quantitative proteome analysis by mass spectrometry offers opportunities for biological research. However, knowing the limit of quantification in biological samples is important to accurately judge the results. By using a high-complexity sample spiked with protein standards of known concentrations, we investigated the quantification limits of label-free and label-based peptide quantification, including an evaluation of precursor mixing and its impact on quantification accuracy by isobaric labels. We suggest limits of allowed precursor interference and believe that this study contributes with information useful in proteome quantification by mass spectrometry.
Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Label-free; Precursor mixing; Quantification; TMT; iTRAQ

Mesh:

Substances:

Year:  2013        PMID: 24211767     DOI: 10.1016/j.jprot.2013.10.035

Source DB:  PubMed          Journal:  J Proteomics        ISSN: 1874-3919            Impact factor:   4.044


  24 in total

1.  Extensive Peptide Fractionation and y1 Ion-Based Interference Detection Method for Enabling Accurate Quantification by Isobaric Labeling and Mass Spectrometry.

Authors:  Mingming Niu; Ji-Hoon Cho; Kiran Kodali; Vishwajeeth Pagala; Anthony A High; Hong Wang; Zhiping Wu; Yuxin Li; Wenjian Bi; Hui Zhang; Xusheng Wang; Wei Zou; Junmin Peng
Journal:  Anal Chem       Date:  2017-02-22       Impact factor: 6.986

2.  Secretome analysis of diarrhea-inducing strains of Escherichia coli.

Authors:  Raja Sekhar Nirujogi; Babylakshmi Muthusamy; Min-Sik Kim; Gajanan J Sathe; P T V Lakshmi; Olga N Kovbasnjuk; T S Keshava Prasad; Mary Wade; Rabih E Jabbour
Journal:  Proteomics       Date:  2017-03-06       Impact factor: 3.984

3.  Comparative Analysis of Quantitative Mass Spectrometric Methods for Subcellular Proteomics.

Authors:  Abla Tannous; Marielle Boonen; Haiyan Zheng; Caifeng Zhao; Colin J Germain; Dirk F Moore; David E Sleat; Michel Jadot; Peter Lobel
Journal:  J Proteome Res       Date:  2020-03-05       Impact factor: 4.466

4.  Improving data quality and preserving HCD-generated reporter ions with EThcD for isobaric tag-based quantitative proteomics and proteome-wide PTM studies.

Authors:  Qing Yu; Xudong Shi; Yu Feng; K Craig Kent; Lingjun Li
Journal:  Anal Chim Acta       Date:  2017-03-16       Impact factor: 6.558

5.  Separation methods in single-cell proteomics: RPLC or CE?

Authors:  Kellye A Cupp-Sutton; Mulin Fang; Si Wu
Journal:  Int J Mass Spectrom       Date:  2022-08-17       Impact factor: 1.934

6.  Improved Sensitivity of Ultralow Flow LC-MS-Based Proteomic Profiling of Limited Samples Using Monolithic Capillary Columns and FAIMS Technology.

Authors:  Michal Greguš; James C Kostas; Somak Ray; Susan E Abbatiello; Alexander R Ivanov
Journal:  Anal Chem       Date:  2020-10-15       Impact factor: 6.986

7.  The Human Tau Interactome: Binding to the Ribonucleoproteome, and Impaired Binding of the Proline-to-Leucine Mutant at Position 301 (P301L) to Chaperones and the Proteasome.

Authors:  C Geeth Gunawardana; Mohadeseh Mehrabian; Xinzhu Wang; Iris Mueller; Isabela B Lubambo; James E N Jonkman; Hansen Wang; Gerold Schmitt-Ulms
Journal:  Mol Cell Proteomics       Date:  2015-08-11       Impact factor: 5.911

8.  Efficient Quantitative Comparisons of Plasma Proteomes Using Label-Free Analysis with MaxQuant.

Authors:  Lynn A Beer; Pengyuan Liu; Bonnie Ky; Kurt T Barnhart; David W Speicher
Journal:  Methods Mol Biol       Date:  2017

Review 9.  Proteomics for systems toxicology.

Authors:  Bjoern Titz; Ashraf Elamin; Florian Martin; Thomas Schneider; Sophie Dijon; Nikolai V Ivanov; Julia Hoeng; Manuel C Peitsch
Journal:  Comput Struct Biotechnol J       Date:  2014-08-27       Impact factor: 7.271

10.  Comparative Analysis of Label-Free and 8-Plex iTRAQ Approach for Quantitative Tissue Proteomic Analysis.

Authors:  Agnieszka Latosinska; Konstantinos Vougas; Manousos Makridakis; Julie Klein; William Mullen; Mahmoud Abbas; Konstantinos Stravodimos; Ioannis Katafigiotis; Axel S Merseburger; Jerome Zoidakis; Harald Mischak; Antonia Vlahou; Vera Jankowski
Journal:  PLoS One       Date:  2015-09-02       Impact factor: 3.240

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