Literature DB >> 28217993

Assessment of Label-Free Quantification in Discovery Proteomics and Impact of Technological Factors and Natural Variability of Protein Abundance.

Mhd Rami Al Shweiki1, Susann Mönchgesang2, Petra Majovsky1, Domenika Thieme1, Diana Trutschel2,3,4, Wolfgang Hoehenwarter1.   

Abstract

We evaluated the state of label-free discovery proteomics focusing especially on technological contributions and contributions of naturally occurring differences in protein abundance to the intersample variability in protein abundance estimates in this highly peptide-centric technology. First, the performance of popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant, and Progenesis QIP, was benchmarked using their default parameters and some modified settings. Beyond this, the intersample variability in protein abundance estimates was decomposed into variability introduced by the entire technology itself and variable protein amounts inherent to individual plants of the Arabidopsis thaliana Col-0 accession. The technical component was considerably higher than the biological intersample variability, suggesting an effect on the degree and validity of reported biological changes in protein abundance. Surprisingly, the biological variability, protein abundance estimates, and protein fold changes were recorded differently by the software used to quantify the proteins, warranting caution in the comparison of discovery proteomics results. As expected, ∼99% of the proteome was invariant in the isogenic plants in the absence of environmental factors; however, few proteins showed substantial quantitative variability. This naturally occurring variation between individual organisms can have an impact on the causality of reported protein fold changes.

Entities:  

Keywords:  MaxQuant; Progenesis QIP; biological variability; experimental variability; label-free quantification; natural variability; protein abundance; shotgun proteomics

Mesh:

Substances:

Year:  2017        PMID: 28217993     DOI: 10.1021/acs.jproteome.6b00645

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  33 in total

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Journal:  Mol Cell Proteomics       Date:  2019-05-16       Impact factor: 5.911

3.  Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments.

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Journal:  Nat Methods       Date:  2021-07-01       Impact factor: 28.547

4.  ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.

Authors:  Jing Tang; Jianbo Fu; Yunxia Wang; Bo Li; Yinghong Li; Qingxia Yang; Xuejiao Cui; Jiajun Hong; Xiaofeng Li; Yuzong Chen; Weiwei Xue; Feng Zhu
Journal:  Brief Bioinform       Date:  2020-03-23       Impact factor: 11.622

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6.  3-D physiomimetic extracellular matrix hydrogels provide a supportive microenvironment for rodent and human islet culture.

Authors:  K Jiang; D Chaimov; S N Patel; J-P Liang; S C Wiggins; M M Samojlik; A Rubiano; C S Simmons; C L Stabler
Journal:  Biomaterials       Date:  2018-09-07       Impact factor: 12.479

7.  Quantitative Proteomics Reveals that Hsp90 Inhibition Dynamically Regulates Global Protein Synthesis in Leishmania mexicana.

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Journal:  mSystems       Date:  2021-05-11       Impact factor: 6.496

8.  The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia.

Authors:  Karin E Borgmann-Winter; Kai Wang; Sabyasachi Bandyopadhyay; Abolfazl Doostparast Torshizi; Ian A Blair; Chang-Gyu Hahn
Journal:  Schizophr Res       Date:  2019-08-13       Impact factor: 4.662

9.  Cellular and molecular characteristics of pollen abortion in chrysanthemum cv. Kingfisher.

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Journal:  Plant Mol Biol       Date:  2018-09-10       Impact factor: 4.076

10.  Plant Phosphopeptide Identification and Label-Free Quantification by MaxQuant and Proteome Discoverer Software.

Authors:  Shalan Li; Haitao Zan; Zhe Zhu; Dandan Lu; Leonard Krall
Journal:  Methods Mol Biol       Date:  2021
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