Literature DB >> 27130639

Targeted proteomics coming of age - SRM, PRM and DIA performance evaluated from a core facility perspective.

Tobias Kockmann1, Christian Trachsel1, Christian Panse1, Asa Wahlander2, Nathalie Selevsek1, Jonas Grossmann1, Witold E Wolski1, Ralph Schlapbach1.   

Abstract

Quantitative mass spectrometry is a rapidly evolving methodology applied in a large number of omics-type research projects. During the past years, new designs of mass spectrometers have been developed and launched as commercial systems while in parallel new data acquisition schemes and data analysis paradigms have been introduced. Core facilities provide access to such technologies, but also actively support the researchers in finding and applying the best-suited analytical approach. In order to implement a solid fundament for this decision making process, core facilities need to constantly compare and benchmark the various approaches. In this article we compare the quantitative accuracy and precision of current state of the art targeted proteomics approaches single reaction monitoring (SRM), parallel reaction monitoring (PRM) and data independent acquisition (DIA) across multiple liquid chromatography mass spectrometry (LC-MS) platforms, using a readily available commercial standard sample. All workflows are able to reproducibly generate accurate quantitative data. However, SRM and PRM workflows show higher accuracy and precision compared to DIA approaches, especially when analyzing low concentrated analytes.
© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  LC-MS; Label-free; Targeted proteomics; Technology

Mesh:

Year:  2016        PMID: 27130639     DOI: 10.1002/pmic.201500502

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  14 in total

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Authors:  Paolo Cifani; Alex Kentsis
Journal:  Proteomics       Date:  2016-12-21       Impact factor: 3.984

Review 2.  Chemical cross-linking with mass spectrometry: a tool for systems structural biology.

Authors:  Juan D Chavez; James E Bruce
Journal:  Curr Opin Chem Biol       Date:  2018-08-30       Impact factor: 8.822

Review 3.  Mass spectrometry-based targeted proteomics for analysis of protein mutations.

Authors:  Tai-Tu Lin; Tong Zhang; Reta B Kitata; Tao Liu; Richard D Smith; Wei-Jun Qian; Tujin Shi
Journal:  Mass Spectrom Rev       Date:  2021-10-31       Impact factor: 9.011

Review 4.  Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques.

Authors:  Jesse G Meyer; Birgit Schilling
Journal:  Expert Rev Proteomics       Date:  2017-05       Impact factor: 3.940

5.  Determining Allele-Specific Protein Expression (ASPE) Using a Novel Quantitative Concatamer Based Proteomics Method.

Authors:  Jian Shi; Xinwen Wang; Huaijun Zhu; Hui Jiang; Danxin Wang; Alexey Nesvizhskii; Hao-Jie Zhu
Journal:  J Proteome Res       Date:  2018-09-04       Impact factor: 4.466

6.  Comparison of Quantitative Mass Spectrometry Platforms for Monitoring Kinase ATP Probe Uptake in Lung Cancer.

Authors:  Melissa A Hoffman; Bin Fang; Eric B Haura; Uwe Rix; John M Koomen
Journal:  J Proteome Res       Date:  2017-11-22       Impact factor: 4.466

Review 7.  Application of targeted mass spectrometry in bottom-up proteomics for systems biology research.

Authors:  Nathan P Manes; Aleksandra Nita-Lazar
Journal:  J Proteomics       Date:  2018-02-13       Impact factor: 4.044

8.  Comparison of data acquisition modes with Orbitrap high-resolution mass spectrometry for targeted and non-targeted residue screening in aquacultured eel.

Authors:  I-Lin Wu; Sherri B Turnipseed; Joseph M Storey; Wendy C Andersen; Mark R Madson
Journal:  Rapid Commun Mass Spectrom       Date:  2020-04-15       Impact factor: 2.586

9.  Time serial transcriptome reveals Cyp2c29 as a key gene in hepatocellular carcinoma development.

Authors:  Qi Wang; Qin Tang; Lijun Zhao; Qiong Zhang; Yuxin Wu; Hui Hu; Lanlan Liu; Xiang Liu; Yanhong Zhu; Anyuan Guo; Xiangliang Yang
Journal:  Cancer Biol Med       Date:  2020-05-15       Impact factor: 4.248

10.  Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry.

Authors:  Pavel Bouchal; Olga T Schubert; Jakub Faktor; Lenka Capkova; Hana Imrichova; Karolina Zoufalova; Vendula Paralova; Roman Hrstka; Yansheng Liu; Holger Alexander Ebhardt; Eva Budinska; Rudolf Nenutil; Ruedi Aebersold
Journal:  Cell Rep       Date:  2019-07-16       Impact factor: 9.423

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