Literature DB >> 26287124

Minimal sample requirement for highly multiplexed protein quantification in cell lines and tissues by PCT-SWATH mass spectrometry.

Shiying Shao1,2, Tiannan Guo2, Chiek Ching Koh2, Silke Gillessen3, Markus Joerger3, Wolfram Jochum4, Ruedi Aebersold2,5.   

Abstract

The amount of sample available for clinical and biological proteomic research is often limited and thus significantly restricts clinical and translational research. Recently, we have integrated pressure cycling technology (PCT) assisted sample preparation and SWATH-MS to perform reproducible proteomic quantification of biopsy-level tissue samples. Here, we further evaluated the minimal sample requirement of the PCT-SWATH method using various types of samples, including cultured cells (HeLa, K562, and U251, 500 000 to 50 000 cells) and tissue samples (mouse liver, heart, brain, and human kidney, 3-0.2 mg). The data show that as few as 50 000 human cells and 0.2-0.5 mg of wet mouse and human tissues produced peptide samples sufficient for multiple SWATH-MS analyses at optimal sample load applied to the system. Generally, the reproducibility of the method increased with decreasing tissue sample amounts. The SWATH maps acquired from peptides derived from samples of varying sizes were essentially identical based on the number, type, and quantity of identified peptides. In conclusion, we determined the minimal sample required for optimal PCT-SWATH analyses, and found smaller sample size achieved higher quantitative accuracy.
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Biomedicine; Mass spectrometry; Pressure cycling technology; SWATH

Mesh:

Substances:

Year:  2015        PMID: 26287124     DOI: 10.1002/pmic.201500161

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


  12 in total

1.  Application of SWATH Proteomics to Mouse Biology.

Authors:  Yibo Wu; Evan G Williams; Ruedi Aebersold
Journal:  Curr Protoc Mouse Biol       Date:  2017-06-19

Review 2.  High-throughput proteomic sample preparation using pressure cycling technology.

Authors:  Xue Cai; Zhangzhi Xue; Chunlong Wu; Rui Sun; Liujia Qian; Liang Yue; Weigang Ge; Xiao Yi; Wei Liu; Chen Chen; Huanhuan Gao; Jing Yu; Luang Xu; Yi Zhu; Tiannan Guo
Journal:  Nat Protoc       Date:  2022-08-05       Impact factor: 17.021

Review 3.  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

4.  Proteome-Wide Evaluation of Two Common Protein Quantification Methods.

Authors:  Jeremy D O'Connell; Joao A Paulo; Jonathon J O'Brien; Steven P Gygi
Journal:  J Proteome Res       Date:  2018-04-19       Impact factor: 4.466

Review 5.  The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer.

Authors:  Claire L Tonry; Emma Leacy; Cinzia Raso; Stephen P Finn; John Armstrong; Stephen R Pennington
Journal:  Diagnostics (Basel)       Date:  2016-07-18

Review 6.  Impact of Phosphoproteomics in the Era of Precision Medicine for Prostate Cancer.

Authors:  Johnny R Ramroop; Mark N Stein; Justin M Drake
Journal:  Front Oncol       Date:  2018-02-16       Impact factor: 6.244

7.  Identification of single amino acid differences in uniformly charged homopolymeric peptides with aerolysin nanopore.

Authors:  Fabien Piguet; Hadjer Ouldali; Manuela Pastoriza-Gallego; Philippe Manivet; Juan Pelta; Abdelghani Oukhaled
Journal:  Nat Commun       Date:  2018-03-06       Impact factor: 14.919

8.  Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines.

Authors:  Tiannan Guo; Augustin Luna; Vinodh N Rajapakse; Ching Chiek Koh; Zhicheng Wu; Wei Liu; Yaoting Sun; Huanhuan Gao; Michael P Menden; Chao Xu; Laurence Calzone; Loredana Martignetti; Chiara Auwerx; Marija Buljan; Amir Banaei-Esfahani; Alessandro Ori; Murat Iskar; Ludovic Gillet; Ran Bi; Jiangnan Zhang; Huanhuan Zhang; Chenhuan Yu; Qing Zhong; Sudhir Varma; Uwe Schmitt; Peng Qiu; Qiushi Zhang; Yi Zhu; Peter J Wild; Mathew J Garnett; Peer Bork; Martin Beck; Kexin Liu; Julio Saez-Rodriguez; Fathi Elloumi; William C Reinhold; Chris Sander; Yves Pommier; Ruedi Aebersold
Journal:  iScience       Date:  2019-10-31

9.  A Bayesian algorithm for detecting differentially expressed proteins and its application in breast cancer research.

Authors:  Tapesh Santra; Eleni Ioanna Delatola
Journal:  Sci Rep       Date:  2016-07-22       Impact factor: 4.379

10.  High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification.

Authors:  Yi Zhu; Tobias Weiss; Qiushi Zhang; Rui Sun; Bo Wang; Xiao Yi; Zhicheng Wu; Huanhuan Gao; Xue Cai; Guan Ruan; Tiansheng Zhu; Chao Xu; Sai Lou; Xiaoyan Yu; Ludovic Gillet; Peter Blattmann; Karim Saba; Christian D Fankhauser; Michael B Schmid; Dorothea Rutishauser; Jelena Ljubicic; Ailsa Christiansen; Christine Fritz; Niels J Rupp; Cedric Poyet; Elisabeth Rushing; Michael Weller; Patrick Roth; Eugenia Haralambieva; Silvia Hofer; Chen Chen; Wolfram Jochum; Xiaofei Gao; Xiaodong Teng; Lirong Chen; Qing Zhong; Peter J Wild; Ruedi Aebersold; Tiannan Guo
Journal:  Mol Oncol       Date:  2019-09-18       Impact factor: 6.603

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