Comparative Analysis of Quantitative Mass Spectrometric Methods for Subcellular Proteomics
Author(s) -
Abla Tannous,
Marielle Boonen,
Haiyan Zheng,
Caifeng Zhao,
Colin J. Germain,
Dirk F. Moore,
David E. Sleat,
Michel Jadot,
Peter Lobel
Publication year - 2020
Publication title -
journal of proteome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/acs.jproteome.9b00862
Subject(s) - proteome , isobaric labeling , chemistry , quantitative proteomics , isobaric process , tandem mass tag , fractionation , mass spectrometry , cell fractionation , proteomics , tandem mass spectrometry , chromatography , biological system , biology , biochemistry , protein mass spectrometry , physics , membrane , gene , thermodynamics
Knowledge of intracellular location can provide important insights into the function of proteins and their respective organelles, and there is interest in combining classical subcellular fractionation with quantitative mass spectrometry to create global cellular maps. To evaluate mass spectrometric approaches specifically for this application, we analyzed rat liver differential centrifugation and Nycodenz density gradient subcellular fractions by tandem mass tag (TMT) isobaric labeling with reporter ion measurement at the MS2 and MS3 level and with two different label-free peak integration approaches, MS1 and data independent acquisition (DIA). TMT-MS2 provided the greatest proteome coverage, but ratio compression from contaminating background ions resulted in a narrower accurate dynamic range compared to TMT-MS3, MS1, and DIA, which were similar. Using a protein clustering approach to evaluate data quality by assignment of reference proteins to their correct compartments, all methods performed well, with isobaric labeling approaches providing the highest quality localization. Finally, TMT-MS2 gave the lowest percentage of missing quantifiable data when analyzing orthogonal fractionation methods containing overlapping proteomes. In summary, despite inaccuracies resulting from ratio compression, data obtained by TMT-MS2 assigned protein localization as well as other methods but achieved the highest proteome coverage with the lowest proportion of missing values.
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