Premium
A critical comparison of three MS‐based approaches for quantitative proteomics analysis
Author(s) -
Taverna Domenico,
Gaspari Marco
Publication year - 2021
Publication title -
journal of mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 121
eISSN - 1096-9888
pISSN - 1076-5174
DOI - 10.1002/jms.4669
Subject(s) - chemistry , proteome , false positive paradox , tandem mass spectrometry , proteomics , quantitative proteomics , mass spectrometry , orbitrap , false discovery rate , reproducibility , computational biology , chromatography , selected reaction monitoring , analytical chemistry (journal) , computer science , artificial intelligence , biochemistry , biology , gene
MS‐based proteomics is expanding its role as a routine tool for biological discovery. Nevertheless, the task of accurately and precisely quantifying thousands of analytes in a single experiment remains challenging. In this study, the diagnostic accuracy of three popular data‐dependent methods for protein relative quantification (label‐free [LF], dimethyl labelling [DML] and tandem mass tags [TMT]) has been assessed using a mixed species proteome (three species) and five experimental replicates per condition. Data were produced using a quadrupole‐Orbitrap mass spectrometer and analysed using a single platform (the MaxQuant/Perseus software suite). The whole comparative analysis was repeated three times over a period of 6 months, in order to assess the consistency of the reported findings. As expected, label‐based methods reproducibly provided a lower false positives rate, whereas TMT and LF performed similarly, and significantly better than DML, in terms of proteome coverage using the same instrument time. Although parameters like proteome coverage and precision were consistent in between replicates, other parameters like sensitivity, intended as the capacity of correctly classifying true positives (regulated proteins), were found to be less reproducible, especially at challenging fold‐changes (1.5). Collectively, data suggest that an increased interest in data reproducibility would be desirable in the quantitative proteomics field.