A critical comparison of three MS‐based approaches for quantitative proteomics analysis. (31st October 2020)

• Record Type: Journal Article
• Authors: Taverna, Domenico; Gaspari, Marco
• Other Names: Giorgi Gianluca guestEditor.; Napoli Anna guestEditor.
• Is Part Of: Journal of mass spectrometry. Volume 56:Number 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-31
• Subjects: dimethyl labelling -- isobaric labelling -- label‐free proteomics -- proteomics benchmarking -- quantitative proteomics; Mass spectrometry -- Periodicals; 543.65
• DOI: 10.1002/jms.4669
• Languages: English
• ISSNs: 1076-5174

Abstract

Abstract: 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.