A critical comparison of three MS‐based approaches for quantitative proteomics analysis

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.

Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools

“…Specificity, sensitivity, and precision values measured in this study after MQ-L analysis were globally in line with previously published results, presented in comparison with those obtained using labeling methods . Furthermore, a recent study showed that MQ-L outperformed other quantitative tools (not including PD) according to several statistical metrics, highlighting the value of the results achieved by the approaches compared in this study.…”

“…A lower CV could be observed in normalization-based methods also when considering background proteins. The CV values measured here for MQ-L quantification were consistent to those reported by other groups in standard protein mixture and cell line experiments.…”

Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling

“…Isobaric labeling with tandem mass tags (TMT) − is a popular multiplexing strategy for mass spectrometry-based proteomics. It labels peptides from different samples with isobaric variants of a mass tag, combines them into a single biological mixture, and profiles the mixture in a single mass spectrometry run. , This strategy allows us to both gain the accuracy of relative protein quantification and increase the sample throughput. , However, since TMT experiments are limited to up to 18 samples in a single mixture, larger-scale cohorts require multiple TMT mixtures. − Increasingly, TMT-based investigations combine repeated measures designs and the use of multiple TMT mixtures. For example, recent studies from the CPTAC consortium compared protein abundances between tumor and adjacent normal tissues in a paired design with more than 100 patients and 20 mixtures. − As another example, Nie et al used a hybrid design to investigate the changes in protein abundances among different organs after SARS-CoV-2 virus infection with 93 patients and 22 mixtures.…”

Illuminating the dark protein-protein interactome

“…label-based proteomic approaches (e.g., SILAC) are expensive and time consuming ( Taverna and Gaspari, 2021 )…”