Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae
Journal article, 2021
Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for predicting genome-wide absolute protein abundances. A known caveat of this approach, however, is the poor technical reproducibility, that is, how consistent predictions are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We demonstrate that our simple normalization and rescaling approach can perform as accurately, yet more precisely, than methods which rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results offer a new benchmark for assessing MS data quality for protein quantification, while also underscoring current limitations in this approach.
absolute proteomics
batch effect
UPS2
iBAQ
mass spectrometry
Author
Benjamín José Sánchez
Novo Nordisk Foundation
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Petri-Jaan Lahtvee
University of Tartu
Kate Campbell
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Novo Nordisk Foundation
S. Kasvandik
University of Tartu
Tao Yu
Novo Nordisk Foundation
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Iván Domenzain Del Castillo Cerecer
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Novo Nordisk Foundation
Aleksej Zelezniak
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Jens B Nielsen
Novo Nordisk Foundation
Technical University of Denmark (DTU)
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Proteomics
1615-9853 (ISSN) 1615-9861 (eISSN)
Vol. 21 6 2000093Subject Categories
Analytical Chemistry
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
DOI
10.1002/pmic.202000093
PubMed
33452728