Abstract
In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.
Highlights
Mass spectrometry-based proteomics has become an essential tool in the study of biological processes because it can provide an overall assessment of the proteomes of organisms, cells, organs, and tissues
A total of 34 out of 48 Universal Proteomics Standard 2 (UPS2) proteins were identified across 5 molar concentrations, which indicates that not all the UPS2 proteins were detectable in our conditions, especially those at the lowest concentration level (0.5 fmol)
These results fit with what was observed by Tsou et al [44], who compared the performance of data-independent acquisition (DIA) methods and data-dependent acquisition (DDA) methods across multiple samples, including a UPS2 sample
Summary
Mass spectrometry-based proteomics has become an essential tool in the study of biological processes because it can provide an overall assessment of the proteomes of organisms, cells, organs, and tissues. Relative protein abundance is determined using a labelfree shotgun approach, which can provide an overall view of proteomes across multiple situations and can detect thousands of proteins The latter is possible because the approach is simpler and more versatile than label-based methods (e.g., iTRAQ, iCAT, TMT). Compared to other quantification methods, SC-based methods, especially emPAI, consistently underperform because they overestimate levels of outlier proteins, notably those that are the most abundant or those with a single peptide spectrum match [5,6]. Another widely used method for comparing proteins within samples is intensity-based absolute quantification (iBAQ) [7], which is equivalent to PAI but uses peptide intensities instead of SC. The accuracy of these methods remains to be evaluated
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