Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

• Published in: Proteomes Vol. 10; no. 1; p. 2
• Main Authors: Millán-Oropeza, Aarón, Blein-Nicolas, Mélisande, Monnet, Véronique, Zivy, Michel, Henry, Céline
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.01.2022; MDPI
• Subjects: Biochemistry, Molecular Biology; Bioinformatics; Computer Science; Datasets; Genomes; Genomics; Isotopes; label free; Life Sciences; Mass spectrometry; metabolic models; Metabolism; Microbiology and Parasitology; Mycology; Peptides; Proteins; Proteomes; Proteomics; quantitative proteomics; Saccharomyces; Scientific imaging; semi-absolute quantification; TPA; France; United States > US; semi-absolute quantification; TPA; metabolic models; label free; Saccharomyces; UPS2; quantitative proteomics
• DOI: 10.3390/proteomes10010002
• ISSN: 2227-7382

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.