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Cited by 28Advanced Proteomics Techniques and ApplicationsBioinformatics and Genomic NetworksAdvanced Biosensing Techniques and Applications

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

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Millán-Oropeza, Aarón, Blein-Nicolas, Mélisande, Monnet, Véronique, Zivy, Michel, Henry, Céline·2022·Proteomes
DOI 10.3390/proteomes10010002PMC PMC8788469

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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.

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Figure 1 (A) Sample preparation and (B) the bioinformatic approaches used to determine absolute protein abundances.
Figure 1 (A) Sample preparation and (B) the bioinformatic approaches used to determine absolute protein abundances.CC BY · PubMed Central
Figure 2 Coefficient of variation (CV) among replicates of the quantification methods (median values; n = 6 samples). Median values are indicated above each boxplot.
Figure 2 Coefficient of variation (CV) among replicates of the quantification methods (median values; n = 6 samples). Median values are indicated above each boxplot.CC BY · PubMed Central
Figure 3 Absolute error among samples for the different quantification methods as obtained by bootstrapping the data for the spiked UPS2 proteins. Results for the two transformation strategies: the UPS2-based approach (A) and TPA (B). The points are displayed as a function of the different UPS2 protein concentrations (fmol). The inner marks indicate median absolute error (n = 144).
Figure 3 Absolute error among samples for the different quantification methods as obtained by bootstrapping the data for the spiked UPS2 proteins. Results for the two transformation strategies: the UPS2-based approach (A) and TPA (B). The points are displayed as a function of the different UPS2 protein concentrations (fmol). The inner marks indicate median absolute error (n = 144).CC BY · PubMed Central
Figure 4 (A) Workflows used for validation. For TPA and the UPS2-based strategy, i refers to a given protein from a given LC-MS/MS sample k. For the standard of reference obtained using the UPS2-based strategy, m represents the slope and a represents the y-intercept. (B) Comparison of the different semi-absolute quantification techniques using the absolute error of the estimated abundance of purified proteins at known concentrations. Results of the t-test comparing the NSAF (UPS2) and PAI (UPS2) methods. The UPS2-based strategy is indicated in the pink boxes. TPA is indicated in the blue boxes.
Figure 4 (A) Workflows used for validation. For TPA and the UPS2-based strategy, i refers to a given protein from a given LC-MS/MS sample k. For the standard of reference obtained using the UPS2-based strategy, m represents the slope and a represents the y-intercept. (B) Comparison of the different semi-absolute quantification techniques using the absolute error of the estimated abundance of purified proteins at known concentrations. Results of the t-test comparing the NSAF (UPS2) and PAI (UPS2) methods. The UPS2-based strategy is indicated in the pink boxes. TPA is indicated in the blue boxes.CC BY · PubMed Central

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Citations

Cites

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Metadata

Authors
Millán-Oropeza, A., Blein-Nicolas, M., Monnet, V., Zivy, M., & Henry, C.
Affiliations
  • AgroParisTech (FR)
  • PAPPSO, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
Millán-Oropeza, Aarón
ORCID
Venue
Proteomes
Publisher
MDPI AG
Published
2022-01-07
Volume
10
Issue
1
Pages
2
ISSN
2227-7382 (ISSN-L)
DOI
10.3390/proteomes10010002
PMC
PMC8788469
OpenAlex
W4226251097
Cited by
28
References
4
Citations by year
2026: 5 · 2025: 7 · 2024: 8 · 2023: 6 · 2022: 2
FWCI
2.07
Type
journal article
Language
en
Open access
Licence
Locations
  • Proteomes (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
European Union’s Horizon 2020 research programme and the CHASSY innovation program (720824)
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
Keywords
absolute protein quantification, label-free quantification, spectral counting, extracted ion chromatograms, protein abundance, Saccharomyces cerevisiae, proteomics, genome-scale metabolic models

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