Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance
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,...
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Published in | Proteomes Vol. 10; no. 1; p. 2 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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07.01.2022
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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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AbstractList | 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. |
Author | Blein-Nicolas, Mélisande Henry, Céline Millán-Oropeza, Aarón Zivy, Michel Monnet, Véronique |
AuthorAffiliation | 1 PAPPSO, Micalis Institute, INRAE, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France; veronique.monnet@inrae.fr (V.M.); celine.henry@inrae.fr (C.H.) 2 PAPPSO, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190 Gif-sur-Yvette, France; melisande.blein-nicolas@universite-paris-saclay.fr (M.B.-N.); michel.zivy@inrae.fr (M.Z.) |
AuthorAffiliation_xml | – name: 1 PAPPSO, Micalis Institute, INRAE, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France; veronique.monnet@inrae.fr (V.M.); celine.henry@inrae.fr (C.H.) – name: 2 PAPPSO, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190 Gif-sur-Yvette, France; melisande.blein-nicolas@universite-paris-saclay.fr (M.B.-N.); michel.zivy@inrae.fr (M.Z.) |
Author_xml | – sequence: 1 givenname: Aarón orcidid: 0000-0001-9740-8683 surname: Millán-Oropeza fullname: Millán-Oropeza, Aarón organization: PAPPSO, Micalis Institute, INRAE, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France – sequence: 2 givenname: Mélisande surname: Blein-Nicolas fullname: Blein-Nicolas, Mélisande organization: PAPPSO, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190 Gif-sur-Yvette, France – sequence: 3 givenname: Véronique surname: Monnet fullname: Monnet, Véronique organization: PAPPSO, Micalis Institute, INRAE, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France – sequence: 4 givenname: Michel surname: Zivy fullname: Zivy, Michel organization: PAPPSO, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190 Gif-sur-Yvette, France – sequence: 5 givenname: Céline surname: Henry fullname: Henry, Céline organization: PAPPSO, Micalis Institute, INRAE, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France |
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Keywords | semi-absolute quantification TPA metabolic models label free Saccharomyces UPS2 quantitative proteomics |
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SubjectTerms | 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 |
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Title | Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance |
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