A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis
Open accessAbstract
Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage. Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun) and the other supporting hypothesis-driven (targeted) proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis, which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.
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Studies in model organisms have shown that variation in mRNA and protein expression levels are often uncorrelated.
“Studies in model organisms have shown that variation in mRNA and protein expression levels are often uncorrelated (5-8).”
From Impact of regulatory variation from RNA to protein (2015) · cites this paper as [7]
Advances in quantitative proteomics allow genome-wide pQTL mapping in model organisms.
“The advance in quantitative proteomics allows a genome-wide map of pQTL model organisms (Foss et al., 2007; Picotti et al., 2013).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Picotti et al., 2013
Picotti et al. used a nearly complete yeast proteome and genetic variation data to identify strong genetic effects on protein abundance and to demonstrate epistatic interactions affecting protein levels.
“Picotti et al. used a nearly complete map of yeast proteome and genetic variation data to identify strong genetic effects of protein abundance, and to demonstrate epistatic interactions affecting protein levels (Picotti et al., 2013).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Picotti et al., 2013
Recent pQTL analyses using precise MS measurement over many samples revealed complex genetic influence on protein levels in yeast and mouse.
“Using precise MS measurement of peptides over a large number of samples, recent pQTL analyses revealed complex genetic influence on the levels of proteins in yeast (Picotti et al., 2013) and mouse (Holdt et al., 2013).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Picotti et al., 2013
Human biology is closely tied to proteins, and mass spectrometry-based proteomics has linked phenotype to protein-level biology.
“Human biology is tightly linked to proteins, and mass spectrometry-based proteomics has established a strong linkage between phenotype and protein-level biology (1, 2).”
From The Blood Proteoform Atlas: A reference map of proteoforms in human hematopoietic cells (2022) · cites this paper as [1]
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Impact of regulatory variation from RNA to protein
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The RNA-binding proteomes from yeast to man harbour conserved enigmRBPs
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“Using precise MS measurement of peptides over a large number of samples, recent pQTL analyses revealed complex genetic influence on the levels of proteins in yeast (Picotti et al., 2013) and mouse (Holdt et al., 2013).”
BackgroundInfluentialA repository of assays to quantify 10,000 human proteins by SWATH-MS
Rosenberger, G., Koh, C. C., Guo, T., Röst, H. L., Kouvonen, P., Collins, B. C., Heusel, M., Liu, Y., Caron, E., Vichalkovski, A., et al. (2014) · Scientific Data · doi:10.1038/sdata.2014.31 · Cited by 413 · see citing works
Self-citationA High-Confidence Human Plasma Proteome Reference Set with Estimated Concentrations in PeptideAtlas
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Self-citationTargeted Quantitation of Proteins by Mass Spectrometry
Liebler, D. C., & Zimmerman, L. J. (2013) · Biochemistry · doi:10.1021/bi400110b · Cited by 362 · see citing works
A complete tool set for molecular QTL discovery and analysis
Delaneau, O., Ongen, H., Brown, A. A., Fort, A., Panousis, N. I., & Dermitzakis, E. T. (2017) · Nature Communications · doi:10.1038/ncomms15452 · Cited by 362 · see citing works
Quantitative variability of 342 plasma proteins in a human twin population
Liu, Y., Buil, A., Collins, B. C., Gillet, L. C., Blum, L. C., Cheng, L., Vitek, O., Mouritsen, J., Lachance, G., Spector, T. D., et al. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145728 · Cited by 328 · see citing works
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Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome
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Self-citationProteomic and interactomic insights into the molecular basis of cell functional diversity
Bludau, I., & Aebersold, R. (2020) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-0231-2 · Cited by 297 · see citing works
Self-citationBuilding high-quality assay libraries for targeted analysis of SWATH MS data
Schubert, O. T., Gillet, L. C. J., Collins, B. C., Navarro, P., Rosenberger, G., Wolski, W., Lam, H., Amodei, D., Mallick, P., MacLean, B., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2015.015 · Cited by 309 · see citing works
Self-citationInverse statistical problems: from the inverse Ising problem to data science
Nguyen, H. C., Zecchina, R., & Berg, J. (2017) · Advances In Physics · doi:10.1080/00018732.2017.1341604 · Cited by 283 · see citing works
Yeast Systems Biology: Model Organism and Cell Factory
Nielsen, J. (2019) · Biotechnology Journal · doi:10.1002/biot.201800421 · Cited by 283 · see citing works
“Omics analysis was pioneered using yeast as a model organism, with the first transcriptome analysis performed in this organism using so-called DNA microarrays (56), followed by many other types of omics analysis, including proteomics, wherein studies with yeast have often been used as a bench mark for new methods (57).”
Quantitative proteomics: challenges and opportunities in basic and applied research
Schubert, O. T., Röst, H. L., Collins, B. C., Rosenberger, G., & Aebersold, R. H. (2017) · Nature Protocols · doi:10.1038/nprot.2017.040 · Cited by 276 · see citing works
Self-citationTrans-Omics: How To Reconstruct Biochemical Networks Across Multiple ‘Omic’ Layers
Yugi, K., Kubota, H., Hatano, A., & Kuroda, S. (2016) · Trends in Biotechnology · doi:10.1016/j.tibtech.2015.12.013 · Cited by 258 · see citing works
“Quantitative trait locus (QTL) analysis, an alternative method for disease-related gene discovery, enables us to identify the genomic regions that affect quantitative phenotypes, such as the amount of transcripts, proteins, and metabolites [115–120].”
MethodBuilding ProteomeTools based on a complete synthetic human proteome
Zolg, D. P., Wilhelm, M., Schnatbaum, K., Zerweck, J., Knaute, T., Delanghe, B., Bailey, D. J., Gessulat, S., Ehrlich, H., Weininger, M., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4153 · Cited by 237 · see citing works
“However, in proteome research the generation or use of such standards is only beginning to be implemented systematically 2–4 .”
BackgroundSelf-citationMultilayered Genetic and Omics Dissection of Mitochondrial Activity in a Mouse Reference Population
Wu, Y., Williams, E. G., Dubuis, S., Mottis, A., Jovaisaite, V., Houten, S. M., Argmann, C. A., Faridi, P., Wolski, W., Kutalik, Z., et al. (2014) · Cell · doi:10.1016/j.cell.2014.07.039 · Cited by 244 · see citing works
“Recent studies in yeast have shown that the proteins and transcripts of genes are typically controlled by different, distinctmechanisms (Albert et al., 2014; Picotti et al., 2013).”
Self-citationBug mapping and fitness testing of chemically synthesized chromosome X
Wu, Y., Li, B., Zhao, M., MITCHELL, L. A., Xie, Z., Lin, Q.-H., Wang, X., Xiao, W., Wāng, Y., Zhou, X., et al. (2017) · Science · doi:10.1126/science.aaf4706 · Cited by 239 · see citing works
Lombard‐Banek, C., Moody, S. A., & Nemes, P. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201510411 · Cited by 214 · see citing works
Paulo, J. A., O’Connell, J. D., Everley, R. A., O’Brien, J. J., Gygi, M. A., & Gygi, S. P. (2016) · Journal of Proteomics · doi:10.1016/j.jprot.2016.07.005 · Cited by 214 · see citing works
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Self-citationRosenberger, G., Bludau, I., Schmitt, U., Heusel, M., Hunter, C. L., Liu, Y., MacCoss, M. J., MacLean, B. X., Nesvizhskii, A. I., Pedrioli, P. G. A., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4398 · Cited by 203 · see citing works
Self-citationOne library to make them all: streamlining the creation of yeast libraries via a SWAp-Tag strategy
Yofe, I., Weill, U., Meurer, M., Chuartzman, S. G., Zalckvar, E., Goldman, O., Ben‐Dor, S., Schütze, C., Wiedemann, N., Knop, M., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3795 · Cited by 202 · see citing works
Genetics of trans-regulatory variation in gene expression
Albert, F. W., Bloom, J. S., Siegel, J. J., Day, L., & Kruglyak, L. (2018) · eLife · doi:10.7554/elife.35471 · Cited by 180 · see citing works
Modules, networks and systems medicine for understanding disease and aiding diagnosis
Gustafsson, M., Nestor, C. E., Zhang, H., Barabási, A.-L., Baranzini, S., Brunak, S., Chung, K. F., Federoff, H. J., Gavin, A.-C., Meehan, R. R., et al. (2014) · Genome Medicine · doi:10.1186/s13073-014-0082-6 · Cited by 194 · see citing works
“However, recent technological advances indicate that targeted proteomics may partly address these limitations and render the analysis of predetermined sets of proteins over large numbers of samples [58,59].”
BackgroundSelf-citationData-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology
Krásný, L., & Huang, P. H. (2020) · Molecular Omics · doi:10.1039/d0mo00072h · Cited by 189 · see citing works
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Applications of targeted proteomics in systems biology and translational medicine
Ebhardt, H. A., Root, A., Sander, C., & Aebersold, R. (2015) · PROTEOMICS · doi:10.1002/pmic.201500004 · Cited by 184 · see citing works
“SRM-MS has been applied to quantify protein levels of liver tissue across 40 strains of BDX mouse [31] and quantitative trait analysis (QTL) of 78 Saccharomyces cerevisiae strains [32].”
MethodSelf-citationGenome-wide SWAp-tag yeast libraries for proteome exploration
Weill, U., Yofe, I., Sass, E., Stynen, B., Davidi, D., Natarajan, J., Ben-Menachem, R., Avihou, Z., Goldman, O., Harpaz, N., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0044-9 · Cited by 167 · see citing works
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“To eliminate the need for tedious SRM assay generation before each experiment, a strategy based on high-throughput synthesis of peptides representing every protein of a proteome has recently been presented for the model organism yeast (Picotti et al., 2013).”
MethodSelf-citationSelevsek, N., Chang, C.-Y., Gillet, L. C. J., Navarro, P., Bernhardt, O. M., Reiter, L., Cheng, L., Vitek, O., & Aebersold, R. H. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.035550 · Cited by 174 · see citing works
“In combination, these developments have supported the creation of mass-spectrometric maps of entire proteomes of selected species including Streptococcus pyogenes, Mycobacterium tuberculosis and Saccharomyces cerevisae [5, 17-19] and the robust use of these resources to quantify specific protein sets across multiple b…”
BackgroundSelf-citationTRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics
Röst, H. L., Liu, Y., D’Agostino, G. A., Zanella, M., Navarro, P., Rosenberger, G., Collins, B. C., Gillet, L. C. J., Testa, G., Malmström, L., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3954 · Cited by 163 · see citing works
Self-citationProtein Phase Separation as a Stress Survival StrategyPreprint
Franzmann, T. M., & Alberti, S. (2019) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a034058 · Cited by 166 · see citing works
“Moreover,many proteins aggregate when, for example, yeast cells are exposed to physiological stress conditions, such as glucose depletion or mild heat stress (Narayanaswamy et al. 2009; Olzscha et al. 2011; Picotti et al. 2013; O’Connell et al. 2014; Wallace et al. 2015).”
BackgroundSelf-citationBioinformatic analysis of proteomics data
Schmidt, A., Forné, I., & Imhof, A. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-s2-s3 · Cited by 170 · see citing works
“The multiplexing capability have been used to quantify several hundreds of proteins in a broad dynamic range, down to proteins present at very low copy number in the cell (~50 copies/cell) in the background of the whole range of protein concentration in eukaryotic cells [18,19].”
BackgroundSelf-citationThe Blood Proteoform Atlas: A reference map of proteoforms in human hematopoietic cells
Melani, R. D., Gerbasi, V. R., Anderson, L. C., Sikora, J. W., Toby, T. K., Hutton, J. E., Butcher, D. S., Negrão, F., Seckler, H. S., Srzentić, K., et al. (2022) · Science · doi:10.1126/science.aaz5284 · Cited by 140 · see citing works
Quantifying Ubiquitin Signaling
Ordureau, A., Münch, C., & Harper, J. W. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.02.020 · Cited by 147 · see citing works
“Historically, MRM has been used to quantify total protein abundance (Picotti et al., 2013).”
Genetics of single-cell protein abundance variation in large yeast populations
Albert, F. W., Treusch, S., Shockley, A. H., Bloom, J. S., & Kruglyak, L. (2014) · Nature · doi:10.1038/nature12904 · Cited by 141 · see citing works
Messner, C. B., Demichev, V., Wang, Z., Hartl, J., Kustatscher, G., Mülleder, M., & Ralser, M. (2022) · PROTEOMICS · doi:10.1002/pmic.202200013 · Cited by 133 · see citing works
Mohammed, Y., Domański, D., Jackson, A., SMITH, D. S. H., Deelder, A. M., Palmblad, M., & Borchers, C. H. (2014) · Journal of Proteomics · doi:10.1016/j.jprot.2014.04.018 · Cited by 130 · see citing works
Impact of the Identification Strategy on the Reproducibility of the DDA and DIA Results
Fernández-Costa, C., Martínez‐Bartolomé, S., McClatchy, D. B., Saviola, A. J., Yu, N., & Yates, J. R. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00153 · Cited by 118 · see citing works
Mass spectrometric protein maps for biomarker discovery and clinical research
Liu, Y., Hüttenhain, R., Collins, B. C., & Aebersold, R. H. (2013) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.2013.845089 · Cited by 123 · see citing works
Self-citationApplication of targeted mass spectrometry in bottom-up proteomics for systems biology research
Manes, N. P., & Nita‐Lazar, A. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.02.008 · Cited by 124 · see citing works
A deep auto-encoder model for gene expression prediction
Xie, R., Wen, J., Quitadamo, A., Cheng, J. J., & Shi, X. M. (2017) · BMC Genomics · doi:10.1186/s12864-017-4226-0 · Cited by 120 · see citing works
“For example, protein quantifications [80, 81], metabolite screening [82, 83] and chromatin accessibility data [84, 85] are available for yeast.”
BackgroundHuffman, R. G., Leduc, A., Wichmann, C., Gioia, M. D., Borriello, F., Specht, H., Derks, J., Khan, S., Khoury, L., Emmott, E., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-01830-1 · Cited by 107 · see citing works
A random forest approach to capture genetic effects in the presence of population structure
Stephan, J., Stegle, O., & Beyer, A. (2015) · Nature Communications · doi:10.1038/ncomms8432 · Cited by 107 · see citing works
“To this end, we note that permutation schemes have been successfully combined with RFs for use in genetics 30,56 .”
BackgroundMethodInfluentialSelf-citationRund, S. S. C., Bonar, N. A., Champion, M. M., Ghazi, J. P., Houk, C. M., Leming, M. T., Syed, Z., & Duffield, G. E. (2013) · Scientific Reports · doi:10.1038/srep02494 · Cited by 100 · see citing works
A Computational Tool to Detect and Avoid Redundancy in Selected Reaction Monitoring
Röst, H. L., Malmström, L., & Aebersold, R. H. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.013045 · Cited by 98 · see citing works
Self-citationParker, S. J., Röst, H. L., Rosenberger, G., Collins, B. C., Malmström, L., Amodei, D., Venkatraman, V., Raedschelders, K., Eyk, J. E. V., & Aebersold, R. H. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o114.042267 · Cited by 93 · see citing works
Self-citationDirect and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring
Lawless, C., Holman, S. W., Brownridge, P., Lanthaler, K., Harman, V. M., Watkins, R. H., Hammond, D. E., Miller, R. L., Sims, P. F. G., Grant, C. M., et al. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.054288 · Cited by 87 · see citing works
“2 B ), which is comparable to or better than similar SRM-based studies (6, 22).”
A large-scale targeted proteomics assay resource based on an in vitro human proteome
Matsumoto, M., Matsuzaki, F., Oshikawa, K., Goshima, N., Mori, M., Kawamura, Y., Ogawa, K., Fukuda, E., Nakatsumi, H., Natsume, T., et al. (2016) · Nature Methods · doi:10.1038/nmeth.4116 · Cited by 91 · see citing works
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Why measure enzyme activities in the era of systems biology?
Stitt, M., & Gibon, Y. (2013) · Trends in Plant Science · doi:10.1016/j.tplants.2013.11.003 · Cited by 87 · see citing works
An Untargeted Metabolomic Workflow to Improve Structural Characterization of Metabolites
Nikolskiy, I., Mahieu, N. G., Chen, Y., Tautenhahn, R., & Patti, G. J. (2013) · Analytical Chemistry · doi:10.1021/ac400751j · Cited by 85 · see citing works
Organ-specific proteome analysis for identification of abiotic stress response mechanism in crop
Komatsu, S., & Hossain, Z. (2013) · Frontiers in Plant Science · doi:10.3389/fpls.2013.00071 · Cited by 85 · see citing works
“Very recently, Picotti et al. (2013) exploited a strategy based on high-throughput peptide synthesis and MS to generate an almost complete reference map (97% of the genome-predicted proteins) of the S. cerevisiae proteome.”
BackgroundThe emergence of proteome-wide technologies: systematic analysis of proteins comes of age
Breker, M., & Schuldiner, M. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3821 · Cited by 82 · see citing works
Simicevic, J., Schmid, A. W., Gilardoni, P. A., Zoller, B., Raghav, S. K., Krier, I., Gubelmann, C., Lisacek, F., Naef, F., Moniatte, M., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2441 · Cited by 83 · see citing works
Breker, M., Gymrek, M., Moldavski, O., & Schuldiner, M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt933 · Cited by 83 · see citing works
“Comparison with transcriptome data (14) demonstrated that up to 60% of proteomic changes could not be predicted from the dynamics of their transcripts, therefore, emphasizing the importance of studying proteins directly (20,21).”
BackgroundThe future of whole-cell modeling
Macklin, D. N., Ruggero, N. A., & Covert, M. W. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.01.012 · Cited by 83 · see citing works
Bianco, L., & Perrotta, G. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms16035803 · Cited by 78 · see citing works
“strategy and technologies have been applied to yeast and mammalian cells [81,82], showing a sharp delay of proteomic research on filamentous fungi when compared to other model systems [79,83,84].”
MethodCurrent trends in quantitative proteomics – an update
Li, H., Han, J., Pan, J., Liu, T., Parker, C. E., & Borchers, C. H. (2017) · Journal of Mass Spectrometry · doi:10.1002/jms.3932 · Cited by 74 · see citing works
The quest for tolerant varieties: the importance of integrating “omics” techniques to phenotyping
Zivy, M., Wienkoop, S., Renaut, J., Pinheiro, C., Goulas, E., & Carpentier, S. (2015) · Frontiers in Plant Science · doi:10.3389/fpls.2015.00448 · Cited by 73 · see citing works
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Tandem mass spectral libraries of peptides and their roles in proteomics research
Shao, W., & Lam, H. (2016) · Mass Spectrometry Reviews · doi:10.1002/mas.21512 · Cited by 69 · see citing works
“Lastly, the key limitation of spectral library searching, that it can only identify previously observed peptides, can be partially overcome by extending libraries with spectra from synthetic peptides (Picotti et al., 2013) and various spectrum prediction methods.”
BackgroundMethodSelf-citationEmerging mass spectrometry techniques for the direct analysis of microbial colonies
Fang, J., & Dorrestein, P. C. (2014) · Current Opinion in Microbiology · doi:10.1016/j.mib.2014.06.014 · Cited by 69 · see citing works
Huang, R., Chen, Z., He, L., He, N., Xi, Z., Li, Z., Deng, Y., & Zeng, X. (2017) · Theranostics · doi:10.7150/thno.20797 · Cited by 68 · see citing works
“One of the applications of the shotgun approach is to generate spectral libraries for mass spectrometric reference maps [41, 42].”
BackgroundModified MuDPIT Separation Identified 4488 Proteins in a System-wide Analysis of Quiescence in Yeast
Webb, K. J., Xu, T., Park, S. K., & Yates, J. R. (2013) · Journal of Proteome Research · doi:10.1021/pr400027m · Cited by 67 · see citing works
Bludau, I., Heusel, M., Frank, M., Rosenberger, G. A., Hafen, R., Banaei‐Esfahani, A., van Drogen, A., Collins, B. C., Gstaiger, M., & Aebersold, R. (2019) · Nature Protocols · doi:10.1038/s41596-020-0332-6 · Cited by 65 · see citing works
Self-citationKusebauch, U., Deutsch, E. W., Campbell, D. S., Sun, Z., Farrah, T., & Moritz, R. L. (2014) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1325s46 · Cited by 64 · see citing works
“…on which the data were acquired, the number of runs performed, the number of proteins and peptides measured, the presence of isotope-labeled peptides to quantify endogenous peptides, the presence of mProphet (Reiter et al., 2011) scores, and the publication in which the study is described.”
BackgroundMethodInfluentialSelf-citationTargeted Proteomics for Studying Pathogenic Bacteria
Saleh, S., Staes, A., Deborggraeve, S., & Gevaert, K. (2019) · PROTEOMICS · doi:10.1002/pmic.201800435 · Cited by 63 · see citing works
Bottom-Up Proteomics (2013–2015): Keeping up in the Era of Systems Biology
Mayne, J., Ning, Z., Zhang, X., Starr, A. E., Chen, R., Deeke, S. A., Chiang, C., Xu, B., Wen, M., Cheng, K., et al. (2015) · Analytical Chemistry · doi:10.1021/acs.analchem.5b04230 · Cited by 63 · see citing works
Champion, M. M., Williams, E. A., Pinapati, R. S., & Champion, P. A. D. (2014) · Journal of Proteome Research · doi:10.1021/pr500484w · Cited by 59 · see citing works
Paulo, J. A., O’Connell, J. D., Gaun, A., & Gygi, S. P. (2015) · Molecular Biology of the Cell · doi:10.1091/mbc.e15-07-0499 · Cited by 60 · see citing works
“cerevisiae via mass spectrometry–based techniques (King et al., 2006; Nagaraj et al., 2012; Picotti et al., 2013; Webb et al., 2013; Hebert et al., 2014; Paulo and Gygi, 2014).”
ResultFaria, S. S., Morris, C. F. M., Silva, A. R., da Fonseca, M. P., Forget, P., Castro, M. S., & Fontes, W. (2017) · Frontiers in Oncology · doi:10.3389/fonc.2017.00013 · Cited by 60 · see citing works
“A classical targeted proteomics assay requires specific MS instruments like the triple quadrupole analyzer, where the first and third quadrupoles work as m/z filters, while the second quadrupole acts as a collision cell (146).”
BackgroundMethodChoi, S. B., Zamarbide, M., Manzini, M. C., & Nemes, P. (2016) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-016-1532-8 · Cited by 58 · see citing works
Quantitative isoform-profiling of highly diversified recognition molecules
Schreiner, D., Simicevic, J., Ahrné, E., Schmidt, A., & Scheiffele, P. (2015) · eLife · doi:10.7554/elife.07794 · Cited by 56 · see citing works
Self-citationWhat are the 100 most cited fungal genera?
Bhunjun, C. S., Chen, Y. J., Phukhamsakda, C., Boekhout, T., Groenewald, J. Z., McKenzi, E. H. C., Francisco, E. C., Frisvad, J. C., Groenewald, M., Hurdeal, V. G., et al. (2024) · Studies in Mycology · doi:10.3114/sim.2024.108.01 · Cited by 57 · see citing works
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Bauer, M., Ahrné, E., Baron, A. P., Glatter, T., Fava, L. L., Santamaría, A., Nigg, E. A., & Schmidt, A. (2014) · Journal of Proteome Research · doi:10.1021/pr500860c · Cited by 56 · see citing works
Self-citationHe, F., Arce, A. L., Schmitz, G., Koornneef, M., Novikova, P. Y., Beyer, A., & de Meaux, J. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw096 · Cited by 54 · see citing works
Self-citationSardi, M. I., Paithane, V., Place, M., Robinson, D., Hose, J., Wohlbach, D. J., & Gasch, A. P. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007217 · Cited by 56 · see citing works
“cataloged natural differences across strains, in transcript abundance [37, 45, 46], 119 protein abundance [47-49], metabolism [50-52], and growth in various 120 environments [32, 36, 37, 42, 52-54].”
BackgroundMulti-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers
Guo, T., Li, L., Zhong, Q., Rupp, N. J., Charmpi, K., Wong, C. E., Wagner, U., Rueschoff, J. H., Jochum, W., Fankhauser, C. D., et al. (2018) · Life Science Alliance · doi:10.26508/lsa.201800042 · Cited by 55 · see citing works
Self-citationTargeted proteomics reveals compositional dynamics of 60S pre‐ribosomes after nuclear export
Altvater, M., Chang, Y., Melnik, A., Occhipinti, L., Schütz, S., Rothenbusch, U., Picotti, P., & Panse, V. G. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.63 · Cited by 55 · see citing works
Self-citationEvolution of Gene Regulation during Transcription and Translation
Wang, Z., Sun, X.-P., Zhao, Y., Guo, X., Jiang, H.-F., Li, H.-Y., & Gu, Z. (2015) · Genome Biology and Evolution · doi:10.1093/gbe/evv059 · Cited by 53 · see citing works
“…than Transcription during Evolution To illustrate the relationship between transcriptome and translatome with respect to the protein product, we first explored the correlation of our transcriptome and translatome data with the whole-cell proteome data in S. cerevisiae (Picotti et al. 2013).”
BackgroundUnderstanding the molecular diversity of synapses
van Oostrum, M., & Schuman, E. M. (2025) · Nature Reviews Neuroscience · doi:10.1038/s41583-024-00888-w · Cited by 49 · see citing works
Zhang, C., Qi, Y., Ma, H.-X., Li, W., Dai, L., & Xiao, D. (2015) · Journal of Industrial Microbiology & Biotechnology · doi:10.1007/s10295-015-1583-z · Cited by 54 · see citing works
“[15] mapped almost entirely the proteome of S.”
BackgroundMass spectrometry-based proteomic quest for diabetes biomarkers
Shao, S., Guo, T., & Aebersold, R. H. (2014) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2014.12.012 · Cited by 54 · see citing works
“The S/MRM assay (reference map) is defined as the collection of fragment ion spectra of peptides corresponding to predicted protein sequences based on the genome [70], containing all essential coordinates of informative peptides.”
BackgroundSelf-citationProtein degradation corrects for imbalanced subunit stoichiometry in OST complex assembly
Mueller, S., Wåhlander, Å., Selevsek, N., Otto, C., Ngwa, E. M., Poljak, K., Frey, A. D., Aebi, M., & Gauss, R. (2015) · Molecular Biology of the Cell · doi:10.1091/mbc.e15-03-0168 · Cited by 54 · see citing works
- Authors
- Picotti, P., Clément-Ziza, M., Lam, H., Campbell, D. S., Schmidt, A., Deutsch, E. W., Röst, H., Sun, Z., Rinner, O., Reiter, L., Shen, Q., Michaelson, J. J., Frei, A., Alberti, S., Kusebauch, U., Wollscheid, B., Moritz, R. L., Beyer, A., & Aebersold, R.
- Affiliations
- ETH Zurich (CH)
- Hong Kong University of Science and Technology (HK)
- Institute for Systems Biology (US)
- University of Basel (CH)
- Chinese Academy of Sciences (CN)
- Biotechnologisches Zentrum (DE)
- Max Planck Institute of Molecular Cell Biology and Genetics (DE)
- University of Zurich (CH)
- Corresponding author
- Picotti, Paola
- Beyer, Andreas
- Aebersold, Ruedi
- ORCID
- Venue
- Nature
- Published
- 2013-01-20
- Volume
- 494
- Issue
- 7436
- Pages
- 266-270
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3951219
- OpenAlex
- W2065096062
- Cited by
- 318
- References
- 73
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 7 · 2023: 10 · 2022: 18 · 2021: 14 · 2020: 10 · 2019: 30 · 2018: 24 · 2017: 44 · 2016: 31 · 2015: 44 · 2014: 40 · 2013: 31 · 2012: 2
- FWCI
- 21.82
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- Université du Luxembourg
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (133670, 107679)
- National Institutes of Health (n01hv28179-12-0-0)
- National Institute of Standards and Technology
- National Heart, Lung, and Blood Institute (N01 HV028179, N01-HV-28179)
- National Human Genome Research Institute (RC2HG005805)
- National Institute of General Medical Sciences (P50GM076547)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Keywords
- proteome reference map, protein abundance, shotgun proteomics, targeted proteomics, genetic polymorphism, protein stoichiometry, selection pressure, yeast strain
- MeSH terms
- Polymorphism, Genetic, Reference Values, Saccharomyces cerevisiae, Selection, Genetic, Mass Spectrometry, Peptide Library, Proteome, Saccharomyces cerevisiae Proteins, Quantitative Trait Loci, Proteomics
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