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Streamlined Tandem Mass Tag (SL-TMT) Protocol: An Efficient Strategy for Quantitative (Phospho)proteome Profiling Using Tandem Mass Tag-Synchronous Precursor Selection-MS3
Open accessAbstract
Mass spectrometry (MS) coupled toisobaric labeling has developed rapidly into a powerful strategy for high-throughput protein quantification. Sample multiplexing and exceptional sensitivity allow for the quantification of tens of thousands of peptides and, by inference, thousands of proteins from multiple samples in a single MS experiment. Accurate quantification demands a consistent and robust sample-preparation strategy. Here, we present a detailed workflow for SPS-MS3-based quantitative abundance profiling of tandem mass tag (TMT)-labeled proteins and phosphopeptides that we have named the streamlined (SL)-TMT protocol. We describe a universally applicable strategy that requires minimal individual sample processing and permits the seamless addition of a phosphopeptide enrichment step ("mini-phos") with little deviation from the deep proteome analysis. To showcase our workflow, we profile the proteome of wild-type Saccharomyces cerevisiae yeast grown with either glucose or pyruvate as the carbon source. Here, we have established a streamlined TMT protocol that enables deep proteome and medium-scale phosphoproteome analysis.
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
The Perseus computational platform for comprehensive analysis of (prote)omics data
Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 8,803
Universal sample preparation method for proteome analysis
Wiśniewski, J. R., Zougman, A., Nagaraj, N., & Mann, M. (2009) · Nature Methods · doi:10.1038/nmeth.1322 · Cited by 8,352
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
A Multiple Comparison Procedure for Comparing Several Treatments with a Control
Dunnett, C. W. (1955) · Journal of the American Statistical Association · doi:10.1080/01621459.1955.10501294 · Cited by 6,094
The MaxQuant computational platform for mass spectrometry-based shotgun proteomics
Tyanova, S., Temu, T., & Cox, J. (2016) · Nature Protocols · doi:10.1038/nprot.2016.136 · Cited by 4,797
Elias, J. E., & Gygi, S. P. (2007) · Nature Methods · doi:10.1038/nmeth1019 · Cited by 3,925
Self-citationPhosphoSitePlus, 2014: mutations, PTMs and recalibrations
Hornbeck, P. V., Zhang, B., Murray, B., Kornhauser, J. M., Latham, V., & Skrzypek, E. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1267 · Cited by 3,411
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Metadata
- Authors
- Navarrete-Perea, J., Yu, Q., Gygi, S. P., & Paulo, J. A.
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-05-07
- Volume
- 17
- Issue
- 6
- Pages
- 2226-2236
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- OpenAlex
- W2799836896
- Cited by
- 339
- References
- 61
- FWCI
- 11.72
- Type
- journal article
- Language
- en
- Open access
- green
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- Navarrete-Perea, J., Yu, Q., Gygi, S. P., & Paulo, J. A. (2018). Streamlined Tandem Mass Tag (SL-TMT) Protocol: An Efficient Strategy for Quantitative (Phospho)proteome Profiling Using Tandem Mass Tag-Synchronous Precursor Selection-MS3. Journal of Proteome Research. https://doi.org/10.1021/acs.jproteome.8b00217
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