Multibatch TMT reveals false positives, batch effects and missing values
Open accessAbstract
Multiplexing strategies for large-scale proteomic analyses have become increasingly prevalent, tandem mass tags (TMT) in particular. Here we used a large iPSC proteomic experiment with twenty-four 10-plex TMT batches to evaluate the effect of integrating multiple TMT batches within a single analysis. We identified a significant inflation rate of protein missing values as multiple batches are integrated and show that this pattern is aggravated at the peptide level. We also show that without normalization strategies to address the batch effects, the high precision of quantitation within a single multiplexed TMT batch is not reproduced when data from multiple TMT batches are integrated.Further, the incidence of false positives was studied by using Y chromosome peptides as an internal control. The iPSC lines quantified in this data set were derived from both male and female donors, hence the peptides mapped to the Y chromosome should be absent from female lines. Nonetheless, these Y chromosome-specific peptides were consistently detected in the female channels of all TMT batches. We then used the same Y chromosome specific peptides to quantify the level of ion coisolation as well as the effect of primary and secondary reporter ion interference. These results were used to propose solutions to mitigate the limitations of multi-batch TMT analyses. We confirm that including a common reference line in every batch increases precision by facilitating normalization across the batches and we propose experimental designs that minimize the effect of cross population reporter ion interference.
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It is not uncommon to have 20-50% of the possible peptide values that are not quantified.
“It is not uncommon to have 20–50% of the possible peptide values that are not quantified (Webb-Robertson et al., 2015; Brenes et al., 2019).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Brenes et al., 2019
Multiplexing methods are limited in how many samples can be tagged and analyzed together, and the same peptides are sampled less frequently across batches than proteins are.
“These multiplexing methods are still limited in the number of samples that can be uniquely tagged, combined, and analyzed at once, and while multiple batches of samples can be acquired, the same peptides are less frequently sampled in different batches compared to proteins.(9)”
From Putting humpty dumpty back together again: what does protein quantification mean in bottom-up proteomics? (2022) · cites this paper as [9]
TMT-MS may still yield missing values across multiplexed batches when analyzing large numbers of samples.
“However, despite these achievements in quantification, TMT-MS may still yield missing values across multiplexed batches when analyzing large numbers of samples [80].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [80]
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Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855 · see citing works
ggplot2: Elegant Graphics for Data Analysis
Ginestet, C. E. (2011) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00676_9.x · Cited by 14,484 · see citing works
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447 · see citing works
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785 · see citing works
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 4,691 · see citing works
Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., et al. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400129-mcp200 · Cited by 4,401 · see citing works
2016 update of the PRIDE database and its related tools
Vizcaíno, J. A., Csordás, A., del‐Toro, N., Dianes, J. A., Griss, J., Lavidas, I., Mayer, G., Pérez‐Riverol, Y., Reisinger, F., Ternent, T., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1145 · Cited by 3,466 · see citing works
ProteomeXchange provides globally coordinated proteomics data submission and dissemination
Vizcaíno, J. A., Deutsch, E. W., Wang, R., Csordás, A., Reisinger, F., Ríos, D., Dianes, J. A., Sun, Z., Farrah, T., Bandeira, N., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2839 · Cited by 2,735 · see citing works
Log-normal distributions across the sciences: keys and clues
LIMPERT, E., STAHEL, W. A., & ABBT, M. (2001) · BioScience · doi:10.1641/0006-3568(2001)051[0341:lndats]2.0.co;2 · Cited by 2,621 · see citing works
Thompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., & Hamon, C. (2003) · Analytical Chemistry · doi:10.1021/ac0262560 · Cited by 2,386 · see citing works
McAlister, G. C., Nusinow, D. P., Jedrychowski, M. P., Wühr, M., Huttlin, E. L., Erickson, B. K., Rad, R., Haas, W., & Gygi, S. P. (2014) · Analytical Chemistry · doi:10.1021/ac502040v · Cited by 1,263 · see citing works
The quantitative proteome of a human cell line
Beck, M., Schmidt, A., Malmstroem, J., Claassen, M., Ori, A., Szymborska, A., Herzog, F., Rinner, O., Ellenberg, J., & Aebersold, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.82 · Cited by 816 · see citing works
A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards
Wiśniewski, J. R., Hein, M. Y., Cox, J., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.037309 · Cited by 690 · see citing works
Common genetic variation drives molecular heterogeneity in human iPSCs
Kilpinen, H., Goncalves, A., Leha, A., Afzal, V., Alasoo, K., Ashford, S., Bala, S., Bensaddek, D., Casale, F. P., Culley, O. J., et al. (2017) · Nature · doi:10.1038/nature22403 · Cited by 586 · see citing works
Self-citationLazar, C., Gatto, L., Ferro, M., Bruley, C., & Bürger, T. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00981 · Cited by 532 · see citing works
iTRAQ Underestimation in Simple and Complex Mixtures: “The Good, the Bad and the Ugly”
Ow, S. Y., Salim, M., Noirel, J., Evans, C. A., Rehman, I., & Wright, P. C. (2009) · Journal of Proteome Research · doi:10.1021/pr900634c · Cited by 490 · see citing works
An Optimized Shotgun Strategy for the Rapid Generation of Comprehensive Human Proteomes
Bekker‐Jensen, D. B., Kelstrup, C. D., Batth, T. S., Buch-Larsen, S. C., Haldrup, C., Bramsen, J. B., Sørensen, K. D., Høyer, S., Orntoft, T. F., Andersen, C. L., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2017.05.009 · Cited by 467 · see citing works
Multidimensional proteomics for cell biology
Larance, M., & Lamond, A. I. (2015) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3970 · Cited by 407 · see citing works
Self-citationA Quantitative Spatial Proteomics Analysis of Proteome Turnover in Human Cells
Boisvert, F.-M., Ahmad, Y., Gierliński, M., Charrière, F., Lamont, D., Scott, M., Barton, G., & Lamond, A. I. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.011429 · Cited by 387 · see citing works
Self-citationMeier, F., Geyer, P. E., Winter, S. V., Cox, J., & Mann, M. (2018) · Nature Methods · doi:10.1038/s41592-018-0003-5 · Cited by 341 · see citing works
Plubell, D. L., Wilmarth, P. A., Zhao, Y., Fenton, A. M., Minnier, J., Reddy, A. P., Klimek, J. E., Yang, X., David, L. L., & Pamir, N. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.065524 · Cited by 337 · see citing works
Webb-Robertson, B.-J. M., Wiberg, H. K., Matzke, M. M., Brown, J. N., Wang, J., McDermott, J. E., Smith, R. D., Rodland, K. D., Metz, T. O., Pounds, J. G., et al. (2015) · Journal of Proteome Research · doi:10.1021/pr501138h · Cited by 294 · see citing works
Technical advances in proteomics: new developments in data-independent acquisition
Hu, A. K., Noble, W. S., & Wolf‐Yadlin, A. (2016) · F1000Research · doi:10.12688/f1000research.7042.1 · Cited by 258 · see citing works
The cytotoxic T cell proteome and its shaping by the kinase mTOR
Hukelmann, J. L., Anderson, K. E., Sinclair, L. V., Grzes, K. M., Murillo, A. B., Hawkins, P. T., Stephens, L. R., Lamond, A. I., & Cantrell, D. A. (2016) · Nature Immunology · doi:10.1038/ni.3314 · Cited by 249 · see citing works
Self-citationGlobal quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease
Ping, L., Duong, D. M., Yin, L., Gearing, M., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2018) · Scientific Data · doi:10.1038/sdata.2018.36 · Cited by 221 · see citing works
Proteome-Wide Evaluation of Two Common Protein Quantification Methods
O’Connell, J. D., Paulo, J. A., O’Brien, J. J., & Gygi, S. P. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00016 · Cited by 174 · see citing works
Paulo, J. A., O’Connell, J. D., & Gygi, S. P. (2016) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-016-1434-9 · Cited by 171 · see citing works
Muntel, J., Kirkpatrick, J. M., Bruderer, R., Huang, T. C., Olga Vitek, A., Ori, A., & Reiter, L. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00898 · Cited by 154 · see citing works
Statistical Inference from Multiple iTRAQ Experiments without Using Common Reference Standards
Herbrich, S. M., Cole, R. N., West, K. P., Schulze, K. J., Yager, J. D., Groopman, J. D., Christian, P. S., Wu, L. S. F., O’Meally, R. N., May, D., et al. (2012) · Journal of Proteome Research · doi:10.1021/pr300624g · Cited by 138 · see citing works
The Association of Biomolecular Resource Facilities Proteomics Research Group 2006 Study
Turck, C. W., Falick, A. M., Kowalak, J. A., Lane, W. S., Lilley, K. S., Phinney, B. S., Weintraub, S. T., Witkowska, H. E., & Yates, N. A. (2007) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m700165-mcp200 · Cited by 107 · see citing works
Hennrich, M. L., Romanov, N., Horn, P., Jaeger, S., Eckstein, V., Steeples, V., Ye, F., Ding, X., Poisa-Beiro, L., Lai, M. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06353-4 · Cited by 101 · see citing works
EASI-tag enables accurate multiplexed and interference-free MS2-based proteome quantification
Winter, S. V., Meier, F., Wichmann, C., Cox, J., Mann, M., & Meissner, F. (2018) · Nature Methods · doi:10.1038/s41592-018-0037-8 · Cited by 97 · see citing works
Phosphoproteomic screening identifies physiological substrates of the CDKL5 kinase
Munoz, I. M., Morgan, M. E., Peltier, J., Weiland, F., Gregorczyk, M., Brown, F., Macartney, T., Toth, R. L., Trost, M., & Rouse, J. W. (2018) · The EMBO Journal · doi:10.15252/embj.201899559 · Cited by 84 · see citing works
The role of protein and peptide separation before mass spectrometry analysis in clinical proteomics
Camerini, S., & Mauri, P. (2014) · Journal of Chromatography A · doi:10.1016/j.chroma.2014.12.035 · Cited by 77 · see citing works
Larance, M., Kirkwood, K. J., Tinti, M., Murillo, A. B., Ferguson, M. A. J., & Lamond, A. I. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o115.055467 · Cited by 64 · see citing works
Self-citationIsasa, M., Rose, C. M., Elsasser, S., Navarrete-Perea, J., Paulo, J. A., Finley, D. J., & Gygi, S. P. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00802 · Cited by 59 · see citing works
Proteomic analysis of the response to cell cycle arrests in human myeloid leukemia cells
Ly, T., Endo, A., & Lamond, A. I. (2015) · eLife · doi:10.7554/elife.04534 · Cited by 52 · see citing works
Self-citationiQuantitator: A tool for protein expression inference using iTRAQ
Schwacke, J. H., Hill, E. G., Krug, E. L., Comte‐Walters, S., & Schey, K. L. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-342 · Cited by 52 · see citing works
Reproducible quantitative proteotype data matrices for systems biology
Röst, H. L., Malmström, L., & Aebersold, R. H. (2015) · Molecular Biology of the Cell · doi:10.1091/mbc.e15-07-0507 · Cited by 46 · see citing works
CONSTANd : A Normalization Method for Isobaric Labeled Spectra by Constrained Optimization
Maes, E., Hadiwikarta, W. W., Mertens, I., Baggerman, G., Hooyberghs, J., & Valkenborg, D. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.056911 · Cited by 41 · see citing works
Myers, S. A., Klaeger, S., Satpathy, S., Viner, R., Choi, J. H., Rogers, J. C., Clauser, K. R., Udeshi, N. D., & Carr, S. A. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00611 · Cited by 29 · see citing works
Quantitative Comparisons of Large Numbers of Human Plasma Samples Using TMT10plex Labeling
Liu, P., Beer, L. A., Ky, B., Barnhart, K. T., & Speicher, D. W. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7057-5_22 · Cited by 8 · see citing works
The iPSC proteomic compendiumPreprint
Brenes, A. J., Bensaddek, D., Hukelmann, J. L., Afzal, V., & Lamond, A. I. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/469916 · Cited by 5 · see citing works
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Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation
Brunner, A., Thielert, M., Vasilopoulou, C., Ammar, C., Coscia, F., Mund, A., Hoerning, O. B., Bache, N., Apalategui, A., Lubeck, M., et al. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110798 · Cited by 544 · see citing works
Unbiased spatial proteomics with single-cell resolution in tissues
Mund, A., Brunner, A.-D., & Mann, M. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.05.022 · Cited by 259 · see citing works
Missing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181 · see citing works
“It is not uncommon to have 20–50% of the possible peptide values that are not quantified (WebbRobertson et al., 2015; Brenes et al., 2019).”
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Rayaprolu, S., Higginbotham, L., Bagchi, P., Watson, C. M., Zhang, T., Levey, A. I., Rangaraju, S., & Seyfried, N. T. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-00840-3 · Cited by 118 · see citing works
Hennrich, M. L., Romanov, N., Horn, P., Jaeger, S., Eckstein, V., Steeples, V., Ye, F., Ding, X., Poisa-Beiro, L., Lai, M. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06353-4 · Cited by 101 · see citing works
Plubell, D. L., Käll, L., Webb-Robertson, B.-J., Bramer, L. M., Ives, A., Kelleher, N. L., Smith, L. M., Montine, T. J., Wu, C. C., & MacCoss, M. J. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00894 · Cited by 85 · see citing works
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Buczak, K., Kirkpatrick, J. M., Truckenmueller, F., Santinha, D., Ferreira, L., Roessler, S., Singer, S., Beck, M., & Ori, A. (2020) · Nature Protocols · doi:10.1038/s41596-020-0356-y · Cited by 78 · see citing works
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Betancourt, L. H., Szasz, A. M., Kuras, M., Rodriguez Murillo, J., Sugihara, Y., Pla, I., Horvath, Z., Pawłowski, K., Rezeli, M., Miharada, K., et al. (2019) · Cancers · doi:10.3390/cancers11121981 · Cited by 24 · see citing works
- Authors
- Brenes, A., Hukelmann, J., Bensaddek, D., & Lamond, A. I.
- Affiliations
- University of Dundee (GB)
- Corresponding author
- Lamond, Angus I.
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 18
- Issue
- 10
- Pages
- 1967-1980
- ISSN
- 1535-9476 (ISSN-L)
- PMC
- PMC6773557
- OpenAlex
- W2963597305
- Cited by
- 196
- References
- 43
- Citations by year
- 2026: 17 · 2025: 31 · 2024: 28 · 2023: 37 · 2022: 23 · 2021: 31 · 2020: 26 · 2019: 2 · 2018: 1
- FWCI
- 8.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Molecular & Cellular Proteomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (publishedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
- Funders
- UK Research and Innovation | Medical Research Council (098503/E/12/Z)
- Wellcome Trust (098503/E/12/Z, 073980/Z/03/Z, 105024/Z/14/Z)
- 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 › Advanced Biosensing Techniques and Applications
- Keywords
- tandem mass tags, batch effects, false positives, induced pluripotent stem cells, data normalization, peptide quantification, multiplexed proteomics
- MeSH terms
- Cells, Cultured, Chromatography, Liquid, Female, Humans, Male, Peptides, Reproducibility of Results, Proteomics, Chromosomes, Human, Y, Tandem Mass Spectrometry, Induced Pluripotent Stem Cells
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