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Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics
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Abstract
Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.
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“Contaminant libraries were used to exclude background signals67.”
From Investigating the neuronal role of the proteasomal ATPase subunit gene PSMC5 in neurodevelopmental proteasomopathies (2025) · cites this paper as [67]
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Cites
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 4,858
A cross-platform toolkit for mass spectrometry and proteomics
Chambers, M., MacLean, B., Burke, R., Amodei, D., Ruderman, D., Neumann, S., Gatto, L., Fischer, B., Pratt, B. S., Egertson, J. D., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2377 · Cited by 4,189
Elias, J. E., & Gygi, S. P. (2007) · Nature Methods · doi:10.1038/nmeth1019 · Cited by 3,925
DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S., & Ralser, M. (2019) · Nature Methods · doi:10.1038/s41592-019-0638-x · Cited by 3,452
MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics
Kong, A. T., Leprevost, F. D. V., Avtonomov, D. M., Mellacheruvu, D., & Nesvizhskii, A. I. (2017) · Nature Methods · doi:10.1038/nmeth.4256 · Cited by 2,406
D’Hulst, G., Soro-Arnaiz, I., Masschelein, E., Veys, K., Fitzgerald, G., Smeuninx, B., Kim, S., Deldicque, L., Blaauw, B., Carmeliet, P., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13889-6 · Cited by 2,310
The CRAPome: a contaminant repository for affinity purification–mass spectrometry data
Mellacheruvu, D., Wright, Z., Couzens, A. L., Lambert, J.-P., St-Denis, N. A., Li, T., Miteva, Y. V., Hauri, S., Sardiu, M. E., Low, T. Y., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2557 · Cited by 1,686
Bruderer, R., Bernhardt, O. M., Gandhi, T., Miladinović, S. M., Cheng, L.-Y., Messner, S., Ehrenberger, T., Zanotelli, V., Butscheid, Y., Escher, C., et al. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.044305 · Cited by 1,238
The minimum information about a proteomics experiment (MIAPE)
Taylor, C. F., Paton, N. W., Lilley, K. S., Binz, P., Julian, R. K., Jones, A. R., Zhu, W., Apweiler, R., Aebersold, R. H., Deutsch, E. W., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1329 · Cited by 769
OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data
Röst, H. L., Rosenberger, G., Navarro, P., Gillet, L. C. J., Miladinović, S. M., Schubert, O. T., Wolski, W., Collins, B. C., Malmström, J. A., Malmström, L., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2841 · Cited by 762
DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics
Tsou, C., Avtonomov, D. M., Larsen, B. G., Tucholska, M., Choi, H., Gingras, A., & Nesvizhskii, A. I. (2015) · Nature Methods · doi:10.1038/nmeth.3255 · Cited by 645
Philosopher: a versatile toolkit for shotgun proteomics data analysis
Leprevost, F. D. V., Haynes, S. E., Avtonomov, D. M., Chang, H.-Y., Shanmugam, A. K., Mellacheruvu, D., Kong, A. T., & Nesvizhskii, A. I. (2020) · Nature Methods · doi:10.1038/s41592-020-0912-y · Cited by 635
Interferences and contaminants encountered in modern mass spectrometry
Keller, B. O., Sui, J., Young, A. B., & Whittal, R. M. (2008) · Analytica Chimica Acta · doi:10.1016/j.aca.2008.04.043 · Cited by 623
Searle, B. C., Pino, L. K., Egertson, J. D., Ting, Y. S., Lawrence, R., MacLean, B., Villén, J., & MacCoss, M. J. (2018) · Nature Communications · doi:10.1038/s41467-018-07454-w · Cited by 528
Bruderer, R., Bernhardt, O. M., Gandhi, T., Xuan, Y., Sondermann, J. R., Schmidt, M., Gómez‐Varela, D., & Reiter, L. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra117.000314 · Cited by 493
High-field asymmetric waveform ion mobility spectrometry: A new tool for mass spectrometry
Guevremont, R. (2004) · Journal of Chromatography A · doi:10.1016/s0021-9673(04)01478-5 · Cited by 441
Chick, J. M., Kolippakkam, D. N., Nusinow, D. P., Zhai, B., Rad, R., Huttlin, E. L., & Gygi, S. P. (2015) · Nature Biotechnology · doi:10.1038/nbt.3267 · Cited by 435
Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studies
Geyer, P. E., Voytik, E., Treit, P. V., Doll, S., Kleinhempel, A., Niu, L., Müller, J. B., Buchholtz, M., Bader, J. M., Teupser, D., et al. (2019) · EMBO Molecular Medicine · doi:10.15252/emmm.201910427 · Cited by 348
Pino, L. K., Just, S., MacCoss, M. J., & Searle, B. C. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.p119.001913 · Cited by 278
Enhanced Global Post-translational Modification Discovery with MetaMorpheus
Solntsev, S. K., Shortreed, M. R., Frey, B. L., & Smith, L. M. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00873 · Cited by 267
MaxDIA enables library-based and library-free data-independent acquisition proteomics
Sinitcyn, P. G., Hamzeiy, H., Soto, F. S., Itzhak, D. N., McCarthy, F., Wichmann, C., Steger, M., Ohmayer, U., Distler, U., Kaspar‐Schoenefeld, S., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00968-7 · Cited by 252
In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
Yang, Y., Liu, X., Shen, C., Lin, Y., Yang, P., & Qiao, L. (2020) · Nature Communications · doi:10.1038/s41467-019-13866-z · Cited by 225
Multiplexed peptide analysis using data-independent acquisition and Skyline
Egertson, J. D., MacLean, B., Johnson, R. S., Xuan, Y., & MacCoss, M. J. (2015) · Nature Protocols · doi:10.1038/nprot.2015.055 · Cited by 214
PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data
Ting, Y. S., Egertson, J. D., Bollinger, J. G., Searle, B. C., Payne, S., Noble, W. S., & MacCoss, M. J. (2017) · Nature Methods · doi:10.1038/nmeth.4390 · Cited by 193
Houel, S., Abernathy, R., Renganathan, K., Meyer-Arendt, K., Ahn, N. G., & Old, W. M. (2010) · Journal of Proteome Research · doi:10.1021/pr1003856 · Cited by 181
A high confidence, manually validated human blood plasma protein reference set
Schenk, S., Schoenhals, G. J., de Souza, G. A., & Mann, M. (2008) · BMC Medical Genomics · doi:10.1186/1755-8794-1-41 · Cited by 181
Muntel, J., Kirkpatrick, J. M., Bruderer, R., Huang, T. C., Vitek, O., Ori, A., & Reiter, L. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00898 · Cited by 155
Cleaning up the masses: Exclusion lists to reduce contamination with HPLC-MS/MS
Hodge, K., ten Have, S., Hutton, L., & Lamond, A. I. (2013) · Journal of Proteomics · doi:10.1016/j.jprot.2013.02.023 · Cited by 152
Improved Quantitative Mass Spectrometry Methods for Characterizing Complex Ubiquitin Signals
Phu, L., Izrael-Tomasevic, A., Matsumoto, M. L., Bustos, D. J., Dynek, J. N., Fedorova, A. V., Bakalarski, C. E., Arnott, D. P., Deshayes, K., Dixit, V. M., et al. (2010) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.003756 · Cited by 144
Strategies to enable large-scale proteomics for reproducible research
Poulos, R. C., Hains, P. G., Shah, R., Lucas, N., Xavier, D., Manda, S. S., Anees, A., Koh, J. M. S., Mahboob, S., Wittman, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17641-3 · Cited by 134
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 130
Parker, 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 94
Trypsin/Lys-C protease mix for enhanced protein mass spectrometry analysis
Saveliev, S. V., Bratz, M., Zubarev, R. A., Szapacs, M., Budamgunta, H., & Urh, M. (2013) · Nature Methods · doi:10.1038/nmeth.f.371 · Cited by 74
Improved Precursor Characterization for Data-Dependent Mass Spectrometry
Hebert, A. S., Thöing, C., Riley, N. M., Kwiecien, N. W., Shiskova, E., Huguet, R., Cardasis, H. L., Kuehn, A., Eliuk, S., Zabrouskov, V., et al. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.7b04808 · Cited by 67
PRIDE Cluster: building a consensus of proteomics data
Griss, J., Foster, J. M., Hermjakob, H., & Vizcaíno, J. A. (2013) · Nature Methods · doi:10.1038/nmeth.2343 · Cited by 57
Fox, K. F., Castanha, E. R., Fox, A., Feigley, C. E., & Salzberg, D. (2007) · Journal of Environmental Monitoring · doi:10.1039/b714802j · Cited by 46
Utility of Keratins as Biomarkers for Human Oral Precancer and Cancer
Vaidya, M. M., Dmello, C., & Mogre, S. (2022) · Life · doi:10.3390/life12030343 · Cited by 40
Rapid Assessment of Contaminants and Interferences in Mass Spectrometry Data Using Skyline
Rardin, M. J. (2018) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-018-1940-z · Cited by 39
Neuronal VCP loss of function recapitulates FTLD-TDP pathology
Wani, A., Zhu, J., Ulrich, J. D., Eteleeb, A. M., Sauerbeck, A. D., Reitz, S. J., Arhzaouy, K., Ikenaga, C., Yuede, C. M., Pittman, S. K., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109399 · Cited by 39
Peptide Identification by Database Search of Mixture Tandem Mass Spectra
Wang, J., Bourne, P. E., & Bandeira, N. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.010017 · Cited by 34
Frankenfield, A. M., Fernandopulle, M. S., Hasan, S., Ward, M. E., & Hao, L. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.0c03107 · Cited by 33
Self-citationTargeted Quantification of the Lysosomal Proteome in Complex Samples
Mosen, P., Sanner, A., Singh, J., & Winter, D. (2021) · Proteomes · doi:10.3390/proteomes9010004 · Cited by 26
Integrated proteomic and metabolomic analyses of the mitochondrial neurodegenerative disease MELAS
Li, H., Uittenbogaard, M. N., Navarro, R., Ahmed, M. J., Gropman, A. L., Chiaramello, A., & Hao, L. (2021) · Molecular Omics · doi:10.1039/d1mo00416f · Cited by 21
Self-citationUromodulin exclusion list improves urinary exosomal protein identification.
Hiemstra, T. F., Charles, P. D., Hester, S., Karet, F. E., & Lilley, K. S. (2011) · PubMed Central · Cited by 16
A stringent approach to improve the quality of nitrotyrosine peptide identifications
Ghesquière, B., Helsens, K., Vandekerckhove, J. S., & Gevaert, K. (2011) · PROTEOMICS · doi:10.1002/pmic.201000526 · Cited by 15
Data Independent Acquisition in Practice: Foundations and Resources for Implementing DIA
Neely, B. A., Kirkpatrick, J. M., Midha, M. K., Stemmer, P. M., Martin, L. R., Wang, Y., Herring, L. E., Phinney, B. S., Shan, B., Chien, A., et al. (2019) · Europe PMC (PubMed Central) · Cited by 2
Flagged first, then by citations
Research works printed but not linked: 3
- Hiemstra T. F., 2011, J. Biomol. Tech., 22, 136
- Neely B., 2019, J. Biomol. Tech. JBT, 30, S46
- 10.1038/nbt.3267.An · DOI
Cited by
Sorted by citations
Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry
Jiang, Y., Balaya, R. D. A., Schuster, D., Neely, B. A., Rosano, G. L., Volkmar, N., Momenzadeh, A., Peters-Clarke, T. M., Egbert, S., Kreimer, S. D., et al. (2024) · ACS Measurement Science Au · doi:10.1021/acsmeasuresciau.3c00068 · Cited by 217
Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale
Lazear, M. R. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.3c00486 · Cited by 93
Expanding the human proteome with microproteins and peptideins
Deutsch, E. W., Kok, L. W., Mudge, J. M., Valls, C., Jungreis, I., Ruiz‐Orera, J., Sun, Z., Kusebauch, U., Fierro-Monti, I., Abelin, J. G., et al. (2026) · Nature · doi:10.1038/s41586-026-10459-x · Cited by 98
A framework for ultra-low-input spatial tissue proteomics
Makhmut, A., Qin, D., Fritzsche, S., Nimo, J., König, J., & Coscia, F. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.003 · Cited by 67
High-resolution electron cryomicroscopy of V-ATPase in native synaptic vesicles
Coupland, C. E., Karimi, R., Bueler, S. A., Liang, Y., Courbon, G. M., Di Trani, J. M., Wong, C. J., Saghian, R., Youn, J.-Y., Wang, L.-Y., et al. (2024) · Science · doi:10.1126/science.adp5577 · Cited by 58
Metadata
- Authors
- Frankenfield, A. M., Ni, J., Ahmed, M., & Hao, L.
- Affiliations
- George Washington University (US)
- Corresponding author
- Hao, Ling
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-07-06
- Volume
- 21
- Issue
- 9
- Pages
- 2104-2113
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- PMC
- PMC10040255
- OpenAlex
- W4284882692
- Cited by
- 321
- References
- 46
- Citations by year
- 2026: 114 · 2025: 115 · 2024: 58 · 2023: 31 · 2022: 3
- FWCI
- 19.59
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Proteome Research (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS121608)
- 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 › Metabolomics and Mass Spectrometry Studies
- Keywords
- protein contaminants, data-dependent acquisition, data-independent acquisition, false discovery rate, protein identification, shotgun proteomics
- MeSH terms
- Software, Mass Spectrometry, Proteome, Proteomics
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