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.
“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 4,691 · see citing works
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 4,038 · see citing works
Elias, J. E., & Gygi, S. P. (2007) · Nature Methods · doi:10.1038/nmeth1019 · Cited by 3,925 · see citing works
DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput
Demichev, V., Messner, C., Vernardis, S., Lilley, K., & Ralser, M. (2019) · Nature Methods · doi:10.1038/s41592-019-0638-x · Cited by 2,961 · see citing works
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 2,344 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 770 · see citing works
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 · see citing works
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 · see citing works
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 622 · see citing works
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 618 · see citing works
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 508 · see citing works
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 479 · see citing works
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 419 · see citing works
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 390 · see citing works
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 · see citing works
High-field asymmetric waveform ion mobility spectrometry: A new tool for mass spectrometry
Guevremont, R. (2004) · Journal of Chromatography A · doi:10.1016/j.chroma.2004.08.119 · Cited by 321 · see citing works
Pino, L. K., Just, S., MacCoss, M. J., & Searle, B. C. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.p119.001913 · Cited by 265 · see citing works
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 263 · see citing works
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 241 · see citing works
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 223 · see citing works
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 211 · see citing works
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 185 · see citing works
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 · see citing works
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 · 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
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 · see citing works
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 · see citing works
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 133 · 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
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 93 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 44 · see citing works
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 · see citing works
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 · see citing works
Frankenfield, A. M., Fernandopulle, M. S., Hasan, S., Ward, M. E., & Hao, L. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.0c03107 · Cited by 34 · see citing works
Self-citationPeptide 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 33 · see citing works
Targeted 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 · see citing works
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 22 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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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 200 · see citing works
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 · see citing works
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 91 · see citing works
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 · see citing works
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 · see citing works
Hasan, S., Fernandopulle, M. S., Humble, S. W., Frankenfield, A. M., Li, H., Prestil, R., Johnson, K. R., Ryan, B. J., Wade‐Martins, R., Ward, M. E., et al. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00673-w · Cited by 47 · see citing works
Self-citationUnifying the analysis of bottom-up proteomics data with CHIMERYS
Frejno, M., Berger, M. T., Tüshaus, J., Hogrebe, A., Seefried, F., Graber, M., Samaras, P., Fredj, S. B., Sukumar, V., Eljagh, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02663-w · Cited by 46 · see citing works
Universal Pretreatment Development for Low-input Proteomics Using Lauryl Maltose Neopentyl Glycol
Konno, R., Ishikawa, M., Nakajima, D., Endo, Y., Ohara, O., & Kawashima, Y. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100745 · Cited by 42 · see citing works
Robust proteome profiling of cysteine-reactive fragments using label-free chemoproteomics
Biggs, G. S., Cawood, E. E., Vuorinen, A., McCarthy, W. J., Wilders, H., Riziotis, I. G., van der Zouwen, A. J., Pettinger, J., Nightingale, L., Chen, P., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55057-5 · Cited by 36 · see citing works
Wünnemann, F., Sicklinger, F., Bestak, K., Nimo, J., Thiemann, T., Amrute, J., Nordbeck, M., Hartmann, N., Ibarra-Arellano, M. A., Tanevski, J., et al. (2025) · Nature Cardiovascular Research · doi:10.1038/s44161-025-00717-y · Cited by 35 · see citing works
C-terminal amides mark proteins for degradation via SCF–FBXO31
Muhar, M., Farnung, J., Cernakova, M., Hofmann, R., Henneberg, L. T., Pfleiderer, M. M., Denoth‐Lippuner, A., Kalčic, F., Nievergelt, A. S., al‐Bayati, M., et al. (2025) · Nature · doi:10.1038/s41586-024-08475-w · Cited by 34 · see citing works
Challenges of MS‐based small extracellular vesicles proteomics
Fochtman, D., Marczak, Ł., Pietrowska, M., & Wojakowska, A. (2024) · Journal of Extracellular Vesicles · doi:10.1002/jev2.70020 · Cited by 33 · see citing works
van Wijk, K. J., Leppert, T., Sun, Z., Kearly, A., Li, M., Mendoza, L., Guzchenko, I., Debley, E. L., Sauermann, G., Routray, P., et al. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.3c00536 · Cited by 30 · see citing works
Targeted degradation of endogenous YAP by nanobody bioPROTAC inhibits tumor progression
Zhou, R., Wang, H., Zhang, G., Liu, Y., Liu, X., Li, Z., Shi, G., Yuan, J., Qu, C., Li, Y., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64426-7 · Cited by 28 · see citing works
Makhmut, A., Qin, D., Hartlmayr, D., Seth, A., & Coscia, F. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100750 · Cited by 27 · see citing works
Coorssen, J. R., & Padula, M. P. (2024) · Proteomes · doi:10.3390/proteomes12020014 · Cited by 27 · see citing works
Ramsperger, A. F. R. M., Wieland, S., Wilde, M. V., Fröhlich, T., Kress, H., & Laforsch, C. (2025) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2025.137647 · Cited by 25 · see citing works
Peptidomics profiling and biological activities of grape pomace protein hydrolysates
Knuf, F., Caspers-Weiffenbach, R., Schieber, A., & Fontana, A. (2024) · Food Chemistry · doi:10.1016/j.foodchem.2024.141032 · Cited by 23 · see citing works
Schneider, S., Kovacevic, A., Mayer, M., Dicke, A., Arévalo, L., Koser, S. A., Hansen, J. N., Young, S., Brenker, C., Kliesch, S., et al. (2023) · eLife · doi:10.7554/elife.86100 · Cited by 23 · see citing works
OmicScope unravels systems-level insights from quantitative proteomics data
Reis‐de‐Oliveira, G., Carregari, V. C., de Sousa, G. R. D. R., & Martins‐de‐Souza, D. (2024) · Nature Communications · doi:10.1038/s41467-024-50875-z · Cited by 22 · see citing works
Zhang, R., & Jia, W. (2024) · Journal of Dairy Science · doi:10.3168/jds.2023-24386 · Cited by 22 · see citing works
Krismer, E., Bludau, I., Strauss, M. T., & Mann, M. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad461 · Cited by 22 · see citing works
Romero, M. L. R., Poehls, J., Kirilenko, A., Richter, D., Jumel, T., Shevchenko, A., & Tóth-Petróczy, Á. (2024) · Nature Communications · doi:10.1038/s41467-024-48387-x · Cited by 21 · see citing works
Declercq, A., Devreese, R., Scheid, J., Jachmann, C., Bossche, T. V. D., Preikschat, A., Gomez‐Zepeda, D., Rijal, J. B., Hirschler, A., Krieger, J. R., et al. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00609 · Cited by 20 · see citing works
Proteomic insights into mental health status: plasma markers in young adults
Afonin, A. М., Piironen, A., Maciel, I. D. S., Ivanova, M., Alatalo, A., Whipp, A. M., Pulkkinen, L., Rose, R. J., KAMP, I. V., Kaprio, J. A., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02751-z · Cited by 20 · see citing works
Zou, X., Wang, L., Chen, Y., Fu, H., Gao, Y., Liu, B., Tan, M., & Zhai, L. (2025) · Clinical Proteomics · doi:10.1186/s12014-025-09572-2 · Cited by 19 · see citing works
Plasma proteomics discovery of mental health risk biomarkers in adolescents
Maciel, I. D. S., Piironen, A., Afonin, A. М., Ivanova, M., Alatalo, A., Jadhav, K. K., Júlvez, J., Foraster, M., KAMP, I. V., & Kanninen, K. M. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00103-2 · Cited by 19 · see citing works
Multiomics Evaluation of Human iPSCs and iPSC-Derived Neurons
Lee, G. B., Mazli, W. N. A. B., & Hao, L. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.3c00790 · Cited by 19 · see citing works
Self-citationAn integrated workflow for quantitative analysis of the newly synthesized proteome
Borteçen, T., Müller, T., & Krijgsveld, J. (2023) · Nature Communications · doi:10.1038/s41467-023-43919-3 · Cited by 19 · see citing works
Plasma-Derived Extracellular Vesicle Proteomics
Tan, Y., Kao, W., Boppart, M. D., & Sweedler, J. V. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5c00316 · Cited by 18 · see citing works
A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data
Hutchings, C., Dawson, C. S., Krueger, T., Lilley, K. S., & Breckels, L. M. (2023) · F1000Research · doi:10.12688/f1000research.139116.1 · Cited by 18 · see citing works
Muntiu, A., Papait, A., Vincenzoni, F., Vitali, A., Lattanzi, W., Romele, P., Cargnoni, A., Silini, A. R., Parolini, O., & Desiderio, C. (2023) · Stem Cell Research & Therapy · doi:10.1186/s13287-023-03557-4 · Cited by 17 · see citing works
Chapman, A., McAfee, A., Tarpy, D. R., Fine, J. D., Rempel, Z., Peters, K., Currie, R., & Foster, L. J. (2024) · Scientific Reports · doi:10.1038/s41598-024-66286-5 · Cited by 16 · see citing works
Engels, I., Burnett, A., Robert, P., Pironneau, C., Abrams, G., Bouwmeester, R., der Plaetsen, P. V., Modica, K. D., Otte, M., Straus, L. G., et al. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00962 · Cited by 16 · see citing works
Electrophilic compound screening identifies GPX4-dependent ferroptosis as a senescence vulnerability
D’Ambrosio, M., White, M. E. H., Gavriil, E.-S., Bousset, L., Birch, J., Gruevska, A., Pasquini, E., Colucci, M., Fong, W., Mosole, S., et al. (2026) · Nature Cell Biology · doi:10.1038/s41556-026-01921-z · Cited by 15 · see citing works
Chisholm, S. A., Flores, V., Kemp, A., Breckels, L., Koreny, L., Pacheco, N. D. S., Barylyuk, K., Kuroshchenkova, A., Lilley, K. S., Rayner, J., et al. (2025) · bioRxiv · doi:10.1038/s41467-026-73664-2 · Cited by 15 · see citing works
Macrophage-induced enteric neurodegeneration leads to motility impairment during gut inflammation
Breßer, M., Siemens, K. D., Schneider, L. H., Lunnebach, J. E., Leven, P., Glowka, T. R., Oberländer, K., Domenico, E. D., Schultze, J. L., Schmidt, J., et al. (2025) · EMBO Molecular Medicine · doi:10.1038/s44321-024-00189-w · Cited by 15 · see citing works
Shoji, T., Tanaka, Y., Nakashima, Y., Mizohata, E., Komaki, M., Sugawara, S., Takaya, J., Yonekura‐Sakakibara, K., Morita, H., Saito, K., et al. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c09392 · Cited by 15 · see citing works
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Stanisheuski, S., Ebrahimi, A., Vaidya, K. A., Jang, H. S., Yang, L., Eddins, A. J., Marean‐Reardon, C. L., Franco, M. C., & Maier, C. S. (2024) · Frontiers in Chemistry · doi:10.3389/fchem.2024.1428547 · Cited by 14 · see citing works
Hou, M., Huang, J., Jia, T., Guan, Y., Yang, F., Zhou, H., Huang, P., Wang, J., Yang, L., & Dai, L. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00785 · Cited by 14 · see citing works
Halim, M. F. A., Fonseca, D. R., Niehaus, T. D., & Costa, K. C. (2023) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2023.105550 · Cited by 13 · see citing works
Ghosh, S., Tuz, A. A., Stenzel, M., Singh, V., Richter, M., Soehnlein, O., Lange, E., Heyer, R., Cibir, Z., Beer, A., et al. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100858 · Cited by 13 · see citing works
Fontana, A., Knuf, F., Monasterio, R. P., & Schieber, A. (2025) · Food Chemistry · doi:10.1016/j.foodchem.2025.143478 · Cited by 13 · see citing works
Wei, X., Person, M. D., & Yang, K. (2022) · DNA repair · doi:10.1016/j.dnarep.2022.103391 · Cited by 13 · see citing works
Horten, P., Song, K., Garlich, J., Hardt, R., Colina-Tenorio, L., Horvath, S. E., Schulte, U., Fakler, B., van der Laan, M., Becker, T., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113772 · Cited by 12 · see citing works
Griffin, K. H., Mizenko, R. R., Arun, V., Carney, R. P., & Leach, J. K. (2024) · Advanced Biology · doi:10.1002/adbi.202300577 · Cited by 12 · see citing works
Govaere, O., Cockell, S., Zatorska, M., Wonders, K., TINIAKOS, D. G., Frey, A. M., Palmowski, P., Walker, R., Porter, A., Trost, M., et al. (2024) · Biochemical Pharmacology · doi:10.1016/j.bcp.2024.116249 · Cited by 12 · see citing works
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Gudgeon, J., Dannoura, A., Chatterjee, R., Sidgwick, F. R., Raymond, B. B. A., Frey, A. M., Marín‐Rubio, J. L., & Trost, M. (2024) · Life Science Alliance · doi:10.26508/lsa.202402760 · Cited by 12 · see citing works
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Bernaleau, L., Drobek, M., Blank, F., Walch, P., Delacrétaz, M., Drobek, A., Tortajada, M., Brož, P., Majer, O., & Rebsamen, M. (2024) · The Journal of Experimental Medicine · doi:10.1084/jem.20240825 · Cited by 12 · see citing works
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Makhmut, A., Dragomir, M. P., Fritzsche, S., Möbs, M., Schmitt, W., TAUBE, E. T., & Coscia, F. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00168-4 · Cited by 12 · see citing works
Hay, B. N., Akinlaja, M. O., Baker, T. C., Houfani, A. A., Stacey, R. G., & Foster, L. J. (2023) · PROTEOMICS · doi:10.1002/pmic.202200278 · Cited by 11 · see citing works
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Lee, E. M., Srinivasan, S., Purvine, S. O., Fiedler, T. L., Leiser, O. P., Proll, S. C., Minot, S. S., Kaiser, B. L. D., & Fredricks, D. N. (2023) · mSystems · doi:10.1128/msystems.00678-22 · Cited by 11 · see citing works
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Bose, U., Buck, S., Sirault, X. R. R., Bahmani, M., Byrne, K. A., Stockwell, S., McWilliam, S. M., Colgrave, M. L., Juhász, A., & Ral, J.-P. F. (2024) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c07669 · Cited by 11 · see citing works
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Li, H., Mazli, W. N. A. B., & Hao, L. (2025) · Journal of Mass Spectrometry · doi:10.1002/jms.5134 · Cited by 11 · see citing works
Self-citationSharifpour, M. F., Sıkder, S., Wong, Y., Koifman, N., Thomas, T., Courtney, R. L., Seymour, J., & Loukas, A. C. (2024) · Journal of Extracellular Biology · doi:10.1002/jex2.70025 · Cited by 10 · see citing works
Wong, Y., Rosa, B. A., Becker, L., Camberis, M., Gros, G. S. L., Zhan, B., Bottazzi, M. E., Fujiwara, R. T., Ritmejerytė, E., Laha, T., et al. (2025) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0012780 · Cited by 10 · see citing works
Zhang, Y. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100583 · Cited by 10 · see citing works
Factors affecting heat resilience of drone honey bees (Apis mellifera) and their sperm
McAfee, A., Metz, B. N., Connor, P., Du, K., Allen, C. W., Frausto, L. A., Swenson, M. P., Phillips, K. S., Julien, M., Rempel, Z., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0317672 · Cited by 9 · see citing works
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Frankenfield, A. M., Yang, K., Mazli, W. N. A. B., Shih, J., Yu, F., Lo, E., Nesvizhskii, A. I., & Hao, L. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2025.100980 · Cited by 9 · see citing works
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Lee, J., Chin, N. Y., Zou, J., Mazli, W. N. A. B., Jarník, M., Saidi, L. F., Xu, Y., Jeong, E., Suh, J., Replogle, J. M., et al. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01738-2 · Cited by 9 · see citing works
Self-citationApaydin, D. C., Sadhnani, G., Carlaw, T., Renziehausen, J., Lizunova, E., Filor, V., Hiller, A. L., Brumhard, S., Halim, V., Brüning, U., et al. (2026) · Cell stem cell · doi:10.1016/j.stem.2026.01.001 · Cited by 9 · see citing works
O’Lone, C. E., Juhász, A., Nye‐Wood, M. G., Moody, D., Dunn, H., Ral, J.-P. F., & Colgrave, M. L. (2024) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c00884 · Cited by 9 · see citing works
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Dorl, S., Winkler, S., Mechtler, K., & Dorfer, V. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00658 · Cited by 9 · see citing works
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Ishorst, N., Hölzel, S., Greve, C., Yilmaz, Ö., Lindenberg, T. T., Lambertz, J., Drichel, D., Zametica, B., Mingardo, E., Kalanithy, J. C., et al. (2024) · European Journal of Human Genetics · doi:10.1038/s41431-024-01775-9 · Cited by 8 · see citing works
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McCaskie, K. J. A., Hutchings, C., Feret, R., Kim, Y.-I., Breckels, L. M., Deery, M. J., & Lilley, K. S. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2025.101047 · Cited by 8 · see citing works
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Klingeberg, M., Krisp, C., Fritzsche, S., Schallenberg, S., Hornburg, D., & Coscia, F. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2025.101489 · Cited by 8 · see citing works
McAfee, A., Chapman, A., Bao, G., Tarpy, D. R., & Foster, L. J. (2024) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2023.2463 · Cited by 8 · see citing works
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Smeriglio, N., Li, H., Mazli, W. N. A. B., Bendel, K., & Hao, L. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.3c05020 · Cited by 8 · see citing works
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Hermosilla-Trespaderne, M., Hu-Yang, M. X., Dannoura, A., Frey, A. M., George, A. L., Trost, M., & Marín‐Rubio, J. L. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100778 · Cited by 8 · see citing works
O’Lone, C. E., Juhász, A., Nye‐Wood, M. G., Dunn, H., Moody, D. B., Ral, J.-P. F., & Colgrave, M. L. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1305381 · Cited by 8 · see citing works
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Devreese, R., Jachmann, C., Puyvelde, B. V., Anagho, H. A., Wolski, W., Webel, H. E., Mattanovich, M., Bittremieux, W., Chaoui, K., Chiva, C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.09.692895 · Cited by 8 · see citing works
Yang, C., Lee, G. B., Hao, L., & Hu, F. (2025) · Communications Biology · doi:10.1038/s42003-025-07752-2 · Cited by 7 · see citing works
Self-citationSenatore, E., Avolio, R., Rinaldi, L., Chiuso, F., Oliva, M. A., DˈAmbrosio, C., Bianco, A. G., Dalla, E., Pagnotta, S. M., Flammia, R., et al. (2025) · EMBO Reports · doi:10.1038/s44319-025-00425-5 · Cited by 7 · see citing works
Frey, A. M., Sidgwick, F. R., Porter, A., Palmowski, P., & Trost, M. (2026) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6c00328 · Cited by 7 · see citing works
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Frankenfield, A. M., Ni, J., & Hao, L. (2022) · Journal of Visualized Experiments · doi:10.3791/64132 · Cited by 7 · see citing works
Self-citationMüller, P., Dietrich, D., Schoch, S., Pitsch, J., Becker, A. J., & Cases‐Cunillera, S. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106364 · Cited by 7 · see citing works
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Tabb, D. L., Kaniyar, M. H., Bringas, O. G. R., Shin, H., Stéfano, L. D., Taylor, M. S., Xie, S., Yilmaz, Ö. H., & LaCava, J. (2024) · Journal of Proteins and Proteomics · doi:10.1007/s42485-024-00166-4 · Cited by 7 · see citing works
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Iwasaki, M., Nishimura, R., Yamakawa, T., Miyamoto, Y., Tabata, T., & Narita, M. (2024) · Cells · doi:10.3390/cells13221848 · Cited by 7 · see citing works
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Franciosa, G., Nieddu, V., Battistini, C., Caffarini, M., Lupia, M., Colombo, N., Fusco, N., Olsen, J. V., & Cavallaro, U. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2025.100965 · Cited by 7 · see citing works
Σιαπέρας, Ρ., Taxeidis, G., Gioti, Α., Nikolaivits, E., & Topakas, E. (2025) · Environmental Pollution · doi:10.1016/j.envpol.2025.126386 · Cited by 7 · see citing works
A conserved chronobiological complex times C. elegans development
Spangler, R. K., Braun, K., Ashley, G. E., van der Does, M., Wruck, D., Coronado, A. R., Ragle, J. M., Iešmantavičius, V., Moya, L. J. M., Daly, K., et al. (2025) · The EMBO Journal · doi:10.1038/s44318-025-00585-z · Cited by 7 · see citing works
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Wong, Y., Pearson, M. S., Fedorova, O. S., Ivanov, V. V., Khmelevskaya, E. S., Tedla, B., Arachchige, B. J., Reed, S., Field, M. A., Laha, T., et al. (2023) · Frontiers in Parasitology · doi:10.3389/fpara.2023.1195457 · Cited by 7 · see citing works
Harjuhaahto, S., Jokela, M. E., Rajendran, J., Rokka, M., Hu, B., Kvist, J., Zhang, F., Zárybnický, T., Haimilahti, K., Euro, L., et al. (2025) · Acta Neuropathologica Communications · doi:10.1186/s40478-025-02039-3 · Cited by 6 · see citing works
Kosa, A., Lopez‐Gutierrez, L., Ando, K., Doeraene, E., Aydin, E., Lasri, H., Wathelet‐Depauw, A., Pieters, K., Morckhoven, D. V., Imbault, V., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70784 · Cited by 6 · see citing works
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Ahmed, B., Salaün, D., Jordan, J. B., Daulat, A. M., Pastorello, C., Brito, T., Yang, Y., Josselin, E., Byrne, D., Betzi, S., et al. (2026) · Molecular Cell · doi:10.1016/j.molcel.2026.04.008 · Cited by 6 · see citing works
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Holstein, T., Verschaffelt, P., Bossche, T. V. D., de Vyver, S. V., Martens, L., Mesuere, B., & Muth, T. (2026) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5c00567 · Cited by 6 · see citing works
Saleem, W., Carpentier, N., Hinnekens, C., Oh, D., Vlierberghe, S. V., Braeckmans, K., & Nauwynck, H. (2024) · Veterinary Research · doi:10.1186/s13567-024-01374-y · Cited by 6 · see citing works
Ibrahim, P., Mitsuhashi, H., Taylor, L., Cleyle, J., Mechawar, N., Nagy, C., & Turecki, G. (2025) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyaf012 · Cited by 6 · see citing works
Barny, L. A., Hermanson, J. N., Garcia, S. K., Stauffer, P. E., & Plate, L. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2025.101036 · Cited by 6 · see citing works
Powell, A. E., Caruso, H., Park, S., Chen, J., O’Rear, J., Ferrer, B., Stieh, D. J., Weiss, A. M., Belnap, D. M., Walker, A. E., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68458-5 · Cited by 6 · see citing works
An updated Bioconductor workflow for correlation profiling subcellular proteomics
Hutchings, C., Krueger, T., Crook, O. M., Gatto, L., Lilley, K. S., & Breckels, L. M. (2025) · F1000Research · doi:10.12688/f1000research.165543.1 · Cited by 5 · see citing works
PARP7 is a proteotoxic stress sensor that labels proteins for degradation
Ikenga, N. J., Vervoorts, J., Lüscher, B., Žaja, R., & Feijs, K. L. H. (2025) · The EMBO Journal · doi:10.1038/s44318-025-00545-7 · Cited by 5 · see citing works
Postmortem interval estimation through skeletal muscle protein degradation: a comprehensive review
Kori, A., Chandran, A., Sangita, M., & Yadav, J. (2026) · International Journal of Legal Medicine · doi:10.1007/s00414-025-03703-y · Cited by 5 · see citing works
McAfee, A., Martinet, B., Przybyla, K., Degueldre, F., Hoover, S. E., Aron, S., & Foster, L. J. (2024) · Molecular Ecology · doi:10.1111/mec.17568 · Cited by 5 · see citing works
Maia, T. M., Haver, D. V., Dufour, S., Linden, M. V. D., Dendooven, A., Impens, F., & Devos, S. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4298-6_20 · Cited by 5 · see citing works
de Lima, I. L., Cataldi, T. R., Brites, C. R., Labate, M. T. V., Vaz, S. N., Deminco, F., da Cunha, G. S., Labate, C. A., & Eberlin, M. N. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00630 · Cited by 5 · see citing works
Considerations and Software for Successful Immune Cell Deconvolution Using Proteomics Data
Zamore, M., Mosquim, S., Andree, S. L., Altunbulakli, C., Lindstedt, M., & Levander, F. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00868 · Cited by 5 · see citing works
Taxeidis, G., Σιαπέρας, Ρ., Foka, K., Ponjavić, M., Runic, J. N., Zerva, A., & Topakas, E. (2025) · Environmental Pollution · doi:10.1016/j.envpol.2025.126214 · Cited by 5 · see citing works
- 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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