Exploratory analysis of the application of animal reduction approaches in proteomics: How much is enough?

Main Article Content

Laura M. Langan
Bryan W. Brooks

Abstract

Animal testing has been the corner stone for chemical safety assessments, but fish embryo assays now represent an alternative. Increases in omics studies offers unparalleled access to examine early molecular responses in organisms in response to environmental stressors and yet reduction in animal usage within this context has been overlooked. For proteomics, there is significant disparity and variability in organismal pool size ranging from 1-2000 embryos per replicate for zebrafish alone. However, it is unknown if varying sample pool size results in higher protein identifications. To examine whether the proteome changes are dependent on this variable, 3 pool sizes (5, 10 or 20 embryos per replicate) were examined using the two most common fish models with appropriate biological replicate number determined based on statistical power analysis (n=7). Samples were analysed by data-independent acquisition (DIA), resulted in 1,946 and 2,770 protein groups identified (1 % FDR) for the fathead minnow and zebrafish, respectively. Proteins were not differentially expressed among pool sizes, and no significant difference was observed among the protein groups identified. However, for the fathead minnow, a decrease in the number of proteins identified was observed with increasing pool size, while only a modest increase of 110 protein identifications was observed in zebrafish between the lowest and highest pool size. Taken together, our observations suggests that a proteome characterization experiment using these fish models can achieve comparable protein identifications using pool sizes of less than 5 per replicate assuming a protein requirement of 50µg or less. 

Article Details

How to Cite
Langan, L. M. and Brooks , B. W. (2021) “Exploratory analysis of the application of animal reduction approaches in proteomics: How much is enough?”, ALTEX - Alternatives to animal experimentation. doi: 10.14573/altex.2107212.
Section
Articles
References

Aballo, T. J., Roberts, D. S., Melby, J. A. et al. (2021). Ultrafast and Reproducible Proteomics from Small Amounts of Heart Tissue Enabled by Azo and timsTOF Pro. J Proteome Res. https://doi.org/10.1021/acs.jproteome.1c00446.

Almeida, A. M., Ali, S. A., Ceciliani, F. et al. (2021). Domestic animal proteomics in the 21st century: A global retrospective and viewpoint analysis. Journal of Proteomics 241, 104220. https://doi.org/10.1016/j.jprot.2021.104220.

Avtonomov, D. M., Kong, A. and Nesvizhskii, A. I. (2019). DeltaMass: Automated Detection and Visualization of Mass Shifts in Proteomic Open-Search Results. J Proteome Res 18, 715–720. https://doi.org/10.1021/acs.jproteome.8b00728.

Ayobahan, S. U., Eilebrecht, S., Baumann, L. et al. (2020). Detection of biomarkers to differentiate endocrine disruption from hepatotoxicity in zebrafish (Danio rerio) using proteomics. Chemosphere 240, 124970. https://doi.org/10.1016/j.chemosphere.2019.124970.

Barkovits, K., Pacharra, S., Pfeiffer, K. et al. (2020). Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition. Mol Cell Proteomics 19, 181–197. https://doi.org/10.1074/mcp.RA119.001714.

Becker, R. A. (2019). Transforming regulatory safety evaluations using New Approach Methodologies: A perspective of an industrial toxicologist. Current Opinion in Toxicology 15, 93–98. https://doi.org/10.1016/j.cotox.2019.07.002.

Benton, H. P., Want, E. J. and Ebbels, T. M. D. (2010). Correction of mass calibration gaps in liquid chromatography–mass spectrometry metabolomics data. Bioinformatics 26, 2488–2489. https://doi.org/10.1093/bioinformatics/btq441.

Blattmann, P., Stutz, V., Lizzo, G. et al. (2019). Generation of a zebrafish SWATH-MS spectral library to quantify 10,000 proteins. Scientific Data 6, 1–11. https://doi.org/10.1038/sdata.2019.11.

Bouhifd, M., Beger, R., Flynn, T. et al. (2015). t4 Workshop Report: quality assurance of metabolomics. ALTEX 32, 319–326

Boyles, R. R., Thessen, A. E., Waldrop, A. et al. (2019). Ontology-based data integration for advancing toxicological knowledge. Current Opinion in Toxicology 16, 67–74. https://doi.org/10.1016/j.cotox.2019.05.005.

Brockmeier, E. K., Hodges, G., Hutchinson, T. H. et al. (2017). The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment. Toxicol Sci 158, 252–262. https://doi.org/10.1093/toxsci/kfx097.

Bruderer, R., Bernhardt, O. M., Gandhi, T. et al. (2017). Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results. Molecular and Cellular Proteomics 16, 2296–2309. https://doi.org/10.1074/mcp.RA117.000314.

Buesen, R., Chorley, B. N., da Silva Lima, B. et al. (2017). Applying ’omics technologies in chemicals risk assessment: Report of an ECETOC workshop. Regul Toxicol Pharmacol 91 Suppl 1, S3–S13. https://doi.org/10.1016/j.yrtph.2017.09.002.

Campos, B., Colbourne, J. K., Brown, J. B. et al. (2018). How omics technologies can enhance chemical safety regulation: perspectives from academia, government, and industry. Environmental Toxicology and Chemistry 37, 1252–1259. https://doi.org/10.1002/etc.4079.

Chambers, M. C., Maclean, B., Burke, R. et al. (2012). A Cross-platform Toolkit for Mass Spectrometry and Proteomics. Nat Biotechnol 30, 918–920. https://doi.org/10.1038/nbt.2377.

Chen, L., Hu, Y., He, J. et al. (2017). Responses of the Proteome and Metabolome in Livers of Zebrafish Exposed Chronically to Environmentally Relevant Concentrations of Microcystin-LR. Environ Sci Technol 51, 596–607. https://doi.org/10.1021/acs.est.6b03990.

Chick, J. M., Kolippakkam, D., Nusinow, D. P. et al. (2015). A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides. Nat Biotechnol 33, 743–749. https://doi.org/10.1038/nbt.3267.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences. 2nd ed. New York: Lawrence Erlbaum Associates. Available at: https://doi.org/10.4324/9780203771587.

Delcourt, N., Quevedo, C., Nonne, C. et al. (2015). Targeted Identification of Sialoglycoproteins in Hypoxic Endothelial Cells and Validation in Zebrafish Reveal Roles for Proteins in Angiogenesis. J Biol Chem 290, 3405–3417. https://doi.org/10.1074/jbc.M114.618611.

Della Torre, C., Maggioni, D., Ghilardi, A. et al. (2018). The interactions of fullerene C60 and Benzo(α)pyrene influence their bioavailability and toxicity to zebrafish embryos. Environmental Pollution 241, 999–1008. https://doi.org/10.1016/j.envpol.2018.06.042.

Dhillon, R. S. and Richards, J. G. (2018). Hypoxia induces selective modifications to the acetylome in the brain of zebrafish (Danio rerio). Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 224, 79–87. https://doi.org/10.1016/j.cbpb.2017.12.018.

Eng, J. K., Searle, B. C., Clauser, K. R. et al. (2011). A Face in the Crowd: Recognizing Peptides Through Database Search. Molecular & Cellular Proteomics 10, R111.009522-R111.009522. https://doi.org/10.1074/mcp.r111.009522.

Feist, P. and Hummon, A. B. (2015). Proteomic challenges: Sample preparation techniques for Microgram-Quantity protein analysis from biological samples. International Journal of Molecular Sciences 16, 3537–3563. https://doi.org/10.3390/ijms16023537.

Fernández-Costa, C., Martínez-Bartolomé, S., McClatchy, D. B. et al. (2020). Impact of the Identification Strategy on the Reproducibility of the DDA and DIA Results. J Proteome Res 19, 3153–3161. https://doi.org/10.1021/acs.jproteome.0c00153.

Frøyset, A. K., Khan, E. A. and Fladmark, K. E. (2016). Quantitative proteomics analysis of zebrafish exposed to sub-lethal dosages of β-methyl-amino-L-alanine (BMAA). Sci Rep 6, 29631. https://doi.org/10.1038/srep29631.

Gatto, L., Gibb, S. and Rainer, J. (2020). MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data. J Proteome Res, acs.jproteome.0c00313. https://doi.org/10.1021/acs.jproteome.0c00313.

Gouveia, D., Almunia, C., Cogne, Y. et al. (2019). Ecotoxicoproteomics: A decade of progress in our understanding of anthropogenic impact on the environment. Journal of Proteomics 198, 66–77. https://doi.org/10.1016/j.jprot.2018.12.001.

Gündel, U., Kalkhof, S., Zitzkat, D. et al. (2012). Concentration–response concept in ecotoxicoproteomics: Effects of different phenanthrene concentrations to the zebrafish (Danio rerio) embryo proteome. Ecotoxicology and Environmental Safety 76, 11–22. https://doi.org/10.1016/j.ecoenv.2011.10.010.

Hagenaars, A., Vergauwen, L., Benoot, D. et al. (2013). Mechanistic toxicity study of perfluorooctanoic acid in zebrafish suggests mitochondrial dysfunction to play a key role in PFOA toxicity. Chemosphere 91, 844–856. https://doi.org/10.1016/j.chemosphere.2013.01.056.

Healy, M. J., Tong, W., Ostroff, S. et al. (2016). Regulatory bioinformatics for food and drug safety. Regulatory Toxicology and Pharmacology 80, 342–347. https://doi.org/10.1016/j.yrtph.2016.05.021.

Helmus, R., ter Laak, T. L., van Wezel, A. P. et al. (2021). patRoon: open-source software platform for environmental mass spectrometry based non-target screening. Journal of Cheminformatics 13, 1. https://doi.org/10.1186/s13321-020-00477-w.

Ives, C., Campia, I., Wang, R.-L. et al. (2017). Creating a Structured Adverse Outcome Pathway Knowledgebase via Ontology-Based Annotations. Applied In Vitro Toxicology 3, 298–311. https://doi.org/10.1089/aivt.2017.0017.

Jeffries, M. K. S., Stultz, A. E., Smith, A. W. et al. (2015). The fish embryo toxicity test as a replacement for the larval growth and survival test: A comparison of test sensitivity and identification of alternative endpoints in zebrafish and fathead minnows. Environ Toxicol Chem 34, 1369–1381. https://doi.org/10.1002/etc.2932.

Käll, L., Canterbury, J. D., Weston, J. et al. (2007). Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods 4, 923–925. https://doi.org/10.1038/nmeth1113.

Kendziorski, C., Irizarry, R. A., Chen, K.-S. et al. (2005). On the utility of pooling biological samples in microarray experiments. PNAS 102, 4252–4257. https://doi.org/10.1073/pnas.0500607102.

Kim, S. and Pevzner, P. A. (2014). MS-GF+ makes progress towards a universal database search tool for proteomics. Nature Communications 5, 1–10. https://doi.org/10.1038/ncomms6277.

Kim, H., Lee, S. & Park, H. Target-small decoy search strategy for false discovery rate estimation. BMC Bioinformatics 20, 438 (2019). https://doi.org/10.1186/s12859-019-3034-8

Kimmel, C. B., Ballard, W. W., Kimmel, S. R. et al. (1995). Stages of embryonic development of the zebrafish. Dev Dyn 203, 253–310. https://doi.org/10.1002/aja.1002030302.

Klont, F., Bras, L., Wolters, J. C. et al. (2018). Assessment of Sample Preparation Bias in Mass Spectrometry-Based Proteomics. Anal Chem 90, 5405–5413. https://doi.org/10.1021/acs.analchem.8b00600.

Knigge, T. (2015). Proteomics in Marine Organisms. Proteomics 15, 3921–3924. https://doi.org/10.1002/pmic.201570213.

Kong, A. T., Leprevost, F. V., Avtonomov, D. M. et al. (2017). MSFragger: ultrafast and comprehensive peptide identification in shotgun proteomics. Nat Methods 14, 513–520. https://doi.org/10.1038/nmeth.4256.

Kristofco, L. A., Haddad, S. P., Chambliss, C. K. et al. (2018). Differential uptake of and sensitivity to diphenhydramine in embryonic and larval zebrafish. Environmental Toxicology and Chemistry 37, 1175–1181. https://doi.org/10.1002/etc.4068.

Kroeger, M. (2006). How omics technologies can contribute to the ‘3R’ principles by introducing new strategies in animal testing. Trends in Biotechnology 24, 343–346. https://doi.org/10.1016/j.tibtech.2006.06.003.

Kwon, O. K., Kim, S. and Lee, S. (2016). Global proteomic analysis of lysine acetylation in zebrafish ( Danio rerio ) embryos: Proteomics and 2-DE. Electrophoresis 37, 3137–3145. https://doi.org/10.1002/elps.201600210.

Lavelle, C., Smith, L. C., Bisesi, J. H. et al. (2018). Tissue-Based Mapping of the Fathead Minnow (Pimephales promelas) Transcriptome and Proteome. Frontiers in Endocrinology 9, 1–18. https://doi.org/10.3389/fendo.2018.00611.

Lazar, C., Gatto, L., Ferro, M. et al. (2016). Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. Journal of Proteome Research 15, 1116–1125. https://doi.org/10.1021/acs.jproteome.5b00981.

Lemeer, S., Jopling, C., Gouw, J. et al. (2008). Comparative Phosphoproteomics of Zebrafish Fyn/Yes Morpholino Knockdown Embryos. Mol Cell Proteomics 7, 2176–2187. https://doi.org/10.1074/mcp.M800081-MCP200.

Lemeer, S., Ruijtenbeek, R., Pinkse, M. W. H. et al. (2007). Endogenous Phosphotyrosine Signaling in Zebrafish Embryos. Mol Cell Proteomics 6, 2088–2099. https://doi.org/10.1074/mcp.M600482-MCP200.

Levin, Y. (2011). The role of statistical power analysis in quantitative proteomics. PROTEOMICS 11, 2565–2567. https://doi.org/10.1002/pmic.201100033.

Li, D., Lu, S., Liu, W. et al. (2018). Optimal Settings of Mass Spectrometry Open Search Strategy for Higher Confidence. Journal of Proteome Research 17, 3719–3729. https://doi.org/10.1021/acs.jproteome.8b00352.

Libiseller, G., Dvorzak, M., Kleb, U. et al. (2015). IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics 16, 118. https://doi.org/10.1186/s12859-015-0562-8.

Lippolis, J. D., Powell, E. J., Reinhardt, T. A. et al. (2019). Symposium review: Omics in dairy and animal science—Promise, potential, and pitfalls*. Journal of Dairy Science 102, 4741–4754. https://doi.org/10.3168/jds.2018-15267.

Lombard-Banek, C., Moody, S. A., Manzini, M. C. et al. (2019). Microsampling Capillary Electrophoresis Mass Spectrometry Enables Single-Cell Proteomics in Complex Tissues: Developing Cell Clones in Live Xenopus laevis and Zebrafish Embryos. Anal Chem 91, 4797–4805. https://doi.org/10.1021/acs.analchem.9b00345.

López-Pedrouso, M., Varela, Z., Franco, D. et al. (2020). Can proteomics contribute to biomonitoring of aquatic pollution? A critical review. Environmental Pollution 267, 115473. https://doi.org/10.1016/j.envpol.2020.115473.

Martinson, J., Bencic, D. C., Toth, G. P. et al. (2021). De novo assembly and annotation of a highly contiguous reference genome of the fathead minnow (Pimephales promelas) reveals an AT-rich repetitive genome with compact gene structure. bioRxiv, 2021.02.24.432777. https://doi.org/10.1101/2021.02.24.432777.

Martyniuk, C. J. and Alvarez, S. (2013). Proteome analysis of the fathead minnow (Pimephales promelas) reproductive testes. Journal of Proteomics 79, 28–42. https://doi.org/10.1016/j.jprot.2012.11.023.

Martyniuk, C. J., Alvarez, S., McClung, S. et al. (2009). Quantitative Proteomic Profiles of Androgen Receptor Signaling in the Liver of Fathead Minnows (Pimephales promelas). J Proteome Res 8, 2186–2200. https://doi.org/10.1021/pr800627n.

May, D. H., Tamura, K. and Noble, W. S. (2017). Param-Medic: A Tool for Improving MS/MS Database Search Yield by Optimizing Parameter Settings. J Proteome Res 16, 1817–1824. https://doi.org/10.1021/acs.jproteome.7b00028.

Meigs, L., Smirnova, L., Rovida, C. et al. (2018). Animal testing and its alternatives – the most important omics is economics. ALTEX 35, 275–305. https://doi.org/10.14573/altex.1807041.

Molinari, N., Roche, S., Peoc’h, K. et al. (2018). Sample Pooling and Inflammation Linked to the False Selection of Biomarkers for Neurodegenerative Diseases in Top–Down Proteomics: A Pilot Study. Front Mol Neurosci 11. https://doi.org/10.3389/fnmol.2018.00477.

Moosa, J. M., Guan, S., Moran, M. F. et al. (2020). Repeat-Preserving Decoy Database for False Discovery Rate Estimation in Peptide Identification. J Proteome Res 19, 1029–1036. https://doi.org/10.1021/acs.jproteome.9b00555.

Moreton, M. L., Lo, B. P., Simmons, D. B. D. et al. (2020). Toxicity of the aquatic herbicide, reward®, on the fathead minnow with pulsed-exposure proteomic profile. Comparative Biochemistry and Physiology Part D: Genomics and Proteomics 33, 100635. https://doi.org/10.1016/j.cbd.2019.100635.

OECD (2013). Test No. 236: Fish Embryo Acute Toxicity (FET) Test, OECD Guidelines for the Testing of Chemicals, Section 2, OECD Publishing. , 1–22. https://doi.org/10.1787/9789264203709-en.

Piehowski, P. D., Petyuk, V. A., Orton, D. J. et al. (2013). Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis. J Proteome Res 12, 2128–2137. https://doi.org/10.1021/pr301146m.

van der Plas-Duivesteijn, S. J., Mohammed, Y., Dalebout, H. et al. (2014). Identifying Proteins in Zebrafish Embryos Using Spectral Libraries Generated from Dissected Adult Organs and Tissues. J Proteome Res 13, 1537–1544. https://doi.org/10.1021/pr4010585.

Purushothaman, K., Das, P. P., Presslauer, C. et al. (2019). Proteomics Analysis of Early Developmental Stages of Zebrafish Embryos. IJMS 20, 6359. https://doi.org/10.3390/ijms20246359.

R Development Core Team (2013). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: http://www.r-project.org/.

Rawlings, J. M., Belanger, S. E., Connors, K. A. et al. (2019). Fish embryo tests and acute fish toxicity tests are interchangeable in the application of the threshold approach. Environmental Toxicology and Chemistry 38, 671–681. https://doi.org/10.1002/etc.4351.

Révész, Á., Milley, M. G., Nagy, K. et al. (2021). Tailoring to Search Engines: Bottom-Up Proteomics with Collision Energies Optimized for Identification Confidence. J Proteome Res 20, 474–484. https://doi.org/10.1021/acs.jproteome.0c00518.

Sadiq, S. T. and Agranoff, D. (2008). Pooling serum samples may lead to loss of potential biomarkers in SELDI-ToF MS proteomic profiling. Proteome Sci 6, 16. https://doi.org/10.1186/1477-5956-6-16.

Sanchez, B. C., Ralston-Hooper, K. and Sepúlveda, M. S. (2011). Review of recent proteomic applications in aquatic toxicology. Environ Toxicol Chem 30, 274–282. https://doi.org/10.1002/etc.402.

Sauer, U. G., Deferme, L., Gribaldo, L. et al. (2017). The challenge of the application of ’omics technologies in chemicals risk assessment: Background and outlook. Regulatory Toxicology and Pharmacology 91, S14–S26. https://doi.org/10.1016/j.yrtph.2017.09.020.

Schaeck, M., Van den Broeck, W., Hermans, K. et al. (2013). Fish as Research Tools: Alternatives to In Vivo Experiments. Altern Lab Anim 41, 219–229. https://doi.org/10.1177/026119291304100305.

Searle, B. C., Turner, M. and Nesvizhskii, A. I. (2008). Improving Sensitivity by Probabilistically Combining Results from Multiple MS/MS Search Methodologies. J Proteome Res 7, 245–253. https://doi.org/10.1021/pr070540w.

Shliaha, P. V., Bond, N. J., Gatto, L. et al. (2013). Effects of Traveling Wave Ion Mobility Separation on Data Independent Acquisition in Proteomics Studies. J Proteome Res 12, 2323–2339. https://doi.org/10.1021/pr300775k.

Smith, C. A., Want, E. J., O’Maille, G. et al. (2006). XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Anal Chem 78, 779–787. https://doi.org/10.1021/ac051437y.

Smith, L. C., Lavelle, C. M., Silva-Sanchez, C. et al. (2018). Early phosphoproteomic changes for adverse outcome pathway development in the fathead minnow (Pimephales promelas) brain. Scientific Reports 8, 1–14. https://doi.org/10.1038/s41598-018-28395-w.

Spivak, M., Weston, J., Bottou, L. et al. (2009). Improvements to the Percolator algorithm for peptide identification from shotgun proteomics data sets. J Proteome Res 8, 3737–3745. https://doi.org/10.1021/pr801109k.

Steele, W. B., Kristofco, L. A., Corrales, J. et al. (2018). Comparative behavioral toxicology with two common larval fish models: Exploring relationships among modes of action and locomotor responses. Science of The Total Environment 640–641, 1587–1600. https://doi.org/10.1016/j.scitotenv.2018.05.402.

Su, T., Lian, D., Bai, Y. et al. (2021). The feasibility of the zebrafish embryo as a promising alternative for acute toxicity test using various fish species: A critical review. Science of The Total Environment 787, 147705. https://doi.org/10.1016/j.scitotenv.2021.147705.

Tabb, D. L., Vega-Montoto, L., Rudnick, P. A. et al. (2010). Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography—Tandem Mass Spectrometry. J Proteome Res 9, 761. https://doi.org/10.1021/pr9006365.

Tautenhahn, R., Böttcher, C. and Neumann, S. (2008). Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9, 504. https://doi.org/10.1186/1471-2105-9-504.

Taylor, K. and Alvarez, L. R. (2019). An Estimate of the Number of Animals Used for Scientific Purposes Worldwide in 2015. Altern Lab Anim 47, 196–213. https://doi.org/10.1177/0261192919899853.

The, M., MacCoss, M. J., Noble, W. S. et al. (2016). Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0. Journal of the American Society for Mass Spectrometry 27, 1719–1727. https://doi.org/10.1007/s13361-016-1460-7.

Tsou, C.-C., Tsai, C.-F., Teo, G. C. et al. (2016). Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers. PROTEOMICS 16, 2257–2271. https://doi.org/10.1002/pmic.201500526.

UK Home Office (2015). Statistics of Scientific Procedures on Living Animals Great Britain 2014. https://doi.org/10/15.

USEPA and Environmental Protection Agencyency (2002). Method 1000.0: Fathead Minnow, Pimephales promelas, Larval Survival and Growth; Chronic Toxicity. Environmental Protection Agenc. Available at: www.epa.gov.

Välikangas, T., Suomi, T. and Elo, L. L. (2018). A systematic evaluation of normalization methods in quantitative label-free proteomics. Brief Bioinform 19, bbw095. https://doi.org/10.1093/bib/bbw095.

Wei, R., Wang, J., Su, M. et al. (2018). Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data. Scientific Reports 8, 1–10. https://doi.org/10.1038/s41598-017-19120-0.

Weng, R. R., Chu, L. J., Shu, H.-W. et al. (2013). Large precursor tolerance database search — A simple approach for estimation of the amount of spectra with precursor mass shifts in proteomic data. Journal of Proteomics 91, 375–384. https://doi.org/10.1016/j.jprot.2013.07.030.

Wiśniewski, J. R. (2016). Quantitative Evaluation of Filter Aided Sample Preparation (FASP) and Multienzyme Digestion FASP Protocols. Analytical Chemistry 88, 5438–5443. https://doi.org/10.1021/acs.analchem.6b00859.

Wit, M. D., Keil, D., Ven, K. van der et al. (2010). An integrated transcriptomic and proteomic approach characterizing estrogenic and metabolic effects of 17 α-ethinylestradiol in zebrafish (Danio rerio). General and Comparative Endocrinology 167, 190–201. https://doi.org/10.1016/j.ygcen.2010.03.003.

Wu, Y., Lou, Q.-Y., Ge, F. et al. (2017). Quantitative Proteomics Analysis Reveals Novel Targets of miR-21 in Zebrafish Embryos. Sci Rep 7, 4022. https://doi.org/10.1038/s41598-017-04166-x.

Yu, G., Wang, L.-G., Han, Y. et al. (2012). clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS: A Journal of Integrative Biology 16, 284–287. https://doi.org/10.1089/omi.2011.0118.

Zhang, W., Liu, Y., Zhang, H. et al. (2012). Proteomic analysis of male zebrafish livers chronically exposed to perfluorononanoic acid. Environment International 42, 20–30. https://doi.org/10.1016/j.envint.2011.03.002.

Zhou, C., Simpson, K. L., Lancashire, L. J. et al. (2012). Statistical Considerations of Optimal Study Design for Human Plasma Proteomics and Biomarker Discovery. J Proteome Res 11, 2103–2113. https://doi.org/10.1021/pr200636x.