Abstract
Antibodies are used in multiple cell biology applications, but there are no standardized methods to assess antibody quality—an absence that risks data integrity and reproducibility. We describe a mass spectrometry–based standard operating procedure for scoring immunoprecipitation antibody quality. We quantified the abundance of all the proteins in immunoprecipitates of 1,124 new recombinant antibodies for 152 chromatin-related human proteins by comparing normalized spectral abundance factors from the target antigen with those of all other proteins. We validated the performance of the standard operating procedure in blinded studies in five independent laboratories. Antibodies for which the target antigen or a member of its known protein complex was the most abundant protein were classified as 'IP gold standard'. This method generates quantitative outputs that can be stored and archived in public databases, and it represents a step toward a platform for community benchmarking of antibody quality.
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References
Bordeaux, J. et al. Antibody validation. Biotechniques 48, 197–209 (2010).
Malovannaya, A. et al. Streamlined analysis schema for high-throughput identification of endogenous protein complexes. Proc. Natl. Acad. Sci. USA 107, 2431–2436 (2010).
Haab, B.B. et al. A reagent resource to identify proteins and peptides of interest in cancer community (a workshop report). Mol. Cell. Proteomics 5, 1996–2007 (2006).
Pope, M.E., Soste, M.V., Eyford, B.A., Anderson, N.L. & Pearson, T.W. Anti-peptide antibody screening: selection of high affinity monoclonal reagents by a refined surface plasmon resonance technique. J. Immunol. Methods 341, 86–96 (2009).
Razavi, M. et al. MALDI Immunoscreening (MiSCREEN): a method for selection of anti-peptide monoclonal antibodies for use in immunoproteomics. J. Immunol. Methods 364, 50–64 (2011).
Boström, T., Johansson, H.J., Lehtiö, J., Uhlén, M. & Hober, S. Investigating the applicability of antibodies generated within the Human Protein Atlas as capture agents in immunoenrichment coupled to mass spectrometry. J. Proteome Res. 13, 4424–4435 (2014).
Marcon, E. et al. Human-chromatin-related protein interactions identify a demethylase complex required for chromosome segregation. Cell Rep. 8, 297–310 (2014).
Florens, L. et al. Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors. Methods 40, 303–311 (2006).
Sowa, M.E., Bennett, E.J., Gygi, S.P. & Harper, J.W. Defining the human deubiquitinating enzyme interaction landscape. Cell 138, 389–403 (2009).
Choi, H. et al. SAINT: probabilistic scoring of affinity purification–mass spectrometry data. Nat. Methods 8, 70–73 (2011).
Collins, S.R. et al. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446, 806–810 (2007).
Guruharsha, K.G. et al. A protein complex network of Drosophila melanogaster. Cell 147, 690–703 (2011).
Zhang, B., Park, B.H., Karpinets, T. & Samatova, N.F. From pull-down data to protein interaction networks and complexes with biological relevance. Bioinformatics 24, 979–986 (2008).
Hart, G.T., Lee, I. & Marcotte, E.R. A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality. BMC Bioinformatics 8, 236 (2007).
Mellacheruvu, D. et al. The CRAPome: a contaminant repository for affinity purification–mass spectrometry data. Nat. Methods 10, 730–736 (2013).
Geiger, T., Wehner, A., Schaab, C., Cox, J. & Mann, M. Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol. Cell. Proteomics 11, M111.014050 (2012).
Wysocka, J. et al. WDR5 associates with histone H3 methylated at K4 and is essential for H3 K4 methylation and vertebrate development. Cell 121, 859–872 (2005).
Gan, Q. et al. WD repeat-containing protein 5, a ubiquitously expressed histone methyltransferase adaptor protein, regulates smooth muscle cell-selective gene activation through interaction with pituitary homeobox 2. J. Biol. Chem. 286, 21853–21864 (2011).
Cesaro, E. et al. The Kruppel-like zinc finger protein ZNF224 recruits the arginine methyltransferase PRMT5 on the transcriptional repressor complex of the aldolase A gene. J. Biol. Chem. 284, 32321–32330 (2009).
Wang, L., Pal, S. & Sif, S. Protein arginine methyltransferase 5 suppresses the transcription of the RB family of tumor suppressors in leukemia and lymphoma cells. Mol. Cell. Biol. 28, 6262–6277 (2008).
Tae, S. et al. Bromodomain protein 7 interacts with PRMT5 and PRC2, and is involved in transcriptional repression of their target genes. Nucleic Acids Res. 39, 5424–5438 (2011).
Liu, H., Sadygov, R.G. & Yates, J.R. III. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76, 4193–4201 (2004).
Geiger, T. et al. Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat. Protoc. 6, 147–157 (2011).
Lu, Y., Bottari, P., Aebersold, R., Turecek, F. & Gelb, M.H. Absolute quantification of specific proteins in complex mixtures using visible isotope-coded affinity tags. Methods Mol. Biol. 359, 159–176 (2007).
Wepf, A., Glatter, T., Schmidt, A., Aebersold, R. & Gstaiger, M. Quantitative interaction proteomics using mass spectrometry. Nat. Methods 6, 203–205 (2009).
Zeiler, M., Straube, W.L., Lundberg, E., Uhlen, M. & Mann, M. A Protein Epitope Signature Tag (PrEST) library allows SILAC-based absolute quantification and multiplexed determination of protein copy numbers in cell lines. Mol. Cell. Proteomics 11, O111.009613 (2012).
Gillet, L.C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 (2012).
Baker, M. Blame it on the antibodies. Nature 521, 274–276 (2015).
Bradbury, A. & Plückthun, A. Standardize antibodies used in research. Nature 518, 27–29 (2015).
Mak, A.B. et al. A lentiviral functional proteomics approach identifies chromatin remodeling complexes important for the induction of pluripotency. Mol. Cell. Proteomics 9, 811–823 (2010).
Porath, J. Immobilized metal ion affinity chromatography. Protein Expr. Purif. 3, 263–281 (1992).
Beckett, D., Kovaleva, E. & Schatz, P.J. A minimal peptide substrate in biotin holoenzyme synthetase-catalyzed biotinylation. Protein Sci. 8, 921–929 (1999).
Cull, M.G. & Schatz, P.J. Biotinylation of proteins in vivo and in vitro using small peptide tags. Methods Enzymol. 326, 430–440 (2000).
Paduch, M. et al. Generating conformation-specific synthetic antibodies to trap proteins in selected functional states. Methods 60, 3–14 (2013).
Miller, K.R. et al. T cell receptor-like recognition of tumor in vivo by synthetic antibody fragment. PLoS ONE 7, e43746 (2012).
Fellouse, F.A. et al. High-throughput generation of synthetic antibodies from highly functional minimalist phage-displayed libraries. J. Mol. Biol. 373, 924–940 (2007).
Liu, G. et al. ProHits: integrated software for mass spectrometry–based interaction proteomics. Nat. Biotechnol. 28, 1015–1017 (2010).
Vizcaíno, J.A. et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 32, 223–226 (2014).
Mosley, A.L., Florens, L., Wen, Z. & Washburn, M.P. A label free quantitative proteomic analysis of the Saccharomyces cerevisiae nucleus. J. Proteomics 72, 110–120 (2009).
Boyer, L.A. et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122, 947–956 (2005).
Rada-Iglesias, A. et al. A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470, 279–283 (2011).
Acknowledgements
The Structural Genomics Consortium (SGC) is a registered charity (number 1097737) that receives funds from AbbVie, Bayer, Boehringer Ingelheim, Canada Foundation for Innovation, Genome Canada, GlaxoSmithKline, Janssen, Lilly Canada, Merck, the Novartis Research Foundation, the Ontario Ministry of Research and Innovation, Pfizer, Takeda and the Wellcome Trust. This research was also supported by the Ontario Research Fund (J.F.G. and A.E.; 489921), by the Canadian Institutes of Health Research (A.-C.G.; MOP-84314) and through a grant from Thermo Fisher Scientific. A.G.P., L.Z., J.J.K. and J.R.W. were supported by the National Cancer Institute of the US National Institutes of Health under award number U24CA160034. A.A.K., S.K. and S.S.S. were supported by the National Cancer Institute (GM094588 and HG006436) and by SGC. R.A. was supported by the European Union 7th Framework project PROSPECTS and European Research Council advanced grant Proteomics v3.0 (233226). R.A., M.G. and B.C.C. were supported by SystemsX.ch Project.
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Contributions
E.M. and A.M.E. designed experiments, developed the methodology, analyzed and interpreted the data and wrote the manuscript. E.M., H.J. and A.B. designed and performed experiments and analyzed the data. H.G. performed MS. S. Phanse and S. Pu performed bioinformatic analysis. G.B., B.C.C., J.J.K., B.K., B.L., Z.-Y.L., M.F.L., G.V., M.S.V., J.R.W. and L.Z. validated the method in blinded studies. M.F., A.H., P.L., M.R., A.S., M.P., S.M., E.D. and N.Z. made the antigens and produced Fabs and IgGs. X.G. helped with the production of Flag-tagged cell lines and performed experiments. G.Z. produced Flag-tagged cell lines. J.B.O. helped in initial analysis of the MS data. T.N. designed and performed IF experiments. R.A., A.-C.G., M.G. and A.G.P. are principal investigators where blinded studies were carried out. S.K., A.A.K. and S.S.S. are principal investigators where recombinant antibody selection was carried out. S.J.W. and A.E. are principal investigator involved in bioinformatics analysis. A.E. is a principal investigator where all the mass spectroscopy analysis was carried out. J.F.G. is the principal investigator where all the validation experiments were carried out and a principal investigator on the grant that supported the project. S.G. supervised and organized the project including antigen design and production, Fab production, validation and data management. C.H.A. and A.M.E. conceived the project, designed the experiments and are principal investigators for the grants that supported this project.
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G.B., B.K., M.F.L., G.V. and M.S.V are employees of Thermo Fisher Scientific, from where the majority of the antibodies are distributed.
Integrated supplementary information
Supplementary Figure 1 Method development.
Titration experiments to determine standard antibody:lysate concentration. Lysates (~2 mg of total proteins) were prepared from cells expressing FLAG- tagged CBX2 (a) or SCMH1 (b) and either anti-Flag or a number of recombinant antibodies were titred in for immunoprecipitation. Shown is a Western blot of the Flag-tagged antigen. The left of each Panel, labeled IP, indicates the proteins captured in the IP and on the right, labeled FT, are the proteins remaining in the lysate after immunoprecipitation. Most of the recombinant Fabs approached saturation when 2 μg of antibody or 10 μl of anti-Flag beads were used per 2 mg of lysate.
Supplementary Figure 2 Determination of a suitable cutoff for the elimination of background contaminants.
(a) The graph shows the number of preys recovered at a given percentage of occurence among all Fab or IgG purifications. From this graph it seems that 8% is an inflection point indicating a good cut-off frequency. Below 8%, increase of percentage causes rapid drop in the number of preys recovered while above 8% the drop in frequency slows down dramatically. This implies that preys appearing in less than 8% purifications are more likely to be specific. Those appearing in more than 8% purification are more likely to be non-specific (regardless of the occurrence, the number of preys is similar). (b) Venn diagrams showing overlap between proteins identified in immunoprecipitations using >1,000 different recombinant antibodies and those from Crapome repository11. On the left, we compared all the proteins detected in all immunoprecipitations and on the right only those proteins that appear in more than 8% of all purifications.
Supplementary Figure 3 Comparison between IP-WB and IP-MS methods.
Fabs or IgGs (phagemid IDs are indicated) against several targets were used to immunoprecipitate their antigens from HEK293 cell extracts expressing their respective Flag-tagged proteins. The immunoprecipitated proteins were then detected using either WB or MS. The spectral counts from IP-MS experiments are indicated below each panel.
Supplementary Figure 4 Comparison of staining patterns.
Flag-tagged cell lines were stained using either anti-Flag antibody or Fabs against 5 different proteins, listed on the right, and the staining pattern was compared. For these Fabs, the localization patterns are very similar to the anti-Flag staining. DAPI staining was used to highlight the nuclei. Flag-tagged WDR5, shows nucleolar staining, but the anti-WDR5 Fab does not; the nucleolar staining is likely due to the over-expression of Flag-tagged WDR5. Scale represents 20 μm.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–4, Supplementary Note and Supplementary Protocol (PDF 3507 kb)
Supplementary Table 1
Total list of proteins found in immunoprecipitations using 1,001 recombinant Fabs (XLSX 210 kb)
Supplementary Table 2
Total list of proteins found in immunoprecipitations using 362 recombinant IgGs. (XLSX 151 kb)
Supplementary Table 3
Representative analysis of composition of individual immunoprecipitations. (XLSX 57 kb)
Supplementary Table 4
Targets with IP-MS positive antibodies and antibody assessment. (XLSX 31 kb)
Supplementary Table 5
Comparison of antibody ranking among five different labs. (XLSX 48 kb)
Supplementary Table 6
Selecting “IP positive” antibodies from in vitro validated antibodies. (XLSX 50 kb)
Supplementary Table 7
Analyzing success rates of Fabs in immunofluorescence. (XLSX 49 kb)
Supplementary Table 8
Analyzing success rates of Fabs in ChIP assays. (XLSX 43 kb)
Supplementary Table 9
All Compass-derived scores used to generate Figure 2b. (XLSX 768 kb)
Supplementary Table 10
List of the raw files available from PRIDE and their names. (XLSX 106 kb)
Supplementary Table 11
Primers used in ChIP-qPCR. (XLSX 45 kb)
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Marcon, E., Jain, H., Bhattacharya, A. et al. Assessment of a method to characterize antibody selectivity and specificity for use in immunoprecipitation. Nat Methods 12, 725–731 (2015). https://doi.org/10.1038/nmeth.3472
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DOI: https://doi.org/10.1038/nmeth.3472
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