Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Assessment of a method to characterize antibody selectivity and specificity for use in immunoprecipitation

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Method overview and summary.
Figure 2: Comparison of antibody performance among five independent laboratories based on CompPASS-derived scores.
Figure 3: Comparison between IP-WB or IP-MS methods.
Figure 4: 'IP gold standard' antibodies are suitable for IF.
Figure 5: IP-MS–validated antibodies in ChIP applications.

References

  1. Bordeaux, J. et al. Antibody validation. Biotechniques 48, 197–209 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Malovannaya, A. et al. Streamlined analysis schema for high-throughput identification of endogenous protein complexes. Proc. Natl. Acad. Sci. USA 107, 2431–2436 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 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).

    CAS  PubMed  Google Scholar 

  4. 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).

    CAS  PubMed  Google Scholar 

  5. 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).

    CAS  PubMed  Google Scholar 

  6. 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).

    PubMed  Google Scholar 

  7. Marcon, E. et al. Human-chromatin-related protein interactions identify a demethylase complex required for chromosome segregation. Cell Rep. 8, 297–310 (2014).

    CAS  PubMed  Google Scholar 

  8. Florens, L. et al. Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors. Methods 40, 303–311 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Sowa, M.E., Bennett, E.J., Gygi, S.P. & Harper, J.W. Defining the human deubiquitinating enzyme interaction landscape. Cell 138, 389–403 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Choi, H. et al. SAINT: probabilistic scoring of affinity purification–mass spectrometry data. Nat. Methods 8, 70–73 (2011).

    CAS  PubMed  Google Scholar 

  11. 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).

    CAS  PubMed  Google Scholar 

  12. Guruharsha, K.G. et al. A protein complex network of Drosophila melanogaster. Cell 147, 690–703 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 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).

    CAS  PubMed  Google Scholar 

  14. 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).

    PubMed  PubMed Central  Google Scholar 

  15. Mellacheruvu, D. et al. The CRAPome: a contaminant repository for affinity purification–mass spectrometry data. Nat. Methods 10, 730–736 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 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).

    PubMed  PubMed Central  Google Scholar 

  17. 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).

    CAS  PubMed  Google Scholar 

  18. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 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).

    CAS  PubMed  Google Scholar 

  23. 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).

    CAS  PubMed  Google Scholar 

  24. 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).

    CAS  PubMed  Google Scholar 

  25. Wepf, A., Glatter, T., Schmidt, A., Aebersold, R. & Gstaiger, M. Quantitative interaction proteomics using mass spectrometry. Nat. Methods 6, 203–205 (2009).

    CAS  PubMed  Google Scholar 

  26. 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).

    PubMed  Google Scholar 

  27. 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).

    PubMed  PubMed Central  Google Scholar 

  28. Baker, M. Blame it on the antibodies. Nature 521, 274–276 (2015).

    CAS  PubMed  Google Scholar 

  29. Bradbury, A. & Plückthun, A. Standardize antibodies used in research. Nature 518, 27–29 (2015).

    CAS  PubMed  Google Scholar 

  30. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Porath, J. Immobilized metal ion affinity chromatography. Protein Expr. Purif. 3, 263–281 (1992).

    CAS  PubMed  Google Scholar 

  32. Beckett, D., Kovaleva, E. & Schatz, P.J. A minimal peptide substrate in biotin holoenzyme synthetase-catalyzed biotinylation. Protein Sci. 8, 921–929 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Cull, M.G. & Schatz, P.J. Biotinylation of proteins in vivo and in vitro using small peptide tags. Methods Enzymol. 326, 430–440 (2000).

    CAS  PubMed  Google Scholar 

  34. Paduch, M. et al. Generating conformation-specific synthetic antibodies to trap proteins in selected functional states. Methods 60, 3–14 (2013).

    CAS  PubMed  Google Scholar 

  35. Miller, K.R. et al. T cell receptor-like recognition of tumor in vivo by synthetic antibody fragment. PLoS ONE 7, e43746 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 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).

    CAS  PubMed  Google Scholar 

  37. Liu, G. et al. ProHits: integrated software for mass spectrometry–based interaction proteomics. Nat. Biotechnol. 28, 1015–1017 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Vizcaíno, J.A. et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 32, 223–226 (2014).

    PubMed  PubMed Central  Google Scholar 

  39. 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).

    CAS  PubMed  Google Scholar 

  40. Boyer, L.A. et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122, 947–956 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Rada-Iglesias, A. et al. A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470, 279–283 (2011).

    CAS  PubMed  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

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.

Corresponding author

Correspondence to Aled M Edwards.

Ethics declarations

Competing interests

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)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.3472

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing