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A consensus platform for antibody characterization

Nature Protocols volume 20, pages 1509–1545 (2025)Cite this article

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Abstract

Antibody-based research applications are critical for biological discovery. Yet there are no industry standards for comparing the performance of antibodies in various applications. We describe a knockout cell line-based antibody characterization platform, developed and approved jointly by industry and academic researchers, that enables the systematic comparison of antibody performance in western blot, immunoprecipitation and immunofluorescence. The scalable protocols, which require minimal technological resources, consist of (1) the identification of appropriate cell lines for antibody characterization studies, (2) development/contribution of isogenic knockout controls, and (3) a series of antibody characterization procedures focused on the most common applications of antibodies in research. We provide examples of expected outcomes to guide antibody users in evaluating antibody performance. Central to our approach is advocating for transparent and open data sharing, enabling a community effort to identify specific antibodies for all human proteins. Mid-level graduate students with training in biochemistry and prior experience in cell culture and microscopy can complete the protocols for a specific protein within 1 month while working part-time on this effort. Antibody characterization is needed to meet standards for resource validation and data reproducibility, which are increasingly required by journals and funding agencies.

Key points

  • YCharOS is a knockout-based consensus platform for antibody characterization developed through a collaboration between academia and industry. The platform enables direct comparisons among research antibodies that target a specific protein in three common applications: western blot, immunoprecipitation and immunofluorescence.

  • The use of the YCharOS consensus protocols and open data dissemination facilitates a community-driven approach to identifying one, or ideally two, renewable and specific antibodies for each human protein.

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Fig. 1: Experimental design of the antibody characterization workflow.
Fig. 2: Interpretation of antibody performance in WB.
Fig. 3: Protein abundance influences antibody performance.
Fig. 4: Identification of an adequate cell line background for KO generation.
Fig. 5: Antibody performance correlates with sample preparation.
Fig. 6: Choice of SDS–PAGE chemistry in WB.
Fig. 7: Interpretation of antibody performance by IP.
Fig. 8: Selection of secondary detection systems for IP-WB experiments.
Fig. 9: Semiautomated analysis of antibody performance in IF.
Fig. 10: Boiling protein samples creates an aggregation artifact.

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Data availability

Underlying data can be found at Zenodo via the following links: https://doi.org/10.5281/zenodo.8356134 (ref. 49), https://doi.org/10.5281/zenodo.10844512 (ref. 50), https://doi.org/10.5281/zenodo.10108291 (ref. 51), https://doi.org/10.5281/zenodo.10149969 (ref. 52), https://doi.org/10.5281/zenodo.12666747 (ref. 53), https://doi.org/10.5281/zenodo.12636746 (ref. 54), https://doi.org/10.5281/zenodo.7459248 (ref. 55), https://doi.org/10.5281/zenodo.10835290 (ref. 56), https://doi.org/10.5281/zenodo.7671286 (ref. 57), https://doi.org/10.5281/zenodo.10835327 (ref. 58), https://doi.org/10.5281/zenodo.7459387 (ref. 59), https://doi.org/10.5281/zenodo.10838677 (ref. 60), https://doi.org/10.5281/zenodo.4724176 (ref. 61), https://doi.org/10.5281/zenodo.10845536 (ref. 62), https://doi.org/10.5281/zenodo.13151151 (ref. 63), https://doi.org/10.5281/zenodo.7971951 (ref. 64), https://doi.org/10.5281/zenodo.10839338 (ref. 65), https://doi.org/10.5281/zenodo.7459541 (ref. 66), https://doi.org/10.5281/zenodo.7671135 (ref. 67), https://doi.org/10.5281/zenodo.10819348 (ref. 68), https://doi.org/10.5281/zenodo.10927535 (ref. 69), https://doi.org/10.5281/zenodo.10819189 (ref. 70) and https://doi.org/10.5281/zenodo.10839647 (ref. 71).

Code availability

YCharOS imaging analysis scripts are available on GitHub via https://github.com/ABIF-McGill/YCharOS_IF_characterization/.

References

  1. Baker, M. When antibodies mislead: the quest for validation. Nature 585, 313–314 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Voskuil, J. L. A. et al. The Antibody Society’s antibody validation webinar series. MAbs 12, 1794421 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ellis, M. J. et al. Identification of high-performing antibodies for the reliable detection of Tau proteoforms by Western blotting and immunohistochemistry. Acta Neuropathol. 147, 87 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Andersson, S. et al. Insufficient antibody validation challenges oestrogen receptor beta research. Nat. Commun. 8, 15840 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Laflamme, C. et al. Implementation of an antibody characterization procedure and application to the major ALS/FTD disease gene C9ORF72. eLife https://doi.org/10.7554/eLife.48363 (2019).

  6. Rot, A. et al. Murine bone marrow macrophages and human monocytes do not express atypical chemokine receptor 1. Cell Stem Cell 29, 1013–1015 (2022).

    Article  CAS  PubMed  Google Scholar 

  7. Virk, H. S. et al. Validation of antibodies for the specific detection of human TRPA1. Sci. Rep. 9, 18500 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Elliott, S. et al. Anti-Epo receptor antibodies do not predict Epo receptor expression. Blood 107, 1892–1895 (2006).

    Article  CAS  PubMed  Google Scholar 

  9. Schrohl, A. S., Pedersen, H. C., Jensen, S. S., Nielsen, S. L. & Brunner, N. Human epidermal growth factor receptor 2 (HER2) immunoreactivity: specificity of three pharmacodiagnostic antibodies. Histopathology 59, 975–983 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Biddle, M. S. & Virk, H. S. YCharOS open antibody characterisation data: lessons learned and progress made. F1000Res 12, 1344 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ayoubi, R. et al. Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications. eLife https://doi.org/10.7554/eLife.91645 (2023).

  12. Biddle, M. et al. Improving the integrity and reproducibility of research that uses antibodies: a technical, data sharing, behavioral and policy challenge. MAbs 16, 2323706 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bandrowski, A., Pairish, M., Eckmann, P., Grethe, J. & Martone, M. E. The Antibody Registry: ten years of registering antibodies. Nucleic Acids Res. 51, D358–D367 (2023).

    Article  CAS  PubMed  Google Scholar 

  14. Davies, P. et al. Comprehensive characterization and optimization of anti-LRRK2 (leucine-rich repeat kinase 2) monoclonal antibodies. Biochem. J. 453, 101–113 (2013).

    Article  CAS  PubMed  Google Scholar 

  15. Lis, P. et al. Development of phospho-specific Rab protein antibodies to monitor in vivo activity of the LRRK2 Parkinson’s disease kinase. Biochem. J. 475, 1–22 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Jong, T., Gehrlein, A., Sidransky, E., Jagasia, R. & Chen, Y. Characterization of novel human β-glucocerebrosidase antibodies for Parkinson’s disease research. J. Parkinsons Dis. 14, 65–78 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Stadler, C. et al. Systematic validation of antibody binding and protein subcellular localization using siRNA and confocal microscopy. J. Proteomics 75, 2236–2251 (2012).

    Article  CAS  PubMed  Google Scholar 

  18. Laflamme, C., Edwards, A. M., Bandrowski, A. E. & McPherson, P. S. Opinion: independent third-party entities as a model for validation of commercial antibodies. N. Biotechnol. 65, 1–8 (2021).

    Article  CAS  PubMed  Google Scholar 

  19. Baker, M. Reproducibility crisis: blame it on the antibodies. Nature 521, 274–276 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Goodman, S. L. The antibody horror show: an introductory guide for the perplexed. N. Biotechnol. 45, 9–13 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Bradbury, A. & Pluckthun, A. Reproducibility: standardize antibodies used in research. Nature 518, 27–29 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Kwon, D. The antibodies don’t work! The race to rid labs of molecules that ruin experiments. Nature 635, 26–28 (2024).

  23. Uhlen, M. et al. A proposal for validation of antibodies. Nat. Methods 13, 823–827 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mitchell, K. G. et al. High-volume hybridoma sequencing on the NeuroMabSeq platform enables efficient generation of recombinant monoclonal antibodies and scFvs for neuroscience research. Sci. Rep. 13, 16200 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bandrowski, A. et al. The Resource Identification Initiative: a cultural shift in publishing. F1000Res 4, 134 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Bandrowski, A. E. & Martone, M. E. RRIDs: a simple step toward improving reproducibility through rigor and transparency of experimental methods. Neuron 90, 434–436 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bandrowski, A., Pairish, M., Eckmann, P., Grethe, J. & Martone, M. E. The antibody registry: ten years of registering antibodies. Nucleic Acids Res. https://doi.org/10.1093/nar/gkac927 (2022).

  28. Longworth, S. YCharOS antibody characterization data is now linked from the CiteAb antibody search engine! CiteAB https://blog.citeab.com/ycharos-antibody-characterization-data-citeab-search-engine/ (2024).

  29. Marx, V. Change-makers bring on recombinant antibodies. Nat. Methods 17, 763–766 (2020).

    Article  CAS  PubMed  Google Scholar 

  30. Schwende, H., Fitzke, E., Ambs, P. & Dieter, P. Differences in the state of differentiation of THP-1 cells induced by phorbol ester and 1,25-dihydroxyvitamin D3. J. Leukoc. Biol. 59, 555–561 (1996).

    Article  CAS  PubMed  Google Scholar 

  31. Bairoch, A. The Cellosaurus, a cell-line knowledge resource. J. Biomol. Tech. 29, 25–38 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ruiz Moleon, V. et al. A guide to selecting high-performing antibodies for Rab27A (UniProt ID: P51159) for use in western blot, immunoprecipitation, and immunofluorescence. Zenodo https://doi.org/10.5281/zenodo.13685126 (2024).

  33. Ayoubi, R. et al. A guide to selecting high-performing antibodies for CSNK1A1 (UniProt ID: P48729) for use in western blot, immunoprecipitation, and immunofluorescence [version 1; peer review: awaiting peer review]. F1000Res https://doi.org/10.12688/f1000research.155928.1 (2024).

  34. Fotouhi, M. et al. Antibody characterization report for valosin-containing protein VCP. Zenodo https://doi.org/10.5281/zenodo.7971904 (2023).

  35. Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Uhlen, M. et al. The human secretome. Sci. Signal https://doi.org/10.1126/scisignal.aaz0274 (2019).

  37. Starr, T., Bauler, T. J., Malik-Kale, P. & Steele-Mortimer, O. The phorbol 12-myristate-13-acetate differentiation protocol is critical to the interaction of THP-1 macrophages with Salmonella typhimurium. PLoS ONE 13, e0193601 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Subedi, P. et al. Comparison of methods to isolate proteins from extracellular vesicles for mass spectrometry-based proteomic analyses. Anal. Biochem. 584, 113390 (2019).

    Article  CAS  PubMed  Google Scholar 

  39. Shirai, A. et al. Global analysis of gel mobility of proteins and its use in target identification. J. Biol. Chem. 283, 10745–10752 (2008).

    Article  CAS  PubMed  Google Scholar 

  40. Marcon, E. et al. Assessment of a method to characterize antibody selectivity and specificity for use in immunoprecipitation. Nat. Methods 12, 725–731 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. Sikorski, K. et al. A high-throughput pipeline for validation of antibodies. Nat. Methods 15, 909–912 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. Stadler, C., Skogs, M., Brismar, H., Uhlen, M. & Lundberg, E. A single fixation protocol for proteome-wide immunofluorescence localization studies. J. Proteomics 73, 1067–1078 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. Pillai-Kastoori, L., Schutz-Geschwender, A. R. & Harford, J. A. A systematic approach to quantitative western blot analysis. Anal. Biochem. 593, 113608 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Wang, M., Herrmann, C. J., Simonovic, M., Szklarczyk, D. & von Mering, C. Version 4.0 of PaxDb: protein abundance data, integrated across model organisms, tissues, and cell-lines. Proteomics 15, 3163–3168 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wang, M. et al. PaxDb, a database of protein abundance averages across all three domains of life. Mol. Cell Proteomics 11, 492–500 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Antonin, W. et al. A SNARE complex mediating fusion of late endosomes defines conserved properties of SNARE structure and function. EMBO J. 19, 6453–6464 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Bonifacino, J. S., Gershlick, D. C. & Dell’Angelica, E. C. Immunoprecipitation. Curr. Protoc. Cell Biol. 71, 7.2.1–7.2.24 (2016).

  48. Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).

    Article  CAS  PubMed  Google Scholar 

  49. Ayoubi, R. et al. Antibody characterization report for calponin-3. Zenodo https://doi.org/10.5281/zenodo.8356134 (2023).

  50. Ayoubi, R. & Laflamme, C. Dataset for the calponin-3 antibody screening study. Zenodo https://doi.org/10.5281/zenodo.10844512 (2024).

  51. Ruiz Moleon, V. et al. Antibody characterization report for PLC-γ-2 (PLCG2). Zenodo https://doi.org/10.5281/zenodo.10108291 (2023).

  52. Southern K. Dataset for the PLC-γ-2 antibody screening study. Zenodo https://doi.org/10.5281/zenodo.10149969 (2023).

  53. Biddle, M. S. et al. A guide to selecting high-performing antibodies for synaptotagmin-1 (Uniprot ID P21579) for use in western blot, immunoprecipitation, immunofluorescence and flow cytometry. Zenodo https://doi.org/10.5281/zenodo.12666747 (2024).

  54. Ayoubi, R. & Laflamme, C. Dataset for the synaptotagmin-1 antibody screening study. Zenodo https://doi.org/10.5281/zenodo.12636746 (2024).

  55. Ayoubi, R. et al. Antibody characterization report for endothelin-converting enzyme 1. Zenodo https://doi.org/10.5281/zenodo.7459248 (2022).

  56. Ayoubi, R. & Laflamme, C. Dataset for the endothelin-converting enzyme 1 antibody screening study. Zenodo https://doi.org/10.5281/zenodo.10835290 (2024).

  57. Ayoubi, R., Worrall, D., McPherson, P. S. & Laflamme, C. Antibody characterization report for angiogenin. Zenodo https://doi.org/10.5281/zenodo.7671286 (2023).

  58. Ayoubi, R. & Laflamme, C. Dataset for the angiogenin antibody screening study. Zenodo https://doi.org/10.5281/zenodo.10835327 (2024).

  59. Ayoubi, R. et al. Antibody characterization report for QPRTase (nicotinate–nucleotide pyrophosphorylase (carboxylating)). Zenodo https://doi.org/10.5281/zenodo.7459387 (2022).

  60. Ayoubi, R. & Laflamme, C. Dataset for the QPRTase (nicotinate–nucleotide pyrophosphorylase (carboxylating)) antibody screening study. Zenodo https://doi.org/10.5281/zenodo.10838677 (2024).

  61. Ayoubi, R. et al. Antibody characterization report for plectin. Zenodo https://doi.org/10.5281/zenodo.4724176 (2021).

  62. Ayoubi. R, Gonzalez Bolivar, S., McPherson, P. S. & Laflamme, C. Antibody characterization report for Fc receptor gamma-chain (FCER1G). Zenodo https://doi.org/10.5281/zenodo.10845536 (2024).

  63. Ayoubi, R. & Laflamme, C. Dataset for the Fc receptor γ-chain (FCER1G) antibody screening study. Zenodo https://doi.org/10.5281/zenodo.13151151 (2024).

  64. Ayoubi, R. et al. Antibody characterization report for Prolow-density lipoprotein receptor-related protein1 (LRP-1). Zenodo https://doi.org/10.5281/zenodo.7971951 (2023).

  65. Ayoubi, R. & Laflamme, C. Dataset for the Prolow-density lipoprotein receptor-related protein1 (LRP-1) antibody screening study. Zenodo https://doi.org/10.5281/zenodo.10839338 (2024).

  66. McDowell, I. et al. Antibody characterization report for ubiquilin-2. Zenodo https://doi.org/10.5281/zenodo.7459541 (2022).

  67. Laflamme, C. Dataset for the ubiquilin-2 antibody screening study. Zenodo https://doi.org/10.5281/zenodo.7671135 (2023).

  68. Ayoubi, R. et al. Antibody characterization report for TGM2 (protein–glutamine γ-glutamyltransferase 2). Zenodo https://doi.org/10.5281/zenodo.10819348 (2024).

  69. Ayoubi, R. & Laflamme, C. Dataset for the TGM2 (protein–glutamine γ-glutamyltransferase 2) antibody screening study. Zenodo https://doi.org/10.5281/zenodo.10927535 (2024).

  70. Ayoubi, R. et al. Antibody characterization report for sphingosine 1-phosphate receptor 1 (S1PR1). Zenodo https://doi.org/10.5281/zenodo.10819189 (2024).

  71. Ayoubi, R. & Laflamme, C. Dataset for the sphingosine 1-phosphate receptor 1 (S1PR1) antibody screening study. Zenodo https://doi.org/10.5281/zenodo.10839647 (2024).

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Acknowledgements

This work was supported by the Emory-Sage-SGC TREAT-AD center established by the National Institute on Aging grant U54AG065187 and additional support by RF1AG057443, by a grant from the Michael J. Fox Foundation for Parkinson’s Research (no. 18331), by a grant from the Motor Neurone Disease Association, the ALS Association and ALS Canada to develop the ALS-Reproducibility Antibody Platform, by the Bill and Melinda Gates Foundation and by the Government of Canada through Genome Canada, Genome Quebec and Ontario Genomics (OGI-210). The Structural Genomics Consortium is a registered charity (no. 1097737) that receives funds from Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute (grant no. OGI-196), the European Union (EU) and European Federation of Pharmaceutical Industries and Associations through the Innovative Medicines Initiative 2 Joint Undertaking (EUbOPEN grant no. 875510), Janssen, Merck (also known as EMD in Canada and the USA), Pfizer and Takeda. R.A. is supported by a Mitacs postdoctoral fellowship. Image processing workflows for this manuscript were developed with the McGill University Advanced BioImaging Facility (RRID: SCR_017697). CMB is supported by grant number 2020-225398 and 2023-329682 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. A.B. is the cofounder and serves as the CEO of SciCrunch Inc, a company that works with publishers to improve the rigor and transparency of scientific manuscripts. We thank A. Bairoch at the University of Geneva and manager of the Cellosaurus database, who helped us extract from Cellosaurus the number of KO cell lines and human genes covered by KO lines.

Author information

Author notes

  1. These authors contributed equally: Matt R. Baker, Alexander R. Ball Jr, Danielle Callahan, Jeffery A. Cooper, Katherine Crosby, Kevin J. Harvey, Douglas W. Houston, Ravindran Kumaran, Meghan Rego, Christine Schofield, Hai Wu.

Authors and Affiliations

  1. Department of Neurology and Neurosurgery, Structural Genomics Consortium, the Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada

    Riham Ayoubi, Sara Gonzalez Bolivar, Charles Alende, Vera Ruiz Moleon, Maryam Fotouhi, Kathleen Southern, Walaa Alshafie, Peter S. McPherson & Carl Laflamme

  2. Advanced BioImaging Facility, McGill University, Montreal, Quebec, Canada

    Joel Ryan & Claire M. Brown

  3. Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada

    Mona Alqazzaz & Aled M. Edwards

  4. Thermo Fisher Scientific, Carlsbad, CA, USA

    Matt R. Baker

  5. GeneTex, Irvine, CA, USA

    Alexander R. Ball Jr

  6. Proteintech Group, Rosemont, IL, USA

    Danielle Callahan

  7. Bio-Techne, Minneapolis, MN, USA

    Jeffery A. Cooper

  8. Cell Signaling Technology, Danvers, MA, USA

    Katherine Crosby

  9. Aviva Systems Biology, San Diego, CA, USA

    Kevin J. Harvey

  10. Developmental Studies Hybridoma Bank, Department of Biology, The University of Iowa, Iowa City, IA, USA

    Douglas W. Houston

  11. Abcam, Cambridge Biomedical Campus, Cambridge, UK

    Ravindran Kumaran

  12. Addgene, Watertown, MA, USA

    Meghan Rego

  13. Revvity, Waterbeach, Cambridge, UK

    Christine Schofield

  14. ABclonal Technology, Woburn, MA, USA

    Hai Wu

  15. NIHR Respiratory Biomedical Research Centre, Department of Respiratory Sciences, University of Leicester, Leicester, UK

    Michael S. Biddle & Harvinder S. Virk

  16. Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA

    Richard A. Kahn

  17. Department of Neuroscience, University of California San Diego, La Jolla, CA, USA

    Anita Bandrowski

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Contributions

R.A., A.M.E., P.S.M. and C.L. conceptualized the workflow. R.A., C.L., J.R. and W.A. established the protocols. R.A., A.R.B. Jr., D.C., J.A.C., K.C., K.J.H., D.W.H., K.R, M.R., C.S., H.W., M.S.B, C.M.B., R.A.K., A.B. and H.S.V. contributed to the final version of the protocols. R.A., S.G.B., C.A., V.R.M., M.F. and K.S. carried out the experiments shown here. R.A. and C.L. wrote the manuscript. All authors were involved in editing of the final manuscript.

Corresponding author

Correspondence to Carl Laflamme.

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The authors declare no competing interests.

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Nature Protocols thanks Fridtjof Lund-Johansen and Cecilia Williams for their contribution to the peer review of this work.

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Key references

Laflamme, C. et al. eLife 8 (2019): https://doi.org/10.7554/eLife.48363

Ayoubi, R. et al. eLife 12 (2023): https://doi.org/10.7554/eLife.91645

Ayoubi, R. et al. F1000Res 12, 810 (2023): https://doi.org/10.12688/f1000research.133899.3

Biddle, M. S. & Virk, H. S. F1000Res 12, 1344 (2023): https://doi.org/10.12688/f1000research.141719.1

Moleon, V.R. et al. F1000Res 12, 1578 (2023): https://doi.org/10.12688/f1000research.143928.2

Extended data

Extended Data Fig. 1 Order of sample loading for antibody screening in WB and IP-WB.

a) A scanned Ponceau S-stained membrane used for antibody screening in WB. Master mixes of MWM, WT and KO lysates were prepared, and samples were loaded in the following order on a 12-well SDS–PAGE gel: MWM, WT and KO lysates. Commercial 12-well gels have two side wells. The right side well was used here for MWM (+). Up to 4 antibodies can be tested in WB on a single 12-well gel. b) Scanned Ponceau S-stained membrane used for antibody screening in IP. Samples are loaded in the following order on a 12-wells SDS–PAGE gel. Up to 3 antibodies can be tested in IP on a single 12-well gel. Transfers from 4-20% TG gels are shown as examples in a) and b). MWM=molecular weight marker, SM=starting material, UB=unbound fraction, IP=immunoprecipitate.

Supplementary information

Supplementary Tables 1–6

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Ayoubi, R., Ryan, J., Gonzalez Bolivar, S. et al. A consensus platform for antibody characterization. Nat Protoc 20, 1509–1545 (2025). https://doi.org/10.1038/s41596-024-01095-8

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