A key component of efforts to address the reproducibility crisis in biomedical research is the development of rigorously validated and renewable protein-affinity reagents. As part of the US National Institutes of Health (NIH) Protein Capture Reagents Program (PCRP), we have generated a collection of 1,406 highly validated immunoprecipitation- and/or immunoblotting-grade mouse monoclonal antibodies (mAbs) to 737 human transcription factors, using an integrated production and validation pipeline. We used HuProt human protein microarrays as a primary validation tool to identify mAbs with high specificity for their cognate targets. We further validated PCRP mAbs by means of multiple experimental applications, including immunoprecipitation, immunoblotting, chromatin immunoprecipitation followed by sequencing (ChIP-seq), and immunohistochemistry. We also conducted a meta-analysis that identified critical variables that contribute to the generation of high-quality mAbs. All validation data, protocols, and links to PCRP mAb suppliers are available at http://proteincapture.org.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


Primary accessions

Gene Expression Omnibus


  1. 1.

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

  2. 2.

    Quality issues of research antibodies. Anal. Chem. Insights 11, 21–27 (2016).

  3. 3.

    & How to avoid pitfalls in antibody use. F1000Res. 4, 691 (2015).

  4. 4.

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

  5. 5.

    & Magic peptides, magic antibodies: guidelines for appropriate controls for immunohistochemistry. J. Comp. Neurol. 465, 161–163 (2003).

  6. 6.

    Antibody can get it right: confronting problems of antibody specificity and irreproducibility. Mol. Endocrinol. 28, 1403–1407 (2014).

  7. 7.

    et al. A high through-put platform for recombinant antibodies to folded proteins. Mol. Cell. Proteomics 14, 2833–2847 (2015).

  8. 8.

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

  9. 9.

    et al. A high-throughput pipeline for the production of synthetic antibodies for analysis of ribonucleoprotein complexes. RNA 22, 636–655 (2016).

  10. 10.

    & Antibodies as valuable neuroscience research tools versus reagents of mass distraction. J. Neurosci. 26, 8017–8020 (2006).

  11. 11.

    et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

  12. 12.

    et al. The NIH Protein Capture Reagents Program (PCRP): a standardized protein affinity reagent toolbox. Nat. Methods 13, 805–806 (2016).

  13. 13.

    et al. Rapid identification of monospecific monoclonal antibodies using a human proteome microarray. Mol. Cell. Proteomics 11, O111.016253 (2012).

  14. 14.

    et al. Identification of new autoantigens for primary biliary cirrhosis using human proteome microarrays. Mol. Cell. Proteomics 11, 669–680 (2012).

  15. 15.

    et al. DNA methylation presents distinct binding sites for human transcription factors. eLife 2, e00726 (2013).

  16. 16.

    et al. Profiling the human protein-DNA interactome reveals ERK2 as a transcriptional repressor of interferon signaling. Cell 139, 610–622 (2009).

  17. 17.

    et al. Construction of human activity-based phosphorylation networks. Mol. Syst. Biol. 9, 655 (2013).

  18. 18.

    et al. Identification of SUMO E3 ligase-specific substrates using the HuProt human proteome microarray. Methods Mol. Biol. 1295, 455–463 (2015).

  19. 19.

    et al. Global identification of SUMO substrates reveals crosstalk between SUMOylation and phosphorylation promotes cell migration. Mol. Cell. Proteomics (2018).

  20. 20.

    et al. Systematic discovery of Xist RNA binding proteins. Cell 161, 404–416 (2015).

  21. 21.

    et al. The Xist lncRNA interacts directly with SHARP to silence transcription through HDAC3. Nature 521, 232–236 (2015).

  22. 22.

    , , & A census of human transcription factors: function, expression and evolution. Nat. Rev. Genet. 10, 252–263 (2009).

  23. 23.

    Cohen's Conjecture, Howard's Hypothesis, and Ptashne's Ptruth: an exploration of the relationship between affinity and specificity. Trends Immunol. 31, 138–143 (2010).

  24. 24.

    & The importance of antibody affinity in the performance of immunoassays for antibody. J. Immunol. Methods 78, 173–190 (1985).

  25. 25.

    , , & Determining kinetics and affinities of protein interactions using a parallel real-time label-free biosensor, the Octet. Anal. Biochem. 377, 209–217 (2008).

  26. 26.

    et al. Fluorescence ImmunoPrecipitation (FLIP): a novel assay for high-throughput IP. Biol. Proced. Online 18, 16 (2016).

  27. 27.

    et al. Lhx2 is an essential factor for retinal gliogenesis and Notch signaling. J. Neurosci. 36, 2391–2405 (2016).

  28. 28.

    & Using Antibodies: A Laboratory Manual (Cold Spring Harbor Laboratory Press, 1998).

  29. 29.

    et al. The European antibody network's practical guide to finding and validating suitable antibodies for research. MAbs 8, 27–36 (2016).

  30. 30.

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

  31. 31.

    et al. Global analysis of protein activities using proteome chips. Science 293, 2101–2105 (2001).

  32. 32.

    , , & The long noncoding RNA Six3OS acts in trans to regulate retinal development by modulating Six3 activity. Neural Dev. 6, 32 (2011).

  33. 33.

    et al. Affinity proteomics reveals human host factors implicated in discrete stages of LINE-1 retrotransposition. Cell 155, 1034–1048 (2013).

  34. 34.

    , , & Poly(A) binding protein C1 is essential for efficient L1 retrotransposition and affects L1 RNP formation. Mol. Cell. Biol. 32, 4323–4336 (2012).

  35. 35.

    , , & Transient mammalian cell transfection with polyethylenimine (PEI). Methods Enzymol. 529, 227–240 (2013).

  36. 36.

    et al. Injury-independent induction of reactive gliosis in retina by loss of function of the LIM homeodomain transcription factor Lhx2. Proc. Natl. Acad. Sci. USA 109, 4657–4662 (2012).

  37. 37.

    et al. Tanycytes of the hypothalamic median eminence form a diet-responsive neurogenic niche. Nat. Neurosci. 15, 700–702 (2012).

  38. 38.

    , & Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002).

Download references


This work was supported by the NIH Common Fund (awards U54HG006434 (to J.D.B., S.B., and H.Z.) and U01DC011485 (to S.A. and G.T.M.)). Cy5-UTP-incorporated cRNA probes of Xist produced by T7-directed transcription were a kind gift from E. Lander's lab (MIT, Cambridge, Massachusetts, USA).

Author information

Author notes

    • Zheng Kuang
    •  & Diane Bayron Kain

    Present addresses: Department of Immunology, University of Texas Southwestern Medical Center, Dallas, Texas, USA (Z.K.); BioReliance, Sigma-Aldrich Corp., Rockville, Maryland, USA (D.B.K.).


  1. Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Anand Venkataraman
    • , Lin Xue
    • , Devlina Ghosh
    • , Brian S Clark
    • , Sooyeon Yoo
    • , Jimmy de Melo
    • , Milanka Stevanovic
    • , Lizhi Jiang
    •  & Seth Blackshaw
  2. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Kun Yang
    •  & Joel S Bader
  3. CDI Laboratories, Mayaguez, PR, USA.

    • Jose Irizarry
    • , Pedro Ramos
    • , Shaohui Hu
    • , Diane Bayron Kain
    • , Edisa Albino
    • , Lillyann Asencio
    • , Leonardo Ramos
    • , Luvir Lugo
    • , Gloriner Morell
    • , Javier Rivera
    • , Kimberly Ruiz
    • , Ruth Almodovar
    • , Luis Nazario
    • , Keven Murphy
    • , Ivan Vargas
    • , Zully Ann Rivera-Pacheco
    • , Christian Rosa
    • , Moises Vargas
    • , Wendy Y Yap
    • , Ignacio Pino
    •  & Daniel J Eichinger
  4. HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA.

    • Mark Mackiewicz
    • , Florencia Pauli Behn
    •  & Richard M Myers
  5. Institute for System Genetics, NYU Langone Health, New York, New York, USA.

    • Paolo Mita
    • , Sarah Keegan
    • , Jef D Boeke
    •  & David Fenyö
  6. Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, New York, USA.

    • Paolo Mita
    • , Zheng Kuang
    • , Sarah Keegan
    • , Jef D Boeke
    •  & David Fenyö
  7. Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Paolo Mita
    • , Jessica McDade
    •  & Jef D Boeke
  8. Department of Pharmacology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Shuang Liu
    • , Richard Saul
    • , Hongyan Zhang
    • , Guang Song
    •  & Heng Zhu
  9. Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA.

    • Simona Colantonio
    •  & Gordon Whiteley
  10. Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Seva G Khambadkone
  11. Eukaryotic Tissue Culture Facility, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Yana Li
  12. NeoBiotechnologies, Inc., Union City, California, USA.

    • Brittany Jones
    •  & Atul Tandon
  13. Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, New Jersey, USA.

    • Elliot Campbell
    • , Gaetano T Montelione
    •  & Stephen Anderson
  14. Center for Advanced Biotechnology and Medicine, Piscataway, New Jersey, USA.

    • Elliot Campbell
    • , Gaetano T Montelione
    •  & Stephen Anderson
  15. Center for Human Systems Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Heng Zhu
    •  & Seth Blackshaw
  16. Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Seth Blackshaw
  17. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Seth Blackshaw
  18. Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Seth Blackshaw


  1. Search for Anand Venkataraman in:

  2. Search for Kun Yang in:

  3. Search for Jose Irizarry in:

  4. Search for Mark Mackiewicz in:

  5. Search for Paolo Mita in:

  6. Search for Zheng Kuang in:

  7. Search for Lin Xue in:

  8. Search for Devlina Ghosh in:

  9. Search for Shuang Liu in:

  10. Search for Pedro Ramos in:

  11. Search for Shaohui Hu in:

  12. Search for Diane Bayron Kain in:

  13. Search for Sarah Keegan in:

  14. Search for Richard Saul in:

  15. Search for Simona Colantonio in:

  16. Search for Hongyan Zhang in:

  17. Search for Florencia Pauli Behn in:

  18. Search for Guang Song in:

  19. Search for Edisa Albino in:

  20. Search for Lillyann Asencio in:

  21. Search for Leonardo Ramos in:

  22. Search for Luvir Lugo in:

  23. Search for Gloriner Morell in:

  24. Search for Javier Rivera in:

  25. Search for Kimberly Ruiz in:

  26. Search for Ruth Almodovar in:

  27. Search for Luis Nazario in:

  28. Search for Keven Murphy in:

  29. Search for Ivan Vargas in:

  30. Search for Zully Ann Rivera-Pacheco in:

  31. Search for Christian Rosa in:

  32. Search for Moises Vargas in:

  33. Search for Jessica McDade in:

  34. Search for Brian S Clark in:

  35. Search for Sooyeon Yoo in:

  36. Search for Seva G Khambadkone in:

  37. Search for Jimmy de Melo in:

  38. Search for Milanka Stevanovic in:

  39. Search for Lizhi Jiang in:

  40. Search for Yana Li in:

  41. Search for Wendy Y Yap in:

  42. Search for Brittany Jones in:

  43. Search for Atul Tandon in:

  44. Search for Elliot Campbell in:

  45. Search for Gaetano T Montelione in:

  46. Search for Stephen Anderson in:

  47. Search for Richard M Myers in:

  48. Search for Jef D Boeke in:

  49. Search for David Fenyö in:

  50. Search for Gordon Whiteley in:

  51. Search for Joel S Bader in:

  52. Search for Ignacio Pino in:

  53. Search for Daniel J Eichinger in:

  54. Search for Heng Zhu in:

  55. Search for Seth Blackshaw in:


A.V., M.M., P.M., Z.K., L.X., Y.L., D.G., S.L., P.R., S.H., D.B.K., H. Zhang, F.P.-B., G.S., E.A., L.A., L.R., L.L., G.M., J.R., K.R., R.A., L.N., K.M., I.V., Z.A.R.-P., C.R., M.V., J.M., B.S.C., S.Y., S.G.K., J.d.M., M.S., L.J., B.J., A.T. and E.C. performed experimental work. R.S. and S.C. performed independent validation of PCRP mAbs. K.Y., J.I. and S.K. designed algorithms and implemented software. W.Y.Y., S.A., G.T.M., R.M.M., J.D.B., D.F., G.W., D.J.E., J.S.B., I.P., H. Zhu and S.B. contributed expertise and supervision. All authors contributed to manuscript preparation.

Competing interests

S.B., H. Zhu, I.P., D.J.E., and J.D.B. are cofounders and shareholders of CDI Labs Inc. J.I., P.R., D.B.K., E.A., L.A., L.R., L.L., G.M., J.R., K.R., R.A., L.N., K.M., I.V., Z.A.R.-P., C.R., M.V., and W.Y.Y. are employees of CDI Labs Inc. A.V. and J.D.B. are consultants to CDI Labs Inc. J.D.B. serves on the Board of Directors of CDI Labs, and J.D.B.'s relationship with CDI Labs is managed by NYU Langone Health's committee on conflicts of interest. B.J. is an employee of NeoBiotechnologies, Inc. A.T. is the founder and sole owner of NeoBiotechnologies, Inc. G.T.M. is founder and shareholder of Nexomics Biosciences, Inc.

Corresponding authors

Correspondence to Ignacio Pino or Daniel J Eichinger or Heng Zhu or Seth Blackshaw.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–5 and Supplementary Note 1

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    List of proteins recognized by the Xist probe on HuProt

  2. 2.

    Supplementary Table 2

    List of approved targets for the PCRP project

  3. 3.

    Supplementary Table 3

    List of recombinant domain antigens produced in E. coli

  4. 4.

    Supplementary Table 4

    List of recombinant full-length proteins produced in yeast

  5. 5.

    Supplementary Table 5

    List of commercially sourced mAbs and PCRP mAbs tested in a competitive IP protocol

  6. 6.

    Supplementary Table 6

    Competitive IP analysis

  7. 7.

    Supplementary Table 7

    List of mAbs that passed in ChIP-seq

  8. 8.

    Supplementary Table 8

    List of all mAbs that passed HuProt, along with all recorded parameters

  9. 9.

    Supplementary Table 9

    List of all targets in the approved target list with corresponding details on passing mAbs at different stages of the pipeline

  10. 10.

    Supplementary Table 10

    List of all parameters and groups used in comparisons for the meta-analysis

  11. 11.

    Supplementary Table 11

    Parametric comparisons and corresponding P values for mAbs generated by immunization with a single versus multiple antigens

  12. 12.

    Supplementary Table 12

    Parametric comparisons and corresponding P values at the levels of targets

  13. 13.

    Supplementary Table 13

    Parametric comparisons and corresponding P values for mAbs generated by intraperitoneal (i.p.) versus footpad (f.p.) immunization

  14. 14.

    Supplementary Table 14

    Parametric comparisons and corresponding P values at the levels of targets

  15. 15.

    Supplementary Table 15

    Parametric comparisons and corresponding P values between mAbs that recognize only their cognate immunized domain and those that recognize their intended full-length target on HuProt

  16. 16.

    Supplementary Table 16

    Parametric comparisons and corresponding P values at the levels of targets

  17. 17.

    Supplementary Table 17

    List of IB+ and IB− mAbs that were tested and the corresponding parameters measured for these mAbs on denatured HuProt

  18. 18.

    Supplementary Table 18

    Summary of mAbs at different stages of the pipeline by target class/subclass

  19. 19.

    Supplementary Table 19

    Summary and success rates of targets at different stages of the pipeline classified by target class/subclass

  20. 20.

    Supplementary Table 20

    Summary of targets and success rates at different stages of the pipeline classified by type and route of immunization used to generate the mAbs

About this article

Publication history






Further reading