Enhanced Validation of Antibodies

Validation of antibodies is an experimental confirmation that an antibody is suitable for its intended use. It is important to understand that antibody validation is application- and context specific. Atlas Antibodies provides highly characterized antibodies further strengthened by enhanced validation, as recommended by leading researchers and described in this paper.

 

Triple A Polyclonals

Triple A Polyclonals are originally devel- oped within the academic Human Protein Atlas (HPA) project (proteinatlas.org). HPA uses the antibodies to characterize all hu- man proteins in the majority of normal and cancerous tissues, as well as on a subcel- lular level. The uniqueness and specific- ity of the antibodies used, originate from a thorough selection of antigen regions, affinity purification of the polyclonal anti- bodies, a stringent selection of approved antibodies and validation using different methods.

PrecisA Monoclonals

Atlas Antibodies also provides a selected number of mouse monoclonal antibodies, under the brand name PrecisA Monoclo- nals. PrecisA Monoclonals are developed with careful antigen design and are iso- typed for multiplex possibilities.

Transparency of Data

  • All antibody characterization data achieved using different applications are freely accessible on the Human Protein Atlas portal.
  • For immunohistochemistry (IHC), every antibody is supplied with 500 images, from 44 normal and 20 cancer tissues.
  • The antigen sequences are provided on the product pages for all antibodies.

Precise epitope information is supplied for PrecisA Monoclonals (where available). • Our scientific support team will be happy to share anything you may like to know about production and quality control of each antibody.

Enhanced Validation of Antibodies

To further strenghten the reliability of our antibodies we apply enhanced valida- tion, based on the recommendations by the International Working Group for Anti- body Validation (IWGAV), as published in Nature Methods, Uhlén et al1. The group consists of researchers from institutions in USA and Canada, such as Stanford University, Yale University, MIT, UCSD, University of Toronto, NIH, EMBL, Niigata University in Japan and the Science for Life laboratory in Sweden.

In the article, five conceptual pillars for antibody validation are suggested to be used in an application specific manner. Based on these pillars, HPA and Atlas Antibodies have developed an Enhanced Validation Strategy, resulting in the follow- ing five methods:

Five Conceptual Methods

  1. Genetic validation:
    Target confirmed by siRNA knock-down.

  2. Orthogonal validation:
    Specificity confirmed by a non-antibody based method.

  3. Independent antibody validation:
    Specificity confirmed by another antibody targeting a different epitope of the protein.

  4. Recombinant expression validation: Target confirmed by an overexpressed version of the protein.

  5. Capture MS validation:
    Presence of target verified by Mass Spectrometry.

At least one of the methods must be used for an antibody to receive Enhanced Vali- dation Status in a specific application.

1. Genetic Validation

The target protein can be down-regulated on a genetic level using siRNA or CRISPR-Cas9. If knock-down (by siRNA) or knock-out ( by CRIS- PR) of the corresponding gene correlates with absence or decrease of antibody signal, the antibody is shown to be specific for its target. This is exemplified in Figure 1 where the Anti-PPIB antibody AMAb91245 is used in Western Blot (WB) analysis in U-251 cells. The antibody signal is down regulated in the cells silenced by PPIB siRNA.

 

2. Orthogonal Validation

By using a non-antibody based method for target quantification, the antibody signal can be validated by comparing the results from the different methods across multiple samples.

One approach is to compare the antibody staining intensities over multiple samples with varying expression of the target gene, with RNA-seq data in the same samples. This is illustrated in Figure 2, using WB analysis in human cell lines SK-MEL-30 and Caco-2 using Anti-RA- B27A antibody HPA001333. Corresponding RAB27A RNA-seq data (TPM values) are presented for the same cell lines.

 

3. Validation by Independent Antibodies

Two antibodies directed against different regions of the same target protein may validate each other when compared in a set of relevant tissues. This is exemplified in Figure 3 with two antibodies directed against different regions of the A2ML1 protein used in the immunohistochemistry application. Both antibodies show positive staining in liver, but are negative in tonsil, colon and kidney.

 

4. Recombinant Expression Validation


An antibody signal can also be validated using an over-expressed or tagged version of the target protein. When over-expressing the target protein in a cell line, the antibody is validated by comparing the signal from the over-expressed version with the unmodified endogenous target protein. This approach is exemplified in Figure 4a.

The target protein may also be tagged by an affinity tag or a fluorescent protein. The pattern displayed by the tagged target protein is matched to the antibody signal.

A match confirms that the antibody recognizes its target protein. Validation by tagged proteins can be applied in
immunocytochemistry-immunofluorescence (ICC-IF) and is shown in Figure 4b.

 

5. Migration Capture MS Validation

In this method, the staining pattern and the protein size detected by the antibody is compared with results obtained by a capture Mass Spectrometry (MS) method. The band generated by the antibody in WB should correlate with the target protein identified by MS in terms of gel migration.

Enhanced Validation Guidelines

 

References

Selected References from the Human Protein Atlas Project:

  1. Uhlén M, Bandrowski A, Carr S, Edwards A, Ellenberg J, Lundberg E, Rimm DL, Rodriguez H, Hiltke T, Snyder M and Yamamoto T. A Proposal for Validation of Antibodies. Nature Methods 2016 13, 823–827.

  2. Uhlen M, Zhang C, Lee S, Sjöstedt E, Fagerberg L, Bidkhori G, Benfeitas R, Arif M, Liu Z, Edfors F, Sanli K, von Feilitzen K, Oksvold P, Lundberg E, Hober S, Nilsson P, Mattsson J, Schwenk JM, Brunnström H, Glimelius B, Sjöblom T, Edqvist PH, Djureinovic D, Micke P, Lindskog C, Mardinoglu A, Ponten F. A pathology atlas of the human cancer transcriptome. Science. 2017 357(6352)

  3. Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, Alm T, Asplund A, Björk L, Breckels LM, Bäckström A, Danielsson F, Fagerberg L, Fall J, Gatto L, Gnann C, Hober S, Hjelmare M, Johansson F, Lee S, Lindskog C, Mulder J, Mulvey CM, Nilsson P, Oksvold P, Rockberg J, Schutten R, Schwenk JM, Sivertsson Å, Sjöstedt E, Skogs M, Stadler C, Sullivan DP, Tegel H, Winsnes C, Zhang C, Zwahlen M, Mardinoglu A, Pontén F, von Feilitzen K, Lilley KS, Uhlén M, Lundberg E. A subcellular map of the human proteome. Science. 2017 356(6340).

  4. Edfors F, Danielsson F, Hallström BM, Käll L, Lundberg E, Pontén F, Forsström B and Uhlén M. Gene-specific correlation of RNA and protein levels in human cells and tissues.Mol Syst Biol. 2016 Oct; 12(10): 883.

  5. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F. Tissue-based map of the human proteome. Science. 2015 Jan;23;347(6220).