So you’ve seen how to select the best antibodies for your application and have learned tips for proper antibody validation. Now let’s see how you can use this information to publish meaningful data and wrap up the series with some antibody best practices.

Part III — publishing meaningful antibody data

Tips for Generating Good Antibody Data

  1. Provide complete antibody information
  2. Always include positive and negative controls in published data
  3. Include validation data for all new antibodies
  4. Present complete data and describe all quantitative methods

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Most journals do not specify reporting criteria for the publication of antibody-generated data. This is highly problematic because many scientists turn to previously published data to inform not only their antibody choice but also the direction of their research. As the reproducibility debate is gaining momentum, more journals are defining stricter reporting criteria (Fosang and Colbran 2015; Until these criteria are universally enforced, it falls to the scientific community to implement minimum guidelines both in their own publications and when participating in the peer-review process.

1. Provide complete antibody information
The full antibody name, vendor, lot number, and antibody concentration, dilution, and incubation time should be provided. If a new in-house antibody is used, include information about how the antibody was generated.
Provide complete antibody information.
2. Always include positive and negative controls in published data
All antibody-generated data should include positive and negative controls, as well as all additional controls required for your particular application (for example, loading controls for western blots, standard curves for ELISAs). Not including these controls makes published data uninterpretable.
Always include positive and negative controls in published data.
3. Include validation data for all new antibodies
When using non-established antibodies or established antibodies for a new application, validation data that determine antibody specificity, sensitivity, and reproducibility should be presented. This information can be included in supplementary data but should not be missing from the published study. Without this crucial information conclusions drawn from presented experiments are difficult to evaluate.
Include validation data for all new antibodies.
4. Present complete data and describe all quantitative methods
Do not crop western blots or splice lanes from different blots into a single image. If lanes need to be cropped out of a blot, crop lines should be clearly indicated. All quantitation using antibodies should be described carefully in the methods or supplementary materials, including how signal intensity was measured, assay linearity was determined, and signal was normalized for quantitation.
Present complete data and describe all quantitative methods.


The antibody quality problem is well documented in the literature and can no longer be ignored. With growing discussion and awareness, vendors and scientists alike must be held to higher validation and reporting standards. We have summarized the minimum best practice guidelines in Table 1 in the hope that they will simplify the antibody search, serve as a starting point for further conversation, and improve the quality of antibody data published until strict antibody reporting standards are agreed upon and universally enforced.

Table 1. Guidelines for antibody best practices.

Pre-Purchase Post-Purchase Publication
Compare antibodies from different vendors Optimize protocols for your application Provide complete antibody information (antibody name, vendor, catalog number, lot number, dilution)
Select antibody type (monoclonal, polyclonal, recombinant) that matches your application needs Test all antibodies for sensitivity, specificity, and reproducibility Include proper controls in all published data
Pick antibodies validated for your application Retest antibodies before using them on an important sample Include validation data for new antibodies
Choose vendors that will work with you Run positive and negative controls with all experiments Present complete data and describe quantitative methods
Look for complete validation data Store antibodies as recommended
Ensure additives are compatible with your application Train new lab personnel on proper antibody etiquette
Review publications critically

For further reading on the issues facing researchers and what they and antibody suppliers can do to ensure proper antibody validation, read Validating Antibodies — the Good, the Bad, and the Necessary.

Bio-Rad’s Solution for Better Antibodies

Learn more about Bio-Rad’s solution for better antibodies with antibody validation.

Related Reading

Crucial Controls and Tips for IHC Experiments

ELISA Controls

Controls in Flow Cytometry

Western Blot Loading Controls

Unstained Controls


Fosang AJ and Colbran RJ (2015). Transparency is the key to quality. J Biol Chem 290, 29,692–29,694.

Veronique Neumeister, Department of Pathology, Yale University School of Medicine, New Haven, CT Poulomi Acharya and Anna Quinlan, Bio-Rad Laboratories, Inc., Hercules, CA

First published as: Acharya P et al. (2017). The ABCs of finding a good antibody: How to find a good antibody, validate it, and publish meaningful data. F1000Res 2017 6, 851.

Bio-Rad is a trademark of Bio-Rad Laboratories, Inc. in certain jurisdictions.

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