Now that you know how to select the best antibodies for your application, let’s move on to the next step in finding a good antibody.

Part II — validating antibodies for your application

Tips for Antibody Validation

  1. Optimize protocols for your specific applications
  2. Test each antibody for specificity, sensitivity, and reproducibility
  3. Run controls with every experiment
  4. Retest antibodies before using them with a limited/precious sample
  5. Store antibodies as recommended by the vendor
  6. Train all new lab personnel

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Once you have selected two to five promising candidates, the time-consuming process of validating these antibodies for your application begins. The temptation to skip this process, especially when an antibody vendor has not provided validation data, should be resisted. However, if a vendor has provided extensive validation data for your application, including data for your specific sample/ closely related sample type, it may not be necessary to test multiple antibodies.

Nevertheless, always test antibodies yourself on your sample, regardless of the antibodies’ source and validation state. Validation data provided by vendors do not always reflect the current antibody lot, antibodies may perform differently in your hands, and although it doesn’t occur frequently, mistakes do happen during antibody production and processing. For example, a research laboratory at an academic center recently encountered unexpected specificity issues within the same lot of an antibody that had been validated and used successfully over an extended period of time. The source of this problem was a packaging error.

1. Optimize protocols for your specific applications.

Always optimize protocols and antibody dilutions and report final concentrations used. It is important to know the concentration of an antibody as dilutions are meaningful only when the stock concentration is known. Contact the vendor, as many will provide this information when queried. If the vendor has tested the antibody using physiologically relevant samples and provides detailed validation protocols, use their experimental conditions as a starting point. This can help considerably to reduce effort and time spent testing for the optimal conditions.

If antibody-based protein evaluation is performed in a quantitative manner, signal-to-noise ratio and dynamic range are two of the most critical objective parameters to define the best antibody concentration for a given assay. Using too much antibody can yield non-specific results, and too little can lead to no data or false-negative results. Based on the antibody application, the critical steps should be outlined and the experiment should have proper positive and negative controls in place to make sure there are no or minimal artifacts. Optimizing assay conditions by conventional DAB/IHC should also be performed using a range of antibody concentrations.

Pay attention to protein-specific IHC method recommendations and antigen retrieval methods, as it is best to follow the vendor’s recommendations when optimizing antibody concentration. If the assay does not perform as expected, different fixation and retrieval methods may yield better results. Note that as you alter retrieval methods the optimal antibody concentration might need to be adjusted as well.

Optimize protocols for your specific applications.
2. Test each antibody for specificity, sensitivity, and reproducibility.

When assessing specificity, sensitivity, and reproducibility it is vital to keep your intended application in mind. Will you be looking at native proteins or denatured proteins, a complex biological sample or a purified protein? These considerations will allow you to set meaningful performance criteria that an antibody must meet. Whenever possible, set quantitative quality control criteria rather than using qualitative measures that are often less reproducible and stringent (Ramos et al. 2016).

The specificity of an antibody can be assessed by comparing its performance in cell lines in which the target protein is expressed or not expressed; signal in knock-out cell lines can be attributed to nonspecific binding (Bordeaux et al. 2010). When knock-out cell lines are not readily available, RNAi can be used to knock down the protein of interest. If the protein shows tissue-specific expression patterns, another easy way to assess specificity is by using samples known to express and not express the protein of interest.

Sensitivity can be assessed by using protein-specific index arrays that contain sample and/or cell lines with varying but known amounts of target protein (Carvajal-Hausdorf et al. 2015; Welsh et al. 2011). A simpler method for assessing the sensitivity of an antibody is to spike a sample that does not express the protein of interest with known amounts of purified protein.

To assess reproducibility, run your validated antibody on 20–40 tissue samples, either as whole tissue sections or represented on a tissue microarray (TMA) for IHC. For western blotting, it is important to run replicates of lysates generated from the same batch of cells. Irrespective of the application, run your experiment in triplicate, using the same lot of antibody on different days and by different operators. In addition, use polyclonal antibodies from different lots to compare lot-to-lot reproducibility. If you have previously used the antibody or trust published data generated using the antibody, compare your results to those data.

Comparing antibodies from different vendors targeting the same protein adds further value to validation and reproducibility assessments. It is, however, important to consider that antibodies raised against different epitopes of the same protein can yield significantly different results, depending on how accessible a given epitope is in a sample of interest.

Perform your validation experiments using the same buffers, sample types, and experimental conditions that will be used for your final experiments. An antibody validated in one buffer system will not necessarily perform similarly in another.

Keep in mind that purified protein is sufficient to benchmark the target protein’s molecular weight in your sample, but it does not allow you to draw conclusions about specificity because purified protein is not a complex biological sample. Purified protein also does not allow you to determine the sensitivity or dynamic range of an antibody unless a dilution curve is set up to establish these parameters. Also bear in mind that purified proteins are often tagged, which changes their molecular weight and in certain cases expression patterns. To facilitate antibody validation, whenever possible choose a vendor that provides a physiologically relevant positive control sample rather than a purified protein or over-expressed/transfected lysate.

Test each antibody for specificity, sensitivity, and reproducibility.
3. Run controls with every experiment.

Every experiment should include a positive and negative control to assess antibody performance, ideally a set of samples with variable expression levels of the protein of interest. Protein-specific TMAs consisting of tissue samples and/or a set of cell lines can also be run alongside the experiments for quality control and reproducibility purposes. Arrays of cell lines with a range of expression levels and target-specific test TMAs can be purchased from a number of vendors. When a protein of interest is not expressed in immortalized cell lines or is expressed only transiently during a specific developmental stage, tissue samples may have to be used to validate an antibody’s performance.

Knock-out or knock-down cells or samples known to not express the protein of interest are also frequently used as negative controls, especially since tools like CRISPR and siRNA have simplified generation of such cell lines. Samples overexpressing the protein of interest, or even purified recombinant proteins, are commonly used as positive controls. However, results from such experiments are not always physiologically relevant, as knockdowns or knockouts can cause compensatory changes in cellular physiology. One way to avoid these pitfalls is to test samples with varying, known endogenous expression levels of the target protein. When researchers are working with freshly isolated primary cells or tissue samples, this becomes particularly important since over expression or knock-down validation is not always feasible.

Depending on your application, additional controls should be included. For example, every quantitative western blot should include a housekeeping protein loading control unless you are performing total protein normalization (TPN), and every ELISA should include a standard curve. In both cases, make sure that your signal is within the assays’ dynamic range. When using TPN, be aware that this method detects proteins by interacting with tryptophans (Trp). If the total amount of Trp in your sample is altered by your experimental treatment, TPN will no longer serve as a reliable control.

Run controls with every experiment.
4. Retest antibodies before using them with a limited/precious sample.

Antibodies have limited shelf lives and are often shared resources in a laboratory. It is therefore wise to aliquot your antibody and to retest aliquots before performing critical experiments. This does not need to be a full validation; in these cases a quick experiment with relevant controls under previously established conditions is sufficient to ensure that an antibody is still performing as expected.

Retest antibodies before using them with a limited/precious sample.
5. Store antibodies as recommended by the vendor.

Carefully review vendor recommendations and store antibodies accordingly. Write the date of first use on the vial to track antibody usage and do not store working dilutions in buffer for later use because this can affect stability; as you dilute your antibody you are also diluting stabilizers added by the vendor. If an antibody has been stored for a long time or has expired, it is best to use it with caution. Validation experiments should be repeated and working concentrations may need to be adjusted as antibody stability decreases over time. If you have altered vendor storage conditions by, for example, removing additives or stabilizers, the antibody shelf life can decrease significantly. It is therefore advisable to carefully mark any alterations in storage or formulation so that both current and future users are aware of these changes.

Store antibodies as recommended by the vendor.
6. Train all new lab personnel.

Take the time to familiarize new lab members with proper antibody etiquette. Ensure that they understand the importance of antibody validation, proper controls, and best practices.

Train all new lab personnel.

Tune in July for Part III to see what information to include in publications to ensure that antibody quality and results can be evaluated by the reader.

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.


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

Carvajal-Hausdorf DE et al. (2015). Measurement of domain-specific HER2 (ERBB2) expression may classify benefit from trastuzumab in breast cancer. J Natl Cancer Inst 107, djv136.

Ramos P et al. (2016). Antibody cross-reactivity testing using the HuProt Human Proteome Microarray., accessed May 24, 2018.

Welsh AW et al. (2011). Standardization of estrogen receptor measurement in breast cancer suggests false-negative results are a function of threshold intensity rather than percentage of positive cells. J Clin Oncol 29, 2,978–2,984.

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.

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