Comprehensive strategy for antibody validation

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Antibodies serve as essential tools in many facets of biological research. They are used as sensitive detection reagents to interrogate cell pathways, to diagnose disease and assess different treatment strategies, and to effect immunotherapeutic responses of their own. Poorly characterized antibodies not only waste time and money but also can lead to flawed research findings and incorrect conclusions [1].

To address these issues, several funding agencies and major journals will begin requiring additional antibody information in 2017, and many antibody suppliers and research labs are unprepared for these changes. Providing guidance to the research community, the International Working Group on Antibody Validation (IWGAV) published a Nature Methods manuscript in September 2016 that described five recommended “conceptual pillars” to guide antibody validation [2]. These include:

  • Genetic strategies: Measure the relevant signal in control cells or tissues in which the target gene has been knocked out or knocked down using techniques such as CRISPR-Cas or RNAi.
  • Orthogonal strategies: Use an antibody-independent method for quantitation across multiple samples, and then examine the correlation between the antibody-based and antibody-independent quantitations.
  • Independent antibody strategies: Use two or more independent antibodies that recognize different epitopes on the target protein and confirm specificity with comparative and quantitative analyses.
  • Expression of tagged proteins: Modify the endogenous target gene to add sequences for an affinity tag or a fluorescent protein. The signal from the tagged protein can then be correlated with antibody-based detection.
  • Immunocapture followed by mass spectrometry (IP-MS): Couple immunocapture (i.e., immunoprecipitation, or IP), the technique of isolating a protein from a solution through binding with a target-specific antibody, with mass spectrometry (MS) analysis to identify proteins that interact directly with the purified antibody as well as proteins that may form a complex with the target protein.

While each of these conceptual pillars may provide evidence of antibody specificity, the IWGAV recommends multiple pillars be used in order to claim a particular antibody has been well validated for use in a specific application. The IWGAV paper has examples of the first four conceptual pillars; however, the paper shows no data for the IP-MS strategy, which is the only validation approach that can verify the true antibody target as well as identify protein modifications, isoforms, off-target(s), and interacting proteins. In order to better characterize Invitrogen antibodies, we have implemented a comprehensive workflow for antibody validation * using immunocapture followed by mass spectrometry (Figure 1). This strategy includes the selection of protein targets, antibody candidates, and cell models, as well as the characterization of cell models by LC-MS, IP-MS sample preparation and analysis, and bioinformatic analysis.

Figure 1. Experimental workflow for antibody validation by immunoprecipitation and mass spectrometry (IP-MS) analysis. Protein targets and antibodies are prioritized for validation based on research areas and literature references. Cell models are then identified based on literature references and RNA expression, and lysates from these cell lines are prepared for MS analysis by cysteine reduction and alkylation, tryptic digestion, high-pH reversed-phase fractionation, and peptide quantitation. Fractionated peptide samples are analyzed by nanoLC-MS/MS on a Thermo Scientific Q Exactive Mass Spectrometer, and peptides are identified and quantified with Thermo Scientific Proteome Discoverer 2.1 and MaxQuant software (v1.5.8, Max Planck Institute). Protein targets are immuno-enriched from cell lysates with the Thermo Scientific Pierce MS-Compatible Magnetic IP Kit (protein A/G) and analyzed by nanoLC-MS/MS to verify and quantify target fold-enrichment. After filtering to remove common background proteins, enriched proteins are submitted for analysis of known interactions with the STRING database (http://string-db.org). These antibodies can be used alone or in combination for enrichment of protein targets prior to MS-based quantitation.

Selecting protein targets, antibody candidates, and cell lines

We choose protein targets based on research areas (e.g., cancer signaling, stem cell differentiation, etc.) and literature references. We then identify candidate cell lines likely to express these proteins based on RNA expression, followed by extensive characterization of the proteomes of those cell lines with liquid chromatography in-line with mass spectrometry (LC-MS). The LC-MS data verify the presence of the target protein in the cell line and provides protein quantitation information. For our initial round of antibody validation, we chose 12 cell lines from the NCI60 cancer cell line panel based on RNA expression data, and identified and quantified 6,000–9,000 proteins from each cell line by LC-MS. For each antibody candidate, we use this information to choose cell lines that express the antibody’s target protein at a midto- low expression level so that we can test the antibody performance in a relevant background. We then immunoprecipitate (IP) the protein target from the cell lysate with each antibody candidate and analyze the enriched proteins by LC-MS. Finally, we verify that the target is detected, and we conduct a bioinformatic analysis of the IP-MS data (Figure 2).

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Figure 2. Selection of cell lines for antibody verification by IP-MS. (A) RNA expression Z-scores from the NCI60 cell line panel were retrieved from Cell Miner (https://discover.nci.nih.gov/cellminer/) and hierarchically clustered for 22 genes in the Thermo Scientific Ion AmpliSeq Colon and Lung Cancer Panel. (B) Venn diagram of the number of proteins identified from five NCI60 cell lines using mass spectrometry analysis of peptides from each lysate.

Assessing antibody selectivity and identifying interacting proteins with IP-MS

MS can be used to detect every protein in an immunoprecipitated sample. However, unlike western blotting or ELISA techniques, MS is not compatible with the use of a blocker, such as milk or albumin, to minimize nonspecific binding. Instead, we can filter out common contaminating proteins from the LC-MS analysis by comparing the immunoprecipitated proteins and their abundance with proteins immunoprecipitated by an antibody to an unrelated protein target, which serves as a negative control. This comparison simplifies the detection of the target protein, as well as any off-targets and interacting proteins.

After subtracting background proteins from the IP-MS data using a negative control, we calculate the fold enrichment of identified proteins after immunoprecipitation relative to the protein expression level in the cell line tested, using the formula:

Fold enrichment =

Target protein abundance in IP sample/Total protein abundance in IP sample
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Target protein abundance in cell lysate/Total protein abundance in cell lysate

Fold enrichment is a measure of the ability of an antibody to selectively capture its target protein. The calculated fold enrichment of each protein captured with an antibody is plotted to assess the capture performance and selectivity of the antibody (Figure 3A). This list of enriched proteins is then analyzed with the STRING database (http://string-db.org), a public repository of known or predicted protein interactions; the STRING database website can provide a diagram of known or suspected protein interactions from the list of immunoprecipitated proteins (Figure 3B). We then color-code the fold-enrichment bar chart to signify the intended target (red) and any observed likely interactors (blue) from the STRING analysis. For example, cadherin-1 (CDH1, E-cadherin) is enriched with an anti–cadherin-1 antibody, as are several proteins that are known to interact with CDH1 including α- and β-catenin (Figure 3), and these results are observed with several standard cell lines. In other experiments, antibodies to β-catenin also enrich CDH1 (data not shown). Several other common background proteins that may stick to tubes and beads, such as myosin (MYO6), are also present, suggesting that this IP-MS analysis does not over-filter common background proteins.

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Figure 3. Fold enrichment of cadherin-1 (CDH1) and interacting proteins. (A) CDH1 was enriched >50-fold from MCF7 cell lysate using anti-CDH1 monoclonal antibody. Identified proteins were submitted to STRING (http://string-db.org) for interactome analysis. Based on the STRING results, we color-coded the fold-enrichment bars for CDH1 (red) and its likely interactors (blue). (B) STRING interactome diagram for proteins enriched with an anti-CDH1 antibody.

Learn more about our IP-MS validated antibodies

IP-MS can be used to verify the antibody target and to identify off-targets, interacting proteins, and protein modifications. This in-depth antibody characterization approach provides verification data that are not available with any other validation technique, and it is only offered by Thermo Fisher Scientific. IP-MS validated antibodies can be used for simultaneous enrichment of low-abundance signaling pathway proteins, as described in “Examine signaling pathways with targeted proteomics” in BioProbes 75 Journal of Cell Biology Applications.

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