An orthogonal strategy for antibody validation involves cross-referencing antibody-based results with data obtained using non-antibody-based methods. The approach has gained traction in recent years, with CiteAb reporting over 14,000 examples of supplier-conducted orthogonal validations for commercial antibodies.
This post explores why an orthogonal strategy is a key part of antibody validation, and provides examples of how orthogonal data is used in-house by CST scientists during our comprehensive antibody development and testing process.
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In statistics, the term orthogonal describes equations in which variables are statistically independent—or, put more simply, when two values are unrelated. The same definition of the concept applies to orthogonal antibody validation, which is when data from an antibody-dependent experiment is corroborated by data derived from a method that does not rely on antibodies. Orthogonal data sources can include mining previously published results, studying expression via ‘omics techniques (genomics, transcriptomics, and proteomics), and using other established antibody-independent methods such as in situ hybridization.
"Orthogonal validation is similar in principle to using a reference standard to verify a measurement," explains Katherine (Katie) Crosby, Sr Director of Antibody Applications & Validation at CST. "Just as you need a different, calibrated weight to check if a scale is working correctly, you need antibody-independent data to cross-reference and verify the results of an antibody-driven experiment. Using data from an unrelated method helps control bias and results in more conclusive evidence of target specificity.”
Orthogonal data is just one of the "five conceptual pillars for antibody validation" recommended by the International Working Group on Antibody Validation’s widely accepted proposal for the validation of antibodies.1 Collectively, CST refers to these pillars as the Hallmarks of Antibody Validation and they include the following experimental methodologies: binary (e.g., knockout validation), ranged, complementary, heterologous, multiple antibody, and orthogonal.
As an example, let's say you would like to use an antibody in western blot (WB) for the first time. You could use the Human Protein Atlas as a source of orthogonal data to select candidate cell lines that have high and low levels of RNA—i.e., a binary experimental model. Western blot results that do not mirror the RNA data should be investigated. A successful western blot experiment conducted as part of the validation effort for Nectin-2/CD112 is illustrated below.
Binary validation strategies evaluate antibody specificity by testing in systems with known positive (+) and negative (-) expression of the target protein, such as endogenous cells, genetic knockouts, or induced/inhibited expression models, to confirm target recognition without cross-reactivity.
"At the highest level, an orthogonal approach means using multiple, independent experimental techniques and cross-referencing data to verify an antibody experiment," explains Srikanth Subramanian, PhD, Senior Scientist at CST. "Doing this extra legwork takes additional effort, but it's well worth the result." The confidence gained in antibody specificity and sensitivity is why CST uses orthogonal data whenever possible in our antibody validation campaigns."
It’s important to note, however, that application-specific validation is critical. While the binary experiment described above can be used to validate the antibody’s specificity and sensitivity in WB, it would not confirm its performance in other applications, such as immunohistochemistry (IHC). The processing steps used to prepare and preserve samples for use in various applications can affect antigens differently, ultimately impacting how an antibody binds to an epitope, and therefore altering its functionality.
The following experimental techniques are examples of methods that can be used to gather orthogonal data about a target protein or its corresponding gene that can be detected or quantified without using antibodies:
The below list includes examples of publicly available and open-source resources that include non-antibody generated data about various disease states, biological models, and tissue types:
Protein expression as measured via WB (antibody-dependent method) corresponds as expected with publicly available ‘omics data from the Human Protein Atlas (antibody-independent method).
The following is an example of the antibody validation data generated when CST scientists validated the recombinant monoclonal antibody clone D8D3F, which targets Nectin-2/CD112, for use in WB.
Nectin-2, also known as CD112, is a multifunctional protein that plays critical roles in diverse processes such as cell adhesion, T-cell signaling, cardiac function, and viral entry. To confirm the specificity of this antibody clone in WB, orthogonal data from the Human Protein Atlas was first used to determine the expected relative expression of Nectin-2 across different cell lines, as shown in Figure 1.
Based on the Human Protein Atlas data, the following four cell lines were selected to test the antibody:
Next, a WB was run using the antibody clone in the four cell line samples. Figure 2 shows the results, where the expected elevated expression levels are clearly noticeable in RT4 and MCF7, and minimal to no expression is seen in HDLM-2 and MOLT-4.
Figure 2. Left: WB analysis of extracts from RT4 , MCF7, HDLM-2, and MOLT-4 using Nectin-2/CD112 (D8D3F) #95333 (upper) and β-Actin (D6A8) (lower).
The experiment outlined above, which leverages both orthogonal data and a binary validation strategy, is sufficient to confirm the specificity of Nectin-2/CD112 (D8D3F) #95333 in WB. However, additional data would be needed to confirm the clone's specificity in other applications—in fact, additional testing by CST scientists has validated the clone for use in immunohistochemistry (IHC), but it is not suggested for use in other applications.
Protein expression analyzed using immunohistochemistry (antibody-dependent method) corresponds with peptide counts as measured by mass spec (antibody-independent method).
Because of the inherent complexity of tissues, antibody validation for IHC can be particularly challenging. At CST, we conduct comprehensive validation testing for our new IHC antibodies to ensure specificity and sensitivity to the intended target. While Liquid Chromatography-Mass Spectrometry (LC-MS) is not readily available in all labs for researcher-led, independent antibody validation, we've found it can be a valuable source of orthogonal data in our in-house validation process.
The LC-MS data was used to validate the CST antibody clone E3J5R, which targets DLL3 (Delta-like ligand 3) for use in IHC. Small cell lung carcinoma samples were subjected to LC-MS analysis, and peptide counts for DLL3 are shown in Figure 3 below. Three samples with high (blue), medium (yellow), and low (green) DLL3 peptide counts were selected for IHC analysis.
Figure 3. Peptide counts of DLL3 in various tissues from LC-MS analysis using a combination of iBAQ (intensity-based absolute quantification) and TOMAHAQ (triggered by offset multiplexed accurate mass high-resolution absolute quantification) methods.
As shown in Figure 4, protein expression of DLL3 as assessed by IHC with E3J5R correlates with DLL3 peptide counts. As expected, the green-highlighted tissue shows minimal to no detection of the target protein in the IHC staining, the tissue highlighted in blue exhibits high abundance of the target protein, and the yellow tissue demonstrates staining of medium abundance.
This comparison between antibody-based IHC and antibody-independent mass spectrometry data demonstrates a strong correlation in protein abundance across the three tissue samples. While this was not the only validation experiment conducted to confirm the performance of #71804, the use of orthogonal data adds another level of assurance of the reliability and performance of this reagent for use in IHC.
The scientific reproducibility crisis has highlighted the need for improved quality control for research reagents.
“Just like one experiment is never enough to ‘prove’ a hypothesis, one test is not enough to confirm an antibody works,” concludes Crosby. “By leveraging an application-specific validation strategy, conducting multiple experiments, and referencing existing literature and publicly accessible knowledge, we can reduce the risk of false findings and improve confidence in results.”
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Steve Gygi, PhD |
“[CST] makes really good antibodies to start with. But they also, you know, validate those antibodies… I've never had a CST antibody that didn't work.” |
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At CST, we’re proud to be known for our rigorous approach to antibody validation. When you use one of our reagents, you can trust we’ve put it through every test we can think of. The orthogonal approach is just one part of the broader Hallmarks of Antibody Validation framework we use across our portfolio.
This level of testing takes time and resources, but we believe there’s nothing more important than a reagent that works. That's why, despite being one of the pioneering companies in the antibody industry since 1999, our product catalog remains focused—it includes just over 13,000 reagents compared to the 100,000+ offered by some other manufacturers. This focus on quality over quantity is what allows us to maintain the highest standards of performance, reproducibility, and scientific rigor—so researchers can spend more time on discovery, and less time troubleshooting.
Learn More about antibody validation at CST:
Read the additional blogs in our Hallmarks of Antibody Validation series:
A version of this blog post was originally published in March 2020. It was updated in April 2025. 25-BRE-03350