In 2015, an analysis indicated that the total prevalence of irreproducible preclinical research exceeded 50%, resulting in approximately $28 billion a year spent on preclinical research that is not reproducible—in the United States alone.1 That same year, a Nature news article pointed to non-validated antibodies as a major driver of this “reproducibility crisis.”2
Although antibodies are a key resource in biomedical research, no community-accepted standards exist to rigorously characterize their quality. In 2019, Laflamme et al. developed a procedure to validate existing antibodies initially focusing on the major amyotrophic lateral sclerosis (ALS) disease gene product, C9ORF72. Results showed that the antibody most cited or used in most studies is not specific to C9ORF72 and that the best antibodies for each of the characterization assays had never appeared in the literature.3
Thus, YCharOS Inc. was created, a collaboration between the laboratory of Peter McPherson, PhD, of the Montreal Neurological Institute-Hospital (The Neuro) at McGill University and the Structural Genomics Consortium (SCG), a public-private partnership based in Europe, the United States, and Canada. This open-science organization performs side-by-side comparisons of commercially-available antibodies to the same target protein and publishes the results for use by scientists globally.
Antibody characterization
Various antibody characterization methods have been proposed.4 “We and others, including antibody manufacturers, have demonstrated that the genetic approach is the most scientifically rigorous,” 5 said Carl Laflamme, PhD, project lead at the SGC Neuro.
“We remove the intended protein target by genetically inhibiting gene expression using the CRISPR-Cas9 gene-editing technology. These knockout (KO) cell lines are then used as an ideal negative control for antibody characterization studies. An antibody is specific for the intended protein target if a complete lack of signal in the KO cell lines stained with the antibody is observed.”
YCharOS has characterized almost 400 antibodies using KO cell lines. A representative characterization report for Syntaxin-4 shows the details. Some antibody manufacturers also provide KO-characterization data. When possible, YCharOS focuses on renewable antibodies, monoclonals, or recombinants. In some cases, for “dark” proteins with just a few PubMed entries, only polyclonals may be available.
Matching the reagent to the application
Laflamme recommends that researchers validate their commercial antibody in their cell system and application of interest.
Antibodies are conceptually fit-for-purpose reagents. They recognize a feature of the target protein, such as a 3-D structure but depending on the research application used, proteins undergo different treatments. These treatments can fully denature proteins, cross-link proteins to each other, or can preserve their native structure. “Antibodies are thought to work in specific applications depending on their ability to recognize denatured or structured features,” said Laflamme. “However, we have some intriguing preliminary data contesting this concept.”
Almost 90% of the antibodies that performed well in immunofluorescence studies also performed well in western blots, which is counterintuitive because the structure of the protein differs in the two applications. The same relationship held true for antibodies that performed well in immunoprecipitation experiments; they also performed well in western blots (unpublished data).
“Typically we would think an antibody would not recognize both a linear conformation and a well-structured epitope,” said Laflamme. “And although one might expect a correlation between performance in immunofluorescence and immunoprecipitation this does not hold true.”
Cell line selection
Protein expression can affect antibody performance, therefore, an important consideration is the choice of a target-expressing cell line to initiate the characterization studies.3 “Researchers need to confirm protein expression and abundance in their own cell system compared to the cells we have used,” said Laflamme.
Selection of an optimal cell line can be challenging. A helpful open database generated by the Broad Institute, the DepMap Portal, provides RNA data and a shotgun proteomics for over 400 cell lines. But even this resource does not always identify cell lines that express high levels of the target protein. YCharOS has seen a poor correlation between protein expression and RNA levels.
“Performing the old-school western blot on lysates of different cell lines is still the best way to identify the most relevant high-expressing cell line,” advises Laflamme.
Resource Identifiers
Research Resource Identifiers (#RRID) are ID numbers assigned to help researchers cite key resources in the biomedical literature to improve transparency and are essential to allow large-scale screening of research articles.
“We have observed that the same catalog number can be used by different companies for different antibodies, for example mAb1234 from supplier A and mAb1234 from Supplier B may be antibodies to two distinct proteins. These two antibodies would have unique RRIDs. RRIDs also identify core antibodies cross-licensed between different companies, helping researchers to avoid purchasing the same antibodies twice,” emphasizes Laflamme.
Antibody characterization can increase transparency in preclinical research and is being used to help address the “reproducibility crisis.”
References
- Freedman LP, Cockburn IM, Simcoe TS (2015) The Economics of Reproducibility in Preclinical Research. PLoS Biol 13(6): e1002165. DOI:10.1371/journal.pbio.1002165
- Baker M (2015) Blame it on the Antibodies. Nature, Vol 521, 274-276
- Laflamme et al., eLife 2019;8:e48363. DOI: https://doi.org/10.7554/eLife.48363
- Uhlen, M., et al., A proposal for validation of antibodies. Nat Methods, 2016. 13(10): p. 823-7 DOI: 10.1038/nmeth.3995
- Laflamme, C., et al., Opinion: Independent third-party entities as a model for validation of commercial antibodies. N Biotechnol, 2021. 65: p. 1-8 DOI: 10.1016/j.nbt.2021.07.001
