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Cross-species reactive monoclonal antibodies against the extracellular domains of the insulin receptor and IGF1 receptor
- Ørstrup, Laura Hvidsten, Slaaby, Rita, Rasch, Morten Grønbech, Rasmussen, Nicolaj, Lund, Søren, Brandt, Jakob, Schluckebier, Gerd, Wang, Zhe, Lützen, Anne, Pedersen, Thomas Åskov, Hvid, Henning, Hansen, Bo Falck, Blume, Niels
- Journal of immunological methods 2019 v.465 pp. 20-26
- cell lines, clones, cross reaction, enzyme-linked immunosorbent assay, epitopes, flow cytometry, humans, insulin, insulin receptors, insulin-like growth factor I receptor, mice, models, monoclonal antibodies, precipitin tests, rabbits, rats, swine
- Translation across species of immunoassay results is often challenging due to the lack of cross-species reactivity of antibodies. In order to investigate the biology of insulin and IGF1 receptors, we generated new versatile monoclonal assay antibodies using the extracellular domain of the insulin/IGF1 hybrid receptor as the bait protein in the Adimab yeast antibody discovery platform and as the antigen in a rabbit monoclonal antibody platform. The resulting antibody clones were screened for receptor specificity as well as cross-species reactivity to both tissue and cell line derived samples. Using these strategies, we were able to identify highly specific insulin receptor monoclonal antibodies that lack cross-reactivity to the IGF1 receptor using the Adimab platform and a highly specific IGF1 receptor monoclonal antibody that lacks cross-reactivity to the insulin receptor using the rabbit antibody platform. Unlike earlier monoclonal antibodies reported in the literature, these antibodies show cross-species reactivity to the extracellular domains of mouse, rat, pig, and human receptors, indicating that they bind conserved epitopes. Furthermore, the antibodies work well in several different assay formats, including ELISA, flow cytometry, and immunoprecipitation, and therefore provide new tools to study insulin and IGF1 receptor biology with translation across several species and experimental model systems.