In need of a specific antibody against the oxytocin receptor for neuropsychiatric research: A KO validation study

Abstract

Antibodies are one of the most utilized tools in biomedical research. However, few of them are rigorously evaluated, as there are no accepted guidelines or standardized methods for determining their validity before commercialization. Often, an antibody is considered validated if it detects a band by Western blot of the expected molecular weight and, in some cases, if blocking peptides result in loss of staining. Neither of these approaches are unquestionable proof of target specificity. Since the oxytocin receptor has recently become a popular target in neuropsychiatric research, the need for specific antibodies to be used in brain has arisen. In this work, we have tested the specificity of six commercially available oxytocin receptor antibodies, indicated by the manufacturers to be suitable for Western blot and with an available image showing the correct size band (45-55 KDa). Antibodies were first tested by Western blot in brain lysates of wild-type and oxytocin receptor knockout mice. Uterus tissue was also tested as control for putative differential tissue specificity. In brain, the six tested antibodies lacked target specificity, as both wild-type and receptor knockout samples resulted in a similar staining pattern, including the expected 45-55 KDa band. Five of the six antibodies detected a selective band in uterus (which disappeared in knockout tissue). These five specific antibodies were also tested for immunohistochemistry in uterus, where only one was specific. However, when the uterine-specific antibody was tested in brain tissue, it lacked specificity. In conclusion, none of the six tested commercial antibodies are suitable to detect oxytocin receptor in brain by either Western blot or immunohistochemistry, although some do specifically detect it in uterus. The present work highlights the need to develop standardized antibody validation methods, including a proper negative control, in order to grant quality and reproducibility of the generated data.