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Kibat, Janek; Schirrmann, Thomas; Knape, Matthias J.; Helmsing, Saskia; Meier, Doris; Hust, Michael; Schröder, Christoph; Bertinetti, Daniela; Winter, Gerhard; Pardes, Khalid; Funk, Mia; Vala, Andrea; Giese, Nathalia; Herberg, Friedrich W.; Dübel, Stefan; Hoheisel, Jörg D. (2016): Utilisation of antibody microarrays for the selection of specific and informative antibodies from recombinant library binders of unknown quality. In: New Biotechnology, Vol. 33, No. 5: pp. 574-581
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Abstract

Many diagnostic and therapeutic concepts require antibodies of high specificity. Recombinant binder libraries and related selection approaches allow the efficient isolation of antibodies against almost every target of interest. Nevertheless, it cannot be guaranteed that selected antibodies perform well and interact specifically enough with analytes unless an elaborate characterisation is performed. Here, we present an approach to shorten this process by combining the selection of suitable antibodies with the identification of informative target molecules by means of antibody microarrays, thereby reducing the effort of antibody characterisation by concentrating on relevant molecules. In a pilot scheme, a library of 456 single-chain variable fragment (scFv) binders to 134 antigens was used. They were arranged in a microarray format and incubated with the protein content of clinical tissue samples isolated from pancreatic ductal adenocarcinoma and healthy pancreas, as well as recurrent and non-recurrent bladder tumours. We observed significant variation in the expression of the E3 ubiquitin-protein ligase (CHFR) as well as the glutamate receptor interacting protein 2 (GRIP2), for example, always with more than one of the scFvs binding to these targets. Only the relevant antibodies were then characterised further on antigen microarrays and by surface plasmon resonance experiments so as to select the most specific and highest affinity antibodies. These binders were in turn used to confirm a microarray result by immunohistochemistry analysis.