A Computational Synaptic Antibody Characterization and Screening Framework for Array Tomography

Abstract

16Application-specific validation of antibodies is a critical prerequisite for their successful use. Here 17 we introduce an automated approach for characterization and screening of antibodies against 18 synaptic molecules for high-resolution immunofluorescence array tomography. The method, based 19 on a probabilistic synapse detection algorithm, is designed to provide a robust, flexible, and 20 efficient tool for antibody characterization. By allowing the user to define the molecular 21 composition and size of synapses expected to contain the antigen, the method is applicable to 22 synaptic molecules with different distribution. The output of the algorithm are automatically 23 computed characteristics such as synapse density and target specificity ratio, which reflect the 24 sensitivity and specificity of immunolabeling with a given antibody. These measurements provide 25 an objective way to characterize and compare the performance of different antibodies against the 26 same target, and can be used to objectively select the antibodies best suited for AT, and potentially 27 also for other immunolabeling applications. 28 29 30Antibodies are an indispensable tool for the modern biologist. Their high-affinity binding to spe-31 cific target molecules makes it possible to detect, isolate, and manipulate the function of these 32 molecules. A staggering number of antibodies are available to the research community, as are 33 many options to make new antibodies. However, since antibodies are biological tools employed in 34 complex systems, they can be very difficult to evaluate and to use in a predictable and reproducible 35 way. Indeed, a large volume of misleading or incorrect data has been published based on results 36 from antibodies that did not perform as assumed (Anderson and Grant, 2006; Baker, 2015; Rhodes 37 and Trimmer, 2006). 38 1 of 21 Preprint for biorxiv.org 39 Recognizing this problem, there has been substantial progress in optimizing antibody produc-40 tion (Nilsson et al., 2005; Gong et al., 2016). The importance of establishing reliable practices for 41 antibody use is now widely recognized, and many companies are adopting transparent practices 42 for rigorous antibody validation (Fritschy et al., 1998; Uhlen et al., 2016). The performance of anti-43 bodies, however, is application-specific (Lorincz and Nusser, 2008), and the reliable performance 44 of an antibody in one application does not guarantee its suitability for another application. For 45 example, an antibody that yields a single band on an immunoblot analysis of a tissue homogenate 46 may prove wholly unsatisfactory for immunohistochemistry on sections of fixed tissue. Moreover, 47 the same antibody that yields a robust and specific signal in immunohistochemical labeling of 48 tissue sections prepared under one set of conditions may yield little or no signal on comparable 49 samples prepared under different conditions (Fritschy et al., 1998; Fukaya and Watanabe, 2000). 50 For practical reasons, commercial antibodie...