A Computational Synaptic Antibody Characterization Tool for Array Tomography.

Published

Journal Article

Application-specific validation of antibodies is a critical prerequisite for their successful use. Here we introduce an automated framework for characterization and screening of antibodies against synaptic molecules for high-resolution immunofluorescence array tomography (AT). The proposed Synaptic Antibody Characterization Tool (SACT) is designed to provide an automatic, robust, flexible, and efficient tool for antibody characterization at scale. SACT automatically detects puncta of immunofluorescence labeling from candidate antibodies and determines whether a punctum belongs to a synapse. The molecular composition and size of the target synapses expected to contain the antigen is determined by the user, based on biological knowledge. Operationally, the presence of a synapse is defined by the colocalization or adjacency of the candidate antibody punctum to one or more reference antibody puncta. The outputs of SACT are automatically computed measurements such as target synapse density and target specificity ratio that reflect the sensitivity and specificity of immunolabeling with a given candidate antibody. These measurements provide an objective way to characterize and compare the performance of different antibodies against the same target, and can be used to objectively select the antibodies best suited for AT and potentially for other immunolabeling applications.

Full Text

Duke Authors

Cited Authors

  • Simhal, AK; Gong, B; Trimmer, JS; Weinberg, RJ; Smith, SJ; Sapiro, G; Micheva, KD

Published Date

  • January 2018

Published In

Volume / Issue

  • 12 /

Start / End Page

  • 51 -

PubMed ID

  • 30065633

Pubmed Central ID

  • 30065633

Electronic International Standard Serial Number (EISSN)

  • 1662-5129

International Standard Serial Number (ISSN)

  • 1662-5129

Digital Object Identifier (DOI)

  • 10.3389/fnana.2018.00051

Language

  • eng