Use of HCA in subproteome-immunization and screening of hybridoma supernatants to define distinct antibody binding patterns

UMMS Affiliation

Department of Cell and Developmental Biology



Document Type



Understanding the properties and functions of complex biological systems depends upon knowing the proteins present and the interactions between them. Recent advances in mass spectrometry have given us greater insights into the participating proteomes, however, monoclonal antibodies remain key to understanding the structures, functions, locations and macromolecular interactions of the involved proteins. The traditional single immunogen method to produce monoclonal antibodies using hybridoma technology are time, resource and cost intensive, limiting the number of reagents that are available. Using a high content analysis screening approach, we have developed a method in which a complex mixture of proteins (e.g., subproteome) is used to generate a panel of monoclonal antibodies specific to a subproteome located in a defined subcellular compartment such as the nucleus. The immunofluorescent images in the primary hybridoma screen are analyzed using an automated processing approach and classified using a recursive partitioning forest classification model derived from images obtained from the Human Protein Atlas. Using an ammonium sulfate purified nuclear matrix fraction as an example of reverse proteomics, we identified 866 hybridoma supernatants with a positive immunofluorescent signal. Of those, 402 produced a nuclear signal from which patterns similar to known nuclear matrix associated proteins were identified. Detailed here is our method, the analysis techniques, and a discussion of the application to further in vivo antibody production.

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Citation: Methods. 2016 Mar 1;96:75-84. doi: 10.1016/j.ymeth.2015.10.021. Epub 2015 Oct 30. Link to article on publisher's site

Related Resources

Link to Article in PubMed


High content analysis, High throughput imaging, Hybridoma, Machine learning, Monoclonal antibody, Nuclear matrix

Journal Title

Methods (San Diego, Calif.)

PubMed ID