Wood, Laura and Wright, Gavin J orcid.org/0000-0003-0537-0863 (2019) High-Content Imaging for Large-Scale Detection of Low-Affinity Extracellular Protein Interactions. SLAS Discovery. pp. 987-999. ISSN 2472-5552
Abstract
Extracellular protein interactions coordinate cellular responses with their local environment and have important roles in pathogen invasion and disease. Due to technical challenges associated with studying binding events at the cell surface, the systematic and reliable identification of novel ligand-receptor pairs remains difficult. Here, we describe the development of a cell-based assay using large-scale transient transfections and high-content imaging (HCI) to detect extracellular binding events. We optimized the parameters for efficient transfection of human cells with cDNA plasmids encoding full-length cell surface receptors in 384-well plates. Using a range of well-characterized structurally diverse low-affinity cell surface interactions, we show that transfected cells probed with highly avid ligands can be used to successfully identify ligand-receptor pairs using an HCI platform and automated image analysis software. To establish the high-throughput potential of this approach, we also screened a pool of ligands against a collection of 2455 cell surface expression clones and found that known ligand-receptor interactions could be robustly and consistently detected across the library using this technology.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | first & last 3 (QQ only),first & last (all papers),parasites and microbes,staffpaper |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Biology (York) The University of York > Faculty of Sciences (York) > Hull York Medical School (York) |
Depositing User: | Pure (York) |
Date Deposited: | 19 Oct 2020 16:20 |
Last Modified: | 14 Dec 2024 00:10 |
Published Version: | https://doi.org/10.1177/2472555219879053 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1177/2472555219879053 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166891 |