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SurfaceGenie: A web-based application for integrating predictive and experimental data for rational candidate surface marker prioritization

View ORCID ProfileMatthew Waas, View ORCID ProfileShana T. Snarrenberg, View ORCID ProfileJack Littrell, View ORCID ProfileRachel A. Jones Lipinski, View ORCID ProfilePolly A. Hansen, View ORCID ProfileJohn A. Corbett, View ORCID ProfileRebekah L. Gundry
doi: https://doi.org/10.1101/575969
Matthew Waas
Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Shana T. Snarrenberg
Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Jack Littrell
Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Rachel A. Jones Lipinski
Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Polly A. Hansen
Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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John A. Corbett
Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Rebekah L. Gundry
Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USACenter for Biomedical Mass Spectrometry Research, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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  • For correspondence: rgundry@mcw.edu
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Abstract

Cell surface proteins play critical roles in a wide range of biological functions and disease processes through mediation of adhesion and signaling between a cell and its environment. Owing to their biological significance and accessibility, cell surface proteomes (i.e. surfaceomes) are a rich source of targets for developing tools and strategies to identify, study, and manipulate specific cell types of interest, from immunophenotyping and immunotherapy to targeted drug delivery and in vivo imaging. Despite their relevance, the unique combination of molecules present at the cell surface are not yet described for most cell types. While modern mass spectrometry approaches have proven invaluable for generating discovery-driven, empirically-derived snapshot views of the surfaceome, significant challenges remain when analyzing these often-large datasets for the purpose of identifying candidate markers that are most applicable for downstream applications. To overcome these challenges, we developed SurfaceGenie, a web-based application that integrates a consensus-based prediction of cell surface localization with user-input data to prioritize candidate cell type specific surface markers. Here, we outline the development of the strategy and demonstrate its utility for analyzing human and rodent data from proteomic and transcriptomic workflows. An easy-to-use web application is freely available at www.cellsurfer.net/surfacegenie.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted March 12, 2019.
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SurfaceGenie: A web-based application for integrating predictive and experimental data for rational candidate surface marker prioritization
Matthew Waas, Shana T. Snarrenberg, Jack Littrell, Rachel A. Jones Lipinski, Polly A. Hansen, John A. Corbett, Rebekah L. Gundry
bioRxiv 575969; doi: https://doi.org/10.1101/575969
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SurfaceGenie: A web-based application for integrating predictive and experimental data for rational candidate surface marker prioritization
Matthew Waas, Shana T. Snarrenberg, Jack Littrell, Rachel A. Jones Lipinski, Polly A. Hansen, John A. Corbett, Rebekah L. Gundry
bioRxiv 575969; doi: https://doi.org/10.1101/575969

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