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.
