Abstract
Cell surface proteins play critical roles in a wide range of biological functions and disease processes through mediation of contacts and signals between a cell and its environment. Owing to their biological significance and accessibility, cell surface proteins are attractive targets for developing tools and strategies to identify, study, and manipulate specific cell types of interest. Applications ranging from immunophenotyping and immunotherapy to targeted drug delivery and in vivo imaging are enabled by exploitation of cell-type specific surface proteins. Despite their utility and 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 surface proteins, 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 GenieScore, a prioritization metric that integrates a consensus-based prediction of cell surface localization with user-input data to rank-order candidate cell-type specific surface markers. In this report, we outline the development of this prioritization strategy and demonstrate its utility for analyzing human and rodent data from proteomic and transcriptomic experiments in the areas of cancer, stem cell, and islet biology. The calculation of GenieScores, as well as additional scoring algorithm permutations that enable prioritization of co-expressed and intracellular cell-type specific candidate markers, is made accessible via the freely available SurfaceGenie web-application at www.cellsurfer.net/surfacegenie.
Footnotes
Additional analyses, figures and data are included. Additional permutations to the GenieScore metric are included.
