Although the classification of cell types often relies on the identification of cell surface proteins as differentiation markers, flow cytometry requires suitable antibodies and currently permits detection of only up to a dozen differentiation markers in a single measurement. We use multiplexed massspectrometric identification of several hundred N-linked glycosylation sites specifically from cell surface-exposed glycoproteins to phenotype cells without antibodies in an unbiased fashion and without a priori knowledge. Our cell surface-capturing (CSC) technology, which covalently labels extracellular glycan moieties on live cells, enables the detection and relative quantitative comparison of the cell surface N-glycoproteomes of T and B cells, as well as monitoring changes in the abundance of cell surface N-glycoprotein markers during T-cell activation and the controlled differentiation of embryonic stem cells into the neural lineage. A snapshot view of the cell surface N-glycoprotein will enable detection of panels of N-glycoproteins as potential differentiation markers that are currently not accessible by other means.The molecular composition of the plasma membrane and its dynamic changes determine how a cell can interact with its environment. Proteins embedded in the membrane that have exposed, extracellular domains are crucial for cell-cell communication, interaction with pathogens, binding of chemical messengers and responses to environmental perturbations 1,2 . As cell surface proteins confer specific cellular functions and are easily accessible, they are often used as markers to classify cell types 3 and as drug targets 4 . By using available antibodies against cell surface proteins, cells are thus often classified or immunophenotyped according to their cell-surface-protein expression profile 5 . This approach has been used to immunophenotype cells of the immune system, and for the development of the cluster of differentiation (CD) nomenclature for antibodies against cell surface molecules. The latter has been used to classify the ~220 currently known cell types 6 .

Cell surface proteins are major targets of biomedical research due to their utility as cellular markers and their extracellular accessibility for pharmacological intervention. However, information about the cell surface protein repertoire (the surfaceome) of individual cells is only sparsely available. Here, we applied the Cell Surface Capture (CSC) technology to 41 human and 31 mouse cell types to generate a mass-spectrometry derived Cell Surface Protein Atlas (CSPA) providing cellular surfaceome snapshots at high resolution. The CSPA is presented in form of an easy-to-navigate interactive database, a downloadable data matrix and with tools for targeted surfaceome rediscovery (http://wlab.ethz.ch/cspa). The cellular surfaceome snapshots of different cell types, including cancer cells, resulted in a combined dataset of 1492 human and 1296 mouse cell surface glycoproteins, providing experimental evidence for their cell surface expression on different cell types, including 136 G-protein coupled receptors and 75 membrane receptor tyrosine-protein kinases. Integrated analysis of the CSPA reveals that the concerted biological function of individual cell types is mainly guided by quantitative rather than qualitative surfaceome differences. The CSPA will be useful for the evaluation of drug targets, for the improved classification of cell types and for a better understanding of the surfaceome and its concerted biological functions in complex signaling microenvironments.

SignificanceDespite the fundamental importance of the surfaceome as a signaling gateway to the cellular microenvironment, it remains difficult to determine which proteoforms reside in the plasma membrane and how they interact to enable context-dependent signaling functions. We applied a machine-learning approach utilizing domain-specific features to develop the accurate surfaceome predictor SURFY and used it to define the human in silico surfaceome of 2,886 proteins. The in silico surfaceome is a public resource which can be used to filter multiomics data to uncover cellular phenotypes and surfaceome markers. By our domain-specific feature machine-learning approach, we show indirectly that the environment (extracellular, cytoplasm, or vesicle) is reflected in the biochemical properties of protein domains reaching into that environment.