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.

Summary The high complexity and large dynamic range of blood plasma proteins currently prohibits the sensitive and high throughput profiling of disease and control plasma proteome sample sets large enough to reliably detect disease indicating differences. To circumvent these technological limitations we describe here a new two stage strategy for the mass spectrometry (MS) assisted discovery, verification and validation of disease biomarkers. In an initial discovery phase N-linked glycoproteins with distinguishable expression patterns in primary normal and diseased tissue are detected and identified. In the second step the proteins identified in the initial phase are subjected to targeted MS analysis in plasma samples, using the highly sensitive and specific selected reaction monitoring (SRM) technology. Since glycosylated proteins, such as those secreted or shed from the cell surface are likely to reside and persist in blood, the two stage strategy is focused on the quantification of tissue derived glycoproteins in plasma. The focus on the N-glycoproteome not only reduces the complexity of the analytes, but also targets an information-rich subproteome which is relevant for remote sensing of diseases in the plasma. The N-glycoprotein based biomarker discovery and validation workflow reviewed here allows for the robust identification of protein candidate panels that can finally be selectively monitored in the blood plasma at high sensitivity in a reliable, non-invasive and quantitative fashion.

The development of plasma biomarkers has proven to be more challenging than initially anticipated. Many studies have reported lists of candidate proteins rather than validated candidate markers with an assigned performance to a specific clinical objective. Biomarker research necessitates a clear rational framework with requirements on a multitude of levels. On the technological front, the platform needs to be effective to detect low abundant plasma proteins and be able to measure them in a high throughput manner over a large amount of samples reproducibly. At a conceptual level, the choice of the technological platform and available samples should be part of an overall clinical study design that depends on a joint effort between basic and clinical research. Solutions to these needs are likely to facilitate more feasible studies. Targeted proteomic workflows based on SRM mass spectrometry show the potential of fast verification of biomarker candidates in plasma and thereby closing the gap between discovery and validation in the biomarker development pipeline. Biological samples need to be carefully chosen based on well-established guidelines either for candidate discovery in the form of disease models with optimal fidelity to human disease or for candidate evaluation as well-designed and annotated clinical cohort groups. Most importantly, they should be representative of the target population and directly address the investigated clinical question. A conceptual structure of a biomarker study can be provided in the form of several sequential phases, each having clear objectives and predefined goals. Furthermore, guidelines for reporting the outcome of biomarker studies are critical to adequately assess the quality of the research, interpretation and generalization of the results. By being attentive to and applying these considerations, biomarker research should become more efficient and lead to directly translatable biomarker candidates into clinical evaluation.