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.

This unit outlines the steps required to prepare a sample for MS analysis following protein separation or enrichment by gel electrophoresis, liquid chromatography, and affinity capture within the context of a bottom‐up proteomics workflow in which the protein is first broken up into peptides, either by chemical or enzymatic digestion, prior to MS analysis. Also included are protocols for enrichment at the peptide level, including phosphopeptide enrichment and reversed‐phase chromatography for sample purification immediately prior to MS analysis. Finally, there is a discussion regarding the types of MS technologies commonly used to analyze proteomics samples, as well as important parameters that should be considered when analyzing the MS data to ensure stringent and robust protein identifications and characterization. Curr. Protoc. Mol. Biol. 88:10.25.1‐10.25.23. © 2009 by John Wiley & Sons, Inc.

The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP’s tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.

Porous graphitized carbon (PGC) based chromatography achieves high-resolution separation of glycan structures released from glycoproteins. This approach is especially valuable when resolving structurally similar isomers and for discovery of novel and/or sample-specific glycan structures. However, the implementation of PGC-based separations in glycomics studies has been limited because system-independent retention values have not been established to normalize technical variation. To address this limitation, this study combined the use of hydrolyzed dextran as an internal standard and Skyline software for post-acquisition normalization to reduce retention time and peak area technical variation in PGC-based glycan analyses. This approach allowed assignment of system-independent retention values that are applicable to typical PGC-based glycan separations and supported the construction of a library containing >300 PGC-separated glycan structures with normalized glucose unit (GU) retention values. To enable the automation of this normalization method, a spectral MS/MS library was developed of the dextran ladder, achieving confident discrimination against isomeric glycans. The utility of this approach is demonstrated in two ways. First, to inform the search space for bioinformatically predicted but unobserved glycan structures, predictive models for two structural modifications, core-fucosylation and bisecting GlcNAc, were developed based on the GU library. Second, the applicability of this method for the analysis of complex biological samples is evidenced by the ability to discriminate between cell culture and tissue sample types by the normalized intensity of N-glycan structures alone. Overall, the methods and data described here are expected to support the future development of more automated approaches to glycan identification and quantitation.

Detailed knowledge of cell-surface proteins for isolating well-defined populations of human pluripotent stem cells (hPSCs) would significantly enhance their characterization and translational potential. Through a chemoproteomic approach, we developed a cell-surface proteome inventory containing 496 N-linked glycoproteins on human embryonic (hESCs) and induced PSCs (hiPSCs). Against a backdrop of human fibroblasts and 50 other cell types, >100 surface proteins of interest for hPSCs were revealed. The >30 positive and negative markers verified here by orthogonal approaches provide experimental justification for the rational selection of pluripotency and lineage markers, epitopes for cell isolation, and reagents for the characterization of putative hiPSC lines. Comparative differences between the chemoproteomic-defined surfaceome and the transcriptome-predicted surfaceome directly led to the discovery that STF-31, a reported GLUT-1 inhibitor, is toxic to hPSCs and efficient for selective elimination of hPSCs from mixed cultures.