Jürgen Cox

Quantitative Phosphoproteomics Reveals Widespread Full Phosphorylation Site Occupancy During Mitosis

Protein phosphorylation during the cell cycle may be an all-or-none process in many instances. All-or-None Phosphorylation Phosphorylation is a key regulatory event that drives many cellular processes, including cell division. Olsen et al. undertook a phosphoproteomic analysis of HeLa cells at various stages in the cell cycle, which linked new phosphorylation sites and kinase substrates to specific stages. Furthermore, they established a method to calculate the fractional occupancy of particular phosphorylation sites (phosphorylation stoichiometry) on a global level and found that, contrary to expectations, many sites on functionally related proteins appeared to be nearly completely phosphorylated at particular stages of the cell cycle. They observed an inverse relationship in the phosphorylation occupancy of some sites in cells undergoing mitosis compared to those in S phase. The authors speculate that a high stoichiometry of phosphorylation may be necessary to inactivate an entire protein population to effectively block activity, whereas function may only require a low stoichiometry of phosphorylation, because only a small fraction of the protein population may be required for full activity. Eukaryotic cells replicate by a complex series of evolutionarily conserved events that are tightly regulated at defined stages of the cell division cycle. Progression through this cycle involves a large number of dedicated protein complexes and signaling pathways, and deregulation of this process is implicated in tumorigenesis. We applied high-resolution mass spectrometry–based proteomics to investigate the proteome and phosphoproteome of the human cell cycle on a global scale and quantified 6027 proteins and 20,443 unique phosphorylation sites and their dynamics. Co-regulated proteins and phosphorylation sites were grouped according to their cell cycle kinetics and compared to publicly available messenger RNA microarray data. Most detected phosphorylation sites and more than 20% of all quantified proteins showed substantial regulation, mainly in mitotic cells. Kinase-motif analysis revealed global activation during S phase of the DNA damage response network, which was mediated by phosphorylation by ATM or ATR or DNA-dependent protein kinases. We determined site-specific stoichiometry of more than 5000 sites and found that most of the up-regulated sites phosphorylated by cyclin-dependent kinase 1 (CDK1) or CDK2 were almost fully phosphorylated in mitotic cells. In particular, nuclear proteins and proteins involved in regulating metabolic processes have high phosphorylation site occupancy in mitosis. This suggests that these proteins may be inactivated by phosphorylation in mitotic cells.

Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ

Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.

Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast

Mass spectrometry is a powerful technology for the analysis of large numbers of endogenous proteins. However, the analytical challenges associated with comprehensive identification and relative quantification of cellular proteomes have so far appeared to be insurmountable. Here, using advances in computational proteomics, instrument performance and sample preparation strategies, we compare protein levels of essentially all endogenous proteins in haploid yeast cells to their diploid counterparts. Our analysis spans more than four orders of magnitude in protein abundance with no discrimination against membrane or low level regulatory proteins. Stable-isotope labelling by amino acids in cell culture (SILAC) quantification was very accurate across the proteome, as demonstrated by one-to-one ratios of most yeast proteins. Key members of the pheromone pathway were specific to haploid yeast but others were unaltered, suggesting an efficient control mechanism of the mating response. Several retrotransposon-associated proteins were specific to haploid yeast. Gene ontology analysis pinpointed a significant change for cell wall components in agreement with geometrical considerations: diploid cells have twice the volume but not twice the surface area of haploid cells. Transcriptome levels agreed poorly with proteome changes overall. However, after filtering out low confidence microarray measurements, messenger RNA changes and SILAC ratios correlated very well for pheromone pathway components. Systems-wide, precise quantification directly at the protein level opens up new perspectives in post-genomics and systems biology.

The Perseus computational platform for comprehensive analysis of (prote)omics data

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseuss arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics

MaxQuant is a quantitative proteomics software package designed for analyzing large mass spectrometric data sets. It is specifically aimed at high-resolution mass spectrometry (MS) data. Currently, Thermo LTQ-Orbitrap and LTQ-FT-ICR instruments are supported and Mascot is used as a search engine. This protocol explains step by step how to use MaxQuant on stable isotope labeling by amino acids in cell culture (SILAC) data obtained with double or triple labeling. Complex experimental designs, such as time series and drug-response data, are supported. A standard desktop computer is sufficient to fulfill the computational requirements. The workflow has been stress tested with more than 1,000 liquid chromatography/mass spectrometry runs in a single project. In a typical SILAC proteome experiment, hundreds of thousands of peptides and thousands of proteins are automatically and reliably quantified. Additional information for identified proteins, such as Gene Ontology, domain composition and pathway membership, is provided in the output tables ready for further bioinformatics analysis. The software is freely available at the MaxQuant home page.

Quantitative, High-Resolution Proteomics for Data-Driven Systems Biology

Systems biology requires comprehensive data at all molecular levels. Mass spectrometry (MS)-based proteomics has emerged as a powerful and universal method for the global measurement of proteins. In the most widespread format, it uses liquid chromatography (LC) coupled to high-resolution tandem mass spectrometry (MS/MS) to identify and quantify peptides at a large scale. This peptide intensity information is the basic quantitative proteomic data type. It is used to quantify proteins between different proteome states, including the temporal variation of the proteome, to determine the complete primary structure of proteins including posttranslational modifications, to localize proteins to organelles, and to determine protein interactions. Here, we describe the principles of analysis and the areas of biology where proteomics can make unique contributions. The large-scale nature of proteomics data and its high accuracy pose special opportunities as well as challenges in systems biology that have been largely untapped so far.

Quantitative Proteomics Reveals Subset-Specific Viral Recognition in Dendritic Cells

Dendritic cell (DC) populations consist of multiple subsets that are essential orchestrators of the immune system. Technological limitations have so far prevented systems-wide accurate proteome comparison of rare cell populations in vivo. Here, we used high-resolution mass spectrometry-based proteomics, combined with label-free quantitation algorithms, to determine the proteome of mouse splenic conventional and plasmacytoid DC subsets to a depth of 5,780 and 6,664 proteins, respectively. We found mutually exclusive expression of pattern recognition pathways not previously known to be different among conventional DC subsets. Our experiments assigned key viral recognition functions to be exclusively expressed in CD4(+) and double-negative DCs. The CD8alpha(+) DCs largely lack the receptors required to sense certain viruses in the cytoplasm. By avoiding activation via cytoplasmic receptors, including retinoic acid-inducible gene I, CD8alpha(+) DCs likely gain a window of opportunity to process and present viral antigens before activation-induced shutdown of antigen presentation pathways occurs.

System-Wide Changes to SUMO Modifications in Response to Heat Shock

The small ubiquitin-like modifier protein SUMO is redistributed among many targets to mediate both short- and long-term signaling events. SUMO Status Revealed Posttranslational modification of proteins through their conjugation to small ubiquitin-like modifier (SUMO) proteins is important in the nucleus for the repair of damaged DNA and the maintenance of chromosome structure, as well as for a number of cytoplasmic processes. Although the machinery involved in attaching SUMO moieties to target proteins is well characterized, less is known about the upstream signals that trigger this modification. Golebiowski et al. designed a highly stringent, quantitative approach, involving protein purification and mass spectrometric techniques, to perform a system-wide analysis of the SUMOylation states of hundreds of proteins in HeLa cells in response to heat shock. The authors also analyzed the dynamic nature of SUMOylation in cells during the subsequent recovery phase. In addition to identifying many previously unknown substrates of SUMO-2, this proteome-wide analysis of SUMOylation revealed a rapid and dramatic redistribution of SUMO-2 among proteins involved in short- or long-term responses to heat stress. This new approach should also prove valuable in systems-wide analysis of other posttranslational modifications. Covalent conjugation of the small ubiquitin-like modifier (SUMO) proteins to target proteins regulates many important eukaryotic cellular mechanisms. Although the molecular consequences of the conjugation of SUMO proteins are relatively well understood, little is known about the cellular signals that regulate the modification of their substrates. Here, we show that SUMO-2 and SUMO-3 are required for cells to survive heat shock. Through quantitative labeling techniques, stringent purification of SUMOylated proteins, advanced mass spectrometric technology, and novel techniques of data analysis, we quantified heat shock–induced changes in the SUMOylation state of 766 putative substrates. In response to heat shock, SUMO was polymerized into polySUMO chains and redistributed among a wide range of proteins involved in cell cycle regulation; apoptosis; the trafficking, folding, and degradation of proteins; transcription; translation; and DNA replication, recombination, and repair. This comprehensive proteomic analysis of the substrates of a ubiquitin-like modifier (Ubl) identifies a pervasive role for SUMO proteins in the biologic response to hyperthermic stress.

Is proteomics the new genomics

Mass spectrometry (MS)-based proteomics has become a formidable tool for the investigation of posttranslational modifications to proteins, protein interactions, and organelles. Is it now ready to tackle comprehensive protein expression analysis?

Ultradeep Human Phosphoproteome Reveals a Distinct Regulatory Nature of Tyr and Ser/Thr-Based Signaling

Regulatory protein phosphorylation controls normal and pathophysiological signaling in eukaryotic cells. Despite great advances in mass-spectrometry-based proteomics, the extent, localization, and site-specific stoichiometry of this posttranslational modification (PTM) are unknown. Here, we develop a stringent experimental and computational workflow, capable of mapping more than 50,000 distinct phosphorylated peptides in a single human cancer cell line. We detected more than three-quarters of cellular proteins as phosphoproteins and determined very high stoichiometries in mitosis or growth factor signaling by label-free quantitation. The proportion of phospho-Tyr drastically decreases as coverage of the phosphoproteome increases, whereas Ser/Thr sites saturate only for technical reasons. Tyrosine phosphorylation is maintained at especially low stoichiometric levels in the absence of specific signaling events. Unexpectedly, it is enriched on higher-abundance proteins, and this correlates with the substrate KM values of tyrosine kinases. Our data suggest that P-Tyr should be considered a functionally separate PTM of eukaryotic proteomes.