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 Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

More than 10 000 proteins were identified by high-resolution mass spectrometry in a human cancer cell line. The data cover most of the functional proteome as judged by RNA-seq data and it reveals the expression range of different protein classes.

Deep proteomic analysis of mammalian cell lines would yield an inventory of the building blocks of the most commonly used systems in biological research. Mass spectrometry-based proteomics can identify and quantify proteins in a global and unbiased manner and can highlight the cellular processes that are altered between such systems. We analyzed 11 human cell lines using an LTQ-Orbitrap family mass spectrometer with a "high field" Orbitrap mass analyzer with improved resolution and sequencing speed. We identified a total of 11,731 proteins, and on average 10,361 ؎ 120 proteins in each cell line. This very high proteome coverage enabled analysis of a broad range of processes and functions. Despite the distinct origins of the cell lines, our quantitative results showed surprisingly high similarity in terms of expressed proteins. Nevertheless, this global similarity of the proteomes did not imply equal expression levels of individual proteins across the 11 cell lines, as we found significant differences in expression levels for an estimated two-third of them. The variability in cellular expression levels was similar for low and high abundance proteins, and even many of the most highly expressed proteins with household roles showed significant differences between cells. Metabolic pathways, which have high redundancy, exhibited variable expression, whereas basic cellular functions such as the basal transcription machinery varied much less. We harness knowledge of these cell line proteomes for the construction of a broad coverage "super-SILAC" quantification standard. Together with the accompanying paper (Schaab, C. MCP 2012, PMID: 22301388) (17) these data can be used to obtain reference expression profiles for proteins of interest both within and across cell line proteomes. Molecular & Cellular Proteomics 11: 10.1074/mcp.M111.014050, 1-11, 2012.Mammalian cell lines are the basis of much of the biological work that examines protein function and cell response to perturbations and they have been indispensable for many of the biological insights obtained in the last decades. In the majority of cases these cell lines were extracted from tumors of different origins, and were then adapted to growth in vitro. These cell lines serve as proxies not only of the original tumors or tissues but also for fundamental biological processes. A system-wide and comparative view of the proteomes of such cell lines can reveal commonalities and discrepancies between cell lines in general and highlight the biological processes and their variations across the cells.So far only very few proteomic studies have attempted to determine shared and distinct features of different cell lines. Burkard et al. defined a "central proteome" in a comparison of seven cell lines (1). It consisted of the 1124 proteins that were identified in all these cell systems and that were preferentially involved in protein expression, metabolism and proliferation. This study identified 2000 -4000 proteins per cell line, and was therefore limited to the more abundant pro...

We describe a method to accurately quantify human tumor proteomes by combining a mixture of five stable-isotope labeling by amino acids in cell culture (SILAC)-labeled cell lines with human carcinoma tissue. This generated hundreds of thousands of isotopically labeled peptides in appropriate amounts to serve as internal standards for mass spectrometry-based analysis. By decoupling the labeling from the measurement, this super-SILAC method broadens the scope of SILAC-based proteomics.

Cellular life depends on continuous transport of lipids and small molecules between mitochondria and the endomembrane system. Recently, endoplasmic reticulum-mitochondrial encounter structure (ERMES) was identified as an important yet nonessential contact for such transport. Using a high-content screen in yeast, we found a contact site, marked by Vam6/Vps39, between vacuoles (the yeast lysosomal compartment) and mitochondria, named vCLAMP (vacuole and mitochondria patch). vCLAMP is enriched with ion and amino-acid transporters and has a role in lipid relay between the endomembrane system and mitochondria. Critically, we show that mitochondria are dependent on having one of two contact sites, ERMES or vCLAMP. The absence of one causes expansion of the other, and elimination of both is lethal. Identification of vCLAMP adds to our ability to understand the complexity of interorganellar crosstalk.