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Perseus is a comprehensive, user-friendly software platform for the biological analysis of quantitative proteomics data. It is intended to help biologists with little bioinformatics training to interpret protein expression, post-translational modification and interaction data. Also in this issue, see the Perspective by Röst et al . 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.

Systems-wide profiling of breast cancer has almost always entailed RNA and DNA analysis by microarray and sequencing techniques. Marked developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analysed 40 oestrogen receptor positive (luminal), Her2 positive and triple negative breast tumours and reached a quantitative depth of >10,000 proteins. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell–cell communication. Furthermore, we derived a signature of 19 proteins, which differ between the breast cancer subtypes, through support vector machine (SVM)-based classification and feature selection. Remarkably, only three proteins of the signature were associated with gene copy number variations and eleven were also reflected on the mRNA level. These breast cancer features revealed by our work provide novel insights that may ultimately translate to development of subtype-specific therapeutics. Breast cancers have been extensively studied at the genomic and transcriptomic levels in the hope of tailoring therapeutic regimens. Here the authors generate deep coverage proteomes from several clinical breast cancer samples, and use machine learning techniques to uncover biological processes altered in specific cancer subtypes.

While the number and identity of proteins expressed in a single human cell type is currently unknown, this fundamental question can be addressed by advanced mass spectrometry (MS)‐based proteomics. Online liquid chromatography coupled to high‐resolution MS and MS/MS yielded 166 420 peptides with unique amino‐acid sequence from HeLa cells. These peptides identified 10 255 different human proteins encoded by 9207 human genes, providing a lower limit on the proteome in this cancer cell line. Deep transcriptome sequencing revealed transcripts for nearly all detected proteins. We calculate copy numbers for the expressed proteins and show that the abundances of >90% of them are within a factor 60 of the median protein expression level. Comparisons of the proteome and the transcriptome, and analysis of protein complex databases and GO categories, suggest that we achieved deep coverage of the functional transcriptome and the proteome of a single cell type. 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.

Endosomes are compositionally dynamic organelles that regulate signaling, nutrient status and organelle quality by specifying whether material entering the cells will be shuttled back to the cell surface or degraded by the lysosome. Recently, membrane contact sites (MCSs) between the endoplasmic reticulum (ER) and endosomes have emerged as important players in endosomal protein sorting, dynamics and motility. Here, we show that PDZD8, a Synaptotagmin-like Mitochondrial lipid-binding Proteins (SMP) domain-containing ER transmembrane protein, utilizes distinct domains to interact with Rab7-GTP and the ER transmembrane protein Protrudin and together these components localize to an ER-late endosome MCS. At these ER-late endosome MCSs, mitochondria are also recruited to form a three-way contact. Thus, our data indicate that PDZD8 is a shared component of two distinct MCSs and suggest a role for SMP-mediated lipid transport in the regulation of endosome function. Membrane contact sites between organelles have been shown to play important biological roles. Here, the authors show that at the ER, PDZD8 associates with Protrudin and also with Rab7 endosomes and recruits mitochondria to form three-way contacts.

Regulation of mRNA translation has a pivotal role in modulating protein levels, and the genome-wide identification of proteins synthesized at a given time is indispensable to our understanding of gene expression. This protocol describes the mass-spectrometric analysis of newly synthesized proteins from cultured cells or whole tissues by using a biotinylated derivative of puromycin, which becomes incorporated into nascent polypeptide chains by ribosome catalysis. In this method, termed puromycin-associated nascent chain proteomics (PUNCH-P), intact ribosome-nascent chain complexes are first recovered from cells by ultracentrifugation, followed by biotin-puromycin labeling of newly synthesized proteins, streptavidin affinity purification and liquid chromatography–tandem mass spectrometry (LC-MS/MS). Unlike methods that require in vivo labeling, the sensitivity and coverage of PUNCH-P depend only on the amount of starting material and not on the duration of labeling, thus enabling the measurement of rapid fluctuations in protein synthesis. The protocol requires 3 d for sample preparation and analysis.

Along the transformation process, cells accumulate DNA aberrations, including mutations, translocations, amplifications, and deletions. Despite numerous studies, the overall effects of amplifications and deletions on the end point of gene expression--the level of proteins--is generally unknown. Here we use large-scale and high-resolution proteomics combined with gene copy number analysis to investigate in a global manner to what extent these genomic changes have a proteomic output and therefore the ability to affect cellular transformation. We accurately measure expression levels of 6,735 proteins and directly compare them to the gene copy number. We find that the average effect of these alterations on the protein expression is only a few percent. Nevertheless, by using a novel algorithm, we find the combined impact that many of these regional chromosomal aberrations have at the protein level. We show that proteins encoded by amplified oncogenes are often overexpressed, while adjacent amplified genes, which presumably do not promote growth and survival, are attenuated. Furthermore, regulation of biological processes and molecular complexes is independent of general copy number changes. By connecting the primary genome alteration to their proteomic consequences, this approach helps to interpret the data from large-scale cancer genomics efforts.

Background The quantitative relations between RNA and protein are fundamental to biology and are still not fully understood. Across taxa, it was demonstrated that the protein-to-mRNA ratio in steady state varies in a direction that lessens the change in protein levels as a result of changes in the transcript abundance. Evidence for this behavior in tissues is sparse. We tested this phenomenon in new data that we produced for the mouse auditory system, and in previously published tissue datasets. A joint analysis of the transcriptome and proteome was performed across four datasets: inner-ear mouse tissues, mouse organ tissues, lymphoblastoid primate samples and human cancer cell lines. Results We show that the protein levels are more conserved than the mRNA levels in all datasets, and that changes in transcription are associated with translational changes that exert opposite effects on the final protein level, in all tissues except cancer. Finally, we observe that some functions are enriched in the inner ear on the mRNA level but not in protein. Conclusions We suggest that partial buffering between transcription and translation ensures that proteins can be made rapidly in response to a stimulus. Accounting for the buffering can improve the prediction of protein levels from mRNA levels.

The development of mass spectrometry-based single-cell proteomics technologies opens unique opportunities to understand the functional crosstalk between cells that drive tumor development.

Mass spectrometry (MS)-based proteomics is increasingly applied in a quantitative format, often based on labeling of samples with stable isotopes that are introduced chemically or metabolically. In the stable isotope labeling by amino acids in cell culture (SILAC) method, two cell populations are cultured in the presence of heavy or light amino acids (typically lysine and/or arginine), one of them is subjected to a perturbation, and then both are combined and processed together. In this study, we describe a different approach—the use of SILAC as an internal or 'spike-in' standard—wherein SILAC is only used to produce heavy labeled reference proteins or proteomes. These are added to the proteomes under investigation after cell lysis and before protein digestion. The actual experiment is therefore completely decoupled from the labeling procedure. Spike-in SILAC is very economical, robust and in principle applicable to all cell- or tissue-based proteomic analyses. Applications range from absolute quantification of single proteins to the quantification of whole proteomes. Spike-in SILAC is especially advantageous when analyzing the proteomes of whole tissues or organisms. The protocol describes the quantitative analysis of a tissue sample relative to super-SILAC spike-in, a mixture of five SILAC-labeled cell lines that accurately represents the tissue. It includes the selection and preparation of the spike-in SILAC standard, the sample preparation procedure, and analysis and evaluation of the results.

Studying the complex relationship between transcription, translation and protein degradation is essential to our understanding of biological processes in health and disease. The limited correlations observed between mRNA and protein abundance suggest pervasive regulation of post-transcriptional steps and support the importance of profiling mRNA levels in parallel to protein synthesis and degradation rates. In this work, we applied an integrative multi-omic approach to study gene expression along the mammalian cell cycle through side-by-side analysis of mRNA, translation and protein levels. Our analysis sheds new light on the significant contribution of both protein synthesis and degradation to the variance in protein expression. Furthermore, we find that translation regulation plays an important role at S-phase, while progression through mitosis is predominantly controlled by changes in either mRNA levels or protein stability. Specific molecular functions are found to be co-regulated and share similar patterns of mRNA, translation and protein expression along the cell cycle. Notably, these include genes and entire pathways not previously implicated in cell cycle progression, demonstrating the potential of this approach to identify novel regulatory mechanisms beyond those revealed by traditional expression profiling. Through this three-level analysis, we characterize different mechanisms of gene expression, discover new cycling gene products and highlight the importance and utility of combining datasets generated using different techniques that monitor distinct steps of gene expression.