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Comprehensive molecular characterization of tumors aims to uncover cancer vulnerabilities, drug resistance mechanisms, and biomarkers. Identification of cancer drivers was suggested as the basis for patient-tailored therapy, and transcriptomic analyses were proposed to reveal the phenotypic outcome of cancer mutations. With the maturation of the proteomic field, studies of protein-RNA discrepancies suggested that RNA analyses are insufficient to predict cellular functions. In this article we discuss the importance of direct mRNA-protein comparisons in clinical cancer studies. We make use of the large amount of data generated by the Clinical Proteomic Tumor Analysis Consortium, which includes protein and mRNA expression analyses from the exact same samples. Analysis of protein-RNA correlations showed marked differences among cancer types, and highlighted the protein-RNA similarities and discrepancies among functional pathways and drug targets. Additionally, unsupervised clustering of the data based on protein or RNA showed substantial differences in tumor classification and the cellular processes that differentiate between clusters. These analyses show the difficulty to predict protein levels from mRNAs, and the critical role of protein analyses for phenotypic tumor characterization., Competing Interests: Conflict of interest G. A. is an employee and T. G. is an advisor of Protai Bio., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Exercise prevents cancer incidence and recurrence, yet the underlying mechanism behind this relationship remains mostly unknown. Here we report that exercise induces the metabolic reprogramming of internal organs that increases nutrient demand and protects against metastatic colonization by limiting nutrient availability to the tumor, generating an exercise-induced metabolic shield. Proteomic and ex vivo metabolic capacity analyses of murine internal organs revealed that exercise induces catabolic processes, glucose uptake, mitochondrial activity, and GLUT expression. Proteomic analysis of routinely active human subject plasma demonstrated increased carbohydrate utilization following exercise. Epidemiologic data from a 20-year prospective study of a large human cohort of initially cancer-free participants revealed that exercise prior to cancer initiation had a modest impact on cancer incidence in low metastatic stages but significantly reduced the likelihood of highly metastatic cancer. In three models of melanoma in mice, exercise prior to cancer injection significantly protected against metastases in distant organs. The protective effects of exercise were dependent on mTOR activity, and inhibition of the mTOR pathway with rapamycin treatment ex vivo reversed the exercise-induced metabolic shield. Under limited glucose conditions, active stroma consumed significantly more glucose at the expense of the tumor. Collectively, these data suggest a clash between the metabolic plasticity of cancer and exercise-induced metabolic reprogramming of the stroma, raising an opportunity to block metastasis by challenging the metabolic needs of the tumor., Significance: Exercise protects against cancer progression and metastasis by inducing a high nutrient demand in internal organs, indicating that reducing nutrient availability to tumor cells represents a potential strategy to prevent metastasis. See related commentary by Zerhouni and Piskounova, p. 4124., (©2022 The Authors; Published by the American Association for Cancer Research.)

Single-cell analyses characterize individual cells, allowing their clustering and characterization in an unsupervised manner. Single-cell genomics and transcriptomics dominate the field of single-cell analysis, however, these often do not accurately reflect cellular functions. In contrast, single-cell protein analyses were until recently only performed using antibody-based approaches. This review aims to highlight the recent developments in mass spectrometry-based single-cell proteomics and discuss the challenges and opportunities. Advances in the field hold the promise to impact biomedical research and contribute to the understanding of complex biological systems., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Purpose: Treatment of metastatic melanoma has dramatically improved in recent years, thanks to the development of immunotherapy and BRAF-MEK-targeted therapies. However, these developments revealed marked heterogeneity in patient response, which is yet to be fully understood. In this work, we aimed to associate the proteomic profiles of metastatic melanoma with the patient clinical information, to identify protein correlates with metastatic location and prior treatments., Experimental Design: We performed mass spectrometry-based proteomic analysis of 185 metastatic melanoma samples and followed with bioinformatics analysis to examine the association of metastatic location, BRAF status, survival, and immunotherapy response with the tumor molecular profiles., Results: Bioinformatics analysis showed a high degree of functional heterogeneity associated with the site of metastasis. Lung metastases presented higher immune-related proteins, and higher mitochondrial-related processes, which were shown previously to be associated with better immunotherapy response. In agreement, epidemiological analysis of data from the National Cancer Database showed improved response to anti-programmed death 1, mainly in patients with lung metastasis. Focus on lung metastases revealed prognostic and molecular heterogeneity and highlighted potential tissue-specific biomarkers. Analysis of the BRAF mutation status and prior treatments with MAPK inhibitors proposed the molecular basis of the effect on immunotherapy response and suggested coordinated combination of immunotherapy and targeted therapy may increase treatment efficacy., Conclusions: Altogether, the proteomic data provided novel molecular determinants of critical clinical features, including the effects of sequential treatments and metastatic locations. These results can be the basis for development of site-specific treatments toward treatment personalization., (©2021 American Association for Cancer Research.)

Glioblastoma (GBM) is the most aggressive form of glioma, with poor prognosis exhibited by most patients, and a median survival time of less than 2 years. We assemble a cohort of 87 GBM patients whose survival ranges from less than 3 months and up to 10 years and perform both high-resolution mass spectrometry proteomics and RNA sequencing (RNA-seq). Integrative analysis of protein expression, RNA expression, and patient clinical information enables us to identify specific immune, metabolic, and developmental processes associated with survival as well as determine whether they are shared between expression layers or are layer specific. Our analyses reveal a stronger association between proteomic profiles and survival and identify unique protein-based classification, distinct from the established RNA-based classification. By integrating published single-cell RNA-seq data, we find a connection between subpopulations of GBM tumors and survival. Overall, our findings establish proteomic heterogeneity in GBM as a gateway to understanding poor survival., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Tumor relapse as a consequence of chemotherapy resistance is a major clinical challenge in advanced stage breast tumors. To identify processes associated with poor clinical outcome, we took a mass spectrometry-based proteomic approach and analyzed a breast cancer cohort of 113 formalin-fixed paraffin-embedded samples. Proteomic profiling of matched tumors before and after chemotherapy, and tumor-adjacent normal tissue, all from the same patients, allowed us to define eight patterns of protein level changes, two of which correlate to better chemotherapy response. Supervised analysis identified two proteins of proline biosynthesis pathway, PYCR1 and ALDH18A1, that were significantly associated with resistance to treatment based on pattern dominance. Weighted gene correlation network analysis of post-treatment samples revealed that these proteins are associated with tumor relapse and affect patient survival. Functional analysis showed that knockdown of PYCR1 reduced invasion and migration capabilities of breast cancer cell lines. PYCR1 knockout significantly reduced tumor burden and increased drug sensitivity of orthotopically injected ER-positive tumor in vivo, thus emphasizing the role of PYCR1 in resistance to chemotherapy., (© 2020 The Authors. Published under the terms of the CC BY 4.0 license.)

Immunotherapy has revolutionized cancer treatment, yet most patients do not respond. Here, we investigated mechanisms of response by profiling the proteome of clinical samples from advanced stage melanoma patients undergoing either tumor infiltrating lymphocyte (TIL)-based or anti- programmed death 1 (PD1) immunotherapy. Using high-resolution mass spectrometry, we quantified over 10,300 proteins in total and ∼4,500 proteins across most samples in each dataset. Statistical analyses revealed higher oxidative phosphorylation and lipid metabolism in responders than in non-responders in both treatments. To elucidate the effects of the metabolic state on the immune response, we examined melanoma cells upon metabolic perturbations or CRISPR-Cas9 knockouts. These experiments indicated lipid metabolism as a regulatory mechanism that increases melanoma immunogenicity by elevating antigen presentation, thereby increasing sensitivity to T cell mediated killing both in vitro and in vivo. Altogether, our proteomic analyses revealed association between the melanoma metabolic state and the response to immunotherapy, which can be the basis for future improvement of therapeutic response., (Copyright © 2019 Elsevier Inc. All rights reserved.)

High-grade ovarian cancer (HGOC) is the leading cause of mortality from gynecological malignancies, because of diagnosis at a metastatic stage. Current screening options fail to improve mortality because of the absence of early-stage-specific biomarkers. We postulated that a liquid biopsy, such as utero-tubal lavage (UtL), may identify localized lesions better than systemic approaches of serum/plasma analysis. Further, while mutation-based assays are challenged by the rarity of tumor DNA within nonmutated DNA, analyzing the proteomic profile, is expected to enable earlier detection, as it reveals perturbations in both the tumor as well as in its microenvironment. To attain deep proteomic coverage and overcome the high dynamic range of this body fluid, we applied our method for microvesicle proteomics to the UtL samples. Liquid biopsies from HGOC patients ( n = 49) and controls ( n = 127) were divided into a discovery and validation sets. Data-dependent analysis of the samples on the Q-Exactive mass spectrometer provided depth of 8578 UtL proteins in total, and on average ∼3000 proteins per sample. We used support vector machine algorithms for sample classification, and crossed three feature-selection algorithms, to construct and validate a 9-protein classifier with 70% sensitivity and 76.2% specificity. The signature correctly identified all Stage I lesions. These results demonstrate the potential power of microvesicle-based proteomic biomarkers for early cancer diagnosis., (© 2019 Barnabas et al.)

Purpose: Bone is the most predominant site of distant metastasis in prostate cancer, and patients have limited therapeutic options at this stage. Experimental Design: We performed a system-wide quantitative proteomic analysis of bone metastatic prostate tumors from 22 patients operated to relieve spinal cord compression. At the time of surgery, most patients had relapsed after androgen-deprivation therapy, while 5 were previously untreated. An extended cohort of prostate cancer bone metastases ( n = 65) was used for immunohistochemical validation. Results: On average, 5,067 proteins were identified and quantified per tumor. Compared with primary tumors ( n = 26), bone metastases were more heterogeneous and showed increased levels of proteins involved in cell-cycle progression, DNA damage response, RNA processing, and fatty acid β-oxidation; and reduced levels of proteins were related to cell adhesion and carbohydrate metabolism. Within bone metastases, we identified two phenotypic subgroups: BM1, expressing higher levels of AR canonical targets, and mitochondrial and Golgi apparatus resident proteins; and BM2, with increased expression of proliferation and DNA repair-related proteins. The two subgroups, validated by the inverse correlation between MCM3 and prostate specific antigen immunoreactivity, were related to disease prognosis, suggesting that this molecular heterogeneity should be considered when developing personalized therapies. Conclusions: This work is the first system-wide quantitative characterization of the proteome of prostate cancer bone metastases and a valuable resource for understanding the etiology of prostate cancer progression. Clin Cancer Res; 24(21); 5433-44. ©2018 AACR ., (©2018 American Association for Cancer Research.)

Breast cancer classification has been the focus of numerous worldwide efforts, analyzing the molecular basis of breast cancer subtypes and aiming to associate them with clinical outcome and to improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles has yet to be elucidated. In this work, we utilized mass spectrometry-based proteomic analysis on more than 130 clinical breast samples to demonstrate intertumor heterogeneity across three breast cancer subtypes and healthy tissue. Unsupervised analysis identified four proteomic clusters, among them, one that represents a novel luminal subtype characterized by increased PI3K signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. These results demonstrate the importance of deep proteomic analysis, which may affect cancer treatment decision making. Significance: These findings utilize extensive proteomics to identify a novel luminal breast cancer subtype, highlighting the added value of clinical proteomics in breast cancer to identify unique features not observable by genomic approaches. Cancer Res; 78(20); 6001-10. ©2018 AACR ., (©2018 American Association for Cancer Research.)

Proteomics technology aims to map the protein landscapes of biological samples, and it can be applied to a variety of samples, including cells, tissues, and body fluids. Because the proteins are the main functional molecules in the cells, their levels reflect much more accurately the cellular phenotype and the regulatory processes within them than gene levels, mutations, and even mRNA levels. With the advancement in the technology, it is possible now to obtain comprehensive views of the biological systems and to study large patient cohorts in a streamlined manner. In this review we discuss the technological advancements in mass spectrometry-based proteomics, which allow analysis of breast cancer tissue samples, leading to the first large-scale breast cancer proteomics studies. Furthermore, we discuss the technological developments in blood-based biomarker discovery, which provide the basis for future development of assays for routine clinical use. Although these are only the first steps in implementation of proteomics into the clinic, extensive collaborative work between these worlds will undoubtedly lead to major discoveries and advances in clinical practice., (Copyright © 2017 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Precise regulation of mRNA translation is critical for proper cell division, but little is known about the factors that mediate it. To identify mRNA-binding proteins that regulate translation during mitosis, we analyzed the composition of polysomes from interphase and mitotic cells using unbiased quantitative mass-spectrometry (LC-MS/MS). We found that mitotic polysomes are enriched with a subset of proteins involved in RNA processing, including alternative splicing and RNA export. To demonstrate that these may indeed be regulators of translation, we focused on heterogeneous nuclear ribonucleoprotein C (hnRNP C) as a test case and confirmed that it is recruited to elongating ribosomes during mitosis. Then, using a combination of pulsed SILAC, metabolic labeling and ribosome profiling, we showed that knockdown of hnRNP C affects both global and transcript-specific translation rates and found that hnRNP C is specifically important for translation of mRNAs that encode ribosomal proteins and translation factors. Taken together, our results demonstrate how proteomic analysis of polysomes can provide insight into translation regulation under various cellular conditions of interest and suggest that hnRNP C facilitates production of translation machinery components during mitosis to provide daughter cells with the ability to efficiently synthesize proteins as they enter G1 phase., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Plasma biomarker discovery necessitates a method for deep proteomic profiling, as well as for highly accurate quantification of the proteins in the sample. Furthermore, to obtain strong candidates for potential biomarkers, the method should be high throughput to enable a large scale analysis. Here we describe in detail PROMIS-Quan (PROteomics of MIcroparticles using Super-SILAC Quantification), a method for a simple and robust fractionation of the plasma samples by extraction of plasma microparticles, followed by SILAC-based relative and absolute quantification.

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.

Vanishing white matter (VWM) is a recessive neurodegenerative disease caused by mutations in translation initiation factor eIF2B and leading to progressive brain myelin deterioration, secondary axonal damage, and death in early adolescence. Eif2b5(R132H/R132H) mice exhibit delayed developmental myelination, mild early neurodegeneration and a robust remyelination defect in response to cuprizone-induced demyelination. In the current study we used Eif2b5(R132H/R132H) mice for mass-spectrometry analyses, to follow the changes in brain protein abundance in normal- versus cuprizone-diet fed mice during the remyelination recovery phase. Analysis of proteome profiles suggested that dysregulation of mitochondrial functions, altered proteasomal activity and impaired balance between protein synthesis and degradation play a role in VWM pathology. Consistent with these findings, we detected elevated levels of reactive oxygen species in mutant-derived primary fibroblasts and reduced 20S proteasome activity in mutant brain homogenates. These observations highlight the importance of tight translational control to precise coordination of processes involved in myelin formation and regeneration and point at cellular functions that may contribute to VWM pathology. Eif2b5(R132H/R132H) mouse model for vanishing white matter (VWM) disease was used for mass spectrometry of brain proteins at two time points under normal conditions and along recovery from cuprizone-induced demyelination. Comparisons of proteome profiles revealed the importance of mitochondrial function and tight coordination between protein synthesis and degradation to myelination formation and regeneration, pointing at cellular functions that contribute to VWM pathology., (© 2015 International Society for Neurochemistry.)

Unbiased proteomic analysis of plasma samples holds the promise to reveal clinically invaluable disease biomarkers. However, the tremendous dynamic range of the plasma proteome has so far hampered the identification of such low abundant markers. To overcome this challenge we analyzed the plasma microparticle proteome, and reached an unprecedented depth of over 3000 plasma proteins in single runs. To add a quantitative dimension, we developed PROMIS-Quan-PROteomics of MIcroparticles with Super-Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) Quantification, a novel mass spectrometry-based technology for plasma microparticle proteome quantification. PROMIS-Quan enables a two-step relative and absolute SILAC quantification. First, plasma microparticle proteomes are quantified relative to a super-SILAC mix composed of cell lines from distinct origins. Next, the absolute amounts of selected proteins of interest are quantified relative to the super-SILAC mix. We applied PROMIS-Quan to prostate cancer and compared plasma microparticle samples of healthy individuals and prostate cancer patients. We identified in total 5374 plasma-microparticle proteins, and revealed a predictive signature of three proteins that were elevated in the patient-derived plasma microparticles. Finally, PROMIS-Quan enabled determination of the absolute quantitative changes in prostate specific antigen (PSA) upon treatment. We propose PROMIS-Quan as an innovative platform for biomarker discovery, validation, and quantification in both the biomedical research and in the clinical worlds., (© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.)

Stable isotope labeling with amino acids in cell culture (SILAC) has risen as a powerful quantification technique in mass spectrometry (MS)-based proteomics in classical and modified forms. Previously, SILAC was limited to cultured cells because of the requirement of active protein synthesis; however, in recent years, it was expanded to model organisms and tissue samples. Specifically, the super-SILAC technique uses a mixture of SILAC-labeled cells as a spike-in standard for accurate quantification of unlabeled samples, thereby enabling quantification of human tissue samples. Here, we highlight the recent developments in super-SILAC and its application to the study of clinical samples, secretomes, post-translational modifications and organelle proteomes. Finally, we propose super-SILAC as a robust and accurate method that can be commercialized and applied to basic and clinical research.

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.

Stable isotope labeling with amino acids in cell culture (SILAC) is considered the most accurate method for proteome quantification by mass spectrometry. As it relies on active protein translation, it was traditionally limited to cells in culture and was not applicable to tissues. We have previously developed the super-SILAC mix, which is a mixture of several cell lines that serves as an internal spike-in standard for the study of human tumor tissue. The super-SILAC mix greatly improves the quantification accuracy while lowering error rates, and it is a simple, economic, and robust technique. Here we describe the design and application of super-SILAC to a broad range of biological systems, for basic biological research as well as clinical one.

Monitoring protein synthesis is essential to our understanding of gene expression regulation, as protein abundance is thought to be predominantly controlled at the level of translation. Mass-spectrometric and RNA sequencing methods have been recently developed for investigating mRNA translation at a global level, but these still involve technical limitations and are not widely applicable. In this study, we describe a novel system-wide proteomic approach for direct monitoring of translation, termed puromycin-associated nascent chain proteomics (PUNCH-P), which is based on incorporation of biotinylated puromycin into newly synthesized proteins under cell-free conditions followed by streptavidin affinity purification and liquid chromatography-tandem mass spectrometry analysis. Using PUNCH-P, we measured cell cycle-specific fluctuations in synthesis for >5000 proteins in mammalian cells, identified proteins not previously implicated in cell cycle processes, and generated the first translational profile of a whole mouse brain. This simple and economical technique is broadly applicable to any cell type and tissue, enabling the identification and quantification of rapid proteome responses under various biological conditions.

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.

MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a user-friendly web interface at http://www.biochem.mpg.de/maxqb.

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.

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.

The orbitrap mass analyzer combines high sensitivity, high resolution, and high mass accuracy in a compact format. In proteomics applications, it is used in a hybrid configuration with a linear ion trap (LTQ-Orbitrap) where the linear trap quadrupole (LTQ) accumulates, isolates, and fragments peptide ions. Alternatively, isolated ions can be fragmented by higher energy collisional dissociation. A recently introduced stand-alone orbitrap analyzer (Exactive) also features a higher energy collisional dissociation cell but cannot isolate ions. Here we report that this instrument can efficiently characterize protein mixtures by alternating MS and "all-ion fragmentation" (AIF) MS/MS scans in a manner similar to that previously described for quadrupole time-of-flight instruments. We applied the peak recognition algorithms of the MaxQuant software at both the precursor and product ion levels. Assignment of fragment ions to co-eluting precursor ions was facilitated by high resolution (100,000 at m/z 200) and high mass accuracy. For efficient fragmentation of different mass precursors, we implemented a stepped collision energy procedure with cumulative MS readout. AIF on the Exactive identified 45 of 48 proteins in an equimolar protein standard mixture and all of them when using a small database. The technique also identified proteins with more than 100-fold abundance differences in a high dynamic range standard. When applied to protein identification in gel slices, AIF unambiguously characterized an immunoprecipitated protein that was barely visible by Coomassie staining and quantified it relative to contaminating proteins. AIF on a benchtop orbitrap instrument is therefore an attractive technology for a wide range of proteomics analyses.

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., Competing Interests: The authors have declared that no competing interests exist.

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.

Kinases play a prominent role in tumor development, pointing to the presence of specific phosphorylation patterns in tumor tissues. Here, we investigate whether recently developed high resolution mass spectrometric (MS) methods for proteome and phosphoproteome analysis can also be applied to solid tumors. As tumor model, we used TG3 mutant mice carrying skin melanomas. At total of 100 microg of solid tumor lysate yielded a melanoma proteome of 4443 identified proteins, including at least 88 putative melanoma markers previously found by cDNA microarray technology. Analysis of 2 mg of lysate from dissected melanoma with titansphere chromatography and 8 mg with strong cation exchange together resulted in the identification of more than 5600 phosphorylation sites on 2250 proteins. The phosphoproteome included many hits from pathways important in melanoma. One-month storage at -80 degrees C did not significantly decrease the number of identified phosphorylation sites. Thus, solid tumor can be analyzed by MS-based proteomics with similar efficiency as cell culture models and in amounts compatible with biopsies.