Proteomics Evidence of a Systemic Response to Desiccation in the Resurrection Plant Haberlea rhodopensis.

Global warming and drought stress are expected to have a negative impact on agricultural productivity. Desiccation-tolerant species, which are able to tolerate the almost complete desiccation of their vegetative tissues, are appropriate models to study extreme drought tolerance and identify novel approaches to improve the resistance of crops to drought stress. In the present study, to better understand what makes resurrection plants extremely tolerant to drought, we performed transmission electron microscopy and integrative large-scale proteomics, including organellar and phosphorylation proteomics, and combined these investigations with previously published transcriptomic and metabolomics data from the resurrection plant Haberlea rhodopensis. The results revealed new evidence about organelle and cell preservation, posttranscriptional and posttranslational regulation, photosynthesis, primary metabolism, autophagy, and cell death in response to desiccation in H. rhodopensis. Different protective intrinsically disordered proteins, such as late embryogenesis abundant (LEA) proteins, thaumatin-like proteins (TLPs), and heat shock proteins (HSPs), were detected. We also found a constitutively abundant dehydrin in H. rhodopensis whose phosphorylation levels increased under stress in the chloroplast fraction. This integrative multi-omics analysis revealed a systemic response to desiccation in H. rhodopensis and certain targets for further genomic and evolutionary studies on DT mechanisms and genetic engineering towards the improvement of drought tolerance in crops.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

Multiomics Study of Bacterial Growth Arrest in a Synthetic Biology Application.

We investigated the scalability of a previously developed growth switch based on external control of RNA polymerase expression. Our results indicate that, in liter-scale bioreactors operating in fed-batch mode, growth-arrested Escherichia coli cells are able to convert glucose to glycerol at an increased yield. A multiomics quantification of the physiology of the cells shows that, apart from acetate production, few metabolic side effects occur. However, a number of specific responses to growth slow-down and growth arrest are launched at the transcriptional level. These notably include the downregulation of genes involved in growth-associated processes, such as amino acid and nucleotide metabolism and translation. Interestingly, the transcriptional responses are buffered at the proteome level, probably due to the strong decrease of the total mRNA concentration after the diminution of transcriptional activity and the absence of growth dilution of proteins. Growth arrest thus reduces the opportunities for dynamically adjusting the proteome composition, which poses constraints on the design of biotechnological production processes but may also avoid the initiation of deleterious stress responses.

Mixotrophic growth of the extremophile Galdieria sulphuraria reveals the flexibility of its carbon assimilation metabolism.

Galdieria sulphuraria is a cosmopolitan microalga found in volcanic hot springs and calderas. It grows at low pH in photoautotrophic (use of light as a source of energy) or heterotrophic (respiration as a source of energy) conditions, using an unusually broad range of organic carbon sources. Previous data suggested that G. sulphuraria cannot grow mixotrophically (simultaneously exploiting light and organic carbon as energy sources), its photosynthetic machinery being repressed by organic carbon. Here, we show that G. sulphuraria SAG21.92 thrives in photoautotrophy, heterotrophy and mixotrophy. By comparing growth, biomass production, photosynthetic and respiratory performances in these three trophic modes, we show that addition of organic carbon to cultures (mixotrophy) relieves inorganic carbon limitation of photosynthesis thanks to increased CO2 supply through respiration. This synergistic effect is lost when inorganic carbon limitation is artificially overcome by saturating photosynthesis with added external CO2 . Proteomic and metabolic profiling corroborates this conclusion suggesting that mixotrophy is an opportunistic mechanism to increase intracellular CO2 concentration under physiological conditions, boosting photosynthesis by enhancing the carboxylation activity of Ribulose-1,5-bisphosphate carboxylase-oxygenase (Rubisco) and decreasing photorespiration. We discuss possible implications of these findings for the ecological success of Galdieria in extreme environments and for biotechnological applications.

An innovative standard for LC-MS-based HCP profiling and accurate quantity assessment: Application to batch consistency in viral vaccine samples.

Biotherapeutics, molecules produced from biological systems, require rigorous purification steps to remove impurities including host cell proteins (HCPs). Regulatory guidelines require manufacturers to monitor process-related impurities along the purification workflow. Mass spectrometry (MS) has recently been considered as a complementary method to the well-established ELISA for HCPs quantification, since it has the advantage of unambiguously identifying individual HCP. In this study, we developed an innovative standard dedicated to MS-based HCP profiling analysis in order to monitor the consistency of viral vaccine intermediate purification samples. This standard, termed the HCP-PROFILER standard, is composed of a water-soluble bead (READYBEADS technology) which, after being added into the sample, releases unlabeled peptides in controlled amounts. The standard meets three desired criteria: (1) it is composed of multiple peptides, at different concentration levels, allowing construction of a calibration curve covering the dynamic range of HCPs present in the target sample, ensuring quantification accuracy; (2) it demonstrates high batch-to-batch reproducibility, ensuring quantification robustness and consistency over time; and (3) it is easy to use and avoids user-induced analytical biases. In this study, we present the use of the HCP-PROFILER standard for vaccine batches comparison and downstream process performance studies.

Systematic quantitative analysis of H2A and H2B variants by targeted proteomics.

BACKGROUND: Histones organize DNA into chromatin through a variety of processes. Among them, a vast diversity of histone variants can be incorporated into chromatin and finely modulate its organization and functionality. Classically, the study of histone variants has largely relied on antibody-based assays. However, antibodies have a limited efficiency to discriminate between highly similar histone variants. RESULTS: In this study, we established a mass spectrometry-based analysis to address this challenge. We developed a targeted proteomics method, using selected reaction monitoring or parallel reaction monitoring, to quantify a maximum number of histone variants in a single multiplexed assay, even when histones are present in a crude extract. This strategy was developed on H2A and H2B variants, using 55 peptides corresponding to 25 different histone sequences, among which a few differ by a single amino acid. The methodology was then applied to mouse testis extracts in which almost all histone variants are expressed. It confirmed the abundance profiles of several testis-specific histones during successive stages of spermatogenesis and the existence of predicted H2A.L.1 isoforms. This methodology was also used to explore the over-expression pattern of H2A.L.1 isoforms in a mouse model of male infertility. CONCLUSIONS: Our results demonstrate that targeted proteomics is a powerful method to quantify highly similar histone variants and isoforms. The developed method can be easily transposed to the study of human histone variants, whose abundance can be deregulated in various diseases.

Down-regulation of NOX2 activity in phagocytes mediated by ATM-kinase dependent phosphorylation.

NADPH oxidases (NOX) have many biological roles, but their regulation to control production of potentially toxic ROS molecules remains unclear. A previously identified insertion sequence of 21 residues (called NIS) influences NOX activity, and its predicted flexibility makes it a good candidate for providing a dynamic switch controlling the NOX active site. We constructed NOX2 chimeras in which NIS had been deleted or exchanged with those from other NOXs (NIS1, 3 and 4). All contained functional heme and were expressed normally at the plasma membrane of differentiated PLB-985 cells. However, NOX2-ΔNIS and NOX2-NIS1 had neither NADPH-oxidase nor reductase activity and exhibited abnormal translocation of p47phox and p67phox to the phagosomal membrane. This suggested a functional role of NIS. Interestingly after activation, NOX2-NIS3 cells exhibited superoxide overproduction compared with wild-type cells. Paradoxically, the Vmax of purified unstimulated NOX2-NIS3 was only one-third of that of WT-NOX2. We therefore hypothesized that post-translational events regulate NOX2 activity and differ between NOX2-NIS3 and WT-NOX2. We demonstrated that Ser486, a phosphorylation target of ataxia telangiectasia mutated kinase (ATM kinase) located in the NIS of NOX2 (NOX2-NIS), was phosphorylated in purified cytochrome b558 after stimulation with phorbol 12-myristate-13-acetate (PMA). Moreover, ATM kinase inhibition and a NOX2 Ser486Ala mutation enhanced NOX activity whereas a Ser486Glu mutation inhibited it. Thus, the absence of Ser486 in NIS3 could explain the superoxide overproduction in the NOX2-NIS3 mutant. These results suggest that PMA-stimulated NOX2-NIS phosphorylation by ATM kinase causes a dynamic switch that deactivates NOX2 activity. We hypothesize that this downregulation is defective in NOX2-NIS3 mutant because of the absence of Ser486.

A community proposal to integrate proteomics activities in ELIXIR.

Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on 'The Future of Proteomics in ELIXIR' that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes. These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR's existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper.

ChloroKB: A Web Application for the Integration of Knowledge Related to Chloroplast Metabolic Network.

Higher plants, as autotrophic organisms, are effective sources of molecules. They hold great promise for metabolic engineering, but the behavior of plant metabolism at the network level is still incompletely described. Although structural models (stoichiometry matrices) and pathway databases are extremely useful, they cannot describe the complexity of the metabolic context, and new tools are required to visually represent integrated biocurated knowledge for use by both humans and computers. Here, we describe ChloroKB, a Web application (http://chlorokb.fr/) for visual exploration and analysis of the Arabidopsis (Arabidopsis thaliana) metabolic network in the chloroplast and related cellular pathways. The network was manually reconstructed through extensive biocuration to provide transparent traceability of experimental data. Proteins and metabolites were placed in their biological context (spatial distribution within cells, connectivity in the network, participation in supramolecular complexes, and regulatory interactions) using CellDesigner software. The network contains 1,147 reviewed proteins (559 localized exclusively in plastids, 68 in at least one additional compartment, and 520 outside the plastid), 122 proteins awaiting biochemical/genetic characterization, and 228 proteins for which genes have not yet been identified. The visual presentation is intuitive and browsing is fluid, providing instant access to the graphical representation of integrated processes and to a wealth of refined qualitative and quantitative data. ChloroKB will be a significant support for structural and quantitative kinetic modeling, for biological reasoning, when comparing novel data with established knowledge, for computer analyses, and for educational purposes. ChloroKB will be enhanced by continuous updates following contributions from plant researchers.

Multiplex and accurate quantification of acute kidney injury biomarker candidates in urine using Protein Standard Absolute Quantification (PSAQ) and targeted proteomics.

There is a need for multiplex, specific and quantitative methods to speed-up the development of acute kidney injury biomarkers and allow a more specific diagnosis. Targeted proteomic analysis combined with stable isotope dilution has recently emerged as a powerful option for the parallelized evaluation of candidate biomarkers. This article presents the development of a targeted proteomic assay to quantify 4 acute kidney injury biomarker candidates in urine samples. The proteins included in the assessed panel consisted of myo-inositol oxygenase (MIOX), phosphoenolpyruvate carboxykinase 1 (PCK1), neutrophil gelatinase-associated lipocalin (NGAL) and liver fatty acid-binding protein (L-FABP). The proteomic assay combined an antibody-free sample preparation and a liquid chromatography-selected reaction monitoring (LC-SRM) analysis pipeline. For accurate quantification of the selected candidates, we used PSAQ (Protein Standard Absolute Quantification) standards which are isotopically labeled versions of the target proteins. When added directly to the biological samples, these standards improve detection specificity and quantification accuracy. The multiplexed assay developed for the 4 biomarker candidates showed excellent analytical performance, in line with the recommendations of health authorities. Tests on urine from two small patient cohorts and a group of healthy donors confirmed the relevance of NGAL and L-FABP as biomarkers for AKI diagnosis. The assay is readily adaptable to other biomarker candidates and should be very useful for the simultaneous and accurate quantification of multiple biomarkers.

Bdf1 Bromodomains Are Essential for Meiosis and the Expression of Meiotic-Specific Genes.

Bromodomain and Extra-terminal motif (BET) proteins play a central role in transcription regulation and chromatin signalling pathways. They are present in unicellular eukaryotes and in this study, the role of the BET protein Bdf1 has been explored in Saccharomyces cerevisiae. Mutation of Bdf1 bromodomains revealed defects on both the formation of spores and the meiotic progression, blocking cells at the exit from prophase, before the first meiotic division. This phenotype is associated with a massive deregulation of the transcription of meiotic genes and Bdf1 bromodomains are required for appropriate expression of the key meiotic transcription factor NDT80 and almost all the Ndt80-inducible genes, including APC complex components. Bdf1 notably accumulates on the promoter of Ndt80 and its recruitment is dependent on Bdf1 bromodomains. In addition, the ectopic expression of NDT80 during meiosis partially bypasses this dependency. Finally, purification of Bdf1 partners identified two independent complexes with Bdf2 or the SWR complex, neither of which was required to complete sporulation. Taken together, our results unveil a new role for Bdf1 -working independently from its predominant protein partners Bdf2 and the SWR1 complex-as a regulator of meiosis-specific genes.

A proteomics assay to detect eight CBRN-relevant toxins in food.

A proteomics assay was set up to analyze food substrates for eight toxins of the CBRN (chemical, biological, radiological and nuclear) threat, namely ricin, Clostridium perfringens epsilon toxin (ETX), Staphylococcus aureus enterotoxins (SEA, SEB and SED), shigatoxins from Shigella dysenteriae and entero-hemorragic Escherichia coli strains (STX1 and STX2) and Campylobacter jejuni cytolethal distending toxin (CDT). The assay developed was based on an antibody-free sample preparation followed by bottom-up LC-MS/MS analysis operated in targeted mode. Highly specific detection and absolute quantification were obtained using isotopically labeled proteins (PSAQ standards) spiked into the food matrix. The sensitivity of the assay for the eight toxins was lower than the oral LD50 which would likely be used in a criminal contamination of food supply. This assay should be useful in monitoring biological threats. In the public-health domain, it opens the way for multiplex investigation of food-borne toxins using targeted LC-MS/MS.

DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics.

DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser. AVAILABILITY AND IMPLEMENTATION: DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project (http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages. CONTACT: samuel.wieczorek@cea.fr, florence.combes@cea.fr, thomas.burger@cea.fr.

hEIDI: An Intuitive Application Tool To Organize and Treat Large-Scale Proteomics Data.

Advances in high-throughput proteomics have led to a rapid increase in the number, size, and complexity of the associated data sets. Managing and extracting reliable information from such large series of data sets require the use of dedicated software organized in a consistent pipeline to reduce, validate, exploit, and ultimately export data. The compilation of multiple mass-spectrometry-based identification and quantification results obtained in the context of a large-scale project represents a real challenge for developers of bioinformatics solutions. In response to this challenge, we developed a dedicated software suite called hEIDI to manage and combine both identifications and semiquantitative data related to multiple LC-MS/MS analyses. This paper describes how, through a user-friendly interface, hEIDI can be used to compile analyses and retrieve lists of nonredundant protein groups. Moreover, hEIDI allows direct comparison of series of analyses, on the basis of protein groups, while ensuring consistent protein inference and also computing spectral counts. hEIDI ensures that validated results are compliant with MIAPE guidelines as all information related to samples and results is stored in appropriate databases. Thanks to the database structure, validated results generated within hEIDI can be easily exported in the PRIDE XML format for subsequent publication. hEIDI can be downloaded from http://biodev.extra.cea.fr/docs/heidi .

Deletion of FtsH11 protease has impact on chloroplast structure and function in Arabidopsis thaliana when grown under continuous light.

The membrane-integrated metalloprotease FtsH11 of Arabidopsis thaliana is proposed to be dual-targeted to mitochondria and chloroplasts. A bleached phenotype was observed in ftsh11 grown at long days or continuous light, pointing to disturbances in the chloroplast. Within the chloroplast, FtsH11 was found to be located exclusively in the envelope. Two chloroplast-located proteins of unknown function (Tic22-like protein and YGGT-A) showed significantly higher abundance in envelope membranes and intact chloroplasts of ftsh11 and therefore qualify as potential substrates for the FtsH11 protease. No proteomic changes were observed in the mitochondria of 6-week-old ftsh11 compared with wild type, and FtsH11 was not immunodetected in these organelles. The abundance of plastidic proteins, especially of photosynthetic proteins, was altered even during standard growth conditions in total leaves of ftsh11. At continuous light, the amount of photosystem I decreased relative to photosystem II, accompanied by a drastic change of the chloroplast morphology and a drop of non-photochemical quenching. FtsH11 is crucial for chloroplast structure and function during growth in prolonged photoperiod.

Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update.

The Chromosome-Centric Human Proteome Project (C-HPP) aims to identify "missing" proteins in the neXtProt knowledgebase. We present an in-depth proteomics analysis of the human sperm proteome to identify testis-enriched missing proteins. Using protein extraction procedures and LC-MS/MS analysis, we detected 235 proteins (PE2-PE4) for which no previous evidence of protein expression was annotated. Through LC-MS/MS and LC-PRM analysis, data mining, and immunohistochemistry, we confirmed the expression of 206 missing proteins (PE2-PE4) in line with current HPP guidelines (version 2.0). Parallel reaction monitoring acquisition and sythetic heavy labeled peptides targeted 36 ≪one-hit wonder≫ candidates selected based on prior peptide spectrum match assessment. 24 were validated with additional predicted and specifically targeted peptides. Evidence was found for 16 more missing proteins using immunohistochemistry on human testis sections. The expression pattern for some of these proteins was specific to the testis, and they could possibly be valuable markers with fertility assessment applications. Strong evidence was also found of four "uncertain" proteins (PE5); their status should be re-examined. We show how using a range of sample preparation techniques combined with MS-based analysis, expert knowledge, and complementary antibody-based techniques can produce data of interest to the community. All MS/MS data are available via ProteomeXchange under identifier PXD003947. In addition to contributing to the C-HPP, we hope these data will stimulate continued exploration of the sperm proteome.

AtMic60 Is Involved in Plant Mitochondria Lipid Trafficking and Is Part of a Large Complex.

The mitochondrion is an organelle originating from an endosymbiotic event and playing a role in several fundamental processes such as energy production, metabolite syntheses, and programmed cell death. This organelle is delineated by two membranes whose synthesis requires an extensive exchange of phospholipids with other cellular organelles such as endoplasmic reticulum (ER) and vacuolar membranes in yeast. These transfers of phospholipids are thought to occur by a non-vesicular pathway at contact sites between two closely apposed membranes. In plants, little is known about the biogenesis of mitochondrial membranes. Contact sites between ER and mitochondria are suspected to play a similar role in phospholipid trafficking as in yeast, but this has never been demonstrated. In contrast, it has been shown that plastids are able to transfer lipids to mitochondria during phosphate starvation. However, the proteins involved in such transfer are still unknown. Here, we identified in Arabidopsis thaliana a large lipid-enriched complex called the mitochondrial transmembrane lipoprotein (MTL) complex. The MTL complex contains proteins located in the two mitochondrial membranes and conserved in all eukaryotic cells, such as the TOM complex and AtMic60, a component of the MICOS complex. We demonstrate that AtMic60 contributes to the export of phosphatidylethanolamine from mitochondria and the import of galactoglycerolipids from plastids during phosphate starvation. Furthermore, AtMic60 promotes lipid desorption from membranes, likely as an initial step for lipid transfer, and binds to Tom40, suggesting that AtMic60 could regulate the tethering between the inner and outer membranes of mitochondria.

Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods.

This data article describes a controlled, spiked proteomic dataset for which the "ground truth" of variant proteins is known. It is based on the LC-MS analysis of samples composed of a fixed background of yeast lysate and different spiked amounts of the UPS1 mixture of 48 recombinant proteins. It can be used to objectively evaluate bioinformatic pipelines for label-free quantitative analysis, and their ability to detect variant proteins with good sensitivity and low false discovery rate in large-scale proteomic studies. More specifically, it can be useful for tuning software tools parameters, but also testing new algorithms for label-free quantitative analysis, or for evaluation of downstream statistical methods. The raw MS files can be downloaded from ProteomeXchange with identifier PXD001819. Starting from some raw files of this dataset, we also provide here some processed data obtained through various bioinformatics tools (including MaxQuant, Skyline, MFPaQ, IRMa-hEIDI and Scaffold) in different workflows, to exemplify the use of such data in the context of software benchmarking, as discussed in details in the accompanying manuscript [1]. The experimental design used here for data processing takes advantage of the different spike levels introduced in the samples composing the dataset, and processed data are merged in a single file to facilitate the evaluation and illustration of software tools results for the detection of variant proteins with different absolute expression levels and fold change values.

Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.

Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We describe a series of comparisons that support our views: For instance, we show that a supposedly "under-performing" method (i.e., giving baseline average results), if applied at the "appropriate" time in the data-processing pipeline (before or after peptide aggregation) on a data set with the "appropriate" nature of missing values, can outperform a blindly applied, supposedly "better-performing" method (i.e., the reference method from the state-of-the-art). This leads us to formulate few practical guidelines regarding the choice and the application of an imputation method in a proteomics context.

Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset.

Proteomic workflows based on nanoLC-MS/MS data-dependent-acquisition analysis have progressed tremendously in recent years. High-resolution and fast sequencing instruments have enabled the use of label-free quantitative methods, based either on spectral counting or on MS signal analysis, which appear as an attractive way to analyze differential protein expression in complex biological samples. However, the computational processing of the data for label-free quantification still remains a challenge. Here, we used a proteomic standard composed of an equimolar mixture of 48 human proteins (Sigma UPS1) spiked at different concentrations into a background of yeast cell lysate to benchmark several label-free quantitative workflows, involving different software packages developed in recent years. This experimental design allowed to finely assess their performances in terms of sensitivity and false discovery rate, by measuring the number of true and false-positive (respectively UPS1 or yeast background proteins found as differential). The spiked standard dataset has been deposited to the ProteomeXchange repository with the identifier PXD001819 and can be used to benchmark other label-free workflows, adjust software parameter settings, improve algorithms for extraction of the quantitative metrics from raw MS data, or evaluate downstream statistical methods. BIOLOGICAL SIGNIFICANCE: Bioinformatic pipelines for label-free quantitative analysis must be objectively evaluated in their ability to detect variant proteins with good sensitivity and low false discovery rate in large-scale proteomic studies. This can be done through the use of complex spiked samples, for which the "ground truth" of variant proteins is known, allowing a statistical evaluation of the performances of the data processing workflow. We provide here such a controlled standard dataset and used it to evaluate the performances of several label-free bioinformatics tools (including MaxQuant, Skyline, MFPaQ, IRMa-hEIDI and Scaffold) in different workflows, for detection of variant proteins with different absolute expression levels and fold change values. The dataset presented here can be useful for tuning software tool parameters, and also testing new algorithms for label-free quantitative analysis, or for evaluation of downstream statistical methods.