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Label-free LC-MS methods are attractive for high-throughput quantitative proteomics, as the sample processing is straightforward and can be scaled to a large number of samples. Label-free methods therefore facilitate biomarker discovery in studies involving dozens of clinical samples. However, despite the increased popularity of label-free workflows, there is a hesitance in the research community to use it in clinical proteomics studies. Therefore, we here discuss pros and cons of label free LC-MS/MS for biomarker discovery, and delineate the main prerequisites for its successful employment. Furthermore, we cite studies where label-free LC-MS/MS was successfully used to identify novel biomarkers, and foresee an increased acceptance of label-free techniques by the proteomics community in the near future. This article is protected by copyright. All rights reserved.

High-throughput multiplexed protein quantification using mass spectrometry is steadily increasing in popularity, with the two major techniques being data-dependent acquisition (DDA) and targeted acquisition using selected reaction monitoring (SRM). However, both techniques involve extensive data processing, which can be performed by a multitude of different software solutions. Analysis of quantitative LC-MS/MS data is mainly performed in three major steps: processing of raw data, normalization, and statistical analysis. To evaluate the impact of data processing steps, we developed two new benchmark data sets, one each for DDA and SRM, with samples consisting of a long-range dilution series of synthetic peptides spiked in a total cell protein digest. The generated data were processed by eight different software workflows and three postprocessing steps. The results show that the choice of the raw data processing software and the postprocessing steps play an important role in the final outcome. Also, the linear dynamic range of the DDA data could be extended by an order of magnitude through feature alignment and a charge state merging algorithm proposed here. Furthermore, the benchmark data sets are made publicly available for further benchmarking and software developments.

Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.

The oomycete Phytophthora infestans is the most harmful pathogen of potato. It causes the disease late blight, which generates increased yearly costs of up to one billion euro in the EU alone and is difficult to control. We have performed a large-scale quantitative proteomics study of six P. infestans life stages with the aim to identify proteins that change in abundance during development, with a focus on preinfectious life stages. Over 10 000 peptides from 2061 proteins were analyzed. We identified several abundance profiles of proteins that were up- or downregulated in different combinations of life stages. One of these profiles contained 59 proteins that were more abundant in germinated cysts and appressoria. A large majority of these proteins were not previously recognized as being appressorial proteins or involved in the infection process. Among those are proteins with putative roles in transport, amino acid metabolism, pathogenicity (including one RXLR effector) and cell wall structure modification. We analyzed the expression of the genes encoding nine of these proteins using RT-qPCR and found an increase in transcript levels during disease progression, in agreement with the hypothesis that these proteins are important in early infection. Among the nine proteins was a group involved in cell wall structure modification and adhesion, including three closely related, uncharacterized proteins encoded by PITG-01131, PITG-01132, and PITG-16135, here denoted Piacwp1-3. Transient silencing of these genes resulted in reduced severity of infection, indicating that these proteins are important for pathogenicity. Our results contribute to further insight into P. infestans biology, and indicate processes that might be relevant for the pathogen while preparing for host cell penetration and during infection. The mass spectrometry data have been deposited to ProteomeXchange via the PRIDE partner repository with the data set identifier PXD002446.

The oomycete Phytophthora infestans is the most harmful pathogen of potato. It causes the disease late blight, which generates increased yearly costs of up to one billion euro in the EU alone and is difficult to control. We have performed a large-scale quantitative proteomics study of six P. infestans life stages with the aim to identify proteins that change in abundance during development, with a focus on preinfectious life stages. Over 10 000 peptides from 2061 proteins were analyzed. We identified several abundance profiles of proteins that were up- or downregulated in different combinations of life stages. One of these profiles contained 59 proteins that were more abundant in germinated cysts and appressoria. A large majority of these proteins were not previously recognized as being appressorial proteins or involved in the infection process. Among those are proteins with putative roles in transport, amino acid metabolism, pathogenicity (including one RXLR effector) and cell wall structure modification. We analyzed the expression of the genes encoding nine of these proteins using RT-qPCR and found an increase in transcript levels during disease progression, in agreement with the hypothesis that these proteins are important in early infection. Among the nine proteins was a group involved in cell wall structure modification and adhesion, including three closely related, uncharacterized proteins encoded by PITG_01131, PITG_01132, and PITG_16135, here denoted Piacwp1–3. Transient silencing of these genes resulted in reduced severity of infection, indicating that these proteins are important for pathogenicity. Our results contribute to further insight into P. infestans biology, and indicate processes that might be relevant for the pathogen while preparing for host cell penetration and during infection. The mass spectrometry data have been deposited to ProteomeXchange via the PRIDE partner repository with the data set identifier PXD002446.

In bottom-up mass spectrometry (MS)-based proteomics, peptide isotopic and chromatographic traces (features) are frequently used for label-free quantification in data-dependent acquisition MS but can also be used for the improved identification of chimeric spectra or sample complexity characterization. Feature detection is difficult because of the high complexity of MS proteomics data from biological samples, which frequently causes features to intermingle. In addition, existing feature detection algorithms commonly suffer from compatibility issues, long computation times, or poor performance on high-resolution data. Because of these limitations, we developed a new tool, Dinosaur, with increased speed and versatility. Dinosaur has the functionality to sample algorithm computations through quality-control plots, which we call a plot trail. From the evaluation of this plot trail, we introduce several algorithmic improvements to further improve the robustness and performance of Dinosaur, with the detection of features for 98% of MS/MS identifications in a benchmark data set, and no other algorithm tested in this study passed 96% feature detection. We finally used Dinosaur to reimplement a published workflow for peptide identification in chimeric spectra, increasing chimeric identification from 26% to 32% over the standard workflow. Dinosaur is operating-system-independent and is freely available as open source on https://github.com/fickludd/dinosaur .

We present a comparison of two-dimensional separation methods and how they affect the degree of coverage of protein expression in complex mixtures. We investigated the relative merits of various protein and peptide separations prior to acidic reversed-phase chromatography directly coupled to an ion trap mass spectrometer. The first dimensions investigated were density gradient organelle fractionation of cell extracts, 1D SDS-PAGE protein separation followed by digestion by trypsin or GluC proteases, strong cation exchange chromatography, and off-gel isoelectric focusing of tryptic peptides. The number of fractions from each first dimension and the total data accumulation RP-HPLC-MS/MS time was kept constant and the experiments were run in triplicate. We find that the most critical parameters are the data accumulation time, which defines the level of under-sampling and the avoidance of peptides from high expression level proteins eluting over the entire gradient.

As high-resolution instruments are becoming standard in proteomics laboratories, label-free quantification using precursor measurements is becoming a viable option, and is consequently rapidly gaining popularity. Several software solutions have been presented for label-free analysis, but to our knowledge no conclusive studies regarding the sensitivity and reliability of each step of the analysis procedure has been described. Here, we use real complex samples to assess the reliability of label-free quantification using four different software solutions. A generic approach to quality test quantitative label-free LC-MS is introduced. Measures for evaluation are defined for feature detection, alignment and quantification. All steps of the analysis could be considered adequately performed by the utilized software solutions, although differences and possibilities for improvement could be identified. The described method provides an effective testing procedure, which can help the user to quickly pinpoint where in the workflow changes are needed.

Background In order to get global molecular understanding of one of the most important crop diseases worldwide, we investigated compatible and incompatible interactions between Phytophthora infestans and potato (Solanum tuberosum). We used the two most field-resistant potato clones under Swedish growing conditions, which have the greatest known local diversity of P. infestans populations, and a reference compatible cultivar. Results Quantitative label-free proteomics of 51 apoplastic secretome samples (PXD000435) in combination with genome-wide transcript analysis by 42 microarrays (E-MTAB-1515) were used to capture changes in protein abundance and gene expression at 6, 24 and 72 hours after inoculation with P. infestans. To aid mass spectrometry analysis we generated cultivar-specific RNA-seq data (E-MTAB-1712), which increased peptide identifications by 17%. Components induced only during incompatible interactions, which are candidates for hypersensitive response initiation, include a Kunitz-like protease inhibitor, transcription factors and an RCR3-like protein. More secreted proteins had lower abundance in the compatible interaction compared to the incompatible interactions. Based on this observation and because the well-characterized effector-target C14 protease follows this pattern, we suggest 40 putative effector targets. Conclusions In summary, over 17000 transcripts and 1000 secreted proteins changed in abundance in at least one time point, illustrating the dynamics of plant responses to a hemibiotroph. Half of the differentially abundant proteins showed a corresponding change at the transcript level. Many putative hypersensitive and effector-target proteins were single representatives of large gene families. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-497) contains supplementary material, which is available to authorized users.

AimsWe tested whether characteristic changes of the plasma lipidome in individuals with comparable total lipids level associate with future cardiovascular disease (CVD) outcome and whether 23 validated gene variants associated with coronary artery disease (CAD) affect CVD associated lipid species.Methods and resultsScreening of the fasted plasma lipidome was performed by top-down shotgun analysis and lipidome compositions compared between incident CVD cases (n = 211) and controls (n = 216) from the prospective population-based MDC study using logistic regression adjusting for Framingham risk factors. Associations with incident CVD were seen for eight lipid species (0.21≤q≤0.23). Each standard deviation unit higher baseline levels of two lysophosphatidylcholine species (LPC), LPC16∶0 and LPC20∶4, was associated with a decreased risk for CVD (P = 0.024-0.028). Sphingomyelin (SM) 38∶2 was associated with increased odds of CVD (P = 0.057). Five triglyceride (TAG) species were associated with protection (P = 0.031-0.049). LPC16∶0 was negatively correlated with the carotid intima-media thickness (P = 0.010) and with HbA1c (P = 0.012) whereas SM38∶2 was positively correlated with LDL-cholesterol (P = 0.0*10(-6)) and the q-values were good (q≤0.03). The risk allele of 8 CAD-associated gene variants showed significant association with the plasma level of several lipid species. However, the q-values were high for many of the associations (0.015≤q≤0.75). Risk allele carriers of 3 CAD-loci had reduced level of LPC16∶0 and/or LPC 20∶4 (P≤0.056).ConclusionOur study suggests that CVD development is preceded by reduced levels of LPC16∶0, LPC20∶4 and some specific TAG species and by increased levels of SM38∶2. It also indicates that certain lipid species are intermediate phenotypes between genetic susceptibility and overt CVD. But it is a preliminary study that awaits replication in a larger population because statistical significance was lost for the associations between lipid species and future cardiovascular events when correcting for multiple testing.

Protein quantification using different LC–MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC–MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC–MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Nontransient hypoxia is strongly associated with malignant lesions, resulting in aggressive behavior and resistance to treatment. We present an analysis of mRNA and protein expression changes in neuroblastoma cell lines occurring upon the transition from normoxia to hypoxia. The correlation between mRNA and protein level changes was poor, although some known hypoxia-driven genes and proteins correlated well. We present previously undescribed membrane proteins expressed under hypoxic conditions that are candidates for evaluation as biomarkers.