WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows.

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

OmicLoupe: facilitating biological discovery by interactive exploration of multiple omic datasets and statistical comparisons.

BACKGROUND: Visual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends. Still, such exploration is challenging as there is a need for visualizations that are tailored for the purpose. RESULTS: The OmicLoupe software was developed to facilitate visual data exploration and provides more than 15 interactive cross-dataset visualizations for omics data. It expands visualizations to multiple datasets for quality control, statistical comparisons and overlap and correlation analyses, while allowing for rapid inspection and downloading of selected features. The usage of OmicLoupe is demonstrated in three different studies, where it allowed for detection of both technical data limitations and biological trends across different omic layers. An example is an analysis of SARS-CoV-2 infection based on two previously published studies, where OmicLoupe facilitated the identification of gene products with consistent expression changes across datasets at both the transcript and protein levels. CONCLUSIONS: OmicLoupe provides fast exploration of omics data with tailored visualizations for comparisons within and across data layers. The interactive visualizations are highly informative and are expected to be useful in various analyses of both newly generated and previously published data. OmicLoupe is available at quantitativeproteomics.org/omicloupe.

High-Resolution Proteomic Profiling Shows Sexual Dimorphism in Zebrafish Heart-Associated Proteins.

Understanding the molecular basis of sexual dimorphism in the cardiovascular system may contribute to the improvement of the outcome in biological, pharmacological, and toxicological studies as well as on the development of sex-based drugs and therapeutic approaches. Label-free protein quantification using high-resolution mass spectrometry was applied to detect sex-based proteome differences in the heart of zebrafish Danio rerio. Out of almost 3000 unique identified proteins in the heart, 79 showed significant abundance differences between male and female fish. The functional differences were mapped using enrichment analyses. Our results suggest that a large amount of materials needed for reproduction (e.g., sugars, lipids, proteins, etc.) may impose extra pressure on blood, vessels, and heart on their way toward the ovaries. In the present study, the female's heart shows a clear sexual dimorphism by changing abundance levels of numerous proteins, which could be a way to safely overcome material-induced elevated pressures. These proteins belong to the immune system, oxidative stress response, drug metabolization, detoxification, energy, metabolism, and so on. In conclusion, we showed that sex can induce dimorphism at the molecular level in nonsexual organs such as heart and must be considered as an important factor in cardiovascular research. Data are available via ProteomeXchange with identifier PXD023506.

Leaf Apoplast of Field-Grown Potato Analyzed by Quantitative Proteomics and Activity-Based Protein Profiling.

Multiple biotic and abiotic stresses challenge plants growing in agricultural fields. Most molecular studies have aimed to understand plant responses to challenges under controlled conditions. However, studies on field-grown plants are scarce, limiting application of the findings in agricultural conditions. In this study, we investigated the composition of apoplastic proteomes of potato cultivar Bintje grown under field conditions, i.e., two field sites in June-August across two years and fungicide treated and untreated, using quantitative proteomics, as well as its activity using activity-based protein profiling (ABPP). Samples were clustered and some proteins showed significant intensity and activity differences, based on their field site and sampling time (June-August), indicating differential regulation of certain proteins in response to environmental or developmental factors. Peroxidases, class II chitinases, pectinesterases, and osmotins were among the proteins more abundant later in the growing season (July-August) as compared to early in the season (June). We did not detect significant differences between fungicide Shirlan treated and untreated field samples in two growing seasons. Using ABPP, we showed differential activity of serine hydrolases and ß-glycosidases under greenhouse and field conditions and across a growing season. Furthermore, the activity of serine hydrolases and ß-glycosidases, including proteins related to biotic stress tolerance, decreased as the season progressed. The generated proteomics data would facilitate further studies aiming at understanding mechanisms of molecular plant physiology in agricultural fields and help applying effective strategies to mitigate biotic and abiotic stresses.

NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis.

Technical biases are introduced in omics data sets during data generation and interfere with the ability to study biological mechanisms. Several normalization approaches have been proposed to minimize the effects of such biases, but fluctuations in the electrospray current during liquid chromatography-mass spectrometry gradients cause local and sample-specific bias not considered by most approaches. Here we introduce a software named NormalyzerDE that includes a generic retention time (RT)-segmented approach compatible with a wide range of global normalization approaches to reduce the effects of time-resolved bias. The software offers straightforward access to multiple normalization methods, allows for data set evaluation and normalization quality assessment as well as subsequent or independent differential expression analysis using the empirical Bayes Limma approach. When evaluated on two spike-in data sets the RT-segmented approaches outperformed conventional approaches by detecting more peptides (8-36%) without loss of precision. Furthermore, differential expression analysis using the Limma approach consistently increased recall (2-35%) compared to analysis of variance. The combination of RT-normalization and Limma was in one case able to distinguish 108% (2597 vs 1249) more spike-in peptides compared to traditional approaches. NormalyzerDE provides widely usable tools for performing normalization and evaluating the outcome and makes calculation of subsequent differential expression statistics straightforward. The program is available as a web server at http://quantitativeproteomics.org/normalyzerde .

Proteomic Analysis of Phytophthora infestans Reveals the Importance of Cell Wall Proteins in Pathogenicity.

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.

Proteomics of PTI and Two ETI Immune Reactions in Potato Leaves.

Plants have a variety of ways to defend themselves against pathogens. A commonly used model of the plant immune system is divided into a general response triggered by pathogen-associated molecular patterns (PAMPs), and a specific response triggered by effectors. The first type of response is known as PAMP triggered immunity (PTI), and the second is known as effector-triggered immunity (ETI). To obtain better insight into changes of protein abundance in immunity reactions, we performed a comparative proteomic analysis of a PTI and two different ETI models (relating to Phytophthora infestans) in potato. Several proteins showed higher abundance in all immune reactions, such as a protein annotated as sterol carrier protein 2 that could be interesting since Phytophthora species are sterol auxotrophs. RNA binding proteins also showed altered abundance in the different immune reactions. Furthermore, we identified some PTI-specific changes of protein abundance, such as for example, a glyoxysomal fatty acid beta-oxidation multifunctional protein and a MAR-binding protein. Interestingly, a lysine histone demethylase was decreased in PTI, and that prompted us to also analyze protein methylation in our datasets. The proteins upregulated explicitly in ETI included several catalases. Few proteins were regulated in only one of the ETI interactions. For example, histones were only downregulated in the ETI-Avr2 interaction, and a putative multiprotein bridging factor was only upregulated in the ETI-IpiO interaction. One example of a methylated protein that increased in the ETI interactions was a serine hydroxymethyltransferase.

Comparative Membrane-Associated Proteomics of Three Different Immune Reactions in Potato.

Plants have evolved different types of immune reactions but large-scale proteomics about these processes are lacking, especially in the case of agriculturally important crop pathosystems. We have established a system for investigating PAMP-triggered immunity (PTI) and two different effector-triggered immunity (ETI; triggered by Avr2 or IpiO) responses in potato. The ETI responses are triggered by molecules from the agriculturally important Phytophthora infestans interaction. To perform large-scale membrane protein-based comparison of these responses, we established a method to extract proteins from subcellular compartments in leaves. In the membrane fractions that were subjected to quantitative proteomics analysis, we found that most proteins regulated during PTI were also regulated in the same way in ETI. Proteins related to photosynthesis had lower abundance, while proteins related to oxidative and biotic stress, as well as those related to general antimicrobial defense and cell wall degradation, were found to be higher in abundance. On the other hand, we identified a few proteins-for instance, an ABC transporter-like protein-that were only found in the PTI reaction. Furthermore, we also identified proteins that were regulated only in ETI interactions. These included proteins related to GTP binding and heterotrimeric G-protein signaling, as well as those related to phospholipase signaling.

Dinosaur: A Refined Open-Source Peptide MS Feature Detector.

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 .

Targeted Proteomics Approach for Precision Plant Breeding.

Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that enables precise quantitation of hundreds of peptides in a single run. This technique provides new opportunities for multiplexed protein biomarker measurements. For precision plant breeding, DNA-based markers have been used extensively, but the potential of protein biomarkers has not been exploited. In this work, we developed an SRM marker panel with assays for 104 potato (Solanum tuberosum) peptides selected using univariate and multivariate statistics. Thereafter, using random forest classification, the prediction markers were identified for Phytopthora infestans resistance in leaves, P. infestans resistance in tubers, and plant yield in potato leaf secretome samples. The results suggest that the marker panel has the predictive potential for three traits, two of which have no commercial DNA markers so far. Furthermore, the marker panel was also tested and found to be applicable to potato clones not used during the marker development. The proposed workflow is thus a proof-of-concept for targeted proteomics as an efficient readout in accelerated breeding for complex and agronomically important traits.

DIANA--algorithmic improvements for analysis of data-independent acquisition MS data.

MOTIVATION: Data independent acquisition mass spectrometry has emerged as a reproducible and sensitive alternative in quantitative proteomics, where parsing the highly complex tandem mass spectra requires dedicated algorithms. Recently, targeted data extraction was proposed as a novel analysis strategy for this type of data, but it is important to further develop these concepts to provide quality-controlled, interference-adjusted and sensitive peptide quantification. RESULTS: We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis. The algorithm is capable of providing accurate quantitative values and increased recall at a controlled false discovery rate, in a complex gold standard dataset. Importantly, we further demonstrate increased confidence gained by the use of two complementary data-independent acquisition targeted analysis algorithms, as well as increased numbers of quantified peptide precursors in complex biological samples. AVAILABILITY AND IMPLEMENTATION: DIANA is implemented in scala and python and available as open source (Apache 2.0 license) or pre-compiled binaries from http://quantitativeproteomics.org/diana. PyProphet can be installed from PyPi (https://pypi.python.org/pypi/pyprophet). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Numerical compression schemes for proteomics mass spectrometry data.

The open XML format mzML, used for representation of MS data, is pivotal for the development of platform-independent MS analysis software. Although conversion from vendor formats to mzML must take place on a platform on which the vendor libraries are available (i.e. Windows), once mzML files have been generated, they can be used on any platform. However, the mzML format has turned out to be less efficient than vendor formats. In many cases, the naïve mzML representation is fourfold or even up to 18-fold larger compared with the original vendor file. In disk I/O limited setups, a larger data file also leads to longer processing times, which is a problem given the data production rates of modern mass spectrometers. In an attempt to reduce this problem, we here present a family of numerical compression algorithms called MS-Numpress, intended for efficient compression of MS data. To facilitate ease of adoption, the algorithms target the binary data in the mzML standard, and support in main proteomics tools is already available. Using a test set of 10 representative MS data files we demonstrate typical file size decreases of 90% when combined with traditional compression, as well as read time decreases of up to 50%. It is envisaged that these improvements will be beneficial for data handling within the MS community.

Representation of selected-reaction monitoring data in the mzQuantML data standard.

The mzQuantML data standard was designed to capture the output of quantitative software in proteomics, to support submissions to public repositories, development of visualization software and pipeline/modular approaches. The standard is designed around a common core that can be extended to support particular types of technique through the release of semantic rules that are checked by validation software. The first release of mzQuantML supported four quantitative proteomics techniques via four sets of semantic rules: (i) intensity-based (MS(1) ) label free, (ii) MS(1) label-based (such as SILAC or N(15) ), (iii) MS(2) tag-based (iTRAQ or tandem mass tags), and (iv) spectral counting. We present an update to mzQuantML for supporting SRM techniques. The update includes representing the quantitative measurements, and associated meta-data, for SRM transitions, the mechanism for inferring peptide-level or protein-level quantitative values, and support for both label-based or label-free SRM protocols, through the creation of semantic rules and controlled vocabulary terms. We have updated the specification document for mzQuantML (version 1.0.1) and the mzQuantML validator to ensure that consistent files are produced by different exporters. We also report the capabilities for production of mzQuantML files from popular SRM software packages, such as Skyline and Anubis.

Data processing has major impact on the outcome of quantitative label-free LC-MS analysis.

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.

Data processing methods and quality control strategies for label-free LC-MS protein quantification.

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.

An adaptive alignment algorithm for quality-controlled label-free LC-MS.

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.

Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets.

High-throughput omics data often contain systematic biases introduced during various steps of sample processing and data generation. As the source of these biases is usually unknown, it is difficult to select an optimal normalization method for a given data set. To facilitate this process, we introduce the open-source tool "Normalyzer". It normalizes the data with 12 different normalization methods and generates a report with several quantitative and qualitative plots for comparative evaluation of different methods. The usefulness of Normalyzer is demonstrated with three different case studies from quantitative proteomics and transcriptomics. The results from these case studies show that the choice of normalization method strongly influences the outcome of downstream quantitative comparisons. Normalyzer is an R package and can be used locally or through the online implementation at http://quantitativeproteomics.org/normalyzer .

Quantitative label-free phosphoproteomics of six different life stages of the late blight pathogen Phytophthora infestans reveals abundant phosphorylation of members of the CRN effector family.

The oomycete Phytophthora infestans is the causal agent of late blight in potato and tomato. Since the underlying processes that govern pathogenicity and development in P. infestans are largely unknown, we have performed a large-scale phosphoproteomics study of six different P. infestans life stages. We have obtained quantitative data for 2922 phosphopeptides and compared their abundance. Life-stage-specific phosphopeptides include ATP-binding cassette transporters and a kinase that only occurs in appressoria. In an extended data set, we identified 2179 phosphorylation sites and deduced 22 phosphomotifs. Several of the phosphomotifs matched consensus sequences of kinases that occur in P. infestans but not Arabidopsis. In addition, we detected tyrosine phosphopeptides that are potential targets of kinases resembling mammalian tyrosine kinases. Among the phosphorylated proteins are members of the RXLR and Crinkler effector families. The latter are phosphorylated in several life stages and at multiple positions, in sites that are conserved between different members of the Crinkler family. This indicates that proteins in the Crinkler family have functions beyond their putative role as (necrosis-inducing) effectors. This phosphoproteomics data will be instrumental for studies on oomycetes and host-oomycete interactions. The data sets have been deposited to ProteomeXchange (identifier PXD000433).

Quantitative proteomics and transcriptomics of potato in response to Phytophthora infestans in compatible and incompatible interactions.

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.

Proteomics and transcriptomics of the BABA-induced resistance response in potato using a novel functional annotation approach.

BACKGROUND: Induced resistance (IR) can be part of a sustainable plant protection strategy against important plant diseases. ß-aminobutyric acid (BABA) can induce resistance in a wide range of plants against several types of pathogens, including potato infected with Phytophthora infestans. However, the molecular mechanisms behind this are unclear and seem to be dependent on the system studied. To elucidate the defence responses activated by BABA in potato, a genome-wide transcript microarray analysis in combination with label-free quantitative proteomics analysis of the apoplast secretome were performed two days after treatment of the leaf canopy with BABA at two concentrations, 1 and 10 mM. RESULTS: Over 5000 transcripts were differentially expressed and over 90 secretome proteins changed in abundance indicating a massive activation of defence mechanisms with 10 mM BABA, the concentration effective against late blight disease. To aid analysis, we present a more comprehensive functional annotation of the microarray probes and gene models by retrieving information from orthologous gene families across 26 sequenced plant genomes. The new annotation provided GO terms to 8616 previously un-annotated probes. CONCLUSIONS: BABA at 10 mM affected several processes related to plant hormones and amino acid metabolism. A major accumulation of PR proteins was also evident, and in the mevalonate pathway, genes involved in sterol biosynthesis were down-regulated, whereas several enzymes involved in the sesquiterpene phytoalexin biosynthesis were up-regulated. Interestingly, abscisic acid (ABA) responsive genes were not as clearly regulated by BABA in potato as previously reported in Arabidopsis. Together these findings provide candidates and markers for improved resistance in potato, one of the most important crops in the world.