MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data

A concise guide to essential R packages for analyses of DNA, RNA, and proteins

R is widely regarded as unrivaled by other high-level programming languages for its statistical functions. The popularity of R as a statistical language has led many to overlook its applications outside the statistical realm. In this brief review, we present a list of R packages for supporting projects that entail analyses of DNA, RNA, and proteins. These R packages span the gamut of important molecular techniques, from routine quantitative polymerase chain reaction (qPCR) and Western blotting to high-throughput sequencing and proteomics generating very large datasets. The text-mining power of R can also be harnessed to facilitate literature reviews and predict future research trends and avenues. We encourage researchers to make full use of R in their work, given the versatility of the language, as well as its straightforward syntax which eases the initial learning curve.

An interactive R-based custom quantification program for semi-quantitative analysis of triacylglycerols in bovine milk

The R programming language, RStudio, and open-source software solutions for analysis of liquid chromatography-mass spectrometry (LC-MS) data have been used with user-written R-based custom quantification programs (CQP) for semi-quantification of triacylglycerols (TAGs) in bovine milk lipid extracts. Using the peak-finding capabilities of the package "xcms" in RStudio, peaks were integrated, and retention times aligned, normalized, and then used for semi-quantitative analysis of a custom set of four extraction internal standards (EISs) and 29 TAG regioisomers using the choice of four analytical internal standards (AISs). Alternating stereospecific numbering (sn) 1,3 TAG regioisomers (standards 1, 3, and 5 of six calibration standards) and sn-1,2 TAG regioisomers (standards 2, 4, and 6 of six standards) were used to make a set of six calibration standards, which were used for quantification using a linear fit model, polynomial fit model, power fit model, level-bracketed linear fit, replicate-bracketed polynomial fit, replicate-bracketed power fit, and replicate- and level-bracketed linear fit and response factors. For example, the linear fit for EIS1 gave an unacceptable coefficient of determination (CoD), r2 = 0.9616, whereas the polynomial fit gave r2 = 0.9908 and the power fit gave r2 = 0.9928, while the double-bracketed linear fit gave CoDs of r2 = 0.9960, 0.9848, and 0.9781 for the three brackets, yet gave the least % difference to known calibration concentrations. For unparalleled transparency, the CQP produced webpages that allowed every step in the data processing and quantification sequence to be verified and reproduced, and contained interactive figures. The data are publicly available using a digital object identifier (DOI). The R code can be downloaded and used with the downloadable data to reproduce the results, to modify the code and further customize the results, or to copy and paste and adapt the code to other quantification applications.

Electro-elution-based purification of covalent DNA-protein cross-links

Covalent DNA-protein cross-links (DPCs) are pervasive DNA lesions that challenge genome stability and can be induced by metabolic or chemotherapeutic cross-linking agents including reactive aldehydes, topoisomerase poisons and DNMT1 inhibitors. The purification of x-linked proteins (PxP), where DNA-cross-linked proteins are separated from soluble proteins via electro-elution, can be used to identify DPCs. Here we describe a versatile and sensitive strategy for PxP. Mammalian cells are collected following exposure to a DPC-inducing agent, embedded in low-melt agarose plugs and lysed under denaturing conditions. Following lysis, the soluble proteins are extracted from the agarose plug by electro-elution, while genomic DNA and cross-linked proteins are retained in the plug. The cross-linked proteins can then be analyzed by standard analytical techniques such as sodium dodecyl-sulfate-polyacrylamide gel electrophoresis followed by western blotting or fluorescent staining. Alternatively, quantitative mass spectrometry-based proteomics can be used for the unbiased identification of DPCs. The isolation and analysis of DPCs by PxP overcomes the limitations of alternative methods to analyze DPCs that rely on precipitation as the separating principle and can be performed by users trained in molecular or cell biology within 2-3 d. The protocol has been optimized to study DPC induction and repair in mammalian cells but may also be adapted to other sample types including bacteria, yeast and tissue samples.

Communicating Mass Spectrometry Quality Information in mzQC with Python, R, and Java

Mass spectrometry is a powerful technique for analyzing molecules in complex biological samples. However, inter- and intralaboratory variability and bias can affect the data due to various factors, including sample handling and preparation, instrument calibration and performance, and data acquisition and processing. To address this issue, the Quality Control (QC) working group of the Human Proteome Organization's Proteomics Standards Initiative has established the standard mzQC file format for reporting and exchanging information relating to data quality. mzQC is based on the JavaScript Object Notation (JSON) format and provides a lightweight yet versatile file format that can be easily implemented in software. Here, we present open-source software libraries to process mzQC data in three programming languages: Python, using pymzqc; R, using rmzqc; and Java, using jmzqc. The libraries follow a common data model and provide shared functionalities, including the (de)serialization and validation of mzQC files. We demonstrate use of the software libraries in a workflow for extracting, analyzing, and visualizing QC metrics from different sources. Additionally, we show how these libraries can be integrated with each other, with existing software tools, and in automated workflows for the QC of mass spectrometry data. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub).

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems, such as cell signaling and epigenetic regulation. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Protein positional isomers refer to a set of proteoforms with identical total mass and set of modifications, but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, the current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for the relative quantification of PI within a single tool. Benchmarking IsoForma's performance against two existing workflows produced comparable results and improvements in speed. Overall, IsoForma provides a streamlined process for quantifying PI, reduces the analysis time, and offers an essential framework for developing customized proteoform analysis workflows. The software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning

For the detection of food adulteration, sensitive and reproducible analytical methods are required. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a highly sensitive method that can be used to obtain analytical fingerprints consisting of a variety of different components. Since the comparability of measurements carried out with different devices and at different times is not given, specific adulterants are usually detected in targeted analyses instead of analyzing the entire fingerprint. However, this comprehensive analysis is desirable in order to stay ahead in the race against food fraudsters, who are constantly adapting their adulterations to the latest state of the art in analytics. We have developed and optimized an approach that enables the separate processing of untargeted LC‑HRMS data obtained from different devices and at different times. We demonstrate this by the successful determination of the geographical origin of honey samples using a random forest model. We then show that this approach can be applied to develop a continuously learning classification model and our final model, based on data from 835 samples, achieves a classification accuracy of 94% for 126 test samples from 6 different countries.

Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference

Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.

Transcription-coupled repair of DNA-protein cross-links depends on CSA and CSB

Covalent DNA-protein cross-links (DPCs) are toxic DNA lesions that block replication and require repair by multiple pathways. Whether transcription blockage contributes to the toxicity of DPCs and how cells respond when RNA polymerases stall at DPCs is unknown. Here we find that DPC formation arrests transcription and induces ubiquitylation and degradation of RNA polymerase II. Using genetic screens and a method for the genome-wide mapping of DNA-protein adducts, DPC sequencing, we discover that Cockayne syndrome (CS) proteins CSB and CSA provide resistance to DPC-inducing agents by promoting DPC repair in actively transcribed genes. Consequently, CSB- or CSA-deficient cells fail to efficiently restart transcription after induction of DPCs. In contrast, nucleotide excision repair factors that act downstream of CSB and CSA at ultraviolet light-induced DNA lesions are dispensable. Our study describes a transcription-coupled DPC repair pathway and suggests that defects in this pathway may contribute to the unique neurological features of CS.

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.

System-wide analysis of RNA and protein subcellular localization dynamics

Although the subcellular dynamics of RNA and proteins are key determinants of cell homeostasis, their characterization is still challenging. Here we present an integrative framework to simultaneously interrogate the dynamics of the transcriptome and proteome at subcellular resolution by combining two methods: localization of RNA (LoRNA) and a streamlined density-based localization of proteins by isotope tagging (dLOPIT) to map RNA and protein to organelles (nucleus, endoplasmic reticulum and mitochondria) and membraneless compartments (cytosol, nucleolus and cytosolic granules). Interrogating all RNA subcellular locations at once enables system-wide quantification of the proportional distribution of RNA. We obtain a cell-wide overview of localization dynamics for 31,839 transcripts and 5,314 proteins during the unfolded protein response, revealing that endoplasmic reticulum-localized transcripts are more efficiently recruited to cytosolic granules than cytosolic RNAs, and that the translation initiation factor eIF3d is key to sustaining cytoskeletal function. Overall, we provide the most comprehensive overview so far of RNA and protein subcellular localization dynamics.

Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection

Recent developments in mass spectrometry-based metabolite profiling allow unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte. This leads to thousands of signals per analysis without satisfactory means of filtering those corresponding to abundant constituents. Generic approaches are therefore needed for the qualitative and quantitative annotation of a broad range of relevant constituents. For this, we used an analytical platform combining liquid chromatography-mass spectrometry (LC-MS) with Charged Aerosol Detection (CAD). We established a generic metabolite profiling for the concomitant recording of qualitative MS data and semiquantitative CAD profiles. The MS features (recorded in high-resolution tandem MS) are grouped and annotated using state-of-the-art tools. To efficiently attribute features to their corresponding extracted and integrated CAD peaks, a custom signal pretreatment and peak-shape comparison workflow is built. This strategy allows us to automatically contextualize features at both major and minor metabolome levels, together with a detailed reporting of their annotation including relevant orthogonal information (taxonomy, retention time). Signals not attributed to CAD peaks are considered minor metabolites. Results are illustrated on an ethanolic extract of Swertia chirayita (Roxb.) H. Karst., a bitter plant of industrial interest, exhibiting the typical complexity of plant extracts as a proof of concept. This generic qualitative and quantitative approach paves the way to automatically assess the composition of single natural extracts of interest or broader collections, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns.

Adding open spectral data to MassBank and PubChem using open source tools to support non-targeted exposomics of mixtures

The term "exposome" is defined as a comprehensive study of life-course environmental exposures and the associated biological responses. Humans are exposed to many different chemicals, which can pose a major threat to the well-being of humanity. Targeted or non-targeted mass spectrometry techniques are widely used to identify and characterize various environmental stressors when linking exposures to human health. However, identification remains challenging due to the huge chemical space applicable to exposomics, combined with the lack of sufficient relevant entries in spectral libraries. Addressing these challenges requires cheminformatics tools and database resources to share curated open spectral data on chemicals to improve the identification of chemicals in exposomics studies. This article describes efforts to contribute spectra relevant for exposomics to the open mass spectral library MassBank (https://www.massbank.eu) using various open source software efforts, including the R packages RMassBank and Shinyscreen. The experimental spectra were obtained from ten mixtures containing toxicologically relevant chemicals from the US Environmental Protection Agency (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT). Following processing and curation, 5582 spectra from 783 of the 1268 ENTACT compounds were added to MassBank, and through this to other open spectral libraries (e.g., MoNA, GNPS) for community benefit. Additionally, an automated deposition and annotation workflow was developed with PubChem to enable the display of all MassBank mass spectra in PubChem, which is rerun with each MassBank release. The new spectral records have already been used in several studies to increase the confidence in identification in non-target small molecule identification workflows applied to environmental and exposomics research.

Intact polar lipidome and membrane adaptations of microbial communities inhabiting serpentinite-hosted fluids

The generation of hydrogen and reduced carbon compounds during serpentinization provides sustained energy for microorganisms on Earth, and possibly on other extraterrestrial bodies (e.g., Mars, icy satellites). However, the geochemical conditions that arise from water-rock reaction also challenge the known limits of microbial physiology, such as hyperalkaline pH, limited electron acceptors and inorganic carbon. Because cell membranes act as a primary barrier between a cell and its environment, lipids are a vital component in microbial acclimation to challenging physicochemical conditions. To probe the diversity of cell membrane lipids produced in serpentinizing settings and identify membrane adaptations to this environment, we conducted the first comprehensive intact polar lipid (IPL) biomarker survey of microbial communities inhabiting the subsurface at a terrestrial site of serpentinization. We used an expansive, custom environmental lipid database that expands the application of targeted and untargeted lipodomics in the study of microbial and biogeochemical processes. IPLs extracted from serpentinite-hosted fluid communities were comprised of >90% isoprenoidal and non-isoprenoidal diether glycolipids likely produced by archaeal methanogens and sulfate-reducing bacteria. Phospholipids only constituted ~1% of the intact polar lipidome. In addition to abundant diether glycolipids, betaine and trimethylated-ornithine aminolipids and glycosphingolipids were also detected, indicating pervasive membrane modifications in response to phosphate limitation. The carbon oxidation state of IPL backbones was positively correlated with the reduction potential of fluids, which may signify an energy conservation strategy for lipid synthesis. Together, these data suggest microorganisms inhabiting serpentinites possess a unique combination of membrane adaptations that allow for their survival in polyextreme environments. The persistence of IPLs in fluids beyond the presence of their source organisms, as indicated by 16S rRNA genes and transcripts, is promising for the detection of extinct life in serpentinizing settings through lipid biomarker signatures. These data contribute new insights into the complexity of lipid structures generated in actively serpentinizing environments and provide valuable context to aid in the reconstruction of past microbial activity from fossil lipid records of terrestrial serpentinites and the search for biosignatures elsewhere in our solar system.

Nontargeted screening of aldehydes and ketones by chemical isotope labeling combined with ultra-high performance liquid chromatography-high resolution mass spectrometry followed by hybrid filtering of features

Aldehydes and ketones are important carbonyl compounds that are widely present in foodstuffs, biological organisms and human living environment. However, it is still challenging to comprehensively detect and capture them using liquid chromatography - mass spectrometry. In this work, a chemical isotope labeling (CIL) coupled with ultra-high performance liquid chromatography - high resolution mass spectrometry (UHPLC-HRMS) strategy was developed for the capture and detection of this class of compounds. 2,4-Dinitrophenylhydrazine (DNPH) and isotope-labeled DNPH (DNPH-d3) were utilized to selectively label the target analytes. To address the difficulties in processing UHPLC-HRMS data, a post-acquisition data processing method called MSFilter was proposed to facilitate the screening and identification aldehydes and ketones in complex matrices. The MSFilter consists of four independent filters, namely statistical characteristic-based filtering, mass defect filtering, CIL paired peaks filtering, and diagnostic fragmentation ion filtering. These filters can be used individually or in combination to eliminate unrelated interfering MS features and efficiently detect DNPH-labeled aldehydes and ketones. The results of a mixture containing 48 model compounds showed that although all individual filtering methods could significantly reduce more than 95% of the raw MS features with acceptable recall rates above 85%, but they had relatively high false positive ratios of over 90%. In comparison, the hybrid filtering method combining four filters is able to eliminate massive interfering features (> 99.5%) with a high recall rate of 81.25% and a much lower false positive ratio of 15.22%. By implementing the hybrid filtering method in MSFilter, a total of 154 features were identified as potential signals of CCs from the original 45,961 features of real tobacco samples, of which 70 were annotated. We believe that the proposed strategy is promising to analyze the potential CCs in complex samples by UHPLC-HRMS.

CIL-ExPMRM: An Ultrasensitive Chemical Isotope Labeling Assisted Pseudo-MRM Platform to Accelerate Exposomic Suspect Screening

Exposome is the future of next-generation environmental health to establish the association between environmental exposure and diseases. However, due to low concentrations of exposure chemicals, exposome has been hampered by lacking an effective analytical platform to characterize its composition. In this study, by combining the benefit of chemical isotope labeling and pseudo-multiple reaction monitoring (CIL-pseudo-MRM), we have developed one highly sensitive and high-throughput platform (CIL-ExPMRM) by isotope labeling urinary exposure biomarkers. Dansyl chloride (DnsCl), N-methylphenylethylamine (MPEA), and their isotope-labeled forms were used to derivatize polar hydroxyl and carboxyl compounds, respectively. We have programmed a series of scripts to optimize MRM transition parameters, curate the MRM database (>70,000 compounds), predict accurate retention time (RT), and automize dynamic MRMs. This was followed by an automated MRM peak assignment, peak alignment, and statistical analysis. A computational pipeline was eventually incorporated into a user-friendly website interface, named CIL-ExPMRM (http://www.exposomemrm.com/). The performance of this platform has been validated with a relatively low false positive rate (10.7%) across instrumental platforms. CIL-ExPMRM has systematically overcome key bottlenecks of exposome studies to some extent and outperforms previous methods due to its independence of MS/MS availability, accurate RT prediction, and collision energy optimization, as well as the ultrasensitivity and automated robust intensity-based quantification. Overall, CIL-ExPMRM has great potential to advance the exposomic studies based on urinary biomarkers.

Mapping diversity in African trypanosomes using high resolution spatial proteomics

African trypanosomes are dixenous eukaryotic parasites that impose a significant human and veterinary disease burden on sub-Saharan Africa. Diversity between species and life-cycle stages is concomitant with distinct host and tissue tropisms within this group. Here, the spatial proteomes of two African trypanosome species, Trypanosoma brucei and Trypanosoma congolense, are mapped across two life-stages. The four resulting datasets provide evidence of expression of approximately 5500 proteins per cell-type. Over 2500 proteins per cell-type are classified to specific subcellular compartments, providing four comprehensive spatial proteomes. Comparative analysis reveals key routes of parasitic adaptation to different biological niches and provides insight into the molecular basis for diversity within and between these pathogen species.

The Rab5 effector FERRY links early endosomes with mRNA localization

Localized translation is vital to polarized cells and requires precise and robust distribution of different mRNAs and ribosomes across the cell. However, the underlying molecular mechanisms are poorly understood and important players are lacking. Here, we discovered a Rab5 effector, the five-subunit endosomal Rab5 and RNA/ribosome intermediary (FERRY) complex, that recruits mRNAs and ribosomes to early endosomes through direct mRNA-interaction. FERRY displays preferential binding to certain groups of transcripts, including mRNAs encoding mitochondrial proteins. Deletion of FERRY subunits reduces the endosomal localization of transcripts in cells and has a significant impact on mRNA levels. Clinical studies show that genetic disruption of FERRY causes severe brain damage. We found that, in neurons, FERRY co-localizes with mRNA on early endosomes, and mRNA loaded FERRY-positive endosomes are in close proximity of mitochondria. FERRY thus transforms endosomes into mRNA carriers and plays a key role in regulating mRNA distribution and transport.

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping

In recent years, feces has surfaced as the matrix of choice for investigating the gut microbiome-health axis because of its non-invasive sampling and the unique reflection it offers of an individual's lifestyle. In cohort studies where the number of samples required is large, but availability is scarce, a clear need exists for high-throughput analyses. Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources and downstream data processing workflows that are as automated and time efficient as possible. We present a dual fecal extraction and ultra high performance liquid chromatography-high resolution-quadrupole-orbitrap-mass spectrometry (UHPLC-HR-Q-Orbitrap-MS)-based workflow that enables widely targeted and untargeted metabolome and lipidome analysis. A total of 836 in-house standards were analyzed, of which 360 metabolites and 132 lipids were consequently detected in feces. Their targeted profiling was validated successfully with respect to repeatability (78% CV < 20%), reproducibility (82% CV < 20%), and linearity (81% R² > 0.9), while also enabling holistic untargeted fingerprinting (15,319 features, CV < 30%). To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. The latter was benchmarked toward vendor-specific targeted and untargeted software and our isotopologue parameter optimization/XCMS-based untargeted pipeline in LifeLines Deep cohort samples (n = 97). TaPEx clearly outperformed the untargeted approaches (81.3 vs 56.7-66.0% compounds detected). Finally, our novel dual fecal metabolomics-lipidomics-TaPEx method was successfully applied to Flemish Gut Flora Project cohort (n = 292) samples, leading to a sample-to-result time reduction of 60%.

LFQ-Based Peptide and Protein Intensity Differential Expression Analysis

Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.

Analysis of context-specific KRAS-effector (sub)complexes in Caco-2 cells

Ras is a key switch controlling cell behavior. In the GTP-bound form, Ras interacts with numerous effectors in a mutually exclusive manner, where individual Ras-effectors are likely part of larger cellular (sub)complexes. The molecular details of these (sub)complexes and their alteration in specific contexts are not understood. Focusing on KRAS, we performed affinity purification (AP)-mass spectrometry (MS) experiments of exogenously expressed FLAG-KRAS WT and three oncogenic mutants ("genetic contexts") in the human Caco-2 cell line, each exposed to 11 different culture media ("culture contexts") that mimic conditions relevant in the colon and colorectal cancer. We identified four effectors present in complex with KRAS in all genetic and growth contexts ("context-general effectors"). Seven effectors are found in KRAS complexes in only some contexts ("context-specific effectors"). Analyzing all interactors in complex with KRAS per condition, we find that the culture contexts had a larger impact on interaction rewiring than genetic contexts. We investigated how changes in the interactome impact functional outcomes and created a Shiny app for interactive visualization. We validated some of the functional differences in metabolism and proliferation. Finally, we used networks to evaluate how KRAS-effectors are involved in the modulation of functions by random walk analyses of effector-mediated (sub)complexes. Altogether, our work shows the impact of environmental contexts on network rewiring, which provides insights into tissue-specific signaling mechanisms. This may also explain why KRAS oncogenic mutants may be causing cancer only in specific tissues despite KRAS being expressed in most cells and tissues.

Membrane Adaptations and Cellular Responses of Sulfolobus acidocaldarius to the Allylamine Terbinafine

Cellular membranes are essential for compartmentalization, maintenance of permeability, and fluidity in all three domains of life. Archaea belong to the third domain of life and have a distinct phospholipid composition. Membrane lipids of archaea are ether-linked molecules, specifically bilayer-forming dialkyl glycerol diethers (DGDs) and monolayer-forming glycerol dialkyl glycerol tetraethers (GDGTs). The antifungal allylamine terbinafine has been proposed as an inhibitor of GDGT biosynthesis in archaea based on radiolabel incorporation studies. The exact target(s) and mechanism of action of terbinafine in archaea remain elusive. Sulfolobus acidocaldarius is a strictly aerobic crenarchaeon thriving in a thermoacidophilic environment, and its membrane is dominated by GDGTs. Here, we comprehensively analyzed the lipidome and transcriptome of S. acidocaldarius in the presence of terbinafine. Depletion of GDGTs and the accompanying accumulation of DGDs upon treatment with terbinafine were growth phase-dependent. Additionally, a major shift in the saturation of caldariellaquinones was observed, which resulted in the accumulation of unsaturated molecules. Transcriptomic data indicated that terbinafine has a multitude of effects, including significant differential expression of genes in the respiratory complex, motility, cell envelope, fatty acid metabolism, and GDGT cyclization. Combined, these findings suggest that the response of S. acidocaldarius to terbinafine inhibition involves respiratory stress and the differential expression of genes involved in isoprenoid biosynthesis and saturation.

Cell lineage-specific mitochondrial resilience during mammalian organogenesis

Mitochondrial activity differs markedly between organs, but it is not known how and when this arises. Here we show that cell lineage-specific expression profiles involving essential mitochondrial genes emerge at an early stage in mouse development, including tissue-specific isoforms present before organ formation. However, the nuclear transcriptional signatures were not independent of organelle function. Genetically disrupting intra-mitochondrial protein synthesis with two different mtDNA mutations induced cell lineage-specific compensatory responses, including molecular pathways not previously implicated in organellar maintenance. We saw downregulation of genes whose expression is known to exacerbate the effects of exogenous mitochondrial toxins, indicating a transcriptional adaptation to mitochondrial dysfunction during embryonic development. The compensatory pathways were both tissue and mutation specific and under the control of transcription factors which promote organelle resilience. These are likely to contribute to the tissue specificity which characterizes human mitochondrial diseases and are potential targets for organ-directed treatments.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis

Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with the interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. We extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across data sets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS data sets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.

Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils

spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications to spectrum plotting from online data repositories and assisting data analysis tasks for dozens of other projects. Here, we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.

VAMS-Based Blood Capillary Sampling for Mass Spectrometry-Based Human Metabolomics Studies

Volumetric absorptive microsampling (VAMS) is a recently developed sample collection method that enables single-drop blood collection in a minimally invasive manner. Blood biomolecules can then be extracted and processed for analysis using several analytical platforms. The integration of VAMS with conventional mass spectrometry (MS)-based metabolomics approaches is an attractive solution for human studies representing a less-invasive procedure compared to phlebotomy with the additional potential for remote sample collection. However, as we recently demonstrated, VAMS samples require long-term storage at -80 °C. This study investigated the stability of VAMS samples during short-term storage and compared the metabolome obtained from capillary blood collected from the fingertip to those of plasma and venous blood from 22 healthy volunteers. Our results suggest that the blood metabolome collected by VAMS samples is stable at room temperature only for up to 6 h requiring subsequent storage at -80 °C to avoid significant changes in the metabolome. We also demonstrated that capillary blood provides better coverage of the metabolome compared to plasma enabling the analysis of several intracellular metabolites presented in red blood cells. Finally, this work demonstrates that with the appropriate pre-analytical protocol capillary blood can be successfully used for untargeted metabolomics studies.

Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism

The perinatal period represents a critical window for cognitive and immune system development, promoted by maternal and infant gut microbiomes and their metabolites. Here, we tracked the co-development of microbiomes and metabolomes from late pregnancy to 1 year of age using longitudinal multi-omics data from a cohort of 70 mother-infant dyads. We discovered large-scale mother-to-infant interspecies transfer of mobile genetic elements, frequently involving genes associated with diet-related adaptations. Infant gut metabolomes were less diverse than maternal but featured hundreds of unique metabolites and microbe-metabolite associations not detected in mothers. Metabolomes and serum cytokine signatures of infants who received regular-but not extensively hydrolyzed-formula were distinct from those of exclusively breastfed infants. Taken together, our integrative analysis expands the concept of vertical transmission of the gut microbiome and provides original insights into the development of maternal and infant microbiomes and metabolomes during late pregnancy and early life.

EASY-FIA: A Readably Usable Standalone Tool for High-Resolution Mass Spectrometry Metabolomics Data Pre-Processing

Flow injection analysis coupled with high-resolution mass spectrometry (FIA-HRMS) is a fair trade-off between resolution and speed. However, free software available for data pre-processing is few, web-based, and often requires advanced user specialization. These tools rarely embedded blank and noise evaluation strategies, and direct feature annotation. We developed EASY-FIA, a free standalone application that can be employed for FIA-HRMS metabolomic data pre-processing by users with no bioinformatics/programming skills. We validated the tool's performance and applicability in two clinical metabolomics case studies. The main functions of our application are blank subtraction, alignment of the metabolites, and direct feature annotation by means of the Human Metabolome Database (HMDB) using a minimum number of mass spectrometry parameters. In a scenario where FIA-HRMS is increasingly recognized as a reliable strategy for fast metabolomics analysis, EASY-FIA could become a standardized and feasible tool easily usable by all scientists dealing with MS-based metabolomics. EASY-FIA was implemented in MATLAB with the App Designer tool and it is freely available for download.

High-throughput proteomics: a methodological mini-review

Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other 'omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.

The Musashi proteins direct post-transcriptional control of protein expression and alternate exon splicing in vertebrate photoreceptors

The Musashi proteins, MSI1 and MSI2, are conserved RNA binding proteins with a role in the maintenance and renewal of stem cells. Contrasting with this role, terminally differentiated photoreceptor cells express high levels of MSI1 and MSI2, pointing to a role for the two proteins in vision. Combined knockout of Msi1 and Msi2 in mature photoreceptor cells abrogated the retinal response to light and caused photoreceptor cell death. In photoreceptor cells the Musashi proteins perform distinct nuclear and cytoplasmic functions. In the nucleus, the Musashi proteins promote splicing of photoreceptor-specific alternative exons. Surprisingly, conserved photoreceptor-specific alternative exons in genes critical for vision proved to be dispensable, raising questions about the selective pressures that lead to their conservation. In the cytoplasm MSI1 and MSI2 activate protein expression. Loss of Msi1 and Msi2 lead to reduction in the levels of multiple proteins including proteins required for vision and photoreceptor survival. The requirement for MSI1 and MSI2 in terminally differentiated photoreceptors alongside their role in stem cells shows that, depending on cellular context, these two proteins can control processes ranging from cell proliferation to sensory perception.

A synthetic C2 auxotroph of Pseudomonas putida for evolutionary engineering of alternative sugar catabolic routes

Acetyl-coenzyme A (AcCoA) is a metabolic hub in virtually all living cells, serving as both a key precursor of essential biomass components and a metabolic sink for catabolic pathways for a large variety of substrates. Owing to this dual role, tight growth-production coupling schemes can be implemented around the AcCoA node. Building on this concept, a synthetic C2 auxotrophy was implemented in the platform bacterium Pseudomonas putida through an in silico-informed engineering approach. A growth-coupling strategy, driven by AcCoA demand, allowed for direct selection of an alternative sugar assimilation route-the phosphoketolase (PKT) shunt from bifidobacteria. Adaptive laboratory evolution forced the synthetic P. putida auxotroph to rewire its metabolic network to restore C2 prototrophy via the PKT shunt. Large-scale structural chromosome rearrangements were identified as possible mechanisms for adjusting the network-wide proteome profile, resulting in improved PKT-dependent growth phenotypes. ¹³C-based metabolic flux analysis revealed an even split between the native Entner-Doudoroff pathway and the synthetic PKT bypass for glucose processing, leading to enhanced carbon conservation. These results demonstrate that the P. putida metabolism can be radically rewired to incorporate a synthetic C2 metabolism, creating novel network connectivities and highlighting the importance of unconventional engineering strategies to support efficient microbial production.

Operation of a TCA cycle subnetwork in the mammalian nucleus

Nucleic acid and histone modifications critically depend on the tricarboxylic acid (TCA) cycle for substrates and cofactors. Although a few TCA cycle enzymes have been reported in the nucleus, the corresponding pathways are considered to operate in mitochondria. Here, we show that a part of the TCA cycle is operational also in the nucleus. Using 13C-tracer analysis, we identified activity of glutamine-to-fumarate, citrate-to-succinate, and glutamine-to-aspartate routes in the nuclei of HeLa cells. Proximity labeling mass spectrometry revealed a spatial vicinity of the involved enzymes with core nuclear proteins. We further show nuclear localization of aconitase 2 and 2-oxoglutarate dehydrogenase in mouse embryonic stem cells. Nuclear localization of the latter enzyme, which produces succinyl-CoA, changed from pluripotency to a differentiated state with accompanying changes in the nuclear protein succinylation. Together, our results demonstrate operation of an extended metabolic pathway in the nucleus, warranting a revision of the canonical view on metabolic compartmentalization.

N1-methylpseudouridine found within COVID-19 mRNA vaccines produces faithful protein products

Synthetic mRNA technology is a promising avenue for treating and preventing disease. Key to the technology is the incorporation of modified nucleotides such as N1-methylpseudouridine (m1Ψ) to decrease immunogenicity of the RNA. However, relatively few studies have addressed the effects of modified nucleotides on the decoding process. Here, we investigate the effect of m1Ψ and the related modification pseudouridine (Ψ) on translation. In a reconstituted system, we find that m1Ψ does not significantly alter decoding accuracy. More importantly, we do not detect an increase in miscoded peptides when mRNA containing m1Ψ is translated in cell culture, compared with unmodified mRNA. We also find that m1Ψ does not stabilize mismatched RNA-duplex formation and only marginally promotes errors during reverse transcription. Overall, our results suggest that m1Ψ does not significantly impact translational fidelity, a welcome sign for future RNA therapeutics.

MobilityTransformR: an R package for effective mobility transformation of CE-MS data

SUMMARY

We present MobilityTransformR, an R/Bioconductor package for the effective mobility scaling of capillary zone electrophoresis-mass spectrometry (CE-MS) data. It uses functionality from different R packages that are frequently used for data processing and analysis in MS-based metabolomics workflows, allowing the subsequent use of reproducible transformed CE-MS data in existing workflows.

AVAILABILITY AND IMPLEMENTATION

MobilityTransformR is implemented in R (Version >= 4.2) and can be downloaded directly from the Bioconductor database (https://bioconductor.org/packages/MobilityTransformR) or GitHub (https://github.com/LiesaSalzer/MobilityTransformR).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Spatial Proteomics Reveals Differences in the Cellular Architecture of Antibody-Producing CHO and Plasma Cell-Derived Cells

Most of the recombinant biotherapeutics employed today to combat severe illnesses, for example, various types of cancer or autoimmune diseases, are produced by Chinese hamster ovary (CHO) cells. To meet the growing demand of these pharmaceuticals, CHO cells are under constant development in order to enhance their stability and productivity. The last decades saw a shift from empirical cell line optimization toward rational cell engineering using a growing number of large omics datasets to alter cell physiology on various levels. Especially proteomics workflows reached new levels in proteome coverage and data quality because of advances in high-resolution mass spectrometry instrumentation. One type of workflow concentrates on spatial proteomics by usage of subcellular fractionation of organelles with subsequent shotgun mass spectrometry proteomics and machine learning algorithms to determine the subcellular localization of large portions of the cellular proteome at a certain time point. Here, we present the first subcellular spatial proteome of a CHO-K1 cell line producing high titers of recombinant antibody in comparison to the spatial proteome of an antibody-producing plasma cell-derived myeloma cell line. Both cell lines show colocalization of immunoglobulin G chains with chaperones and proteins associated in protein glycosylation within the endoplasmic reticulum compartment. However, we report differences in the localization of proteins associated to vesicle-mediated transport, transcription, and translation, which may affect antibody production in both cell lines. Furthermore, pairing subcellular localization data with protein expression data revealed elevated protein masses for organelles in the secretory pathway in plasma cell-derived MPC-11 (Merwin plasma cell tumor-11) cells. Our study highlights the potential of subcellular spatial proteomics combined with protein expression as potent workflow to identify characteristics of highly efficient recombinant protein-expressing cell lines. Data are available via ProteomeXchange with identifier PXD029115.

Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform ( www.metaboanalyst.ca ). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min.

Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity

A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genetic heterogeneity and epistasis, would be expected to increase in severity with the geographical range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French Arabidopsis thaliana population (less than 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than the previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits. This article is part of the theme issue 'Genetic basis of adaptation and speciation: from loci to causative mutations'.

Deciphering the immunopeptidome in vivo reveals new tumour antigens

Immunosurveillance of cancer requires the presentation of peptide antigens on major histocompatibility complex class I (MHC-I) molecules1-5. Current approaches to profiling of MHC-I-associated peptides, collectively known as the immunopeptidome, are limited to in vitro investigation or bulk tumour lysates, which limits our understanding of cancer-specific patterns of antigen presentation in vivo6. To overcome these limitations, we engineered an inducible affinity tag into the mouse MHC-I gene (H2-K1) and targeted this allele to the KrasLSL-G12D/+Trp53fl/fl mouse model (KP/KbStrep)7. This approach enabled us to precisely isolate MHC-I peptides from autochthonous pancreatic ductal adenocarcinoma and from lung adenocarcinoma (LUAD) in vivo. In addition, we profiled the LUAD immunopeptidome from the alveolar type 2 cell of origin up to late-stage disease. Differential peptide presentation in LUAD was not predictable by mRNA expression or translation efficiency and is probably driven by post-translational mechanisms. Vaccination with peptides presented by LUAD in vivo induced CD8+ T cell responses in naive mice and tumour-bearing mice. Many peptides specific to LUAD, including immunogenic peptides, exhibited minimal expression of the cognate mRNA, which prompts the reconsideration of antigen prediction pipelines that triage peptides according to transcript abundance8. Beyond cancer, the KbStrep allele is compatible with other Cre-driver lines to explore antigen presentation in vivo in the pursuit of understanding basic immunology, infectious disease and autoimmunity.

Prior Signal Acquisition Software Versions for Orbitrap Underestimate Low Isobaric Mass Tag Intensities, Without Detriment to Differential Abundance Experiments

Tandem mass tags (TMTs) enable simple and accurate quantitative proteomics for multiplexed samples by relative quantification of tag reporter ions. Orbitrap quantification of reporter ions has been associated with a characteristic notch region in intensity distribution, within which few reporter intensities are recorded. This has been resolved in version 3 of the instrument acquisition software Tune. However, 47% of Orbitrap Fusion, Lumos, or Eclipse submissions to PRIDE were generated using prior software versions. To quantify the impact of the notch on existing quantitative proteomics data, we generated a mixed species benchmark and acquired quantitative data using Tune versions 2 and 3. Intensities below the notch are predominantly underestimated with Tune version 2, leading to overestimation of the true differences in intensities between samples. However, when summarizing reporter ion intensities to higher-level features, such as peptides and proteins, few features are significantly affected. Targeted removal of spectra with reporter ion intensities below the notch is not beneficial for differential peptide or protein testing. Overall, we find that the systematic quantification bias associated with the notch is not detrimental for a typical proteomics experiment.

Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier Transform Mass Spectrometry Experiments

We present a novel, scan-centric method for characterizing peaks from direct injection multi-scan Fourier transform mass spectra of complex samples that utilizes frequency values derived directly from the spacing of raw m/z points in spectral scans. Our peak characterization method utilizes intensity-independent noise removal and normalization of scan-level data to provide a much better fit of relative intensity to natural abundance probabilities for low abundance isotopologues that are not present in all of the acquired scans. Moreover, our method calculates both peak- and scan-specific statistics incorporated within a series of quality control steps that are designed to robustly derive peak centers, intensities, and intensity ratios with their scan-level variances. These cross-scan characterized peaks are suitable for use in our previously published peak assignment methodology, Small Molecule Isotope Resolved Formula Enumeration (SMIRFE).

Characterisation of a nucleo-adhesome

In addition to central functions in cell adhesion signalling, integrin-associated proteins have wider roles at sites distal to adhesion receptors. In experimentally defined adhesomes, we noticed that there is clear enrichment of proteins that localise to the nucleus, and conversely, we now report that nuclear proteomes contain a class of adhesome components that localise to the nucleus. We here define a nucleo-adhesome, providing experimental evidence for a remarkable scale of nuclear localisation of adhesion proteins, establishing a framework for interrogating nuclear adhesion protein functions. Adding to nuclear FAK's known roles in regulating transcription, we now show that nuclear FAK regulates expression of many adhesion-related proteins that localise to the nucleus and that nuclear FAK binds to the adhesome component and nuclear protein Hic-5. FAK and Hic-5 work together in the nucleus, co-regulating a subset of genes transcriptionally. We demonstrate the principle that there are subcomplexes of nuclear adhesion proteins that cooperate to control transcription.

VIQoR: a web service for visually supervised protein inference and protein quantification

MOTIVATION

In quantitative bottom-up mass spectrometry (MS)-based proteomics, the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization.

RESULTS

In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well-established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in datasets with replicated design.

AVAILABILITY AND IMPLEMENTATION

VIQoR is accessible at: http://computproteomics.bmb.sdu.dk/Apps/VIQoR/. The source code is available at: https://bitbucket.org/veitveit/viqor/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In silico deconjugation of glucuronide conjugates enhances tandem mass spectra library annotation of human samples

Mass spectral library annotation of liquid chromatography-high resolution tandem mass spectrometry (LC-HRMS/MS) data is a reliable approach for fast identification of organic contaminants and toxicants in complex environmental and biological matrices. While determining the exposure of humans or mammals, it is indispensable to include phase I and phase II metabolites (conjugates) along with the parent compounds, but often, tandem mass spectra for these are unavailable. In this study, we present and evaluate a strategy for annotating glucuronide conjugates in LC-HRMS/MS scans by applying a neutral loss search for detection, then truncating the spectra which we refer to as in silico deconjugation, and finally searching these against mass spectral libraries of the aglycones. The workflow was tested on a dataset of in vitro-generated glucuronides of reference standard mixtures and a dataset of 51 authentic urine samples collected from patients with known medication status, acquired on different instrumentations. A total number of 75 different glucuronidated molecular structures were identified by in silico deconjugation and spectral library annotation. We also identified specific molecular structures (sulfonamides, ether bonds, di-glucuronides), which resulted in slightly different fragmentation patterns between the glucuronide and the unconjugated compound. This led to a decreased spectral matching score and in some cases to a false-negative identification. Still, by applying this method, we revealed a reliable annotation of most common glucuronides, leading to a new strategy reducing the need for deconjugation steps or for recording many reference glucuronide spectra for screening approaches.

MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics

Data-independent acquisition-mass spectrometry (DIA-MS) is the method of choice for deep, consistent, and accurate single-shot profiling in bottom-up proteomics. While classic workflows for targeted quantification from DIA-MS data require auxiliary data-dependent acquisition (DDA) MS analysis of subject samples to derive prior-knowledge spectral libraries, library-free approaches based on in silico prediction promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses are however limited, in part, by the large library size and persistent deviations from the experimental data. We present MSLibrarian, a new workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library and analysis approach. Predicted-vs-observed comparisons enabled optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups, and optimization of the library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded experiment of species-mixed proteins and quantitative ratio-validation confirmed gains of up to 13% on peptide and 8% on protein level at equivalent FDR control and validation criteria. MSLibrarian is made available as an open-source R software package, including step-by-step user instructions, at https://github.com/MarcIsak/MSLibrarian.

MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics

Summary

Mass spectrometry-based proteomics is increasingly employed in biology and medicine. To generate reliable information from large datasets and ensure comparability of results, it is crucial to implement and standardize the quality control of the raw data, the data processing steps and the statistical analyses. MSPypeline provides a platform for importing MaxQuant output tables, generating quality control reports, data preprocessing including normalization and performing exploratory analyses by statistical inference plots. These standardized steps assess data quality, provide customizable figures and enable the identification of differentially expressed proteins to reach biologically relevant conclusions.

Availability and implementation

The source code is available under the MIT license at https://github.com/siheming/mspypeline with documentation at https://mspypeline.readthedocs.io. Benchmark mass spectrometry data are available on ProteomeXchange (PXD025792).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Genome-Guided Discovery of Natural Products through Multiplexed Low-Coverage Whole-Genome Sequencing of Soil Actinomycetes on Oxford Nanopore Flongle

Genome mining is an important tool for discovery of new natural products; however, the number of publicly available genomes for natural product-rich microbes such as actinomycetes, relative to human pathogens with smaller genomes, is small. To obtain contiguous DNA assemblies and identify large (ca. 10 to greater than 100 kb) biosynthetic gene clusters (BGCs) with high GC (>70%) and high-repeat content, it is necessary to use long-read sequencing methods when sequencing actinomycete genomes. One of the hurdles to long-read sequencing is the higher cost. In the current study, we assessed Flongle, a recently launched platform by Oxford Nanopore Technologies, as a low-cost DNA sequencing option to obtain contiguous DNA assemblies and analyze BGCs. To make the workflow more cost-effective, we multiplexed up to four samples in a single Flongle sequencing experiment while expecting low-sequencing coverage per sample. We hypothesized that contiguous DNA assemblies might enable analysis of BGCs even at low sequencing depth. To assess the value of these assemblies, we collected high-resolution mass spectrometry data and conducted a multi-omics analysis to connect BGCs to secondary metabolites. In total, we assembled genomes for 20 distinct strains across seven sequencing experiments. In each experiment, 50% of the bases were in reads longer than 10 kb, which facilitated the assembly of reads into contigs with an average N50 value of 3.5 Mb. The programs antiSMASH and PRISM predicted 629 and 295 BGCs, respectively. We connected BGCs to metabolites for N,N-dimethyl cyclic-di-tryptophan, two novel lasso peptides, and three known actinomycete-associated siderophores, namely, mirubactin, heterobactin, and salinichelin. IMPORTANCE Short-read sequencing of GC-rich genomes such as those from actinomycetes results in a fragmented genome assembly and truncated biosynthetic gene clusters (often 10 to >100 kb long), which hinders our ability to understand the biosynthetic potential of a given strain and predict the molecules that can be produced. The current study demonstrates that contiguous DNA assemblies, suitable for analysis of BGCs, can be obtained through low-coverage, multiplexed sequencing on Flongle, which provides a new low-cost workflow ($30 to 40 per strain) for sequencing actinomycete strain libraries.

protti: an R package for comprehensive data analysis of peptide- and protein-centric bottom-up proteomics data

Summary

We present a flexible, user-friendly R package called protti for comprehensive quality control, analysis and interpretation of quantitative bottom-up proteomics data. protti supports the analysis of protein-centric data such as those associated with protein expression analyses, as well as peptide-centric data such as those resulting from limited proteolysis-coupled mass spectrometry analysis. Due to its flexible design, it supports analysis of label-free, data-dependent, data-independent and targeted proteomics datasets. protti can be run on the output of any search engine and software package commonly used for bottom-up proteomics experiments such as Spectronaut, Skyline, MaxQuant or Proteome Discoverer, adequately exported to table format.

Availability and implementation

protti is implemented as an open-source R package. Release versions are available via CRAN (https://CRAN.R-project.org/package=protti) and work on all major operating systems. The development version is maintained on GitHub (https://github.com/jpquast/protti). Full documentation including examples is provided in the form of vignettes on our package website (jpquast.github.io/protti/).

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.

Putative Biomarkers for Acute Pulmonary Embolism in Exhaled Breath Condensate

Current diagnostic markers for pulmonary embolism (PE) are unspecific. We investigated the proteome of the exhaled breath condensate (EBC) in a porcine model of acute PE in order to identify putative diagnostic markers for PE. EBC was collected at baseline and after the induction of autologous intermediate-risk PE in 14 pigs, plus four negative control pigs. The protein profiles of the EBC were analyzed using label-free quantitative nano liquid chromatography-tandem mass spectrometry. A total of 897 proteins were identified in the EBCs from the pigs. Alterations were found in the levels of 145 different proteins after PE compared with the baseline and negative controls: albumin was among the most upregulated proteins, with 14-fold higher levels 2.5 h after PE (p-value: 0.02). The levels of 49 other proteins were between 1.3- and 17.1-fold higher after PE. The levels of 95 proteins were lower after PE. Neutrophil gelatinase-associated lipocalin (fold change 0.3, p-value < 0.01) was among the most reduced proteins 2.5 h after PE. A prediction model based on penalized regression identified five proteins including albumin and neutrophil gelatinase-associated lipocalin. The model was capable of discriminating baseline samples from EBC samples collected 2.5 h after PE correctly in 22 out of 27 samples. In conclusion, the EBC from pigs with acute PE contained several putative diagnostic markers of PE.