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Targeted protein degradation (TPD) strategies exploit bivalent small molecules to bridge substrate proteins to an E3 ubiquitin ligase to induce substrate degradation. Few E3s have been explored as degradation effectors due to a dearth of E3-binding small molecules. We show that genetically induced recruitment to the GID4 subunit of the CTLH E3 complex induces protein degradation. An NMR-based fragment screen followed by structure-guided analog elaboration identified two binders of GID4

Targeted, untargeted, and data-independent acquisition (DIA) metabolomics workflows are often hampered by ambiguous identification based on either MS1 information alone or relatively few MS2 fragment ions. While DIA methods have been popularized in proteomics, it is less clear whether they are suitable for metabolomics workflows due to their large precursor isolation windows and complex coisolation patterns. Here, we quantitatively investigate the conditions necessary for unique metabolite detection in complex backgrounds using precursor and fragment ion mass-to-charge (m/z) separation, comparing three benchmarked mass spectrometry (MS) methods [MS1, MRM (multiple reaction monitoring), and DIA]. Our simulations show that DIA outperformed MS1-only and MRM-based methods with regards to specificity by factors of ∼2.8-fold and ∼1.8-fold, respectively. Additionally, we show that our results are not dependent on the number of transitions used or the complexity of the background matrix. Finally, we show that collision energy is an important factor in unambiguous detection and that a single collision energy setting per compound cannot achieve optimal pairwise differentiation of compounds. Our analysis demonstrates the power of using both high-resolution precursor and high-resolution fragment ion m/z for unambiguous compound detection. This work also establishes DIA as an emerging MS acquisition method with high selectivity for metabolomics, outperforming both data-dependent acquisition (DDA) and MRM with regards to unique compound identification potential.

Motivation Mass spectrometry (MS) data, used for proteomics and metabolomics analyses, have seen considerable growth in the last years. Aiming at reducing the associated storage costs, dedicated compression algorithms for MS data have been proposed, such as MassComp and MSNumpress. However, these algorithms focus on either lossless or lossy compression, respectively, and do not exploit the additional redundancy existing across scans contained in a single file. We introduce mspack, a compression algorithm for MS data that exploits this additional redundancy and that supports both lossless and lossy compression, as well as the mzML and the legacy mzXML formats. mspack applies several preprocessing lossless transforms and optional lossy transforms with a configurable error, followed by the general purpose compressors gzip or bsc to achieve a higher compression ratio. Results We tested mspack on several datasets generated by commonly used MS instruments. When used with the bsc compression backend, mspack achieves on average 76% smaller file sizes for lossless compression and 94% smaller file sizes for lossy compression, as compared with the original files. Lossless mspack achieves 10–60% lower file sizes than MassComp, and lossy mspack compresses 36–60% better than the lossy MSNumpress, for the same error, while exhibiting comparable accuracy and running time. Availability and implementation mspack is implemented in C++ and freely available at https://github.com/fhanau/mspack under the Apache license. Supplementary information Supplementary data are available at Bioinformatics online.

SmartPeak is an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of CE-, GC- and LC-MS(/MS) data, and HPLC data for targeted and semi-targeted metabolomics, lipidomics, and fluxomics experiments.HighlightsNovel algorithms for retention time alignment, calibration curve fitting, and peak integrationEnables reproducibility by reducing operator bias and ensuring high QC/QAAutomated pipeline for high throughput targeted and/or quantitative metabolomics, lipidomics, and fluxomics data processing from multiple analytical instrumentsManually curated data set of LC-MS/MS, GC-MS, and HPLC integrated peaks for further algorithm development and benchmarking

diaPASEF makes use of the correlation between the ion mobility and the m/z of peptides to trap and release precursor ions in a TIMS-TOF mass spectrometer for an almost complete sampling of the precursor ion beam with data-independent acquisition.Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass analysis. Here, we make use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by inclusion of the ion mobility dimension for both signal extraction and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.

1AbstractMass spectrometry is the method of choice in large-scale proteomics studies. One common method is data-independent acquisition (DIA), which allows for high-throughput analysis of biological samples, but also produces complex data. Methods of peptide separation, in addition to retention time, improve data analysis and there has been increasing interest in separating peptides based on collisional cross section (CCS), which is a measure of the size of a peptide. However, existing libraries that are used during data analysis lack CCS measurements, and this data is expensive and time-consuming to acquire. This has led to the desire to predict library values for mass spectrometry analysis. Here we compare three deep learning architectures, LSTM, CNN, and transformer, for the tasks of retention time and collisional cross section prediction. We show that the LSTM and CNN models perform similarly and that the transformer has a lower performance than expected.

Male sex is a risk factor for progression of diabetic kidney disease, but the reasons for this predilection are unclear. Here, we demonstrate that cell sex and sex hormones alter the metabolic phenotype of human proximal tubular epithelial cells (PTECs). Male PTECs displayed increased glycolysis, mitochondrial respiration, oxidative stress, apoptosis, and high glucose-induced injury, compared to female PTECs. This phenotype was enhanced by dihydrotestosterone (DHT) and linked to increased mitochondrial utilization of glucose and glutamine. Studies in vivo pointed towards increased glutamine anaplerosis in diabetic male kidneys. Male sex was linked to increased levels of glutamate, TCA cycle, and glutathione cycle metabolites, in PTECs and in the blood metabolome of healthy youth and youth with type 2 diabetes. Conversely, female PTECs displayed increased levels of pyruvate, glutamyl-cysteine, cysteinylglycine, and a higher GSH/GSSG ratio than male PTECs, indicative of enhanced redox homeostasis. Finally, we identified transcriptional mechanisms that control kidney metabolism in a sex-specific fashion. While X-linked demethylase KDM6A facilitated metabolic homeostasis in female PTECs, transcription factor HNF4A mediated the deleterious effects of DHT in male PTECs. This work uncovers the role of sex in glucose/glutamine metabolism, that may explain the roots of sex dimorphism in the healthy and diabetic kidney.

Summary Metabolism during pregnancy is a dynamic and precisely programmed process, the failure of which can bring devastating consequences to the mother and fetus. To define a high-resolution temporal profile of metabolites during healthy pregnancy, we analyzed the untargeted metabolome of 784 weekly blood samples from 30 pregnant women. Broad changes and a highly choreographed profile were revealed: 4,995 metabolic features (of 9,651 total), 460 annotated compounds (of 687 total), and 34 human metabolic pathways (of 48 total) were significantly changed during pregnancy. Using linear models, we built a metabolic clock with five metabolites that time gestational age in high accordance with ultrasound (R = 0.92). Furthermore, two to three metabolites can identify when labor occurs (time to delivery within two, four, and eight weeks, AUROC ≥ 0.85). Our study represents a weekly characterization of the human pregnancy metabolome, providing a high-resolution landscape for understanding pregnancy with potential clinical utilities., Graphical Abstract, Highlights • Weekly metabolome of maternal blood changes dynamically through healthy pregnancy • A metabolic clock of five blood metabolites accurately predicts gestational age • Two to three metabolites identify labor onset within two, four, and eight weeks • Women with metabolic clocks that outpaced ultrasound evaluation tend to deliver earlier, Identification of blood metabolites in pregnant women that can accurately predict gestational age and provide insights into pregnancy variations undetected by ultrasound.

Data-independent acquisition (DIA) has become an important approach in global, mass spectrometric proteomic studies because it provides in-depth insights into the molecular variety of biological systems. However, DIA data analysis remains challenging due to the high complexity and large data and sample size, which require specialized software and large computing infrastructures. Most available open-source DIA software necessitate basic programming skills and cover only a fraction of the analysis steps, often yielding a complex of multiple software tools, severely limiting usability and reproducibility. To overcome this hurdle, we have integrated a suite of DIA tools in the Galaxy framework for reproducible and version-controlled data processing. The DIA suite includes OpenSwath, PyProphet, diapysef and swath2stats. We have compiled functional Galaxy pipelines for DIA processing, which provide a web-based graphical user interface to these pre-installed and pre-configured tools for their usage on freely accessible, powerful computational resources of the Galaxy framework. This approach also enables seamless sharing workflows with full configuration in addition to sharing raw data and results. We demonstrate usability of the all-in-one DIA pipeline in Galaxy by the analysis of a spike-in case study dataset. Additionally, extensive training material is provided, to further increase access for the proteomics community.

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

SWATH-MS has been widely used for proteomics analysis given its high-throughput and reproducibility but ensuring consistent quantification of analytes across large-scale studies of heterogeneous samples such as human-plasma remains challenging. Heterogeneity in large-scale studies can be caused by large time intervals between data-acquisition, acquisition by different operators or instruments, intermittent repair or replacement of parts, such as the liquid chromatography column, all of which affect retention time (RT) reproducibility and successively performance of SWATH-MS data analysis. Here, we present a novel algorithm for retention time alignment of SWATH-MS data based on direct alignment of raw MS2 chromatograms using a hybrid dynamic programming approach. The algorithm does not impose a chronological order of elution and allows for alignment of elution-order swapped peaks. Furthermore, allowing RT-mapping in a certain window around coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperforms the current state-of-the-art approaches. In addition, on a real-world clinical data, our approach outperforms global alignment methods by mapping 98% of peaks compared to 67% cumulatively and DIAlignR can reduce alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy has also been demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.Abbreviations:AUCArea Under the CurveDIAData-independent acquisitionLCLiquid chromatographyLOESSLocal weighted regressionRSEResidual Standard ErrorRTRetention timeXICExtracted ion chromatogramsData Availability:Raw chromatograms and features extracted by OpenSWATH are available on PeptideAtlas.Servername: ftp.peptideatlas.orgUsername: PASS01280Password: KQ2592b

Proteome analysis revealed signatures of co-expressed upregulated metabolism proteins highly conserved between primary and non-small cell lung cancer (NSCLC) patient-derived xenograft tumors (Li et al. 2014, Nat. Communications 5:5469). The C10 signature is encoded by seven genes (ADSS, ATP2A2, CTPS1, IMPDH2, PKM2, PTGES3, SGPL1) and DNA alterations in C10-encoding genes are associated with longer survival in a subset of NSCLC. To explore the C10 signature as an oncogenic driver and address potential mechanisms of action, C10 protein expression and protein-protein interactions were determined. In independent NSCLC cohorts, the coordinated expression of C10 proteins was significant and mutations in C10 genes were associated with better outcome. Affinity purification-mass spectrometry and in vivo proximity-based biotin identification defined a C10 interactome involving 667 proteins including candidate drug targets and clusters associated with glycolysis, calcium homeostasis, and nucleotide and sphingolipid metabolism. DNA alterations in genes encoding C10 interactome components were also found to be associated with better survival. These data support the notion that the coordinated upregulation of the C10 signature impinges metabolic processes that collectively function as an oncogenic driver in NSCLC.

In bottom-up mass spectrometry based proteomics, deep proteome coverage is limited by high cofragmentation rates. This occurs when more than one analyte is isolated by the quadrupole and the subsequent fragmentation event produces fragment ions of heterogeneous origin. One strategy to reduce cofragmentation rates is through effective peptide separation techniques such as chromatographic separation and, the more recently popularized, ion mobility (IM) spectrometry which separates peptides by their collisional cross section. Here we investigate the capability of the Trapped Ion Mobility Spectrometry (TIMS) device to effectively separate peptide ions and quantify the separation power of the TIMS device in the context of a Parallel Accumulation-Serial Fragmentation (PASEF) workflow. We found that TIMS IM separation increases the number of interference-free MS1 features 9.2-fold, while decreasing the average peptide density in precursor spectra 6.5 fold. In a Data Dependent Acquisition (DDA) PASEF workflow, IM separation increased the number of spectra without cofragmentation by a factor of 4.1 and the number of high quality spectra 17-fold. This observed decrease in spectral complexity results in a substantial increase in peptide identification rates when using our data-driven model. In the context of a Data Independent Acquisition (DIA), the reduction in spectral complexity resulting from IM separation is estimated to be equivalent to a 4-fold decrease in isolation window width (from 25Da to 6.5Da). Our study shows that TIMS IM separation dramatically reduces cofragmentation rates leading to an increase in peptide identification rates.

Data-independent acquisition (DIA) is a powerful method to acquire spectra from all ionized precursors of a sample. Considering the complexity of the highly multiplexed spectral data, sophisticated workflows have been developed to obtain peptides quantification. Here we describe an open-source and easy-to-use workflow to obtain a quantitative matrix from multiple DIA runs. This workflow requires as prior information an "assay library," which contains the MS coordinates of peptides. It consists of OpenSWATH, pyProphet, and DIAlignR software. For the ease of installation and to isolate operating system-related dependency, docker-based containerization is utilized in this workflow.

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. Main advantages include greater reproducibility, sensitivity and dynamic range compared to data-dependent acquisition (DDA). However, data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics a multi-functional, automated high-throughput pipeline implemented in Nextflow that allows to easily process proteomics and peptidomics DIA datasets on diverse compute infrastructures. Central components are well-established tools such as the OpenSwathWorkflow for DIA spectral library search and PyProphet for false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and carry out retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from statistical post-processing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is open-source software and available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

Approximately, 35% of women with Gestational Diabetes (GDM) progress to Type 2 Diabetes (T2D) within 10 years. However, links between GDM and T2D are not well understood. We used a well-characterised GDM prospective cohort of 1035 women following up to 8 years postpartum. Lipidomics profiling covering >1000 lipids was performed on fasting plasma samples from participants 6–9 week postpartum (171 incident T2D vs. 179 controls). We discovered 311 lipids positively and 70 lipids negatively associated with T2D risk. The upregulation of glycerolipid metabolism involving triacylglycerol and diacylglycerol biosynthesis suggested activated lipid storage before diabetes onset. In contrast, decreased sphingomyelines, hexosylceramide and lactosylceramide indicated impaired sphingolipid metabolism. Additionally, a lipid signature was identified to effectively predict future diabetes risk. These findings demonstrate an underlying dyslipidemia during the early postpartum in those GDM women who progress to T2D and suggest endogenous lipogenesis may be a driving force for future diabetes onset.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Data Independent Acquisition (DIA) is a powerful method to acquire spectra from all ionized precursors of a sample. Considering the complexity of the highly multiplexed spectral data, sophisticated workflows have been developed to obtain peptides quantification. Here we describe an open-source and easy-to-use workflow to obtain a quantitative matrix from multiple DIA runs. This workflow requires as prior information an “assay library”, which contains the MS coordinates of peptides. It consists of OpenSWATH, pyProphet and DIAlignR software. For the ease of installation and to isolate operating system related dependency, docker-based containerization is utilized in this workflow.

Multi-run alignment is widely used in proteomics to establish analyte correspondence across runs. Generally alignment algorithms return a cumulative score, which may not be easily interpretable for each peptide. Here we present a novel tool, DrawAlignR, to visualize each chromatographic alignment for DIA/SWATH data. Furthermore, we have developed a novel C++ based implementation of raw chromatogram alignment which is 35 times faster than the previously published algorithm. This not only enables users to plot alignment interactively by DrawAlignR, but also allows other software platforms to use the algorithm. DrawAlignR is an open-source web application using R Shiny that can be hosted using the source-code available at https://github.com/Roestlab/DrawAlignR.

Python is a versatile scripting language that is widely used in bioinformatics and has generated increasing interest in the proteomics and mass spectrometry community. Computing and data analysis in mass spectrometry is very diverse and in many cases must be tailored to a specific experiment. This makes Python an excellent programming language for the task due to its flexibility, visualization capabilities and large number of powerful libraries. Python can be used to quickly prototype software, combine existing libraries into powerful analysis workflows while avoiding the trap of re-inventing the wheel for a new project. Here, we will describe data analysis and software prototyping of mass spectrometric data with Python using the pyOpenMS package. pyOpenMS is an open-source Python library for mass spectrometry, specifically built for the analysis of proteomics and metabolomics data in Python. pyOpenMS facilitates the execution of common tasks in protoemics (and other mass spectrometric fields) such as file handling, chemistry (mass calculation, peptide fragmentation, isotopic abundances), signal processing (smoothing, filtering, de-isotoping, retention time correction and peak-picking), identification analysis (including peptide search, PTM analysis, cross-linked analytes, FDR control, RNA oligonucleotide search and small molecule search tools), quantitative analysis (including label-free, metabolomics, SILAC, iTRAQ and SWATH/DIA analysis tools), chromatogram analysis (chromatographic peak picking, smoothing, elution profiles and peak scoring for SRM/MRM/PRM/SWATH/DIA data) as well as providing an interface for interacting with common tools in proteomics and metabolomics.

Computational mass spectrometry is plagued by a multitude of issues, including a heterogeneous software environment, complex workflows and proprietary tools. OpenMS addresses these challenges by providing robust open-source software for users and an open, well-designed software environment for developers. OpenMS is an open-source software C++ library for LC-MS data management and analyses using modern C++11. It offers an infrastructure for rapid development of mass spectrometry related software. OpenMS is free software available under the three clause BSD license. It comes with a variety of pre-built and ready-to-use tools for high-throughput Proteomics and Metabolomics data analysis (TOPPTools), covering most MS and LC-MS data processing and mining tasks, as well as visualization (TOPPView). OpenMS offers automated analyses for various quantitation protocols, including label-free quantitation, SILAC, iTRAQ, TMT, SRM, SWATH. It provides built-in algorithms for de novo identification and database search, as well as adapters to other tools like X!Tandem, Mascot, OMSSA, SIRIUS. It supports easy integration of OpenMS built tools into workflow engines like KNIME, Galaxy, WS-Pgrade, and TOPPAS. OpenMS supports the Proteomics Standard Initiative (PSI) formats for MS data including mzML, mzXML, mzIdentXML, pepXML. With pyOpenMS, OpenMS offers Python bindings to a large part of the OpenMS API to enable rapid algorithm development.

While molecules that promote the growth of animal cells have been identified, it remains unclear how such signals are orchestrated to determine a characteristic target size for different cell types. It is increasingly clear that cell size is determined by size checkpoints-mechanisms that restrict the cell cycle progression of cells that are smaller than their target size. Previously, we described a p38 MAPK-dependent cell size checkpoint mechanism whereby p38 is selectively activated and prevents cell cycle progression in cells that are smaller than a given target size. In this study, we show that the specific target size required for inactivation of p38 and transition through the cell cycle is determined by CDK4 activity. Our data suggest a model whereby p38 and CDK4 cooperate analogously to the function of a thermostat: while p38 senses irregularities in size, CDK4 corresponds to the thermostat dial that sets the target size.

Bottom-up proteomics produces complex peptide populations that are identified and quantified at the precursor or fragment ion level. Data dependent acquisition methods sequentially isolate and fragment particular precursors, whereas data independent acquisition (DIA) modes isolate and concurrently fragment populations of different precursors by cycling deterministically through segments of a predefined precursor m/z range. Although the selection windows of DIA collectively cover the entire mass range of interest, only a few percent of the ion current are sampled due to the consecutive selection of acquisition windows. Making use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro), we here devise a novel scan mode that samples up to 100% of the peptide precursor ion current. We analyze the acquired data by extending established targeted data extraction workflow for the analysis of DIA data by the additional ion mobility dimension, providing additional specificity in the precursor identification. Data acquired from simple protein mixtures verify the expected data completeness and data in single runs of a whole proteome digest demonstrate deep proteome coverage and a very high degree of reproducibility and quantitative accuracy, even from 10 ng sample amounts.

Nature Methods, 13 (9), ISSN:1548-7105, ISSN:1548-7091

The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows setups. For consistent evaluation we developed LFQbench, an R-package to calculate metrics of precision and accuracy in label-free quantitative MS, and report the identification performance, robustness and specificity of each software tool. Our reference datasets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.

Liquid Chromatography coupled to Tandem Mass Spectrometry (LC-MS/MS) based methods are currently the top choice for high-throughput, quantitative measurements of the proteome. While traditional proteomics LC-MS/MS methods can suffer from issues such as low reproducibility and quantitative accuracy due to its stochastic nature, recent improvements in acquisition protocols have resulted in methods that can overcome these challenges. Data-independent acquisition (DIA) is a novel mass spectrometric method that does so by using a deterministic acquisition strategy. These new approaches will allow researchers to apply MS on more complex samples, however, existing heuristic and expert-knowledge based methods will struggle with keeping pace of the increasing complexity of the resulting data. Deep learning (DL) based methods have been shown to be more adept at handling large amounts of complex data than traditional methods in many other fields, such as computer vision and natural language processing. Proteomics is also entering a phase where the size and complexity of the data will require us to look towards scalable and data-driven DL pipelines.

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

International audience; Software Containers are changing the way scientists and researchers develop, deploy and exchange scientific software. They allow labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. However, containers and software packages should be produced under certain rules and standards in order to be reusable, compatible and easy to integrate into pipelines and analysis workflows. Here, we presented a set of recommendations developed by the BioContainers Community to produce standardized bioinformatics packages and containers. These recommendations provide practical guidelines to make bioinformatics software more discoverable, reusable and transparent. They are aimed to guide developers, organisations, journals and funders to increase the quality and sustainability of research software.

Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host–microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states., Deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, alongside changes in the microbiome, in samples from individuals with and without prediabetes reveal insights into inter-individual variability and associations between changes in the microbiome and other factors.

BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice. ispartof: BMC MEDICINE vol:16 issue:1 ispartof: location:England status: published

The interpretation of fragmentation patterns in tandem mass spectrometry is crucial for peptide sequencing, but the relative intensities of these patterns are difficult to predict computationally. Two groups have applied deep neural networks to address this long-standing problem in the proteomics field, extending theoretical spectra with an additional dimension of high-accuracy fragment ion intensities.

Chemical cross-linking in combination with mass spectrometry generates distance restraints of amino acid pairs in close proximity on the surface of native proteins and protein complexes. In this study we used quantitative mass spectrometry and chemical cross-linking to quantify differences in cross-linked peptides obtained from complexes in spatially discrete states. We describe a generic computational pipeline for quantitative cross-linking mass spectrometry consisting of modules for quantitative data extraction and statistical assessment of the obtained results. We used the method to detect conformational changes in two model systems: firefly luciferase and the bovine TRiC complex. Our method discovers and explains the structural heterogeneity of protein complexes using only sparse structural information.

SummaryMycobacterium tuberculosis remains a health concern due to its ability to enter a non-replicative dormant state linked to drug resistance. Understanding transitions into and out of dormancy will inform therapeutic strategies. We implemented a universally applicable, label-free approach to estimate absolute cellular protein concentrations on a proteome-wide scale based on SWATH mass spectrometry. We applied this approach to examine proteomic reorganization of M. tuberculosis during exponential growth, hypoxia-induced dormancy, and resuscitation. The resulting data set covering >2,000 proteins reveals how protein biomass is distributed among cellular functions during these states. The stress-induced DosR regulon contributes 20% to cellular protein content during dormancy, whereas ribosomal proteins remain largely unchanged at 5%–7%. Absolute protein concentrations furthermore allow protein alterations to be translated into changes in maximal enzymatic reaction velocities, enhancing understanding of metabolic adaptations. Thus, global absolute protein measurements provide a quantitative description of microbial states, which can support the development of therapeutic interventions.

Whole exome sequencing (WES) is increasingly utilized in both clinical and non-clinical settings, but little is known about the utility of WES in healthy individuals. In order to determine the frequency of both medically actionable and non-actionable but medically relevant exome findings in the general population we assessed the exomes of 70 participants who have been extensively characterized over the past several years as part of a longitudinal integrated multi-omics profiling study at Stanford University. We assessed exomes for rare likely pathogenic and pathogenic variants in genes associated with Mendelian disease in the Online Mendelian Inheritance in Man (OMIM) database. We used American College of Medical Genetics (ACMG) guidelines were used for the classification of rare sequence variants, and additionally we assessed pharmacogenetic variants. Twelve out of 70 (17%) participants had medically actionable findings in Mendelian disease genes, including 6 (9%) with mutations in genes not currently included in the ACMG’s list of 59 actionable genes. This number is higher than that reported in previous studies and suggests added benefit from utilizing expanded gene lists and manual curation to assess actionable findings. A total of 60 participants (89%) had non-actionable findings identified including 57 who were found to be mutation carriers for recessive diseases and 21 who have increased Alzheimer’s disease risk due to heterozyg ous or homozygousAPOEe4 alleles (18 participants had both). These results suggest that exome sequencing may have considerably more utility for health management in the general population than previously thought.

Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.

Targeted mass spectrometry comprises a set of methods able to quantify protein analytes in complex mixtures with high accuracy and sensitivity. These methods, e.g., Selected Reaction Monitoring (SRM) and SWATH MS, use specific mass spectrometric coordinates (assays) for reproducible detection and quantification of proteins. In this protocol, we describe how to analyze, in a targeted manner, data from a SWATH MS experiment aimed at monitoring thousands of proteins reproducibly over many samples. We present a standard SWATH MS analysis workflow, including manual data analysis for quality control (based on Skyline) as well as automated data analysis with appropriate control of error rates (based on the OpenSWATH workflow). We also discuss considerations to ensure maximal coverage, reproducibility, and quantitative accuracy.

In this Perspective, we discuss developments in mass-spectrometry-based proteomic technology over the past decade from the viewpoint of our laboratory. We also reflect on existing challenges and limitations, and explore the current and future roles of quantitative proteomics in molecular systems biology, clinical research and personalized medicine. ISSN:1750-2799 ISSN:1754-2189

pyOpenMS is an open-source, Python-based interface to the C++ OpenMS library, providing facile access to a feature-rich, open-source algorithm library for MS-based proteomics analysis. It contains Python bindings that allow raw access to the data structures and algorithms implemented in OpenMS, specifically those for file access (mzXML, mzML, TraML, mzIdentML among others), basic signal processing (smoothing, filtering, de-isotoping, and peak-picking) and complex data analysis (including label-free, SILAC, iTRAQ, and SWATH analysis tools). pyOpenMS thus allows fast prototyping and efficient workflow development in a fully interactive manner (using the interactive Python interpreter) and is also ideally suited for researchers not proficient in C++. In addition, our code to wrap a complex C++ library is completely open-source, allowing other projects to create similar bindings with ease. The pyOpenMS framework is freely available at https://pypi.python.org/pypi/pyopenms while the autowrap tool to create Cython code automatically is available at https://pypi.python.org/pypi/autowrap (both released under the 3-clause BSD licence).

Mass spectrometry-based proteomics using soft ionization techniques has been used successfully to identify large numbers of proteins from complex biological samples. However, reproducible quantification across a large number of samples is still highly challenging with commonly used “shotgun proteomics” which uses stochastic sampling of the peptide analytes (data dependent acquisition; DDA) to analyze samples. Recently, data independent acquisition (DIA) methods have been investigated for their potential for reproducible protein quantification, since they deterministically sample all peptide analytes in every single run. This increases reproducibility and sensitivity, reduces the number of missing values and removes stochasticity from the acquisition process. However, one of the major challenges for wider adoption of DIA has been data analysis. In this chapter we will introduce the five most well-known of these techniques, as well as their data analysis methods, classified either as targeted or untargeted; then, we will discuss briefly the meaning of the false discovery rate (FDR) in DIA experiments, to finally close the chapter with a review of the current challenges in this subject.

Exome sequencing is increasingly utilized in both clinical and nonclinical settings, but little is known about its utility in healthy individuals. Most previous studies on this topic have examined a small subset of genes known to be implicated in human disease and/or have used automated pipelines to assess pathogenicity of known variants. To determine the frequency of both medically actionable and nonactionable but medically relevant exome findings in the general population we assessed the exomes of 70 participants who have been extensively characterized over the past several years as part of a longitudinal integrated multiomics profiling study. We analyzed exomes by identifying rare likely pathogenic and pathogenic variants in genes associated with Mendelian disease in the Online Mendelian Inheritance in Man (OMIM) database. We then used American College of Medical Genetics (ACMG) guidelines for the classification of rare sequence variants. Additionally, we assessed pharmacogenetic variants. Twelve out of 70 (17%) participants had medically actionable findings in Mendelian disease genes. Five had phenotypes or family histories associated with their genetic variants. The frequency of actionable variants is higher than that reported in most previous studies and suggests added benefit from utilizing expanded gene lists and manual curation to assess actionable findings. A total of 63 participants (90%) had additional nonactionable findings, including 60 who were found to be carriers for recessive diseases and 21 who have increased Alzheimer's disease risk because of heterozygous or homozygous APOE e4 alleles (18 participants had both). Our results suggest that exome sequencing may have considerably more utility for health management in the general population than previously thought.

Motivation: Targeted mass spectrometry comprises a set of powerful methods to obtain accurate and consistent protein quantification in complex samples. To fully exploit these techniques, a cross-platform and open-source software stack based on standardized data exchange formats is required. Results: We present TAPIR, a fast and efficient Python visualization software for chromatograms and peaks identified in targeted proteomics experiments. The input formats are open, community-driven standardized data formats (mzML for raw data storage and TraML encoding the hierarchical relationships between transitions, peptides and proteins). TAPIR is scalable to proteome-wide targeted proteomics studies (as enabled by SWATH-MS), allowing researchers to visualize high-throughput datasets. The framework integrates well with existing automated analysis pipelines and can be extended beyond targeted proteomics to other types of analyses. Availability and implementation: TAPIR is available for all computing platforms under the 3-clause BSD license at https://github.com/msproteomicstools/msproteomicstools. Contact: lars@imsb.biol.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.

SummaryResearch advancing our understanding of Mycobacterium tuberculosis (Mtb) biology and complex host-Mtb interactions requires consistent and precise quantitative measurements of Mtb proteins. We describe the generation and validation of a compendium of assays to quantify 97% of the 4,012 annotated Mtb proteins by the targeted mass spectrometric method selected reaction monitoring (SRM). Furthermore, we estimate the absolute abundance for 55% of all Mtb proteins, revealing a dynamic range within the Mtb proteome of over four orders of magnitude, and identify previously unannotated proteins. As an example of the assay library utility, we monitored the entire Mtb dormancy survival regulon (DosR), which is linked to anaerobic survival and Mtb persistence, and show its dynamic protein-level regulation during hypoxia. In conclusion, we present a publicly available research resource that supports the sensitive, precise, and reproducible quantification of virtually any Mtb protein by a robust and widely accessible mass spectrometric method.

Emerging studies have linked the ribosome to more selective control of gene regulation. However, an outstanding question is whether ribosome heterogeneity at the level of core ribosomal proteins (RPs) exists and enables ribosomes to preferentially translate specific mRNAs genome-wide. Here, we measured the absolute abundance of RPs in translating ribosomes and profiled transcripts that are enriched or depleted from select subsets of ribosomes within embryonic stem cells. We find that heterogeneity in RP composition endows ribosomes with differential selectivity for translating subpools of transcripts, including those controlling metabolism, cell cycle, and development. As an example, mRNAs enriched in binding to RPL10A/uL1-containing ribosomes are shown to require RPL10A/uL1 for their efficient translation. Within several of these transcripts, this level of regulation is mediated, at least in part, by internal ribosome entry sites. Together, these results reveal a critical functional link between ribosome heterogeneity and the post-transcriptional circuitry of gene expression.

Selected reaction monitoring (SRM), also called multiple reaction monitoring, has become an invaluable tool for targeted quantitative proteomic analyses, but its application can be compromised by nonoptimal selection of transitions. In particular, complex backgrounds may cause ambiguities in SRM measurement results because peptides with interfering transitions similar to those of the target peptide may be present in the sample. Here, we developed a computer program, the SRMCollider, that calculates nonredundant theoretical SRM assays, also known as unique ion signatures (UIS), for a given proteomic background. We show theoretically that UIS of three transitions suffice to conclusively identify 90% of all yeast peptides and 85% of all human peptides. Using predicted retention times, the SRMCollider also simulates time-scheduled SRM acquisition, which reduces the number of interferences to consider and leads to fewer transitions necessary to construct an assay. By integrating experimental fragment ion intensities from large scale proteome synthesis efforts (SRMAtlas) with the information content-based UIS, we combine two orthogonal approaches to create high quality SRM assays ready to be deployed. We provide a user friendly, open source implementation of an algorithm to calculate UIS of any order that can be accessed online at http://www.srmcollider.org to find interfering transitions. Finally, our tool can also simulate the specificity of novel data-independent MS acquisition methods in Q1-Q3 space. This allows us to predict parameters for these methods that deliver a specificity comparable with that of SRM. Using SRM interference information in addition to other sources of information can increase the confidence in an SRM measurement. We expect that the consideration of information content will become a standard step in SRM assay design and analysis, facilitated by the SRMCollider.

Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals., Molecular Biology of the Cell, 26 (22), ISSN:1939-4586, ISSN:1059-1524

Accurate knowledge of retention time (RT) in liquid chromatography-based mass spectrometry data facilitates peptide identification, quantification, and multiplexing in targeted and discovery-based workflows. Retention time prediction is particularly important for peptide analysis in emerging data-independent acquisition (DIA) experiments such as SWATH-MS. The indexed RT approach, iRT, uses synthetic spiked-in peptide standards (SiRT) to set RT to a unit-less scale, allowing for normalization of peptide RT between different samples and chromatographic set-ups. The obligatory use of SiRTs can be costly and complicates comparisons and data integration if standards are not included in every sample. Reliance on SiRTs also prevents the inclusion of archived mass spectrometry data for generation of the peptide assay libraries central to targeted DIA-MS data analysis. We have identified a set of peptide sequences that are conserved across most eukaryotic species, termed Common internal Retention Time standards (CiRT). In a series of tests to support the appropriateness of the CiRT-based method, we show: (1) the CiRT peptides normalized RT in human, yeast, and mouse cell lysate derived peptide assay libraries and enabled merging of archived libraries for expanded DIA-MS quantitative applications; (2) CiRTs predicted RT in SWATH-MS data within a 2-min margin of error for the majority of peptides; and (3) normalization of RT using the CiRT peptides enabled the accurate SWATH-MS-based quantification of 340 synthetic isotopically labeled peptides that were spiked into either human or yeast cell lysate. To automate and facilitate the use of these CiRT peptide lists or other custom user-defined internal RT reference peptides in DIA workflows, an algorithm was designed to automatically select a high-quality subset of datapoints for robust linear alignment of RT for use. Implementations of this algorithm are available for the OpenSWATH and Skyline platforms. Thus, CiRT peptides can be used alone or as a complement to SiRTs for RT normalization across peptide spectral libraries and in quantitative DIA-MS studies.

The open XML format mzML, used for representation of mass spectrometry (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 naive mzML representation is 4-fold or even up to 18-fold larger compared to 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.