MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

The network response to Egf is tissue-specific

Epidermal growth factor receptor (Egfr)-driven signaling regulates fundamental homeostatic processes. Dysregulated signaling via Egfr is implicated in numerous disease pathologies and distinct Egfr-associated disease etiologies are known to be tissue-specific. The molecular basis of this tissue-specificity remains poorly understood. Most studies of Egfr signaling to date have been performed in vitro or in tissue-specific mouse models of disease, which has limited insight into Egfr signaling patterns in healthy tissues. Here, we carried out integrated phosphoproteomic, proteomic, and transcriptomic analyses of signaling changes across various mouse tissues in response to short-term stimulation with the Egfr ligand Egf. We show how both baseline and Egf-stimulated signaling dynamics differ between tissues. Moreover, we propose how baseline phosphorylation and total protein levels may be associated with clinically relevant tissue-specific Egfr-associated phenotypes. Altogether, our analyses illustrate tissue-specific effects of Egf stimulation and highlight potential links between underlying tissue biology and Egfr signaling output.

A nonenzymatic dependency on inositol-requiring enzyme 1 controls cancer cell cycle progression and tumor growth

Endoplasmic-reticulum resident inositol-requiring enzyme 1α (IRE1) supports protein homeostasis via its cytoplasmic kinase-RNase module. Known cancer dependency on IRE1 entails its enzymatic activation of the transcription factor XBP1s and regulated RNA decay. We discovered that some cancer cells surprisingly require IRE1 but not its enzymatic activity. IRE1 knockdown but not enzymatic IRE1 inhibition or XBP1 disruption attenuated cell cycle progression and tumor growth. IRE1 silencing led to activation of TP53 and CDKN1A/p21 in conjunction with increased DNA damage and chromosome instability, while decreasing heterochromatin as well as DNA and histone H3K9me3 methylation. Immunoelectron microscopy detected endogenous IRE1 at the nuclear envelope. Thus, cancer cells co-opt IRE1 either enzymatically or nonenzymatically, which has significant implications for IRE1's biological role and therapeutic targeting.

Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

QuickProt: A bioinformatics and visualization tool for DIA and PRM mass spectrometry-based proteomics datasets

Mass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Processing of MS-derived raw data, including deconvolution, alignment, and peptide-protein prediction, has been achieved through various software platforms. However, the downstream analysis, including quality control, visualizations, and interpretation of proteomics results remains challenging due to the lack of integrated tools to facilitate the analyses. To address this challenge, we developed QuickProt, a series of Python-based Google Colab notebooks for analyzing data-independent acquisition (DIA) and parallel reaction monitoring (PRM) proteomics datasets. These pipelines are designed so that users with no coding expertise can utilize the tool. Furthermore, as open-source code, QuickProt notebooks can be customized and incorporated into existing workflows. As proof of concept, we applied QuickProt to analyze in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets from a time-course study of human erythropoiesis. The analysis resulted in annotated tables and publication-ready figures revealing a dynamic rearrangement of the proteome during erythroid differentiation, with the abundance of proteins linked to gene regulation, metabolic, and chromatin remodeling pathways increasing early in erythropoiesis. Altogether, these tools aim to automate and streamline DIA and PRM-MS proteomics data analysis, making it more efficient and less time-consuming.

A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis

The exponential increase in proteomics data presents critical challenges for conventional processing workflows. These pipelines often consist of fragmented software packages, glued together using complex in-house scripts or error-prone manual workflows running on local hardware, which are costly to maintain and scale. The MSAID Platform offers a fully automated, managed proteomics data pipeline, consolidating formerly disjointed functions into unified, API-driven services that cover the entire process from raw data to biological insights. Backed by the cloud-native search algorithm CHIMERYS, as well as scalable cloud compute instances and data lakes, the platform facilitates efficient processing of large data sets, automation of processing via the command line, systematic result storage, analysis, and visualization. The data lake supports elastically growing storage and unified query capabilities, facilitating large-scale analyses and efficient reuse of previously processed data, such as aggregating longitudinally acquired studies. Users interact with the platform via a web interface, CLI client, or API, providing flexible, automated access. Readily available tools for accessing result data include browser-based interrogation and one-click visualizations for statistical analysis. The platform streamlines research processes, making advanced and automated proteomic workflows accessible to a broader range of scientists. The MSAID Platform is globally available via https://platform.msaid.io.

ProteoArk: A One-Pot Proteomics Data Analysis and Visualization Tool for Biologists

ProteoArk is a web-based tool that offers a range of computational pipelines for comprehensive analysis and visualization of mass spectrometry-based proteomics data. The application comprises four primary sections designed to address various aspects of mass spectrometry data analysis in a single platform, including label-free and labeled samples (SILAC/iTRAQ/TMT), differential expression analysis, and data visualization. ProteoArk supports postprocessing of Proteome Discoverer, MaxQuant, and MSFragger search results. The tool also includes functional enrichment analyses such as gene ontology, protein-protein interactions, pathway analysis, and differential expression analysis, which incorporate various statistical tests. By streamlining workflows and developing user-friendly interfaces, we created a robust and accessible solution for users with basic bioinformatics skills in proteomic data analysis. Users can easily create manuscript-ready figures with a single click, including principal component analysis, heatmaps (K-means and hierarchical), MA plots, volcano plots, and circular bar plots. ProteoArk is developed using the Django framework and is freely available for users [https://ciods.in/proteoark/]. Users can also download and run the standalone version of ProteoArk using Docker as described in the instructions [https://ciods.in/proteoark/dockerpage]. The application code, input data, and documentation are available online at https://github.com/ArokiaRex/proteoark. A tutorial video is available on YouTube: https://www.youtube.com/watch?v=WFMKAZ9Slq4&ab_channel=RexD.A.B.

Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach

MOTIVATION

Bottom-up mass spectrometry-based proteomics studies changes in protein abundance and structure across conditions. Since the currency of these experiments are peptides, i.e. subsets of protein sequences that carry the quantitative information, conclusions at a different level must be computationally inferred. The inference is particularly challenging in situations where the peptides are shared by multiple proteins or post-translational modifications. While many approaches infer the underlying abundances from unique peptides, there is a need to distinguish the quantitative patterns when peptides are shared.

RESULTS

We propose a statistical approach for estimating protein abundances, as well as site occupancies of post-translational modifications, based on quantitative information from shared peptides. The approach treats the quantitative patterns of shared peptides as convex combinations of abundances of individual proteins or modification sites, and estimates the abundance of each source in a sample together with the weights of the combination. In simulation-based evaluations, the proposed approach improved the precision of estimated fold changes between conditions. We further demonstrated the practical utility of the approach in experiments with diverse biological objectives, ranging from protein degradation and thermal proteome stability, to changes in protein post-translational modifications.

AVAILABILITY AND IMPLEMENTATION

The approach is implemented in an open-source R package MSstatsWeightedSummary. The package is currently available at https://github.com/Vitek-Lab/MSstatsWeightedSummary (doi: 10.5281/zenodo.14662989). Code required to reproduce the results presented in this article can be found in a repository https://github.com/mstaniak/MWS_reproduction (doi: 10.5281/zenodo.14656053).

EndoMAP.v1, a Structural Protein Complex Landscape of Human Endosomes

Early/sorting endosomes are dynamic organelles that play key roles in proteome control by triaging plasma membrane proteins for either recycling or degradation in the lysosome1,2,3. These events are coordinated by numerous transiently-associated regulatory complexes and integral membrane components that contribute to organelle identity during endosome maturation4. While a subset of the several hundred protein components and cargoes known to associate with endosomes have been studied at the biochemical and/or structural level, interaction partners and higher order molecular assemblies for many endosomal components remain unknown. Here, we combine cross-linking and native gel mass spectrometry5-8 of purified early endosomes with AlphaFold9,10 and computational analysis to create a systematic human endosomal structural interactome. We present dozens of structural models for endosomal protein pairs and higher order assemblies supported by experimental cross-links from their native subcellular context, suggesting structural mechanisms for previously reported regulatory processes. Using induced neurons, we validate two candidate complexes whose interactions are supported by crosslinks and structural predictions: TMEM230 as a subunit of ATP8/11 lipid flippases11 and TMEM9/9B as subunits of CLCN3/4/5 chloride-proton antiporters12. This resource and its accompanying structural network viewer provide an experimental framework for understanding organellar structural interactomes and large-scale validation of structural predictions.

prolfquapp ─ A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative Proteomics

Mass spectrometry is a cornerstone of quantitative proteomics, enabling relative protein quantification and differential expression analysis (DEA) of proteins. As experiments grow in complexity, involving more samples, groups, and identified proteins, interactive differential expression analysis tools become impractical. The prolfquapp addresses this challenge by providing a command-line interface that simplifies DEA, making it accessible to nonprogrammers and seamlessly integrating it into workflow management systems. Prolfquapp streamlines data processing and result visualization by generating dynamic HTML reports that facilitate the exploration of differential expression results. These reports allow for investigating complex experiments, such as those involving repeated measurements or multiple explanatory variables. Additionally, prolfquapp supports various output formats, including XLSX files, SummarizedExperiment objects and rank files, for further interactive analysis using spreadsheet software, the exploreDE Shiny application, or gene set enrichment analysis software, respectively. By leveraging advanced statistical models from the prolfqua R package, prolfquapp offers a user-friendly, integrated solution for large-scale quantitative proteomics studies, combining efficient data processing with insightful, publication-ready outputs.

Sex-specific response of intramuscular fat to diet-induced obesity in rats

Metabolic abnormalities associated with excess adiposity in obesity contribute to many noncommunicable diseases, including sarcopenic obesity. Sarcopenic obesity is the loss of muscle mass coupled with excess fat mass and fatty infiltrations in muscle tissue called myosteatosis. A diet-induced obesity model was developed to study fat infiltration in muscle tissue. Only male rats have been considered in these investigations neglecting that female rats might respond differently. The objective of this study was to determine if the response to diet-induced obesity can be generalized to both sexes, or whether sex affects the response to the HFS diet, as indicated by markers of metabolic syndrome and changed in muscle integrity. Using a combination of histological staining techniques, quantitative proteomics, and measures of metabolic syndrome and inflammation, it was determined that the diet-induced obesity model in female Sprague-Dawley rats is a viable model with pronounced effects on the musculoskeletal system. We found sex-dependent and muscle-specific differences in intramuscular fat infiltration between male and female rats receiving the obesogenic diet. Including females in research may allow for identifying distinct causes of the mechanistic relationship between diet, obesity, metabolic syndrome, and the sex-dependent differential effects of these factors on adaptation and degeneration of musculoskeletal tissues.

Quorum sensing mediates morphology and motility transitions in the model archaeon Haloferax volcanii

Quorum sensing (QS) is a mechanism of intercellular communication that enables microbes to alter gene expression and adapt to the environment. This cell-cell signaling is necessary for intra- and interspecies behaviors such as virulence and biofilm formation. While QS has been extensively studied in bacteria, little is known about cell-cell communication in archaea. Here we established an archaeal model system to study QS. We showed that for Haloferax volcanii, the transition from motile rods to non-motile disks is dependent on a possibly novel, secreted small molecule present in cell-free conditioned medium (CM). Moreover, we determined that this putative QS molecule fails to induce the morphology transition in mutants lacking the regulatory factors, DdfA and CirA. Using quantitative proteomics of wild-type cells, we detected significant differential abundances of 236 proteins in the presence of CM. Conversely, in the ΔddfA mutant, addition of CM resulted in only 110 proteins of significant differential abundances. These results confirm that DdfA is involved in CM-dependent regulation. CirA, along with other proteins involved in morphology and swimming motility transitions, is among the proteins regulated by DdfA. These discoveries significantly advance our understanding of microbial communication within archaeal species.

Synergy of Analytical Characterization and Biocompatible Extractions for the Enhancement of High-Quality Biorefinery Products from Medicago sativa

This study presents the development of an analytical characterization strategy tailored to end products derived from an alfalfa (Medicago sativa)-based biorefinery with particular emphasis on protein concentrates and phenolic-enriched fractions. Our approach began with a comprehensive full-factorial experimental design aimed at optimizing the extraction process, taking care to design a biocompatible extraction protocol. Liquid chromatography with tandem mass spectrometry (LC-MS/MS) techniques were used to characterize the molecular profile of the extracts. In particular, the extracts showed a significant relative abundance of flavonoids and isoflavonoids in both their aglycone and glycosylated forms, in which antioxidant activity was evaluated. In addition, we elucidated the proteomic profiles of the protein concentrates. This proteomic characterization served as a valuable resource for understanding the differences between these end products, providing insights that can guide informed decisions about their potential applications.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition has become a widely used strategy for peptide and protein quantification in liquid chromatography-tandem mass spectrometry-based proteomics studies. The integration of ion mobility separation into data-independent acquisition analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using data-independent acquisition. We introduce diaTracer, a spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (mass to charge ratio, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-tandem mass spectra", facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer, cerebrospinal fluid, and plasma samples, data from phosphoproteomics and human leukocyte antigens immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

Identification of Disease-Relevant, Sex-Based Proteomic Differences in iPSC-Derived Vascular Smooth Muscle Cells

The prevalence of cardiovascular disease varies with sex, and the impact of intrinsic sex-based differences on vasculature is not well understood. Animal models can provide important insights into some aspects of human biology; however, not all discoveries in animal systems translate well to humans. To explore the impact of chromosomal sex on proteomic phenotypes, we used iPSC-derived vascular smooth muscle cells from healthy donors of both sexes to identify sex-based proteomic differences and their possible effects on cardiovascular pathophysiology. Our analysis confirmed that differentiated cells have a proteomic profile more similar to healthy primary aortic smooth muscle cells than iPSCs. We also identified sex-based differences in iPSC-derived vascular smooth muscle cells in pathways related to ATP binding, glycogen metabolic process, and cadherin binding as well as multiple proteins relevant to cardiovascular pathophysiology and disease. Additionally, we explored the role of autosomal and sex chromosomes in protein regulation, identifying that proteins on autosomal chromosomes also show sex-based regulation that may affect the protein expression of proteins from autosomal chromosomes. This work supports the biological relevance of iPSC-derived vascular smooth muscle cells as a model for disease, and further exploration of the pathways identified here can lead to the discovery of sex-specific pharmacological targets for cardiovascular disease.

Improving design and normalization of multiplex proteomics study

Advances in multiplex mass spectrometry-based technologies have enabled high-throughput, quantitative proteome profiling of large cohort. However, certain experimental design configurations can amplify sample variability and introduce systematic biases. To address these challenges, we incorporated two novel features in a recent proteogenomic investigation: (1) the inclusion of two reference samples within each mass spectrometry run to serve as internal standards, and (2) the analysis of each specimen as technical replicates across two distinct mass spectrometry runs. Building on these enhancements, we present ProMix, a flexible analytical framework designed to fully leverage these supplementary experimental components. Using both simulated and real-world datasets, we demonstrate the improved performance of ProMix and highlight the advantages conferred by these refined experimental design strategies.

Intra-strain colony biofilm heterogeneity in uropathogenic Escherichia coli and the effect of the NlpI lipoprotein

Biofilm growth facilitates the interaction of uropathogenic Escherichia coli (UPEC) with the host environment. The extracellular polymeric substances (EPS) of UPEC biofilms are composed prominently of curli amyloid fiber and cellulose polysaccharide. When the organism is propagated as a colony biofilm on agar media, these macromolecules can generate pronounced macroscopic structures. Moreover, curli/cellulose associate tightly with Congo red, generating a characteristic pink-to-red staining pattern when the media is supplemented with this dye. Among different clinical isolates of UPEC, changes in the abundance of curli/cellulose can lead to diverse colony biofilm phenotypes on a strain-by-strain basis. Nevertheless, for any given isolate, these phenotypes are classically homogenous throughout the colony biofilm. Here, we report that a subset of clinical UPEC isolates display heterogenous 'peppermint' colony biofilms, with distinct pale and red subpopulations. Through isolation of these subpopulations and whole genome sequencing, we demonstrate various emergent mutations associated with the phenomenon, including within the gene encoding the outer membrane lipoprotein nlpI. Deletion of nlpI within independent strain-backgrounds increased biofilm rugosity, while its overexpression induced the peppermint phenotype. Upregulation of EPS-associated proteins and transcripts was likewise observed in the absence of nlpI. Overall, these results demonstrate that EPS elaboration in UPEC is impacted by nlpI. More broadly, this phenomenon of intra-strain colony biofilm heterogeneity may be leveraged as a tool to identify additional members within the broad collection of genes that regulate or otherwise affect biofilm formation.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer (TNBC), cerebrospinal fluid (CSF), and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

Unveiling the proteome of the fasting heart: Insights into HIF-1 pathway regulation

Fasting is a common dietary intervention known for its protective effects against metabolic and cardiovascular diseases. While its effects are mostly systemic, understanding tissue-specific changes in the heart is crucial for the identification of the mechanisms underlying fasting-induced cardioprotection. In this study, we performed a proteomic analysis of the fasting heart and attempted to clarify the molecular basis of fasting-induced cardioprotection. Our investigation identified a total of 4,652 proteins, with 127 exhibiting downregulation and 118 showing upregulation after fasting. Annotation analysis highlighted significant changes in processes such as lipid metabolism, the peroxisome pathway, and reactive oxygen species metabolism. Notably, the HIF-1 signaling pathway emerged as one of the focal points, with various HIF-1 targets exhibiting differential responses to fasting. Further experiments demonstrated downregulation of HIF-1α at both transcript and protein levels. Intriguingly, while gene expression of Egln3 decreased, its protein product PHD3 remained unaffected by fasting. The unchanged levels of pro-inflammatory cytokines indicated that the observed reduction in Hif1a expression did not stem from a decrease in basal inflammation. These findings underscore the complex regulation of the well-established cardioprotective HIF-1 signaling within the heart during 3-day fasting.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

Empowering Green Energy Storage Systems with MXene for a Sustainable Future

Green energy storage systems play a vital role in enabling a sustainable future by facilitating the efficient integration and utilization of renewable energy sources. The main problems related to two-dimensional (2D) materials are their difficult synthesis process, high cost, and bulk production, which hamper their performance. In recent years, MXenes have emerged as highly promising materials for enhancing the performance of energy storage devices due to their unique properties, including their high surface area, excellent electrical and thermal conductivity, and exceptional chemical stability. This paper presents a comprehensive scientific approach that explores the potential of MXenes for empowering green energy storage systems. Which indicates the novelty of the article. The paper reviews the latest advances in MXene synthesis techniques. Furthermore, investigates the application of MXenes in various energy storage technologies, such as lithium-ion batteries, supercapacitors, and emerging energy storage devices. The utilization of MXenes as electrodes in flexible and transparent energy storage devices is also discussed. Moreover, the paper highlights the potential of MXenes in addressing key challenges in energy storage, including enhancing energy storage capacity, improving cycling stability, and promoting fast charging and discharging rates. Additionally, industrial application and cost estimation of MXenes are explored. As the output of the work, we analyzed that HF and modified acid (LiF and HCl) are the established methods for synthesis. Due to high electrical conductivity, MXene materials are showing extraordinary results in energy storage and related applications. Making a composite hydrothermal method is one of the established methods. This scientific paper underscores the significant contributions of MXenes in advancing green energy storage systems, paving the way for a sustainable future driven by renewable energy sources. To facilitate the research, this article includes technical challenges and future recommendations for further research gaps in the topic.

A Proteomic Analysis of Nasopharyngeal Carcinoma in a Moroccan Subpopulation

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a distinct cancer of the head and neck that is highly prevalent in Southeast Asia and North Africa. Though an extensive analysis of environmental and genetic contributors has been performed, very little is known about the proteome of this disease. A proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissues can provide valuable information on protein expression and molecular patterns for both increasing our understanding of the disease and for biomarker discovery. To date, very few NPC proteomic studies have been performed, and none focused on patients from Morocco and North Africa.

METHODS

Label-free Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) was used to perform a proteomic analysis of FFPE tissue samples from a cohort of 41 NPC tumor samples of Morocco and North Africa origins. The LC-MS/MS data from this cohort were analyzed alongside 21 healthy controls using MaxQuant 2.4.2.0. A differential expression analysis was performed using the MSstats package in R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotations were carried out using the DAVID bioinformatic tool.

RESULTS

3341 proteins were identified across our NPC cases, revealing three main clusters and five DEPs with prognostic significance. The sex disparity of NPC was investigated from a proteomic perspective in which 59 DEPs were found between males and females, with significantly enriched terms associated with the immune response and gene expression. Furthermore, 26 DEPs were observed between patients with early and advanced stages of NPC with a significant cluster related to the immune response, implicating up-regulated DEPs such as IGHA, IGKC, and VAT1. Across both datasets, 6532 proteins were quantified between NPC patients and healthy controls. Among them, 1507 differentially expressed proteins (DEPs) were observed. GO and KEGG pathway analyses showed enriched terms of DEPs related to increased cellular activity, cell proliferation, and survival. PI3K and MAPK proteins as well as RAC1 BCL2 and PPIA were found to be overexpressed between cancer tissues and healthy controls. EBV infection was also one of the enriched pathways implicating its latent genes like LMP1 and LMP2 that activate several proteins and signaling pathways including NF-Kappa B, MAPK, and JAK-STAT pathways.

CONCLUSION

Our findings unveil the proteomic landscape of NPC for the first time in the Moroccan population. These studies additionally may provide a foundation for identifying potential biomarkers. Further research is still needed to help develop tools for the early diagnosis and treatment of NPC in Moroccan and North African populations.

Fully defined NGN2 neuron protocol reveals diverse signatures of neuronal maturation

NGN2-driven induced pluripotent stem cell (iPSC)-to-neuron conversion is a popular method for human neurological disease modeling. In this study, we present a standardized approach for generating neurons utilizing clonal, targeted-engineered iPSC lines with defined reagents. We demonstrate consistent production of excitatory neurons at scale and long-term maintenance for at least 150 days. Temporal omics, electrophysiological, and morphological profiling indicate continued maturation to postnatal-like neurons. Quantitative characterizations through transcriptomic, imaging, and functional assays reveal coordinated actions of multiple pathways that drive neuronal maturation. We also show the expression of disease-related genes in these neurons to demonstrate the relevance of our protocol for modeling neurological disorders. Finally, we demonstrate efficient generation of NGN2-integrated iPSC lines. These workflows, profiling data, and functional characterizations enable the development of reproducible human in vitro models of neurological disorders.

Primate cerebrospinal fluid CHI3L1 reflects brain TREM2 agonism

INTRODUCTION

Triggering receptor expressed on myeloid cells 2 (TREM2) agonists are being clinically evaluated as disease-modifying therapeutics for Alzheimer's disease. Clinically translatable pharmacodynamic (PD) biomarkers are needed to confirm drug activity and select the appropriate therapeutic dose in clinical trials.

METHODS

We conducted multi-omic analyses on paired non-human primate brain and cerebrospinal fluid (CSF), and stimulation of human induced pluripotent stem cell-derived microglia cultures after TREM2 agonist treatment, followed by validation of candidate fluid PD biomarkers using immunoassays. We immunostained microglia to characterize proliferation and clustering.

RESULTS

We report CSF soluble TREM2 (sTREM2) and CSF chitinase-3-like protein 1 (CHI3L1/YKL-40) as PD biomarkers for the TREM2 agonist hPara.09. The respective reduction of sTREM2 and elevation of CHI3L1 in brain and CSF after TREM2 agonist treatment correlated with transient microglia proliferation and clustering.

DISCUSSION

CSF CHI3L1 and sTREM2 reflect microglial TREM2 agonism and can be used as clinical PD biomarkers to monitor TREM2 activity in the brain.

HIGHLIGHTS

CSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2) reflects brain target engagement for a novel TREM2 agonist, hPara.09. CSF chitinase-3-like protein 1 reflects microglial TREM2 agonism. Both can be used as clinical fluid biomarkers to monitor TREM2 activity in brain.

OmicScope unravels systems-level insights from quantitative proteomics data

Shotgun proteomics analysis presents multifaceted challenges, demanding diverse tool integration for insights. Addressing this complexity, OmicScope emerges as an innovative solution for quantitative proteomics data analysis. Engineered to handle various data formats, it performs data pre-processing - including joining replicates, normalization, data imputation - and conducts differential proteomics analysis for both static and longitudinal experimental designs. Empowered by Enrichr with over 224 databases, OmicScope performs Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). Additionally, its Nebula module facilitates meta-analysis from independent datasets, providing a systems biology approach for enriched insights. Complete with a data visualization toolkit and accessible as Python package and a web application, OmicScope democratizes proteomics analysis, offering an efficient and high-quality pipeline for researchers.

Increasing the Throughput and Reproducibility of Activity-Based Proteome Profiling Studies with Hyperplexing and Intelligent Data Acquisition

Intelligent data acquisition (IDA) strategies, such as a real-time database search (RTS), have improved the depth of proteome coverage for experiments that utilize isobaric labels and gas phase purification techniques (i.e., SPS-MS3). In this work, we introduce inSeqAPI, an instrument application programing interface (iAPI) program that enables construction of novel data acquisition algorithms. First, we analyze biotinylated cysteine peptides from ABPP experiments to demonstrate that a real-time search method within inSeqAPI performs similarly to an equivalent vendor method. Then, we describe PairQuant, a method within inSeqAPI designed for the hyperplexing approach that utilizes protein-level isotopic labeling and peptide-level TMT labeling. PairQuant allows for TMT analysis of 36 conditions in a single sample and achieves ∼98% coverage of both peptide pair partners in a hyperplexed experiment as well as a 40% improvement in the number of quantified cysteine sites compared with non-RTS acquisition. We applied this method in the ABPP study of ligandable cysteine sites in the nucleus leading to an identification of additional druggable sites on protein- and DNA-interaction domains of transcription regulators and on nuclear ubiquitin ligases.

I'm Walking into Spiderwebs: Making Sense of Protein-Protein Interaction Data

Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.

Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Trichomonas vaginalis extracellular vesicles up-regulate and directly transfer adherence factors promoting host cell colonization

Trichomonas vaginalis, a common sexually transmitted parasite that colonizes the human urogenital tract, secretes extracellular vesicles (TvEVs) that are taken up by human cells and are speculated to be taken up by parasites as well. While the crosstalk between TvEVs and human cells has led to insight into host:parasite interactions, roles for TvEVs in infection have largely been one-sided, with little known about the effect of TvEV uptake by T. vaginalis. Approximately 11% of infections are found to be coinfections of multiple T. vaginalis strains. Clinical isolates often differ in their adherence to and cytolysis of host cells, underscoring the importance of understanding the effects of TvEV uptake within the parasite population. To address this question, our lab tested the ability of a less adherent strain of T. vaginalis, G3, to take up fluorescently labeled TvEVs derived from both itself (G3-EVs) and TvEVs from a more adherent strain of the parasite (B7RC2-EVs). Here, we showed that TvEVs generated from the more adherent strain are internalized more efficiently compared to the less adherent strain. Additionally, preincubation of G3 parasites with B7RC2-EVs increases parasite aggregation and adherence to host cells. Transcriptomics revealed that TvEVs up-regulate expression of predicted parasite membrane proteins and identified an adherence factor, heteropolysaccharide binding protein (HPB2). Finally, using comparative proteomics and superresolution microscopy, we demonstrated direct transfer of an adherence factor, cadherin-like protein, from TvEVs to the recipient parasite's surface. This work identifies TvEVs as a mediator of parasite:parasite communication that may impact pathogenesis during mixed infections.

Top-down proteomics

Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.

Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems

Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport.

Spatiotemporal proteomic profiling of cellular responses to NLRP3 agonists

Nucleotide-binding domain and leucine-rich repeat pyrin-domain containing protein 3 (NLRP3) is an innate immune sensor that forms an inflammasome in response to various cellular stressors. Gain-of-function mutations in NLRP3 cause autoinflammatory diseases and NLRP3 signalling itself exacerbates the pathogenesis of many other human diseases. Despite considerable therapeutic interest, the primary drivers of NLRP3 activation remain controversial due to the diverse array of signals that are integrated through NLRP3. Here, we mapped subcellular proteome changes to lysosomes, mitochondrion, EEA1-positive endosomes, and Golgi caused by the NLRP3 inflammasome agonists nigericin and CL097. We identified several common disruptions to retrograde trafficking pathways, including COPI and Shiga toxin-related transport, in line with recent studies. We further characterized mouse NLRP3 trafficking throughout its activation using temporal proximity proteomics, which supports a recent model of NLRP3 recruitment to endosomes during inflammasome activation. Collectively, these findings provide additional granularity to our understanding of the molecular events driving NLRP3 activation and serve as a valuable resource for cell biological research. We have made our proteomics data accessible through an open-access Shiny browser to facilitate future research within the community, available at: https://harperlab.connect.hms.harvard.edu/inflame/. We will display anonymous peer review for this manuscript on pubpub.org (https://harperlab.pubpub.org/pub/nlrp3/) rather than a traditional journal. Moreover, we invite community feedback on the pubpub version of this manuscript, and we will address criticisms accordingly.

Fasting upregulates the monocarboxylate transporter MCT1 at the rat blood-brain barrier through PPAR δ activation

BACKGROUND

The blood-brain barrier (BBB) is pivotal for the maintenance of brain homeostasis and it strictly regulates the cerebral transport of a wide range of endogenous compounds and drugs. While fasting is increasingly recognized as a potential therapeutic intervention in neurology and psychiatry, its impact upon the BBB has not been studied. This study was designed to assess the global impact of fasting upon the repertoire of BBB transporters.

METHODS

We used a combination of in vivo and in vitro experiments to assess the response of the brain endothelium in male rats that were fed ad libitum or fasted for one to three days. Brain endothelial cells were acutely purified and transcriptionaly profiled using RNA-Seq. Isolated brain microvessels were used to assess the protein expression of selected BBB transporters through western blot. The molecular mechanisms involved in the adaptation to fasting were investigated in primary cultured rat brain endothelial cells. MCT1 activity was probed by in situ brain perfusion.

RESULTS

Fasting did not change the expression of the main drug efflux ATP-binding cassette transporters or P-glycoprotein activity at the BBB but modulated a restrictive set of solute carrier transporters. These included the ketone bodies transporter MCT1, which is pivotal for the brain adaptation to fasting. Our findings in vivo suggested that PPAR δ, a major lipid sensor, was selectively activated in brain endothelial cells in response to fasting. This was confirmed in vitro where pharmacological agonists and free fatty acids selectively activated PPAR δ, resulting in the upregulation of MCT1 expression. Moreover, dosing rats with a specific PPAR δ antagonist blocked the upregulation of MCT1 expression and activity induced by fasting.

CONCLUSIONS

Altogether, our study shows that fasting affects a selected set of BBB transporters which does not include the main drug efflux transporters. Moreover, we describe a previously unknown selective adaptive response of the brain vasculature to fasting which involves PPAR δ and is responsible for the up-regulation of MCT1 expression and activity. Our study opens new perspectives for the metabolic manipulation of the BBB in the healthy or diseased brain.

Proteomics applications in next generation induced pluripotent stem cell models

INTRODUCTION

Induced pluripotent stem (iPS) cell technology has transformed biomedical research. New opportunities now exist to create new organoids, microtissues, and body-on-a-chip systems for basic biology investigations and clinical translations.

AREAS COVERED

We discuss the utility of proteomics for attaining an unbiased view into protein expression changes during iPS cell differentiation, cell maturation, and tissue generation. The ability to discover cell-type specific protein markers during the differentiation and maturation of iPS-derived cells has led to new strategies to improve cell production yield and fidelity. In parallel, proteomic characterization of iPS-derived organoids is helping to realize the goal of bridging in vitro and in vivo systems.

EXPERT OPINIONS

We discuss some current challenges of proteomics in iPS cell research and future directions, including the integration of proteomic and transcriptomic data for systems-level analysis.

Ketone flux through BDH1 supports metabolic remodeling of skeletal and cardiac muscles in response to intermittent time-restricted feeding

Time-restricted feeding (TRF) has gained attention as a dietary regimen that promotes metabolic health. This study questioned if the health benefits of an intermittent TRF (iTRF) schedule require ketone flux specifically in skeletal and cardiac muscles. Notably, we found that the ketolytic enzyme beta-hydroxybutyrate dehydrogenase 1 (BDH1) is uniquely enriched in isolated mitochondria derived from heart and red/oxidative skeletal muscles, which also have high capacity for fatty acid oxidation (FAO). Using mice with BDH1 deficiency in striated muscles, we discover that this enzyme optimizes FAO efficiency and exercise tolerance during acute fasting. Additionally, iTRF leads to robust molecular remodeling of muscle tissues, and muscle BDH1 flux does indeed play an essential role in conferring the full adaptive benefits of this regimen, including increased lean mass, mitochondrial hormesis, and metabolic rerouting of pyruvate. In sum, ketone flux enhances mitochondrial bioenergetics and supports iTRF-induced remodeling of skeletal muscle and heart.

VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) is a Bioinformatics Resource Center funded by the National Institutes of Health with additional funding from the Wellcome Trust. VEuPathDB supports >600 organisms that comprise invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Since 2004, VEuPathDB has analyzed omics data from the public domain using contemporary bioinformatic workflows, including orthology predictions via OrthoMCL, and integrated the analysis results with analysis tools, visualizations, and advanced search capabilities. The unique data mining platform coupled with >3000 pre-analyzed data sets facilitates the exploration of pertinent omics data in support of hypothesis driven research. Comparisons are easily made across data sets, data types and organisms. A Galaxy workspace offers the opportunity for the analysis of private large-scale datasets and for porting to VEuPathDB for comparisons with integrated data. The MapVEu tool provides a platform for exploration of spatially resolved data such as vector surveillance and insecticide resistance monitoring. To address the growing body of omics data and advances in laboratory techniques, VEuPathDB has added several new data types, searches and features, improved the Galaxy workspace environment, redesigned the MapVEu interface and updated the infrastructure to accommodate these changes.

Interactors and effects of overexpressing YlxR/RnpM, a conserved RNA binding protein in cyanobacteria

Throughout the tree of life RNA-binding proteins play important roles, but they are poorly characterized in cyanobacteria. Overexpression of the predicted RNA-binding protein Ssr1238 in the cyanobacterium Synechocystis 6803 for 24 h led to higher levels of RNase P RNA, tRNAs, and stress-related mRNAs. Co-immunoprecipitation of proteins followed by MS analysis and sequencing of UV crosslinked, co-immunoprecipitated RNA samples identified potential interaction partners of Ssr1238. The most enriched transcript was RNase P RNA, and RnpA, the protein component of RNase P, was among the most highly enriched proteins. A second highly enriched transcript is derived from gene ssl3177, which encodes a central enzyme in cell wall remodelling during cell division. The data also showed a strong connection to the RNA maturation and modification system indicated by co-precipitation of RNA modifying enzymes, riboendonuclease E and enolase. Surprisingly, cyanophycin synthetase and urease were highly enriched as well. In conclusion, Ssr1238 specifically binds to two different transcripts and could be involved in the coordination of RNA maturation, translation, cell division, and aspects of nitrogen metabolism. Our results are consistent with recent findings that the B. subtilis YlxR protein functions as an RNase P modulator (RnpM), extending its proposed role to the phylum cyanobacteria, and suggesting additional functionalities.

The proteomic landscape of synaptic diversity across brain regions and cell types

Neurons build synaptic contacts using different protein combinations that define the specificity, function, and plasticity potential of synapses; however, the diversity of synaptic proteomes remains largely unexplored. We prepared synaptosomes from 7 different transgenic mouse lines with fluorescently labeled presynaptic terminals. Combining microdissection of 5 different brain regions with fluorescent-activated synaptosome sorting (FASS), we isolated and analyzed the proteomes of 18 different synapse types. We discovered ∼1,800 unique synapse-type-enriched proteins and allocated thousands of proteins to different types of synapses (https://syndive.org/). We identify shared synaptic protein modules and highlight the proteomic hotspots for synapse specialization. We reveal unique and common features of the striatal dopaminergic proteome and discover the proteome signatures that relate to the functional properties of different interneuron classes. This study provides a molecular systems-biology analysis of synapses and a framework to integrate proteomic information for synapse subtypes of interest with cellular or circuit-level experiments.

Evaluating Proteomics Imputation Methods with Improved Criteria

Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, "downstream-centric" criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization

BACKGROUND

The analysis of mass spectrometry-based quantitative proteomics data can be challenging given the variety of established analysis platforms, the differences in reporting formats, and a general lack of approachable standardized post-processing analyses such as sample group statistics, quantitative variation and even data filtering. We developed tidyproteomics to facilitate basic analysis, improve data interoperability and potentially ease the integration of new processing algorithms, mainly through the use of a simplified data-object.

RESULTS

The R package tidyproteomics was developed as both a framework for standardizing quantitative proteomics data and a platform for analysis workflows, containing discrete functions that can be connected end-to-end, thus making it easier to define complex analyses by breaking them into small stepwise units. Additionally, as with any analysis workflow, choices made during analysis can have large impacts on the results and as such, tidyproteomics allows researchers to string each function together in any order, select from a variety of options and in some cases develop and incorporate custom algorithms.

CONCLUSIONS

Tidyproteomics aims to simplify data exploration from multiple platforms, provide control over individual functions and analysis order, and serve as a tool to assemble complex repeatable processing workflows in a logical flow. Datasets in tidyproteomics are easy to work with, have a structure that allows for biological annotations to be added, and come with a framework for developing additional analysis tools. The consistent data structure and accessible analysis and plotting tools also offers a way for researchers to save time on mundane data manipulation tasks.