Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows

MS based foodomics: An edge tool integrated metabolomics and proteomics for food science

Foodomics has become a popular methodology in food science studies. Mass spectrometry (MS) based metabolomics and proteomics analysis played indispensable roles in foodomics research. So far, several methodologies have been developed to detect the metabolites and proteins in diets and consumers, including sample preparation, MS data acquisition, annotation and interpretation. Moreover, multiomics analysis integrated metabolomics and proteomics have received considerable attentions in the field of food safety and nutrition, because of more comprehensive and deeply. In this context, we intended to review the emerging strategies and their applications in MS-based foodomics, as well as future challenges and trends. The principle and application of multiomics were also discussed, such as the optimization of data acquisition, development of analysis algorithm and exploration of systems biology.

Integrated proteogenomic analysis for inherited bone marrow failure syndrome

Recent advances in in-depth data-independent acquisition proteomic analysis have enabled comprehensive quantitative analysis of >10,000 proteins. Herein, an integrated proteogenomic analysis for inherited bone marrow failure syndrome (IBMFS) was performed to reveal their biological features and to develop a proteomic-based diagnostic assay in the discovery cohort; dyskeratosis congenita (n = 12), Fanconi anemia (n = 11), Diamond-Blackfan anemia (DBA, n = 9), Shwachman-Diamond syndrome (SDS, n = 6), ADH5/ALDH2 deficiency (n = 4), and other IBMFS (n = 18). Unsupervised proteomic clustering identified eight independent clusters (C1-C8), with the ribosomal pathway specifically downregulated in C1 and C2, enriched for DBA and SDS, respectively. Six patients with SDS had significantly decreased SBDS protein expression, with two of these not diagnosed by DNA sequencing alone. Four patients with ADH5/ALDH2 deficiency showed significantly reduced ADH5 protein expression. To perform a large-scale rapid IBMFS screening, targeted proteomic analysis was performed on 417 samples from patients with IBMFS-related hematological disorders (n = 390) and healthy controls (n = 27). SBDS and ADH5 protein expressions were significantly reduced in SDS and ADH5/ALDH2 deficiency, respectively. The clinical application of this first integrated proteogenomic analysis would be useful for the diagnosis and screening of IBMFS, where appropriate clinical screening tests are lacking.

Data Independent Acquisition to Inform the Development of Targeted Proteomics Assays Using a Triple Quadrupole Mass Spectrometer

Mass spectrometry based targeted proteomics methods provide sensitive and high-throughput analysis of selected proteins. To develop a targeted bottom-up proteomics assay, peptides must be evaluated as proxies for the measurement of a protein or proteoform in a biological matrix. Candidate peptide selection typically relies on predetermined biochemical properties, data from semi-stochastic sampling, or by empirical measurements. These strategies require extensive testing and method refinement due to the difficulties associated with prediction of peptide response in the biological matrix of interest. Gas-phase fractionated (GPF) narrow window data-independent acquisition (DIA) aids in the development of reproducible selected reaction monitoring (SRM) assays by providing matrix-specific information on peptide detectability and quantification by mass spectrometry. To demonstrate the suitability of DIA data for selecting peptide targets, we reimplement a portion of an existing assay to measure 98 Alzheimer's disease proteins in cerebrospinal fluid (CSF). Peptides were selected from GPF-DIA based on signal intensity and reproducibility. The resulting SRM assay exhibits similar quantitative precision to published data, despite the inclusion of different peptides between the assays. This workflow enables development of new assays without additional up-front data acquisition, demonstrated here through generation of a separate assay for an unrelated set of proteins in CSF from the same dataset.

Environment modulates protein heterogeneity through transcriptional and translational stop codon readthrough

Stop codon readthrough events give rise to longer proteins, which may alter the protein's function, thereby generating short-lasting phenotypic variability from a single gene. In order to systematically assess the frequency and origin of stop codon readthrough events, we designed a library of reporters. We introduced premature stop codons into mScarlet, which enabled high-throughput quantification of protein synthesis termination errors in E. coli using fluorescent microscopy. We found that under stress conditions, stop codon readthrough may occur at rates as high as 80%, depending on the nucleotide context, suggesting that evolution frequently samples stop codon readthrough events. The analysis of selected reporters by mass spectrometry and RNA-seq showed that not only translation but also transcription errors contribute to stop codon readthrough. The RNA polymerase was more likely to misincorporate a nucleotide at premature stop codons. Proteome-wide detection of stop codon readthrough by mass spectrometry revealed that temperature regulated the expression of cryptic sequences generated by stop codon readthrough in E. coli. Overall, our findings suggest that the environment affects the accuracy of protein production, which increases protein heterogeneity when the organisms need to adapt to new conditions.

Galectin-10 in serum extracellular vesicles reflects asthma pathophysiology

BACKGROUND

Novel biomarkers (BMs) are urgently needed for bronchial asthma (BA) with various phenotypes and endotypes.

OBJECTIVE

We sought to identify novel BMs reflecting tissue pathology from serum extracellular vesicles (EVs).

METHODS

We performed data-independent acquisition of serum EVs from 4 healthy controls, 4 noneosinophilic asthma (NEA) patients, and 4 eosinophilic asthma (EA) patients to identify novel BMs for BA. We confirmed EA-specific BMs via data-independent acquisition validation in 61 BA patients and 23 controls. To further validate these findings, we performed data-independent acquisition for 6 patients with chronic rhinosinusitis without nasal polyps and 7 patients with chronic rhinosinusitis with nasal polyps.

RESULTS

We identified 3032 proteins, 23 of which exhibited differential expression in EA. Ingenuity pathway analysis revealed that protein signatures from each phenotype reflected disease characteristics. Validation revealed 5 EA-specific BMs, including galectin-10 (Gal10), eosinophil peroxidase, major basic protein, eosinophil-derived neurotoxin, and arachidonate 15-lipoxygenase. The potential of Gal10 in EVs was superior to that of eosinophils in terms of diagnostic capability and detection of airway obstruction. In rhinosinusitis patients, 1752 and 8413 proteins were identified from EVs and tissues, respectively. Among 11 BMs identified in EVs and tissues from patients with chronic rhinosinusitis with nasal polyps, 5 (including Gal10 and eosinophil peroxidase) showed significant correlations between EVs and tissues. Gal10 release from EVs was implicated in eosinophil extracellular trapped cell death in vitro and in vivo.

CONCLUSION

Novel BMs such as Gal10 from serum EVs reflect disease pathophysiology in BA and may represent a new target for liquid biopsy approaches.

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.

Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome

Membrane-bound particles in plasma are composed of exosomes, microvesicles, and apoptotic bodies and represent ~1-2% of the total protein composition. Proteomic interrogation of this subset of plasma proteins augments the representation of tissue-specific proteins, representing a "liquid biopsy," while enabling the detection of proteins that would otherwise be beyond the dynamic range of liquid chromatography-tandem mass spectrometry of unfractionated plasma. We have developed an enrichment strategy (Mag-Net) using hyper-porous strong-anion exchange magnetic microparticles to sieve membrane-bound particles from plasma. The Mag-Net method is robust, reproducible, inexpensive, and requires <100 μL plasma input. Coupled to a quantitative data-independent mass spectrometry analytical strategy, we demonstrate that we can collect results for >37,000 peptides from >4,000 plasma proteins with high precision. Using this analytical pipeline on a small cohort of patients with neurodegenerative disease and healthy age-matched controls, we discovered 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer's disease dementia (ADD) from those without ADD. Our method also discovered 310 proteins that were different between Parkinson's disease and those with either ADD or healthy cognitively normal individuals. Using machine learning we were able to distinguish between ADD and not ADD with a mean ROC AUC = 0.98 ± 0.06.

LC-MS/DIA-based strategy for comprehensive flavonoid profiling: an Ocotea spp. applicability case

We introduce a liquid chromatography - mass spectrometry with data-independent acquisition (LC-MS/DIA)-based strategy, specifically tailored to achieve comprehensive and reliable glycosylated flavonoid profiling. This approach facilitates in-depth and simultaneous exploration of all detected precursors and fragments during data processing, employing the widely-used open-source MZmine 3 software. It was applied to a dataset of six Ocotea plant species. This framework suggested 49 flavonoids potentially newly described for these plant species, alongside 45 known features within the genus. Flavonols kaempferol and quercetin, both exhibiting O-glycosylation patterns, were particularly prevalent. Gas-phase fragmentation reactions further supported these findings. For the first time, the apigenin flavone backbone was also annotated in most of the examined Ocotea species. Apigenin derivatives were found mainly in the C-glycoside form, with O. porosa displaying the highest flavone : flavonol ratio. The approach also allowed an unprecedented detection of kaempferol and quercetin in O. porosa species, and it has underscored the untapped potential of LC-MS/DIA data for broad and reliable flavonoid profiling. Our study annotated more than 50 flavonoid backbones in each species, surpassing the current literature.

Trans-omic analysis reveals opposite metabolic dysregulation between feeding and fasting in liver associated with obesity

Dysregulation of liver metabolism associated with obesity during feeding and fasting leads to the breakdown of metabolic homeostasis. However, the underlying mechanism remains unknown. Here, we measured multi-omics data in the liver of wild-type and leptin-deficient obese (ob/ob) mice at ad libitum feeding and constructed a differential regulatory trans-omic network of metabolic reactions. We compared the trans-omic network at feeding with that at 16 h fasting constructed in our previous study. Intermediate metabolites in glycolytic and nucleotide metabolism decreased in ob/ob mice at feeding but increased at fasting. Allosteric regulation reversely shifted between feeding and fasting, generally showing activation at feeding while inhibition at fasting in ob/ob mice. Transcriptional regulation was similar between feeding and fasting, generally showing inhibiting transcription factor regulations and activating enzyme protein regulations in ob/ob mice. The opposite metabolic dysregulation between feeding and fasting characterizes breakdown of metabolic homeostasis associated with obesity.

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme

In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

L-type amino acid transporter 1 inhibitor JPH203 prevents the growth of cabazitaxel-resistant prostate cancer by inhibiting cyclin-dependent kinase activity

L-type amino acid transporter 1 (LAT1, SLC7A5) is an amino acid transporter expressed in various carcinomas, and it is postulated to play an important role in the proliferation of cancer cells through the uptake of essential amino acids. Cabazitaxel is a widely used anticancer drug for treating castration-resistant prostate cancer (CRPC); however, its effectiveness is lost when cancer cells acquire drug resistance. In this study, we investigated the expression of LAT1 and the effects of a LAT1-specific inhibitor, JPH203, in cabazitaxel-resistant prostate cancer cells. LAT1 was more highly expressed in the cabazitaxel-resistant strains than in the normal strains. Administration of JPH203 inhibited the growth, migration, and invasive ability of cabazitaxel-resistant strains in vitro. Phosphoproteomics using liquid chromatography-mass spectrometry to comprehensively investigate changes in phosphorylation due to JPH203 administration revealed that cell cycle-related pathways were affected by JPH203, and that JPH203 significantly reduced the kinase activity of cyclin-dependent kinases 1 and 2. Moreover, JPH203 inhibited the proliferation of cabazitaxel-resistant cells in vivo. Taken together, the present study results suggest that LAT1 might be a valuable therapeutic target in cabazitaxel-resistant prostate cancer.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics

We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition

Mass spectrometry (MS)-based proteomics aims to characterize comprehensive proteomes in a fast and reproducible manner. Here we present the narrow-window data-independent acquisition (nDIA) strategy consisting of high-resolution MS1 scans with parallel tandem MS (MS/MS) scans of ~200 Hz using 2-Th isolation windows, dissolving the differences between data-dependent and -independent methods. This is achieved by pairing a quadrupole Orbitrap mass spectrometer with the asymmetric track lossless (Astral) analyzer which provides >200-Hz MS/MS scanning speed, high resolving power and sensitivity, and low-ppm mass accuracy. The nDIA strategy enables profiling of >100 full yeast proteomes per day, or 48 human proteomes per day at the depth of ~10,000 human protein groups in half-an-hour or ~7,000 proteins in 5 min, representing 3× higher coverage compared with current state-of-the-art MS. Multi-shot acquisition of offline fractionated samples provides comprehensive coverage of human proteomes in ~3 h. High quantitative precision and accuracy are demonstrated in a three-species proteome mixture, quantifying 14,000+ protein groups in a single half-an-hour run.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Hippocampus Glutathione S Reductase Potentially Confers Genetic Resilience to Cognitive Decline in the AD-BXD Mouse Population

Alzheimer's disease (AD) is a prevalent and costly age-related dementia. Heritable factors account for 58-79% of variation in late-onset AD, but substantial variation remains in age-of- onset, disease severity, and whether those with high-risk genotypes acquire AD. To emulate the diversity of human populations, we utilized the AD-BXD mouse panel. This genetically diverse resource combines AD genotypes with multiple BXD strains to discover new genetic drivers of AD resilience. Comparing AD-BXD carriers to noncarrier littermates, we computed a novel quantitative metric for resilience to cognitive decline in the AD-BXDs. Our quantitative AD resilience trait was heritable and genetic mapping identified a locus on chr8 associated with resilience to AD mutations that resulted in amyloid brain pathology. Using a hippocampus proteomics dataset, we nominated the mitochondrial glutathione S reductase protein (GR or GSHR) as a resilience factor, finding that the DBA/2J genotype was associated with substantially higher GR abundance. By mapping protein QTLs (pQTLs), we identified synaptic organization and mitochondrial proteins coregulated in trans with a cis-pQTL for GR. We found four coexpression modules correlated with the quantitative resilience score in aged 5XFAD mice using paracliques, which were related to cell structure, protein folding, and postsynaptic densities. Finally, we found significant positive associations between human GSR transcript abundance in the brain and better outcomes on AD-related cognitive and pathology traits in the Religious Orders Study/Memory and Aging project (ROSMAP). Taken together, these data support a framework for resilience in which neuronal antioxidant pathway activity provides for stability of synapses within the hippocampus.

Single extraction and integrated non-target data acquisition with data mining workflow for analysis of hazardous substances in agricultural plant products

Identifying contaminants in agricultural plant food products (APFPs) is a major problem. In this study, we developed a single-step extraction and integrated non-target data acquisition (INDA) workflow for increasing hazardous substances coverage. D-optimal experimental designs were applied to optimize filter plate extraction (FPE) for one-single extraction of multipolar hazardous substances. The vDIA mode was used to collect all precursor ion fragments within the range to supplement data loss caused by DDA mode. The underlying principle of vDIA is to increase the utilization rate of MS2 spectra that are likely to identify a maximum number and minimum amounts of hazardous substances. Compared with traditional DDA mode alone, a combination of the two modes increased the rate of identification of hazardous substances by 18.5%. The molecular network of hazardous substance provided by GNPS could enable some metabolites and structure-related products to discover potentially hazardous substance.

Skeletal muscle TFEB signaling promotes central nervous system function and reduces neuroinflammation during aging and neurodegenerative disease

Skeletal muscle has recently arisen as a regulator of central nervous system (CNS) function and aging, secreting bioactive molecules known as myokines with metabolism-modifying functions in targeted tissues, including the CNS. Here, we report the generation of a transgenic mouse with enhanced skeletal muscle lysosomal and mitochondrial function via targeted overexpression of transcription factor E-B (TFEB). We discovered that the resulting geroprotective effects in skeletal muscle reduce neuroinflammation and the accumulation of tau-associated pathological hallmarks in a mouse model of tauopathy. Muscle-specific TFEB overexpression significantly ameliorates proteotoxicity, reduces neuroinflammation, and promotes transcriptional remodeling of the aged CNS, preserving cognition and memory in aged mice. Our results implicate the maintenance of skeletal muscle function throughout aging in direct regulation of CNS health and disease and suggest that skeletal muscle originating factors may act as therapeutic targets against age-associated neurodegenerative disorders.

An Optimized and High-Throughput Method for Histone Propionylation and Data-Independent Acquisition Analysis for the Identification and Quantification of Histone Post-translational Modifications

Histones are DNA binding proteins that allow for packaging of the DNA into the nucleus. They are abundantly present across the genome and thus serve as a major site of epigenetic regulation through the use of post-translational modifications (PTMs). Aberrations in histone expression and modifications have been implicated in a variety of human diseases and thus are a major focus of disease etiology studies. A well-established method for studying histones and PTMs is through the chemical derivatization of isolated histones followed by liquid chromatography and mass spectrometry analysis. Using such an approach has allowed for a swath of discoveries to be found, leading to novel therapeutics such as histone deacetylase (HDAC) inhibitors that have already been applied in the clinic. However, with the rapid improvement in instrumentation and data analysis pipelines, it remains important to temporally re-evaluate the established protocols to improve throughput and ensure data quality. Here, we optimized the histone derivatization procedure to increase sample throughput without compromising peptide quantification. An implemented spike-in standard peptide further serves as a quality control to evaluate the propionylation and digestion efficiencies as well as reproducibility in chromatographic retention and separation. Last, the application of various data-independent acquisition (DIA) strategies was explored to ensure low variation between runs. The output of this study is a newly optimized derivatization protocol and mass spectrometry method that maintains high identification and quantification of histone PTMs while increasing sample throughput.

Elucidating the cellular determinants of targeted membrane protein degradation by lysosome-targeting chimeras

Targeted protein degradation can provide advantages over inhibition approaches in the development of therapeutic strategies. Lysosome-targeting chimeras (LYTACs) harness receptors, such as the cation-independent mannose 6-phosphate receptor (CI-M6PR), to direct extracellular proteins to lysosomes. In this work, we used a genome-wide CRISPR knockout approach to identify modulators of LYTAC-mediated membrane protein degradation in human cells. We found that disrupting retromer genes improved target degradation by reducing LYTAC recycling to the plasma membrane. Neddylated cullin-3 facilitated LYTAC-complex lysosomal maturation and was a predictive marker for LYTAC efficacy. A substantial fraction of cell surface CI-M6PR remains occupied by endogenous M6P-modified glycoproteins. Thus, inhibition of M6P biosynthesis increased the internalization of LYTAC-target complexes. Our findings inform design strategies for next-generation LYTACs and elucidate aspects of cell surface receptor occupancy and trafficking.

Translating Senotherapeutic Interventions into the Clinic with Emerging Proteomic Technologies

Cellular senescence is a state of irreversible growth arrest with profound phenotypic changes, including the senescence-associated secretory phenotype (SASP). Senescent cell accumulation contributes to aging and many pathologies including chronic inflammation, type 2 diabetes, cancer, and neurodegeneration. Targeted removal of senescent cells in preclinical models promotes health and longevity, suggesting that the selective elimination of senescent cells is a promising therapeutic approach for mitigating a myriad of age-related pathologies in humans. However, moving senescence-targeting drugs (senotherapeutics) into the clinic will require therapeutic targets and biomarkers, fueled by an improved understanding of the complex and dynamic biology of senescent cell populations and their molecular profiles, as well as the mechanisms underlying the emergence and maintenance of senescence cells and the SASP. Advances in mass spectrometry-based proteomic technologies and workflows have the potential to address these needs. Here, we review the state of translational senescence research and how proteomic approaches have added to our knowledge of senescence biology to date. Further, we lay out a roadmap from fundamental biological discovery to the clinical translation of senotherapeutic approaches through the development and application of emerging proteomic technologies, including targeted and untargeted proteomic approaches, bottom-up and top-down methods, stability proteomics, and surfaceomics. These technologies are integral for probing the cellular composition and dynamics of senescent cells and, ultimately, the development of senotype-specific biomarkers and senotherapeutics (senolytics and senomorphics). This review aims to highlight emerging areas and applications of proteomics that will aid in exploring new senescent cell biology and the future translation of senotherapeutics.

A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.

nf-encyclopedia: A Cloud-Ready Pipeline for Chromatogram Library Data-Independent Acquisition Proteomics Workflows

Data-independent acquisition (DIA) mass spectrometry methods provide systematic and comprehensive quantification of the proteome; yet, relatively few open-source tools are available to analyze DIA proteomics experiments. Fewer still are tools that can leverage gas phase fractionated (GPF) chromatogram libraries to enhance the detection and quantification of peptides in these experiments. Here, we present nf-encyclopedia, an open-source NextFlow pipeline that connects three open-source tools, MSConvert, EncyclopeDIA, and MSstats, to analyze DIA proteomics experiments with or without chromatogram libraries. We demonstrate that nf-encyclopedia is reproducible when run on either a cloud platform or a local workstation and provides robust peptide and protein quantification. Additionally, we found that MSstats enhances protein-level quantitative performance over EncyclopeDIA alone. Finally, we benchmarked the ability of nf-encyclopedia to scale to large experiments in the cloud by leveraging the parallelization of compute resources. The nf-encyclopedia pipeline is available under a permissive Apache 2.0 license; run it on your desktop, cluster, or in the cloud: https://github.com/TalusBio/nf-encyclopedia.

MetaboMSDIA: A tool for implementing data-independent acquisition in metabolomic-based mass spectrometry analysis

Data-dependent acquisition (DDA) is the most widely used mode in untargeted metabolomic analysis despite its limited tandem mass spectrometry (MS2) detection coverage. We present MetaboMSDIA for complete processing of data-independent acquisition (DIA) files by the extraction of multiplexed MS2 spectra and further identification of metabolites in open libraries. In the analysis of polar extracts from lemon and olive fruits, DIA allows one to obtain multiplexed MS2 spectra for 100% of precursor ions compared to 64% of precursor ions from average MS2 acquisition in DDA. MetaboMSDIA is compatible with MS2 repositories and homemade libraries prepared by analysis of standards. An additional option is based on filtering molecular entities by searching for selective fragmentation patterns according to selective neutral losses or product ions to target the annotation of families of metabolites. Combining both options, the applicability of MetaboMSDIA was tested by annotating 50 and 35 metabolites in polar extracts from lemon and olive fruit, respectively. MetaboMSDIA is particularly proposed to increase the acquisition coverage in untargeted metabolomics and to improve spectral quality, which are two critical pillars for the tentative annotation of metabolites. The R script used in MetaboMSDIA workflow is available at github repository (https://github.com/MonicaCalSan/MetaboMSDIA).

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Organism-wide, cell-type-specific secretome mapping of exercise training in mice

There is a significant interest in identifying blood-borne factors that mediate tissue crosstalk and function as molecular effectors of physical activity. Although past studies have focused on an individual molecule or cell type, the organism-wide secretome response to physical activity has not been evaluated. Here, we use a cell-type-specific proteomic approach to generate a 21-cell-type, 10-tissue map of exercise training-regulated secretomes in mice. Our dataset identifies >200 exercise training-regulated cell-type-secreted protein pairs, the majority of which have not been previously reported. Pdgfra-cre-labeled secretomes were the most responsive to exercise training. Finally, we show anti-obesity, anti-diabetic, and exercise performance-enhancing activities for proteoforms of intracellular carboxylesterases whose secretion from the liver is induced by exercise training.

Strategies for consistent and automated quantification of HDL proteome using data-independent acquisition (DIA)

The introduction of mass spectrometry-based proteomics has revolutionized HDL field, with the description, characterization and implication of HDL-associated proteins in an array of pathologies. However, acquiring robust, reproducible data is still a challenge in the quantitative assessment of HDL proteome. Data-independent acquisition (DIA) is a mass spectrometry methodology that allows the acquisition of reproducible data, but data analysis remains a challenge in the field. Up to date, there is no consensus in how to process DIA-derived data for HDL proteomics. Here, we developed a pipeline aiming to standardize HDL proteome quantification. We optimized instrument parameters, and compared the performance of four freely available, user-friendly software tools (DIA-NN, EncyclopeDIA, MaxDIA and Skyline) in processing DIA data. Importantly, pooled samples were used as quality controls throughout our experimental setup. A carefully evaluation of precision, linearity, and detection limits, first using E. coli background for HDL proteomics, and second using HDL proteome and synthetic peptides, was undertaken. Finally, as a proof of concept, we employed our optimized and automated pipeline to quantify the proteome of HDL and apolipoprotein B (APOB)-containing lipoproteins. Our results show that determination of precision is key to confidently and consistently quantify HDL proteins. Taking this precaution, any of the available software tested here would be appropriate for quantification of HDL proteome, although their performance varied considerably.

Brain proteomic analysis implicates actin filament processes and injury response in resilience to Alzheimer's disease

Resilience to Alzheimer's disease is an uncommon combination of high disease burden without dementia that offers valuable insights into limiting clinical impact. Here we assessed 43 research participants meeting stringent criteria, 11 healthy controls, 12 resilience to Alzheimer's disease and 20 Alzheimer's disease with dementia and analyzed matched isocortical regions, hippocampus, and caudate nucleus by mass spectrometry-based proteomics. Of 7115 differentially expressed soluble proteins, lower isocortical and hippocampal soluble Aβ levels is a significant feature of resilience when compared to healthy control and Alzheimer's disease dementia groups. Protein co-expression analysis reveals 181 densely-interacting proteins significantly associated with resilience that were enriched for actin filament-based processes, cellular detoxification, and wound healing in isocortex and hippocampus, further supported by four validation cohorts. Our results suggest that lowering soluble Aβ concentration may suppress severe cognitive impairment along the Alzheimer's disease continuum. The molecular basis of resilience likely holds important therapeutic insights.

A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer's disease

Alzheimer's disease (AD) is a looming public health disaster with limited interventions. Alzheimer's is a complex disease that can present with or without causative mutations and can be accompanied by a range of age-related comorbidities. This diverse presentation makes it difficult to study molecular changes specific to AD. To better understand the molecular signatures of disease we constructed a unique human brain sample cohort inclusive of autosomal dominant AD dementia (ADD), sporadic ADD, and those without dementia but with high AD histopathologic burden, and cognitively normal individuals with no/minimal AD histopathologic burden. All samples are clinically well characterized, and brain tissue was preserved postmortem by rapid autopsy. Samples from four brain regions were processed and analyzed by data-independent acquisition LC-MS/MS. Here we present a high-quality quantitative dataset at the peptide and protein level for each brain region. Multiple internal and external control strategies were included in this experiment to ensure data quality. All data are deposited in the ProteomeXchange repositories and available from each step of our processing.

Global Discovery and Temporal Changes of Human Albumin Modifications by Pan-Protein Adductomics: Initial Application to Air Pollution Exposure

Assessing personal exposure to environmental toxicants is a critical challenge for predicting disease risk. Previously, using human serum albumin (HSA)-based biomonitoring, we reported dosimetric relationships between adducts at HSA Cys³⁴ and ambient air pollutant levels (Smith et al., Chem. Res. Toxicol. 2021, 34, 1183). These results provided the foundation to explore modifications at other sites in HSA to reveal novel adducts of complex exposures. Thus, the Pan-Protein Adductomics (PPA) technology reported here is the next step toward an unbiased, comprehensive characterization of the HSA adductome. The PPA workflow requires <2 μL serum/plasma and uses nanoflow-liquid chromatography, gas-phase fractionation, and overlapping-window data-independent acquisition high-resolution tandem mass spectrometry. PPA analysis of albumin from nonsmoking women exposed to high levels of air pollution uncovered 68 unique location-specific modifications (LSMs) across 21 HSA residues. While nearly half were located at Cys³⁴ (33 LSMs), 35 were detected on other residues, including Lys, His, Tyr, Ser, Met, and Arg. HSA adduct relative abundances spanned a ∼400 000-fold range and included putative products of exogenous (SO₂, benzene, phycoerythrobilin) and endogenous (oxidation, lipid peroxidation, glycation, carbamylation) origin, as well as 24 modifications without annotations. PPA quantification revealed statistically significant changes in LSM levels across the 84 days of monitoring (∼3 HSA lifetimes) in the following putative adducts: Cys³⁴ trioxidation, β-methylthiolation, benzaldehyde, and benzene diol epoxide; Met³²⁹ oxidation; Arg¹⁴⁵ dioxidation; and unannotated Cys³⁴ and His¹⁴⁶ adducts. Notably, the PPA workflow can be extended to any protein. Pan-Protein Adductomics is a novel and powerful strategy for untargeted global exploration of protein modifications.

Data-independent acquisition phosphoproteomics of urinary extracellular vesicles enables renal cell carcinoma grade differentiation

Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated (GPF) library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease (CKD), and healthy control (HC) individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2,584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications.

Profiling mouse brown and white adipocytes to identify metabolically relevant small ORFs and functional microproteins

Microproteins (MPs) are a potentially rich source of uncharacterized metabolic regulators. Here, we use ribosome profiling (Ribo-seq) to curate 3,877 unannotated MP-encoding small ORFs (smORFs) in primary brown, white, and beige mouse adipocytes. Of these, we validated 85 MPs by proteomics, including 33 circulating MPs in mouse plasma. Analyses of MP-encoding mRNAs under different physiological conditions (high-fat diet) revealed that numerous MPs are regulated in adipose tissue in vivo and are co-expressed with established metabolic genes. Furthermore, Ribo-seq provided evidence for the translation of Gm8773, which encodes a secreted MP that is homologous to human and chicken FAM237B. Gm8773 is highly expressed in the arcuate nucleus of the hypothalamus, and intracerebroventricular administration of recombinant mFAM237B showed orexigenic activity in obese mice. Together, these data highlight the value of this adipocyte MP database in identifying MPs with roles in fundamental metabolic and physiological processes such as feeding.

Transcriptome and proteome analysis of dogs with precursor targeted immune-mediated anemia treated with splenectomy

Precursor-targeted immune-mediated anemia (PIMA) in dogs is characterized by persistent non-regenerative anemia and ineffective erythropoiesis, and it is suspected to be an immune-mediated disease. Most affected dogs respond to immunosuppressive therapies; however, some are resistant. In this study, we carried out splenectomy as an alternative therapy for refractory PIMA in dogs, and analyzed gene expression levels in the spleen of dogs with or without PIMA and in serum before and after splenectomy. A total of 1,385 genes were found to express differentially in the spleens from dogs with PIMA compared with healthy dogs by transcriptome analysis, of which 707 genes were up-regulated, including S100A12, S100A8, and S100A9 that are linked directly to the innate immune system and have been characterized as endogenous damage-associated molecular patterns. Furthermore, immunohistochemistry confirmed that S100A8/A9 protein expression levels were significantly higher in dogs with PIMA compared with those in healthy dogs. A total of 22 proteins were found to express differentially between the serum samples collected before and after splenectomy by proteome analysis, of which 12 proteins were up-regulated in the samples before. The lectin pathway of complement activation was identified by pathway analysis in pre-splenectomy samples. We speculated that S100A8/9 expression may be increased in the spleen of dogs with PIMA, resulting in activation of the lectin pathway before splenectomy. These findings further our understanding of the pathology and mechanisms of splenectomy for PIMA.

Identification of trypsin-degrading commensals in the large intestine

Increased levels of proteases, such as trypsin, in the distal intestine have been implicated in intestinal pathological conditions^1–3. However, the players and mechanisms that underlie protease regulation in the intestinal lumen have remained unclear. Here we show that Paraprevotella strains isolated from the faecal microbiome of healthy human donors are potent trypsin-degrading commensals. Mechanistically, Paraprevotella recruit trypsin to the bacterial surface through type IX secretion system-dependent polysaccharide-anchoring proteins to promote trypsin autolysis. Paraprevotella colonization protects IgA from trypsin degradation and enhances the effectiveness of oral vaccines against Citrobacter rodentium. Moreover, Paraprevotella colonization inhibits lethal infection with murine hepatitis virus-2, a mouse coronavirus that is dependent on trypsin and trypsin-like proteases for entry into host cells^4,5. Consistently, carriage of putative genes involved in trypsin degradation in the gut microbiome was associated with reduced severity of diarrhoea in patients with SARS-CoV-2 infection. Thus, trypsin-degrading commensal colonization may contribute to the maintenance of intestinal homeostasis and protection from pathogen infection.

Colonization of trypsin-degrading commensal bacteria may contribute to the maintenance of intestinal homeostasis and protection against pathogen infection in humans and mice.

Serum plays an important role in reprogramming the seasonal transcriptional profile of brown bear adipocytes

Understanding how metabolic reprogramming happens in cells will aid the progress in the treatment of a variety of metabolic disorders. Brown bears undergo seasonal shifts in insulin sensitivity, including reversible insulin resistance in hibernation. We performed RNA-sequencing on brown bear adipocytes and proteomics on serum to identify changes possibly responsible for reversible insulin resistance. We observed dramatic transcriptional changes, which depended on both the cell and serum season of origin. Despite large changes in adipocyte gene expression, only changes in eight circulating proteins were identified as related to the seasonal shifts in insulin sensitivity, including some that have not previously been associated with glucose homeostasis. The identified serum proteins may be sufficient for shifting hibernation adipocytes to an active-like state.

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Hibernation in grizzly bears is marked by insulin resistance

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Bear adipocytes were stimulated with active and hibernating bear blood serum

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Serum elicited dramatic gene expression responses related to insulin signaling

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Eight serum proteins were implicated in driving this transcriptional response

Cullin-independent recognition of HHARI substrates by a dynamic RBR catalytic domain

RING-between-RING (RBR) E3 ligases mediate ubiquitin transfer through an obligate E3-ubiquitin thioester intermediate prior to substrate ubiquitination. Although RBRs share a conserved catalytic module, substrate recruitment mechanisms remain enigmatic, and the relevant domains have yet to be identified for any member of the class. Here we characterize the interaction between the auto-inhibited RBR, HHARI (AriH1), and its target protein, 4EHP, using a combination of XL-MS, HDX-MS, NMR, and biochemical studies. The results show that (1) a di-aromatic surface on the catalytic HHARI Rcat domain forms a binding platform for substrates and (2) a phosphomimetic mutation on the auto-inhibitory Ariadne domain of HHARI promotes release and reorientation of Rcat for transthiolation and substrate modification. The findings identify a direct binding interaction between a RING-between-RING ligase and its substrate and suggest a general model for RBR substrate recognition.

Lysosomal targetomics of ghr KO mice shows chaperone-mediated autophagy degrades nucleocytosolic acetyl-coA enzymes

Mice deficient in GHR (growth hormone receptor; ghr KO) have a dramatic lifespan extension and elevated levels of hepatic chaperone-mediated autophagy (CMA). Using quantitative proteomics to identify protein changes in purified liver lysosomes and whole liver lysates, we provide evidence that elevated CMA in ghr KO mice downregulates proteins involved in ribosomal structure, translation initiation and elongation, and nucleocytosolic acetyl-coA production. Following up on these initial proteomics findings, we used a cell culture approach to show that CMA is necessary and sufficient to regulate the abundance of ACLY and ACSS2, the two enzymes that produce nucleocytosolic (but not mitochondrial) acetyl-coA. Inhibition of CMA in NIH3T3 cells has been shown to lead to aberrant accumulation of lipid droplets. We show that this lipid droplet phenotype is rescued by knocking down ACLY or ACSS2, suggesting that CMA regulates lipid droplet formation by controlling ACLY and ACSS2. This evidence leads to a model of how constitutive activation of CMA can shape specific metabolic pathways in long-lived endocrine mutant mice.Abbreviations: CMA: chaperone-mediated autophagy; DIA: data-independent acquisition; ghr KO: growth hormone receptor knockout; GO: gene ontology; I-WAT: inguinal white adipose tissue; KFERQ: a consensus sequence resembling Lys-Phe-Glu-Arg-Gln; LAMP2A: lysosomal-associated membrane protein 2A; LC3-I: non-lipidated MAP1LC3; LC3-II: lipidated MAP1LC3; PBS: phosphate-buffered saline; PI3K: phosphoinositide 3-kinase.

Resolving isobaric interferences in direct infusion tandem mass spectrometry

RATIONALE

The co-fragmentation of precursors in direct infusion (DI) tandem high-resolution mass spectrometry (HRMS) can complicate the fragment spectra and consequently lead to false hits during compound identification.

METHODS

The method herein described, termed IQAROS (incremental quadrupole acquisition to resolve overlapping spectra), modulates the intensities of precursors and fragments by stepwise movement of the quadrupole isolation window over the mass-to-charge (m/z) range of the precursors. The modulated signals are then deconvoluted by a linear regression model to reconstruct the fragment spectra with less interference. The hardware to demonstrate the use of IQAROS was an orbitrap with electrospray ionization (ESI) or secondary electrospray ionization (SESI), although the method can also be applied to other ionization techniques or mass analyzers.

RESULTS

Assessing the performance of IQAROS with isobaric standards revealed that the reconstructed fragment spectra match with spectra acquired from the pure standards and that more compounds were correctly identified compared with the classical approach with the quadrupole centered at the m/z value of the precursor of interest. Moreover, the strength of IQAROS is exemplified by the identification of two isobaric biomarkers directly from a breath sample with SESI-HRMS.

CONCLUSIONS

With IQAROS, cleaner fragment spectra of co-fragmenting isobars during DI-HRMS analysis can be obtained. IQAROS can easily be set up by the standard graphical user interface of the instrument. Therefore, it facilitates the characterization of features of interest in samples analyzed by DI-HRMS, for example, in high-throughput or real-time metabolomics.

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.

Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data

Advances in library-based methods for peptide detection from data-independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high-quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico, but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate-controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide-window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

Ultrastructural Assessment and Proteomic Analysis in Myofibrillogenesis in the Heart Primordium After Heartbeat Initiation in Rats

Myofibrillogenesis is an essential process for cardiogenesis and is closely related to excitation-contraction coupling and the maintenance of heartbeat. It remains unclear whether the formation of myofibrils and sarcomeres is associated with heartbeat initiation in the early embryonic heart development. Here, we investigated the association between the ultrastructure of myofibrils assessed by transmission electron microscopy and their proteomic profiling assessed by data-independent acquisition mass spectrometry (DIA-MS) in the rat heart primordia before and after heartbeat initiation at embryonic day 10.0, when heartbeat begins in rats, and in the primitive heart tube at embryonic day 11.0. Bundles of myofilaments were scattered in a few cells of the heart primordium after heartbeat initiation, whereas there were no typical sarcomeres in the heart primordia both before and after heartbeat initiation. Sarcomeres with Z-lines were identified in cells of the primitive heart tube, though myofilaments were not aligned. DIA-MS proteome analysis revealed that only 43 proteins were significantly upregulated by more than 2.0 fold among a total of 7,762 detected proteins in the heart primordium after heartbeat initiation compared with that before heartbeat initiation. Indeed, of those upregulated proteins, 12 (27.9%) were constituent proteins of myofibrils and 10 (23.3%) were proteins that were accessories and regulators for myofibrillogenesis, suggesting that upregulated proteins that are associated with heartbeat initiation were enriched in myofibrillogenesis. Collectively, our results suggest that the establishment of heartbeat is induced by development of bundles of myofilaments with upregulated proteins associated with myofibrillogensis, whereas sarcomeres are not required for the initial heartbeat.

Single-Shot 10K Proteome Approach: Over 10,000 Protein Identifications by Data-Independent Acquisition-Based Single-Shot Proteomics with Ion Mobility Spectrometry

The evolution of mass spectrometry (MS) and analytical techniques has led to the demand for proteome analysis with high proteome coverage in single-shot measurements. Focus has been placed on data-independent acquisition (DIA)-MS and ion mobility spectrometry as techniques for deep proteome analysis. We aimed to expand the proteome coverage by single-shot measurements using optimizing high-field asymmetric waveform ion mobility spectrometry parameters in DIA-MS. With our established proteome analysis system, more than 10,000 protein groups were identified from HEK293 cell digests within 120 min of MS measurement time. Additionally, we applied our approach to the analysis of host proteins in mouse feces and detected as many as 892 host protein groups (771 upregulated/121 downregulated proteins) in a mouse model of repeated social defeat stress (R-SDS) used in studying depression. Interestingly, 285 proteins elevated by R-SDS were related to mental disorders. The fecal host protein profiling by deep proteome analysis may help us understand mental illness pathologies noninvasively. Thus, our approach will be helpful for an in-depth comparison of protein expression levels for biological and medical research because it enables the analysis of highly proteome coverage in a single-shot measurement.

Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes

The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments; however, due to the additional analysis time required, it has not been widely adopted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 45% increase in nonredundant amino acid detections. Nontryptic peptides enabled noncanonical protein isoform determination and resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as protein isoform analysis.

In-Depth Serum Proteomics by DIA-MS with In Silico Spectral Libraries Reveals Dynamics during the Active Phase of Systemic Juvenile Idiopathic Arthritis

In serum proteomics using mass spectrometry, the number of detectable proteins is reduced due to high-abundance proteins, such as albumin. However, recently developed data-independent acquisition mass spectrometry (DIA-MS) proteomics technology has made it possible to remarkably improve the number of proteins in a serum analysis by removing high-abundance proteins. Using this technology, we analyzed sera from patients with systemic juvenile idiopathic arthritis (sJIA), a rare pediatric disease. As a result, we identified 2727 proteins with a wide dynamic range derived from various tissue leakages. We also selected 591 proteins that differed significantly in their active phases. These proteins were involved in many inflammatory processes, and we also identified immunoproteasomes, which were not previously found in serum, suggesting that they may be involved in the pathogenesis of sJIA. A detailed high-depth DIA-MS proteomic analysis of serum may be useful for understanding the pathogenesis of sJIA and may provide clues for the development of new biomarkers.

Activity dependent dissociation of the Homer1 interactome

Neurons encode information by rapidly modifying synaptic protein complexes, which changes the strength of specific synaptic connections. Homer1 is abundantly expressed at glutamatergic synapses, and is known to alter its binding to metabotropic glutamate receptor 5 (mGlu5) in response to synaptic activity. However, Homer participates in many additional known interactions whose activity-dependence is unclear. Here, we used co-immunoprecipitation and label-free quantitative mass spectrometry to characterize activity-dependent interactions in the cerebral cortex of wildtype and Homer1 knockout mice. We identified a small, high-confidence protein network consisting of mGlu5, Shank2 and 3, and Homer1–3, of which only mGlu5 and Shank3 were significantly reduced following neuronal depolarization. We identified several other proteins that reduced their co-association in an activity-dependent manner, likely mediated by Shank proteins. We conclude that Homer1 dissociates from mGlu5 and Shank3 following depolarization, but our data suggest that direct Homer1 interactions in the cortex may be more limited than expected.

Recurrent Co-Option and Recombination of Cytokine and Three Finger Proteins in Multiple Reproductive Tissues Throughout Salamander Evolution

Reproductive proteins evolve at unparalleled rates, resulting in tremendous diversity of both molecular composition and biochemical function between gametes of different taxonomic clades. To date, the proteomic composition of amphibian gametes is largely a molecular mystery, particularly for Urodeles (salamanders and newts) for which few genomic-scale resources exist. In this study, we provide the first detailed molecular characterization of gametes from two salamander species (Plethodon shermani and Desmognathus ocoee) that are models of reproductive behavior. Long-read PacBio transcriptome sequencing of testis and ovary of both species revealed sex-specific expression of many genes common to vertebrate gametes, including a similar expression profile to the egg coat genes of Xenopus oocytes. In contrast to broad conservation of oocyte genes, major testis transcripts included paralogs of salamander-specific courtship pheromones (PRF, PMF, and SPF) that were confirmed as major sperm proteins by mass spectrometry proteomics. Sperm-specific paralogs of PMF and SPF are likely the most abundant secreted proteins in P. shermani and D. ocoee, respectively. In contrast, sperm PRF lacks a signal peptide and may be expressed in cytoplasm. PRF pheromone genes evolved independently multiple times by repeated gene duplication of sperm PRF genes with signal peptides recovered through recombination with PMF genes. Phylogenetic analysis of courtship pheromones and their sperm paralogs support that each protein family evolved for these two reproductive contexts at distinct evolutionary time points between 17 and 360 million years ago. Our combined phylogenetic, transcriptomic and proteomic analyses of plethodontid reproductive tissues support that the recurrent co-option and recombination of TFPs and cytokine-like proteins have been a novel driving force throughout salamander evolution and reproduction.

Mitochondrial Inorganic Polyphosphate (polyP) Is a Potent Regulator of Mammalian Bioenergetics in SH-SY5Y Cells: A Proteomics and Metabolomics Study

Inorganic polyphosphate (polyP) is an ancient, ubiquitous, and well-conserved polymer which is present in all the studied organisms. It is formed by individual subunits of orthophosphate which are linked by structurally similar bonds and isoenergetic to those found in ATP. While the metabolism and the physiological roles of polyP have already been described in some organisms, including bacteria and yeast, the exact role of this polymer in mammalian physiology still remains poorly understood. In these organisms, polyP shows a co-localization with mitochondria, and its role as a key regulator of the stress responses, including the maintenance of appropriate bioenergetics, has already been demonstrated by our group and others. Here, using Wild-type (Wt) and MitoPPX (cells enzymatically depleted of mitochondrial polyP) SH-SY5Y cells, we have conducted a comprehensive study of the status of cellular physiology, using proteomics and metabolomics approaches. Our results suggest a clear dysregulation of mitochondrial physiology, especially of bioenergetics, in MitoPPX cells when compared with Wt cells. Moreover, the effects induced by the enzymatic depletion of polyP are similar to those present in the mitochondrial dysfunction that is observed in neurodegenerative disorders and in neuronal aging. Based on our findings, the metabolism of mitochondrial polyP could be a valid and innovative pharmacological target in these conditions.

Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework

BACKGROUND

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 owing to the high complexity and large data and sample size, which require specialized software and vast computing infrastructures. Most available open-source DIA software necessitates basic programming skills and covers only a fraction of a complete DIA data analysis. In consequence, DIA data analysis often requires usage of multiple software tools and compatibility thereof, severely limiting the usability and reproducibility.

FINDINGS

To overcome this hurdle, we have integrated a suite of open-source 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 use 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 the usability of an 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.

CONCLUSION

The integration of an open-source DIA analysis suite in the web-based and user-friendly Galaxy framework in combination with extensive training material empowers a broad community of researches to perform reproducible and transparent DIA data analysis.

Does Data-Independent Acquisition Data Contain Hidden Gems? A Case Study Related to Alzheimer's Disease

One of the potential benefits of using data-independent acquisition (DIA) proteomics protocols is that information not originally targeted by the study may be present and discovered by subsequent analysis. Herein, we reanalyzed DIA data originally recorded for global proteomic analysis to look for isomerized peptides, which occur as a result of spontaneous chemical modifications to long-lived proteins. Examination of a large set of human brain samples revealed a striking relationship between Alzheimer's disease (AD) status and isomerization of aspartic acid in a peptide from tau. Relative to controls, a surprising increase in isomer abundance was found in both autosomal dominant and sporadic AD samples. To explore potential mechanisms that might account for these observations, quantitative analysis of proteins related to isomerization repair and autophagy was performed. Differences consistent with reduced autophagic flux in AD-related samples relative to controls were found for numerous proteins, including most notably p62, a recognized indicator of autophagic inhibition. These results suggest, but do not conclusively demonstrate, that lower autophagic flux may be strongly associated with loss of function in AD brains. This study illustrates that DIA data may contain unforeseen results of interest and may be particularly useful for pilot studies investigating new research directions. In this case, a promising target for future investigations into the therapy and prevention of AD has been identified.

Optimization of metabolomic data processing using NOREVA

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.