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Fiehn, O.

Publications and source records attributed to Fiehn, O..

At least 19 recordsLinked to original sources

Multi-Tissue Metabolomic Signatures of Five Longevity Interventions Converge on Ergothioneine and Lipid Remodeling in Male UM-HET3 Mice

The pace of aging can be delayed by mutations, dietary manipulations, and drugs, yet the metabolic mechanisms underlying longevity interventions remain poorly understood. Here we present a multi-tissue metabolomic analysis of male UM-HET3 mice treated from 4 to 12 months of age with five validated longevity interventions: rapamycin, acarbose, 17-estradiol, canagliflozin, or caloric restriction. Using a feature-stabilized XGBoost pipeline applied to seven tissues, we show that metabolomic profiles can identify treated mice as likely recipients of a lifespan-extending intervention well before survival differences emerge. A leave-one-intervention-out procedure confirmed that models trained on any four interventions successfully classified mice from a fifth, unseen intervention, implying shared metabolic alterations across mechanistically distinct treatments. The most influential metabolites -- defined as the minimum set explaining 50% of cumulative model gain -- differed substantially across tissues. Only ergothioneine, a dietary antioxidant, ranked highly in more than two tissues: it was elevated by all five interventions in plasma and brain, and by four of five in muscle. Enrichment analyses further identified coordinated remodeling of lipid classes in plasma, perigonadal fat, and kidney. These findings reveal tissue-specific metabolic reprogramming shared across mechanistically distinct longevity interventions and, pending validation against interventions that do not extend lifespan, suggest a path toward metabolomic screening of candidate anti-aging drugs.

Molecular Biology↗

Dysregulation of innate immunity and cellular metabolism through virus-induced deISGylation

Interferon-stimulated gene 15 (ISG15) regulates diverse cellular responses including antiviral immunity through its conjugation to proteins, a process known as ISGylation. Several pathogens, including SARS-CoV-2, subvert ISGylation by encoding deISGylating enzymes. However, the direct targets and physiological consequences of coronaviral deISGylation remain poorly defined. Here, we ablated the deISGylating activity of the SARS-CoV-2 papain-like protease (PLpro) and found that loss of deISGylation boosted innate immune activation, attenuated virus replication, and promoted viral clearance in human cells and in mice. Through untargeted metabolomics and ISGylome proteomics analyses, combined with functional studies, we discovered in molecular detail how the activities of key metabolic enzymes in glycolysis, the pentose phosphate pathway, and oxidative stress are controlled by PLpro deISGylation. These findings provide fundamental new insight into how reversible ISGylation regulates immunity and metabolic processes at the molecular level and highlight viral deISGylation as a major viral tactic for rewiring immunometabolism.

microbiology↗

Discrimination of Normal from Slow-Aging Mice by Plasma Metabolomic and Proteomic Features

Tests that can predict whether a drug is likely to extend mouse lifespan could speed up the search for anti-aging drugs. We have applied a machine learning algorithm, XGBoost regression, to seek sets of plasma metabolites that can discriminate control mice from mice treated with an anti-aging diet (caloric restriction) or any of four anti-aging drugs. When the model is trained on any four of these five interventions, it predicts significantly higher lifespan extension in mice exposed to the intervention which was not included in the training set. Plasma peptide data sets also succeed at this task. Models trained on drug-treated normal mice also discriminate long-lived mutant mice from their respective controls, and models trained on males can discriminate drug-treated from control females. Triglycerides are over-represented among the most influential features in the regression models. Triglycerides with longer fatty acid chains tend to be higher in the slow-aging mice, while triglycerides with shorter fatty acid chains tend to decrease. Plasma metabolite patterns may help to select the most promising anti-aging drugs in mice or in humans, and may give new leads into physiological and enzymatic targets relevant to discovery of new anti-aging drugs.

bioinformatics↗

Dynamic Lipidome Reorganization in Response to Heat Shock Stress

The heat shock response (HSR) is a conserved cellular mechanism critical for adaptation to environmental and physiological stressors, with broad implications for cell survival, immune responses, and cancer biology. While the HSR has been extensively studied at the proteomic and transcriptomic levels, the role of lipid metabolism and membrane reorganization remains underexplored. Here, we integrate mass spectrometry-based lipidomics with RNA sequencing to characterize global lipidomic and transcriptomic changes in HeLa cells exposed to three conditions: control, heat shock (HS), and HS with eight hours of recovery. Heat shock-induced extensive lipid remodeling, including significant increases in fatty acids, glycerophospholipids, and sphingolipids, with partial normalization during recovery. Transcriptomic analysis identified over 2,700 upregulated and 2,300 downregulated genes under heat shock, with GO enrichment suggesting potential transcriptional contributions to lipid metabolism. However, transcriptional changes alone did not fully explain the observed lipidomic shifts, suggesting additional layers of regulation. Joint pathway analysis revealed enrichment in glycerophospholipid and sphingolipid metabolism, while network analysis identified lipid transport regulators (STAB2, APOB), stress-linked metabolic nodes (KNG1), and persistent sphingolipid enrichment during recovery. These findings provide a comprehensive framework for understanding lipid-mediated mechanisms of the HSR and highlight the importance of multi-omics integration in stress adaptation and disease biology.

bioinformatics↗

Heat Shock-Induced PI(4)P Increase Drives HSPA1A Translocation to the Plasma Membrane in Cancer and Stressed Cells through PI4KIII Alpha Activation

HSPA1A, a major heat shock protein, is known to translocate to the plasma membrane (PM) in response to cellular stress and cancer, where it plays protective roles in membrane integrity and stress resistance. Although phosphatidylinositol 4-phosphate [PI(4)P] is essential in this translocation, the signals that trigger and facilitate HSPA1As movement remain undefined.Given that membrane lipid composition dynamically shifts during stress, we hypothesized that heat shock-induced PI(4)P changes are crucial for HSPA1As PM localization. To test this hypothesis, we investigated the mechanisms driving PI(4)P changes and HSPA1A PM localization under heat shock. Lipidomic analysis, enzyme-linked immunosorbent assay (ELISA), and confocal imaging revealed a rapid PI(4)P increase at the PM post-heat shock, with levels peaking at 0 hours and declining by 8 hours. RNA sequencing and protein quantification indicated no transcriptional increase in PI4KIII alpha, the kinase responsible for PI(4)P synthesis, suggesting an alternative regulatory mechanism. Hypothesizing that heat shock enhances PI4KIII alpha activity, we performed ELISA coupled with immunoprecipitation, confirming a significant rise in PI4KIII alpha activity following heat shock. Functional analyses further demonstrated that RNAi-mediated PI4KIII alpha depletion or pharmacological PI(4)P reduction, using GSK-A1, impairs HSPA1As localization to the PM, confirming that HSPA1A translocation is PI(4)P-dependent. Our findings identify PI4KIII alpha activity as a key regulator of PI(4)P accumulation and subsequent HSPA1A recruitment to the PM in stressed and cancer cells. This lipid-mediated response offers new insights into stress adaptation and potentially modifiable pathways for therapeutic interventions to control HSPA1A function in cancer.

biochemistry↗

Coordinated variation in the immune systems and microbiomes of healthy humans is linked to tonic interferon states

Human immune systems are highly variable, with most variation attributable to non-genetic sources. The gut microbiome crucially shapes the immune system; however, its relationship with the baseline immune states of healthy humans remains incompletely understood. Therefore, we performed multi-omic profiling of 110 healthy participants through the ImmunoMicrobiome study. A factor-based integrative approach identified coordinated variation, revealing that the tonic interferon response was amongst the most variable immune features in healthy participants. Microbiome composition, pathways, and stool metabolites varied concomitantly with interferon response pathways. Distinct transcriptional programs involving inflammation and TGF-{beta} in SIGLEC-1high monocytes and CD69high activated MAIT and NK cells were representative of these programs. Our study provides extensive data to examine the relationship between the immune states and microbiomes of healthy individuals at steady state, which paves the way for delineating inter-individual differences relevant for disease susceptibility and responses to therapy.

immunology↗

Heat-Induced Phosphatidylserine Changes Drive HSPA1A's Plasma Membrane Localization

Heat shock protein A1A (HSPA1A) is a molecular chaperone crucial in cell survival. In addition to its cytosolic functions, HSPA1A translocates to heat-shocked and cancer cells plasma membrane (PM). In cancer, PM-localized HSPA1A (mHSPA1A) is associated with increased tumor aggressiveness and therapeutic resistance, suggesting that preventing its membrane localization could have therapeutic value. This translocation depends on HSPA1As interaction with PM phospholipids, including phosphatidylserine (PS). Although PS binding regulates HSPA1As membrane localization, the exact trigger for this movement remains unclear. Given that lipid modifications are a cancer hallmark, we hypothesized that PS is a crucial lipid driving HSPA1A translocation and that heat-induced changes in PS levels trigger HSPA1As PM localization in response to heat stress. We tested this hypothesis using pharmacological inhibition and RNA interference (RNAi) targeting PS synthesis, combined with confocal microscopy, lipidomics, and western blotting. Lipidomic analysis and PS-specific biosensors confirmed a heat shock-induced PS increase, peaking immediately post-stress. Inhibition of PS synthesis with fendiline and RNAi significantly reduced HSPA1As PM localization, while depletion of cholesterol or fatty acids had minimal effects, confirming specificity for PS. Further experiments showed that PS saturation and elongation changes did not significantly impact HSPA1As PM localization, indicating that the total PS increase, rather than specific PS species, is the critical factor. These findings reshape current models of HSPA1A trafficking, demonstrating that PS is a crucial regulator of HSPA1As membrane translocation during the heat shock response. This work offers new insights into lipid-regulated protein trafficking and highlights the importance of PS in controlling cellular responses to stress.

cell biology↗

Composition of saliva metabolome is significantly associated with SARS-CoV2 infection and with severity of COVID-19 disease

BackgroundThe metabolome of COVID-19 patients has been studied sparsely, with most research focusing on a limited number of plasma metabolites or small cohorts. This is the first study to test saliva metabolites in COVID-19 patients in a comprehensive way, revealing significant changes linked to disease severity and highlighting saliva is potential as a non-invasive diagnostic tool. MethodsWe included 30 asymptomatic subjects with no prior COVID-19 infection or vaccination, 102 patients with mild SARS-CoV-2 infection, and 61 hospitalized patients with confirmed SARS-CoV-2 status. Saliva samples were analyzed using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS/MS) in positive and negative ionization modes. ResultsSignificant changes in metabolites were identified in COVID-19 patients, with distinct patterns based on disease severity. Healthy individuals exhibited a well-regulated bacterial network, while severe cases showed disordered microbial networks. Elevated dipeptides such as Val-Glu and Met-Gln in moderate cases suggest specific protease activity related to SARS-CoV-2. Increased acetylated amino acids like N-Acetylserine and N-Acetylhistidine indicate potential biomarkers for stress and disease severity. Bacterial metabolites, including muramic acid and indole-3-carboxaldehyde, were higher in mild-moderate cases, indicating oral microbiota changes. In severe cases, polyamines and organ damage-related metabolites, such as N-acetylspermine and 3-methylcytidine, were significantly increased. Interestingly, metabolites reduced in moderate cases were elevated in severe cases. ConclusionsSaliva metabolomics offers insight into disease progression and potential biomarkers for COVID-19.

bioinformatics↗

APOE Genotype and Biological Age Impact Inter-Omic Associations Related to Bioenergetics

Apolipoprotein E (APOE) modifies human aging; specifically, the {varepsilon}2 and {varepsilon}4 alleles are among the strongest genetic predictors of longevity and Alzheimers disease (AD) risk, respectively. However, detailed mechanisms for their influence on aging remain unclear. Herein, we analyzed inter-omic, context-dependent association patterns across APOE genotypes, sex, and health axes in 2,229 community-dwelling individuals to test APOE genotypes for variation in metabolites and metabolite-associations tied to a previously-validated metric of biological aging (BA) based on blood biomarkers. Our analysis, supported by validation in an independent cohort, identified top APOE-associated plasma metabolites as diacylglycerols, which were increased in {varepsilon}2-carriers and trended higher in {varepsilon}4-carriers compared to {varepsilon}3-homozygotes, despite the known opposing aging effects of the allele variants. Omics association patterns of {varepsilon}2-carriers and increased biological age were also counter-intuitively similar, displaying increased associations between insulin resistance markers and energy-generating pathway metabolites. These results provide an atlas of APOE-related omic associations and support the involvement of bioenergetic pathways in mediating the impact of APOE on aging.

systems biology↗

Introducing identification probability for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

biochemistry↗

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluated the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. Lung lobes from male and female BALB/c mice were cryosectioned and acquired by high resolution mass spectrometry imaging (MSI). Processed MSI peak annotations were validated by LC-MS/MS data from scraped tissue slides and microdissected lung tissue. Images were normalized and segmented into clusters. Interestingly, segmented clusters overlapped with stained serial tissue sections, enabling statistical analysis across biological replicates for morphologically relevant lung regions. Spatially distinct lipids had higher overall degree of unsaturated fatty acids in distal lung regions compared to proximal regions. Furthermore, the airway and alveolar epithelium exhibited significantly decreased sphingolipid and glycerophospholipid abundance in females, but not in males. We demonstrate the potential role of lipid saturation in healthy lung function and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future MSI experiments capable of relative quantification across biological replicates and expansion to multiple sample types, including human tissue.

pharmacology and toxicology↗

The lipidome of posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) can develop after trauma exposure. Some studies report that women develop PTSD at twice the rate of men, despite greater trauma exposure in men. Lipids and their metabolites (lipidome) regulate a myriad of key biological processes and pathways such as membrane integrity, oxidative stress, and neuroinflammation in the brain by maintaining neuronal connectivity and homeostasis. In this study, we analyzed the lipidome of 40 individuals with PTSD and 40 trauma-exposed non-PTSD individuals. Plasma samples were analyzed for lipidomics using Quadrupole Time-of-Flight (QToF) mass spectrometry. Additionally, [~] 90 measures were collected, on sleep, mental and physical health indices. Sleep quality worsened as PTSD severity increased in both sexes. The lipidomics analysis identified a total of 348 quantifiable known lipid metabolites and 1951 lipid metabolites that are yet unknown; known metabolites were part of 13 classes of lipids. After adjusting for sleep quality, in women with PTSD, only one lipid subclass, phosphatidylethanolamine (PE) was altered, whereas, in men with PTSD, 9 out of 13 subclasses were altered compared to non-PTSD women and men, respectively. Severe PTSD was associated with 22% and 5% of altered lipid metabolites in men and women, respectively. Of the changed metabolites, only 0.5% measures (2 PEs and cholesterol) were common between women and men with PTSD. Several sphingomyelins, PEs, ceramides, and triglycerides were increased in men with severe PTSD. The triglycerides and ceramide metabolites that were most highly increased were correlated with cholesterol metabolites and systolic blood pressure in men but not always in women with PTSD. Alterations in triglycerides and ceramides are linked with cardiac health and metabolic function in humans. Thus, disturbed sleep and higher weight may have contributed to changes in the lipidome found in PTSD.

systems biology↗

Lipid metabolism drives allele-specific early-stage hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) results from pathogenic variants in sarcomeric protein genes, that increase myocyte energy demand and lead to cardiac hypertrophy. But it is unknown whether a common metabolic trait underlies the cardiac phenotype at early disease stage. This study characterized two HCM mouse models (R92W-TnT, R403Q-MyHC) that demonstrate differences in mitochondrial function at early disease stage. Using a combination of cardiac phenotyping, transcriptomics, mass spectrometry-based metabolomics and computational modeling, we discovered allele-specific differences in cardiac structure/function and metabolic changes. TnT-mutant hearts had impaired energy substrate metabolism and increased phospholipid remodeling compared to MyHC-mutants. TnT-mutants showed increased incorporation of saturated fatty acid residues into ceramides, cardiolipin, and increased lipid peroxidation, that could underlie allele-specific differences in mitochondrial function and cardiomyopathy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=172 HEIGHT=200 SRC="FIGDIR/small/564562v1_ufig1.gif" ALT="Figure 1"> O_LINKSMALLFIG WIDTH=185 HEIGHT=200 SRC="FIGDIR/small/564562v1_ufig2.gif" ALT="Figure 1"> O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/564562v1_ufig3.gif" ALT="Figure 1"> View larger version (90K): org.highwire.dtl.DTLVardef@195b1d7org.highwire.dtl.DTLVardef@cead88org.highwire.dtl.DTLVardef@e2bf35org.highwire.dtl.DTLVardef@776765_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

IDSL.GOA: Gene Ontology Analysis for Metabolomics

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2,393 metabolic GO terms and associated 3,144 genes, 1,492 EC annotations, and 2,621 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR <0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/

bioinformatics↗

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

systems biology↗

Adipocytes reprogram glucose metabolism in cancer cells promoting metastasis

In the tumor microenvironment, adipocytes function as an alternate fuel source for cancer cells. However, whether adipocytes influence macromolecular biosynthesis in cancer cells is unknown. Here, we systematically characterized the bi-directional interaction between primary human adipocytes and ovarian cancer (OvCa) cells using multi-platform metabolomics, imaging mass spectrometry, [13C]-glucose isotope tracing, and gene expression analysis. We report that omental tumor explants and OvCa cells co-cultured with adipocytes divert part of the glucose from glycolysis and TCA cycle towards glycerol-3-phosphate (G3P) synthesis. Normoxic HIF1 protein, stabilized by adipokines, regulate this altered flow of glucose-derived carbons in cancer cells, resulting in increased synthesis of glycerophospholipids (GPL) and triacylglycerols. Blocking adipocyte-induced HIF1 expression increases lipid peroxidation levels in cancer cells and sensitizes them to ferroptosis-mediated cell death. Subsequently, the knockdown of HIF1 or G3P acyltransferase 3 (a regulatory enzyme of GPL synthesis) reduced metastasis in xenograft models of OvCa. In summary, we show that in an adipose-rich tumor microenvironment, cancer cells generate G3P as a precursor for critical membrane and signaling components, thereby promoting metastasis. Targeting biosynthetic processes specific to adipose-rich tumor microenvironments might be an effective strategy against metastasis.

cancer biology↗

The metabolite alpha-ketobutyrate increases health and life spans by activating AMPK

Aging is a complex process that is directly related to human health and disease. The extraordinary finding that aging is malleable, as shown in model organisms whose life and health spans are extended by specific gene mutations or dietary or pharmacological perturbations 1-3, has offered enormous hope for our understanding and treatment of aging and related diseases. Although many molecules have been identified that can extend the lifespan of model organisms, few have been shown to alleviate age-related symptoms or illness in mammals 4. Here we show that supplementation with the endogenous metabolite -ketobutyrate (-KB) increases the lifespan of adult C. elegans. Using Gelfree DARTS-PROTOMAP, we identified microtubule-actin cross-linking factor (MACF1) that was protected against proteolysis in the presence of -KB. MACF1 belongs to the spectraplakin family of giant, evolutionarily conserved proteins with versatile functions 5, but their link to longevity regulation has not been explored. -KBs longevity effect in C. elegans is abrogated by loss-of-function mutation in vab-10, encoding the worm ortholog of mammalian MACF1 6. Like -KB treatment, vab-10 knockdown activates AMP-activated protein kinase (AMPK), and AMPK is required for -KB effects on longevity. The findings suggest a model in which -KB increases longevity by activating AMPK via VAB-10/MACF1 modulation. -KB also delays aging in mammals, increasing the lifespan of aged male mice and the healthspan of both male and female animals. Targeting of broadly expressed scaffolding proteins in connection to cellular energy homeostasis seems to be a clever way that nature has devised for metabolite signals to impinge upon multiple organ and tissue systems, which may have utility for controlling aging and related diseases.

physiology↗

A Universal Language for Finding Mass Spectrometry Data Patterns

Even though raw mass spectrometry data is information rich, the vast majority of the data is underutilized. The ability to interrogate these rich datasets is handicapped by the limited capability and flexibility of existing software. We introduce the Mass Spec Query Language (MassQL) that addresses these issues by enabling an expressive set of mass spectrometry patterns to be queried directly from raw data. MassQL is an open-source mass spectrometry query language for flexible and mass spectrometer manufacturer-independent mining of MS data. We envision the flexibility, scalability, and ease of use of MassQL will empower the mass spectrometry community to take fuller advantage of their mass spectrometry data and accelerate discoveries.

bioinformatics↗