Quantification of Urine and Plasma Porphyrin Precursors Using LC-MS in Acute Hepatic Porphyrias: Improvement in Routine Diagnosis and in the Monitoring of Kidney Failure Patients.

BACKGROUND: The quantification of delta-aminolevulinic acid (ALA) and porphobilinogen (PBG) in urine are the first-line tests for diagnosis and monitoring of acute hepatic porphyrias (AHP). Ion-exchange chromatography (IEC), which is time- and staff-consuming and limited to urine, is still the preferred method in many specialized laboratories, despite the development of mass spectrometry-based methods. METHODS: We describe a new LC-MS method that allows for rapid and simple quantification of ALA and PBG in urine and plasma with an affordable instrument that was used to analyze 2260 urine samples and 309 blood samples collected in 2 years of routine activity. The results were compared to those obtained with IEC, and urine reference ranges and concentrations in asymptomatic carriers were determined. Plasma concentrations were measured in healthy subjects and subgroups of symptomatic and asymptomatic AHP carriers. RESULTS: In urine, the clinical decision limits were not impacted by the change of method despite discrepancies in low absolute concentrations, leading to lower normal values. Two-thirds of asymptomatic AHP carriers (with the exception of coproporphyria carriers) showed an increased urine PBG concentration. Urine and plasma levels showed a good correlation except in patients with kidney disease in whom the urine/plasma ratio was relatively low. CONCLUSION: We described an LC-MS based method for the routine diagnosis and monitoring of AHP that allows for the detection of more asymptomatic carriers than the historical method. Blood analysis appears to be particularly relevant for patients with kidney disease, where urine measurement underestimates the increase in ALA and PBG levels.

Development of an Untargeted Metabolomics Strategy to Study the Metabolic Rewiring of Dendritic Cells upon Lipopolysaccharide Activation.

Dendritic cells (DCs) are essential immune cells for defense against external pathogens. Upon activation, DCs undergo profound metabolic alterations whose precise nature remains poorly studied at a large scale and is thus far from being fully understood. The goal of the present work was to develop a reliable and accurate untargeted metabolomics workflow to get a deeper insight into the metabolism of DCs when exposed to an infectious agent (lipopolysaccharide, LPS, was used to mimic bacterial infection). As DCs transition rapidly from a non-adherent to an adherent state upon LPS exposure, one of the leading analytical challenges was to implement a single protocol suitable for getting comparable metabolomic snapshots of those two cellular states. Thus, a thoroughly optimized and robust sample preparation method consisting of a one-pot solvent-assisted method for the simultaneous cell lysis/metabolism quenching and metabolite extraction was first implemented to measure intracellular DC metabolites in an unbiased manner. We also placed special emphasis on metabolome coverage and annotation by using a combination of hydrophilic interaction liquid chromatography and reverse phase columns coupled to high-resolution mass spectrometry in conjunction with an in-house developed spectral database to identify metabolites at a high confidence level. Overall, we were able to characterize up to 171 unique meaningful metabolites in DCs. We then preliminarily compared the metabolic profiles of DCs derived from monocytes of 12 healthy donors upon in vitro LPS activation in a time-course experiment. Interestingly, the resulting data revealed differential and time-dependent activation of some particular metabolic pathways, the most impacted being nucleotides, nucleotide sugars, polyamines pathways, the TCA cycle, and to a lesser extent, the arginine pathway.

Metabolomic profiling of cardiac allografts after controlled circulatory death.

BACKGROUND: Assessment of myocardial viability during ex situ heart perfusion (ESHP) is based on the measurement of lactate concentrations. As this provides with limited information, we sought to investigate the metabolic signature associated with donation after circulatory death (DCD) and the impact of ESHP on the myocardial metabolome. METHODS: Porcine hearts were retrieved either after warm ischemia (DCD group, N = 6); after brain-stem death (BSD group, N = 6); or without DCD nor BSD (Control group, N = 6). Hearts were perfused using normothermic oxygenated blood for 240 minutes. Plasma and myocardial samples were collected respectively every 30 and 60 minutes, and analyzed by an untargeted metabolomic approach using liquid chromatography coupled to high-resolution mass spectrometry. RESULTS: Median duration of warm ischemia was 23 minutes [19-29] in DCD animals. Lactate level within myocardial biopsies was not significantly different between groups at T0 (p = 0.281), and remained stable over the 4-hour period of ESHP. More than 300 metabolites were detected in plasma and heart biopsy samples. Compared to BSD animals, metabolomics changes involving energy and nucleotide metabolisms were observed in plasma samples of DCD animals before initiation of ESHP, whereas 2 metabolites (inosine monophosphate and methylbutyrate) exhibited concentration changes in biopsy samples. Normalization of DCD metabolic profile was remarkable after 4 hours of ESHP. CONCLUSION: A specific metabolic profile was observed in DCD hearts, mainly characterized by an increased nucleotide catabolism. DCD and BSD metabolomes proved normalized during ESHP. Complementary investigations are needed to correlate these findings to cardiac performances.

Correlative radioimaging and mass spectrometry imaging: a powerful combination to study 14C-graphene oxide in vivo biodistribution.

Research on graphene based nanomaterials has flourished in the last decade due their unique properties and emerging socio-economic impact. In the context of their potential exploitation for biomedical applications, there is a growing need for the development of more efficient imaging techniques to track the fate of these materials. Herein we propose the first correlative imaging approach based on the combination of radioimaging and mass spectrometry imaging for the detection of Graphene Oxide (GO) labelled with carbon-14 in mice. In this study, 14C-graphene oxide nanoribbons were produced from the oxidative opening of 14C-carbon nanotubes, and were then intensively sonicated to provide nano-size 14C-GO flakes. After Intravenous administration in mice, 14C-GO distribution was quantified by radioimaging performed on tissue slices. On the same slices, MS-imaging provided a highly resolved distribution map of the nanomaterial based on the detection of specific radical anionic carbon clusters ranging from C2˙- to C9˙- with a base peak at m/z 72 (12C) and 74 (14C) under negative laser desorption ionization mass spectrometry (LDI-MS) conditions. This proof of concept approach synergizes the strength of each technique and could be advantageous in the pre-clinical development of future Graphene-based biomedical applications.

Sympathetic nervous activation, mitochondrial dysfunction and outcome in acutely decompensated cirrhosis: the metabolomic prognostic models (CLIF-C MET).

BACKGROUND AND AIMS: Current prognostic scores of patients with acutely decompensated cirrhosis (AD), particularly those with acute-on-chronic liver failure (ACLF), underestimate the risk of mortality. This is probably because systemic inflammation (SI), the major driver of AD/ACLF, is not reflected in the scores. SI induces metabolic changes, which impair delivery of the necessary energy for the immune reaction. This investigation aimed to identify metabolites associated with short-term (28-day) death and to design metabolomic prognostic models. METHODS: Two prospective multicentre large cohorts from Europe for investigating ACLF and development of ACLF, CANONIC (discovery, n=831) and PREDICT (validation, n=851), were explored by untargeted serum metabolomics to identify and validate metabolites which could allow improved prognostic modelling. RESULTS: Three prognostic metabolites strongly associated with death were selected to build the models. 4-Hydroxy-3-methoxyphenylglycol sulfate is a norepinephrine derivative, which may be derived from the brainstem response to SI. Additionally, galacturonic acid and hexanoylcarnitine are associated with mitochondrial dysfunction. Model 1 included only these three prognostic metabolites and age. Model 2 was built around 4-hydroxy-3-methoxyphenylglycol sulfate, hexanoylcarnitine, bilirubin, international normalised ratio (INR) and age. In the discovery cohort, both models were more accurate in predicting death within 7, 14 and 28 days after admission compared with MELDNa score (C-index: 0.9267, 0.9002 and 0.8424, and 0.9369, 0.9206 and 0.8529, with model 1 and model 2, respectively). Similar results were found in the validation cohort (C-index: 0.940, 0.834 and 0.791, and 0.947, 0.857 and 0.810, with model 1 and model 2, respectively). Also, in ACLF, model 1 and model 2 outperformed MELDNa 7, 14 and 28 days after admission for prediction of mortality. CONCLUSIONS: Models including metabolites (CLIF-C MET) reflecting SI, mitochondrial dysfunction and sympathetic system activation are better predictors of short-term mortality than scores based only on organ dysfunction (eg, MELDNa), especially in patients with ACLF.

PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management.

INTRODUCTION: Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. OBJECTIVES: To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. METHODS: We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. RESULTS: PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. CONCLUSION: PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .

The Human Meconium Metabolome and Its Evolution during the First Days of Life.

Meconium represents the first newborn stools, formed from the second month of gestation and excreted in the first days after birth. As an accumulative and inert matrix, it accumulates most of the molecules transferred through the placenta from the mother to the fetus during the last 6 months of pregnancy, and those resulting from the metabolic activities of the fetus. To date, only few studies dealing with meconium metabolomics have been published. In this study, we aimed to provide a comprehensive view of the meconium metabolic composition using 33 samples collected longitudinally from 11 healthy newborns and to analyze its evolution during the first 3 days of life. First, a robust and efficient methodology for metabolite extraction was implemented. Data acquisition was performed using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS), using two complementary LC-HRMS conditions. Data preprocessing and treatment were performed using the Workflow4Metabolomics platform and the metabolite annotation was performed using our in-house database by matching accurate masses, retention times, and MS/MS spectra to those of pure standards. We successfully identified up to 229 metabolites at a high confidence level in human meconium, belonging to diverse chemical classes and from different origins. A progressive evolution of the metabolic profile was statistically evidenced, with sugars, amino acids, and some bacteria-derived metabolites being among the most impacted identified compounds. Our implemented analytical workflow allows a unique and comprehensive description of the meconium metabolome, which is related to factors, such as maternal diet and environment.

Metabolomics in the understanding and management of hepatic encephalopathy.

Metabolomics refers to the study of biological components below 1000 Daltons (Da) involved in metabolic pathways as substrates, products or effectors. According to the interconnected metabolic disturbances that have been described in the pathophysiology of hepatic encephalopathy (HE), this technique appears to be well adapted to study and better delineate the disease. This review will focus on recent advances in metabolomics in the field of HE. Thus, after a brief overview of the general principles of metabolomics, we will discuss metabolomics as a potentially efficient tool for unraveling new HE pathophysiological insights, biomarkers identification, or as a predicting tool for treatment response or outcome prognosis. Finally, we will give our vision on the prospects offered by metabolomics for improving care of HE patients.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests.

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

Nanodiagnostics to Face SARS-CoV-2 and Future Pandemics: From an Idea to the Market and Beyond.

The COVID-19 pandemic made clear how our society requires quickly available tools to address emerging healthcare issues. Diagnostic assays and devices are used every day to screen for COVID-19 positive patients, with the aim to decide the appropriate treatment and containment measures. In this context, we would have expected to see the use of the most recent diagnostic technologies worldwide, including the advanced ones such as nano-biosensors capable to provide faster, more sensitive, cheaper, and high-throughput results than the standard polymerase chain reaction and lateral flow assays. Here we discuss why that has not been the case and why all the exciting diagnostic strategies published on a daily basis in peer-reviewed journals are not yet successful in reaching the market and being implemented in the clinical practice.

Trapped ion mobility spectrometry time-of-flight mass spectrometry for high throughput and high resolution characterization of human milk oligosaccharide isomers.

The microbiome and immune system of infants are shaped by various bioactive components of human breastmilk, notably human milk oligosaccharides (HMOs). HMOs represent the third component of breastmilk and exhibit extremely high structural diversity with many isomers. Here, we propose a high throughput and high resolution approach to characterize main oligosaccharides present in breastmilk with high identification level thanks to ion mobility spectrometry. Four pairs of standard HMO isomers, that are (LNT/LNnT), (LNFP I/LNFP V), (3'-SL/6'-SL) and (2'-FL/3-FL), were first investigated under both positive and negative ionization mode using direct introduction-trapped ion mobility spectrometry-time-of-flight mass spectrometry (TIMS-TOF). By examining all the ionic species formed (i.e. protonated and deprotonated ions as well as adduct species), every isomer pair could be distinguished through the separation of at least one species, even with a small difference in collision cross section values (as small as 1.5%) thanks to the flexible resolution capacity of the TIMS instrument. Although multiple mobility peaks resulting from different glycan anomeric conformers, open-ring and/or different ionic isomer structures (i.e. various charge site locations), could be observed for some HMO species. The reduction at the reducing-end of HMOs did not significantly facilitate the isomer distinction. Finally, the unambiguous identification of the studied HMOs in a breastmilk sample showed the potential of the approach combining ion mobility separation and MS/MS experiments for high throughput distinction of HMO isomers in complex breastmilk samples without laborious sample preparation.

Evidence for the systemic diffusion of (2-chloroethyl)-ethyl-sulfide, a sulfur mustard analog, and its deleterious effects in brain.

Sulfur mustard, a chemical warfare agent known to be a vesicant of skin, readily diffuses in the blood stream and reaches internal organs. In the present study, we used the analog (2-chloroethyl)-ethyl-sulfide (CEES) to provide novel data on the systemic diffusion of vesicants and on their ability to induce brain damage, which result in neurological disorders. SKH-1 hairless mice were topically exposed to CEES and sacrificed at different time until 14 days after exposure. A plasma metabolomics study showed a strong systemic impact following a self-protection mechanism to alleviate the injury of CEES exposure. This result was confirmed by the quantification of specific biomarkers in plasma. Those were the conjugates of CEES with glutathione (GSH-CEES), cysteine (Cys-CEES) and N-acetyl-cysteine (NAC-CEES), as well as the guanine adduct (N7Gua-CEES). In brain, N7Gua-CEES could be detected both in DNA and in organ extracts. Similarly, GSH-CEES, Cys-CEES and NAC-CEES were present in the extracts until day14. Altogether, these results, based on novel exposure markers, confirm the ability of vesicants to induce internal damage following dermal exposure. The observation of alkylation damage to glutathione and DNA in brain provides an additional mechanism to the neurological insult of SM.

Right Ventricle Remodeling Metabolic Signature in Experimental Pulmonary Hypertension Models of Chronic Hypoxia and Monocrotaline Exposure.

INTRODUCTION: Over time and despite optimal medical management of patients with pulmonary hypertension (PH), the right ventricle (RV) function deteriorates from an adaptive to maladaptive phenotype, leading to RV failure (RVF). Although RV function is well recognized as a prognostic factor of PH, no predictive factor of RVF episodes has been elucidated so far. We hypothesized that determining RV metabolic alterations could help to understand the mechanism link to the deterioration of RV function as well as help to identify new biomarkers of RV failure. METHODS: In the current study, we aimed to characterize the metabolic reprogramming associated with the RV remodeling phenotype during experimental PH induced by chronic-hypoxia-(CH) exposure or monocrotaline-(MCT) exposure in rats. Three weeks after PH initiation, we hemodynamically characterized PH (echocardiography and RV catheterization), and then we used an untargeted metabolomics approach based on liquid chromatography coupled to high-resolution mass spectrometry to analyze RV and LV tissues in addition to plasma samples from MCT-PH and CH-PH rat models. RESULTS: CH exposure induced adaptive RV phenotype as opposed to MCT exposure which induced maladaptive RV phenotype. We found that predominant alterations of arginine, pyrimidine, purine, and tryptophan metabolic pathways were detected on the heart (LV+RV) and plasma samples regardless of the PH model. Acetylspermidine, putrescine, guanidinoacetate RV biopsy levels, and cytosine, deoxycytidine, deoxyuridine, and plasmatic thymidine levels were correlated to RV function in the CH-PH model. It was less likely correlated in the MCT model. These pathways are well described to regulate cell proliferation, cell hypertrophy, and cardioprotection. These findings open novel research perspectives to find biomarkers for early detection of RV failure in PH.

Untargeted lipidomics uncovers lipid signatures that distinguish severe from moderate forms of acutely decompensated cirrhosis.

BACKGROUND & AIMS: Acute decompensation (AD) of cirrhosis is a heterogeneous clinical entity associated with moderate mortality. In some patients, this condition develops quickly into the more deadly acute-on-chronic liver failure (ACLF), in which other organs such as the kidneys or brain fail. The aim of this study was to characterize the blood lipidome in a large series of patients with cirrhosis and identify specific signatures associated with AD and ACLF development. METHODS: Serum untargeted lipidomics was performed in 561 patients with AD (518 without and 43 with ACLF) (discovery cohort) and in 265 patients with AD (128 without and 137 with ACLF) in whom serum samples were available to perform repeated measurements during the 28-day follow-up (validation cohort). Analyses were also performed in 78 patients with AD included in a therapeutic albumin trial (43 patients with compensated cirrhosis and 29 healthy individuals). RESULTS: The circulating lipid landscape associated with cirrhosis was characterized by a generalized suppression, which was more manifest during AD and in non-surviving patients. By computing discriminating accuracy and the variable importance projection score for each of the 223 annotated lipids, we identified a sphingomyelin fingerprint specific for AD of cirrhosis and a distinct cholesteryl ester and lysophosphatidylcholine fingerprint for ACLF. Liver dysfunction and infections were the principal net contributors to these fingerprints, which were dynamic and interchangeable between patients with AD whose condition worsened to ACLF and those who improved. Notably, blood lysophosphatidylcholine levels increased in these patients after albumin therapy. CONCLUSIONS: Our findings provide insights into the lipid landscape associated with decompensation of cirrhosis and ACLF progression and identify unique non-invasive diagnostic biomarkers of advanced cirrhosis. LAY SUMMARY: Analysis of lipids in blood from patients with advanced cirrhosis reveals a general suppression of their levels in the circulation of these patients. A specific group of lipids known as sphingomyelins are useful to distinguish between patients with compensated and decompensated cirrhosis. Another group of lipids designated cholesteryl esters further distinguishes patients with decompensated cirrhosis who are at risk of developing organ failures.

Multiplatform metabolomics for an integrative exploration of metabolic syndrome in older men.

BACKGROUND: Metabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis. METHODS: A case/control study was designed within the NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted strategy to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men. FINDINGS: We characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo- and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…). INTERPRETATION: These findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS. FUNDING: The NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIHR; MOP-62842). The actual NuAge Database and Biobank, containing data and biologic samples of 1,753 NuAge participants (from the initial 1,793 participants), are supported by the Fonds de recherche du Québec (FRQ; 2020-VICO-279753), the Quebec Network for Research on Aging, a thematic network funded by the Fonds de Recherche du Québec - Santé (FRQS) and by the Merck-Frost Chair funded by La Fondation de l'Université de Sherbrooke. All metabolomics and lipidomics analyses were funded and performed within the metaboHUB French infrastructure (ANR-INBS-0010). All authors had full access to the full data in the study and accept responsibility to submit for publication.

The Microbiome Meets Nanotechnology: Opportunities and Challenges in Developing New Diagnostic Devices.

Monitoring of the human microbiome is an emerging area of diagnostics for personalized medicine. Here, the potential of different nanomaterials and nanobiosensing technologies is reviewed for the development of novel diagnostic devices for the detection and measurement of microbiome-related biomarkers. Moreover, the current and future landscape of microbiome-based diagnostics is defined by exploring the advantages and disadvantages of current nanotechnology-based approaches, especially in the context of developing point-of-care (PoC) devices that would meet the international guidelines known as REASSURED (Real-time connectivity; Ease of specimen collection; Affordability; Sensitivity; Specificity; User-friendliness; Rapid & robust operation; Equipment-free; and Deliverability). Finally, the strategies of the latest international scientific consortia working in this field are analyzed, the current microbiome diagnostics market are reported and the principal ethical, legal, and societal issues related to microbiome R&D and innovation are discussed.

Quantitative Assessment of SARS-CoV-2 Virus in Nasopharyngeal Swabs Stored in Transport Medium by a Straightforward LC-MS/MS Assay Targeting Nucleocapsid, Membrane, and Spike Proteins.

Alternative methods to RT-PCR for SARS-CoV-2 detection are investigated to provide complementary data on viral proteins, increase the number of tests performed, or identify false positive/negative results. Here, we have developed a simple mass spectrometry assay for SARS-CoV-2 in nasopharyngeal swab samples using common laboratory reagents. The method employs high sensitivity and selectivity targeted mass spectrometry detection, monitoring nine constitutive peptides representative of the three main viral proteins and a straightforward pellet digestion protocol for convenient routine applications. Absolute quantification of N, M, and S proteins was achieved by addition of isotope-labeled versions of best peptides. Limit of detection, recovery, precision, and linearity were thoroughly evaluated in four representative viral transport media (VTM) containing distinct total protein content. The protocol was sensitive in all swab media with limit of detection determined at 2 × 103 pfu/mL, corresponding to as low as 30 pfu injected into the LC-MS/MS system. When tested on VTM-stored nasopharyngeal swab samples from positive and control patients, sensitivity was similar to or better than rapid immunoassay dipsticks, revealing a corresponding RT-PCR detection threshold at Ct ∼ 24. The study represents the first thorough evaluation of sensitivity and robustness of targeted mass spectrometry in nasal swabs, constituting a promising SARS-CoV-2 antigen assay for the first-line diagnosis of COVID-19 and compatible with the constraints of clinical settings. The raw files generated in this study can be found on PASSEL (Peptide Atlas) under data set identifier PASS01646.