Development of an Online Isotope Dilution CE/ICP-MS Method for the Quantification of Sulfur in Biological Compounds.

We report an analytical methodology for the quantification of sulfur in biological molecules via a species-unspecific postcolumn isotope dilution (online ID) approach using capillary electrophoresis (CE) coupled online with inductively coupled plasma-mass spectrometry (online ID CE/ICP-MS). The method was optimized using a mixture of standard compounds including sulfate, methionine, cysteine, cystine, and albumin, yielding compound recoveries between 98 and 105%. The quantity of sulfur is further converted to the quantity of the compounds owing to the prior knowledge of the sulfur content in the molecules. The limit of detection and limit of quantification of sulfur in the compounds were 1.3-2.6 and 4.1-8.4 mg L-1, respectively, with a correlation coefficient of 0.99 within the concentration range of sulfur of 5-100 mg L-1. The capability of the method was extended to quantify albumin in its native matrix (i.e., in serum) using experimentally prepared serum spiked with a pure albumin standard for validation. The relative expanded uncertainty of the method for the quantification of albumin was 6.7% (k = 2). Finally, we tested the applicability of the method on real samples by the analysis of albumin in bovine and human sera. For automated data assessment, a software application (IsoCor)─which was developed by us in a previous work─was developed further for handling of online ID data. The method has several improvements compared to previously published setups: (i) reduced adsorption of proteins onto the capillary wall owing to a special capillary-coating procedure, (ii) baseline separation of the compounds in less than 30 min via CE, (iii) quantification of several sulfur species within one run by means of the online setup, (iv) SI traceability of the quantification results through online ID, and (v) facilitated data processing of the transient signals using the IsoCor application. Our method can be used as an accurate approach for quantification of proteins and other biological molecules via sulfur analysis in complex matrices for various fields, such as environmental, biological, and pharmaceutical studies as well as clinical diagnosis.

Endometabolic profiling of pigmented glacier ice algae: the impact of sample processing.

INTRODUCTION: Glacier ice algae, mainly Ancylonema alaskanum and Ancylonema nordenskiöldi, bloom on Greenland Ice Sheet bare ice surfaces. They significantly decrease surface albedo due to their purple-brown pigmentation, thus increasing melt. Little is known about their metabolic adaptation and factors controlling algal growth dynamics and pigment formation. A challenge in obtaining such data is the necessity of melting samples, which delays preservation and introduces bias to metabolomic analysis. There is a need to evaluate the physiological response of algae to melting and establish consistent sample processing strategies for metabolomics of ice microbial communities. OBJECTIVES: To address the impact of sample melting procedure on metabolic characterization and establish a processing and analytical workflow for endometabolic profiling of glacier ice algae. METHODS: We employed untargeted, high-resolution mass spectrometry and tested the effect of sample melt temperature (10, 15, 20 °C) and processing delay (up to 49 h) on the metabolome and lipidome, and complemented this approach with cell counts (FlowCam), photophysiological analysis (PAM) and diversity characterization. RESULTS AND CONCLUSION: We putatively identified 804 metabolites, with glycerolipids, glycerophospholipids and fatty acyls being the most prominent superclasses (> 50% of identified metabolites). Among the polar metabolome, carbohydrates and amino acid-derivatives were the most abundant. We show that 8% of the metabolome is affected by melt duration, with a pronounced decrease in betaine membrane lipids and pigment precursors, and an increase in phospholipids. Controlled fast melting at 10 °C resulted in the highest consistency, and is our recommendation for future supraglacial metabolomics studies.

Machine learning methods for compound annotation in non-targeted mass spectrometry-A brief overview of fingerprinting, in silico fragmentation and de novo methods.

Non-targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance-based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field. In this work, we illustrate which ML-based methods are available for compound annotation in non-targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies.

Identification of transformation products from fluorinated lithium-ion battery additives TPFPB and TPFPP: forever chemicals of tomorrow?

Fluorinated organic compounds (FOCs) represent a class of synthetic chemicals distinguished by their resilient carbon-fluorine bonds, which demonstrate an ability to withstand environmental degradation over an extended period. The integration of FOCs into cutting-edge applications, including lithium-ion batteries (LiBs), presents considerable potential for environmental harm that has not yet been sufficiently addressed. This study focuses on the environmental fate of two fluorinated aromatics, tris(pentafluorophenyl)borane (TPFPB) and tris(pentafluorophenyl)phosphine (TPFPP), given their important role in improving the performance of LiBs. To achieve this, laboratory simulation methods including total oxidizable precursor assay, electrochemistry (EC), Fenton reaction, UV-C irradiation, and hydrolysis were employed. Liquid chromatography and gas chromatography coupled with high-resolution mass spectrometry were used for identification of transformation products (TPs) and prediction of their molecular formulae. Despite the structural similarity between TPFPB and TPFPP, distinct differences in electrochemical behavior and degradation pathways were observed. TPFPB readily underwent hydroxylation and hydrolysis, resulting in a wide range of 49 TPs. A total of 28 TPs were newly identified, including oligomers and highly toxic dioxins. In contrast, TPFPP degraded exclusively under harsh conditions, requiring the development of innovative conditioning protocols for EC. In total, the simulation experiments yielded nine structurally different compounds, including seven previously undescribed, partially defluorinated TPs. This study highlights the potential risks associated with the use of FOCs in LiBs and provides insight into the complex environmental behavior of FOCs.

eCerto-versatile software for interlaboratory data evaluation and documentation during reference material production.

The statistical tool eCerto was developed for the evaluation of measurement data to assign property values and associated uncertainties of reference materials. The analysis is based on collaborative studies of expert laboratories and was implemented using the R software environment. Emphasis was put on comparability of eCerto with SoftCRM, a statistical tool based on the certification strategy of the former Community Bureau of Reference. Additionally, special attention was directed towards easy usability from data collection through processing, archiving, and reporting. While the effects of outlier removal can be flexibly explored, eCerto always retains the original data set and any manipulation such as outlier removal is (graphically and tabularly) documented adequately in the report. As a major reference materials producer, the Bundesanstalt für Materialforschung und -prüfung (BAM) developed and will maintain a tool to meet the needs of modern data processing, documentation requirements, and emerging fields of RM activity. The main features of eCerto are discussed using previously certified reference materials.

Quantification of Ergot Alkaloids via Lysergic Acid Hydrazide-Development and Comparison of a Sum Parameter Screening Method.

Ergot alkaloids are a group of mycotoxins occurring in products derived from various grasses (e.g., rye) and have been regulated in the EU recently. The new maximum levels refer to the sum of the six most common ergot alkaloids in their two stereoisomeric forms in different food matrices. Typically, these twelve compounds are individually quantified via HPLC-MS/MS or -FLD and subsequently summed up to evaluate food safety in a time-consuming process. Since all these structures share the same ergoline backbone, we developed a novel sum parameter method (SPM) targeting all ergot alkaloids simultaneously via lysergic acid hydrazide. After extraction and clean-up, in analogy to the current European standard method EN 17425 (ESM) for ergot alkaloid quantitation, the samples were derivatized by an optimized hydrazinolysis protocol, which allowed quantitative conversion after 20 min at 100 °C. The new SPM was evaluated against another established HPLC-FLD-based method (LFGB) and the HPLC-MS/MS-based ESM using six naturally contaminated rye and wheat matrix reference materials. While the SPM provided comparable values to the ESM, LFGB showed deviating results. Determined recovery rates, limits of detection and quantification of all three employed methods confirm that the new SPM is a promising alternative to the classical approaches for ergot alkaloid screening in food.

Analysis of electrochemical and liver microsomal transformation products of lasalocid by LC/HRMS.

RATIONALE: Lasalocid (LAS), an ionophore, is used in cattle and poultry farming as feed additive for its antibiotic and growth-promoting properties. Literature on transformation products (TP) resulting from LAS degradation is limited. So far, only hydroxylation is found to occur as the metabolic reaction during the LAS degradation. To investigate potential TPs of LAS, we used electrochemistry (EC) and liver microsome (LM) assays to synthesize TPs, which were identified using liquid chromatography high-resolution mass spectrometry (LC/HRMS). METHODS: Electrochemically produced TPs were analyzed online by direct coupling of the electrochemical cell to the electrospray ionization (ESI) source of a Sciex Triple-TOF high resolution mass spectrometer. Then, EC-treated LAS solution was collected and analyzed offline using LC/HRMS to confirm stable TPs and improve their annotation with a chemical structure due to informative MS/MS spectra. In a complementary approach, TPs formed by rat and human microsomal incubation were investigated using LC/HRMS. The resulting data were used to investigate LAS modification reactions and elucidate the chemical structure of obtained TPs. RESULTS: The online measurements identified a broad variety of TPs, resulting from modification reactions like (de-)hydrogenation, hydration, methylation, oxidation as well as adduct formation with methanol. We consistently observed different ion complexations of LAS and LAS-TPs (Na+ ; 2Na+ K+ ; NaNH4 + ; KNH4 + ). Two stable methylated EC-TPs were found, structurally annotated, and assigned to a likely modification reaction. Using LM incubation, seven TPs were formed, mostly by oxidation/hydroxylation. After the identification of LM-TPs as Na+ -complexes, we identified LM-TPs as K+ -complexes. CONCLUSION: We identified and characterized TPs of LAS using EC- and LM-based methods. Moreover, we found different ion complexes of LAS-based TPs. This knowledge, especially the different ion complexes, may help elucidate the metabolic and environmental degradation pathways of LAS.

Transcriptomic and Metabolomic Analysis of a Pseudomonas-Resistant versus a Susceptible Arabidopsis Accession.

Accessions of one plant species may show significantly different levels of susceptibility to stresses. The Arabidopsis thaliana accessions Col-0 and C24 differ significantly in their resistance to the pathogen Pseudomonas syringae pv. tomato (Pst). To help unravel the underlying mechanisms contributing to this naturally occurring variance in resistance to Pst, we analyzed changes in transcripts and compounds from primary and secondary metabolism of Col-0 and C24 at different time points after infection with Pst. Our results show that the differences in the resistance of Col-0 and C24 mainly involve mechanisms of salicylic-acid-dependent systemic acquired resistance, while responses of jasmonic-acid-dependent mechanisms are shared between the two accessions. In addition, arginine metabolism and differential activity of the biosynthesis pathways of aliphatic glucosinolates and indole glucosinolates may also contribute to the resistance. Thus, this study highlights the difference in the defense response strategies utilized by different genotypes.

Approaches and techniques to characterize cancer metabolism in vitro and in vivo.

Cancer metabolism is wired to sustain uncontrollable cell proliferation and ensure cell survival. Given the multitude of available approaches to study metabolic alterations it remains a challenging task to select the most appropriate method. In this mini-review we describe how cancer metabolism can be studied in vitro and in vivo providing an overview of available approaches and techniques, discussing their advantages and drawbacks and guiding through selection of an appropriate method to address particular research needs. This work is particularly intended to those cancer researchers who are new in the field but want to investigate metabolic alterations in their cancer model systems.

Structural annotation of electro- and photochemically generated transformation products of moxidectin using high-resolution mass spectrometry.

Moxidectin (MOX) is a widely used anthelmintic drug for the treatment of internal and external parasites in food-producing and companion animals. Transformation products (TPs) of MOX, formed through metabolic degradation or acid hydrolysis, may pose a potential environmental risk, but only few were identified so far. In this study, we therefore systematically characterized electro- and photochemically generated MOX TPs using high-resolution mass spectrometry (HRMS). Oxidative electrochemical (EC) TPs were generated in an electrochemical reactor and photochemical (PC) TPs by irradiation with UV-C light. Subsequent HRMS measurements were performed to identify accurate masses and deduce occurring modification reactions of derived TPs in a suspected target analysis. In total, 26 EC TPs and 59 PC TPs were found. The main modification reactions were hydroxylation, (de-)hydration, and derivative formation with methanol for EC experiments and isomeric changes, (de-)hydration, and changes at the methoxime moiety for PC experiments. In addition, several combinations of different modification reactions were identified. For 17 TPs, we could predict chemical structures through interpretation of acquired MS/MS data. Most modifications could be linked to two specific regions of MOX. Some previously described metabolic reactions like hydroxylation or O-demethylation were confirmed in our EC and PC experiments as reaction type, but the corresponding TPs were not identical to known metabolites or degradation products. The obtained knowledge regarding novel TPs and reactions will aid to elucidate the degradation pathway of MOX which is currently unknown. Graphical abstract.

Non-invasive metastasis prognosis from plasma metabolites in stage II colorectal cancer patients: The DACHS study.

Metastasis is the main cause of death from colorectal cancer (CRC). About 20% of stage II CRC patients develop metastasis during the course of disease. We performed metabolic profiling of plasma samples from non-metastasized and metachronously metastasized stage II CRC patients to assess the potential of plasma metabolites to serve as biomarkers for stratification of stage II CRC patients according to metastasis risk. We compared the metabolic profiles of plasma samples prospectively obtained prior to metastasis formation from non-metastasized vs. metachronously metastasized stage II CRC patients of the German population-based case-control multicenter DACHS study retrospectively. Plasma samples were analyzed from stage II CRC patients for whom follow-up data including the information on metachronous metastasis were available. To identify metabolites distinguishing non-metastasized from metachronously metastasized stage II CRC patients robust supervised classifications using decision trees and support vector machines were performed and verified by 10-fold cross-validation, by nested cross-validation and by traditional validation using training and test sets. We found that metabolic profiles distinguish non-metastasized from metachronously metastasized stage II CRC patients. Classification models from decision trees and support vector machines with 10-fold cross-validation gave average accuracy of 0.75 (sensitivity 0.79, specificity 0.7) and 0.82 (sensitivity 0.85, specificity 0.77), respectively, correctly predicting metachronous metastasis in stage II CRC patients. Taken together, plasma metabolic profiles distinguished non-metastasized and metachronously metastasized stage II CRC patients. The classification models consisting of few metabolites stratify non-invasively stage II CRC patients according to their risk for metachronous metastasis.

Lipid metabolism in cancer cells under metabolic stress.

Cancer cells are often exposed to a metabolically challenging environment with scarce availability of oxygen and nutrients. This metabolic stress leads to changes in the balance between the endogenous synthesis and exogenous uptake of fatty acids, which are needed by cells for membrane biogenesis, energy production and protein modification. Alterations in lipid metabolism and, consequently, lipid composition have important therapeutic implications, as they affect the survival, membrane dynamics and therapy response of cancer cells. In this article, we provide an overview of recent insights into the regulation of lipid metabolism in cancer cells under metabolic stress and discuss how this metabolic adaptation helps cancer cells thrive in a harsh tumour microenvironment.

Cancer cell lipid class homeostasis is altered under nutrient-deprivation but stable under hypoxia.

BACKGROUND: Cancer cells modify the balance between fatty acid (FA) synthesis and uptake under metabolic stress, induced by oxygen/nutrient deprivation. These modifications were shown to alter the levels of individual triglyceride (TG) or phospholipid sub-species. To attain a holistic overview of the lipidomic profiles of cancer cells under stress we performed a broad lipidomic assay, comprising 244 lipids from six major classes. This assay allowed us to perform robust analyses and assess the changes in averages of broader lipid-classes, stratified on the basis of saturation index of their fatty-acyl side chains. METHODS: Global lipidomic profiling using Liquid Chromatography-Mass Spectrometry was performed to assess lipidomic profiles of biologically diverse cancer cell lines cultivated under metabolically stressed conditions. RESULTS: Neutral lipid compositions were markedly modified under serum-deprived conditions and, strikingly, the cellular level of triglyceride subspecies decreased with increasing number of double bonds in their fatty acyl chains. In contrast and unexpectedly, no robust changes were observed in lipidomic profiles of hypoxic (2% O2) cancer cells despite concurrent changes in proliferation rates and metabolic gene expression. CONCLUSIONS: Serum-deprivation significantly affects lipidomic profiles of cancer cells. Although, the levels of individual lipid moieties alter under hypoxia (2% O2), the robust averages of broader lipid classes remain unchanged.

Pharmacokinetics of Daclatasvir, Sofosbuvir, and GS-331007 in a Prospective Cohort of Hepatitis C Virus-Positive Kidney Transplant Recipients.

BACKGROUND: Limited data exist on the pharmacokinetic profile of novel direct-acting antivirals in kidney transplant recipients. Daclatasvir is primarily eliminated through the biliary route and sofosbuvir through the renal route; here, we report the pharmacokinetic profile of combined treatment with these compounds in a prospective study of hepatitis C virus (HCV)-positive kidney transplant recipients (EudraCT: 2014-004551-32). METHODS: In this study, plasma samples of 16 HCV-positive kidney transplant recipients receiving daclatasvir and sofosbuvir were collected at 4 time points at days 1, 7, 14, 21, 56, and 84 after start of treatment. Inclusion criteria were stable graft function and an estimated glomerular filtration rate (eGFR) >30 mL/min/1.73 m. Daclatasvir, sofosbuvir, and GS-331007 (inactive metabolite of sofosbuvir) plasma concentrations were determined using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry. RESULTS: All patients showed a rapid virological response with HCV RNA below the detection limit 21 days after the start of therapy (medium time to viral clearance). No difference of the areas under the concentration-time curve (AUC) of daclatasvir, sofosbuvir, and GS-331007 was observed between patients with an eGFR below or ≥60 mL/min. For GS-331007, no relevant changes of trough levels were observed over time. Mean GS-331007 trough levels were 339.5 ± 174.9 ng/mL in patients with an eGFR ≥60 mL/min and 404.3 ± 226 ng/mL in patients with an eGFR <60 mL/min at day 7 (P = 0.52). At day 84, GS-331007 trough levels were 357.8 ± 200.8 and 404.2 ± 70.2 ng/mL in patients with an eGFR ≥60 mL/min and in patients with an eGFR <60 mL/min, respectively (P = 0.51). The accumulation ratios of renally eliminated GS-331007 for AUC and Cmax did not significantly differ between the 2 eGFR groups at day 7. CONCLUSIONS: An impaired eGFR (30-60 mL/min) does not lead to a dose accumulation of daclatasvir, sofosbuvir, and GS-331007. This study provides the rationale for future studies investigating the pharmacokinetic profile of sofosbuvir-based HCV treatment in kidney transplant recipients with an eGFR <30 mL/min.

Synthetic lethal metabolic targeting of cellular senescence in cancer therapy.

Activated oncogenes and anticancer chemotherapy induce cellular senescence, a terminal growth arrest of viable cells characterized by S-phase entry-blocking histone 3 lysine 9 trimethylation (H3K9me3). Although therapy-induced senescence (TIS) improves long-term outcomes, potentially harmful properties of senescent tumour cells make their quantitative elimination a therapeutic priority. Here we use the Eµ-myc transgenic mouse lymphoma model in which TIS depends on the H3K9 histone methyltransferase Suv39h1 to show the mechanism and therapeutic exploitation of senescence-related metabolic reprogramming in vitro and in vivo. After senescence-inducing chemotherapy, TIS-competent lymphomas but not TIS-incompetent Suv39h1(-) lymphomas show increased glucose utilization and much higher ATP production. We demonstrate that this is linked to massive proteotoxic stress, which is a consequence of the senescence-associated secretory phenotype (SASP) described previously. SASP-producing TIS cells exhibited endoplasmic reticulum stress, an unfolded protein response (UPR), and increased ubiquitination, thereby targeting toxic proteins for autophagy in an acutely energy-consuming fashion. Accordingly, TIS lymphomas, unlike senescence models that lack a strong SASP response, were more sensitive to blocking glucose utilization or autophagy, which led to their selective elimination through caspase-12- and caspase-3-mediated endoplasmic-reticulum-related apoptosis. Consequently, pharmacological targeting of these metabolic demands on TIS induction in vivo prompted tumour regression and improved treatment outcomes further. These findings unveil the hypercatabolic nature of TIS that is therapeutically exploitable by synthetic lethal metabolic targeting.

Prediction of Transformation Products of Monensin by Electrochemistry Compared to Microsomal Assay and Hydrolysis.

The knowledge of transformation pathways and identification of transformation products (TPs) of veterinary drugs is important for animal health, food, and environmental matters. The active agent Monensin (MON) belongs to the ionophore antibiotics and is widely used as a veterinary drug against coccidiosis in broiler farming. However, no electrochemically (EC) generated TPs of MON have been described so far. In this study, the online coupling of EC and mass spectrometry (MS) was used for the generation of oxidative TPs. EC-conditions were optimized with respect to working electrode material, solvent, modifier, and potential polarity. Subsequent LC/HRMS (liquid chromatography/high resolution mass spectrometry) and MS/MS experiments were performed to identify the structures of derived TPs by a suspected target analysis. The obtained EC-results were compared to TPs observed in metabolism tests with microsomes and hydrolysis experiments of MON. Five previously undescribed TPs of MON were identified in our EC/MS based study and one TP, which was already known from literature and found by a microsomal assay, could be confirmed. Two and three further TPs were found as products in microsomal tests and following hydrolysis, respectively. We found decarboxylation, O-demethylation and acid-catalyzed ring-opening reactions to be the major mechanisms of MON transformation.

Nontargeted Identification of Tracer Incorporation in High-Resolution Mass Spectrometry.

"Fluxomics" refers to the systematic analysis of metabolic fluxes in a biological system and may uncover novel dynamic properties of metabolism that remain undetected in conventional metabolomic approaches. In labeling experiments, tracer molecules are used to track changes in the isotopologue distribution of metabolites, which allows one to estimate fluxes in the metabolic network. Because unidentified compounds cannot be mapped on pathways, they are often neglected in labeling experiments. However, using recent developments in de novo annotation may allow to harvest the information present in these compounds if they can be identified. Here, we present a novel tool (HiResTEC) to detect tracer incorporation in high-resolution mass spectrometry data sets. The software automatically extracts a comprehensive, nonredundant list of all compounds showing more than 1% tracer incorporation in a nontargeted fashion. We explain and show in an example data set how mass precision and other filter heuristics, calculated on the raw data, can efficiently be used to reduce redundancy and noninformative signals by 95%. Ultimately, this allows to quickly investigate any labeling experiment for a complete set of labeled compounds (here 149) with acceptable false positive rates. We further re-evaluate a published data set from liquid chromatography-electrospray ionization (LC-ESI) to demonstrate broad applicability of our tool and emphasize importance of quality control (QC) tests. HiResTEC is provided as a package in the open source software framework R and is freely available on CRAN.

A naturally occurring promoter polymorphism of the Arabidopsis FUM2 gene causes expression variation, and is associated with metabolic and growth traits.

Fumarate and malate are known intermediates of the TCA cycle, a mitochondrial metabolic pathway generating NADH for respiration. Arabidopsis thaliana and other Brassicaceae contain an additional cytosolic fumarase (FUM2) that functions in carbon assimilation and nitrogen use. Here, we report the identification of a hitherto unknown FUM2 promoter insertion/deletion (InDel) polymorphism found between the Col-0 and C24 accessions, which also divides a large number of Arabidopsis accessions carrying either the Col-0 or the C24 allele. The polymorphism consists of two stretches of 2.1 and 3.8 kb, which are both absent from the promotor region of Col-0 FUM2. By analysing mutants as well as mapping and natural populations with contrasting FUM2 alleles, the promotor insertion was linked to reduced FUM2 mRNA expression, reduced fumarase activity and reduced fumarate/malate ratio in leaves. In a large population of 174 natural accessions, the polymorphism was also found to be associated with the fumarate/malate ratio, malate and fumarate levels, and with dry weight at 15 days after sowing (DAS). The association with biomass production was confirmed in an even larger (251) accession population for dry weight at 22 DAS. The dominant Col-0 allele that results in increased fumarate/malate ratios and enhanced biomass production is predominantly found in central/eastern European accessions, whereas the C24 type allele is prevalent on the Iberian Peninsula, west of the Rhine and in the British Isles. Our findings support the role of FUM2 in diurnal carbon storage, and point to a growth advantage of accessions carrying the FUM2 Col-0 allele.

Compound annotation in liquid chromatography/high-resolution mass spectrometry based metabolomics: robust adduct ion determination as a prerequisite to structure prediction in electrospray ionization mass spectra.

RATIONALE: A bottleneck in metabolic profiling of complex biological extracts is confident, non-supervised annotation of ideally all contained, chemically highly diverse small molecules. Recent computational strategies combining sum formula prediction with in silico fragmentation achieve confident de novo annotation, once the correct neutral mass of a compound is known. Current software solutions for automated adduct ion assignment, however, are either publicly unavailable or have been validated against only few experimental electrospray ionization (ESI) mass spectra. METHODS: We here present findMAIN (find Main Adduct IoN), a new heuristic approach for interpreting ESI mass spectra. findMAIN scores MS1 spectra based on explained intensity, mass accuracy and isotope charge agreement of adducts and related ionization products and annotates peaks of the (de)protonated molecule and adduct ions. The approach was validated against 1141 ESI positive mode spectra of chemically diverse standard compounds acquired on different high-resolution mass spectrometric instruments (Orbitrap and time-of-flight). Robustness against impure spectra was evaluated. RESULTS: Correct adduct ion assignment was achieved for up to 83% of the spectra. Performance was independent of compound class and mass spectrometric platform. The algorithm proved highly tolerant against spectral contamination as demonstrated exemplarily for co-eluting compounds as well as systematically by pairwise mixing of spectra. When used in conjunction with MS-FINDER, a state-of-the-art sum formula tool, correct sum formulas were obtained for 77% of spectra. It outperformed both 'brute force' approaches and current state-of-the-art annotation packages tested as potential alternatives. Limitations of the heuristic pertained to poorly ionizing compounds and cationic compounds forming [M]+ ions. CONCLUSIONS: A new, validated approach for interpreting ESI mass spectra is presented, filling a gap in the nontargeted metabolomics workflow. It is freely available in the latest version of R package InterpretMSSpectrum.

Automated Annotation and Evaluation of In-Source Mass Spectra in GC/Atmospheric Pressure Chemical Ionization-MS-Based Metabolomics.

Gas chromatography using atmospheric pressure chemical ionization coupled to mass spectrometry (GC/APCI-MS) is an emerging metabolomics platform, providing much-enhanced capabilities for structural mass spectrometry as compared to traditional electron ionization (EI)-based techniques. To exploit the potential of GC/APCI-MS for more comprehensive metabolite annotation, a major bottleneck in metabolomics, we here present the novel R-based tool InterpretMSSpectrum assisting in the common task of annotating and evaluating in-source mass spectra as obtained from typical full-scan experiments. After passing a list of mass-intensity pairs, InterpretMSSpectrum locates the molecular ion (M0), fragment, and adduct peaks, calculates their most likely sum formula combination, and graphically summarizes results as an annotated mass spectrum. Using (modifiable) filter rules for the commonly used methoximated-trimethylsilylated (MeOx-TMS) derivatives, covering elemental composition, typical substructures, neutral losses, and adducts, InterpretMSSpectrum significantly reduces the number of sum formula candidates, minimizing manual effort for postprocessing candidate lists. We demonstrate the utility of InterpretMSSpectrum for 86 in-source spectra of derivatized standard compounds, in which rank-1 sum formula assignments were achieved in 84% of the cases, compared to only 63% when using mass and isotope information on the M0 alone. We further use, for the first time, automated annotation to evaluate the purity of pseudospectra generated by different metabolomics preprocessing tools, showing that automated annotation can serve as an integrative quality measure for peak picking/deconvolution methods. As an R package, InterpretMSSpectrum integrates flexibly into existing metabolomics pipelines and is freely available from CRAN ( https://cran.r-project.org/ ).