Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. Using metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering . Our proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

The reaction products of electrophiles in vivo can be measured as adducts to the abundant proteins, hemoglobin (Hb), and human serum albumin (HSA), in human blood samples. During the last decade, methods for untargeted screening of such adducts, called “adductomics”, have used liquid chromatography-mass spectrometry to detect large numbers of previously unknown Hb and HSA adducts. This review presents methodologies that were developed and used in our laboratories for Hb and HSA adductomics, respectively. We discuss critical aspects regarding choice of target protein, sample preparation, mass spectrometry, data evaluation, and strategies for identification of detected unknown adducts. With this review we give an overview of these two methodologies used for protein adductomics and the precursor electrophiles that have been elucidated from the adducts.

Although benzene has long been recognized as a cause of human leukemia, the mechanism by which this simple molecule causes cancer has been problematic. A complicating factor is benzene metabolism, which produces many reactive intermediates, some specific to benzene and others derived from redox processes. Using archived serum from 20 nonsmoking Chinese workers, 10 with and 10 without occupational exposure to benzene (exposed: 3.2–88.9 ppm, controls: 0.002–0.020 ppm), we employed an adductomic pipeline to characterize protein modifications at Cys34 of human serum albumin, a nucleophilic hotspot in extracellular fluids. Of the 47 measured human serum albumin modifications, 39 were present at higher concentrations in benzene-exposed workers than in controls and many of the exposed-control differences were statistically significant. Correlation analysis identified three prominent clusters of adducts, namely putative modifications by benzene oxide and a benzene diolepoxide that grouped with other measures of benzene exposure, adducts of reactive oxygen and carbonyl species, and Cys34 disulfides of small thiols that are formed following oxidation of Cys34. Benzene diolepoxides are potent mutagens and carcinogens that have received little attention as potential causes of human leukemia. Reactive oxygen and carbonyl species—generated by redox processes involving polyphenolic benzene metabolites and by Cyp2E1 regulation following benzene exposure—can modify DNA and proteins in ways that contribute to cancer. The fact that these diverse human serum albumin modifications differed between benzene-exposed and control workers suggests that benzene can increase leukemia risks via multiple pathways involving a constellation of reactive molecules.

Electrophilically reactive compounds have the ability to form adducts with nucleophilic sites in DNA and proteins, constituting a risk for toxic effects. Mass spectrometric detection of adducts to N-terminal valine in hemoglobin (Hb) after detachment by modified Edman degradation procedures is one approach for in vivo monitoring of exposure to electrophilic compounds/metabolites. So far, applications have been limited to one or a few selected reactive species, such as acrylamide and its metabolite glycidamide. This article presents a novel screening strategy for unknown Hb adducts to be used as a basis for an adductomic approach. The method is based on a modified Edman procedure, FIRE, specifically developed for LC–MS/MS analysis of N-terminal valine adducts in Hb detached as fluorescein thiohydantoin (FTH) derivatives. The aim is to detect and identify a priori unknown Hb adducts in human blood samples. Screening of valine adducts was performed by stepwise scanning of precursor ions in small mass increments, monitoring four fragments common for the FTH derivative of valine with different N-substitutions in the multiple-reaction mode, covering a mass range of 135 Da (m/z 503–638). Samples from six smokers and six nonsmokers were analyzed. Control experiments were performed to compare these results with known adducts and to check for artifactual formation of adducts. In all samples of smokers and nonsmokers, seven adducts were identified, of which six have previously been studied. Nineteen unknown adducts were observed, and 14 of those exhibited fragmentation patterns similar to earlier studied FTH derivatives of adducts to valine. Identification of the unknown adducts will be the focus of future work. The presented methodology is a promising screening tool using Hb adducts to indicate exposure to potentially toxic electrophilic compounds and metabolites.

Introduction: Standardized commercial kits enable targeted metabolomics analysis and may thus provide an attractive complement to the more explorative approaches. The kits are typically developed for triple quadrupole mass spectrometers using serum and plasma. Objectives: Here we measure the concentrations of preselected metabolites in cerebrospinal fluid (CSF) using a kit developed for high-resolution mass spectrometry (HRMS). Secondarily, the study aimed to investigate metabolite alterations in patients with secondary progressive multiple sclerosis (SPMS) compared to controls. Methods: We performed targeted metabolomics in human CSF on twelve SPMS patients and twelve age and sex-matched healthy controls using the Absolute IDQ-p400 kit (Biocrates Life Sciences AG) developed for HRMS. The extracts were analysed using two methods; liquid chromatography-mass spectrometry (LC-HRMS) and flow injection analysis-MS (FIA-HRMS). Results: Out of 408 targeted metabolites, 196 (48%) were detected above limit of detection and 35 were absolutely quantified. Metabolites analyzed using LC-HRMS had a median coefficient of variation (CV) of 3% and 2.5% between reinjections the same day and after prolonged storage, respectively. The corresponding results for the FIA-HRMS were a median CV of 27% and 21%, respectively. We found significantly (p < 0.05) elevated levels of glycine, asymmetric dimethylarginine (ADMA), glycerophospholipid PC-O (34:0) and sum of hexoses in SPMS patients compared to controls. Conclusion: The Absolute IDQ-p400 kit could successfully be used for quantifying targeted metabolites in the CSF. Metabolites quantified using LC-HRMS showed superior reproducibility compared to FIA-HRMS.