When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomics method. We first group multiple features arising from the same analyte, which we call "degenerate features", using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ∼2961. We then applied an orthogonal approach to remove nonbiological features from the data using the C-based credentialing technology. This further reduced the number of unique analytes to less than 1000. Our 90% reduction in data is 5-fold greater than previously published studies. On the basis of the results, we propose an alternative approach to untargeted metabolomics that relies on thoroughly annotated reference data sets. To this end, we introduce the creDBle database ( http://creDBle.wustl.edu ), which contains accurate mass, retention time, and MS/MS fragmentation data as well as annotations of all credentialed features.

It is well established that lactate secreted by fermenting cells can be oxidized or used as a gluconeogenic substrate by other cells and tissues. Within the fermenting cell itself, however, it is generally assumed that lactate is produced to replenish NAD+ and then is secreted. Here we explored the possibility that cytosolic lactate is metabolized by the mitochondria of fermenting mammalian cells. We found that fermenting HeLa and H460 cells utilize exogenous lactate carbon to synthesize a large percentage of their lipids. With high-resolution mass spectrometry, we found that both 13C and 2-2H labels from enriched lactate enter the mitochondria. The lactate dehydrogenase (LDH) inhibitor oxamate decreased respiration of isolated mitochondria incubated in lactate, but not isolated mitochondria incubated in pyruvate. Additionally, transmission electron microscopy (TEM) showed that LDHB localizes to the mitochondria. Taken together, our results demonstrate a link between lactate metabolism and the mitochondria of fermenting mammalian cells.

The immense molecular diversity of neurons challenges our ability to understand the genetic and cellular etiology of neuropsychiatric disorders. Leveraging knowledge from neurobiology may help parse the genetic complexity: identifying genes important for a circuit that mediates a particular symptom of a disease may help identify polymorphisms that contribute to risk for the disease as a whole. The serotonergic system has long been suspected in disorders that have symptoms of repetitive behaviors and resistance to change, including autism. We generated a bacTRAP mouse line to permit translational profiling of serotonergic neurons. From this, we identified several thousand serotonergic-cell expressed transcripts, of which 174 were highly enriched, including all known markers of these cells. Analysis of common variants near the corresponding genes in the AGRE collection implicated the RNA binding protein CELF6 in autism risk. Screening for rare variants in CELF6 identified an inherited premature stop codon in one of the probands. Subsequent disruption of Celf6 in mice resulted in animals exhibiting resistance to change and decreased ultrasonic vocalization as well as abnormal levels of serotonin in the brain. This work provides a reproducible and accurate method to profile serotonergic neurons under a variety of conditions and suggests a novel paradigm for gaining information on the etiology of psychiatric disorders.

An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. Validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.

The aim of untargeted metabolomics is to profile as many metabolites as possible, yet a major challenge is comparing experimental method performance on the basis of metabolome coverage. To date, most published approaches have compared experimental methods by counting the total number of features detected. Due to artifactual interference, however, this number is highly variable and therefore is a poor metric for comparing metabolomic methods. Here we introduce an alternative approach to benchmarking metabolome coverage which relies on mixed Escherichia coli extracts from cells cultured in regular and 13C-enriched media. After mass spectrometry-based metabolomic analysis of these extracts, we “credential” features arising from E. coli metabolites on the basis of isotope spacing and intensity. This credentialing platform enables us to accurately compare the number of nonartifactual features yielded by different experimental approaches. We highlight the value of our platform by reoptimizing a published untargeted metabolomic method for XCMS data processing. Compared to the published parameters, the new XCMS parameters decrease the total number of features by 15% (a reduction in noise features) while increasing the number of true metabolites detected and grouped by 20%. Our credentialing platform relies on easily generated E. coli samples and a simple software algorithm that is freely available on our laboratory Web site (). We have validated the credentialing platform with reversed-phase and hydrophilic interaction liquid chromatography as well as Agilent, Thermo Scientific, AB SCIEX, and LECO mass spectrometers. Thus, the credentialing platform can readily be applied by any laboratory to optimize their untargeted metabolomic pipeline for metabolite extraction, chromatographic separation, mass spectrometric detection, and bioinformatic processing.