Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma samples using untargeted metabolomics.

INTRODUCTION: Clinical metabolomics has utility as a screen for inborn errors of metabolism (IEM) and variant classification in patients with rare disease. It is important to understand and characterize preanalytical factors that influence assay performance during patient sample testing. OBJECTIVES: To evaluate the impact of extended thawing of human EDTA plasma samples on ice prior to extraction as well as repeated freeze-thaw cycling of samples to identify compounds that are unstable prior to metabolomic analysis. METHODS: Twenty-four (24) donor EDTA plasma samples were collected and immediately frozen at - 80 °C. Twelve samples were thawed on ice and extracted for analysis at time 0, 2, 4, and 6 h. Twelve other donor samples were repeatedly thawed and frozen up to four times and analyzed at each cycle. Compound levels at each time point/freeze-thaw cycle were compared to the control samples using matched-paired t tests to identify analytes affected by each condition. RESULTS: We identified 1026 biochemicals across all samples. Incubation of thawed EDTA plasma samples on ice for up to 6 h resulted in < 1% of biochemicals changing significantly. Freeze-thaw cycles affected a greater percentage of the metabolome; ~ 2% of biochemicals changed after 3 freeze-thaw cycles. CONCLUSIONS: Our study highlights that the number and magnitude of these changes are not as widespread as other aspects of improper sample handling. In total, < 3% of the metabolome detected on our clinical metabolomics platform should be disqualified when multiple freeze-thaw cycles or extended thawing at 4 °C are performed on a given sample.

An ambient-temperature storage and stabilization device performs comparably to flash-frozen collection for stool metabolomics in infants.

BACKGROUND: Stool metabolites provide essential insights into the function of the gut microbiome. The current gold standard for storage of stool samples for metabolomics is flash-freezing at - 80 °C which can be inconvenient and expensive. Ambient temperature storage of stool is more practical, however no available methodologies adequately preserve the metabolomic profile of stool. A novel sampling kit (OMNImet.GUT; DNA Genotek, Inc.) was introduced for ambient temperature storage and stabilization of feces for metabolomics; we aimed to test the performance of this kit vs. flash-freezing. To do this stool was collected from an infant's diaper was divided into two aliquots: 1) flash-frozen and 2) stored in an OMNImet.GUT tube at ambient temperature for 3-4 days. Samples from the same infant were collected at 2 different time points to assess metabolite changes over time. Subsequently, all samples underwent metabolomic analysis by liquid chromatography - tandem mass spectrometry (LC-MS/MS). RESULTS: Paired fecal samples (flash-frozen and ambient temperature) from 16 infants were collected at 2 time points (32 individual samples, 64 aliquots). Similar numbers of metabolites were detected in both the frozen and ambient temperature samples (1126 in frozen, 1107 in ambient temperature, 1064 shared between sample types). Metabolite abundances were strongly correlated between storage methods (median Spearman correlation Rs = 0.785 across metabolites). Hierarchical clustering analysis and principal component analysis showed that samples from the same individuals at a given time point clustered closely, regardless of the storage method. Repeat samples from the same individual were compared by paired t-test, separately for the frozen and OMNImet.GUT. The number of metabolites in each biochemical class that significantly changed (p < 0.05) at timepoint 2 relative to timepoint 1 was similar in flash-frozen versus ambient temperature storage. Changes in microbiota modified metabolites over time were also consistent across both methodologies. CONCLUSION: Ambient temperature storage and stabilization of stool in the OMNImet.GUT device yielded comparable metabolomic results to flash freezing in terms of 1) the identity and abundance of detected biochemicals 2) the distinct metabolomic profiles of subjects and 3) changes in metabolites over time that are plausibly microbiota-induced. This method potentially provides a more convenient, less expensive home collection and storage option for stool metabolomic analysis.

Precision of a Clinical Metabolomics Profiling Platform for Use in the Identification of Inborn Errors of Metabolism.

BACKGROUND: The application of whole-exome sequencing for the diagnosis of genetic disease has paved the way for systems-based approaches in the clinical laboratory. Here, we describe a clinical metabolomics method for the screening of metabolic diseases through the analysis of a multi-pronged mass spectrometry platform. By simultaneously measuring hundreds of metabolites in a single sample, clinical metabolomics offers a comprehensive approach to identify metabolic perturbations across multiple biochemical pathways. METHODS: We conducted a single- and multi-day precision study on hundreds of metabolites in human plasma on 4, multi-arm, high-throughput metabolomics platforms. RESULTS: The average laboratory coefficient of variation (CV) on the 4 platforms was between 9.3 and 11.5% (median, 6.5-8.4%), average inter-assay CV on the 4 platforms ranged from 9.9 to 12.6% (median, 7.0-8.3%) and average intra-assay CV on the 4 platforms ranged from 5.7 to 6.9% (median, 3.5-4.4%). In relation to patient sample testing, the precision of multiple biomarkers associated with IEM disorders showed CVs that ranged from 0.2 to 11.0% across 4 analytical batches. CONCLUSIONS: This evaluation describes single and multi-day precision across 4 identical metabolomics platforms, comprised each of 4 independent method arms, and reproducibility of the method for the measurement of key IEM metabolites in patient samples across multiple analytical batches, providing evidence that the method is robust and reproducible for the screening of patients with inborn errors of metabolism.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing.

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

Elucidation of the complex metabolic profile of cerebrospinal fluid using an untargeted biochemical profiling assay.

We sought to determine the molecular composition of human cerebrospinal fluid (CSF) and identify the biochemical pathways represented in CSF to understand the potential for untargeted screening of inborn errors of metabolism (IEMs). Biochemical profiles for each sample were obtained using an integrated metabolomics workflow comprised of four chromatographic techniques followed by mass spectrometry. Secondarily, we wanted to compare the biochemical profile of CSF with those of plasma and urine within the integrated mass spectrometric-based metabolomic workflow. Three sample types, CSF (N=30), urine (N=40) and EDTA plasma (N=31), were analyzed from retrospectively collected pediatric cohorts of equivalent age and gender characteristics. We identified 435 biochemicals in CSF representing numerous biological and chemical/structural families. Sixty-three percent (273 of 435) of the biochemicals detected in CSF also were detected in urine and plasma, another 32% (140 of 435) were detected in either plasma or urine, and 5% (22 of 435) were detected only in CSF. Analyses of several metabolites showed agreement between clinically useful assays and the metabolomics approach. An additional set of CSF and plasma samples collected from the same patient revealed correlation between several biochemicals detected in paired samples. Finally, analysis of CSF from a pediatric case with dihydropteridine reductase (DHPR) deficiency demonstrated the utility of untargeted global metabolic phenotyping as a broad assessment to screen samples from patients with undifferentiated phenotypes. The results indicate a single CSF sample processed with an integrated metabolomics workflow can be used to identify a large breadth of biochemicals that could be useful for identifying disrupted metabolic patterns associated with IEMs.

Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites.

Genetic factors modifying the blood metabolome have been investigated through genome-wide association studies (GWAS) of common genetic variants and through exome sequencing. We conducted a whole-genome sequencing study of common, low-frequency and rare variants to associate genetic variations with blood metabolite levels using comprehensive metabolite profiling in 1,960 adults. We focused the analysis on 644 metabolites with consistent levels across three longitudinal data collections. Genetic sequence variations at 101 loci were associated with the levels of 246 (38%) metabolites (P ≤ 1.9 × 10-11). We identified 113 (10.7%) among 1,054 unrelated individuals in the cohort who carried heterozygous rare variants likely influencing the function of 17 genes. Thirteen of the 17 genes are associated with inborn errors of metabolism or other pediatric genetic conditions. This study extends the map of loci influencing the metabolome and highlights the importance of heterozygous rare variants in determining abnormal blood metabolic phenotypes in adults.

Metabolomic Profiling of Human Urine as a Screen for Multiple Inborn Errors of Metabolism.

AIMS: We wished to determine the efficacy of using urine as an analyte to screen for a broad range of metabolic products associated with multiple different types of inborn errors of metabolism (IEMs), using an automated mass spectrometry-based assay. Urine was compared with plasma samples from a similar cohort analyzed using the same assay. Specimens were analyzed using two different commonly utilized urine normalization methods based on creatinine and osmolality, respectively. METHODS: Biochemical profiles for each sample (from both affected and unaffected subjects) were obtained using a mass spectrometry-based platform and population-based statistical analyses. RESULTS: We identified over 1200 biochemicals from among 100 clinical urine samples and identified clear biochemical signatures for 16 of 18 IEM diseases tested. The two diseases that did not result in clear signatures, X-linked creatine transporter deficiency and ornithine transcarbamylase deficiency, were from individuals under treatment, which masked biomarker signatures. Overall the process variability and coefficient of variation for isolating and identifying biochemicals by running technical replicates of each urine sample was 10%. CONCLUSIONS: A single urine sample analyzed with our integrated metabolomic platform can identify signatures of IEMs that are traditionally identified using many different assays and multiple sample types. Creatinine and osmolality-normalized data were robust to the detection of the disorders and samples tested here.

Equation of State of the Two-Dimensional Hubbard Model.

The subtle interplay between kinetic energy, interactions, and dimensionality challenges our comprehension of strongly correlated physics observed, for example, in the solid state. In this quest, the Hubbard model has emerged as a conceptually simple, yet rich model describing such physics. Here we present an experimental determination of the equation of state of the repulsive two-dimensional Hubbard model over a broad range of interactions 0≲U/t≲20 and temperatures, down to k_{B}T/t=0.63(2) using high-resolution imaging of ultracold fermionic atoms in optical lattices. We show density profiles, compressibilities, and double occupancies over the whole doping range, and, hence, our results constitute benchmarks for state-of-the-art theoretical approaches.

Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism.

Global metabolic profiling currently achievable by untargeted mass spectrometry-based metabolomic platforms has great potential to advance our understanding of human disease states, including potential utility in the detection of novel and known inborn errors of metabolism (IEMs). There are few studies of the technical reproducibility, data analysis methods, and overall diagnostic capabilities when this technology is applied to clinical specimens for the diagnosis of IEMs. We explored the clinical utility of a metabolomic workflow capable of routinely generating semi-quantitative z-score values for ~900 unique compounds, including ~500 named human analytes, in a single analysis of human plasma. We tested the technical reproducibility of this platform and applied it to the retrospective diagnosis of 190 individual plasma samples, 120 of which were collected from patients with a confirmed IEM. Our results demonstrate high intra-assay precision and linear detection for the majority compounds tested. Individual metabolomic profiles provided excellent sensitivity and specificity for the detection of a wide range of metabolic disorders and identified novel biomarkers for some diseases. With this platform, it is possible to use one test to screen for dozens of IEMs that might otherwise require ordering multiple unique biochemical tests. However, this test may yield false negative results for certain disorders that would be detected by a more well-established quantitative test and in its current state should be considered a supplementary test. Our findings describe a novel approach to metabolomic analysis of clinical specimens and demonstrate the clinical utility of this technology for prospective screening of IEMs.

Chemoselective reactions of 3-benzyloxy-1,2-o-quinone with organometallic reagents.

Chemoselective additions of organometallic reagents to 3-benzyloxy-1,2-o-quinone are described. Various nucleophiles are shown to undergo selective 1,2-addition, 1,4-addition, and etherification. Selective 1,2-additions provide stable, nondimerizing o-quinols as a novel alternative to oxidative dearomatization.

Redox-modulated recognition of tetrazines using thioureas.

Reduced 1,2,4,5-tetrazines serve as two-point hydrogen-bonding acceptors for thiourea. This host-guest system does not exhibit significant binding in the neutral state, making the complex an electrochemical "on/off" switch.

An investigation of the absolute configuration of the potent histamine H3 receptor antagonist GT-2331 using vibrational circular dichroism.

GT-2331 [(+)-1] is one of the most potent members of a class of chiral drug substances used to regulate the synthesis and release of histamine by the histamine H3 receptor, and as such, is an important biomarker for pharmaceutical companies conducting research in this field. In addition to overall structural features, the bioactivity of this molecule has also been found to be highly dependent on absolute stereochemistry, making the reliable assignment of this property a necessity. X-ray diffraction studies have provided conflicting data, leaving its three-dimensional structure uncertain. In view of this, its absolute configuration was investigated by vibrational circular dichroism. Results from this study provided independent assignment of this important molecule as the (1S,2S)-enantiomer.

Automated mass correction and data interpretation for protein open-access liquid chromatography-mass spectrometry.

Characterization of recombinant protein purification fractions and final products by liquid chromatography-mass spectrometry (LC/MS) are requested more frequently each year. A protein open-access (OA) LC/MS system was developed in our laboratory to meet this demand. This paper compares the system that we originally implemented in our facilities in 2003 to the one now in use, and discusses, in more detail, recent enhancements that have improved its robustness, reliability, and data reporting capabilities. The system utilizes instruments equipped with reversed-phase chromatography and an orthogonal accelerated time-of-flight mass spectrometer fitted with an electrospray source. Sample analysis requests are accomplished using a simple form on a web-enabled laboratory information management system (LIMS). This distributed form is accessible from any intranet-connected company desktop computer. Automated data acquisition and processing are performed using a combination of in-house (OA-Self Service, OA-Monitor, and OA-Analysis Engine) and vendor-supplied programs (AutoLynx, and OpenLynx) located on acquisition computers and off-line processing workstations. Analysis results are then reported via the same web-based LIMS. Also presented are solutions to problems not addressed on commercially available, small-molecule OA-LC/MS systems. These include automated transforming of mass-to-charge (m/z) spectra to mass spectra and automated data interpretation that considers minor variants to the protein sequence-such as common post-translational modifications (PTMs). Currently, our protein OA-LC/MS platform runs on five LC/MS instruments located in three separate GlaxoSmithKline R&D sites in the US and UK. To date, more than 8000 protein OA-LC/MS samples have been analyzed. With these user friendly and highly automated OA systems in place, mass spectrometry plays a key role in assessing the quality of recombinant proteins, either produced at our facilities or bought from external sources, without dedicating extensive amounts of analyst resource.

Protein open-access liquid chromatography/mass spectrometry.

Each year increasing numbers of proteins are submitted for routine characterization by liquid chromatography/mass spectrometry (LC/MS). This paper reports a solution that transforms routine LC/MS analysis of proteins into a fully automated process that significantly reduces analyst intervention. The solution developed, protein open-access (OA) LC/MS, consists of web-enabled sample submission and registration, automated data processing, data interpretation, and report generation. Sample submissions and results are recorded in a LIMS that utilizes an Oracle database. The protein sequence is captured during the sample submission process, stored in the database, and utilized to determine the theoretical protein molecular weight. This calculated mass is used to set the parameters for transformation of the mass-to-charge spectra to the mass domain and evaluate the presence or absence of the desired protein. Three protein OA-LC/MS instruments have been deployed in our facility to support protein characterization, purification, and modification efforts.

Using accurate mass electrospray ionization-time-of-flight mass spectrometry with in-source collision-induced dissociation to sequence peptide mixtures.

Although data-dependent LC-MS-MS with database searching has become au courant for identifying proteins, the technique is constrained by duty-cycle inefficiency and the inability of most tandem mass analyzers to accurately measure peptide product ion masses. In this work, a novel approach is presented for simultaneous peptide fragmentation and accurate mass measurement using in-source collision-induced dissociation (CID) on electrospray ionization (ESI)-time-of-flight (TOF) MS. By employing internal mass reference compounds, mass measurement accuracy within +/-5 ppm for tryptic peptide precursors and +/-10 ppm for most sequence-specific product ions was consistently achieved. Analysis of a complex solution containing several digested protein standards did not adversely affect instrument performance.