Pooled Quality Control Research Articles

A Comprehensive Spatially Resolved Metabolomics Dataset for Lampreys

As one of the two most ancient groups of extant vertebrates, lamprey has become an important model organism in various fields of biology. In this paper, we present a comprehensive tissue-wide spatial metabolomics dataset for lampreys, where 14 distinct tissues were analyzed using liquid chromatography-mass spectrometry (LC-MS) in both positive and negative ion modes. The dataset has been fully validated using internal standard and pooled quality control samples and is readily accessible at the UCSD Metabolomics Workbench. This dataset serves as a valuable resource for researchers using lampreys as a model organism. Additionally, it acts as a benchmark metabolomics dataset for evaluating new algorithms and software tools and comparing them with previously published results. A lamprey spatial metabolomics database is also provided to support studies utilizing lampreys as an animal model, and to complement and validate other spatial metabolomics studies on lampreys conducted with mass spectrometry imaging or other techniques.

Generating pooled quality control samples of volatile organic compounds

Untargeted analysis of volatile organic compounds (VOCs) from exhaled breath and culture headspace are influenced by several confounding factors not represented in reference standards. In this study, we propose a method of generating pooled quality control (QC) samples for untargeted VOC studies using a split-recollection workflow with thermal desorption tubes. Sample tubes were desorbed and split from each sample and recollected onto a single tube, generating a pooled QC sample. This QC sample was then repeatedly desorbed and recollected with a sequentially lower split ratio allowing injection of multiple QC samples. We found pooled QC samples to be representative of complex mixtures using principal component analysis and may be useful in future longitudinal, multi-centre, and validation studies to assess data quality and adjust for batch effects.

Dual column chromatography combined with high-resolution mass spectrometry improves coverage of non-targeted analysis of plant root exudates

BackgroundWithin the plant kingdom, there is an exceptional amount of chemical diversity that has yet to be annotated. It is for this reason that non-targeted analysis is of interest for those working in novel natural products. To increase the number and diversity of compounds observable in root exudate extracts, several workflows which differ at three key stages were compared: 1) sample extraction, 2) chromatography, and 3) data preprocessing. ResultsPlants were grown in Hoagland's solution for two weeks, and exudates were initially extracted with water, followed by a 24-h regeneration period with subsequent extraction using methanol. Utilizing the second extraction showed improved results with less ion suppression and reduced retention time shifting compared to the first extraction. A single column method, utilizing a pentafluorophenyl column, paired with high-resolution mass spectrometry ionized and correctly identified 34 mock root exudate compounds, while the dual column method, incorporating a pentafluorophenyl column and a porous graphitic carbon column, retained and identified 43 compounds. In a pooled quality control sample of exudate extracts, the single column method detected 1,444 compounds. While the dual method detected fewer compounds overall (1,050), it revealed a larger number of small polar compounds. Three preprocessing methods (targeted, proprietary, and open source) successfully identified 43, 31, and 38 mock root exudate compounds to confidence level 1, respectively. SignificanceEnhancing signal strength and analytical method stability involves removing the high ionic strength nutrient solution before sampling root exudate extracts. Despite signal intensity loss, a dual column method enhances compound coverage, particularly for small polar metabolites. Open-source software proves a viable alternative for non-targeted analysis, even surpassing proprietary software in peak picking.

Toward a More Comprehensive Approach for Dissolved Organic Matter Chemical Characterization Using an Orbitrap Fusion Tribrid Mass Spectrometer Coupled with Ion and Liquid Chromatography Techniques.

Dissolved organic matter (DOM) represents one of the largest active organic carbon pools in the global carbon cycle. Although extensively studied, only <10% of DOM has been chemically characterized into individual dissolved compounds due to its molecular complexity. This study introduced a more comprehensive DOM characterization method by coupling both ion chromatography (IC) and liquid chromatography (LC) with high mass accuracy and resolution mass spectrometry. We presented a new on-the-fly mass calibration of the Orbitrap technique by utilizing the "lock mass" function in the Orbitrap Fusion Tribrid mass spectrometer (OT-FTMS), which assures high mass accuracy at every scan by a postcolumn introduction of internal labeled standards. With both IC and LC, tested unlabeled standards of amino acids, small peptides, and organic acids were consistently below 1.0 ppm mass error, giving the OT-FTMS the potential of reaching mass accuracy of the Fourier-transform ion cyclotron resonance mass spectrometer. In addition to mass accuracy, a pooled quality control sample (QC) was used to increase reproducibility by applying systematic error removal using random forest (SERRF). Using an untargeted mass spectrometry approach, estuarine DOM samples were analyzed by OT-FTMS coupled to IC in negative mode and LC in positive mode detection to cover a wide range of highly cationic to highly anionic molecules. As a proof of concept, we focused on elucidating the structures of three distinct DOM compound classes with varied acidities and basicities. In UPLC-OT-FTMS, a total of 915 compounds were detected. We putatively elucidated 44 small peptides and 33 deaminated peptides of these compounds. With IC-OT-FTMS, a total of 1432 compounds were detected. We putatively elucidated 20 peptides, 268 deaminated peptides, and 188 organic acids. Except for five compounds, all putatively elucidated compounds were uniquely detected in their corresponding chromatography technique. These results highlight the need for combining these two techniques to provide a more comprehensive method for DOM characterization. Application of the combined IC and LC techniques is not limited to DOM chemical characterization. It can analyze other complex compound mixtures, such as metabolites, and anthropogenic pollutants, such as pesticides and endocrine-disrupting chemicals, in environmental and biological samples.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Abstract 18472: Plasma Proteome Changes in Heart Failure and Non-Heart Failure Patients Undergoing Cardiac Surgery

Introduction: Patients with and without heart failure display different clinical courses after cardiac surgery. Hypothesis: Unbiased analyses of the plasma proteome from extreme phenotypes in myocardial function may identify differences in candidate proteins differentially modified by cardiac surgery using cardiopulmonary bypass (CPB). Methods: We combined two prospectively-collected datasets: Left ventricular assist device (LVAD) recipients from the INSPIRE-FLO trial (N=94, NCT03081052) and coronary artery bypass graft (CABG) surgery patients with normal biventricular function (N=50). For each population, we obtained EDTA-plasma from arterial blood sampling in the operating room and on postoperative day 1. Including quality control pools, 304 total samples were digested in a 96-well format with trypsin. Peptides were analyzed by microflow liquid chromatography coupled to tandem mass spectrometry using data-independent acquisition. Proteins were identified and quantified at a 1% false discovery rate (FDR) in Spectronaut. Statistical analyses utilized a t-test with FDR correction (significance defined by log2FC&gt;1 and q&lt;0.05). Results: There were 943 protein groups quantified in at least one plasma sample. Patients undergoing LVAD surgery had significantly higher levels of 135 proteins including acute phase reactants (CRP, SAA1, SAA2), putative neutrophil-derived proteins (S100A8/9) and markers of renal insufficiency (B2M and REG3A) (Figure 1). Cardiac surgery using cardiopulmonary bypass increased acute phase and inflammatory markers, and cardiac proteins (ENOB, MYG and KCRM), with NGAL, TIMP1 and PAI1 among proteins with higher absolute increase after LVAD implantation versus CABG surgery ( Fig.1 ). Conclusions: Unbiased analyses of the plasma proteome identifies baseline and postoperative differences associated with extreme phenotypes of myocardial function undergoing cardiac surgery using CPB.

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry.

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.

Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses

Proteomics research has been transformed due to high-throughput liquid chromatography (LC-MS/MS) tandem mass spectrometry instruments combined with highly sophisticated automated sample preparation and multiplexing workflows. However, scaling proteomics experiments to large sample cohorts (hundreds to thousands) requires thoughtful quality control (QC) protocols. Robust QC protocols can help with reproducibility, quantitative accuracy, and provide opportunities for more decisive troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of N = 335 patient samples using tandem mass tag (TMTpro) 16-plex batches. Over the course of a 10-month data acquisition period for this cohort we collected 271 pooled QC LC-MS/MS result files obtained from MS/MS analysis of a patient-derived pooled plasma sample, representative of the entire cohort population. This sample was tagged with TMTzero or TMTpro reagents and used to inform the daily performance of the LC-MS/MS instruments and to allow within and across sample batch normalization. Analytical variability of a number of instrumental and data analysis metrics including protein and peptide identifications, peptide spectral matches (PSMs), number of obtained MS/MS spectra, average peptide abundance, percent of peptides with a Δ m/z between ±0.003 Da, percent of MS/MS spectra obtained at the maximum injection time, and the retention time of selected tracking peptides were evaluated to help inform the design of a robust LC-MS/MS QC workflow for use in future cohort studies. This study also led to general tips for using selected metrics to inform real-time troubleshooting of LC-MS/MS performance issues with daily QC checks.

Large-Scale metabolomics: Predicting biological age using 10,133 routine untargeted LC-MS measurements.

Untargeted metabolomics is the study of all detectable small molecules, and in geroscience, metabolomics has shown great potential to describe the biological age-a complex trait impacted by many factors. Unfortunately, the sample sizes are often insufficient to achieve sufficient power and minimize potential biases caused by, for example, demographic factors. In this study, we present the analysis of biological age in ~10,000 toxicologic routine blood measurements. The untargeted screening samples obtained from ultra-high pressure liquid chromatography-quadruple time of flight mass spectrometry (UHPLC- QTOF) cover + 300 batches and + 30 months, lack pooled quality controls, lack controlled sample collection, and has previously only been used in small-scale studies. To overcome experimental effects, we developed and tested a custom neural network model and compared it with existing prediction methods. Overall, the neural network was able to predict the chronological age with an rmse of 5.88 years (r2 =0.63) improving upon the 6.15 years achieved by existing normalization methods. We used the feature importance algorithm, Shapley Additive exPlanations (SHAP), to identify compounds related to the biological age. Most importantly, the model returned known aging markers such as kynurenine, indole-3-aldehyde, and acylcarnitines along with a potential novel aging marker, cyclo (leu-pro). Our results validate the association of tryptophan and acylcarnitine metabolism to aging in a highly uncontrolled large-s cale sample. Also, we have shown that by using robust computational methods it is possible to deploy large LC-MS datasets for metabolomics studies to reduce the risk of bias and empower aging studies.

The complete change in bile acids and steroids in systematic metabolomics applied to the intrahepatic cholestasis of pregnancy.

Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific hepatobiliary disease, leading to an abnormal increase in total bile acid in the blood of pregnant women. To systematically explore the similarities and differences in metabolites and metabolic pathways among three types of biological samples from ICP women, a study of 18 ICP and 6 healthy (as a normal control) pregnant women was performed to investigate their clinical information and biochemical features. Based on validated LC-MS/MS methods 1-5 for hydrophilic and hydrophobic metabolites (molecular weight <2000 Dalton), an untargeted-metabolomic strategy was applied to 24 pregnant women to determine the metabolites from 22 serum, 15 placental and 22 urine samples. Then 1137 metabolites from serum, 876 metabolites from placental tissue and 311 metabolites from urine with a coefficient of variation <30% in the pooled quality control samples were found. Furthermore, orthogonal partial least squares-discriminate analysis (OPLS-DA), correlation analysis, chemical enrichment analysis and metabolic pathway analysis were carried out by a bioinformatics process. On the OPLS-DA model analysis, the metabolites in urine were better than those in serum or placental tissue to reflect the metabolic changes of ICP disease. Some metabolites were significantly changed in serum (n = 71), placental tissue (n = 46) and urine (n = 36), such as bile acids, triacylglycerols, lysoPCs, and steroids. Primary bile acid biosynthesis was the main metabolic pathway in ICP disease, and taurine and hypotaurine metabolism and sphingolipid metabolism were also found. More specifically, bile acids increased and steroids decreased in the serum, placental and urine samples. For complex metabolic diseases such as ICP disease, untargeted-metabolomic analysis of multiple biological samples could provide a systematic understanding of the changes in metabolic types and pathways.

Normalization Approach by a Reference Material to Improve LC-MS-Based Metabolomic Data Comparability of Multibatch Samples.

Large cohorts of samples from multiple batches are usually required for global metabolomic studies to characterize the metabolic state of human disease. As such, it is critical to eliminate systematic variation and truly reveal the biologically associated alterations. In this study, we proposed a reference material-based approach (Ref-M) for data correction by liquid chromatography-mass spectrometry and represented by an analysis of multibatch human serum samples. The reference material was generated by mixing serum from healthy donors and distributed to each extraction batch of subject samples. Pooled quality control samples and isotopic internal standards were then applied in each acquisition batch for data quality control. Finally, each metabolite in subject samples was normalized by its counterpart in the reference serum. We demonstrated that Ref-M significantly enhanced the numbers of efficient features and effectively eliminated the batch variation of 522 serum samples of healthy individuals, benign pulmonary nodules, and lung cancer patients. Twenty differential metabolites were identified to distinguish lung cancer from healthy controls in the training set. The discriminant model was validated in an independent data set with an area under the receiver operating characteristics (ROC) curve (AUC) of 0.853. Another 40 serum samples further tested with Ref-M were achieved an AUC of 0.843 by the established model. Our results showed that the reference material-based approach presents the potential to improve the data comparability and precision for biomarker discovery in large-scale metabolomic studies.

Development and Application of an LC-MS/MS Untargeted Exposomics Method with a Separated Pooled Quality Control Strategy.

Pooled quality controls (QCs) are usually implemented within untargeted methods to improve the quality of datasets by removing features either not detected or not reproducible. However, this approach can be limiting in exposomics studies conducted on groups of exposed and nonexposed subjects, as compounds present at low levels only in exposed subjects can be diluted and thus not detected in the pooled QC. The aim of this work is to develop and apply an untargeted workflow for human biomonitoring in urine samples, implementing a novel separated approach for preparing pooled quality controls. An LC-MS/MS workflow was developed and applied to a case study of smoking and non-smoking subjects. Three different pooled quality controls were prepared: mixing an aliquot from every sample (QC-T), only from non-smokers (QC-NS), and only from smokers (QC-S). The feature tables were filtered using QC-T (T-feature list), QC-S, and QC-NS, separately. The last two feature lists were merged (SNS-feature list). A higher number of features was obtained with the SNS-feature list than the T-feature list, resulting in identification of a higher number of biologically significant compounds. The separated pooled QC strategy implemented can improve the nontargeted human biomonitoring for groups of exposed and nonexposed subjects.

Quality assurance and harmonization for targeted biomonitoring measurements of environmental organic chemicals across the Children's Health Exposure Analysis Resource laboratory network.

A consortium of laboratories established under the Children's Health Exposure Analysis Resource (CHEAR) used a multifaceted quality assurance program to promote measurement harmonization for trace organics analyses of human biospecimens that included: (1) participation in external quality assurance (EQA)/proficiency testing (PT) programs; (2) analyses of a urine-based CHEAR common quality control (QC) pool with each analytical batch across all participating laboratories; (3) method validation against NIST Standard Reference Materials® (SRMs); and (4) analyses of blinded duplicates and other project-specific QC samples. The capability of five CHEAR laboratories in organic chemical analysis increased across the 4-year period, and performance in the external PT program improved over time - recent challenges reporting >90% analytes with satisfactory performance. The CHEAR QC pools were analyzed for several classes of organic chemicals including phthalate metabolites and environmental phenols by the participating laboratories with every batch of project samples, which provided a rich source of measurement data for the assessment of intra- and inter-laboratory variance. Within-laboratory and overall variabilities in measurements across laboratories were calculated for target chemicals in urine QC pools; the coefficient of variation (CV) was generally below 25% across batches, studies and laboratories and indicated acceptable analytical imprecision. The suite of organic chemicals analyzed in the CHEAR QC pool was broader than those reported for commercially available reference materials. The accuracy of each of the laboratories' methods was verified through the analysis of several NIST SRMs and was, for example, 97±5.2% for environmental phenols and 95±11% for phthalates. Analysis of blinded duplicate samples showed excellent agreement and reliability of measurements. The intra-class correlation coefficients (ICC) for phthalate metabolites analyzed in various batches across three CHEAR laboratories showed excellent reliability (typically >0.90). Overall, the multifaceted quality assurance protocols followed among the CHEAR laboratories ensured reliable and reproducible data quality for several classes of organic chemicals. Increased participation in external PT programs through inclusion of additional target analytes will further enhance the confidence in data quality.

Evaluating inter-study variability in phthalate and trace element analyses within the Children\u2019s Health Exposure Analysis Resource (CHEAR) using multivariate control charts

BackgroundThe Children’s Health Exposure Analysis Resource (CHEAR) program allows researchers to expand their research goals by offering the assessment of environmental exposures in their previously collected biospecimens. Samples are analyzed in one of CHEAR’s network of six laboratory hubs with the ability to assess a wide array of environmental chemicals. The ability to assess inter-study variability is important for researchers who want to combine datasets across studies and laboratories.ObjectiveHerein we establish a process of evaluating inter-study variability for a given analytic method.MethodsCommon quality control (QC) pools at two concentration levels (A and B) in urine were created within CHEAR for insertion into each batch of samples tested at a rate of three samples of each pool per 100 study samples. We assessed these QC pool results for seven phthalates analyzed for five CHEAR studies by three different lab hubs utilizing multivariate control charts to identify out-of-control runs or sets of samples associated with a given QC sample. We then tested the conditions that would lead to an out-of-control run by simulating outliers in an otherwise “in-control” set of 12 trace elements in blood QC samples (NIST SRM 955c).ResultsWhen phthalates were assessed within study, we identified a single out-of-control run for two of the five studies. Combining QC results across lab hubs, all of the runs from these two studies were now in-control, while multiple runs from two other studies were pushed out-of-control. In our simulation study we found that 3–6 analytes with outlier values (5xSD) within a run would push that run out of control in 65–83% of simulations, respectively.SignificanceWe show how acceptable bounds of variability can be established for a given analytic method by evaluating QC materials across studies using multivariate control charts.

Phenolic profiles of raw mono- and polyfloral honeys from Latvia

The phenolic compound profiles of 393 raw mono- and polyfloral Latvian honey samples were investigated using a targeted ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) method. This strategy allowed simultaneous determination of the content of 11 phenolic acids, 18 flavonoids, 2 plant hormones, and 3 water-soluble vitamins in honey samples of botanically diverse origin, including buckwheat (Fagopyrum esculentum), heather (Calluna vulgaris), linden (Tilia cordata), and rapeseed (Brassica napus). Honey samples were collected directly from the beekeepers and were analyzed in their natural, raw form. A selective high-throughput analysis was implemented using sugaring-out assisted liquid-liquid extraction (SULLE) combined with the pentafluorophenyl (PFP) stationary phase. The robustness of the method was evaluated by using the design of experiments (DoE) approach and validated. Pooled quality control (QC) samples allowed monitoring of the method performance across the analysis sequences and principal components analysis (PCA) of QC samples resulted in a small amount of total variance in the total projection, indicating that the data were of good quality. The acquired occurrence data were compiled and compared with other studies. To the best of our knowledge, this study is the first of this scale and purpose, providing unique insights into the phenolic compound composition of fresh, regional European honey.

DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data

Existing data acquisition modes such as full-scan, data-dependent (DDA), and data-independent acquisition (DIA) often present limited capabilities in capturing metabolic information in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. In this work, we proposed a novel metabolomic data acquisition workflow that combines DDA and DIA analyses to achieve better metabolomic data quality, including enhanced metabolome coverage, tandem mass spectrometry (MS2) coverage, and MS2 quality. This workflow, named data-dependent-assisted data-independent acquisition (DaDIA), performs untargeted metabolomic analysis of individual biological samples using DIA mode and the pooled quality control (QC) samples using DDA mode. This combination takes advantage of the high-feature number and MS2 spectral coverage of the DIA data and the high MS2 spectral quality of the DDA data. To analyze the heterogeneous DDA and DIA data, we further developed a computational program, DaDIA.R, to automatically extract metabolic features and perform streamlined metabolite annotation of DaDIA data set. Using human urine samples, we demonstrated that the DaDIA workflow delivers remarkably improved data quality when compared to conventional DDA or DIA metabolomics. In particular, both the number of detected features and annotated metabolites were greatly increased. Further biological demonstration using a leukemia metabolomics study also proved that the DaDIA workflow can efficiently detect and annotate around 4 times more significant metabolites than DDA workflow with broad MS2 coverage and high MS2 spectral quality for downstream statistical analysis and biological interpretation. Overall, this work represents a critical development of data acquisition mode in untargeted metabolomics, which can greatly benefit untargeted metabolomics for a wide range of biological applications.

Limitations of deuterium-labeled internal standards for quantitative electrospray ionization mass spectrometry analysis of fatty acid metabolites.

The electrospray ionization mass spectrometry (ESI-MS) methodology often shows poor ionization reproducibility in the analysis of biological samples. Therefore, normalization of the measured peak intensities is essential. It is believed that quantitative data with high reproducibility can be obtained by adding a constant amount of an internal standard (IS) material labeled with stable isotopes to each sample, thus allowing the correction of the quantitative value of the target compound by that of the IS. We investigated whether the presence or absence of a labeled IS improves the accuracy of these quantitative values. Triple quadrupole MS coupled with liquid chromatography was used to analyze fatty acid metabolites in biological samples as target compounds. Two independent systems were used to provide a measure of reproducibility in two different laboratories. Data having poor reproducibility in the raw peak areas were efficiently normalized using the IS, but, crucially, the IS method using stable isotopes was not always necessary. In some cases, the reproducibility was relatively good even without using the IS. In a contaminant matrix, the MS response behavior of the target compound and its stable isotope-labeled material was complicated. Since ion suppression by matrix contaminants was dependent on the concentration of the target compound, the added amounts of the ISs were also important, Furthermore, an equivalent normalization effect was obtained by using a pooled quality control sample as an external standard, thus obviating the need for labeled IS samples, which are often expensive and sometimes not commercially available. Our results raise the question as to whether the quantitative method using stable-isotope-labeled ISs is always necessary and beneficial. However, the results obtained in this study cannot be generalized because only fatty acid metabolites were examined using ESI-MS and only a highly substituted deuterium-labeled IS was used.

High-Throughput and Sensitive Analysis of Free and Total 8-Isoprostane in Urine with Isotope-Dilution Liquid Chromatography-Tandem Mass Spectrometry.

Oxidative stress (OS) plays a major role in the pathogenesis of various diseases in humans. OS is a result of an imbalance between reactive oxygen species (ROS) and the biologically available antioxidants that prevent or repair damage that ROS inflict on the host cells. ROS are naturally generated during normal mitochondrial respiration and by oxidative burst during the immune response. Many factors may influence OS, including genetics, diet, exercise, and exposure to environmental toxicants (e.g., tobacco smoke). A nonenzymatic peroxidation product of arachidonic acid (AA), 8-iso-PGF2α (8-isoprostane), is a validated biomarker of OS that is present in urine as both glucuronide conjugate and free acid. Previous studies report that the conjugated forms of 8-isoprostane can vary between 30 and 80% of the total 8-isoprostane levels. By hydrolyzing the conjugated forms, it is possible to obtain a total (free + conjugated) measurement of 8-isoprostane in urine samples. Here, we describe a robust, automated, and high-throughput method for measuring total urinary 8-isoprostane using a polymeric weak anion-exchange solid-phase extraction (SPE) and isotope-dilution ultrahigh performance liquid chromatography electrospray ionization–tandem mass spectrometry (UHPLC–MS/MS). This method, using a 96-well plate platform, showed good sensitivity (8.8 pg/mL LOD) and used only 400 μL of the sample volume with a cycle time of 11 min. The inter- and intraday precision, calculated from 20 repeated measurements of two quality control pools, varied from 4 to 10%. Accuracy, calculated from the recovery percentage at three spiking levels, ranged from 92.7 to 106.7%. We modified this method to allow for the exclusive measurement of free 8-isoprostane by removing the hydrolysis step. We measured both free and total 8-isoprostane in urine collected from 30 cigarette smokers (free: 460 ± 78.8 pg/mL; total: 704 ± 108 pg/mL) and 30 nonusers of tobacco products (free: 110 ± 24.2 pg/mL; total: 161 ± 38.7 pg/mL). This method is robust, accurate, and easily adaptable for large population studies.

Normalizing Untargeted Periconceptional Urinary Metabolomics Data: A Comparison of Approaches.

Metabolomics studies of the early-life exposome often use maternal urine specimens to investigate critical developmental windows, including the periconceptional period and early pregnancy. During these windows changes in kidney function can impact urine concentration. This makes accounting for differential urinary dilution across samples challenging. Because there is no consensus on the ideal normalization approach for urinary metabolomics data, this study’s objective was to determine the optimal post-analytical normalization approach for untargeted metabolomics analysis from a periconceptional cohort of 45 women. Urine samples consisted of 90 paired pre- and post-implantation samples. After untargeted mass spectrometry-based metabolomics analysis, we systematically compared the performance of three common approaches to adjust for urinary dilution—creatinine adjustment, specific gravity adjustment, and probabilistic quotient normalization (PQN)—using unsupervised principal components analysis, relative standard deviation (RSD) of pooled quality control samples, and orthogonal partial least-squares discriminant analysis (OPLS-DA). Results showed that creatinine adjustment is not a reliable approach to normalize urinary periconceptional metabolomics data. Either specific gravity or PQN are more reliable methods to adjust for urinary concentration, with tighter quality control sample clustering, lower RSD, and better OPLS-DA performance compared to creatinine adjustment. These findings have implications for metabolomics analyses on urine samples taken around the time of conception and in contexts where kidney function may be altered.

PseudoQC: A Regression-Based Simulation Software for Correction and Normalization of Complex Metabolomics and Proteomics Datasets.

Various types of unwanted and uncontrollable signal variations in MS-based metabolomics and proteomics datasets severely disturb the accuracies of metabolite and protein profiling. Therefore, pooled quality control (QC) samples are often employed in quality management processes, which are indispensable to the success of metabolomics and proteomics experiments, especially in high-throughput cases and long-term projects. However, data consistency and QC sample stability are still difficult to guarantee because of the experimental operation complexity and differences between experimenters. To make things worse, numerous proteomics projects do not take QC samples into consideration at the beginning of experimental design. Herein, a powerful and interactive web-based software, named pseudoQC, is presented to simulate QC sample data for actual metabolomics and proteomics datasets using four different machine learning-based regression methods. The simulated data are used for correction and normalization of the two published datasets, and the obtained results suggest that nonlinear regression methods perform better than linear ones. Additionally, the above software is available as a web-based graphical user interface and can be utilized by scientists without a bioinformatics background. pseudoQC is open-source software and freely available at https://www.omicsolution.org/wukong/pseudoQC/.