Low-abundance Metabolites Research Articles

Enhancement of On-tissue Chemical Derivatization by Laser-Assisted Tissue Transfer for MALDI MS Imaging.

The development of on-tissue chemical derivatization methods for matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) of small endogenous metabolites in tissues has attracted great attention for their advantages in improving detection sensitivity and ionization efficiency of poorly ionized and low abundant metabolites. Herein, a laser-assisted tissue transfer (LATT) technique was developed to enhance on-tissue derivatization of small molecules. Using a focused blue laser, a thin-layer tissue film (∼1 μm) was transferred to an acceptor slide from a 6 μm dry tissue section preliminarily coated with derivatization and matrix reagents. The acceptor slide with its ablated constituents was then imaged by MALDI MS. On-tissue chemical derivatization with amino-specific derivatization reagent 4-hydroxy-3-methoxycinnamaldehyde (CA) was carried out on LATT system. 20-235 folds increase in signal intensity for CA derivatized metabolites such as amino acids, neurotransmitters, and dipeptides were observed from rat brain tissues in comparison with conventional incubation-based derivatization. This technique was further extended to derivatize steroids with Girard reagent T (GirT). The remarkable derivatization efficiency can mainly be attributed to the minimization of ion suppression effects due to the reduced thickness of tissue section and endogenous components. Additionally, shorter derivatization time with no obvious metabolite delocalization was achieved using LATT method. These results demonstrate the advantages of LATT in the enhancement of on-tissue derivatization for the more specific and sensitive imaging of small metabolites in tissues with MALDI MS.

Chemometrics-assisted optimization of liquid chromatography-quadrupole-time-of-flight mass spectrometry analysis for targeted metabolomics

Mass spectrometry-based metabolomics is characterized by a vast number of variables leading to a great degree of complexity. In this work, we aimed to simplify this process with a stepped chemometric optimization of the both funnel technology (funnel exit DC, FDC; funnel RF LP, FLC; funnel RF HP, FRP) and ion source parameters (Octopolo, Oct; and Fragmentor, Frag) of a quadrupole-time of flight (qTOF) for a human urinary metabolites. The workflow comprised a Box-Behnken experimental design with 47 experiments followed by the identification and quantification of a set of metabolites using high-resolution full-scan MS mode and feature extraction with an inclusion list. Metabolite peak areas were grouped according to abundance (high and low) and modeled by Random Forest regression (variance explained >85%). The full three-level factorial design consisting in 243 experiments was predicted and top 10 solutions for desirability function and those comprising the Pareto front were extracted and investigated. To guarantee the quality of results, we compared the Pareto front solutions with those achieved by standard instrumental parameters suggested by the manufacturer. A set of five solutions were identified that increased the mean peak area by 56–59% and 17%, for high- and low-abundance metabolites, respectively. The optimal parameters were determined to be: FLP, 100 V; FDC, 40 and 30 V; Frag, 275 and 400 V; and Oct, 600 and 800 V. The methodology applied throughout this work represents a flexible strategy to optimize instrumental parameters and exploit the performance of a qTOF MS detector.

Detection of Low-Abundance Metabolites in Live Cells Using an RNA Integrator

Genetically encoded biosensors are useful tools for detecting the presence and levels of diverse biomolecules in living cells. However, low-abundance targets are difficult to detect because they are often unable to bind and activate enough biosensors to detect using standard microscopic imaging approaches. Here we describe a type of RNA-based biosensor, an RNA integrator, which enables detection of low-abundance targets invitro and in living cells. The RNA integrator is an RNA sequence comprising a ribozyme and an unfolded form of thefluorogenic aptamer Broccoli. Upon binding itstarget, the ribozyme undergoes cleavage and releases Broccoli, which subsequently folds and becomes fluorescent. Importantly, each target molecule can bind and induce cleavage of multiple copies of the integrator sensor, resulting in an amplified signal. We show that this approach can be generalized to numerous different ribozyme types for the detection of various small molecules.

Plasma Derived Exosomal Biomarkers of Exposure to Ionizing Radiation in Nonhuman Primates.

Exposure to ionizing radiation induces a cascade of molecular events that ultimately impact endogenous metabolism. Qualitative and quantitative characterization of metabolomic profiles is a pragmatic approach to studying the risks of radiation exposure since it provides a phenotypic readout. Studies were conducted in irradiated nonhuman primates (NHP) to investigate metabolic changes in plasma and plasma-derived exosomes. Specifically, rhesus macaques (Macaca mulatta) were exposed to cobalt-60 gamma-radiation and plasma samples were collected prior to and after exposure to 5.8 Gy or 6.5 Gy radiation. Exosomes were isolated using ultracentrifugation and analyzed by untargeted profiling via ultra-performance liquid chromatography mass spectrometry (UPLC-MS) based metabolomic and lipidomic analyses, with the goal of identifying a molecular signature of irradiation. The enrichment of an exosomal fraction was confirmed using quantitative ELISA. Plasma profiling showed markers of dyslipidemia, inflammation and oxidative stress post-irradiation. Exosomal profiling, on the other hand, enabled detection and identification of low abundance metabolites that comprise exosomal cargo which would otherwise get obscured with plasma profiling. We discovered enrichment of different classes of metabolites including N-acyl-amino acids, Fatty Acid ester of Hydroxyl Fatty Acids (FAHFA’s), glycolipids and triglycerides as compared to the plasma metabolome composition with implications in mediation of systemic response to radiation induced stress signaling.

Non-targeted metabolomics reveals distinct chemical compositions among different grades of Bai Mudan white tea.

So far, the chemical quality of different grades of white tea has largely remained unexplored. The objective of this study was to establish a model for quality evaluation of different grades of Bai Mudan white tea. We applied non-targeted ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry in combination with multivariate analysis and analyzed four different grades of Bai Mudan white tea. We found that the metabolite composition from the super-grade and the first-grade shared higher similarity compared to the second-grade or the third-grade white tea, and the Partial Least Square-Discriminant Analysis model showed high capability to explain the sample variation (R2Y = 0.998, Q2 = 0.95 in negative ionization modes). In total, 93 metabolites were structurally identified, wherein 21 low abundant metabolites showed distinct changes in abundance that were closely correlated with tea grade variation. These findings suggested their potential as markers to discriminate different grades of Bai Mudan white tea.

PJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra.

MotivationData processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (“pJRES Binning Algorithm”), which aims to extend the applicability of SRV to pJRES spectra.ResultsThe performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model buildingAvailability and implementationThe algorithm is implemented using the MWASTools R/Bioconductor package.Supplementary information Supplementary data are available at Bioinformatics online.

Development of a metabolic pathway-based pseudo-targeted metabolomics method using liquid chromatography coupled with mass spectrometry

The pseudo-targeted metabolomics approach was developed recently which combined the advantages of untargeted and targeted analysis. However, the current pseudo-targeted analysis method has limitations due to the technical characteristics. In this study, a novel metabolic pathway-based pseudo-targeted approach was proposed for urine metabolomics analysis using an ultra-high-performance liquid chromatography (UPLC)-MS/MS system operated in the multiple reaction monitoring (MRM) mode. MRM ion pairs were acquired from urine samples through untargeted analysis using UPLC-HRMS, as well as by searching for metabolites in related pathways in relevant databases and from previous relevant research, including amino acids, fatty acids, nucleosides, carnitines, glycolysis metabolites, and steroids. This improved pseudo-targeted method exhibited good repeatability and precision, and no complicated peak alignment was required. As a proof of concept, the developed novel method was applied to the discovery of urine biomarkers for patients with esophageal squamous cell carcinoma (ESCC). The results showed that ESCC patients had altered acylcarnitines, amino acids, nucleosides, and steroid derivative levels et al. compared to those of healthy controls. The novelty of this study lies in the fact that it provides an approach for acquiring MRM ion pairs not only from untargeted MS analysis but also from targeted searching for metabolites in related metabolic pathways. By improving the detection limit of low-abundance metabolites, it enlarges the range for the discovery of potential biomarkers. Our work provides a foundation for achieving pseudo-targeted metabolomics analysis on the widely used LC-MS/MS MRM platform.

A Survey of Orbitrap All Ion Fragmentation Analysis Assessed by an R MetaboList Package to Study Small-Molecule Metabolites

Leukemia cell and melanoma tumor tissue extracts were studied for small (mostly m/z < 250) polar metabolites by LC-ESI-HRMSn analysis powered by a hybrid Quadrupole-Orbitrap. MS data were simultaneously acquired in fast polarity switching mode operating in MS1 and MS/MS (All Ion Fragmentation, AIF) full-scan analyses at high mass resolution. Positive metabolite assignments were achieved by AIF analysis considering at least two characteristic transitions. Targeted metabolite profiling was achieved by the relative quantification of 18 metabolites through spiking of their respective deuterated counterparts. Manual data processing of MS1 and AIF scans were compared for the accurate determination of natural metabolites and their deuterated analogs by chromatographic alignment and peak area integration. Evaluation of manual and automated (MetaboList R package) AIF data processing yielded comparable results. The versatility of AIF analysis also enabled the untargeted metabolite profiling of leukemia and melanoma samples in which 22 and 53 compounds were, respectively, identified outside those studied by labeling. The main limitation of this method was that low abundance metabolites with scan rates below 8 scans/peak could not be accurately quantified by AIF analysis. The combination of AIF analysis with MetaboList R package represents an opportunity to move towards automated, faster, and more global metabolomics approaches supported by an entirely flexible open source data processing platform freely available from Comprehensive R Archive Network (CRAN, https://CRAN.R-project.org/package=MetaboList ).

Improved phosphometabolome profiling applying isotope dilution strategy and capillary ion chromatography-tandem mass spectrometry

The phosphometabolome is comprised of all phosphorylated metabolites including the major metabolite classes sugar phosphates and nucleoside phosphates. Phosphometabolites are invaluable in any cell as a part of primary- and energy- metabolism, and as building blocks in the biosynthesis of macromolecules. Here, we report quantitative profiling of the phosphometabolome by applying capillary ion chromatography-tandem mass spectrometry (capIC-MS/MS), ensuring improved chromatographic separation, robustness and quantitative precision. Baseline separation was achieved for six out of eight tested hexose phosphates. Quantitative precision and reproducibility was improved by introducing a fully uniformly (U) 13C–labeled biological extract and applying an isotope dilution (ID) correction strategy. A 13C–labeled biological extract does in principle contain internal standards (IS) for all metabolites, but low abundant metabolites pose a challenge, and solutions to this are discussed. The extreme reproducibility and reliability of this capIC-MS/MS method was demonstrated by running the instrumentation continuously for ten days.

Metabonomic analysis of the urine from rat model with abnormal sapra syndrome

To investigate the correlation between the change in metabolic components of urine and the abnormal sapra syndrome by using a rat model of abnormal sapra syndrome. Methods: Multiple factors, such as dry environment, dry feed, and chronic electrical stimulation, were used to establish the abnormal sapra syndrome in Wistar rats by Uyghur medicine. The differences in metabolites were detected through the metabonomics method. Results: The urine of rats in abnormal sapra syndrome group showed significant high abundance metabolites as follows: Leucine, isoleucine, and glycoprotein. And that significant low abundance metabolites as follows: Glutamine, creatine, citric acid, and phenylalanine. Conclusion: The urine of rats with the abnormal sapra syndrome displays abnormal energy metabolism. It is likely that the dysfunctional metabolisms of three major nutrients might be the molecular basis for the abnormal sapra syndrome.

Thread based electrofluidic platform for direct metabolite analysis in complex samples

The application of electrophoresis upon commercial threads is investigated for development of low-cost diagnostics assays, designed for the matrix separation and quantification of low abundance metabolites in complex samples – in this work riboflavin in human urine. Zone electrophoresis was evaluated upon 8 commercially available threads, with several synthetic threads exhibiting higher electroosmotic flow (EOF) and increased electrophoretic mobility of the rhodamine 6G, rhodamine B, and fluorescein. Of those tested, a nylon bundle was selected as the best platform, offering less band dispersion and higher resolution, a high relative EOF, whilst minimising the contribution of joule heating. A novel 3D printed platform was designed, based on a modular system, facilitating the electrophoresis process and rapid assembly, whilst offering the potential for multiplexed analysis or investigation of more complex systems. Using the thread-based electrophoresis system, riboflavin was determined in less than 2 min. The device exhibited a linear working range from 0.1 to 15 μg/mL of riboflavin in urine, and was in good agreement with capillary electrophoresis measurements.

Enhanced MS/MS coverage for metabolite identification in LC-MS-based untargeted metabolomics by target-directed data dependent acquisition with time-staggered precursor ion list

Metabolite identification is one of the major bottlenecks in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics owing to the difficulty of acquiring MS/MS information of most metabolites detected. Data dependent acquisition (DDA) has been currently used to acquire MS/MS data in untargeted metabolomics. When dealing with the complex biological samples, top-n-based DDA method selects only a small fraction of the ions for fragmentation, leading to low MS/MS coverage of metabolites in untargeted metabolomics. In this study, we proposed a novel DDA method to improve the performance of MS/MS acquisition in LC-MS-based untargeted metabolomics using target-directed DDA (t-DDA) with time-staggered precursor ion lists (ts-DDA). Full scan-based untargeted analysis was applied to extract the target ions. After peak alignment, ion filtration, and ion fusion, the target precursor ion list was generated for subsequent t-DDA and ts-DDA. Compared to the conventional DDA, the ts-DDA exhibits the better MS/MS coverage of metabolomes in a plasma sample, especially for the low abundant metabolites. Even in high co-elution zones, the ts-DDA also showed the superiority in acquiring MS/MS information of co-eluting ions, as evidenced by better MS/MS coverage and MS/MS efficiency, which was mainly attributed to the pre-selection of precursor ion and the reduced number of concurrent ions. The newly developed method might provide more informative MS/MS data of metabolites, which will be helpful to increase the confidence of metabolite identification in untargeted metabolomics.

Rapid discrimination of strain-dependent fermentation characteristics among Lactobacillus strains by NMR-based metabolomics of fermented vegetable juice.

In this study, we investigated the applicability of NMR-based metabolomics to discriminate strain-dependent fermentation characteristics of lactic acid bacteria (LAB), which are important microorganisms for fermented food production. To evaluate the discrimination capability, six type strains of Lactobacillus species and six additional L. brevis strains were used focusing on i) the difference between homo- and hetero-lactic fermentative species and ii) strain-dependent characteristics within L. brevis. Based on the differences in the metabolite profiles of fermented vegetable juices, non-targeted principal component analysis (PCA) clearly separated the samples into those inoculated with homo- and hetero-lactic fermentative species. The separation was primarily explained by the different levels of dominant metabolites (lactic acid, acetic acid, ethanol, and mannitol). Orthogonal partial least squares discrimination analysis, based on a regions-of-interest (ROIs) approach, revealed the contribution of low-abundance metabolites: acetoin, phenyllactic acid, p-hydroxyphenyllactic acid, glycerophosphocholine, and succinic acid for homolactic fermentation; and ornithine, tyramine, and γ-aminobutyric acid (GABA) for heterolactic fermentation. Furthermore, ROIs-based PCA of seven L. brevis strains separated their strain-dependent fermentation characteristics primarily based on their ability to utilize sucrose and citric acid, and convert glutamic acid and tyrosine into GABA and tyramine, respectively. In conclusion, NMR metabolomics successfully discriminated the fermentation characteristics of the tested strains and provided further information on metabolites responsible for these characteristics, which may impact the taste, aroma, and functional properties of fermented foods.

A Comparative Study of Pentafluorophenyl and Octadecylsilane Columns in High-throughput Profiling of Biological Fluids

In high-throughput metabolomic profiling, chromatographic separation is crucial because a well-performed chromatographic separation may reduce signal suppression from complex biological matrices and improve the discoverability of low-abundance metabolites. We compared the performance of pentafluorophenyl (PFP)- and octadecylsilane (ODS)-based columns in profiling biological fluids. Peak resolutions and consistencies were acquired using several reversed-phase columns and were evaluated. Total and extracted ion chromatograms demonstrated that the PFP column achieved better analyte separations than the ODS column. Low relative standard deviations on peak areas and retention times (<10.2 and <0.9%, respectively) acquired using the PFP column evidenced the high reproducibility and consistency of this column. In our study, a PFP column was used for profiling metabolomes extracted from urine and serum samples. Metabolomic study revealed a metabolome difference in normal and overweight participants. In total, 26 lipid species were significantly perturbed and further identified. Choline-containing lipids were the most abundant perturbed lipidome in overweight participants, followed by sphingolipids and various phospholipids. We recommend the use of PFP columns in high-throughput metabolomic analysis to promote the development of basic biological and clinical research in the future.

Detecting Low-Abundance Molecules at Single-Cell Level by Repeated Ion Accumulation in Ion Trap Mass Spectrometer.

Low-abundance metabolites or proteins in single-cell samples are usually undetectable by mass spectrometry (MS) due to the limited amount of substances in single cells. This limitation inspired us to further enhance the sensitivity of commercial mass spectrometers. Herein, we developed a technique named repeated ion accumulation by ion trap MS, which is capable of enhancing the sensitivity by selectively and repeatedly accumulating ions in a linear ion trap for up to 25 cycles. The increase in MS sensitivity was positively correlated with the number of repeated cycles. When ions were repeatedly accumulated for 25 cycles, the sensitivity of adenosine triphosphate detection was increased by 22-fold within 1.8 s. Our technique could stably detect low-abundance ions, especially MSn ions, at the single-cell level, such as 5-methylcytosine hydrolyzed from sample equivalent to ∼0.2 MCF7 cell. The strategy presented in this study offers the possibility to aid single-cell analysis by enhancing MS detection sensitivity.

XMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.

Improved analytical technologies and data extraction algorithms enable detection of >10 000 reproducible signals by liquid chromatography-high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of samples with common properties and publicly available chemical, metabolic, and environmental databases to assign confidence levels to annotation results. Evaluation results show that the algorithm achieves an F1-measure of 0.8 for a data set with known targets and is more robust than previously reported results for cases when database size is much greater than the actual number of metabolites. MS/MS evaluation of a set of randomly selected 210 metabolites annotated using xMSannotator in an untargeted metabolomics human data set shows that 80% of features with high or medium confidence scores have ion dissociation patterns consistent with the xMSannotator annotation. The algorithm has been incorporated into an R package, xMSannotator, which includes utilities for querying local or online databases such as ChemSpider, KEGG, HMDB, T3DB, and LipidMaps.

Analysis of low abundant trehalose-6-phosphate and related metabolites in Medicago truncatula by hydrophilic interaction liquid chromatography–triple quadrupole mass spectrometry

Trehalose-6-phosphate (T6P) is an important signaling metabolite involved in plant growth control that inhibits the sucrose nonfermenting-1-related protein kinase 1 (SnRK1), a key regulator of energy and carbon metabolism in plants. The quantification of T6P in plant tissues is fundamental to improve our understanding of sugar signaling and the links between plant growth and development in response to stress conditions. However, the almost undetectable levels of T6P together with the complex plant matrix and the presence of T6P isomers such as sucrose-6-phosphate (S6P), makes the detection of this metabolite challenging. This work describes the development and validation of a hydrophilic interaction chromatography (HILIC) method for the on-line coupling with negative ion electrospray (ESI) triple quadrupole tandem mass spectrometry (MS/MS) in the highly sensitive and selective multiple reaction monitoring (MRM) mode for the target analysis of metabolic intermediates of the biosynthesis of trehalose, including glucose-6-phosphate (G6P), uridine 5-diphospho-glucose (UDPG), T6P (and its isomer S6P). Enhanced signal in the MRM mode and improved chromatographic separation for each compound were obtained using piperidine and methylphosphonic acid as additives in the HILIC mobile phase. The optimized HILIC-ESI-QqQ-MS/MS method increases the range of sensitive analytical methodologies for the quantification of key low-abundant metabolites, and was applied to quantify the fluctuations of S6P, T6P and G6P in Medicago truncatula plants in response to environmental stress. The levels of S6P, T6P, and G6P in M. truncatula plant tissues (roots and leaves) exposed to a water deficit and recovery treatment, ranged from 30 to 150pmolg−1 FW, 16–120pmolg−1 FW, and 330–1690pmolg−1 FW, respectively.

Development and validation of the simultaneous measurement of four vitamin D metabolites in serum by LC-MS/MS for clinical laboratory applications.

The quantification of serum 25-hydroxyvitamin D [25(OH)D] as an indicator of vitaminD status is currently primarily conducted by immunoassays, yet LC-MS/MS would allow more accurate determination. Furthermore, LC-MS/MS would allow simultaneous measurement of multiple analytes. The aim of this study was to develop and validate an LC-MS/MS method to simultaneously measure four vitaminD metabolites (25(OH)D3, 3-epi-25(OH)D3, 25(OH)D2, and 24,25(OH)2D3) in serum for clinical laboratory applications. Serum samples were first prepared in a 96-well supported liquid extraction plate and the eluate was derivatized using the Cookson-type reagent 4-(4'-dimethylaminophenyl)-1,2,4-triazoline-3,5-dione (DAPTAD), which rapidly and quantitatively reacts with the s-cis-diene structure of vitaminD metabolites. The derivatized samples were subjected to LC-MS/MS, ionized by electrospray ionization (positive-ion mode), and detected by selected reaction monitoring. The lower limits of quantification for 25(OH)D3, 3-epi-25(OH)D3, 25(OH)D2, and 24,25(OH)2D3 were 0.091, 0.020, 0.013, and 0.024ng/mL, respectively. The accuracy values and the extraction recoveries for these four metabolites were satisfactory. Serum 25(OH)D levels determined by our LC-MS/MS were compared with those obtained by conventional radioimmunoassay (RIA) that cannot distinguish 25(OH)D3 and 25(OH)D2. The values obtained by the RIA method exhibited a mean bias of about 8.35ng/mL, most likely as a result of cross reaction of the antibody with low-abundance metabolites, including 24,25(OH)2D3. Various preanalytical factors, such as long sample sitting prior to serum separation, repeated freeze-thaw cycles, and the presence of anticoagulants, had no significant effects on these determinations. This high-throughput LC-MS/MS simultaneous assay of the four vitaminD metabolites 25(OH)D3, 3-epi-25(OH)D3, 25(OH)D2, and 24,25(OH)2D3 required as little as 20μL serum. This method will aid further understanding of low-abundance vitaminD metabolites, as well as the accurate determination of 25(OH)D3 and 25(OH)D2.

Organic Transistor Arrays Integrated with Finger-Powered Microfluidics for Multianalyte Saliva Testing.

A compact multianalyte biosensing platform is reported, composed of an organic electrochemical transistor (OECT) microarray integrated with a pumpless "finger-powered" microfluidic, for quantitative screening of glucose, lactate, and cholesterol levels. A biofunctionalization method is designed, which provides selectivity towards specific metabolites as well as minimization of any background interference. In addition, a simple method is developed to facilitate multi-analyte sensing and avoid electrical crosstalk between the different transistors by electrically isolating the individual devices. The resulting biosensing platform, verified using human samples, offers the possibility to be used in easy-to-obtain biofluids with low abundance metabolites, such as saliva. Based on our proposed method, other types of enzymatic biosensors can be integrated into the array to achieve multiplexed, noninvasive, personalized point-of-care diagnostics.

Investigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer.

Untargeted metabolomics was used in case-control studies of adenocarcinoma (ADC) lung cancer to develop and test metabolite classifiers in serum and plasma as potential biomarkers for diagnosing lung cancer. Serum and plasma were collected and used in two independent case-control studies (ADC1 and ADC2). Controls were frequency matched for gender, age, and smoking history. There were 52 adenocarcinoma cases and 31 controls in ADC1 and 43 adenocarcinoma cases and 43 controls in ADC2. Metabolomics was conducted using gas chromatography time-of-flight mass spectrometry. Differential analysis was performed on ADC1 and the top candidates (FDR < 0.05) for serum and plasma used to develop individual and multiplex classifiers that were then tested on an independent set of serum and plasma samples (ADC2). Aspartate provided the best accuracy (81.4%) for an individual metabolite classifier in serum, whereas pyrophosphate had the best accuracy (77.9%) in plasma when independently tested. Multiplex classifiers of either 2 or 4 serum metabolites had an accuracy of 72.7% when independently tested. For plasma, a multimetabolite classifier consisting of 8 metabolites gave an accuracy of 77.3% when independently tested. Comparison of overall diagnostic performance between the two blood matrices yielded similar performances. However, serum is most ideal given higher sensitivity for low-abundant metabolites. This study shows the potential of metabolite-based diagnostic tests for detection of lung adenocarcinoma. Further validation in a larger pool of samples is warranted. These biomarkers could improve early detection and diagnosis of lung cancer.