Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox.

Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.

Examining functional group-dependent effects on the ionization of lignin monomers using supercritical fluid chromatography/electrospray ionization mass spectrometry.

The chemical and biological conversion of biomass-derived lignin is a promising pathway for producing valuable low molecular weight aromatic chemicals, such as vanillin or guaiacol, known as lignin monomers (LMs). Various methods employing chromatography and electrospray ionization-mass spectrometry (ESI-MS) have been developed for LM analysis, but the impact of LM chemical properties on analytical performance remains unclear. This study systematically optimized ESI efficiency for 24 selected LMs, categorized by functionality. Fractional factorial designs were employed for each LM to assess ESI parameter effects on ionization efficiency using ultra-high-performance supercritical fluid chromatography/ESI-MS (UHPSFC/ESI-MS). Molecular descriptors were also investigated to explain variations in ESI parameter responses and chromatographic retention among the LMs. Structural differences among LMs led to complex optimal ESI settings. Notably, LMs with two methoxy groups benefited from higher gas and sheath gas temperatures, likely due to their lower log P and higher desolvation energy requirements. Similarly, vinyl acids and ketones showed advantages at elevated gas temperatures. The retention in UHPSFC using a diol stationary phase was correlated with the number of hydrogen bond donors. In summary, this study elucidates structural features influencing chromatographic retention and ESI efficiency in LMs. The findings can aid in developing analytical methods for specific technical lignins. However, the absence of an adequate number of LM standards limits the prediction of LM structures solely based on ESI performance data.

Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification.

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening has identified a growing number of novel per- and polyfluoroalkyl substances (PFASs) in the environment. However, without analytical standards, the fraction of overall PFAS exposure accounted for by these suspects remains ambiguous. Fortunately, recent developments in ionization efficiency (IE) prediction using machine learning offer the possibility to quantify suspects lacking analytical standards. In the present work, a gradient boosted tree-based model for predicting log IE in negative mode was trained and then validated using 33 PFAS standards. The root-mean-square errors were 0.79 (for the entire test set) and 0.29 (for the 7 PFASs in the test set) log IE units. Thereafter, the model was applied to samples of liver from pilot whales (n = 5; East Greenland) and white beaked dolphins (n = 5, West Greenland; n = 3, Sweden) which contained a significant fraction (up to 70%) of unidentified organofluorine and 35 unquantified suspect PFASs (confidence level 2-4). IE-based quantification reduced the fraction of unidentified extractable organofluorine to 0-27%, demonstrating the utility of the method for closing the fluorine mass balance in the absence of analytical standards.

Mobile phase and column chemistry selection for high sensitivity non-targeted LC/ESI/HRMS screening of water.

Systematic selection of mobile phase and column chemistry type can be critical for achieving optimal chromatographic separation, high sensitivity, and low detection limits in liquid chromatography electrospray high resolution mass spectrometry (LC/MS). However, the selection process is challenging for non-targeted screening where the compounds of interest are not preselected nor available for method optimization. To provide general guidance, twenty different mobile phase compositions and four columns were compared for the analysis of 78 compounds with a wide range of physicochemical properties (logP range from -1.46 to 5.48), and analyte sensitivity was compared between methods. The pH, additive type, column, and organic modifier had significant effects on the analyte response factors, and acidic mobile phases (e.g. 0.1% formic acid) yielded highest sensitivity. In some cases, the effect was attributable to the difference in organic modifier content at the time of elution, depending on the mobile phase and column chemistry. Based on these findings, 0.1% formic acid, 0.1% ammonia and 5.0 mM ammonium fluoride were further evaluated for their performance in non-targeted LC/ESI/HRMS analysis of wastewater treatment plan influent and effluent, using a data dependent MS2 acquisition and two different data processing workflows (MS-DIAL, patRoon 2.1) to compare number of detected features and sensitivity. Both data-processing workflows indicated that 0.1% formic acid yielded the highest number of features in full scan spectrum (MS1), as well as the highest number of features that triggered fragmentation spectra (MS2) when dynamic exclusion was used.

Electrospray Ionization Efficiency Predictions and Analytical Standard Free Quantification for SFC/ESI/HRMS.

Supercritical fluid chromatography (SFC) is a promising, sustainable, and complementary alternative to liquid chromatography (LC) and has often been coupled with high resolution mass spectrometry (HRMS) for nontarget screening (NTS). Recent developments in predicting the ionization efficiency for LC/ESI/HRMS have enabled quantification of chemicals detected in NTS even if the analytical standards of the detected and tentatively identified chemicals are unavailable. This poses the question of whether analytical standard free quantification can also be applied in SFC/ES/HRMS. We evaluate both the possibility to transfer an ionization efficiency predictions model, previously trained on LC/ESI/HRMS data, to SFC/ESI/HRMS as well as training a new predictive model on SFC/ESI/HRMS data for 127 chemicals. The response factors of these chemicals ranged over 4 orders of magnitude in spite of a postcolumn makeup flow, expectedly enhancing the ionization of the analytes. The ionization efficiency values were predicted based on a random forest regression model from PaDEL descriptors and predicted values showed statistically significant correlation with the measured response factors (p < 0.05) with Spearman's rho of 0.584 and 0.669 for SFC and LC data, respectively. Moreover, the most significant descriptors showed similarities independent of the chromatography used for collecting the training data. We also investigated the possibility to quantify the detected chemicals based on predicted ionization efficiency values. The model trained on SFC data showed very high prediction accuracy with median prediction error of 2.20×, while the model pretrained on LC/ESI/HRMS data yielded median prediction error of 5.11×. This is expected, as the training and test data for SFC/ESI/HRMS have been collected on the same instrument with the same chromatography. Still, the correlation observed between response factors measured with SFC/ESI/HRMS and predicted with a model trained on LC data hints that more abundant LC/ESI/HRMS data prove useful in understanding and predicting the ionization behavior in SFC/ESI/HRMS.

A Spike-Control Approach that Evaluates High Resolution Mass Spectrometry-Based Sequence Variant Analytical Method Performance for Therapeutic Proteins.

An amino acid sequence variant (SV) is defined as an unintended amino acid substitution in protein drug products. SVs contribute to product heterogeneity and can potentially impact product quality, safety, immunogenicity, and efficacy. The analysis of biotherapeutics for SVs is important throughout the product life cycle including clone selection, development of nutrient feed strategies, commercial manufacturing process, and post-approval changes to monitor product quality. The proposed analytical procedure for SVs consists of both qualitative (identification of SVs) and quantitative (quantitation of identified SVs) components. The complexities of SV analysis and the variety of current procedures highlight the need for a systematic approach for assessing the capability of these methodologies to reliably identify and quantitate SVs in biotherapeutics. We described here a "spike-control" approach for evaluating SV analytical procedure. The concept was adopted from quality control samples routinely used in analytical procedure validation. One FDA approved monoclonal antibody (mAb) was spiked with accurate amounts of highly homologous mAb to create mAb samples containing low yet accurate levels of "artificial" SVs. Spike-control samples were denatured, reduced, alkylated, digested and then analyzed by high resolution Orbitrap mass spectrometry. In silico analysis revealed four single amino acid differences between the two mAbs that could be used to represent SVs in the spike-control samples. All four "artificial" SVs were reliably identified by the current workflow. Analytical range (0.01% to 2%), accuracy and precision of identified SVs have also been evaluated. Overall, spike-control sample(s) helped to demonstrate that the SV analytical procedure (i.e., sample preparation, LC separation, mass spectrometry determinations and bioinformatic software) was fit for purpose and suitable for the identification and quantitation of SVs at a pre-determined threshold.

Gigaohm and Teraohm Resistors in Femtoamp and Picoamp Electrospray Ionization.

The femtoamp electrospray ionization (femtoESI) mode has been shown to exhibit unique characteristics that may facilitate ionization efficiency studies and experiments requiring low ion beam flux. Investigation of femtoESI was hindered by a tiny, applied voltage window of 10-100 V, beyond which ionization currents quickly jumped to nanoamps. This window was difficult to locate because the exact onset voltage fluctuates due to variations in ion source alignments. Large resistors (0.1-100 TΩ) in series effectively expanded the femtoESI applied voltage range, up to 1400 V. By swapping resistors, rapid alternation allows for the comparison of both ESI modes under the same alignment. In peptide mixtures, analytes with lower surface activity are suppressed in the nanoESI mode whereas the femtoESI mode shows signal enhancement of less surface-active species. For protein solutions, there is little change in the charge states generated but the femtoESI mode does show a decrease in the average charge state of protein peaks. Peptides and proteins analyzed in the femtoESI mode also tend to generate higher intensity sodiated peaks over protonated peaks at specific charge states compared with nanoESI mode operation.

Supercharging reagents in LC-MS/MS hormone analyses: Enhancing ionization, not limit of quantification.

One of the critical steps during LC-MS/MS hormone analyses that affects the sensitivity of the assay is the ionization process. Enhancing ionization efficiencies by the addition of supercharging reagents might be one way to improve sensitivity and reduce the limit of quantification (LOQ). Therefore, we investigated whether the addition of the supercharging reagents m-nitrobenzyl alcohol (m-NBA), sulfolane, propylene carbonate, and o-nitroanisole (o-NA) increased ionization efficiency and improved assay LOQ of insulin, oxytocin, sex steroids, and corticosteroids in test solutions. Additionally, the influence of the supercharging reagents was tested in serum samples after sample pretreatment to determine whether ionization would be enhanced similarly in routine analyses and, subsequently, lead to improved sensitivity. The screening experiments showed that the impact of the supercharging reagents varied for each hormone; although the addition of m-NBA increased the signal of all hormones, the other reagents only enhanced ionization efficiencies for some hormones. While the addition of 0.05 v/v% m-NBA and 0.05 v/v% o-NA did result in an increase in peak area in both test solutions and serum samples, it did not significantly improve the signal-to-noise ratio, as a simultaneous increase in noise was observed. In conclusion, even though supercharging reagents can enhance ionization efficiencies of hormones significantly, the addition of these reagents does not result in an improved LOQ for hormone measurements with LC-MS/MS.

Estimation of the concentrations of hydroxylated polychlorinated biphenyls in human serum using ionization efficiency prediction for electrospray.

Hydroxylated PCBs are an important class of metabolites of the widely distributed environmental contaminants polychlorinated biphenyls (PCBs). However, the absence of authentic standards is often a limitation when subject to detection, identification, and quantification. Recently, new strategies to quantify compounds detected with non-targeted LC/ESI/HRMS based on predicted ionization efficiency values have emerged. Here, we evaluate the impact of chemical space coverage and sample matrix on the accuracy of ionization efficiency-based quantification. We show that extending the chemical space of interest is crucial in improving the performance of quantification. Therefore, we extend the ionization efficiency-based quantification approach to hydroxylated PCBs in serum samples with a retraining approach that involves 14 OH-PCBs and validate it with an additional four OH-PCBs. The predicted and measured ionization efficiency values of the OH-PCBs agreed within the mean error of 2.1 × and enabled quantification with the mean error of 4.4 × or better. We observed that the error mostly arose from the ionization efficiency predictions and the impact of matrix effects was of less importance, varying from 37 to 165%. The results show that there is potential for predictive machine learning models for quantification even in very complex matrices such as serum. Further, retraining the already developed models provides a timely and cost-effective solution for extending the chemical space of the application area.

Characterization of Spray Modes and Factors Affecting the Ionization Efficiency of Paper Spray Ionization.

In this study, we systematically evaluated the factors affecting the ionization efficiency of paper spray ionization (PSI), such as electric field, solvent supply rate, and paper thickness and hydrophobicity. The observed paper spray plume was classified into three modes: single cone-jet, multi-jet, and rim-jet modes. With the increase in the spraying voltage, the spray plume appeared in order of single cone-jet, multi-jet, and rim-jet modes. The rim-jet mode exhibited the lowest standard deviation and high ionization efficiency among the three spray modes. The main parameter determining the spray mode was the charge density of the droplets generated by paper spray, which depends on the electric field and solvent supply rate. A thicker paper reduced the electric repulsion between the jets and lowered the threshold voltage to reach the rim-jet mode. Lowering the solvent supply rate caused mode transitions from the single cone-jet to the rim-jet, possibly due to the increased droplet charge density. The hydrophobic modification on a paper substrate led to a different ionization mechanism or electrostatic spray ionization at low applied voltages.

A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS.

There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography-high-resolution mass spectrometry. The quantitative structure-property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/ .

Fragmentation and Ionization Efficiency of Positional and Functional Isomers of Paeoniflorin Derivatives in Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry.

Paeoniflorin and albiflorin, which are functional isomers, are the major constituents of an herbal medicine derived from Paeonia lactiflora. Those functional isomers and their galloylated derivatives, which are positional isomers, were studied by matrix-assisted laser desorption/ionization-tandem mass spectrometry (MALDI-MS/MS). The resulting mass spectra are discussed based on the fragmentation patterns of the sodium adducts. The product ion spectra of 4-O-galloylalbiflorin and 4'-O-galloylpaeoniflorin differed, even though they were positional isomers. The fragmentations of the ester parts were influenced by the neighboring hydroxyl groups. The ionization efficiency of the sodium adduct of albiflorin was higher than that for paeoniflorin. These results indicate that the carboxylic ester group has a higher affinity for sodium ions than the acetal group, which can be attributed to the carbonyl oxygen being negatively polarized, allowing it to function as a Lewis base.

Characterizing Oligomeric Hydroxyl Silicon Oils by MALDI-TOF MS With the Pyridine-Modified Matrix.

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF) is a powerful technique for analysis of various polymers, but it is still very difficult to characterize silicone oil due to its poor ionization efficiency. In this work, oligomeric hydroxyl silicone oils were successfully characterized by MALDI-TOF, by using pyridine-modified 2,5-dihydroxylbenzoic acid (DHB) as the matrix. Furthermore, the mixed crystal of DHB and hydroxyl silicone oil was analyzed by scanning electron microscopy (SEM) and energy disperse spectroscopy (EDS), and the analytical results verified that modification with pyridine could remarkably improve the solubility of hydroxyl silicone oil in DHB, leading to the enhancement of its ionization efficiency in MALDI. The analysis of the MS spectra of a series of hydroxyl silicone oils indicated that they tended to be ionized by the attachment with Na+, and the average molecular weight and the degree of polymerization were measured for several oligomeric hydroxyl silicon oils.

Risk-based prioritization of suspects detected in riverine water using complementary chromatographic techniques.

Surface waters are widely used as drinking water sources and hence their quality needs to be continuously monitored. However, current routine monitoring programs are not comprehensive as they generally cover only a limited number of known pollutants and emerging contaminants. This study presents a risk-based approach combining suspect and non-target screening (NTS) to help extend the coverage of current monitoring schemes. In particular, the coverage of NTS was widened by combining three complementary separations modes: Reverse phase (RP), Hydrophilic interaction liquid chromatography (HILIC) and Mixed-mode chromatography (MMC). Suspect lists used were compiled from databases of relevant substances of very high concern (e.g., SVHCs) and the concentration of detected suspects was evaluated based on ionization efficiency prediction. Results show that suspect candidates can be prioritized based on their potential risk (i.e., hazard and exposure) by combining ionization efficiency-based concentration estimation, in vitro toxicity data or, if not available, structural alerts and QSAR.based toxicity predictions. The acquired information shows that NTS analyses have the potential to complement target analyses, allowing to update and adapt current monitoring programs, ultimately leading to improved monitoring of drinking water sources.

Mapping the distribution of perfluoroalkyl substances in zebrafishes by liquid extraction surface analysis mass spectrometry.

Investigation on the distribution of persistent organic pollutants (POPs) in aquatic organisms is of great importance for exploring the biological toxicity and health risks of environmental pollutants. In this study, a liquid extraction surface analysis mass spectrometry (LESA-MS) method was developed for rapid and in situ analysis of the spatial distribution of perfluoroalkyl substances (PFASs) in zebrafish. By combining the high-precision automated moving platform of LESA device and the high-resolution MS, quantitative analysis of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) in zebrafish tissue section were easily achieved. A tissue-specific ionization efficiency factor (TSF) strategy was also proposed to correct the matrix effect in different parts of zebrafish tissue. By using the developed method, high sensitive and efficient imaging of PFOA and PFOS in zebrafish tissue was achieved, and the distributions of PFOA and PFOS in descending order were gills, organs, roes, pelvic fin, muscle, and brain. The experimental results demonstrated that the coupling of LESA-MS method with TFS strategy is an efficient and reliable approach for monitoring the content distribution of environmental pollutants in biological tissues.

Improvement in Ionization Efficiency Using Metal Oxide Nanoparticles in Laser Desorption/Ionization Mass Spectrometry of a Cancer Drug.

Mass spectrometry imaging (MSI) without labeling has the potential for faster screening in drug development. Matrix-assisted laser desorption/ionization (MALDI) is typically used, but it has a large matrix size and uneven drug distribution. Surface-assisted laser desorption/ionization (SALDI) using nanoparticles (NPs) may overcome these issues. Here, the influence of NPs, solvent ratio, and order of dropping of NPs on SALDI-MSI of protoporphyrin IX (PpIX), a cancer drug, are reported. A solution of PpIX in a 50% aqueous solution of 50% acetonitrile at a concentration of 10 µM was used. The NPs include ZnO, Fe3O4, and four types of TiO2. The NPs were fabricated by dissolving them on an aqueous 90% acetonitrile solution. Mass spectra were obtained with a time-of-flight mass spectrometer using a Nd:YAG laser at a 355-nm wavelength. The signal intensity using TiO2 at a 0.5 mg/mL concentration in 50% acetonitrile was increased by 1.6-fold compared to that without TiO2. Changing the solvent to 90% acetonitrile gave a uniform TiO2 distribution and a 9-fold increase in the signal intensity for PpIX. Among the four types of TiO2 with different particle sizes and crystal structures, TiO2 with a smaller particle size and a rutile crystal structure produced the highest signal intensity. Forming a layer on top of the PpIX also resulted in an increased signal intensity. Hence, SALDI using TiO2 provides effective ionization of the drug. In the future, we plan to investigate a spray method for the ionization of PpIX using TiO2 for the MSI of various drugs.

Machine learning guided prediction of liquid chromatography-mass spectrometry ionization efficiency for genotoxic impurities in pharmaceutical products.

The limitation and control of genotoxic impurities (GTIs) has continued to receive attention from pharmaceutical companies and authorities for several decades. Because GTIs have the ability to damage deoxyribonucleic acid (DNA) and the potential to cause cancer, low-level quantitation is required to protect patients. A quick and easy method of determining the liquid chromatography-mass spectrometry (LC/MS) conditions for high-sensitivity analysis of GTIs may prospectively accelerate pharmaceutical development. In this study, a quantitative structure-property relationship (QSPR) model was developed for predicting the ionization efficiency of compounds using liquid-chromatography-mass spectrometry (LC/MS) parameters and molecular descriptors. Before implementing the QSPR prediction model, linear regression analysis was performed to model the relationship between the ionization efficiency and the LC/MS parameters for each compound. Comparison of the predicted peak areas with the experimentally observed peak areas showed good agreement based on the coefficient of determination (R2 > 0.96). The machine learning-based QSPR approach begins with computation of the molecular descriptors expressing the physicochemical properties of a compound, followed by a genetic algorithm-based feature selection. Linear and nonlinear regression were performed, and support vector machine (SVM) was selected as the best machine learning algorithm for the prediction. The SVM algorithm was developed and optimized using 1031 experimental data points for nine compounds, including well-known GTIs. Validation of the model by comparison of the predicted and observed relative ionization efficiencies (RIE) showed a high coefficient of determination (R2 = 0.96) and low root mean squared error value (RMSE = 0.118). Finally, this established prediction model was applied to hydrophilic interaction liquid chromatography coupled with MS for a new compound in new mobile phase compositions and new MS parameter settings. The RMSE of the predicted versus observed RIE was 0.203. This prediction accuracy was sufficient to determine the starting point of the LC/MS method development. The methodology demonstrated in this study can be used to determine the LC/MS conditions for high sensitivity analysis of GTIs.

Novel approach to enhance sensitivity while retaining morphology in fragile tissue sections for mass spectrometry imaging.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging is an effective tool for investigating the distribution of molecules. However, cryosections are made from non-fixed tissues, causing difficulties in preparing sections from fragile, high-water content tissues such as those from tadpoles. Here, we introduce a new method for preparing cryosections using an adhesive tape followed by transfer onto glass slides for MALDI-MS imaging. Signals obtained from the transferred sections were higher than those from other sections, and the transferred sections had high optical quality. This novel approach could be an effective tool for MALDI-MS imaging of aquatic animals.

Characterization of wines with liquid chromatography electrospray ionization mass spectrometry: Quantification of amino acids via ionization efficiency values.

Quantification of analysis results for the suspect and non-targeted screening is essential for obtaining meaningful insight from the measurements. Ionization efficiency predictions is a possible approach to enable quantitation without standard substances. This is, however, especially challenging for the analysis carried out by combining the full scan mode either with fragmentation experiments in data-dependent or data-independent acquisition mode. Here we investigate the correlation of ionization efficiency values measured in full scan mode with the response factors measured in multiple reaction monitoring (MRM) mode for derivatized amino acids. We observe good correlation (R2 of 0.80) for 6-Aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) derivatized amino acids. This encourages the use of the measured ionization efficiency values to estimate amino acid concentrations in different beverages. We apply the measured ionization efficiency values for estimating the concentration of amino acids for measurements done both in full scan as well as in MRM mode in wines and beers. We show that the calculated concentrations are in very good correlation with measured values (R2 of 0.71 to 1.00). The method possesses average trueness of 70.5% and shows an insignificant matrix effect.

Impact of matrix effects and ionization efficiency in non-quantitative untargeted metabolomics.

INTRODUCTION: LC-MS-based untargeted metabolomics has become increasingly popular due to the vast amount of information gained in a single analysis. Many studies utilize metabolomics to profile metabolic changes in various representative biofluids, tissues, or other sample types. Most analyses are performed measuring changes in the metabolic pool of a single biological matrix due to an altered phenotype, such as disease versus normal. Measurements in such experiments are typically highly reproducible with little variation due to analytical and technological advancements in mass spectrometry. With the expanded application of metabolomics into various non-analytical scientific disciplines, the emergence of studies comparing the signal intensities of specific analytes across different biological matrices (e.g. plasma vs. urine) is becoming more common, but the matrix effect between sample types is often neglected. Additionally, the practice of comparing the signal intensities of different analytes and correlating to relative abundance is also increasingly prevalent, but the response ratio between analytes due to differences in ionization efficiency is not always accounted for in data analysis. This report serves to communicate and raise awareness of these two well-recognized issues to prevent improper data interpretation in the field of metabolomics. OBJECTIVES: We demonstrate the impact of matrix effects and ionization efficiency with labeled analytical standards in human plasma, serum, and urine and describe how the direct comparison of non-quantitative signal intensities between biofluids, as well as between different analytes in the same biofluid, in untargeted metabolomics is inaccurate without proper response corrections. METHODS: Human plasma, serum, and urine (n = 4 technical replicates per biofluid) were spiked with a panel of labeled internal standards all at identical concentrations, simultaneously extracted, and analyzed by UHPLC-HRMS. Signal intensities were compared for demonstration of the impact of matrix effects in untargeted metabolomics. A neat mixture of two co-eluting, structurally-similar labeled standards at the same concentration was also analyzed to demonstrate the effect of ionization efficiency on signal intensity. RESULTS: Despite being spiked at identical concentrations, labeled standards we examined in this study showed significant differences in their signal intensities between biofluids, as well as from each other in the same biofluid, due to matrix effects. Co-eluting standards were also found to yield significantly different signal intensities at identical concentrations due to differences in ionization efficiency. CONCLUSIONS: Due to the presence of matrix effects in untargeted, non-quantitative metabolomics, the signal intensity of any single analyte cannot be directly compared to the signal intensity of that same analyte (or any other analyte) between any two different matrices. Due to differences in ionization efficiency, the signal intensity of any single analyte cannot be directly compared to the signal intensity of any other analyte, even in the same matrix.