Mass Spectral Library Research Articles

Improving annotation of known-unknowns with accurately reconstructed mass spectra

Chemical profiling with gas chromatography/mass spectrometry (GC/MS) full-spectrum acquisition mode often leads to the discovery of known-unknown components. These are non-identified components which arise from the limitation of the data processing method or limited breadth of mass spectral libraries. The recent introduction of the NIST Hybrid Search sought to relieve the latter limitation by providing an improved class annotation technique. Herein, we demonstrate the importance of using a precise mass spectral reconstruction technique to increase the confidence of detecting the presence of known-unknowns and subsequently identifying them successfully with NIST Hybrid Search function. We compared the AMDIS algorithm against the rBTEM algorithm and found that the latter could more accurately reconstruct the mass spectra of co-eluting known-unknown components. This has a far-reaching implication to increase the number of identified compounds in GC/MS scan data.

Label-free Proteomics for Discovering Biomarker Candidates for Controlling Krypton Misuse in Castrated Horses (Geldings).

Recent advances in label-free quantitative proteomics may support its application in identifying and monitoring biomarkers for the purpose of doping control in equine sports. In this study, we developed a workflow of label-free quantitative proteomics to propose plasma protein biomarkers in horses after administration with krypton (Kr), a potential erythropoiesis-stimulating agent. Plasma proteomes were profiled by using nanoliquid chromatography-high-resolution mass spectrometry. An in-house mass spectral library consisting of 1121 proteins was compiled using samples collected from geldings (castrated horses) in the administration trial and geldings in training. A data-independent acquisition method was used to quantify an array of plasma proteins across plasma samples from the administration trial. Statistical analyses proposed a profile of 83 biomarker candidates that successfully differentiated Kr-administered samples from control samples, with the ability to detect Kr exposure for up to 13 days (the last sample collected in the administration trial). The model also correctly classified 32 in-training geldings as untreated controls. This is significantly longer than the 1 h detection time of plasma Kr using headspace gas chromatography-tandem mass spectrometry. Bioinformatic analyses enriched biomarker candidates relevant to complement activation and iron metabolism. The upregulation of transferrin receptor protein 1, one of the candidates related to iron metabolism, in plasma after Kr administration was validated by selected reaction monitoring of corresponding peptides. These results have demonstrated label-free quantitative proteomics as a promising approach to propose plasma protein biomarkers to enhance doping control. Data are available via ProteomeXchange with identifier PXD017262.

Conversion method for linear retention indices in gas chromatography acquired using n-alkanes and n-fatty acid methyl esters

Retention index (RI) is a powerful tool for gas chromatography-based structure elucidation of various compounds. In this study, linear RIs based on n-alkanes and n-fatty acid methyl esters in different temperature programs were fitted. Nearly identical first-order linearities were acquired with different temperature programs. The linear equations were y=1.0051x+318.51 (r2=1) and y=1.0362x+562.519 (r2=1) for polar (packed with polyethylene glycol) and semi-standard non-polar (packed with 5% diphenyl/95% dimethylpolysiloxane) columns, respectively. The variables x and y in the equations stand for RIs based on n-fatty acid methyl esters and n-alkanes, respectively. The established linearity was then verified using the reference RIs collected in the NIST mass spectral library, Golm metabolite library, and Fiehnmetabolite library. The RI values converted using both the acquired equations finely matched those in the references. The established relationship between the two kinds of RIs can be used to expand the RI references and decrease the number of RI acquisition experiments.

Application of Stable Isotope Tracing to Elucidate Metabolic Dynamics During Yarrowia lipolytica α-Ionone Fermentation.

SummaryTargeted metabolite analysis in combination with 13C-tracing is a convenient strategy to determine pathway activity in biological systems; however, metabolite analysis is limited by challenges in separating and detecting pathway intermediates with current chromatographic methods. Here, a hydrophilic interaction chromatography tandem mass spectrometry approach was developed for improved metabolite separation, isotopologue analysis, and quantification. The physiological responses of a Yarrowia lipolytica strain engineered to produce ∼400 mg/L α-ionone and temporal changes in metabolism were quantified (e.g., mevalonate secretion, then uptake) indicating bottleneck shifts in the engineered pathway over the course of fermentation. Dynamic labeling results indicated limited tricarboxylic acid cycle label incorporation and, combined with a measurable ATP shortage during the high ionone production phase, suggested that electron transport and oxidative phosphorylation may limit energy supply and strain performance. The results provide insights into terpenoid pathway metabolic dynamics of non-model yeasts and offer guidelines for sensor development and modular engineering.

A Preliminary Study on Metabolome Profiles of Buffalo Milk and Corresponding Mozzarella Cheese: Safeguarding the Authenticity and Traceability of Protected Status Buffalo Dairy Products.

The aim of this study is to combine advanced GC-MS and metabolite identification in a robust and repeatable technology platform to characterize the metabolome of buffalo milk and mozzarella cheese. The study utilized eleven dairies located in a protected designation of origin (PDO) region and nine dairies located in non-PDO region in Italy. Samples of raw milk (100 mL) and mozzarella cheese (100 g) were obtained from each dairy. A total of 185 metabolites were consistently detected in both milk and mozzarella cheese. The PLS-DA score plots clearly differentiated PDO and non-PDO milk and mozzarella samples. For milk samples, it was possible to divide metabolites into two classes according to region: those with lower concentrations in PDO samples (galactopyranoside, hydroxybuthyric acid, allose, citric acid) and those with lower concentrations in non-PDO samples (talopyranose, pantothenic acid, mannobiose, etc.,). The same was observed for mozzarella samples with the proportion of some metabolites (talopyranose, 2, 3-dihydroxypropyl icosanoate, etc.,) higher in PDO samples while others (tagatose, lactic acid dimer, ribitol, etc.,) higher in non-PDO samples. The findings establish the utility of GC-MS together with mass spectral libraries as a powerful technology platform to determine the authenticity, and create market protection, for “Mozzarella di Bufala Campana.”

A complex matrix characterization approach, applied to cigarette smoke, that integrates multiple analytical methods and compound identification strategies for non-targeted liquid chromatography with high-resolution mass spectrometry.

RationaleFor the characterization of the chemical composition of complex matrices such as tobacco smoke, containing more than 6000 constituents, several analytical approaches have to be combined to increase compound coverage across the chemical space. Furthermore, the identification of unknown molecules requiring the implementation of additional confirmatory tools in the absence of reference standards, such as tandem mass spectrometry spectra comparisons and in silico prediction of mass spectra, is a major bottleneck.MethodsWe applied a combination of four chromatographic/ionization techniques (reversed‐phase (RP) – heated electrospray ionization (HESI) in both positive (+) and negative (−) modes, RP – atmospheric pressure chemical ionization (APCI) in positive mode, and hydrophilic interaction liquid chromatography (HILIC) – HESI positive) using a Thermo Q Exactive™ liquid chromatography/high‐resolution accurate mass spectrometry (LC/HRAM‐MS) platform for the analysis of 3R4F‐derived smoke. Compound identification was performed by using mass spectral libraries and in silico predicted fragments from multiple integrated databases.ResultsA total of 331 compounds with semi‐quantitative estimates ≥100 ng per cigarette were identified, which were distributed within the known chemical space of tobacco smoke. The integration of multiple LC/HRAM‐MS‐based chromatographic/ionization approaches combined with complementary compound identification strategies was key for maximizing the number of amenable compounds and for strengthening the level of identification confidence. A total of 50 novel compounds were identified as being present in tobacco smoke. In the absence of reference MS2 spectra, in silico MS2 spectra prediction gave a good indication for compound class and was used as an additional confirmatory tool for our integrated non‐targeted screening (NTS) approach.ConclusionsThis study presents a powerful chemical characterization approach that has been successfully applied for the identification of novel compounds in cigarette smoke. We believe that this innovative approach has general applicability and a huge potential benefit for the analysis of any complex matrices.

Summarized criteria of chemical compounds identification using the chromatography-mass spectrometry

Criteria of chemical identification used in target and non-target analysis during chromatography-mass spectrometry are reviewed. In both cases, the final criteria are comparable and have a close similarity of both chromatographic retention values (times or relative times) and mass spectra (individual ion masses, their relative peak intensities) of identified analytes and analytical standards. Similarity, usually, means maximum permissible deviations of those values from the corresponding standard values assigned to the known compounds. Identification criteria used are contained in the general guides on the chemical analysis issued by the international or national regulatory organizations. In the case of non-target identification, the analytes and the corresponding standards are unknown before the analysis. In the first stages of this kind of analysis, the candidate compounds are selected for the identification and then the candidate features are compared to those of the standard compounds with the use of the criteria suggested for the target analysis. During the selection of the candidates, one uses high resolution chromatography – tandem mass spectrometry, advanced computer methods of data processing and prediction, and performs searches in chemical data bases, collections of retention indices, and mass spectral libraries. The use of these techniques and workflows can be considered as a reliable condition of the candidates’ selection for the identification. The progress in non-target analysis depends on technique innovations in mass spectrometry, first, by improving the accuracy and reproducibility of ion masses. The development of computer technologies is no less important, which should ensure the emergence of more complete libraries of mass spectra and more accurate methods for predicting the mass spectrometric and chromatographic data. Keywords : chemical identification, mass spectrometry, chromatography, computers, quantitative criteria of identification DOI: http://dx.doi.org/10.15826/analitika.2020.24.3.003 B.L.Milman 1 , I.K. Zhurkovich 2 1 Institute of Experimental Medicine, ul. Akad. Pavlova 12, Saint Petersburg 197376, Russian Federation 2 Institute of Toxicology, ul. Bekhtereva 1, Saint Petersburg 192019, Russian Federation

METLIN: A Tandem Mass Spectral Library of Standards.

Untargeted mass spectrometry metabolomics studies rely on accurate databases for the identification of metabolic features. Leveraging unique fragmentation patterns as well as characteristic dissociation routes allows for structural information to be gained for specific metabolites and molecular classes, respectively. Here we describe the evolution of METLIN as a resource for small molecule analysis as well as the tools (e.g., Fragment Similarity Search and Neutral Loss Search) used to query the database and their workflows for the identification of molecular entities. Additionally, we will discuss the functionalities of isoMETLIN, a database of isotopic metabolites, and the latest addition to the METLIN family, METLIN-MRM, which facilitates the analysis of quantitative mass spectrometry data generated with triple quadrupole instrumentation.

Screening of halogenated phenolic compounds in plasma and serum from marine wildlife

The growing knowledge of the impact of halogenated phenolic compounds on hormonal and metabolic systems has led to an increased interest in the exposure and potential effects of these compounds in wildlife. In the present study, a screening procedure was developed to detect and quantify halogenated phenolic compounds in serum and plasma from marine wildlife. A mass spectral library containing selective ion monitoring data was created using gas chromatography electron capture negative ionization mass spectrometry. The selective ion monitoring data in the library were accompanied with retention indices to increase the specificity of each entry in the library. The library together with the developed extraction procedure and optimized instrumental settings can be used for the detection of 52 different halogenated phenolic compounds of environmental concern, including 23 hydroxylated polychlorinated biphenyls and 24 hydroxylated polybrominated diphenyl ethers. The instrument limit of detection for the compounds included in the library ranged from 30 to 320 fg/injection, with a median detection limit of 90 fg/injection. The average recovery of 11 different halogenated phenolic compounds, from four species of marine wildlife, was 66 ± 14%. A full-scan mass spectral library was also created containing an additional seven compounds. Gray seals, long-tailed ducks, and two species of fish from the Baltic Sea were screened for halogenated phenolic compounds using the developed procedure. A total of 33 compounds included in the library were detected and quantified.

Suspect screening of 200 hazardous substances in plastic toys using ultra-high-performance liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry

There is an urgent need for the development of efficient and comprehensive analytical methods for organic chemical compounds due to their increasing number and diversity in children's toy products. The presence of these chemicals in toys poses an extreme risk for the health and development of children. In this study, an analytical methodology has been developed using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). This hybrid instrumentation together with an in-house accurate-mass database and a mass spectral library, allows for wide-scope screening and identification of hazardous substances in plastic toys. A total number of 200 compounds belonging to eight chemical families were investigated, such as coloring agents, plasticizers, fragrance allergens, nitrosamines, primary aromatic amines, flame retardants, perfluorinated compounds, and endocrine disruptors. Following a straightforward and efficient dissolution/precipitation method for sample preparation, chemical screening and confirmation were conducted by comparing the experimentally measured exact mass, retention time, and isotopic pattern with the accurate-mass database and by matching the acquired MS/MS spectra against the mass spectral library. The matrix effect, linearity, sensitivity, precision, and recovery of the proposed method were properly evaluated. The obtained limits of detection (LODs) and quantitation (LOQs) were in the range of 0.01–0.98 mg kg−1 and 0.03–2.99 mg kg−1, respectively. The applicability of the developed protocol was verified through the analysis of 55 real plastic toy products.

Application of the fentanyl analog screening kit toward the identification of emerging synthetic opioids in human plasma and urine by LC-QTOF

Human exposures to fentanyl analogs, which significantly contribute to the ongoing U.S. opioid overdose epidemic, can be confirmed through the analysis of clinical samples. Our laboratory has developed and evaluated a qualitative approach coupling liquid chromatography and quadrupole time-of-flight mass spectrometry (LC-QTOF) to address novel fentanyl analogs and related compounds using untargeted, data-dependent acquisition. Compound identification was accomplished by searching against a locally-established mass spectral library of 174 fentanyl analogs and metabolites. Currently, our library can identify 150 fentanyl-related compounds from the Fentanyl Analog Screening (FAS) Kit), plus an additional 25 fentanyl-related compounds from individual purchases. Plasma and urine samples fortified with fentanyl-related compounds were assessed to confirm the capabilities and intended use of this LC-QTOF method. For fentanyl, 8 fentanyl-related compounds and naloxone, lower reportable limits (LRL100), defined as the lowest concentration with 100 % true positive rate (n = 12) within clinical samples, were evaluated and range from 0.5 ng/mL to 5.0 ng/mL for urine and 0.25 ng/mL to 2.5 ng/mL in plasma. The application of this high resolution mass spectrometry (HRMS) method enables the real-time detection of known and emerging synthetic opioids present in clinical samples.

Gradient boosting for the prediction of gas chromatographic retention indices

The estimation of gas chromatographic retention indices based on compounds structures is an importantproblem. Predicted retention indices can be used in a mass spectral library search for the identificationof unknowns. Various machine learning methods are used for this task, but methods based on decisiontrees, in particular gradient boosting, are not used widely. The aim of this work is to examine the usability ofthis method for the retention index prediction. 177 molecular descriptors computed with Chemistry Development Kit are used as the input representation of a molecule. Random subsets of the whole NIST 17 database are used as training, test and validation sets. 8000 trees with 6 leaves each are used. A neural network with one hidden layer (90 hidden nodes) is used for the comparison. The same data sets and the set of descriptors are used for the neural network and gradient boosting. The model based on gradient boosting outperforms the neural network with one hidden layer for subsets of NIST 17 and for the set of essential oils.The performance of this model is comparable or better than performance of other modern retention prediction models. The average relative deviation is ~3.0%, the median relative deviation is ~1.7% for subsets of NIST 17. The median absolute deviation is ~34 retention index units. Only non-polar liquid stationary phases (such as polydimethylsiloxane, 5% phenyl 95% polydimethylsiloxane, squalane) are considered. Errors obtained with different machine learning algorithms and with the same representation of the molecule strongly correlate with each other.

Creating a Reliable Mass Spectral-Retention Time Library for All Ion Fragmentation-Based Metabolomics.

Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chemical spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liquid chromatography coupled to mass spectrometry (LC–MS) methods across laboratories. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across laboratories. This library construction strategy improves the confidence in annotation for AIF data in LC–MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.

Constructing a Tandem Mass Spectral Library for Forensic Ricin Identification.

Ricin, a protein found in castor seeds, is a lethal toxin that is designated as a category 2 select agent, and cases of attempted ricin poisoning are relatively common. Many methods to detect protein toxins such as ricin use targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify toxin peptides, usually tryptic peptides. The successful use of untargeted methods has also been reported. However, the use of untargeted proteomics methods, including database search, for peptide and protein identification is less common in forensic practice and may be unfamiliar to forensic science practitioners. Here, we propose a method to create spectral libraries of tryptic ricin peptides and use these libraries for ricin identification by spectral library search, which may be more familiar to forensic scientists because of the use of spectral libraries in small molecule identification. Peptide spectral libraries offer a direct comparison to an authentic standard, a key element of forensic analysis, but have not previously been used in a forensic context. To construct these spectral libraries, two pure ricin samples (one from a proposed standard reference material) were digested with trypsin and analyzed using a standard shotgun LC-MS/MS protocol. Spectral libraries were created from resulting tryptic peptides identified from filtered search results from four database search tools. The library was then used in a search using SpectraST on forensically realistic castor seed extracts. These castor seed samples were made using the crude methods commonly encountered in real-world ricin cases. Analysis showed that the spectral library search resulted in more peptides identified from crude castor seed samples compared to MS-GF+ and Sequest plus Percolator database searches. These results, the first published use of spectral library search to detect protein toxins in forensically relevant samples, suggest that computational comparison of putative ricin peptide spectra to library spectra can be an effective method to detect ricin in an unknown sample. Data are available via ProteomeXchange with identifier PXD013711.

Identification of Degradation By-Products of Selected Pesticides During Oxidation and Chlorination Processes

Abstract Advanced oxidation processes (AOPs) are considered to be one of the most effective methods for the decomposition of a wide range of hardly-biodegradable organic compounds, including pesticides. The implementation of such processes in the water streams treatment often leads to the formation of decomposition by-products of micropollutants occurring in water. These compounds, even in concentrations of a few ng/dm3, may negatively affect the water quality. Therefore, there is a need for detailed analyses that will allow to identify intermediates found in the AOP solutions and to assess their impact on the aquatic environment. The paper presents an attempt to identify by-products of three pesticides: triclosan, triallat and oxadiazon during ozonation, chlorination and UV irradiation of their water solutions. The identification of compounds was performed based on the results of the GC-MS analysis using the NIST v17 mass spectral library. It has been shown that during all of tested advanced oxidation processes, incomplete degradation of pesticides occurs. The number of micropollutant decomposition by-products increases with the increase of the applied ozone dose and UV exposure time. During the chlorination process Cl− atoms were added to the tested compound molecules. In the case of triclosan, it led to the generation of compounds containing four or five chlorine atoms in their structure. The toxicological analysis performed by the use of the Microtox® and Lemna sp. Growth Inhibition Test showed the toxic nature of post-process solutions. The decomposition by-products of triclosan and triallate, generated during the UV irradiation process, were highly toxic against the test organisms (toxic effect > 75 %). This makes it impossible to drain these solutions into the natural environment.

Mass Spectrometry Fingerprints of Small-Molecule Metabolites in Biofluids: Building a Spectral Library of Recurrent Spectra for Urine Analysis

A large fraction of ions observed in electrospray liquid chromatography-mass spectrometry (LC-ESI-MS) experiments of biological samples remain unidentified. One of the main reasons for this is that spectral libraries of pure compounds fail to account for the complexity of the metabolite profiling of complex materials. Recently, the NIST Mass Spectrometry Data Center has been developing a novel type of searchable mass spectral library that includes all recurrent unidentified spectra found in the sample profile. These libraries, in conjunction with the NIST tandem mass spectral library, allow analysts to explore most of the chemical space accessible to LC-MS analysis. In this work, we demonstrate how these libraries can provide a reliable fingerprint of the material by applying them to a variety of urine samples, including an extremely altered urine from cancer patients undergoing total body irradiation. The same workflow is applicable to any other biological fluid. The selected class of acylcarnitines is examined in detail, and derived libraries and related software are freely available. They are intended to serve as online resources for continuing community review and improvement.

A Data-Challenge Case Study of Analyte Detection and Identification with Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry (GC×GC-MS)

This case study describes data analysis of a chromatogram distributed for the 2019 GC×GC Data Challenge for the Tenth Multidimensional Chromatography Workshop (Liege, Belgium). The chromatogram resulted from chemical analysis of a terpene-standards sample by comprehensive two-dimensional chromatography with mass spectrometry (GC×GC-MS). First, several aspects of the data quality are assessed, including detector saturation and oscillation, and operations to prepare the data for analyte detection and identification are described, including phase roll for modulation-cycle alignment and baseline correction to account for the non-zero detector baseline. Then, the case study presents operations for analyte detection with filtering, a new method to flag false detections, interactive review to confirm detected peaks, and ion-peaks detection to reveal peaks that are obscured by noise or coelution. Finally, the case study describes analyte identification including mass-spectral library search with a new method for optimizing spectra extraction, retention-index calibration from preliminary identifications, and expression-based identification checks. Processing of the first 40 min of data detected 144 analytes, 21 of which have at least one percent response, plus an additional 20 trace and/or coeluted analytes.

Model-Based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids.

By circumventing the need for a pure colony, MALDI-TOF mass spectrometry of bacterial membrane glycolipids (lipid A) has the potential to identify microbes more rapidly than protein-based methods. However, currently available bioinformatics algorithms (e.g., dot products) do not work well with glycolipid mass spectra such as those produced by lipid A, the membrane anchor of lipopolysaccharide. To address this issue, we propose a spectral library approach coupled with a machine learning technique to more accurately identify microbes. Here, we demonstrate the performance of the model-based spectral library approach for microbial identification using approximately a thousand mass spectra collected from multi-drug-resistant bacteria. At false discovery rates < 1%, our approach identified many more bacterial species than the existing approaches such as the Bruker Biotyper and characterized over 97% of their phenotypes accurately. As the diversity in our glycolipid mass spectral library increases, we anticipate that it will provide valuable information to more rapidly treat infected patients.

False Discovery Rate Estimation for Hybrid Mass Spectral Library Search Identifications in Bottom-up Proteomics.

We present a method for FDR estimation of mass spectral library search identifications made by a recently developed method for peptide identification, the hybrid search, based on an extension of the target-decoy approach. In addition to estimating confidence for a given identification, this allows users to compare and integrate identifications from the hybrid mass spectral library search method with other peptide identification methods, such as a sequence database-based method. In addition to a score, each hybrid score is associated with a "DeltaMass" value, which is the difference in mass of the search and library peptide, which can correspond to the mass of a modification. We explored the relation between FDR and DeltaMass using 100 concatenated random decoy libraries and discovered that a small number of DeltaMass values were especially likely to result from decoy searches. Using these values, FDR values could be adjusted for these specific values and a reliable FDR generated for any DeltaMass value. Finally, using this method, we find and examine common, reliable identifications made by the hybrid search for a range of proteomic studies.

Generating Fatty Acid Profiles in the Gas Phase: Fatty Acid Identification and Relative Quantitation Using Ion/Ion Charge Inversion Chemistry.

Representing the most fundamental lipid class, fatty acids (FA) play vital biological roles serving as energy sources, cellular signaling molecules, and key architectural components of complex lipids. Direct infusion electrospray ionization spectrometry, also known as shotgun lipidomics, has emerged as a rapid and powerful toolbox for lipid analysis. While shotgun lipidomics can be a sensitive approach to FA detection, the diverse molecular structure of FA presents challenges for unambiguous identification and the relative quantification of isomeric contributors. In particular, pinpointing double bond position(s) in unsaturated FA and determining the relative contribution of double bond isomers has limited the application of the shotgun approach. Recently, we reported the use of gas-phase ion/ion reactions to facilitate the identification of FA. Briefly, singly deprotonated FA anions undergo charge inversion when reacted in the gas phase with tris-phenanthroline magnesium dications by forming [FA - H + MgPhen]+ complex ions. These charge-inverted FA complex cations fragment upon ion-trap collision-induced dissociation (CID) to generate product ion spectra unique to individual FA isomers. Herein, we report the development of a mass spectral library comprised of [FA - H + MgPhen]+ product ion spectra. The developed FA library permits confident FA identification, including polyunsaturated FA isomers. Furthermore, we demonstrate the ability to determine relative contributions of isomeric FA using multiple linear regression analysis paired with gas-phase ion/ion reactions. We successfully applied the presented method to generate a FA profile for bovine liver phospholipidome based entirely on gas-phase chemistries.