bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2022‒04‒03
24 papers selected by
Sofia Costa
Icahn School of Medicine at Mount Sinai

  1. J Chromatogr A. 2022 Mar 15. pii: S0021-9673(22)00150-9. [Epub ahead of print]1670 462952
      LC-MS metabolomic analysis in complex biological matrices may be complicated by degeneracy when using large-bore columns. Degeneracy is the detection of multiple mass spectral peaks from the same analyte due to adduction of salts to the metabolite, dimerization, or loss of neutrals. This introduces interferences to the MS spectra, diminishes quantification, and increases the rate of false identifications. Analysis using 2.1 mm inner diameter (i.d.) columns typically leads to degenerate peaks whereas nanospray using capillary columns (25, 50, and 75 µm i.d.) reduces degeneracy. Optimization of chromatographic parameters of capillary LC for amino acid standards showed the lowest HETP at 1.25 mm/sec across all capillary i.d. columns. Results suggest mass-sensitive detection below the optimum velocity. At faster velocities, concentration-dependent detection occurred across all capillaries. The 2.1 mm i.d. analytical scale column showed the greatest level of degeneracy, particularly in the low signal intensity range. 25 µm i.d. columns showed higher levels of metabolite annotation for the same signal intensity range. It also provided the lowest level of degeneracy, making it best suited for untargeted analysis. The 25 µm i.d. column achieved a peak capacity (nc) of 144 in a 30-minute gradient method with nc decreasing as the column i.d. increased. 75 µm i.d. capillary columns showed the highest signal intensity, which is beneficial for targeted analysis. These effects of chromatographic performance, resolution, and degeneracy profile of capillary and analytical scale columns were compared for metabolomic analyses in complex serum and cell lysate matrices.
    Keywords:  Capillary LC; Degeneracy; E. coli; HPLC; Human serum; Ion trap; MS; Metabolomics; Nanospray; Optimization; Orbitrap
  2. Front Mol Biosci. 2022 ;9 841373
      Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.
    Keywords:  experimental network; graph-based analysis; knowledge network; metabolic network; metabolism; systems biology; untargeted metabolomics
  3. Bioinformatics. 2022 Mar 31. pii: btac197. [Epub ahead of print]
      MOTIVATION: Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility (IM) increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross section (CCS).RESULTS: We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class.
    AVAILABILITY: All relevant source code is available at
    SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.
  4. Anal Chim Acta. 2022 Apr 15. pii: S0003-2670(22)00238-0. [Epub ahead of print]1202 339667
      This research reports on the development of a comprehensive two-dimensional liquid chromatography (2D-LC) method hyphenated to inline DAD-UV and ESI-QTOF-MS/MS-detection for the separation of conjugated polyunsaturated fatty acid isomers and structurally related (saturated, unconjugated, oxidized) compounds. In pharmaceutical lipid formulations conjugated fatty acids can be found as impurities, generated by oxidation of polyunsaturated fatty acids. Due to the structural complexity of resultant multi-component samples one dimensional liquid chromatography may be suboptimal for quality control and impurity profiling. The screened reversed-phase columns showed a lack of selectivity for the conjugated fatty acid isomers but the resolutions improved with the shape selectivity of the stationary phases (C18- < C30- < cholesteryl-ether-bonded). Further enhanced selectivity for the non-chiral conjugated FAs could be achieved with amylose/cellulose-based chiral stationary phases (CSPs) which harbor cavities for selective inclusion depending on E/Z configurations of the double bonds of the analytes. Amylose-based CSPs showed higher selectivity for conjugated fatty acids than the cellulose-based polysaccharide CSPs. Hyphenating the chiral and reversed-phase columns in a comprehensive 2D-LC-setup was favorable since they showed orthogonality and good compatibility, because both were operated under RP-conditions. The chiral dimension (1D) mainly separated the different isomers, while the reversed-phase dimension (2D) separated according to number of double bonds and degree of oxidation. Using this setup, advanced structural annotation of unknowns was possible based on UV-, MS1- and MS2-spectra. Data-independent acquisition (by SWATH) enabled differentiation of positional isomers of oxidized lipids by characteristic MS2-fragments and elucidation of co-eluted compounds by selective extracted ion chromatograms of fragment ions (MS2 EICs).
    Keywords:  Data-independent acquisition; Food analysis; LC×LC; Lipidomics; Oxylipins; Pharmaceutical analysis
  5. Horm Metab Res. 2022 Mar 29.
      Estrogens and androgens are important regulators of sexual development and physiological processes in men and women, acting on numerous organs throughout the body. Moreover, they can contribute to a variety of pathologies, including osteoporosis, cancer, and cardiovascular and neurologic diseases. Analysis of estrogens and androgens in biological samples has been commonly performed using immunoassays for many years. However, these assays are suboptimal, as there is cross-reactivity with similar analytes, and they have moderate specificity and sensitivity. Thus, there is a clinical need to develop highly sensitive and specific methods for the accurate measurement of estrogen and androgen concentrations. Herein, we describe the development of three liquid chromatography coupled tandem mass spectrometry-based methods that incorporate the use of a Triple Quadrupole Mass Spectrometer for quantitative measurement of endogenous concentrations of various steroid hormones in human serum samples: (1) the simultaneous measurement of testosterone, androstenedione, and cortisol, (2) dehydroepiandrosterone (DHEA), and (3) 17β-estradiol (E2). The use of derivatizing reagents, Girard's reagent P and dansyl chloride, allowed for significant gains in sensitivity in the analysis of DHEA and E2, respectively, relative to the underivatized analyte. These procedures proved efficient and adequately sensitive for steroid hormone analysis in extracted patient sera samples from older men and postmenopausal women, providing reliable data down to low nanogram/ml and sub-nanogram/ml levels. Moreover, utilizing the combination of highly specific mass transitions associated with these analytes and their respective internal deuterated standards provided a high degree of specificity to the identity of these hormones.
  6. Anal Chim Acta. 2022 Apr 22. pii: S0003-2670(22)00222-7. [Epub ahead of print]1203 339651
      The response to the demand for biomedical testing on small volumes of biofluids has led to a range of new microsampling devices and related techniques. Simple cost-effective sampling devices are available, but most do not incorporate sample clean-up and necessitate extensive sample processing by the analyst. To address both cleanup and analyte stability, a porous polymeric thin film made of methacrylic acid (MAA) and ethylene glycol dimethacrylate (EGDMA) coated (5 × 18 mm2) on a stainless steel substrate was used for the extraction of seven tricyclic antidepressants (TCAs) from plasma spots, with analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Essential factors such as sample volume, extraction time, matrix effects, and the desorption process were investigated. The optimized method is comprised of a quick 3-min extraction from 10 μL of plasma, a wash (10 s in 1 mL of 1% aqueous triethylamine (TEA) to remove plasma matrix interferences, and 2-min desorption (200 μL of ACN with 0.1% formic acid (FA)). For the purpose of sample archiving, thin film devices containing extracted TCAs were stored for 30 days at room temperature and showed a consistent analyte recovery. Inter-device reproducibility was evaluated without internal standard (%RSD 8.2-19.3%), and using two methods of introducing a single deuterated internal standard (imipramine-D3) either prior to (%RSDs 5.6-13.9%) or after (%RSDs 4.9-10.2%) sample loading to the device. Although the intention of this study was to introduce a single use device for rapid and easy analysis, reusability showed the feasibility of 15 consecutive extractions using same device without any performance loss. The optimized method revealed excellent linearity (R2 > 0.99) in the range of 1-1000 ng mL-1, with good intra- and inter-day accuracy (81.4-118%) and precision (≤12%) in human plasma.
    Keywords:  Extracted biofluid spot; Liquid chromatography-tandem mass spectrometry; Microsampling; Porous polymers; Sample preparation; Tricyclic antidepressants
  7. Compr Rev Food Sci Food Saf. 2022 Mar 29.
      Food fraud is currently a growing global concern with far-reaching consequences. Food authenticity attributes, including biological identity, geographical origin, agricultural production, and processing technology, are susceptible to food fraud. Metabolic markers and their corresponding authentication methods are considered as a promising choice for food authentication. However, few metabolic markers were available to develop robust analytical methods for food authentication in routine control. Untargeted metabolomics by liquid chromatography-mass spectrometry (LC-MS) is increasingly used to discover metabolic markers. This review summarizes the general workflow, recent applications, advantages, advances, limitations, and future needs of untargeted metabolomics by LC-MS for identifying metabolic markers in food authentication. In conclusion, untargeted metabolomics by LC-MS shows great efficiency to discover the metabolic markers for the authenticity assessment of biological identity, geographical origin, agricultural production, processing technology, freshness, cause of animals' death, and so on, through three main steps, namely, data acquisition, biomarker discovery, and biomarker validation. The application prospects of the selected markers by untargeted metabolomics require to be valued, and the selected markers need to be eventually applicable at targeted analysis assessing the authenticity of unknown food samples.
    Keywords:  authentication; food; fraud; liquid chromatography; marker; mass spectrometry; metabolomics
  8. Anal Chem. 2022 Mar 28.
      Because of their diverse functionalities in cells, lipids are of primary importance when characterizing molecular profiles of physiological and disease states. Imaging mass spectrometry (IMS) provides the spatial distributions of lipid populations in tissues. Referenced Kendrick mass defect (RKMD) analysis is an effective mass spectrometry (MS) data analysis tool for classification and annotation of lipids. Herein, we extend the capabilities of RKMD analysis and demonstrate an integrated method for lipid annotation and chemical structure-based filtering for IMS datasets. Annotation of lipid features with lipid molecular class, radyl carbon chain length, and degree of unsaturation allows image reconstruction and visualization based on each structural characteristic. We show a proof-of-concept application of the method to a computationally generated IMS dataset and validate that the RKMD method is highly specific for lipid components in the presence of confounding background ions. Moreover, we demonstrate an application of the RKMD-based annotation and filtering to matrix-assisted laser desorption/ionization (MALDI) IMS lipidomic data from human kidney tissue analysis.
  9. Se Pu. 2022 Apr;40(4): 354-363
      A method for the determination of 14 polybrominated diphenyl ethers (PBDEs) in human serum using isotope internal standard-gas chromatography-high resolution dual-focus magnetic mass spectrometry (GC-HRMS) was developed. After thawed to room temperature, 0.5 mL serum samples were mixed with 13C-labeled isotopic internal standard. Subsequently, methanol was added to precipitate the proteins in the samples. The effects of three kinds of acids on the removal of cellulite from the serum samples and the corresponding recoveries were compared, and the results revealed that sulfuric acid was the most optimal. The target compounds were extracted by liquid-liquid extraction (LLE), and the effects of different extraction solvents on recoveries were compared. The results indicated that n-hexane (6 mL)-methyl tert-butyl ether (6 mL) was the best extraction solvent. The extracts were cleaned and eluted using solid phase extraction cartridges. Furthermore, the factors that influenced the cleanup effects and recoveries, including the solid phase extraction columns and elution solvents, were investigated in detail. The results indicated that the optimal conditions were cleanup with a silica gel column and elution with hexane-dichloromethane (1∶1, v/v). The eluate was re-dissolved in hexane after being blown to near dryness using nitrogen. The detection of PBDEs was performed using GC-HRMS. The instrument conditions were optimized, and the capillary column used was an Rtx-1614 column (30 m×0.25 mm×0.1 μm). Helium was used as the carrier gas at a flow rate of 1.0 mL/min. The injector temperature was 290 ℃, and the oven temperature was programmed as follows: 150 ℃ for 2 min, 150 ℃ to 250 ℃ at 15 ℃/min, held for 1 min, 250 ℃ to 290 ℃ at 25 ℃/min, held for 3 min, and 290 ℃ to 320 ℃ at 25 ℃/min, held for 12.5 min. The injection volume was 1 μL in splitless mode. The samples were ionized in the positive electron ionization (EI) mode at 35 eV. Precursor ions and the production of each compound were identified using a voltage-selective ion detection (VSIR) program with a resolution of 10000. The ionization temperature was set at 280 ℃, and the transmission line temperature was set at 320 ℃. To ensure the integrity of the separation of low-brominated components, the column separation time was shortened, the response of high-boiling components was improved (BDE-190 and BDE-209), the decomposition of BDE-209 on the chromatographic column was effectively prevented, and the requirement of the simultaneous determination of multiple PBDEs was met. The method demonstrated good linearity in the range of 0.40 to 25 μg/L for BDE-209, and 0.08 to 5 μg/L for the other 13 PBDEs, with correlation coefficients greater than 0.995. The method detection limits (MDLs) were in the range of 0.01 to 0.51 μg/L, and the limits of quantification (LOQs) ranged from 0.04 to 1.70 μg/L. The recoveries of the 14 compounds ranged from 75.5% to 120.7%. The intra-day relative standard deviations (RSDs) were within 3.8%-10.9% (n=6) and the inter-day RSDs were within 4.2% to 12.4% (n=6). This method was successfully applied to the determination of 14 PBDEs in 15 serum samples from an adolescent population in an area. Notably, 1.86 to 4.66 ng/g lipid BDE-47 was detected in the serum samples with a detection frequency of 100%, and the other compounds were not detected. The results imply that the adolescent population in this region was exposed to some PBDE. Compared with the existing methods reported, this method has less sample demand and higher sensitivity and accuracy, can simultaneously determine 14 PBDEs, including BDE-209 in human serum, and effectively improve the efficiency of detection. This study offers a new method for studying the impact of polybrominated diphenyl ethers on population health in China.
    Keywords:  biological monitoring; gas chromatography-high resolution dual-focus magnetic mass spectrometry (GC-HRMS); liquid-liquid extraction (LLE); polybrominated diphenyl ethers; serum
  10. Sci Rep. 2022 Mar 30. 12(1): 5384
      As the pervasive, standardized format for interchange and deposition of raw mass spectrometry (MS) proteomics and metabolomics data, text-based mzML is inefficiently utilized on various analysis platforms due to its sheer volume of samples and limited read/write speed. Most research on compression algorithms rarely provides flexible random file reading scheme. Database-developed solution guarantees the efficiency of random file reading, but nevertheless the efforts in compression and third-party software support are insufficient. Under the premise of ensuring the efficiency of decompression, we propose an encoding scheme "Stack-ZDPD" that is optimized for storage of raw MS data, designed for the format "Aird", a computation-oriented format with fast accessing and decoding time, where the core compression algorithm is "ZDPD". Stack-ZDPD reduces the volume of data stored in mzML format by around 80% or more, depending on the data acquisition pattern, and the compression ratio is approximately 30% compared to ZDPD for data generated using Time of Flight technology. Our approach is available on AirdPro, for file conversion and the Java-API Aird-SDK, for data parsing.
  11. Anal Chem. 2022 Mar 31.
      Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at
  12. J Pharmacol Toxicol Methods. 2022 Mar 26. pii: S1056-8719(22)00016-8. [Epub ahead of print] 107169
      Thyroid hormones and their derivatives are structurally related to the non-essential amino acid tyrosine (4-hydroxyphenylalanine). However, there are physicochemical differences that make it difficult to apply an analytical method for their simultaneous detection. This work focused on the optimization of a method using liquid chromatography-electrospray ionization mass spectrometry to measure eight compounds related to levothyroxine (T4). In addition, the influence of the additives to the mobile phase, the solvents for liquid-liquid extraction and the influence of the hydrolysis of the conjugated analytes were studied. Optimization of MRM transitions and collision energy for analytes and capillary voltage, nebulizer gas pressure, nozzle voltage, sheath gas flow, sheath gas temperature, drying gas flow and drying gas temperature for ionization source was done. The recovery of analytes was studied using five solvents and six solvent systems to introduce them into the liquid-liquid extraction and matrix cleanup steps. Different additives to the mobile phase were evaluated as well as the effectiveness of enzymatic and chemical hydrolysis. The best MRM transitions and source parameters were settled in order to generate an optimized instrumental method. The addition of ammonium formate, ammonium fluoride, and acetic acid to the mobile phase showed no improvement in responses compared to classic 0.1% formic acid. The use of tert-butyl methyl ether: isopropanol (75: 25, V: V) showed a suitable recovery of analytes to perform a liquid-liquid extraction, and n-hexane might be an appropriate solvent if a cleanup step is needed. The stability of T3, T4 and thyronine, checked after the hydrolysis with extracts of E. coli and Helix pomatia showed good results, contrary to chemical hydrolysis that showed a total degradation of T3 and T4.
    Keywords:  Enzymatic hydrolysis; LC-MS; Levothyroxine; Optimization; Serum; Thyroid hormones; Urine
  13. J Pharm Biomed Anal. 2022 Mar 21. pii: S0731-7085(22)00157-1. [Epub ahead of print]214 114736
      A possibility of application of solid-phase analytical derivatization (SPAD) for the quantification of seven steroid hormones (testosterone, dihydrotestosterone, cortisone, cortisol, progesterone, 11α-hydroxyprogesterone, and estrone) in human urine was evaluated. To prepare urine samples for instrumental analysis, SPAD with hydroxylamine was applied after enzymatic hydrolysis of the sample. To achieve high recovery values, extraction and derivatization conditions were optimized. Cartridges packed with end-capped octadecylsilyl silica sorbent provided optimum extraction of target analytes, while the reaction with hydroxylamine in the cartridge was found as a simple and efficient way for the chemical derivatization of steroids. The obtained derivatives were detected by using reversed-phase ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The proposed procedure was validated and applied to the analysis of real urine samples to prove the applicability of the proposed method for the routine analysis.
    Keywords:  Human urine; Hydroxylamine; Liquid chromatography; Mass spectrometry; Solid-phase analytical derivatization; Steroid hormones
  14. Anal Chem. 2022 Mar 28.
      Non-targeted metabolomics via high-resolution mass spectrometry methods, such as direct infusion Fourier transform-ion cyclotron resonance mass spectrometry (DI-FT-ICR MS), produces data sets with thousands of features. By contrast, the number of samples is in general substantially lower. This disparity presents challenges when analyzing non-targeted metabolomics data sets and often requires custom methods to uncover information not always accessible via classical statistical techniques. In this work, we present a pipeline that combines a convolutional neural network with traditional statistical approaches and an adaptation of a genetic algorithm. The developed method was applied to a lifestyle intervention cohort data set, where subjects at risk of type 2 diabetes underwent an oral glucose tolerance test. Feature selection is the final result of the pipeline, achieved through classification of the data set via a neural network, with a precision-recall score of over 0.9 on the test set. The features most relevant for the described classification were then chosen via a genetic algorithm. The output of the developed pipeline encompasses approximately 200 features with high predictive scores, providing a fingerprint of the metabolic changes in the prediabetic class on the data set. Our framework presents a new approach which allows to apply complex modeling based on convolutional neural networks for the analysis of high-resolution mass spectrometric data.
  15. J Sep Sci. 2022 Mar 27.
      Free fatty acids involved in many metabolic regulations in human body. In this work, an ultra-fast screening method was developed for the analysis of free fatty acids using trapped ion mobility spectrometry coupled with mass spectrometry. Thirty-three free fatty acids possessing different unsaturation degrees and different carbon chain lengths were baseline separated and characterized within milliseconds. Saturated, monounsaturated, and polyunsaturated free fatty acids showed different linearities between collision cross section values and m/z. Establishment of correlations between structures and collision cross section values provided additional qualitative information and made it possible to determine free fatty acids which were out of the standards pool but possessed the confirmed linearity. Gas-phase separation made the quantitative analysis reliable and repeatable at a much lower time cost than chromatographic methods. The sensitivity was comparable to and even better than the reported results. The method was validated and applied to profiling free fatty acids in human plasma. Saturated free fatty acids abundance in the fasting state was found to be lower than that in the postprandial state, while unsaturated species abundance was found higher. The method was fast and robust with minimum sample pretreatment, so it was promising in high-throughput screening of free fatty acids. This article is protected by copyright. All rights reserved.
    Keywords:  Free fatty acids; High-throughput analysis; Mass spectrometry; Trapped ion mobility spectrometry
  16. J Am Soc Mass Spectrom. 2022 Mar 31.
      Mass spectrometry imaging is a technique uniquely suited to localize and identify lipids in a tissue sample. Using an atmospheric pressure (AP-) matrix-assisted laser desorption ionization (MALDI) source coupled to an Orbitrap Elite, numerous lipid locations and structures can be determined in high mass resolution spectra and at cellular spatial resolution, but careful sample preparation is necessary. We tested 11 protocols on serial brain sections for the commonly used MALDI matrices CHCA, norharmane, DHB, DHAP, THAP, and DAN in combination with tissue washing and matrix additives to determine the lipid coverage, signal intensity, and spatial resolution achievable with AP-MALDI. In positive-ion mode, the most lipids could be detected with CHCA and THAP, while THAP and DAN without additional treatment offered the best signal intensities. In negative-ion mode, DAN showed the best lipid coverage and DHAP performed superiorly for gangliosides. DHB produced intense cholesterol signals in the white matter. One hundred fifty-five lipids were assigned in positive-ion mode (THAP) and 137 in negative-ion mode (DAN), and 76 peaks were identified using on-tissue tandem-MS. The spatial resolution achievable with DAN was 10 μm, confirmed with on tissue line-scans. This enabled the association of lipid species to single neurons in AP-MALDI images. The results show that the performance of AP-MALDI is comparable to vacuum MALDI techniques for lipid imaging.
    Keywords:  AP-MALDI; lipids; mass spectrometry imaging; sample preparation; tandem-MS
  17. Talanta. 2022 Mar 24. pii: S0039-9140(22)00192-8. [Epub ahead of print]244 123396
      A computational method for the untargeted determination of cycling yeast metabolites using a comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) dataset is presented. The yeast metabolomic cycle for the diploid yeast strain CEN.PK with a 5 h cycle period relative to the O2 concentration level is comprehensively examined to determine the metabolites that exhibit cycling. Samples were collected over only two cycles (10 h with a total of 24 time-point sampling intervals at 25 min each) as an experimental constraint. Due to the limited number of cycles expressed in the dataset, a computational method was devised to determine with statistical significance whether or not a given metabolite exhibited a temporal signal pattern that constituted cycling in the context of the 5 h cycle period. The computational method we report compares the experimentally obtained 24 time-point metabolite signal sequences to randomly generated signal sequences coupled with statistically based confidence level LOF metrics to determine whether or not a given metabolite expresses cycling, and if so, what is the phase of the cycling. Initially the GC×GC-TOFMS dataset was analyzed using tile-based Fisher ratio (F-ratio) analysis. Since there were 24 time-point intervals, this constituted 24 sample classes in the F-ratio calculation which produced 672 metabolite hits. Next, application of the computational method determined that there were 210 of the 672 metabolites exhibiting cycling: 55 identified metabolites and 155 unknown metabolites. Furthermore, the 210 cycling metabolites were categorized into four groups, and where applicable, a phase determined: 1 cycle/5 h period (106 metabolites), 2 cycles/5 h period (13 metabolites), spiky pattern (12 metabolites), or multimodal pattern (79 metabolites).
    Keywords:  Comprehensive two-dimensional gas chromatography; Cycling determination; Tile-based Fisher ratio analysis; Time-of-flight mass spectrometry; Yeast metabolomic cycle
  18. Anal Chim Acta. 2022 Apr 15. pii: S0003-2670(22)00230-6. [Epub ahead of print]1202 339659
      The primary treatment of breast cancer is the surgical removal of the tumor with an adequate healthy tissue margin. An intraoperative method for assessing surgical margins could optimize tumor resection. Differential ion mobility spectrometry (DMS) is applicable for tissue analysis and allows for the differentiation of malignant and benign tissues. However, the number of cancer cells necessary for detection remains unknown. We studied the detection threshold of DMS for cancer cell identification with a widely characterized breast cancer cell line (BT-474) dispersed in a human myoma-based tumor microenvironment mimicking matrix (Myogel). Predetermined, small numbers of cultured BT-474 cells were dispersed into Myogel. Pure Myogel was used as a zero sample. All samples were assessed with a DMS-based custom-built device described as "the automated tissue laser analysis system" (ATLAS). We used machine learning to determine the detection threshold for cancer cell densities by training binary classifiers to distinguish the reference level (zero sample) from single predetermined cancer cell density levels. Each classifier (sLDA, linear SVM, radial SVM, and CNN) was able to detect cell density of 3700 cells μL-1 and above. These results suggest that DMS combined with laser desorption can detect low densities of breast cancer cells, at levels clinically relevant for margin detection, from Myogel samples in vitro.
  19. Rapid Commun Mass Spectrom. 2022 Mar 30. e9308
      RATIONALE: Analyte quantitation by mass spectrometry underpins a diverse range of scientific endeavors. The fast growing field of mass spectrometer development has resulted in several targeted and untargeted acquisition modes suitable for these applications. By characterizing the acquisition methods available on an ion mobility (IM) enabled orthogonal acceleration time-of-flight (oa-ToF) instrument, the optimum modes for analyte semi-quantitation can be deduced.METHODS: Serial dilutions of commercial metabolite, peptide, or crosslinked peptide analytes were prepared in matrices of human urine or E. coli digest. Each analyte dilution was introduced into an IM separation enabled oa-ToF mass spectrometer by reversed phase liquid chromatography and electrospray ionization. Data were acquired for each sample in duplicate using nine different acquisition modes, including four IM enabled acquisitions modes, available on the mass spectrometer.
    RESULTS: Five (metabolite) or seven (peptide/crosslinked peptide) point calibration curves were prepared for analytes across each of the acquisition modes. A non-linear response was observed at high concentrations for some modes, attributed to saturation effects. Two correction methods, one MS1 isotope-correction and one MS2 ion intensity-correction, were applied to address this observation, resulting in an up to two-fold increase in dynamic range. By averaging the semi-quantitative results across analyte classes, two parameters, linear dynamic range (LDR) and lower limit of quantitation (LLOQ), were determined to evaluate each mode.
    CONCLUSION: Comparison of the acquisition modes revealed that data independent acquisition and parallel reaction monitoring methods are most robust for semi-quantitation when considering achievable LDR and LLOQ. IM enabled modes exhibited sensitivity increases, but a simultaneous reduction in dynamic range which required correction methods to recover. These findings will assist users in identifying the optimum acquisition mode for their analyte quantitation needs, supporting a diverse range of applications and providing guidance for future acquisition mode developments.
  20. J Chromatogr A. 2022 Mar 18. pii: S0021-9673(22)00177-7. [Epub ahead of print]1670 462979
      A simple and accurate method of ultra high performance liquid chromatography (UHPLC) coupled with quadrupole-high field Orbitrap high-resolution mass spectrometry (QE HF HRMS) has been developed for analyzing 8 trace-level (µg/L) carbapenems in milk. Mass spectrometry conditions, chromatographic conditions, extraction solvent and QuEChERS procedures were optimized for determination of 8 carbapenems in milk. Samples were extracted and purified by modified QuEChERS procedure. Good separation for 8 carbapenems s was achieved with a PFP column at 8 min. Method validation results showed the linear ranges are 1-100 µg/L to 10-1,000 µg/L, the correlation coefficients are more than 0.995, and the recoveries of spiked samples are 79.3%-104% with a relative standard deviation less than 15%. This method successfully applied to monitor residue of carbapenems in real milk, four kinds of carbapenems have been detected in 8 sample of 79 collected milks with concentration ranged between 15 µg/L∼3,325 µg/L.
    Keywords:  Carbapenems; Milk; QuEChERS; Ultra-performance liquid chromatography high-field quadrupole-orbitrap high-resolution mass spectrometry
  21. J Anal Toxicol. 2022 Mar 26. pii: bkac020. [Epub ahead of print]
      Drugs of abuse are constantly evolving, while new synthetised substances are constantly emerging to avoid regulations. However, traditional drugs such as cocaine and amphetamine are still two of the most consumed drugs in the world. It is important, therefore, to provide suitable multiresidue methods for determining a wide range of drugs for use in toxicological and forensic analyses. The aim of this study is to develop a method for determining several families of drugs of abuse, including classic drugs, new psychoactive substances and some of their metabolites, in urine by liquid chromatography-tandem mass spectrometry. Urine is one of the most common biological matrices used in drug analysis because of its easy collection and wide window of detection. In this study, we used solid-phase extraction to remove interferences and extract analytes from urine. Four different mixed-mode cation exchange commercial sorbents were evaluated. The best results, in terms of apparent recoveries, were achieved with one of the strong cationic sorbents, i.e. ExtraBond SCX. The method achieved detection limits from 0.003 to 0.500 ng mL-1 and quantification limits from 0.050 to 1.500 ng mL-1, which are suitable for determining these compounds at the usual levels found in the urine of drug users. Applicability of this method was demonstrated by analysing real urine specimens from women following a detoxification program. Our results showed that the drug most consumed was cocaine, since it was detected in most urine specimens together with its main metabolite, benzoylecgonine. The polyconsumption of drugs from different families was also observed in some urine samples analysed.
    Keywords:  Cation-exchange sorbents; Drugs of abuse; Human urine; LC-MS/MS; Toxicological analysis
  22. Se Pu. 2022 Apr;40(4): 333-342
      Antibiotics are emerging contaminants that have recently attracted attention. They have been detected in natural water and pose health concerns owing to potential antibiotic resistance. Antibiotics are ubiquitous in aquatic environments, with a wide spectrum and trace levels. It is difficult to detect all types of antibiotics with completely different physicochemical properties. Solid phase extraction (SPE) is a common sample preparation procedure. For a fast and high-throughput continuous on-line analysis of these emerging contaminants, a method for the determination of 42 antibiotics (grouped into seven categories: sulfonamides, fluoroquinolones, lincosamides, macrolides, tetracyclines, cephalosporins, and chloramphenicols) in environmental water was developed based on ultra high performance liquid chromatography combined with tandem mass spectrometry (UHPLC-MS/MS) involving large volume direct injection without sample enrichment and cleanup. The collected water samples were filtered through a 0.22-μm filter membrane, their pH levels were adjusted to 6.0-8.0 after adding Na2EDTA, and then the solutions were mixed with an internal standard. The addition of Na2EDTA contributed to the release of tetracyclines and fluoroquinolones from the metal chelate. Improved recoveries were observed for all the compounds when the pH of the aqueous solution was set at 6.0-8.0. The optimized UHPLC conditions were as follows: chromatographic column, Phenomenex Kinetex C18 column (50 mm×30 mm, 2.6 μm); mobile phase, acetonitrile and 0.1% (v/v) formic acid aqueous solution; flow rate, 0.4 mL/min; injection volume, 100 μL. In the UHPLC-MS/MS experiment, chloramphenicol, thiamphenicol, and florfenicol were analyzed in the negative ionization scheduled multiple reaction monitoring mode (scheduled-MRM), while the other 39 antibiotics were analyzed in the positive scheduled-MRM mode. This acquisition method improved the response of each target compound by dividing the time of the analysis test cycle and scanning the ion channels of chromatographic peaks at different time periods. The ionspray voltage was set at 5500 and -4500 V in positive and negative modes, respectively. The source temperature for both ionization modes was set at 500 ℃, which was optimized to improve the sensitivity. Instrumental parameters like collision energy and declustering potential were also optimized. Good linearity was observed for all the tested antibiotics, with a correlation coefficient (r) greater than 0.995. The method detection limits (MDLs) were 0.015-3.561 ng/L. The average recoveries ranged from 80.1% to 125%, while the relative standard deviations (RSDs) were between 0.8% and 12.2%. The method was successfully applied to the determination of 10 source water samples and 5 tap water samples. Twelve antibiotics, viz. sulfachloropyridazine, sulfadiazine, sulfamethazine, sulfamethoxazole, sulfisomidine, clindamycin, lincomycin, roxithromycin, clarithromycin, erythromycin, thiamphenicol, and forfenicol, were detected in the 10 water samples with a detection frequency of 100%. The total antibiotic content in each sample ranged from not detected to 80.3 ng/L. Lincosamides and chloramphenicols were the predominant antibiotics in the water samples, with contents in the ranges of 3.83-13.7 and 4.23-33.6 ng/L, respectively. Therefore, the large volume direct injection method exhibited good performance in terms of MDL and recovery compared to standard methods and those reported previously. Compared with traditional pretreatment methods, the large volume direct injection method is simpler, more rapid, more precise, and more accurate. It is a viable alternative to SPE, and can be used for the determination of the 42 antibiotics at trace levels in cleaner water bodies, such as surface water, groundwater, and tap water.
    Keywords:  antibiotics; large volume direct injection; ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS); water body
  23. Talanta. 2022 Mar 26. pii: S0039-9140(22)00211-9. [Epub ahead of print]244 123415
      DeepResolution (Deep learning-assisted multivariate curve Resolution) has been proposed to solve the co-eluting problem for GC-MS data. However, DeepResolution models must be retrained when encountering unknown components, which is undoubtedly time-consuming and burdensome. In this study, a new pipeline named DeepResoution2 was proposed to overcome these limitations. DeepResolution2 utilizes deep neural networks to divide the profile into segments, estimate the number of components in each segment, and predict the elution region of each component. Subsequently, the information obtained by these deep learning models is used to assist the multivariate curve resolution procedure. Only seven models (1 + 1 + 5) are required to automate the whole analysis procedure of untargeted GC-MS data, which is an important improvement over DeepResolution. These seven models are stable and universal. Once established, they can be used to resolve most GC-MS data. Compared with MS-DIAL, ADAP-GC, and AMDIS, DeepResolution2 can obtain more reasonable mass spectra, chromatograms and peak areas to identify and quantify compounds. DeepResoution2 (0.955) outperformed AMDIS (0.939), MS-DIAL (0.948) and ADAP-GC (0.860) in terms of the linear correlation between concentrations and peak areas on overlapped peaks in fatty acid dataset. In real biological samples of human male infertility plasma, the peak areas and mass spectra of 136 untargeted GC-MS files were automatically extracted by DeepResolution2 without any prior information and manual intervention. DeepResolution2 includes all the functions for analyzing untargeted GC-MS datasets from the feature extraction of raw data files to the establishment of discriminant models.
    Keywords:  Automatic resolution; Deep learning; GC-MS; Multivariate curve resolution
  24. Biochem J. 2022 Mar 31. 479(6): 787-804
      Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.
    Keywords:  computational models; metabolic flux; metabolism; trans-omics