bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2019‒12‒22
thirty papers selected by
Sofia Costa
Cold Spring Harbor Laboratory

  1. Metabolites. 2019 Dec 17. pii: E308. [Epub ahead of print]9(12):
      Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.
    Keywords:  data processing; experimental design; liquid chromatography–mass spectrometry (LC-MS); metabolic pathway and network analysis; metabolism; metabolite identification; sample preparation; univariate and multivariate statistics; untargeted metabolomics
  2. J Pharm Biomed Anal. 2019 Nov 28. pii: S0731-7085(19)31998-3. [Epub ahead of print]180 113018
      Altered serotonergic neurotransmission is a key factor in several neurologic and psychiatric disorders such as migraine. Human and animal studies suggest that chronically low interictal serotonin levels of plasma and brain may facilitate increased activity of the trigeminovascular pathway, and may contribute to development of repeated migraine attacks. However, brain serotonin synthesis is affected by the concentration of tryptophan, its metabolites and a number of amino acids. In this work a simple and robust LC-MS/MS method for the quantitative determination of valine, leucine, isoleucine, phenylalanine, tyrosine, tryptophan, serotonin and kynurenine in human plasma has been developed and validated. Sample preparation was achieved by protein precipitation, using trifluoroacetic acid. Chromatographic separation was carried out on a Supelco Ascentis® Express C18 column (3.0 mm i.d. × 150 mm, 2.7 μm) equipped with an Agilent Zorbax Eclipse XDB C8 guard-column under isocratic conditions at a flow rate of 0.4 mL/min, over a 6.5 min run time. Mobile phase was 0.2% trifluoroacetic acid - acetonitrile (85:15, v/v). The eight analytes and two internal standards were ionized by positive electrospray ionization and detected in multiple reaction monitoring mode. A "fit-for-purpose" validation approach was adopted using surrogate matrix for the preparation of calibration samples. The calibration curves of all analytes showed excellent linearities with a correlation coefficient (r2) of 0.998 or better. Spiked surrogate matrix samples and pooled human plasma were used as quality control samples. Intra-day and inter-day precisions were less than 11.8% and 14.3%, and accuracies were within the ranges of 87.4-114.3% and 87.7-113.3%, respectively. Stability of the components in standard solutions, surrogate matrix, pooled plasma and processed samples were found to be acceptable under all relevant conditions. No significant carryover effect was observed. The surrogate matrix behaved parallel to human plasma when assessed by standard addition method and diluting the authentic matrix with surrogate matrix. The method was successfully applied for analysis of 800 human plasma samples to support a clinical study.
    Keywords:  Amino acid; Fit-for-purpose validation; Kynurenine; LC–MS/MS; Serotonin; Surrogate matrix
  3. Biomed Chromatogr. 2019 Dec 17. e4781
      A volumetric microsampling (VAMS) device (20 μL) was evaluated and validated for the analysis of gamma-hydroxybutyric acid (GHB) in venous blood using a simple ultra high pressure liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method. GHB was extracted from VAMS device by acetonitrile, after a re-hydration step in a temperature controlled ultrasonic bath at 60°C for 10 min. Chromatographic analysis was carried out on a Kinetex C18 column using 0.1% formic acid in water and acetonitrile as binary gradient mobile phase (from 5 to 95% of acetonitrile from 1 to 2.5 min) at a flow rate of 0.3 mL/min. VAMS method was fully validated according to current guidelines with satisfactory results in terms of linearity, selectivity, precision, absolute recovery, matrix effect and stability. The linearity was determined from 0.5 to 200 μg/mL and the lower limit of quantitation (LLOQ) was 0.5 μg/mL. The novel VAMS-UHPLC-MS/MS method has been successfully compared with plasma-based method in a GHB-treated patient as a proof of concept.
    Keywords:  VAMS; gamma-hydroxybutyric acid; ultra high performance liquid chromatography-tandem mass spectrometry
  4. J Chromatogr B Analyt Technol Biomed Life Sci. 2019 Dec 09. pii: S1570-0232(19)31038-4. [Epub ahead of print]1136 121929
      Steroids are essential hormones that play a crucial role in homeostasis of many biological processes including sexual development, spermatogenesis, sperm physiology and fertility. Although steroids have been largely studied in many biological matrices (such as urine and plasma), there is very limited information of the steroid content and their study as potential indicators of the quality of the seminal fluid. In this study, a LC-HRMS (liquid chromatography-high resolution mass spectrometry) strategy has been developed in order to obtain the extended steroid profile of human seminal fluid. A comparison between supported liquid extraction (SLE) and solid liquid extraction (SPE) was carried out and the chosen SPE method was further optimized to evidence the largest possible number of compounds. Steroids were automatically annotated by using DynaStI, a publicly available retention time prediction tool developed in our lab, to match the experimental data (i.e. accurate mass and tR). Altogether, these resources allowed us to develop a post-targeted approach able to consistently detect 41 steroids in seminal fluid (with half of them being androgens). Such steroid pattern was found to be stable across different extraction times and injection days. In addition to accurate mass and retention time, the identity of 70% of the steroids contained in such steroid profile was confirmed by comparing their fragmentation patterns in real samples to those of pure commercial standards. Finally, the workflow was applied to compare and distinguish the steroid profile in seminal fluid from healthy volunteers (n = 7, with one of them being a vasectomized subject). In all, the developed steroidomics strategy allows to reliably monitor an extended panel of 41 steroids in human seminal fluid.
    Keywords:  LC-HRMS; Seminal fluid; Steroid profile
  5. Phytochem Anal. 2019 Dec 17.
      INTRODUCTION: Organic molecules that bind to cannabinoid receptors are called cannabinoids, and they have similar pharmacological properties like the plant, Cannabis sativa L. Hyphenated liquid chromatography (LC), incorporating high-performance liquid chromatography (HPLC) and ultra-performance liquid chromatography (UPLC, also known as ultrahigh-performance liquid chromatography, UHPLC), usually coupled to an ultraviolet (UV), UV-photodiode array (PDA) or mass spectrometry (MS) detector, has become a popular analytical tool for the analysis of naturally occurring cannabinoids in various matrices.OBJECTIVE: To review literature on the use of various LC-based analytical methods for the analysis of naturally occurring cannabinoids published since 2010.
    METHODOLOGY: A comprehensive literature search was performed utilising several databases, like Web of Knowledge, PubMed and Google Scholar, and other relevant published materials including published books. The keywords used, in various combinations, with cannabinoids being present in all combinations, in the search were Cannabis, hemp, cannabinoids, Cannabis sativa, marijuana, analysis, HPLC, UHPLC, UPLC, quantitative, qualitative and quality control.
    RESULTS: Since 2010, several LC methods for the analysis of naturally occurring cannabinoids have been reported. While simple HPLC-UV or HPLC-UV-PDA-based methods were common in cannabinoids analysis, HPLC-MS, HPLC-MS/MS, UPLC (or UHPLC)-UV-PDA, UPLC (or UHPLC)-MS and UPLC (or UHPLC)-MS/MS, were also used frequently. Applications of mathematical and computational models for optimisation of different protocols were observed, and pre-analyses included various environmentally friendly extraction protocols.
    CONCLUSIONS: LC-based analysis of naturally occurring cannabinoids has dominated the cannabinoids analysis during the last 10 years, and UPLC and UHPLC methods have been shown to be superior to conventional HPLC methods.
    Keywords:  Cannabis; Cannabis sativa; HPLC; UHPLC; UPLC; analysis; cannabinoids; detection; hempLC-MSLC-PDAliquid chromatography (LC)marijuana
  6. Anal Chem. 2019 Dec 16.
      This article is devoted to the application of machine learning, namely convolutional neural networks to solve problems in the initial steps of the common pipeline for data analysis in metabolomics. These steps are the peak detection and the peak integration in raw liquid chromatography - mass spectrometry (LC-MS) data. Widely used algorithms suffer from rather poor precision for these tasks, yielding many false positive signals. In the present work, we developed an algorithm named peakonly, which has high flexibility for the detection or exclusion of low-intensity noisy peaks, and shows excellent quality in the detection of true positive peaks, approaching the highest possible precision. The current approach was developed for the analysis of high-resolution LC-MS data for the purposes of metabolomics, but potentially it can be applied with several adaptations in other fields, which utilize high-resolution GC- or LC-MS techniques. Peakonly is freely available on GitHub ( under MIT license.
  7. Anal Chem. 2019 Dec 19.
      Among the numerous unknown metabolites representative of our exposure, focusing on toxic compounds should provide more relevant data to link exposure and health. For that purpose, we developed and applied a global method using data independent acquisition (DIA) in mass spectrometry to profile specifically electrophilic compounds originating metabolites. These compounds are most of the time toxic, due to their chemical reactivity towards nucleophilic sites present in bio-macromolecules. The main line of cellular defense against these electrophilic molecules is conjugation to glutathione, then metabolization into mercapturic acid conjugates (MACs). Interestingly, MACs display a characteristic neutral loss in MS/MS experiments, that makes possible to detect all the metabolites displaying this characteristic loss, thanks to the DIA mode, and therefore to highlight the corresponding reactive metabolites. As a proof of concept, our workflow was applied to the toxicological issue of the oxidation of dietary polyunsaturated fatty acids, leading in particular to the formation of toxic alkenals, which lead to MACs upon glutathione conjugation and metabolization. By this way, dozens of MACs were detected and identified. Interestingly, multivariate statistical analyses carried out only on extracted HRMS signals of MACs yield a better characterization of the studied groups compared to results obtained from a classic untargeted metabolomics approach.
  8. Nat Commun. 2019 Dec 20. 10(1): 5811
      Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70[Formula: see text] of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.
  9. Bioanalysis. 2019 Dec;11(24): 2297-2318
      Metabolomics is the comprehensive study of small-molecule metabolites. Obtaining a wide coverage of the metabolome is challenging because of the broad range of physicochemical properties of the small molecules. To study the compounds of interest spectroscopic (NMR), spectrometric (MS) and separation techniques (LC, GC, supercritical fluid chromatography, CE) are used. The choice for a given technique is influenced by the sample matrix, the concentration and properties of the metabolites, and the amount of sample. This review discusses the most commonly used analytical techniques for metabolomic studies, including their advantages, drawbacks and some applications.
    Keywords:  multiplatform approaches; separation techniques; spectrometric techniques; spectroscopic techniques; targeted metabolomics; untargeted metabolomics
  10. BMC Bioinformatics. 2019 Dec 20. 20(Suppl 24): 673
      BACKGROUND: With the rise of metabolomics, the development of methods to address analytical challenges in the analysis of metabolomics data is of great importance. Missing values (MVs) are pervasive, yet the treatment of MVs can have a substantial impact on downstream statistical analyses. The MVs problem in metabolomics is quite challenging and can arise because the metabolite is not biologically present in the sample, or is present in the sample but at a concentration below the lower limit of detection (LOD), or is present in the sample but undetected due to technical issues related to sample pre-processing steps. The former is considered missing not at random (MNAR) while the latter is an example of missing at random (MAR). Typically, such MVs are substituted by a minimum value, which may lead to severely biased results in downstream analyses.RESULTS: We develop a Bayesian model, called BayesMetab, that systematically accounts for missing values based on a Markov chain Monte Carlo (MCMC) algorithm that incorporates data augmentation by allowing MVs to be due to either truncation below the LOD or other technical reasons unrelated to its abundance. Based on a variety of performance metrics (power for detecting differential abundance, area under the curve, bias and MSE for parameter estimates), our simulation results indicate that BayesMetab outperformed other imputation algorithms when there is a mixture of missingness due to MAR and MNAR. Further, our approach was competitive with other methods tailored specifically to MNAR in situations where missing data were completely MNAR. Applying our approach to an analysis of metabolomics data from a mouse myocardial infarction revealed several statistically significant metabolites not previously identified that were of direct biological relevance to the study.
    CONCLUSIONS: Our findings demonstrate that BayesMetab has improved performance in imputing the missing values and performing statistical inference compared to other current methods when missing values are due to a mixture of MNAR and MAR. Analysis of real metabolomics data strongly suggests this mixture is likely to occur in practice, and thus, it is important to consider an imputation model that accounts for a mixture of missing data types.
    Keywords:  Bayesian; Data augmentation; MAR; MNAR; Markov chain Monte Carlo; Metabolomics; Missing values; Truncated normal distribution
  11. Int J Cancer. 2019 Dec 21.
      Clear cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. Intraoperative frozen section (IFS) analysis is used to confirm the diagnosis during partial nephrectomy (PN). However, surgical margin evaluation using IFS analysis is time consuming and unreliable, leading to relatively low utilization. In this study, we demonstrated the use of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) as a molecular diagnostic and prognostic tool for ccRCC. DESI-MSI was conducted on fresh-frozen 23 normal-tumor paired nephrectomy specimens of ccRCC. An independent validation cohort of 17 normal-tumor pairs were analyzed. DESI-MSI provides two-dimensional molecular images of tissues with mass spectra representing small metabolites, fatty acids, and lipids. These tissues were subjected to histopathologic evaluation. A set of metabolites that distinguish ccRCC from normal kidney were identified by performing least absolute shrinkage and selection operator (Lasso) and log-ratio Lasso analysis. Lasso analysis with leave-one-patient-out cross validation selected 57 peaks from over 27,000 metabolic features across 37,608 pixels obtained using DESI-MSI of ccRCC and normal tissues. Baseline Lasso of metabolites predicted the class of each tissue to be normal or cancerous tissue with an accuracy of 94% and 76%, respectively. Combining the baseline Lasso with the ratio of glucose to arachidonic acid could potentially reduce scan time and improve accuracy to identify normal (82%) and ccRCC (88%) tissue. DESI-MSI allows rapid detection of metabolites associated with normal and ccRCC with high accuracy. As this technology advances, it could be used for rapid intraoperative assessment of surgical margin status. This article is protected by copyright. All rights reserved.
    Keywords:  Clear cell renal cell carcinoma; electrospray ionization; histopathology; metabolome; nephrectomy; surgical margins
  12. J Chromatogr B Analyt Technol Biomed Life Sci. 2019 Dec 09. pii: S1570-0232(19)31178-X. [Epub ahead of print]1136 121931
      Oxidative RNA damage has been found to be associated with a variety of diseases, and 8-hydroxyguanosine (8-OHG) is a typical marker of oxidative modification of RNA. This guanosine modification is an emerging biomarker for disease detection and determination of 8-OHG in human urine is favored because it is noninvasive to patients. However, due to its poor ionization efficiency in mass spectrometry and trace amount in urine, accurate quantification of this modified nucleoside is still challenging. Herein, a rapid, accurate, sensitive and robust method using solid-phase extraction (SPE) combined with isotope dilution ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) was developed for detection of this oxidative RNA modification in human urine. The limit of detection can reach 1.5 fmol and the method exhibits good precision on intra-day (1.8-3.3%) and inter-day (0.6-1.2%) analyses. Satisfactory recovery (87.5-107.2%) at three spiked levels was achieved by using HLB cartridge for urine pretreatment. Using this method, we quantified 8-OHG in urine from 65 colorectal cancer (CRC) patients and 76 healthy volunteers. The measured level of urinary 8-OHG for CRC patients and healthy controls is 1.91 ± 0.63 nmol/mmol creatinine and 1.33 ± 0.35 nmol/mmol creatinine, respectively. We found the content of 8-OHG in urine was raised in CRC patients patients, implying this oxidative RNA modification marker could act as a potential noninvasive indicator for early screening of CRC. In addition, this study will make contributions to the investigations of the influences of oxidative stress on the formation and development of CRC.
    Keywords:  8-Hydroxyguanosine; Biomarker; Colorectal cancer; Oxidative RNA modification; UPLC-MS/MS
  13. Sci Rep. 2019 Dec 17. 9(1): 19313
      There is a growing interest concerning the relevance of salivary cortisone levels in stress-related research. However, studies investigating morning patterns and day-to-day variability of cortisone versus cortisol levels are lacking. Cortisol and cortisone analysis by liquid chromatography-tandem mass spectroscopy (LC-MS/MS) has been widely used for routine laboratory measurements in the last years. The aim of this study was to develop an ultra-performance LC-MS/MS method for the simultaneous quantification of salivary cortisol and cortisone levels for assessing the temporal variability of these hormones. Saliva samples were collected from 18 healthy volunteers at 0, 15, and 30 min after awakening on each day for 1 week and analysed with the newly developed method. We used a novel atmospheric pressure ionization source, which resulted in high sensitivity and specificity for both cortisol and cortisone as well as higher peak values and signal-to-noise ratio as compared with the electrospray ionization source. Cortisone showed similar morning patterns as cortisol: a 25% and 49% increase in levels at 15 and 30 min after awakening, respectively. Most cortisone indices showed somewhat lower day-to-day variability and were less affected by state-related covariates. We recommend further exploration of the potential of salivary cortisone as a biomarker in stress-related research.
  14. Rapid Commun Mass Spectrom. 2019 Dec 16. e8701
      RATIONALE Linear MALDI-TOF MS is a widely used in analytical and biomedical applications. The use of delayed extraction increases the resolution, but the roughness of the matrix crystals and the target plate misalignment in order of a few μm cause substantial spread in the ion time-of-flight values and decrease mass accuracy. METHODS: The method of mass spectra correction based on the correlation of matrix fragments peaks in MALDI mass spectra was used. The experiments were performed with the MALDI-TOF instrument CMI-1600 built by Guangzhou Hexin Instrument Co., Ltd. SIMION 8.1 and MATLAB were used for ion motion simulations. Data analysis was done using the home built custom developed software and MATLAB. RESULTS: It was shown that the drift of peak position in the MALDI-TOF MS mass spectra depends linearly on the time-of-flight in a wide mass range. While using the linear correction of the time-of-flight scale, an increase in m/z accuracy of more than 10 times was achieved. The mass accuracy is limited by the resolution of the fast ADC used. CONSLUSION It is expected that the proposed method will significantly increase the dynamic range, since it becomes possible to sum up corrected individual mass spectra without significant loss of resolution. The time scale adjusting can be used both for the linear time-of-flight instruments and for the reflector systems of various configurations.
  15. Molecules. 2019 Dec 15. pii: E4590. [Epub ahead of print]24(24):
      Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purification and denoising will cost a lot of algorithm time. In this paper, we propose a model based on convolutional neural network (CNN) which can analyze the chemical peak information in the tandem mass spectrometry (MS/MS) data. Compared with traditional analyzing methods, CNN can reduce steps in data preprocessing. This model can extract features of different compounds and classify multi-label mass spectral data. When dealing with MS data of mixtures based on the Human Metabolome Database (HMDB), the accuracy can reach at 98%. In 600 MS test data, 451 MS data were fully detected (true positive), 142 MS data were partially found (false positive), and 7 MS data were falsely predicted (true negative). In comparison, the number of true positive test data for support vector machine (SVM) with principal component analysis (PCA), deep neural network (DNN), long short-term memory (LSTM), and XGBoost respectively are 282, 293, 270, and 402; the number of false positive test data for four models are 318, 284, 198, and 168; the number of true negative test data for four models are 0, 23, 7, 132, and 30. Compared with the model proposed in other literature, the accuracy and model performance of CNN improved considerably by separating the different compounds independent MS/MS data through three-channel architecture input. By inputting MS data from different instruments, adding more offset MS data will make CNN models have stronger universality in the future.
    Keywords:  compounds recognition; convolutional neural network; multi-label classification; tandem mass spectra
  16. J Agric Food Chem. 2019 Dec 19.
      This study aimed to determine α-T and its thermal oxidation products simultaneously. A novel method based on an ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was developed. This approach was achieved by means of a BEH C18 analytical column under gradient elution conditions with eluent of acetonitrile/isopropanol (1:9, v/v) and acetonitrile/water (4:6, v/v). Compounds were elucidated through exact molecular mass and fragmention ions obtained from Q-TOF-MS detector. Two oxidation products, α-tocopheryl quinone and 5-formyl-γ-tocopherol were identified and one new compound was determined. This approach offered a simple, precise and reliable method to determine oxidation products of α-T, which may give a way to understand the mechanism of thermal oxidative process of α-T.
  17. Metabolites. 2019 Dec 14. pii: E304. [Epub ahead of print]9(12):
      Tumor spheroids are important model systems due to the capability of capturing in vivo tumor complexity. In this work, the experimental design of metabolomics workflows using three-dimensional multicellular tumor spheroid (3D MTS) models is addressed. Non-scaffold based cultures of the HCT116 colon carcinoma cell line delivered highly reproducible MTSs with regard to size and other key parameters (such as protein content and fraction of viable cells) as a prerequisite. Carefully optimizing the multiple steps of sample preparation, the developed procedure enabled us to probe the metabolome of single MTSs (diameter range 790 ± 22 µm) in a highly repeatable manner at a considerable throughput. The final protocol consisted of rapid washing of the spheroids on the cultivation plate, followed by cold methanol extraction. 13C enriched internal standards, added upon extraction, were key to obtaining the excellent analytical figures of merit. Targeted metabolomics provided absolute concentrations with average biological repeatabilities of <20% probing MTSs individually. In a proof of principle study, MTSs were exposed to two metal-based anticancer drugs, oxaliplatin and the investigational anticancer drug KP1339 (sodium trans-[tetrachloridobis(1H-indazole)ruthenate(III)]), which exhibit distinctly different modes of action. This difference could be recapitulated in individual metabolic shifts observed from replicate single MTSs. Therefore, biological variation among single spheroids can be assessed using the presented analytical strategy, applicable for in-depth anticancer drug metabolite profiling.
    Keywords:  IT-139; KP1339; LC-MS; metabolomics; metallodrugs; method development; multicellular tumor spheroids; oxaliplatin
  18. J Biomed Opt. 2019 12;25(1): 1-14
      Conventional techniques are insufficient precisely to describe the internal structure, the heterogeneous cell populations, and the dynamics of biological processes occurring in diseased liver during surgery. There is a need for a rapid and safe method for the successful diagnosis of liver disease in order to plan surgery and to help avoid postoperative liver failure. We analyze the progression of both acute (cholestasis) and chronic (fibrosis) liver pathology using multiphoton microscopy with fluorescence lifetime imaging and second-harmonic generation modes combined with time-of-flight secondary ion mass spectrometry chemical analysis to obtain new data about pathological changes to hepatocytes at the cellular and molecular levels. All of these techniques allow the study of cellular metabolism, lipid composition, and collagen structure without staining the biological materials or the incorporation of fluorescent or other markers, enabling the use of these methods in a clinical situation. The combination of multiphoton microscopy and mass spectrometry provides more complete information about the liver structure and function than could be assessed using either method individually. The data can be used both to obtain new criteria for the identification of hepatic pathology and to develop a rapid technique for liver quality analysis in order to plan surgery and to help avoid postoperative liver failure in clinic.
    Keywords:  fluorescence lifetime imaging; liver; metabolic imaging; multiphoton microscopy; time-of-flight secondary ion mass spectrometry
  19. Biochim Biophys Acta Mol Cell Biol Lipids. 2019 Dec 13. pii: S1388-1981(19)30238-0. [Epub ahead of print] 158587
      BACKGROUND: To our knowledge, we lack a complete understanding of the lipidomes alterations caused by maternal supraphysiological hypercholesterolemia (MSPH) at the third trimester.OBJECTIVES: The aim of this study was to investigate lipidomes alterations in maternal and umbilical venous (UV) serum and explore the association between these alterations and MSPH.
    METHODS: We conducted a nest case-control study between maternal physiological hypercholesterolemia (MPH) and MSPH subjects during pregnancy. Lipidomic profiling of maternal serum at the first trimester of gestation and UV serum was performed by ultra-high-performance liquid chromatography system connected to a quadrupole time-of-light/mass spectrometer.
    RESULTS: Several glycerophospholipids and sphingolipids (C18 sphingoid base) species were distinctly altered in maternal serum and/or UV serum with MSPH versus MPH. Glycerophospholipid metabolism, sphingolipid metabolism and propanoate metabolism were the main pathways that involved the most of discriminate metabolites. Higher HDL-c and phosphatidylcholine (16:0/0:0) (PC (16:0/0:0)) during pregnancy, higher PC (16:0/0:0) and lower cholesterol ester 20:4(8Z,11Z,14Z,17Z) (CE (20:4(8Z,11Z,14Z,17Z))) in the UV serum may be the risk factors for the increased placental circulation resistance. The total cholesterol levels of maternal serum both at the first trimester and at the third trimester were significantly correlated with some lipid species of UV serum.
    CONCLUSION: This study clarifies the differential lipid profiles to distinguish MSPH from MPH and the pathway which is influenced under the condition of MSPH. Also, it provides a resource to look for potential therapeutic targets for MSPH.
    Keywords:  Cholesterol; Gestation; Lipidomics; Supraphysiological hypercholesterolemia; Umbilical venous
  20. Trends Analyt Chem. 2019 Nov;120 115280
      In mammalian systems "sterolomics" can be regarded as the quantitative or semi-quantitative profiling of all metabolites derived from cholesterol and its cyclic precursors. The system can be further complicated by metabolites derived from ingested phytosterols or pharmaceuticals, but this is beyond the scope of this article. "Sterolomics" can be performed on either an unbiased global format, or more usually, exploiting a targeted format. Here we discuss the different mass spectrometry-based analytical techniques used in "sterolomics" giving specific examples in the context of neurodegenerative disease and for the diagnosis of inborn errors of metabolism. We pay particular attention to the profiling of cholesterol metabolites in the bile acid biosynthesis pathways, although the analytical techniques discussed are also appropriate for analysis of hormonal steroids.
    Keywords:  Alzheimer's disease; Bile acids; Gas chromatography – mass spectrometry; Inborn errors of metabolism; Liquid chromatography – mass spectrometry; Mass spectrometry; Niemann Pick disease; Oxysterols
  21. Bioanalysis. 2020 Jan;12(1): 23-34
      Aim: Microflow tandem mass spectrometry-based methods have been proposed as options to improve sensitivity and selectivity while improving sample utility and solvent consumption. Here, we evaluate a newly introduced microflow source, OptiFlow™, for quantitative performance. Results/methodology: We performed a comparison of the OptiFlow and IonDrive™ sources, respectively, on the same triple quadrupole mass spectrometer. The comparison used a neat cocktail of commercially available drugs and extracted plasma samples monitoring midazolam and alprazolam metabolites. Microflow produced a 2-4× signal increase for the neat drug cocktail and a 5-10× increase for extracted plasma samples. Conclusion: The OptiFlow method consistently gave increased signal response relative to the IonDrive method and enabled a better lower limit of quantitation for defining phamacokinetics.
    Keywords:  LC–MS/MS; microflow; microsampling; pharmacokinetics
  22. J Mass Spectrom. 2019 Dec 20. e4491
      The specific matrix used in matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) can have an effect on the molecules ionized from a tissue sample. The sensitivity for distinct classes of biomolecules can vary when employing different MALDI matrices. Here, we compare the intensities of various lipid sub-classes measured by Fourier transform ion cyclotron resonance (FT-ICR) IMS of murine liver tissue when using 9-Aminoacridine (9AA), 5-Chloro-2-mercaptobenzothiazole (CMBT), 1,5-Diaminonaphthalene (DAN), 2,5-Dihydroxyacetophenone (DHA), and 2,5-Dihydroxybenzoic acid (DHB). Principal component analysis and receiver operating characteristic curve analysis revealed significant matrix effects on the relative signal intensities observed for different lipid sub-classes and adducts. Comparison of spectral profiles and quantitative assessment of the number and intensity of species from each lipid sub-class showed that each matrix produces unique lipid signals. In positive ion mode, matrix application methods played a role in the MALDI analysis for different cationic species. Comparisons of different methods for the application of DHA showed a significant increase in the intensity of sodiated and potassiated analytes when using an aerosol sprayer. In negative ion mode, lipid profiles generated using DAN were significantly different than all other matrices tested. This difference was found to be driven by modification of phosphatidylcholines during ionization that enables them to be detected in negative ion mode. These modified phosphatidylcholines are isomeric with common phosphatidylethanolamines confounding MALDI IMS analysis when using DAN. These results show an experimental basis of MALDI analyses when analyzing lipids from tissue and allow for more informed selection of MALDI matrices when performing lipid IMS experiments.
    Keywords:  Imaging mass spectrometry; MALDI IMS; MALDI matrix; lipid imaging; matrix-assisted laser desorption/ionization mass spectrometry
  23. J Chromatogr A. 2019 Dec 09. pii: S0021-9673(19)31226-9. [Epub ahead of print] 460778
      Sealants, incorporated in the lids of food cans to ensure the can is hermetically sealed, are formulated from a wide variety of compounds. These compounds and associated non-intentionally added substances (NIAS) could migrate to the food contained in the can. In this work, ion mobility quadrupole time-of-flight mass spectrometry coupled to ultra-high performance liquid chromatography (UHPLC-IM-QTOF-MS) has been used to obtain ion mobility filtered extracted ion chromatograms. Subsequently, accurate mass precursor ions and their fragments have been used to identify the compounds migrating from the sealant to the content of the cans. Moreover, the correlation between the collision cross-section (CCS) values and m/z of the compounds was used to increase the level of confidence of the identification. Seven compounds were found to have migrated to the food simulants. The compounds bis(2-hydroxy-3-tert-butyl-5-methylphenyl)dicyclopentane,1-tetradecanesulfonic acid, 1-pentadecanesulfonic acid, 1-hexadecanesulfonic acid and naphthalene-2-sulfonic acid (whose migration was over the specific migration limit established by the European Regulation 10/2011/EU) were identified as NIAS in the food simulants studied.
  24. Bioanalysis. 2020 Jan;12(1): 35-52
      Aim: Routine therapeutic drug monitoring is highly recommended since common antidepressant combinations increase the risk of drug-drug interactions or overlapping toxicity. Materials & methods: A magnetic solid-phase extraction by using C18-functionalized magnetic silica nanoparticles (C18-Fe3O4@SiO2 NPs) as sorbent was proposed for rapid extraction of venlafaxine, paroxetine, fluoxetine, norfluoxetine and sertraline from clinical plasma and urine samples followed by ultra-HPLC-MS/MS assay. Results: The synthesized C18-Fe3O4@SiO2 NPs showed high magnetization and efficient extraction for the analytes. After cleanup by magnetic solid-phase extraction, no matrix effects were found in plasma and urine matrices. The analytes showed LODs among 0.15-0.75 ng ml-1, appropriate linearity (R ≥ 0.9990) from 2.5 to 1000 ng ml-1, acceptable accuracies 89.1-110.9% with precisions ≤11.0%. The protocol was successfully applied for the analysis of patients' plasma and urine samples. Conclusion: It shows high potential in routine therapeutic drug monitoring of clinical biological samples.
    Keywords:  MSPE; UHPLC–MS/MS; antidepressants; magnetic solid-phase extraction; octadecyl-functionalized magnetic silica nanoparticles; plasma; urine
  25. Bioanalysis. 2019 Dec 19.
    Keywords:  LC–MS; LC–MS/MS quantitation; internal standard; matrix effect; validation
  26. Horm Cancer. 2019 Dec 19.
      Breast cancer is the second leading cause of cancer mortality among women. Mammography and tumor biopsy followed by histopathological analysis are the current methods to diagnose breast cancer. Mammography does not detect all breast tumor subtypes, especially those that arise in younger women or women with dense breast tissue, and are more aggressive. There is an urgent need to find circulating prognostic molecules and liquid biopsy methods for breast cancer diagnosis and reducing the mortality rate. In this study, we systematically evaluated metabolites and proteins in blood to develop a pipeline to identify potential circulating biomarkers for breast cancer risk. Our aim is to identify a group of molecules to be used in the design of portable and low-cost biomarker detection devices. We obtained plasma samples from women who are cancer free (healthy) and women who were cancer free at the time of blood collection but developed breast cancer later (susceptible). We extracted potential prognostic biomarkers for breast cancer risk from plasma metabolomics and proteomics data using statistical and discriminative power analyses. We pre-processed the data to ensure the quality of subsequent analyses, and used two main feature selection methods to determine the importance of each molecule. After further feature elimination based on pairwise dependencies, we measured the performance of logistic regression classifier on the remaining molecules and compared their biological relevance. We identified six signatures that predicted breast cancer risk with different specificity and selectivity. The best performing signature had 13 factors. We validated the difference in level of one of the biomarkers, SCF/KITLG, in plasma from healthy and susceptible individuals. These biomarkers will be used to develop low-cost liquid biopsy methods toward early identification of breast cancer risk and hence decreased mortality. Our findings provide the knowledge basis needed to proceed in this direction.
    Keywords:  Breast cancer risk; Circulating biomarker; Feature selection; Liquid biopsy; Machine learning
  27. Anal Chem. 2019 Dec 18.
      Apart from the pure methodological process of implementing a comprehensive two-dimensional gas chromatography (GC×GC) method, surely it is the excitement engendered by a well-designed separation that has captured the attention, fascinated and sustained proponents and users throughout the decades since Liu and Phillips1 were equally enthralled by what they produced, the first time they witnessed their result. This technology update largely addresses developments over the last 2 years (late 2017 - 2019) since the Synovec group presented their review.2 Progressive advances in instrumentation, software and data analysis tools, fundamental relationships and capabilities, and the numerous repertoire of applications to which GC×GC methods have been applied will be included. We attempt to offer an insight into the dynamic nature of the field, and hopefully many newer converts to the field will be recognized for their emerging interest and contributions.
  28. Biosystems. 2019 Dec 12. pii: S0303-2647(19)30446-0. [Epub ahead of print] 104081
      Metabolic networks can model the behavior of metabolism in the cell. Since analyzing the whole metabolic networks is not easy, network modulation is an important issue to be investigated. Decomposing metabolic networks is a strategy to obtain better insight into metabolic functions. Additionally, decomposing these networks facilitates using computational methods, which are very slow when applied to the original genome-scale network. Several methods have been proposed for decomposing of the metabolic network. Therefore, it is necessary to evaluate these methods by suitable criteria. In this study, we introduce a web server package for decomposing of metabolic networks with 10 different methods, 9 datasets and the ability of computing 12 criteria, to evaluate and compare the results of different methods using ten previously defined and two new criteria which are based on Chebi ontology and Co-expression_of_Enzymes information. This package visualizes the obtained modules via "gephi" software. The ability of this package is that the user can examine whether two metabolites or reactions are in the same module or not. The functionality of the package can be easily extended to include new datasets and criteria. It also has the ability to compare the results of novel methods with the results of previously developed methods. The package is implemented in python and is available at
    Keywords:  Criteria; Decomposition; Metabolic network; Package