bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2020‒09‒06
sixteen papers selected by
Sofia Costa
Cold Spring Harbor Laboratory

  1. J Am Soc Mass Spectrom. 2020 Jan 02. 31(1): 155-163
      Mass Spectrometry Imaging (MSI) is an established and powerful MS technique that enables molecular mapping of tissues and cells finding widespread applications in academic, medical, and pharmaceutical industries. As both the applications and MSI technology have undergone rapid growth and improvement, the challenges associated both with analyzing large datasets and identifying the many detected molecular species have become apparent. The lack of readily available and comprehensive software covering all necessary data analysis steps has further compounded this challenge. To address this issue we developed LipostarMSI, comprehensive and vendor-neutral software for targeted and untargeted MSI data analysis. Through user-friendly implementation of image visualization and co-registration, univariate and multivariate image and spectral analysis, and for the first time, advanced lipid, metabolite, and drug metabolite (MetID) automated identification, LipostarMSI effectively streamlines biochemical interpretation of the data. Here, we introduce LipostarMSI and case studies demonstrating the versatility and many capabilities of the software.
    Keywords:  bioinformatics; chemometrics; lipidomics; mass spectrometry imaging; metabolomics
  2. Anal Chem. 2020 Aug 31.
      Data quality in global metabolomics is of great importance for biomarker discovery and systems biology studies. However, comprehensive metrics and methods to evaluate and compare the data quality of global metabolomics data sets are lacking. In this work, we combine newly developed metrics, along with well-known measures, to comprehensively and quantitatively characterize the data quality across two similar LC-MS platforms, with the goal of providing an efficient and improved ability to evaluate the data quality in global metabolite profiling experiments. A pooled human serum sample was run 50 times on two high-resolution LC-QTOF-MS platforms to provide profile and centroid MS data. These data were processed using Progenesis Qi software and then analyzed using five important data quality measures, including retention time drift, number of compounds detected, missing values and MS reproducibility (2 measures). The detected compounds were fit to a gamma distribution versus compound abundance, which was normalized to allow comparison of different platforms. To evaluate missing values, characteristic curves were obtained by plotting the compound detection percentage versus extraction frequency. To characterize reproducibility, the accumulative coefficient of variation (CV) versus percentage of total compounds detected and intra-class correlation coefficient (ICC) versus compound abundance were investigated. Key findings include significantly better performance using profile mode data compared to centroid mode as well quantitatively better performance from the newer, higher resolution instrument. A summary table of results gives a snapshot of the experimental results and provides a template to evaluate the global metabolite profiling workflow. In total, these measures give a good overall view of data quality in global profiling and allow comparisons of data acquisition strategies and platforms as well as optimization of parameters.
  3. J Pharm Biomed Anal. 2020 Aug 21. pii: S0731-7085(20)31464-3. [Epub ahead of print]190 113578
      When using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) to quantify In Vivo samples, an internal standard (ISTD) is key in correcting for variability within the sample extraction process and injection volume. Just as important is the ability of the internal standard to identify any matrix effects, which can artificially suppress or enhance the signal of the compound of interest. To properly do this, the internal standard should co-elute with the compound. A common source of potential matrix effects with In Vivo studies is from the excipient(s) used to formulate the compound for dosing. In the world of high-throughput discovery bioanalysis, a lab can quantitate over a hundred compounds each week, many of which are evaluated once, and rarely is a stable-isotope labeled (SIL) internal standard available (the industry gold standard). Finding a suitable and easy-to-use alternative LC-MS/MS method is important to providing high quality data. To overcome this challenge, a homologous series of compounds was synthesized to improve the chromatographic range for co-eluting ISTD's. This novel mix of internal standards was shown to have key characteristics making it ideal for use as a near universal internal standard mix including but not limited to: they ionize in both positive and negative modes, they are susceptible to signal perturbation from common formulation excipients, and they cover a wide range of retention times.
    Keywords:  Bioanalysis; Formulation; In vivo quantitation; Internal standard; Ion suppression; Liquid chromatography-tandem mass spectrometry
  4. J Pharm Biomed Anal. 2020 Aug 21. pii: S0731-7085(20)31455-2. [Epub ahead of print]191 113569
      Benzodiazepines (BZDs) and Z-drugs have been particularly important treatments for sleeping and anxiety disorders for many years. However, recently, a number of new benzodiazepines (named designer benzodiazepines, DBZDs) were synthesised, but some of them have never been used in the clinic; they reached the black drug market as new psychoactive substances and are used for recreational purposes. The abuse of these substances has led to many crimes and even deaths. Therefore, it is necessary to develop new methods for their quantification for forensic and clinical toxicology. A liquid chromatography-tandem mass spectrometry-based method was developed for the simultaneous determination of 20 classical BZDS, 4 DBZDs and 3 Z-hypnotic drugs in human whole blood. As a sample preparation step, liquid-liquid extraction requiring the use of only 0.5 mL of blood sample and 1 mL of extraction solvent was applied. The selectivity, linearity, carry-over effects, limits of detection (LOD) and quantification (LOQ), precision, accuracy (both intra- and inter-day assays) and recovery were evaluated for validation. Calibration curves were linear with r values > 0.98. The LODs ranged from 0.01 to 0.33, and the LOQs were assumed to be 1 ng/mL. Inter-day precisions and accuracies were in the ranges of 87.8% - 108.5% and 1.8% - 11.2%, respectively. The recovery values ranged from 81.0% to 106.7%. The developed method proved to be sensitive, specific, simple, and fast and can be quickly modified and expanded for new compounds by the optimization of MRM. The method was applied for analysis of blood samples in 145 toxicological cases over a three-year study (2017 - 2019), which allowed us to obtain information on the prevalence of the use of these substances. The most frequently determined compounds were nordazepam (87 cases; 60%), diazepam (81 cases; 55.9%), temazepam (72 cases; 49.7%), oxazepam (56 cases; 38.7%), and midazolam (36 cases; 24.8%). The ranges of concentrations were wide and are presented as box plots. The results were used for the preparation of medico-legal opinions, which proved the utility of the method for routine toxicology analyses.
    Keywords:  Benzodiazepines; Blood; Drug analysis; LC-MS/MS; Liquid-liquid extraction; Z-drugs
  5. J Mass Spectrom. 2020 Oct;55(10): e4613
      Ultra-high-resolution mass spectrometry, in the absence of chromatography, is finding its place for direct analyses of highly complex mixtures, such as those encountered during untargeted metabolomics screening. Advances, however, have been tempered by difficulties such as uneven signal suppression experienced during electrospray ionization. Moreover, ultra-high-resolution mass spectrometers that use Orbitrap and ICR analyzers both suffer from limited ion trapping capacities, owing principally to space-charge effects. This study has evaluated and contrasted the above two types of Fourier transform mass spectrometers for their abilities to detect and identify by accurate mass measurement, small molecule metabolites present in complex mixtures. For these direct introduction studies, the Orbitrap Fusion showed a major advantage in terms of speed of analysis, enabling detection of 218 of 440 molecules (<2 ppm error, 500 000 resolution at m/z 200) present in a complex mixture in 5 min. This approach is the most viable for high-throughput workflows, such as those used in investigations involving very large cohorts of metabolomics samples. From the same mixture, 183 unique molecules were observed by FT-ICR in the broadband mode, but this number was raised to 235 when "selected ion monitoring-stitching" (SIM-stitching) was employed (<0.1 ppm error, 7 T magnet with dynamic harmonization cell, 1.8 million resolution at m/z 200, both cases). SIM-stitching FT-ICR thus offered the most complete detection, which may be of paramount importance in situations where it is essential to obtain the most complete metabolic profile possible. This added completeness, however, came at the cost of a more lengthy analysis time (120 min including manual treatment). Compared to the data presented here, future automation of processing, plus the use of absorption mode detection, segmented ion detection (stepwise detection of smaller width m/z sections), and higher magnetic field strengths, can substantially reduce FT-ICR acquisition times.
    Keywords:  SIM-stitching; ion cyclotron resonance; metabolomics; orbitrap mass spectrometry; space-charge effects
  6. Metabolites. 2020 Aug 27. pii: E348. [Epub ahead of print]10(9):
      This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.
    Keywords:  broad metabolite coverage; dynamic range; hybrid approaches; identification; metabolomics; quantitative analysis; repeatability
  7. Talanta. 2020 Nov 01. pii: S0039-9140(20)30601-9. [Epub ahead of print]219 121310
      Phytocannabinoids are a broad class of compounds uniquely synthesized by the various strains of Cannabis sativa. Up to date, most investigation on phytocannabinoids have been addressed to the most abundant species, Δ9-tetrahydrocannabinol and cannabidiol, for their well-known wide range of pharmaceutical activities. However, in the recent years a large number of minor constituents have been reported, whose role in cannabis pharmacological effects is of current scientific interest. With the purpose of gaining knowledge on major and minor species and furnishing a strategy for their untargeted analysis, in this study we present an innovative approach for comprehensively identifying phytocannabinoids based on high-resolution mass spectrometry in negative ion mode, which allows discrimination of the various isomeric species. For a faster and more reliable manual validation of the tandem mass spectra of known and still unknown species, an extensive database of phytocannabinoid derivatives was compiled and implemented on Compound Discoverer software for the setup of a dedicated data analysis tool. The method was applied to extracts of the Italian FM-2 medicinal cannabis, resulting in the identification of 121 phytocannabinoids, which is the highest number ever reported in a single analysis. Among those, many known and still unknown unconventional phytocannabinoids have been tentatively identified, another piece in the puzzle of unravelling the many uncharted applications of this matrix.
    Keywords:  Cannabis sativa; Compound discoverer; High-resolution mass spectrometry; Phytocannabinoids; Untargeted analysis
  8. Talanta. 2020 Nov 01. pii: S0039-9140(20)30476-8. [Epub ahead of print]219 121185
      An original, selective and automated method, for the microextraction by packed sorbent (MEPS) of Δ9-tetrahydrocannabinol (THC), 11-hydroxy-Δ9-tetrahydrocannabinol (THC-OH), and 11-nor-Δ9-tetrahydrocannabinol-9-carboxylic acid (THC-COOH) from human urine, was developed by using (i) a catechin-molded molecularly imprinted polymer (MIP), (ii) a new lab-made MEPS device easily repackable with any commercial or lab-made sorbent, and (iii) a lab-made multi syringe autosampler. Analyses were performed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and the developed method proved to be precise, accurate and showed good linearity. Determination coefficients ranged from 0.96 to 0.99, in the range of 5-250 ng mL-1. Limits of detection and quantification ranged between 1.0 and 5.0 ng mL-1 and 5.0 and 20.0 ng mL-1. The method was successfully applied in the analysis of real urine samples. The same packed syringe was effectively used over 90 consecutive extractions without carry-over effects.
    Keywords:  Automated microextraction; Cannabinoids; Microextraction by packed sorbent (MEPS); Molecularly imprinted polymer (MIP); Tandem mass spectrometry; Urine
  9. Anal Chem. 2020 Sep 01. 92(17): 11818-11825
      Preliminary compound identification and peak annotation in gas chromatography-mass spectrometry is usually made using mass spectral databases. There are a few algorithms that enable performing a search of a spectrum in a large mass spectral library. In many cases, a library search procedure returns a wrong answer even if a correct compound is contained in a library. In this work, we present a deep learning driven approach to a library search in order to reduce the probability of such cases. Machine learning ranking (learning to rank) is a class of machine learning and deep learning algorithms that perform a comparison (ranking) of objects. This work introduces the usage of deep learning ranking for small molecules identification using low-resolution electron ionization mass spectrometry. Instead of simple similarity measures for two spectra, such as the dot product or the Euclidean distance between vectors that represent spectra, a deep convolutional neural network is used. The deep learning ranking model outperforms other approaches and enables reducing a fraction of wrong answers (at rank-1) by 9-23% depending on the used data set. Spectra from the Golm Metabolome Database, Human Metabolome Database, and FiehnLib were used for testing the model.
  10. Exp Mol Pathol. 2020 Sep 01. pii: S0014-4800(20)30802-9. [Epub ahead of print] 104526
      Pathologic examination of clinical tissue samples is time consuming and often does not involve the comprehensive analysis of the whole specimen. Automated tissue analysis systems have potential to make the workflow of a pathologist more efficient and to support in clinical decision-making. So far, these systems have been based on application of mass spectrometry imaging (MSI). MSI provides high fidelity and the results in tissue identification are promising. However, the high cost and need for maintenance limit the adoption of MSI in the clinical setting. Thus, there is a need for new innovations in the field of pathological tissue imaging. In this study, we show that differential ion mobility spectrometry (DMS) is a viable option in tissue imaging. We demonstrate that a DMS-driven solution performs with up to 92% accuracy in differentiating between two grossly distinct animal tissues. In addition, our model is able to classify the correct tissue with 81% accuracy in an eight-class setting. The DMS-based system is a significant innovation in a field dominated by mass-spectrometry-based solutions. By developing the presented platform further, DMS technology could be a cost-effective and helpful tool for automated pathological analysis.
    Keywords:  Diathermy; Differential ion mobility spectrometry; Imaging; Mass spectrometry; Tissue mapping
  11. J Chromatogr B Analyt Technol Biomed Life Sci. 2020 Aug 21. pii: S1570-0232(20)30088-X. [Epub ahead of print]1157 122339
      The aim of this study was to develop a new approach to sample preparation of biological material based on a combination of the Dried Blood Spot (DBS) method and capillary electrophoresis coupled with mass spectrometry (CE-MS) for the analysis of blood samples collected in vivo or post-mortem. The proposed approach allowed the identification of typical drugs from different groups, such as tricyclic antidepressants (amitriptyline, imipramine), selective serotonin reuptake inhibitors (citalopram), benzodiazepines (tetrazepam) and hypnotics (zolpidem). In this study, a blood sample was spotted on FTA DMPK C cards, then dried, and 6-mm discs were cut out. The sample preparation procedure involved microwave-assisted extraction (MAE). Various extraction agents, temperatures and durations of extraction were examined in order to achieve the highest efficiency of the process. The method was subjected to a validation procedure. Limits of detection (LOD = 1.76 - 14.7 ng/mL) and quantification (LOQ = 5.25 - 49.0 ng/mL), inter- (CV = 1.31 - 9.43%) and intra- (CV = 3.26 - 18.52%) day precision of the determinations, recovery (RE = 85.0-105.4%) and matrix effect on ionization of analytes (ME = 98.6-105.5%) were determined. Furthermore, the developed DBS/MAE/CM-MS method was selective and analytes present in the blood applied on DBS cards were found to be stable after 7 and after 14 days. Moreover, the developed method was successfully applied to the analysis of both post-mortem samples and blood samples taken from patients treated with the analyzed drugs.
    Keywords:  Blood samples; Capillary electrophoresis (CE); Dried blood spot (DBS); Green analysis; Mass Spectrometry (MS); Microwave-assisted extraction (MAE); Toxicological analysis
  12. J Chromatogr B Analyt Technol Biomed Life Sci. 2020 Jan 18. pii: S1570-0232(19)31172-9. [Epub ahead of print]1154 121982
      Short and medium fatty acids derived from either dietary sources, gut microbiota, and liver production might play a role in the modulation of metabolism and inflammation. The outcome of different autoimmune or inflammatory diseases could be related to microbiota composition and consequently fatty acids production. Their analytical detection, historically completed by GC, was herein investigated using a sensitive approach of LC-MS/MS with straightforward chemical derivatization, using 3-NPH, to the respective acylhydrazines. An isopropanol protein precipitation coupled to LC-MS/MS analysis allowed to separate and quantify butyric, valeric, hexanoic acid and their branched forms. The serum physiological ranges of short and medium chain fatty acids were determined in a heterogeneous healthy population (n = 54) from 18 to 85 years finding a concentration of 935.6 ± 246.5 (butyric), 698.8 ± 204.7 (isobutyric), 62.9 ± 15.3 (valeric), 1155.0 ± 490.4 (isovaleric) and 468.7 ± 377.5 (hexanoic) ng/mL respectively (mean ± SD). As expected, the biological levels in human serum are reasonably wide-ranging depending on several factors such as body-weight, gut microbiome dysbiosis, gut permeability, cardiometabolic dysregulation, and diet.
    Keywords:  LC-MS/MS; Medium chain fatty acid; Organic acids; Short chain fatty acid
  13. Metabolites. 2020 Aug 28. pii: E351. [Epub ahead of print]10(9):
      Gut microbiota plays essential roles in maintaining gut homeostasis. The composition of gut microbes and their metabolites are altered in response to diet and remedial agents such as antibiotics. However, little is known about the effect of antibiotics on the gut microbiota and their volatile metabolites. In this study, we evaluated the impact of a moderate level of ampicillin treatment on volatile fatty acids (VFAs) of gut microbial cultures using an optimized real-time secondary electrospray ionization coupled with high-resolution mass spectrometry (SESI-HRMS). To evaluate the ionization efficiency, different types of electrospray solvents and concentrations of formic acid as an additive (0.01, 0.05, and 0.1%, v/v) were tested using VFAs standard mixture (C2-C7). As a result, the maximum SESI-HRMS signals of all studied m/z values were observed from water with 0.01% formic acid than those from the aqueous methanolic solutions. Optimal temperatures of sample inlet and ion chamber were set at 130 °C and 85 °C, respectively. SESI spray pressure at 0.5 bar generated the maximum intensity than other tested values. The optimized SESI-HRMS was then used for the analysis of VFAs in gut microbial cultures. We detected that the significantly elevated C4 and C7 VFAs in the headspace of gut microbial cultures six hours after ampicillin treatment (1 mg/L). In conclusion, our results suggested that the optimized SESI-HRMS method can be suitable for the analysis of VFAs from gut microbes in a rapid, sensitive, and non-invasive manner.
    Keywords:  antibiotics; gut microbial metabolism; secondary electrospray ionization (SESI); volatile fatty acids (VFAs)
  14. Clin Chem. 2020 Sep 01. 66(9): 1181-1189
      BACKGROUND: For high-volume assays, optimizing throughput reduces test cost and turn-around time. One approach for liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays is sample multiplexing, wherein the analyte of interest is derivatized in different specimens with reagents of different molecular weight (differential mass tagging). Specimens can then be combined and simultaneously analyzed within a single injection to improve throughput. Here we developed and validated a quantitative, sample-multiplexed LC-MS/MS assay for serum total testosterone (TT) based on this approach.METHODS: For the sample-multiplexed assay, calibrators, controls, and patient specimens were first extracted separately. After mass tagging with either methoxyamine or hydroxylamine, they were combined and injected into the LC-MS/MS system. To evaluate assay performance, we determined limit of quantification (LOQ), linearity, recovery, and imprecision. A method-comparison study was also performed, comparing the new assay with the standard LC-MS/MS assay in 1574 patient specimens.
    RESULTS: The method was linear from 2.5 to 2000 ng/dL, with accuracies from 93% to 104% for both derivatives. An LOQ of 1.0 ng/dL was achieved. Intra-assay and total CVs across 4 quality control concentrations were less than 10%. The assay demonstrated good agreement (Deming regression, 1.03x + 6.07) with the standard LC-MS/MS assay for the patient specimens tested (TT, 3 to 4862 ng/dL).
    CONCLUSION: Sample multiplexing by differential mass tagging of TT increases LC-MS/MS throughput 2-fold without compromising analytical accuracy and sensitivity.
  15. J Pharm Biomed Anal. 2020 Aug 24. pii: S0731-7085(20)31417-5. [Epub ahead of print]191 113531
      Untargeted metabolomics provides a comprehensive investigation of metabolites and enables the discovery of biomarkers. Improvements in sample preparation, chromatographic separation and raw data processing procedure greatly enhance the metabolome coverage. In addition, database-dependent software identification is also essential, upon which enhances the identification confidence and benefits downstream biological analysis. Herein, we developed an improved detection and identification strategy for untargeted metabolomics based on UPLC-MS. In this work, sample preparation was optimized by considering chemical properties of different metabolites. Chromatographic separation was done by two different columns and MS detection was performed under positive and negative ion modes regarding to the different polarities of metabolites. According to the characteristics of the collected data, an improved identification and evaluation strategy was developed involving fragment simulation and MS/MS library search based on two commonly used databases, HMDB and METLIN. Such combination integrated information from different databases and was aimed to enhance identification confidence by considering the rationality of fragmentation, biological sources and functions comprehensively. In addition, decision tree analysis and lab-developed database were also introduced to assist the data processing and enhance the identification confidence. Finally, the feasibility of the developed strategy was validated by liver samples of obesity mice and controls. 238 metabolites were accurately detected, which was beneficial for the subsequent biomarker discovery and downstream pathway analysis. Therefore, the developed strategy remarkably facilitated the identification accuracy and the confirmation of metabolites in untargeted metabolomics.
    Keywords:  Fragment simulation; MS/MS library search; UPLC-MS; Untargeted metabolomics
  16. J Am Soc Mass Spectrom. 2020 Sep 02. 31(9): 2006-2010
      The Proteomics Society, India (PSI), hosted the Metabolomics workshop on experimental and data analysis training for untargeted metabolomics in December 2019. The workshop included six tutorial lectures and hands-on data analysis training sessions presented by seven speakers from across the globe. The tutorials and hands-on data analysis sessions focused on workflows for liquid chromatography-mass spectrometry (LC-MS) based on untargeted metabolomics. We review here three main topics from the workshop, which were uniquely identified as bottlenecks for new researchers: (a) experimental design, (b) quality controls during sample preparation and instrumental analysis, and (c) data quality evaluation using open source tools. Our objective here is to present common challenges faced by novice researchers and present guidelines to address them. We provide resources and good practices for researchers who are at the initial stage of setting up metabolomics workflows in their laboratories.
    Keywords:  LC-MS/MS analysis; quality control; untargeted metabolomics; workshop report