scholarly journals Benchmarking Non-Targeted Metabolomics Using Yeast-Derived Libraries

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 160
Author(s):  
Evelyn Rampler ◽  
Gerrit Hermann ◽  
Gerlinde Grabmann ◽  
Yasin El Abiead ◽  
Harald Schoeny ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Protein Identification ◽  
Regulatory Elements ◽  
Yeast Fermentation ◽  
Compound Identification ◽  
Targeted Metabolomics ◽  
Targeted Analysis ◽  
Orthogonal State ◽  
Starting Point

Non-targeted analysis by high-resolution mass spectrometry (HRMS) is an essential discovery tool in metabolomics. To date, standardization and validation remain a challenge. Community-wide accepted cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of >200 identified metabolites based on compound identification by accurate mass, matching retention times, and MS/MS, as well as a comprehensive literature search. The library includes metabolites from the classes of (1) organic acids and derivatives (2) nucleosides, nucleotides, and analogs, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds, and (7) benzoids at expected concentrations ranges of sub-nM to µM. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the human metabolome database (HMDB). Orthogonal state-of-the-art reversed-phase (RP-) and hydrophilic interaction chromatography mass spectrometry (HILIC-MS) non-targeted analysis and authentic standards revealed that 104 out of the 206 confirmed metabolites were reproducibly recovered and stable over the course of three years when stored at −80 °C. Overall, 67 out of these 104 metabolites were identified with comparably stable areas over all three yeast fermentation and are the ideal starting point for benchmarking experiments. The provided yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments but also allowed in-house routines for instrumental performance tests. Transferring the quality control strategy of proteomics workflows based on the number of protein identification in HeLa extracts, metabolite IDs in the yeast benchmarking material can be used as metabolomics quality control. Finally, the benchmark material opens new avenues for batch-to-batch corrections in large-scale non-targeted metabolomics studies.

scholarly journals Benchmarking non-targeted metabolomics using yeast derived libraries

2020 ◽  
Author(s):  
Evelyn Rampler ◽  
Gerrit Hermann ◽  
Gerlinde Grabmann ◽  
Yasin El Abiead ◽  
Harald Schoeny ◽  
...  
Keyword(s):  
Large Scale ◽  
High Resolution Mass Spectrometry ◽  
Chemical Space ◽  
Large Fraction ◽  
Cost Effective ◽  
Regulatory Elements ◽  
Instrumental Performance ◽  
Compound Identification ◽  
Targeted Metabolomics ◽  
Targeted Analysis

AbstractNon-targeted analysis by high-resolution mass spectrometry (HRMS) is the essential discovery tool in metabolomics. Up to date, standardization and validation remain a challenge. Community wide accepted, cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts, derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of > 200 metabolites, reproducibly recovered in ethanolic extracts by orthogonal LCHRMS methods, different fermentations (over three years) and different laboratories. More specifically, compound identification was based on accurate mass, matching retention times, and MS/MS spectra as compared to authentic standards and internal databases. The library includes metabolites from the classes of 1) organic acids and derivatives (2) nucleosides, nucleotides and analogues, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds and (7) benzoids at expected concentrations ranges of sub-nM to µM. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the Human metabolome data base (HMDB). A large fraction of metabolites was found to be stable for several years when stored at −80°C. Thus, the yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments, but enabled in-house routines for instrumental performance tests. Finally, the benchmark material opens new avenues for batch to batch corrections in large scale non-targeted metabolomics studies.

scholarly journals Non-Targeted Analysis Using Gas Chromatography-Mass Spectrometry for Evaluation of Chemical Composition of E-Vapor Products

2021 ◽  
Vol 9 ◽  
Author(s):  
Niti H. Shah ◽  
Michael R. Noe ◽  
Kimberly A. Agnew-Heard ◽  
Yezdi B. Pithawalla ◽  
William P. Gardner ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Volatile Compounds ◽  
Tobacco Product ◽  
Gas Chromatography Mass Spectrometry ◽  
Chemical Compositions ◽  
Compound Identification ◽  
Mass Spectral ◽  
Targeted Analysis ◽  
Chromatography Mass Spectrometry

The Premarket Tobacco Product Applications (PMTA) guidance issued by the Food and Drug Administration for electronic nicotine delivery systems (ENDSs) recommends that in addition to reporting harmful and potentially harmful constituents (HPHCs), manufacturers should evaluate these products for other chemicals that could form during use and over time. Although e-vapor product aerosols are considerably less complex than mainstream smoke from cigarettes and heated tobacco product (HTP) aerosols, there are challenges with performing a comprehensive chemical characterization. Some of these challenges include the complexity of the e-liquid chemical compositions, the variety of flavors used, and the aerosol collection efficiency of volatile and semi-volatile compounds generated from aerosols. In this study, a non-targeted analysis method was developed using gas chromatography-mass spectrometry (GC-MS) that allows evaluation of volatile and semi-volatile compounds in e-liquids and aerosols of e-vapor products. The method employed an automated data analysis workflow using Agilent MassHunter Unknowns Analysis software for mass spectral deconvolution, peak detection, and library searching and reporting. The automated process ensured data integrity and consistency of compound identification with >99% of known compounds being identified using an in-house custom mass spectral library. The custom library was created to aid in compound identifications and includes over 1,100 unique mass spectral entries, of which 600 have been confirmed from reference standard comparisons. The method validation included accuracy, precision, repeatability, limit of detection (LOD), and selectivity. The validation also demonstrated that this semi-quantitative method provides estimated concentrations with an accuracy ranging between 0.5- and 2.0-fold as compared to the actual values. The LOD threshold of 0.7 ppm was established based on instrument sensitivity and accuracy of the compounds identified. To demonstrate the application of this method, we share results from the comprehensive chemical profile of e-liquids and aerosols collected from a marketed e-vapor product. Applying the data processing workflow developed here, 46 compounds were detected in the e-liquid formulation and 55 compounds in the aerosol sample. More than 50% of compounds reported have been confirmed with reference standards. The profiling approach described in this publication is applicable to evaluating volatile and semi-volatile compounds in e-vapor products.

scholarly journals A Mass Spectrometry Based Metabolite Profiling Workflow for Selecting Abundant Specific Markers and Their Structurally Related Multi-Component Signatures in Traditional Chinese Medicine Multi‐Herb Formulae

2020 ◽  
Vol 11 ◽  
Author(s):  
Joëlle Houriet ◽  
Pierre-Marie Allard ◽  
Emerson Ferreira Queiroz ◽  
Laurence Marcourt ◽  
Arnaud Gaudry ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Chinese Medicine ◽  
High Resolution ◽  
Traditional Chinese Medicine ◽  
Secondary Metabolites ◽  
Metabolite Profiling ◽  
Specific Marker ◽  
Feature Maps ◽  
Starting Point

In Traditional Chinese Medicine (TCM), herbal preparations often consist of a mixture of herbs. Their quality control is challenging because every single herb contains hundreds of components (secondary metabolites). A typical 10 herb TCM formula was selected to develop an innovative strategy for its comprehensive chemical characterization and to study the specific contribution of each herb to the formula in an exploratory manner. Metabolite profiling of the TCM formula and the extract of each single herb were acquired with liquid chromatography coupled to high-resolution mass spectrometry for qualitative analyses, and to evaporative light scattering detection (ELSD) for semi-quantitative evaluation. The acquired data were organized as a feature-based molecular network (FBMN) which provided a comprehensive view of all types of secondary metabolites and their occurrence in the formula and all single herbs. These features were annotated by combining MS/MS-based in silico spectral match, manual evaluation of the structural consistency in the FBMN clusters, and taxonomy information. ELSD detection was used as a filter to select the most abundant features. At least one marker per herb was highlighted based on its specificity and abundance. A single large-scale fractionation from the enriched formula enabled the isolation and formal identification of most of them. The obtained markers allowed an improved annotation of associated features by manually propagating this information through the FBMN. These data were incorporated in the high-resolution metabolite profiling of the formula, which highlighted specific series of related components to each individual herb markers. These series of components, named multi-component signatures, may serve to improve the traceability of each herb in the formula. Altogether, the strategy provided highly informative compositional data of the TCM formula and detailed visualizations of the contribution of each herb by FBMN, filtered feature maps, and reconstituted chromatogram traces of all components linked to each specific marker. This comprehensive MS-based analytical workflow allowed a generic and unbiased selection of specific and abundant markers and the identification of multiple related sub-markers. This exploratory approach could serve as a starting point to develop more simple and targeted quality control methods with adapted marker specificity selection criteria to given TCM formula.

scholarly journals Revisiting Five Years of CASMI Contests with EPA Identification Tools

Metabolites ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 260
Author(s):  
Andrew D. McEachran ◽  
Alex Chao ◽  
Hussein Al-Ghoul ◽  
Charles Lowe ◽  
Christopher Grulke ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution Mass Spectrometry ◽  
Critical Assessment ◽  
Tandem Mass ◽  
Chemical Identification ◽  
Compound Identification ◽  
Software Applications ◽  
Targeted Analysis ◽  
The Us ◽  
Fit For Purpose

Software applications for high resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) continue to enhance chemical identification capabilities. Given the variety of available applications, determining the most fit-for-purpose tools and workflows can be difficult. The Critical Assessment of Small Molecule Identification (CASMI) contests were initiated in 2012 to provide a means to evaluate compound identification tools on a standardized set of blinded tandem mass spectrometry (MS/MS) data. Five CASMI contests have resulted in recommendations, publications, and invaluable datasets for practitioners of HRMS-based screening studies. The US Environmental Protection Agency’s (EPA) CompTox Chemicals Dashboard is now recognized as a valuable resource for compound identification in NTA studies. However, this application was too new and immature in functionality to participate in the five previous CASMI contests. In this work, we performed compound identification on all five CASMI contest datasets using Dashboard tools and data in order to critically evaluate Dashboard performance relative to that of other applications. CASMI data was accessed via the CASMI webpage and processed for use in our spectral matching and identification workflow. Relative to applications used by former contest participants, our tools, data, and workflow performed well, placing more challenge compounds in the top five of ranked candidates than did the winners of three contest years and tying in a fourth. In addition, we conducted an in-depth review of the CASMI structure sets and made these reviewed sets available via the Dashboard. Our results suggest that Dashboard data and tools would enhance chemical identification capabilities for practitioners of HRMS-based NTA.

Biopharmaceutical quality control with mass spectrometry

Bioanalysis ◽  
2021 ◽  
Author(s):  
Shulei Liu ◽  
Benjamin L Schulz
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Protein Identification ◽  
Structural Integrity ◽  
Cell Protein ◽  
Manufacturing Processes ◽  
Host Cell Protein ◽  
Post Translational Modifications ◽  
Biochemical Studies

Mass spectrometry (MS) is a powerful technique for protein identification, quantification and characterization that is widely applied in biochemical studies, and which can provide data on the quantity, structural integrity and post-translational modifications of proteins. It is therefore a versatile and widely used analytic tool for quality control of biopharmaceuticals, especially in quantifying host-cell protein impurities, identifying post-translation modifications and structural characterization of biopharmaceutical proteins. Here, we summarize recent advances in MS-based analyses of these key quality attributes of the biopharmaceutical development and manufacturing processes.

Unrestrictive protein modification localization and quality control for open search of mass spectra

2018 ◽  
Author(s):  
Zhiwu An ◽  
Fuzhou Gong ◽  
Yan Fu
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Tandem Mass Spectrometry ◽  
Mass Spectra ◽  
Protein Modification ◽  
Software Tool ◽  
Tandem Mass ◽  
Simulation Data ◽  
Post Translational Modifications

We have developed PTMiner, a first software tool for automated, confident filtering, localization and annotation of protein post-translational modifications identified by open (mass-tolerant) search of large tandem mass spectrometry datasets. The performance of the software was validated on carefully designed simulation data. <br>

Mass Spectrometry Techniques: Principles and Practices for Quantitative Proteomics

2020 ◽  
Vol 21 ◽  
Author(s):  
Rocco J. Rotello ◽  
Timothy D. Veenstra
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Protein Identification ◽  
Dynamic Nature ◽  
Complete Coverage ◽  
The Past ◽  
Proteome Level ◽  
A Cell ◽  
Quantitative Changes ◽  
Made In

: In the current omics-age of research, major developments have been made in technologies that attempt to survey the entire repertoire of genes, transcripts, proteins, and metabolites present within a cell. While genomics has led to a dramatic increase in our understanding of such things as disease morphology and how organisms respond to medications, it is critical to obtain information at the proteome level since proteins carry out most of the functions within the cell. The primary tool for obtaining proteome-wide information on proteins within the cell is mass spectrometry (MS). While it has historically been associated with the protein identification, developments over the past couple of decades have made MS a robust technology for protein quantitation as well. Identifying quantitative changes in proteomes is complicated by its dynamic nature and the inability of any technique to guarantee complete coverage of every protein within a proteome sample. Fortunately, the combined development of sample preparation and MS methods have made it capable to quantitatively compare many thousands of proteins obtained from cells and organisms.

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