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

Simón‐Manso, Yamil; Marupaka, Ramesh; Yan, Xiaojun; Liang, Yuxue; Telu, Kelly H.; Mirokhin, Yuri A.; Stein, Stephen E.

doi:10.1021/acs.analchem.9b02977

Cited by 18 publications

(22 citation statements)

References 36 publications

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“…The standardization and harmonization of comparisons across participants thus demands new strategies that address these complex issues while causing minimum disruption to the usual workflows. The appropriate use of certified reference materials, spectral libraries [20,21], and hybrid searches [22,23,24] may be a reasonable approach for these purposes.…”

Section: Resultsmentioning

confidence: 99%

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite

et al. 2019

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There is a lack of experimental reference materials and standards for metabolomics measurements, such as urine, plasma, and other human fluid samples. Reasons include difficulties with supply, distribution, and dissemination of information about the materials. Additionally, there is a long lead time because reference materials need their compositions to be fully characterized with uncertainty, a labor-intensive process for material containing thousands of relevant compounds. Furthermore, data analysis can be hampered by different methods using different software by different vendors. In this work, we propose an alternative implementation of reference materials. Instead of characterizing biological materials based on their composition, we propose using untargeted metabolomic data such as nuclear magnetic resonance (NMR) or gas and liquid chromatography-mass spectrometry (GC-MS and LC-MS) profiles. The profiles are then distributed with the material accompanying the certificate, so that researchers can compare their own metabolomic measurements with the reference profiles. To demonstrate this approach, we conducted an interlaboratory study (ILS) in which seven National Institute of Standards and Technology (NIST) urine Standard Reference Material®s (SRM®s) were distributed to participants, who then returned the metabolomic data to us. We then implemented chemometric methods to analyze the data together to estimate the uncertainties in the current measurement techniques. The participants identified similar patterns in the profiles that distinguished the seven samples. Even when the number of spectral features is substantially different between platforms, a collective analysis still shows significant overlap that allows reliable comparison between participants. Our results show that a urine suite such as that used in this ILS could be employed for testing and harmonization among different platforms. A limited quantity of test materials will be made available for researchers who are willing to repeat the protocols presented here and contribute their data.

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Section: Resultsmentioning

confidence: 99%

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite

et al. 2019

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“…Therefore, acquisition and thorough annotation (or putative identification) of MS/MS data from biologically relevant and recurrently observed metabolite features absent from databases and chemical provider catalogs is required for their further implementation and storage in specific shared databases. Such a strategy has been recently used to build spectral libraries of unidentified but annotated recurrent spectra derived from NIST urine samples [ 96 ], and could be extended to unknown signals returned by meta-analysis at the pathway level with software tools such as Mummichog [ 97 ].…”

Section: Toward Inter-operable and Reusable Metabolomics Data For Biomarker Discovery: From Appropriate Project Design And Sample Collectmentioning

confidence: 99%

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

et al. 2021

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Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine. Graphical abstract

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“…In addition to producing the NIST MS/MS Library, the NIST Mass Spectrometry Data Center (MSDC) has recently begun creating materialoriented libraries that are generated from the analysis of complex mixtures such as human plasma and urine (https://chemdata.nist.gov/ dokuwiki/doku.php?id = chemdata:arus) to address the issue of unknown metabolites (metabolites not identified by library searching), identify cross-platform metabolite signatures, and catalogue all spectra associated with a particular material of interest (Mallard et al, 2014;Remoroza et al, 2018;Simon-Manso et al, 2013;Simon-Manso et al, 2019;S. Stein, 2012;Telu et al, 2016).…”

Section: ------------------------------------------------------------mentioning

confidence: 99%

“…A comprehensive library of both known and unidentified CHO cell metabolites will be beneficial to the field of CHO cell metabolite analysis. In addition to producing the NIST MS/MS Library, the NIST Mass Spectrometry Data Center (MSDC) has recently begun creating material‐oriented libraries that are generated from the analysis of complex mixtures such as human plasma and urine (https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:arus) to address the issue of unknown metabolites (metabolites not identified by library searching), identify cross‐platform metabolite signatures, and catalogue all spectra associated with a particular material of interest (Mallard et al, 2014; Remoroza et al, 2018; Simon‐Manso et al, 2013; Simon‐Manso et al, 2019; S. Stein, 2012; Telu et al, 2016). These material‐oriented libraries contain recurrent spectra (spectra that occur repeatedly in the sample) for all detectable metabolites, both known and unknown that are processed to produce high‐quality consensus spectra for the library.…”

Section: Introductionmentioning

confidence: 99%

Creation and filtering of a recurrent spectral library of CHO cell metabolites and media components

Telu

Marupaka

Simón‐Manso

et al. 2021

Biotech & Bioengineering

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This paper reports the first implementation of a new type of mass spectral library for the analysis of Chinese hamster ovary (CHO) cell metabolites that allows users to quickly identify most compounds in any complex metabolite sample. We also describe an annotation methodology developed to filter out artifacts and low‐quality spectra from recurrent unidentified spectra of metabolites. CHO cells are commonly used to produce biological therapeutics. Metabolic profiles of CHO cells and media can be used to monitor process variability and look for markers that discriminate between batches of product. We have created a comprehensive library of both identified and unidentified metabolites derived from CHO cells that can be used in conjunction with tandem mass spectrometry to identify metabolites. In addition, we present a workflow that can be used for assigning confidence to a NIST MS/MS Library search match based on prior probability of general utility. The goal of our work is to annotate and identify (when possible), all liquid chromatography‐mass spectrometry generated metabolite ions as well as create automatable library building and identification pipelines for use by others in the field.

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

Cited by 18 publications

References 36 publications

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Creation and filtering of a recurrent spectral library of CHO cell metabolites and media components

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