1
|
Alexandersson E, Sandström C, Meijer J, Nestor G, Broberg A, Röhnisch HE. Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates. Metabolomics 2023; 20:11. [PMID: 38141081 DOI: 10.1007/s11306-023-02073-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
INTRODUCTION The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy. OBJECTIVES To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals. METHODS An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix. RESULTS The extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated. CONCLUSIONS The extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.
Collapse
Affiliation(s)
- Elin Alexandersson
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
| | - Corine Sandström
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Johan Meijer
- Department of Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Gustav Nestor
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Anders Broberg
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Hanna E Röhnisch
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| |
Collapse
|
2
|
Canlet C, Deborde C, Cahoreau E, Da Costa G, Gautier R, Jacob D, Jousse C, Lacaze M, Le Mao I, Martineau E, Peyriga L, Richard T, Silvestre V, Traïkia M, Moing A, Giraudeau P. NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows. Metabolomics 2023; 19:65. [PMID: 37418094 DOI: 10.1007/s11306-023-02028-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023]
Abstract
INTRODUCTION Absolute quantification of individual metabolites in complex biological samples is crucial in targeted metabolomic profiling. OBJECTIVES An inter-laboratory test was performed to evaluate the impact of the NMR software, peak-area determination method (integration vs. deconvolution) and operator on quantification trueness and precision. METHODS A synthetic urine containing 32 compounds was prepared. One site prepared the urine and calibration samples, and performed NMR acquisition. NMR spectra were acquired with two pulse sequences including water suppression used in routine analyses. The pre-processed spectra were sent to the other sites where each operator quantified the metabolites using internal referencing or external calibration, and his/her favourite in-house, open-access or commercial NMR tool. RESULTS For 1D NMR measurements with solvent presaturation during the recovery delay (zgpr), 20 metabolites were successfully quantified by all processing strategies. Some metabolites could not be quantified by some methods. For internal referencing with TSP, only one half of the metabolites were quantified with a trueness below 5%. With peak integration and external calibration, about 90% of the metabolites were quantified with a trueness below 5%. The NMRProcFlow integration module allowed the quantification of several additional metabolites. The number of quantified metabolites and quantification trueness improved for some metabolites with deconvolution tools. Trueness and precision were not significantly different between zgpr- and NOESYpr-based spectra for about 70% of the variables. CONCLUSION External calibration performed better than TSP internal referencing. Inter-laboratory tests are useful when choosing to better rationalize the choice of quantification tools for NMR-based metabolomic profiling and confirm the value of spectra deconvolution tools.
Collapse
Affiliation(s)
- Cécile Canlet
- Toxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France
| | - Catherine Deborde
- INRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France
| | - Edern Cahoreau
- TBI, Université de Toulouse, CNRS, INRAE, INSA, MetaboHUB - MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Grégory Da Costa
- Univ. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France
| | - Roselyne Gautier
- Toxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France
| | - Daniel Jacob
- INRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France
| | - Cyril Jousse
- Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut de Chimie de Clermont-Ferrand. Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France
| | - Mélia Lacaze
- Toxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France
| | - Inès Le Mao
- Univ. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France
| | - Estelle Martineau
- Nantes Université, CNRS, CEISAM UMR 6230, 44000, Nantes, France
- CAPACITES SAS, 44200, Nantes, France
| | - Lindsay Peyriga
- TBI, Université de Toulouse, CNRS, INRAE, INSA, MetaboHUB - MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Tristan Richard
- Univ. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France
| | | | - Mounir Traïkia
- Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut de Chimie de Clermont-Ferrand. Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France
| | - Annick Moing
- INRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France.
| | | |
Collapse
|
3
|
Chhaganlal MN, Underhaug J, Mjøs SA. Evaluation of NMR predictors for accuracy and ability to reveal trends in 1 H NMR spectra of fatty acids. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:318-332. [PMID: 36759332 DOI: 10.1002/mrc.5336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/04/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
Four different nuclear magnetic resonance (NMR) predictors have been evaluated for their ability to predict 600-MHz 1 H spectra of free fatty acids and fatty acid methyl esters of 20 common fatty acids. The predictors were evaluated on two main criteria: (1) their accuracy in direct prediction of the spectra (absolute accuracy) and (2) the ability to reveal trends or predict the change that occurs in the spectra as a result of a change in the fatty acid carbon chain, or by esterification of the free fatty acids to methyl esters (relative accuracy). The absolute accuracy in chemical shift prediction for fatty acids was good, compared with previous reports on a broader range of compounds. All four predictors had median prediction errors for chemical shifts of the signals in fatty acid methyl esters well below 0.1 ppm and as low as 0.015 ppm for one of the predictors. However, all predictors also had outliers with errors far above the upper interquartile range. In general, they also fail to reproduce trends of diagnostic value that were observed in the experimental data or properly predict the result of a minor change in molecular structure. All four predictors depend on experimental data from different origins. This may be a limiting factor for the relative accuracy of the predictors.
Collapse
Affiliation(s)
| | - Jarl Underhaug
- Department of Chemistry, University of Bergen, Bergen, Norway
| | - Svein A Mjøs
- Department of Chemistry, University of Bergen, Bergen, Norway
| |
Collapse
|
4
|
Li DW, Bruschweiler-Li L, Hansen A, Brüschweiler R. DEEP Picker1D and Voigt Fitter1D: a versatile tool set for the automated quantitative spectral deconvolution of complex 1D-NMR spectra. MAGNETIC RESONANCE (GOTTINGEN, GERMANY) 2023; 4:19-26. [PMID: 37904796 PMCID: PMC10539790 DOI: 10.5194/mr-4-19-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/16/2022] [Indexed: 11/01/2023]
Abstract
The quantitative deconvolution of 1D-NMR spectra into individual resonances or peaks is a key step in many modern NMR workflows as it critically affects downstream analysis and interpretation. Depending on the complexity of the NMR spectrum, spectral deconvolution can be a notable challenge. Based on the recent deep neural network DEEP Picker and Voigt Fitter for 2D NMR spectral deconvolution, we present here an accurate, fully automated solution for 1D-NMR spectral analysis, including peak picking, fitting, and reconstruction. The method is demonstrated for complex 1D solution NMR spectra showing excellent performance also for spectral regions with multiple strong overlaps and a large dynamic range whose analysis is challenging for current computational methods. The new tool will help streamline 1D-NMR spectral analysis for a wide range of applications and expand their reach toward ever more complex molecular systems and their mixtures.
Collapse
Affiliation(s)
- Da-Wei Li
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, USA
| | - Lei Bruschweiler-Li
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, USA
| | - Alexandar L. Hansen
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, USA
| | - Rafael Brüschweiler
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, USA
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210, USA
| |
Collapse
|
5
|
Pillai MS, Paritala ST, Shah RP, Sharma N, Sengupta P. Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics. Drug Metab Rev 2022; 54:401-426. [PMID: 36351878 DOI: 10.1080/03602532.2022.2125987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.
Collapse
Affiliation(s)
- Megha Sajakumar Pillai
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Sree Teja Paritala
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Ravi P Shah
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Nitish Sharma
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Pinaki Sengupta
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| |
Collapse
|
6
|
Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research. Metabolites 2022; 12:metabo12100963. [PMID: 36295865 PMCID: PMC9609461 DOI: 10.3390/metabo12100963] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 12/02/2022] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.
Collapse
|
7
|
Wöhl J, Kopp WA, Yevlakhovych I, Bahr L, Koß HJ, Leonhard K. Completely Computational Model Setup for Spectroscopic Techniques: The Ab Initio Molecular Dynamics Indirect Hard Modeling Approach. J Phys Chem A 2022; 126:2845-2853. [PMID: 35476427 DOI: 10.1021/acs.jpca.2c01061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The spectroscopic quantification of mixture compositions usually requires pure compounds and mixtures of known compositions for calibration. Since they are not always available, methods to fill such gaps have evolved, which are, however, not generally applicable. Therefore, calibration can be extremely challenging, especially when multiple unstable species, for example, intermediates, exist in a system. This study presents a new calibration approach that uses ab initio molecular dynamics (AIMD)-simulated spectra to set up and calibrate models for the physics-based spectral analysis method indirect hard modeling (IHM). To demonstrate our approach called AIMD-IHM, we analyze Raman spectra of ternary hydrogen-bonding mixtures of acetone, methanol, and ethanol. The derived AIMD-IHM pure-component models and calibration coefficients are in good agreement with conventionally generated experimental results. The method yields compositions with prediction errors of less than 5% without any experimental calibration input. Our approach can be extended, in principle, to infrared and NMR spectroscopy and allows for the analysis of systems that were hitherto inaccessible to quantitative spectroscopic analysis.
Collapse
Affiliation(s)
- Justus Wöhl
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany
| | - Wassja A Kopp
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany
| | - Iryna Yevlakhovych
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany
| | - Leo Bahr
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany
| | - Hans-Jürgen Koß
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany
| | - Kai Leonhard
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany
| |
Collapse
|
8
|
Schultheiss UT, Kosch R, Kotsis F, Altenbuchinger M, Zacharias HU. Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses. Metabolites 2021; 11:460. [PMID: 34357354 PMCID: PMC8304377 DOI: 10.3390/metabo11070460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 12/14/2022] Open
Abstract
Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.
Collapse
Affiliation(s)
- Ulla T. Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Robin Kosch
- Computational Biology, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Fruzsina Kotsis
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Michael Altenbuchinger
- Institute of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany;
| | - Helena U. Zacharias
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| |
Collapse
|