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Manning JE, Harris E, Mathieson H, Sorensen L, Luqmani R, McGettrick HM, Morgan AW, Young SP, Mackie SL. Polymyalgia rheumatica shows metabolomic alterations that are further altered by glucocorticoid treatment: Identification of metabolic correlates of fatigue. J Autoimmun 2024; 147:103260. [PMID: 38797046 DOI: 10.1016/j.jaut.2024.103260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/17/2024] [Accepted: 05/21/2024] [Indexed: 05/29/2024]
Abstract
OBJECTIVE In polymyalgia rheumatica (PMR), glucocorticoids (GCs) relieve pain and stiffness, but fatigue may persist. We aimed to explore the effect of disease, GCs and PMR symptoms in the metabolite signatures of peripheral blood from patients with PMR or the related disease, giant cell arteritis (GCA). METHODS Nuclear magnetic resonance spectroscopy was performed on serum from 40 patients with untreated PMR, 84 with new-onset confirmed GCA, and 53 with suspected GCA who later were clinically confirmed non-GCA, and 39 age-matched controls. Further samples from PMR patients were taken one and six months into glucocorticoid therapy to explore relationship of metabolites to persistent fatigue. 100 metabolites were identified using Chenomx and statistical analysis performed in SIMCA-P to examine the relationship between metabolic profiles and, disease, GC treatment or symptoms. RESULTS The metabolite signature of patients with PMR and GCA differed from that of age-matched non-inflammatory controls (R2 > 0.7). There was a smaller separation between patients with clinically confirmed GCA and those with suspected GCA who later were clinically confirmed non-GCA (R2 = 0.135). In PMR, metabolite signatures were further altered with glucocorticoid treatment (R2 = 0.42) but did not return to that seen in controls. Metabolites correlated with CRP, pain, stiffness, and fatigue (R2 ≥ 0.39). CRP, pain, and stiffness declined with treatment and were associated with 3-hydroxybutyrate and acetoacetate, but fatigue did not. Metabolites differentiated patients with high and low fatigue both before and after treatment (R2 > 0.9). Low serum glutamine was predictive of high fatigue at both time points (0.79-fold change). CONCLUSION PMR and GCA alter the metabolite signature. In PMR, this is further altered by glucocorticoid therapy. Treatment-induced metabolite changes were linked to measures of inflammation (CRP, pain and stiffness), but not to fatigue. Furthermore, metabolite signatures distinguished patients with high or low fatigue.
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Affiliation(s)
- Julia E Manning
- Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15-2TT, UK.
| | - Emma Harris
- School of Medicine, University of Leeds, Leeds, LS7 4SA, UK and School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK.
| | - Hannah Mathieson
- School of Medicine, University of Leeds, Leeds, LS7 4SA, UK and Leeds NIHR Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - Louise Sorensen
- School of Medicine, University of Leeds, Leeds, LS7 4SA, UK.
| | - Raashid Luqmani
- NIHR Musculoskeletal Biomedical Research Unit, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Science, University of Oxford, Oxford, UK.
| | - Helen M McGettrick
- Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15-2TT, UK.
| | - Ann W Morgan
- School of Medicine, University of Leeds, Leeds, School of Human and Health Sciences, University of Huddersfield, Huddersfield, And Leeds NIHR Medtech and in Vitro Diagnostics Co-operative, Leeds Teaching Hospitals NHS Trust, Leeds, LS7 4SA, UK.
| | - Stephen P Young
- Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15-2TT, UK.
| | - Sarah L Mackie
- School of Medicine, University of Leeds, Leeds, LS7 4SA, UK and School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK.
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Carrell T, McDougall MP. Multi-channel magnetic resonance spectroscopy graphical user interface (McMRSGUI). PLoS One 2024; 19:e0299142. [PMID: 38416774 PMCID: PMC10901321 DOI: 10.1371/journal.pone.0299142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/06/2024] [Indexed: 03/01/2024] Open
Abstract
This work introduces an open-sourced graphical user interface (GUI) software enabling the combination of multi-channel magnetic resonance spectroscopy data with different literature-based methods for the improvement of the quality and reliability of combined spectra. The multi-channel magnetic resonance spectroscopy graphical user interface (McMRSGUI) is a MATLAB-based spectroscopy processing GUI equipped to load multi-channel MRS data, pre-process, combine, and export combined data for evaluation with open-source quantification software (jMRUI). A literature-based, decision-tree process was incorporated into the combination type selection to serve as a guide to minimize spectral distortion in selecting between weighting methods. Multi-channel, simulated spectra were combined with the different combination techniques and evaluated for spectral distortion to validate the code. The incorporation of the combination methods into a single processing software enables multi-channel magnetic resonance spectroscopy (MRS) data to be combined and compared for improved spectral quality with little user knowledge of combination techniques. Through the spectral peak distortion simulation of the combination methods, combined signal-to-noise ratio (SNR) values from the literature were verified. The spectral peak distortion simulation provides a secondary tool for researchers to estimate the spectral SNR levels when spectral distortion could occur and use this knowledge to further guide the selection of their combination technique. The McMRSGUI provides a software toolkit for evaluating multi-channel MRS data and their combination. Simulations evaluating spectral distortion at different noise levels were performed for each combination method to validate the GUI and demonstrate a method for researchers to assess the combined SNR levels at which they could be introducing spectral distortion.
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Affiliation(s)
- Travis Carrell
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas, United States of America
| | - Mary P McDougall
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas, United States of America
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3
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Rout M, Lipfert M, Lee BL, Berjanskii M, Assempour N, Fresno RV, Cayuela AS, Dong Y, Johnson M, Shahin H, Gautam V, Sajed T, Oler E, Peters H, Mandal R, Wishart DS. MagMet: A fully automated web server for targeted nuclear magnetic resonance metabolomics of plasma and serum. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:681-704. [PMID: 37265034 DOI: 10.1002/mrc.5371] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/12/2023] [Accepted: 05/17/2023] [Indexed: 06/03/2023]
Abstract
Nuclear magnetic resonance (NMR) spectral analysis of biofluids can be a time-consuming process, requiring the expertise of a trained operator. With NMR becoming increasingly popular in the field of metabolomics, there is a growing need to change this paradigm and to automate the process. Here we introduce MagMet, an online web server, that automates the processing and quantification of 1D 1 H NMR spectra from biofluids-specifically, human serum/plasma metabolites, including those associated with inborn errors of metabolism (IEM). MagMet uses a highly efficient data processing procedure that performs automatic Fourier Transformation, phase correction, baseline optimization, chemical shift referencing, water signal removal, and peak picking/peak alignment. MagMet then uses the peak positions, linewidth information, and J-couplings from its own specially prepared standard metabolite reference spectral NMR library of 85 serum/plasma compounds to identify and quantify compounds from experimentally acquired NMR spectra of serum/plasma. MagMet employs linewidth adjustment for more consistent quantification of metabolites from higher field instruments and incorporates a highly efficient data processing procedure for more rapid and accurate detection and quantification of metabolites. This optimized algorithm allows the MagMet webserver to quickly detect and quantify 58 serum/plasma metabolites in 2.6 min per spectrum (when processing a dataset of 50-100 spectra). MagMet's performance was also assessed using spectra collected from defined mixtures (simulating other biofluids), with >100 previously measured plasma spectra, and from spiked serum/plasma samples simulating known IEMs. In all cases, MagMet performed with precision and accuracy matching the performance of human spectral profiling experts. MagMet is available at http://magmet.ca.
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Affiliation(s)
- Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Nazanin Assempour
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Rosa Vazquez Fresno
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Arnau Serra Cayuela
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Ying Dong
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Mathew Johnson
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Honeya Shahin
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Tanvir Sajed
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Rupasri Mandal
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
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Wang J, Ji B, Lei Y, Liu T, Mao H, Yang X. Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS. Med Phys 2023; 50:7955-7966. [PMID: 37947479 PMCID: PMC10872746 DOI: 10.1002/mp.16831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 10/05/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time. PURPOSE We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS. METHODS The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA = 192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising. RESULTS With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. CONCLUSIONS The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.
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Affiliation(s)
- Jing Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Bing Ji
- Department of Radiology and Imaging Science and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Tian Liu
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Hui Mao
- Department of Radiology and Imaging Science and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
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Eltemur D, Robatscher P, Oberhuber M, Scampicchio M, Ceccon A. Applications of Solution NMR Spectroscopy in Quality Assessment and Authentication of Bovine Milk. Foods 2023; 12:3240. [PMID: 37685173 PMCID: PMC10486658 DOI: 10.3390/foods12173240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/07/2023] [Accepted: 08/18/2023] [Indexed: 09/10/2023] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising technique for the analysis of bovine milk, primarily due to its non-destructive nature, minimal sample preparation requirements, and comprehensive approach to untargeted milk analysis. These inherent strengths of NMR make it a formidable complementary tool to mass spectrometry-based techniques in milk metabolomic studies. This review aims to provide a comprehensive overview of the applications of NMR techniques in the quality assessment and authentication of bovine milk. It will focus on the experimental setup and data processing techniques that contribute to achieving accurate and highly reproducible results. The review will also highlight key studies that have utilized commonly used NMR methodologies in milk analysis, covering a wide range of application fields. These applications include determining milk animal species and feeding regimes, as well as assessing milk nutritional quality and authenticity. By providing an overview of the diverse applications of NMR in milk analysis, this review aims to demonstrate the versatility and significance of NMR spectroscopy as an invaluable tool for milk and dairy metabolomics research and hence, for assessing the quality and authenticity of bovine milk.
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Affiliation(s)
- Dilek Eltemur
- Laimburg Research Centre, Laimburg 6—Pfatten (Vadena), 39040 Auer, Italy (A.C.)
- Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Piazza Unversità 5, 39100 Bolzano, Italy
| | - Peter Robatscher
- Laimburg Research Centre, Laimburg 6—Pfatten (Vadena), 39040 Auer, Italy (A.C.)
| | - Michael Oberhuber
- Laimburg Research Centre, Laimburg 6—Pfatten (Vadena), 39040 Auer, Italy (A.C.)
| | - Matteo Scampicchio
- Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Piazza Unversità 5, 39100 Bolzano, Italy
| | - Alberto Ceccon
- Laimburg Research Centre, Laimburg 6—Pfatten (Vadena), 39040 Auer, Italy (A.C.)
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6
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Romano A, Rižner TL, Werner HMJ, Semczuk A, Lowy C, Schröder C, Griesbeck A, Adamski J, Fishman D, Tokarz J. Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review. Front Oncol 2023; 13:1120178. [PMID: 37091170 PMCID: PMC10118013 DOI: 10.3389/fonc.2023.1120178] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/06/2023] [Indexed: 04/09/2023] Open
Abstract
Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.
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Affiliation(s)
- Andrea Romano
- Department of Gynaecology, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
- GROW – School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- *Correspondence: Andrea Romano, ; Tea Lanišnik Rižner,
| | - Tea Lanišnik Rižner
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- *Correspondence: Andrea Romano, ; Tea Lanišnik Rižner,
| | - Henrica Maria Johanna Werner
- Department of Gynaecology, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
- GROW – School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Andrzej Semczuk
- Department of Gynaecology, Lublin Medical University, Lublin, Poland
| | | | | | | | - Jerzy Adamski
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Dmytro Fishman
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Quretec Ltd., Tartu, Estonia
| | - Janina Tokarz
- Institute for Diabetes and Cancer, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
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Wishart DS, Rout M, Lee BL, Berjanskii M, LeVatte M, Lipfert M. Practical Aspects of NMR-Based Metabolomics. Handb Exp Pharmacol 2023; 277:1-41. [PMID: 36271165 DOI: 10.1007/164_2022_613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.
| | - Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Marcia LeVatte
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
- Reference Standard Management & NMR QC, Lonza Group AG, Visp, Switzerland
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Shaver AO, Garcia BM, Gouveia GJ, Morse AM, Liu Z, Asef CK, Borges RM, Leach FE, Andersen EC, Amster IJ, Fernández FM, Edison AS, McIntyre LM. An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics. Front Mol Biosci 2022; 9:930204. [PMID: 36438654 PMCID: PMC9682135 DOI: 10.3389/fmolb.2022.930204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 10/10/2022] [Indexed: 11/27/2022] Open
Abstract
Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.
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Affiliation(s)
- Amanda O. Shaver
- Department of Genetics, University of Georgia, Athens, GA, United States,Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States
| | - Brianna M. Garcia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Chemistry, University of Georgia, Athens, GA, United States
| | - Goncalo J. Gouveia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Biochemistry, University of Georgia, Athens, GA, United States
| | - Alison M. Morse
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States
| | - Zihao Liu
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States
| | - Carter K. Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - Ricardo M. Borges
- Walter Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Franklin E. Leach
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Environmental Health Science, University of Georgia, Athens, GA, United States
| | - Erik C. Andersen
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, United States
| | - I. Jonathan Amster
- Department of Chemistry, University of Georgia, Athens, GA, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - Arthur S. Edison
- Department of Genetics, University of Georgia, Athens, GA, United States,Complex Carbohydrate Research Center, University of Georgia, Athens, GA, United States,Department of Biochemistry, University of Georgia, Athens, GA, United States
| | - Lauren M. McIntyre
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, United States,University of Florida Genetics Institute, University of Florida, Gainesville, FL, United States,*Correspondence: Lauren M. McIntyre,
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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Duzdevich D, Carr CE, Ding D, Zhang SJ, Walton TS, Szostak JW. Competition between bridged dinucleotides and activated mononucleotides determines the error frequency of nonenzymatic RNA primer extension. Nucleic Acids Res 2021; 49:3681-3691. [PMID: 33744957 PMCID: PMC8053118 DOI: 10.1093/nar/gkab173] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 02/12/2021] [Accepted: 03/05/2021] [Indexed: 12/15/2022] Open
Abstract
Nonenzymatic copying of RNA templates with activated nucleotides is a useful model for studying the emergence of heredity at the origin of life. Previous experiments with defined-sequence templates have pointed to the poor fidelity of primer extension as a major problem. Here we examine the origin of mismatches during primer extension on random templates in the simultaneous presence of all four 2-aminoimidazole-activated nucleotides. Using a deep sequencing approach that reports on millions of individual template-product pairs, we are able to examine correct and incorrect polymerization as a function of sequence context. We have previously shown that the predominant pathway for primer extension involves reaction with imidazolium-bridged dinucleotides, which form spontaneously by the reaction of two mononucleotides with each other. We now show that the sequences of correctly paired products reveal patterns that are expected from the bridged dinucleotide mechanism, whereas those associated with mismatches are consistent with direct reaction of the primer with activated mononucleotides. Increasing the ratio of bridged dinucleotides to activated mononucleotides, either by using purified components or by using isocyanide-based activation chemistry, reduces the error frequency. Our results point to testable strategies for the accurate nonenzymatic copying of arbitrary RNA sequences.
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Affiliation(s)
- Daniel Duzdevich
- To whom correspondence should be addressed. Tel: +1 617 726 5102; Fax: +1 617 643 332;
| | - Christopher E Carr
- Department of Molecular Biology, Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Dian Ding
- Department of Molecular Biology, Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Stephanie J Zhang
- Department of Molecular Biology, Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Travis S Walton
- Department of Molecular Biology, Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jack W Szostak
- Department of Molecular Biology, Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
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11
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Abstract
Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.
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12
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Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring. Metabolites 2021; 11:metabo11030157. [PMID: 33803350 PMCID: PMC8001003 DOI: 10.3390/metabo11030157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 02/24/2021] [Accepted: 03/05/2021] [Indexed: 12/23/2022] Open
Abstract
High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.
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13
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Kumar PR, Mishra SK, Srivastava S. Computational Metabolomics. Adv Bioinformatics 2021. [DOI: 10.1007/978-981-33-6191-1_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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14
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Kreis R, Boer V, Choi I, Cudalbu C, de Graaf RA, Gasparovic C, Heerschap A, Krššák M, Lanz B, Maudsley AA, Meyerspeer M, Near J, Öz G, Posse S, Slotboom J, Terpstra M, Tkáč I, Wilson M, Bogner W. Terminology and concepts for the characterization of in vivo MR spectroscopy methods and MR spectra: Background and experts' consensus recommendations. NMR IN BIOMEDICINE 2020; 34:e4347. [PMID: 32808407 PMCID: PMC7887137 DOI: 10.1002/nbm.4347] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 05/20/2020] [Accepted: 05/21/2020] [Indexed: 05/04/2023]
Abstract
With a 40-year history of use for in vivo studies, the terminology used to describe the methodology and results of magnetic resonance spectroscopy (MRS) has grown substantially and is not consistent in many aspects. Given the platform offered by this special issue on advanced MRS methodology, the authors decided to describe many of the implicated terms, to pinpoint differences in their meanings and to suggest specific uses or definitions. This work covers terms used to describe all aspects of MRS, starting from the description of the MR signal and its theoretical basis to acquisition methods, processing and to quantification procedures, as well as terms involved in describing results, for example, those used with regard to aspects of quality, reproducibility or indications of error. The descriptions of the meanings of such terms emerge from the descriptions of the basic concepts involved in MRS methods and examinations. This paper also includes specific suggestions for future use of terms where multiple conventions have emerged or coexisted in the past.
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Affiliation(s)
- Roland Kreis
- Department of Radiology, Neuroradiology, and Nuclear Medicine and Department of Biomedical ResearchUniversity BernBernSwitzerland
| | - Vincent Boer
- Danish Research Centre for Magnetic Resonance, Funktions‐ og Billeddiagnostisk EnhedCopenhagen University Hospital HvidovreHvidovreDenmark
| | - In‐Young Choi
- Department of Neurology, Hoglund Brain Imaging CenterUniversity of Kansas Medical CenterKansas CityKansasUSA
| | - Cristina Cudalbu
- Centre d'Imagerie Biomedicale (CIBM)Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Robin A. de Graaf
- Department of Radiology and Biomedical Imaging & Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
| | | | - Arend Heerschap
- Department of Radiology and Nuclear MedicineRadboud University Medical CenterNijmegenThe Netherlands
| | - Martin Krššák
- Division of Endocrinology and Metabolism, Department of Internal Medicine III & High Field MR Centre, Department of Biomedical Imaging and Image guided TherapyMedical University of ViennaViennaAustria
| | - Bernard Lanz
- Laboratory of Functional and Metabolic Imaging (LIFMET)Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
| | - Andrew A. Maudsley
- Department of Radiology, Miller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Martin Meyerspeer
- Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
- High Field MR CenterMedical University of ViennaViennaAustria
| | - Jamie Near
- Douglas Mental Health University Institute and Department of PsychiatryMcGill UniversityMontrealCanada
| | - Gülin Öz
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Stefan Posse
- Department of NeurologyUniversity of New Mexico School of MedicineAlbuquerqueNew MexicoUSA
| | - Johannes Slotboom
- Department of Radiology, Neuroradiology, and Nuclear MedicineUniversity Hospital BernBernSwitzerland
| | - Melissa Terpstra
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Ivan Tkáč
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Martin Wilson
- Centre for Human Brain Health and School of PsychologyUniversity of BirminghamBirminghamUK
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
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15
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Ranjan R, Sinha N. Nuclear magnetic resonance (NMR)-based metabolomics for cancer research. NMR IN BIOMEDICINE 2019; 32:e3916. [PMID: 29733484 DOI: 10.1002/nbm.3916] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 02/01/2018] [Accepted: 02/12/2018] [Indexed: 06/08/2023]
Abstract
Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.
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Affiliation(s)
- Renuka Ranjan
- Centre of Biomedical Research, SGPGIMS Campus, Raebarelly Road, Lucknow, India
- School of Biotechnology, Institute of Science Banaras Hindu University, Varanasi, India
| | - Neeraj Sinha
- Centre of Biomedical Research, SGPGIMS Campus, Raebarelly Road, Lucknow, India
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16
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Raulf AP, Butke J, Küpper C, Großerueschkamp F, Gerwert K, Mosig A. Deep representation learning for domain adaptable classification of infrared spectral imaging data. Bioinformatics 2019; 36:287-294. [DOI: 10.1093/bioinformatics/btz505] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 05/28/2019] [Accepted: 06/13/2019] [Indexed: 02/05/2023] Open
Abstract
AbstractMotivationApplying infrared microscopy in the context of tissue diagnostics heavily relies on computationally preprocessing the infrared pixel spectra that constitute an infrared microscopic image. Existing approaches involve physical models, which are non-linear in nature and lead to classifiers that do not generalize well, e.g. across different types of tissue preparation. Furthermore, existing preprocessing approaches involve iterative procedures that are computationally demanding, so that computation time required for preprocessing does not keep pace with recent progress in infrared microscopes which can capture whole-slide images within minutes.ResultsWe investigate the application of stacked contractive autoencoders as an unsupervised approach to preprocess infrared microscopic pixel spectra, followed by supervised fine-tuning to obtain neural networks that can reliably resolve tissue structure. To validate the robustness of the resulting classifier, we demonstrate that a network trained on embedded tissue can be transferred to classify fresh frozen tissue. The features obtained from unsupervised pretraining thus generalize across the large spectral differences between embedded and fresh frozen tissue, where under previous approaches separate classifiers had to be trained from scratch.Availability and implementationOur implementation can be downloaded from https://github.com/arnrau/SCAE_IR_Spectral_Imaging.Supplementary informationSupplementary data are available at Bioinformatics online.
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Affiliation(s)
- Arne P Raulf
- Center for Protein Diagnostics (ProDi), 44801 Bochum, Germany
- Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universität Bochum, 44801 Bochum, Germany
| | - Joshua Butke
- Center for Protein Diagnostics (ProDi), 44801 Bochum, Germany
- Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universität Bochum, 44801 Bochum, Germany
| | - Claus Küpper
- Center for Protein Diagnostics (ProDi), 44801 Bochum, Germany
- Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universität Bochum, 44801 Bochum, Germany
| | - Frederik Großerueschkamp
- Center for Protein Diagnostics (ProDi), 44801 Bochum, Germany
- Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universität Bochum, 44801 Bochum, Germany
| | - Klaus Gerwert
- Center for Protein Diagnostics (ProDi), 44801 Bochum, Germany
- Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universität Bochum, 44801 Bochum, Germany
| | - Axel Mosig
- Center for Protein Diagnostics (ProDi), 44801 Bochum, Germany
- Chair of Biophysics, Department for Biology and Biotechnology, Ruhr-Universität Bochum, 44801 Bochum, Germany
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17
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Hatzakis E. Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review. Compr Rev Food Sci Food Saf 2018; 18:189-220. [PMID: 33337022 DOI: 10.1111/1541-4337.12408] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/28/2018] [Accepted: 10/18/2018] [Indexed: 12/15/2022]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.
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Affiliation(s)
- Emmanuel Hatzakis
- Dept. of Food Science and Technology, The Ohio State Univ., Parker Building, 2015 Fyffe Rd., Columbus, OH, U.S.A.,Foods for Health Discovery Theme, The Ohio State Univ., Parker Building, 2015 Fyffe Rd., Columbus, OH, U.S.A
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18
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Emwas AH, Saccenti E, Gao X, McKay RT, dos Santos VAPM, Roy R, Wishart DS. Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine. Metabolomics 2018; 14:31. [PMID: 29479299 PMCID: PMC5809546 DOI: 10.1007/s11306-018-1321-4] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Accepted: 01/09/2018] [Indexed: 12/11/2022]
Abstract
1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.
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Affiliation(s)
- Abdul-Hamid Emwas
- Imaging and Characterization Core Lab, KAUST, Thuwal, 23955-6900 Kingdom of Saudi Arabia
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955 Kingdom of Saudi Arabia
| | - Ryan T. McKay
- Department of Chemistry, University of Alberta, Edmonton, Canada
| | - Vitor A. P. Martins dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Raja Roy
- Centre of Biomedical Research, Formerly, Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Lucknow, India
| | - David S. Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
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19
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Euceda LR, Giskeødegård GF, Bathen TF. Preprocessing of NMR metabolomics data. Scandinavian Journal of Clinical and Laboratory Investigation 2015; 75:193-203. [PMID: 25738209 DOI: 10.3109/00365513.2014.1003593] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Metabolomics involves the large scale analysis of metabolites and thus, provides information regarding cellular processes in a biological sample. Independently of the analytical technique used, a vast amount of data is always acquired when carrying out metabolomics studies; this results in complex datasets with large amounts of variables. This type of data requires multivariate statistical analysis for its proper biological interpretation. Prior to multivariate analysis, preprocessing of the data must be carried out to remove unwanted variation such as instrumental or experimental artifacts. This review aims to outline the steps in the preprocessing of NMR metabolomics data and describe some of the methods to perform these. Since using different preprocessing methods may produce different results, it is important that an appropriate pipeline exists for the selection of the optimal combination of methods in the preprocessing workflow.
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Affiliation(s)
- Leslie R Euceda
- Department of Circulation and Medical Imaging, Faculty of Medicine, The Norwegian University of Science and Technology (NTNU) , Trondheim , Norway
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20
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Mohamed A, Nguyen CH, Mamitsuka H. Current status and prospects of computational resources for natural product dereplication: a review. Brief Bioinform 2015; 17:309-21. [PMID: 26153512 DOI: 10.1093/bib/bbv042] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Indexed: 01/08/2023] Open
Abstract
Research in natural products has always enhanced drug discovery by providing new and unique chemical compounds. However, recently, drug discovery from natural products is slowed down by the increasing chance of re-isolating known compounds. Rapid identification of previously isolated compounds in an automated manner, called dereplication, steers researchers toward novel findings, thereby reducing the time and effort for identifying new drug leads. Dereplication identifies compounds by comparing processed experimental data with those of known compounds, and so, diverse computational resources such as databases and tools to process and compare compound data are necessary. Automating the dereplication process through the integration of computational resources has always been an aspired goal of natural product researchers. To increase the utilization of current computational resources for natural products, we first provide an overview of the dereplication process, and then list useful resources, categorizing into databases, methods and software tools and further explaining them from a dereplication perspective. Finally, we discuss the current challenges to automating dereplication and proposed solutions.
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21
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Xu J, Jiang H, Li J, Cheng KK, Dong J, Chen Z. 1H NMR-based metabolomics investigation of copper-laden rat: a model of Wilson's disease. PLoS One 2015; 10:e0119654. [PMID: 25849323 PMCID: PMC4388371 DOI: 10.1371/journal.pone.0119654] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2014] [Accepted: 02/02/2015] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND AND PURPOSE Wilson's disease (WD), also known as hepatoleticular degeneration (HLD), is a rare autosomal recessive genetic disorder of copper metabolism, which causes copper to accumulate in body tissues. In this study, rats fed with copper-laden diet are used to render the clinical manifestations of WD, and their copper toxicity-induced organ lesions are studied. To investigate metabolic behaviors of 'decoppering' process, penicillamine (PA) was used for treating copper-laden rats as this chelating agent could eliminate excess copper through the urine. To date, there has been limited metabolomics study on WD, while metabolic impacts of copper accumulation and PA administration have yet to be established. MATERIALS AND METHODS A combination of 1HNMR spectroscopy and multivariate statistical analysis was applied to examine the metabolic profiles of the urine and blood serum samples collected from the copper-laden rat model of WD with PA treatment. RESULTS Copper accumulation in the copper-laden rats is associated with increased lactate, creatinine, valine and leucine, as well as decreased levels of glucose and taurine in the blood serum. There were also significant changes in p-hydroxyphenylacetate (p-HPA), creatinine, alpha-ketoglutarate (α-KG), dimethylamine, N-acetylglutamate (NAG), N-acetylglycoprotein (NAC) in the urine of these rats. Notably, the changes in p-HPA, glucose, lactate, taurine, valine, leucine, and NAG were found reversed following PA treatment. Nevertheless, there were no changes for dimethylamine, α-KG, and NAC as a result of the treatment. Compared with the controls, the concentrations of hippurate, formate, alanine, and lactate were changed when PA was applied and this is probably due to its side effect. A tool named SMPDB (Small Molecule Pathway Database) is introduced to identify the metabolic pathway influenced by the copper-laden diet. CONCLUSION The study has shown the potential application of NMR-based metabolomic analysis in providing further insights into the molecular mechanism underlying disorder due to WD.
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Affiliation(s)
- Jingjing Xu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, P. R. China
| | - Huaizhou Jiang
- Anhui University of Chinese Medicine, Hefei, 230031, P. R. China
| | - Jinquan Li
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, P. R. China
| | - Kian-Kai Cheng
- Department of Bioprocess Engineering & Innovation Centre in Agritechnology, Universiti Teknologi Malaysia, Johor Bahru, 81310, Malaysia
| | - Jiyang Dong
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, P. R. China
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, P. R. China
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22
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Alonso A, Marsal S, Julià A. Analytical methods in untargeted metabolomics: state of the art in 2015. Front Bioeng Biotechnol 2015; 3:23. [PMID: 25798438 PMCID: PMC4350445 DOI: 10.3389/fbioe.2015.00023] [Citation(s) in RCA: 388] [Impact Index Per Article: 43.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 02/18/2015] [Indexed: 12/20/2022] Open
Abstract
Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.
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Affiliation(s)
- Arnald Alonso
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
- Department of Automatic Control (ESAII), Polytechnic University of Catalonia, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
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23
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Izquierdo-Garcia JL, Viswanath P, Eriksson P, Chaumeil MM, Pieper RO, Phillips JJ, Ronen SM. Metabolic reprogramming in mutant IDH1 glioma cells. PLoS One 2015; 10:e0118781. [PMID: 25706986 PMCID: PMC4338038 DOI: 10.1371/journal.pone.0118781] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 01/07/2015] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Mutations in isocitrate dehydrogenase (IDH) 1 have been reported in over 70% of low-grade gliomas and secondary glioblastomas. IDH1 is the enzyme that catalyzes the oxidative decarboxylation of isocitrate to α-ketoglutarate while mutant IDH1 catalyzes the conversion of α-ketoglutarate into 2-hydroxyglutarate. These mutations are associated with the accumulation of 2-hydroxyglutarate within the tumor and are believed to be one of the earliest events in the development of low-grade gliomas. The goal of this work was to determine whether the IDH1 mutation leads to additional magnetic resonance spectroscopy (MRS)-detectable changes in the cellular metabolome. METHODS Two genetically engineered cell models were investigated, a U87-based model and an E6/E7/hTERT immortalized normal human astrocyte (NHA)-based model. For both models, wild-type IDH1 cells were generated by transduction with a lentiviral vector coding for the wild-type IDH1 gene while mutant IDH1 cells were generated by transduction with a lentiviral vector coding for the R132H IDH1 mutant gene. Metabolites were extracted from the cells using the dual-phase extraction method and analyzed by 1H-MRS. Principal Component Analysis was used to analyze the MRS data. RESULTS Principal Component Analysis clearly discriminated between wild-type and mutant IDH1 cells. Analysis of the loading plots revealed significant metabolic changes associated with the IDH1 mutation. Specifically, a significant drop in the concentration of glutamate, lactate and phosphocholine as well as the expected elevation in 2-hydroxyglutarate were observed in mutant IDH1 cells when compared to their wild-type counterparts. CONCLUSION The IDH1 mutation leads to several, potentially translatable MRS-detectable metabolic changes beyond the production of 2-hydroxyglutarate.
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Affiliation(s)
- Jose L. Izquierdo-Garcia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America
- * E-mail:
| | - Pavithra Viswanath
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America
| | - Pia Eriksson
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America
| | - Myriam M. Chaumeil
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America
| | - Russell O. Pieper
- Department of Neurological Surgery, Helen Diller Research Center, University of California San Francisco, San Francisco, California, United States of America
- Brain Tumor Research Center, University of California San Francisco, San Francisco, California, United States of America
| | - Joanna J. Phillips
- Department of Neurological Surgery, Helen Diller Research Center, University of California San Francisco, San Francisco, California, United States of America
- Brain Tumor Research Center, University of California San Francisco, San Francisco, California, United States of America
| | - Sabrina M. Ronen
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America
- Brain Tumor Research Center, University of California San Francisco, San Francisco, California, United States of America
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Brennan L. NMR-based metabolomics: from sample preparation to applications in nutrition research. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2014; 83:42-9. [PMID: 25456316 DOI: 10.1016/j.pnmrs.2014.09.001] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Revised: 09/28/2014] [Accepted: 09/29/2014] [Indexed: 05/24/2023]
Abstract
Metabolomics is the study of metabolites present in biological samples such as biofluids, tissue/cellular extracts and culture media. Measurement of these metabolites is achieved through use of analytical techniques such as NMR and mass spectrometry coupled to liquid chromatography. Combining metabolomic data with multivariate data analysis tools allows the elucidation of alterations in metabolic pathways under different physiological conditions. Applications of NMR-based metabolomics have grown in recent years and it is now widely used across a number of disciplines. The present review gives an overview of the developments in the key steps involved in an NMR-based metabolomics study. Furthermore, there will be a particular emphasis on the use of NMR-based metabolomics in nutrition research.
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Affiliation(s)
- Lorraine Brennan
- UCD Institute of Food and Health, Belfield, UCD, Dublin 4, Ireland.
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Pan X, Wilson M, McConville C, Arvanitis TN, Griffin JL, Kauppinen RA, Peet AC. Increased unsaturation of lipids in cytoplasmic lipid droplets in DAOY cancer cells in response to cisplatin treatment. Metabolomics 2013; 9:722-729. [PMID: 23678346 PMCID: PMC3651531 DOI: 10.1007/s11306-012-0483-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2012] [Accepted: 11/19/2012] [Indexed: 11/28/2022]
Abstract
Increases in 1H nuclear magnetic resonance spectroscopy (NMR) visible lipids are a well-documented sign of treatment response in cancers. Lipids in cytoplasmic lipid droplets (LDs) are the main contributors to the NMR lipid signals. Two human primitive neuroectodermal tumour cell lines with different sensitivities to cisplatin treatment were studied. Increases in NMR visible saturated and unsaturated lipids in cisplatin treated DAOY cells were associated with the accumulation of LDs prior to DNA fragmentation due to apoptosis. An increase in unsaturated fatty acids (UFAs) was detected in isolated LDs from DAOY cells, in contrast to a slight decrease in UFAs in lipid extracts from whole cells. Oleic acid and linoleic acid were identified as the accumulating UFAs in LDs by heteronuclear single quantum coherence spectroscopy (HSQC). 1H NMR lipids in non-responding PFSK-1 cells were unchanged by exposure to 10 μM cisplatin. These findings support the potential of NMR detectable UFAs to serve as a non-invasive marker of tumour cell response to treatment.
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Affiliation(s)
- Xiaoyan Pan
- Cancer Sciences, University of Birmingham, Birmingham, NH UK
- Birmingham Children’s Hospital NHS Foundation Trust, Birmingham, NH UK
| | - Martin Wilson
- Cancer Sciences, University of Birmingham, Birmingham, NH UK
- Birmingham Children’s Hospital NHS Foundation Trust, Birmingham, NH UK
| | | | - Theodoros N. Arvanitis
- Electronic, Electrical and Computer Engineering, University of Birmingham, Birmingham, NH UK
| | - Julian L. Griffin
- Department of Biochemistry and the Cambridge Systems Biology Centre, University of Cambridge, Cambridge, NH UK
| | - Risto A. Kauppinen
- Clinical Research and Imaging Centre and Department of Experimental Psychology, University of Bristol, Bristol, NH UK
| | - Andrew C. Peet
- Cancer Sciences, University of Birmingham, Birmingham, NH UK
- Birmingham Children’s Hospital NHS Foundation Trust, Birmingham, NH UK
- Institute of Child Health, Whittall Street, Birmingham, B4 6NH NH UK
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Huang CC, McDermott MM, Liu K, Kuo CH, Wang SY, Tao H, Tseng YJ. Plasma metabolomic profiles predict near-term death among individuals with lower extremity peripheral arterial disease. J Vasc Surg 2013; 58:989-96.e1. [PMID: 23688629 DOI: 10.1016/j.jvs.2013.04.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2012] [Revised: 02/19/2013] [Accepted: 04/15/2013] [Indexed: 10/26/2022]
Abstract
BACKGROUND Individuals with peripheral arterial disease (PAD) have a nearly two-fold increased risk of all-cause and cardiovascular disease mortality compared to those without PAD. This pilot study determined whether metabolomic profiling can accurately identify patients with PAD who are at increased risk of near-term mortality. METHODS We completed a case-control study using (1)H NMR metabolomic profiling of plasma from 20 decedents with PAD, without critical limb ischemia, who had blood drawn within 8 months prior to death (index blood draw) and within 10 to 28 months prior to death (preindex blood draw). Twenty-one PAD participants who survived more than 30 months after their index blood draw served as a control population. RESULTS Results showed distinct metabolomic patterns between preindex decedent, index decedent, and survivor samples. The major chemical signals contributing to the differential pattern (between survivors and decedents) arose from the fatty acyl chain protons of lipoproteins and the choline head group protons of phospholipids. Using the top 40 chemical signals for which the intensity was most distinct between survivor and preindex decedent samples, classification models predicted near-term all-cause death with overall accuracy of 78% (32/41), a sensitivity of 85% (17/20), and a specificity of 71% (15/21). When comparing survivor with index decedent samples, the overall classification accuracy was optimal at 83% (34/41) with a sensitivity of 80% (16/20) and a specificity of 86% (18/21), using as few as the top 10 to 20 chemical signals. CONCLUSIONS Our results suggest that metabolomic profiling of plasma may be useful for identifying PAD patients at increased risk for near-term death. Larger studies using more sensitive metabolomic techniques are needed to identify specific metabolic pathways associated with increased risk of near-term all-cause mortality among PAD patients.
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Affiliation(s)
- Chiang-Ching Huang
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Ill
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27
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Liu Y, Ning Y, Cai W, Shao X. Micro-analysis by near-infrared diffuse reflectance spectroscopy with chemometric methods. Analyst 2013; 138:6617-22. [DOI: 10.1039/c3an01232h] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Wang KC, Wang SY, Kuo CH, Tseng YJ. Distribution-based classification method for baseline correction of metabolomic 1D proton nuclear magnetic resonance spectra. Anal Chem 2012; 85:1231-9. [PMID: 23249210 DOI: 10.1021/ac303233c] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Baseline distortion in 1D (1)H NMR data complicates the quantification of individual components of biofluids in metabolomic experiments. Current 1D (1)H NMR baseline correction methods usually require manual parameter and filter tuning by experienced users to obtain desirable results from complex metabolomic spectra, thus becoming prone to correction variation and biased quantification. We present a novel alternative method, BaselineCorrector, for automatically estimating the baselines of 1D (1)H NMR metabolomic data. By collecting the standard deviations of spectral intensities, using a moving window to slide through a spectrum, BaselineCorrector can model the distribution of noise standard deviation as a derived chi-squared distribution in each window and then determine optimal parameters for least-error classification of signal and noise. Due to the universal property of noise distributions, BaselineCorrector can robustly recognize the baseline segments in various spectra. In addition to the commonly used 1D NOESY and CPMG pulse sequences, BaselineCorrector also provides an algorithm for correcting diffusion-edited NMR spectra. Using its classification model, BaselineCorrector is able to preserve low signal peaks and correctly handle wide, overlapping peaks in complex metabolomic spectra.
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Affiliation(s)
- Kuo-Ching Wang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Sec. 4, Roosevelt Road, Taipei, Taiwan 106
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29
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Quantitative NMR for bioanalysis and metabolomics. Anal Bioanal Chem 2012; 404:1165-79. [PMID: 22766756 DOI: 10.1007/s00216-012-6188-z] [Citation(s) in RCA: 110] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2012] [Revised: 06/04/2012] [Accepted: 06/08/2012] [Indexed: 01/16/2023]
Abstract
Over the last several decades, significant technical and experimental advances have made quantitative nuclear magnetic resonance (qNMR) a valuable analytical tool for quantitative measurements on a wide variety of samples. In particular, qNMR has emerged as an important method for metabolomics studies where it is used for interrogation of large sets of biological samples and the resulting spectra are treated with multivariate statistical analysis methods. In this review, recent developments in instrumentation and pulse sequences will be discussed as well as the practical considerations necessary for acquisition of quantitative NMR experiments with an emphasis on their use for bioanalysis. Recent examples of the application of qNMR for metabolomics/metabonomics studies, the characterization of biologicals such as heparin, antibodies, and vaccines, and the analysis of botanical natural products will be presented and the future directions of qNMR discussed.
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O'Callaghan S, De Souza DP, Isaac A, Wang Q, Hodkinson L, Olshansky M, Erwin T, Appelbe B, Tull DL, Roessner U, Bacic A, McConville MJ, Likić VA. PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools. BMC Bioinformatics 2012; 13:115. [PMID: 22647087 PMCID: PMC3533878 DOI: 10.1186/1471-2105-13-115] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2012] [Accepted: 04/17/2012] [Indexed: 01/06/2023] Open
Abstract
Background Gas chromatography–mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines. Results PyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS). Conclusions PyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. In real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. We demonstrate data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. Automated sample processing and quantitation with PyMS can provide substantial time savings compared to more traditional interactive software systems that tightly integrate data processing with the graphical user interface.
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Affiliation(s)
- Sean O'Callaghan
- Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, Australia
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Bao Q, Feng J, Chen F, Mao W, Liu Z, Liu K, Liu C. A new automatic baseline correction method based on iterative method. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2012; 218:35-43. [PMID: 22578553 DOI: 10.1016/j.jmr.2012.03.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2011] [Revised: 03/13/2012] [Accepted: 03/16/2012] [Indexed: 05/31/2023]
Abstract
A new automatic baseline correction method for Nuclear Magnetic Resonance (NMR) spectra is presented. It is based on an improved baseline recognition method and a new iterative baseline modeling method. The presented baseline recognition method takes advantages of three baseline recognition algorithms in order to recognize all signals in spectra. While in the iterative baseline modeling method, besides the well-recognized baseline points in signal-free regions, the 'quasi-baseline points' in the signal-crowded regions are also identified and then utilized to improve robustness by preventing the negative regions. The experimental results on both simulated data and real metabolomics spectra with over-crowded peaks show the efficiency of this automatic method.
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Affiliation(s)
- Qingjia Bao
- Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
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Abstract
Metabolomics is the relatively new field in bioinformatics that uses measurements on metabolite abundance as a tool for disease diagnosis and other medical purposes. Although closely related to proteomics, the statistical analysis is potentially simpler since biochemists have significantly more domain knowledge about metabolites. This chapter reviews the challenges that metabolomics poses in the areas of quality control, statistical metrology, and data mining.
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Affiliation(s)
- Alexander Korman
- Department of Statistical Science, Duke University, Durham, NC, USA
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McKenzie JS, Donarski JA, Wilson JC, Charlton AJ. Analysis of complex mixtures using high-resolution nuclear magnetic resonance spectroscopy and chemometrics. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2011; 59:336-59. [PMID: 22027342 DOI: 10.1016/j.pnmrs.2011.04.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2011] [Accepted: 04/27/2011] [Indexed: 05/16/2023]
Affiliation(s)
- James S McKenzie
- The Food and Environment Research Agency, Sand Hutton, York YO41 1LZ, United Kingdom
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A metabolomic approach for diagnosis of experimental sepsis. Intensive Care Med 2011; 37:2023-32. [PMID: 21976186 DOI: 10.1007/s00134-011-2359-1] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2010] [Accepted: 08/16/2011] [Indexed: 10/17/2022]
Abstract
BACKGROUND The search for reliable diagnostic biomarkers of sepsis remains necessary. Assessment of global metabolic profiling using quantitative nuclear magnetic resonance (NMR)-based metabolomics offers an attractive modern methodology for fast and comprehensive determination of multiple circulating metabolites and for defining the metabolic phenotype of sepsis. OBJECTIVE To develop a novel NMR-based metabolomic approach for diagnostic evaluation of sepsis. METHODS Male Sprague-Dawley rats (weight 325-375 g) underwent cecal ligation and puncture (n = 14, septic group) or sham procedure (n = 14, control group) and 24 h later were euthanized. Lung tissue, bronchoalveolar lavage (BAL) fluid, and serum samples were obtained for (1)H NMR and high-resolution magic-angle spinning analysis. Unsupervised principal components analysis was performed on the processed spectra, and a predictive model for diagnosis of sepsis was constructed using partial least-squares discriminant analysis. RESULTS NMR-based metabolic profiling discriminated characteristics between control and septic rats. Characteristic metabolites changed markedly in septic rats as compared with control rats: alanine, creatine, phosphoethanolamine, and myoinositol concentrations increased in lung tissue; creatine increased and myoinositol decreased in BAL fluid; and alanine, creatine, phosphoethanolamine, and acetoacetate increased whereas formate decreased in serum. A predictive model for diagnosis of sepsis using these metabolites classified cases with sensitivity and specificity of 100%. CONCLUSIONS NMR metabolomic analysis is a potentially useful technique for diagnosis of sepsis. The concentrations of metabolites involved in energy metabolism and in the inflammatory response change in this model of sepsis.
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Izquierdo-García JL, Villa P, Kyriazis A, del Puerto-Nevado L, Pérez-Rial S, Rodriguez I, Hernandez N, Ruiz-Cabello J. Descriptive review of current NMR-based metabolomic data analysis packages. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2011; 59:263-270. [PMID: 21920221 DOI: 10.1016/j.pnmrs.2011.02.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2010] [Accepted: 02/14/2011] [Indexed: 05/31/2023]
Affiliation(s)
- Jose L Izquierdo-García
- CIBERES, CIBER Enfermedades Respiratorias, Departartamento Química-Física II, Facultad Farmacia, Universidad Complutense de Madrid, Madrid, Spain.
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36
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Pan X, Wilson M, Mirbahai L, McConville C, Arvanitis TN, Griffin JL, Kauppinen RA, Peet AC. In vitro metabonomic study detects increases in UDP-GlcNAc and UDP-GalNAc, as early phase markers of cisplatin treatment response in brain tumor cells. J Proteome Res 2011; 10:3493-500. [PMID: 21644796 DOI: 10.1021/pr200114v] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
O-linked β-N-acetylglucosamine glycosylation (O-GlcNAcylation) is important in a number of biological processes and diseases including transcription, cell stress, diabetes, and neurodegeneration and may be a marker of tumor metastasis. Uridine diphospho-N-acetylglucosamine (UDP-GlcNAc), the donor molecule in O-GlcNAcylation, can be detected by (1)H nuclear magnetic resonance spectroscopy ((1)H NMR), giving the potential to measure its level noninvasively, providing a novel biomarker of prognosis and treatment monitoring. In this in vitro metabonomic study, four brain cancer cell lines were exposed to cisplatin and studied for metabolic responses using (1)H NMR. The Alamar blue assay and DAPI staining were used to assess cell sensitivity to cisplatin treatment and to confirm cell death. It is shown that in the cisplatin responding cells, UDP-GlcNAc and uridine diphospho-N-acetylgalactosamine (UDP-GalNAc), in parallel with (1)H NMR detected lipids, increased with cisplatin exposure before or at the onset of the microscopic signs of evolving cell death. The changes in UDP-GlcNAc and UDP-GalNAc were not detected in the nonresponders. These glycosylated UDP compounds, the key substrates for glycosylation of proteins and lipids, are commonly implicated in cancer proliferation and malignant transformation. However, the present study mechanistically links UDP-GlcNAc and UDP-GalNAc to cancer cell death following chemotherapeutic treatment.
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Affiliation(s)
- Xiaoyan Pan
- Cancer Sciences, University of Birmingham, Birmingham, United Kingdom
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Yoon S, Kim J, Hum J, Kim H, Park S, Kladwang W, Das R. HiTRACE: high-throughput robust analysis for capillary electrophoresis. ACTA ACUST UNITED AC 2011; 27:1798-805. [PMID: 21561922 DOI: 10.1093/bioinformatics/btr277] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
MOTIVATION Capillary electrophoresis (CE) of nucleic acids is a workhorse technology underlying high-throughput genome analysis and large-scale chemical mapping for nucleic acid structural inference. Despite the wide availability of CE-based instruments, there remain challenges in leveraging their full power for quantitative analysis of RNA and DNA structure, thermodynamics and kinetics. In particular, the slow rate and poor automation of available analysis tools have bottlenecked a new generation of studies involving hundreds of CE profiles per experiment. RESULTS We propose a computational method called high-throughput robust analysis for capillary electrophoresis (HiTRACE) to automate the key tasks in large-scale nucleic acid CE analysis, including the profile alignment that has heretofore been a rate-limiting step in the highest throughput experiments. We illustrate the application of HiTRACE on 13 datasets representing 4 different RNAs, 3 chemical modification strategies and up to 480 single mutant variants; the largest datasets each include 87 360 bands. By applying a series of robust dynamic programming algorithms, HiTRACE outperforms prior tools in terms of alignment and fitting quality, as assessed by measures including the correlation between quantified band intensities between replicate datasets. Furthermore, while the smallest of these datasets required 7-10 h of manual intervention using prior approaches, HiTRACE quantitation of even the largest datasets herein was achieved in 3-12 min. The HiTRACE method, therefore, resolves a critical barrier to the efficient and accurate analysis of nucleic acid structure in experiments involving tens of thousands of electrophoretic bands.
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Affiliation(s)
- Sungroh Yoon
- School of Electrical Engineering, Korea University, Seoul 136-713, Republic of Korea.
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Simpson AJ, McNally DJ, Simpson MJ. NMR spectroscopy in environmental research: from molecular interactions to global processes. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2011; 58:97-175. [PMID: 21397118 DOI: 10.1016/j.pnmrs.2010.09.001] [Citation(s) in RCA: 114] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2010] [Accepted: 09/17/2010] [Indexed: 05/30/2023]
Affiliation(s)
- André J Simpson
- Environmental NMR Center, Department of Chemistry, University of Toronto, Ontario, Canada.
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Mercier P, Lewis MJ, Chang D, Baker D, Wishart DS. Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra. JOURNAL OF BIOMOLECULAR NMR 2011; 49:307-323. [PMID: 21360156 DOI: 10.1007/s10858-011-9480-x] [Citation(s) in RCA: 93] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2010] [Accepted: 11/29/2010] [Indexed: 05/30/2023]
Abstract
Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.
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Prakash BD, Wei YC. A fully automated iterative moving averaging (AIMA) technique for baseline correction. Analyst 2011; 136:3130-5. [DOI: 10.1039/c0an00778a] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Schleif FM, Riemer T, Börner U, Schnapka-Hille L, Cross M. Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications. Bioinformatics 2010; 27:524-33. [PMID: 21123223 DOI: 10.1093/bioinformatics/btq661] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The analysis of metabolic processes is becoming increasingly important to our understanding of complex biological systems and disease states. Nuclear magnetic resonance spectroscopy (NMR) is a particularly relevant technology in this respect, since the NMR signals provide a quantitative measure of the metabolite concentrations. However, due to the complexity of the spectra typical of biological samples, the demands of clinical and high-throughput analysis will only be fully met by a system capable of reliable, automatic processing of the spectra. An initial step in this direction has been taken by Targeted Profiling (TP), employing a set of known and predicted metabolite signatures fitted against the signal. However, an accurate fitting procedure for (1)H NMR data is complicated by shift uncertainties in the peak systems caused by measurement imperfections. These uncertainties have a large impact on the accuracy of identification and quantification and currently require compensation by very time consuming manual interactions. Here, we present an approach, termed Extended Targeted Profiling (ETP), that estimates shift uncertainties based on a genetic algorithm (GA) combined with a least squares optimization (LSQO). The estimated shifts are used to correct the known metabolite signatures leading to significantly improved identification and quantification. In this way, use of the automated system significantly reduces the effort normally associated with manual processing and paves the way for reliable, high-throughput analysis of complex NMR spectra. RESULTS The results indicate that using simultaneous shift uncertainty correction and least squares fitting significantly improves the identification and quantification results for (1)H NMR data in comparison to the standard targeted profiling approach and compares favorably with the results obtained by manual expert analysis. Preservation of the functional structure of the NMR spectra makes this approach more realistic than simple binning strategies.
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Affiliation(s)
- F-M Schleif
- Department of Computer Science, University of Bielefeld, Bielefeld, Germany.
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Barkauskas DA, Rocke DM. A general-purpose baseline estimation algorithm for spectroscopic data. Anal Chim Acta 2010; 657:191-7. [PMID: 20005331 DOI: 10.1016/j.aca.2009.10.043] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2009] [Revised: 10/16/2009] [Accepted: 10/19/2009] [Indexed: 11/29/2022]
Abstract
A common feature of many modern technologies used in proteomics--including nuclear magnetic resonance imaging and mass spectrometry--is the generation of large amounts of data for each subject in an experiment. Extracting the signal from the background noise, however, poses significant challenges. One important part of signal extraction is the correct identification of the baseline level of the data. In this article, we propose a new algorithm (the "BXR algorithm") for baseline estimation that can be directly applied to different types of spectroscopic data, but also can be specifically tailored to different technologies. We then show how to adapt the algorithm to a particular technology--matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry--which is rapidly gaining popularity as an analytic tool in proteomics. Finally, we compare the performance of our algorithm to that of existing algorithms for baseline estimation. The BXR algorithm is computationally efficient, robust to the type of one-sided signal that occurs in many modern applications (including NMR and mass spectrometry), and improves on existing baseline estimation algorithms. It is implemented as the function baseline in the R package FTICRMS, available either from the Comprehensive R Archive Network (http://www.r-project.org/) or from the first author.
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Affiliation(s)
- Donald A Barkauskas
- Children's Oncology Group, 440 E. Huntington Drive Suite 402, Arcadia, CA 91006, USA.
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Izquierdo-García JL, Rodríguez I, Kyriazis A, Villa P, Barreiro P, Desco M, Ruiz-Cabello J. A novel R-package graphic user interface for the analysis of metabonomic profiles. BMC Bioinformatics 2009; 10:363. [PMID: 19874601 PMCID: PMC2774703 DOI: 10.1186/1471-2105-10-363] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2008] [Accepted: 10/29/2009] [Indexed: 11/26/2022] Open
Abstract
Background Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries. Results The package offers the following options: Raw 1-dimensional spectra processing: phase, baseline correction and normalization. Importing processed spectra. Including/excluding spectral ranges, optional binning and bucketing, detection and alignment of peaks. Sorting of metabolites based on their ability to discriminate, metabolite selection, and outlier identification. Multivariate unsupervised analysis: principal components analysis (PCA). Multivariate supervised analysis: partial least squares (PLS), linear discriminant analysis (LDA), k-nearest neighbor classification. Neural networks. Visualization and overlapping of spectra. Plot values of the chemical shift position for different samples. Furthermore, the "Metabonomic" GUI includes a console to enable other kinds of analyses and to take advantage of all R statistical tools. Conclusion We made complex multivariate analysis user-friendly for both experienced and novice users, which could help to expand the use of NMR-based metabonomics.
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Zhang S, Zheng C, Lanza IR, Nair KS, Raftery D, Vitek O. Interdependence of signal processing and analysis of urine 1H NMR spectra for metabolic profiling. Anal Chem 2009; 81:6080-8. [PMID: 19950923 PMCID: PMC2789356 DOI: 10.1021/ac900424c] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Metabolic profiling of urine presents challenges because of the extensive random variation of metabolite concentrations and the dilution resulting from changes in the overall urine volume. Thus statistical analysis methods play a particularly important role; however, appropriate choices of these methods are not straightforward. Here we investigate constant and variance-stabilization normalization of raw and peak picked spectra, for use with exploratory analysis (principal component analysis) and confirmatory analysis (ordinary and Empirical Bayes t-test) in (1)H NMR-based metabolic profiling of urine. We compare the performance of these methods using urine samples spiked with known metabolites according to a Latin square design. We find that analysis of peak picked and logarithm-transformed spectra is preferred, and that signal processing and statistical analysis steps are interdependent. While variance-stabilizing transformation is preferred in conjunction with principal component analysis, constant normalization is more appropriate for use with a t-test. Empirical Bayes t-test provides more reliable conclusions when the number of samples in each group is relatively small. Performance of these methods is illustrated using a clinical metabolomics experiment on patients with type 1 diabetes to evaluate the effect of insulin deprivation.
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Affiliation(s)
- Shucha Zhang
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN 47907, USA
| | - Cheng Zheng
- Department of Statistics, Purdue University, 250 N. University Street, West Lafayette, IN 47907, USA
| | - Ian R. Lanza
- Division of Endocrinology, Mayo Clinic College of Medicine, 200 First St. S.W., Joseph 5-194, Rochester, MN 55905, USA
| | - K. Sreekumaran Nair
- Division of Endocrinology, Mayo Clinic College of Medicine, 200 First St. S.W., Joseph 5-194, Rochester, MN 55905, USA
| | - Daniel Raftery
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN 47907, USA
| | - Olga Vitek
- Department of Statistics, Purdue University, 250 N. University Street, West Lafayette, IN 47907, USA
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Barkauskas DA, Kronewitter SR, Lebrilla CB, Rocke DM. Analysis of MALDI FT-ICR mass spectrometry data: a time series approach. Anal Chim Acta 2009; 648:207-14. [PMID: 19646586 DOI: 10.1016/j.aca.2009.06.064] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2009] [Revised: 06/24/2009] [Accepted: 06/25/2009] [Indexed: 10/20/2022]
Abstract
Matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry is a technique for high mass-resolution analysis of substances that is rapidly gaining popularity as an analytic tool. Extracting signal from the background noise, however, poses significant challenges. In this article, we model the noise part of a spectrum as an autoregressive, moving average (ARMA) time series with innovations given by a generalized gamma distribution with varying scale parameter but constant shape parameter and exponent. This enables us to classify peaks found in actual spectra as either noise or signal using a reasonable criterion that outperforms a standard threshold criterion.
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Wilson M, Davies NP, Brundler MA, McConville C, Grundy RG, Peet AC. High resolution magic angle spinning 1H NMR of childhood brain and nervous system tumours. Mol Cancer 2009; 8:6. [PMID: 19208232 PMCID: PMC2651110 DOI: 10.1186/1476-4598-8-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2008] [Accepted: 02/10/2009] [Indexed: 11/10/2022] Open
Abstract
Background Brain and nervous system tumours are the most common solid cancers in children. Molecular characterisation of these tumours is important for providing novel biomarkers of disease and identifying molecular pathways which may provide putative targets for new therapies. 1H magic angle spinning NMR spectroscopy (1H HR-MAS) is a powerful tool for determining metabolite profiles from small pieces of intact tissue and could potentially provide important molecular information. Methods Forty tissue samples from 29 children with glial and primitive neuro-ectodermal tumours were analysed using HR-MAS (600 MHz Varian gHX nanoprobe). Tumour spectra were fitted to a library of individual metabolite spectra to provide metabolite values. These values were then used in a two tailed t-test and multi-variate analysis employing a principal component analysis and a linear discriminant analysis. Classification accuracy was estimated using a leave-one-out analysis and B632+ bootstrapping. Results Glial tumours had significantly (two tailed t-test p < 0.05) higher creatine and glutamine and lower taurine, phosphoethanolamine, phosphorylcholine and choline compared with primitive neuro-ectodermal tumours. Classification accuracy was 90%. Medulloblastomas (n = 9) had significantly (two tailed t-test p < 0.05) higher creatine, glutamine, phosphorylcholine, glycine and scyllo-inositol than neuroblastomas (n = 7), classification accuracy was 94%. Supratentorial primitive neuro-ectodermal tumours had metabolite profiles in keeping with other primitive neuro-ectodermal tumours whilst ependymomas (n = 2) had metabolite profiles intermediate between pilocytic astrocytomas (n = 10) and primitive neuro-ectodermal tumours. Conclusion HR-MAS identified key differences in the metabolite profiles of childhood brain and nervous system improving the molecular characterisation of these tumours. Further investigation of the underlying molecular pathways is required to assess their potential as targets for new agents.
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Affiliation(s)
- Martin Wilson
- Cancer Sciences, University of Birmingham, Birmingham, UK.
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Environmental metabolomics: new insights into earthworm ecotoxicity and contaminant bioavailability in soil. Anal Bioanal Chem 2009; 394:137-49. [DOI: 10.1007/s00216-009-2612-4] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2008] [Revised: 12/23/2008] [Accepted: 01/08/2009] [Indexed: 12/14/2022]
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48
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Barkauskas DA, An HJ, Kronewitter SR, de Leoz ML, Chew HK, de Vere White RW, Leiserowitz GS, Miyamoto S, Lebrilla CB, Rocke DM. Detecting glycan cancer biomarkers in serum samples using MALDI FT-ICR mass spectrometry data. Bioinformatics 2008; 25:251-7. [PMID: 19073586 DOI: 10.1093/bioinformatics/btn610] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The development of better tests to detect cancer in its earliest stages is one of the most sought-after goals in medicine. Especially important are minimally invasive tests that require only blood or urine samples. By profiling oligosaccharides cleaved from glycosylated proteins shed by tumor cells into the blood stream, we hope to determine glycan profiles that will help identify cancer patients using a simple blood test. The data in this article were generated using matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI FT-ICR MS). We have developed novel methods for analyzing this type of mass spectrometry data and applied it to eight datasets from three different types of cancer (breast, ovarian and prostate). RESULTS The techniques we have developed appear to be effective in the analysis of MALDI FT-ICR MS data. We found significant differences between control and cancer groups in all eight datasets, including two structurally related compounds that were found to be significantly different between control and cancer groups in all three types of cancer studied.
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Affiliation(s)
- Donald A Barkauskas
- Graduate Group in Biostatistics with a Designated Emphasis in Biotechnology, School of Medicine, University of California, Davis, CA 95616, USA.
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