Publications
592 results found
Serrano-Contreras JI, Lindon JC, Frost G, et al., 2024, Implementation of pure shift 1 H NMR in metabolic phenotyping for structural information recovery of biofluid metabolites with complex spin systems, NMR in Biomedicine, Vol: 37, ISSN: 0952-3480
NMR spectroscopy is a mainstay of metabolic profiling approaches to investigation of physiological and pathological processes. The one-dimensional proton pulse sequences typically used in phenotyping large numbers of samples generate spectra that are rich in information but where metabolite identification is often compromised by peak overlap. Recently developed pure shift (PS) NMR spectroscopy, where all J-coupling multiplicities are removed from the spectra, has the potential to simplify the complex proton NMR spectra that arise from biosamples and hence to aid metabolite identification. Here we have evaluated two complementary approaches to spectral simplification: the HOBS (band-selective with real-time acquisition) and the PSYCHE (broadband with pseudo-2D interferogram acquisition) pulse sequences. We compare their relative sensitivities and robustness for deconvolving both urine and serum matrices. Both methods improve resolution of resonances ranging from doublets, triplets and quartets to more complex signals such as doublets of doublets and multiplets in highly overcrowded spectral regions. HOBS is the more sensitive method and takes less time to acquire in comparison with PSYCHE, but can introduce unavoidable artefacts from metabolites with strong couplings, whereas PSYCHE is more adaptable to these types of spin system, although at the expense of sensitivity. Both methods are robust and easy to implement. We also demonstrate that strong coupling artefacts contain latent connectivity information that can be used to enhance metabolite identification. Metabolite identification is a bottleneck in metabolic profiling studies. In the case of NMR, PS experiments can be included in metabolite identification workflows, providing additional capability for biomarker discovery.
Jaggard MKJ, Boulange CL, Graca G, et al., 2023, The effect of liquid-liquid extraction on metabolite detection and analysis using NMR spectroscopy in human synovial fluid, JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS, Vol: 226, ISSN: 0731-7085
Harvey N, Takis PG, Lindon JC, et al., 2023, Optimization of diffusion-ordered NMR spectroscopy experiments for high-throughput automation in human metabolic phenotyping, Analytical Chemistry, Vol: 95, Pages: 3147-3152, ISSN: 0003-2700
The diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY) experiment allows the calculation of diffusion coefficient values of metabolites in complex mixtures. However, this experiment has not yet been broadly used for metabolic profiling due to lack of a standardized protocol. Here we propose a pipeline for the DOSY experimental setup and data processing in metabolic phenotyping studies. Due to the complexity of biological samples, three experiments (a standard DOSY, a relaxation-edited DOSY, and a diffusion-edited DOSY) have been optimized to provide DOSY metabolic profiles with peak-picked diffusion coefficients for over 90% of signals visible in the one-dimensional 1H general biofluid profile in as little as 3 min 36 s. The developed parameter sets and tools are straightforward to implement and can facilitate the use of DOSY for metabolic profiling of human blood plasma and urine samples.
Masuda R, Lodge S, Whiley L, et al., 2022, Exploration of Human Serum Lipoprotein Supramolecular Phospholipids Using Statistical Heterospectroscopy in n-Dimensions (SHY-n): Identification of Potential Cardiovascular Risk Biomarkers Related to SARS-CoV-2 Infection., Anal Chem
SARS-CoV-2 infection causes a significant reduction in lipoprotein-bound serum phospholipids give rise to supramolecular phospholipid composite (SPC) signals observed in diffusion and relaxation edited 1H NMR spectra. To characterize the chemical structural components and compartmental location of SPC and to understand further its possible diagnostic properties, we applied a Statistical HeterospectroscopY in n-dimensions (SHY-n) approach. This involved statistically linking a series of orthogonal measurements made on the same samples, using independent analytical techniques and instruments, to identify the major individual phospholipid components giving rise to the SPC signals. Thus, an integrated model for SARS-CoV-2 positive and control adults is presented that relates three identified diagnostic subregions of the SPC signal envelope (SPC1, SPC2, and SPC3) generated using diffusion and relaxation edited (DIRE) NMR spectroscopy to lipoprotein and lipid measurements obtained by in vitro diagnostic NMR spectroscopy and ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). The SPC signals were then correlated sequentially with (a) total phospholipids in lipoprotein subfractions; (b) apolipoproteins B100, A1, and A2 in different lipoproteins and subcompartments; and (c) MS-measured total serum phosphatidylcholines present in the NMR detection range (i.e., PCs: 16.0,18.2; 18.0,18.1; 18.2,18.2; 16.0,18.1; 16.0,20.4; 18.0,18.2; 18.1,18.2), lysophosphatidylcholines (LPCs: 16.0 and 18.2), and sphingomyelin (SM 22.1). The SPC3/SPC2 ratio correlated strongly (r = 0.86) with the apolipoprotein B100/A1 ratio, a well-established marker of cardiovascular disease risk that is markedly elevated during acute SARS-CoV-2 infection. These data indicate the considerable potential of using a serum SPC measurement as a metric of cardiovascular risk based on a single NMR experiment. This is of specific interest in relation to understanding the potential for in
Nitschke P, Lodge S, Kimhofer T, et al., 2022, J-edited dIffusional proton nuclear magnetic resonance spectroscopic measurement of glycoprotein and supramolecular phospholipid biomarkers of inflammation in human serum., Analytical Chemistry, Vol: 94, Pages: 1333-1341, ISSN: 0003-2700
Proton nuclear magnetic resonance (NMR) N-acetyl signals (Glyc) from glycoproteins and supramolecular phospholipids composite peak (SPC) from phospholipid quaternary nitrogen methyls in subcompartments of lipoprotein particles) can give important systemic metabolic information, but their absolute quantification is compromised by overlap with interfering resonances from lipoprotein lipids themselves. We present a J-Edited DIffusional (JEDI) proton NMR spectroscopic approach to selectively augment signals from the inflammatory marker peaks Glyc and SPCs in blood serum NMR spectra, which enables direct integration of peaks associated with molecules found in specific compartments. We explore a range of pulse sequences that allow editing based on peak J-modulation, translational diffusion, and T2 relaxation time and validate them for untreated blood serum samples from SARS-CoV-2 infected patients (n = 116) as well as samples from healthy controls and pregnant women with physiological inflammation and hyperlipidemia (n = 631). The data show that JEDI is an improved approach to selectively investigate inflammatory signals in serum and may have widespread diagnostic applicability to disease states associated with systemic inflammation.
Holmes E, Wist J, Masuda R, et al., 2021, Incomplete Systemic Recovery and Metabolic Phenoreversion in Post-Acute-Phase Nonhospitalized COVID-19 Patients: Implications for Assessment of Post-Acute COVID-19 Syndrome, Journal of Proteome Research, Vol: 20, Pages: 3315-3329, ISSN: 1535-3893
Jaggard MKJ, Boulange CL, Graca G, et al., 2021, The influence of sample collection, handling and low temperature storage upon NMR metabolic profiling analysis in human synovial fluid, JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS, Vol: 197, ISSN: 0731-7085
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- Citations: 3
Lodge S, Nitschke P, Kimhofer T, et al., 2021, Diffusion and relaxation edited proton NMR spectroscopy of plasma reveals a high-fidelity supramolecular biomarker signature of SARS-CoV-2 infection, Analytical Chemistry, Vol: 93, Pages: 3976-3986, ISSN: 0003-2700
We have applied nuclear magnetic resonance spectroscopy based plasma phenotyping to reveal diagnostic molecular signatures of SARS-CoV-2 infection via combined diffusional and relaxation editing (DIRE). We compared plasma from healthy age-matched controls (n = 26) with SARS-CoV-2 negative non-hospitalized respiratory patients and hospitalized respiratory patients (n = 23 and 11 respectively) with SARS-CoV-2 rRT-PCR positive respiratory patients (n = 17, with longitudinal sampling time-points). DIRE data were modelled using principal component analysis and orthogonal projections to latent structures discriminant analysis (O-PLS-DA), with statistical cross-validation indices indicating excellent model generalization for the classification of SARS-CoV-2 positivity for all comparator groups (area under the receiver operator characteristic curve = 1). DIRE spectra show biomarker signal combinations conferred by differential concentrations of metabolites with selected molecular mobility properties. These comprise the following: (a) composite N-acetyl signals from α-1-acid glycoprotein and other glycoproteins (designated GlycA and GlycB) that were elevated in SARS-CoV-2 positive patients [p = 2.52 × 10–10 (GlycA) and 1.25 × 10–9 (GlycB) vs controls], (b) two diagnostic supramolecular phospholipid composite signals that were identified (SPC-A and SPC-B) from the –+N–(CH3)3 choline headgroups of lysophosphatidylcholines carried on plasma glycoproteins and from phospholipids in high-density lipoprotein subfractions (SPC-A) together with a phospholipid component of low-density lipoprotein (SPC–B). The integrals of the summed SPC signals (SPCtotal) were reduced in SARS-CoV-2 positive patients relative to both controls (p = 1.40 × 10–7) and SARS-CoV-2 negative patients (p = 4.52 × 10–8) but were not significantly different between controls and SARS-CoV-2 negative patients. The identity of the SPC signal comp
Lodge S, Nitschke P, Kimhofer T, et al., 2021, NMR spectroscopic windows on the systemic effects of SARS-CoV-2 infection on plasma lipoproteins and metabolites in relation to circulating cytokines., Journal of Proteome Research, Vol: 20, Pages: 1382-1396, ISSN: 1535-3893
To investigate the systemic metabolic effects of SARS-CoV-2 infection, we analyzed 1H NMR spectroscopic data on human blood plasma and co-modeled with multiple plasma cytokines and chemokines (measured in parallel). Thus, 600 MHz 1H solvent-suppressed single-pulse, spin-echo, and 2D J-resolved spectra were collected on plasma recorded from SARS-CoV-2 rRT-PCR-positive patients (n = 15, with multiple sampling timepoints) and age-matched healthy controls (n = 34, confirmed rRT-PCR negative), together with patients with COVID-19/influenza-like clinical symptoms who tested SARS-CoV-2 negative (n = 35). We compared the single-pulse NMR spectral data with in vitro diagnostic research (IVDr) information on quantitative lipoprotein profiles (112 parameters) extracted from the raw 1D NMR data. All NMR methods gave highly significant discrimination of SARS-CoV-2 positive patients from controls and SARS-CoV-2 negative patients with individual NMR methods, giving different diagnostic information windows on disease-induced phenoconversion. Longitudinal trajectory analysis in selected patients indicated that metabolic recovery was incomplete in individuals without detectable virus in the recovery phase. We observed four plasma cytokine clusters that expressed complex differential statistical relationships with multiple lipoproteins and metabolites. These included the following: cluster 1, comprising MIP-1β, SDF-1α, IL-22, and IL-1α, which correlated with multiple increased LDL and VLDL subfractions; cluster 2, including IL-10 and IL-17A, which was only weakly linked to the lipoprotein profile; cluster 3, which included IL-8 and MCP-1 and were inversely correlated with multiple lipoproteins. IL-18, IL-6, and IFN-γ together with IP-10 and RANTES exhibited strong positive correlations with LDL1-4 subfractions and negative correlations with multiple HDL subfractions. Collectively, these data show a distinct pattern indicative of a multilevel cellular immune resp
Lodge S, Nitschke P, Loo RL, et al., 2021, Low volume in vitro diagnostic proton NMR spectroscopy of human blood plasma for lipoprotein and metabolite analysis: application to SARS-CoV-2 biomarkers., Journal of Proteome Research, Vol: 20, Pages: 1415-1423, ISSN: 1535-3893
The utility of low sample volume in vitro diagnostic (IVDr) proton nuclear magnetic resonance (1H NMR) spectroscopic experiments on blood plasma for information recovery from limited availability or high value samples was exemplified using plasma from patients with SARS-CoV-2 infection and normal controls. 1H NMR spectra were obtained using solvent-suppressed 1D, spin-echo (CPMG), and 2-dimensional J-resolved (JRES) spectroscopy using both 3 mm outer diameter SampleJet NMR tubes (100 μL plasma) and 5 mm SampleJet NMR tubes (300 μL plasma) under in vitro diagnostic conditions. We noted near identical diagnostic models in both standard and low volume IVDr lipoprotein analysis (measuring 112 lipoprotein parameters) with a comparison of the two tubes yielding R2 values ranging between 0.82 and 0.99 for the 40 paired lipoprotein parameters samples. Lipoprotein measurements for the 3 mm tubes were achieved without time penalty over the 5 mm tubes as defined by biomarker recovery for SARS-CoV-2. Overall, biomarker pattern recovery for the lipoproteins was extremely similar, but there were some small positive offsets in the linear equations for several variables due to small shimming artifacts, but there was minimal degradation of the biological information. For the standard untargeted 1D, CPMG, and JRES NMR experiments on the same samples, the reduced signal-to-noise was more constraining and required greater scanning times to achieve similar differential diagnostic performance (15 min per sample per experiment for 3 mm 1D and CPMG, compared to 4 min for the 5 mm tubes). We conclude that the 3 mm IVDr method is fit-for-purpose for quantitative lipoprotein measurements, allowing the preparation of smaller volumes for high value or limited volume samples that is common in clinical studies. If there are no analytical time constraints, the lower volume experiments are equally informative for untargeted profiling.
Akhbari P, Jaggard MK, Boulange CL, et al., 2021, Differences between infected and noninfected synovial fluid, BONE & JOINT RESEARCH, Vol: 10, Pages: 85-95, ISSN: 2046-3758
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- Citations: 3
Jaggard MKJ, Boulange CL, Graca G, et al., 2020, Can metabolic profiling provide a new description of osteoarthritis and enable a personalised medicine approach?, CLINICAL RHEUMATOLOGY, Vol: 39, Pages: 3875-3882, ISSN: 0770-3198
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- Citations: 4
Loo RL, Lodge S, Kimhofer T, et al., 2020, Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples, JOURNAL OF PROTEOME RESEARCH, Vol: 19, Pages: 4428-4441, ISSN: 1535-3893
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- Citations: 29
Vonhof EV, Piotto M, Holmes E, et al., 2020, Improved spatial resolution of metabolites in tissue biopsies using high-resolution magic-angle-spinning slice localization NMR spectroscopy., Analytical Chemistry, Vol: 92, Pages: 11516-11519, ISSN: 0003-2700
High-resolution magic-angle-spinning 1H NMR spectroscopy (HR-MAS NMR) is a well-established technique for assessing the biochemical composition of intact tissue samples. In this study, we utilized a method based on HR-MAS NMR spectroscopy with slice localization (SLS) to achieve spatial resolution of metabolites. The obtained 7 slice spectra from each of the model samples (i.e., chicken thigh muscle with skin and murine renal biopsy including medulla (M) and cortex (C)) showed distinct metabolite compositions. Furthermore, we analyzed previously acquired 1H HR-MAS NMR spectra of separated cortex and medulla samples using multivariate statistical methods. Concentrations of glycerophosphocholine (GPC) were found to be significantly higher in the renal medulla compared to the cortex. Using GPC as a biomarker, we identified the tissue slices that were predominantly the cortex or medulla. This study demonstrates that HR-MAS SLS combined with multivariate statistics has the potential for identifying tissue heterogeneity and detailed biochemical characterization of complex tissue samples.
Garcia Perez I, Posma JM, Serrano Contreras JI, et al., 2020, Identifying unknown metabolites using NMR-based metabolic profiling techniques, Nature Protocols, Vol: 15, Pages: 2538-2567, ISSN: 1750-2799
Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes, but is hindered by a lack of automated annotation and standardised methods for structure elucidation of candidate disease biomarkers. Here, we describe a system for identifying molecular species derived from NMR spectroscopy based metabolic phenotyping studies, with detailed info on sample preparation, data acquisition, and data modelling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as STOCSY, STORM and RED-STORM to identify other signals in the NMR spectra relating to the same molecule. It also utilizes 2D-NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multidimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take two or three days. This approach to biomarker discovery is efficient, cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. Finally, it requires basic understanding of Matlab in order to perform statistical spectroscopic tools and analytical skills to perform Solid Phase Extraction, LC-fraction collection, LC-NMR-MS and 1D and 2D NMR experiments.
Akhbari P, Karamchandani U, Jaggard MKJ, et al., 2020, Can joint fluid metabolic profiling (or "metabonomics") reveal biomarkers for osteoarthritis and inflammatory joint disease? A SYSTEMATIC REVIEW, BONE & JOINT RESEARCH, Vol: 9, Pages: 108-119, ISSN: 2046-3758
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- Citations: 17
Bradshaw P, Richards S, Wilson I, et al., 2020, Kinetic modelling of acyl glucuronide and glucoside reactivity and development of structure-property relationships, Organic and Biomolecular Chemistry, Vol: 18, Pages: 1389-1401, ISSN: 1477-0520
Acyl glucuronide metabolites have been implicated in the toxicity of several carboxylic acid-containing drugs, and the rate of their degradation via intramolecular transacylation and hydrolysis has been associated with the degree of protein adduct formation. Although not yet proven, the formation of protein adducts in vivo-and subsequent downstream effects -has been proposed as amechanism of toxicity for carboxylic acid-containing xenobiotics capable of forming acyl glucuronides. A structurally-related series of metabolites, the acyl glucosides, have also been shown to undergo similar degradation reactions and consequently the potential todisplay a similar mode of toxicity.Here we reportdetailedkinetic modelsof each transacylation and hydrolysis reaction for a series of phenylacetic acid acyl glucuronides and their analogous acyl glucosides. Differences in reactivity were observed for the individual transacylation steps between the compound series; our findings suggest that the chargedcarboxylateion and neutral hydroxyl group in the glucuronideand glucoside conjugates, respectively, are responsible for these differences. The transacylation reaction was modelled using density functional theory and the calculated activation energy for this reaction showed a close correlation with thedegradation rateof the 1-banomer.Comparison of optimised geometries between the two series of conjugates revealed differences in hydrogen bondingwhich may further explain the differencesin reactivity observed. Together, these models may find application in drug discovery for prediction ofacyl glucuronide and glucoside metabolitebehaviour.
Akhbari P, Jaggard MK, Boulange CL, et al., 2019, Differences in the composition of hip and knee synovial fluid in osteoarthritis: a nuclear magnetic resonance (NMR) spectroscopy study of metabolic profiles, OSTEOARTHRITIS AND CARTILAGE, Vol: 27, Pages: 1768-1777, ISSN: 1063-4584
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- Citations: 5
Everett JR, Holmes E, Veselkov KA, et al., 2019, A uified conceptual framework for metabolic phenotyping in diagnosis and prognosis, Trends in Pharmacological Sciences, Vol: 40, Pages: 763-773, ISSN: 0165-6147
Understanding metabotype (multicomponent metabolic characteristics) variation can help to generate new diagnostic and prognostic biomarkers, as well as models, with potential to impact on patient management. We present a suite of conceptual approaches for the generation, analysis, and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of (i) metabolite levels, (ii) metabolic trajectories, (iii) metabolic entropies, and (iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalized medicine, with the aim of improving clinical outcomes for patients, but the framework will have value and utility in areas of metabolic profiling well beyond this exemplar.
Tzoulaki I, Castagné R, Boulangé CL, et al., 2019, Serum metabolic signatures of coronary and carotid atherosclerosis and subsequent cardiovascular disease, European Heart Journal, Vol: 40, Pages: 2883-2896, ISSN: 1522-9645
Aims: To characterise serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). Methods and Results: We used untargeted one-dimensional (1D) serum metabolic profiling by proton (1H) nuclear magnetic resonance (NMR) spectroscopy among 3,867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3,569 participants from the Rotterdam and LOLIPOP Studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 NMR measured metabolites were associated with CAC and/or IMT, P =1.3x10-14 to 6.5x10-6 (discovery), P =4.2x10-14 to 4.4x10-2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched-chain and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide and lactate as well as apolipoprotein B (P <0.05). Conclusion: Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclero
McGill D, Chekmeneva E, Lindon J, et al., 2019, Application of novel solid phase extraction-NMR protocols for metabolic profiling of human urine, Faraday Discussions, Vol: 218, Pages: 395-416, ISSN: 1359-6640
Metabolite identification and annotation procedures are necessary for the discovery of biomarkers indicative of phenotypes or disease states, but these processes can be bottlenecked by the sheer complexity of biofluids containing thousands of different compounds. Here we describe low-cost novel SPE-NMR protocols utilising different cartridges and conditions, on both natural and artifical urine mixtures, which produce unique retention profiles useful to metabolic profiling. We find that different SPE methods applied to biofluids such as urine can be used to selectively retain metabolites based on compound taxonomy or other key functional groups, reducing peak overlap through concentration and fractionation of unknowns and hence promising greater control over the metabolite annotation/identification process.
Jaggard MKJ, Boulange CL, Akhbari P, et al., 2019, A systematic review of the small molecule studies of osteoarthritis using nuclear magnetic resonance and mass spectroscopy, OSTEOARTHRITIS AND CARTILAGE, Vol: 27, Pages: 560-570, ISSN: 1063-4584
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- Citations: 11
Graça G, Serrano Contreras JI, Chekmeneva E, 2019, NMR Spectroscopy, Techniques, Pulse Sequences for Structural Elucidation of Small Molecules, Encyclopedia of Analytical Science, 3rd edition, Volume 7, ISBN: 9780081019832
NMR spectroscopy is the most comprehensive analytical tool for chemical structure elucidation and verification. This article aims at introducing and explaining the basics of the most useful NMR pulse sequences for structural elucidation of small organic molecules such as metabolites, drugs and natural products. Step by step we introduce the experiments that are needed to determine the backbone structure and stereochemistry, terminating with a brief description of some of the latest developments in pulse sequences for improving spectral resolution and acquisition. The described experiments are available in most modern NMR spectrometers, from high-resolution systems to benchtop systems.
Boulange CL, Rood IM, Posma JM, et al., 2019, NMR and MS urinary metabolic phenotyping in kidney diseases is fit-for-purpose in the presence of a protease inhibitor, Molecular Omics, Vol: 15, Pages: 39-49, ISSN: 2515-4184
Nephrotic syndrome with idiopathic membranous nephropathy as a major contributor, is characterized by proteinuria, hypoalbuminemia and oedema. Diagnosis is based on renal biopsy and the condition is treated using immunosuppressive drugs; however nephrotic syndrome treatment efficacy varies among patients. Multi-omic urine analyses can discover new markers of nephrotic syndrome that can be used to develop personalized treatments. For proteomics, a protease inhibitor (PI) is sometimes added at sample collection to conserve proteins but its impact on urine metabolic phenotyping needs to be evaluated. Urine from controls (n = 4) and idiopathic membranous nephropathy (iMN) patients (n = 6) were collected with and without PI addition and analysed using 1H NMR spectroscopy and UPLC-MS. PI-related data features were observed in the 1H NMR spectra but their removal followed by a median fold change normalisation, eliminated the PI contribution. PI-related metabolites in UPLC-MS data had limited effect on metabolic patterns specific to iMN. When using an appropriate data processing pipeline, PI-containing urine samples are appropriate for 1H NMR and MS metabolic profiling of patients with nephrotic syndrome.
Lindon JC, Nicholson JK, 2019, Nuclear magnetic resonance (NMR) spectroscopy, Clarke's Analysis of Drugs and Poisons, Editors: Moffatt, Osselton, Widdop, London, Publisher: Pharmaceutical Press
Bonner FW, Maslen L, Lindon JC, et al., 2019, Conception, Implementation and Operation of Large-Scale Metabolic Phenotyping Centres: Phenome Centres, HANDBOOK OF METABOLIC PHENOTYPING, Editors: Lindon, Nicholson, Holmes, Publisher: ELSEVIER SCIENCE BV, Pages: 385-405, ISBN: 978-0-12-812293-8
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Lindon JC, Holmes E, Nicholson JK, 2019, Metabolic Phenotyping: History, Status, and Prospects, HANDBOOK OF METABOLIC PHENOTYPING, Editors: Lindon, Nicholson, Holmes, Publisher: ELSEVIER SCIENCE BV, Pages: 571-583, ISBN: 978-0-12-812293-8
Holmes E, Wilson ID, Lindon JC, 2019, An Overview of Metabolic Phenotyping and Its Role in Systems Biology, HANDBOOK OF METABOLIC PHENOTYPING, Editors: Lindon, Nicholson, Holmes, Publisher: ELSEVIER SCIENCE BV, Pages: 1-51, ISBN: 978-0-12-812293-8
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- Citations: 9
Lindon JC, Holmes E, Nicholson JK, 2019, The Handbook of Metabolic Phenotyping Preface, HANDBOOK OF METABOLIC PHENOTYPING, Editors: Lindon, Nicholson, Holmes, Publisher: ELSEVIER SCIENCE BV, Pages: XXI-XXII, ISBN: 978-0-12-812293-8
Jimenez B, Holmes E, Heude C, et al., 2018, Quantitative lipoprotein subclass and low molecular weight metabolite analysis in human serum and plasma by 1H NMR spectroscopy in a multilaboratory trial, Analytical Chemistry, Vol: 90, Pages: 11962-11971, ISSN: 0003-2700
We report an extensive 600 MHz NMR trial of a quantitative lipoprotein and small molecule measurements in human blood serum and plasma. Five centers with eleven 600 MHz NMR spectrometers were used to analyze 98 samples including: 20 QCs, 37 commercially sourced, paired serum and plasma samples and 2 National Institute of Science and Technology, NIST, reference material 1951c replicates. Samples were analyzed using rigorous protocols for sample preparation and experimental acquisition. A commercial lipoprotein subclass analysis was used to quantify 105 lipoprotein subclasses and 24 low molecular weight metabolites from the nuclear magnetic resonance, NMR, spectra. For all spectrometers, the instrument specific variance in measuring internal quality controls, QCs, was lower than the percentage described by the National Cholesterol Education Program, NCEP, criteria for lipid testing (triglycerides<2.7%, cholesterol<2.8%; LDL-cholesterol<2.8%; HDL-cholesterol<2.3%), showing exceptional reproducibility for direct quantitation of lipoproteins in both matrices. The average RSD for the 105 lipoprotein parameters in the 11 instruments was 4.6% and 3.9% for the two NIST samples while it was 38% and 40% for the 37 commercially sourced plasmas and sera, respectively, showing negligible analytical compared to biological variation. The coefficient of variance, CV, obtained for the quantification of the small molecules across the 11 spectrometers was below 15% for 20 out of the 24 metabolites analyzed. This study provides further evidence of the suitability of NMR for high-throughput lipoprotein subcomponent analysis and small molecule quantitation with the exceptional reproducibility required for clinical and other regulatory settings.
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