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For any enquiries related to analytical infrastructure, please contact:

Dr Matthew Lewis
Chief Operating Officer (NPC), Director Metabolic Profiling
matthew.lewis@imperial.ac.uk
+44 (0)20 7594 3108

A world-class analytical facility for metabolic phenotyping

The Division of CSM hosts one of the largest and most well-equipped academic facilities for biospectroscopy in the world, with multiple high-resolution analytical platforms including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) instrumentation. 

NMR spectroscopy and MS are both very well established analytical tools, and permit the interrogation of chemical structure in diverse and complementary ways. Alongside information derived from other (commonly integrated/hyphenated) analytical devices (e.g. UV/vis detection, liquid chromatography or ion-mobility separations), the analyst can collate sufficient evidence to conclusively confirm metabolite structure. Researchers in CSM have contributed substantially to the development and application of these analytical tools, particularly in the field of small molecule analysis, which underpins metabolic phenotyping and drug metabolism studies.

The Division benefits from direct links to two substantial allied analytical resources which have sufficient high-throughput capacity for large-scale clinical and epidemiological sample sets:

  • MRC-NIHR National Phenome Centre (NPC) - the flagship for world-class capability in metabolic phenotyping, positioned to benefit the whole UK translational medicine community

Find out more about our key focus areas:

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Large-Scale Metabolic Phenotyping Platforms

Researchers in CSM have pioneered high-resolution spectroscopic methods for global metabolic profiling of biofluids and tissues, particularly those based on nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), typically used in conjunction with ultraperformance liquid chromatography (UPLC).  The complementarity of NMR and UPLC-MS analytical platforms affords broad coverage of small-molecule metabolites, and together provide deep phenotype information for use in data-driven studies. 

The Division has been at the forefront of initiatives to apply metabolic phenotyping to large-scale pre-clinical, clinical and epidemiological sample sets in order to unlock the benefits of understanding individual-level metabolic variation in the context of population.

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Analytical Method Development and Structure Elucidation

The annotation of spectral features in metabolic profile data is central to our ability to efficiently use data-driven analyses to formulate specific, testable hypotheses relating to biological function. Accurate spectral assignment, and the ability to elucidate the structure of unknown compounds is therefore a critical aspect of the metabolic profiling workflow. Spectral data generated by the main analytical platforms used for routine metabolome analysis - nuclear magnetic resonance (NMR) spectroscopy, and mass spectrometry (MS) - contain a wealth of information relating to the chemical structure, which not only provides the basis for their utility in profiling, but can be used to positively identify the components of complex biological matrices. 

De novo structure elucidation of unknown small molecules is often a rate-limiting step in the metabonomics analysis pipeline, as routinely-collected profile data rarely contains sufficient features for unambiguous assignment, and further experimentation is often required. NMR and MS are both very well established tools, and permit the interrogation of chemical structure in diverse and complementary ways. Alongside information derived from other (commonly integrated/hyphenated) analytical devices (e.g. UV/vis detection, liquid chromatography or ion-mobility separations), the analyst can collate sufficient evidence to conclusively confirm metabolite structure.

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Crystallisation and Crystallography

Led by Prof. Naomi Chayen, research into crystallization for 3D structure determination is a key element of CSM and led to the development of pioneering methods and products for X-ray diffraction based protein structure elucidation. Prof. Chayen is recognised as a world leader in crystallization with a strong track record in methodological advances. The Chayen group within CSM have concentrated on optimising nucleation, the pre-requisite and first step which determines the outcome of the crystallisation process and developed novel methods for the robust and reproducible production of high-quality crystals. 

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Knowledge of the 3D crystal structure of a protein is a fundamental part of characterisation and can help understand function. This is immensely important in both systems biology approaches in human health, and rational drug design. High quality protein crystals are required for efficient and accurate protein structure determination by X-ray diffraction. There is a current requirement for robust and reproducible methods for producing such crystals, and to tackle the issue of nucleation that determines crystal quality. 

Prof. Naomi Chayen has developed a variety of novel methods for efficient protein crystallisation, and with her team and collaborators, recently designed smart materials and nanomaterials for producing high-quality protein crystals. This work has great impact in the context of rational drug design has produced several commercial products e.g. ‘Naomi’s Nucleant’ - sold by Molecular Dimensions under licence from Imperial Innovations, 'Chayen Reddy MIP' and other crystallization kits and tools.


Key members within Analytical Infrastructure

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  • JOURNAL ARTICLE
    Alexander J, Gildea L, Balog J, Speller A, McKenzie J, Muirhead L, Scott A, Kontovounisios C, Rasheed S, Teare J, Hoare J, Veselkov K, Goldin R, Tekkis P, Darzi A, Nicholson J, Kinross J, Takats Zet al., 2017,

    A novel methodology for in vivo endoscopic phenotyping of colorectal cancer based on real-time analysis of the mucosal lipidome: a prospective observational study of the iKnife

    , SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES, Vol: 31, Pages: 1361-1370, ISSN: 0930-2794
  • JOURNAL ARTICLE
    Inglese P, McKenzie JS, Mroz A, Kinross J, Veselkov K, Holmes E, Takats Z, Nicholson JK, Glen RCet al., 2017,

    Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

    , CHEMICAL SCIENCE, Vol: 8, Pages: 3500-3511, ISSN: 2041-6520
  • JOURNAL ARTICLE
    Nanev CN, Saridakis E, Chayen NE, 2017,

    Protein crystal nucleation in pores

    , SCIENTIFIC REPORTS, Vol: 7, ISSN: 2045-2322
  • JOURNAL ARTICLE
    Ouchemoukh S, Amessis-Ouchemoukh N, Gómez-Romero M, Aboud F, Giuseppe A, Fernández-Gutiérrez A, Segura-Carretero Aet al., 2017,

    Characterisation of phenolic compounds in Algerian honeys by RP-HPLC coupled to electrospray time-of-flight mass spectrometry

    , LWT - Food Science and Technology, Vol: 85, Pages: 460-469, ISSN: 0023-6438

    © 2016 Elsevier Ltd A total of 35 honey samples from different regions of Algeria were studied to determine their phenolic profiles. Phenolic compounds, products of the secondary metabolism of plants, were extracted with amberlite XAD-4 and analysed by liquid chromatography, with diode array detection and electrospray ionisation mass spectrometry in negative ion polarity. By using colorometric assays, Erica honeys showed the highest content of phenolic compounds and flavonoids (245 ± 54 mg GAE/100 g and 29 ± 6 mg QE/100 g, respectively). More than 30 compounds were identified in the honey samples studied including 14 phenolic acids and 16 flavonoids. In general, honey samples showed different chromatographic profiles. It has been shown that 4-hydroxybenzoic acid, apigenin, chrysin, galangin, kaempferol, isorhamnetin, luteolin and pinocembrin were present in all honey extracts. Moreover, caffeic, p-coumaric and vanillic acids; abscisic and syringic acids; and benzoic acid were detected in 34, 33 and 32 honey samples, respectively. The members of flavonol subclass were the most abundant of the identified flavonoids. Gallic and homovanillic acids, daidzein and myricetin were less present: 7, 5, 7 and 6 in honey samples, respectively. Caffeic and p-coumaric acids were potential floral markers for Capparis spinosa and Trifoliumn honeys, respectively.

  • JOURNAL ARTICLE
    Posma JM, Garcia Perez I, Heaton JC, Burdisso P, Mathers JC, Draper J, Lewis M, Lindon JC, Frost G, Holmes E, Nicholson JKet al., 2017,

    An integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers

    , Analytical Chemistry, Vol: 89, Pages: 3300-3309, ISSN: 1086-4377

    A major purpose of exploratory metabolic profiling is for the identification of molecular species that are statistically associated with specific biological or medical outcomes; unfortunately the structure elucidation process of unknowns is often a major bottleneck in this process. We present here new holistic strategies that combine different statistical spectroscopic and analytical techniques to improve and simplify the process of metabolite identification. We exemplify these strategies using study data collected as part of a dietary intervention to improve health and which elicits a relatively subtle suite of changes from complex molecular profiles. We identify three new dietary biomarkers related to the consumption of peas (N-methyl nicotinic acid), apples (rhamnitol) and onions (N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide) that can be used to enhance dietary assessment and assess adherence to diet. As part of the strategy, we introduce a new probabilistic statistical spectroscopy tool, RED-STORM (Resolution EnhanceD SubseT Optimization by Reference Matching), that uses 2D J-resolved ¹H-NMR spectra for enhanced information recovery using the Bayesian paradigm to extract a subset of spectra with similar spectral signatures to a reference. RED-STORM provided new information for subsequent experiments (e.g. 2D-NMR spectroscopy, Solid-Phase Extraction, Liquid Chromatography prefaced Mass Spectrometry) used to ultimately identify an unknown compound. In summary, we illustrate the benefit of acquiring J-resolved experiments alongside conventional 1D ¹H-NMR as part of routine metabolic profiling in large datasets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resource.

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

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