Metabolomics/Analytical Methods

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  1. NMR
  2. Mass Spectrometry
  3. Sample Preparation
  4. Fluids
  5. Tissues
  6. Statistical Methods

Analytical Methods IntroductionEdit

Metabolomics is a field where the discovery of specific cellular metabolites is essential to the field. Without the tools and methods to discover the metabolites, metabolite profiles and cellular processes unique to organisms cannot be discovered. Methods for discovering metabolomes range from technical chemical methods such as NMR to statistical analysis.

The Sumner GroupEdit

The Sumner Group is involved in the research of plant metabolomics. This is challenging because of 3 primary limitations. Profiling the metabolome is difficult because of its chemical diversity. Genomic and proteomic variance are limited by the basic components (nucleotides and amino acids), whereas metabolomes lack any sort of consistency. Another problem is dynamic range, which deals with measurement tolerance and sensitivity. The third problem is analytical and biological variance, which deals with deviations in measurement and in composition. The Sumner group is developing a way to get around these problems by dividing metabolites into different groups for analysis. The analytical techniques they develop are used to research disease in plants, and also to develop computational model organisms of plants.

Terms

transcriptome – the set of all transcripts produced by either a single cell or an entire organism principal component analysis – a statistical technique used to separate data into classes hierarchical clustering – a method to determine relationships or similarity between data sets. GC/MS (gas chromatography coupled to mass spectrometry ) - a way to profile large numbers of metabolites by separation (GS) and then measurement (MS) HPLC (high performance liquid chromatography) – a technique to separate both volatile and nonvolatile elements. Coupled to MS, it is another way to profile a large number of metabolites.

Relevance

The research of this group looks at metabolic regulation and its relationship to diseases in plants. This course looks at metabolic regulation, and problems within a pathway that can lead to disease. The area that is more relevant is the group's efforts at creating a model organism. The comprehensive understanding of an organism's metabolic regulation, depending on environmental conditions, relates to the regulatory mechanisms covered in class thus far.

Three Dimensional Cellular Microarray for High-Throughput Toxicology AssaysEdit

Three-dimensional cellular microarray for high-throughput toxicology assays

The main purpose of this paper was to develop a miniaturized 3D cell-culture array (DataChip) that would screen drug candidates for toxicity of a particular drug at the early stages of drug development. This is important due to amount of research put into drug development in today's world. The DataChip will hopefully select for drugs that cause adverse effects, remove them from development and help bring more drugs into the development process. This DataChip will be able to rapidly identify metabolic activation or deactivation of xenobiotics before they ever enter a human being. Xenobiotics are metabolized in the liver and in most cases, the liver is adversely affected by drugs. The DataChip’s aim is to significantly reduce the number of cases of liver failure due to xenobiotic metabolism, which is the leading cause of liver failure.

Terms:

Xenobiotics – chemical which is found in an organism but which is not normally produced or expected to be present in it

Microarray – biological assay

Alginate - viscous gum that is abundant in the cell walls of brown algae

P450 isoforms – (CYP1A2, CYP2D6, and CYP3A4) enzymes that are a part of the mixed-function oxidase system

IC50 values - or the half maximal inhibitory concentration

Hep3B cells – Human hepatoma cells

Fluorogenic - a process in which fluorescence is generated

Ketoconazole - synthetic antifungal drug used to prevent and treat skin and fungal infections

Relevance: In class, we were discussing different metabolic pathways in the human body and the different products they produce. We were also learning how all these processes are linked to one another. This paper talks about new ways to detect harmful side effects caused by the metabolism of a substance. Xenobiotics are broken down in the liver. Glycolysis and gluconeogenesis both take place in the liver as well. If the liver if damaged by xenobiotic metabolism, then glycolysis and gluconeogenesis would also be disrupted. This paper describes other metabolic process that occurs in the body and the possible side effects of the metabolites.

Recommended ReadingEdit

High Resolution Separations and Improved Ion Production and Transmission in Metabolomics

Metabolomics involves the detection and quantification of as many sample components as reasonably possible in order to identify compounds that can be used to characterize the samples under study. In electrospray ionization, ions are produced for analysis my mass spectrometry (MS). When applying this technique to metabolomics, it is important that metabolome sample constituents are efficiently separated prior to ion production, so that ionization suppression is minimized. This will ensure an extensive dynamic range of the measurement, as well as coverage of the metabolome. Measurement sensitivity may be increased by optimizing the MS inlet and interface.

Advancements in front end liquid chromatography (LC) separations, electrospray ionization and ion transmission efficiency have increased the sensitivity of liquid chromatography-mass spectrometry (LC-MS)-based measurements. An extended and more reproducible coverage of the metabolome has resulted in the detection of larger numbers of features characterized by accurately measured masses and retention times. Ionization efficiency has been improved with small i.d. columns that provide lower flow rates. This has led to greater sensitivity and better quantification. However, longer columns that provide high separation peak capacities produce lengthy analysis times that are not amenable in the analysis of large numbers of samples, which are required for statistically significant metabolomics studies. Alternative and complementary approaches, such as ionization electrospray-mass spectrometry (IMS-MS) or liquid chromatography-ionization microspray-mass spectrometry (LC-IMS-MS) are in development. These methods would provide higher throughput, which would allow for a moderately high coverage analysis of several hundred samples over the duration of a few days.


Molecular formula and METLIN Personal Metabolite Database matching applied to the identification of compounds generated by LC/TOF-MS

In an effort to simplify and streamline compound identification from metabolomics data generated by liquid chromatography time-of-flight mass spectrometry, software has been developed for constructing Personalized Metabolite Databases with content from over 15,000 compounds pulled from the public METLIN database. Extra functionalities have been added that permit the addition of user-defined retention times as an orthogonal searchable parameter; and allow interfacing to separate software, a Molecular Formula Generator (MFG), that facilitates reliable interpretation of any database matches from the accurate mass spectral data. In an effort to assess the utility of this identification strategy, retention times have been added to a subset of masses in this database that represent a mixture of 78 synthetic urine standards. The mixture was analyzed and screened against this METLIN urine database, which resulted in 46 accurate mass and retention time matches. Human urine samples had been analyzed under the same analytical conditions and were screened against this database. Another 374 had an accurate mass match to the database, where 163 of those masses had the highest MFG score. What’s more, MFG calculated a formula for an additional 849 ions that had no database matches. These results indicate that the METLIN Personal Metabolite database and MFG software offer an exemplary strategy for confirming the formula of database matches. If no database match is found, possible formulas are suggested.

Since a mass determination cannot assign elemental composition with absolute certainty, it has been suggested to complement database assignment of high mass accuracy data with other techniques such as isotope ratios and RT. It has been demonstrated that the METLIN Personal Metabolite Database software can be used to assign the correct elemental compositions for a set of urine metabolite standards. Including RT as a separate, orthogonal variable will allow for rapid, positive identification of the temporally resolved masses. Combining MFG capacity with mass and RT database matching is expected to increase the confidence with which both known and unknown compounds are assigned a correct elemental composition.


Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry

Fourier Transform Cyclotron Resonance Mass Spectrometry (FTICR-MS) offers unparalleled mass resolution, mass accuracy, and superb detection sensitivity. With these features, FTICR-MS is capable of becoming a powerful technique for high-throughput metabolomics analysis. This study examines properties of ultrahigh-field 12-Tesla (12T) in order to identify and quantify human plasma metabolites, and for the untargeted metabolic fingerprinting of inbred-strain mouse serum by direct infusion (DI). Rational elemental compositions (incorporated unlimited C, H, N and O, and a maximum of two S, three P, two Na, and one K per formula) of approximately 250 out of 570 metabolite features were detected in a 3-min infusion analysis of aqueous extract of human plasma, and were able to identify more than 100 metabolites. Isotopically-labeled internal standards were used to obtain high-quality calibration curves for the absolute quantitation of choline with sub-pmol sensitivity. This required 500 times less sample than previous LC/MS analyses. Optimal serum dilution conditions allowed for chemical compounds that were spiked into mouse serum as metabolite mimics to produce a linear response that spanned over a 600-fold concentration range. DI/FTICR-MS analysis was conducted on serum from 26 mice from 2 inbred strains, with and without acute trichloroethylene (TCE) treatment. This method had also been extended to the metabolomic fingerprinting of serum samples from 49 mice from 5 inbred strains that were involved in an acute alcohol toxicity study, with positive and negative electrospray ionization (ESI). It was demonstrated that more upwards of 400 metabolites could be profiled within 24 hrs, when applying DI/FTICR-MS to these samples.

Ultrahigh-field FTICR-MS has proven to be a potentially invaluable tool for qualitative metabolomics. The superior mass resolution facilitates the detection of hundreds of metabolites in complex samples in the DI mode. The mass accuracy frequently allows the identification of low-mass metabolites that are based solely on mass. This is accomplished by generating elemental compositions and then searching available metabolome databases.

Ultrahigh-field FTICR-MS can also be used to rapidly generate differential metabolite profiles in samples taken from large studies. However, DI/FTICR-MS is not comprehensive and therefore will not likely eliminate the need for chromatographic separations to achieve thorough metabolome coverage and to differentiate metabolite isomers. Software is currently being developed to enhance the potential of FTICR-MS for high-throughput metabolomics. This software would facilitate analyses by automatic detection of differentially-abundant metabolites, followed by their automated identification via elemental composition determination and metabolome database searching.

Articles and Web Pages for Review and InclusionEdit

Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements


Metabolomics approach for determining growth-specific metabolites based on Fourier transform ion cyclotron resonance mass spectrometry


LC-MS-based metabolomics in drug metabolism

Last modified on 2 April 2011, at 11:37