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Discovery-based relative quantitation allows a scientist to determine relative metabolite changes in one sample group relative to other sample groups without prior knowledge of the metabolites involved. Here we describe the most commonly used technique for relative quantitation of metabolites.

The metabolome is the entire collection of low molecular weight metabolites in a biological system, such as cells, tissues, fluids or whole organisms. Untargeted metabolomics refers to the global relative quantitative analysis of the metabolome.  The goals of untargeted metabolomics are to determine the metabolomic profiles of phenotypic diseases and healthy states, involving a complex set of genomic and proteomic changes. Presently, healthy and diseased states are followed using a limited number of specific assays that analyze only a few markers of distinct phenotypes. Untargeted metabolomics makes it possible to analyze hundreds to thousands of individual metabolites. Such detailed metabolic profiles can be the most direct reflection of the current state of a biological system and are the basis for the concept of personalized medicine. Other applications of untargeted metabolomics include agroscience and plant science;1 biomarkers;2,3 cardiovascular, diabetes and obesity disease research;4 drug discovery;5 environmental health;6 food safety;7 microbiology;8,9 nutrition;10 and systems biology.11,12

Discovery-based relative quantitation allows a scientist to determine relative metabolite changes in one sample group relative to other sample groups without prior knowledge of the metabolites involved. Here we describe the most commonly used technique for relative quantitation of metabolites.
 

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Overview

Untargeted Metabolomics Workflow Overview


Untargeted metabolomics studies involve comparing the relative abundances of metabolites in multiple samples without prior identification. Samples are run individually and common chromatographic features are used to align the various runs with software. Signals corresponding to individual metabolite ions are integrated over the LC time scale and compared between runs. This untargeted quantitation method is a powerful technique for identifying and quantifying relative changes in complex samples. For this technique, the mass spectrometer must have high sensitivity and be capable of measuring a wide dynamic range of analytes ranging from very hydrophilic to hydrophobic metabolites. Orbitrap-based mass spectrometers provide the high-resolution, accurate-mass measurements required for LC-MS relative quantitation experiments.





Workflows_UntargetMetabolomics.jpg

 




resources

Metabolomic Profiling in Drug Discovery: Understanding the Factors that Influence a Metabolomics Stu...

Sanders M, Hnatyshyn S, Robertson D, Reily M, McClure T, Athanas M, Wang J, Yang P, Huang H, Peake D.
Application Note 562

 
Metabolite Profiling and Identification Employing High Resolution MS Strategies and Dedicated Softwa...

Welchman H, Portwood D, Seymour M, Baxter C, Earll M, Ament Z, Peake D, Seymour G, Hodgman C, Hornshaw M, Oppermann M.
Application Note 550

Accurate and Sensitive All-Ions Quantitation Using Ultra-High Resolution LC/MS

Sanders M, Ruzicka J, McHale K, Shipkova P.
Application Note 476
 

Thermo Scientific SIEVE Software for Differential Expression Analysis: Automated, label-free, semi-q...

Thermo Fisher Scientific
Software Brochure

Sample Preparation

Sample Preparation Workflow for Untargeted Metabolomics

 

Samples for untargeted metabolomics studies come from a variety of sources ranging from microbes to yeast, plants or animals. Sample types generally include cell pellets, plasma, urine or tissues and are typically prepared using liquid – liquid extraction or plasma precipitation. The goal here is to prepare a fraction of the sample containing the water soluble metabolites and lipids, free of proteins. However, because each sample is run individually and samples are typically extremely complex, all conditions up to and including LC analysis must be highly reproducible. Therefore, meticulous sample handling and sample preparation, reproducible chromatography between technical and biological replicates, and sensitive, high-resolution, accurate-mass MS are all essential.

Sample preparation procedures can vary widely. Some experiments may not require any sample preparation, for example, the metabolomics analysis of wine.1 Cell pellets are typically extracted using cold methanol and mechanical agitation.2 Plant samples are pulverized in liquid nitrogen and the powdered plant tissue is then extracted using aqueous methanol.3

A common sample preparation strategy for plasma metabolomics analysis4 is the following:

MWorkflows_UntarMeta_SP_.jpg


References


1. Determination of the Molecular Transformations and Pathways that Occur During the Winemaking Proc...
Dreyer M, Tarr P, et al.
ASMS 2012 Poster

 
2. Metabolic pro�ling of the �ssion yeast S. pombe: quanti�cation of compounds under different...
Pluskal T, Nakamura T, et al.
Mol Biosyst. 2010 Jan;6(1):182-98.

 
3. Mass spectrometry-based metabolomics application to identify quantitative resistance-related meta...
Bollina V, Kumaraswamy GK, et al.
Mol Plant Pathol. 2010 Nov;11(6):769-82.

 
4. Alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a no...
Gall WE, Beebe K, et al.
PLoS One. 2010 May 28;5(5):e10883.

Data Analysis

Data Analysis Workflow for Untargeted Metabolomics

 

Thermo Scientific SIEVE software is a key facilitator of untargeted metabolomics quantitation. The automated software finds related ion features that are grouped into a single monoisotopic component identified by its exact molecular weight and retention time. The ratios of chromatographic peak areas are used for the relative quantitation, and statistical analyses are done to find those components that change reliably between the sample groups.
 

SIEVE™ software version 2.0 enables processing of multiple sample groups and provides a correction for the average data from solvent blanks, thereby reducing the overall number of data points for processing by as much as 98%. Related ions are then grouped together based on the accurate mass differences between adducts, dimers and isotopes. The monoisotopic molecular weight is determined from the largest adduct for the base peak component, which is stored in the component table, along with the related ions. The data is treated statistically and those components that vary significantly between the sample groups are determined.
 

The metabolomic differences between lean and obese Zucker diabetic fatty (ZDF) rat serum, a common model for Type II diabetes, illustrate the use of Q Exactive MS and SIEVE software for metabolomics analysis.1 Pooled rat serum was extracted in triplicate and analyzed by HR/AM LC-MS using the Q Exactive MS at 70K resolution. The separation of the lean and obese groups in the figure below indicates statistically significant changes in the obese rats relative to the lean controls. The compounds responsible for these changes were putatively identified via a ChemSpider search of their monoisotopic molecular weights. Analysis of the ZDF fatty rat serum compared to normal rat serum served as a benchmarking study to determine the merits of the technology platform.  We observed an increase in acylcarnitines, branched chain amino acids2, phospholipids, fatty acids and conjugated bile acids (P<0.001) in fatty ZDF rat plasma relative to normal ZDF rats (Table 1). Identification of the metabolites of interest is then performed by comparing the MS-MS spectrum of the analytes with the spectrum obtained from authentic standards.
 

UnTag_Met_DA-Fig1.jpg


Figure 1. The Principle Components Analysis (PCA) from SIEVE software version 2 shows the lean and obese ZDF rat serum groups are statistically significantly different.

 

UnTag_Met_data(1).jpg

Table 1. Compounds identified via ChemSpider database search, with metabolites that significantly decrease shown in purple and those that increase shown in blue in ZDF Obese vs. Normal Rats (p< 0.001)

 

References


1. Applying Q Exactive Benchtop Orbitrap LC-MS/MS and SIEVE Software for Cutting Edge Metabolomics a...

Athanas M, Peake DA, et al.
Metabolomics Society Poster, Washington DC, June 25-28, 2012

 

2. Metabolomics applied to diabetes research: moving from information to knowledge

Bain JR, Stevens RD, et al.
Diabetes. 2009 Nov;58(11):2429-43.
 

  
For more information on SIEVE 2, including a free 30 day trial, please visit the Thermo Scientific Proteomics Software Portal.  

Mass Spectrometry

Mass Spectrometry Workflow for Untargeted Metabolomics

 

The samples (3 to 5 µL) are injected onto an efficient 2.1 x 150 mm UHPLC, 2 µm C18 (or C18 aQ) column.  Two separate analyses are performed for each sample, one by positive and one by negative ion mode. For positive ion mode, the mobile phases are typically 0.1% formic acid in either water or acetonitrile. For negative ion mode, the mobile phases are 6.5 mM ammonium bicarbonate (basic pH 8.0) in either water or methanol.1

 

Quantitation results from untargeted metabolomics experiments are based on the relative precursor ion intensities of each metabolite across multiple runs. Because of this, high-resolution LC separation should be used to reduce the number of co-eluting species, and all LC runs must be highly reproducible between technical and biological replicates. Also imperative are high-resolution and accurate-mass (HR/AM) analyses to resolve the co-eluting isobaric species to reduce quantitation interference. This is especially important for samples of high complexity and/or high dynamic range.
 

The precision and accuracy obtained in the UHPLC-benchtop Orbitrap MS analysis of rat serum from a fasting study (AN 562 [YX3]) is illustrated below. HR/AM analysis using the Q Exactive MS operated in full scan (m/z 65-850), positive and negative ion modes at 70,000 resolution results in 14-15 [YX4] data points across a 3.5 second-wide peak.  This provides a sufficient number of data points for accurate relative quantitation, as illustrated in Figure 1 below. In addition, mass measurement is excellent in a single scan and obviates the need for averaging spectra to obtain good mass accuracy.


UnTag_Met_DA_MassSpec.jpg

Figure 1. Mass and response stability of N-benzoyl-D5-glycine, with external calibration, resolution 82,000. See Application Note 562 for more details: Metabolomic Profiling in Drug Discovery: Understanding the Factors that Influence a Metabolomics Stu....
 

References

 

1. Alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a no...

Gall WE, Beebe K, et al.
PLoS One. 2010 May 28;5(5):e10883.

REFERENCES

References Related to Introduction


1. Applications of liquid chromatography coupled to mass spectrometry-based metabolomics in clinical...

Roux A, Lison D, et al.
Clin Biochem. 2011 Jan;44(1):119-35.

 
2. Mass spectrometry based metabolomics to identify potential biomarkers for resistance in barley ag...

Kumaraswamy KG, Kushalappa AC, et al.
J Chem Ecol. 2011 Aug;37(8):846-56.

 

3. Towards an unbiased metabolic profiling of protozoan parasites: optimisation of a Leishmania samp...

t'Kindt R, Jankevics A, et al.
Anal Bioanal Chem. 2010 Nov;398(5):2059-69.


4. Metabolomic characterization of the salt stress response in Streptomyces coelicolor
Kol S, Merlo ME, et al.

Appl Environ Microbiol. 2010 Apr;76(8):2574-81.



5. Plasma metabolomic profile in nonalcoholic fatty liver disease

Kalhan SC, Guo L, et al.
Metabolism. 2011 Mar;60(3):404-13.

 
6. Early hepatic insulin resistance in mice: a metabolomics analysis

Li LO, Hu YF, et al.
Mol Endocrinol. 2010 Mar;24(3):657-66.


7. Metabolomics to Unveil and Understand Phenotypic Diversity between Pathogen Populations

t'Kindt R, Scheltema RA, et al.
PLoS Negl Trop Dis. 2010 Nov 30;4(11):e904.
 

 
8. Metabolomics profiling of extracellular metabolites in recombinant Chinese Hamster Ovary fed-batc...

Chong WP, Goh LT, et al.
Rapid Commun Mass Spectrom. 2009 Dec;23(23):3763-71.

 

9. Systems-level metabolic flux profiling elucidates a complete, bifurcated tricarboxylic acid cycle...

Amador-Noguez D, Feng XJ, et al.
J Bacteriol. 2010 Sep;192(17):4452-61.

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