Metabolomics: Basic Principles and Strategies

Sinem Nalbantoglu

### Abstract

Metabolomics is the study of metabolome within cells, biofluids, tissues, or organisms to comprehensively identify and quantify all endogenous and exogenous low-molecular-weight (<1 kDa) small molecules/metabolites in a biological system in a high-throughput manner. Metabolomics has several applications in health and disease including precision/personalized medicine, single cell, epidemiologic population studies, metabolic phenotyping, and metabolome-wide association studies (MWAS), precision metabolomics, and in combination with other omics disciplines as integrative omics, biotechnology, and bioengineering. Mass spectrometry (MS) based metabolomics/lipidomics provides a useful approach for both identification of disease-related metabolites in biofluids or tissue and also encompasses classification and/or characterization of disease or treatment-associated molecular patterns generated from metabolites. Here, in this review, we provide a brief overview of the current status of promising MS-based metabolomics strategies and their emerging roles, as well as possible challenges.

Keywords: metabolomics, untargeted metabolomics, targeted metabolomics, omics, mass spectrometry

### 1. Introduction

Metabolomics is an evolving field to comprehensively identify and quantify all endogenous and exogenous low-molecular-weight (<1 kDa) small molecules/ metabolites in a biological system in a high-throughput manner. The composition of these endogenous compounds is affected by the upstream influence of the proteome and genome as well as environmental factors, lifestyle factors, medication, and underlying disease. Metabolomics is reported as the reflection of the phenotype. The metabolome is downstream of the transcriptome and proteome and is considered to be complementary to genomics, transcriptomics, and proteomics. It has been reported that due to the close relation of metabolome to the genotype, physiology, and environment of an organism, genotype-phenotype as well as genotypeenvirotype relationships could be successfully documented by metabolomics [1–3].

Metabolomics is the study of metabolome within cells, biofluids, tissues, or organisms and applied in molecular and personalized medicine involved in clinical chemistry, transplant monitoring, newborn screening, pharmacology, and toxicology. Metabolome can be defined as the small molecules and their interactions within a biological system which has been estimated as 3000–20,000 global metabolite profiles under a given genetic, nutritional, environmental conditions. Since the metabolome is the final downstream product, changes and interactions between gene expression, protein expression, and the environment are directly reflected in metabolome making it more physically and chemically complex than the other "omes." The metabolome is closest to the phenotype among other omics approaches, and metabolomics best modulates and represents the molecular phenotype of health and disease [4]. It has been demonstrated that relations of genotype-genomics and phenotype-metabolomics refer to specific gene variations and resultant metabolite changes which ultimately give information about genetic epigenetic phenotypic changes [1, 5–8]. In this regard, metabolomics is a brilliant source for biomarker discovery with advantages over other omics approaches.

An overview of first metabolomics experiments has demonstrated metabolite quantitation in biofluids which was first utilized in 1971 [9, 10]. After that, in the same year, the first use of the definition of "metabolic profiling" was observed [9, 11], while "metabolome" was first used in 1998 [9, 12]. In 1999, the term "metabonomics" was described as the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification [13].

Due to the complexity of the metabolome, a wide variety of chemically diverse compounds such as lipids, organic acids, carbohydrates, amino acids, nucleotides, and steroids, among others, have not yet been validated and reported totally; thus, we do not know the complete number of metabolites present in the human [14]. Human Metabolome Database (HMDB; http://www.hmdb.ca, version 4.0, 2018) reported 114,098 (April 2019) metabolite entries including both water-soluble and lipid-soluble metabolites as well as metabolites that would be regarded as either abundant (>1 uM) or relatively rare (<1 nM). Those small molecules, which have been identified and experimentally confirmed in various human tissues and biofluids, have been suggested as only 20% of the total metabolome [15]. Additionally, 5702 protein sequences are linked to these metabolite entries. The database also contained listing normal and abnormal concentrations of different metabolites for 23 different biospecimens.

features do not comply with fingerprint and footprint metabolomics. Metabolomics strategies for validation purposes refer to quantitative tandem/targeted analysis and diagnostic analysis of a known clinical associated compound/biomarker [16]. Untargeted and targeted approaches should be performed consecutively in order to achieve an accurate identification and absolute quantitation of the metabolites [9]. Here, in this review, we provide a brief overview of the current status of promising MS-based metabolomics strategies and their emerging roles, as well as possible

MS is used to identify and quantify metabolites even at very low concentrations (femtomolar to attomolar) with high resolution, sensitivity, and dynamic range [17]. MS-based analyses basically include sample preparation, extraction, capillary electrophoresis (CE), and/or chromatographic separation, introduction of sample for ionization process (charged molecules), and detection of possible metabolites on the basis of their mass-to-charge ratio (m/z). In this review, the main aspects of MS-based untargeted metabolomics strategies are briefly outlined below:

1.Sample acquisition: Metabolome analysis can be performed in various biological samples including tissue, biofluids (blood, urine, feces, seminal fluid, saliva, bile, cerebrospinal fluid), and cell culture [18] (Table 1). Careful sampling, sample preparation and management, and sample biobanking/ biorepositories together with sample labeling are critical and essential for an

2. Basic workflow of MS-based metabolomics

Metabolomics workflow with main bulleted points.

Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

challenges.

139

Figure 1.

Metabolomics strategies cover two primary analysis platforms including "untargeted-discovery-global" and "targeted-validation-tandem" based on the objective of the study (Figure 1). In order to systematically identify and quantify metabolites from a biological sample and achieve comprehensive characterization of biomarker targets, the analysis considers both endometabolome and exometabolome. Untargeted discovery metabolomics has a hypothesis-generating manner and allows for full scanning of the metabolome, pattern identification, and "metabolic fingerprinting" for the global classification of phenotypes with interacting pathway interactions. Targeted metabolomics is hypothesis testing and generally performed for validation of an untargeted analysis. In the targeted approaches (tandem-MS/MS), using a known standard, a quantitative analysis is performed on specific small molecules/metabolites or perturbations along a metabolic pathway [9] also known as "biased or directed metabolomics" or "metabolic profiling." Hypothesis-generating metabolomics covers different strategies as (i) nontargeted profiling, (ii) fingerprinting, and (iii) footprinting [16], while hypothesis-testing strategies are target analysis and diagnostic analysis. Nontargeted global metabolomics profiling refers to comprehensive metabolite/ small molecule analysis. This analysis performs semiquantitative analysis with putative identifications of the detected features. Metabolomics fingerprint examines global snapshot of the intracellular metabolome enabling classification and screening, while metabolomics footprint analysis explores global snapshot of the extracellular fluid metabolome (secretions from cells or changes in metabolites consumed from the exometabolome). Quantification and identification of the

### Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

metabolome is the final downstream product, changes and interactions between gene expression, protein expression, and the environment are directly reflected in metabolome making it more physically and chemically complex than the other "omes." The metabolome is closest to the phenotype among other omics

approaches, and metabolomics best modulates and represents the molecular phenotype of health and disease [4]. It has been demonstrated that relations of genotype-genomics and phenotype-metabolomics refer to specific gene variations and resultant metabolite changes which ultimately give information about genetic epigenetic phenotypic changes [1, 5–8]. In this regard, metabolomics is a brilliant source for biomarker discovery with advantages over other omics approaches. An overview of first metabolomics experiments has demonstrated metabolite quantitation in biofluids which was first utilized in 1971 [9, 10]. After that, in the same year, the first use of the definition of "metabolic profiling" was observed [9, 11], while "metabolome" was first used in 1998 [9, 12]. In 1999, the term "metabonomics" was described as the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli

Due to the complexity of the metabolome, a wide variety of chemically diverse compounds such as lipids, organic acids, carbohydrates, amino acids, nucleotides, and steroids, among others, have not yet been validated and reported totally; thus, we do not know the complete number of metabolites present in the human [14]. Human Metabolome Database (HMDB; http://www.hmdb.ca, version 4.0, 2018) reported 114,098 (April 2019) metabolite entries including both water-soluble and lipid-soluble metabolites as well as metabolites that would be regarded as either abundant (>1 uM) or relatively rare (<1 nM). Those small molecules, which have been identified and experimentally confirmed in various human tissues and

biofluids, have been suggested as only 20% of the total metabolome [15]. Additionally, 5702 protein sequences are linked to these metabolite entries. The database also contained listing normal and abnormal concentrations of different metabolites for

Metabolomics strategies cover two primary analysis platforms including "untargeted-discovery-global" and "targeted-validation-tandem" based on the objective of the study (Figure 1). In order to systematically identify and quantify metabolites from a biological sample and achieve comprehensive characterization

exometabolome. Untargeted discovery metabolomics has a hypothesis-generating manner and allows for full scanning of the metabolome, pattern identification, and

interacting pathway interactions. Targeted metabolomics is hypothesis testing and generally performed for validation of an untargeted analysis. In the targeted approaches (tandem-MS/MS), using a known standard, a quantitative analysis is performed on specific small molecules/metabolites or perturbations along a metabolic pathway [9] also known as "biased or directed metabolomics" or "metabolic profiling." Hypothesis-generating metabolomics covers different strategies as (i) nontargeted profiling, (ii) fingerprinting, and (iii) footprinting [16], while hypothesis-testing strategies are target analysis and diagnostic analysis.

Nontargeted global metabolomics profiling refers to comprehensive metabolite/ small molecule analysis. This analysis performs semiquantitative analysis with putative identifications of the detected features. Metabolomics fingerprint examines global snapshot of the intracellular metabolome enabling classification and screening, while metabolomics footprint analysis explores global snapshot of the extracellular fluid metabolome (secretions from cells or changes in metabolites consumed from the exometabolome). Quantification and identification of the

of biomarker targets, the analysis considers both endometabolome and

"metabolic fingerprinting" for the global classification of phenotypes with

or genetic modification [13].

Molecular Medicine

23 different biospecimens.

138


#### Figure 1. Metabolomics workflow with main bulleted points.

features do not comply with fingerprint and footprint metabolomics. Metabolomics strategies for validation purposes refer to quantitative tandem/targeted analysis and diagnostic analysis of a known clinical associated compound/biomarker [16]. Untargeted and targeted approaches should be performed consecutively in order to achieve an accurate identification and absolute quantitation of the metabolites [9]. Here, in this review, we provide a brief overview of the current status of promising MS-based metabolomics strategies and their emerging roles, as well as possible challenges.

## 2. Basic workflow of MS-based metabolomics

MS is used to identify and quantify metabolites even at very low concentrations (femtomolar to attomolar) with high resolution, sensitivity, and dynamic range [17]. MS-based analyses basically include sample preparation, extraction, capillary electrophoresis (CE), and/or chromatographic separation, introduction of sample for ionization process (charged molecules), and detection of possible metabolites on the basis of their mass-to-charge ratio (m/z). In this review, the main aspects of MS-based untargeted metabolomics strategies are briefly outlined below:

1.Sample acquisition: Metabolome analysis can be performed in various biological samples including tissue, biofluids (blood, urine, feces, seminal fluid, saliva, bile, cerebrospinal fluid), and cell culture [18] (Table 1). Careful sampling, sample preparation and management, and sample biobanking/ biorepositories together with sample labeling are critical and essential for an


optimum, reproducible, and high-throughput analysis with high recovery,

2.Sample preparation/extraction: During sample preparation different extraction solvents/methods are used for high recovery of both polar and nonpolar compounds based on nontargeted and targeted approaches on

Systematic collection and storage of human samples for metabolomics/lipidomics strategies.

Types of biospecimen Sample acquisition and preparation

Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

• Centrifuge at 1000–3000 rcf for 5 min

chemicals such as pilocarpine

with distilled water

• The skin area is cleaned with ethanol and then

• The sweat is collected by using a micropipette (from 20 to 200 μL at least 100 μL) • The minimum sweat rate demanded to obtain a valid sweat sampling is 1 g/m<sup>2</sup> per min • The use of deodorants, perfumes, and cosmetics was excluded at least 1 day prior to sweat

fasting including smoking is performed • Collecting time for EBC is 20 min, producing approximately 1 mL condensate sample • Commercial collection samplers, bags, and

• Supernatant (seminal plasma) is separated • Supernatant seminal plasma is recentrifuged at

• Removing supernatant

Saliva • 3 mL saliva samples collected into sample tubes under fasting conditions

• Aliquoting • Storage at 80°C • Freeze-thaw cycles avoided

• Aliquoting • Storage at 80°C Sweat • Sweat secretion is stimulated by exercise, heat, or

> collection • Aliquoting • Storage at 80°C • Freeze-thaw cycles avoided

• Storage at 80°C Breath • Exhaled breath condensate (EBC) upon 8 h of

> devices are used • Compatible with GC, GC-MS

• Aliquoting • Storage at 80°C • Freeze-thaw cycles avoided

Seminal plasma • Semen is centrifuged at 700g, 4°C, 10 min

• Aliquoting • Storage at 80°C • Freeze-thaw cycles avoided

10,000g, 60 min, 4°C

Bile • 2 mL gallbladder bile is collected intraoperatively • Aliquoting • Storage at 80°C • Freeze-thaw cycles avoided

Feces • A single spot 1–2 g of feces

extraction, and enriched metabolite coverage.

Table 1.

141

### Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

Types of biospecimen Sample acquisition and preparation

Cells (intracellularfingerprint-metabolite

Cell medium (extracellularfootprint-metabolite

profiling)

profiling)

Suspensioncultured mammalian cells

Molecular Medicine

140

Tissue • Mechanical and nonmechanical homogenization

biological samples

bicarbonate + methanol

• Snap freeze in liquid nitrogen

• Aliquoting • Storage at 80°C • Freeze-thaw cycles avoided

the cells

• Aliquoting • Storage at 80°C • Freeze-thaw cycles avoided

cells)

• Aliquoting • Storage at 80°C • Freeze-thaw cycles avoided

Biofluids Serum • Blood samples should be collected into serum

• Remove supernatant • Snap freeze in liquid nitrogen

metabolic activity

temperature

Plasma • Blood samples collected into heparin, citrate,

conditions

• Aliquoting • Storage at 80°C • Freeze-thaw cycles avoided

Cerebrospinal fluid (CSF) • CSF sampling via lumbar puncture • Aliquoting • Storage at 80°C • Freeze-thaw cycles avoided

Urine • Sodium azide addition to the sample as

any particulates

can be performed for tissues, cells, and other

homogenizer, ultrasound, microwave, manual grinding, ball mill, and grinding in a liquid nitrogen-cooled mortar and pestle

• Quenching with ice-cold 50 mM ammonium

• Centrifuge the media at 500g for 5 min (remove

separator tubes and incubate 30 min and no longer than 60 min for clotting procedure on ice instead of room temperature to minimize residual

• At the end of the clotting time, the blood sample is centrifuged or 20 min at 1100–1300g at ambient

• The serum visible in the upper layer of the tube as supernatant is collected and stored at 80°C, and

or EDTA-containing tubes under fasting

• Various coagulants such as heparin, citrate, or EDTA represent different retention time peaks

• To obtain highly resolved chromatograms, careful handling is essential in case of blood-

bacteriostatic agent during sample storage • 0.2 μm filtration for each sample in order to avoid

contaminated CSF sample

freeze-thaw cycles are avoided

• Centrifuge at 13,000g, 15 min, 4°C • Supernatant isolation and removal

• Centrifuge at 1000g for 1 min at 20°C • Removal of the media/quenching solution from

• Mechanical homogenization include


Table 1.

Systematic collection and storage of human samples for metabolomics/lipidomics strategies.

optimum, reproducible, and high-throughput analysis with high recovery, extraction, and enriched metabolite coverage.

2.Sample preparation/extraction: During sample preparation different extraction solvents/methods are used for high recovery of both polar and nonpolar compounds based on nontargeted and targeted approaches on

different biological samples involving tissue, blood, plasma, serum, cells, urine, etc. Basically applied approaches include optimized methanol-waterchloroform combinations to extract both hydrophilic and hydrophobic compounds. For high recovery of both hydrophilic and hydrophobic compounds, separate extraction applications give better results. During sample preparation after the centrifugation process, a biphasic mixture of the upper (aqueous) and lower (organic) layers is extracted separately. In the two sequential or two-phase extraction applications optimized for both polar and nonpolar metabolites such as lipids, an aqueous extraction using polar organic solvents (e.g., methanol or acetonitrile) mixed with water followed by organic extraction (lipid extraction) with dichloromethane or chloroformmethanol is carried out [16, 19]. The first one of the two-phase extractions involves aqueous solvent (e.g., methanol-water) followed by extraction with a nonpolar solvent (e.g., chloroform) of the centrifuged pellet.

evaporate. Then the resultant evaporated droplets transfer the charge to the analytes and ionize them both in the positive and negative mode via charge transfer [26]. Polarity of the ionization/ion sources has great importance for avoiding metabolites' losses. Based on the polarity of the molecule, applied ionization sources include electron ionization (EI), chemical ionization (CI), electrospray ionization source (ESI), atmospheric pressure chemical ionization (APCI), atmospheric pressure photo-ionization (APPI), and matrix assisted

5.Detection: High-resolution mass spectrum composed of mass-to-charge (m/z) ratios of fragment ions created by ionized biomolecules is detected by MS at

6.Data analysis and metabolite identification: The large amounts of complex raw data involving specific metabolic signals are extracted from MS and analyzed in specialized software to properly interpret the data and identify the metabolite of interest. Commercially available and free software bioinformatic analysis tools automatically perform processing of peak selection, assessment, and relative quantitation. Raw data signal spectrum preprocessing includes background spectral filtering (noise elimination), retention time correction, appropriate peak assignment for the same compound (identification of matching m/z and assigning adducts appropriately), peak detection, peak alignment (matching peaks across multiple samples) and peak normalization (adjusting peak intensities and reducing analytical drift), and chromatogram alignment. Following this, data

preparation includes data integrity checking, data normalization, and compound name identification using the univariate, multivariate, clustering, and classification statistical analyses [32]. Following data processing, data interpretation, and metabolite identification from mass spectrum can be performed with the following: functional interpretation, enrichment analysis, pathway analysis, and metabolite pathway networks mapping. Commonly used tools include XCMS [33], Metaboanalyst [34], Progenesis [35], MetaCore [36], and 3Omics [37], with different analysis capabilities. The software processes raw mass spectrum data, perform various statistical analyses to find significantly altered ions/features, and for metabolite identifications connect to the metabolite database search such as Human Metabolome Database (HMDB) [14], Metabolite and Tandem MS Database (METLIN) [38], LIPID MAPS [39], Madison Metabolomics Consortium Database (MMCD) [40], BiGG [41], SetupX [42], KNApSAcK [43], and MetaboLights [44]. Compound or compound-specific databases include PubChem [45], Chemical Entities of Biological Interest (ChEBI) [46], ChemSpider [47], The KEGG GLYCAN [48],

KEGG COMPOUND [49], and In Vivo/In Silico Metabolites Database (IIMDB) [50]. Metabolic pathway databases include Kyoto Encyclopedia of Genes and Genomes (KEGG) [51], BiKEGG [52], KEGG PATHWAY [53], MetaCyc [54], BioCyc [54], Model SEED [55], Reactome [56], and Ingenuity

sub-femtomole levels. Mass analyzers include time of flight (TOF), quadrupole time of flight (QTOF), quadrupole, ion trap, and orbitrap. For targeted metabolomics analysis, tandem or MS/MS is performed for validation of potentially discovered metabolites during untargeted analysis. For MS/MS, ion trap or triple quadrupole (QQQ) with multiple reaction monitoring (MRM) is generally used with high sensitivity, mass resolution, and accuracy

laser desorption ionization (MALDI) [27–31].

(<1 ppm mass error) [9].

Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

Pathway Analysis (IPA) [57].

143

3.Separation: Chromatographic separation techniques including liquid chromatography (LC) and gas chromatography (GC) are used coupling to MS systems (GC-MS, HPLC-MS, UPLC-MS), while direct injection techniques include direct infusion MS [20] and direct analysis in real-time MS (DART-MS) [21, 22]. In addition, capillary electrophoresis (CE) coupled to MS systems (CE-MS) is an important technique for separation and profiling of polar metabolites in biological samples. Reversed-phase LC using C18 columns is used for separation of nonpolar compounds, while hydrophilic interaction chromatography (HILIC) is used for separation of polar compounds [23].

Gas chromatography/mass spectrometry (GC/MS) is one of the widely used untargeted and targeted metabolomics platforms which offer high chromatographic resolution. In addition to other compounds, volatile organic compounds (VOCs) such as fatty acids and organic acids which are important biomarker candidates in biological samples can be successfully achieved by GC-MS. Analysis by GC-MS requires derivatization with reactions of alkylation, acylation, and silylation in order to increase detection or retention of the compound [24].

Volatile organic compounds are important components of the metabolome and include metabolites such as alcohols, alkanes, aldehydes, furans, ketones, pyrroles, and terpenes. Volatilomics is a new field with adductomics into the metabolomics. For the extraction of VOCs, solvent-free sample preparation/extraction method "solid-phase microextraction (SPME)" is used, which enables extraction of organic compounds from gaseous, aqueous, and solid materials [24].

LC/MS and GC-MS have different sensitivities for detecting metabolites with high recovery/coverage. Compared to GC/MS, a wide range of molecular features can be analyzed via LC-MS. GC-MS is capable of analyzing less polar biomolecules involving alkylsilyl derivatives, eicosanoids, essential oils, esters, perfumes, terpenes, waxes, volatiles, carotenoids, flavonoids, and lipids. LC-MS is capable of analyzing more polar biomolecules involving organic acids, organic amines, nucleosides, ionic species, nucleotides, and polyamines and does not require derivatization. Both LC-MS and GC-MS are able to analyze alcohols, alkaloids, amino acids, catecholamines, fatty acids, phenolics, polar organics, prostaglandins, and steroids [25].

4.Ionization: After chromatographic separation, samples are pumped through MS capillary to obtain positive or negative electrically charged ions in gas phase. Introduction of heat and dry nitrogen in the MS cause the droplets to

different biological samples involving tissue, blood, plasma, serum, cells, urine, etc. Basically applied approaches include optimized methanol-waterchloroform combinations to extract both hydrophilic and hydrophobic compounds. For high recovery of both hydrophilic and hydrophobic

solvents (e.g., methanol or acetonitrile) mixed with water followed by organic extraction (lipid extraction) with dichloromethane or chloroformmethanol is carried out [16, 19]. The first one of the two-phase extractions involves aqueous solvent (e.g., methanol-water) followed by extraction with a

nonpolar solvent (e.g., chloroform) of the centrifuged pellet.

to increase detection or retention of the compound [24].

compounds from gaseous, aqueous, and solid materials [24].

and steroids [25].

Molecular Medicine

142

3.Separation: Chromatographic separation techniques including liquid

chromatography (LC) and gas chromatography (GC) are used coupling to MS systems (GC-MS, HPLC-MS, UPLC-MS), while direct injection techniques include direct infusion MS [20] and direct analysis in real-time MS (DART-MS) [21, 22]. In addition, capillary electrophoresis (CE) coupled to MS systems (CE-MS) is an important technique for separation and profiling of polar metabolites in biological samples. Reversed-phase LC using C18 columns is used for separation of nonpolar compounds, while hydrophilic interaction chromatography (HILIC) is used for separation of polar compounds [23]. Gas chromatography/mass spectrometry (GC/MS) is one of the widely used untargeted and targeted metabolomics platforms which offer high chromatographic resolution. In addition to other compounds, volatile organic compounds (VOCs) such as fatty acids and organic acids which are important biomarker candidates in biological samples can be successfully achieved by GC-MS. Analysis by GC-MS requires derivatization with reactions of alkylation, acylation, and silylation in order

Volatile organic compounds are important components of the metabolome and include metabolites such as alcohols, alkanes, aldehydes, furans, ketones, pyrroles, and terpenes. Volatilomics is a new field with adductomics into the metabolomics. For the extraction of VOCs, solvent-free sample preparation/extraction method "solid-phase microextraction (SPME)" is used, which enables extraction of organic

LC/MS and GC-MS have different sensitivities for detecting metabolites with high recovery/coverage. Compared to GC/MS, a wide range of molecular features can be analyzed via LC-MS. GC-MS is capable of analyzing less polar biomolecules involving alkylsilyl derivatives, eicosanoids, essential oils, esters, perfumes, terpenes, waxes, volatiles, carotenoids, flavonoids, and lipids. LC-MS is capable of analyzing more polar biomolecules involving organic acids, organic amines, nucleosides, ionic species, nucleotides, and polyamines and does not require derivatization. Both LC-MS and GC-MS are able to analyze alcohols, alkaloids, amino acids, catecholamines, fatty acids, phenolics, polar organics, prostaglandins,

4.Ionization: After chromatographic separation, samples are pumped through MS capillary to obtain positive or negative electrically charged ions in gas phase. Introduction of heat and dry nitrogen in the MS cause the droplets to

compounds, separate extraction applications give better results. During sample preparation after the centrifugation process, a biphasic mixture of the upper (aqueous) and lower (organic) layers is extracted separately. In the two sequential or two-phase extraction applications optimized for both polar and nonpolar metabolites such as lipids, an aqueous extraction using polar organic

evaporate. Then the resultant evaporated droplets transfer the charge to the analytes and ionize them both in the positive and negative mode via charge transfer [26]. Polarity of the ionization/ion sources has great importance for avoiding metabolites' losses. Based on the polarity of the molecule, applied ionization sources include electron ionization (EI), chemical ionization (CI), electrospray ionization source (ESI), atmospheric pressure chemical ionization (APCI), atmospheric pressure photo-ionization (APPI), and matrix assisted laser desorption ionization (MALDI) [27–31].


### 3. Challenges and affecting factors

Though there are extensive tools and databases for analysis and identification of metabolites, challenges remain in the field of data analysis/integration, pathway analysis, and metabolite identification in untargeted metabolomics due to the highthroughput heterogeneous omics data which essentially requires improved bioinformatics and computational techniques to comprehensively evaluate the metabolomic profiles and completion of the human metabolome [32, 58–62].

4. Metabolomics in health and disease

Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

Metabolomics has several applications in health and disease including precision/

metabolomics, and in combination with other omics disciplines as integrative omics [69]. Single-cell metabolomics and single-cell lipidomics technologies allow highdimensional characterizations of individual cells, disease heterogeneity and complexity, identification, expression and abundance of disease-associated small molecules, metabolites, and by LC-MS/MS, GC-MS/MS, and live single-cell mass spectrometry (LSCMS) [70]. Imaging MS analysis of human breast cancer samples at the single-cell level revealed cell-cell interactions and tumor heterogeneity [71]. Clinical biomarkers and different metabotypes of disease severity correlated to

personalized medicine, single cell, epidemiologic population studies, metabolic phenotyping and metabolome-wide association studies (MWAS), precision

exposures [72], and biological outcomes [73] have been studied and identified through metabolomics profiling, MWAS, and metabolomics fingerprinting and footprinting techniques in individuals and populations which will enable precision medicine and public healthcare [74–79]. Studies moving from genome-wide association studies (GWAS) to metabolome-wide association studies (MWAS) were first described in 2008 as "environmental and genomic influences to investigate the connections between phenotype variation and disease risk factors" [78–80]. Rattray

and colleagues suggested exposotypes on single individual phenotypes and populations, in epidemiologic research, and disease risk using metabolome-wide

In conclusion, MS-based metabolomics/lipidomics provides a useful approach for both identification of disease-related metabolites in biofluids or tissue and also encompasses classification and/or characterization of disease or treatment-

Molecular Oncology Laboratory, Gene Engineering and Biotechnology Institute,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

TUBITAK Marmara Research Center, Kocaeli, Turkey

provided the original work is properly cited.

\*Address all correspondence to: nalbantoglusinem@gmail.com

association studies and impacts on precision medicine [79].

Author details

145

Sinem Nalbantoglu

associated molecular patterns generated from metabolites [81, 82].

Chromatographic resolution, absolute MS signals, and compound identification can be affected during ionization process due to polarity, ion sources, ion suppression, flow rates, and MS vacuum, sample preparation strategies, purity and temperature (prechilled) of reagents and solvents, different laboratory staff and techniques in different analysis days (typical sources of variance), different mass analyzers, chromatographic separation columns and compositions that cause forming of different fragments of the same molecule, frequent detection of the most abundant molecules, ion suppression, and day-day variation. Another challenge is the existence of isomers with identical masses and highly similar spectra which complicates distinguishing and differentiation during metabolite assignment to spectrum features [63, 64].

During the analytical process, a plot of the area internal standard (IS) response ratio vs. analyte concentration of each sample within a batch is performed to obtain calibration curves, which are needed to control for accuracy and reproducibility of the system. Performance monitoring in terms of plots of absolute MS signals, retention time points, and mass patterns/chromatographic peak shape of the analytes/ISs are required for maintaining sensitivity, integrity, and robustness of the analytical results. It has been reported that high system pressure and short columns with small particle sizes (<2 μm) lead to better signal:noise ratios than columns with larger particle sizes. Small particle-sized columns also affect the fastness of screening feature of MS [65–68].

Quality control (QC) sample plots of analytical parameters vs. retention times and peak shapes during calibration, sample preparation, and the analysis belonging to each batch as a quality check are essential for optimum standardization. All QC samples of each batch collected from each sample to create the pooled QC should be identical to each other and placed 1 in 5–1 in 10 samples in the analyses [16]. During sample preparation, a general contamination due to matrix compounds occurs and degrades the analytical system. In order to eradicate those possibilities, monitorization and randomization using technical QC and pooled QC samples which involve small aliquots of each biological sample, batch samples, and blanks considering peak shapes and retention times should be performed. Using a set of ISs, control samples, and blanks, performance of the method and monitorization have to be performed. In addition, randomization has to be performed on both sample preparation and the analysis order of system.

In order to eliminate or minimize methodological challenges, systematic errors, and bias factor due to analytical drift such as batch effect and obtain analytical sensitivity and specificity, reproducibility, accurate quantitation, and high recovery + low metabolite losses, each analysis should be carried out specifically under conditions of method optimization and optimum performance monitoring. Furthermore, using quality control samples and ISs during sample preparation, injection, extraction, fractionation, separation, detection, and normalization periods is essential. Generally, stable isotopes are used as the most identical ISs to compounds of interest in order to obtain accurate identification and to eliminate metabolite losses and ion suppression.

3. Challenges and affecting factors

Molecular Medicine

to spectrum features [63, 64].

fastness of screening feature of MS [65–68].

sample preparation and the analysis order of system.

losses and ion suppression.

144

Though there are extensive tools and databases for analysis and identification of metabolites, challenges remain in the field of data analysis/integration, pathway analysis, and metabolite identification in untargeted metabolomics due to the highthroughput heterogeneous omics data which essentially requires improved bioin-

Chromatographic resolution, absolute MS signals, and compound identification can be affected during ionization process due to polarity, ion sources, ion suppression, flow rates, and MS vacuum, sample preparation strategies, purity and temperature (prechilled) of reagents and solvents, different laboratory staff and techniques in different analysis days (typical sources of variance), different mass analyzers, chromatographic separation columns and compositions that cause forming of different fragments of the same molecule, frequent detection of the most abundant molecules, ion suppression, and day-day variation. Another challenge is the existence of isomers with identical masses and highly similar spectra which complicates distinguishing and differentiation during metabolite assignment

During the analytical process, a plot of the area internal standard (IS) response ratio vs. analyte concentration of each sample within a batch is performed to obtain calibration curves, which are needed to control for accuracy and reproducibility of the system. Performance monitoring in terms of plots of absolute MS signals, retention time points, and mass patterns/chromatographic peak shape of the analytes/ISs are required for maintaining sensitivity, integrity, and robustness of the analytical results. It has been reported that high system pressure and short columns with small particle sizes (<2 μm) lead to better signal:noise ratios than columns with larger particle sizes. Small particle-sized columns also affect the

Quality control (QC) sample plots of analytical parameters vs. retention times and peak shapes during calibration, sample preparation, and the analysis belonging to each batch as a quality check are essential for optimum standardization. All QC samples of each batch collected from each sample to create the pooled QC should be identical to each other and placed 1 in 5–1 in 10 samples in the analyses [16]. During sample preparation, a general contamination due to matrix compounds occurs and

In order to eliminate or minimize methodological challenges, systematic errors, and bias factor due to analytical drift such as batch effect and obtain analytical sensitivity and specificity, reproducibility, accurate quantitation, and high recovery + low metabolite losses, each analysis should be carried out specifically under conditions of method optimization and optimum performance monitoring. Furthermore, using quality control samples and ISs during sample preparation, injection, extraction, fractionation, separation, detection, and normalization periods is essential. Generally, stable isotopes are used as the most identical ISs to compounds of interest in order to obtain accurate identification and to eliminate metabolite

degrades the analytical system. In order to eradicate those possibilities, monitorization and randomization using technical QC and pooled QC samples which involve small aliquots of each biological sample, batch samples, and blanks considering peak shapes and retention times should be performed. Using a set of ISs, control samples, and blanks, performance of the method and monitorization have to be performed. In addition, randomization has to be performed on both

formatics and computational techniques to comprehensively evaluate the metabolomic profiles and completion of the human metabolome [32, 58–62].

### 4. Metabolomics in health and disease

Metabolomics has several applications in health and disease including precision/ personalized medicine, single cell, epidemiologic population studies, metabolic phenotyping and metabolome-wide association studies (MWAS), precision metabolomics, and in combination with other omics disciplines as integrative omics [69]. Single-cell metabolomics and single-cell lipidomics technologies allow highdimensional characterizations of individual cells, disease heterogeneity and complexity, identification, expression and abundance of disease-associated small molecules, metabolites, and by LC-MS/MS, GC-MS/MS, and live single-cell mass spectrometry (LSCMS) [70]. Imaging MS analysis of human breast cancer samples at the single-cell level revealed cell-cell interactions and tumor heterogeneity [71].

Clinical biomarkers and different metabotypes of disease severity correlated to exposures [72], and biological outcomes [73] have been studied and identified through metabolomics profiling, MWAS, and metabolomics fingerprinting and footprinting techniques in individuals and populations which will enable precision medicine and public healthcare [74–79]. Studies moving from genome-wide association studies (GWAS) to metabolome-wide association studies (MWAS) were first described in 2008 as "environmental and genomic influences to investigate the connections between phenotype variation and disease risk factors" [78–80]. Rattray and colleagues suggested exposotypes on single individual phenotypes and populations, in epidemiologic research, and disease risk using metabolome-wide association studies and impacts on precision medicine [79].

In conclusion, MS-based metabolomics/lipidomics provides a useful approach for both identification of disease-related metabolites in biofluids or tissue and also encompasses classification and/or characterization of disease or treatmentassociated molecular patterns generated from metabolites [81, 82].

### Author details

Sinem Nalbantoglu Molecular Oncology Laboratory, Gene Engineering and Biotechnology Institute, TUBITAK Marmara Research Center, Kocaeli, Turkey

\*Address all correspondence to: nalbantoglusinem@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## References

[1] Tsoukalas D, Alegakis A, Fragkiadaki P, Papakonstantinou E, Nikitovic D, Karataraki A, et al. Application of metabolomics: Focus on the quantification of organic acids in healthy adults. International Journal of Molecular Medicine. 2017;40(1):112-120

[2] Sun J, Beger DR, Schnackenberg KL. Metabolomics as a tool for personalizing medicine: 2012 update. Personalized Medicine. 2013;10:149-161. DOI: 10.2217/pme.13.8

[3] McKillop AM, Flatt PR. Emerging applications of metabolomic and genomic profiling in diabetic clinical medicine. Diabetes Care. 2011;34: 2624-2630. DOI: 10.2337/dc11-0837

[4] Guijas C, Montenegro-Burke JR, Warth B, Spilker ME, Siuzdak G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nature Biotechnology. 2018; 36(4):316-320. DOI: 10.1038/nbt.4101

[5] Astarita G, Langridge J. An emerging role for metabolomics in nutrition science. Journal of Nutrigenetics and Nutrigenomics. 2013;6:181-200. DOI: 10.1159/000354403

[6] Baraldi E, Carraro S, Giordano G, Reniero F, Perilongo G, Zacchello F. Metabolomics: Moving towards personalized medicine. Italian Journal of Pediatrics. 2009;35:30. DOI: 10.1186/ 1824-7288-35-30

[7] Vander Heiden MG. Targeting cancer metabolism: A therapeutic window opens. Nature Reviews. Drug Discovery. 2011;10(9):671-684

[8] Gowda GA, Zhang S, Gu H, Asiago V, Shanaiah N, Raftery D. Metabolomics-based methods for early disease diagnostics. Expert Review of Molecular Diagnostics. 2008;8(5): 617-633

[9] Wang JH, Byun J, Pennathur S. Analytical approaches to metabolomics and applications to systems biology. Seminars in Nephrology. 2010;30(5): 500-511. DOI: 10.1016/j.semnephrol. 2010.07.007

[17] Marshall DD, Powers R. Beyond the

Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

> Henrique R, et al. Volatile metabolomic signature of bladder cancer cell lines based on gas chromatography-mass spectrometry. Metabolomics. 2018; 14(5):62. DOI: 10.1007/s11306-018-

[25] Hofmann A, Clokie S, editor. In: Wilson and Walker's Principles and Techniques of Biochemistry and Molecular Biology. 8th edition. Cambridge University Press; 2018. 956 p. ISBN-10: 1107162270

[26] Kebarle P. A brief overview of the present status of the mechanisms involved in electrospray mass spectrometry. Journal of Mass Spectrometry. 2000;35:804-817

[28] Pitt JJ. Principles and applications of

spectrometry in clinical biochemistry. Clinical Biochemist Reviews. 2009;

[29] Rosenberg E. The potential of organic (electrospray- and atmospheric pressure chemical ionisation) mass spectrometric techniques coupled to liquid-phase separation for speciation analysis. Journal of Chromatography. A.

[30] Byrdwell WC. Atmospheric pressure chemical ionization mass spectrometry for analysis of lipids.

[31] Dally JE, Gorniak J, Bowie R, Bentzley CM. Quantitation of underivatized free amino acids in mammalian cell culture media using matrix assisted laser desorption ionization time-of-flight mass

[27] Edwards JL, Kennedy RT. Metabolomic analysis of eukaryotic tissue and prokaryotes using negative mode MALDI time-of-flight mass spectrometry. Analytical Chemistry.

liquid chromatography-mass

2005;77(7):2201-2209

30(1):19-34

2003;1000:841-889

Lipids. 2001;36:327-346

1361-9

spectrometry and nuclear magnetic resonance for metabolomics. Progress in

[18] Wishart DS. Metabolomics: The principles and potential applications to transplantation. American Journal of Transplantation. 2005;5(12):2814-2820

[19] Masson P, Alves AC, Ebbels TM, Nicholson JK, Want EJ. Optimization and evaluation of metabolite extraction protocols for untargeted metabolic profiling of liver samples by UPLC-MS. Analytical Chemistry. 2010;82(18):

[20] Lin L, Yu Q, Yan X, Hang W, Zheng J, Xing J, et al. Direct infusion

chromatography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer. The Analyst. 2010;135:2970-2978

[21] Vermeersch KA, Styczynski MP. Applications of metabolomics in cancer research. Journal of Carcinogenesis.

[22] Zhou M, Guan W, Walker LD, Mezencev R, Benigno BB, Gray A, et al. Rapid mass spectrometric metabolic profiling of blood sera detects ovarian cancer with high accuracy. Cancer Epidemiology, Biomarkers & Prevention. 2010;19:2262-2271

[23] Tolstikov VV, Fiehn O. Analysis of highly polar compounds of plant origin:

spectrometry. Analytical Biochemistry. 2002;301(2):298-307. DOI: 10.1006/

[24] Rodrigues D, Pinto J, Araújo AM,

Monteiro-Reis S, Jerónimo C,

Combination of hydrophilic interaction chromatography and electrospray ion trap mass

abio.2001.5513

147

mass spectrometry or liquid

7779-7786

2013;12:9

paradigm: Combining mass

Nuclear Magnetic Resonance Spectroscopy. 2017;100:1-16. DOI: 10.1016/j.pnmrs.2017.01.001

[10] Pauling L, Robinson AB, Teranishi R, Cary P. Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proceedings of the National Academy of Sciences of the United States of America. 1971;68(10):2374-2376

[11] Horning EC, Horning MG. Metabolic profiles: Gas-phase methods for analysis of metabolites. Clinical Chemistry. 1971;17(8):802-809

[12] Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis of the yeast genome. Trends in Biotechnology. 1998;16(9):373-378

[13] Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29(11):1181-1189

[14] Wishart DS, Knox C, Guo AC, et al. HMDB: A knowledgebase for the human metabolome. Nucleic Acids Research. 2009;37(Database issue): D603-D610

[15] Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez-Fresno R, et al. HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Research. 2018;46(D1):D608- D617

[16] Hyötyläinen T, Wiedmer S, editors. Chromatographic Methods in Metabolomics. United Kingdom: The Royal Society of Chemistry; 2013. DOI: 10.1039/9781849737272

Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

References

Molecular Medicine

10.2217/pme.13.8

10.1159/000354403

1824-7288-35-30

2011;10(9):671-684

617-633

146

[8] Gowda GA, Zhang S, Gu H, Asiago V, Shanaiah N, Raftery D. Metabolomics-based methods for early disease diagnostics. Expert Review of Molecular Diagnostics. 2008;8(5):

[1] Tsoukalas D, Alegakis A,

Fragkiadaki P, Papakonstantinou E, Nikitovic D, Karataraki A, et al. Application of metabolomics: Focus on the quantification of organic acids in healthy adults. International Journal of Molecular Medicine. 2017;40(1):112-120 [9] Wang JH, Byun J, Pennathur S. Analytical approaches to metabolomics and applications to systems biology. Seminars in Nephrology. 2010;30(5): 500-511. DOI: 10.1016/j.semnephrol.

[10] Pauling L, Robinson AB, Teranishi R, Cary P. Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proceedings of the National Academy of

Sciences of the United States of America. 1971;68(10):2374-2376

[11] Horning EC, Horning MG.

of the yeast genome. Trends in Biotechnology. 1998;16(9):373-378

pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29(11):1181-1189

D603-D610

D617

Metabolic profiles: Gas-phase methods for analysis of metabolites. Clinical Chemistry. 1971;17(8):802-809

[12] Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis

[13] Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': Understanding the metabolic responses of living systems to

[14] Wishart DS, Knox C, Guo AC, et al. HMDB: A knowledgebase for the human metabolome. Nucleic Acids Research. 2009;37(Database issue):

[15] Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez-Fresno R,

metabolome database for 2018. Nucleic Acids Research. 2018;46(D1):D608-

[16] Hyötyläinen T, Wiedmer S, editors.

Metabolomics. United Kingdom: The Royal Society of Chemistry; 2013. DOI:

et al. HMDB 4.0: The human

Chromatographic Methods in

10.1039/9781849737272

2010.07.007

[2] Sun J, Beger DR, Schnackenberg KL. Metabolomics as a tool for personalizing medicine: 2012 update. Personalized Medicine. 2013;10:149-161. DOI:

[3] McKillop AM, Flatt PR. Emerging applications of metabolomic and genomic profiling in diabetic clinical medicine. Diabetes Care. 2011;34: 2624-2630. DOI: 10.2337/dc11-0837

[4] Guijas C, Montenegro-Burke JR, Warth B, Spilker ME, Siuzdak G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nature Biotechnology. 2018; 36(4):316-320. DOI: 10.1038/nbt.4101

[5] Astarita G, Langridge J. An emerging role for metabolomics in nutrition science. Journal of Nutrigenetics and Nutrigenomics. 2013;6:181-200. DOI:

[6] Baraldi E, Carraro S, Giordano G, Reniero F, Perilongo G, Zacchello F. Metabolomics: Moving towards

personalized medicine. Italian Journal of Pediatrics. 2009;35:30. DOI: 10.1186/

[7] Vander Heiden MG. Targeting cancer metabolism: A therapeutic window opens. Nature Reviews. Drug Discovery. [17] Marshall DD, Powers R. Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics. Progress in Nuclear Magnetic Resonance Spectroscopy. 2017;100:1-16. DOI: 10.1016/j.pnmrs.2017.01.001

[18] Wishart DS. Metabolomics: The principles and potential applications to transplantation. American Journal of Transplantation. 2005;5(12):2814-2820

[19] Masson P, Alves AC, Ebbels TM, Nicholson JK, Want EJ. Optimization and evaluation of metabolite extraction protocols for untargeted metabolic profiling of liver samples by UPLC-MS. Analytical Chemistry. 2010;82(18): 7779-7786

[20] Lin L, Yu Q, Yan X, Hang W, Zheng J, Xing J, et al. Direct infusion mass spectrometry or liquid chromatography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer. The Analyst. 2010;135:2970-2978

[21] Vermeersch KA, Styczynski MP. Applications of metabolomics in cancer research. Journal of Carcinogenesis. 2013;12:9

[22] Zhou M, Guan W, Walker LD, Mezencev R, Benigno BB, Gray A, et al. Rapid mass spectrometric metabolic profiling of blood sera detects ovarian cancer with high accuracy. Cancer Epidemiology, Biomarkers & Prevention. 2010;19:2262-2271

[23] Tolstikov VV, Fiehn O. Analysis of highly polar compounds of plant origin: Combination of hydrophilic interaction chromatography and electrospray ion trap mass spectrometry. Analytical Biochemistry. 2002;301(2):298-307. DOI: 10.1006/ abio.2001.5513

[24] Rodrigues D, Pinto J, Araújo AM, Monteiro-Reis S, Jerónimo C,

Henrique R, et al. Volatile metabolomic signature of bladder cancer cell lines based on gas chromatography-mass spectrometry. Metabolomics. 2018; 14(5):62. DOI: 10.1007/s11306-018- 1361-9

[25] Hofmann A, Clokie S, editor. In: Wilson and Walker's Principles and Techniques of Biochemistry and Molecular Biology. 8th edition. Cambridge University Press; 2018. 956 p. ISBN-10: 1107162270

[26] Kebarle P. A brief overview of the present status of the mechanisms involved in electrospray mass spectrometry. Journal of Mass Spectrometry. 2000;35:804-817

[27] Edwards JL, Kennedy RT. Metabolomic analysis of eukaryotic tissue and prokaryotes using negative mode MALDI time-of-flight mass spectrometry. Analytical Chemistry. 2005;77(7):2201-2209

[28] Pitt JJ. Principles and applications of liquid chromatography-mass spectrometry in clinical biochemistry. Clinical Biochemist Reviews. 2009; 30(1):19-34

[29] Rosenberg E. The potential of organic (electrospray- and atmospheric pressure chemical ionisation) mass spectrometric techniques coupled to liquid-phase separation for speciation analysis. Journal of Chromatography. A. 2003;1000:841-889

[30] Byrdwell WC. Atmospheric pressure chemical ionization mass spectrometry for analysis of lipids. Lipids. 2001;36:327-346

[31] Dally JE, Gorniak J, Bowie R, Bentzley CM. Quantitation of underivatized free amino acids in mammalian cell culture media using matrix assisted laser desorption ionization time-of-flight mass

spectrometry. Analytical Chemistry. 2003;75(19):5046-5053

[32] Cambiaghi A, Ferrario M, Masseroli M. Analysis of metabolomic data: Tools, current strategies and future challenges for omics data integration. Briefings in Bioinformatics. 2017;18(3): 498-510

[33] Smith CA, Want EJ, O'Maille G, et al. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry. 2006;78:779-787

[34] Xia J, Psychogios N, Young N, et al. MetaboAnalyst: A web server for metabolomic data analysis and interpretation. Nucleic Acids Research. 2009;37:652-660

[35] Progenesis QI. Available from: h ttp://www.nonlinear.com/progenesis/ qi/ [Accessed: 25 February 2016]

[36] Thomson Reuters. MetaCoreTM 2004. Available from: http://lsresearch. thom sonreuters.com/ [Accessed: 25 February 2016]

[37] Kuo T-C, Tian T-F, Tseng YJ. 3Omics: A web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Systems Biology. 2013;7:64

[38] Smith CA, O'Maille G, Want EJ, et al. METLIN: A metabolite mass spectral database. Therapeutic Drug Monitoring. 2005;27:747-751

[39] Fahy E, Sud M, Cotter D, Subramaniam S. LIPID MAPS online tools for lipid research. Nucleic Acids Research. 2007;35(Web Server issue): W606-W612. DOI: 10.1093/nar/gkm324

[40] Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J, Schulte CF, et al. Metabolite identification via the

Madison Metabolomics Consortium Database. Nature Biotechnology. 2008; 26:162-164

compound biochemical database for nontargeted metabolomics. Journal of Chemical Information and Modeling. 2013;53(9):2483-2492. DOI: 10.1021/

Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

> [59] Taylor PJ. Matrix effects: The Achilles heel of quantitative highperformance liquid chromatography-

spectrometry. Clinical Biochemistry.

metabolomics. Current Protocols in Molecular Biology. 2012;30:2

[61] Annesley TM. Ion suppression in mass spectrometry. Clinical Chemistry.

[62] Matuszewski BK, Constanzer ML, Chavez-Eng CM. Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS. Analytical Chemistry. 2003;75:3019-3030

[63] Oglesbee D, Sanders KA, Lacey JM, Magera MJ, Casetta B, Strauss KA, et al. Second-tier test for quantification of alloisoleucine and branched-chain amino acids in dried blood spots to improve newborn screening for maple syrup urine disease. Clinical Chemistry.

[64] Pitt JJ, Eggington M, Kahler SG. Comprehensive screening of urine samples for inborn errors of metabolism

spectrometry. Clinical Chemistry. 2002;

[65] Nordstrom A, O'Maille G, Qin C, Siuzdak G. Nonlinear data alignment for

[66] Aronov PA, Hall LM, Dettmer K, Stephensen CB, Hammock BD.

Metabolic profiling of major vitamin D

derivatization and ultra-performance liquid chromatography-tandem mass

metabolites using Diels-Alder

UPLC-MS and HPLC-MS based metabolomics: Quantitative analysis of endogenous and exogenous metabolites in human serum. Analytical Chemistry.

by electrospray tandem mass

electrospray-tandem mass

[60] Roberts LD, Souza LA, Gerszten RE, et al. Targeted

2005;38:328-334

2003;49:1041-1044

2008;54:542-549

48:1970-1980

2006;78:3289-3295

[51] https://www.genome.jp/kegg/

the BiGG and KEGG databases. Molecular BioSystems. 2016;12(11): 3459-3466. DOI: 10.1039/c6mb00532b

show\_pathway?map01100

[52] Jamialahmadi O, Motamedian E, Hashemi-Najafabadi S. BiKEGG: A COBRA toolbox extension for bridging

[53] https://www.genome.jp/kegg-bin/

[55] Devoid S, Overbeek R, DeJongh M,

[56] Jupe S, Akkerman JW, Soranzo N, Ouwehand WH. Reactome—A curated knowledgebase of biological pathways: Megakaryocytes and platelets. Journal of Thrombosis and Haemostasis. 2012; 10(11):2399-2402. DOI: 10.1111/ j.1538-7836.2012.04930.x

[57] Ingenuity. IPA: Ingenuity Pathway Analysis. Available from: http://www.in genuity.com/products/ipa/ [Accessed:

[58] Nanda T, Das M, Tripathy K, Ravi Teja Y. Metabolomics: The future of systems biology. Journal of Computer Science and Systems Biology. 2011:S13.

25 February 2016]

149

DOI: 10.4172/jcsb.S13-003

[54] Caspi R, Altman T, Dreher K, Fulcher CA, Subhraveti P, Keseler IM,

et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/ genome databases. Nucleic Acids Research. 2012;40(Database issue): D742-D753. DOI: 10.1093/nar/gkr1014

Vonstein V, Best AA, Henry C. Automated genome annotation and metabolic model reconstruction in the SEED and model SEED. Methods in Molecular Biology. 2013;985:17-45. DOI:

10.1007/978-1-62703-299-5\_2

ci400368v

[41] King ZA, Lu J, Dräger A, Miller P, Federowicz S, Lerman JA, et al. BiGG models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Research. 2016; 44(D1):D515-D522. DOI: 10.1093/nar/ gkv1049

[42] Scholz M, Fiehn O. SetupX—A public study design database for metabolomic projects. Pacific Symposium on Biocomputing. 2007: 169-180

[43] KNApSAcK: A Comprehensive Species-Metabolite Relationship Database. Available from: http://kanaya .aist-nara.ac.jp/KNApSAcK/

[44] Kale NS, Haug K, Conesa P, Jayseelan K, Moreno P, Rocca-Serra P, et al. MetaboLights: An open-access database repository for metabolomics data. Current Protocols in Bioinformatics. 2016;53:14.13.1-14.1318. DOI: 10.1002/ 0471250953.bi1413s53

[45] https://pubchem.ncbi.nlm.nih.gov/

[46] Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, et al. ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Research. 2016;44(D1):D1214-D1219. DOI: 10.1093/nar/gkv1031

[47] https://www.chemspider.com/Data sourceDetails.aspx?id=84

[48] https://www.genome.jp/kegg/ glycan/

[49] https://www.genome.jp/kegg/c ompound/

[50] Menikarachchi LC, Hill DW, Hamdalla MA, Mandoiu II, Grant DF. In silico enzymatic synthesis of a 400,000

Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

compound biochemical database for nontargeted metabolomics. Journal of Chemical Information and Modeling. 2013;53(9):2483-2492. DOI: 10.1021/ ci400368v

[51] https://www.genome.jp/kegg/

spectrometry. Analytical Chemistry.

Madison Metabolomics Consortium Database. Nature Biotechnology. 2008;

[41] King ZA, Lu J, Dräger A, Miller P, Federowicz S, Lerman JA, et al. BiGG models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Research. 2016; 44(D1):D515-D522. DOI: 10.1093/nar/

[42] Scholz M, Fiehn O. SetupX—A public study design database for metabolomic projects. Pacific Symposium on Biocomputing. 2007:

[43] KNApSAcK: A Comprehensive Species-Metabolite Relationship

.aist-nara.ac.jp/KNApSAcK/

0471250953.bi1413s53

10.1093/nar/gkv1031

glycan/

ompound/

sourceDetails.aspx?id=84

[44] Kale NS, Haug K, Conesa P, Jayseelan K, Moreno P, Rocca-Serra P, et al. MetaboLights: An open-access database repository for metabolomics data. Current Protocols in Bioinformatics. 2016;53:14.13.1-14.1318. DOI: 10.1002/

Database. Available from: http://kanaya

[45] https://pubchem.ncbi.nlm.nih.gov/

[47] https://www.chemspider.com/Data

[48] https://www.genome.jp/kegg/

[49] https://www.genome.jp/kegg/c

[50] Menikarachchi LC, Hill DW, Hamdalla MA, Mandoiu II, Grant DF. In silico enzymatic synthesis of a 400,000

[46] Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, et al. ChEBI in 2016: Improved services

and an expanding collection of metabolites. Nucleic Acids Research. 2016;44(D1):D1214-D1219. DOI:

26:162-164

gkv1049

169-180

Masseroli M. Analysis of metabolomic data: Tools, current strategies and future challenges for omics data integration. Briefings in Bioinformatics. 2017;18(3):

[33] Smith CA, Want EJ, O'Maille G, et al. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak

alignment, matching, and identification. Analytical Chemistry. 2006;78:779-787

[34] Xia J, Psychogios N, Young N, et al. MetaboAnalyst: A web server for metabolomic data analysis and

interpretation. Nucleic Acids Research.

[35] Progenesis QI. Available from: h ttp://www.nonlinear.com/progenesis/ qi/ [Accessed: 25 February 2016]

[36] Thomson Reuters. MetaCoreTM 2004. Available from: http://lsresearch. thom sonreuters.com/ [Accessed: 25

[37] Kuo T-C, Tian T-F, Tseng YJ. 3Omics: A web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC

[38] Smith CA, O'Maille G, Want EJ, et al. METLIN: A metabolite mass spectral database. Therapeutic Drug

[40] Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J, Schulte CF, et al. Metabolite identification via the

Systems Biology. 2013;7:64

Monitoring. 2005;27:747-751

[39] Fahy E, Sud M, Cotter D, Subramaniam S. LIPID MAPS online tools for lipid research. Nucleic Acids Research. 2007;35(Web Server issue): W606-W612. DOI: 10.1093/nar/gkm324

148

2003;75(19):5046-5053

Molecular Medicine

498-510

2009;37:652-660

February 2016]

[32] Cambiaghi A, Ferrario M,

[52] Jamialahmadi O, Motamedian E, Hashemi-Najafabadi S. BiKEGG: A COBRA toolbox extension for bridging the BiGG and KEGG databases. Molecular BioSystems. 2016;12(11): 3459-3466. DOI: 10.1039/c6mb00532b

[53] https://www.genome.jp/kegg-bin/ show\_pathway?map01100

[54] Caspi R, Altman T, Dreher K, Fulcher CA, Subhraveti P, Keseler IM, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/ genome databases. Nucleic Acids Research. 2012;40(Database issue): D742-D753. DOI: 10.1093/nar/gkr1014

[55] Devoid S, Overbeek R, DeJongh M, Vonstein V, Best AA, Henry C. Automated genome annotation and metabolic model reconstruction in the SEED and model SEED. Methods in Molecular Biology. 2013;985:17-45. DOI: 10.1007/978-1-62703-299-5\_2

[56] Jupe S, Akkerman JW, Soranzo N, Ouwehand WH. Reactome—A curated knowledgebase of biological pathways: Megakaryocytes and platelets. Journal of Thrombosis and Haemostasis. 2012; 10(11):2399-2402. DOI: 10.1111/ j.1538-7836.2012.04930.x

[57] Ingenuity. IPA: Ingenuity Pathway Analysis. Available from: http://www.in genuity.com/products/ipa/ [Accessed: 25 February 2016]

[58] Nanda T, Das M, Tripathy K, Ravi Teja Y. Metabolomics: The future of systems biology. Journal of Computer Science and Systems Biology. 2011:S13. DOI: 10.4172/jcsb.S13-003

[59] Taylor PJ. Matrix effects: The Achilles heel of quantitative highperformance liquid chromatographyelectrospray-tandem mass spectrometry. Clinical Biochemistry. 2005;38:328-334

[60] Roberts LD, Souza LA, Gerszten RE, et al. Targeted metabolomics. Current Protocols in Molecular Biology. 2012;30:2

[61] Annesley TM. Ion suppression in mass spectrometry. Clinical Chemistry. 2003;49:1041-1044

[62] Matuszewski BK, Constanzer ML, Chavez-Eng CM. Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS. Analytical Chemistry. 2003;75:3019-3030

[63] Oglesbee D, Sanders KA, Lacey JM, Magera MJ, Casetta B, Strauss KA, et al. Second-tier test for quantification of alloisoleucine and branched-chain amino acids in dried blood spots to improve newborn screening for maple syrup urine disease. Clinical Chemistry. 2008;54:542-549

[64] Pitt JJ, Eggington M, Kahler SG. Comprehensive screening of urine samples for inborn errors of metabolism by electrospray tandem mass spectrometry. Clinical Chemistry. 2002; 48:1970-1980

[65] Nordstrom A, O'Maille G, Qin C, Siuzdak G. Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: Quantitative analysis of endogenous and exogenous metabolites in human serum. Analytical Chemistry. 2006;78:3289-3295

[66] Aronov PA, Hall LM, Dettmer K, Stephensen CB, Hammock BD. Metabolic profiling of major vitamin D metabolites using Diels-Alder derivatization and ultra-performance liquid chromatography-tandem mass

spectrometry. Analytical and Bioanalytical Chemistry. 2008;391: 1917-1930

[67] Ventura R, Roig M, Montfort N, Saez P, Berges R, Segura J. Highthroughput and sensitive screening by ultra-performance liquid chromatography tandem mass spectrometry of diuretics and other doping agents. European Journal of Mass Spectrometry (Chichester, England). 2008;14:191-200

[68] Licea-Perez H, Wang S, Szapacs ME, Yang E. Development of a highly sensitive and selective UPLC/MS/ MS method for the simultaneous determination of testosterone and 5alpha-dihydrotestosterone in human serum to support testosterone replacement therapy for hypogonadism. Steroids. 2008;73:601-610

[69] Khoomrung S, Wanichthanarak K, Nookaew I, Thamsermsang O, Seubnooch P, Laohapand T, et al. Metabolomics and integrative omics for the development of Thai traditional medicine. Frontiers in Pharmacology. 2017;8:474. DOI: 10.3389/fphar.2017. 00474

[70] Emara S, Amer S, Ali A, Abouleila Y, Oga A, Masujima T. Singlecell metabolomics. Advances in Experimental Medicine and Biology. 2017;965:323-343. DOI: 10.1007/978-3- 319-47656-8\_13

[71] Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature Methods. 2014;11(4):417-422. DOI: 10.1038/ nmeth.2869

[72] Walker DI, Pennell KD, Uppal K, Xia X, Hopke PK, Utell MJ, et al. Pilot metabolome-wide association study of benzo(a)pyrene in serum from military personnel. Journal of Occupational and

Environmental Medicine. 2016;58: S44-S52

Understanding exposotypes through metabolomics. Human Genomics. 2018; 12(1):4. DOI: 10.1186/s40246-018-

Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

[81] Liu R, Zhang G, Sun M, Pan X, Yang Z. Integrating a generalized data analysis workflow with the single-probe mass spectrometry experiment for single cell metabolomics. Analytica Chimica Acta. 2019;1064:71-79. DOI:

10.1016/j.aca.2019.03.006

[82] Yang K, Han X. Lipidomics:

Techniques, applications, and outcomes related to biomedical sciences. Trends in Biochemical Sciences. 2016;41(11): 954-969. DOI: 10.1016/j.tibs.2016.

0134-x

08.010

151

[73] Bictash M, Ebbels TM, Chan Q, Loo RL, Yap IKS, Brown IJ, et al. Opening up the "black box": Metabolic phenotyping and metabolome-wide association studies in epidemiology. Journal of Clinical Epidemiology. 2010; 63:970-979

[74] Holmes E, Nicholson JK. Human metabolic phenotyping and metabolome wide association studies. Ernst Schering Foundation Symposium Proceedings. 2007;4:227-249

[75] Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell. 2008;134(5):714-717. DOI: 10.1016/j.cell.2008.08.026

[76] Nicholson JK, Holmes E, Elliott P. The metabolome-wide association study: A new look at human disease risk factors. Journal of Proteome Research. 2008;7:3637-3638

[77] Tolstikov V. Metabolomics: Bridging the gap between pharmaceutical development and population health. Metabolites. 2016;6(3). DOI: 10.3390/ metabo6030020.

[78] Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan JA, Cascante M, et al. Metabolomics enables precision medicine: "A White Paper, Community Perspective". Metabolomics. 2016; 12(10):149. DOI: 10.1007/s11306-016- 1094-6

[79] Chadeau-Hyam M, Ebbels TM, Brown IJ, Chan Q, Stamler J, et al. Metabolic profiling and the metabolome-wide association study: Significance level for biomarker identification. Journal of Proteome Research. 2010;9:4620-4627

[80] Rattray NJW, Deziel NC, Wallach JD, Khan SA, Vasiliou V, Ioannidis JPA, et al. Beyond genomics: Metabolomics: Basic Principles and Strategies DOI: http://dx.doi.org/10.5772/intechopen.88563

Understanding exposotypes through metabolomics. Human Genomics. 2018; 12(1):4. DOI: 10.1186/s40246-018- 0134-x

spectrometry. Analytical and Bioanalytical Chemistry. 2008;391:

ultra-performance liquid chromatography tandem mass spectrometry of diuretics and other doping agents. European Journal of Mass Spectrometry (Chichester, England). 2008;14:191-200

[68] Licea-Perez H, Wang S,

MS method for the simultaneous determination of testosterone and 5alpha-dihydrotestosterone in human

serum to support testosterone

Nookaew I, Thamsermsang O, Seubnooch P, Laohapand T, et al. Metabolomics and integrative omics for the development of Thai traditional medicine. Frontiers in Pharmacology. 2017;8:474. DOI: 10.3389/fphar.2017.

[70] Emara S, Amer S, Ali A,

cell metabolomics. Advances in Experimental Medicine and Biology. 2017;965:323-343. DOI: 10.1007/978-3-

Steroids. 2008;73:601-610

00474

319-47656-8\_13

nmeth.2869

150

Szapacs ME, Yang E. Development of a highly sensitive and selective UPLC/MS/

replacement therapy for hypogonadism.

[69] Khoomrung S, Wanichthanarak K,

Abouleila Y, Oga A, Masujima T. Single-

[71] Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature Methods. 2014;11(4):417-422. DOI: 10.1038/

[72] Walker DI, Pennell KD, Uppal K, Xia X, Hopke PK, Utell MJ, et al. Pilot metabolome-wide association study of benzo(a)pyrene in serum from military personnel. Journal of Occupational and

[67] Ventura R, Roig M, Montfort N, Saez P, Berges R, Segura J. Highthroughput and sensitive screening by Environmental Medicine. 2016;58:

[73] Bictash M, Ebbels TM, Chan Q, Loo RL, Yap IKS, Brown IJ, et al. Opening up the "black box": Metabolic phenotyping and metabolome-wide association studies in epidemiology. Journal of Clinical Epidemiology. 2010;

[74] Holmes E, Nicholson JK. Human metabolic phenotyping and metabolome wide association studies. Ernst Schering Foundation Symposium Proceedings.

[75] Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell. 2008;134(5):714-717. DOI:

[76] Nicholson JK, Holmes E, Elliott P. The metabolome-wide association study: A new look at human disease risk factors. Journal of Proteome Research.

[77] Tolstikov V. Metabolomics: Bridging

[78] Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan JA, Cascante M, et al.

medicine: "A White Paper, Community Perspective". Metabolomics. 2016; 12(10):149. DOI: 10.1007/s11306-016-

[79] Chadeau-Hyam M, Ebbels TM, Brown IJ, Chan Q, Stamler J, et al. Metabolic profiling and the

metabolome-wide association study: Significance level for biomarker identification. Journal of Proteome Research. 2010;9:4620-4627

[80] Rattray NJW, Deziel NC, Wallach JD, Khan SA, Vasiliou V, Ioannidis JPA, et al. Beyond genomics:

the gap between pharmaceutical development and population health. Metabolites. 2016;6(3). DOI: 10.3390/

Metabolomics enables precision

S44-S52

63:970-979

2007;4:227-249

2008;7:3637-3638

metabo6030020.

1094-6

10.1016/j.cell.2008.08.026

1917-1930

Molecular Medicine

[81] Liu R, Zhang G, Sun M, Pan X, Yang Z. Integrating a generalized data analysis workflow with the single-probe mass spectrometry experiment for single cell metabolomics. Analytica Chimica Acta. 2019;1064:71-79. DOI: 10.1016/j.aca.2019.03.006

[82] Yang K, Han X. Lipidomics: Techniques, applications, and outcomes related to biomedical sciences. Trends in Biochemical Sciences. 2016;41(11): 954-969. DOI: 10.1016/j.tibs.2016. 08.010

## *Edited by Sinem Nalbantoglu and Hakima Amri*

Molecular medicine is an applied science focused on human genes/transcripts, proteins, metabolites, and metabolic networks that describes molecular and cellular processes of health and disease onset and progression. Molecular medicine-based integrative identification and characterization of biomarker targets and their clinical translations is essential to explain/decipher the mechanism(s) underlying physiological pathways and pathological conditions, and acquire cell-targeted early interventional and therapeutic strategies in the context of precision medicine and public health. Principally, *Molecular Medicine* provides an overview of the latest headlines/ developments of systems and molecular medicine, highlighting the emerging highthroughput technologies, promising potential applications, and progress in biomedical research and development strategies.

Published in London, UK © 2019 IntechOpen © kirstypargeter / iStock

Molecular Medicine

Molecular Medicine

*Edited by Sinem Nalbantoglu and Hakima Amri*