**Meet the editor**

Dr. Jeevan K. Prasain received his PhD in 1998 from the Toyama Medical and Pharmaceutical University, Japan, and then joined the Biotechnology Research Institute, Montreal, Canada, as a visiting fellow where he contributed to the development of high-throughput screening of bioactive natural products and their analysis by tandem mass spectrometry. He was a postdoctoral fellow

in 2000 at the Institute of Physical and Chemical Research (RIKEN), Japan. Currently, Dr. Prasain is an assistant professor at the Department of Pharmacology & Toxicology, University of Alabama at Birmingham, and his research interests include metabolomics/lipidomics, biological evaluation of dietary supplements in vitro and in vivo, metabolism, bioavailability, and pharmacokinetic evaluation of active metabolites.

## Contents

#### **Preface XI**


#### **X** Contents


## Preface

Chapter 7 **Impact of Metabolomics in Symbiosis Research 139** Alba Chavez-Dozal and Michele K. Nishiguchi

Emmanuel Ibarra-Estrada, Ramón Marcos Soto-Hernández and

Chapter 9 **Application of Metabolomics for the Diagnosis and Traditional Chinese Medicine Syndrome Differentiation of Chronic**

Juan Wang, Jianxin Chen, Huihui Zhao and Wei Wang

Luis Aldámiz-Echevarría, Fernando Andrade, Marta Llarena and

Chapter 10 **A Metabolomics Approach to Metabolic Diseases 185**

Chapter 8 **Metabolomics as a Tool in Agriculture 147**

**Section 3 Biomedical and Clinical Metabolomics 169**

Mariana Palma-Tenango

**VI** Contents

**Heart Failure 171**

Domingo González-Lamuño

Mina H. Hanna and Patrick D. Brophy

Chapter 11 **Metabolomics in Neonatology 195**

Metabolomics being system-level analyses of a large number of metabolites in response to biological stimuli or environmental perturbations is an emerging powerful technique with a wide range of applications including clinical chemistry. Over the next decade, metabolomics is expected to provide more insights into quality of foods, disease onset, and progression and identification of biomarkers and thus may revolutionize the future healthcare strategy. Given its potential applications, there is still a dearth of books that capture the current status of our knowledge in various aspects of metabolomics. This book is an attempt to understand basic principles and applications of metabolomics. It contains 11 chapters divided into three main sections covering basic principles, analytical techniques, data analysis and applications in foods, plant metabolism, agriculture, and biomedical and clinical sciences.

Thanks are expressed to the contributing authors who have attempted to provide updated review from the basic principles to practical application of metabolomics. Finally, I would like to thank Ms. Dajana Pemac, Publishing Process Manager, and other InTech publication staffs for their valuable support.

> **Jeevan K. Prasain, PhD** Department of Pharmacology & Toxicology University of Alabama at Birmingham USA

## **Analysis and Data Processing**

#### **Chapter 1 Provisional chapter**

#### **Marine Environmental Metabolomics Marine Environmental Metabolomics**

Francisco Javier Toledo Marante

Francisco Javier Toledo Marante

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66246

#### **Abstract**

Marine environmental metabolomics studies the interactions of marine organisms with their environment using metabolomics to characterise these interactions. There are many advantages in using this method to study interactions between organisms and the environment and to assess the function and health of organisms at the molecular level. In fact, metabolomics is finding an increasing number of applications in the marine sciences. These range from understanding the response of organisms to abiotic pressure to researching the response of organisms to other biota. These interactions can be studied at different levels, from individuals to populations for more traditional eco‐ physiological or ecological studies. Marine organisms have developed a high diversity of chemical defences to avoid predators and parasites. This study therefore highlights the complexity of chemical interactions in the marine environment. The research methods include 1 H‐ and 13C‐NMR spectroscopy, mass spectrometry, analytical and preparative chromatography, and a multitude of bio‐assays.

**Keywords:** metabolomics, marine, environmental, allomones, kairomones, phero‐ mones

## **1. Introduction**

Environmental metabolomics is the application of metabolomics for characterising the interactions of organisms with their environment [1]. Marine environmental metabolomics is the application of metabolomics to characterise the interactions of marine organisms with their environment [2].

As Greek scholars claimed, everything is born out of struggle and need. The pressing need of organisms is to adapt to the environment or adapt the environment to the interests of the species. The survival and progress of the different living entities represent a secret driving

© 2016 The Author(s). Licensee InTech. 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. © 2017 The Author(s). Licensee InTech. 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.

force that harnesses metabolites to trigger sophisticated chemical interactions. Although the production of these bio‐active substances requires an enormous effort from the organism in terms of energy, the adaptive advantage gained in return is as spectacular as it is necessary for survival.

The progressive degradation of the marine environment on the other hand (animal/plant pests, pollution, etc.) leads us to the need to protect it, and to do so, we have to understand it. Scientists are increasingly clear that our understanding of the marine environment is incomplete without a deeper understanding of its metabolomics. There is even talk of a new science, although its name is not clear. Some call it chemical ecology [3], others ecological bio‐chemistry [4], and others consider it part of synecology or biocenotics. According to the principle of science itself "given enough time, only the necessary will survive" [3] so one can expect that the day will come when chemists and biologists will decide on the best name for it.

## **2. Terminology**

Coactones or semiochemicals are the compounds released by an organism which evoke a reaction in another organism of a different or the same species. When these compounds act at a distance, they are called allelochemicals, and their interaction is known as allelopathy [5].

Interactions can be inter‐specific or intra‐specific, depending on whether they affect individ‐ uals of different or the same species, respectively. The chemical factors that affect organisms of different species can in turn be allomones or kairomones, while the chemical factors that affect individuals of the same species can be by auto‐toxins, or pheromones [3].

Allomones are semiochemicals that favour the emitter, but not for the receiver. Examples in the marine environment include toxins, digestibility reducing factors, repellents, feeding deterrents, anti‐fouling compounds, escape substances, suppressors—antibiotics and cytotox‐ ins [6].

Kairomones are semiochemicals that favour the receiver. Examples from the marine environ‐ ment include predator attractors or substances that predators use to locate their prey, adapta‐ tion inducers, like the spine‐development factor in rotifers, warnings signals of danger or toxicity, which benefit the receiver, such as colourants that generate bright colours with characteristic designs on the most toxic animals, growth stimulators, etc. [6].

Pheromones are the semiochemicals released into the environment to influence behaviour or some biological function in the same species. These include sexual/social/warning/territorial marking/trace and communication pheromones [7]. One particular and highly important case of the latter refers to migration pheromones or tele‐mediators [8].

## **3. Toxins**

Animals that are mobile or have hard shells or spines are typically not defended by noxious or toxic chemicals. This is the case of the sea urchin or the spiny lobster. Contrarily, the spotted trunkfish (*Lactophrys bicaudalis*) secretes a colourless toxin from glands on its skin when touched. Predators as large as nurse sharks can die as a result of eating a trunkfish. Methane chemical ionisation gas chromatography‐mass spectrometry was used to study pahutoxin (**1**) and choline chloride esters of 16C (**2**), 17C (**3**) and 18C (**4**) fatty acids from Caribbean trunkfish (*Lactophrys triqueter*) toxin (**Figure 1**) [9].

**Figure 1.** Caribbean trunkfish toxins.

force that harnesses metabolites to trigger sophisticated chemical interactions. Although the production of these bio‐active substances requires an enormous effort from the organism in terms of energy, the adaptive advantage gained in return is as spectacular as it is necessary for

The progressive degradation of the marine environment on the other hand (animal/plant pests, pollution, etc.) leads us to the need to protect it, and to do so, we have to understand it. Scientists are increasingly clear that our understanding of the marine environment is incomplete without a deeper understanding of its metabolomics. There is even talk of a new science, although its name is not clear. Some call it chemical ecology [3], others ecological bio‐chemistry [4], and others consider it part of synecology or biocenotics. According to the principle of science itself "given enough time, only the necessary will survive" [3] so one can expect that the day will

Coactones or semiochemicals are the compounds released by an organism which evoke a reaction in another organism of a different or the same species. When these compounds act at a distance, they are called allelochemicals, and their interaction is known as allelopathy [5]. Interactions can be inter‐specific or intra‐specific, depending on whether they affect individ‐ uals of different or the same species, respectively. The chemical factors that affect organisms of different species can in turn be allomones or kairomones, while the chemical factors that

Allomones are semiochemicals that favour the emitter, but not for the receiver. Examples in the marine environment include toxins, digestibility reducing factors, repellents, feeding deterrents, anti‐fouling compounds, escape substances, suppressors—antibiotics and cytotox‐

Kairomones are semiochemicals that favour the receiver. Examples from the marine environ‐ ment include predator attractors or substances that predators use to locate their prey, adapta‐ tion inducers, like the spine‐development factor in rotifers, warnings signals of danger or toxicity, which benefit the receiver, such as colourants that generate bright colours with

Pheromones are the semiochemicals released into the environment to influence behaviour or some biological function in the same species. These include sexual/social/warning/territorial marking/trace and communication pheromones [7]. One particular and highly important case

Animals that are mobile or have hard shells or spines are typically not defended by noxious or toxic chemicals. This is the case of the sea urchin or the spiny lobster. Contrarily, the spotted

affect individuals of the same species can be by auto‐toxins, or pheromones [3].

characteristic designs on the most toxic animals, growth stimulators, etc. [6].

of the latter refers to migration pheromones or tele‐mediators [8].

come when chemists and biologists will decide on the best name for it.

survival.

4 Metabolomics - Fundamentals and Applications

**2. Terminology**

ins [6].

**3. Toxins**

**Figure 2.** Jaspisamide A, a nudibranch egg ribbon toxin.

On the other hand, marine snails are strongly protected. Nudibranchs, or sea slugs as they are also known, are an example that is commonly quoted of how organisms with powerful chemical defences have little need for a physical defence like a hard shell to protect them from predators. Nudibranchs usually obtain their chemical defences from the sponges, bryozoans and sea squirts that they eat, but cases have been reported in which these are produced by *de novo* bio‐genesis [10]. Nudibranchs also put defensive compounds in their soft egg ribbons. One known example is the case of the jaspisamides A (**5**), B and C, chemical compounds that, while produced by the Okinawan sponge *Jaspis* sp., have been isolated from nudibranch egg masses (**Figure 2**) [11]. These trisoxazole macrolides are cytotoxic and antifungal metabolites initially isolated from the egg ribbons of the *Hexabranchus* nudibranch [12]. They possess a characteristic macrolide portion, comprising three contiguous oxazole units. Trisoxazole macrolides depolymerise F‐actin and form a 1:1 complex with G‐actin, thereby exhibiting potent toxicity towards eukaryotic cells [13].

## **4. Repellents/feeding deterrents**

Sponges are an abundant group of coral‐reef invertebrates that are very chemically rich. Recent studies have shown that many sponge chemicals effectively repel potential predators, and many of the distasteful compounds have now been isolated and structurally characterised [14]. Bioassays are usually run to locate sponges that accumulate repellents. As an experimental methodology, *Preference assays* offer a range of potential prey species to common predators. Species that are avoided by predators frequently have chemical defences. *Caging experiments* also cast light on the role played by predators in habitats, such as coral reefs, where they abound, in eliminating poorly defended prey. Ecologists first identify low preference prey species, before determining whether they are equipped with a chemical defence by adding chemical extracts taken from them to a food item that predators readily eat. This feeding experiment is simple: each fish (from a typical sample of 10–15 individuals) is offered a food pellet containing the extract and an identical food pellet without the extract. The numbers of control and treatment pellets eaten are recorded in tables and graphs and then compared to see whether the fish find the extract distasteful. The assay is then made more realistic from an ecological point of view by placing control and extract‐treated foods on the reef where many different species of fish can feed on them.

Soft fleshy seaweeds found where herbivorous fish and invertebrates abound typically deter herbivory by producing distasteful secondary metabolites. *Halimeda* spp. are among the most common seaweeds on tropical reefs where herbivory is intense. These calcified seaweeds produce feeding deterrents. Halimedatrial (**6**) a structurally unprecedented diterpenoid trialdehyde was identified as the major secondary metabolite in six species of these calcareous reef‐building alga (**Figure 3**) [15]. In laboratory bioassays, this metabolite is toxic to reef fish, significantly reducing feeding in herbivorous fishes, and has cytotoxic and anti‐microbial activities. When plants from most of the *Halimeda* spp. on Guam suffer grinding or crushing damage, they immediately convert halimedatetraacetate, a less‐deterrent secondary metabo‐ lite, into halimedatrial, a more powerful deterrent to feeding (**6**). The conversion process would be triggered when fish bite or chew *Halimeda* plants. The process of rapid conversion is known as activation. Extracts from injured plants contained more halimedatrial and were more deterrent towards herbivorous fishes than extracts from control plants. Herbivore‐activated defences are common in many families of terrestrial plants, but this was the first example of an activated defence in a marine plant [16].

**Figure 3.** Halimedatrial.

On the other hand, marine snails are strongly protected. Nudibranchs, or sea slugs as they are also known, are an example that is commonly quoted of how organisms with powerful chemical defences have little need for a physical defence like a hard shell to protect them from predators. Nudibranchs usually obtain their chemical defences from the sponges, bryozoans and sea squirts that they eat, but cases have been reported in which these are produced by *de novo* bio‐genesis [10]. Nudibranchs also put defensive compounds in their soft egg ribbons. One known example is the case of the jaspisamides A (**5**), B and C, chemical compounds that, while produced by the Okinawan sponge *Jaspis* sp., have been isolated from nudibranch egg masses (**Figure 2**) [11]. These trisoxazole macrolides are cytotoxic and antifungal metabolites initially isolated from the egg ribbons of the *Hexabranchus* nudibranch [12]. They possess a characteristic macrolide portion, comprising three contiguous oxazole units. Trisoxazole macrolides depolymerise F‐actin and form a 1:1 complex with G‐actin, thereby exhibiting

Sponges are an abundant group of coral‐reef invertebrates that are very chemically rich. Recent studies have shown that many sponge chemicals effectively repel potential predators, and many of the distasteful compounds have now been isolated and structurally characterised [14]. Bioassays are usually run to locate sponges that accumulate repellents. As an experimental methodology, *Preference assays* offer a range of potential prey species to common predators. Species that are avoided by predators frequently have chemical defences. *Caging experiments* also cast light on the role played by predators in habitats, such as coral reefs, where they abound, in eliminating poorly defended prey. Ecologists first identify low preference prey species, before determining whether they are equipped with a chemical defence by adding chemical extracts taken from them to a food item that predators readily eat. This feeding experiment is simple: each fish (from a typical sample of 10–15 individuals) is offered a food pellet containing the extract and an identical food pellet without the extract. The numbers of control and treatment pellets eaten are recorded in tables and graphs and then compared to see whether the fish find the extract distasteful. The assay is then made more realistic from an ecological point of view by placing control and extract‐treated foods on the reef where many

Soft fleshy seaweeds found where herbivorous fish and invertebrates abound typically deter herbivory by producing distasteful secondary metabolites. *Halimeda* spp. are among the most common seaweeds on tropical reefs where herbivory is intense. These calcified seaweeds produce feeding deterrents. Halimedatrial (**6**) a structurally unprecedented diterpenoid trialdehyde was identified as the major secondary metabolite in six species of these calcareous reef‐building alga (**Figure 3**) [15]. In laboratory bioassays, this metabolite is toxic to reef fish, significantly reducing feeding in herbivorous fishes, and has cytotoxic and anti‐microbial activities. When plants from most of the *Halimeda* spp. on Guam suffer grinding or crushing damage, they immediately convert halimedatetraacetate, a less‐deterrent secondary metabo‐ lite, into halimedatrial, a more powerful deterrent to feeding (**6**). The conversion process would

potent toxicity towards eukaryotic cells [13].

**4. Repellents/feeding deterrents**

6 Metabolomics - Fundamentals and Applications

different species of fish can feed on them.

Gorgonians, a type of soft coral, are close relatives of hard corals, but they do not have a hard calcium carbonate skeleton. Their soft texture seems to make them a target for a range of reef predators, but the many novel compounds they produce act as an effective defence to protect them from these predators. Hence, for example, *Erythropodium caribaeorum* and *Verru‐ cella umbraculum* produce several diterpenes B, **7** (**Figure 4**) [17–20].

**Figure 4.** Erythrolide B.

Biomass screening of a new marine‐derived strain of *Penicillium roqueforti*, as produced by liquid‐state fermentation, led to the identification of the compound 4‐hydroxy‐benzaldehyde **8** (0.92%) [21]. This natural product is a feeding deterrent factor that restrains the greatest predator of the *Isodictya erinacea* sponge, the *Perknaster fuscus* starfish [22]. Although it is the first time that this has been described in fungi, this substance is structurally related to other previously known metabolites in these organisms that are involved in the shikimic acid pathway, such as oxime‐2‐(4‐hydroxyphenyl)‐2‐oxo acetaldehyde **9**, a metabolite previously isolated from *P. olsonii* (**Figure 5**) [23].

**Figure 5.** Allomones of *Penicillium roqueforti* (**8**) and *Penicillium olsonii* (**9**).

This seems to suggest that the shikimic acid pathway allows fungi to produce their allo‐ mones by *de novo* bio‐genesis. After these and many other analogous discoveries [2], the idea began to take hold that these allomones are not produced by sponges, but by some symbiotic fungus that lives on them. One fact that supports this idea is that other fungal allomones have been identified among the components of others sponges [24], oysters [25] and algae [26, 27].

#### **5. Anti‐fouling compounds**

Many sessile marine organisms are surprisingly clean given the abundance of algal spores and invertebrate larvae that could settle and grow on them. Some seaweeds and invertebrates produce compounds that deter or kill larvae and spores attempting to colonise them as a way of keeping clean. Zosteric acid **10** (**Figure 6**), isolated from the young shoots of a seagrass, is an example of a potent natural anti‐fouling compound [28, 29].

**Figure 6.** Zosteric acid.

#### **6. Kairomones**

Saponins act as repellents in sea cucumbers, and many species produce these cytotoxic secondary metabolites. Despite the deterrent, they are still colonised by multiple symbiotic organisms, including the Harlequin crab, *Lissocarcinus orbicularis*, which is one of the most widespread in the Indo‐Pacific Ocean. The authors have identified the nature of the molecules secreted by sea cucumbers that attract symbionts for the first time. The kairomones recognised by the crabs are saponins—like holothurin A, **11** (**Figure 7**)—ensuring symbiosis. The success of this symbiosis is due to the ability that crabs showed during evolution to overcome the sea cucumber's chemical defences, with their repellents evolving into powerful attractants [30].

**Figure 7.** Holothurin A.

pathway, such as oxime‐2‐(4‐hydroxyphenyl)‐2‐oxo acetaldehyde **9**, a metabolite previously

This seems to suggest that the shikimic acid pathway allows fungi to produce their allo‐ mones by *de novo* bio‐genesis. After these and many other analogous discoveries [2], the idea began to take hold that these allomones are not produced by sponges, but by some symbiotic fungus that lives on them. One fact that supports this idea is that other fungal allomones have been identified among the components of others sponges [24], oysters [25] and algae [26, 27].

Many sessile marine organisms are surprisingly clean given the abundance of algal spores and invertebrate larvae that could settle and grow on them. Some seaweeds and invertebrates produce compounds that deter or kill larvae and spores attempting to colonise them as a way of keeping clean. Zosteric acid **10** (**Figure 6**), isolated from the young shoots of a seagrass, is

Saponins act as repellents in sea cucumbers, and many species produce these cytotoxic secondary metabolites. Despite the deterrent, they are still colonised by multiple symbiotic

isolated from *P. olsonii* (**Figure 5**) [23].

8 Metabolomics - Fundamentals and Applications

**5. Anti‐fouling compounds**

**Figure 6.** Zosteric acid.

**6. Kairomones**

**Figure 5.** Allomones of *Penicillium roqueforti* (**8**) and *Penicillium olsonii* (**9**).

an example of a potent natural anti‐fouling compound [28, 29].

#### **7. Pheromones**

Pheromones are secreted or excreted chemicals that trigger a social response in members of the same species. In the marine environment, there are some well‐known examples that affect behaviour or physiology: alarm pheromones, sex pheromones, etc. There are papers in the field of alarm pheromones reporting how the nudibranchs *Tambje* spp. use alarm pheromones,

**Figure 8.** Tambjamine B (**12**) and ectocarpene (**13**).

such as tambjamine B, **12** (**Figure 8**), to alert others to a threat [31]. Similarly, there are papers in the field of sex pheromones that report how male copepods can follow a three‐dimensional pheromone trail left by a swimming female [32], and male gametes of many brown algae use a pheromone, such as ectocarpene, **13,** (**Figure 8**) to help find a female gamete for fertilisation, a phenomenon known as chemotaxis [33, 34], in fact there are even videos of this on internet.

## **8. Research methods**

Isolating and identifying natural products requires the use of physio‐chemical fractionation and purification techniques (**Figure 9**). These products can be explored once enough biological matter is obtained, either from organisms collected directly from their natural habitat or using bio‐processes (fermentation, photo‐bio‐reaction) or marine aquaculture to grow them. The biomass obtained can be frozen, freeze‐dried or chemically set in a dissolvent to conserve it.

**Figure 9.** Methodology to identify bioactive substances.

The preliminary separation techniques used in laboratories are performed with adsorption chromatography, using gravity flow columns at low or medium pressure, or using liquid‐ liquid partition methods [35]. The latter technique can be applied simply with the help of a decanting funnel, and using one of several variants, it can provide low, medium or high‐ polarity fractions. This was the case of the fractions obtained from the growth medium used for *P. roqueforti* fungus [36]. Throughout the process to separate the components, there is always the option of using a *Sephadex‐*type molecular size exclusion chromatography, which separates families of chemical components of similar molecular size. There are several resins on the market under the common name of the technique (*Gel Filtration*). A very popular one is *LH‐20* [37]. Other separation techniques can then be used, such as adsorption chromatography in flash‐type columns, either in normal phase or in reverse phase, allowing us to work on a scale of grams, or the case of preparatory plates for thin‐layer chromatography (HPTLC/TLC). Finally, the isolated component is crystallised to get pure crystals [38].

such as tambjamine B, **12** (**Figure 8**), to alert others to a threat [31]. Similarly, there are papers in the field of sex pheromones that report how male copepods can follow a three‐dimensional pheromone trail left by a swimming female [32], and male gametes of many brown algae use a pheromone, such as ectocarpene, **13,** (**Figure 8**) to help find a female gamete for fertilisation, a phenomenon known as chemotaxis [33, 34], in fact there are even videos of this on internet.

Isolating and identifying natural products requires the use of physio‐chemical fractionation and purification techniques (**Figure 9**). These products can be explored once enough biological matter is obtained, either from organisms collected directly from their natural habitat or using bio‐processes (fermentation, photo‐bio‐reaction) or marine aquaculture to grow them. The biomass obtained can be frozen, freeze‐dried or chemically set in a dissolvent to conserve it.

The preliminary separation techniques used in laboratories are performed with adsorption chromatography, using gravity flow columns at low or medium pressure, or using liquid‐

**8. Research methods**

10 Metabolomics - Fundamentals and Applications

**Figure 9.** Methodology to identify bioactive substances.

Structural elucidation using spectroscopic methods requires perfectly pure substances crystalline, amorphous or oily. If crystals are successfully obtained, an X‐ray diffraction study could then be performed. If not, if the purified component has a non‐crystalline structure, then the literature recommends obtaining 1 H‐ and 13C‐nuclear magnetic resonance spectra and mass spectra. Once we have the spectra and these are studied, the researcher proposes the structure of the component. This process requires a meticulous revision of the structures previously described in the literature.

Because of the immense number of known products, it is much easier to resort to a powerful database. In these days, many databases on the subject are available to the scientific community online, from the more conventional *Chemical Abstracts Registry*, now part of *Scifinder*, to more specific ones like *MarinLit*® published by the University of Canterbury (New Zealand), which encompasses all the literature published on natural marine products. Mention must also be made of the antibase (*Chemical Concepts*), which deals solely with natural products isolated from micro‐organisms and higher fungi.

Once the bibliographic background has been checked and the conclusion has been drawn that the component isolated is new, a more refined structural elucidation has to be carried out. A second high‐resolution mass spectrum (HRMS) is required for this to determine the exact molecular formula of both the molecule and the fragments of it that form in the apparatus' injection block. With this information, the two‐dimensional structure of the new metabolite isolated can be deduced [39].

Obtaining the three‐dimensional structure of molecules requires high‐resolution nuclear magnetic resonance techniques. Such spectra provide data on coupling between nuclei that are close together in space and their dihedral angles, using the coupling constants *J*HH. Complex two‐dimensional resonance experiments called COSY, TOCSY, NOESY, HSQC, HMBC, DEPT‐ 90, DEPT‐135, etc. provide all the other necessary information [40, 41].

Apart from the techniques indicated above, analytical methods provide important tools in the qualitative and quantitative analysis of substances allowing us to establish their identity and the precise quantity of each component of a given mixture [36]. Instrumental techniques include high‐resolution liquid chromatography (HPLC or UHPLC) and gas chromatography

(GC). Once connected to modern mass spectrometry, these tools resolve countless analytical problems (UHPLC‐MS/MS or GC‐MS) [21].

## **9. Results and discussion**

A new *Paecilomyces variotii* strain was isolated from the marine habitat. The fungal biomass necessary for the chemical study was successfully produced on a laboratory scale. Twenty‐ eight structural groups were identified from volatile compounds, a large part of which are lipid compounds involved in the fatty acid pathway, fragments from its catabolism, terpenoids and a metabolite from the shikimic acid pathway. Two other non‐volatile compounds, olein and ergosterol peroxide, were also isolated and identified using spectroscopy [42].

The screening of the biomass of a new marine‐derived strain of *Penicillium roqueforti*, produced by liquid‐state fermentation, led to the identification of several volatile organic compounds active in the fatty acid pathway, together with fragments produced as a result of their catabolism, terpenoids, and metabolites from the shikimic acid route. In addition, three non‐ volatile organic compounds: 9(11)‐dehydroergosterol peroxide, 4‐hydroxy‐benzaldehyde and D‐mannitol were isolated and identified using spectroscopy. The results have shown that this fungal strain produces no mycotoxin in the culture conditions applied and thus is useful for industrial applications where high value‐added biomolecules are generated [21].

A GC‐MS chemical screening on the biomass of a marine protist of the *Schizochytrium* genus enables the authors [43] to identify 24 kinds of organic compounds belonging to the *n*‐alkanes, 1‐alkenes, 1‐alkanols, free fatty acids, methyl and ethyl esters of saturated and unsaturated fatty acids, saturated and unsaturated glycerides, wax esters, sterols, mono‐, sesqui‐ and tri‐ terpenes. Thus, this organism from the base of the food chain, which accumulates so many nutrients and does not produce toxins, has been proposed as a very interesting specimen for modern functional nutrition.

The chemical constituents of the fermentation broth of the marine‐derived fungus *Penicillium roqueforti* were determined. Several volatile organic compounds involved in the fatty acid pathway were identified, along with a terpene and a cyclic dipeptide. Three kinds of non‐ volatile metabolites were also identified by spectroscopy: alkanes, fatty acids and 1‐alkanols. The results showed that the fermented broth of this fungal strain does not produce mycotoxins in the growing conditions used, which is an important factor, given the importance of this species for nutraceuticals [36].

A recent proposal is to study *Spirulina* metabolites (a blue‐green alga) as a laboratory practise for bio‐organic/bio‐chemistry students. They propose that students tackle the separation and analysis of metabolites of nutritional interest using simple thin‐layer chromatography (TLC) plates [44].

## **10. Conclusions and future direction**

(GC). Once connected to modern mass spectrometry, these tools resolve countless analytical

A new *Paecilomyces variotii* strain was isolated from the marine habitat. The fungal biomass necessary for the chemical study was successfully produced on a laboratory scale. Twenty‐ eight structural groups were identified from volatile compounds, a large part of which are lipid compounds involved in the fatty acid pathway, fragments from its catabolism, terpenoids and a metabolite from the shikimic acid pathway. Two other non‐volatile compounds, olein

The screening of the biomass of a new marine‐derived strain of *Penicillium roqueforti*, produced by liquid‐state fermentation, led to the identification of several volatile organic compounds active in the fatty acid pathway, together with fragments produced as a result of their catabolism, terpenoids, and metabolites from the shikimic acid route. In addition, three non‐ volatile organic compounds: 9(11)‐dehydroergosterol peroxide, 4‐hydroxy‐benzaldehyde and D‐mannitol were isolated and identified using spectroscopy. The results have shown that this fungal strain produces no mycotoxin in the culture conditions applied and thus is useful for

A GC‐MS chemical screening on the biomass of a marine protist of the *Schizochytrium* genus enables the authors [43] to identify 24 kinds of organic compounds belonging to the *n*‐alkanes, 1‐alkenes, 1‐alkanols, free fatty acids, methyl and ethyl esters of saturated and unsaturated fatty acids, saturated and unsaturated glycerides, wax esters, sterols, mono‐, sesqui‐ and tri‐ terpenes. Thus, this organism from the base of the food chain, which accumulates so many nutrients and does not produce toxins, has been proposed as a very interesting specimen for

The chemical constituents of the fermentation broth of the marine‐derived fungus *Penicillium roqueforti* were determined. Several volatile organic compounds involved in the fatty acid pathway were identified, along with a terpene and a cyclic dipeptide. Three kinds of non‐ volatile metabolites were also identified by spectroscopy: alkanes, fatty acids and 1‐alkanols. The results showed that the fermented broth of this fungal strain does not produce mycotoxins in the growing conditions used, which is an important factor, given the importance of this

A recent proposal is to study *Spirulina* metabolites (a blue‐green alga) as a laboratory practise for bio‐organic/bio‐chemistry students. They propose that students tackle the separation and analysis of metabolites of nutritional interest using simple thin‐layer chromatography (TLC)

and ergosterol peroxide, were also isolated and identified using spectroscopy [42].

industrial applications where high value‐added biomolecules are generated [21].

problems (UHPLC‐MS/MS or GC‐MS) [21].

**9. Results and discussion**

12 Metabolomics - Fundamentals and Applications

modern functional nutrition.

species for nutraceuticals [36].

plates [44].

There are an estimated 22,000 known marine metabolites. Their value as potential drugs for industry—cosmetic, nutraceutical and pharmaceutical—is well documented; in fact in recent years, companies have appeared such as the Spanish company *Pharmamar*, that are trying to sustainably exploit the issue.

However, only a few marine metabolites have been developed commercially. This is perhaps due to the fact that marine environmental metabolomics is scarcely 60 years old, apart from the fact that the major bio‐technology and/or pharmaceutical companies have invested very few resources in this field. But irrespective of whether or not marine metabolites have an industrial application, an understanding of their three‐dimensional chemical structures and the bio‐genetic pathways that living creatures use to produce them is already of great value in the field of marine chemical ecology.

Marine organisms use chemistry for many different purposes. The obvious objectives are to form cellular structures, genetic expression (DNA) and primary metabolism, which guarantees their basic welfare. There is also a secondary metabolism, controlled by enzymes, which is used by organisms to produce, accumulate and disseminate active biological substances into the environment that are essential for the survival of both the organism itself and others of the same or a different species.

For some time, these metabolites were classed under the definition of marine natural products (MNPs), but this definition is defective as it ignores the ecological function or role that they have. That is why more precise words such as allomone, kairomone or pheromone are increasingly applied to them, as we have explained in this chapter.

However, the future is promising, as there is an increasing awareness of the need to study the marine environment in‐depth. The proof of this is the creation of four faculties of marine sciences in Spain in recent decades, which means that there is now a bachelor's degree in marine sciences, along with a range of Masters and PhD courses that focus on the sea as their field of study. Subjects like "Chemistry of Marine Natural Products" have suddenly appeared on our syllabuses. At the same time, our students are presenting their degree/master projects or their doctoral theses on marine environmental metabolomics, all of which augers a promising future for this exciting field of science.

## **Author details**

Francisco Javier Toledo Marante

Address all correspondence to: franciscojavier.toledo@ulpgc.es

Chemistry Department, University of Las Palmas de Gran Canaria, Gran Canaria, Spain

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#### **Biofunctionality of Carotenoid Metabolites: An Insight into Qualitative and Quantitative Analysis Biofunctionality of Carotenoid Metabolites: An Insight into Qualitative and Quantitative Analysis**

Bangalore Prabhashankar Arathi, Poorigali Raghavendra-Rao Sowmya, Kariyappa Vijay, Vallikannan Baskaran and Rangaswamy Lakshminarayana Bangalore Prabhashankar Arathi, Poorigali Raghavendra-Rao Sowmya, Kariyappa Vijay, Vallikannan Baskaran and Rangaswamy Lakshminarayana

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66210

#### **Abstract**

Epidemiological and clinical studies have shown that dietary intake of carotenoid-rich fruits and vegetables is positively correlated with reduction in age-related eye diseases, atherosclerosis, certain cancers and chronic diseases. Carotenoids consist of unique chemical characteristics and are highly vulnerable to structural modifications, leading to the formation of various derivatives under physiological conditions. The identification of these molecules is necessary before addressing their biological functions. Carotenoid metabolomics is believed to be highly complex to fingerprint due to instability and interference with complex biological matrices. Noteworthy, progress has been made in understanding carotenoid metabolism or its biotransformation in biological samples. In this regard, the chapter highlights the concept of metabolomics and their related bio-analytical techniques pertaining to the detection of carotenoids and their derived products to elucidate their bio-transformation on targeted biological functions. Further, this chapter highlights the various hyphenated analytical tools and their optimization.

**Keywords:** metabolomics, hyphenated technique, bio-functionality, metabolites

## **1. Introduction**

Considerable research has made in understanding the potential role of carotenoids in plants and animals. Carotenoids are tetra-terpenoids, a group of natural compounds, found

predominantly in photosynthesizing organisms such as green plants, algae and some bacteria [1]. There are over 750 carotenoids identified in nature; among them, major carotenoids like α-carotene, β-carotene, lycopene, lutein, zeaxanthin, astaxanthin, fucoxanthin and β-cryptoxanthin are well explored. In plants, carotenoids and their derivatives (apo-carotenoids) are believed to involve in photosynthesis, photo-protection, the precursor for hormones and production of aroma and flavor [2]. The diversity of carotenoids in nature is formed from the common biosynthetic pathway. The exploration of each distinctive carotenoid pigment from various dietary sources or non-dietary sources is the milestone in carotenoids biochemistry and metabolism. In animals and human, dietary ingestion is the only source of carotenoids obtained from plants (vegetables and fruits) or animals (meat, fish and poultry products) or other nutraceuticals sources. They are categorized based on biological functions as provitamin A or non-provitamin A carotenoids. Further, they classified as carotenes (β-carotene and lycopene) and xanthophylls with epoxy- (violaxanthin, neoxanthin, fucoxanthin), hydroxy- (lutein and zeaxanthin), keto- (astaxanthin and canthaxanthin) and methoxy- (spirilloxanthin) functional groups. In plants and certain microorganisms, under various environmental conditions, the biotransformation of carotenoids occurs as a necessary adaptation. Carotenoids research is an active area of research due to the characteristics of different chemical nature with unique biological function [3]. Epidemiological and clinical trials have correlated that consumption of carotenoids rich fruits and green vegetables decreases vitamin A deficiency problems, cancer, cardiovascular diseases and age-related macular degeneration [4]. Although diversified carotenoids with unique characteristics structures exist in nature, the relation between structure function needs to explore by developing appropriate analytical protocols and techniques. Only a few carotenoids are studied and detailed from past three decades, due to their presence in common dietary sources and human serum and tissues. Many of the bioactive compounds execute their function either by intact or by its derivatives [5, 6]. In many cases, carotenoids daughter molecules exist in nature as a secondary compound, or they possibly formed over series of reaction that occurs in physiological condition [7]. Carotenoids consist of long polyene chain with series of conjugated double bonds and functional groups that make them highly reactive. Thus, carotenoids are more prone to various modifications such as hydrogenation, dehydrogenation, double-bond migration, chain shortening or extension, rearrangement, isomerization, oxidation or combinations of these processes under different conditions. Apart from these, carotenoid metabolites may also form due to the presence of enzymes monooxygenase, cycloxygenase and dioxygenase [8]. Investigation on β-carotene metabolism and their conversion to vital active molecules, that is, retinyl palmitate, retinal, retinoic acids and apo-carotenoids, has made a greater contribution to the concept of metabolomics. Further, these molecules augmented with cellular function on vision, growth, immune response and related chronic health problems. With this discovery, subsequently many notable researchers worked on other non-provitamin A carotenoids and succeeded partly in the identification of bioactive metabolites in vivo. Therefore, metabolomics has attracted and motivated in an identification of carotenoid bioactive metabolites. In continuation, several studies revealed the potential role of carotenoid metabolite or oxidative products in vitro and in vivo and supported the concept that biological functions mediated through their metabolites [6–9]. With this background, we highlight the possible biotransformation of carotenoids (β-carotene, lycopene, lutein, astaxanthin, fucoxanthin and other carotenoids) and the related analytical techniques used for their determination. Also, biofuctionality of carotenoids metabolites discussed. Further, this chapter details the improvement of analytical techniques and their hyphenation for evaluation of carotenoids metabolite (beneficial or harmful effect). The advancement in analytical tools and the discovery of unknown carotenoids metabolites broaden the scope of carotenoids research. Hence, omics instruments and their methods perform untargeted and targeted profiling of carotenoids in foods and human samples. Apart from these, this chapter also emphasizes on the suitability and optimization of analytical methods to determine carotenoid metabolites.

## **2. Biotransformation of carotenoids**

predominantly in photosynthesizing organisms such as green plants, algae and some bacteria [1]. There are over 750 carotenoids identified in nature; among them, major carotenoids like α-carotene, β-carotene, lycopene, lutein, zeaxanthin, astaxanthin, fucoxanthin and β-cryptoxanthin are well explored. In plants, carotenoids and their derivatives (apo-carotenoids) are believed to involve in photosynthesis, photo-protection, the precursor for hormones and production of aroma and flavor [2]. The diversity of carotenoids in nature is formed from the common biosynthetic pathway. The exploration of each distinctive carotenoid pigment from various dietary sources or non-dietary sources is the milestone in carotenoids biochemistry and metabolism. In animals and human, dietary ingestion is the only source of carotenoids obtained from plants (vegetables and fruits) or animals (meat, fish and poultry products) or other nutraceuticals sources. They are categorized based on biological functions as provitamin A or non-provitamin A carotenoids. Further, they classified as carotenes (β-carotene and lycopene) and xanthophylls with epoxy- (violaxanthin, neoxanthin, fucoxanthin), hydroxy- (lutein and zeaxanthin), keto- (astaxanthin and canthaxanthin) and methoxy- (spirilloxanthin) functional groups. In plants and certain microorganisms, under various environmental conditions, the biotransformation of carotenoids occurs as a necessary adaptation. Carotenoids research is an active area of research due to the characteristics of different chemical nature with unique biological function [3]. Epidemiological and clinical trials have correlated that consumption of carotenoids rich fruits and green vegetables decreases vitamin A deficiency problems, cancer, cardiovascular diseases and age-related macular degeneration [4]. Although diversified carotenoids with unique characteristics structures exist in nature, the relation between structure function needs to explore by developing appropriate analytical protocols and techniques. Only a few carotenoids are studied and detailed from past three decades, due to their presence in common dietary sources and human serum and tissues. Many of the bioactive compounds execute their function either by intact or by its derivatives [5, 6]. In many cases, carotenoids daughter molecules exist in nature as a secondary compound, or they possibly formed over series of reaction that occurs in physiological condition [7]. Carotenoids consist of long polyene chain with series of conjugated double bonds and functional groups that make them highly reactive. Thus, carotenoids are more prone to various modifications such as hydrogenation, dehydrogenation, double-bond migration, chain shortening or extension, rearrangement, isomerization, oxidation or combinations of these processes under different conditions. Apart from these, carotenoid metabolites may also form due to the presence of enzymes monooxygenase, cycloxygenase and dioxygenase [8]. Investigation on β-carotene metabolism and their conversion to vital active molecules, that is, retinyl palmitate, retinal, retinoic acids and apo-carotenoids, has made a greater contribution to the concept of metabolomics. Further, these molecules augmented with cellular function on vision, growth, immune response and related chronic health problems. With this discovery, subsequently many notable researchers worked on other non-provitamin A carotenoids and succeeded partly in the identification of bioactive metabolites in vivo. Therefore, metabolomics has attracted and motivated in an identification of carotenoid bioactive metabolites. In continuation, several studies revealed the potential role of carotenoid metabolite or oxidative products in vitro and in vivo and supported the concept that biological functions mediated through their metabolites [6–9]. With this background, we highlight the possible biotransformation of

20 Metabolomics - Fundamentals and Applications

Even though more than 750 carotenoids are identified and predicted in the natural source, only a few carotenoids (β-carotene, lycopene, lutein, astaxanthin, fucoxanthin, canthaxanthin) addressed by using routine analytical techniques. Presently, carotenoids research is mainly focused on the analysis of dietary carotenoids and linked to biological functions. Since carotenoids are unstable molecules, it undergoes various modifications (discussed elsewhere), and hence, it is challenging to analyze such carotenoids or its metabolites. β-Carotene metabolites such as retinol, retinal and retinoic acid are studied extensively due to its significance in human health. Therefore, creating an awareness to study the metabolism of carotenoids and their bio-functions is currently warranted. The carotenoid metabolites may be involved in differential gene expression, cell-to-cell communication and cell differentiation. Furthermore, it is interesting to address the molecular interaction of carotenoids metabolites with free radicals, protein and lipids at cellular levels. Studies also show that carotenoids oxidative products may be involved in oxidative stress and act as pro-oxidant [10]. The identification of oxidative metabolites is considered to be more important before addressing their biological activity. Hence, rapid and sensitive analytical techniques are given priority. The bioconversion of major carotenoids such as β-carotene, lycopene, lutein, astaxanthin and fucoxanthin is discussed in this section.

#### **2.1. Hydrocarbon carotenoids**

β-Carotene is the most abundant provitamin A carotenoid found in human diet and tissues. It exerts a beneficial function in mammals, including humans, due to its ability to convert to vitamin A. Even though β-carotene considered as a safer form of vitamin A, under circumstances detrimental effects also ascribed [11]. A better understanding metabolism of the βcarotene and their derivatives of (retinoids) are still needed to unequivocally discriminate the beneficial or detrimental effects under various physiological conditions and thus enable the formulation of adequate dietary recommendations for different age groups of individuals. Symmetric oxidative cleavage of β-carotene by the enzyme β-carotene-15, 15′-monooxygenase (BCMO1) generates two molecules of retinaldehyde, and these molecules further oxidized to form retinoic acids by aldehyde dehydrogenase. The oxidation of retinoic acid conversion into more polar compound 4-oxo retinoic acid by cytochrome P450 enzymes is believed to be transcriptionally inactive. Alternatively, different forms of alcohol dehydrogenase and a variety of retinol dehydrogenases reduce retinaldehyde to retinol. Subsequently, these molecules further get esterified into retinyl esters by the enzyme lecithin retinol acyltransferase. Also, apo-carotenals can be generated by eccentric cleavage of β-carotene. The cleavage at the 9′, 10′ double bond is catalyzed by β-carotene 9′, 10′-oxygenase 2 (CMO II) and leads to the formation of β-apo-10′-carotenal and β-ionone. Apo-carotenals are ultimately converted into one molecule of retinaldehyde, and the mechanism of this conversion is not completely elucidated [12].

**Figure 1.** Molecular structures of hydrocarbon carotenoids and their metabolites.

Lycopene is another major hydrocarbon carotenoid extensively studied due to its potential role in the reduction in certain chronic diseases including cancer and cardiovascular disease. Recently, metabolism of lycopene has made a greater insight into the biological role of its derivates. These observations raised an important question about the effect of lycopene on various cellular functions and signaling pathways are a result of the direct actions of intact lycopene or its derivatives. Considerable efforts have been expended to identify its biological and physiochemical properties. β-Carotene and lycopene have the same molecular mass and chemical formula, yet lycopene is an open-polyene chain lacking the β-ionone ring structure. The metabolism of β-carotene studied extensively, but biological activities of lycopene are not detailed. Derivates of lycopene formed due to shortening the carbon chain by removal of fragments from one or both the ends. Recent studies have shown that BCMO II enzyme catalyzes lycopene eccentrically to form apo-10-carotenoids. Cleavage of cis-lycopene by BCO II may occur at either 9, 10 or 9′, 10′ double bond to produce apo-10′-lycopenal, which can be oxidized to apo-10′-lycopenoic acid or reduced to apo-10′-lycopenol. Lycopene metabolites, including apo-6-, apo-8′-, apo-10′-, apo-12′- and apo-14′-lycopenal, were detected in the plasma of humans [13]. However, the identification of cleaved lycopene metabolites in vivo is challenging. Previously, Khachik et al. [9] identified a group of lycopene oxidative products, 2,6-cyclolycopene-1,5-diol in human serum and tissues. In animal studies, several metabolites were detected in lung tissue of lycopene-supplemented ferrets. The intermediate primary cleavage product apo-10′-lycopenal could be either reduced apo-10-lycopenol or oxidized to apo-10′-lycopenoic acid. The apo-10′-lycopenal was further converted into apo-10′-lycopenoic acid in the presence of NAD+. Similarly in presence of NADH lycopene was converted into both apo-10′-lycopenoic acid and apo-10′-lycopenol [14]. Gajic et al. [15] reported apo-8′- and apo-12′-lycopenal as well as other unidentified polar metabolites of lycopene in the liver of rats when supplemented with lycopene-rich diet. Interestingly, a recent study indicated that apo-10′- and apo-14′-lycopenoic acid have a remarkable ability to upregulate BCO II expression [16]. These results showed that lycopene converted to apo-10′-lycopenoids in mammalian tissues both in vitro and in vivo. These observations presumed that lycopene metabolites play an important biological functions related to human health. The molecular structures of hydrocarbon carotenoids and their metabolites are shown in **Figure 1**.

#### **2.2. Hydroxyl carotenoids**

transcriptionally inactive. Alternatively, different forms of alcohol dehydrogenase and a variety of retinol dehydrogenases reduce retinaldehyde to retinol. Subsequently, these molecules further get esterified into retinyl esters by the enzyme lecithin retinol acyltransferase. Also, apo-carotenals can be generated by eccentric cleavage of β-carotene. The cleavage at the 9′, 10′ double bond is catalyzed by β-carotene 9′, 10′-oxygenase 2 (CMO II) and leads to the formation of β-apo-10′-carotenal and β-ionone. Apo-carotenals are ultimately converted into one molecule of retinaldehyde, and the mechanism of this conversion is not completely

**Figure 1.** Molecular structures of hydrocarbon carotenoids and their metabolites.

Lycopene is another major hydrocarbon carotenoid extensively studied due to its potential role in the reduction in certain chronic diseases including cancer and cardiovascular disease. Recently, metabolism of lycopene has made a greater insight into the biological role of its derivates. These observations raised an important question about the effect of lycopene on various cellular functions and signaling pathways are a result of the direct actions of intact lycopene or its derivatives. Considerable efforts have been expended to identify its biological and physiochemical properties. β-Carotene and lycopene have the same molecular mass and chemical formula, yet lycopene is an open-polyene chain lacking the β-ionone ring structure. The metabolism of β-carotene studied extensively, but biological activities of lycopene are not detailed. Derivates of lycopene formed due to shortening the carbon chain by removal of fragments from one or both the ends. Recent studies have shown that BCMO II enzyme catalyzes lycopene eccentrically to form apo-10-carotenoids. Cleavage of cis-lycopene by BCO II may occur at either 9, 10 or 9′, 10′ double bond to produce apo-10′-lycopenal, which can be oxidized to apo-10′-lycopenoic acid or reduced to apo-10′-lycopenol. Lycopene metabolites, including apo-6-, apo-8′-, apo-10′-, apo-12′- and apo-14′-lycopenal, were detected in the plasma of humans [13]. However, the identification of cleaved lycopene metabolites in vivo is

elucidated [12].

22 Metabolomics - Fundamentals and Applications

Lutein is one of the major xanthophylls present in green leafy vegetables. Lutein and its isomer zeaxanthin selectively accumulated in the macula of the human retina. They are recognized as antioxidants and as blue light filters [17] to protect the eyes from sunlight exposure and other lifestyle-related oxidative stress, which can lead to age-related macular degeneration and cataracts. Khachik et al. [9] identified lutein metabolites in human tissues such as plasma, milk, liver and retina. Yonekura et al. [18] observed remarkable accumulation of lutein metabolites, 3′-Hydroxy-ε,ε-caroten-3-one along with ε,ε-carotene-3,3′-dione in the plasma, liver, kidney and adipose tissues of mice fed with lutein. Further, findings indicated that mice actively convert lutein to keto-carotenoids by oxidizing the secondary hydroxyl group. 3′-oxolutein is the metabolite of lutein detected in human plasma and retina [19]. Similarly, 3′-epilutein identified in human retina and presumed that this product might form by a reduction of 3′ oxolutein from lutein. Other lutein derivatives like meso-zeaxanthin detected only in the retina and formed due to double-bond migration from lutein [20]. The dehydration products of lutein such as, 3-hydroxy-3′, 4′-didehydro-β,γ-carotene and 3-hydroxy-2′,3′-didehydro-β,ε-carotene may be formed non-enzymatically in stomach under acidic conditions [9]. Also, studies demonstrated and indicated the cleavage reaction of xanthophylls occurred in mammals by BCO II by cleaving double bond at C-9′ and C-10′ of xanthophylls [21].

#### **2.3. Epoxy- and keto-carotenoids**

The fucoxanthin is a marine carotenoid found in brown seaweeds, macroalgae and diatoms, with biological properties. The bioconversion of fucoxanthin to fucoxanthinol and amarouciaxanthin A was found in the plasma and liver of mice fed with fucoxanthin, whereas fucoxanthin was not detected [22]. Fucoxanthinol hydrolyzed from fucoxanthin in the intestinal tract, circulated in the body and then oxidatively converted into amarouciaxanthin A. The conversion of fucoxanthinol into amarouciaxanthin A found in human hepatoma HepG2 cells. Moreover, oxidative conversion of xanthophylls in mouse liver microsomal fractions required NAD+ as a co-factor, and this result demonstrates the metabolic conversion of the 3-hydroxyl end group at the level of enzyme reaction [23].

**Figure 2.** Molecular structures of hydroxyl-, epoxy- and keto-carotenoids and their metabolites.

Astaxanthin (ASTX) is a major keto-carotenoid metabolized into 3-hydroxy-4-oxo-β-ionone and 3-hydroxy-4-oxo-7,8-dihydro-β-ionone in primary rat hepatocytes [24]. However, enzymes involved in the synthesis of these metabolites and their potential biological functions are not elucidated. However, ASTX incubated with microsomes containing cytochrome P450 (CYPs) did not generate ASTX metabolites and induction of CYP activity in hepatocytes [25]. Therefore, the CYPs enzymes are not responsible for the production of ASTX metabolites. Hence, investigation on ASTX metabolites and their metabolism needs to explore. However, there are no detailed in vivo studies available on astaxanthin metabolites except isomers detected in human serum samples [26]. Canthaxanthin, (4,4′-diketo-β,β-carotene), is a ketocarotenoid found in green algae, crustaceans and certain microorganism. This pigment also used as poultry feed to extend the dominant pigments in the skin and egg yolk of chickens. Analysis of tissues from chicks revealed that a portion of canthaxanthin reduced to 4-hydroxyechinenone (4-hydroxy-4′-keto-β,β-carotene) and iso-zeaxanthin (4,4′-dihydroxy-β,βcarotene). Ratio of canthaxanthin to metabolites depends on type of tissue, but in general metabolites concentrated in the skin [27]. The molecular structure of hydroxyl-, epoxy- and keto-carotenoids and their metabolites is shown in **Figure 2**.

## **3. Sample preparation for carotenoids and their metabolites**

fractions required NAD+

24 Metabolomics - Fundamentals and Applications

of the 3-hydroxyl end group at the level of enzyme reaction [23].

**Figure 2.** Molecular structures of hydroxyl-, epoxy- and keto-carotenoids and their metabolites.

Astaxanthin (ASTX) is a major keto-carotenoid metabolized into 3-hydroxy-4-oxo-β-ionone and 3-hydroxy-4-oxo-7,8-dihydro-β-ionone in primary rat hepatocytes [24]. However, enzymes involved in the synthesis of these metabolites and their potential biological functions are not elucidated. However, ASTX incubated with microsomes containing cytochrome P450 (CYPs) did not generate ASTX metabolites and induction of CYP activity in hepatocytes [25]. Therefore, the CYPs enzymes are not responsible for the production of ASTX metabolites. Hence, investigation on ASTX metabolites and their metabolism needs to explore. However, there are no detailed in vivo studies available on astaxanthin metabolites except isomers detected in human serum samples [26]. Canthaxanthin, (4,4′-diketo-β,β-carotene), is a keto-

as a co-factor, and this result demonstrates the metabolic conversion

Biological samples used for analysis of carotenoid and their metabolites need to store in −80°C under argon or nitrogen gas to prevent carotenoids deterioration. Before analysis, the tissues samples thawed at room temperature before homogenization using suitable solvents, and then, organic phase collected after vortexing and centrifugation. To this appropriate amount of anhydrous, sodium sulfate should be added to remove traces of moisture. The organic phase evaporated with argon or nitrogen gas, and the extract was reconstituted in mobile phase and injected into the LC/MS/MS equipment. Other than the sample preparation adequate care should be taken to avoid light, heat and exposure to atmospheric oxygen, and the high quality solvents used for extraction and analysis, proper storage of samples under inert conditions, addition of antioxidants and completion of analysis within short run time applied with sophisticated instruments.

## **4. Analysis of carotenoids and their isomers, cleavage products/oxidation products or metabolites in food and biological samples**

HPLC with C18 and C30 stationary phases either with reverse phase or normal phase used extensively for the analysis of a diverse group of carotenoids in various natural sources including food and biological samples [28, 29]. In general, the separation and resolution of carotenoids are better with gradient than isocratic solvents system. However, these methods require higher analysis time and solvent consumption. Further, these conditions and stationary phase were successfully employed in the analysis of carotenoids and their isomers or/and related derived products in biological fluids and tissues samples [30, 31]. Others have attempted and identified several types of carotenoids and their metabolites/oxidative products by employing gas chromatography and mass spectrometry. Stratton et al. [32] determined β-carotene oxidation products as β-ionone, β-apo-l4′-carotenal, β-apo-10′ carotenal, β-apo-8′-carotenal and β-carotene 5,8-endoperoxide by using RP-HPLC and GC-MS. Wyss and Bucheli [33] developed an HPLC method with automated column switching for the simultaneous determination of endogenous levels of 13-cis-retinoic acid, all-transretinoic acid and their 4-oxo metabolites in human and animals tissue samples. Khachik et al. [9] separated, identified, quantified and compared 34 carotenoids, including 13 geometrical isomers and 8 metabolites in breast milk and serum of lactating mothers by HPLC-

PDA-MS. Wolz et al. [24] investigated the astaxanthin metabolites in primary cultures of rat hepatocytes by GC-MS. Siems et al. [34] demonstrated the oxidation products of β-carotene by using capillary gas-liquid chromatography and HPLC. The method developed was linear in the range of 0.3–100 ng/mL with a lower quantification limit. Kim et al. [35] isolated and analyzed auto-oxidation products of lycopene by GC-MS. Subsequently, Bernstein et al. [36] identified and quantified the dietary lutein and their geometrical (E/Z) isomers and related metabolites by HPLC in tissues of the human eye. Dachtler et al. [37] identified carotenoid stereoisomers in spinach and human retina samples by using HPLC online coupled to mass spectrometry and nuclear magnetic resonance spectroscopy. Aust et al. [38] have reported lycopene oxidative product 2,7,11-trimethyltetradecahexaene-1,14-dial by using GC-MS. Sommerburg et al. [39] separated and identified 5,6-epoxi-β-ionone, ionene, βcyclocitral, β-ionone, dihydroactinidiolide and 4-oxo-β-ionone as major cleavage products of β-carotene mediated by hypochlorous acid using GC-MS.

Simultaneously from the late 2000 s, spectroscopic and mass spectrometric techniques have being used for qualitative and quantitative analysis for structurally different carotenoids and their derived products. The carotenoid analysis is done by using different ionization modes such as matrix-assisted laser desorption/ionization (MALDI), electrospray (ESI), atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI). These methods are employed successfully to target the mass with better ionization and high resolution for the identification of broad range of natural organic molecules including carotenoids [40–42]. Further, this application is extended to explore several metabolites and oxidative products of carotenoids in rodents and human biological samples [18, 43–45]. Initially, van Breemen [46] developed an APCI-LC-MS method and analyzed all-trans-retinol and all-transretinyl palmitate with the lower limit of detection (LOD) of 34 fmoles/μL and 36 fmoles/μL (on-column), and limit of quantitation (LOQ) was 500 fmoles/μl and 250 fmoles/μl (oncolumn), respectively. Later, Zhu et al. [47] developed LC/APCI-MS a negative ion mode for the measurement of labeled and unlabeled β-carotene in human serum and feces to demonstrate bioefficacy of orally administered β-carotene, and the limit of detection of 0.25 pmol (on injection of 20 μL of 0.0125 μM β-carotene) and LOQ was 1.0 pmol (on the injection of 20 μL of 0.050 μM β-carotene) with the linear range of 1.1–2179 pmoles on-column. Further, they believed that linear range with low LOD and LOQ facilitated sensitive and selective analysis of provitamin A carotenoids. Fraser et al. [40] used MALDI/TOF-MS to detect and quantify plant carotenoids, and other metabolites (m/z) in complex biological systems. Schäffer et al. [48] validated RP-HPLC-DAD method for simultaneous quantification of retinol, retinyl esters, tocopherols and selected carotenoids in the lung, liver and plasma of various animal samples. Würtinger and Oberacher [49] demonstrated influence of analytical parameters like constituents of mobile phase, including the modifiers added (acids, bases, dopants, metals and salts) and other experimental conditions (collision energy, flow collision gas, temperatures, etc.) on the ionization of the analyses (e.g., ESI, APCI, APPI, FAB). Consequently, Giuffrida et al. [50] for the first time analyzed and identified 52 carotenoids among the various cultivars of Capsicum using HPLC-DAD-APCI-MS. Further, Rivera et al. [51] studied the effect of ionization of carotenes, oxygenated carotenoids using ESI, APCI and APPI and reported that 12 of the 16 carotenoids exhibited strongest signal strength with APCI.

PDA-MS. Wolz et al. [24] investigated the astaxanthin metabolites in primary cultures of rat hepatocytes by GC-MS. Siems et al. [34] demonstrated the oxidation products of β-carotene by using capillary gas-liquid chromatography and HPLC. The method developed was linear in the range of 0.3–100 ng/mL with a lower quantification limit. Kim et al. [35] isolated and analyzed auto-oxidation products of lycopene by GC-MS. Subsequently, Bernstein et al. [36] identified and quantified the dietary lutein and their geometrical (E/Z) isomers and related metabolites by HPLC in tissues of the human eye. Dachtler et al. [37] identified carotenoid stereoisomers in spinach and human retina samples by using HPLC online coupled to mass spectrometry and nuclear magnetic resonance spectroscopy. Aust et al. [38] have reported lycopene oxidative product 2,7,11-trimethyltetradecahexaene-1,14-dial by using GC-MS. Sommerburg et al. [39] separated and identified 5,6-epoxi-β-ionone, ionene, βcyclocitral, β-ionone, dihydroactinidiolide and 4-oxo-β-ionone as major cleavage products

Simultaneously from the late 2000 s, spectroscopic and mass spectrometric techniques have being used for qualitative and quantitative analysis for structurally different carotenoids and their derived products. The carotenoid analysis is done by using different ionization modes such as matrix-assisted laser desorption/ionization (MALDI), electrospray (ESI), atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI). These methods are employed successfully to target the mass with better ionization and high resolution for the identification of broad range of natural organic molecules including carotenoids [40–42]. Further, this application is extended to explore several metabolites and oxidative products of carotenoids in rodents and human biological samples [18, 43–45]. Initially, van Breemen [46] developed an APCI-LC-MS method and analyzed all-trans-retinol and all-transretinyl palmitate with the lower limit of detection (LOD) of 34 fmoles/μL and 36 fmoles/μL (on-column), and limit of quantitation (LOQ) was 500 fmoles/μl and 250 fmoles/μl (oncolumn), respectively. Later, Zhu et al. [47] developed LC/APCI-MS a negative ion mode for the measurement of labeled and unlabeled β-carotene in human serum and feces to demonstrate bioefficacy of orally administered β-carotene, and the limit of detection of 0.25 pmol (on injection of 20 μL of 0.0125 μM β-carotene) and LOQ was 1.0 pmol (on the injection of 20 μL of 0.050 μM β-carotene) with the linear range of 1.1–2179 pmoles on-column. Further, they believed that linear range with low LOD and LOQ facilitated sensitive and selective analysis of provitamin A carotenoids. Fraser et al. [40] used MALDI/TOF-MS to detect and quantify plant carotenoids, and other metabolites (m/z) in complex biological systems. Schäffer et al. [48] validated RP-HPLC-DAD method for simultaneous quantification of retinol, retinyl esters, tocopherols and selected carotenoids in the lung, liver and plasma of various animal samples. Würtinger and Oberacher [49] demonstrated influence of analytical parameters like constituents of mobile phase, including the modifiers added (acids, bases, dopants, metals and salts) and other experimental conditions (collision energy, flow collision gas, temperatures, etc.) on the ionization of the analyses (e.g., ESI, APCI, APPI, FAB). Consequently, Giuffrida et al. [50] for the first time analyzed and identified 52 carotenoids among the various cultivars of Capsicum using HPLC-DAD-APCI-MS. Further, Rivera et al. [51] studied the effect of ionization of carotenes, oxygenated carotenoids using ESI, APCI and APPI and reported that

of β-carotene mediated by hypochlorous acid using GC-MS.

26 Metabolomics - Fundamentals and Applications

12 of the 16 carotenoids exhibited strongest signal strength with APCI.

Furthermore, MS/MS is considered to be an efficient method for carotenoids identification by the use of transitions through precursor and daughter ions. This approach is also suitable for the determination of carotenoids with the same molecular mass such as structural isomers. Prasain et al. [52] developed a sensitive and specific ESI-MS/MS method using MRM for the detection of carotenoid oximes (zeaxanthin oxidation products) in a human eye sample. Further, this provides invaluable information on the characterization and quantification of carotenoids and their oxidized products formed during in vitro and in vivo studies. Crupi et al. [41] investigated carotenoids in a typical wine grape variety by using RP-HPLC-DAD-MS (ESI+ ) method. Due to an unusual ionization process, their mass spectra of carotenoids comprised both protonated molecules and molecular ion radicals. Further, they were subjected for the selective collision-induced dissociation (CID) to differentiate structural and geometrical isomers such as lutein isomers (zeaxanthin, 9Z and 9′Z-lutein) and a cis-isomer of β-carotene (9Z- β-carotene), 5,6-epoxy xanthophylls, violaxanthin, (9′Z) neoxanthin, lutein-5,6-epoxide and 5,8-epoxy xanthophylls diastereoisomers (neochrome, auroxanthin, luteoxanthin, flavoxanthin, chrysanthemaxanthin). Kopec et al. [13] performed HPLC-MS/MS using APCI−ve mode to separate and detect the apo-6′-, apo-8′-, apo-10′-, apo-12′-, apo-14′- and apo-15′-lycopenal products formed by in vitro oxidation reaction. The quantitative analysis of carotenoids and other fat-soluble compounds performed simultaneously on a C30 column and detected by APCI-MS/MS with operation of selected reaction monitoring (SRM) mode. Also, this method calibrated to shown a less variability in intraand inter-day precision analysis. Recently, ultrahigh performance liquid chromatography (UPLC/UHPLC) has attracted much attention due to faster analysis and higher sensitivity with 2 μm particle size stationary phases, thereby increasing column efficiency, decreasing band broadening, and increasing resolution [4]. Granado-Lorencio et al. [53] assessed the suitability of UHPLC for the simultaneous determination of biomarkers of vitamins including vitamin A (retinol, retinyl esters) and major carotenoids in human serum. This method allowed a better resolution for carotenes and xanthophylls isomers provides better sensitivity and reproducibility in peak area and retention time than the HPLC, with mean RSDs. Rivera et al. [51] analyzed various carotenoids by UHPLC-MS/MS detection. Further, they compared three different ionization techniques (ESI, APCI and APPI) to ionize the carotenoids and concluded that APCI has a powerful technique to ionize carotenoids. They also used dopants (acetone, toluene, anisole and chlorobenzene) that allowed the enhancement of the carotenoid signals strength up to 178-fold. Delpino-Rius et al. [54] analyzed simultaneously epoxy carotenoids, hydroxyl carotenoids and carotenes in fresh homemade and industrially processed fruit products by UPLC. They identified 27 carotenoids eluted within 17 min; furthermore, this method allowed to differentiate the carotenoid profiles and 5,6- to 5,8-epoxycarotenoids. Separation of carotenoids on UHPLC columns illustrates less analysis time compared to HPLC C30 column; however, the separation is better in C30 column. Hence, there is a requirement for appropriate UHPLC column for rapid and sensitive analysis of carotenoids with better separation. Zhao et al. [55] developed a quick and simple ultrahigh performance supercritical fluid chromatography-photodiode array detector (UHPSFC-PDA) method and validated the determination of carotenoids in dietary supplements using Acquity UPC2HSS column by gradient elution with carbon dioxide and solvent system. We have developed a rapid UPLC-MS/MS method for analysis of lycopene isomers and their fragmentation pattern with CID and demonstrated the importance of ion mobility to differentiate carotenoids geometrical isomers [56]. Dong et al. [57] characterized and distinguished geometrical isomers of lycopene and β-carotene using ion mobility spectrometry. Raphael et al. [58] identified canthaxanthin oxidation products and compared the similarity of β-carotene like oxidation products by using both LC–MS and GC–MS chromatograms.

**Figure 3.** Hyphenated analytical techniques for the characterization of carotenoids and their metabolites. The details of these techniques are discussed in Section 4.

Apart from these, Orbitrap MS a high-resolution mass spectrometry was also exploited for carotenoids analysis to generate mass spectra with a resolving power up to 100,000 at fullwidth half-maximum and mass accuracies within two parts per million (ppm). Due to its high mass resolution and exact mass screening detectors, probable molecular formulae of the ions and fragments were elucidated [59]. In continuation, Van Meulebroek et al. [60] developed a full-scan high-resolution Orbitrap MS method enabling the metabolomic screening for carotenoids in tomato fruit tissue. The validation demonstrated the excellent performance in terms of linearity, repeatability and higher range of mean corrected recovery. Additionally, a well-established detection technique, that is, MS/MS and ultraviolet-visible spectroscopy photodiode array, indicated superior performance of high-resolution Orbitrap MS (with limits of detection ranging from 1.0 to 3.8 pg μL−1). Contemporarily, 2D-LC and multi-dimensional chromatography have emerged as a tool for carotenoid analysis. This provides an excellent separation and resolution for analysis of complex matrices. In this regard, Cacciola, et al. [61] developed and applied a comprehensive normal-phase × reversed-phase liquid chromatography (NP-LC × RP-LC) system for analysis of the intact carotenoid composition of red chili peppers, with photodiode array and mass spectrometry detection. A total of 33 compounds separated into 10 diverse chemical classes in the 2-D space and identified by accurate IT-TOF (ion trap-time of flight) MS. Apart from these, the robust technique LC-NMR offers 1-D and 2-D NMR spectra for the components separated by HPLC. Recently, LC-NMR is used because of improved sensitivity due to higher magnetic fields. Further, NMR provides information about conformational geometry for structural elucidation. LC-NMR is established as a method of analyzing major carotenoids and metabolites in food and biological samples [62]. The NMR studies on carotenoids metabolites are scanty due to quantitative limitations. Therefore, LC-MS-APCI studies are widely used in the characterization of carotenoids metabolites or oxidative cleavage products. The approach of LC-MS techniques and ionization modes for characterization of carotenoids and their metabolites are shown in **Figure 3**.

vent system. We have developed a rapid UPLC-MS/MS method for analysis of lycopene isomers and their fragmentation pattern with CID and demonstrated the importance of ion mobility to differentiate carotenoids geometrical isomers [56]. Dong et al. [57] characterized and distinguished geometrical isomers of lycopene and β-carotene using ion mobility spectrometry. Raphael et al. [58] identified canthaxanthin oxidation products and compared the similarity of β-carotene like oxidation products by using both LC–MS and GC–MS chroma-

**Figure 3.** Hyphenated analytical techniques for the characterization of carotenoids and their metabolites.

Apart from these, Orbitrap MS a high-resolution mass spectrometry was also exploited for carotenoids analysis to generate mass spectra with a resolving power up to 100,000 at fullwidth half-maximum and mass accuracies within two parts per million (ppm). Due to its high mass resolution and exact mass screening detectors, probable molecular formulae of the ions and fragments were elucidated [59]. In continuation, Van Meulebroek et al. [60] developed a full-scan high-resolution Orbitrap MS method enabling the metabolomic screening for carotenoids in tomato fruit tissue. The validation demonstrated the excellent performance in terms of linearity, repeatability and higher range of mean corrected recovery. Additionally, a well-established detection technique, that is, MS/MS and ultraviolet-visible spectroscopy photodiode array, indicated superior performance of high-resolution Orbitrap MS (with limits of detection ranging from 1.0 to 3.8 pg μL−1). Contemporarily, 2D-LC and multi-dimensional chromatography have emerged as a tool for carotenoid analysis. This provides an excellent separation and resolution for analysis of complex matrices. In this regard, Cacciola, et al. [61] developed and applied a comprehensive normal-phase × reversed-phase liquid chromatography (NP-LC × RP-LC) system for analysis of the intact carotenoid composition of red chili peppers, with photodiode array and mass spectrometry detection. A total of 33 compounds separated into 10 diverse chemical classes in the 2-D space and identified by accurate IT-TOF (ion trap-time of flight) MS. Apart from these, the robust technique LC-NMR offers 1-D and

The details of these techniques are discussed in Section 4.

tograms.

28 Metabolomics - Fundamentals and Applications

Even advancement in hyphenated analytical techniques and inconsistency of results may arise due to several pre-chromatographic (samples or tissues, the nature of carotenoids, sample preparation, incomplete extraction, solvent incompatibility, isomerization/oxidation, physical losses of carotenoids/metabolites and its accountability) and post-chromatographic (low recovery, less stability, inaccurate method validation, co-elution, unavailability of standards, selection of improper mode of ionization, carotenoid/metabolites with same molecular mass) errors in the carotenoids and their metabolites analysis [4]. The qualitative and quantitative analyses of carotenoids need a proper method validation as per the ICH (International Council on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use) and IUPAC (International Union of Pure and Applied Chemistry) guidelines. The method validation mainly comprises of precision, specificity, LOD, LOQ, linearity, sensitivity, range and robustness. Carotenoid standard curves prepared for subsequent quantitation by HPLC–PDA. The amounts of carotenoids calculated based on the regression equations. The LOD and LOQ calculated for each standard on the basis of the signal-to-noise ratio (3:1 for LOD and 10:1 for LOQ). LOD is the amount that resulted in a peak with a height three times that of the baseline noise, respectively, and the LOQ determined as lowest injected amount which could be quantifiable reproducibility (RSD ≤ 5%). The precision evaluated by the relative coefficient of variation (%). The inter-and intra-day relative standard deviations (RSD) for retention times of individual carotenoid are considered for standard concentration to check the reproducibility of the method. The accuracy of the extraction method is assessed by determining recovery of carotenoids with a mean value, respectively.

## **5. Biofunctionality of hydrocarbon carotenoid metabolites**

β-Carotene is one of the most potent vitamin A precursor among other provitamin A carotenoids (α-carotene, β-cryptoxanthin and γ-carotene). Chemically, vitamin A refers to all isoprenoid compounds that possess the biological activity of all-trans-retinol. The parent structure of most retinoids contains a substituted β-ionone ring with a side chain of three isoprenoid units linked at the 6-position of the β-ionone ring. Retinol plays a role in vision, differentiation and proliferation of a wide range of epithelial cells, bone growth, reproduction and embryonic development. β-Carotene conversion to retinal, retinoic acid and other active forms of the vitamin A family is well documented. Retinoic acids serve as ligands for nuclear retinoic acid receptors (RARs), namely RXR and RAR, mediate vitamin A dependent activities. Several studies have reported the biological properties of β-apocarotenoids. Zile et al. [63]

demonstrated that retinol β-D-glucuronide (a metabolite of vitamin A) could arrest HL-60 cell proliferation and induces differentiation into mature granulocytes; it may act by itself or by being hydrolyzed to retinoic acid. Others have reported that β-carotene cleavage products, such as β-apo-14′-carotenoic acid, stimulate the differentiation of U937 leukemia cells, and β-apo-12′ carotenoic acid inhibits the proliferation of HL-60 cells [64, 65]. Hu et al. [66] characterized a polar oxidation product of β-carotene 5,8-endoperoxy-2,3-dihydro-β-apocarotene-13-one and demonstrated the inhibition of cell growth and cholesterol synthesis in MCF-7 cells. Kawada et al. [67] suggested that the inhibitory action of adipocyte differentiation by carotenoids and retinoids exhibited through the RAR upregulation and the suppression of PPARγ. Tibaduiza et al. [68] tested the effect of synthetic eccentric cleavage products of β-carotene such as βapocarotenoid acids, including β-apo-8′-, β-apo-10′-, β-apo-12′- and β-apo-14′-carotenoic acid on the growth of the human breast cancer cells. Further, they observed β-apo-14′ and β-apo-12′ carotenoic acid significantly inhibited MCF-7 growth, and only β-apo-14′-carotenoic acid inhibited Hs578T growth. Further, they observed none of these treatments inhibited the growth of MDA-MB-231 cells. Also, authors concluded that apocarotenoid acids exhibit antiproliferative effects by downregulation of cell cycle regulatory proteins and inhibition of AP-1 transcriptional activity. Rühl et al. [69] also reported that β-carotene is involved in activation of human pregnane receptor (PXR) a ligand-activated transcription factors involved in xenobiotic detoxification in the liver. Ziouzenkova et al. [70] demonstrated that β-apo-14′ carotenal, but not other structurally related apo-carotenals, represses peroxisome proliferatoractivated receptors (PPAR) and RXR activation. Eroglu et al. [71] demonstrated that none of the β-apocarotenoids significantly activated RARs. However, β-apo-14′-carotenal, β-apo-14′ carotenoic acid and β-apo-13-carotenone antagonized ATRA-induced transactivation of RARs.

Lycopene is another non-provitamin A hydrocarbon carotenoid present in human serum and tissues. Recently, lycopene has attracted much attention due to its association with a decreased risk of certain cancers, cardiovascular diseases and other chronic problems [72]. Several studies from in vivo and in vitro suggest that lycopene induces apoptosis in cancer cells [73–75]. Structurally, lycopene consists of open-polyene chain lacking the β-ionone ring compared to β-carotene and share a same molecular mass and chemical formula. Even though the metabolism of β-carotene extensively studied, the metabolism and biological activities of lycopene are not detailed. Several studies support the concept that biological activities of lycopene are mediated by its oxidative products/cleavage products [76–78]. The characterization of BCO II enzyme has demonstrated that this enzyme can catalyze the eccentric cleavage of lycopene to form apo-10′-lycopenoids [14]. Several lycopene metabolites have been identified in vivo and in vitro [9, 13, 15, 35, 79], but their biological activities and mechanism of action need elucidation. The discovery of various oxidation products or metabolites of carotenoids has questioned the active role of them compared to intact carotenoid molecules in combating various diseases. However, metabolites or oxidative products of LYC on health benefits are warranted. Nara et al. [80] have demonstrated that autoxidation mixtures of LYC inhibited the HL-60 cell growth effectively than LYC. Similarly, Zhang et al. [81] identified a cleavage product of LYC (E, E, E)-4-methyl-8-oxo-2,4,6-nonatrienal and evaluated its apoptosis-inducing activity in HL-60 cells. Consequently, Aust et al. [38] reported the role of LYC degraded products in enhancing cell communication and cell signaling. Lian et al. [5] demonstrated that apo-10′-lycopenoic acid is a biologically active metabolite of lycopene with potential chemopreventive agent against lung tumorigenesis. Ford et al. [82] demonstrated that lycopene and apo-12′-lycopenal reduce the proliferation of prostate cancer cells. In the recent study, apo-10′-lycopenoic acid led to the increase in SIRT1 (Sirtuin 1) enzyme activity by treatment with this metabolite in mice, resulting in prevention of fatty liver [83]. Due to its high antioxidant activity, lycopene is more unstable and needs more advanced analytical techniques to identify and characterize lycopene metabolites and their biological activities.

#### **6. Biofunctionality of oxygenated carotenoid metabolites**

demonstrated that retinol β-D-glucuronide (a metabolite of vitamin A) could arrest HL-60 cell proliferation and induces differentiation into mature granulocytes; it may act by itself or by being hydrolyzed to retinoic acid. Others have reported that β-carotene cleavage products, such as β-apo-14′-carotenoic acid, stimulate the differentiation of U937 leukemia cells, and β-apo-12′ carotenoic acid inhibits the proliferation of HL-60 cells [64, 65]. Hu et al. [66] characterized a polar oxidation product of β-carotene 5,8-endoperoxy-2,3-dihydro-β-apocarotene-13-one and demonstrated the inhibition of cell growth and cholesterol synthesis in MCF-7 cells. Kawada et al. [67] suggested that the inhibitory action of adipocyte differentiation by carotenoids and retinoids exhibited through the RAR upregulation and the suppression of PPARγ. Tibaduiza et al. [68] tested the effect of synthetic eccentric cleavage products of β-carotene such as βapocarotenoid acids, including β-apo-8′-, β-apo-10′-, β-apo-12′- and β-apo-14′-carotenoic acid on the growth of the human breast cancer cells. Further, they observed β-apo-14′ and β-apo-12′ carotenoic acid significantly inhibited MCF-7 growth, and only β-apo-14′-carotenoic acid inhibited Hs578T growth. Further, they observed none of these treatments inhibited the growth of MDA-MB-231 cells. Also, authors concluded that apocarotenoid acids exhibit antiproliferative effects by downregulation of cell cycle regulatory proteins and inhibition of AP-1 transcriptional activity. Rühl et al. [69] also reported that β-carotene is involved in activation of human pregnane receptor (PXR) a ligand-activated transcription factors involved in xenobiotic detoxification in the liver. Ziouzenkova et al. [70] demonstrated that β-apo-14′ carotenal, but not other structurally related apo-carotenals, represses peroxisome proliferatoractivated receptors (PPAR) and RXR activation. Eroglu et al. [71] demonstrated that none of the β-apocarotenoids significantly activated RARs. However, β-apo-14′-carotenal, β-apo-14′ carotenoic acid and β-apo-13-carotenone antagonized ATRA-induced transactivation of RARs.

30 Metabolomics - Fundamentals and Applications

Lycopene is another non-provitamin A hydrocarbon carotenoid present in human serum and tissues. Recently, lycopene has attracted much attention due to its association with a decreased risk of certain cancers, cardiovascular diseases and other chronic problems [72]. Several studies from in vivo and in vitro suggest that lycopene induces apoptosis in cancer cells [73–75]. Structurally, lycopene consists of open-polyene chain lacking the β-ionone ring compared to β-carotene and share a same molecular mass and chemical formula. Even though the metabolism of β-carotene extensively studied, the metabolism and biological activities of lycopene are not detailed. Several studies support the concept that biological activities of lycopene are mediated by its oxidative products/cleavage products [76–78]. The characterization of BCO II enzyme has demonstrated that this enzyme can catalyze the eccentric cleavage of lycopene to form apo-10′-lycopenoids [14]. Several lycopene metabolites have been identified in vivo and in vitro [9, 13, 15, 35, 79], but their biological activities and mechanism of action need elucidation. The discovery of various oxidation products or metabolites of carotenoids has questioned the active role of them compared to intact carotenoid molecules in combating various diseases. However, metabolites or oxidative products of LYC on health benefits are warranted. Nara et al. [80] have demonstrated that autoxidation mixtures of LYC inhibited the HL-60 cell growth effectively than LYC. Similarly, Zhang et al. [81] identified a cleavage product of LYC (E, E, E)-4-methyl-8-oxo-2,4,6-nonatrienal and evaluated its apoptosis-inducing activity in HL-60 cells. Consequently, Aust et al. [38] reported the role of LYC degraded products in enhancing cell communication and cell signaling. Lian et al. [5] demonstrated that apo-10′-lycopenoic

Lutein and its isomer zeaxanthin are the two major carotenoids found in the human eye associated with vision protective properties. Dietary consumption of lutein-rich fruits and vegetables positively associated in decreasing AMD and cataract [8, 17]. Although conversions of β-carotene to retinoids documented in animals and humans, only little is known about the metabolism of xanthophyll carotenoids. In general, carotenoid metabolites are involved in chemoprevention of cancer [6]. Lutein metabolites such as 3′-epilutein, 3′-oxolutein, 3′-dehydrolutein, meso-zeaxanthin, methoxy-zeaxanthin, oxime derivatives of 3-hydroxy-β-ionone and 3-hydroxy-14-apocarotenal reported in human tissues and serum [9, 43, 52]. Characterization of these lutein and zeaxanthin metabolites in vitro and in vivo is warranted to address their biological functions. The health benefit of these metabolites is not detailed. The oxidized lutein may be highly reactive when combined with similar reactive oxygen species and presumed to enhance antioxidant property [6], but the mechanism of action of these xanthophylls (lutein) metabolites remains unanswered. Several noteworthy studies have explored lutein metabolites in vivo [9, 18, 43, 44, 52]. However, mechanism of action and other functional aspects of these metabolites/oxidative cleavage products need further research. We reported the possible protective effect of lutein oxidation products in cervical cancer cell lines [84]. Further, we also elucidated the formation of lutein oxidation products mediated through peroxyl radicals and screened the antioxidant and cytotoxic effects of oxidized lutein on HeLa cancer cells [6]. Previously, we identified the apocarotenals, diepoxides and other oxidative degradation products of lutein in liver. Further, we presumed that these products are formed ay due to the peroxyl radical-mediated oxidation in the body [6, 44]. These results are significant in chain breaking peroxyl radical or quenching of singlet oxygen. Furthermore, the existence of these oxidized molecules in vivo is important in free radical chemistry and oxidative stress [85]. Possibly, oxidized lutein may reduce the cancer cells viability through induction of apoptosis. Further, result from our study demonstrated the inhibitory effect of these compounds on cancer cell growth, which may be due to the effect on signaling pathway involved in apoptosis. The biological activity of intact lutein may be different than its metabolites. Hence, it is important to address that the beneficial role of lutein and zeaxanthin in delaying and possibly protecting against ascribed chronic diseases may be due to their metabolites. In the case of other oxygenated carotenoids, such as astaxanthin, Wolz et al. [24] identified the metabolized products such as 3-hydroxy-4-oxo-β-ionone and 3-hydroxy-4-oxo-7,8-dihydro-

β-ionone in primary rat hepatocytes. Later, Kistler et al. [25] reported four radiolabeled metabolites of astaxanthin including 3-hydroxy-4-oxo-β-ionol, 3-hydroxy-4-oxo-β-ionone, 3-hydroxy-4-oxo-7,8-dihydro-β-ionol and 3-hydroxy-4-oxo-7,8-dihydro-β-ionone in human and primary human hepatocytes. Further, they demonstrated that incubation of astaxanthin with microsomes from liver containing detoxifying enzymes did not generate astaxanthin metabolites. Sangeetha and Baskaran [86] hypothesized that astaxanthin might be converted into retinol via β-carotene in retinol-deficient rats. However, the formation of astaxanthin metabolites and their biological functions is not detailed. Further, studies related to astaxanthin metabolites and their biological functions are not detailed and explored.

Fucoxanthin is a major epoxy carotenoid found in the marine source (seaweeds) and explored for its anticancer, anti-allergic and anti-obese activities [87, 88]. Dietary fucoxanthin is metabolized to fucoxanthinol in the gastrointestinal tract by digestive enzymes, and further, it is converted to amarouciaxanthin in liver [23]. The bioactivity of fucoxanthin is attributed to its metabolites fucoxanthinol and amarouciaxanthin A. There are reports demonstrated that fucoxanthinol is effective and plays an important role in health benefits than intact fucoxanthin [89, 90]. Anti-proliferative and cancer preventing influences of fucoxanthin and fucoxanthinol are mediated through different signaling pathways [90]. Also, others have reported fucoxanthin, and its metabolites regulate adipogenic gene expression and inhibit the adipocyte differentiation of 3T3-L1 cells through downregulation of PPARγ. The suppressive effect of fucoxanthinol is superior on adipocyte differentiation than its parent molecule [91]. In MDA-MB-231 cells, fucoxanthinol reduced nuclear levels of NF-κB members and indicated an effective for the treatment and/or prevention of breast cancer [89]. However, there are no much reports on epoxy carotenoids identified in blood and tissues and may be due to the less dietary importance. This is an active area of research and deserves further study. The overview of biofunctionality of carotenoid cleavage products/metabolites is shown in **Table 1**.



**Table 1.** Biofunctionality of carotenoid cleavage products/metabolites.

## **7. Conclusion**

β-ionone in primary rat hepatocytes. Later, Kistler et al. [25] reported four radiolabeled metabolites of astaxanthin including 3-hydroxy-4-oxo-β-ionol, 3-hydroxy-4-oxo-β-ionone, 3-hydroxy-4-oxo-7,8-dihydro-β-ionol and 3-hydroxy-4-oxo-7,8-dihydro-β-ionone in human and primary human hepatocytes. Further, they demonstrated that incubation of astaxanthin with microsomes from liver containing detoxifying enzymes did not generate astaxanthin metabolites. Sangeetha and Baskaran [86] hypothesized that astaxanthin might be converted into retinol via β-carotene in retinol-deficient rats. However, the formation of astaxanthin metabolites and their biological functions is not detailed. Further, studies related to astaxanthin metabolites and their biological functions are not detailed and ex-

Fucoxanthin is a major epoxy carotenoid found in the marine source (seaweeds) and explored for its anticancer, anti-allergic and anti-obese activities [87, 88]. Dietary fucoxanthin is metabolized to fucoxanthinol in the gastrointestinal tract by digestive enzymes, and further, it is converted to amarouciaxanthin in liver [23]. The bioactivity of fucoxanthin is attributed to its metabolites fucoxanthinol and amarouciaxanthin A. There are reports demonstrated that fucoxanthinol is effective and plays an important role in health benefits than intact fucoxanthin [89, 90]. Anti-proliferative and cancer preventing influences of fucoxanthin and fucoxanthinol are mediated through different signaling pathways [90]. Also, others have reported fucoxanthin, and its metabolites regulate adipogenic gene expression and inhibit the adipocyte differentiation of 3T3-L1 cells through downregulation of PPARγ. The suppressive effect of fucoxanthinol is superior on adipocyte differentiation than its parent molecule [91]. In MDA-MB-231 cells, fucoxanthinol reduced nuclear levels of NF-κB members and indicated an effective for the treatment and/or prevention of breast cancer [89]. However, there are no much reports on epoxy carotenoids identified in blood and tissues and may be due to the less dietary importance. This is an active area of research and deserves further study. The overview of

biofunctionality of carotenoid cleavage products/metabolites is shown in **Table 1**.

**Lycopene** Oxidation products of lycopene Inhibits the growth of HL-60 human

2,7,11-Trimethyltetradecahexaene-1,14-dial

(E,E,E)-4-methyl-8-oxo-2,4,6 nonatrienal (MON)

**Carotenoid Metabolites identified Bio-functionality of carotenoid metabolites References**

Acyclo retinoic acid Activates RAR to inhibit mammary cancer cells growth

HL-60 cells

Apo-10′-lycopenoic acid Influence on SIRT1 enzyme activity and

Apo-10′-lycopenoic acid Chemopreventive effect on lung carcinogenesis [5] Apo-12′-lycopenal Reduced androgen-independent prostate cancer cells

prevention of fatty liver

promyelocytic leukemia cells

Enhance gap-junction communication [38]

Induce apoptosis by downregulation of Bcl-2 and Bcl-XL, and activated caspase cascades in [80]

[92]

[81]

[82]

[83]

plored.

32 Metabolomics - Fundamentals and Applications

In concern to the application of analytical techniques in carotenoid, research needs a more sophisticated instrumentation to increase sensitivity, precision, specificity and speed of analysis. The increase in analytical hyphenation considered as cutting edge with multidimensional or techniques that may support to decipher the carotenoids found in natural sources and their effects on human health. Further, metabolomics corresponds to prevailing analytical platforms to obtain detailed and complete information on the composition of food components and their existence in biological entities. Further, the target in carotenoid/ metabolites analysis is to understand the role of these compounds at the molecular levels (i.e.,their interaction with genes and their subsequent effect on proteins and metabolites). This information will provide a rational design of strategies to manipulate cell functions through diet/nutraceuticals, which is expected to have an extraordinary impact on human health. The development of the framework in genomics, transcriptomics, proteomics and metabolomics has given rise to opportunities for increasing our understanding of different issues that can be addressed by profiling carotenoid metabolites.

## **Acknowledgements**

Authors acknowledge Department of Science and Technology, Govt. of India, Grant Reference numbers (WOS-A: F.NO.SR/WOS-A/LS-35/2012) and (SB/EMEQ-233/2013). Authors also acknowledge the Department of Biotechnology-Bangalore University for their encouragement and support.

## **Author details**

Bangalore Prabhashankar Arathi1 , Poorigali Raghavendra-Rao Sowmya1 , Kariyappa Vijay1 , Vallikannan Baskaran2 and Rangaswamy Lakshminarayana1\*

\*Address all correspondence to: rlnarn21@gmail.com

1 Department of Biotechnology, Jnana Bharathi Campus, Bangalore University, Bangalore, India

2 Department of Biochemistry and Nutrition, Central Food Technological Research Institute, CSIR, Mysuru, India

## **References**


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metabolites analysis is to understand the role of these compounds at the molecular levels (i.e.,their interaction with genes and their subsequent effect on proteins and metabolites). This information will provide a rational design of strategies to manipulate cell functions through diet/nutraceuticals, which is expected to have an extraordinary impact on human health. The development of the framework in genomics, transcriptomics, proteomics and metabolomics has given rise to opportunities for increasing our understanding of different issues that can be

Authors acknowledge Department of Science and Technology, Govt. of India, Grant Reference numbers (WOS-A: F.NO.SR/WOS-A/LS-35/2012) and (SB/EMEQ-233/2013). Authors also acknowledge the Department of Biotechnology-Bangalore University for their encouragement

and Rangaswamy Lakshminarayana1\*

1 Department of Biotechnology, Jnana Bharathi Campus, Bangalore University, Bangalore,

2 Department of Biochemistry and Nutrition, Central Food Technological Research Institute,

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#### **Metabolomic and Proteomic Analysis of the Mesenchymal Stem Cells' Secretome Metabolomic and Proteomic Analysis of the Mesenchymal Stem Cells' Secretome**

Galya Ivanova, Tiago Pereira, Ana Rita Caseiro, Petia Georgieva and Ana Colette Maurício Galya Ivanova, Tiago Pereira, Ana Rita Caseiro, Petra Georgieva and Ana Colette Maurício

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66101

#### **Abstract**

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42 Metabolomics - Fundamentals and Applications

Mesenchymal stem cells (MSCs) are multipotent stromal cells with a strong potential in human regenerative medicine due to their ability to renew themselves and differentiate into various specialized cell types under certain physiological or experimental conditions. MSCs secrete a broad spectrum of autocrine and paracrine factors (MSCs' secretome) that could exert significant effects on cells in their vicinity. MSCs have been clinically tested and have displayed a great potential in the treatment of bone/cartilage fractures and disorders, diabetes, cardiovascular diseases and immune, neurodegener‐ ative and inflammatory diseases. The therapeutic efficacy of MSCs was initially attributed to their multipotent character and ability to engraft and differentiate at the site of injury. However, in recent years, it has been revealed that either undifferentiated or differentiated MSCs' secretome plays an important role in the therapeutic potential of MSCs. The deciphering of the composition of MSCs' secretome through proteomic and metabolic analyses and implementation of certain advanced analytical (nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), chromatography, etc.) and immunological methods could contribute to the understanding of the mechanisms underlying the therapeutic effects of MSCs.

**Keywords:** secretome, mesenchymal stem cells, proteomics, metabolomics, umbilical cord blood plasma, growth factors

#### **1. Introduction**

Mesenchymal stem cells (MSCs) are one of the most promising types of stem cells for cell‐ based therapies. The continuously increasing interest in the therapeutic application of MSCs

© 2016 The Author(s). Licensee InTech. 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. © 2017 The Author(s). Licensee InTech. 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.

to various diseases can be linked to their capacity to self‐renew and differentiate into several cell types, to secret soluble factors with paracrine actions and their immunosuppressive and immunomodulatory properties [1–5]. In addition, MSCs can be easily isolated from a wide range of ethically acceptable adult tissues and demonstrate a significant capacity for *ex vivo* expansion [6, 7].

Mesenchymal stem cells (MSCs) were first isolated and described by Friedenstein et al. in the early 1970s as a population of multipotent cells found in the bone marrow that can differentiate into osteocytes, chondrocytes, adipocytes and myoblasts [8, 9]. Currently, MSCs can be found and isolated from various foetal and adult tissues including bone marrow, adipose tissue, skeletal muscle, umbilical cord blood (UCB), umbilical cord tissue or matrix (UCT, Wharton's jelly), peripheral blood, dental pulp and amniotic fluid [10–12]. Despite their presence in different tissue sources, the isolated MSCs exhibit a similar characteristic phenotype [2] with some additional features that reflect their tissue of origin [13].

MSCs are able to renew themselves and differentiate into various specialized cell types such as bone, cartilage, muscle and fat cells under certain physiological or experimental conditions and may serve as a renewable source of cell and tissue replacements [1, 2]. According to the definition agreed by the International Society for Cellular Therapy (ISCT) in 2006, mesenchy‐ mal stem cells are characterized by (a) their capacity to adhere to plastic; (b) their expression of specific surface markers, namely, CD73, CD90 and CD105, and no expression of CD14, CD19, CD34, CD45 and HLA‐DR. Additionally, according to the ISCT, MSCs are able to undergo tri‐ lineage differentiation into adipocytes, chondrocytes and osteoblasts [2].

Besides their multipotency, MSCs possess unique characteristics, such as the ability to migrate to sites of injury, inflammation or cancer, the capacity to secrete a broad spectrum of autocrine/ paracrine factors that could exert significant effects on cells in their vicinity and the ability to modulate inflammatory and regenerative processes and the immune system. MSCs have been clinically tested for treatment of a variety of pathologies and diseases such as brain paralysis, cardiovascular diseases and myocardial infarction, type I diabetes, multiple sclerosis, Crohn's disease, bone fractures, graft versus host disease (GVHD) in bone marrow transplantation, osteoarthritis and rheumatoid arthritis [3, 14, 15].

The therapeutic effects of MSCs were initially attributed to the ability to migrate to sites of injury and inflammation, engraft into the damaged tissues and differentiate into specialised cell types. However, many studies provide strong evidences that the therapeutic efficacy of transplanted MSCs is not dependent on the physical proximity of the transplanted cells to the damaged tissue and propose that MSCs exert their therapeutic activity through MSCs' secretome [16, 17]. Although these studies, and many others, indicate the potency of MSC‐ secreted factors in mediating tissue repair and regeneration, the mechanisms of MSCs' action are not completely clear. The scientific community has tried to understand MSCs' biological mechanisms of action considering their capacity to secret soluble factors with paracrine actions and unveil their potential in cell therapy and regenerative medicine.

Proteomics and metabolomics are omics techniques that employ state of the art analytical instrumentation in conjunction with pattern recognition techniques for comprehensive and simultaneous systematic analysis of biological systems and monitoring of cellular and systemic proteomic and metabolic fluctuations in response to xenobiotic exposure, environmental factors, physiological stimuli and genetic modifications [18–23].

The studies of the composition of secretion products of MSCs through proteomic and metabolic analyses could contribute to the understanding of the mechanisms underlying the therapeutic effects of MSCs. Currently, certain advanced analytical (nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), chromatography, immunological assays, etc.) and chemometric techniques exist for proteomic and metabolic analyses of bio‐fluids, which can be applied in MSC secretomics [20, 22–24].

## **2. MSCs' conditioned medium and secreted products**

to various diseases can be linked to their capacity to self‐renew and differentiate into several cell types, to secret soluble factors with paracrine actions and their immunosuppressive and immunomodulatory properties [1–5]. In addition, MSCs can be easily isolated from a wide range of ethically acceptable adult tissues and demonstrate a significant capacity for *ex vivo*

Mesenchymal stem cells (MSCs) were first isolated and described by Friedenstein et al. in the early 1970s as a population of multipotent cells found in the bone marrow that can differentiate into osteocytes, chondrocytes, adipocytes and myoblasts [8, 9]. Currently, MSCs can be found and isolated from various foetal and adult tissues including bone marrow, adipose tissue, skeletal muscle, umbilical cord blood (UCB), umbilical cord tissue or matrix (UCT, Wharton's jelly), peripheral blood, dental pulp and amniotic fluid [10–12]. Despite their presence in different tissue sources, the isolated MSCs exhibit a similar characteristic phenotype [2] with

MSCs are able to renew themselves and differentiate into various specialized cell types such as bone, cartilage, muscle and fat cells under certain physiological or experimental conditions and may serve as a renewable source of cell and tissue replacements [1, 2]. According to the definition agreed by the International Society for Cellular Therapy (ISCT) in 2006, mesenchy‐ mal stem cells are characterized by (a) their capacity to adhere to plastic; (b) their expression of specific surface markers, namely, CD73, CD90 and CD105, and no expression of CD14, CD19, CD34, CD45 and HLA‐DR. Additionally, according to the ISCT, MSCs are able to undergo tri‐

Besides their multipotency, MSCs possess unique characteristics, such as the ability to migrate to sites of injury, inflammation or cancer, the capacity to secrete a broad spectrum of autocrine/ paracrine factors that could exert significant effects on cells in their vicinity and the ability to modulate inflammatory and regenerative processes and the immune system. MSCs have been clinically tested for treatment of a variety of pathologies and diseases such as brain paralysis, cardiovascular diseases and myocardial infarction, type I diabetes, multiple sclerosis, Crohn's disease, bone fractures, graft versus host disease (GVHD) in bone marrow transplantation,

The therapeutic effects of MSCs were initially attributed to the ability to migrate to sites of injury and inflammation, engraft into the damaged tissues and differentiate into specialised cell types. However, many studies provide strong evidences that the therapeutic efficacy of transplanted MSCs is not dependent on the physical proximity of the transplanted cells to the damaged tissue and propose that MSCs exert their therapeutic activity through MSCs' secretome [16, 17]. Although these studies, and many others, indicate the potency of MSC‐ secreted factors in mediating tissue repair and regeneration, the mechanisms of MSCs' action are not completely clear. The scientific community has tried to understand MSCs' biological mechanisms of action considering their capacity to secret soluble factors with paracrine actions

Proteomics and metabolomics are omics techniques that employ state of the art analytical instrumentation in conjunction with pattern recognition techniques for comprehensive and

some additional features that reflect their tissue of origin [13].

osteoarthritis and rheumatoid arthritis [3, 14, 15].

lineage differentiation into adipocytes, chondrocytes and osteoblasts [2].

and unveil their potential in cell therapy and regenerative medicine.

expansion [6, 7].

44 Metabolomics - Fundamentals and Applications

The paracrine effect of MSCs and their ability to synthesise and secrete a broad spectrum of growth factors, chemokines and cytokines that could exert significant effects on cells in their vicinity were first described by Haynesworth et al. [25]. This study was followed by other publications in the scientific literature describing therapeutic effects of these secreted factors [26, 27].

The MSCs' secreted factors are principal molecules for intercellular communication involved in most physiological processes, such as cell signalling, differentiation, invasion, metastasis, cell adhesion and binding, angiogenesis and apoptosis [18]. Growth factors, extracellular matrix proteins and extracellular matrix remodelling enzymes, pro‐inflammatory, anti‐ inflammatory and pleiotropic cytokines, chemokines and angiogenic factors have been recently identified in stem cell secretomes including MSCs' one [19].

The compounds synthesised in the process of the proliferation and differentiation of MSCs could be secreted from the cells either constitutively or in the regulated manner and determine the composition of the extracellular environment [28]. Cell treatments *in vitro* could initiate alterations in the amount of particular factors secreted in the culture media [29, 30]. The secreted molecules could act as a possible replacement of stem cells for therapeutic applications that allow precise dosing, low biological variability and overcome of several stem cell related issues including cell origin and immunocompatibility [20]. The therapeutic application of MSCs' secretome could reveal new safe and effective strategies with predictable outcomes as an alternative to the cell therapy.

#### **2.1. Preparation of MSCs' conditioned medium samples**

MSCs' secretome studies involve a number of defined stages: (i) cell isolation and characteri‐ sation; (ii) cell culture in an appropriate culture medium; (iii) cell expansion and differentiation and (iv) collection of conditioned media.

The preparation of MSCs' conditioned medium has been performed by our research group and published elsewhere and here it is resumed and described [31]. Different batches of human MSCs from Wharton's jelly umbilical cord cryopreserved cells are *in* *vitro* cultured and maintained in a humidified atmosphere with 5% CO2 at 37°C. This process can be applied to previously isolated, expanded and cryopreserved MSCs from a diversity of tissues, with the advantage of a higher number of MSCs obtained in a shorter culture time, not dependent on donor's availability and ethic committee's authorization. During expansion, the cells become long spindle‐shaped, and their phenotype is con‐ firmed by flow cytometry analysis for a comprehensive panel of markers, such as platelet endothelial cell adhesion molecule‐1 (PECAM‐1, CD31), homing cell adhesion molecule (HCAM, CD44), CD45 and Endoglin (CD105). Characterization is also performed with the following antibodies and their respective isotypes: PE anti‐human CD105; APC anti‐hu‐ man CD73; PE anti‐human CD90; PerCP/Cy5.5 anti‐human CD45: FITC anti‐human CD34; PerCP/Cy5.5 anti‐human CD14; Pacific Blue anti‐human CD19 and pacific‐blue anti‐hu‐ man HLA‐DR. The karyotype of the MSCs should also be determined to ensure that no structural alterations are found, which demonstrate the absence of neoplastic characteris‐ tics in these cells, as well as chromosomal stability of the somatic and sexual chromo‐ somes due to the cell culture procedures. Two mesenchymal stem cell media have been tested for the conditioning, namely, PromoCell® medium (LabClinics, Promocell, C‐28010, so‐called commercial medium further on) and Dulbecco's Modified Eagle Medium (DMEM, Gibco®) supplemented with 10% of foetal bovine serum (FBS, Gibco®), 2 mM glutamine (sigma), 100 U/ml of penicillin and 100 µg/ml of streptomycin (Sigma). Condi‐ tioned medium (CM) is normally collected from passage 4 (P4) MSCs. To obtain the de‐ sired CM, 4000 cell/cm2 are plated and allowed to grow until reaching a minimum of 80% confluence. At this stage, the commercial medium is removed from the T‐flasks, and after five washing cycles with Dulbecco's Phosphate Buffered Saline 1× (DPBS) without calcium (Ca2+) and magnesium (Mg2+) (Gibco®), Dulbecco's Modified Eagle Medium/Nutrient Mix‐ ture (DMEM, Gibco®) supplemented with 100 U/ml of penicillin and 100 µg/ml of strepto‐ mycin (Sigma) is added. The cell culture T‐flasks are maintained in a humidified atmosphere with 5% CO2 at 37°C, allowing the adherent cells to be in contact with a se‐ rum‐free basal medium. The culture medium, added after the confluence is reached, is not renewed and it is collected at different time points (24 and 48 h). For conditioning with Dulbecco's Modified Eagle Medium/Nutrient Mixture (DMEM, Gibco®) supplemented with 100 U/ml of penicillin and 100 µg/ml of streptomycin (Sigma), samples of the medi‐ um are also collected at 24 and 48 h. Upon collection, the CM is frozen at –20°C, being later on thawed on the day of the experiments. 1 H‐NMR spectra and Multiplexing LASER Bead analysis were acquired from both 24 and 48 h samples.

## **3. Human umbilical cord blood plasma as a potential supplement for MSCs' culture and conditioning**

In search of a suitable alternative (and animal‐component‐free) culturing medium for the referred MSCs, we employed similar analytical techniques in order to better characterize hUCBP and assess its viability as a substitute for standard usage in FBS. The utilization of FBS in cellular culture has a series of ethical, technical and biological issues that made challenging its most extended usage in clinical setting. Since exogenous protein and other factors' supple‐ mentation is still a facilitating point in MSCs' expansion (as chemically defined media are mostly expensive and occasionally underperforming), research has focused on finding suitable replacements for the FBS [31–34].

#### **3.1. Preparation of hUCBP samples**

*vitro* cultured and maintained in a humidified atmosphere with 5% CO2 at 37°C. This process can be applied to previously isolated, expanded and cryopreserved MSCs from a diversity of tissues, with the advantage of a higher number of MSCs obtained in a shorter culture time, not dependent on donor's availability and ethic committee's authorization. During expansion, the cells become long spindle‐shaped, and their phenotype is con‐ firmed by flow cytometry analysis for a comprehensive panel of markers, such as platelet endothelial cell adhesion molecule‐1 (PECAM‐1, CD31), homing cell adhesion molecule (HCAM, CD44), CD45 and Endoglin (CD105). Characterization is also performed with the following antibodies and their respective isotypes: PE anti‐human CD105; APC anti‐hu‐ man CD73; PE anti‐human CD90; PerCP/Cy5.5 anti‐human CD45: FITC anti‐human CD34; PerCP/Cy5.5 anti‐human CD14; Pacific Blue anti‐human CD19 and pacific‐blue anti‐hu‐ man HLA‐DR. The karyotype of the MSCs should also be determined to ensure that no structural alterations are found, which demonstrate the absence of neoplastic characteris‐ tics in these cells, as well as chromosomal stability of the somatic and sexual chromo‐ somes due to the cell culture procedures. Two mesenchymal stem cell media have been tested for the conditioning, namely, PromoCell® medium (LabClinics, Promocell, C‐28010, so‐called commercial medium further on) and Dulbecco's Modified Eagle Medium (DMEM, Gibco®) supplemented with 10% of foetal bovine serum (FBS, Gibco®), 2 mM glutamine (sigma), 100 U/ml of penicillin and 100 µg/ml of streptomycin (Sigma). Condi‐ tioned medium (CM) is normally collected from passage 4 (P4) MSCs. To obtain the de‐ sired CM, 4000 cell/cm2 are plated and allowed to grow until reaching a minimum of 80% confluence. At this stage, the commercial medium is removed from the T‐flasks, and after five washing cycles with Dulbecco's Phosphate Buffered Saline 1× (DPBS) without calcium (Ca2+) and magnesium (Mg2+) (Gibco®), Dulbecco's Modified Eagle Medium/Nutrient Mix‐ ture (DMEM, Gibco®) supplemented with 100 U/ml of penicillin and 100 µg/ml of strepto‐ mycin (Sigma) is added. The cell culture T‐flasks are maintained in a humidified atmosphere with 5% CO2 at 37°C, allowing the adherent cells to be in contact with a se‐ rum‐free basal medium. The culture medium, added after the confluence is reached, is not renewed and it is collected at different time points (24 and 48 h). For conditioning with Dulbecco's Modified Eagle Medium/Nutrient Mixture (DMEM, Gibco®) supplemented with 100 U/ml of penicillin and 100 µg/ml of streptomycin (Sigma), samples of the medi‐ um are also collected at 24 and 48 h. Upon collection, the CM is frozen at –20°C, being

H‐NMR spectra and Multiplexing LASER

later on thawed on the day of the experiments. 1

**MSCs' culture and conditioning**

46 Metabolomics - Fundamentals and Applications

Bead analysis were acquired from both 24 and 48 h samples.

**3. Human umbilical cord blood plasma as a potential supplement for**

In search of a suitable alternative (and animal‐component‐free) culturing medium for the referred MSCs, we employed similar analytical techniques in order to better characterize hUCBP and assess its viability as a substitute for standard usage in FBS. The utilization of FBS in cellular culture has a series of ethical, technical and biological issues that made challenging These samples were collected from different consenting donors that clinically evaluated according to the Portuguese law 12/2009 (Diário da República, lei 12/2009 de 26 de Março de 2009) and analysed by flow cytometry and for microbiological contamination for aerobic and anaerobic microorganisms and fungi.

Umbilical cord blood (UCB) was collected from the umbilical vein by gravity into a 150 ml volume simple bag (reference 1385.13, Suru, Portugal), containing 21 ml of citrate‐phosphate‐ dextrose (CPD), and stored at 4 ± 2°C until processing for cryopreservation. UCB samples were transported to the laboratory at refrigerated temperatures ranging between 4 and 22°C, within 48 h after collection. The collected UCB is subjected to a volume reduction process using AXP system® (thermogenesis). During the two‐step centrifugation, whole blood is separated into three layers that are delivered into a red blood cell (RBC) bag and a freezing bag. Plasma remains in the processing bag which is also known as plasma bag. Samples of the UCB are taken by sampling pillows integrated within the kit for flow cytometry analysis. The total nucleated cell (TNC) count, CD34+ cell counts, CD34+ cell viability and leucocyte (CD45+) viability are determined by samples obtained from the UCB before volume reduction. Microbiological controls are usually performed after volume reduction and before cryopre‐ servation and tested for microbiological contamination using an automated blood culture system (BacT/ALERT®, bioMérieux) at 35°C for 14 days. TNC and the number of white blood cells (WBC) are counted with a haematology autoanalyser (Ac T diff2™, Beckman Coulter, Inc.). The CD34+ cell number and the CD34+ viability are quantified by flow cytometry (BD FACSCalibur™ 3 CA Becton Dickinson, BD Biosciences); the software for acquisition and analysis was BD CellQuest™ and BDCellQuest™ Pro Templates, respectively. The clusters of differentiation (CD) were used to enumerate the total number of CD34+ cells and the total number of leucocytes (CD45+), and the 7‐amino‐actinomycin D (7AAD) nucleic acid dye was used for viability measure (BD Stem Cell Enumeration kit, Becton Dickinson, BD Biosciences), according to the manufacture's protocol. The BD stem cell enumeration simultaneously enumerates the total viable dual‐positive (CD45+/CD34+) haematopoietic stem cells in absolute counts of CD34+ cells per µl and the percentage (%) of viable leucocytes (CD45+) that are CD34 positive (CD34+).

The sample collection, sample storage and sample preparation are very important steps in proteomics and metabolomics. If the samples are not collected properly or the samples are not stored or processed uniformly, the metabolomics data generated from these samples could be invalid. Hence, the sample collection, storage and processing procedures are critical for conducting successful metabolomics studies.

## **4. Proteomic techniques**

Proteomics is the omics technique that focuses on proteins and their functions in biological systems. Proteins are involved in all processes of living organisms and possess a complex structure, interactions, dynamics in concentration, degradation and/or modification which are the key factors defining the behaviour of biological systems. Therefore, the main goal of proteomics is the identification, quantification and characterisation of the protein content of biological samples, such as organs, tissues, cells and biological fluids [22].

Well‐established methodologies exist for proteomic analysis of bio‐fluids that include separa‐ tion and protein identification techniques, and chemometric methods for data interpretation and visualization [18, 20, 24]. The extensive development of advanced analytical techniques and instrumentation in the last decade has enabled the proteomic analysis of cell secretome and several profiles from different cell types, body fluids and physiological conditions that have been studied and established [20, 35]. The continuously increasing interest in secretome proteomics has been raised due to the pivotal role of these secreted proteins in all physiological processes in living systems, including physiological, pathophysiological and genetic transfor‐ mations, degenerative processes, disease conditions and progression [23, 24, 28].

#### **4.1. Proteomic analysis of MSCs' secretome**

The proteomic profiling of biological systems, including cell secretomes is mainly centred on mass spectrometry (MS), chromatography (liquid chromatography, LC), immunological assays and chemometric techniques [20, 24]. *In vivo* and *ex vivo* studies of MSCs' secretome encounter significant technical difficulties due to the low abundance of secreted proteins relative to total proteins in tissue/bio‐fluid, the presence of different cell types including the cells of interest, endothelial cells and fibroblast cells, possibility of contamination by blood and the difficulty of accessing the extracellular medium in a tissue section [20, 23]. Therefore, *in vitro* strategies have been developed to probe the secretome under physiological conditions, where culture medium is conditioned by cells for a certain period of time followed by collection, preparation and processing of the medium for proteomic analysis. The proteomic analysis of MSCs' secreted products involves MSCs' isolation and characterisation; culture medium preparation; MSCs' conditioning; isolation of the conditioned medium; implemen‐ tation of appropriate proteomic analytical techniques and protocols and data analysis. Proteomic studies of MSCs' secretome are currently performed under *in vitro* conditions as shown in **Figure 1** [20, 23].

Proteomic analysis based on LC, MS, LC‐MS, immunological assays and bioinformatics has already been applied in the studies of protein and peptide separation, identification and quantification, screening of posttranslational modifications and characterisation of secreted products [20, 22, 23, 36, 37]. Proteomic studies performed on MSCs' secretome have been recently revised considering the cell origin, conditioning protocols and the analytical techni‐ ques used [20]. The studies of the secreted products of MSCs isolated from various foetal and adult tissues including bone marrow, adipose tissue, skeletal muscle, umbilical cord blood and umbilical cord tissue have been performed. Proteomic profiles of secreted products obtained from culturing of a wide range of human and animal MSCs have been established through implementation of proteomic analytical techniques such as MS, LC, LC/MS, immunological assays and bioinformatics [20, 38–42].

**4. Proteomic techniques**

48 Metabolomics - Fundamentals and Applications

**4.1. Proteomic analysis of MSCs' secretome**

shown in **Figure 1** [20, 23].

Proteomics is the omics technique that focuses on proteins and their functions in biological systems. Proteins are involved in all processes of living organisms and possess a complex structure, interactions, dynamics in concentration, degradation and/or modification which are the key factors defining the behaviour of biological systems. Therefore, the main goal of proteomics is the identification, quantification and characterisation of the protein content of

Well‐established methodologies exist for proteomic analysis of bio‐fluids that include separa‐ tion and protein identification techniques, and chemometric methods for data interpretation and visualization [18, 20, 24]. The extensive development of advanced analytical techniques and instrumentation in the last decade has enabled the proteomic analysis of cell secretome and several profiles from different cell types, body fluids and physiological conditions that have been studied and established [20, 35]. The continuously increasing interest in secretome proteomics has been raised due to the pivotal role of these secreted proteins in all physiological processes in living systems, including physiological, pathophysiological and genetic transfor‐

The proteomic profiling of biological systems, including cell secretomes is mainly centred on mass spectrometry (MS), chromatography (liquid chromatography, LC), immunological assays and chemometric techniques [20, 24]. *In vivo* and *ex vivo* studies of MSCs' secretome encounter significant technical difficulties due to the low abundance of secreted proteins relative to total proteins in tissue/bio‐fluid, the presence of different cell types including the cells of interest, endothelial cells and fibroblast cells, possibility of contamination by blood and the difficulty of accessing the extracellular medium in a tissue section [20, 23]. Therefore, *in vitro* strategies have been developed to probe the secretome under physiological conditions, where culture medium is conditioned by cells for a certain period of time followed by collection, preparation and processing of the medium for proteomic analysis. The proteomic analysis of MSCs' secreted products involves MSCs' isolation and characterisation; culture medium preparation; MSCs' conditioning; isolation of the conditioned medium; implemen‐ tation of appropriate proteomic analytical techniques and protocols and data analysis. Proteomic studies of MSCs' secretome are currently performed under *in vitro* conditions as

Proteomic analysis based on LC, MS, LC‐MS, immunological assays and bioinformatics has already been applied in the studies of protein and peptide separation, identification and quantification, screening of posttranslational modifications and characterisation of secreted products [20, 22, 23, 36, 37]. Proteomic studies performed on MSCs' secretome have been recently revised considering the cell origin, conditioning protocols and the analytical techni‐ ques used [20]. The studies of the secreted products of MSCs isolated from various foetal and adult tissues including bone marrow, adipose tissue, skeletal muscle, umbilical cord blood and umbilical cord tissue have been performed. Proteomic profiles of secreted products obtained

biological samples, such as organs, tissues, cells and biological fluids [22].

mations, degenerative processes, disease conditions and progression [23, 24, 28].

**Figure 1.** Schematic representation of the steps followed in a proteomic analysis of conditioned medium obtained from MSCs' *in vitro* expansion.

We have recently disclosed proteomic study of several MSCs' conditioned media based on Multiplexing LASER Bead Technology which enable the simultaneous testing of numerous analytic categories such as cytokines, chemokines and growth factors in a single assay [31]. Human Primary Cytokine Array/Chemokine Array 41‐Plex Panel (Eve Technologies, Calgary, Alberta, Canada) has been performed to analyse the conditioned media, including the following cytokines, chemokines and growth factors: epidermal growth factor (EGF), eotaxin‐ 1, fibroblast growth factor 2 (FGF‐2), fms‐related tyrosine kinase 3 ligand (Flt‐3L), fractalkine, granulocyte colony‐stimulating factor (G‐CSF), granulocyte macrophage colony‐stimulating factor (GM‐CSF), GRO(pan), interferon‐ alpha 2 (IFNα2), interferon‐gamma (IFN*γ*), several interleukins (IL‐1α, IL‐1β, IL‐1ra, IL‐2, IL‐3, IL‐4, IL‐5, IL‐6, IL‐7, IL‐8, IL‐9, IL‐10, IL‐12 (p40), IL‐12 (p70), IL‐13, IL‐15, IL‐17A), interferon gamma‐induced protein 10 (IP‐10), monocyte chemotactic protein‐1 (MCP‐1), monocyte chemotactic protein‐3 (MCP‐3), macrophage‐ derived chemokine (MDC), macrophage inflammatory protein‐1 alpha (MIP‐1α), macrophage inflammatory protein‐1 beta (MIP‐1β), platelet‐derived growth factor‐AA (PDGF‐AA), platelet‐derived growth factor‐AB/BB (PDGF‐AB/BB), chemokin (C‐C motif) ligand 5 (RANTES or CCL5), soluble CD40 ligand (sCD40L), transforming growth factor alpha (TGFα), tumour necrosis factor alpha (TNFα), tumour necrosis factor beta TNFβ and vascular endothelial growth factor A (VEGF‐A). TGF‐β 3‐Plex Array Multi‐Species (Eve Technologies, Calgary, Alberta, Canada) was also performed to analyse the tumour growth factor beta 1, 2 and 3 (TGF‐β 1, 2 and 3).

**Figure 2.** Proliferative and antiapoptotic growth factors, immunomodulatory, immunosupressive cytokines and che‐ mokines concentrations in unconditioned (Com. Medium\* and DMEM) and conditioned media (24/48 h Com. Medium and DMEM). (Multiplexing LASER Bead Analysis (Eve Technologies, Calgary, Alberta, Canada). \*Commercial medium from PromoCell (LabClinics, Promocell, reference C‐28010), adapted from [31].

The results from the Human Primary Cytokine Array/Chemokine Array 41‐Plex Panel together with the TGF‐β 3‐Plex Array allowed the comprehensive analysis of the cytokines, chemokines and growth factors included in conditioned media in comparison with unconditioned culture media [43]. It was confirmed that the concentration of important proliferative and anti‐ apoptotic factors increases the process of cell conditioning. This was particularly evident for TGF‐β1, EGF, G‐CSF, GM‐CSF, PDGF‐AA and VEGF. Some of these factors like EGF were even absent in unconditioned media. It was also confirmed that the content of factors such as TGF‐ β1 and G‐CSF depends on the duration of the conditioning process (**Figure 2**).

#### **4.2. Proteomic analysis of hUCBP**

platelet‐derived growth factor‐AB/BB (PDGF‐AB/BB), chemokin (C‐C motif) ligand 5 (RANTES or CCL5), soluble CD40 ligand (sCD40L), transforming growth factor alpha (TGFα), tumour necrosis factor alpha (TNFα), tumour necrosis factor beta TNFβ and vascular endothelial growth factor A (VEGF‐A). TGF‐β 3‐Plex Array Multi‐Species (Eve Technologies, Calgary, Alberta, Canada) was also performed to analyse the tumour growth factor beta 1, 2

**Figure 2.** Proliferative and antiapoptotic growth factors, immunomodulatory, immunosupressive cytokines and che‐ mokines concentrations in unconditioned (Com. Medium\* and DMEM) and conditioned media (24/48 h Com. Medium and DMEM). (Multiplexing LASER Bead Analysis (Eve Technologies, Calgary, Alberta, Canada). \*Commercial medium

The results from the Human Primary Cytokine Array/Chemokine Array 41‐Plex Panel together with the TGF‐β 3‐Plex Array allowed the comprehensive analysis of the cytokines, chemokines and growth factors included in conditioned media in comparison with unconditioned culture media [43]. It was confirmed that the concentration of important proliferative and anti‐ apoptotic factors increases the process of cell conditioning. This was particularly evident for TGF‐β1, EGF, G‐CSF, GM‐CSF, PDGF‐AA and VEGF. Some of these factors like EGF were even

from PromoCell (LabClinics, Promocell, reference C‐28010), adapted from [31].

and 3 (TGF‐β 1, 2 and 3).

50 Metabolomics - Fundamentals and Applications

Following an identical analytical protocol hUCBP depicted impressively high detection levels for TGF‐β family members 1–3. Fair concentrations of other factors like VEGF, PDGF‐AA, PDGF‐BB and EGF were also observed. Other immunossupressive/immunomodulatory factors can also be found at interesting levels in hUCBP (**Figure 3**).

**Figure 3.** Proliferative and anti‐apoptotic growth factors (A), immunomodulatory, immunosuppressive cytokines (B) and chemokines (C) concentrations in UCB Plasma and unconditioned media (Com. Medium\* and DMEM). (Multi‐ plexing LASER Bead Analysis (Eve Technologies, Calgary, Alberta, Canada). \*Commercial medium from PromoCell (LabClinics, Promocell, C‐28010), as in [31].

## **5. Metabolomics techniques**

Metabolomics is a powerful approach for multicomponent analysis of biological samples that allow the comprehensive and simultaneous systematic profiling of multiple metabolite concentrations and their cellular and systemic fluctuations in response to xenobiotic exposure, environment, stimuli and genetic modulations. Metabolomics is important and well‐estab‐ lished technique that encompasses the multicomponent analysis of metabolites in biological fluids, tissue and cell extracts using advanced analytical techniques [21, 43–46].

**Figure 4.** Schematic representation of the steps followed in a metabolomic analysis of conditioned medium obtained from MSCs' *in vitro* expansion.

An analysis of the metabolite spectrum in a biological system provides a detailed information and specific view into cellular metabolic processes under normal and altered conditions. The study of the entire multicomponent metabolic profile of biological systems is a challenging task and relies on the use of reproducible analytical technologies that provide sufficiently high sensitivity, high resolution and wide dynamic range. Metabolomics is mainly based on NMR spectroscopy (NMR) and mass spectrometry (MS), with chromatographic separation usually involved in the latter [43–46]. Both spectroscopic techniques could give valuable information on the structure and quantitative distribution of metabolites in biological systems.

Metabolomics studies usually result in large number of complex multivariate data sets that are typically analysed using chemometric and bioinformatics methods for data interpretation and visualization. These studies have been extensively expanded after implementation of pattern recognition (PR), expert systems and related bio‐informatics tools to interpret and classify complex metabolic data sets. Well‐established methodologies and protocols for metabolic analysis of biological samples have been extensively developed [21, 43–48].

The metabolomics analysis of biological samples involves several basic steps (**Figures 1** and **4**): sample collection; sample manipulation and storage; sample preparation for metabolic analysis; application of appropriate analytical method(s); data acquisition; data analysis (metabolites identification and quantification) and pattern recognition of spectral data (data interpretation and visualisation).

The development of advanced analytical techniques and instrumentation in the last decades has enabled a comprehensive metabolic analysis of diverse and complex biological samples, such as tissues, cells, tissues and cellular extracts, bio‐fluids, etc. Certain advanced analytical techniques such as NMR, MS, LC, LC‐MS and bioinformatics exist for metabolic analysis of biological systems, which can be applied in MSC secretomics [21, 43–48].

Studies of the composition of MSCs' secretome could contribute to the understanding of the mechanisms underlying the therapeutic effects of MSCs and assessment of the potential of MSCs' secretome as an alternative to MSCs' cell therapy.

#### **5.1. Metabolic analysis of MSCs' secretome by NMR spectroscopy**

NMR spectroscopy being a high reproducible and quantitative analytical technique, with minimal or no sample preparation and the sample can be recovered following the analysis, is one of the main tools for metabolic profiling, identification and quantification of known and unknown metabolites in biological samples. It offers unique opportunities for improving the structural and functional characterization of metabolome, which can be essential for the understanding of many biological processes and physiological changes in biological systems [43–48].

NMR spectroscopy as a quantitative non‐destructive, non‐invasive analytical technique can be used effectively to screen for metabolite profiles and to monitor structural and physiological changes in biological systems [21, 43, 44]. Nowadays, high‐resolution NMR spectroscopy is one of the most powerful analytical techniques available for metabolic profiling of biological systems and could be suitable for metabolic analysis of MSC secretome. One of the advantages of NMR spectroscopy over other methods is that it can generate a large quantity of information concerning the metabolic composition of samples, making possible the simultaneous identi‐ fication and quantification of structurally diverse metabolites, without the need for individual isolation or no special sample preparation [43, 44].

#### *5.1.1. Samples collection and preparation*

concentrations and their cellular and systemic fluctuations in response to xenobiotic exposure, environment, stimuli and genetic modulations. Metabolomics is important and well‐estab‐ lished technique that encompasses the multicomponent analysis of metabolites in biological

**Figure 4.** Schematic representation of the steps followed in a metabolomic analysis of conditioned medium obtained

An analysis of the metabolite spectrum in a biological system provides a detailed information and specific view into cellular metabolic processes under normal and altered conditions. The study of the entire multicomponent metabolic profile of biological systems is a challenging task and relies on the use of reproducible analytical technologies that provide sufficiently high sensitivity, high resolution and wide dynamic range. Metabolomics is mainly based on NMR spectroscopy (NMR) and mass spectrometry (MS), with chromatographic separation usually involved in the latter [43–46]. Both spectroscopic techniques could give valuable information

on the structure and quantitative distribution of metabolites in biological systems.

from MSCs' *in vitro* expansion.

fluids, tissue and cell extracts using advanced analytical techniques [21, 43–46].

52 Metabolomics - Fundamentals and Applications

As for the proteomic analysis, the sample collection, storage and processing procedures are critical for conducting successful metabolomics studies. As previously studied, the collected samples of CM or for other biological fluids like umbilical cord blood plasma [31] should be centrifuged to remove cellular detritus and stored at –20 or even –80°C. The cellular systems used in the metabolomics and secretome profile studies should be previously characterised, including the flow cytometry or immunecitochemistry profile and karyotype.

At the end of the cell conditioning period, the conditioned medium is collected and centrifu‐ gation and/or filtration are usually applied to remove cell residues. In some cases, larger volumes of conditioned medium can be concentrated to succeed optimal experimental conditions. Upon collection and purification, the conditioned medium can be frozen and stored at –20°C, being later on thawed on the day of the NMR experiments.

The preparation of samples for NMR analysis can involve the addition of buffer to stabilize the pH, deuterated water (D2O) as a magnetic field lock signal and reference compound to be used as a chemical shift and quantitation standard. The reference compound used in aqueous media is usually sodium trimethylsilyl‐[2,2,3,3‐d4]‐propionate (TSP), which can be used as an internal or external standard. Absolute concentrations of metabolites can be obtained by the use of internal standard of known concentration added to the sample.

#### *5.1.2. NMR techniques*

The metabolomic study of bio‐fluids is usually based of 1 H NMR spectroscopy. The NMR data for all samples need to be acquired and processed uniformly. The NMR experimental condi‐ tions and procedures are critical for conducting successful metabolomics studies and need to be uniform. The large interfering NMR signal arising from water in all bio‐fluids can be eliminated by the use of standard NMR solvent suppression pulse sequences. 1 H NMR spectra using a 1D NOESY (noesygppr1d) pulse sequence (recycle delay‐90°‐t1‐90°‐tm‐ acquire) and a Carr‐Purcell‐Meiboom‐Gill (CPMG, cpmgpr1d) pulse sequence (recycle delay‐90°‐[τ‐180°‐ τ]n‐ acquire) can be acquired. The 1D NOESY experiment generates spectra with improved solvent peak (at 4.70 ppm) suppression. Relaxation edited 1 H NMR spectra with T2 (spin‐spin relaxation time) filter using CPMG pulse sequence and suppression of water resonance were acquired to facilitate the identification of low molecular weight metabolites, reducing signals from high molecular weight species or systems in intermediate chemical exchange. The generated NMR data should be processed spectra were processed uniformly.

#### *5.1.3. NMR data analysis*

NMR spectra of biological systems are usually extremely complex because of the large number of components in the samples, resulting in spectra with complex line shapes and significant overlap of the resonance signals. The analysis of NMR spectra and identification of the metabolites in biological samples may involve a number of spectral techniques, information from databases of known metabolite spectra and NMR assignment software. Though very complex, the NMR spectra of biological samples (in particular 1 H NMR) allow direct assign‐ ment of resonance signals of some metabolites based on their chemical shifts, multiplicity and intensity and spectral analysis of appropriate NMR spectroscopic techniques such as two (2D) or multidimensional NMR techniques. 2D NMR spectroscopy can be useful to increase the signal dispersion in spectra with significant overlap of the resonances and reveal the connec‐ tivity between signals (1 H/1H Correlation spectroscopy (COSY); 1 H/1 H Total Correlation spectroscopy (TOCSY), 1 H/13C or 1 H/15N Hetero‐correlation spectroscopy (HSQC, HMBC)) and/or to define the multiplicity and coupling pattern of resonances (2D J‐resolved spectro‐ scopy), thereby helping to identify metabolites in biological samples [21, 43, 44, 48].

#### *5.1.4. Bioinformatics*

samples of CM or for other biological fluids like umbilical cord blood plasma [31] should be centrifuged to remove cellular detritus and stored at –20 or even –80°C. The cellular systems used in the metabolomics and secretome profile studies should be previously characterised,

At the end of the cell conditioning period, the conditioned medium is collected and centrifu‐ gation and/or filtration are usually applied to remove cell residues. In some cases, larger volumes of conditioned medium can be concentrated to succeed optimal experimental conditions. Upon collection and purification, the conditioned medium can be frozen and stored

The preparation of samples for NMR analysis can involve the addition of buffer to stabilize the pH, deuterated water (D2O) as a magnetic field lock signal and reference compound to be used as a chemical shift and quantitation standard. The reference compound used in aqueous media is usually sodium trimethylsilyl‐[2,2,3,3‐d4]‐propionate (TSP), which can be used as an internal or external standard. Absolute concentrations of metabolites can be obtained by the

for all samples need to be acquired and processed uniformly. The NMR experimental condi‐ tions and procedures are critical for conducting successful metabolomics studies and need to be uniform. The large interfering NMR signal arising from water in all bio‐fluids can be

using a 1D NOESY (noesygppr1d) pulse sequence (recycle delay‐90°‐t1‐90°‐tm‐ acquire) and a Carr‐Purcell‐Meiboom‐Gill (CPMG, cpmgpr1d) pulse sequence (recycle delay‐90°‐[τ‐180°‐ τ]n‐ acquire) can be acquired. The 1D NOESY experiment generates spectra with improved

relaxation time) filter using CPMG pulse sequence and suppression of water resonance were acquired to facilitate the identification of low molecular weight metabolites, reducing signals from high molecular weight species or systems in intermediate chemical exchange. The

NMR spectra of biological systems are usually extremely complex because of the large number of components in the samples, resulting in spectra with complex line shapes and significant overlap of the resonance signals. The analysis of NMR spectra and identification of the metabolites in biological samples may involve a number of spectral techniques, information from databases of known metabolite spectra and NMR assignment software. Though very

ment of resonance signals of some metabolites based on their chemical shifts, multiplicity and intensity and spectral analysis of appropriate NMR spectroscopic techniques such as two (2D) or multidimensional NMR techniques. 2D NMR spectroscopy can be useful to increase the

H NMR spectroscopy. The NMR data

H NMR spectra with T2 (spin‐spin

H NMR) allow direct assign‐

H NMR spectra

including the flow cytometry or immunecitochemistry profile and karyotype.

at –20°C, being later on thawed on the day of the NMR experiments.

use of internal standard of known concentration added to the sample.

eliminated by the use of standard NMR solvent suppression pulse sequences. 1

generated NMR data should be processed spectra were processed uniformly.

The metabolomic study of bio‐fluids is usually based of 1

solvent peak (at 4.70 ppm) suppression. Relaxation edited 1

complex, the NMR spectra of biological samples (in particular 1

*5.1.2. NMR techniques*

54 Metabolomics - Fundamentals and Applications

*5.1.3. NMR data analysis*

In NMR metabolomics, the interpretation and classification of the complex metabolic data sets typically require the use of chemometric and pattern recognition (PR) techniques, and related bio‐informatics tools. Pattern recognition techniques such as principal component analysis (PCA) allow for the analysis and classification of the large number of complex NMR spectro‐ scopic data usually acquired for metabolic profiling of biological samples [44, 47, 48].

#### *5.1.5. Applications of NMR–based metabolomics*

NMR‐metabolomics has been applied to monitor cellular and systemic metabolic fluctuations in response to xenobiotic exposure, environmental factors, physiological stimuli and genetic modifications [21, 43–48]. Many applications of NMR‐metabolomics have been published, including the extensive study of physiological variation in experimental animals, age‐related changes, dietary modulation, diurnal effects and phenotyping of mutant and transgenic animals [44, 47, 49].

NMR‐based metabolomics has found many applications in metabolic analysis of a wide diversity of biological samples and has been recently used for characterization, monitoring and optimisation of stem cell culture medium preparation [50]. NMR spectroscopy was used for metabolomics analysis of human embryonic stem cell (hESCs) conditioning medium characterization. A number of metabolites were identified and quantified in conditioned media. Significantly, higher concentrations for certain metabolites (lactate, alanine, glutamine, glucose and formate) and lower for others (tryptophan, folate and niacinamide) were detected in the conditioned media compared to unconditioned media. Multivariate statistical analysis was applied to classify the data and assess the main metabolic changes in the process of cell conditioning. It was confirmed that NMR could be an accurate and valuable tool for monitor‐ ing, controlling and optimising hESC culture medium preparation. In this study, the detailed scrutiny of metabolites involved in a variety of biochemical pathways for potential tissue engineering applications has been facilitated [50].

In a more recent study by the application of proton NMR spectroscopy and implementation of appropriate one‐dimensional (1D) and two‐dimensional (2D) NMR techniques, it was confirmed that the metabolic composition of Wharton's jellies derived MSCs' conditioned media and changes in the metabolic profile of the culture media in the process of stem cell expansion and differentiation. These changes were found to be affected mainly by the type of the culture media and the duration of the conditioning time [31]. Typical 1 H NMR spectra of two different types of unconditioned and MSCs' conditioned media are presented in **Figure 5**.

**Figure 5.** Typical 600 MHz 1 H NMR spectra recorded in H2O:D2O (9:1) of (A) Unconditioned medium I, (B) MSCs' con‐ ditioned medium I; (C) Unconditioned medium II, (D) MSCs' conditioned medium II. Characteristic signals of metabo‐ lites in the samples are marked.

**Figure 6.** Representative 2D NMR experiments of MSCs' conditioned medium (conditioned medium I): (A) 1H/1H CO‐ SY spectrum, (B) 1H/1H TOCSY spectrum, (C) 1H/13C HSQC Spectrum.

The metabolomics study of different types culture media and the corresponding Wharton's jelly derived MSCs' conditioned media isolated at different time points was based on 1 H NMR spectroscopy. Sodium trimethylsilyl‐[2,2,3,3‐d4]‐propionate (TSP) was used as an internal reference for the calibration and quantification of NMR spectra. 1 H NMR spectra with suppression of the water resonance signal using a 1D NOESY and CPMG pulse sequences were acquired. The NMR spectra were acquired and processed uniformly using TOPSPIN 3.2 (Bruker Biospin).

Additionally, to confirm the chemical shift assignment of 1 H NMR spectra and facilitate the identification of metabolites presented in both unconditioned and MSCs' conditioned media, the studied 2D 1 H/1 H COSY, 1 H/1 H TOCSY and 1 H/13C HSQC spectra were obtained and analysed (**Figure 6**). The results have been compared to the data published in the literature [51].

**Figure 5.** Typical 600 MHz 1

lites in the samples are marked.

56 Metabolomics - Fundamentals and Applications

H NMR spectra recorded in H2O:D2O (9:1) of (A) Unconditioned medium I, (B) MSCs' con‐

ditioned medium I; (C) Unconditioned medium II, (D) MSCs' conditioned medium II. Characteristic signals of metabo‐

**Figure 6.** Representative 2D NMR experiments of MSCs' conditioned medium (conditioned medium I): (A) 1H/1H CO‐

The metabolomics study of different types culture media and the corresponding Wharton's

H NMR

jelly derived MSCs' conditioned media isolated at different time points was based on 1

SY spectrum, (B) 1H/1H TOCSY spectrum, (C) 1H/13C HSQC Spectrum.


**Table 1.** <sup>1</sup> H NMR chemical shifts and multiplicity of the main metabolites observed in the spectra of culture medium and MSCs' secretome.

From the analysis of the 1H and 2D (1 H/1 H homonuclear and 1 H, 13C heteronuclear correlation spectra) NMR spectra, certain number of metabolites have been identified. The main metab‐ olites identified in the samples are listed in **Table 1** with an assignment of the corresponding resonance signals and their multiplicity in 1 H NMR spectra recorded.

**Figure 7.** (A) Relative distribution (%) of the main metabolites detected in the culture media I and II and the corre‐ sponding MSCs' conditioned media I and II [31]; (B) Relative distribution (%) of the main metabolites detected in the culture media I and II and the corresponding MSCs' conditioned media I and II, and umbilical cord blood plasma (hUCBP) [31]; (C) Typical 600 MHz 1 H NMR spectra recorded in H2O:D2O (9:1) of two plasma samples obtained from umbilical cord blood (hUCBP) and foetal bovine serum (FBS) from three different manufacturers; (D) The quantitative distribution (ppm) of metabolites determined from the integral intensity of characteristic signals in 1 H NMR spectra considering the number of protons contributing to the intensity of the signal in umbilical cord blood plasma (hUCBP) and foetal bovine serum (SBF) from two different fabricants used of *in vitro* culture of MSCs from the umbilical cord matrix (MSCs\_SBF) and dental pulp mesenchymal stem cells (DPSCs\_SBF).

The metabolite content in two different types of culture media (culture media I and II) and the corresponding Wharton's jelly derived MSCs' conditioned media collected at different time points was defined by quantitative NMR analysis, using the relative quantitative method [52]. The quantitative distribution of the NMR‐visible metabolites was determined from the relative integral intensity of characteristic signals in 1 H NMR spectra of the samples referenced to the integral intensity of TSP (internal standard), considering the number of the contributing nuclei for that particular resonance. The relative quantitative distribution of the main metabolites detected in the 1 H NMR spectra of the two different types of culture media (culture medium I and culture medium II) and the corresponding Wharton's jelly derived MSCs' conditioned media collected after 24 h of conditioning is presented graphically in **Figure 7A**.

The results from the NMR metabolomics analysis reveal the presence of important amino acids, glucose and low molecular weight (lactate, acetate, pyruvate and formate) compounds in the basal culture media (types I and II) and the corresponding conditioned media.

Different contents of glucose, lactate and certain amino acids were detected in the two basal culture media (types I and II) and the corresponding conditioned media (**Figures 3** and **7A**). Gathering the results of unconditioned media (types I and II) and comparing them globally with the corresponding conditioned media, it is possible to attest not only to metabolite depletion through cell metabolisation (as the glucose example), but also to some metabolites (like lactate, isoleucine, leucine, valine, arginine, lysine, glutamate, formate or pyruvate) presented in higher concentration, reflecting the production and secretion of metabolites. Metabolic differences between samples collected at different time points (24 and 48 h) were also considered [31]. This is because conditioned media could be achieved at different stages after reaching minimal cell confluence.

#### **5.2. Metabolic analysis of hUCBP by NMR spectroscopy**

From the analysis of the 1H and 2D (1

58 Metabolomics - Fundamentals and Applications

(hUCBP) [31]; (C) Typical 600 MHz 1

detected in the 1

resonance signals and their multiplicity in 1

H/1

H homonuclear and 1

H NMR spectra recorded.

spectra) NMR spectra, certain number of metabolites have been identified. The main metab‐ olites identified in the samples are listed in **Table 1** with an assignment of the corresponding

**Figure 7.** (A) Relative distribution (%) of the main metabolites detected in the culture media I and II and the corre‐ sponding MSCs' conditioned media I and II [31]; (B) Relative distribution (%) of the main metabolites detected in the culture media I and II and the corresponding MSCs' conditioned media I and II, and umbilical cord blood plasma

umbilical cord blood (hUCBP) and foetal bovine serum (FBS) from three different manufacturers; (D) The quantitative

considering the number of protons contributing to the intensity of the signal in umbilical cord blood plasma (hUCBP) and foetal bovine serum (SBF) from two different fabricants used of *in vitro* culture of MSCs from the umbilical cord

The metabolite content in two different types of culture media (culture media I and II) and the corresponding Wharton's jelly derived MSCs' conditioned media collected at different time points was defined by quantitative NMR analysis, using the relative quantitative method [52]. The quantitative distribution of the NMR‐visible metabolites was determined from the relative

integral intensity of TSP (internal standard), considering the number of the contributing nuclei for that particular resonance. The relative quantitative distribution of the main metabolites

I and culture medium II) and the corresponding Wharton's jelly derived MSCs' conditioned

The results from the NMR metabolomics analysis reveal the presence of important amino acids, glucose and low molecular weight (lactate, acetate, pyruvate and formate) compounds in the

media collected after 24 h of conditioning is presented graphically in **Figure 7A**.

basal culture media (types I and II) and the corresponding conditioned media.

H NMR spectra of the two different types of culture media (culture medium

distribution (ppm) of metabolites determined from the integral intensity of characteristic signals in 1

matrix (MSCs\_SBF) and dental pulp mesenchymal stem cells (DPSCs\_SBF).

integral intensity of characteristic signals in 1

H NMR spectra recorded in H2O:D2O (9:1) of two plasma samples obtained from

H NMR spectra of the samples referenced to the

H, 13C heteronuclear correlation

H NMR spectra

The metabolite profiles of the unconditioned and MSCs' conditioned media (types I and II) were further compared with those of human umbilical cord blood plasma (hUCBP) (**Fig‐ ure 7B**) which has been considered as a promising culture medium supplement due to the fact that UCB is rich in soluble growth factors that support the growth, proliferation and differen‐ tiation of mesenchymal and hematopoietic stem cells [31, 33, 34].

The comparative study of the metabolic profiles of unconditioned media, MSCs' conditioned media and hUCBP reveals the presence of essential proteinogenic amino acids (alanine, arginine, isoleucine, histidine, leucine, lysine, phenylalanine, proline, tyrosine, valine), organic acids and derivatives (lactate, formate and creatine), glucose and nucleotides (adenosine, uridine and guanosine species) in the last. The results strongly suggest that hUCBP might be used as a culture medium supplement in the processes of cell isolation, expansion and cryopreservation and even as an alternative in the cellular therapy [31]. Moreover, hUCBP could be considered as an animal sera substituent that could solve important economic, ethical and scientific issues that have severe complications in the utilization of foetal bovine serum (FBS) for cell culture. In addition, hUCBP are attractive alternatives to FBS due to the world‐ wide increase in the number of cryopreserved UCB units in Public and Private Cord Blood Banks. However, the substitute medium supplement must possess the animal sera character‐ istics crucial for the cell expansion and preservation.

Recently, a comparative study of the metabolic composition of hUCBP and FBS, based on 1 H NMR spectroscopy and multivariate statistical analysis has been performed to evaluate the capability of hUCBP as a culture medium supplement and FBS substituent for *in vitro* proliferation and cryopreservation of MSCs. **Figure 7C** shows typical 1 H NMR spectra of hUCBP and FBS.

However, 1D and 2D NMR techniques have been used to identify and quantify the detectible metabolites in the samples. The quantitative distribution of metabolites presented in **Figure 7D** was determined from the integral intensity of characteristic signals in 1 H NMR spectra considering the number of protons contributing to the intensity of the signal.

The results show that the differences between the metabolic composition of hUCBP and FBS are due mainly to the different content of glucose, lactate, acetate, glutamate and alanine. The much higher content of glucose determined for hUCBP has been attributed to the UCB function to provide energetic support for the foetus during intrauterine development. From the analysis of the NMR spectra it was confirmed the presence of essential proteinogenic amino acids such as alanine, arginine, creatine, glutamine, glutamate, isoleucine, histidine, leucine, lysine, phenylalanine, proline, threonine, tyrosine and valine in hUBCP as well as in FBS. These proteinogenic amino acids are precursors to proteins and could play a crucial role in the growth, proliferation and differentiation of cells. Significantly, higher levels of alanine, glutamate, isoleucine, leucine and valine in the commercial FBS as compared to hUBCP are determined. This seems to be the main difference between the amino acids profiles of human UBC plasma and foetal bovine sera. The increased levels of alanine and glutamine could due to the use of supplements such as Glutamax® in the commercial FBS sera [50]. Similar or slightly higher contents of arginine, citrulline, creatine, histidine, lysine, phenylalanine and tyrosine, nucleotides and lipids were found in hUBCP as compared to the FBS samples studied.

**Figure 8.** A and B—PCA statistics. PCs distribution of NMR data matrix (A) and Score plots (PC1 versus PC2) (B) and —PC1 and PC2 loading plots (C).

Principal component analysis has been used to interpret and classify the NMR spectral data obtained for a certain number of hUCBP (samples of 11 donors) and FBS samples (MSCs\_FBS (3 samples of MSCs FBS and three samples of DPSCS FBS) to evaluate the main factors contributing to the discrimination between the two groups of samples.

The contributions of the principal components (PCs) of the NMR spectral data are depicted in **Figure 8A** and **B**. The first two PCs accumulate more than 85 % of data variance. Therefore statistical analysis based on PC1 and PC2 is legitimate.

The 2D (PC1 versus PC2) PCA score plot is depicted in **Figure 8B**. The results illustrate the close similarity of MSCs\_FBS and DPSCS\_FBS data sets (a well‐defined cluster with overlap‐ ping samples) and reveal their separation from the human plasma data (a scattered cluster).

The corresponding loading plots in **Figure 8C** provide evidence that the glucose, lactate and acetates are the metabolites contributing to the divergence between the human plasma and FBS data sets. The results also reveal the close content of essential proteinogenic amino acids in the human plasma and foetal bovine sera. Loading plots around zero line suggest close metabolic profiles in the spectral region from 9.0 to 5.3 ppm of the three groups.

## **6. Conclusions**

much higher content of glucose determined for hUCBP has been attributed to the UCB function to provide energetic support for the foetus during intrauterine development. From the analysis of the NMR spectra it was confirmed the presence of essential proteinogenic amino acids such as alanine, arginine, creatine, glutamine, glutamate, isoleucine, histidine, leucine, lysine, phenylalanine, proline, threonine, tyrosine and valine in hUBCP as well as in FBS. These proteinogenic amino acids are precursors to proteins and could play a crucial role in the growth, proliferation and differentiation of cells. Significantly, higher levels of alanine, glutamate, isoleucine, leucine and valine in the commercial FBS as compared to hUBCP are determined. This seems to be the main difference between the amino acids profiles of human UBC plasma and foetal bovine sera. The increased levels of alanine and glutamine could due to the use of supplements such as Glutamax® in the commercial FBS sera [50]. Similar or slightly higher contents of arginine, citrulline, creatine, histidine, lysine, phenylalanine and tyrosine, nucleotides and lipids were found in hUBCP as compared to the FBS samples studied.

**Figure 8.** A and B—PCA statistics. PCs distribution of NMR data matrix (A) and Score plots (PC1 versus PC2) (B) and

Principal component analysis has been used to interpret and classify the NMR spectral data obtained for a certain number of hUCBP (samples of 11 donors) and FBS samples (MSCs\_FBS (3 samples of MSCs FBS and three samples of DPSCS FBS) to evaluate the main factors

contributing to the discrimination between the two groups of samples.

—PC1 and PC2 loading plots (C).

60 Metabolomics - Fundamentals and Applications

In brief conclusion, proteomic and metabolomics studies have demonstrated the richness of MSC' conditioned medium in terms of growth factors with *in situ* action in disease and regeneration processes, as well as in other metabolites that interact with the environment surrounding the mesenchymal cellular populations. Hence, as knowledge is gained in its composition and potential, thought these omics techniques and other assays, their potential on the application for the treatment of number of diseases increases alike.

As for the hUCBP, given its similarity and even superiority (in supplementary proteins and other contents) to promote and sustain MSCs' expansion, it might become a standardized human derived supplement for cellular populations aiming at the production of secretome or direct application in effective clinical treatments.

## **Acknowledgements**

This research was supported by Programa Operacional Regional do Norte (ON.2 ‐ O Novo Norte), QREN, FEDER with the project "iBone Therapies: Terapias inovadoras para a regen‐ eração óssea", ref. NORTE‐01‐0247‐FEDER‐003262, and by the program COMPETE ‐ Programa Operacional Factores de Competitividade, Projects PEst‐OE/AGR/UI0211/2011, PEst‐C/EME/ UI0285/2013, and Pest‐C/EQB/LA0006/2013 funding from FCT. This research was also sup‐ ported by Programa Operacional Competitividade e Internacionalização (P2020), Fundos Europeus Estruturais e de Investimento (FEEI) and FCT with the project "BioMate ‐ A novel bio‐manufacturing system to produce bioactive scaffolds for tissue engineering" with refer‐ ence PTDC/EMS‐SIS/7032/2014 and by COMPETE 2020, from ANI ‐ Projectos ID…T Empresas em Copromoção, Programas Operacionais POCI, by the project "insitu.Biomas ‐ Reinvent biomanufacturing systems by using an usability approach for *in situ* clinic temporary implants fabrication" with the reference POCI‐01‐0247‐FEDER‐017771. Ana Rita Caseiro (SFRH/BD/ 101174/2014) acknowledges FCT, for financial support. This work also received financial under the framework of QREN through Project NORTE‐07‐0124‐FEDER‐000066. The Bruker Avance III 600 HD spectrometer was purchased under the framework of QREN, through Project NORTE‐07‐0162‐FEDER‐000048, and is part of the Portuguese NMR Network created with support of FCT through Contract REDE/1517/RMN/2005, with funds from POCI 2010 (FED‐ ER).

## **Author details**

Galya Ivanova1 , Tiago Pereira2,3, Ana Rita Caseiro2,3,4, Petia Georgieva5 and Ana Colette Maurício1,4\*

\*Address all correspondence to: ana.colette@hotmail.com

1 REQUIMTE‐UCIBIO, Department of Chemistry and Biochemistry, Faculty of Sciences, Porto University, Porto, Portugal

2 Department of Veterinary Clinics, Abel Salazar Institute of Biomedical Sciences (ICBAS), Porto University (UP), Porto, Portugal

3 Animal Science Studies Centre (CECA), Agro‐Alimentary Technologies and Sciences Insti‐ tute, (ICETA), Porto, Portugal

4 CEMUC, Department of Materials and Metallurgic Engineering, Faculty of Engineering, Porto University, Porto, Portugal

5 Department of Electronics Telecommunications and Informatics, IEETA, University of Aveiro, Portugal

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#### **Processing and Visualization of Metabolomics Data Using R Processing and Visualization of Metabolomics Data Using R**

Stephen C. Grace and Dane A. Hudson Stephen C. Grace and Dane A. Hudson

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Metabolomics provides a wealth of information about the biochemical status of cells, tissues, and other biological systems. However, for many researchers, processing the large quantities of data generated in typical metabolomics experiments poses a formidable challenge. Robust computational tools are required for all data processing steps, from handling raw data to high level statistical analysis and interpretation. This chapter describes several established methods for processing and analyzing metabolo‐ mics data within the R statistical programming environment. The focus is on processing LCMS data but the methods can be applied virtually to any analytical platform. We provide a step‐by‐step workflow to demonstrate how to integrate, analyze, and visualize LCMS‐based metabolomics data using computational tools available in R. These concepts and methods will allow specialists and nonspecialists alike to develop and evaluate their own data more critically.

**Keywords:** liquid chromatography mass spectrometry (LCMS), R, data processing, multivariate analysis, data visualization

#### **1. Introduction**

Metabolomics is a rapidly growing discipline focusing on the global study of small molecule metabolites in biological systems. Through the characterization of metabolite dynamics, interactions, and responses to genetic or environmental perturbations, metabolomics can provide a comprehensive picture of both baseline physiology and global biochemical responses to genetic, abiotic, and biotic factors [1].

© 2016 The Author(s). Licensee InTech. 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. © 2017 The Author(s). Licensee InTech. 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.

As the diversity in abundance and chemical properties of metabolites varies greatly in organisms, a range of analytical techniques must be utilized to survey the entire metabolome. A number of methods have been developed for the extraction, detection, identification, and quantification of the metabolome [2]. Mass spectrometry coupled with gas chromatography (GC‐MS) or liquid chromatography (LC‐MS) are the most common analytical platforms, although capillary electrophoresis mass spectrometry (CE‐MS) and nuclear magnetic reso‐ nance (NMR) are also widely used in metabolomics research [3–6].

Since metabolomics experiments typically produce large amounts of data, sophisticated bioinformatic tools are needed for efficient and high‐throughput data processing to remove systematic bias and to explore biologically significant findings. Both multivariate statistical analysis and data visualization play a critical role in extracting relevant information and interpreting the results of metabolomics experiments.

The data generated in a metabolomics experiment generally can be represented as a matrix of intensity values containing *N* observations (samples) of *K* variables (peaks, bins, etc.). Addi‐ tional information, such as experimental group, genotype, time point, gender, etc., is also required for some statistical procedures. For multivariate analysis, very few mathematical constraints are placed on the values contained in the data matrix. Therefore, a common set of statistical tools can be used to analyze metabolomics data of almost any type. However, as discussed below, multiple preprocessing steps are often necessary to yield interpretable results [7, 8].

The focus of this chapter is on describing methods for processing and visualizing metabolomics data obtained by liquid chromatography mass spectrometry (LCMS). LCMS is the most widely used method in metabolomics research due to its dynamic range, coverage, ease of sample preparation, and high information content [3–5]. We present a standard workflow for handling LCMS data, from raw data processing to downstream statistical analysis using open source tools available within the R software environment.

#### **1.1. What is R?**

R is a software environment for statistical computing, data analysis, and graphics, which has become an essential tool in all areas of bioinformatics research. A major advantage of R over commercial software is that it is open source and free to all users. The base distribution of R and a large number of user contributed packages are available under the terms of the Free Software Foundation's GNU General Public License in source code form. There are versions of R for Unix, Windows, and Macintosh at the official CRAN website (http://cran.r‐ project.org/).

In addition to being a popular language for performing high level statistics, R has a wide array of graphical tools that make it an ideal environment for exploratory data analysis and gener‐ ating publication quality figures. All work is done using the command line‐based text functions with user‐defined scripts. Although R can be challenging for new users, it is quite flexible once the basic commands, functions, and data structures have been learned. A detailed description of every function with examples can be obtained by typing *help* followed by the name of the function, i.e., *help(plot)*. In addition, there are ample online resources to help users learn the basics of R as well as solve a wide range of common data analysis problems [9–11].

R has a powerful set of functions for creating graphics, from fairly simple graphs using base graphics commands to highly sophisticated graphs using the one of several advanced graphics packages [12]. The focus of this chapter is on using the ggplot2 package, a high‐level platform for creating graphics that is especially powerful for working with high‐dimensional data [13]. The basic idea in ggplot2 is to build graphs by adding successive layers that include visual representations as well as statistical summaries of the data [14]. A layer is defined as an R data frame or matrix, a specification mapping columns of that frame into aesthetic properties, a statistical approach to summarize the rows of that frame, a geometric object to visually represent that summary, and an optional position adjustment to move overlapping geometric objects. This approach allows great flexibility in producing highly customizable graphs by combining layers that describe single or multiple data objects. We will demonstrate these ideas throughout this chapter.

#### **1.2. Open source tools for metabolomics**

As the diversity in abundance and chemical properties of metabolites varies greatly in organisms, a range of analytical techniques must be utilized to survey the entire metabolome. A number of methods have been developed for the extraction, detection, identification, and quantification of the metabolome [2]. Mass spectrometry coupled with gas chromatography (GC‐MS) or liquid chromatography (LC‐MS) are the most common analytical platforms, although capillary electrophoresis mass spectrometry (CE‐MS) and nuclear magnetic reso‐

Since metabolomics experiments typically produce large amounts of data, sophisticated bioinformatic tools are needed for efficient and high‐throughput data processing to remove systematic bias and to explore biologically significant findings. Both multivariate statistical analysis and data visualization play a critical role in extracting relevant information and

The data generated in a metabolomics experiment generally can be represented as a matrix of intensity values containing *N* observations (samples) of *K* variables (peaks, bins, etc.). Addi‐ tional information, such as experimental group, genotype, time point, gender, etc., is also required for some statistical procedures. For multivariate analysis, very few mathematical constraints are placed on the values contained in the data matrix. Therefore, a common set of statistical tools can be used to analyze metabolomics data of almost any type. However, as discussed below, multiple preprocessing steps are often necessary to yield interpretable

The focus of this chapter is on describing methods for processing and visualizing metabolomics data obtained by liquid chromatography mass spectrometry (LCMS). LCMS is the most widely used method in metabolomics research due to its dynamic range, coverage, ease of sample preparation, and high information content [3–5]. We present a standard workflow for handling LCMS data, from raw data processing to downstream statistical analysis using open source

R is a software environment for statistical computing, data analysis, and graphics, which has become an essential tool in all areas of bioinformatics research. A major advantage of R over commercial software is that it is open source and free to all users. The base distribution of R and a large number of user contributed packages are available under the terms of the Free Software Foundation's GNU General Public License in source code form. There are versions of R for Unix, Windows, and Macintosh at the official CRAN website (http://cran.r‐

In addition to being a popular language for performing high level statistics, R has a wide array of graphical tools that make it an ideal environment for exploratory data analysis and gener‐ ating publication quality figures. All work is done using the command line‐based text functions with user‐defined scripts. Although R can be challenging for new users, it is quite flexible once the basic commands, functions, and data structures have been learned. A detailed description of every function with examples can be obtained by typing *help* followed by the name of the

nance (NMR) are also widely used in metabolomics research [3–6].

interpreting the results of metabolomics experiments.

68 Metabolomics - Fundamentals and Applications

tools available within the R software environment.

results [7, 8].

**1.1. What is R?**

project.org/).

A number of free software tools are available for processing, visualization, and statistical analysis of metabolomics data. Some of the more popular platforms are presented in **Table 1**.

XCMS is a powerful R‐based software for LCMS data processing. As with any R‐based package, it is command line driven and requires some background knowledge of the R programming language. XCMS uses nonlinear retention time correction, matched filtration, peak detection, and peak matching to extract relevant information from raw LCMS data [15]. Peak detection parameters can be optimized to process the raw data in an appropriate and efficient manner. As shown in the following sections, XCMS can be combined with base R functions and additional R packages to provide a complete solution to LCMS data processing needs. Statistical analysis and data visualization can all be incorporated into the scripts to quickly process the large amounts of data from start to finish.

XCMS online is a web‐based version of XCMS that provides many of the advantages of the traditional R package without the use of a command line‐based environment [16]. It allows limited control over processing parameters and gives interactive graphs of univariate and multivariate analyses [17].

MetaboAnalyst is a popular web‐based resource that provides an easy to use, comprehensive interface for metabolomics data analysis [18]. It includes a variety of data preprocessing and statistical tools for univariate and multivariate analysis and generates high resolution, interactive graphics. Depending on the type of data being analyzed, it can also be used for biomarker analysis, enrichment analysis, pathway analysis, and more [19].

Haystack is a web server‐based processing tool that uses mass bins to filter and extract information from raw LCMS data [20]. Haystack also provides graphical tools to visualize raw and processed data and incorporates some exploratory statistical analysis tools. Because extracted features are based on mass bins, missing values due to redundant or missing peaks are absent from the processed data. Processed data files are downloadable in .csv format, which can be imported into analysis software of the user's preference.


**Table 1.** Open source and web‐based platforms for metabolomics data analysis.

MZmine 2 is a Java‐based platform that allows for flexible MS data processing through a user‐ friendly graphical interface and customizable parameters for data processing and visualiza‐ tion [21].

Metabolomics Ion‐Based Data Extraction Algorithm (MET‐IDEA) is a large‐scale metabolo‐ mics data processing program generally used for GCMS data but can also be used for LCMS data. It performs peak alignment, annotation, and integration of hyphenated mass spectrom‐ etry data and allows visualization of integrated peaks along with their accompanying mass spectra [22].

#### **1.3. Data processing for metabolomics**

Robust computational tools are essential to analyze and interpret metabolomics data. The first step in data processing, especially in untargeted metabolomics, is to convert the raw data into a numerical format that can be used for downstream statistical analysis. For LCMS data, this involves multiple steps, including filtering, feature detection, alignment, and normalization [23, 24]. Filtering methods aim to remove effects like measurement or baseline noise. Feature detection is used to identify measured ions from the raw signal. Alignment methods cluster measurements from across different samples and normalization removes unwanted systematic variation between samples.

Once the data have been converted to a numerical matrix, statistical tools are used to reveal patterns in the data, determine class membership, and identify relevant biological features. Univariate methods are often used as a first step to obtain a rough ranking of potentially important features before applying more sophisticated statistical techniques [25]. Familiar examples include fold change differences, *t*‐tests, and volcano plots.

Multivariate methods treat multiple, often correlated, variables simultaneously, and attempt to model relationships between variables and observations [25–27]. Well known examples include principal component analysis (PCA) and partial least squares (PLS). PCA is an unsupervised method meaning that only the data matrix itself is used to model the data. Since class membership is not considered, PCA provides an unbiased summary of the data structure. For exploratory studies where differences between experimental groups may be unknown or unpredictable, it is appropriate to apply PCA as a first step to reveal patterns in the data and relationships between groups.

A shortcoming of PCA is that it can only reveal group differences when within‐group variation is sufficiently less than between‐group variation [26]. To overcome this problem, supervised forms of discriminant analysis such as PLS that rely on class membership are also routinely applied in metabolomics studies [28]. The primary goal of these methods is to identify class differences from a multivariate data set and to identify biologically important features that account for these differences. Often, the results of a PCA are used to formulate a hypothesis that PLS or other supervised methods can test or verify in more detail.

In the following sections, we will provide an overview of the data processing steps used in a typical metabolomics experiment. We do not aim to provide a thorough description of the statistical methods but rather introduce the basic concepts behind these methods and demon‐ strate how to perform them in the R computing environment.

## **2. Experimental methods**

are absent from the processed data. Processed data files are downloadable in .csv format, which

**Platform Description Advantages Disadvantages Reference**

‐Requires knowledge of R

‐Not as customizable as the R

‐Relies on pre‐processed data ‐Limited options for customizing graphics

‐Graphics not customizable ‐Does not take into account peak retention time

‐Aimed more for GCMS data ‐Low‐quality graphics

‐Limited options for customizing graphics ‐Numerous options can be

overwhelming

[15]

[16, 17]

[18, 19]

[20]

[21]

[22]

‐Command line based

language

version

‐Adjustable parameters ‐Streamlined workflow

‐Cloud storage and sharing ‐Relatively easy to use

‐Wide variety of statistical tests

‐Not dependent on quality of

‐Works well with very large data

MZmine 2 is a Java‐based platform that allows for flexible MS data processing through a user‐ friendly graphical interface and customizable parameters for data processing and visualiza‐

Metabolomics Ion‐Based Data Extraction Algorithm (MET‐IDEA) is a large‐scale metabolo‐ mics data processing program generally used for GCMS data but can also be used for LCMS data. It performs peak alignment, annotation, and integration of hyphenated mass spectrom‐ etry data and allows visualization of integrated peaks along with their accompanying mass

Robust computational tools are essential to analyze and interpret metabolomics data. The first step in data processing, especially in untargeted metabolomics, is to convert the raw data into

‐Optional manual integration

‐Easy to use

available ‐Interactive plots

‐Unbiased ‐No zero values

chromatography

‐Java based ‐User friendly ‐Project batching

sets

**Table 1.** Open source and web‐based platforms for metabolomics data analysis.

can be imported into analysis software of the user's preference.

XCMS R‐based platform for

70 Metabolomics - Fundamentals and Applications

XCMS online Web‐based graphical

MetaboAnalyst Online statistical analysis

Haystack Raw data processing

MZmine 2 Raw data processing

MET‐IDEA Raw data processing

data

tion [21].

spectra [22].

mass bins

and visualization

for GCMS and LCMS

**1.3. Data processing for metabolomics**

of XCMS

raw LCMS data processing and visualization

user interface version

and visualization using

To illustrate the concepts and methods presented in this chapter, we produced a data set from two tomato varieties harvested at two developmental stages using untargeted LCMS. The varieties used were "Manapal" and its nearly isogenic counterpart, the *h*igh *p*igment‐2 *dark green* (hp‐2dg) mutant. Hp‐2dg plants have a mutation in the tomato homolog of the DEE‐ TIOLATED‐1 gene involved in light‐mediated signal transduction and plant photomorpho‐ genesis [29]. These plants display a number of interesting traits, including shorter stature, slower growth rates, darker foliage, and elevated levels of certain metabolites such as flavonoids and carotenoids [30–32]. Since many of these phytochemicals are important for human health, there is great interest in understanding the molecular mechanisms that underlie the altered phenotype of the hp‐2dg mutation.

Fully expanded fruits were harvested at green and red stages, lyophilized, and stored at −80°C until analysis. Samples from 10 individual fruits were extracted in 80% methanol and analyzed using an Agilent 1100 HPLC/MSD‐VI Ion Trap mass spectrometer with an electrospray ionization (ESI) source. Chromatograms were saved in netCDF format using the instrument software.

The data were analyzed using R as described in the following sections. A summary of the data processing workflow is presented in **Figure 1**. The full source code for all procedures is available online at https://github.com/ualr‐Rgroup/metabolomics‐in‐r.

**Figure 1.** Summary of metabolomic data processing workflow.

#### **2.1. Data processing with XCMS**

While web‐based tools such as MetaboAnalyst and XCMS online are inarguably convenient, learning data analysis procedures in R gives researchers much greater flexibility not only in processing and analyzing their data but also in creating high‐quality custom graphics. Here, we demonstrate how to perform raw data processing in R using the XCMS package. XCMS is a powerful and flexible software package that has gained widespread use for untargeted metabolomic studies [15]. It is available through Bioconductor and can be installed in R using the following commands:

```
source("http://bioconductor.org/biocLite.R") 
1.1.1.1. biocLite("xcms")
```
#### **2.2. Data import and visualization**

XCMS requires data in an open access nonproprietary format such as Network Common Data Form (NetCDF) or mzXML. File conversion often can be done within the operating software of the instrument. A popular online tool, ProteoWizard (http://proteowizard.sourceforge.net), is also available that can be used to convert raw LCMS data into an open data format. The converted data files are placed in a subdirectory named "cdf" within the R working directory. From here the data can be imported, processed, and visualized.

An LCMS data file is a series of successively recorded mass spectra over a range of *m/z* values. The total intensity of all ions at each time point is known as the total ion chromatogram (TIC). The *xcmsRaw* function is used to read data files into R's memory environment. The *plotTIC* function can be used to produce a TIC or base peak chromatogram (BPC). This function can also be used to obtain the numerical data for custom plotting. The following example demonstrates how to do this in R:

library(xcms) # load the xcms library cdffiles <-list.files("./cdf", recursive=T, full=T) # define data xr1 <- xcmsRaw(cdffiles[6]) # extract raw data t1 <- xr1@scantime # extract time vector int1 <- xr1@tic # extract TIC intensity vector s1 <- rep("HG5", 1896) # create sample vector g1 <- rep("hp-2dg Green", 1896) # create group vector tic1 <- data.frame(t1, int1, s1, g1)

This script loads the xcms package, defines the raw data files, and creates an xcmsRaw object (xr1) from file number 6. This object stores information in the raw data that can be extracted and combined into a data frame. We add factor columns for sample and group that will be used to make a custom plot with ggplot2.

This process is repeated for additional raw data files. The individual data frames are combined into a single data frame with the *rbind* function.

```
tic.data <- rbind(tic1, tic2, tic3, tic4)
```
Once the data have been combined into a single data frame, a multipanel plot can be produced using the facet\_wrap function in ggplot2. The result is shown in **Figure 2**. Variation in peak profiles can be readily observed between the different groups.

#### **2.3. Raw data processing**

flavonoids and carotenoids [30–32]. Since many of these phytochemicals are important for human health, there is great interest in understanding the molecular mechanisms that underlie

Fully expanded fruits were harvested at green and red stages, lyophilized, and stored at −80°C until analysis. Samples from 10 individual fruits were extracted in 80% methanol and analyzed using an Agilent 1100 HPLC/MSD‐VI Ion Trap mass spectrometer with an electrospray ionization (ESI) source. Chromatograms were saved in netCDF format using the instrument

The data were analyzed using R as described in the following sections. A summary of the data processing workflow is presented in **Figure 1**. The full source code for all procedures is

While web‐based tools such as MetaboAnalyst and XCMS online are inarguably convenient, learning data analysis procedures in R gives researchers much greater flexibility not only in processing and analyzing their data but also in creating high‐quality custom graphics. Here, we demonstrate how to perform raw data processing in R using the XCMS package. XCMS is a powerful and flexible software package that has gained widespread use for untargeted metabolomic studies [15]. It is available through Bioconductor and can be installed in R using

source("http://bioconductor.org/biocLite.R")

XCMS requires data in an open access nonproprietary format such as Network Common Data Form (NetCDF) or mzXML. File conversion often can be done within the operating software

available online at https://github.com/ualr‐Rgroup/metabolomics‐in‐r.

the altered phenotype of the hp‐2dg mutation.

72 Metabolomics - Fundamentals and Applications

**Figure 1.** Summary of metabolomic data processing workflow.

*1.1.1.1. biocLite("xcms")* 

**2.1. Data processing with XCMS**

the following commands:

**2.2. Data import and visualization**

software.

XCMS uses several algorithms to process LCMS data. The first step is to filter and detect ion peaks using the *xcmsSet* method. The peak detection algorithm is based on cutting the data into slices of predefined mass widths and then finding peaks in the chromatographic time domain by applying a Gaussian model peak matched filter. Although the default arguments for the *xcmsSet* method may provide acceptable results in some cases, it is recommended that the parameters used for peak selection be optimized for this step. Without optimization, common problems that may arise include oversampling, i.e., assigning the same metabolite to multiple peaks, and missing values, i.e., failure to detect certain peaks in some samples that can interfere with downstream statistical analysis [8].

**Figure 2.** Representative total ion chromatograms (TICs) of green fruits (A, C) and red fruits (B, D) of the hp‐2dg (A, B) and Manapal (C, D) tomato varieties.

The next step after filtration and peak identification is matching peaks across samples. Peaks representing the same analyte across samples must be placed into groups. This is accomplished with the *group* function, which returns a new xcmsSet object. After matching peaks into groups, XCMS can use those groups to identify and correct correlated drifts in retention time using the *retcor* function. The aligned peaks can then be used for a second pass of peak grouping which will be more accurate than the first.

After the second pass of peak grouping, there will still be missing peaks from some of the samples. This can occur because peaks were missed during peak identification or because an analyte was not present or below the detection limit in a sample. Missing values can be problematic for statistical methods that require a fully defined data matrix. Those missing data points can be filled in by re‐reading the raw data files and integrating them in the regions of missing peaks using the *fillPeaks* function.

XCMS can generate a report showing fold change differences in analyte intensities and their statistical significance using the *diffreport* function. However, we recommend obtaining the raw peak integration results using the *groupval* function. This function returns a numerical matrix in which each row represents a peak defined by its mass and retention time and each column represents a different sample. In an example used here, 308 peaks were identified with 1.4% missing values. This matrix provides the starting point for downstream statistical analysis.

```