Metabolic Phenotyping Using Capillary Electrophoresis Mass Spectrometry

Chapter 5

Metabolic Phenotyping Using Capillary Electrophoresis Mass Spectrometry Joanna Godzien, A´ngeles Lo´pez-Gonza´lvez, Antonia Garcı´a and Coral Barbas CEMBIO (Centre for Metabolomics and Bioanalysis), Facultad de Farmacia, Universidad San Pablo CEU, Madrid, Spain

Chapter Outline 1 Introduction 1.1 Instrumentation 1.2 Principles of Capillary Zone Electrophoresis 1.3 Conditions That Can Be Modified to Optimize the Separation and Limitations When Coupled to MS 1.4 Strengths 1.5 Weakness 1.6 Coupling to MS 2 Workflow 2.1 Design 2.2 Sample

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2.3 Separation and Detection 2.4 Data 2.5 Statistics 2.6 Identification 2.7 Meaning 3 Applications 3.1 Cancer 3.2 Metabolic Disorders 3.3 Neurodegenerative Diseases and Brain Disorders 3.4 Others 4 Conclusions and Future Trends References

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INTRODUCTION

Capillary electrophoresis (CE) is, as its name suggests, a separation technique based on electrophoretic rather than chromatographic principles. The movement of analytes along the capillary is due to the electric field, conferring upon this technique certain strengths and weaknesses, both of which are discussed in this chapter. In general terms, when working with mass spectrometry, the separation technique selected to be coupled with the mass spectrometer (MS) determines The Handbook of Metabolic Phenotyping. https://doi.org/10.1016/B978-0-12-812293-8.00005-0 © 2019 Elsevier Inc. All rights reserved.

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the type of samples that can be introduced and the compounds that will be analyzed and detected. In this regard, capillary electrophoresis, a technique very tolerant of complex sample matrices and oriented to polar and ionic compounds, provides a complementary tool in metabolic phenotyping to improve metabolite coverage. This is particularly true for those compounds that are usually more problematic in liquid chromatography coupled to mass spectrometry (LCMS) (amino acids, amines, peptides, acylcarnitines, nucleosides, organic acids, etc.). Furthermore, CE-MS is especially well suited for samples with a high saline content such as cell culture media [1]. There are different types of CE methods [2], but this chapter focuses on the conditions that are commonly used when CE is coupled to MS for nontargeted metabolic phenotyping.

1.1 Instrumentation A CE system is quite simple and comprises (i) a silica capillary (usually with an internal diameter of 25–75 μm and a length of 50–100 cm), although special conditions could require different types of capillaries, covered with polyimide to prevent the silica from breaking; (ii) a power source able to produce potentials as high as 30 kV at the ends of the capillary; (iii) a cooling system able to eliminate the heat produced inside the capillary due to the Joule effect while applying the potential; (iv) an injection system and a detector, which in the case of MS requires an interface.

1.2 Principles of Capillary Zone Electrophoresis Capillary electrophoresis is a family of electrokinetic separation methods and among them Capillary Zone Electrophoresis (CZE) is characterized by the use of relatively low viscosity buffer systems where analyte molecules move according to the vector sum of electrophoretic and electroosmotic mobility. In CZE, the capillary, after conditioning, is filled with a conductive buffer, the background electrolyte (BGE). The sample is then introduced either by elevating the corresponding vial at the inlet or by pressure, and the vial at the inlet is replaced with another one containing BGE before a high voltage is applied between the two ends. In the so-called normal polarity, the injection takes place at the positive end (anode) and the detector is located at the negative end (cathode). Under these conditions, the compounds are subjected to two different forces: electrophoretic and electro-osmotic mobility (Fig. 1). Electrophoretic mobility, μep (Eq. 1), can be approximated from DebeyeHuckel-Henry theory. It is directly proportional to the charge and inversely proportional to the size of the analyte. It leads ions toward the electrode with opposite polarity. μep ¼ q=6πηR

(1)

where q is the net charge, R is the Stokes radius, and η is the viscosity.

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FIG. 1 Graphical representation of electro-osmotic flow (panel A), electrophoretic mobility (panel B), and total mobility (panel C).

The electrophoretic velocity, vep (Eq. 2), is the product of the μep and the electric field strength, E. vep ¼ μep E

(2)

Electroosmotic flow μeo is described by Eq. (3); although the theoretical pKa for silanol groups is around six, except in very acidic conditions, the silica wall has some negative charge. The buffer inside the capillary is neutral but positive ions accumulate closer to the capillary wall, forming the Stern layer. The accumulation of cations progressively dissipates further away from the wall, but this layer is important enough to move toward the negative end pulling the liquid and all compounds inside toward the cathode. veo ¼ ð2 ζ=4πηÞE

(3)

Here, 2 is the dielectric constant, η is the viscosity of the buffer, and ζ is the zeta potential measured at the shearing plane close to the liquid-solid interface. The final velocity is the sum, considering the corresponding directions, of both velocities and therefore positive ions, for which vep and veo add, are the first to reach the detector. Those with higher positive charge and smaller size arrive first, followed by those that are larger or have a lower charge. Next, neutral substances appear. They move only due to μeo and therefore their resolution is poor. Finally, some negative ions can reach the detector, but only those that are large or have a small charge, with small μep, moving in response to the difference between veo and vep. Negative ions with higher vep than veo do not reach the detector because they move toward the injection end and their analysis requires different conditions. Therefore, to analyze negative ions, polarity can be reversed and a coated capillary is used to avoid μep in the opposite direction, because the coating is a polymer that has no charge or even polymers introducing a positive charge in the wall could be used to increase the mobility of negative ions. Some coatings include polyacrylamide, polyvinyl alcohol, polyethylene glycol, and polyvinylpyrrolidone [3].

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1.3 Conditions That Can Be Modified to Optimize the Separation and Limitations When Coupled to MS It is commonly said that if a separation is not achieved with CE, it is because you did not try hard enough, such is the variety of parameters that can be optimized, namely, buffer composition and pH, temperature, addition of different solvents, addition of modifiers, micelle formation, among others. However, when coupled to MS, there are important limitations; only substances that can be made volatile are compatible and this constraint greatly limits the number of buffers and additives that can be used. Buffers recommended for use in CE-MS are volatile acid-base types such as formic acid (the most common in metabolic phenotyping), ammonium formate, acetic acid, and ammonium acetate. Some authors have used a system known as partial filling, where an additive such as a chiral selector is added, filling only one part of the capillary and instrumental conditions impede it from reaching the detector. However, these methods are not robust enough to be used in metabolic phenotyping.

1.4 Strengths – Only a small volume of sample is required. Although the vial needs to contain several microliters to allow the capillary and electrode to be introduced, only a few nanoliters are injected and therefore sample consumption is minimal, which is very important for many experiments in which only a small volume of sample is available (e.g., from small animals, tears, and other similar fluids), as well as for multiplatform studies or cases where many different studies need to be performed. – CE can be considered as a separation technique orthogonal to LC, because the principles of the separations are different and this can be especially valuable for the confirmation and identification of metabolites. In addition, information about the charge state of the analyte can be obtained through the migration time (MT). – Sample preparation is minimal because after obtaining the signal for the desired compounds, the capillary can be rinsed out. – Ionic and polar compounds in complex matrices, even those containing different salts as is the case of culture media, can be analyzed better and more easily than with any other technique.

1.5 Weaknesses – The small amount of sample injected (around 1000 times lower than in LC for the same detector) is also a drawback in terms of sensitivity. Although some authors say that CE is very sensitive because it can detect picograms, detection should be expressed in concentration units and pg/nL is in the millimolar to micromolar range. In fact, the limits of detection are not as poor as 1000 times lower than LC because the electroosmotic mobility is driven by

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the electric field, which is constant across the capillary section, rather than the classical hydrodynamic, parabolic profile in LC. This provides lower dispersion within the peak signal, and increases both resolution and sensitivity. This factor partially compensates for the small volume of injection. – Reproducibility: the uncoated silica wall easily adsorbs proteins and other substances in complex samples, which modifies its surface and therefore the EOF. Time drift is a common problem to deal with when comparing CE profiles in metabolic phenotyping.

1.6 Coupling to MS When combined with other detection techniques such as UV or fluorescence, the inlet and outlet of the capillary are immersed in vials with BGE. However, since MS is an off-capillary detector, the outlet vial should be replaced by the entrance to the MS with the expected difficulties in matching two voltages with differences of several orders of magnitude (CE and MS), and challenges maintaining a stable spray with a very low volume of fluid. This is accomplished with an interface, which is a continuous source of research in CE-MS for improvement. The interface connects the capillary to the electrospray (ESI), which is the most common ion source in CE-MS (Fig. 2). There are two types of interfaces: with or without an additional liquid flow (sheath-liquid or sheathless interfaces, respectively) [4–6]. Achieving stable electrospray operation with an added liquid often involves balancing multiple parameters, such as capillary position, sheath liquid flow rate and composition, gas sheath flow rate, and ESI conditions [7]. In addition, sheath-liquid substantially dilutes the CE eluent resulting in a corresponding decrease in sensitivity. However, despite the development of numerous sheathless interfaces with proven higher sensitivity, robustness is still compromised. This is a

FIG. 2 Graphical representation of CE coupled to MS, indicating the most important components of both pieces of equipment.

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disadvantage in metabolic phenotyping and may explain why there are no nontargeted studies published in which this tool is used. Optimization of the ion introduction voltage and fragment voltage is based on the same concept as optimization in LC-MS. When analyzing small molecules, the standard fragment voltage is 100 V. Regarding the mass analyzer, all commercially available instruments can be connected to CE and time of flight (TOF) is the one with the most published applications. The reason TOF is widely used is that it provides an accurate mass, high resolution, and high scanning speed (duty cycle) to obtain enough points over the peak.

2 WORKFLOW The entire process of metabolic phenotyping can be represented through a workflow scheme, made up of consecutive stages that, despite differences between laboratories, can be simplified into several phases (Fig. 3). Some of these stages include the design of the experiment, sample collection and preparation, analysis including separation and detection, data reprocessing, and data analysis, followed by identification of significant signals and final data interpretation. The metabolic phenotyping workflow for CE-MS is not very different from any other; however, due to the specificity of the platform itself, there are some differences, which underlie the challenges and advantages associated with its use.

2.1 Design The very first step is the design of experiment (DoE), which aims to construct a hypothesis (that may be very general in nature) and to plan the research as a whole based on a defined goal. This particular stage is often skipped, even though it is by no means trivial and should be performed with adequate attention [8]. A clearly determined research objective is crucial to appropriately select the experimental groups, number of individuals and type of sample, as well as to define all the steps in the work plan, thereby minimizing the risk of serious problems or even failure.

2.2 Sample CE is particularly suitable for projects in which biological changes are expected to occur at the ionic/polar compound level. Therefore, every sample containing polar metabolites can be successfully analyzed by means of CE-MS. This has been demonstrated in a range of biological samples including urine [9], plasma [10], serum [11], cells and cell media [12], gut bacteria [13], breast milk [14], saliva [15], feces [16] and different organs such as liver [10], brain [17], lung [18], heart [19], stomach [20], as well as aorta [21]. All samples, regardless of the type, must be collected following previously defined requirements that have

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FIG. 3 Metabolic phenotyping workflow with a general characterization of each stage with special focus on CE-MS-specific aspects.

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been set out in standard operating procedures (SOP) [22]. This is crucial since nontargeted metabolic phenotyping reveals relative differences based on intergroup comparison. Therefore, all compared samples have to be collected in the same way to avoid unwanted variation that could lead to spurious correlation. Although consistency is important, sample collection and manipulation (preparation, order of analysis, etc.) have to be performed in a randomized way. This means that all samples must be collected following the same requirements, and collection, preparation and analysis cannot be performed separately for controls and cases. This helps to prevent bias due to systematic drift over time and minimizes the risk of correlation to class structure. Each type of biological sample has to be collected following certain guidelines optimized for the specific type of biological material involved; however, all samples have to be quenched to halt all metabolic processes. This is very important since many changes in metabolism happen in a matter of seconds and, therefore, the quenching process should stop metabolism quicker than the turnover of metabolites in order to preserve metabolite concentrations. The type of samples should be selected considering the previously defined goal of the experiment. To understand the mechanism of disease, tissues are preferred, while for the discovery of biomarkers, biofluid should be measured. The main requirements and steps for the preparation of urine, feces, plasma, cells, and tissue samples are summarized in Fig. 4. However, a general rule, regardless of sample type, is that sample treatment should be as simple as possible, assuring extraction of metabolites with simultaneous elimination of unwanted or redundant elements such as proteins and particles. Proteins can bind to the capillary wall disturbing electro-osmotic flow and can therefore affect migration time. For this reason, all samples containing them have to be deproteinized. The most common deprotenization method is protein precipitation with ice-cold organic solvents (e.g., methanol, acetonitrile, or a mixture of methanol and ethanol). Despite its efficiency, the applicability of this method in electrophoresisbased studies is limited since it requires solvent evaporation and subsequent resuspension in water-based solution, which may result in a loss of information [23]. This is because organic solvents have low conductivity and affect current stability. For this reason, an alternative method such as ultrafiltration is commonly used, which allows proteins and peptides to be separated from other sample components using devices with a particular cut-off [24]. Protein-free samples can be simply diluted (if necessary), then filtered (usually at 0.22 μm) or centrifuged, and analyzed directly. Examples of such samples are urine, saliva, or tears, although it is important to highlight that no samples are completely protein free. Samples considered to be protein free do in fact contain some proteins but at such low level that deprotenization is not mandatory, e.g., urine (100 mg/L) [25] or tears (yawn 120 mg/L and eye-wash 20 mg/L) [26], unlike blood-derived samples (70 g/L) [27] or breast milk (10 g/L) [28]. However, it should be noted that this may not be the case under certain conditions and samples might contain significantly

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FIG. 4 Scheme of main steps and requirements for sample preparation for CE-MS nontargeted metabolic phenotyping.

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increased levels of proteins, such as the presence of blood in urine in the case of kidney damage for instance. An important aspect of sample preparation, directly related to analysis and data pretreatment, is the preparation of quality control (QC) samples. QC is a representation of samples being analyzed, which is usually achieved by mixing aliquots of all samples [29]. Such a pool is analyzed at the beginning of the batch to reach an equilibrium and then again at regular intervals. The purpose of this is to control the stability of the system performance, since any differences in the signal reflect analytical variation. Nowadays, the use of QCs in nontargeted metabolic phenotyping is compulsory for several reasons including control of analytical stability over the duration of the work list, revision and filtration of the quality of acquired data, and/or signal correction between batches. In this regard, the use of QC in CE-MS studies is no different from any other platform. In terms of the time needed to reach an equilibrium, CE ranks in between GC and LC, where GC requires very little conditioning (three to five injections of QC), while LC requires much longer (usually 10 injections or more). A big advantage of CE is that only a small amount of sample is required per analysis. Consequently, one single QC sample can be used even for very long batches [30].

2.3 Separation and Detection As already stated, there are several different separation methodologies available but CZE coupled to mass spectrometry is the one discussed here. The reason for this is that over 90% of the applications described in the literature regarding nontargeted metabolic phenotyping are based on this type of electrophoretic separation. To explain both separation and detection occurring during typical global metabolite analysis, a protocol-type description is given, including materials, new capillary conditioning, as well as the method itself.

2.3.1 Materials 1. Fused-silica capillary: 100 cm total length, 50 μm internal diameter. 2. BGE: 1 M formic acid in 10% MeOH (v/v). 3. Sheath liquid: MeOH:H2O (1:1) containing 1.0 mM formic acid and purine m/z 121.0509) and hexakis (1H,1H,3H([C5H4N4 + H]+, tetrafluoropropoxy)-phosphazene ([C18H18O6N3P3F24 + H]+, m/z 922.0098) as reference masses for MS analysis. 4. New capillary conditioning solution: 1 M NaOH. 2.3.2 Capillary Preparation 1. The capillary length must be between 60 and 120 cm. In our laboratory, the length of the capillary is around 100 cm.

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2. Cut the capillary to the required length using a diamond blade capillary cutter. A good spray formation depends on the quality of the cut. Observe the end of the capillary under the microscope by rotating it. If it is not a clean cut, then sand with the cutter until it looks right. 3. Burn both ends using a lighter to remove the polyamide coating: 5 mm from the sample injection side and 2 cm from the source MS side. Clean the polyamide debris with a particle-free cloth impregnated with IPA:H2O (1:1) solution. 4. Sonicate both ends for several minutes in IPA:H2O (1:1) solution. 5. The new capillary is mounted on the electrophoresis cassette and inserted into the CE. It has to be conditioned (with no connection to the MS), for 30 min with 1 M sodium hydroxide, then 30 min with H2O and finally 30 min with BGE. 6. Insert and adjust the capillary end in the ESI-MS sprayer. Adequate sensitivity is achieved by adjusting the ring on the sprayer. This adjustment is very important to obtain a good electrical contact and ensure optimal spraying.

2.3.3 CE-MS Method 1. The separation is performed in normal polarity with a BGE containing 1.0 M formic acid in 10% MeOH (v/v) at 20°C. 2. Before each analysis, the BGE content of the inlet home vial is refilled. 3. Before each analysis, the capillary is conditioned by flushing with the BGE for 5 min at 950 mbar. 4. Samples are hydrodynamically injected at 50 mbar for 10–50 s, depending on the sample type and metabolite concentration. A stacking effect is then established by applying the background electrolyte at 100 mbar for 20 s. 5. The separation conditions are set up at 30 kV with an internal pressure of 25 mbar for a run time of 30 min. 6. The sheath liquid is continuously infused (6 μL/min). 7. The MS parameters are: fragmentor 125 V, skimmer 65 V, octopole 750 V, drying gas temperature 200°C, nebulizer pressure 10 psig, flow rate 10 L/min and capillary voltage 3500 V. The data are acquired in positive mode with a full scan from m/z 85 to 1000 at a rate of 1.41 scans per second. Although the most commonly used combination is positive polarization (CE) with positive ionization (MS), some researchers have used other configurations in order to augment the metabolite coverage with anionic compounds and nucleocompounds. Soga’s group has developed different cationic coated capillaries, Smile [31, 32] and Cosmos [33], with reversed polarity and negative ionization of MS using 50 mM ammonium acetate at pH 8.5 as BGE. They described that these configurations require the use of a platinum ESI needle instead of a stainless steel ESI spray needle because of the formation of a discharge crown.

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2.4 Data During analysis, two types of information are acquired: CE and MS, stored as electropherogram and mass spectrum, respectively. These data must be reprocessed in order to combine both types of information and to represent recorded signals in a three-dimensional way, including m/z, migration time, and signal abundance/intensity. This can be performed using very different software and tools that are either open-licensed or vendor-related. During this step, background noise is removed while all coeluting related signals are identified, considering different adducts (e.g., [M + H]+, [M + Na]+, [M + K]+), charges (e.g., [M + 2H]2+, [M + 3H]3+, [M + H + Na]2+), multimers ([2 M + H]+, [3 M + H]+), and often fragments. Then, all of these types of signals can be either summed giving a single value (feature) or all additional signals can be ignored leaving only [M + H]+ as the main signal. Both of these strategies are equally common and the approach chosen depends on the software and the number of multisignals formed. This is very important especially for CE-MS data since the presence of salts causes formation of many different adducts and clusters. Differences in the concentration of salt between samples induce differences in the number and abundance of different adducts formed. Therefore, the best way to estimate the amount of a particular compound in the sample is to sum all of the ions into one value. However, since the behavior of different ions in the source can be different, some authors recommend working only with a single, “representative” signal. Exact reprocessing parameters depend on in-source conditions (especially fragmentor voltage and gas temperature), the composition of buffer and sheath liquid (presence of modifiers), as well as the composition of the sample itself (especially pH and the presence of salts). Extracted features have to be aligned to match the same metabolites across different samples. This step is no different from other separation techniques, but is more challenging in the case of CE-MS. This is due to the known migration time shift, which, depending on the exact conditions and length of batch, can vary from <1 min up to 5 min or more. Moreover, the shift along the electropherogram is not the same for all compounds: compounds with faster electrophoretic mobility tend to have a smaller MT shift than compounds with slower electrophoretic mobility, especially those close to the neutral molecules [34]. This makes it necessary to take special care to align features. This is even more important considering the fact that many metabolites with the same monoisotopic mass are separated and the difference in MT between them is often within the window of shift of MT (e.g., betaine and valine or alanine and sarcosine). For this reason, either (i) MT correction (Mass Profiler Professional, Agilent Technologies) must be performed prior to the alignment itself or (ii) the visualization of aligned features must be available to inspect and eventually correct all wrong integrations and misalignments (Mass Hunter Profinder, Agilent Technologies). Another solution to deal with irregular MT shift is the use of an open-source algorithm for data in mzXML format called msalign2, which

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applies a genetic algorithm and breakpoints of the piecewise function. So far, the most successful, and consequently the most popular, strategy for CE-MS data is dynamic time warping. This algorithm, with its many modifications, tends to find corresponding signals across different samples to produce warping functions; for example, the Ordered Bijective Interpolated Warping algorithm in the XCMS Metabolomic platform (Scripps Research Institute, CA, USA) [35] and reference peak warping function in MsXelerator [36]. To deal with all of the aforementioned limitations, many research groups have developed their own tools, usually employing Matlab or R. Many studies have been performed using Mass Hunter and Mass Profiler Professional (Agilent Technologies), Master Hands (KEIO University), or XCMS (Metabolomic Platform) and Data Analysis. There are many other tools used in CE-MS, although they make a more minor contribution to the field; such tools include Analyst, MathDAMP, Mzmine, PeakView, MSXelerator, XCMS Metabolomic Platform, and Human Metabolome Technologies [1]. When it comes to metabolic phenotyping, more data do not always equate to more information. Some regions of the electropherogram do not contain useful or meaningful information. Usually two such areas can be distinguished: the first is related to the peak (relatively wide and flat-topped) containing different salt clusters and the second corresponds to the electro-osmotic flow. This area (peak) can be more or less abundant depending on the amount of neutral and negative compounds and can be eliminated from the electropherogram by changing the range of acquired time. Alternatively data recorded on that region should be eliminated from the data analysis due to their low reliability, similar to what occurs with the peak containing salts clusters. Currently a lot of attention is paid to the phenomenon of in-source fragmentation since small polar metabolites are very fragile and tend to easily break up in the source. This can be very problematic taking into account the fact that a fragment of one metabolite can have the same mass as a completely different compound; e.g., m/z 130.086 is a mass of pipecolic acid and the most abundant fragment of lysine, and m/z 116.071 corresponds to both proline and a fragment of ornithine [37]. As multiadducts and multicharges were already considered in the algorithms for data reprocessing, fragmentation still requires some systematic solutions.

2.5 Statistics In this section, two aspects of statistics will be considered: data pretreatment and data treatment. Data pretreatment aims to prepare data for statistical analysis, while data treatment aims to find significant metabolites responsible for the differences between compared conditions. Generally speaking, this is not very different from any of the other platforms; indeed, it is the most similar step of all.

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Data pretreatment includes any type of data standardization to minimize any variation other than those that are induced biologically. Such nonbiological disparities may be due to either sampling (such as differences in cell numbers, weight of tissue, or volume of urine collected) or analytical drift over batch duration. To deal with analytical drift, internal standards can be used, which might also be applied to deal with the MT shift. Sampling differences can be corrected by normalizing data that has been already acquired to the weight of the sample for tissue, cell numbers, total amount of proteins or DNA for cells [38], amount of creatinine for urine [39], etc. Another solution is to adjust the concentration of the sample prior to the analysis. This is more challenging and can only be performed when there is reliable information about the samples. One of the characteristics of MS-derived metabolic phenotyping data is the detection of signals within a wide range of abundances. Although this range is narrower for CE-MS data than for LC-MS, it still might be problematic for further data analysis. This is of particular importance for multivariate analysis where metabolites with high abundance make a stronger contribution to the model than those with lower intensity, which is not necessarily correct from a biological point of view [40]. For this reason, several data scaling scenarios can be used with UV (unit variance) and Par (pareto) being the most popular ones. These scaling scenarios either give equal variance to all metabolites (UV) or minimize the impact of abundant signals, while increasing the impact of those with a low-level signal (Par) [41]. Both UV and Par are commonly used but it is important to highlight that in the case of UV scaling, data are analyzed on the basis of correlations, while for Par scaling, the analysis is based on covariance. Furthermore, UV scaling is very sensitive to large deviations from the sample mean, which in practice means that the presence of outliers may skew the scaling coefficient and therefore the shape of the data. Ideally, the best solution would be to perform outlier detection. However, scaling is often combined with either power or logarithmic transformation, which aims to achieve more normal distribution and therefore minimize the impact of outliers [42]. Further information can be found in Chapters 8 and 9. Univariate analysis can be performed by computing the P value through parametric or nonparametric tests using either asymptotic or permutation methods. Regardless of the option selected, multiple testing correction is required to minimize the impact of false-positives, which are considered one of the biggest problems in data analysis (see also Chapters 8 and 9). Indeed, this phenomenon can be very problematic, especially in the case of biomarker discovery studies. However, it is again important to note that this problem is much smaller in CE-MS studies than in LC-MS metabolic phenotyping, but is still bigger than it is for GC-MS studies. This is because the number of detected signals ranked CE between the GC and LC platforms. Correlation analysis is often applied either to find connections between the metabolites measured or to find relationships between metabolites and some previously measured biochemical parameters such as levels of enzymes, etc. This approach is very useful and can

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reveal many important findings. However, it is important to highlight that this kind of analysis can be easily perturbed by the presence of multisignals including different adducts and/or fragments formed in the source. Therefore, in the case of studies in which correlation analysis plays a crucial role, redundant signals must be identified and removed from the data set to minimize the risk of spurious correlations. Multivariate analysis most often involves the use of either unsupervised (principal component analysis, PCA) or supervised (PLS-DA, PLS-regression, OPLS) models, to reduce the multidimensional data matrix to a readable plot to visualize inter- and intragroup relationships (further details of these techniques are found in Chapter 9). Unsupervised methods are very suitable to find and visualize spontaneous sample clustering and/or spreading. For this reason, they provide very valuable information about outliers and general magnitude of the intergroup differences, which can be used to track either biological or analytical trends (e.g., QC). Supervised models work well to either select significant metabolites or to test separation power of previously selected compounds. This is particularly useful to test the predictive power of proposed markers for classification of new, unassigned samples. Unsupervised models are very sensitive to missing values, so data containing a large number of “zeros” should be carefully analyzed. On the other hand, supervised models are sensitive to multisignals arising from the same molecule. Depending on whether such a molecule is significant or not, its multiple presence in the data matrix will either improve or worsen the separation or parameters of the model. Therefore, multiadducts and/or fragments should be identified and removed from the matrix prior to the statistical analysis.

2.6 Identification Significant signals, selected through statistical analysis, have to be assigned to the metabolites to interpret the obtained results and to give biological meaning to the data. Broadly speaking, identification can be divided into two concepts: metabolite annotation (also called tentative or putative identification) and identification sensu stricto. Metabolite annotation refers to the identifier (ID) assignment based on the mass accuracy within a selected window. This assignment is also often supported by isotopic distribution matching to the proposed formula, as well as MT information. Identification sensu stricto is based on the comparison of two independent, orthogonal characteristics of experimental signal with an authentic standard. Although this method is undeniably the most reliable, it is seriously limited due to the low number of commercially available standards. Moreover, compared to the LC-MS platform, MS/MS analysis is not very common, which reduces the identification power of CE-MS. It is due to the very low amount of injected sample, which is often not sufficient to gain enough signal for tandem MS. Although there are several publications where CE was coupled to a

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QTOF analyzer, CE-TOF is still the most common where MS/MS analysis is not available. For this reason, the most popular identification method is the use of standards that are analyzed under identical conditions to biological samples. Moreover, due to the differences in the MT and differences between the behavior of a compound in a standard solution versus its behavior in a complex sample, sample spiking is compulsory for unambiguous ID confirmation. Therefore, analysis of the standard, the biological sample, and then the biological sample spiked with the standard gives sufficient evidence for final ID confirmation, and is considered, according to The Chemical Analysis Working Group of the Metabolomics Society Initiative (MSI), as the first-choice, most reliable approach [43]. However, as mentioned earlier, standards are not available for all metabolites, and the price of those that are available is often not affordable. Therefore, other sources of information have to be used. Using these alternatives, only the second MSI level can be achieved referring to putatively annotated compounds. This level covers comparison of any physicochemical properties and spectral similarity of libraries without reference to authentic standards. In nontargeted metabolic phenotyping, identification power is directly related to the mass accuracy of measured metabolites: higher accuracy means more precise identification. For this purpose, a suitable window has to be set to search the particular mass against databases. Mass analyzers such as TOF, which work using reference masses, provide not only accurate but continuously accurate mass via continuous accuracy correction. For this reason, most annotations are successfully performed with a tolerance of 2–5 ppm. This annotation can be supported by isotopic distribution, providing information about the chemical composition of molecules. It is true that, in most cases, the difference in the isotopes is too small to be used for annotation purposes. However, there are some compounds that can be successfully and robustly recognized on the spectral information level, for example, sulfur-containing molecules, such as adenosylmethionine and glutathione disulfide. The MT also provides very valuable information about separated metabolites, which can be successfully used for identification purposes. The easiest and most robust solution is to compare the MT of a standard with the MT of a particular metabolite, both measured under identical conditions. However, as already mentioned, this method can only be applied when an authentic standard is available. Another strategy is to use prediction of MT based on the behavior of previously analyzed standards. Considering information such as the pH of the sample and buffer, chemical formula and pKa of the metabolite and its mass, charge formation can be predicted and therefore migration order can be established. Such prediction has already been applied in some studies, either performed without using any automated tool (by applying defined rules) or employing previously developed tools [44, 45]. This strategy can provide a very reliable prediction and can be improved over time by analysis of large numbers of

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metabolites and by improvement of the prediction algorithm. Moreover, in contrast to LC-MS where separation is achieved in a gradient, the conditions applied are relatively stable over the course of the run. The following three tools are examples where such algorithms are employed: the tool developed by Sugimoto et al. using artificial neural networks to predict MT from the structure of the molecule, charge, pKa and Molecular Operating Environment descriptors; a tool using support vector regression based on the molecular descriptors (calculated using Molecular Operating Environment (Chemical Computing Group, Canada)) and net charge (computed based on the pKa and the buffer pH); and Simul software, which uses physicochemical properties of metabolites like absolute mobility (based on a Hubbard-Onsanger model using molecular volume and intrinsic valence charge) and pKa to predict the behavior of analyte electromigration. Another interesting approach is the use of relative migration time (RMT) instead of its absolute value. RMT is computed by comparison of the MT of particular metabolites to the MT of used internal standard(s). This strategy can minimize MT variations within a given batch or between batches, thereby improving ID assignment. However, it is important to mention that this method can only be applied to samples analyzed under the same conditions keeping the sample composition exactly the same, since the presence or absence of some compounds can affect the MT of other metabolites. It is important to highlight that the identification power of RMT is always higher than the predicted MT and therefore using a reliable internal standard is highly recommended. The most frequently used internal standard is methyl sulfone, followed by ethane sulfonic acid, 2-N-morpholino-ethanesulfonic acid, camphor-10-sulfonic acid, 1,4-piperazinediethane sulfonic acid, and HEPES. Regardless of the usefulness of employing MT in metabolite annotation, its limitation is quite obvious when considering isobaric or structurally similar compounds. In such cases, MS/MS analysis seems to be the best solution. However, as previously mentioned, its applicability is very limited with CE-MS-based metabolic phenotyping. Nevertheless, MS/MS analysis has already been used in several studies employing QTOF, e.g., Bruker Daltonics, micrOTOF ESI-TOF-MS and Bruker Daltonics, maXis ESI-Qq-TOF-MS/MS series [46–48]. Structural elucidation was then performed either comparing the obtained fragmentation pattern with standard (1st level, identified compound) or a spectral database such as Metlin (2nd level, putatively annotated compound) [43]. The fact that in-source fragmentation is a common phenomenon in CE-MS studies performed by means of CE-TOF-MS is a point that has already been made use of for identification purposes. The fragmentor voltage can be intentionally raised to enhance the fragmentation of molecules in the source and therefore to produce more abundant fragments/product ions. Without previous precursor ion selection, all metabolites present in the source undergo fragmentation resulting in a mixture of many fragments. Therefore, for the assignment

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of particular fragments to their corresponding precursor, correlation analysis can be used. It has already been proven that successful fragment assignment can be obtained with a correlation coefficient above 0.9 [37].

2.7 Meaning A list of significant and identified metabolites is then used for the last stage, which is biochemical interpretation. This step and the identification step are regarded as being the most difficult ones in the entire metabolic phenotyping workflow. This difficulty stems from the lack of fully automated tools to help interpret the results obtained. The interpretation of data usually involves the arduous and time-consuming process of searching for and reviewing the literature, to properly understand observed changes and to draw reliable conclusions. Some algorithms, such as natural language processes (NLP), can be used to deal with this issue. Prior to this, significant metabolites are searched for in a defined database (e.g., abstracts in PubMed), and then relationships are established between these significant compounds and others reported in metabolite publications. Clearly, the results of such an analysis must be carefully inspected and curated by the researcher; however, this approach is very helpful and represents a useful starting point for data interpretation. Another useful solution is the use of pathway generators, which match identified metabolites into selected metabolic maps. The limitation of this strategy is the lack of identifiers, e.g., KEGG, CAS, HMDB, ChEBI ID, etc. because not all metabolites have been assigned with a unique identifier. This limitation is not overly problematic for CE-MS derived data since the majority of small and polar metabolites have already been described in detail. However, it is much more problematic for lipidic compounds, the majority of which do not have any ID. In this regard, CE-MS data are relatively easy to interpret and use for the construction of pathways or intermetabolite relationships. For this purpose, automated tools can be applied, such as pathway analysis integrating enrichment analysis and pathway topology analysis in MetaboAnalyst [49]. Some pathways are disease-specific pathways, which can only be found in particular cases, such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, or Sudden Infant Death Syndrome Susceptibility. Other pathways are not as specific but are related to the same particular perturbations such as DNA damage response or oxidative stress. There is also a list of pathways that are very common in CE-MS metabolic phenotyping due to the nature of the detected compounds. For this reason, any pathway with a relevant number of amino acids, organic acids, or nucleotides can be easily explored by means of CE, such as glycolysis and gluconeogenesis, TCA Krebs’ cycle, urea cycle, oxidative phosphorylation, or pentose phosphate pathway. The biggest limitations occur with nonpolar and lipidic compounds, which have to be analyzed by means of an alternative platform.

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APPLICATIONS

To give a broader picture of CE-MS coverage and therefore its utility in understanding of biological processes, CE-MS-based metabolic phenotyping studies have been eviewed and a list of published metabolites has been created. Based on this analysis, a chart has been prepared (Fig. 5) to illustrate the contributions of different metabolite classes within the general “CE-metabolome.” It is important to highlight that the coverage of CE is bigger than the one presented here, since in the majority of nontargeted metabolic phenotyping studies, only statistically significant metabolites are reported, not all of those that are detected. Amino acids ( 37%) constitute the biggest group of CE-measurable metabolites followed by generally understood organic acids

Amine Amide

Other Choline

2

3

1 Nucleo-compound

Amino acid

Carnitine Sugar phosphate

Organic acid and derivative

1 Nucleo-compound Nucleobase

2 Other

3 Amine and amide

Coenzyme Amine Phosphate

Nucleoside Heterocyclic Disulfide

Amide

Glycerophospholipid Azole Nucleotide

Sulfate Phenol

Dinucleotide Dinucleotide phosphate Purine Pyridine and derivatives

Polyamine

Organic nitrogen Organic oxygen Glycerolipid Benzenoid Sulfoxide Diazine Oxide

FIG. 5 Structure of chemical classes of metabolites reported by CE-MS-based metabolic phenotyping.

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and their derivatives ( 31%). Nucleocompounds represent another large group ( 13%). This category covers nucleobases ( 10%), nucleosides ( 22%), nucleotides ( 58%), dinucleotides ( 5%), and dinucleotide phosphates ( 5%). Analysis of these compounds provides information on the metabolism of purines (adenine- and guanine-based metabolites) and pyrimidines (cytosine-, uracil-, and thymine-based metabolites). Both purines and pyrimidines are components of nucleic acids, although they are synthesized in different ways. Moreover, the products of pyrimidine degradation are water soluble and thus can be detected particularly well by CE-MS. For example, the degradation cascade of cytosine can produce uracil, N-carbamoyl-beta-alanine, and beta-alanine, all readily detectable with CE-MS. Both purine and pyrimidine biosynthetic pathways employ glutamine and 5-phosphoribosyl diphosphate (PRPP), derived from the pentose phosphate pathway, to provide the sugar phosphate backbone to this group of compounds. Sugar phosphates themselves can be detectable using CE-MS, primarily in negative mode. Although fatty acids themselves are not measurable by CE-MS, this platform provides valuable information about carnitines ( 3%), which are important to understand fatty acid oxidation. Regardless of the exact MT, the conditions employed in CE-MS allow the separation of all carnitines and their detection following identical ionization conditions. Thus, performing ratio analyses between them is highly feasible. Amines and amides encompass a wide range of compounds, both endogenous and exogenous to biological systems. For example, nicotinamide and related compounds represent one of the most abundant classes of amides in organisms, involved in the synthesis of NAD and NAD(P)H. This class of compounds can also be related to nucleocompounds through the synthesis of nicotinamide ribotide, an important intermediate of nicotinate and nicotinamide metabolism. Amines as a group encompass a huge array of biologically endogenous compounds covering certain amino acids and polyamines, among others. Polyamines such as putrescine, ornithine, spermidine, and spermine, which are all synthesized from arginine as the initial precursor, are a biologically interesting group of compounds with important functions in cell growth, regulation and transport mechanisms. They are known for their interactions with DNA and are involved in the process of translation as well as in the control of permeability in membranes and signaling. For many years, epigenetic modification has been known to play a vital role in the phenotype, bridging the gap between gene expression and metabolic profile. Several alterations such as acetylation, methylation, and phosphorylation have been studied in depth for histone post-translational modifications. These modifications are currently of particular interest in terms of metabolite modifications. For this reason, from available general metabolomes, an epimetabolome has been described covering modified metabolites with specific biological functions, and so far includes around 1000 metabolites. This number covers only those “modified” metabolites that have been confirmed; however, theoretically, there are thousands of different modifications. The biological or

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pathological importance of such metabolites has been demonstrated. For example, diacetyl-spermine and methyl-glycine have been connected to cancer, dimethyl-arginine to asthma, and oxylipins to inflammation. Methylnicotinamide has been previously related to the regulation of pluripotency. The epigenome can provide even more information when compared to the rest of the metabolome: firstly in the comparison of experimental groups, by providing valuable information and, secondly, in the performance of ratio analysis between nonmodified and modified metabolites. The power of this strategy points to the fact that within sample analysis ignores interindividual differences. However, for this strategy to be successful, all of the metabolites to be compared must be analyzed under identical conditions. This requirement can be easily fulfilled by CE since, as already mentioned, the separation condition remains constant for the duration of the analysis, in contrast to LC where the mobile-phase gradient affects not only separation itself, but also ionization. In LC-MS separation and ionization, conditions can change over time making even semiquantitative comparison of compounds from different chromatogram regions difficult. Out of all the modifications reported so far for CE-MS metabolic phenotyping, methylation and acetylation are the most frequent, followed by amination and hydroxylation, and oxylation. All other modifications constitute <5% and are listed in Table 1, together with examples for each modification. Other “pairs” of metabolites that are important to mention are methionine and homocysteine as well as S-adenosyl methionine and S-adenosyl homocysteine. Although they do not strictly fit in with the epimetabolome concept, the relationship between their amount (or ratio) gives very valuable information about one of the main biochemical processes, the folate cycle, which can be easily obtained through CE-MS [50]. From this point of view, CE-MS seems to be an ideal solution for studying many metabolites and their modifications. As mentioned earlier, any type of sample containing metabolites can be successfully analyzed by means of CE. Garcia et al. obtained detailed information about the type of samples used in nontargeted CE/MS-based metabolic phenotyping. According to this publication, the majority of analyses are performed on tissues ( 19%), cells ( 17%), urine ( 17%), serum ( 16%), and plasma ( 10%), followed by many more type of samples. Higher number of research performed with serum samples compared with plasma samples is related to clinical rather than analytical aspects, since both types of sample are very suitable for CE. Selection of the sample type mainly depends on the main objective of the study: biomarkers are searched for in easily collectable biofluids, while new pathologies or diseases are explored using tissues, to observe metabolic changes occurring at the “site of action.” Regardless of the type of sample, most research was performed using human models (this was the case for almost 50% of all CE-MS metabolic phenotyping studies). Animal models are also often used including either wild-type or knockout rat and mouse. CE-MS metabolic phenotyping is also successfully applied to explore the metabolome of unicellular organisms such as bacteria (e.g., Escherichia coli, Bacillus subtilis,

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TABLE 1 Illustration of the Epimetabolome and Examples of Particular Modifications Modification

Examples

Methyl

Methylthreonine, dimethylarginine, trimethyllysine

Acetyl

Acetyllysine, acetylhistidine, acetylasparagine

Amino

Aminohippuric acid, aminoadipic acid, diaminopimelic acid

Hydroxy

Hydroxyglutaric acid, hydroxycotinine

Oxo

Oxoglutaric acid, oxoacetic acid, oxoproline

Phosphoryl

Phosphorylcholine, phosphogluconic acid, bisphosphoglyceric acid

Phenyl

Phenylacetylglutamine

Ethyl

Ethylglutamine

Hydro

Dihydrothymine, dihydrouracil, tetrahydrobiopterin

Carboxy

Carboxyethyllysine, carboxymethyllysine, carboxyethylarginine

Succinyl

Succinylornithine, succinylcitrulline, succinylbenzoic acid

Benzoyl

Benzoylformic acid, benzoylphosphoadenosine, benzylaminopurine

Butyryl

Butyrobetaine

Methoxy

Methoxytryptamine, methoxytyramine, methoxycatechol sulfate

Bifidobacterium animalis), yeasts, and parasites (Leishmania and Fasciola hepatica). Importantly, the analysis of bacteria does not have to be limited to one particular strain; a group of different species can be used, such as gastrointestinal microbiome. The results of the number of studies based on the type of organism and the type of diseases or disorders that have so far been subjected to CE-MS-based metabolic phenotyping are summarized in Fig. 6A and B, respectively. As the figure illustrates, many disorders have already been explored, mainly different types of cancer studies covering bladder, colon, breast, stomach, lung, pancreas and prostate cancers and tumors. Diabetes mellitus, including both types of this disease (type 1 and 2), has been investigated both in human and in animal models. A very interesting group of disorders is neurodisease, which covers Alzheimer’s disease, neurodegenerative dementia, cortical spreading depression and schizophrenia. The “other” group includes different ailments such as atopic dermatitis, complex regional pain syndrome, ischemia-reperfusion injury, myopia, systemic lupus erythematosus, and fatigue. The biggest group of all corresponds to studies of the effect of treatment or administration. This group includes the effect of drug administration either to

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Mouse

Other

Bacteria

Human

Parasite Yeast

Rat

(A)

Other Treatment administration Obesity Neurodisease Urinary track disease Gastrointestinal track disease Diabetes mellitus Respiratory track disease

Cancer

(B) FIG. 6 Pie chart based on the number of studies involving specific types of organisms (panel A) and main groups of diseases and disorders (panel B) that have been subjected to CE-MS based metabolic phenotyping.

track the effect of the drug itself or to understand the mechanism of resistance. Other studies focus on acute intoxications, dietary consumption, alcohol intake, and smoking. CE-MS was proven to be a powerful tool for comparative analysis of different conditions to find and define differences. However, in many studies, CE-MS

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was applied to analyze entire samples without investigating any particular disease. This strategy was used either to profile the entire metabolite content of the samples or to test new analytical solutions, either for sample preparation or electrophoretic separation. Such research has already been performed for several different sample types from different organisms, e.g., CSF and liver from mouse, urine from human, and bacteria cells. Onjiko et al. recently described “microprobe single-cell CE-ESI-MS” to make possible, for the first time, the in situ analysis of metabolites in single cells in the freely developing live frog embryo by integrating capillary microsampling, microscale metabolite extraction, and CE-ESI-MS [51]. The authors of this study differentiated 230 different molecular features (positive ion mode), including 70 known metabolites, in single dorsal and ventral cells in 8- to 32-cell embryos with demonstrated enhanced detection sensitivity. Soga et al. quantified the metabolome of Bacillus subtilis obtaining information about almost 1700 metabolites. Such great coverage was achieved by analysis under three different conditions adjusted exclusively for cationic and anionic metabolites followed by nucleotides and coenzyme A [31]. The main differences concern the type of capillary and consequently BGE: fused silica capillary with 1 M formic acid for ESI + for cations; cationic polymer coated SMILE (+) capillary with 50 mM ammonium acetate solution pH 8.5 for ESI- for anions; and GC polydimethylsiloxane capillary with 50 mM ammonium acetate solution pH 7.5 for ESI-for nucleotides. This demonstrates that although CE is not as flexible as LC, extensive metabolite coverage can be obtained by changing some basic parameters. Out of all the signals detected, almost 170 were quantified but also relative changes were obtained for B. subtilis collected in different growth phases. In this way, both the qualitative and quantitative characteristics of CE were tested. Untargeted metabolic phenotyping based on CE-MS is used to obtain information about similarities and differences in metabolite composition between case and controls for different purposes: identification of biomarkers and altered biochemical pathways in diagnosis, prognosis, evolution of different severities of a disease, unveiling the mechanism of an administered drug or food intake, or even personalized medicine. To date, the most common application of CE-MS untargeted metabolic phenotyping has been the first one, that is, biomarker discovery by the analysis of many different types of biological matrices. Over the last 15 years, the comparison of several hundred metabolites in these types of biological samples has facilitated the acquisition of greater knowledge in animal and human studies in preclinical and clinical settings. The role and discoveries of CE-MS-based metabolic phenotyping have been described in several reviews [1, 52–55]. Some relevant findings for common diseases such as cancer, metabolic disorders, neurodegenerative diseases and brain disorders, among others, are described here.

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3.1 Cancer CE-MS provides reliable information regarding critical small molecules related to cancer metabolism and inflammation that could represent targets for the development of cancer therapy. The analysis of metabolites such as charged metabolites from the pentose phosphate pathway, glycolysis, TCA, and urea cycles, as well as amino acid and nucleotide metabolisms has facilitated the association of cancers with low glucose and high lactate concentrations (Warburg effect) along with an increment of all amino acids except glutamine, which suggests of glutaminolysis. This is an interesting finding achieved from the analysis of the tumor microenvironment rather than from analyzing biofluids [56]. Hirayama’s group [57] analyzed tumor and surrounding grossly normal-appearing tissues obtained from 16 colon and 12 stomach cancer patients after surgical treatment. Regarding biofluids, urine from colorectal cancer (CRC) patients was studied by Chen and colleagues [58]. The main metabolic pathways related to CRC included glycolysis (lactic acid), serine metabolism (serine), and the tricarboxylic acid (TCA) cycle (succinic, citric and malic acids). The levels of valine and isoleucine were lower in the advanced stage than in the early CRC group. Other types of cancer, such as urothelial bladder cancer, oral, pancreatic, breast, prostate, osteosarcoma, lung, etc., have also been studied. An aminoacid-rich metabolome is a characteristic hallmark of bladder tumor development [59]. In this regard, Alberice and colleagues [56], using the LC-MS and CE-MS multiplatforms, were able to classify the urines from 48 patients classified into four groups according to whether or not they suffered a tumor recurrence as well as their risk group according to tumor grade and stage. Human saliva from 215 individuals (69 oral, 18 pancreatic, and 30 breast cancer patients; 11 periodontal disease patients; and 87 healthy controls) was analyzed by Sugimoto’s group [15] and 28 statistically different metabolites were found both in the case of oral cancer and for breast cancer, with 48 found for pancreatic cancer and 27 for periodontal disease. Soliman et al. described sarcosine and its related metabolites as potential prostate cancer biomarkers by analyzing them by CE-MS [60].

3.2 Metabolic Disorders After quantifying 74 metabolites in serum, glycerophosphate level in the fasting condition was found as a predictor of glucose intolerance in type 2 diabetes mellitus in Japanese male adults [61]. Regarding metabolic disorders, Barbas’ group in the San Pablo CEU University has performed several studies related to type 1 diabetes mellitus in rats, type 2 diabetes (T2DM), gestational diabetes, and insulin resistance (IR) with children and adult patients [9, 11, 62]. Mastrangelo et al. [62], in a study with

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serum from 60 prepubertal obese children with and without IR (boys and girls), found several metabolites related to inflammation and central carbon metabolism, together with the contribution of the gut microbiota, via a multiplatform MS-based metabolic phenotyping study in which CE-MS provided exceptional information about the relevance of amino acids, as shown in Fig. 7A. In a T2DM study with 197 serum samples from normal weight, overweight, and obese individuals, some branched chain amino acids, lysine, and acetylcarnitine and methionine were found to be potential classifiers, all independent of BMI. In several metabolic phenotyping studies on hypercholesterolemia and the effect of functional ingredients in the diet, Gonza´lez-Pen˜a et al. [10, 63] employed a multiplatform MS approach for obtaining plasma and liver fingerprints in rats being fed with different diets. Some metabolites—such as amino acids, organic acids, and purine and pyridine derivatives—differentiated the groups of samples taken from rats fed with a control diet (C), high-cholesterol diet (HC), and high-cholesterol enriched with onion diet (HCO). Of these metabolites, carnitine and derivatives were specially affected by the HC diet, showing a decrease in liver and plasma concentrations. Moreover, according to the amino acid profiles, there was an alteration in the arginine metabolism and amino acid transport across membranes as a result of the HC feeding (see Fig. 7B) along with a possible alteration of the methionine pathway due to the cholesterol and cholic acid overload, which showed a tendency to be lower in the HCO group compared with the HC group.

3.3 Neurodegenerative Diseases and Brain Disorders Migraine, schizophrenia, Alzheimer’s disease (AD), Huntington’s disease (HD) [64], and bipolar disorder (BD), among other diseases, have been studied by CE-MS metabolic phenotyping with human and animal samples. Shyti studied migraine in transgenic mice [65], while Koike’s group investigated schizophrenia in plasma samples from patients with schizophrenia and autism spectrum disorders [66]. Their approach involved exploring the global metabolomic alterations that characterize the onset of schizophrenia and identifying biomarkers with robust changes (increased creatine and decreased betaine, nonanoic acid, benzoic acid, and perillic acid). Altered levels of these metabolites are consistent with well-known hypotheses regarding abnormalities of homocysteine metabolism, creatine kinase-emia, and oxidative stress. In addition, biomarkers in plasma from drug-free patients with bipolar disorder and schizophrenia were studied by Kageyama and colleagues [67]. Moreover, human brain samples including frontal cortex and hippocampus were studied and the mean levels of 29 metabolites were significantly different between the schizophrenia and control groups in the frontal cortex. In both the hippocampus and the frontal cortex, glycyl-glycine, a dipeptide, was significantly higher in schizophrenia (P  0.001, nominal P-value) [68].

´106 3.6 3.4 3.2 3 2.8 2.6 2.4 2.2 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 8

8.5

9

9.5

10

´106 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

1. Alanine/sarcosine 2. Valine 3. Piperidine 4. Proline 5. Tyrosine 6. Betaine

12 12.5 11 11.5 Counts vs acquisition time (min)

10.5

13

13.5

14

14.5

15

15.5

2 1 4

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(A)

12.4 12.6

12.8

13

13.2

13.4 13.6 13.8 14 14.2 Counts vs acquisition time (min)

14.4

14.6

5

6

14.8

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15.2

´106 4.25 4 3.75 3.5 3.25 3 2.75 2.5 2.25 2 1.75 1.5 1.25 1 0.75 0.5 8

9

10

11

12

13

14

15 16 17 18 19 20 Counts vs acquisition time (min)

´105 5.5

1. Spermidine 2. Choline 3. Pipecolic acid 4. Histidine 5. Glycine 6. Canitine 7. Serine 8. Adenosine 9. Threonine 10. Aspartic acid

21

22

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25

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27

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(B)

9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23

Counts vs acquisition time (min) ´10

6

2.6 2.4 2.2 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 7.5

8

8.5

1. Choline 2. Ornithine 3. Arginine 4. Histidine 5. Imidazole/lactate 6. Citrulline 7. Alanine/sarcosine 8. Betaine/valine 9. Leucine

(C)

9

9.5

10

10.5

11

11.5 12 12.5 13 13.5 14 Counts vs acquisition time (min)

14.5

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10 10.2 10.4 10.6 10.8 11 11.2 11.4 11.6 11.8 12 12.2 12.4 12.6 12.8 13 13.2 13.4 13.6 13.8 14 14.2 14.4 14.6

Counts vs acquisition time (min)

FIG. 7 Representative total ion chromatograms (TIC) (upper panel) and extracted ion chromatograms for discriminating compounds (lower panel) in various pathophysiological situations and different types of samples comparing case (red) and control (blue): (A) extract of serum from obese child with and without insulin resistance; (B) liver extract from hypercholesterolemic (HC) and healthy rats (C); (C) extract of Leishmania infantum nontreated and treated with antimonial drug.

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Alzheimer’s disease (AD) is currently the most prevalent form of dementia, and the estimated number of cases worldwide is expected to exceed 100 million by 2050 [69]. CSF and human sera from patients with altered cognitive status related to AD progression were analyzed and 10 biomarkers, mainly amino acids, were proposed as biomarkers, two of which were choline and dimethylarginine.

3.4 Others 3.4.1 Infectious Diseases In parasitic diseases such as leishmaniasis, CE-MS untargeted metabolic phenotyping has been used to uncover the action of antimonial drugs and to reveal the underlying basis of resistance to them [70]. L. infantum promastigotes were analyzed by LC, GC, and CE all coupled to MS after optimization of the sample treatment. In the comparison by CE-MS, the amino acids, peptides, and conjugates were the largest group (67%) found to be altered, followed by purines, pyrimidines, and their conjugates (13%) and organic acids (10%). Amino acids were found to be decreased, when antimony-treated samples were compared with nontreated ones, as shown in Fig. 7C. In addition, Soga and colleagues [71] explored the capabilities of CE-MS in the analysis of serum of patients with liver disease and were able to differentiate infectious diseases at different disease stages. Forty nine statistically significant metabolites were described and the models revealed a high discriminatory capacity based mainly on γ-glutamyl dipeptides alone or in combination with other biochemical parameters. 3.4.2 Alcohol Abuse The associations between ethanol intake and plasma concentration of metabolites were examined by Harada et al. using untargeted metabolic phenotyping [72]. Samples from alcohol drinkers and control individuals were compared and 19 metabolites were associated with alcohol intake, and three biomarker candidates (threonine, guanidinosuccinate, and glutamine) of alcohol-induced liver injury were identified. The glutamate/glutamine ratio was also proposed to be a good biomarker candidate. 3.4.3 Clinical Approaches For clinical applications, robustness is a must and the suitability of untargeted metabolic phenotyping-based CE-MS is unclear. Although the clinical utility of CE-MS in proteomics and biomarker discovery has been demonstrated in several reviews [73, 74], which included the analysis of more than 10,000 human urine samples in a long-term study [75], their utility in metabolic phenotyping studies is still by no means established, especially with new CE-MS interfaces

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that still need to be evaluated in more extended studies analyzing large numbers of diverse clinical samples [76]. For clinical applications, high-throughput analysis is required. BritzMcKibbin’s group [77] has developed a strategy for CE-MS-TOF, based on consecutive multisegment injections of plugs of samples separated by plugs of background electrolyte buffer as a high throughput platform for metabolic phenotyping without ion suppression. Boizard and colleagues [78] agree that very few studies actually used CE-MS to identify clinically useful body fluid metabolites. They therefore explored the use of CE-MS and endogenous stable urinary metabolites for long-term, reproducible, and comparable analysis of the urinary metabolome, by using a beveled tip capillary that improves the sensitivity of detection over a flat tip and also allows a highly reproducible comparison of the same sample analyzed nearly 130 times over a period of 4 years. The methodology also includes a novel normalization procedure based on the use of endogenous stable urinary metabolites identified in the combined metabolome of 75 different urine samples from healthy and diseased individuals [78].

4

CONCLUSIONS AND FUTURE TRENDS

To date, CE-MS has not been one of the most common tools in metabolic phenotyping. However, it can offer valuable and sometimes unique information, especially regarding cations, anions, nucleotides, and epimetabolomics, in any kind of biological sample in any condition, as illustrated by the examples provided in previous sections of this chapter. Now is the time to make the most of previous research and also explore new possibilities for very small samples: biofluids, tissues, or even a single cell. Even though clinical applications are still a long way off, several biomarkers of important diseases have been revealed and validated with large cohorts. This technique has, without a doubt, contributed extensively to deepening our understanding of altered pathways and the mechanisms underpinning them.

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