Liquid chromatographic methods combined with mass spectrometry in metabolomics

C H A P T E R

9 Liquid chromatographic methods combined with mass spectrometry in metabolomics Georgios A. Theodoridisa,b,c, Helen G. Gikab,c,d, Robert Plumbe, Ian D. Wilsonf a

b

Department of Chemistry, Aristotle University, Thessaloniki, Greece BIOMIC_AUTh, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center, Thessaloniki, Greece c FoodOmicsGR Research Infrastructure, Aristotle University Node, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center, Thessaloniki, Greece d Department of Medicine, Aristotle University, Thessaloniki, Greece e Waters Corporation, Milford, MA, United States f Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, United Kingdom

O U T L I N E Introduction Chromatographic methods for metabolite profiling Reversed-phase LC separations Hydrophilic interaction liquid chromatography (HILIC) Other approaches to the profiling of polar and ionic metabolites Miniaturized LC systems

Proteomic and Metabolomic Approaches to Biomarker Discovery https://doi.org/10.1016/B978-0-12-818607-7.00009-8

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Multicolumn and multidimensional separations

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Ion mobility spectrometry combined with LC-MS

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Detection

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Quality control, data analysis, and biomarker detection

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Copyright # 2020 Elsevier Inc. All rights reserved.

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Metabolite identification and biomarker validation

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Conclusions

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Introduction Liquid chromatography (LC), in both untargeted and targeted forms, is probably the most widely used format for metabolic phenotyping (metabolomic/metabonomic profiling) as applied to the task of obtaining metabolic information on the composition of biological fluids/ tissues. The aim of such studies is to obtain as comprehensive a set of data as possible in order to understand the biological systems under study, and also discover biomarkers that provide novel insights into mechanism of physiological change, disease, toxicity, etc.1,2 For this task, direct infusion mass spectrometry (DIMS) nuclear magnetic resonance (NMR, especially 1 H NMR) spectroscopy and chromatography have been widely deployed. In the case of analysis linked to a separation technique, a large number of different modes of analysis, currently including liquid chromatography (LC)-based methods, gas chromatography (GC), capillary electrophoresis (CE) and, to some extent, supercritical fluid chromatography (SFC), are used. However, the rise to dominance of LC-MS in this field, from their beginnings in the early 2000s (e.g., see refs3,4) has been rapid. The combination of the ease of use of LC, deriving from its compatibility with the (generally) involatile analytes present aqueous biological samples, such as urine or blood plasma/serum etc., allows analysis with minimal sample preparation. Then, the relatively easy hyphenation of LC to mass spectrometry provides sensitive detection and the potential for identification. These attractive features of LC-MS have driven the rapid uptake of the technique in many areas of bioanalysis, and this includes metabolomics/metabonomics.5–7 Increasingly, the adoption of ultra high performance (UHPLC or UPLC) has displaced the

References

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Further reading

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original conventional HPLC methods based on separations employing 3 to 5 μm particles. Thus, the improved performance resulting from separations on the sub-2-μm particles used in UHPLC8,9 provided such clear and unequivocal advantages for complex mixture analysis compared to HPLC that the adoption of the former technique represents an obvious choice. In addition to UHPLC, there have continued to be innovations in the capillary and miniaturized LC systems that have been applied to metabotyping. While all of these positive features make the use of LC-MS very attractive for metabonomic studies, there is no doubt that its use in this application remains problematic in certain areas. For example, sample-dependent ion suppression/ enhancement still represents a limitation, while the identity of many of the peaks detected in biological samples is still unknown pending further detailed studies. Factors such as ion suppression can, of course, be minimized to some extent by increasing the amount of “chromatographic space” used to resolve analytes from each other via longer analysis times. However, increased analysis time per sample inevitably leads to a reduction in throughput, which can severely impact on the delivery of data to informaticians searching for biomarkers in the metabolic “soup.” Clearly, there will always some degree of conflict between the requirements for both the most comprehensive metabolite profiles that can reasonably be obtained and the shortest possible analysis times, and some compromise is inevitable. One popular approach for maximizing throughput is to remove the separation step entirely and simply use DIMS. However, in our experience, with the exception of very simple matrices, there are considerable disadvantages in such “zero separation” systems, as they clearly

Chromatographic methods for metabolite profiling

are guaranteed to maximize matrix effects such as ion suppression/enhancement. Even in samples/matrices where these effects are minimal, distinguishing between isobaric species and structural isomers still remains as a problem for DIMS. Currently, obtaining the most comprehensive metabolite profiles requires, in our view, chromatographic separations prior to MS. However, the desire to minimize analysis time still remains a priority and has indeed been a major benefit of the more widespread use of ultra (high) performance LC (UHPLC). The narrow chromatographic peaks resulting from UHPLC, generally between 2 and 5 s in width at the base, provide much greater peak capacity than the equivalent HPLC separations for the same analysis times. This means that even shorter, highthroughput, UHPLC separations of ca. 2–3 min can potentially achieve metabolome coverage equivalent to a much longer (10–15 min) HPLC analysis (e.g., 9–11) enabling 20 to 30 samples/h to be processed. Alternatively, and as seen in the very earliest demonstrations of the technique for metabolic phenotyping,8 the greater efficiency of UHPLC combined with separations occurring over ca. 10–15 min can be used to obtain increased coverage compared to the same analysis time for HPLC. Such UPLC-MS systems enable reasonable throughput combined with excellent chromatographic resolution and a range of methods and protocols for various different sample types (e.g., serum/plasma, urine, tissue extracts, etc.) have been proposed.12–16 Where very detailed, in depth, investigations of the metabolic phenotypes of a small set of samples are needed, in order to more fully characterize the metabolome, and maximum peak resolution is required, then longer separations, or very-high-resolution LC systems employing multidimensional or capillary LC can be deployed. With respect to the modes of chromatography used to effect these separations, and the stationary phases employed in metabolomic/metabonomic applications, it is still the case that systems based on reversed-phase (RP) and hydrophilic interaction (HILIC) chromatography

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are the most popular. RP-based methods are used for “medium” to nonpolar metabolites, while HILIC is employed for the more polar metabolites that are not well retained in RP systems. Although HILIC is very popular for polar compounds, it is sometimes necessary to use alternative methods such as ion-pair (IPLC), ionexchange (IEC), or aqueous normal-phase chromatography (ANPC) to accommodate particularly troublesome analytes. Here an overview of the current practice of LC-MS for metabolic phenotyping is provided that considers the various options presently available, as well as considerations such as to how separations can be optimized for the matrix to be analyzed. Thus, as described later, methods optimized for urine, with its highly polar metabolite content, are unlikely to be suitable for a lipid-rich matrix such as serum/ plasma or tissue, etc.

Chromatographic methods for metabolite profiling Reversed-phase LC separations As indicated earlier, RPLC is currently the most widely used separation mode for metabolomic/metabonomic profiling, stemming partly from the fact that it is well suited to the analysis of aqueous samples (e.g., urine, bile, and protein-precipitated plasma/serum). Typical separations using RP-HPLC-based methods of analysis for metabotyping use C-18 bonded stationary phases with 3 to 5-μm-sized particles in 2.1 to 4.6 mm i.d. columns of 5 to 15 cm in length and elution via gradient chromatography. Generally, analysis times of 10–30 min. are used and for samples such as e.g., urine typical conditions for analysis would employ a 2.1 mm i.d.
10 cm column containing a C-18 bonded stationary phase (e.g., 3.5 μm, C18bonded Symmetry) at a temperature of 40°C, and flow rates of ca. 600 μL/min, e.g.4,15,17 The equivalent UHPLC methods use sub 2-μm stationary phases packed in 1 or 2.1 mm i.d. columns with

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lengths of 5–15cm, e.g.8,9,12–14 While the smaller particle sizes used in UHPLC require much higher operating pressures than those used in HPLC the other conditions, such as mobile phases, gradient elution profiles, and flow rates are generally similar. For both UHPLC and HPLC, solvent gradients range from 5 to 30min, depending on the method, and include column washing and re-equilibration steps totaling 2 to 5 min at the end of the run. A typical method for urine might employ 0.1% aqueous formic acid as solvent A and acetonitrile (also containing 0.1% formic acid) as solvent B, with a starting composition of 100% Solvent A, held for 0.5 min at the start of the analysis before increasing, in a linear gradient, to 20% solvent B at 4 min and then continuing to 95% solvent B at 8 min. Typically, this solvent composition

would be held for a further minute to wash strongly retained contaminants from the column before returning to 100% solvent A for a re-equilibration period prior to injection of the next sample. For urine, plasma/serum, and tissue extracts various protocols using RPLC-MS analysis have been described, e.g.12–14 Often only minimal sample preparation is needed for human urine, (generally only centrifugation and dilution13,16), but our experience is that rodent urine can benefit from treatment with methanol to precipitate protein, followed by centrifugation. Acetonitrile should be avoided for this precipitation step as phase separation can occur with some samples (presumably due to high salt content). A typical gradient RP-UPLC separation of urine, and a urine extract, is shown in Fig. 1. For proteinaceous samples such as

FIG. 1 UHPLC-TOFMS (+ESI) of rodent urine on an Acquity BEH C18 2.1
100 mm 1.7 μM column (maintained at 40°C) using a solvent gradient of 0.1% aqueous formic acid versus acetonitrile (containing 0.1% formic acid). The upper trace shows the metabolites recovered from a “blood spot” paper and the lower trace is of the unextracted urine. Details can be found in Michopoulos F, Theodoridis G, Smith CJ, et al. Metabolite profiles from dried biofluids spots for metabonomics studies using UPLC combined with oaToF-MS. J Proteome Res 2010;9:3328-34.

Chromatographic methods for metabolite profiling

such as plasma/serum,12,18,19 it is clearly necessary to remove proteins before chromatographic analysis is attempted and, similarly, the procedures designed to extract metabolites from semisolid samples such as tissues must also denature and eliminate proteins20–22 in order to avoid damaging the column. Clearly, as already indicated, the quantities and proportions of polar and nonpolar metabolites are very different for urine compared to blood-derived samples, and indeed tissues vary considerably in their metabolite composition. As a result, there is no “universal” metabolic profiling system and bespoke optimization of the solvent gradient for different classes of sample is essential to maximize metabolome coverage. Consequently, the gradient elution programs employed for the analysis of lipid-rich samples often begin with a greater proportion of organic modifier than those used for predominantly polar metabolite-containing samples such as urine. The strong retention of lipids on RPLC columns, in addition to starting the gradients with higher proportions of organic modifier, generally requires more eluotropic solvent for analyte elution and column wash steps. Methanol and, for lipidomic applications, acetonitrile and isopropanol represent suitable solvents with which to analyze nonpolar analytes such as lipids. A representative UPLC analysis of a sample such as protein precipitated plasma or serum would use a 1.7 μm, 2.1
100 mm, Acuity BEH column, or equivalent, at 50°C and a solvent flow rate of at 0.4 mL/min. The gradient conditions would be formed from 0.1% aqueous formic acid as solvent A and methanol (with 0.1% formic acid v/v) as solvent B with, with an initial composition of 95% A for 0.5 min. This initial step is then be followed by a linear gradient to 40% B at 2.5 min, then 70% B at 4.5 min before rising to 100% B at 10min (held for 2 min, then returning to 95% A for 2.5 min). Clearly, in RPLC, retention during gradient elution is generally controlled by changing mobile phase composition, but it is also

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possible to use temperature to achieve analyte elution. While rarely used, high-temperature (HT)LC has been used successfully for profiling urine, which as indicated, is composed largely of polar metabolites. Performing separations at high temperature reduces the viscosity of the solvent, facilitating the use of high flow, and also increases the eluotropic strength of the solvent, reducing the amount of organic modifiers required. Examples of the use of HT-UPLC for metabotyping include the isothermal profiling of urine at 90°C23 with a solvent gradient employed for elution, while a second example involved the elimination of the organic solvent entirely and elution solely via a thermal gradient.24 Given the importance of lipids as a class of metabolites, the development of the subfield of lipidomics has resulted in the development of well-optimized, “lipid-friendly” gradient RPLC methods (e.g.,25–27) such as acetonitrile-aqueous ammonium formate (10 mM) 2:3 v/v versus acetonitrile-isopropanol 1:9 v/v plus 10 mM ammonium formate,26,27 or 10 mM ammonium acetate versus acetonitrile-isopropanol 5:2 v/v plus 10 mM ammonium formate.28 A flow rate of 0.4 mL min, at 55°C, and an Acquity HSS T3 column (2.1 mm i.d.
100 mm) was used for the separation, with elution achieved via a series of linear gradients rising first from 100% of the aqueous solvent to 40% of the organic-rich mobile phase over 3 min, then to 100% organic solvent over the next 10 min (held for 2 min) before returning to the initial solvent composition for 3 min before beginning the analysis of the next sample.28 (see Fig. 2 for an example). As indicated, urine does not usually present a major challenge for metabolite profiling by LC-based methods, with sample preparation often merely centrifugation, to remove column blocking particulates, and subsequent dilution. However, with protein-rich samples, the proteins must be removed by techniques such as solvent precipitation, effected by means of

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PC, SM, PE & DG 5.03 Positive ESI ion mode LPC & LPE

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Base peak ion (BPI) chromatogram of a human plasma extract. Retention time windows for the lipid classes are seen in the UPLC-TOF MS chromatogram. Analysis was performed on a 1.8-μm particle 100
2.1 mm i.d. Waters Acquity HSS T3 column at 55°C using gradient of acetonitrile:water (40:60, v/v) 10 mM ammonium acetate (eluent A) versus acetonitrile: isopropanol (10:90, v/v) 10 mM ammonium acetate (eluent B). Elution started with 60% A; for 10 min the gradient was ramped in a linear fashion to 100% B, where it was held for 2 min. The flow rate was 0.4 mL/min and the injection volume was 10 μL. MS detection was performed on SYNAPT HDMS, in electrospray in both positive (top pane) and negative (bottom pane) ionization. Reprinted with permission from Castro-Perez JM, Kamphorst J, DeGroot J, et al. Comprehensive LC-MSE lipidomic analysis using a shotgun approach and its application to biomarker detection and identification in osteoarthritis patients. J Proteome Res 2010;9:2377-89. The American Chemical Society.

Chromatographic methods for metabolite profiling

adding 3 or 4 volumes of methanol or acetonitrile to samples such as plasma/serum and removing the precipitate via centrifugation. Extraction techniques such as solid-phase extraction (SPE) are also effective and can be used for obtaining protein-free samples,29 while turbulent flow chromatography (TFC) provides an online means of protein removal.30 In TFC samples, such as plasma, first pass through a short column, at high flow rates, containing particles of 25–50 μm in size. This column retains small molecules while larger ones, such as proteins, pass through unretained and are diverted to waste. The analytes retained on the TFC column are then eluted onto a UHPLC column via a solvent gradient for analysis without compromising chromatographic performance. A preliminary investigation demonstrated that the methodology could be used for metabotyping,30 while also revealing significant differences between the profiles seen using TFC compared, e.g., to those obtained via methanol precipitation. These differences included a tenfold reduction in the quantities of phospholipids detected (presumably because these lipids are normally transported on proteins). Other sample preparation techniques for e.g., blood, urine, or bile, have made use of the “dried blood spot” method, where the sample is collected onto a paper matrix for subsequent solvent extraction, and this also effectively eliminates protein. Such blood spots have been the subject of a number of exploratory metabonomic studies but, despite advantages of convenience of sample collection/extraction and ease of storage, need to be used with care because of contamination and stability issues.31,32

Hydrophilic interaction liquid chromatography (HILIC) The more polar/ionizable metabolites present in samples, e.g., sugars, amines, amino acids, and organic acids, are often poorly retained by RPLC column packings, eluting at, or near, the void

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volume of the column. The analysis of these polar analytes by LC-MS-based approaches currently revolves around the technique known as HILIC, with the separations employing mobile phases containing a high proportion of organic solvents. While water is used in HILIC to modify retention, it should constitute less than 50% of the total mobile phase composition. A consequence of the high organic content of HILIC mobile phases is that ionization in the ESI source is more efficient, providing excellent sensitivity, exceeding that of RP methods. The use of HILIC for the profiling polar metabolites is now widespread as shown by numerous applications, e.g.,33–38 and HILIC, when used in combination with RPLC in untargeted metabotyping, currently provides the most pragmatic means of maximizing metabolome coverage by LC-based methods. Sample preparation for HILIC does not need to be extensive and, e.g., urine samples can be injected following dilution with water (1:9 v/v), centrifugation (13,000 g), and then mixing with 1:9 (v/v) with acetonitrile. Alternatively, a technique such as SPE can be used to separate polar and nonpolar metabolites to enable separate analysis. Such an approach was used in the analysis of rat urine33 with the fraction of the sample eluted unretained from the cartridge analyzed using HPLC on a ZIC-HILIC column (100
2.1 mm, 3.5 mm) and the SPE-extracted material by RPLC. Typical examples of HILICbased metabotyping in the HPLC format include separations performed with the ZIC-HILIC 37 or Aphera NH2 polymer phases (150
2 mm, 5 μm)38 and, in the case of UHPLC methods using HILIC, on e.g., the Acquity BEH HILIC (2.1
50 mm, 1.7 μm) material, would include profiling animal disease models,34 toxicological investigations,39 or cancer biomarker discovery.40 In the toxicological study quoted, the endogenous metabolites excreted via the urine of rats administered with the hepatotoxin galactosamine were investigated by HILIC-based UPLC on a 2.1
100 mm, 1.7 μm Acquity BEH HILIC column. A solvent gradient employing

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0.1% (v/v) formic acid and 10 mM ammonium acetate in acetonitrile-water 95:5 v/v formed the initial organic-rich solvent, with 0.1% aqueous formic acid and 10 mM ammonium acetate in acetonitrile-water 50:50 v/v as the aqueous solvent. A flow rate of 0.4 mL/min was used, combined with a column temperature of 40°C. The starting solvent composition of 99.0% organic and 1% aqueous solvent was held for 1.0 min and then a linear gradient was used to increase proportion of the aqueous solvent over the next 11.0 min. After completion, the column was re-equilibrated for 4 min prior to analysis of the next sample. The applications of HILIC are now numerous, in both HPLC and UHPLC-based modes (reviewed in Spagou et al.33 and Tan et al.36) and HILIC can now be considered an established and essential part of the metabolic phenotyping toolbox. A comparison of the results obtained for rodent urine using both RPLC and HILIC-based UHPLC-MS is shown in Fig. 3.

Other approaches to the profiling of polar and ionic metabolites Although HILIC has been widely used for polar compounds as discussed previously, it does not yet provide a complete answer to the analysis of polar, ionic compounds and it is arguable that techniques such ion exchange chromatography (IEC) should provide another approach to the separation of such metabolites. In a recent example, the use of anion exchange chromatography couple to MS was used for both targeted and untargeted metabolic profiling of cancer cells for analytes such as organic acids, sugars, sugar phosphates, and nucleotides in 25 min.41 The results for studies on drugsensitive vs resistant SW480 cancer cells found different metabotypes for the different cell lines. Another alternative to both HILIC and IEC is ion-pair liquid chromatography (IPLC), but it has to be recognized that this methodology has

long-term consequences for MS-based applications as a result of the contamination of the instrument by the ion pair reagent. This contamination can prove extremely resistant to cleaning, and it may therefore be necessary to dedicate a mass spectrometer solely to this type of analysis. However, where the profiling of polar acidic metabolites is required, the disadvantages of IPLC may be compensated for by the utility of the method. Though many examples of the use of the IPLC technique involve targeted analysis, an example of this type of method, with detection via an Orbitrap mass spectrometer that was used for the sensitive and specific detection of both unknown and known metabolites used RP-IPLC on a Synergi Hydro-RP 2.5 μm C18 column (100 mm
2 mm i.d.) at a flow rate of 200 μL/min.42 Studies on a genetically engineered yeast showed that a gene of unknown function (YKL215C) was oxoprolinase as a result of changes in the metabolome.42 This method used tributylamine (TBA) (10 mM) (and 15 mM acetic acid) as the IP reagent and a water-methanol gradient starting at 97:3 v/v water/methanol for 2.5 min, rising to 20% methanol at 5 min, followed at 7.5 min by an increase to 55% methanol at 13 min, with a further increase from 15.5 min to 95% methanol at 18.5 min. After a further 0.5 min at 95% methanol, the composition was returned to the initial gradient starting conditions. TBA was also the IP-reagent employed in the profiling of metabolites in cell extracts of Methylobacterium extorquens (using a nanoscale LC-MS system43). Another amine, hexylamine, has been used for the IPLC44 to profile bacterial cell extracts obtained from L. plantarum, E. coli, and B. subtilis. IPLC. A further example of IPLCMS includes its use for the analysis and profiling of samples from a model of arthritis in the muse, where itaconic acid was identified as a potential biomarker of the disease.45 While requiring special measures for its use, such as the dedication of an instrument, IPLC-

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FIG. 3 Profiles from RP (upper) and HILIC (lower) -ToF-MS analysis of urine, obtained from Zucker lean and fat rats, for the ion m/z160 showing the better retention of these polar substances on the HILIC phase. The PCA plots to the right of the chromatograms show a clear separation (based on the whole metabolite profiles) between the two groups regardless of which method of profiling was used (but based on different markers). Red ¼ lean; black ¼ fat.

MS appears to provide a robust and reliable alternative approach to the profiling of polar acidic metabolites and, if no more practicable alternative emerges may well, by default, become the method of choice in this area. Possible contenders for the analysis of polar compounds include porous graphitic carbon

(but see ref.45) and aqueous normal-phase chromatography (ANPC).46 The latter technique has been used to separate polar metabolites in human urine and plant extracts. Chromatography was performed on a Cogent Diamond Hydride column (100
2.1 mm, 4 μm) using gradient elution with 15.9 mM ammonium

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formate or 13.0 mM ammonium acetate and acetonitrile-water (containing 15.9 mM ammonium formate or 13.0 mM ammonium acetate) 90:10 v/v at 0.4 mL/min as mobile phases.47 A more recent study has compared the coverage of this approach with both RPLC and HILIC as applied to human urine.48

Miniaturized LC systems While standard HPLC-MS configurations are clearly capable of delivering excellent results in metabolic phenotyping situations, sensitivity may become an important consideration where sample availability is limited (or where many analyses are to be performed on a relatively small sample). While MS sensitivity is generally increasing with each new generation of mass spectrometer, another useful technical option that can be explored, providing benefits in minimizing sample consumption and improved sensitivity, as well as reducing solvent use, etc. is obtained from miniaturized LC column formats. These include both microbore columns, having internal diameters from 0.5 to 1.0 mm and capillary columns of varying lengths. In the case of microbore LC, reducing the column diameter from 2.1 to 1 mm can result in an improvement in sensitivity ranging from 2 to 3.5-fold for the same mass of sample and enable a ca. 5 fold reduction in sample and mobilephase consumption. These advantages have been illustrated by the use of 100 mm by 1 mm i.d. column to obtain profile RPLC-MS profiles of rat urine obtained during an investigation of pravastatin toxicity in the rat.49 A recent study directly compared the results obtained for the analysis of rat urine by RPLC using both 1 mm and 2.1 mm i.d. columns, again demonstrating the value of the microbore format in terms of sample and solvent use reduction.10 In addition to employing reduced column internal diameters, a rapid microbore metabolic profiling method was developed with a short RPLC

gradient performed on short 1 mm i.d. column. This confirmed the utility of the highthroughput screening/analysis of urine samples10 and has recently been supplemented with a similar HILIC method, which also incorporated ion mobility spectrometry to aid both resolution of analytes and identification (see later).11 The successful implementation of microbore LC methods does however require some reoptimization of the UPLC-MS system with respect to, e.g., the internal diameters of the tubing, which must be reduced to avoid unwanted extra-column peak broadening (e.g., see10). With respect to capillary-based (cap-LC) separations, to date uptake of these methods has been limited; however, a number of metabolic profiling studies have demonstrated the potential of these methods.50–52 The Cap-LC profiling of extracts of Arabidopsis thaliana50 on various 2 mm i.d., monolithic columns containing C18bonded silica, ranging in length from 30 to 90 cm, with MS detection provides an early example. Another is provided by an impressive, high-resolution study based on RP-cap-LC on a 200-cm-long, 50-μm i.d., fused silica capillary containing 3-μm porous C18-bonded particles. When applied to profiling the contents of extracts of Shewanella oneidensis51 via RP gradient LC (at 20 Kpsi) over 5000 metabolites were detected, although that analysis took over 2000 min to perform. While such long capillaries can result in the detection of large numbers of peaks, there are still benefits resulting from the use of shorter columns, as seen for the metabolic profiling of urine rodent urine.52 Thus, when applied to samples obtained from several strains of Zucker rat (a model for Type II diabetes) capillary gradient RPLC capillary, using a 10-cm-long 320-μ m i.d. column containing a 3.5-μm C18-bonded stationary phase52 provided a more comprehensive profile was obtained than seen with HPLCMS using same stationary phase and column length. The use of cap-LC analysis also provided

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ca. double the features detected by HPLC with the higher number of ions detected presumably the result of reduced ion suppression compared to the conventional HLPC analysis. The recent introduction of capillary-scale microfluidic systems containing the 1.7-μm Acquity BEH stationary phase in a 10-cm-long, 300-μmi.d.separationschannel53 providesanother means performing cap-LC separations for metabolic phenotyping. This is illustrated in Fig. 4, which shows a separation performed on a 1-μL samples of rat urine via a 5 to 95% RP-gradient formed from acetonitrile and 0.1% aqueous formic acid over 10 min at a flow rate of 12 μL/min. A recent review of nanoscale LC-ESI that discusses the potential of this approach for improving metabolome coverage has recently been published.54

Multicolumn and multidimensional separations As comprehensive metabolome coverage generally requires the use of more than one type of chromatographic system combining, e.g., HILIC and RP-based analysis in a multicolumn, or multidimensional separation is potentially an attractive option for untargeted metabolic profiling. As such, it enables the analysis of samples for polar, “midpolar,” and nonpolar metabolites in a single analysis and avoids the need to 1.18 1.90

remove analytes that are duplicated when using separate runs where they are detected in both analyses. Examples of column switching,55–57 coupledcolumns, 58–64 and simultaneous parallel separations65 have all been described for metabolic phenotyping applications. Multidimensional LC for metabolome analysis using column switching-based methods have employed HILIC55,56 or graphitized carbon57 to separate polar metabolites in the first dimension, with apolar compounds trapped and then separated on a C-18-bonded phase using RP chromatography. One of the HILIC-RPLC combinations resulted in a combined analysis time for both polar and apolar metabolites of ca. 50 min/sample.55 When applied to the metabolic phenotyping of urine samples from lung cancer patients ca. 840 metabolites were seen, with ca. 580 metabolites present in the HILIC analysis and the rest (ca. 260 metabolites) detected in the second, RP, dimension.56 Although the HILIC-RP combination is an obvious choice for multidimensional metabolomics it is by no means the only option and combining the highly retentive graphitized porous carbon (GPC) phase with conventional C18 material provides one such alternative 2-dimensional option.57 This approach, using 2.1
150 mm GPC HPLC and 2.1 mm
100 mm U(H)PLC columns (packed with 3- and 1.8-μm materials,

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respectively), was used for a combined untargeted and targeted analysis of human plasma collected from cancer patients. In this method, the metabolites trapped on the C18 phase were eluted first, and were followed by the more polar metabolites that had been retained on the GPC phase. The analysis of both polar and nonpolar phenols present in wine has been demonstrated using a combination of Poroshell C18 RP and ZIC HILIC columns linked in series with polar compounds, unretained on the RP phase, collected on the HILIC phase.58 Retention on the HILIC phase was accomplished by the expedient of modifying the eluent from the C18 column with ACN (from a second pump) via a T-piece fitted in between the columns. This setup resulted in a combined analysis time for both nonpolar and polar metabolites in ca. 27 min.58 Similarly, the use of serially coupled UP-RPLC and ZICHILIC was used in the separation of both TCA cycle intermediates and bile acids, as well as being applied to the profiling of a beer extract.59 The overall analysis time for this combination was ca. 37 min. In addition to these examples, HILIC and RP columns have been coupled in series for the metabolic phenotyping of samples such as mouse serum,60 the analysis of plasma collected from apolipoprotein E-deficient mice,61 or for the determination of the human urinary metabolome have also been described.62 As an alternative to serially coupling columns, a system for performing these separations simultaneously on RP and HILIC columns connected in parallel has been devised. This approach has been successfully used in the analysis selected central carbon metabolites,63 requiring only 15 min., for the combined analysis to be performed. While these multidimensional approaches are technically very interesting, there has, to date, been little uptake of these methods by the wider metabolic phenotyping community. Further descriptions of methods for multidimensional LC-MS, including offline approaches, have recently been reviewed.64

Ion mobility spectrometry combined with LC-MS Ion mobility (IM), which can be considered perhaps as a form of gas-phase electrophoresis, enables the very rapid separation of charged molecules, based on their size and shape, in a low-pressure gas under the influence of an electric field. This mechanism, as well as providing a separation, can also enable a “collision cross section” (CCS) for a molecule to be determined, which, as a specific property of that compound, can aid the identification of unknown metabolites. Thus, the increased presence of ion mobility-enabled MS instruments offers a number of possibilities in metabolome characterization by providing both an orthogonal dimension of separation, allowing the resolution of coeluting metabolites with different CCS values prior to MS detection. This can result in improved mass-spectral data increasing the quality of the spectra and aiding metabolite characterization/identification. An early example of the potential of RP-LC-IMS-MS is seen in an application to rat urine,65 and this was followed by RP-UHPLC-IMS-MS to investigate changes in the metabolites present after exercise in human saliva.66 In the latter study, δ-valerolactam was identified via the use of both retention time, IM drift time, and MS as a potential exercise marker. More recently, the effects of column length and analysis time on the number of peaks detected for human urine analyzed using RP-UPLC-IMS-MS have been studied, showing the advantages of the addition of IM in terms of the significantly increased number of features detected and improvements in spectra (Fig. 5).67 In addition, HILIC-IMS-MS employing a rapid gradient for metabolic phenotyping has been reported.11 A key development enabling the more rapid implementation of the CCS values that can be obtained is this work is the emergence of databases of CCS data, based on experimental determination of this property from authentic

Detection

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FIG. 5 Extracted ion chromatogram for urinary tryptophan using a 2.1
30 mm column and a gradient duration of 3 min. The upper chromatogram illustrates the ion mobility-enabled data, while the lower chromatogram depicts the DIA analysis data. The upper MS data show a much “cleaner” spectrum for tryptophan. From Rainville PD, Wilson ID, Nicholson JK, et al. Ion mobility spectrometry combined with ultra performance liquid chromatography/mass spectrometry for metabolic phenotyping of urine: effects of column length, gradient duration and ion mobility spectrometry on metabolite detection. Anal Chim Acta 2017;982:1–8.

standards, and the ability to accurately calculate such values based on, e.g., machine learning.68–71 These developments mean that the potential of IMS as an adjunct to LC-MS methods is now widely accepted as a major advance for both metabolomics and lipidomics,72–75 making us confident that it will rapidly gain significant traction in these fields over the next few years.

Detection A limitation of MS is that compound detection depends on ionization, and the efficiency of this property is compound specific and may also depend on matrix-specific factors that reduce or promote ionization (so called matrix

effects). Indeed ionization efficiency can differ significantly even among structurally quite similar analytes. At the present moment, the most popular method for the MS of metabolites is ionization using electrospray (ESI), often in combination with time-of-flight mass spectrometry (TOF-MS). ESI is required to be employed in both positive and negative modes in order to detect both positively and negatively charged metabolites and maximize metabolome coverage. In addition to ESI ionization techniques such as APCI (atmospheric pressure chemical ionization), also in positive and negative modes, can be used to advantage with nonpolar metabolite analysis. However, to date, APCI has not been widely used in metabotyping. As well as TOF-MS, linear ion trap and hybrid IT-TOF instruments76 or the QTRAP15,76 have been used

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for metabolomic applications. In cases where very high mass resolution is desired, this can be provided via the use of Fourier transform ion cyclotron (FT-ICR) or “Orbitrap”-MS enabling high mass accuracy to be obtained. Such data can provide more accurate assessment of atomic composition and this in turn can aid metabolite identification. However, another difficulty associated with MS is that different mass spectrometers do not necessarily provide the same responses (both in terms of signal intensity and fragmentation). This compounds the problem of comparing results obtained from different laboratories based on LC-MS analysis with different LC systems and methods and mass spectrometers (even if nominally the same chromatography is used). This problem has been exemplified in an investigation that employed both QTOF and QTRAP mass spectrometers to simultaneously detect the metabolites eluting from the same column with the same eluent equally divided to each MS.77 This study revealed that while the data from for the urinary metabolites present in samples from control and drug-dosed animals and detected by these instruments could readily differentiate the two classes using principal components analysis (PCA), the compounds responsible for this separation were not necessarily the same but were dependent on the MS used.77

Quality control, data analysis, and biomarker detection In order to properly compare metabolic profiles between samples and study groups, the generation of repeatable results is central to enabling subsequent data analysis. Consequently, retention time, peak shape, mass accuracy, and analyte response must ideally be kept stable for the term of the analytical run (and ideally between runs). In practice, this may require consistent instrument performance to be routinely maintained for extended periods, often in excess of 24 h. While modern UPLC methods

can demonstrate enviable performance, the nature of the samples (minimally processed biological matrices) can be challenging, and the type of sample matrix, the amount of sample applied, and previous exposure to samples can adversely affect column lifetime and performance. Another factor that needs to be taken into account is the fact that achieving retention time stability may require a number of injections of sample matrix to stabilize (equilibrate, or “condition”) the column before sample analysis can commence.12–16,18,19 In practice we, and others, have observed that the number of sample injections required to condition the system is often matrix dependent, such that samples such as urine in general require fewer injections than serum/plasma in order to achieve retention time stability. After a suitable number of conditioning matrix samples have been injected, retention is generally stable but, with time, contamination of the ion source in the MS builds up and an overall decline in sensitivity occurs and, at some point, cleaning is required. In practice, and unsurprisingly, the lifetime of the column depends on the type of sample being profiled, with e.g., the analysis of a matrix such as urine associated with longer column lifetimes (often thousands of injections) than serum/plasma before replacement is required. Obviously any analytical variability that is caused by alterations in the properties of the profiling system is a major source of problems for successful metabolic phenotyping. As indicated, the acquisition of LC-MS data can suffer from variability due to column degradation, leading to, e.g., gradual loss of performance via changes in peak shape/ retention time etc., or due to increasing column pressure leading to overpressure failure all the way up to catastrophic changes such as column blocking. In the case of changes in mass spectrometer performance, these are most often the result of loss of signal intensity but, less often, in mass accuracy. These changes in system performance should be monitored and appropriate corrective action taken to avoid problems in subsequent data analysis.

Quality control, data analysis, and biomarker detection

A (now) common approach to monitoring the performance of untargeted methods is the use of so-called quality control (QC) samples.12–16,18,19,77 Suitable QCs are easily prepared by pooling aliquots of the study samples to provide a bulk sample that effectively a “mean” sample that is representative of the “population” being analyzed. Where it is not possible to prepare a QC from the study samples, it may be possible to obtain a bulk sample of the same matrix of, e.g., plasma/ serum from another source (e.g., a blood bank or commercial supplier). In addition, it may be of value to prepare a “phenotypic QC” where samples from the individual study groups (e.g., test and control) are combined to ensure that group-specific “biomarkers” are not lost by being “diluted out” in the bulk “population QC.” Once prepared, the bulk QCs are then analyzed at regular intervals (e.g., every 5 or 10 injections) through the run, with the phenotypic QCs perhaps analyzed at a lesser frequency. When analyzed, the data for the bulk QCs, in a perfect analysis, should be identical, but of course no method is perfect. However, by examining the data from the QCs, trends in the results indicating variability can be determined (e.g., time-dependent runorder effects) and the overall quality of the resulting metabolic phenotypes can be assessed. Clearly, a large amount of LC-MS data will be acquired for each of the samples analyzed in a metabolic phenotyping experiment based on retention time, signal intensity, and the m/z value (over the range  70 to 1200 amu) for each of the “features” detected, which can amount to many thousands of peaks. The data for these features are generally in the form of full scan mass spectra, which include associated adducts, isotope peaks, and general systematic noise. Processing the large amounts of data that comprise these files in order to be able to obtain useful information requires sophisticated and specialized software. As a result, many different types of software package, including manufacturer-specific, internet-based freeware, and in-house programs, are available to

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accomplish the required data reduction. These have been developed to enable the removal of instrument noise, baseline correction, peak picking, deisotoping, adduct removal, peak integration, peak alignment, centering, normalization, etc., to be undertaken thereby allowing the metabolite peaks to be revealed. Based on this data processing, a peak table can be constructed listing the samples, the ions for the various peaks corresponding to the metabolites, peak intensities, and the 3-dimensional retention time/mass/intensity information data for each feature reduced to two dimensions by combining the mass and retention time data into a single property. Further data analysis can then be performed via either the software provided by the manufacturer or, if the data are converted into a format such as netCDF or mzXML, an open source program, e.g., MZmine,78 MetAlign,3,79 and XCMS,80 which are freely available. The main advantage of the open source software is that, as it is possible to vary parameters such as peak width thereby accommodating particularly broad or narrow peaks, etc., the user can “customize” it for their own particular application. Generally metabolic phenotyping data are most commonly examined, in the first instance, using multivariate analysis (MVA) with PCA as a means of highlighting any differences between test and control groups. This form of “unsupervised” statistical analysis can then be followed by supervised methods, such as OPLS-DA (orthogonal projection to latent structure-discriminant analysis). Based on the results from the MVA univariate statistical analysis, combined with manual examination, of the data can increase confidence that these features truly represent discriminating factors for the condition under investigation. Next to metabolite identification (see later), data analysis remains a very time-consuming step in any metabolic phenotyping experiment. However, great care must be taken over this particular aspect of the work if the investigator(s) are to be rewarded by the possibility of discovering genuine associations of metabolites with the condition

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being investigated (the use of MS-based data analysis for metabolic phenotyping is reviewed in81).

Metabolite identification and biomarker validation Once the data have been deemed acceptable and MVA and features that represent metabolites that have the potential to be biomarkers have been identified via the data analysis, then unambiguous structural identification must be performed. As a result of the variety of metabolite classes and structures that can be present in biological samples, this type of “natural product identification” may require much time- and resource-consuming effort. This problem still represents one of the major bottlenecks for biomarker discovery using LC-MS-based methods. Clearly much can be gleaned from the mass spectral (and ion mobility-derived CCS) data, including accurate mass data to provide elemental composition and fragmentation data. Possibly the metabolites of interest can be identified by comparison to those present in, laboriously constructed, in-house databases. If not, the next step would be to use the available data in combination with searching the various metabolite databases available online (e.g., METLIN,82 lipidmaps,83 and the Human Metabolome Database (HMDB)84,85 and Chemspider,86 etc.). Such searches must, however, be used with caution, as a “hit” is not a positive identification, but only an indication of a possible one (and many such “identifications” can easily be excluded on the basis of biological implausibility). These databases can, however, help to narrow the potential candidates to a more manageable number, which can be further reduced by comparison with the information in hand, such as the chromatographic properties (which can give a clue as to polarity via retention time), MS fragmentation, molecular formulae, and CCS data. Another important factor to take into account is the likely involvement of any of the highlighted metabolites in the condition being

investigated. Eventually, however, when the list of possible structures has been narrowed down to a few candidates, confirmation requires either purchase of the compound (if available) and its comparison with the “unknown,” or its isolation for further characterization by, e.g., NMR spectroscopy. After positive identification of the putative biomarkers has been performed, the metabolic phenotyping study can be used to generate new hypotheses as to why these molecules biomark the condition under study. A first step is, however, to confirm the results of the untargeted analysis with a validated targeted method, possibly providing more information on, e.g., the pathways identified in the untargeted work. The reanalysis of the samples used to find the putative biomarkers with such a validated, specific, and quantitative method should confirm that the changes are a genuine result, and not an artifact resulting from, e.g., selective matrix effects. There are many example of metabolic phenotyping investigations following this type of approach as illustrated by, e.g., the metabolites 5-oxoproline and ophthalmic acid, which have been suggested as being biomarkers of glutathione depletion as a result of exposing both animals and hepatocytes to acetaminophen (paracetamol). The use of specific, quantitative, LC–MS assays for both of these compounds87,88 confirmed that they represented biomarkers of glutathione depletion due to reactive metabolite formation, and allowed a “systems biology” approach to be developed to model and explain this.89 As another example, in the INTERMAP (INTERnational collaborative study of MAcronutrients, micronutrients, and blood Pressure, or INTERMAP), epidemiological study blood pressure was apparently correlated with a number of urinary metabolites.90 Their quantification using UPLC-MS provided concentration data for phenylacetylglutamine, 4-cresyl sulfate, and hippurate enabling reference ranges for their 24-h urinary excretion to be provided.90 In Fig. 6, the metabolic profiling and data analysis workflow of the type that would

Metabolite identification and biomarker validation

FIG. 6

A typical workflow for LC-MS-based metabolomics.

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typically be used in the type of metabolic phenotyping experiments described earlier is illustrated.

5.

Conclusions It is now quite clearly the case that LC-MS-based methods have acquired a dominant position for performing untargeted metabolic phenotyping. However, it remains the case that no single mode of chromatography can provide comprehensive metabolic profiles and it is still the case that, at a minimum, analysis using both RPLC- and HILIC-MS is necessary (using both positive and negative modes of ESI). While ESI-based methods of MS dominate, it is arguable that there would be value in also performing APCI for particular types of analyte. Advances in multidimensional/multicolumn separations and miniaturization also hold promise for metabolome analysis, as does the incorporation of IMS into metabotyping workflows and protocols. There is also an emerging consensus about quality control measures for ensuring the validity of the results of these LC-MS-based analyses.91 However, there remain clear difficulties, especially in finding efficient methods for the characterization of compounds identified as potential biomarkers in metabolic phenotyping studies and innovation in these areas is still required.

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Further reading

68. Zhou Z, Shen X, Tu J, et al. Large-scale prediction of collision cross-section values for metabolites in ion mobility-mass spectrometry. Anal Chem 2016;88:11084–91. 69. Blazˇenovic I, Kind T, Ji J, et al. Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites 2018;8:31. 70. Zheng X, Aly NA, Zhou Y, et al. A structural examination and collision cross section database for over 500 metabolites and xenobiotics using drift tube ion mobility spectrometry. Chem Sci 2017;8:7724–36. 71. Zhou Z, Tu J, Zhu Z-J. Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era. Curr Opin Chem Biol 2018;42:34–41. 72. Zhang X, Kew K, Reisdorph R, et al. Performance of a high-pressure liquid chromatography-ion mobility-mass spectrometry system for metabolic profiling. Anal Chem 2017;89:6384–91. 73. Paglia G, Astarita G. Metabolomics and lipidomics using traveling-wave ion mobility mass spectrometry. Nat Protoc 2017;12:797–813. 74. Sinclair E, Hollywood KA, Yan C, et al. Mobilising ion mobility mass spectrometry for metabolomics. Analyst 2018;143:4783–8. 75. Mairinger T, Causon TJ, Hann S. The potential of ion mobility–mass spectrometry for non-targeted metabolomics. Curr Opin Chem Biol 2018;42:9–15. 76. Loftus N, Miseki K, Iida J, et al. Profiling and biomarker identification in plasma from different Zucker rat strains via high mass accuracy multistage mass spectrometric analysis using liquid chromatography/mass spectrometry with a quadrupole ion trap-time of flight mass spectrometer. Rapid Commun Mass Spectrom 2008;22:2547–54. 77. Gika HG, Theodoridis GA, Earll M, et al. Does the mass spectrometer define the marker? A comparison of global metabolite profiling data generated simultaneously via UPLC-MS on two different mass spectrometers. Anal Chem 2010;82:8226–34. 78. Katajamaa M, Miettinen J, Oresˇic M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 2006;22:634–6. 79. Lommen A. MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data reprocessing. Anal Chem 2011;81:3079–86.

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Further reading Broadhurst D, Goodacre R, Reinke SN, et al. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics 2019;14:72.