Toward Merging Untargeted and Targeted Methods in Mass

Dec 4, 2015 - E-mail: [email protected]. This article is part of the Fundamental and Applied Reviews in Analytical Chemistry 2016 special issue. Cite...
9 downloads 19 Views 6MB Size
Download PDF
Review pubs.acs.org/ac

Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics Tomas Cajka† and Oliver Fiehn*,†,‡ †

UC Davis Genome Center−Metabolomics, University of California Davis, 451 Health Sciences Drive, Davis, California 95616, United States ‡ King Abdulaziz University, Faculty of Science, Biochemistry Department, P.O. Box 80203, Jeddah 21589, Saudi Arabia

■

CONTENTS

Sample Extraction Extraction of Polar Metabolites (Metabolomics) Extraction of Lipids (Lipidomics) Combined Extraction of Amphiphilic and Lipophilic Metabolites Mass Spectrometry-Based Metabolomics and Lipidomics Direct Infusion MS Ion Mobility-Mass Spectrometry (IM-MS) Liquid Chromatography−Mass Spectrometry (LC−MS) Reversed-Phase Liquid Chromatography (RPLC) Hydrophilic Interaction Chromatography (HILIC) Normal-Phase Liquid Chromatography (NPLC) Supercritical Fluid Chromatography (SFC) Two-Dimensional Liquid Chromatography (2D-LC) Mass Spectrometric Detection Data Processing Quality Control Conclusions Author Information Corresponding Author Notes Biographies Acknowledgments References

spectrometry (GC−MS) used for analysis of volatile metabolites and for primary metabolites after derivatization and capillary electrophoresis−mass spectrometry (CE−MS) for analysis of polar, charged metabolites as reviewed in references 5, 6. Metabolomics and lipidomics strategies can be divided into untargeted and targeted approaches, each with their own advantages and limitations.7 Untargeted metabolomics/lipidomics focuses on the analysis of all the detectable metabolites in a sample, including chemical unknowns. By contrast, targeted metabolomics/lipidomics is the measurement of defined groups of metabolites. As Figure 1 shows, these approaches can be

525 525 527 527 528 528 529 533 533 534 535 535 535 536 540 541 541 542 542 542 542 542 542

Figure 1. Untargeted and targeted metabolomics/lipidomics methods in relation to the number of detected metabolites and reliability of quantitative results.

characterized by the number of detected metabolites as well as the reliability of quantification of particular method. Here, reliability is presented either as the accuracy of absolute quantifications, typically expressed in micromolar units, or as precision given by semiquantitative assessments in arbitrary (normalized) units. The best method accuracy should be theoretically achieved when an isotopically labeled internal standard of a particular metabolite is spiked into the sample during the extraction in various concentrations (isotope dilution mass spectrometry). A slightly less reliable method uses a calibration curve of a particular standard spiked into the sample at different concentration levels, normalized to a spiked constant concentration of an (isotope labeled) internal standard

A

dvances in mass spectrometry (MS) and data processing have led to discoveries in regulation of cellular metabolism by using metabolomics and lipidomics approaches.1,2 Mass spectrometry is by far the dominating analytical platform in metabolomics and lipidomics, surpassing the use of nuclear magnetic resonance (NMR) at a 5:2 ratio according to our citation analysis.3 While NMR can quantify metabolites in the micromolar range, use of MS permits detection of up to picoand nanomolar concentrations.4 Mass spectrometry is also easily linked with chromatographic separation, reducing matrix effects of biological samples as well as limiting the complexity of analytes at the moment of detection. Liquid chromatography− mass spectrometry (LC−MS) has become the most applied chromatography−MS tool for analysis of both polar and nonpolar metabolites followed by gas chromatography−mass © 2015 American Chemical Society

Special Issue: Fundamental and Applied Reviews in Analytical Chemistry 2016 Published: December 4, 2015 524

DOI: 10.1021/acs.analchem.5b04491 Anal. Chem. 2016, 88, 524−545

Analytical Chemistry

Review

of tissue, 105−107 cells, or 10−250 μL of biofluids per analysis are required.13−15 Development of efficient sample preparation procedures is even more challenging for studies where the sample amount for the analysis is limited. In addition, the amount of sample for the analysis depends also on the concentration of metabolites covered by particular analytical methods. For instance, lower amount (10−30 μL) of plasma/serum are sufficient for untargeted lipidomics profiling covering main lipid classes,16−18 while higher amount (up to 250 μL) is needed for trace analysis of oxylipins19 or vitamin D metabolites.20 While each metabolomic platform has different requirements with regard to sample pretreatment, deproteinization is required for all methods because the presence of proteins can seriously influence precision, accuracy, and instrument lifetime.13 In order to maintain wide metabolite coverage in untargeted metabolomics and lipidomics discovery studies, biological samples are typically analyzed with minimal pretreatment. For this purpose, nonselective sample-pretreatment methods, such as solvent−protein precipitation (e.g., plasma/ serum) and dilute-and-shoot (e.g., urine but also used for microbial high-throughput methods21), are often used.22,23 On the contrary, for hypothesis-driven targeted validation analyses, sample pretreatment includes solvent−protein precipitation, often followed by liquid−liquid extraction (LLE) and/or solidphase extraction (SPE) for the selective isolation and enrichment of specific target metabolites and removal of interfering matrix components.24 These cleanup steps serve advancements in both selectivity and dynamic range; however, we will discuss if similar improvements in total separation and quantification capacity can be achieved by combination of novel techniques such as ion mobility and high-resolution, fast scanning mass spectrometers. For both targeted and untargeted methods, the final step during sample pretreatment typically involves evaporation in order to concentrate the metabolites and resuspension of the dry extracts using an analysis-compatible solvent.13,14 Lipidomics is clearly a subsection of metabolomics. However, as Figure 2 shows, the continued distinction of lipophilic and hydrophilic metabolites remains very useful because comprehensive metabolomics and lipidomics target metabolites covering ∼40 orders of magnitude on the octanol/water coefficient scale.25,26 Extraction efficiency is clearly enhanced if two separate extraction steps and/or use of separate extraction solvent mixtures are used compared to a single solvent extraction. However, the increased number of operations requires more time, which reduces sample throughput of the laboratory, a critical aspect during large-scale metabolomics and lipidomics studies. Historically, metabolomics and lipidomics sample preparation protocols were used separately. Over the last couple years, there has been an attempt to use a single extraction method to isolate both polar metabolites as well as polar and nonpolar lipids followed by fractionations and analysis of the polar and lipophilic fractions (aka “metabolomics” and “lipidomics”) under different separation conditions.11,12 Below, we discuss trends in sample extractions for polar metabolites, lipids, and combined metabolomics/ lipidomics extraction procedures. Extraction of Polar Metabolites (Metabolomics). In metabolomics, the most commonly used extraction methods are organic solvent-based protein precipitation followed by centrifugation or membrane-based techniques (ultrafiltration).13 Organic solvent-based protein precipitation extracts both hydrophilic and hydrophobic compounds, but the extent of recovery of different metabolite classes depends on the nature

of similar molecular structure. This approach, however, is limited to a very small number of metabolites because the number of commercially available isotope labeled standards is far smaller than the number of metabolites that are typically analyzed. In practice, absolute quantifications are therefore often performed by using only one internal standard per metabolite or lipid class, compromising accuracies in comparison to truly quantitative methods. Nevertheless, such quantifications are still more reliable, and (more importantly), comparable between different laboratories and studies than untargeted metabolomics. While the strength of targeted metabolomics is therefore validating one or several hypotheses, untargeted metabolomics offers the opportunity to discover novel compounds that led to a range of breakthroughs in understanding human disease risks.8−10 Untargeted analyses can be accomplished with or without internal standard addition. When internal standards are added into the samples, the method can provide pseudoconcentration results for particular metabolites or for metabolites with similar physicochemical properties (e.g., lipids). Although these results are not truly quantitative, they can be accurate enough for case/ control comparisons. Hence, in terms of quantifications and comparability of results between studies, there is a range of options for analytical chemists between fully untargeted and fully targeted approaches, depending on the aim and scope of the underlying research questions. In this review, we will discuss whether novel techniques in mass spectrometry, from ultrahigh resolution detection to dataindependent MS/MS and ion mobility methods, have advanced so far that selectivity and sensitivity of untargeted analyses have indeed reached a point at which hypothesis-driven validation studies can be conducted by accurate mass profiling methods rather than classic triple-quadrupole multiple-reaction monitoring. To this end, we reviewed original MS-based metabolomics and lipidomics papers published mainly in years 2012−2015 with focus on sample extraction, LC separation, MS detection, and data processing.

■

SAMPLE EXTRACTION Untargeted metabolomics and lipidomics applications are broad scale techniques that require fast and reproducible sample preparation methods. At the same time, these methods have to cover a wide range of target molecules spanning swaths of lipophilicity ranges, from highly water-soluble sugars to very lipophilic triacylglycerols.11,12 Unlike classic target strategies that aim for accurate analysis of very select compounds, lipidomics and metabolomics methods aim for increased coverage of metabolites, often leading to suboptimal recoveries for specific compounds. It is important to recognize that there is no single method or platform that can cover the full metabolome: hence, the task for method developments in untargeted analyses is to cover the metabolome with as few platforms as possible, while maintaining precision and accuracy for the metabolite classes that are detected by the chosen platforms. Second, the term platform is here mostly defined by sample extraction and chromatography techniques, because mass spectrometry approaches may differ mostly between accurate mass discovery (or profiling) techniques and multiplexed single reaction monitoring target validation methods. Metabolomics and lipidomics studies are focused on complex biological matrixes such as plasma or serum, animal and plant tissues, cells, and urine.13 Depending on the limit of detection used and the abundance of specific metabolite classes, 1−100 mg 525

DOI: 10.1021/acs.analchem.5b04491 Anal. Chem. 2016, 88, 524−545

Analytical Chemistry

Review

Figure 2. Predicted octanol/water partition coefficient (X log P) range of common metabolites (data for representative metabolites taken from ref 25) in blood plasma and polarity index of solvents26 used for sample extraction. Typical solvents or solvent mixtures (in gray color) used in metabolomics and lipidomics indicate the polarity range of isolated metabolites with high recovery. Extraction ranges increase if ternary mixtures are used that include water, or if water is added in simultaneous extraction/fractionation procedures similar to the classic Bligh−Dyer42 or Matyash− Schwudke43 protocols. Legend: Cer, ceramides; Chol, cholesterol; CholE, cholesteryl esters; CL, cardiolipins; DG, diacylglycerols; FAHFA, fatty acid esters of hydroxyl fatty acids; LPA, lysophosphatidic acids; LPC, lysophosphatidylcholines; LPE, lysophosphatidylethanolamines; MG, monoacylglycerols; PA, phosphatidic acids; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PG, phosphatidylglycerols; PI, phosphatidylinositols; PS, phosphatidylserines; PUR, purines; PYR, pyrimidines; SM, sphingomyelins; TG, triacylglycerols; TMAO, trimethylamine N-oxide.

Each organic solvent-based protein precipitation method can be evaluated in terms of protein-removal efficiency, metabolite coverage, and precision. For human plasma, Bruce et al. showed that precipitation with acetonitrile (ACN) or acetone works better for protein removal,32 whereas solvent-based protein precipitation with methanol (MeOH) or MeOH/ethanol (EtOH), MeOH/ACN, MeOH/ACN/acetone mixtures result in improved metabolic coverage. For human serum, on the other hand, Want et al. showed that acetone and MeOH were efficient for protein removal compared to pure ACN with MeOH being the most efficient extraction solvent for isolation of polar metabolites.33 For large cohort studies, organic solvent-based protein precipitation may become a bottleneck because of the number of required manual operations. Recently, automated solvent-based protein precipitation using 96-well-plate formats became available [Captiva (Agilent), HyperSep (Thermo Scientific), Impact (Phenomenex), Protein Precipitation Filter Plate (Supleco), Sirocco (Waters)]. Biofluids are pipetted to the protein precipitation plates followed by addition of organic solvents (e.g., methanol, ACN/H2O, or MeOH/H2O mixtures). The plates are then capped and mixed, subsequently placed on a vacuum manifold and filtered to remove any precipitated material. The filtrate is collected into another 96-well plate and aliquots (or the entire volume) are used either for direct injection or used for further processing (e.g., evaporation, derivatization).34−36 These 96-well-plate formats are also available

of the solvent or solvent mixtures as well as solvent/sample ratios. Indeed, it is interesting to note that procedures in which samples are extracted by multiple consecutive extractions of the same type of solvent have been rarely published,27−29 despite the well-known Nernst equation that predicts that extraction efficiencies (and compound recoveries) will be more exhaustive if several extraction steps are combined.30 It appears that authors use single extraction steps for two reasons, to speed up total sample pretreatments for high-throughput studies and to limit the extraction efficiency for unwanted metabolite classes. As an example, if a simple methanol extraction is used on plasma samples, triglycerides and cholesteryl esters will not be efficiently extracted. Nevertheless, these compounds will be detectable in samples, and hence, further cleanup of the extracted samples is often required as the supernatant still contains many components, including lipids, which may interfere with subsequent MS analysis and may reduce the lifetime of columns (for example, in GC/MS analysis). Thus, the combination of protein and lipid removal is often used to improve both metabolite coverage and reproducibility.13,31 During metabolomics method development, metabolite losses may occur due to coprecipitation with proteins, due to poor solubility in the extraction solvent used or due to solvent saturation effects that depend on the total metabolome content of a sample. Overall, these effects are matrix-dependent, which is the major reason why different metabolomics extraction methods have been developed for different biological samples. 526

DOI: 10.1021/acs.analchem.5b04491 Anal. Chem. 2016, 88, 524−545

Analytical Chemistry

Review

compared to conventional Bligh−Dyer extraction. This modified Bligh−Dyer extraction did not show any artifacts resulting from hydrolysis of natural abundant phospholipids. Lofgren et al.45 developed an automated chloroform-free 96-well extraction method for isolation of plasma lipids. In the first step the plasma is mixed with a butanol (BuOH)/MeOH mixture (3:1, v/v) followed by two-phase extraction using heptane/EtOAc (3:1, v/v) and 1% acetic acid.45 This method showed comparable recovery for main lipid classes (CholE, cholesterol, TG, PC, SM, Cer, DG, LPC) with the Folch method. The protocol introduced by Alshehry et al. includes extraction of blood plasma with the BuOH/MeOH mixture (1:1, v/v) resulting in efficient extraction of major lipid classes including sterols, glycerolipids, glycerophospholipids, and sphingolipids.46 This extraction mixture was found to be compatible with initial conditions of the LC−MS method, thus, avoiding evaporation of the extraction solvent and resuspension of dry extracts. Precipitation with organic solvents such as MeOH or ACN (or mixtures with water) can also be used, but extraction efficiencies for a range of lipids will be lower than recoveries from the classic Bligh−Dyer42 or Matyash−Schwudke43 protocols. On the other hand, Sarafian et al. showed that isopropanol used for protein precipitation permits isolation of a broad range of plasma lipids with high recovery compared to other protein precipitation methods (MeOH, ACN, isopropanol−ACN) and liquid−liquid extractions [MeOH/chloroform (2:2, v/v), MeOH/ dichloromethane (2:2, v/v), MeOH/MTBE/H2O (1:5:1.5, v/v/v), and MeOH/isopropanol/hexane (3:7:1, v/v/v)].47 SPE methods have also been used for lipidomic profiling, enabling fractionation of lipids.19 The 96-well-plate formats with SPE-sorbents were used for bile acid profiling and oxylipins in human plasma.48,49 In general, SPE is the method of choice for removing specific lipid fractions that interfere with subsequent LC−MS analysis or for studies where in-depth characterization of lipid classes is required. Combined Extraction of Amphiphilic and Lipophilic Metabolites. Use of LLE methods such as MeOH/chloroform/H2O or MTBE/MeOH/H2O leads to formation of two phases: one containing mostly nonpolar metabolites (lipids) and the other mostly consisting of polar metabolites. The problem in any biphasic fractionation procedure is, of course, the matrix dependent fractionation of semipolar compounds that separate in relevant amounts into both the hydrophilic and the lipophilic solvent. Nevertheless, with optimal conditions such as appropriate resuspension solvent or solvent mixture and LC−MS configuration, increased metabolome coverage can be obtained by using a single extraction for a specific matrix, as long as the total matrix composition is not severely biased between different biological groups within a study. A simple thought experiment may illuminate this problem: triglyceride contents differ massively in plasma between fasted and postprandial subjects, driving semilipophilic compounds from the lipophilic fractionation phase into the hydrophilic fraction. Without internal standards correcting for this matrix effect, statistical assessments become very complicated. However, if such matrix effects can be excluded, combined extraction of amphiphilic and lipophilic metabolites appear quite advantageous. Table 1 summarizes methods used for such combined metabolomic and lipidomic extractions.11,12,37,50−54 In studies conducted by Godzien et al.11 and Whiley et al.,12 these combined extraction methods were streamlined even further by performing the plasma extraction using a highperformance liquid chromatography (HPLC) vial with a 0.3 mL

with various SPE-sorbents, thus, removing bulk phospoholipids and isolating metabolites or drugs from biofluids.37 Another automated procedure for removing proteins is turbulent flow chromatography (TFC). This online technique enables the direct injection of crude biological samples onto a column (e.g., 0.5−1 mm × 50 mm i.d.) packed with large particles (25−50 μm). The sample is injected into a turbo flow column with high flow rate (1.5−5.0 mL/min) generating turbulent flow conditions inside the column. Over a short time period (∼0.5 min), proteins are washed to waste while small molecules are retained into pores of the turbulent flow column followed by their elution onto the conventional analytical column for further separation.38 Michopoulos et al. compared TFC and solvent-based protein precipitation using methanol on blood plasma.39 Large differences of metabolite profiles were observed between these two methods, specifically for phospholipids (∼10-fold reduction for TFC).39 In a very recent study, Mousavi et al. focused on evaluation of different solid phase microextraction (SPME) coating chemistries for extraction of a wide range of both hydrophobic and hydrophilic cellular metabolites produced by a model organism, Escherichia coli.40 An equal-ratio coating of the solid fibers with polystyrene-divinylbenzene sorbent and weak anion exchange:hydrophilic−lipophilic balance material (PS-DVB-WAX:HLB) extracted the highest number of metabolites with a broader logP range than other previously developed coatings.40 Over 200 cellular E. coli metabolites were separated and detected including amino acids, peptides, nucleotides, carbohydrates, poly(carboxylic acid)s, vitamins, phosphorylated compounds, and lipids such as hydrophobic phospholipids, prenol lipids, and fatty acids using the developed 96-blade SPME-LC−MS method.40 Extraction of Lipids (Lipidomics). Isolation of lipids relies on general extraction procedures introduced by Folch et al.41 and Bligh and Dyer,42 frequently with modifications such as scaling of solvent volumes and matrices analyzed. The Folch method uses a mixture of chloroform/MeOH (2:1, v/v) for the extraction while the Bligh−Dyer method employs a mixture of chloroform/MeOH (1:2, v/v) with subsequent addition of 1 volume of chloroform and 1 volume of water. Chloroform may be replaced with dichloromethane (DCM) as a less toxic alternative. Using these traditional extraction methods, problems may occur while collecting the lipophilic bottom fraction because the glass pipet or plastic tip needs to breach a layer of insoluble matrix between two phases and may get contaminated. This problem has been overcome by employing methyl tert-butyl ether (MTBE) by Matyash et al.43 This protocol involves addition of MeOH and MTBE (1.5:5, v/v) to the blood plasma sample followed by adding water (1.25 ratio comped to MeOH and MTBE) to induce phase separation. Because of the low density of the lipid-containing organic phase that forms the upper layer during phase separation, its collection is greatly simplified. Furthermore, MTBE is nontoxic and noncarcinogenic compared to chloroform. These extraction protocols were shown to extract the main lipid classes (PC, SM, PE, LPC, Cer, CholE, TG) in blood plasma with high recovery.43 Recently, Triebl et al. pointed out low recovery of lysophosphatidic acid (LPA) and phosphatidic acid (PA) when using Bligh−Dyer and MTBE/MeOH protocols.44 A modified Bligh−Dyer extraction by addition of 0.1 M hydrochloric acid resulted in a ≈1.2-fold increase of recovery for the seven PA species analyzed and a more than 15-fold increase for the six LPA molecular species of a commercially available natural mix 527

DOI: 10.1021/acs.analchem.5b04491 Anal. Chem. 2016, 88, 524−545

Analytical Chemistry

Review

Table 1. Combined Extraction Methods for the Analysis of Hydrophilic, Amphiphilic, and Lipophilic Metabolites matrix

extraction method

phase collected

resuspension solvent(s)

plant tissue

MeOH/MTBE/H2O

liver and muscle tissue

MeOH/MTBE/H2O

MTBE/MeOH fraction H2O/MeOH fraction MTBE/MeOH fraction

MeOH/MTBE/H2O

MTBE/MeOH fraction and H2O/MeOH fraction (2:1 ratio) MTBE/MeOH fraction

LC−MS

ACN/IPA (7:3, v/v) water chloroform/MeOH mixture (2:1, v/v) diluted in ACN/ IPA/H2O (65:30:5, v/v/v) 20% MeOH

C8 column ESI(+) C18 column ESI(+) C8 column ESI(+)

MeOH

plasma

MeOH/MTBE/H2O

MTBE/MeOH fraction H2O/MeOH fraction

IPA water (0.1% formic acid)

plasma

SPE 96-well plates Step 1: ACN Step 2: chloroform/MeOH (2:1, v/v)

ACN fraction

MeOH/H2O (1:1, v/v)

serum spot

MeOH/MTBE/H2O

chloroform/MeOH fraction MTBE/MeOH fraction H2O/MeOH fraction

chloroform/MeOH (2:1, v/v) direct injection direct injection

C18 column ESI(+) C18 column ESI(+) C18 column ESI(+)

plasma

MeOH/MTBE/H2O

H2O/MeOH fraction

5% ACN

MTBE/MeOH fraction

Direct injection

H2O/MeOH fraction

direct injection

51

C18 column ESI(+) C18 column ESI(+), ESI(−) HILIC column ESI(+), ESI(−) C8 column ESI(+), ESI(−) C18 column ESI(+), ESI(−) C18 column ESI(+) C18-PFP column ESI(+) C18 column ESI(+)

plasma

ref 50

52

11,12

53

37

54

Direct Infusion MS. For direct infusion MS, electrospray ionization (ESI) is the most common method used for ionization. ESI-MS fingerprinting can be adapted to analyze both polar and lipophilic extracts in both positive and negative ion modes to provide comprehensive coverage of a wide range of metabolites.55,56 In LC−MS, signal drifts may pose a problem that can be related to gradual deterioration or contamination of chromatography columns over long sequences of use. In comparison, in direct infusion MS methods contaminations may be easier to control as these are caused mainly by contamination of the ion source. In direct infusion ESI-MS, samples can be introduced either using classic LC autosamplers into a running solvent as a plug flow in a short (2−3 min) data acquisition run or by continuously infusing samples by chip-based nozzles or syringes. ESI-MS fingerprint data can be generated using either unit resolution or high-resolution MS instruments such as time-offlight (TOF), orbitral ion trap, or Fourier transform ion cyclotron resonance (FTICR).56 For direct-infusion ESI-MS metabolomic profiling, tandem mass spectrometry employing either a triple-quadrupole (QqQ) or a quadrupole/linear ion trap (QLIT) is typically used. The most popular method represents commercially available kits (Biocrates) permitting quantitative analysis of polar metabolites (acylcarnitines, amino acids, hexoses) and polar lipids (sphingolipids, glycerophospholipids) simultaneously.57−59 Direct-infusion ESI-MS lipidomic profiling mostly uses either multidimensional MS/MS approaches (MDMS) or high-resolution mass spectrometry.15,60 MDMS methods are regularly used as tandem MS-based shotgun lipidomics due to its simplicity, efficiency, high sensitivity, ease of management, and inexpensive instrumental requirements. All individual lipid species in a particular class can be detected in one MS/MS acquisition directly from a total lipid extract with a QqQ type mass spectrometer. MDMS shotgun lipidomics maximally exploits the unique chemistries inherent in discrete lipid classes for the analysis of lipids, including low-abundance molecular species. MDMS includes multiplex lipid extractions, different MS/MS

glass insert. After phase separation induced by centrifugation, the upper, nonpolar phase and the lower, polar phase were injected onto the LC−MS system from the same vial/insert by changing the needle position for each of two separate runs. When using the upper MTBE/MeOH phase for injection, more attention should be made during the data acquisition and if reinjection is needed this should be conducted immediately because evaporation of MTBE occurs very quickly compared to other type of resuspension solvent(s). It is interesting to note, though, that amphiphilic acylcarnitines are detectable in both fractions: low-chain acylcarnitines (2 to 10) will be predominately detected in the polar fraction while long-chain acylcarnitines (12 to 20) will be mostly fractionated into the nonpolar phase of the MTBE/MeOH/H2O extraction. To be able to detect both low-carbon as well as high-carbon acylcarnitines within a single LC run Chen et al. mixed the upper, nonpolar and lower, polar phases (2:1, v/v) followed by evaporation, resuspension of the dry extracts, and separation of metabolites on a C18 column.51 Overall, analysis of both phases of biphase extractions under different LC−MS conditions significantly increases polar metabolite and lipidome coverage. For typical matrixes such as blood, animal or plant tissues, the lipophilic phase contains different lipids classes from polar long-chain acylcarnitines and phospholipids to nonpolar acylglycerols and cholesteryl esters, while the amphiphilic phase comprises hydrophilic lipids such as low-chain acylcarnitines and hydrophilic metabolites (e.g., amino acids, organic acids, polyamines, TMAO, monosaccharides, flavonoids).10

■

MASS SPECTROMETRY-BASED METABOLOMICS AND LIPIDOMICS Profiling methods for comprehensive metabolome analysis can in principle be divided between separation-based techniques prior to use of mass spectrometry or direct ionization and mass spectrometry analyses of complex mixtures. Each procedure has its advantages and pitfalls. Here, we highlight trends and examples of these methods but do not attempt to detail each method variant of the main approaches. 528

DOI: 10.1021/acs.analchem.5b04491 Anal. Chem. 2016, 88, 524−545

Analytical Chemistry

Review

Figure 3. Positive- and negative-ion ESI mass spectra of the mouse myocardium lipid extract acquired under weak acidic, neutral, and weak basic conditions. Positive- and negative-ion ESI mass spectra were acquired after direct infusion in the presence of 10% acetic acid (panels A and B), 5 mM ammonium acetate (panels C and D), and 10 μM lithium hydroxide (panels E and F) in the infused solution. IS and Ac stand for “internal standard” and “acetate”, respectively. Reproduced from Multidimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses, Han, X.; Yang, K.; Gross, R.W., Mass Spectrom. Rev., Vol. 31, Issue 1 (ref 55). Copyright 2011 Wiley.

ranges between 5 and 20 min, which allows determination of up to 400 lipids.60 In contrast to ESI-MS metabolomic fingerprinting for which quantification is normally limited to expression of signal intensities as relative ratios in arbitrary units (e.g., in relation to total ion current of each sample),56 shotgun ESI-MS lipidomic fingerprinting typically uses a series of class-specific internal standards to achieve absolute quantification. Because these internal standards are usually added during the sample extraction, they also adjust for lower recovery for some lipid classes.15 Ion Mobility-Mass Spectrometry (IM-MS). Ion mobilitymass spectrometry (IM-MS) represents technology with a great potential in metabolomics and lipidomics analysis. Using IM-MS, ions are separated by their size, shape, charge and mass depending on different mobility in low or high electric fields. This provides a new dimension in the LC−MS workflow. Specifically, (1) separation of isobars, (2) background noise reduction, and (3) increase of selectivity through the separation

scan modes (precursor-ion scanning, neutral-loss scanning, product-ion scanning) and chemical modifications. For instance, in the multiplexed extraction approach, the lipid extract is divided into different fractions to performed data acquisition in ESI(−), ESI(+), and also after spiking with acidic or basic modifiers that enhance protonation or deprotonation, respectively, thus, favoring detection of lipids otherwise present at low signal intensity. Using this multiplexed extraction approach, anionic, weakly anionic, and neutral polar lipids can be detected (Figure 3).55 Alternatively to MDMS, high mass accuracybased shotgun lipidomics uses either orbital ion trap or (less frequently) quadrupole/time-of-flight (QTOF) mass spectrometry to collecting high-resolution MS fingerprints for quantitative analysis and product-ion spectrum of each protonated/deprotonated molecule ion for lipid identifications. Additionally, coupling of direct infusion with ion mobility MS system has been shown to further enhance the identification and quantification potential in lipidomics. The typical analysis time 529

DOI: 10.1021/acs.analchem.5b04491 Anal. Chem. 2016, 88, 524−545

Analytical Chemistry

a

Reprinted with permission from Lanucara, F.; Holman, S. W.; Gray, C. J.; Eyers, C. E. Nat. Chem. 2014, 6, 281−294 (ref 61). Copyright 2014 Nature Publishing Group.

• The percentage of ions detected relative to those generated following ionization (that is, the duty cycle) is relatively low when operated under conditions where the compensation voltage (CV) is ramped (CV scanning mode), reducing sensitivity

• CCS cannot be determined

• CCS determination requires calibration of the drift time through the TWIMS cell, ideally using a calibrant of similar physical and chemical properties • Relatively low resolving power (≤∼45 as defined by Ω/ΔΩ at fwhm) Disadvantages

• Ion heating can occur as ions are injected into the TWIMS cell which may affect gas-phase conformation. Unless carefully controlled, the process of measurement may therefore perturb analyte structure

• Relatively straightforward to transfer the ion mobility device between different mass spectrometers • Can be used for mobility separation of product ions generated either by collision-induced dissociation or by electron-transfer dissociation

FAIMS/DMS

• High resolving power (≤∼100 as defined by Ω/ΔΩ at fwhm) • Rotationally averaged CCS can be determined

TWIMS DTIMS

• Rotationally averaged collisional cross-section (CCS; Ω), that is, “shape” can be measured (Å2) • Can be used to separate species of very similar mobility; high resolving power (>100 as defined by Ω/ΔΩ measured at full width at half-maximum (fwhm)) • The geometric configuration of current commercial DTIMS-MS instruments means that they can only be used to separate analytes immediately postionization • Gating-type instruments are susceptible to ion losses when transferred from atmospheric pressure during ionization to the reduced pressure required for analysis

Table 2. Comparison of the Three Main Types of Ion-Mobility Spectrometrya 530

Advantages

of matrix interferences from target analytes are the main application areas of this technique.61,62 Conceptually, three IM-MS techniques are available: (1) drift-time ion mobility spectrometry (DTIMS), (2) travelingwave ion mobility spectrometry (TWIMS), and (3) fieldasymmetric ion mobility spectrometry (FAIMS) or differential mobility spectrometry (DMS).61 In DTIMS, ions migrate through a buffer gas in the presence of an axial, linear, electric-field gradient. In TWIMS, a sequence of applied voltages generates a traveling wave that propels the ions through the buffer gas.62 Both DTIMS and TWIMS contain the mobility cell present between the end part of ion source and the analyzer. In contrast, FAIMS operates by varying compensation voltage, to filter selected ions in a space-dispersive manner.62 In this case, the hardware part is located at the front part of the ion source. Advantages and limitations of each IM-MS technique are summarized in Table 2. From the time required for a given ion to cross the chamber, the collision cross-section value (CCS) can be derived. CCS values are unique physicochemical properties of compounds and could therefore be predicted based on molecular structures and used for compound annotation. Consequently, it has been suggested that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches.55 Indeed, recent papers reported CCS values for small molecules (lipids, polar metabolites), which can be used for metabolite confirmation.63−65 Recently, different configurations of IM-MS such as coupling to direct infusion MS, online chromatographic separation−MS, data-independent acquisition, pre- and postmobility fragmentation, and MS-based imaging were exploited in metabolomics and lipidomics.64,66−70 Coupling ultrahigh-resolution liquid chromatography (UHPLC) with IM-MS might be a promising analytical technique due the complementary nature of different physical properties that are used for separation, namely, lipophilicity (chromatographic behavior), shape (ion mobility), and mass (MS). While retention times in liquid chromatography can drift due to matrix effects and subtle differences of buffers and column conditions, ion mobility drift times did not undergo matrix effects when analyzing different matrixes (urine, plasma, platelet, red blood cells).65 Paglia et al. acquired CCS values for more than 200 lipids in ESI(+) and ESI(−) across different laboratories.65 The CCS values showed high reproducibility with an interlaboratory RSD