Metabolomics in Practice E-Book
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- Herausgeber: Wiley-VCH
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Unlike other handbooks in this emerging field, this guide focuses on the challenges and critical parameters in running a metabolomics study, including such often-neglected issues as sample preparation, choice of separation and detection method, recording and evaluating data as well as method validation. By systematically covering the entire workflow, from sample preparation to data processing, the insight and advice offered here helps to clear the hurdles in setting up and running a successful analysis, resulting in high-quality data from every experiment. Based on more than a decade of practical experience in developing, optimizing and validating metabolomics approaches as a routine technology in the academic and industrial research laboratory, the lessons taught here are highly relevant for all systems-level approaches, whether in systems biology, biotechnology, toxicology or pharmaceutical sciences. From the Contents: * Sampling and Sample Preparation in Microbial Metabolomics * Tandem Mass Spectrometry Hyphenated with HPLC and UHPLC for Targeted Metabolomics * GC-MS, LC-MS, CE-MS and Ultrahigh Resolution MS (FTICR-MS) in Metabolomics * NMR-based metabolomics analysis * Potential of Microfluidics and Single Cell Analysis in Metabolomics * Data Processing in Metabolomics * Validation and Measurement Uncertainty in Metabolomic Studies * Metabolomics and its Role in the Study of Mammalian Systems and in Plant Sciences * Metabolomics in Biotechnology and Nutritional Metabolomics and more.
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Veröffentlichungsjahr: 2013
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Table of Contents
Related Titles
Title Page
Copyright
List of Contributors
Preface
Chapter 1: The Sampling and Sample Preparation Problem in Microbial Metabolomics
1.1 Introduction
1.2 Microorganisms and Their Properties
1.3 Sampling Methods
1.4 Quenching
1.5 Metabolite Extraction
1.6 Application of 13C-Labeled Internal Standards
1.7 Conclusions
References
Chapter 2: Tandem Mass Spectrometry Hyphenated with HPLC and UHPLC for Targeted Metabolomics
2.1 Introduction
2.2 LC-MS-Based Targeted Metabolomics
2.3 Liquid Chromatography
2.4 Mass Spectrometry
2.5 Sample Preparation
2.6 Relative and Absolute Quantification
2.7 Applications
2.8 Synopsis
References
Chapter 3: Uncertainty of Measurement in Quantitative Metabolomics
3.1 Introduction
3.2 Uncertainties of Quantitative MS Experiments
3.3 Concluding Remarks
Acknowledgment
References
Chapter 4: Gas Chromatography and Comprehensive Two-Dimensional Gas Chromatography Hyphenated with Mass Spectrometry for Targeted and Nontargeted Metabolomics
4.1 Introduction and Scope
4.2 Sample Preparation for GC-Based Metabolite Profiling
4.3 GC–MS and GC × GC–TOFMS Instrumentation for Metabolomics
4.4 Data Analysis Strategies and Software
4.5 Illustrative Examples and Concluding Remarks
References
Chapter 5: LC-MS-Based Nontargeted Metabolomics
5.1 Introduction
5.2 LC-MS-Based Untargeted Metabolomics
5.3 Study Design
5.4 Sample Preparation
5.5 Analytical Strategies
5.6 Data Analysis
5.7 Metabolite Identification
5.8 Applications
5.9 Synopsis
References
Chapter 6: The Potential of Ultrahigh Resolution MS (FTICR-MS) in Metabolomics
6.1 Introduction
6.2 Metabolomics Technologies
6.3 Principles of FTICR-MS
6.4 Proceeding in Metabolomics
6.5 Application Example in Metabolomics Using FTICR-MS Exhaled Breath Condensate
6.6 Conclusion and Remarks
References
Chapter 7: The Art and Practice of Lipidomics
7.1 Introduction
7.2 Lipid Diversity
7.3 Tackling the Lipidome: State-of-the-Art
7.4 LC-MS-Based Lipidomics
7.5 GC-MS-Based Lipidomics
7.6 Conclusion
References
Chapter 8: The Role of CE–MS in Metabolomics
8.1 Introduction
8.2 CE–MS
8.3 Sample Pretreatment
8.4 Data Analysis
8.5 Applications
8.6 Conclusions and Perspectives
References
Chapter 9: NMR-Based Metabolomics Analysis
9.1 Introduction
9.2 Platforms for Metabolomics
9.3 NMR for Metabolomics
9.4 Applications of NMR-Based Metabolomics
9.5 Future Prospects and Conclusions
References
Chapter 10: Potential of Microfluidics and Single Cell Analysis in Metabolomics (Micrometabolomics)
10.1 Introduction
10.2 Sample Processing for Metabolomics
10.3 Microfluidic Separations for Metabolic Analysis
10.4 Microfluidics for Cellular Analysis
10.5 A Look Forward
References
Chapter 11: Data Processing in Metabolomics
11.1 Introduction and Scope
11.2 Characteristics of Metabolomics Data
11.3 Types of Biological Questions Asked
11.4 Validation
11.5 Overview of Methods
References
Chapter 12: Metabolic Flux Analysis
12.1 Introduction
12.2 Prerequisites for Flux Studies
12.3 Stoichiometric Flux Analysis
12.4 Labeling Studies Using Isotopes
12.5 State-of-Art 13C Flux Analysis
12.6 Application of Metabolic Flux Analysis
12.7 Recent Advances in the Field
12.8 Concluding Remarks
Acknowledgments
References
Chapter 13: Metabolomics: Application in Plant Sciences
13.1 Introduction
13.2 Sample Preparation
13.3 Analytical Methods
13.4 Metabolite Identification
13.5 Structural Elucidation of Novel Metabolites and Validation of Model
13.6 Conclusion and Perspectives
Acknowledgments
References
Chapter 14: Metabolomics and Its Role in the Study of Mammalian Systems
14.1 Introduction – From Early Beginnings
14.2 Hypothesis Generation or Hypothesis-Testing Studies
14.3 Untargeted, Semi-Targeted, and Targeted Analytical Experiments
14.4 Study and Experimental Design
14.5 Sample Types
14.6 Quality Assurance and Quality Control
14.7 Metabolite Annotation and Identification
14.8 Applications
Acknowledgments
References
Chapter 15: Metabolomics in Biotechnology (Microbial Metabolomics)
15.1 Introduction
15.2 Analytical Methods Applied for Microbial Metabolomics
15.3 Custom-Made Separation for Microbial Metabolomics
15.4 Microbial Metabolomics with Higher Throughput
15.5 Application of Microbial Metabolomics
15.6 Conclusion
References
Chapter 16: Nutritional Metabolomics
16.1 Introduction
16.2 The Metabolome of Human Plasma and Urine: General Considerations
16.3 The Food Metabolome and its Signature in Human Samples
16.4 The Variability of the Human Metabolome in Health and Disease States
16.5 The Dynamic Nature of the Metabolome
16.6 The Future of Nutritional Metabolomics and Research Needs
References
Index
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The Editor
Prof. Dr. Michael Lämmerhofer
Eberhard Karls University Tübingen
Pharmazeutisches Institut
Auf der Morgenstelle 8
72076 Tübingen
Germany
Prof. Dr. Wolfram Weckwerth
Universität Wien
Molekulare Systembiologie
Althanstr. 14
1090 Wien
österreich
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© 2013 Wiley-VCH Verlag & Co. KGaA, Boschstr. 12, 69469 Weinheim, Germany
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List of Contributors
Preface
Metabolomics is a rapidly growing and expanding subdiscipline in systems biology (Figure 1). The latter attempts to look at biological systems in a holistic manner and tries to analyze the entirety of complex molecular systems in a dynamically comprehensive way. The metabolome is downstream to the genome, transcriptome, and proteome and thus provides a great deal of information on the functional status of a cell or organism. Often it is assumed that the low molecular complement of genes and proteins is easier to analyse, and recent developments of powerful analytical techniques such as nuclear magnetic resonance spectroscopy (NMR) and, in particular, mass spectrometry (MS), hyphenated with separation technologies like liquid chromatography (LC) or gas chromatography (GC) have greatly contributed to the wide popularity of metabolomics technologies in analyzing biological systems. While NMR techniques are still preferentially used by chemists, MS, LC-MS, and GC-MS are more favoured for use by biologists as well. Developments, in particular, in MS technology are extremely rapid and continuous training is needed. Users of metabolomics techniques are seldom specialists in all methodologies and techniques that are needed or are helpful and utilized to address metabolomics questions. This may be related either to separation technologies, mass spectrometry, chemometric data processing or simply to the design of metabolomic studies. Some researchers are experts in a certain application field but are less interested in spending a lot of time to optimize analytical technologies. On the other hand, analytical chemists are sometimes not as familiar with biological background as it would be desirable. Thus, it may occur that one or the other mentioned aspect of significant relevance in metabolomics research is treated more like a black box due to lack of knowledge and user-friendly tools that are currently available. Critical issues may be overlooked and this may lead to suboptimal results. This book aims at presenting current methodologies from an analytical chemist's point of view, with the goal to raise the awareness for strengths, weaknesses, pitfalls, shortcomings, problems of methodologies, and solutions how to overcome them. Usually, methodological shortcomings and problems can only be recognized if suitable quality control measures are implemented in workflows. This book also attempts to draw emphasis on quality assurance and validation of analytical methods in metabolomics research. It should support the idea of implementing method validation as a tool to improve the quality and reliability of the produced data, and ascertain that conclusions from metabolomics data are significant. We hope that this book is of significant interest for readers who are new in this field but also for those performing metabolomics analyses for a while.
Figure 1 Gain in popularity of metabolomics research as measured by no. of publications with key word “metabolomics” in SciFinder database (as of May 2012).
We have sub-structured the book in two parts. The first part deals with the most common methods utilized in metabolomics and targeted metabolite analysis. The second part focuses on the application of those methods in the major fields of metabolomics.
Inadequate sample preparation can destroy everything in metabolomics research and make results meaningless. To stress their importance, the book starts with a chapter on sampling and sample preparation in microbial metabolomics (Chapter 1). While this chapter specifically focuses on samples from microbial cultivations, it deals with important basic and general ideas that may be of relevance for other samples as well. It is clear that sampling and sample preparation has to be developed specifically for each type of sample, and that questions addressed are certainly different for plant metabolomics, microbial metabolomics, and red biotechnology. Specifics in these fields will be dealt with in the application part. Metabolomics research aims at measuring variances of substances over time, space or populations. The results should not be blurred by excessive analytical variances. It is well known that variances of individual steps in the entire analytical process are additive. Those of early steps in analytical process are usually much larger. This holds, in particular, for sampling and sample preparation. Hence, the importance of this step must not be underestimated and the first chapter emphasises this. The following chapters in the book are dedicated to targeted metabolomics (Chapter 2). This subdiscipline is often thought to be not a real metabolomics approach because only a preselected set of metabolites and not the entirety of the metabolome is analyzed comprehensively. However, we included it in this book because, on the one hand, issues and errors related to analytical methodologies can be better pinpointed by quantitative measurements, for example, ion suppression effects, chemical, isomeric, and isotopic interferences, and so forth. On the other hand, at the end, accurate metabolite quantitation is the final goal of most metabolomics studies. To convey how errors propagate in individual steps to the total uncertainty in the entire metabolomics process, the concept of measurement uncertainty and of uncertainty budgets is presented in the next chapter (Chapter 3). Chapters dealing with methodological aspects of non-targeted metabolomics by GC-MS, GCxGC-MS and LC-MS/MS follow (Chapters 4–6). These techniques are the current workhorses in metabolomics research. Some of these chapters focus more on technological aspects and others on quality assurance issues of analytical measurements. In one chapter, methods employed in lipidomics, a subdiscipline of metabolomics, are described in more detail (Chapter 7). The lipidome is particularly complex and thus it was deemed justified to deal with this topic in more detail. Techniques less broadly used, such as capillary electrophoresis, hyphenated with MS and miniaturized technologies such as microfluidics (micro-metabolomics) as well as NMR based metabolomics follow in the next chapters (Chapters 8–10). All the above techniques, in particular those being utilized in untargeted profiling methods generate a huge body of data. It needs appropriate statistical tools such as multivariate analysis techniques to filter useful information from a noisy background. The most common statistical methods utilized in metabolomics are described in an illustrative manner in the next chapter (Chapter 11). Metabolism is a highly dynamic process with rapid turnover of metabolic intermediates. While most of the above described methods focus on analysis of metabolic samples where the metabolic state is frozen by quenching, metabolic flux analysis deals with the dynamic change of metabolism. Considerations of primary importance in “fluxomics” are described in a separate chapter (Chapter 12). There are certainly many more techniques of relevance in metabolomics research such as imaging methods but are beyond the primary scope of this book.
The second part of the book deals with applications. The application fields covered comprise plant metabolomics (green metabolomics) (Chapter 13), mammalian metabolomics (red metabolomics) (Chapter 14), and metabolomics in biotechnology and food science (white metabolomics) (Chapters 15 and 16). Pecularities in different application fields are pointed out in these chapters including sample preparation aspects and design considerations of metabolomics experiments and studies. They should make users aware of certain problems, outline which methods are most appropriate and provide an insight to what can be achieved.
The book should give a state-of-the-art picture of the most important techniques written from the analytical chemist's point of view, thus focusing on methodological aspects and technical details, and last but not least quality assurance aspects. We hope that the book is useful and enjoyable to read for both beginners and experts in the field. We greatly appreciate the efforts of all contributing authors and would like to thank them for their excellent articles. Without their valuable contributions this book would not have been possible.
Michael Lämmerhofer
Wolfram Weckwerth
Tübingen, Germany
Vienna, Austria
Chapter 1
The Sampling and Sample Preparation Problem in Microbial Metabolomics
Walter M. van Gulik, André B. Canelas, Reza M. Seifar, and Joseph J. Heijnen
1.1 Introduction
The reproducibility and accuracy of analysis methods has expanded rapidly over the past years. Hyphenated methods, for example, GC-MS and LC-MS/MS (liquid chromatography-mass spectrometry), are now almost routinely used for (semi-) quantitative analysis of tens to hundreds of metabolites in biological samples. It should be realized, however, that the quality of the applied analytical techniques alone is not sufficient to obtain meaningful results. Other important aspects, which are often overlooked, are the methods that are applied to withdraw the samples from a microbial cultivation and the subsequent sample processing procedures. Immediately after withdrawal of the samples, the metabolism should be arrested to preserve the metabolic snapshot represented by the sample by preventing further metabolic conversions. Subsequently, an appropriate extraction method should be applied, which guarantees complete and unbiased release of all metabolites from the cells without significant degradation and inactivation of enzymatic activity. Finally, an accurate and high throughput analytical platform is required for metabolite quantification. In this chapter, we focus on the essential requirements of the sampling and sample processing procedures as well as the methods to validate if these requirements are met in practice.
1.2 Microorganisms and Their Properties
Microorganisms show very large differences in properties, for example, size, structure, degree of complexity, heterogeneity, physiology, nutrient requirements, and so on. These aspects should be considered beforehand because they can be important for the choice of the sampling and sample processing methods to be used. They also determine the meaning of the obtained results, for example, the interpretation of metabolite measurements in cell extracts of prokaryotic microorganisms, which contain only one compartment (cytosol), is more straightforward compared to measurements in cell extracts of eukaryotes. As eukaryotic (micro)organisms contain several compartments (cytosol, mitochondria, peroxisomes, etc.) in which the concentrations of metabolites can be very different, the interpretation of the measurements, which are in fact whole cell averages, can be problematic if the metabolites quantified are present in more than one cellular compartment.
1.3 Sampling Methods
1.3.1 The Need for Rapid Sampling
As quantitative metabolomics aims at obtaining accurate snapshots of intracellular metabolite levels, the withdrawal of samples from the microbial culture under study should be sufficiently fast to prevent significant changes in metabolite levels arising from ongoing metabolic activity. This is particularly important if samples are taken from cultures for which one of the medium components is the growth-limiting nutrient (e.g., chemostat or fed-batch cultivations). If the sampling procedure requires too much time, the small amount of growth-limiting nutrient present in the sample will be rapidly exhausted, which will result in changes in metabolic fluxes and consequently in changes in intracellular metabolite levels. Similarly, if samples are taken from aerobic cultivations, the amount of oxygen dissolved in the liquid phase will be rapidly exhausted because of the consumption of oxygen by the cells combined with the very limited solubility of oxygen in water.
It should be realized that the speed at which the ongoing metabolic activity of the cells in the culture sample occurs, and thus the time of exhaustion of the limiting nutrient and/or the available oxygen in the liquid phase, directly depends on the biomass density and the growth rate of the culture from which the sample is withdrawn. For relatively high density and fast growing cultures, this could happen in a few seconds. Considering the pool sizes of intracellular metabolites, which range from 10−3 to 102 μmol g−1 dry cell mass [1–3] and the fluxes through the metabolic network, the apparent turnover times of metabolite pools range from fractions of a second to tens of minutes [4]. This implies that to obtain meaningful results a dedicated rapid sampling system is required, whereby the residence time of the sampled culture broth in the sampling system is in the subsecond range.
Furthermore, the metabolic activity of the cells should be arrested as soon as the sample enters the sampling vial. This has generally been accomplished by introducing the culture broth with sufficient velocity (i.e., as a liquid jet) into a dedicated quenching solution (Section 1.4) such that it is instantaneously mixed. A common approach is to use a cold aqueous solvent mixture, for example, 60% aqueous methanol, to cool down the sample below −20 °C, thereby minimizing the enzymatic activity. It has been shown that with this method effective quenching of metabolism is obtained only if the mixing of the sample with the cold aqueous quenching liquid is instantaneous [5]. Another approach is to combine quenching with extraction, by sampling directly into the extraction solution (e.g., perchloric acid) or by integrating extraction in the sampling mechanism itself [6].
1.3.2 Sampling Systems
An overview of different rapid sampling systems, both manually operated and fully automated, has been provided by Schaedel and Lara [7]. Apart from being sufficiently fast, other important requirements for rapid sampling systems are that the amount of sample withdrawn is reproducible; the dead volume is negligible (to prevent that stagnant liquid residing in the system is mixed up with the sample); and especially for sampling during highly dynamic experiments, the time between subsequent samples is sufficiently small to fully capture the dynamics.
A convenient manually operated rapid sampling system for withdrawal of broth samples from bench scale bioreactors has been described by Lange et al. [8]. This system consists of a sampling port, inserted in the wall of the bioreactor, with an internal diameter of 1 mm, which is connected to a tube adapter. Sampling is started by removing the dead volume by flushing into the waste. Subsequently, the sample tube is evacuated and directly thereafter, the sample is withdrawn from the bioreactor, facilitated by the vacuum in the sample vial and a slight overpressure in the bioreactor. The liquid flows and evacuation of the sample tube are controlled by electromagnetic pinch valves operated by a timer, allowing the sample volume to be precisely adjusted, that is, with a standard deviation of less than 2%. The authors reported that with this system, they could withdraw samples of 1 ml from a bioreactor, operated at an overpressure of 0.3 bar, within 0.7 s. The residence time of the sample in the system was below 100 ms. However, with this system, the sampling frequency could not be increased much above 1 sample per 5 s because of the many manual handlings that had to be performed. Therefore Schaefer et al. [9] developed a completely automated sampling device, whereby the sampling vials were fixed in transport racks, which were moved by a step engine underneath a continuous jet of culture broth, with a flow rate of 3.3 ml s−1, from a stirred tank bioreactor. In this way, each sampling vial, which contained a cold quenching solution, could be filled within 220 ms resulting in a sampling rate of approximately 4.5 samples per second. This automated sampling device was applied to investigate the intracellular metabolite dynamics of glycolysis in Escherichia coli after rapid glucose addition to a glucose-limited steady state culture. A disadvantage of this approach is the large amount of broth that is withdrawn, requiring a relatively large bioreactor.
A different approach to integrated sampling, quenching, and extraction from a bioreactor culture has been published by Schaub et al. [6]. Hereby, short time heating of the sample is used as the procedure to quench all metabolic activity, at the same time extracting the metabolites from the cells. This is achieved by using a helical coil heat exchanger that allowed continuous withdrawal of the sample from a bioreactor, whereby the broth was rapidly heated to 95 °C. The helical geometry was chosen to enhance radial mixing, thus improving the plug-flow characteristics of the system. After extraction, the cell debris can be removed by filtration. This sampling device allows withdrawing approximately 5 samples per second. The method has been applied to the analysis of the growth-rate-dependent in vivo dynamics of glycolysis in E. coli [10].
1.4 Quenching
1.4.1 Quenching Procedures and Their Properties
Different sampling techniques can be combined with various quenching procedures. An essential property of a quenching procedure is that it should accomplish that all metabolic activity is arrested in the moment of sampling. This can be achieved in various ways, for example, by injecting the sample in a solution with an extreme pH (highly acidic or alkaline), by fast heating and subsequent cooling of the sample (see above), by fast cooling to a temperature below −20 °C or by combining different approaches. Rapid cooling can be achieved by injecting the sample in liquid nitrogen or into a cold quenching solution. Examples are cold perchloric acid ( − 20 °C), 1 M alcoholic KOH 50% (v/v) methanol at −20 °C or cold aqueous 60% (v/v) methanol at −40 °C.
An important aspect in deciding which quenching method should be used for a particular case is whether the complete broth sample could be extracted (i.e., cells with the surrounding medium) or whether the cells should be separated from the surrounding medium before extraction. Hereby, it should be realized that in common laboratory-scale bioreactor cultivations, the volume of the surrounding medium is 2 orders of magnitude larger than the total cell volume.
This implies that even low concentrations of metabolites outside the cells could compromise the quantification of the intracellular metabolite levels, if the metabolite measurements are carried out in extracts from total broth samples. This is illustrated by the results of metabolite measurements carried out in glucose-limited chemostats of E. coli, of which the results are shown in Table 1.1. As shown in Table 1.1, the measured metabolite concentrations in the culture filtrate are 2–3 orders of magnitude lower than the intracellular concentrations. Nevertheless, if the intracellular and extracellular levels are expressed as amounts per gram biomass dry weight (DW) present, it appears that for some metabolites (in this example malate and pyruvate) the amount present in the extracellular medium can be quite significant (around 50%). Thus, if metabolite measurements are carried out in extracts of total broth samples, the levels of certain metabolites will be significantly overestimated.
Table 1.1 Example of Extracellular and Intracellular Metabolite Levels Measured in a Glucose-Limited Chemostat of Escherichia Coli K12 MG1655 [3]. To Obtain Intracellular Concentrations a Cellular Volume of 2.5 ml g−1 of Dry Weight was Used
In order to separate the cells from the surrounding medium before the metabolite extraction step, a quenching procedure must be used, during which the cell membrane integrity is retained. This clearly excludes quenching procedures employing extremely alkaline or acidic solutions, or short time heating is used to arrest metabolic activity. Rapid freezing in liquid nitrogen is also thought to compromise cell integrity because of the formation of ice crystals.
The most commonly used procedure that enables separation of the cells from the cultivation medium is cold methanol quenching [11], combined with cold centrifugation. This method has been used, for example, in combination with a manual rapid sampling system [8], whereby 1 ml of sample is immediately injected in 5 ml of aqueous 60% methanol at −40 °C. After a cold centrifugation step, whereby the temperature of the quenched sample is kept below −20 °C, and an optional washing step, the obtained cell pellet can be extracted for quantification of intracellular metabolites, without interference of extracellular compounds. It should be noted here that this procedure can only be applied successfully if the metabolites are contained within the cells and do not leak out into the quenching/washing solution.
1.4.2 Validation of the Quenching Procedure and Minimization of Metabolite Leakage
From metabolite measurements carried out in the supernatant after cold centrifugation of the quenched sample, de Koning and van Dam [11] concluded that metabolite leakage during cold methanol quenching of yeast was insignificant. This was later confirmed by other authors [8, 12–14]. Contrary to this, Villas-Bôas et al. [15] reported significant leakage of several metabolite classes, especially organic acids and a few amino acids from yeast cells after cold methanol quenching.
Later on a systematic study was performed to trace the fate of a large number of metabolites during cold methanol quenching of yeast culture samples [1]. The authors carried out metabolite measurements in all different sample fractions, that is, total broth, culture filtrate, cell pellet, and quenching solutions. To obtain accurate quantification of metabolites in all sample fractions, isotope dilution mass spectrometry (IDMS) was applied (Section 1.6). This approach allowed them to check the mass balance for each metabolite, and in this way verify the consistency of the measurements, that is, the total amount of each metabolite, as measured in a total broth sample, should be quantitatively found back in the different sample fractions (cell pellet, culture filtrate, and quenching/washing solution). From the obtained results, the authors concluded that quenching of Saccharomyces cerevisiae samples in aqueous 60% methanol at −40 °C induced metabolite leakage to such an extent that for most metabolites the levels are underestimated by at least twofold. By varying the methanol content and quenching temperature, they found the optimal condition for yeast quenching and reported that metabolite leakage is entirely prevented by quenching in 100% methanol at a temperature ≤ − 40 °C, with a sample/quenching solution ratio ≤ 1:5.
The same approach has been used to quantify metabolite leakage during cold methanol quenching, and subsequently optimize the procedure, for other eukaryotic microorganisms. de Jonge et al. [16] found that quenching of Penicillium chrysogenum in 60% aqueous methanol at −40 °C also resulted in significant metabolite leakage into the quenching solution. In contrast to what was observed for S. cerevisiae, increasing the methanol content of the quenching liquid resulted in increased instead of decreased metabolite leakage. The authors reported that metabolite leakage was minimal if mycelium was quenched in 40% aqueous methanol at a temperature of −20 °C. For cold methanol quenching of Pichia pastoris, it was observed that the methanol content of the quenching solution was not very critical, and for most metabolites measured, leakage was acceptable for methanol contents between 40 and 100% and quenching temperatures between −27 and −40 °C [17]. However, from a critical evaluation, it was found that quenching in 60% methanol at a temperature of −27 °C resulted in the lowest metabolite leakage.
The above findings illustrate that apparently no single condition exists, which is suitable for the quenching of different eukaryotic microorganisms, possibly because of differences in membrane composition. Consequently, it seems necessary to validate and optimize the quenching procedure for each different organism. Although modifications might be required, cold methanol quenching appears to be applicable for quantitative metabolomics of different eukaryotic microorganisms and can thus be used to separate the cells from the surrounding medium to avoid interference with metabolites present therein.
1.4.3 Quenching Procedure for Determination of Intracellular Metabolites in the Presence of Extracellular Abundance
In some cases, it is important to quantify levels of intracellular metabolites that are present in high concentrations in the surrounding medium. Typical examples are excreted products such as antibiotics or organic acids by high producing industrial strains. In this case, proper quantification of the intracellular levels requires complete removal of the amounts present outside the cells and thus efficient removal of the extracellular medium before metabolite extraction. This could be attempted by increasing the number of washing steps after cold methanol quenching and centrifugation.
However, for efficient removal of highly abundant extracellular metabolites, repeated steps of resuspension of the quenched cell pellet in a cold washing solution and subsequent cold centrifugation would be required because of the carryover of remaining medium after decantation. This is not only a laborious procedure but could also lead to increased metabolite leakage because of the prolonged exposure to the cold methanol containing quenching/washing solution or to partial cell disruption due to the repeated centrifugation steps. For this reason, Douma et al. [18] compared the efficiency of cold centrifugation combined with several washing steps with a procedure whereby cold methanol quenching was combined with rapid cold filtration and subsequent filtration-based washing. They applied these procedures to quantify the metabolites involved in the penicillin biosynthesis pathway, in a high producing strain of P. chrysogenum. For several of these metabolites, for example, the side-chain precursor phenylacetic acid (PAA), the end product penicillin G (penG), and several by-products, the amounts present in the extracellular medium are 3–4 orders of magnitude higher than the amounts present inside the cells.
In the centrifugation-based method, removal of the extracellular metabolites from the cells was achieved by means of multiple centrifugation and resuspension steps with the cold quenching solution. The cold filtration method was found to be highly superior to the centrifugation method to determine intracellular amounts of metabolites related to penG biosynthesis and indeed allowed the quantification of compounds of which the extracellular amounts were 3–4 orders of magnitude higher than the intracellular amounts.
1.4.4 Quenching of Bacteria
Also for bacteria, cold methanol quenching has been applied. Bolten and coworkers [19] reported that cold methanol quenching of different gram-positive and gram-negative bacteria resulted in a loss of more than 60% of most metabolites from the cells.
From a thorough validation study of cold methanol quenching for E. coli [3], whereby metabolite levels were measured in different sample fractions, it was found that a major part of the intracellular metabolites were lost from the cells and ended up in the quenching solution. The authors observed no positive effect of buffering or increasing the ionic strength of the quenching solution. Therefore, they proposed, an alternative to measure the metabolite levels in the total broth sample as well as in the culture supernatant, whereafter the intracellular levels can be obtained by subtraction. This method was successfully applied to quantify intracellular metabolite levels of E. coli grown in an aerobic glucose-limited chemostat at a dilution rate of 0.1 h−1 [3] and during short term highly dynamic conditions following a glucose pulse [20, 21].
1.5 Metabolite Extraction
1.5.1 Extraction Methods and Their Properties
After obtaining a quenched and washed cell pellet, the metabolites contained within the cells should be extracted completely and quantitatively from them, to allow meaningful quantification. Quite a number of different extraction methods have been described in literature and are based on heating, extreme pH, organic solvents and mechanical disruption, or combinations of these. In earlier works, mainly perchloric acid extraction [22, 23] or hot extraction methods have been applied, for example, short-term boiling in water [24, 25] or aqueous ethanol [26, 27]. More recently, milder methods have been advocated, for example, freeze thaw cycles in cold methanol [28], cold chloroform-methanol (CM) extraction [11], and acidic acetonitrile-methanol (AANM) extraction [29].
Essential requirements that a proper extraction method should fulfill are completeness of extraction, that is, all metabolites should be completely released from the cells; complete denaturation of all enzymes present to prevent further interconversion of metabolites during subsequent sample processing steps; and at the same time, no significant degradation and/or chemical conversion. Finally, the obtained extract should be compatible with the analysis techniques, which will be used for quantification.
Several authors have attempted to evaluate different methods for the extraction of metabolites from different organisms. A summary is given in Table 1.2. It can be seen from this table that the methods that are considered “good” or “best” by some authors are considered as “poor” by others. Clearly, the completeness of extraction might well be different for different microorganisms; however, in some cases, different methods are recommended for the same organism, for example, for E. coli (Table 1.2). Important requirements such as the denaturation of enzyme activity and the absence of metabolite degradation should, in principle, not be dependent on the organism used.
Table 1.2 Survey of Comparisons of Extraction Procedures for Intracellular Metabolite Analysis in Microorganisms
1.5.2 Validation of Extraction Methods for Yeast Metabolomics
With the aim to identify the most appropriate extraction method for yeast metabolomics, Canelas et al. [39] carried out a quantitative evaluation of the essential requirements, that is, completeness of extraction, enzyme denaturation, and absence of metabolite degradation for five different methods, namely, extraction with hot water (HW), boiling ethanol (BE), CM, freezing-thawing in methanol (FTM), and AANM. The procedure followed is shown schematically in Figure 1.1.
Figure 1.1 Schematic representation of the experimental procedure used for the quantitative evaluation of the applicability of five different extraction procedures for yeast metabolomics. (Source: Figure from Canelas et al. [39].) IC, ion-pair LC.
It should be noted that completeness of extraction can never be guaranteed because the exact metabolite contents of the cells cannot be known beforehand. The only way to determine this is to compare how a number of different extraction methods perform with respect to the amounts of metabolites obtained, under the assumption that at least one of the methods will achieve complete extraction.
The second essential property, denaturation of enzymatic activity, can be evaluated by monitoring whether any interconversion of metabolites takes place after the extract has been obtained, that is, during the subsequent sample processing steps. The third requirement, absence of metabolite degradation, can be evaluated by quantifying the metabolite losses during extraction.
Canelas et al. [39] evaluated both metabolite conversion as well as degradation by comparing the obtained metabolite levels after extraction of the same sample in the presence of fully U-13C metabolite mixture with the levels obtained after extraction in the absence of the U-13C mixture (see also the Section 1.6). In the latter case, the U-13C mixture was added after extraction, thus allowing analysis with IDMS. With this procedure, each metabolite is quantified relative to a U-13C-labeled standard, thus correcting for analytical artifacts, for example, sample matrix effects. If the U-13C-labeled standard mix is added before extraction, partial metabolite degradation can also be effectively corrected for because the 12C and U-13C labeled forms of each metabolite are chemically identical and therefore their ratio will not change if partial degradation occurs [40, 41]. This procedure effectively corrects for partial degradation of metabolites during the extraction procedure.
By comparing the metabolite levels in cell extracts obtained in the presence and absence of the U-13C standard mix, the overall process recovery can be quantified for each metabolite analyzed.
1.1
where the superscript BA indicates that the U-13C standard mix was added before analysis, that is, extraction was carried out in the absence of internal standards (ISs), and AQ indicates that the standard mix was added directly after quenching, that is, the extraction was carried out in the presence of ISs.
This recovery includes nonspecific losses sample handling, metabolite degradation as well as (inter)conversion. Furthermore, to take into account the time that the samples have to stay in a cooled auto sampler before analysis, the extracts were incubated for 8–9 h at a temperature of 4–5 °C. If only nonspecific losses would occur, a very similar recovery for all metabolites with a value above 90% would be expected. Significantly lower recoveries would be an indication for metabolite degradation, whereas if recoveries well above as well as well below 100% would occur, this would strongly indicate the occurrence of interconversion of metabolites because of the remaining enzymatic activity.
Canelas et al. determined the overall process recoveries for the extraction methods shown in Figure 1.1 for 44 metabolites in cell samples of S. cerevisiae grown in two different ways, namely in glucose-limited chemostats and in unlimited batch cultures on glucose [39]. The results are shown in Figure 1.2. It can be seen from this figure that the overall process recoveries for the first three extraction methods (HW, BE, and CM) are close to one for the majority of the quantified metabolites, whereby BE shows the flattest profile. For the other two methods (FTM and AANM), however, large deviations were observed. In case of the FTM method, recoveries were found which were either much higher or much lower than one. This shows that interconversion of metabolites has occurred. It should be noted that the FTM method (freeze-thawing cycles in 50% (v/v) aqueous methanol) is a mild extraction method that will cause cell disruption and release of metabolites but is unlikely to result in complete denaturation of enzymes.
Figure 1.2 Overall process recoveries (calculated from Eq. (1.1)) for 44 metabolites analyzed, in the order of increasing molecular weight, for each of the extraction methods, under two growth conditions (glucose limited chemostat and batch cultivation). Data are averages and standard deviations of duplicate samples each analyzed twice. Legend: ∇, chemostat; Δ, batch. Dashed vertical lines are for guidance only. (Source: Figure from Canelas et al. [39].)
The AANM method did not result in recoveries above 100%, and hence no indications were obtained for interconversion of metabolites. However, especially for the large metabolites (i.e., on the basis of a larger mw), the recoveries were poor (Figure 1.2). The reason for this is not clear. Also, this method seems to be relatively mild because it is carried out at a low temperature and the time of exposure to acidic conditions is not extremely long (45–60 min). From the observation that especially polar charged metabolites exhibit low recoveries, Canelas et al. [39] suggested that these could have been caused by poor solubility of these compounds in the cold solvent-rich mixture. If this results in precipitation, a part would be lost during the centrifugation and filtration steps of the sample processing procedure. For further details on the evaluation of these five extraction methods for yeast metabolomics, we refer to the paper of Canelas et al. [39]. The final conclusion of these authors was that, at least for yeast, from the five methods tested, BE and CM should be considered as the most reliable methods in terms of completeness of extraction, prevention of metabolite conversion, and metabolite stability.
1.6 Application of 13C-Labeled Internal Standards
Finally, the determination of the metabolites in the obtained cell extracts can be performed with different analytical techniques. Before the application of mass spectrometry for quantification of compounds became common practice, mainly enzymatic assays and chromatographic methods, for example, high-performance liquid chromatography (HPLC), were applied. The advantage of enzymatic assays is their specificity. Their disadvantage is that for each metabolite a different assay has to be used, which makes the analysis of a large number of metabolites very laborious while the amount of sample required is relatively large.
At present, high-throughput hyphenated mass spectrometric techniques, whereby liquid- or gas-chromatographic separation is coupled to mass spectrometric detection, are routinely used for the measurement of small molecules. These methods allow the simultaneous identification and quantification of a large number of metabolites with high selectivity, adequate sensitivity, and minimal sample use (e.g., 5–10 μl for the quantification of several tens of compounds versus 100 μl or more for the enzymatic assay of a single metabolite).
In particular, liquid chromatography/electrospray ionization mass spectrometry (LC–ESI-MS) is nowadays widely used for the quantification of phosphorylated carbon compounds, which are intermediates of primary metabolic pathways [42, 43]. However, the performance of LC–ESI-MS can be compromised by ion suppression effects [44] that result in a changing signal response of the analyte, owing to changes in the sample matrix. The composition of the sample matrix is influenced by, for example, the applied quenching and extraction procedures, the species of microorganism used, and the composition of the cultivation medium.
This drawback of LC–ESI-MS quantification can be tackled by applying the isotope dilution technique, whereby for each analyte a stable isotope analog (i.e., isotopolog) is used as an IS in the MS analysis [45]. This technique has found a large variety of applications, as reviewed by Baillie [46]. Because of the high physical-chemical similarities between the labeled IS and the analyte, degradation during sample preparation, instrument drift, and ion suppression effects in LC–ESI-MS can be compensated for. This technique has been widely applied to different research areas, for example, pharmacological, medical studies [46–48]. Also, for microbial metabolomics, it has been shown that the application of IDMS is indispensible to accomplish accurate and reproducible quantification of metabolites [40, 41, 49, 50].
To apply this method, an isotopolog has to be included as IS for each metabolite to be analyzed. Because these isotopologs are not commercially available for most metabolites, they must be produced by cultivating microorganisms on 100% U-13C labeled medium and subsequent extraction to obtain a cell extract containing a 100% U-13C labeled metabolite mixture. If this cell extract is added to each sample before metabolite extraction, partial degradation of metabolites, losses during sample handling as well as instrument drift, and ion suppression effects during LC-MS analysis can be effectively corrected for.
A suitable procedure for the production of the 13C extract is (small-scale) fed-batch cultivation [41]. The advantage of this cultivation method is that the growth rate of the culture can be set to a desired value by controlling the medium feed rate. Cultivation of cells at a certain growth rate can be relevant, for example, to obtain certain desired metabolite levels. Examples are producing strains for which product formation only occurs within a particular range of growth rates (e.g., a high producing strain of P. chrysogenum for which the rate of penicillin production, and consequently the intracellular concentrations of the intermediates of the biosynthesis pathway, is highest at a specific growth rate of around 0.03 h−1 [51]). Another example is baker's yeast (S. cerevisiae) that should be cultivated below the so-called critical growth rate ( ∼ 0.3 h−1) because otherwise massive formation of ethanol occurs, resulting in a much lower biomass yield on the supplied U-13C glucose and thus much lower concentrations of U-13C labeled metabolites in the obtained cell extract.
Harvesting of the U-13C-labeled cells can be performed by quenching portions of, for example, 100 ml of broth in 500 ml of cold ( − 40 °C) aqueous methanol followed by extraction in 75% aqueous BE, as described for the production of U-13C yeast extract [41].
Metabolite measurement with IDMS, whereby U-13C labeled cell extract is used as IS, requires the addition of an amount of 13C extract to the quenched cell samples before extraction. To enable proper quantification, a calibration line has to be made, whereby a fixed amount of the 13C extract is added to different dilutions of an unlabeled standard mixture of the metabolites to be quantified. LC-MS analysis of the calibration mixtures yields a calibration line, whereby for each compound the 12C/13C peak area ratio is plotted against the (known) concentration of the unlabeled metabolite in the different dilutions of the standard mixtures. If the volume fraction of the 13C extract in the calibration mixture is identical to the volume ratio of the 13C extract added to each samples versus the volume of the final cell extract, calibration line can be directly applied to convert the measured 12C/13C peak area ratio's to the concentrations of unlabeled metabolites in the samples.
It should be noted that partial degradation of metabolites during extraction and/or matrix and ion suppression effects will not result in changes in the 12C/13C metabolite ratios because chemical degradation, matrix and ion suppression effects of labeled and unlabeled metabolite analogs should be the same (isotopologs are chemically identical). Also, volume losses due to sample handling will not affect the 12C/13C ratio. Therefore, these issues will not affect the outcome of the quantification. Examples of calibration lines for the adenosine nucleotides AMP, ADP, and ATP are shown in Figure 1.3. It can be clearly seen from this figure that the constructed calibration lines based on peak areas of unlabeled standards were highly nonlinear compared with the calibration lines based on the ID approach. This shows that the application of U-13C-labeled isotopologs as ISs improves the quality of MS-based metabolome quantification by correcting for partial losses during sample preparation as well as through improvement of the precision of the LC-EI-MS based analytics. It should be realized that enzymatic (inter)conversions of metabolites in the presence of 13C IS mix will result in changes in 12C/13C ratios of the metabolites involved, and thus the application of this standard mix cannot correct for this kind of conversions. The applied extraction procedure should therefore result in complete enzyme denaturation to prevent enzymatic (inter)conversions to occur.
Figure 1.3 Comparison of obtained calibration curves based on measured peak areas of unlabeled nucleotide standards (closed triangles) and peak area ratios of unlabeled to labeled internal standards (open triangles) for monophosphate (AMP), diphosphate (ADP), and triphosphate (ATP) adenosine nucleotides. a.u., arbitrary units. (Source: Figure from Seifar et al. [52].)
1.7 Conclusions
To obtain meaningful snapshots of the microbial metabolome, rapid sampling and instantaneous quenching of all metabolic activity to prevent further interconversion of metabolites is essential.
An important aspect is the separation of the cells from the surrounding medium, before the extraction procedure, as in many cases intracellular metabolites are also present outside the cells. Although the metabolite concentrations in the extracellular medium are usually low, the much larger volume fraction of the medium in a culture sample, compared to the volume occupied by the cells, could easily result in overestimation of metabolite levels. To avoid this, a quenching procedure should be applied, which does not induce metabolite leakage from the cells and thus allows separation of cells and surrounding liquid after quenching without causing metabolite losses.
So far, no single condition has been found that is suitable for the quenching of different eukaryotic microorganisms; possibly, because of differences in membrane composition. Consequently, it seems necessary to validate and optimize the quenching procedure for each different organism. Although modifications might be required, cold methanol quenching appears to be applicable for quantitative metabolomics of different eukaryotic microorganisms and can thus be used to separate the cells from the surrounding medium to avoid interference with metabolites present therein.
In contrast to this, no suitable quenching procedure has been reported, which allows removal of extracellular metabolites in case of bacterial cultures. Although cold methanol quenching can be applied to bacteria, subsequent cold centrifugation and washing will result in significant loss of metabolites, because of substantial diffusion from the cells into the cold methanol solution, during and after quenching.
The second step after obtaining the cell samples is an extraction procedure to release the metabolites from the cells. Essential requirements that a proper extraction method should fulfill are completeness of extraction, that is, all metabolites should be completely released from the cells; complete denaturation of all enzymes present to prevent further interconversion of metabolites during subsequent sample processing steps; and at the same time no significant degradation and/or chemical conversion. Several methods have been described in the literature, which do not fulfill these requirements. At present, hot aqueous ethanol boiling seems to be the most common procedure, while HW boiling and CM extraction yield comparable results. These three methods are common in that the enzymes present in the cell samples are effectively denaturated, and therefore enzymatic (inter)conversion of metabolites does not occur.
Metabolite quantification is mostly accomplished with MS-based methods (mainly LC- and GC-MS). This allows the application of IDMS, using stable isotope labeling, for example, 13C, to improve the reproducibility and accuracy of the measurements.
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