Sample Preparation For Metabolomics Research Articles

Metabolomics meets systems immunology.

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Bias caused by incomplete metabolite extraction and matrix effect: Evaluation of critical factors for plasma sample preparation prior to metabolomics

Metabolomics is an omics strategy to study the metabolite alteration in the biological system. Unbiased observation of the metabolite level is essential for targeted metabolite quantification and untargeted metabolic profiling. State-of-the-art instruments and versatile tools have been developed for accurate observation of metabolic alterations in various studies. Several analytical pitfalls, such as sample overloading and signal-saturation-induced bias, have been revealed and addressed. In this study, we proposed incomplete-metabolite-extraction-caused bias is also an important issue that results in biased observation when performing metabolomics. In the demonstration example, numerous metabolites exhibited no significant difference between extracted plasma samples with different plasma contents, which is attributed to incomplete-metabolite-extraction-caused bias and matrix effect. Matrix effect is a well-known factor that result in biased observation, it can be reduced by sample dilution and compensated by using stable isotope labelled internal standards. The detection of metabolite signals in the following consecutive extractions provided further evidence of incomplete metabolite extraction. The completeness of metabolite extraction is crucial for unbiased observation of metabolic profile changes. To address this issue, we optimized the extraction time and methanol volume to reduce the incomplete-metabolite-extraction-caused bias and evaluated the metabolite signals in consecutive extractions. Methanol extraction performed with a plasma-to-methanol ratio of 1:14 resulted in metabolite responses of less than 18.1 % in the second extractions observed by metabolomic profiling. Finally, the optimized sample preparation procedure and untargeted profiling platform were applied to detect metabolite alterations associated with patients with cerebrovascular diseases and several features with significant difference were successfully identified. This study revealed and evaluated the bias caused by incomplete metabolite extraction and matrix effect in the commonly used methanol extraction method for human plasma sample preparation for metabolomics. We anticipate the proposed metabolite extraction evaluation method could benefit more clinical and biological metabolomics studies.

Fast filtration with a vacuum manifold system as a rapid and robust metabolome sampling method for Saccharomyces cerevisiae

For metabolome sample preparation, rapid quenching of cellular metabolism without severe loss of intracellular metabolites is important. Here, we comparatively evaluated the two sampling methods, cold methanol quenching and fast filtration, for one of the most important industrial microorganisms, yeast Saccharomyces cerevisiae. The latter, using a vacuum manifold system capable of processing up to twenty samples simultaneously, was developed here. The results showed that the cold methanol quenching method resulted in severe losses of intracellular metabolites but that sugars and sugar phosphates were highly preserved. In comparison, the fast filtration method better preserved most metabolites with less extracellular contamination. Our results suggested that the fast filtration method with a vacuum manifold system can be widely applied to yeast metabolome analysis due to its simplicity, robustness, and lack of significant intracellular metabolite loss.

Metabolomic Analysis of Yeast and Human Cells: Latest Advances and Challenges.

Liquid chromatography-mass spectrometry (LC-MS) based nontargeted metabolomics has been applied to a wide range of biological samples and can provide information on thousands of compounds. However, reliable identification of the compounds remains a challenge affecting result interpretation. In this protocol, we describe comparable yeast cell and whole blood metabolome sample preparation for extracting similar compound groups, and we present a LC-MS method using the all ion fragmentation (AIF) approach for the purposes of increasing accuracy in metabolite annotation. Our method enables database-dependent targeted as well as nontargeted metabolomics analysis from the same data acquisition, while simultaneously improving the accuracy in metabolite identification to increase the quality of the resulting biological information.

Metabolomics for clinical use and research in chronic kidney disease.

Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.

Atmospheric vs. anaerobic processing of metabolome samples for the metabolite profiling of a strict anaerobic bacterium, Clostridium acetobutylicum.

Well-established metabolome sample preparation is a prerequisite for reliable metabolomic data. For metabolome sampling of a Gram-positive strict anaerobe, Clostridium acetobutylicum, fast filtration and metabolite extraction with acetonitrile/methanol/water (2:2:1, v/v) at -20°C under anaerobic conditions has been commonly used. This anaerobic metabolite processing method is laborious and time-consuming since it is conducted in an anaerobic chamber. Also, there have not been any systematic method evaluation and development of metabolome sample preparation for strict anaerobes and Gram-positive bacteria. In this study, metabolome sampling and extraction methods were rigorously evaluated and optimized for C. acetobutylicum by using gas chromatography/time-of-flight mass spectrometry-based metabolomics, in which a total of 116 metabolites were identified. When comparing the atmospheric (i.e., in air) and anaerobic (i.e., in an anaerobic chamber) processing of metabolome sample preparation, there was no significant difference in the quality and quantity of the metabolomic data. For metabolite extraction, pure methanol at -20°C was a better solvent than acetonitrile/methanol/water (2:2:1, v/v/v) at -20°C that is frequently used for C. acetobutylicum, and metabolite profiles were significantly different depending on extraction solvents. This is the first evaluation of metabolite sample preparation under aerobic processing conditions for an anaerobe. This method could be applied conveniently, efficiently, and reliably to metabolome analysis for strict anaerobes in air.

Infrared biospectroscopy for a fast qualitative evaluation of sample preparation in metabolomics

Liquid chromatography-mass spectrometry (LC-MS) has been increasingly used in biomedicine to study the dynamic metabolomic responses of biological systems under different physiological or pathological conditions. To obtain an integrated snapshot of the system, metabolomic methods in biomedicine typically analyze biofluids (e.g. plasma) that require clean-up before being injected into LC-MS systems. However, high resolution LC-MS is costly in terms of resources required for sample and data analysis and care must be taken to prevent chemical (e.g. ion suppression) or statistical artifacts. Because of that, the effect of sample preparation on the metabolomic profile during metabolomic method development is often overlooked. This work combines an Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) and a multivariate exploratory data analysis for a cost-effective qualitative evaluation of major changes in sample composition during sample preparation. ATR-FTIR and LC-time of flight mass spectrometry (TOFMS) data from the analysis of a set of plasma samples precipitated using acetonitrile, methanol and acetone performed in parallel were used as a model example. Biochemical information obtained from the analysis of the ATR-FTIR and LC-TOFMS data was thoroughly compared to evaluate the strengths and shortcomings of FTIR biospectroscopy for assessing sample preparation in metabolomics studies. Results obtained show the feasibility of ATR-FTIR for the evaluation of major trends in the plasma composition changes among different sample pretreatments, providing information in terms of e.g., amino acids, proteins, lipids and carbohydrates overall contents comparable to those found by LC-TOFMS.

Yeast metabolomics: sample preparation for a GC/MS-based analysis.

Metabolome sample preparation is one of the key factors in metabolomics analyses. The quality of the metabolome data will depend on the suitability of the experimental procedures to the cellular system (e.g., yeast cells) and the analytical performance. Here, we summarize a protocol for metabolome analysis of yeast cells using gas chromatography-mass spectrometry (GC-MS). First, the main phases of a metabolomics analysis are identified: sample preparation, metabolite extraction, and analysis. We also provide an overview on different methods used to quench samples and extract intracellular metabolites from yeast cells. This protocol provides a detailed description of a GC-MS-based analysis of yeast metabolome, in particular for metabolites containing amino and/or carboxyl groups, which represent most of the compounds participating in the central carbon metabolism.

Evaluation of metabolome sample preparation methods regarding leakage reduction for the oleaginous yeast Yarrowia lipolytica

Yarrowia lipolytica is the well-known oleaginous yeast able to accumulate amounts of lipids and a variety of high value products. The knowledge of its intracellular metabolites concentrations, which are directly linked to the sample preparation of corresponding intracellular metabolites, is of fundamental importance for the characterization of its metabolism. Therefore, in this study, we evaluated current sampling and extraction methods used in gas chromatography–mass spectrometry (GC–MS) analysis of the intracellular metabolites of microorganism. Mainly, the comparison between the methyl chloroformate (MCF) derivatization and the N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) derivatization applied to the analysis of a broad variety of Y. lipolytica's metabolites was performed. More importantly, the dimethyl sulfoxide (DMSO) solution quenching method was attempted with the purpose of reducing the intracellular metabolites leakage during quenching and adapting the leakage-free quenching method to both kinds of derivatization. The results obtained showed that the DMSO quenching method permits measurement of the concentration of intracellular metabolites and can be applicable for the analysis of multiple metabolites of important pathways in Y. lipolytica by available routine GC–MS platform.

Evaluation and Optimization of Metabolome Sample Preparation Methods for Saccharomyces cerevisiae

Metabolome sampling is one of the most important factors that determine the quality of metabolomics data. The main steps in metabolite sample preparation include quenching and metabolite extraction. Quenching with 60% (v/v) cold methanol at -40 °C has been most commonly used for Saccharomyces cerevisiae, and this method was recently modified as "leakage-free cold methanol quenching" using pure methanol at -40 °C. Boiling ethanol (75%, v/v) and cold pure methanol are the most widely used extraction solvents for S. cerevisiae. In the present study, metabolome sampling protocols, including the above methods, were evaluated by analyzing 110 identified intracellular metabolites of S. cerevisiae using gas chromatography/time-of-flight mass spectrometry. According to our results, fast filtration followed by washing with an appropriate volume of water can minimize the metabolite loss due to cell leakage as well as the contamination by extracellular metabolites. For metabolite extraction, acetonitrile/water mixture (1:1, v/v) at -20 °C was the most effective. These results imply that the systematic evaluation of existing methods and the development of customized methods for each microorganism are critical for metabolome sample preparation to facilitate the reliable and accurate analysis of metabolome.

U13C cell extract of Pichia pastoris – a powerful tool for evaluation of sample preparation in metabolomics

Quantitative metabolic profiling is preceded by dedicated sample preparation protocols. These multistep procedures require detailed optimization and thorough validation. In this work, a uniformly (13)C-labeled (U(13)C) cell extract was used as a tool to evaluate the recoveries and repeatability precisions of the cell extraction and the extract treatment. A homogenous set of biological replicates (n = 15 samples of Pichia pastoris) was prepared for these fundamental experiments. A range of less than 30 intracellular metabolites, comprising amino acids, nucleotides, and organic acids were measured both in monoisotopic (12)C and U(13)C form by LC-MS/MS employing triple quadrupole MS, reversed phase chromatography, and HILIC. Recoveries of the sample preparation procedure ranging from 60 to 100% and repeatability precisions below 10% were obtained for most of the investigated metabolites using internal standardization approaches. Uncertainty budget calculations revealed that for this complex quantification task, in the optimum case, total combined uncertainty of 12% could be achieved. The optimum case would be represented by metabolites, easy to extract from yeast with high and precise recovery. In other cases the total combined uncertainty was significantly higher.

In Vivo Solid‐Phase Microextraction in Metabolomics: Opportunities for the Direct Investigation of Biological Systems

Sample preparation has a strong impact on the quality of metabolomics studies. The use of solid-phase microextraction (SPME), particularly its in vivo format, enables the capture of a more representative metabolome and presents opportunities to detect low-abundance, short-lived, and/or unstable species not easily captured by traditional methods. The technique is ideally suited for temporal, spatial, and longitudinal studies of the same living system, as well as multicompartmental studies of the same organism. SPME is useful for the investigation of biological systems ranging in complexity from cells to mammalian tissues. Selected examples are highlighted in this Minireview in order to place the technique within the context of conventional methods of sample preparation for metabolomics.

Evaluation of Sampling and Extraction Methodologies for the Global Metabolic Profiling of Saccharophagus degradans

Metabolomics is based on the unbiased and global analysis of metabolites of organisms at specific time points. Therefore, the nonselective and reproducible recovery of all existing metabolites while preventing their transformation is the ideal criterion for metabolome sample preparation. We evaluated currently used sampling methods and extraction solvents for global metabolic profiling of a gram-negative bacterium, Saccharophagus degradans, using gas chromatography-time-of-flight mass spectrometry (GC-TOF MS) with an emphasis on three steps: the sampling method, which consisted of cold methanol quenching or fast filtration; the subsequent washing step; and the extraction solvents. After cold methanol quenching with 70% (v/v) methanol at -70 degrees C, washing with 2.3% NaCl produced a serious loss of intracellular metabolites. In addition, when cold methanol quenching and fast filtration were compared, severe cell leakage caused by cold methanol quenching resulted in a significant loss of intracellular metabolites, which was confirmed by spectrometric analysis at 260 and 280 nm. Upon evaluation of extraction solvents such as pure methanol (MeOH), acetonitrile/water (50ACN; 1:1, v/v), acetonitrile/methanol/water mixture (AMW; 2:2:1), and water/isopropanol/methanol (WiPM; 2:2:5). AMW and WiPM were found to be superior extraction solvents for S. degradans based on the total peak intensities of the metabolites, the total number of metabolite peaks, and the reproducibility of recovered metabolite quantities; however, the metabolite profiles differed significantly between AMW and WiPM. This is the first evaluation of each step of sample preparation involved in global scale metabolic analysis by GC-TOF MS, which can be used as a model in the preparation of organism-specific samples for metabolome analysis.