**Preface to "Sample Preparation in Metabolomics"**

Metabolomics aims at the comprehensive analysis of all low molecular weight metabolites present in a biological system under consideration. This is a demanding task due to the chemical, physicochemical, and biological diversity of metabolites; the broad range of rapidly changing concentrations; and the limited stability of some compound classes. Furthermore, the choice of the sample preparation method needs to be made in alignment with the study aim and the selected analytical approach. To generate high-quality data, sample preparation is a particularly important aspect, as, generally, a major source of error in analytical results is associated with pre-analytical steps. Despite its importance, sample preparation is often an overlooked aspect of studies involving metabolomics, so the spotlight of this book is on the latest developments in sample collection, preparation, and optimization for targeted and untargeted analysis using different analysis platforms in a diverse field of applications.

Expanding metabolome coverage to include complex lipids and polar metabolites is critical for the generation of well-founded hypotheses in biological assays. The performance of single-step sample preparation methods for the simultaneous extraction of the complex lipidome and polar metabolome from human plasma was evaluated [1]. Method performance was assessed using high-coverage hydrophilic interaction liquid chromatography-ESI coupled with tandem mass spectrometry (HILIC-ESI-MS/MS) targeting a panel of 1159 lipids and 374 polar metabolites. Among the tested methods, the isopropanol (IPA) and 1-butanol:methanol (BUME) mixtures were selected as the best compromises for the simultaneous extraction of complex lipids and polar metabolites, allowing for the detection of 584 lipid species and 116 polar metabolites in plasma.

The analysis of highly polar and small molecules in complex biolfuids is especially troublesome as widely employed reversed-phase liquid chromatography methods do not achieve adequate retention and co-elution with matrix compounds, eventually leading to strong ion suppression effects when using mass spectrometry detection. A smart set-up involves the electromembrane extraction of five polar metabolites from several plasma samples, in parallel, dramatically increasing the extraction throughput. Extracted compounds were analyzed using a multi-segment injection—capillary electrophoresis—mass spectrometry (MSI-CE-MS) method and fast-liquid chromatography—tandem mass spectrometry, achieving recoveries of up to 100% with variability as low as 2% [2].

Nuclear magnetic resonance (NMR) is one of the principal spectroscopic methods employed in metabolomics studies for the analysis of biofluids. Recently, high-resolution micro-magic angle spinning (HR-µMAS), has been introduced, paving the way for investigating microscopic specimens (<500 μg) with NMR spectroscopy. Sample preparation for HR-μMAS is challenging due to the μgscale specimen. This book addresses this topic with a special emphasis on three specimen types: biofluids, fluid matrices, and tissues [3]. In biomedical research, the use of cell lines is frequently employed to gain insights into mechanisms of diseases, as well as new treatment approaches. Dedicated protocols for cell harvesting, disruption, and metabolite quenching and extraction were recently developed and critically assessed [3–6]. Using Caulobacter crescentus as a model for Gramnegative bacteria, eight different sample preparation techniques were evaluated using a full-factorial experimental design in combination with the ANOVA multiblock orthogonal partial least squares (AMOPLS) algorithm for decomposing the contribution of each studied factor. All of the main effects of the studied factors were found to significantly contribute to the total observed variability, with cell retrieval having the biggest impact. The protocol showing the best performance in terms of recovery, versatility, and variability was centrifugation for cell retrieval in

combination with MeOH:H2O (8:2) as quenching and extraction solvent, and freeze–thaw cycles as the cell disrupting mechanism [4].

Another study evaluated the effect of the choice of drying technique, i.e., centrifugal evaporation under vacuum vs. lyophilization, on the retrieved NMR spectroscopic profiles of hydrophilic extracts of three human pancreatic cancer cell lines [4]. Between 40 and 50 metabolites showed statistically significant differences in abundance in redissolved sample extract depending on the drying technique employed. Metabolite coverage was also differing, with a set of metabolites being exclusively detected in samples subjected to each drying technique. Hence, these observations showcase the impact of sample preparation on the final result. A three-dimensional multicellular tumor spheroid (3D MTS) model was the focus of another study [6]. Careful optimization of multiple steps of sample preparation enabled the probing of the metabolome of single MTSs in a highly repeatable manner at a considerable throughput and provided absolute concentrations with average biological repeatability of <20%. In a proof-of-principle study, distinct metabolic shifts upon MTS exposure to two metal-based anticancer drugs, which exhibit distinctly different modes of action, were retrieved. Therefore, biological variation among single spheroids can be assessed using the presented analytical strategy that is applicable for in-depth anticancer drug metabolite profiling. Finally, a sample preparation strategy for gas chromatography–mass spectrometry (GC-MS)-untargeted metabolomics of adherent cells grown under high (20%) fetal calf serum conditions was developed [7]. The reproducibility of using different proportions of methanol for the quenching of cells was compared for sample harvest, and the efficiency and reproducibility of intracellular metabolite extraction were tested by employing different volumes and ratios of chloroform. Additionally, the use of total protein amount versus cell mass for normalization purposes was assessed.

The last chapters of this book provide an overview of the state of the art in a selection of fields of applications. Cell-secreted extracellular vesicles (EVs) have rapidly gained prominence as sources of biomarkers, owing to their ubiquity across human biofluids and physiological stability. Many studies have been devoted to their protein, nucleic acid, lipid, and glycan content, but more recently the metabolomic profile of EV content has also gained attention. Beyond clinical applications, metabolomics has also elucidated possible mechanisms of action underlying EV function. There are challenges inherent to working with EVs, particularly concerning sample production and preparation. This chapter outlines recent advances in EV metabolomics whilst highlighting practical pitfalls in applying metabolomics to EV studies [8].

Human milk (HM) is the gold standard for infant nutrition, and, as such, it is an outstandingly complex biofluid with a dynamically changing composition. The use of novel, cutting-edge techniques involving different metabolomics platforms has permitted the expansion of knowledge on HM composition. This chapter presents the state of the art in untargeted metabolomic studies of HM, with an emphasis on sampling, extraction, and analysis approaches with a special focus on the achievable metabolome coverage. Finally, current knowledge gaps and potential future research directions are pointed out [9].

Plant-derived natural products are a valuable source of active compounds. Natural extracts are rich in different classes of metabolites, whereby the bioactivity of natural extracts can be a synergistic effect of several compounds. In recent years, metabolomics has emerged as an indispensable tool for the analysis of crude natural extracts, leading to a paradigm shift in natural products drug research. In this chapter, current advancements in plant sample preparation, analysis, and data processing are presented alongside several case studies of the successful applications of these processes in plant natural product drug discovery [10].

Finally, the last chapter is focused on the peculiarities of forest tree metabolomics. Tree tissues are intrinsically complex matrices, and the presence of several compounds, such as oleoresins and cellulose, might interfere during their analysis. Additionally, in this field of application, experimental design, tissue harvest conditions, and sample preparation are crucial factors to ensure consistency and reproducibility among datasets. This chapter discusses the main challenges when setting up a forest tree metabolomics experiment for mass spectrometry (MS)-based analysis covering technical aspects of all stages of the workflow. The importance of forest tree metadata standardization in metabolomics studies is also highlighted [11].

#### **References**

[1] Single-Step Extraction Coupled with Targeted HILIC-MS/MS Approach for Comprehensive Analysis of Human Plasma Lipidome and Polar Metabolome

[2] Electromembrane Extraction of Highly Polar Compounds: Analysis of Cardiovascular Biomarkers in Plasma

[3] General Guidelines for Sample Preparation Strategies in HR-μMAS NMR-based Metabolomics of Microscopic Specimens

[4] Choosing an Optimal Sample Preparation in Caulobacter crescentus for Untargeted Metabolomics Approaches

[5] Influence of Drying Method on NMR-Based Metabolic Profiling of Human Cell Lines

[6] Single Spheroid Metabolomics: Optimizing Sample Preparation of Three-Dimensional Multicellular Tumor Spheroids

[7] Modified Protocol of Harvesting, Extraction, and Normalization Approaches for Gas

Chromatography–Mass Spectrometry-Based Metabolomics Analysis of Adherent Cells Grown Under High Fetal Calf Serum Conditions

[8] Metabolomics Applied to the Study of Extracellular Vesicles

[9] Current Practice in Untargeted Human Milk Metabolomics

[10]Metabolomics in the Context of Plant Natural Products Research: From Sample Preparation to Metabolite Analysis

[11]Experimental Design and Sample Preparation in Forest Tree Metabolomics

#### **Julia Kuligowski, Guillermo Quint´as**

*Editors*

*Article*
