Next Article in Journal
Comprehensive Quantitative Analysis of Coal-Based Liquids by Mask R-CNN-Assisted Two-Dimensional Gas Chromatography
Previous Article in Journal
Fabrications and Properties of Heteroatom-Based Co-Doped Biochar for Environmental Application: A Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Separations
Volume 12
Issue 2
10.3390/separations12020021
separations-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Plant Sample Preparation for Metabolomics, Lipidomics, Ionomics, Fluxomics, and Peptidomics

by
Walace Breno da Silva
†,
Gabriel Felipe Hispagnol
†,
Emanuel Victor dos Santos Nunes
†,
Ian Castro-Gamboa
and
Alan Cesar Pilon
*
Institute of Chemistry, Department of Biochemistry and Organic Chemistry, São Paulo State University (UNESP), Araraquara 14800-060, SP, Brazil
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Separations 2025, 12(2), 21; https://doi.org/10.3390/separations12020021
Submission received: 27 November 2024 / Revised: 20 January 2025 / Accepted: 21 January 2025 / Published: 24 January 2025
Browse Figure
Versions Notes

Abstract

:
Plant metabolomics, lipidomics, ionomics, fluxomics, and peptidomics are essential approaches for exploring how plants respond to epigenetic, pathological, and environmental stimuli through comprehensive chemical profiling. Over the past decades, significant progress has been made in protocols and methodologies to address the challenges in sample collection and extraction. Despite these advancements, sample preparation remains intricate, with ongoing debates about the most effective strategies. This review emphasizes the importance of clear research questions and well-designed experiments to minimize complexity, save time, and enhance reproducibility. It provides an overview of the key steps in these fields, including harvesting, drying, extraction, and data pre-acquisition for major analytical platforms. By discussing best practices and common challenges, this review aims to streamline methods and promote more consistent and reliable research outcomes.

1. Introduction

In recent years, advancements in omics technologies have greatly enhanced our understanding of the biochemical and physiological processes in plants. Approaches such as metabolomics [1,2,3,4,5], lipidomics [1,4,5], ionomics [6,7,8], peptidomics [9,10], fluxomics [11], and the more recent single-cell metabolomics [12,13] have collectively provided a more comprehensive overview of the molecular mechanisms that drive plant growth, adaptation, variation in genetic expression, disease resistance, phenotypic traits, and environmental interactions. While each of these omics approaches offers unique insights, they also present significant challenges, particularly in terms of sample preparation.
To address these challenges, various global initiatives have been established to develop standardized protocols for sample handling and analysis. Organizations like the Metabolomics Standards Initiative (MSI) [14], COordination of Standards in MetabOlomicS (COSMOS) [15], and platforms like Metabolomics Workbench [16] have been instrumental in developing guidelines that promote reproducibility and consistency across laboratories. Quality assurance (QA) and quality control (QC) protocols remain pivotal for reliable and replicable metabolomics data [17]. Supporting this effort, the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) provides comprehensive guidelines to enhance data reliability, and is accessible at https://www.mqacc.org/outputs.
The choice of sample preparation protocols is closely linked to the analytical approach—whether targeted, untargeted, fingerprinting, or profiling—and the specific omics layer being studied, such as the complete metabolome or specific classes of compounds [5,18,19,20,21,22,23,24]. Each method requires different levels of precision, accuracy, and chemical coverage. For instance, untargeted methods aim to capture as many metabolites as possible, providing a comprehensive snapshot of the plant’s biochemical state. In contrast, targeted approaches focus on the precise quantification of specific metabolites.
Sample preparation presents several challenges, mainly due to the chemical diversity and concentration range of metabolites, the influence of the plant’s ontogenetic stage, and the specific edaphoclimatic conditions in which the plants are grown/cultivated [1,5,21,23,24,25,26]. These factors vary between controlled environments, such as growth chambers, natural, and open-field settings. Furthermore, constraints related to data acquisition and instrument settings also play a role. No single analytical technique can comprehensively analyze the full range of metabolites—from highly polar to non-polar—in a qualitative or quantitative study [27,28,29]. As a result, combining complementary orthogonal approaches is often necessary. For instance, liquid chromatography–mass spectrometry (LC-MS) is widely used to cover a broad spectrum of semi-polar compounds, while nuclear magnetic resonance (NMR) excels in quantifying a wider range of metabolites.
In lipidomics, efficiently extracting a range of lipids—from membrane lipids to signaling molecules—while preventing degradation during extraction is a core challenge. Techniques like LC-MS and GC-MS are commonly used for these analyses due to their sensitivity and precision [1,4,5]. Ionomics refers to the study of minerals and trace elements in plant and animal systems according to an external change, it is an approach that performs genomic correlations to understand how changes in gene expression influence ionic profiles in organisms [30,31]. Avoiding contamination is critical, as even trace elements can distort the ionic profile. ICP-MS and ICP-OES are frequently employed, providing the necessary specificity and sensitivity to accurate quantification of elemental composition [6,7].
Fluxomics refers to the quantification of the flow of metabolites through their pathways in living organisms and presents the challenge of capturing the real-time movement of these metabolites through biochemical pathways. This omics approach combines experimental techniques in order to simulate metabolic fluxes under stress or normal conditions by using isotopic labeling with mathematical models [32,33]. This requires rapid quenching to halt metabolic activity to preserve the integrity of metabolic fluxes. LC-MS and GC-MS, especially when combined with isotope-labeling techniques, enable the tracking of dynamic metabolic flow [11,34,35]. In peptidomics, which focuses on the profiling of small peptides (<10 kDa), LC-MS/MS is typically used to identify peptides and their post-translational modifications. Ensuring peptide integrity during sample preparation is essential, often requiring protease inhibitors or rapid freezing to prevent degradation [9,10]. Single-cell metabolomics faces the unique challenge of isolating individual cells without contamination from surrounding tissues [36,37].
Overall, selecting an analytical technique depends primarily on the type of sample, but also on factors like metabolite concentration, chemical properties, and the sample volume available. To illustrate the complexities involved, Table 1 compares the main techniques based on sensitivity, reproducibility, resolution, quantification capabilities, and the difficulty of metabolite identification.
With the main challenges in sample preparation across omics fields—metabolomics, lipidomics, ionomics, peptidomics, fluxomics, and single-cell metabolomics—we can delve into experimental design and the specific steps involved in optimizing sample preparation for plant-based studies. Scheme 1 outlines the key steps hierarchically, from defining the research hypothesis to power analysis, the design of experiments (DOE), and sample collection, storage, and transport. This scheme highlights the essential precautions and conditions for each step, allowing adaptations based on the unique requirements of each omics layer.

2. Experimental Hypothesis and Statistical Power Analysis

A strong research hypothesis (RH) is the foundation of a well-designed experiment. It should be directly linked to the metabolic pathways and metabolites of interest, guiding the choice of the most appropriate analytical tools [2].
When designing the experiment, it is important to consider the biological levels involved. For example, metabolite concentrations can vary significantly between leaves on the same branch, different branches, or even between individual plants. Consistent sampling across growth stages and environmental conditions is vital to maintain data integrity and ensure reliable results. In this context, replication is a key factor for boosting an experiment’s statistical power. However, it is critical to employ true replication [22]. Sampling different parts of the same plant or multiple samples from a single source leads to pseudo-replication, which fails to provide independent data points. True replication uses independent experimental units, such as different plants, to capture genuine biological variation. Clearly defining biological units (BUs), experimental units (EUs), and observational units (OUs) within the experimental design ensures accurate data interpretation and avoids replication errors [38].
Randomization is another key factor for controlling potential biases. By randomizing the order of sample collection or treatment application, systematic effects are evenly distributed, minimizing bias in the results. However, randomization is more effective when the sample size is large enough. Statistical validity in experimental studies requires careful power analysis and proper sample size determination. Hypothesis testing can be affected by false positives (type I) and false negatives (type II), which can lead to misleading conclusions. Power analysis helps to identify the minimum sample size needed to achieve the desired effect and level of significance, reducing the likelihood of such errors [22].
In metabolomics, determining the right sample size is particularly challenging due to the high dimensionality in data and the multicollinearity between variables. Optimizing workflows and ensuring consistency in sample collection, extraction, and data analysis are critical to managing these complexities. Moreover, employing quality control (QC) samples at regular intervals helps monitor drift and variability in data, which is essential for maintaining the robustness of experimental results. This aspect is particularly relevant when determining the power analysis and sample size required for untargeted metabolomics studies, where sample heterogeneity is often high [22,39,40,41]. Tools like MetSizeR [42] and MetaboAnalyst [43,44] offer practical methods for calculating sample size and power analysis, addressing the high-dimensional data challenges common in metabolomics.
As in metabolomics, the experimental design for lipidomics studies must consider the complexity of the samples and biological variability. Lipidomics faces challenges due to the variety of lipids, their differences in polarity, size, and solubility. Extraction methods must be standardized. Statistical power analysis is sensitive to the quality of analytical methods and technical variability. The use of quality control samples is crucial. Tools such as LipidQC and MS-DIAL can help in normalization and statistical power analysis, ensuring greater reliability in studies [45,46,47].
In the statistical power analysis of fluxomics, it is challenging to integrate metabolic and isotopic data from stable isotope experiments. It is important to have clear hypotheses about metabolic fluxes and to standardize the collection time to minimize variations. Replication is essential, as small variations in isotope incorporation can introduce errors. Using tools such as 13CFlux and INCA helps to calculate statistical power, ensuring adequate sample sizes to detect changes in fluxes [48,49]. Peptidomics requires careful sample collection and preparation, as peptides degrade easily. Randomization and replication are essential to avoid bias and capture biological variation. Assessing statistical power is challenging due to instrument sensitivity and data complexity. Multicollinearity between peptides can complicate interpretation. The use of QC and tools such as Skyline and MaxQuant helps monitor analytical accuracy and optimize sample sizes in research [50,51].
In ionomic analysis, it is important to consider the variety of ion concentrations in different parts of the plant, such as roots, leaves, and fruits. This diversity is influenced by genotype, evolutionary stage, and environment. Standardizing cultivation and ion extraction is essential for consistent results. Randomization and replication help reduce bias and detect biological variations. The use of tools such as ionomicQC and MetaboAnalyst is crucial to manage high-dimensional data and minimize type I and II errors [52,53].

3. Design of Experiments

The design of experiments (DOE) is essential for ensuring accuracy and reproducibility in metabolomics by systematically identifying and minimizing errors throughout the experimental process [2,54,55,56]. It focuses on key variables and responses relevant to the research hypothesis. Screening designs, such as Fractional, Factorial Designs (FDs) or Plackett–Burman Designs (PBDs) helps pinpoint significant variables with fewer experiments, while optimization designs like Box–Behnken (BB) or Central Composite Design (CCD) refine experimental conditions and create response surfaces to predict optimal setups [56]. DOE has been widely applied across various omics fields, enhancing sample preparation, method development, and data processing. Table 2 illustrates several examples of DOE application in metabolomics, lipidomics, ionomics, peptidomics, and other omics studies.
In metabolomics, DOE frequently optimizes extraction protocols. For example, a Box–Behnken Design in a GC-MS analysis of apple fruit improved the extraction and derivatization of polar compounds, enhancing quantification accuracy [57]. In lipidomics, a Headspace Solid–Phase Microextraction (HS-SPME)-GC-MS study on grape berries used DOE to optimize fiber type, extraction time, and temperature, achieving comprehensive lipid recovery [58]. Ionomics leverages DOE to refine digestion methods for dried fruits using ICP-OES, comparing wet, dry, and microwave digestion techniques to minimize contamination and enhance trace element extraction [59]. Similarly, peptidomics benefits from DOE when optimizing solvent ratios for small peptide extraction, as demonstrated by a Taguchi-based design in an LC-MS study on lichens, which improved peptide recovery [60].
In fluxomics, DOE was used for refining quenching techniques to preserve real-time metabolic fluxes. A Plackett–Burman design applied to a GC-MS study of olive oil optimized temperature and column flow, enabling accurate measurement of dynamic metabolic processes [61]. For single-cell metabolomics, DOE helps refine micro-level extraction and preparation. For example, a fractional factorial design was used to optimize extraction parameters in studies of individual pancreatic progenitor cells, enhancing metabolite recovery [62].
Table 2. Metabolomics studies using DOE for sample preparation, method development and validation, and data processing in omics studies.
Table 2. Metabolomics studies using DOE for sample preparation, method development and validation, and data processing in omics studies.
DOE ApproachAnalytical TechniqueOptimizationPlantRef
Sample Preparation
Box–Behnken designGC-MSExtraction and derivatization of polar compoundsApple fruit[57]
Central composite designLC-UVExtraction (solvent, time, and temperature)Saffron[63]
D-optimalUPLC-MSAccelerated solvent extractionsCamellia sinensis[64]
GC-MSExtraction and derivatization of compoundsArabidopsis thaliana[65]
HS-SPME-GC-MSType of fiber, extraction time, equilibration time, and temperatureGrape berry[66]
ICP-OESDigestion method (wet, dry, microwave)Dried fruits[59]
Full factorial and Box–Behnken designsNMRExtraction (solvent, time, power, and solvent/material ratio)Apricots[67]
Simplex centroidLC-DADMixture of solvents of extractionJatropha species[68]
Taguchi-based designsLC-MSExtraction (gridding solvent/material ratio, and stirring)Lichens[60]
Method Development and Validation
Box–Behnken DesignLC-MSFunnel technology and ion-source parametersStandards[69]
Box–Behnken DesignLC-MSFour parameters for LC and six for ESI-MSMeconopsi species[70]
Central composite and factorial designLC-DADLC solvent, injection volume, temperatureJatropha species[68]
Plackett–Burman and Factorial DesignGC-MSTemperature, ramp rate, split, column flowOlive oils[60]
Data processing
Plackett–Burman and Central Composite DesignLC-MSXCMS parametersStandards[71]
Modified full factorialLC-MSXCMS parametersPoplar[72]

4. Plant Ontology, Harvesting, and Sampling

Plant metabolomics, lipidomics, peptidomics, and others involve a wide range of environmental variations, genetic expression changes, and ontological factors, all of which can influence study results and conclusions [19,73]. Before any harvesting or study design, it is paramount that the plant species under investigation be unequivocally identified, as improper identification can undermine all subsequent results [74].
In studies where plants are collected from different locations, regions, or biomes, considering the plant’s ontogenetic stage—whether flowering or at another developmental phase—is crucial [75,76,77]. Additionally, with the rise of single-cell metabolomics, accurately selecting and sectioning tissues demands a strong understanding of plant anatomy [76]. The Plant Ontology Consortium [76] offers a structured vocabulary covering aspects like plant taxa, development, anatomy, and genetic data.
Harvesting plays a significant role in determining the results of plant omics studies. Factors such as circadian and seasonal cycles can dramatically influence a plant’s chemical profile. It is crucial to schedule multiple harvests at consistent times of the day or season to capture representative metabolite profiles. Additionally, the specific plant organ or tissue harvested and its position—whether exposed to full sunlight or shade—can affect metabolite levels [19,78]. For example, research on Arabidopsis thaliana under ultraviolet light stress revealed higher levels of primary metabolites, such as ascorbate derivatives, and increased production of flavonoids and phenolics [69].
It is also important to consider whether plant material is sourced from greenhouses or natural environments. In controlled greenhouse settings, even small variations in factors like light intensity, irrigation, temperature, and CO2 gradients can affect the metabolic profiles of genetically identical plants [19,23,73]. For studies targeting specific metabolic pathways, these environmental differences can contribute to data variability. To minimize such “edge effects” in greenhouses, redistributing plants between the center and peripheral locations regularly can help maintain uniform conditions [23,79].
In natural settings, where eco-metabolomic research is often conducted, controlling environmental conditions is much more challenging. The use of global positioning systems (GPSs) or tools like Google Maps helps accurately delineate plant locations. However, understanding the exact environmental conditions—such as soil type, herbivory frequency, or the ontogenetic stage of the plant—can be difficult, adding complexity to the interpretation of results. Ideally, sampling regions should be chosen where factors such as precipitation, soil characteristics, altitude, ultraviolet radiation, and diurnal temperature variations are well documented [75,79].

5. Enzymatic Inhibition

After plant material is collected, enzymatic activity must be immediately inhibited to preserve metabolic integrity, also commonly referred to as quenching. This is particularly important for preventing the degradation of sensitive metabolites like ATP and NADH, which can rapidly break down when exposed to heat and light [80,81,82].
The quenching process is vital for ensuring accurate results. For example, Moreira and coworkers [83] demonstrated that the accumulation of chlorogenic acid in certain plants used in traditional Chinese medicine was influenced not only by anabolic processes but also by catabolism and turnover. This highlights how postharvest treatments can shape metabolic profiles.
When performing enzymatic denaturation, a few key principles must be followed: (i) minimal leakage: even as proteins are denaturated, cell structures should remain intact to prevent the release of oxidative species that could degrade metabolites during the extraction process; (ii) energy charge: monitoring the balance between ATP, ADP, and AMP can provide insight into the energy state of the cell, serving as an indicator of metabolic inactivation; (iii) residues from quenching: any solvents, temperature conditions, or equipment used for quenching must not interfere with later steps like lyophilization or extraction [80].
There are several quenching methods available, including the use of hot or cold solvents, acidic or basic conditions, or by freeze-drying. Liquid nitrogen at −180 °C is a widely used method due to its rapid thermal shock, which effectively halts enzymatic activity. Being inert, nitrogen can be easily removed during freeze-drying or storage. However, extreme freezing can sometimes lead to the formation of ice crystals, which might cause cell lysis and the mixing of endogenous and exogenous compounds, including oxidative species [75,82].
Milder quenching methods involve using hot or cold organic solvents like ethanol and methanol, typically at controlled pH values (3–8). However, hot conditions may not completely inhibit enzymatic activity and can potentially degrade sensitive compounds or create artifacts. Cold solvents, such as methanol at −20 to −40 °C or dry ice (−78 °C), are effective for both plant and microbial metabolomics [19,80,82]. For instance, Faijes and colleagues [84] compared different quenching strategies and found that a mixture of ammonium carbonate–methanol provided efficient inactivation with minimal cell lysis (under 10%), and minimal interference with downstream analysis.
In peptidomics, the rapid inhibition of protease activity is crucial, as proteases can degrade endogenous peptides, leading to the loss of valuable information. Common methods to preserve peptide integrity include heating, such as microwave heating [85], as well as freezing [86], and the use of protease inhibitors [87]. These approaches help to ensure that peptides remain intact for subsequent analysis.
Despite its importance, systematic investigations of enzymatic inhibition and quenching procedures in plant omics are still limited. In contrast, extended research has been conducted for other biological matrices. For example, Yang and colleagues [88] tested different quenching and extraction methods in terms of cell membrane integrity and the metabolite abundance of intracellular metabolites, and concluded that for their matrix, mild quenching methods (20% MeOH at −4 °C) wielded better results than harsher conditions. Wang et al. [89] contributed with a systematic evaluation of cell quenching and metabolite extraction of the HeLa carcinoma cell line was performed by combining the GC-MS and LC-MS analytical tools for a wide range of substances, reporting the optimal overall coverage of intracellular metabolites to be a combination of liquid nitrogen and 50% acetonitrile. Canelas et al. [90] evaluated protocols for yeasts, and reported better results with comparatively more abrasive methods, utilizing boiling methanol and chloroform–methanol, and did not discriminate in terms of metabolite classes.
Given the variety of strategies available for selecting a quenching method in plant-based matrices, it is not always clear which is best suited to a researcher’s objectives. When prior knowledge about the plant species being studied is limited, smaller-scale pilot studies are recommended to assess the impact on various substances. However, due to the practical challenges of field sample collection, such studies are not always feasible. In such cases, researchers should choose field-friendly quenching methods, mindful that the appropriate solvent conditions (in terms of temperature and composition) may not be achievable in the field. The selected method should be aligned with the study’s goals, as it may affect data acquisition in terms of detection sensitivity for the targeted substances.

6. Drying, Homogenization, and Storage

The presence of water in plant matrices can significantly impact various stages of plant metabolomics studies. Water not only affects the efficiency of organic solvent extraction but also influences sample storage and can interfere with subsequent instrumental analyses [19,75,79]. Moreover, residual water content can prolong enzymatic activity and promote the growth of bacteria and fungi, further altering the chemical profile [19,91,92]. This is particularly problematic in 1H NMR experiments, where broad water signals can distort signals and complicate data interpretation [19,79].
To mitigate these effects, various drying methods are commonly used, including oven drying with circulating air at temperatures of 40 °C, air drying at room temperature, and vacuum drying, which may involve speed-vacuum systems or be combined with sublimation processes like freeze-drying [19,21,24,79,90,93]. Table 3 expands each method’s advantages and limitations. In the field or in eco-metabolomics studies, where infrastructure may not be accessible, alternative drying methods like using silica gel in paper bags can suffice until samples can be transported to a laboratory for proper drying [94].
In controlled laboratory settings, freeze-drying is the most used technique, particularly for samples that are sensitive to heat [23,24,95]. This process involves two stages: (i) cooling the sample, followed by (ii) sublimation at reduced pressure and around −40 °C. While freeze-drying produces highly reproducible results, it has drawbacks. Rapid sublimation or excessive thermal shock can cause metabolites to adhere to cell walls, affecting extraction quality and yield. Additionally, freeze-drying under reduced pressure can lead to the loss of volatile compounds, altering the sample’s chemical profile. Air drying and oven drying are more common in phytochemistry, but they must be used cautiously in metabolomics to avoid errors related to water content or inconsistent exposure to light or heat, which can result in chemical imbalances.
Homogenization is another critical step to ensure sample consistency and comparability [19,21,79,91]. Crushing or grinding reduces tissue size, which enhances uniformity and solvent contact. Standardizing this process, known as granulometric control, is essential. Mortar and pestle are commonly employed, but for larger sample sets, equipment like ball mills, cryogenics, or vertical homogenizers provides rapid and uniform sample comminution [18,25]. For example, research by Zheng et al. (2020) [96] demonstrated how thermal processing, such as the baking and frying of blueberry-filled bakery products, impacted their phytochemical profile, especially anthocyanins. This highlights the importance of controlling sample processing steps that involve heat, as new compounds can form, affecting results.
Ideally, samples should be processed through grinding, drying, and extraction soon after harvesting. If immediate processing is not possible, storage at −80 °C is recommended. Lower temperatures like −20 °C or 4 °C are often insufficient, as biochemical reactions may continue, especially in samples with salts or organic solvents. Proper storage helps prevent unwanted chemical changes and preserve the sample’s metabolic profile [85,97].
For transport, especially from field sites, using dry ice or liquid nitrogen in well-insulated containers helps maintain low temperatures and prevents sample degradation. Packaging should be designed to sustain −60 °C for at least a week, and freeze-dried samples should be sealed in plastic bags with desiccants like silica gel to prevent moisture absorption. It is best to avoid storage temperatures between 0 and 40 °C, as metabolites may concentrate in residual aqueous phases, altering their properties [23].

7. Extraction Process

Until this point, the steps discussed are relevant to various analytical platforms, regardless of whether the compounds are volatile, non-volatile, polar, or non-polar. However, with the extraction process, the choice of analytical techniques becomes crucial. The extraction method used determines chemical coverage, polarity, and solubility, as well as the suitability for specific analytical platforms. It is crucial to keep extractions as simple, rapid, and minimal as possible, as metabolomics typically requires highly reproducible protocols for large-scale experiments.
A range of extraction techniques are available, including solvent extraction (maceration), steam distillation, supercritical fluid extraction, ionic liquids, and solvent extraction under high temperatures and pressures [18,20,75,85,98]. Device-assisted extractions like ultrasound-assisted extraction offer high efficiency [99]. Unlike microwave-assisted extraction, ultrasound minimizes the overheating or degradation of volatile compounds, making it a preferred method due to its simplicity, speed, and solvent flexibility. That said, longer extraction times and greater temperature fluctuations may yield more compounds but also increase the risk of reactions and degradation [85].
Liquid pressurization extraction (LPE) uses high temperatures and pressures to increase extraction efficiency, reducing solvent consumption in closed systems. Enzymatic methods use enzymes to release bioactive compounds from complex matrices without the need for aggressive solvents and stand out for their specificity. Adsorption on activated carbon is widely used due to its selective adsorption. Membrane extraction uses selective barriers to separate compounds based on physicochemical characteristics, while cold plasma-assisted extraction appears to be a promising alternative, disrupting cellular structures quickly and efficiently. Hyperbaric extraction, in turn, uses extreme pressures to access compounds that are difficult to extract, increasing the recovery of bioactive compounds [100,101,102,103,104,105].
Organic solvents combined with ultrasound-assisted extractions are frequently employed due to their compatibility with LC-MS, GC-MS, and NMR. Environmental concerns have spurred the development of more sustainable extraction techniques, like Natural Deep Eutectic Solvents (NADESs), supercritical CO2, and greener organic solvents [98,106,107]. While optimizing the extraction step is important, certain methods can hinder comparisons with other studies or databases due to differences in solubilizing power, toxicity, and selectivity.
Methanol–water mixtures (50–90% methanol) are commonly used for broad metabolic coverage, extracting sugars, organic acids, alkaloids, and phenolic compounds [85,87,108]. Ethanol is also a greener alternative. Controlling pH (around 6–7) during extraction helps prevent artifact formation from oxidative reactions. In NMR, pH, water, and other solvents can shift chemical signals and affect retention times in HPLC and ionization in mass spectrometry, particularly with electrospray ionization. Buffers help minimize these effects in NMR, while in LC-MS, it is important to avoid metal salt precipitation in the ionization source [109].
Methanol, water, and chloroform (2:2:1) effectively extract both polar and non-polar compounds. For example, Lisec et al. (2006) [110] developed a protocol for GC-MS plant metabolomics, optimized to minimize enzyme activity. This protocol has been effective in extracting a wide range of metabolites, including primary metabolites and specialized compounds like glucosinolates, alkaloids, and polyamines [111]. LC-MS is particularly well-suited to semi-polar metabolite profiling, as shown by De Vos et al. (2007) [112], who used acidity methanol (formic acid) and sonication to extract plant tissues like A. thaliana, tomato, and strawberry [113,114].
The Lipidomics Standards Initiative (LSI) was stablished to allow for the standardization and comparison of results across different lipidomic studies (https://lipidomicstandards.org) (accessed on 20 October 2024). Popular lipid extraction protocols include Folch [115], Bligh and Dyer [116], Matyash [117], and the BUME method [118,119]. These methods, mainly developed for animal tissues, involve varying solvent ratios. For instance, some protocols use methanol followed by MTBE, adding water to induce phase separation. This chloroform-free method offers high recovery rates across lipid types. The BUME method, suited for high throughput extractions, uses smaller solvent volumes. Since these protocols were developed for animal matrices, it is recommended to perform pilot studies on plant organs, as results may vary for leaves, bark, seed, or fruits. Creydt and colleagues (2018) [120] found that the Folch protocol was optimal for their matrix, while Romsdahl et al. (2022) [121] and Hu et al. (2021) [122] identified Matyash as most suitable for their lipid analyses of seeds and oilseeds. Table 4 shows some analytical methods applied to different type of compounds.
Kim et al. (2010) [19] focused on the 1H NMR analysis of primary and secondary metabolites, utilizing labeled solvents like deuterated methanol and water, to enhance signal clarity. Advances now allow for single-cell mass spectrometry for rapid metabolite detection, enabling the real-time analysis of individual plant cells. Fujii et al. (2015) [99] developed a protocol involving nanospray microcapillary tips and optical microscopy for single-cell metabolomics, allowing for precise detection from plant cells with minimal manipulation. Table 5 shows some protocols commonly used in plant omics analysis.

8. Pre-Processing to Acquisition

With extracts prepared, it is essential to address potential interferences and residues that could affect data acquisition. The pre-processing steps vary depending on the analytical platform and the metabolites being analyzed. For example, when examining polar compounds from aqueous alcohol extracts with GC-MS, an additional methoxylation step is often required. This step involves the cyclization of sugars and protection of carbonyl groups, followed by silylation to enhance compound volatility. BSTFA and MSTFA are common silylating agents, with BSTFA derivatives typically resulting in longer retention times.
A factor to be considered in LC-MS analysis is the matrix effect, which is caused by competition between the sample constituents with the analyte(s) of interest; this affects the ionization efficiency, which means that the generated signal can be increased due to ion enrichment or decreased due to ion suppression. Especially at low concentrations, the matrix effect can cause a reduction in the accuracy and precision of results. Considering the complexity of the sample, some procedures can be included to eliminate or at least minimize the matrix effect. This can be achieved by changing the ionization source usually used, improving the chromatographic conditions with the use of corrective calibration methods, optimizing the extraction method and sample dilution [134,135].
For LC-MS analysis, extracts from chloroform–methanol mixtures frequently need further cleanup. Since many secondary metabolites are analyzed in reversed-phase mode, it is helpful to remove non-polar compounds like fatty acids using reversed-phase cartridges (C8, C18, or similar), preventing column clogging, and enhancing efficiency and reproducibility. Filtering before injections is crucial, with 0.45 µm and 0.22 µm PTFE filters being widely used to eliminate particulates and prevent system blockages.
In fluxomics analysis, sample preparation requires careful use of isotopic markers, such as (13C), (15N), or (2H), to monitor metabolic flux. Pre-processing involves eliminating cellular debris. Filtration and centrifugation help reduce metabolite losses. It is important to separate non-reactive tracers prior to analysis [136,137]. Preprocessing for peptidomics analysis often includes enzymatic digestion to convert proteins into peptides, with trypsin being widely used for specific cuts. After digestion, cleanup is performed using techniques such as solid-phase extraction or protein precipitation. Complex samples may require fractionation by high-performance liquid chromatography. Desalination is important prior to LC-MS/MS analysis to remove salts that may affect ionization and mass spectra [138,139]. Lipidomics requires the careful extraction and storage of samples to avoid lipid oxidation. Solvents such as methanol–chloroform–water are used to separate phases. The removal of unwanted lipids and the addition of antioxidants are important. Modifications in ESI ionization are necessary to improve analysis [140,141].
The goal of preprocessing for ionomics is to eliminate inorganic pollutants that could impair trace element detection by ICP-MS or ICP-OES. Plant samples are digested with potent acids to release ions of interest [142]. In NMR or ICP (OES or MS) analysis, removing particulates is equally important to ensure shimming quality and clear chemical shift resolution. Centrifugation at 13,000× g or filtration aid in this, while cartridge filters or suppression pulse sequences address interferences from high concentrations of sugars, fatty acids, and phthalates, helping maintain data integrity.

9. Minimum Reports and Best Practices in mQACC

Standardization is critical for ensuring comparability and reliability in metabolomics research. While targeted metabolomics benefits from established guidelines by organizations like COSMOS, Metabolomics Workbench and MSI for metabolite quantification, untargeted metabolomics faces challenges due to its exploratory nature. The mQACC consortium addresses these gaps by promoting QA/QC practices, aiming to harmonize quality standards and enhance reproducibility and confidence in results [17].
Key recommendations include minimum reporting standards that ensure basic but essential information, such as instrument details, sample preparation methods, and quality control criteria. Best practices build on this foundation, encouraging the comprehensive documentation of experimental parameters and, where feasible, making raw and processed data available. These practices strengthen methodological integrity and align with mQACC’s broader goal of advancing metabolomics research through transparent and reproducible methods [143].
Quality control (QC) samples play a vital role in maintaining analytical reliability. These include pooled QC samples for monitoring system stability and correcting drift, internal standards for addressing fluctuations during analysis, and long-term reference materials for evaluating instrument stability across runs. Blanks and system suitability samples further ensure data quality by identifying contaminants and verifying system performance [143,144].
Together, these practices provide a structured framework that integrates QC measures with robust reporting standards, enabling researchers to navigate methodological challenges and deliver reliable metabolomics data. Table 6 provides a simplified overview of the concepts of best practices and minimum reports.
Quality assurance and control should include clear acceptance criteria, such as analysis frequency, calculation of Relative Standard Deviation (RSD), and intra- and inter-batch variabilities for reported compounds. Whenever possible, both raw and processed data should be made available, along with batch drift corrections, including details on the software and parameters used. QC performance can be monitored manually, but automated scripts are recommended for system suitability checks, evaluating parameters like baseline stability [17,143,144].
Sample preparation is a critical process that includes metabolism quenching to preserve metabolite integrity, along with strict control over storage conditions like temperature and aliquot size. Standardized thawing and aliquoting schemes ensure consistent handling of both experimental and quality control (QC) samples. Key steps such as solvent selection, extraction, derivatization, centrifugation, and filtration must follow rigorous protocols to maintain data quality [144,145].
Different analytical technologies, including LC-MS, GC-MS, and NMR, have distinct requirements. For LC-MS, careful parameter documentation, ionization settings, and the use of standards are vital for data integrity. GC-MS requires compound derivatization and precise temperature control, while NMR relies on chemical shift calibration and stringent control of magnetic field strength. Despite these variations, robust QC protocols, including the use of QC samples and adherence to standardized operating procedures (SOPs), are fundamental across all platforms to ensure reproducibility and reliability [120].
The control of systematic block effects, arising from experimental or environmental variations, is another essential aspect. Using control samples helps monitor accuracy and identify anomalies. Statistical tools such as Principal Component Analysis (PCA) assist in isolating patterns and managing batch effects, enhancing data quality. Uniform adoption of SOPs further strengthens validation and replication efforts, ensuring reliable and interpretable analytical results [17,145].

10. Concluding Remarks

Despite the rapid advances in chromatography, mass spectrometry (MS), and NMR technologies, plant metabolomics and their related fields still face challenges, especially in harvesting, storage, and the extracting the wide range of metabolites in plant matrices. For a successful study, it is critical to focus on sample preparation by: (i) developing a clear and focused research hypothesis that considers the study’s objectives and all potential confounding factors; (ii) implementing an experimental design aligned with the study’s aims; and (iii) minimizing random errors, from enzymatic activity to drying, storage, and extraction.
Emerging technologies, like the miniaturization of live single-cell analysis and ion mobility for MS separation, offer new ways to explore the biochemical mechanisms in plant metabolomics. The expansion of metabolomic databases and advanced analytical tools also promises to streamline the identification of the vast array of signals and molecules encountered in these studies, making the process more accurate and efficient.

Author Contributions

W.B.d.S. was involved in investigation, data curation, and writing—review and editing. G.F.H. contributed to investigation, data curation, and writing—review and editing. E.V.d.S.N. was responsible for investigation, data curation, and writing—review and editing. I.C.-G. provided supervision and revision. A.C.P. was responsible for conceptualization, investigation, data curation, writing—original draft, review and editing, and validation. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financed, in part, by the São Paulo Research Foundation (FAPESP), Brasil, (Process Numbers #14/50926-0, #20/02207-5, 20/11967-3 and #19/15889-0). We also are grateful for funding from the Ministry of Health and the University of Brasília (TED 74/2016 and TED 42/2017) and CNPq (Process Number 421682/2023-0). The authors also thank CAPES for scholarships awarded to W.B.S. (88887.940697/2024-00), G.F.H.B. (88887.992411/2024-00), and E.V.S. (88887.833848/2023-00).

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Rakusanova, S.; Cajka, T. Tips and Tricks for LC–MS-Based Metabolomics and Lipidomics Analysis. TrAC Trends Anal. Chem. 2024, 180, 117940. [Google Scholar] [CrossRef]
  2. Jacyna, J.; Kordalewska, M.; Markuszewski, M.J. Design of Experiments in Metabolomics-Related Studies: An Overview. J. Pharm. Biomed. Anal. 2019, 164, 598–606. [Google Scholar] [CrossRef] [PubMed]
  3. Pilon, A.C.; Carneiro, R.L.; Carnevale Neto, F.; Da, S.; Bolzani, V.; Castro-Gamboa, I. Interval Multivariate Curve Resolution in the Dereplication of HPLC-DAD Data from Jatropha Gossypifolia. Phytochem. Anal. 2013, 24, 401–406. [Google Scholar] [CrossRef]
  4. Köfeler, H.C.; Ahrends, R.; Baker, E.S.; Ekroos, K.; Han, X.; Hoffmann, N.; Holčapek, M.; Wenk, M.R.; Liebisch, G. Recommendations for Good Practice in MS-Based Lipidomics. J. Lipid Res. 2021, 62, 100138. [Google Scholar] [CrossRef] [PubMed]
  5. Züllig, T.; Trötzmüller, M.; Köfeler, H.C. Lipidomics from Sample Preparation to Data Analysis: A Primer. Anal. Bioanal. Chem. 2020, 412, 2191–2209. [Google Scholar] [CrossRef] [PubMed]
  6. Zhang, Y.; Xu, Y.; Zheng, L. Disease Ionomics: Understanding the Role of Ions in Complex Disease. Int. J. Mol. Sci. 2020, 21, 8646. [Google Scholar] [CrossRef] [PubMed]
  7. Filipiak, M.; Filipiak, Z.M. Application of Ionomics and Ecological Stoichiometry in Conservation Biology: Nutrient Demand and Supply in a Changing Environment. Biol. Conserv. 2022, 272, 109622. [Google Scholar] [CrossRef]
  8. Lin, G.; Ma, L.; He, X.; Tang, J.; Wang, L. Gene Regulation and Ionome Homeostasis in Rice Plants in Response to Arsenite Stress: Potential Connection between Transcriptomics and Ionomics. BioMetals 2023, 36, 1157–1169. [Google Scholar] [CrossRef] [PubMed]
  9. Schrader, M. Origins, Technological Development, and Applications of Peptidomics; Humana Press: New York, NY, USA, 2018; pp. 3–39. [Google Scholar] [CrossRef]
  10. Schrader, M.; Fricker, L.D. Current Challenges and Future Directions in Peptidomics; Humana Press: New York, NY, USA, 2024; pp. 485–498. [Google Scholar] [CrossRef]
  11. Emwas, A.-H.; Szczepski, K.; Al-Younis, I.; Lachowicz, J.I.; Jaremko, M. Fluxomics—New Metabolomics Approaches to Monitor Metabolic Pathways. Front. Pharmacol. 2022, 13, 805782. [Google Scholar] [CrossRef]
  12. Ali, A.; Davidson, S.; Fraenkel, E.; Gilmore, I.; Hankemeier, T.; Kirwan, J.A.; Lane, A.N.; Lanekoff, I.; Larion, M.; McCall, L.-I.; et al. Single Cell Metabolism: Current and Future Trends. Metabolomics 2022, 18, 77. [Google Scholar] [CrossRef]
  13. de Souza, L.P.; Borghi, M.; Fernie, A. Plant Single-Cell Metabolomics—Challenges and Perspectives. Int. J. Mol. Sci. 2020, 21, 8987. [Google Scholar] [CrossRef]
  14. Fiehn, O.; Robertson, D.; Griffin, J.; van der Werf, M.; Nikolau, B.; Morrison, N.; Sumner, L.W.; Goodacre, R.; Hardy, N.W.; Taylor, C.; et al. The Metabolomics Standards Initiative (MSI). Metabolomics 2007, 3, 175–178. [Google Scholar] [CrossRef]
  15. Salek, R.M.; Neumann, S.; Schober, D.; Hummel, J.; Billiau, K.; Kopka, J.; Correa, E.; Reijmers, T.; Rosato, A.; Tenori, L.; et al. COordination of Standards in MetabOlomicS (COSMOS): Facilitating Integrated Metabolomics Data Access. Metabolomics 2015, 11, 1587–1597. [Google Scholar] [CrossRef] [PubMed]
  16. Sud, M.; Fahy, E.; Cotter, D.; Azam, K.; Vadivelu, I.; Burant, C.; Edison, A.; Fiehn, O.; Higashi, R.; Nair, K.S.; et al. Metabolomics Workbench: An International Repository for Metabolomics Data and Metadata, Metabolite Standards, Protocols, Tutorials and Training, and Analysis Tools. Nucleic Acids Res. 2016, 44, D463–D470. [Google Scholar] [CrossRef] [PubMed]
  17. Lippa, K.A.; Aristizabal-Henao, J.J.; Beger, R.D.; Bowden, J.A.; Broeckling, C.; Beecher, C.; Clay Davis, W.; Dunn, W.B.; Flores, R.; Goodacre, R.; et al. Reference Materials for MS-Based Untargeted Metabolomics and Lipidomics: A Review by the Metabolomics Quality Assurance and Quality Control Consortium (mQACC). Metabolomics 2022, 18, 24. [Google Scholar] [CrossRef] [PubMed]
  18. Álvarez-Sánchez, B.; Priego-Capote, F.; de Castro, M.L. Metabolomics Analysis I. Selection of Biological Samples and Practical Aspects Preceding Sample Preparation. TrAC Trends Anal. Chem. 2010, 29, 111–119. [Google Scholar] [CrossRef]
  19. Kim, H.K.; Choi, Y.H.; Verpoorte, R. NMR-Based Metabolomic Analysis of Plants. Nat. Protoc. 2010, 5, 536–549. [Google Scholar] [CrossRef] [PubMed]
  20. Álvarez-Sánchez, B.; Priego-Capote, F.; de Castro, M.L. Metabolomics Analysis II. Preparation of Biological Samples Prior to Detection. TrAC Trends Anal. Chem. 2010, 29, 120–127. [Google Scholar] [CrossRef]
  21. Rodrigues, A.M.; Ribeiro-Barros, A.I.; António, C. Experimental Design and Sample Preparation in Forest Tree Metabolomics. Metabolites 2019, 9, 285. [Google Scholar] [CrossRef]
  22. Blaise, B.J.; Correia, G.; Tin, A.; Young, J.H.; Vergnaud, A.-C.; Lewis, M.; Pearce, J.T.M.; Elliott, P.; Nicholson, J.K.; Holmes, E.; et al. Power Analysis and Sample Size Determination in Metabolic Phenotyping. Anal. Chem. 2016, 88, 5179–5188. [Google Scholar] [CrossRef] [PubMed]
  23. Biais, B.; Bernillon, S.; Deborde, C.; Cabasson, C.; Rolin, D.; Tadmor, Y.; Burger, J.; Schaffer, A.A.; Moing, A. Precautions for Harvest, Sampling, Storage, and Transport of Crop Plant Metabolomics Samples; Humana Press: New York, NY, USA, 2012; pp. 51–63. [Google Scholar] [CrossRef]
  24. William Allwood, J.; Winder, C.L.; Dunn, W.B.; Goodacre, R. Considerations in Sample Preparation, Collection, and Extraction Approaches Applied in Microbial, Plant, and Mammalian Metabolic Profiling. In Methodologies for Metabolomics; Cambridge University Press: Cambridge, UK, 2013; pp. 79–118. [Google Scholar] [CrossRef]
  25. Lin, C.Y.; Wu, H.; Tjeerdema, R.S.; Viant, M.R. Evaluation of Metabolite Extraction Strategies from Tissue Samples Using NMR Metabolomics. Metabolomics 2007, 3, 55–67. [Google Scholar] [CrossRef]
  26. Römisch-Margl, W.; Prehn, C.; Bogumil, R.; Röhring, C.; Suhre, K.; Adamski, J. Procedure for Tissue Sample Preparation and Metabolite Extraction for High-Throughput Targeted Metabolomics. Metabolomics 2012, 8, 133–142. [Google Scholar] [CrossRef]
  27. Segers, K.; Declerck, S.; Mangelings, D.; Heyden, Y.V.; Eeckhaut, A.V. Analytical Techniques for Metabolomic Studies: A Review. Bioanalysis 2019, 11, 2297–2318. [Google Scholar] [CrossRef] [PubMed]
  28. Manickam, S.; Rajagopalan, V.R.; Kambale, R.; Rajasekaran, R.; Kanagarajan, S.; Muthurajan, R. Plant Metabolomics: Current Initiatives and Future Prospects. Curr. Issues Mol. Biol. 2023, 45, 8894–8906. [Google Scholar] [CrossRef] [PubMed]
  29. Vinay, C.M.; Udayamanoharan, S.K.; Prabhu Basrur, N.; Paul, B.; Rai, P.S. Current Analytical Technologies and Bioinformatic Resources for Plant Metabolomics Data. Plant Biotechnol. Rep. 2021, 15, 561–572. [Google Scholar] [CrossRef]
  30. Salt, D.E.; Baxter, I.; Lahner, B. Ionomics and the Study of the Plant Ionome. Annu. Rev. Plant Biol. 2008, 59, 709–733. [Google Scholar] [CrossRef]
  31. Yin, H.; Yan, Y.; Hu, W.; Liu, G.; Zeng, H.; Wei, Y.; Shi, H. Genome-Wide Association Studies Reveal Genetic Basis of Ionomic Variation in Cassava. Plant J. 2022, 112, 1212–1223. [Google Scholar] [CrossRef] [PubMed]
  32. Mishra, M.K.; Kumar, A. Fluxomics and Metabolic Flux Analysis. In Multi-Omics Analysis of the Human Microbiome: From Technology to Clinical Applications; Mani, I., Singh, V., Eds.; Springer Nature: Singapore, 2024; pp. 171–180. [Google Scholar] [CrossRef]
  33. Klein, S.; Heinzle, E. Isotope Labeling Experiments in Metabolomics and Fluxomics. WIREs Syst. Biol. Med. 2012, 4, 261–272. [Google Scholar] [CrossRef]
  34. Tian, B.; Chen, M.; Liu, L.; Rui, B.; Deng, Z.; Zhang, Z.; Shen, T. 13C Metabolic Flux Analysis: Classification and Characterization from the Perspective of Mathematical Modeling and Application in Physiological Research of Neural Cell. Front. Mol. Neurosci. 2022, 15, 883466. [Google Scholar] [CrossRef] [PubMed]
  35. de Falco, B.; Giannino, F.; Carteni, F.; Mazzoleni, S.; Kim, D.-H. Metabolic Flux Analysis: A Comprehensive Review on Sample Preparation, Analytical Techniques, Data Analysis, Computational Modelling, and Main Application Areas. RSC Adv. 2022, 12, 25528–25548. [Google Scholar] [CrossRef] [PubMed]
  36. Pandian, K.; Matsui, M.; Hankemeier, T.; Ali, A.; Okubo-Kurihara, E. Advances in Single-Cell Metabolomics to Unravel Cellular Heterogeneity in Plant Biology. Plant Physiol. 2023, 193, 949–965. [Google Scholar] [CrossRef]
  37. Cole, B.; Bergmann, D.; Blaby-Haas, C.E.; Blaby, I.K.; Bouchard, K.E.; Brady, S.M.; Ciobanu, D.; Coleman-Derr, D.; Leiboff, S.; Mortimer, J.C.; et al. Plant Single-Cell Solutions for Energy and the Environment. Commun. Biol. 2021, 4, 962. [Google Scholar] [CrossRef]
  38. Lazic, S.E.; Clarke-Williams, C.J.; Munafò, M.R. What Exactly Is ‘N’ in Cell Culture and Animal Experiments? PLoS Biol. 2018, 16, e2005282. [Google Scholar] [CrossRef] [PubMed]
  39. Dudzik, D.; Barbas-Bernardos, C.; García, A.; Barbas, C. Quality Assurance Procedures for Mass Spectrometry Untargeted Metabolomics. a Review. J. Pharm. Biomed. Anal. 2018, 147, 149–173. [Google Scholar] [CrossRef] [PubMed]
  40. Godzien, J.; Alonso-Herranz, V.; Barbas, C.; Armitage, E.G. Controlling the Quality of Metabolomics Data: New Strategies to Get the Best out of the QC Sample. Metabolomics 2015, 11, 518–528. [Google Scholar] [CrossRef]
  41. Mosley, J.D.; Schock, T.B.; Beecher, C.W.; Dunn, W.B.; Kuligowski, J.; Lewis, M.R.; Theodoridis, G.; Ulmer Holland, C.Z.; Vuckovic, D.; Wilson, I.D.; et al. Establishing a Framework for Best Practices for Quality Assurance and Quality Control in Untargeted Metabolomics. Metabolomics 2024, 20, 20. [Google Scholar] [CrossRef]
  42. Nyamundanda, G.; Gormley, I.; Fan, Y.; Gallagher, W.M.; Brennan, L. MetSizeR: Selecting the Optimal Sample Size for Metabolomic Studies Using an Analysis Based Approach. BMC Bioinform. 2013, 14, 338. [Google Scholar] [CrossRef] [PubMed]
  43. Pang, Z.; Lu, Y.; Zhou, G.; Hui, F.; Xu, L.; Viau, C.; Spigelman, A.F.; MacDonald, P.E.; Wishart, D.S.; Li, S.; et al. MetaboAnalyst 6.0: Towards a Unified Platform for Metabolomics Data Processing, Analysis and Interpretation. Nucleic Acids Res. 2024, 52, W398–W406. [Google Scholar] [CrossRef]
  44. Xia, J.; Wishart, D.S. Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis. Curr. Protoc. Bioinform. 2016, 55, 14.10.1–14.10.91. [Google Scholar] [CrossRef]
  45. Ulmer, C.Z.; Ragland, J.M.; Koelmel, J.P.; Heckert, A.; Jones, C.M.; Garrett, T.J.; Yost, R.A.; Bowden, J.A. LipidQC: Method Validation Tool for Visual Comparison to SRM 1950 Using NIST Interlaboratory Comparison Exercise Lipid Consensus Mean Estimate Values. Anal. Chem. 2017, 89, 13069–13073. [Google Scholar] [CrossRef]
  46. Tsugawa, H.; Ikeda, K.; Takahashi, M.; Satoh, A.; Mori, Y.; Uchino, H.; Okahashi, N.; Yamada, Y.; Tada, I.; Bonini, P.; et al. A Lipidome Atlas in MS-DIAL 4. Nat. Biotechnol. 2020, 38, 1159–1163. [Google Scholar] [CrossRef]
  47. Can Eylem, C.; Nemutlu, E.; Dogan, A.; Acik, V.; Matyar, S.; Gezercan, Y.; Altintas, S.; Okten, A.I.; Basci Akduman, N.E. Optimized High-Throughput Protocols for Comprehensive Metabolomic and Lipidomic Profiling of Brain Sample. Talanta 2025, 282, 126953. [Google Scholar] [CrossRef] [PubMed]
  48. Bednarski, T.K.; Rahim, M.; Young, J.D. In Vivo 2H/13C Flux Analysis in Metabolism Research. Curr. Opin. Biotechnol. 2021, 71, 1–8. [Google Scholar] [CrossRef]
  49. Ahn, E.; Kumar, P.; Mukha, D.; Tzur, A.; Shlomi, T. Temporal Fluxomics Reveals Oscillations in TCA Cycle Flux throughout the Mammalian Cell Cycle. Mol. Syst. Biol. 2017, 13, 953. [Google Scholar] [CrossRef] [PubMed]
  50. Hellinger, R.; Sigurdsson, A.; Wu, W.; Romanova, E.V.; Li, L.; Sweedler, J.V.; Süssmuth, R.D.; Gruber, C.W. Peptidomics. Nat. Rev. Methods Primers 2023, 3, 25. [Google Scholar] [CrossRef] [PubMed]
  51. Gabriel, W.; Picciani, M.; The, M.; Wilhelm, M. Deep Learning-Assisted Analysis of Immunopeptidomics Data. In Peptidomics: Methods and Strategies; Schrader, M., Fricker, L.D., Eds.; Springer: New York, NY, USA, 2024; pp. 457–483. [Google Scholar] [CrossRef]
  52. Yang, Y.; Saand, M.A.; Huang, L.; Abdelaal, W.B.; Zhang, J.; Wu, Y.; Li, J.; Sirohi, M.H.; Wang, F. Applications of Multi-Omics Technologies for Crop Improvement. Front. Plant Sci. 2021, 12, 563953. [Google Scholar] [CrossRef]
  53. Anguita-Maeso, M.; Haro, C.; Montes-Borrego, M.; De La Fuente, L.; Navas-Cortés, J.A.; Landa, B.B. Metabolomic, Ionomic and Microbial Characterization of Olive Xylem Sap Reveals Differences According to Plant Age and Genotype. Agronomy 2021, 11, 1179. [Google Scholar] [CrossRef]
  54. Bruns, R.; Scarminio, I.; de Barros Neto, B. Statistical Design—Chemimetrics, 1st ed.; Bruns, R., Scarmínio, I., de Barros Neto, B., Eds.; Elsevier Science: Amsterdam, The Netherlands, 2006. [Google Scholar]
  55. Leardi, R. Experimental Design in Chemistry: A Tutorial. Anal. Chim. Acta 2009, 652, 161–172. [Google Scholar] [CrossRef] [PubMed]
  56. Brereton, R.G.; Jansen, J.; Lopes, J.; Marini, F.; Pomerantsev, A.; Rodionova, O.; Roger, J.M.; Walczak, B.; Tauler, R. Chemometrics in Analytical Chemistry—Part I: History, Experimental Design and Data Analysis Tools. Anal. Bioanal. Chem. 2017, 409, 5891–5899. [Google Scholar] [CrossRef] [PubMed]
  57. Bekele, E.A.; Annaratone, C.E.P.; Hertog, M.L.; Nicolai, B.M.; Geeraerd, A.H. Multi-Response Optimization of the Extraction and Derivatization Protocol of Selected Polar Metabolites from Apple Fruit Tissue for GC–MS Analysis. Anal. Chim. Acta 2014, 824, 42–56. [Google Scholar] [CrossRef]
  58. Li, J.; Zhang, S.; Zhang, M.; Sun, B. Novel Approach for Extraction of Grape Skin Antioxidants by Accelerated Solvent Extraction: Box–Behnken Design Optimization. J. Food Sci. Technol. 2019, 56, 4879–4890. [Google Scholar] [CrossRef] [PubMed]
  59. Altundag, H.; Tuzen, M. Comparison of Dry, Wet and Microwave Digestion Methods for the Multi Element Determination in Some Dried Fruit Samples by ICP-OES. Food Chem. Toxicol. 2011, 49, 2800–2807. [Google Scholar] [CrossRef] [PubMed]
  60. Parrot, D.; Peresse, T.; Hitti, E.; Carrie, D.; Grube, M.; Tomasi, S. Qualitative and Spatial Metabolite Profiling of Lichens by a LC-MS Approach Combined with Optimised Extraction. Phytochem. Anal. 2015, 26, 23–33. [Google Scholar] [CrossRef] [PubMed]
  61. Boggia, R.; Borgogni, C.; Hysenaj, V.; Leardi, R.; Zunin, P. Direct GC–(EI)MS Determination of Fatty Acid Alkyl Esters in Olive Oils. Talanta 2014, 119, 60–67. [Google Scholar] [CrossRef]
  62. Merz, M.; Fischer, L.; Sia, J.; Born, P.; Weiss, R.; Grieb, N.; Rade, M.; Boldt, A.; Fricke, S.; Franz, P.; et al. Single Cell Multi-Omic Dissection of Response and Resistance to Chimeric Antigen Receptor T Cells Against BCMA in Relapsed Multiple Myeloma. Blood 2023, 142 (Suppl. S1), 453. [Google Scholar] [CrossRef]
  63. Sarfarazi, M.; Jafari, S.M.; Rajabzadeh, G. Extraction Optimization of Saffron Nutraceuticals Through Response Surface Methodology. Food Anal. Methods 2015, 8, 2273–2285. [Google Scholar] [CrossRef]
  64. Kellogg, J.J.; Wallace, E.D.; Graf, T.N.; Oberlies, N.H.; Cech, N.B. Conventional and Accelerated-Solvent Extractions of Green Tea (Camellia sinensis) for Metabolomics-Based Chemometrics. J. Pharm. Biomed. Anal. 2017, 145, 604–610. [Google Scholar] [CrossRef] [PubMed]
  65. Gullberg, J.; Jonsson, P.; Nordström, A.; Sjöström, M.; Moritz, T. Design of Experiments: An Efficient Strategy to Identify Factors Influencing Extraction and Derivatization of Arabidopsis Thaliana Samples in Metabolomic Studies with Gas Chromatography/Mass Spectrometry. Anal. Biochem. 2004, 331, 283–295. [Google Scholar] [CrossRef]
  66. Fedrizzi, B.; Carlin, S.; Franceschi, P.; Vrhovsek, U.; Wehrens, R.; Viola, R.; Mattivi, F. D-Optimal Design of an Untargeted HS-SPME-GC-TOF Metabolite Profiling Method. Analyst 2012, 137, 3725. [Google Scholar] [CrossRef]
  67. Tsiaka, T.; Fotakis, C.; Lantzouraki, D.Z.; Tsiantas, K.; Siapi, E.; Sinanoglou, V.J.; Zoumpoulakis, P. Expanding the Role of Sub-Exploited DOE-High Energy Extraction and Metabolomic Profiling towards Agro-Byproduct Valorization: The Case of Carotenoid-Rich Apricot Pulp. Molecules 2020, 25, 2702. [Google Scholar] [CrossRef]
  68. Pilon, A.C.; Carnevale Neto, F.; Freire, R.T.; Cardoso, P.; Carneiro, R.L.; Da Silva Bolzani, V.; Castro-Gamboa, I. Partial Least Squares Model and Design of Experiments toward the Analysis of the Metabolome of Jatropha Gossypifolia Leaves: Extraction and Chromatographic Fingerprint Optimization. J. Sep. Sci. 2016, 39, 1023–1030. [Google Scholar] [CrossRef]
  69. Kusano, M.; Fukushima, A.; Redestig, H.; Saito, K. Metabolomic Approaches toward Understanding Nitrogen Metabolism in Plants. J. Exp. Bot. 2011, 62, 1439–1453. [Google Scholar] [CrossRef] [PubMed]
  70. Zhou, Y.; Song, J.-Z.; Choi, F.F.-K.; Wu, H.-F.; Qiao, C.-F.; Ding, L.-S.; Gesang, S.-L.; Xu, H.-X. An Experimental Design Approach Using Response Surface Techniques to Obtain Optimal Liquid Chromatography and Mass Spectrometry Conditions to Determine the Alkaloids in Meconopsi Species. J. Chromatogr. A 2009, 1216, 7013–7023. [Google Scholar] [CrossRef]
  71. Zheng, H.; Clausen, M.R.; Dalsgaard, T.K.; Mortensen, G.; Bertram, H.C. Time-Saving Design of Experiment Protocol for Optimization of LC-MS Data Processing in Metabolomic Approaches. Anal. Chem. 2013, 85, 7109–7116. [Google Scholar] [CrossRef] [PubMed]
  72. Eliasson, M.; Rännar, S.; Madsen, R.; Donten, M.A.; Marsden-Edwards, E.; Moritz, T.; Shockcor, J.P.; Johansson, E.; Trygg, J. Strategy for Optimizing LC-MS Data Processing in Metabolomics: A Design of Experiments Approach. Anal. Chem. 2012, 84, 6869–6876. [Google Scholar] [CrossRef]
  73. Teahan, O.; Gamble, S.; Holmes, E.; Waxman, J.; Nicholson, J.K.; Bevan, C.; Keun, H.C. Impact of Analytical Bias in Metabonomic Studies of Human Blood Serum and Plasma. Anal. Chem. 2006, 78, 4307–4318. [Google Scholar] [CrossRef]
  74. Chen, S.; Pang, X.; Song, J.; Shi, L.; Yao, H.; Han, J.; Leon, C. A Renaissance in Herbal Medicine Identification: From Morphology to DNA. Biotechnol. Adv. 2014, 32, 1237–1244. [Google Scholar] [CrossRef] [PubMed]
  75. Pilon, A.C.; Selegato, D.M.; Fernandes, R.P.; Bueno, P.C.P.; Pinho, D.R.; Neto, F.C.; Freire, R.T.; Castro-Gamboa, I.; Bolzani, V.S.; Lopes, N.P. Metabolômica de Plantas: Métodos e Desafios. Química Nova 2020, 43, 329–354. [Google Scholar] [CrossRef]
  76. Consortium, T.P.O. The Plant Ontology Consortium and Plant Ontologies. Comp. Funct. Genom. 2002, 3, 137–142. [Google Scholar] [CrossRef] [PubMed]
  77. Walls, R.L.; Cooper, L.; Elser, J.; Gandolfo, M.A.; Mungall, C.J.; Smith, B.; Stevenson, D.W.; Jaiswal, P. The Plant Ontology Facilitates Comparisons of Plant Development Stages Across Species. Front. Plant Sci. 2019, 10, 631. [Google Scholar] [CrossRef] [PubMed]
  78. Urbanczyk-Wochniak, E.; Baxter, C.; Kolbe, A.; Kopka, J.; Sweetlove, L.J.; Fernie, A.R. Profiling of Diurnal Patterns of Metabolite and Transcript Abundance in Potato (Solanum tuberosum) Leaves. Planta 2005, 221, 891–903. [Google Scholar] [CrossRef] [PubMed]
  79. Selegato, D.M.; Freire, R.T.; Pilon, A.C.; Biasetto, C.R.; de Oliveira, H.C.; de Abreu, L.M.; Araujo, A.R.; da Silva Bolzani, V.; Castro-Gamboa, I. Improvement of Bioactive Metabolite Production in Microbial Cultures—A Systems Approach by OSMAC and Deconvolution-Based 1HNMR Quantification. Magn. Reson. Chem. 2019, 57, 458–471. [Google Scholar] [CrossRef] [PubMed]
  80. Pinu, F.; Villas-Boas, S.; Aggio, R. Analysis of Intracellular Metabolites from Microorganisms: Quenching and Extraction Protocols. Metabolites 2017, 7, 53. [Google Scholar] [CrossRef]
  81. Teng, Q.; Huang, W.; Collette, T.W.; Ekman, D.R.; Tan, C. A Direct Cell Quenching Method for Cell-Culture Based Metabolomics. Metabolomics 2009, 5, 199–208. [Google Scholar] [CrossRef]
  82. Mushtaq, M.Y.; Choi, Y.H.; Verpoorte, R.; Wilson, E.G. Extraction for Metabolomics: Access to The Metabolome. Phytochem. Anal. 2014, 25, 291–306. [Google Scholar] [CrossRef]
  83. Moreira, F.D.L.; Riul, T.B.; Moreira, M.D.L.; Pilon, A.C.; Dias-Baruffi, M.; Araújo, M.S.S.; Lopes, N.P.; de Oliveira, A.R.M. Leishmanicidal Effects of Piperlongumine (Piplartine) and Its Putative Metabolites. Planta Medica 2018, 84, 1141–1148. [Google Scholar] [CrossRef]
  84. Faijes, M.; Mars, A.E.; Smid, E.J. Comparison of Quenching and Extraction Methodologies for Metabolome Analysis of Lactobacillus Plantarum. Microb. Cell Factories 2007, 6, 27. [Google Scholar] [CrossRef] [PubMed]
  85. Sköld, K.; Svensson, M.; Norrman, M.; Sjögren, B.; Svenningsson, P.; Andrén, P.E. The Significance of Biochemical and Molecular Sample Integrity in Brain Proteomics and Peptidomics: Stathmin 2-20 and Peptides as Sample Quality Indicators. Proteomics 2007, 7, 4445–4456. [Google Scholar] [CrossRef]
  86. Hu, J.; Lu, J.; Zhang, X.; Wang, C.; Ren, K.; Chang, Q.; Ji, M.; Pan, W.; Ma, B.; Fan, W. Peptidomic Analysis on Synovial Tissue Reveals Galectin-1 Derived Peptide as a Potential Bioactive Molecule against Rheumatoid Arthritis. Cytokine 2020, 131, 155020. [Google Scholar] [CrossRef]
  87. Barashkova, A.S.; Rogozhin, E.A. Isolation of Antimicrobial Peptides from Different Plant Sources: Does a General Extraction Method Exist? Plant Methods 2020, 16, 143. [Google Scholar] [CrossRef] [PubMed]
  88. Yang, Q.; Tao, R.; Yang, B.; Zhang, H.; Chen, Y.Q.; Chen, H.; Chen, W. Optimization of the Quenching and Extraction Procedures for a Metabolomic Analysis of Lactobacillus plantarum. Anal. Biochem. 2018, 557, 62–68. [Google Scholar] [CrossRef] [PubMed]
  89. Wang, T.; Wang, X.; Zhuang, Y.; Wang, G. A Systematic Evaluation of Quenching and Extraction Procedures for Quantitative Metabolome Profiling of HeLa Carcinoma Cell under 2D and 3D Cell Culture Conditions. Biotechnol. J. 2023, 18, 2200444. [Google Scholar] [CrossRef]
  90. Canelas, A.B.; Ten Pierick, A.; Ras, C.; Seifar, R.M.; Van Dam, J.C.; Van Gulik, W.M.; Heijnen, J.J. Quantitative Evaluation of Intracellular Metabolite Extraction Techniques for Yeast Metabolomics. Anal. Chem. 2009, 81, 7379–7389. [Google Scholar] [CrossRef]
  91. Jenkins, H.; Hardy, N.; Beckmann, M.; Draper, J.; Smith, A.R.; Taylor, J.; Fiehn, O.; Goodacre, R.; Bino, R.J.; Hall, R.; et al. A Proposed Framework for the Description of Plant Metabolomics Experiments and Their Results. Nat. Biotechnol. 2004, 22, 1601–1606. [Google Scholar] [CrossRef]
  92. Athersuch, T. Metabonomics. In Unraveling the Exposome: A Practical Review; Springer: Cham, Switzerland, 2018; Volume 455, pp. 141–181. [Google Scholar] [CrossRef]
  93. Harbourne, N.; Marete, E.; Jacquier, J.C.; O’Riordan, D. Effect of Drying Methods on the Phenolic Constituents of Meadowsweet (Filipendula ulmaria) and Willow (Salix alba). LWT-Food Sci. Technol. 2009, 42, 1468–1473. [Google Scholar] [CrossRef]
  94. Moonlight, P.W.; Banda-R, K.; Phillips, O.L.; Dexter, K.G.; Pennington, R.T.; Baker, T.R.; de Lima, H.C.; Fajardo, L.; González, M.R.; Linares-Palomino, R.; et al. Expanding Tropical Forest Monitoring into Dry Forests: The DRYFLOR Protocol for Permanent Plots. Plants People Planet 2021, 3, 295–300. [Google Scholar] [CrossRef]
  95. Wolfender, J.L.; Marti, G.; Thomas, A.; Bertrand, S. Current Approaches and Challenges for the Metabolite Profiling of Complex Natural Extracts. J. Chromatogr. A 2015, 1382, 136–164. [Google Scholar] [CrossRef]
  96. Zheng, J.; Wu, Z.; Yang, N.; Zhou, K.; Hu, W.; Ou, S.; Liu, P. Widely Targeted UHPLC-MS/MS Metabolomic Analysis on the Chemical Variation in Blueberry-Filled Pastries During Processing. Front. Nutr. 2020, 7, 569172. [Google Scholar] [CrossRef] [PubMed]
  97. de Almeida, R.T.R.; do Prado, R.M.; Porto, C.; dos Santos, G.T.; Huws, S.A.; Pilau, E.J. Exploring the Rumen Fluid Metabolome Using Liquid Chromatography-High-Resolution Mass Spectrometry and Molecular Networking. Sci. Rep. 2018, 8, 17971. [Google Scholar] [CrossRef] [PubMed]
  98. Dai, Y.; Witkamp, G.-J.; Verpoorte, R.; Choi, Y.H. Natural Deep Eutectic Solvents as a New Extraction Media for Phenolic Metabolites in Carthamus tinctorius L. Anal. Chem. 2013, 85, 6272–6278. [Google Scholar] [CrossRef]
  99. Fujii, T.; Matsuda, S.; Tejedor, M.L.; Esaki, T.; Sakane, I.; Mizuno, H.; Tsuyama, N.; Masujima, T. Direct Metabolomics for Plant Cells by Live Single-Cell Mass Spectrometry. Nat. Protoc. 2015, 10, 1445–1456. [Google Scholar] [CrossRef]
  100. Višnjevec, A.M.; Barp, L.; Lucci, P.; Moret, S. Pressurized Liquid Extraction for the Determination of Bioactive Compounds in Plants with Emphasis on Phenolics. TrAC Trends Anal. Chem. 2024, 173, 117620. [Google Scholar] [CrossRef]
  101. Gligor, O.; Mocan, A.; Moldovan, C.; Locatelli, M.; Crișan, G.; Ferreira, I.C. Enzyme-Assisted Extractions of Polyphenols—A Comprehensive Review. Trends Food Sci. Technol. 2019, 88, 302–315. [Google Scholar] [CrossRef]
  102. Wong, S.; Ngadi, N.; Inuwa, I.M.; Hassan, O. Recent Advances in Applications of Activated Carbon from Biowaste for Wastewater Treatment: A Short Review. J. Clean. Prod. 2018, 175, 361–375. [Google Scholar] [CrossRef]
  103. Kabir, A.; Olayanju, B. Chapter 5—Membrane-Based Extraction Techniques. In Green Analytical Chemistry; Locatelli, M., Kaya, S., Eds.; Elsevier: Amsterdam, The Netherlands, 2025; pp. 119–144. [Google Scholar] [CrossRef]
  104. Van Eygen, G.; Van der Bruggen, B.; Buekenhoudt, A.; Luis Alconero, P. Efficient Membrane-Based Affinity Separations for Chemical Applications: A Review. Chem. Eng. Process.-Process Intensif. 2021, 169, 108613. [Google Scholar] [CrossRef]
  105. Khan, S.A.; Aslam, R.; Makroo, H.A. High Pressure Extraction and Its Application in the Extraction of Bio-Active Compounds: A Review. J. Food Process Eng. 2019, 42, e12896. [Google Scholar] [CrossRef]
  106. Funari, C.S.; Sutton, A.T.; Carneiro, R.L.; Fraige, K.; Cavalheiro, A.J.; da Silva Bolzani, V.; Hilder, E.F.; Arrua, R.D. Natural Deep Eutectic Solvents and Aqueous Solutions as an Alternative Extraction Media for Propolis. Food Res. Int. 2019, 125, 108559. [Google Scholar] [CrossRef] [PubMed]
  107. Borges, M.S.; Zanatta, A.C.; Souza, O.A.; Pelissari, J.H.; Camargo, J.G.S.; Carneiro, R.L.; Funari, C.S.; Bolzani, V.S.; Rinaldo, D. A Green and Sustainable Method for Monitoring the Chemical Composition of Soybean: An Alternative for Quality Control. Phytochem. Anal. 2021, 32, 562–574. [Google Scholar] [CrossRef] [PubMed]
  108. Want, E.J.; Masson, P.; Michopoulos, F.; Wilson, I.D.; Theodoridis, G.; Plumb, R.S.; Shockcor, J.; Loftus, N.; Holmes, E.; Nicholson, J.K. Global Metabolic Profiling of Animal and Human Tissues via UPLC-MS. Nat. Protoc. 2013, 8, 17–32. [Google Scholar] [CrossRef]
  109. Maltese, F.; Van Der Kooy, F.; Verpoorte, R. Solvent Derived Artifacts in Natural Products Chemistry. Nat. Prod. Commun. 2009, 4, 447–454. [Google Scholar] [CrossRef]
  110. Lisec, J.; Schauer, N.; Kopka, J.; Willmitzer, L.; Fernie, A.R. Gas Chromatography Mass Spectrometry-Based Metabolite Profiling in Plants. Nat. Protoc. 2006, 1, 387–396. [Google Scholar] [CrossRef] [PubMed]
  111. Higgs, R.E.; Zahn, J.A.; Gygi, J.D.; Hilton, M.D. Rapid Method to Estimate the Presence of Secondary Metabolites in Microbial Extracts. Appl. Environ. Microbiol. 2001, 67, 371–376. [Google Scholar] [CrossRef]
  112. De Vos, R.C.H.; Moco, S.; Lommen, A.; Keurentjes, J.J.B.; Bino, R.J.; Hall, R.D. Untargeted Large-Scale Plant Metabolomics Using Liquid Chromatography Coupled to Mass Spectrometry. Nat. Protoc. 2007, 2, 778–791. [Google Scholar] [CrossRef] [PubMed]
  113. Moco, S.; Bino, R.J.; Vorst, O.; Verhoeven, H.A.; de Groot, J.; van Beek, T.A.; Vervoort, J.; de Vos, C.H.R. A Liquid Chromatography-Mass Spectrometry-Based Metabolome Database for Tomato. Plant Physiol. 2006, 141, 1205–1218. [Google Scholar] [CrossRef]
  114. Vorst, O.; de Vos, C.H.R.; Lommen, A.; Staps, R.V.; Visser, R.G.F.; Bino, R.J.; Hall, R.D. A Non-Directed Approach to the Differential Analysis of Multiple LC–MS-Derived Metabolic Profiles. Metabolomics 2005, 1, 169–180. [Google Scholar] [CrossRef]
  115. Folch, J.; Lees, M.; Stanley, G.H.S. A Simple Method for The Isolation and Purification of Total Lipides from Animal Tissues. J. Biol. Chem. 1957, 226, 497–509. [Google Scholar] [CrossRef] [PubMed]
  116. Bligh, E.G.; Dyer, W.J. A Rapid Method of Total Lipid Extraction and Purification. Can. J. Biochem. Physiol. 1959, 37, 911–917. [Google Scholar] [CrossRef] [PubMed]
  117. Matyash, V.; Liebisch, G.; Kurzchalia, T.V.; Shevchenko, A.; Schwudke, D. Lipid Extraction by Methyl-Tert-Butyl Ether for High-Throughput Lipidomics. J. Lipid Res. 2008, 49, 1137–1146. [Google Scholar] [CrossRef] [PubMed]
  118. Löfgren, L.; Ståhlman, M.; Forsberg, G.-B.; Saarinen, S.; Nilsson, R.; Hansson, G.I. The BUME Method: A Novel Automated Chloroform-Free 96-Well Total Lipid Extraction Method for Blood Plasma. J. Lipid Res. 2012, 53, 1690–1700. [Google Scholar] [CrossRef] [PubMed]
  119. Löfgren, L.; Forsberg, G.-B.; Ståhlman, M. The BUME Method: A New Rapid and Simple Chloroform-Free Method for Total Lipid Extraction of Animal Tissue. Sci. Rep. 2016, 6, 27688. [Google Scholar] [CrossRef]
  120. Creydt, M.; Arndt, M.; Hudzik, D.; Fischer, M. Plant Metabolomics: Evaluation of Different Extraction Parameters for Nontargeted UPLC-ESI-QTOF-Mass Spectrometry at the Example of White Asparagus Officinalis. J. Agric. Food Chem. 2018, 66, 12876–12887. [Google Scholar] [CrossRef] [PubMed]
  121. Romsdahl, T.B.; Cocuron, J.-C.; Pearson, M.J.; Alonso, A.P.; Chapman, K.D. A Lipidomics Platform to Analyze the Fatty Acid Compositions of Non-Polar and Polar Lipid Molecular Species from Plant Tissues: Examples from Developing Seeds and Seedlings of Pennycress (Thlaspi arvense). Front. Plant Sci. 2022, 13, 1038161. [Google Scholar] [CrossRef]
  122. Hu, A.; Wei, F.; Huang, F.; Xie, Y.; Wu, B.; Lv, X.; Chen, H. Comprehensive and High-Coverage Lipidomic Analysis of Oilseeds Based on Ultrahigh-Performance Liquid Chromatography Coupled with Electrospray Ionization Quadrupole Time-of-Flight Mass Spectrometry. J. Agric. Food Chem. 2021, 69, 8964–8980. [Google Scholar] [CrossRef] [PubMed]
  123. Sedio, B.E.; Boya, P.C.A.; Rojas Echeverri, J.C. A Protocol for High-Throughput, Untargeted Forest Community Metabolomics Using Mass Spectrometry Molecular Networks. Appl. Plant Sci. 2018, 6, e1033. [Google Scholar] [CrossRef]
  124. Bijttebier, S.; Van der Auwera, A.; Foubert, K.; Voorspoels, S.; Pieters, L.; Apers, S. Bridging the Gap between Comprehensive Extraction Protocols in Plant Metabolomics Studies and Method Validation. Anal. Chim. Acta 2016, 935, 136–150. [Google Scholar] [CrossRef] [PubMed]
  125. Álvarez-Urdiola, R.; Borràs, E.; Valverde, F.; Matus, J.T.; Sabidó, E.; Riechmann, J.L. Peptidomics Methods Applied to the Study of Flower Development. In Flower Development: Methods and Protocols; Riechmann, J.L., Ferrándiz, C., Eds.; Springer: New York, NY, USA, 2023; pp. 509–536. [Google Scholar] [CrossRef]
  126. Van Dijck, A.; Hayakawa, E.; Landuyt, B.; Baggerman, G.; Van Dam, D.; Luyten, W.; Schoofs, L.; De Deyn, P.P. Comparison of Extraction Methods for Peptidomics Analysis of Mouse Brain Tissue. J. Neurosci. Methods 2011, 197, 231–237. [Google Scholar] [CrossRef] [PubMed]
  127. Ernst, M.; Silva, D.B.; Silva, R.; Monge, M.; Semir, J.; Vêncio, R.Z.N.; Lopes, N.P. A Metabolomic Protocol for Plant Systematics by Matrix-Assisted Laser-Desorption/Ionization Time-of Flight Mass Spectrometry. Anal. Chim. Acta 2015, 859, 46–58. [Google Scholar] [CrossRef] [PubMed]
  128. Kruger, N.J.; Troncoso-Ponce, M.A.; Ratcliffe, R.G. 1H NMR Metabolite Fingerprinting and Metabolomic Analysis of Perchloric Acid Extracts from Plant Tissues. Nat. Protoc. 2008, 3, 1001–1012. [Google Scholar] [CrossRef]
  129. Wu, X.; Li, N.; Li, H.; Tang, H. An Optimized Method for NMR-Based Plant Seed Metabolomic Analysis with Maximized Polar Metabolite Extraction Efficiency, Signal-to-Noise Ratio, and Chemical Shift Consistency. Analyst 2014, 139, 1769–1778. [Google Scholar] [CrossRef]
  130. Chaparro, J.M.; Jones, R.R.; Mitchell, S.B.; Broeckling, C.D.; Heuberger, A.L.; Shafizadeh, T.; Watkins, S.; Prenni, J.E. Multielement Profiling of Diverse Food Samples. ACS Food Sci. Technol. 2023, 3, 459–464. [Google Scholar] [CrossRef]
  131. Nardin, R.; Tamasi, G.; Baglioni, M.; Bisozzi, F.; Consumi, M.; Costa, J.; Fattori, G.; Tozzi, C.; Riccaboni, A.; Rossi, C. Determination of Elemental Content in Vineyard Soil, Leaves, and Grapes of Sangiovese Grapes from the Chianti Region Using ICP-MS for Geographical Identification. ACS Food Sci. Technol. 2024, 4, 2585–2599. [Google Scholar] [CrossRef] [PubMed]
  132. Beyzi, E. Chemometric Assessment of the Chemical Profile of Tea Seed (Camellia sinensis) with Different Size Determined by GC and ICP/OES. Eur. Food Res. Technol. 2024, 250, 1229–1237. [Google Scholar] [CrossRef]
  133. Kruszewska, J.; Kur, A.; Kulpińska, D.; Grabowska-Jadach, I.; Matczuk, M.; Keppler, B.K.; Timerbaev, A.R.; Jarosz, M. An Improved Protocol for ICP-MS-Based Assessment of the Cellular Uptake of Metal-Based Nanoparticles. J. Pharm. Biomed. Anal. 2019, 174, 300–304. [Google Scholar] [CrossRef]
  134. Gonçalves, E.S.; Borges, R.M.; De Carvalho, L.V.B.; Alves, S.R.; André, L.C.; Moreira, J.C. Estratégias analíticas com cromatografia e espectrometria de massas para biomonitorização da exposição ao benzeno pela determinação do ácido S-fenilmercaptúrico urinário. Rev. Bras. Saúde Ocup. 2017, 42, e1s. [Google Scholar] [CrossRef]
  135. Nasiri, A.; Jahani, R.; Mokhtari, S.; Yazdanpanah, H.; Daraei, B.; Faizi, M.; Kobarfard, F. Overview, Consequences, and Strategies for Overcoming Matrix Effects in LC-MS Analysis: A Critical Review. Analyst 2021, 146, 6049–6063. [Google Scholar] [CrossRef]
  136. Balcells, C.; Foguet, C.; Tarragó-Celada, J.; de Atauri, P.; Marin, S.; Cascante, M. Tracing Metabolic Fluxes Using Mass Spectrometry: Stable Isotope-Resolved Metabolomics in Health and Disease. TrAC Trends Anal. Chem. 2019, 120, 115371. [Google Scholar] [CrossRef]
  137. Babele, P.K.; Young, J.D. Applications of Stable Isotope-Based Metabolomics and Fluxomics toward Synthetic Biology of Cyanobacteria. WIREs Syst. Biol. Med. 2020, 12, e1472. [Google Scholar] [CrossRef]
  138. Kašička, V. Peptide Mapping of Proteins by Capillary Electromigration Methods. J. Sep. Sci. 2022, 45, 4245–4279. [Google Scholar] [CrossRef] [PubMed]
  139. Foreman, R.E.; George, A.L.; Reimann, F.; Gribble, F.M.; Kay, R.G. Peptidomics: A Review of Clinical Applications and Methodologies. J. Proteome Res. 2021, 20, 3782–3797. [Google Scholar] [CrossRef] [PubMed]
  140. Rund, K.M.; Carpanedo, L.; Lauterbach, R.; Wermund, T.; West, A.L.; Wende, L.M.; Calder, P.C.; Schebb, N.H. LC-ESI-HRMS—Lipidomics of Phospholipids. Anal. Bioanal. Chem. 2024, 416, 925–944. [Google Scholar] [CrossRef]
  141. Liakh, I.; Sledzinski, T.; Kaska, L.; Mozolewska, P.; Mika, A. Sample Preparation Methods for Lipidomics Approaches Used in Studies of Obesity. Molecules 2020, 25, 5307. [Google Scholar] [CrossRef]
  142. ul Ain, Q.; Hocking, R.K.; Mahon, P.J.; Bhave, M.; Butardo, V. Chapter 3—Ionomics-Based Imaging, Localization and Quantification of Zinc and Other Micronutrients in Rice Grains for Biofortification Research. In Genetic Engineering and Genome Editing for Zinc Biofortification of Rice; Swamy, B.P.M., Macovei, A., Trijatmiko, K.R., Eds.; Academic Press: Cambridge, MA, USA, 2023; pp. 31–43. [Google Scholar] [CrossRef]
  143. Dunn, W.B.; Kuligowski, J.; Lewis, M.; Mosley, J.D.; Schock, T.; Holland, C.U.; Zanetti, K.A.; Vuckovic, D. Metabolomics 2022 Workshop Report: State of QA/QC Best Practices in LC–MS-Based Untargeted Metabolomics, Informed through mQACC Community Engagement Initiatives. Metabolomics 2023, 19, 93. [Google Scholar] [CrossRef] [PubMed]
  144. Broeckling, C.D.; Beger, R.D.; Cheng, L.L.; Cumeras, R.; Cuthbertson, D.J.; Dasari, S.; Davis, W.C.; Dunn, W.B.; Evans, A.M.; Fernández-Ochoa, A.; et al. Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples. Anal. Chem. 2023, 95, 18645–18654. [Google Scholar] [CrossRef]
  145. Kirwan, J.A.; Gika, H.; Beger, R.D.; Bearden, D.; Dunn, W.B.; Goodacre, R.; Theodoridis, G.; Witting, M.; Yu, L.-R.; Wilson, I.D. Quality Assurance and Quality Control Reporting in Untargeted Metabolic Phenotyping: mQACC Recommendations for Analytical Quality Management. Metabolomics 2022, 18, 70. [Google Scholar] [CrossRef] [PubMed]
Separations 12 00021 sch001
Scheme 1. A representation of the different critical steps from experimental hypothesis, power analysis, DOE, harvest, sample treatment, storage, and the transport of plant samples.
Scheme 1. A representation of the different critical steps from experimental hypothesis, power analysis, DOE, harvest, sample treatment, storage, and the transport of plant samples.
Separations 12 00021 sch001
Table 1. A comparison of the analytical techniques in metabolomic and related areas. For metabolites we used the symbols + for low levels and +++ for the maximal contribution.
Table 1. A comparison of the analytical techniques in metabolomic and related areas. For metabolites we used the symbols + for low levels and +++ for the maximal contribution.
FeatureAnalytical Technique
LC-MSGC-MSCE-MSICP-MSMALDI-MSNMR
Amount of sample preparation++++++++++++++
Volume for injection+++++++++++++
Range of metabolitesRP: non-polar
HILIC: polar		Volatile and thermostablePolarElementsPolar and non-polarPolar and non-polar
Sensitivity++++++++++++++
Resolution++++++++++++++
QuantificationIS neededISISISIS
Reproducibility<NMR<NMRHigh<NMRHigh
Identification of metabolitesDifficult (few or no standard libraries)Easy (large spectral libraries)Difficult (databases with few data)EasyDifficult (few or no standard libraries)Easy
Table 3. Advantages and limitations of drying methods.
Table 3. Advantages and limitations of drying methods.
Drying MethodAdvantagesLimitationsRef.
Oven DryingEfficient and cost-effective; suitable for large sample volumes.Prolonged exposure to heat may degrade heat-sensitive metabolites; risk of inconsistent drying if temperature fluctuates.[19,75,79,93]
Air DryingLow-cost; no specialized equipment needed.Longer drying times; potential for microbial contamination in humid conditions.[19,21,79,94]
Vacuum DryingMinimizes oxidation and thermal degradation; effective for heat-sensitive compounds.Equipment cost; potential loss of volatile compounds.[19,24,79,85]
Freeze-DryingPreserves heat-sensitive metabolites; produces highly reproducible results.Expensive; loss of volatiles; thermal shock may affect metabolite adherence.[23,24,79,95]
Drying with Silica GelPortable; suitable for fieldwork.Limited capacity; inconsistent drying efficiency compared to lab-based methods.[19,21,94]
Table 4. Extraction methods for different types of compounds.
Table 4. Extraction methods for different types of compounds.
CharacteristicsAnalytical MethodRef.
Volatile- Steam distillation- Microwave-assisted extraction- Cold plasma-assisted extraction- Liquid pressurization extraction (LPE)- Hyperbaric extraction[18,85,99,109]
Non-Volatile- Solvent extraction (maceration)- Supercritical fluid extraction (e.g., CO2)- Membrane extraction- Ultrasound-assisted extraction[18,85,98,106,107]
Polar- Methanol–water mixtures (50–90% methanol)- Methanol, water, and chloroform (2:2:1)- Buffers for NMR to control pH[85,87,108,109,110]
Non-Polar- Chloroform-free methods (e.g., MTBE-based protocols)- Lipid extraction protocols (e.g., Folch, Bligh and Dyer, Matyash, BUME) [108,109,110]- Supercritical CO2[115,116,117,118,119]
Table 5. Plant metabolomics protocols for CG-MS, LC-MS, NMR, ICP-MS or OES, and MALDI-MS.
Table 5. Plant metabolomics protocols for CG-MS, LC-MS, NMR, ICP-MS or OES, and MALDI-MS.
Analytical MethodCharacteristicsRef.
GC-ToF-MSEstablished protocol with a large database for compound identification. Difficult to detect secondary metabolites.[110]
GC-MS and LC-MSFocused on pre- and post-treatment data.[13]
LC-QToF-MSEstablished protocol for analysis of semi-polar metabolites, mainly secondary metabolites.[112]
UHPLC-MSProtocol for analysis of large data sets using molecular networking (identifying tool).[123]
LC-MSOptimization of extraction steps evaluating efficiency, repeatability, and ionization efficiency[124,125,126]
MALDI-ToF MSProtocol for MALDI-TOF-MS with multivariate analysis and taxonomic approach.[127]
NMRUse perchloric acid as extraction solvent. Used mainly for primary metabolism of plants.[128]
NMRRelatively simple protocol including different groups of primary/secondary metabolites.[19]
NMRQuality for extraction of tissues for NMR. Also applied for quantitative metabolomics.[129]
LC-MSUsed for live single-cell metabolomics.[87]
ICP-OES and ICP-MSUsed for elemental analysis of plant material.[130,131,132]
ICP-MSAnalysis of metals in nanoparticles[133]
Table 6. Minimum Reports and Best Practices Based on https://www.mqacc.org/outputs (accessed on 6 November 2024).
Table 6. Minimum Reports and Best Practices Based on https://www.mqacc.org/outputs (accessed on 6 November 2024).
Quality Assurance and ControlMinimum ReportsBest Practices
QC
Sample types		Specify the types of QC samples (e.g., pooled samples).Use multiple QC types (e.g., intra- and inter-batch) and ensure consistency.
Blanks and
System Suitability		Identify blank types used to detect contamination.Differentiate between true blanks and process blanks; provide details of composition, suppliers, and preparation methods.
Internal
Standards		Report concentration and source of internal standards used.Include internal standard normalization details, signal tracking over time, and exact time of addition in analysis.
Preparation and
Storage		Report preparation and storage conditions, including freeze–thaw cycles.Include detailed aliquoting information and monitor freeze–thaw stability with evidence of sample integrity.
Data Normalization and ScalingProvide basic normalization details, e.g., total area or selected features.Report all normalization, scaling, and transformation steps applied, with justification for each.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

da Silva, W.B.; Hispagnol, G.F.; dos Santos Nunes, E.V.; Castro-Gamboa, I.; Pilon, A.C. Plant Sample Preparation for Metabolomics, Lipidomics, Ionomics, Fluxomics, and Peptidomics. Separations 2025, 12, 21. https://doi.org/10.3390/separations12020021

AMA Style

da Silva WB, Hispagnol GF, dos Santos Nunes EV, Castro-Gamboa I, Pilon AC. Plant Sample Preparation for Metabolomics, Lipidomics, Ionomics, Fluxomics, and Peptidomics. Separations. 2025; 12(2):21. https://doi.org/10.3390/separations12020021

Chicago/Turabian Style

da Silva, Walace Breno, Gabriel Felipe Hispagnol, Emanuel Victor dos Santos Nunes, Ian Castro-Gamboa, and Alan Cesar Pilon. 2025. "Plant Sample Preparation for Metabolomics, Lipidomics, Ionomics, Fluxomics, and Peptidomics" Separations 12, no. 2: 21. https://doi.org/10.3390/separations12020021

APA Style

da Silva, W. B., Hispagnol, G. F., dos Santos Nunes, E. V., Castro-Gamboa, I., & Pilon, A. C. (2025). Plant Sample Preparation for Metabolomics, Lipidomics, Ionomics, Fluxomics, and Peptidomics. Separations, 12(2), 21. https://doi.org/10.3390/separations12020021

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop