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Article

Optimization of an Extraction Protocol for Untargeted Metabolomics of Vitis vinifera L. Leaves

by
Laura Sánchez-Ochoa
,
Teresa Garde-Cerdán
*,
Itziar Sáenz de Urturi
,
Miriam González-Lázaro
and
Eva P. Pérez-Álvarez
*
Grupo VIENAP, Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Ctra. de Burgos, Km. 6, 26007 Logroño, Spain
*
Authors to whom correspondence should be addressed.
Plants 2026, 15(5), 696; https://doi.org/10.3390/plants15050696
Submission received: 7 February 2026 / Revised: 23 February 2026 / Accepted: 24 February 2026 / Published: 26 February 2026
(This article belongs to the Section Phytochemistry)
Browse Figures
Versions Notes

Abstract

Viticulture faces increasing challenges due to the susceptibility of Vitis vinifera L. to biotic and abiotic stresses, which trigger defense responses involving the synthesis of secondary metabolites. Untargeted metabolomics has become a powerful tool to explore these metabolic changes; however, the efficiency and reproducibility of metabolomic studies strongly depend on the extraction protocol used. Current literature shows variability in sample handling, solvent composition, and extraction conditions. This study aimed to optimize an extraction protocol for secondary metabolites in grapevine leaves to ensure high recovery of compounds relevant to untargeted metabolomics. Leaves of Vitis vinifera L. cv. Tempranillo were collected, pooled, frozen, and ground under liquid nitrogen. A factorial design was used to evaluate the effects of sample mass, sample-to-solvent ratio, and solvent type on extraction efficiency. Extracts were analyzed using UHPLC-QTOF-MS in both positive and negative ionization modes, and multivariate statistical tools (PCA and OPLS-DA) were used to identify discriminant metabolites. Optimal extraction was achieved using 750 mg of leaf powder, a sample-to-solvent ratio of 100 mg/Ml, and methanol 80% acidified with 0.1% of formic acid. This protocol maximizes the recovery of metabolites and provides a robust basis for future untargeted metabolomics studies of grapevine responses.

Graphical Abstract

1. Introduction

Viticulture is an important economic and traditional social activity in many countries. However, it faces significant challenges due to the high susceptibility of grapevines to a wide range of biotic and abiotic stresses. Biotic stresses include important diseases caused by pathogens, such as viruses, bacteria, insects, and fungi, including powdery mildew, downy mildew, and gray mold [1,2,3]. In addition, grapevines are affected by several abiotic stresses, such as salinity, drought, extreme temperatures, or even nutrient imbalances, which are being intensified by climate change, making stress conditions more unpredictable and severe [4,5].
As a response to biotic and abiotic stresses, grapevines activate defense mechanisms. The early phase of the mechanism involves the accumulation of reactive oxygen species (ROS) [2], followed by the synthesis of pathogenesis-related proteins, enzymes, and antimicrobial compounds, such as phytoalexins [2,5,6,7]. Phytoalexins are produced as part of the plant’s active defense response, where they inhibit or limit the growth of invading microorganisms [5]. In addition, other phytohormones, for instance salicylic acid, jasmonic acid, and ethylene, which are plant-derived signaling molecules that regulate growth, development, and stress responses, are synthesized and play a key role in stress signaling [2,6]. Under drought conditions, grapevines increase the concentration of various secondary metabolites, which can be grouped into three families: phenolic, aromatic, and nitrogen compounds [8].
All these metabolic changes can be identified through untargeted metabolomics techniques, which allow the simultaneous analysis of a wide range of metabolites found in plant tissues without targeting any special metabolite [9]. The study of the diversity of metabolites can develop a metabolic fingerprint that permits us to evaluate the biochemical differences by recognizing patterns and biomarkers [10]. For instance, Chitarrini et al. [11] observed differences in the metabolome after the inoculation of Plasmospara viticola (fungus responsible for downy mildew disease in grapevines) and identified some biomarkers of plants’ defense response to fungal infection.
Most of the analytical techniques used are gas and liquid chromatography coupled to mass spectrometry (GC-MS and LC-MS) [10]. In particular, mass spectrometry combined with high-resolution chromatography improves the separation and characterization of the metabolites of leaf tissue, providing deeper insight into the biochemical responses triggered during pathogen attack or environmental stress exposure. The study of secondary compounds contributes to a deeper understanding of the induced defense mechanisms in grapevines [12].
Metabolomics analysis strongly depends on efficient sample preparation. In the case of Vitis vinifera L. leaves, extraction protocols described in the literature show considerable methodological variability. Differences are observed in sample processing methods, with studies using either frozen or lyophilized leaves [13], and sample masses ranged from 5 mg [13] to 1 g [14]. Most extraction solvents consist of methanol–water mixtures (from 80% [15] to 100% [16]), often acidified with formic acid [14] or acetic acid [13]. Common extraction and homogenization techniques include sonication [17], shaking [18], and the use of the Ultra-Turrax system [19].
The objective of this study was to evaluate the influence of sample mass, sample-to-solvent ratio, and extractant composition on the extraction efficiency of secondary metabolites from Vitis vinifera L. leaves, with the aim of establishing a standardized and reproducible protocol suitable for untargeted metabolomics and future studies of defense responses.

2. Results and Discussion

2.1. Preliminary Analysis

The optimal dilution and injection volumes were established through an evaluation of chromatograms by a visual inspection (Figure S1), considering overall signal response and the absence of detector saturation, as recommended by the instrument specialist. A 1:5 dilution and an injection volume of 0.5 µL were selected, since the chromatograms showed comparable signal patterns to those obtained with a 1:10 dilution, while allowing better visualization of low-intensity compounds.
Sample stability over a 24 h interval was evaluated in both positive and negative ionization modes using orthogonal partial least squares discriminant analysis (OPLS-DA; Figure 1). Although a separation between injections was observed, the analyses focused on the compounds responsible for the differences between the first injection and the second injection performed 24 h apart were significant.
In positive mode, only five metabolites showed variation between the first and second injections, only two of which can be identified. These compounds were quercetin-4′-O-β-D-glucoside, a flavonoid commonly found in plants of Vitis vinifera L., and tephrowatsin A, a flavonoid related to plants of the genus Tephrosia [20] (see Table S1 for chemical structures). This compound has not previously been reported in Vitis vinifera. As the annotation was based on database matching, the identification should be considered tentative. In negative mode, ten signals showed significant differences between injections. Only two of them have been identified as guattegumerine, an alkaloid presented in grapevines, and heptaprenyl diphosphate, which is an isoprenoid present in Vitis vinifera L. species. All the identifications were supported by the PlantCyC database, based on the compounds annotated for Vitis vinifera L.
These results indicate that changes in the metabolic profile of the leaf extracts during the analytical interval were minimal, supporting the validity of using the same sample for both ionization modes.

2.2. Extraction Optimization

A greater number of putative compounds were detected in positive ionization (5945 compounds), whereas fewer putative compounds were identified in negative mode (3912 compounds).
Figure 2 presents the principal component analysis (PCA) plots for each ionization mode, with samples color-coded according to the extraction factors studied. In negative mode, Function 1 explained 54.37% of the variance, while Function 2 explained 14.66%, accounting for a total of 69.03% of the total variance among samples (Figure 2a,c,e). In positive mode, Function 1 explained 60.90% of the variance, and Function 2 explained 9.72%, explaining a total of 70.62% of the total variance among samples (Figure 2b,d,f).
In the plots grouped by the sample-to-solvent ratio, a clear separation between groups was observed in both ionization modes (Figure 2a,b). When color-coded by the extraction solvents used, samples extracted with MeOH were more distinctly separated from the rest, situated slightly higher in the plot, particularly in the negative ionization mode (Figure 2e). The PCA showed better separation of the samples based on the sample-to-solvent ratio than according to the mass of the sample and extraction solvent. This was further confirmed by the OPLS-DA graphical representation (Figure 3), which showed improved separation of the samples when evaluating the sample-to-solvent factor, indicating its significant influence.
Discriminant analyses (OPLS-DA) were conducted for each extraction factor, and the compounds contributing the most to the observed differences were identified. In positive mode, glutathione, phytosphingosine, and quercetin were the differentiating putative compounds. In negative mode, the metabolites that contributed most to the differentiation among extraction conditions were quercetin-3-glucuronide, melibiose, vitamin C, isoquercetin, tartaric acid, glucuronic acid, rutin, kaempferol-3-galactoside, kaempferol-3-O-β-glucoside, myricetin-3-O-glucoside, and steviolbioside (see Table S1 for chemical structures).
Among these metabolites, glutathione is a peptide present in plants and animals [21] and plays an important role in different defense mechanisms, particularly due to its antioxidant capacity [22]. Glutathione participates in detoxification of plant cells from ROS [23]. Vitamin C (ascorbic acid) is also a key antioxidant molecule involved in plant protection from biotic and abiotic stresses. In addition, it also acts as an intermediate in many physiological processes, including photosynthesis and cell division [24,25]. Ascorbic acid is a component in the ascorbate–glutathione cycle that modulates reactive oxygen species (ROS). This regulation prevents oxidative cell damage while maintaining ROS at a low concentration that supports their role as signaling molecules, since high concentrations of ROS can trigger negative reactions [26].
Phytosphingosine, as a sphingolipid, plays important structural and signaling functions in plants. Sphingolipids are essential components in the cellular membrane system [27] and are messengers to stomata closure [28]. Phytosphingosine has been associated with plant defense responses, as it can delay fungal growth after infection [29]. Flavonols are important secondary metabolites involved in plant defense—they act as antioxidants and their accumulation on leaves is associated with solar radiation, as they filter UV rays and allow visible light to pass through [30,31]. Latouche et al. [32] observed that the leaves with more flavonols were less susceptible to fungal diseases, such as mildew caused by Plasmopara viticola fungus. Glucuronic acid, especially in its nucleotide form, acts as a precursor of nucleotide sugars involved in hemicellulose synthesis, the principal component of the cellular wall [33]. It can also conjugate with flavonols, facilitating their storage [34], and has been proposed as an indicator of the leaf age, as older leaves tend to accumulate flavonol glucosides compared to flavonol glucuronides [31].
Melibiose and stelviolbioside are sugars, mainly derived from primary metabolism associated with photosynthesis [35]. For instance, drought reduces the photosynthetic activity due to stomata closure and degradation of chlorophyll [36]. Under drought conditions, starch is also degraded into soluble sugars to contribute stress tolerance [37]. Therefore, sugar profiles are useful for assessing physiological leaf status. Tartaric acid is considered a specialized primary metabolite derived from primary metabolism, with ascorbic acid as a precursor. It is synthetized in both young berries (until veraison) and leaves [38,39]. Tartaric acid concentration in grape berries is important because it influences organoleptic properties, as well as wine preservation and stability [40,41]. Although most genes involved in tartaric acid biosynthesis are not strongly affected by environmental conditions [38], abiotic stresses can lead to reduced acidity in grapes [41]. Additionally, accumulation of tartaric acid may serve as an alternative defense mechanism for herbivores by contributing to fruit unpalatability until the seed is mature, a strategy also reported in other plant species, such as Urtica dioiaca, whose leaf hairs contain tartaric acid and can induce a pain response [38].
As many of these metabolites are directly involved in plant defense mechanisms, identifying the extraction conditions that maximize their recovery is essential for selecting an optimal protocol for subsequent untargeted metabolomic analyses.
These compounds were grouped by chemical family, and statistical comparisons of their relative abundance among extraction conditions were conducted using the ionization mode in which each compound exhibited the highest signal intensity: positive mode for glutathione and phytosphingosine, and negative mode for vitamin C, flavonols, acids (tartaric and glucuronic acids), and sugars (melibiose and steviolbioside). This approach should be considered as semi-quantitative, since signal intensity can vary depending on the ionization behavior of each compound and the sample matrix.
Figure 4 shows the abundance of glutathione extracted using different extraction conditions. The highest relative abundance of this compound was observed in the extraction performed with 750 mg of leaf powder at a sample-to-solvent ratio of 200 mg/mL, using acidified methanol (MeOH 80% with 0.1% of formic acid) as the extraction solvent. Slightly lower but still high abundances were obtained using 750 and 500 mg of leaf powder, with a 100 mg/mL sample-to-solvent ratio and 80% methanol acidified. Also, comparable results were observed with 750 mg of leaf powder and MeOH 80%, regardless of the sample-to-solvent ratio used (200 or 100 mg/mL). It is also noteworthy that the minor abundances were detected in the extractions carried out with 500 mg and a ratio of 200 mg/mL with MeOH and MeOH 80%.
Figure 5 shows the abundance of phytosphingosine resulting from each different extraction method. A better extraction of this compound was carried out by using 750 mg of leaf powder with a sample-to-solvent ratio of 100 mg/mL, regardless of the extractant used—MeOH or MeOH 80% acidified. These extracts did not differ from extraction with a 100 mg/mL sample-to-solvent ratio and 750 or 500 mg of leaf powder and MeOH 80% as extractants, nor from extractions using 500 mg, 100 mg/mL, and MeOH 80% acidified. Worse extractions of phytosphingosine were observed using MeOH as a solvent, a 200 mg/mL sample-to-solvent ratio, and sample masses of 750 and 500 mg. These extracts also did not differ between using 500 mg and 100 mg/mL sample-to-solvent ratios and MeOH as an extractant, or using 200 mg/mL, regardless of the sample mass used (750 or 500 mg) and the extractant (MeOH 80% or MeOH 80% acidified with formic acid).
The extraction of the phytoesphingosine was not reproducible under the conditions using 750 mg of leaf powder at 100 mg/mL and MeOH—a large variation was observed between duplicates. This variability was only detected for this compound. This issue does not affect the choice of the optimal extraction protocol, as the conditions showing this variability were not those that gave the most balance overall in metabolite recovery, as it compromised the recovery of acids and vitamin C.
A higher abundance of vitamin C was mainly observed in extractions performed with MeOH 80% acidified with 0.1% of formic acid with respect the rest of the extractants, except for the combination of 500 mg of sample and a sample-to-solvent ratio of 200 mg/mL using the same extractant. A comparable extraction of this compound was also observed using 750 mg of sample with MeOH 80%, regardless of the sample-to-solvent ratio used. Lower abundances of vitamin C were obtained in the extracts performed mostly with 500 mg of leaf powder, except for the combination of 500 mg of sample and a sample-to-solvent ratio of 100 mg/mL using methanol 80% acidified with 0.1% of formic acid as the extractant. In addition, this bad extraction was also observed using 750 mg of sample, a sample-to-solvent ratio of 200 mg/mL, and MeOH as the extractant (Figure 6a).
Regarding the flavonol family, a more efficient extraction was achieved with 750 mg of sample and a sample-to-solvent ratio of 100 mg/mL, regardless of whether the MeOH 80% extractant was acidified or not, comparable with the recovery obtained with MeOH, 100 mg/mL, and both sample masses (750 and 500 mg). Lower yields were observed on the extracts carried out by using 200 mg/mL regardless of the mass of the sample (750 and 500 mg) or extractants used (MeOH, MeOH 80%, or MeOH 80% Form 0.1%; Figure 6b).
For acids, the best extraction was obtained by using 750 mg of leaf powder, a ratio of 100 mg/mL, and MeOH 80% acidified with 0.1% of formic acid as the extractant. Lower yields were observed with MeOH, except for the extraction using 500 mg of leaf powder at a 100 mg/mL sample-to-solvent ratio (Figure 6c).
Finally, sugar extraction was most effective with 750 mg of sample at a sample-to-solvent ratio of 100 mg/mL, with no significant differences observed among the extractants used (MeOH, MeOH 80%, or MeOH 80% Form 0.1%). Worse extractions were observed extracting at a 200 mg/mL sample-to-solvent ratio for both mases and all extractants, except for 750 mg of leaf powder extracted with methanol (Figure 6d).
Overall, considering the recovery of each studied metabolite, the extraction performed using 750 mg of leaf powder at a 100 mg/mL sample-to-solvent ratio with methanol 80% acidified with 0.1% of formic acid provided a balanced recovery of metabolites without compromising the recovery of any compound that showed significant differences between extraction conditions.

3. Materials and Methods

3.1. Plant Material

For the extraction optimization phase, leaves of Vitis vinifera L. cv. Tempranillo plants were sampled for the analysis. The Tempranillo grapevines, grafted onto R-110 rootstock, were planted in 2020 and grown in pot conditions. For each plant, four young healthy leaves were collected, including leaf blade and petiole, around the seventh node. A total of 58 leaves were collected from 15 healthy plants without nutritional deficiency, in order to obtain a homogeneous and representative pool with enough material to carry out the study. Immediately after collection, the leaves were frozen in liquid nitrogen to minimize metabolomics changes suffered from cutting stress and to preserve their native metabolites. Samples were stored at −80 °C until processing.
For metabolite extraction, only leaf blades were used. To prevent the degradation of sensitive metabolites, the frozen leaf blades were grounded in a mortar, pre-cooled with liquid nitrogen. Leaf petioles were discarded to avoid the formation of a white, fibrous mass, which is composed mainly of cellulose [42], which could compromise the efficient homogenization of the leaf blades. The resulting powders were combined into a unique pool. This method allowed for efficient homogenization. The resulting powder was kept frozen (−80 °C) until extraction.

3.2. Extraction Optimization Methodology

3.2.1. Metabolite Extraction

The solid–liquid extraction process was optimized by evaluating three factors that may potentially influence extraction efficiency and in accordance with that observed in [14,15,16]: sample mass, sample-to-solvent ratio, and extraction solvent type. Two different masses (500 and 750 mg of leaf powder) and two sample-to-solvent ratios (100 and 200 mg/mL) were tested. Additionally, three methanolic solvents were evaluated: pure methanol (MeOH; PanReac AppliChem, ITW Reagents, Monza, Italy), 80% methanol (MeOH 80%), and 80% methanol acidified with 0.1% of formic acid (MeOH 80% Form 0.1%; Labkem, Barcelona, Spain). The combination of these factors resulted in 12 experimental conditions, and each condition was analyzed in duplicate, as is presented in Table 1.
For each extraction, the corresponding amount of leaf powder (500 or 750 mg) was weighed into a Falcon tube wrapped in aluminum foil to minimize light exposure and protect photosensitive compounds. The appropriate volume of the selected extraction solvent was added, and the mixture was manually homogenized. Then, samples were sonicated for 30 min in an ultrasonic bath (DU-100, ArgoLab, Milano, Italy) maintained below 15 °C to avoid thermal degradation of metabolites. Finally, the extracts were centrifuged at 4 °C and 18,000 rpm for 20 min, allowing the separation of the supernatant from the solid residue. The obtained extracts were stored at −80 °C until the processing for metabolomic analysis.

3.2.2. Preliminary Analysis Processing

To ensure data reliability and protect the instrument, a dilution study was conducted. Two dilution ratios (1:5 and 1:10) were evaluated using ultrapure water (Milli-Q System, Milipore, Bedford, MA, USA) acidified with 0.1% of formic acid as the diluent. In addition, two injection volumes (1 µL and 0.5 µL) were tested using an ultra-high-pressure liquid chromatography (UHPLC; Waters Acquity I-Class, Waters Corporation, Milford, CT, USA) coupled to a high-resolution quadrupole/time-of-flight mass spectrometer (QTOF; Waters SYNAPT XS HDM, Waters Corporation, Wilmslow, UK).
Sample stability over 24 h was also evaluated, as the chromatographic method requires analysis in both positive and negative ionization modes. Because certain metabolites ionize more efficiently in one mode than the other, thus, separate runs in the UHPLC-QTOF were required. Due to the time needed for instrument setup, these analyses must be carried out on consecutive days, therefore making it essential to confirm sample stability during this period.

3.2.3. Sample Preparation for the Injection of the Extraction Optimization

After cold storage (−80 °C) of the extracts, precipitate formation was observed. To eliminate insoluble residues and protect the analytical system, a second centrifugation was performed under the same conditions as the initial one (18,000 rpm for 20 min). The supernatants were then diluted at a 1:5 ratio using ultrapure water acidified with 0.1% of formic acid, to minimize the risk of detector saturation. Finally, each diluted extract was filtered through 0.2 µm syringe filters and transferred to amber vials (Agilent, Palo Alto, CA, USA) for the immediate chromatographic injection.

3.3. Metabolomic Analysis

3.3.1. Chromatographic Separation

The chromatographic method proposed by Avesani et al. [15] was adapted to our equipment. Separation of leaf metabolites was performed using UHPLC (Waters Acquity I-Class) equipped with a Waters Acquity HSS T3 column (100 mm × 2.1 mm, 1.8 µm particle size; Waters Corporation, Milford, CT, USA), coupled to a QTOF detector (Waters SYNAPT XS HDM). Each of the assays was analyzed in both positive and negative ionization modes with identical chromatographic conditions.
Chromatographic separation was performed with a flow rate of 0.4 mL/min and an injection volume of 0.5 µL of the diluted extract. The temperatures of the autosampler and the column oven were maintained at 8 °C and 40 °C, respectively. The mobile phases consisted of ultrapure water obtained from a Milli-Q system with 0.1% of formic acid (Phase A) and acetonitrile (Carlo Erba) with 0.1% of formic acid (Phase B). The gradient was as follows: 0–12 min, 5–50% Phase B; 12–13 min, 50–95% Phase B; 13–16 min, 95% Phase B (isocratic); 16–17 min, 95–5% Phase B; 17–20 min, 5% Phase B (isocratic), with Phase A completing the total composition in each step. Quality control (QC) samples, prepared from a pooled mixture of all analyzed extracts, were injected throughout the sequence to ensure instrument stability and data reliability.
Mass spectrometry was performed using an electrospray ionization (ESI) source in both positive (ESI+) and negative (ESI−) modes, with a detection range of 50–1200 m/z. For ESI+, capillary voltage and sampling cone voltage were set at 0.70 kV and 40 V, respectively, whereas, for ESI−, the capillary voltage was set at 1.75 kV. The source and desolvation temperatures were maintained at 120 °C and 500 °C, respectively, with a desolvation gas flow rate of 800 L/h. The mass spectrometer acquired mass data using a scan time of 0.2 s. Fragment ion information was obtained using a collision energy ramp from 20 to 40 V.
A real-time mass correction was achieved by injection of leucine enkephalin (Waters, Milford, CT, USA) at 10 µL/min as a reference mass (positive and negative) every 30 s.

3.3.2. Compound Identification and Analysis

Chromatographic data were processed using Progenesis QI software (version 2.4, Waters Corporation, Milford, CT, USA). At first, peak alignment was performed using the QC injections as the reference to correct for retention time drift. Compound identification was carried out within the same software, using the following specialized databases: Food and Agriculture Organization of the United Nations, Phenol-Explorer, Web of Science, FooDB, PlantCyc, Human Metabolome Database, BioCyc, ChEBI, KEGG, MassBank, and LIPID MAPS. An 8 ppm mass tolerance and 90% isotopic similarity were applied as identification criteria.
For statistical analysis, the EZinfo tool integrated into Progenesis QI was used to assess differences between the experimental factors. This analysis included both principal component analysis (PCA), to visualize the overall distribution of samples, and orthogonal partial least squares discriminant analysis (OPLS-DA), to identify the compounds showing significant variation among the different sample groups. Compounds that exhibited statistically significant differences in this analysis were subsequently exported to SPSS software (version 20.0, SPSS Inc., Chicago, IL, USA), where their normalized abundances were analyzed to determine significant variations among compound families and to assess the combined effects of the experimental factors.

4. Conclusions

The optimization of the leaf sample extraction protocol allowed the identification of experimental conditions that maximize the recovery of secondary metabolites from Vitis vinifera L. leaves for untargeted metabolomics studies. Among the factors evaluated (sample mass, sample-to-solvent ratio, and extraction solvent type), the sample-to-solvent ratio proved to be the most influential parameter in extraction efficiency, as demonstrated by multivariate analyses (PCA and OPLS-DA).
The combination of 750 mg of leaf powder, a 100 mg/mL sample-to-solvent ratio, and 80% methanol with 0.1% of formic acid resulted in the most efficient extraction, maximizing the recovery of the differential metabolites, such as glutathione, phytosphingosine, vitamin C, flavonols, acids, and sugars. This provides a robust basis for future untargeted metabolomic analysis of grapevine responses.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15050696/s1. Figure S1: Representative base peak chromatograms obtained with 1:5 and 1:10 dilutions, used for the visual evaluation and selection of the injection volume and sample dilution. Table S1: Chemical structures of the selected compounds included in this study.

Author Contributions

Conceptualization, T.G.-C. and E.P.P.-Á.; methodology, T.G.-C. and E.P.P.-Á.; formal analysis, L.S.-O.; investigation, L.S.-O., T.G.-C., I.S.d.U., M.G.-L. and E.P.P.-Á.; resources, T.G.-C. and E.P.P.-Á.; funding acquisition, E.P.P.-Á. and T.G.-C.; data curation, L.S.-O. and T.G.-C.; writing—original draft preparation, L.S.-O.; writing—review and editing, T.G.-C. and E.P.P.-Á. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministerio de Ciencia e Innovación through the Project PID2022-138780OB-C21.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Acknowledgments

L.S.-O. thanks the Ministerio de Ciencia e Innovación for her predoctoral contract funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MeOHMethanol
MeOH 80% Form 0.1%80% methanol acidified with 0.1% of formic acid
UHPLCUltra-high-pressure liquid chromatography
QTOF-MSQuadrupole/time-of-flight mass spectrometer
ESIElectrospray ionization
PCAPrincipal component analysis
OPLS-DAOrthogonal partial least squares discriminant analysis
v1100 mg/mL
v2200 mg/mL
ROSReactive oxygen species

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Plants 15 00696 g001
Figure 1. OPLS-DA color-coded by the injections (first injection and second injection). (a) Negative ionization and (b) positive ionization.
Figure 1. OPLS-DA color-coded by the injections (first injection and second injection). (a) Negative ionization and (b) positive ionization.
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Plants 15 00696 g002
Figure 2. Principal component analysis (PCA). (a) Negative-mode PCA color-coded by the sample-to-solvent ratio (200 and 100 mg/mL). (b) Positive-mode PCA color-coded by the sample-to-solvent ratio (200 and 100 mg/mL). (c) Negative-mode PCA color-coded by the mass used (500 and 750 mg). (d) Positive-mode PCA color-coded by the mass used (500 and 750 mg). (e) Negative-mode PCA color-coded by the extraction solvents used (MeOH, MeOH 80%, and MeOH 80% Form 0.1%). (f) Positive-mode PCA color-coded by the extraction solvents used (MeOH, MeOH 80%, and MeOH 80% Form 0.1%). MeOH: pure methanol, MeOH 80%: methanol 80%, and MeOH 80% Form 0.1%: methanol 80% acidified with 0.1% of formic acid.
Figure 2. Principal component analysis (PCA). (a) Negative-mode PCA color-coded by the sample-to-solvent ratio (200 and 100 mg/mL). (b) Positive-mode PCA color-coded by the sample-to-solvent ratio (200 and 100 mg/mL). (c) Negative-mode PCA color-coded by the mass used (500 and 750 mg). (d) Positive-mode PCA color-coded by the mass used (500 and 750 mg). (e) Negative-mode PCA color-coded by the extraction solvents used (MeOH, MeOH 80%, and MeOH 80% Form 0.1%). (f) Positive-mode PCA color-coded by the extraction solvents used (MeOH, MeOH 80%, and MeOH 80% Form 0.1%). MeOH: pure methanol, MeOH 80%: methanol 80%, and MeOH 80% Form 0.1%: methanol 80% acidified with 0.1% of formic acid.
Plants 15 00696 g002
Plants 15 00696 g003
Figure 3. OPLS-DA in positive ionization mode. Samples are color-coded by sample-to-solvent ratio: v1 (100 mg/mL, black) and v2 (200 mg/mL, red).
Figure 3. OPLS-DA in positive ionization mode. Samples are color-coded by sample-to-solvent ratio: v1 (100 mg/mL, black) and v2 (200 mg/mL, red).
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Plants 15 00696 g004
Figure 4. Representation of glutathione abundances in positive mode for each of the different methods of extraction: mass of the sample (500 and 750 mg), sample-to-solvent ratio (100 and 200 mg/mL), and the different extraction solvents (MeOH, MeOH 80%, and MeOH 80% Form 0.1%). Abundances are presented as means, each with its standard deviation (n = 2). Different letters indicate significant differences among samples at p ≤ 0.05. MeOH: pure methanol, MeOH 80%: methanol 80%, and MeOH 80% Form 0.1%: methanol 80% acidified with 0.1% of formic acid.
Figure 4. Representation of glutathione abundances in positive mode for each of the different methods of extraction: mass of the sample (500 and 750 mg), sample-to-solvent ratio (100 and 200 mg/mL), and the different extraction solvents (MeOH, MeOH 80%, and MeOH 80% Form 0.1%). Abundances are presented as means, each with its standard deviation (n = 2). Different letters indicate significant differences among samples at p ≤ 0.05. MeOH: pure methanol, MeOH 80%: methanol 80%, and MeOH 80% Form 0.1%: methanol 80% acidified with 0.1% of formic acid.
Plants 15 00696 g004
Plants 15 00696 g005
Figure 5. Representation of phytosphingosine abundances in positive mode for each of the different methods of extraction: mass of the sample (500 and 750 mg), sample-to-solvent ratio (100 and 200 mg/mL), and the different extraction solvents (MeOH, MeOH 80%, and MeOH 80% Form 0.1%). Abundances are presented as means, each with its standard deviation (n = 2). Different letters indicate significant differences among samples at p ≤ 0.05. MeOH: pure methanol, MeOH 80%: methanol 80%, and MeOH 80% Form 0.1%: methanol 80% acidified with 0.1% of formic acid.
Figure 5. Representation of phytosphingosine abundances in positive mode for each of the different methods of extraction: mass of the sample (500 and 750 mg), sample-to-solvent ratio (100 and 200 mg/mL), and the different extraction solvents (MeOH, MeOH 80%, and MeOH 80% Form 0.1%). Abundances are presented as means, each with its standard deviation (n = 2). Different letters indicate significant differences among samples at p ≤ 0.05. MeOH: pure methanol, MeOH 80%: methanol 80%, and MeOH 80% Form 0.1%: methanol 80% acidified with 0.1% of formic acid.
Plants 15 00696 g005
Plants 15 00696 g006
Figure 6. Representation of abundances in the negative mode of vitamin C (a), flavonols (b), acids (c), and sugars (d) for each extraction factor: mass of the sample (500 and 750 mg), sample-to-solvent ratio (100 and 200 mg/mL), and the different extraction solvents (MeOH, MeOH 80%, and MeOH 80% Form 0.1%). All values are presented as means, each with its standard deviation (n = 2). Different letters indicate significant differences among samples at p ≤ 0.05. MeOH: pure methanol, MeOH 80%: methanol 80%, and MeOH 80% Form 0.1%: methanol 80% acidified with 0.1% of formic acid.
Figure 6. Representation of abundances in the negative mode of vitamin C (a), flavonols (b), acids (c), and sugars (d) for each extraction factor: mass of the sample (500 and 750 mg), sample-to-solvent ratio (100 and 200 mg/mL), and the different extraction solvents (MeOH, MeOH 80%, and MeOH 80% Form 0.1%). All values are presented as means, each with its standard deviation (n = 2). Different letters indicate significant differences among samples at p ≤ 0.05. MeOH: pure methanol, MeOH 80%: methanol 80%, and MeOH 80% Form 0.1%: methanol 80% acidified with 0.1% of formic acid.
Plants 15 00696 g006
Table 1. Experimental conditions tested for each extraction optimization: mass of the sample, sample-to-solvent ratio, and type of solvent.
Table 1. Experimental conditions tested for each extraction optimization: mass of the sample, sample-to-solvent ratio, and type of solvent.
Mass of Sample
(mg)		Sample-to-Solvent
Ratio (mg/mL)		Extraction Solvent
750100MeOH *
750200MeOH
500100MeOH
500200MeOH
750100MeOH 80% *
750200MeOH 80%
500100MeOH 80%
500200MeOH 80%
750100MeOH 80% Form 0.1% *
750200MeOH 80% Form 0.1%
500100MeOH 80% Form 0.1%
500200MeOH 80% Form 0.1%
* MeOH: pure methanol; MeOH 80%: methanol 80%; MeOH 80% Form 0.1%: methanol 80% acidified with 0.1% of formic acid.
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Sánchez-Ochoa, L.; Garde-Cerdán, T.; Sáenz de Urturi, I.; González-Lázaro, M.; Pérez-Álvarez, E.P. Optimization of an Extraction Protocol for Untargeted Metabolomics of Vitis vinifera L. Leaves. Plants 2026, 15, 696. https://doi.org/10.3390/plants15050696

AMA Style

Sánchez-Ochoa L, Garde-Cerdán T, Sáenz de Urturi I, González-Lázaro M, Pérez-Álvarez EP. Optimization of an Extraction Protocol for Untargeted Metabolomics of Vitis vinifera L. Leaves. Plants. 2026; 15(5):696. https://doi.org/10.3390/plants15050696

Chicago/Turabian Style

Sánchez-Ochoa, Laura, Teresa Garde-Cerdán, Itziar Sáenz de Urturi, Miriam González-Lázaro, and Eva P. Pérez-Álvarez. 2026. "Optimization of an Extraction Protocol for Untargeted Metabolomics of Vitis vinifera L. Leaves" Plants 15, no. 5: 696. https://doi.org/10.3390/plants15050696

APA Style

Sánchez-Ochoa, L., Garde-Cerdán, T., Sáenz de Urturi, I., González-Lázaro, M., & Pérez-Álvarez, E. P. (2026). Optimization of an Extraction Protocol for Untargeted Metabolomics of Vitis vinifera L. Leaves. Plants, 15(5), 696. https://doi.org/10.3390/plants15050696

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