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Brief Report

A Simple High-Throughput Procedure for Microscale Extraction of Bioactive Compounds from the Flowers of Saint John’s Wort (Hypericum perforatum L.)

by
Mila Rusanova
1,*,
Krasimir Rusanov
1,2,
Marina Alekova
1,
Liliya Georgieva
1,2,
Pavlina Georgieva
1,
Tzvetelina Zagorcheva
1 and
Ivan Atanassov
1,2
1
Department of Agrobiotechnology, AgroBioInstitute, Agricultural Academy, 8 Dragan Tsankov Blvd., 1164 Sofia, Bulgaria
2
Centre of Competence “Sustainable Utilization of Bio-Resources and Waste of Medicinal and Aromatic Plants for Innovative Bioactive Products” (BIORESOURCES BG), 1000 Sofia, Bulgaria
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(13), 7334; https://doi.org/10.3390/app15137334
Submission received: 1 June 2025 / Revised: 25 June 2025 / Accepted: 28 June 2025 / Published: 30 June 2025
(This article belongs to the Special Issue Biosynthesis and Applications of Natural Products)
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Abstract

We report the development of a procedure for ultrasound-assisted microscale extraction of metabolites from the flowers of Saint John’s wort (Hypericum perforatum L.), designed for comparative metabolite analysis of plants from genetic resource collections and natural and segregating populations. The procedure involves high-throughput methanol extraction of metabolites from ground-frozen flowers at a selected stage of flower development, which is carried out in a standard 2 mL Eppendorf tube. A total of 18 compounds, including chlorogenic acid, catechins, glycosylated flavonoids, hypericins, and hyperforin, were identified based on LC/DAD/QTOF analysis, of which 16 could be detected in the UV-Vis spectrum. Two alternative versions of the procedure were evaluated: the “single-flower” procedure, including repeated collection and analysis of single flowers from the tested plant, and the “bulk-flower” procedure, employing the collection of a bulk flower sample from the tested plant and analysis of a portion of the ground sample. The results showed excellent technical reproducibility of the “single-flower” procedure when used with the suggested combination of the peak areas for the proto- and stable forms of pseudohypericin and hypericin. Application of the developed “single-flower” procedure for comparison of the plants derived from seed progeny of the apomictic line Hp93 revealed significantly lower metabolite variation among the apomictic progeny plants compared to the variation observed among plants belonging to different genotypes.

1. Introduction

St. John’s wort (Hypericum perforatum L.) is a perennial medicinal plant that has long been known for its antioxidant [1,2,3], antimicrobial [4,5,6], antiviral [6,7,8], antidepressant [6,9,10,11], anti-inflammatory [12,13], and anticancer [14,15] properties and, recently, for its antinociceptive [16], analgesic [17,18], and neuroprotective properties [19]. The species has a wide geographic distribution spanning Europe, North America, and Western Asia. Many of the biological activities of H. perforatum are related to its high content of biologically active compounds such as hypericin, hyperforin, and phenolic antioxidants [1,2,3,4,5,6,7,8,9,10,11,12,13,14,17,18]. The highest content of hypericin and hyperforin—two of the major compounds responsible for the biological activity of H. perforatum—is found in the plant buds and flowers [16,20,21,22,23,24]. The interest in developing natural products based on St John’s wort extracts has increased over the last decade, leading to many commercially available products on the market [25]. The growing interest in this species requires the development of elite cultivars based on the existing genetic and metabolic diversity in the natural populations. A modern breeding program in H. perforatum would require correct metabolite characterization of large plant sets, processing a large number of samples, and, therefore, the application of a highly reproducible, high-throughput procedure for comparative metabolite analysis of single plants from the populations and genetic resource collections.
In addition to the plant genotype, the plant metabolome is significantly influenced by several factors, including the developmental stage of the plant and the plant organ sampled [26,27,28], as well as by numerous other environmental factors. When comparing the performances of different genotypes, the influence of all other factors that could affect the composition of the plant metabolome should be excluded or minimized as much as possible. Ideally, in such cases, the plants should be planted in the same site or experimental field, and the plant material should be collected at the same stage of plant/organ development, within a short time, stored properly for further processing, and used for the extraction of metabolites in preferably smaller volumes when dealing with a large number of samples [29,30]. The sample preparation procedures currently applied for metabolite analysis of Hypericum species typically involve the processing of relatively large amounts of plant material (e.g., tens to hundreds of grams), dried beforehand [23,24,31,32,33,34,35]. This becomes a significant challenge when the number of samples exceeds a few hundred, making it impossible to conduct accurate comparative analyses of individual plants from large collections. Scaling down the sample preparation procedures to support the application of a highly reproducible, high-throughput analysis of the metabolite composition is crucial in such cases. The analytical methods employed for the quantitative and qualitative characterizations of Hypericum perforatum extracts are primarily designed to ensure quality control across various commercial products. The chemically diverse bioactive metabolites in St. John’s wort—most notably phloroglucinols and naphthodianthrones—necessitate the use of multiple chromatographic and detection techniques. Thin-layer chromatography (TLC) and high-performance thin-layer chromatography (HPTLC) serve as fundamental tools for routine qualitative analysis [36,37,38,39,40,41]. For quantitative assessment, high-performance liquid chromatography with diode array detector (HPLC-DAD) is the most widely utilized method due to its versatility [41,42,43]. Meanwhile, liquid chromatography–mass spectrometry (LC-MS) is gaining increasing significance, particularly in pharmacokinetic studies, owing to its superior sensitivity [44,45,46,47,48].
Apomixis is a form of asexual reproduction and a common phenomenon often observed in H. perforatum, where seeds are developed without fertilization. As a result of it, the offspring is genetically identical to the mother plant [49]. Since the progeny is genetically identical, it is expected that the variation in traits, including flower metabolite composition, would be low. Although apomixis has been extensively studied in H. perforatum, to our knowledge, only one study has reported on the variation in metabolites among H. perforatum plants developed through apomixis [32]. Accordingly, the impact of apomixis on the metabolite composition of offspring plants remains poorly studied.
Here, we present the development and testing of a highly repetitive, high-throughput procedure for comparative metabolite analysis of H. perforatum based on a microscale ultrasound-assisted extraction of biologically active compounds from flowers of single plants employing two sampling strategies and using an LC/DAD/MS analytical method. The developed procedure is applied for the characterization of the metabolite variation across three different genotypes and within the seed progeny of one apomictic line of H. perforatum.

2. Materials and Methods

2.1. Plant Material

Field-grown plants of Hypericum perforatum L., specifically lines 3_29, 90_44, 139_28, and 205, and seed progeny of the apomictic line Hp93, which were previously selected from different regions in Bulgaria and are currently part of the AgroBioInstitute’s collection (Kostinbrod, Bulgaria), were used in the experiments. The plants were cultivated in the experimental field during 2023 and 2024 and sampled in the second year at the active blooming phase.

2.2. Determination of Flower Fresh Weight

Three buds or flowers representing each one of the developmental stages 1 to 5 (Figure 1) were collected in triplicate from plants 3_29, 90_44, and 139_28. Using tweezers, the samples were carefully picked and placed into pre-weighed 2 mL Eppendorf tubes (Eppendorf AG, Hamburg, Germany), which were immediately weighed on an analytical balance to determine their fresh weight.

2.3. Collection and Extraction of a Bulk Flower Sample

A bulk flower sample consisting of flowers at developmental stage 4 (Figure 1) was collected from line 205 in the following manner: Fifty to sixty flowers were carefully picked with tweezers and snap-frozen in a 50 mL Falcon™ tube (Falcon, Corning Inc., Corning, NY, USA) immersed in liquid nitrogen in a Cryogenic Dewar flask. The bulk sample was transported to the lab, ground for 1 min at 25 Hz in stainless steel jars using TissueLyser II (QIAGEN AG, Steinhausen, Switzerland), and stored at −80 °C until extraction. Seventy milligrams of frozen ground material were weighed in triplicate in precooled 2 mL Eppendorf tubes and kept frozen in liquid nitrogen until the addition of the solvent. For testing of the optimal proportion of the extracted material and the solvent, methanol (100%) (Macron Fine Chemicals™; VWR, Radnor, PA, USA) was added in a 1/5, 1/10, 1/15, 1/20, or 1/25 (weight/volume) ratio. The tubes containing the obtained mixtures were sonicated using a sweep mode in the dark in an ultrasound water bath (Elmasonic P70H, Elma Schmidbauer GmbH, Singen, Germany) set at 30 °C, 37 kHz, and a power of 100 W. Testing of the extraction duration included periods of 10, 20, 30, 40, 50, and 60 min. During ultrasonic treatment, an external cooling water bath (WCR-8, WITEG Labortechnik GmbH, Wertheim, Germany) was used for the circulation of water cooled down to 4 °C through a hose placed in the ultrasound bath, thus preventing the temperature in the ultrasound bath from exceeding 34 °C. After each extraction, the 2 mL Eppendorf tubes with the obtained methanol extracts were centrifuged at 10,000 rpm for 5 min at room temperature (2-16PK, Sigma Laborzentrifugen GmbH, Osterode am Harz, Germany). The supernatants were filtered through 45 µm membrane filters (CHROMAFIL® PTFE-45/15 MS, Macherey-Nagel GmbH & Co. KG, Düren, Germany) and transferred to 1.5 mL amber vials (Macherey-Nagel GmbH & Co. KG, Düren, Germany) for LC/DAD/MS analysis.

2.4. Collection and Extraction of Single Flower Samples

From each of the plants (3_29, 90_44, 139_28, and apomictic line Hp93) in quadruple repetition, a single fully open flower (stage 4) was placed in a 2 mL Eppendorf tube containing two grinding 3 mm Tungsten Carbide Beads (QIAGEN AG, Steinhausen, Switzerland). The samples were snap-frozen in liquid nitrogen and stored at −80 °C until extraction. Before the extraction, the samples were finely ground using the 2 mL Adapter Sets of TissueLyser II (QIAGEN AG, Steinhausen, Switzerland) at 25 Hz for 30 s and maintained frozen until the solvent addition. One thousand and fifty microliters of 100% methanol was added to each sample, and the ultrasound-assisted extraction was conducted as described in Section 2.3 for the bulk-flower sample procedure, with a duration of 20 min. The obtained extracts were prepared for LC/UV-Vis analysis following the steps described in Section 2.3 for sample preparation for LC/DAD/MS analysis in batches of 16 samples and immediately loaded on the LC autosampler.

2.5. LC/DAD/MS Analysis

LC/DAD/MS analysis was carried out using an Agilent Technologies 1260 Infinity II LC system (Agilent Technologies, Inc., Santa Clara, CA, USA), equipped with a quaternary pump, autosampler, multicolumn thermostat, WR Diode Array Detector (DAD), and an Agilent 6546 Quadrupole Time-Of-Flight (QTOF) mass spectrometer. ESI-MS spectra were acquired in negative ion mode ([M−H]−), over an m/z range of 50 to 1500, with the fragmentor voltage set to 120 V. For MS/MS analyses, collision energies of 10, 20, and 40 V were applied. Chromatographic separation was performed on a Knauer Eurospher II 100-2 C18 column (150 × 2 mm, 2 μm particle size; Knauer Wissenschaftliche Geräte GmbH, Berlin, Germany). The column temperature was maintained at 25 °C. The autosampler temperature was set to 10 °C. The mobile phase consisted of eluent A, 0.1% aqueous formic acid (Merck, KGaA, Darmstadt, Germany), and eluent B, 0.1% formic acid in acetonitrile (Macron Fine Chemicals™; VWR, Radnor, PA, USA). The eluent flow rate was set at 0.2 mL/min, and the injection volume was 2 µL. The gradient elution program was as follows: 0–50 min, 10% B; 50–62 min, 100% B; and 62–72 min, 10% B. The DAD monitored wavelengths from 190 to 950 nm, with peak detection at 270 nm and 590 nm. Chromatograms were visualized using Agilent MassHunter Qualitative Analysis software, version 10.0. Compound identification was based on the UV absorption profile, the exact mass of the pseudomolecular ion (as referenced in the Agilent METLIN master accurate mass compound database and accurate mass MS/MS spectral library v.B.08.00, Agilent Technologies, Inc., Santa Clara, CA, USA), and external databases such as the Human Metabolome Database (HMDB) (http://www.hmdb.ca, accessed on 23 January 2025), MassBank (https://massbank.eu/MassBank/, accessed on 23 January 2025), PubChem (https://pubchem.ncbi.nlm.nih.gov/, accessed on 21 January 2025) and the MS/MS fragmentation patterns.

2.6. LC/UV-Vis Analysis

LC/UV-Vis analysis was performed on an Agilent Technologies 1260 Infinity II LC system (Agilent Technologies, Inc., Santa Clara, CA, USA) including a quaternary pump, autosampler, multicolumn thermostat, and an Agilent 1260 Infinity II Multiple Wavelength Detector. Detection of the compounds was performed at 270 and 590 nm. The chromatographic conditions were as for the LC/DAD/MS analysis. OpenLab CDS ver. 2.6 (Agilent Technologies, Inc., Santa Clara, CA, USA) was used to visualize the chromatograms and integrate the chromatographic peaks.

2.7. Statistical Analysis

Each experimental variant in the study was performed as a parallel extraction, with three replicates for the bulk-flower procedure and four replicates for the single-flower procedure. Statistical analyses were conducted using IBM SPSS Statistics version 26 (IBM, Armonk, NY, USA). Mean values were compared using one-way ANOVA and the Tukey post hoc test to assess the variations in fresh weights at different flowering stages, as well as to evaluate the effects of different w/v ratios and extraction durations. Mass Profiler Professional 15.1 (Agilent Technologies, Inc., Santa Clara, CA, USA) was used for statistical analyses applying ANOVA and Student’s t-test.

3. Results

3.1. Selection of the Stage of Flower Development for Analysis

To select the stage of flower development, we assessed the variation in fresh weight of flowers across five different stages according to Tekel’ová et al. [16] for three different genotypes. Stage 4 showed the lowest intra- and inter-genotypic variation in flower weight (Table 1). H. perforatum flowers at stage 4 were reported to accumulate higher levels of bioactive phenolics, characteristic of this species [16]. Moreover, stage 4 is morphologically easy to distinguish from the other sampled stages because of the fully open flower compared to stages 2 and 3, and the light-yellow color of the anthers, which sets it apart from stage 5, where flowers are overblown with brown anthers. Therefore, flowers at stage 4 of development were used for further analysis.

3.2. Bulk-Flower Extraction and LC-DAD-QTOF Analysis

3.2.1. LC-DAD-QTOF Analysis and Identification of the Compounds

The procedure for LC-DAD-QTOF analysis was developed using bulk-flower extraction of flowers at stage 4. A total of 18 compounds were identified based on their exact mass, molecular ion fragmentation, and UV/Vis absorption spectrum, as presented in Table 2. Out of them, 16 were detected in the UV/Vis spectrum, allowing for their use in a comparative analysis applying only a UV/Vis detector (Table 2 and Figure 2). These compounds were well separated except for the collective peak of querciturone + isoquercitrin (compounds 9 and 10, Figure 2). Accordingly, the two compounds were reported collectively in the comparative LC/UV-Vis analysis.

3.2.2. Testing Different Material-to-Solvent Ratios

The possible impact of the proportion of the extracted plant material to the volume of the applied solvent was evaluated by testing different w/v ratios (Table S1). The lowest variation and calculated average relative standard deviation (RSD) in the relative abundances of the analyzed compounds was observed for the 1/15 ratio, which was then selected for further application in the bulk-flower extraction procedure.

3.2.3. Testing Different Durations of the Extraction

Using the bulk flower sample, six different extraction durations were tested (Table S2). The area of each peak/compound was normalized to the sum of all 16 detected compounds. The calculated means and RSD values of compound abundances obtained after each extraction duration are shown in Table S2. The lowest variation and calculated average RSD value were observed for the 20 min duration, which was selected for both the “bulk” and “single-flower” extraction procedures. The observed overall RSD values for all tested variants were below 15%, indicating low deviation from the mean and high reproducibility of the procedure.

3.3. Single-Flower Extraction

The reproducibility of this procedure was evaluated by comparing the results of the LC/UV-Vis analysis of plants from three different genotypes. The results for the mean values of the 16 compounds for each genotype, represented as a percentage of the sum of the peak areas of the analyzed compounds, are shown in Table 3. In terms of differences among the three tested genotypes, the performed ANOVA with a Tukey post hoc test showed significant differences for all compounds except for quercitrin, protopseudohypericin, and pseudohypericin.

3.4. Analysis of Plants Obtained Through Apomixis Using the “Single-Flower” Procedure

We further applied the developed “single-flower” procedure to study the variation in metabolites in the flowers at stage 4 in 19 plants grown from seeds derived from the apomictic line H. perforatum Hp93. The apomixis origin of the plants was proven by analysis with 10 SSR markers, which resulted in uniform SSR profiles. Individual extractions from four flowers at stage 4 were performed for each plant using the developed procedure. A summary of the obtained results is presented in Table 4. As can be seen from Table 4, eleven of the identified compounds showed low variation among the samples with RSD values below 15%, including chlorogenic acid, catechin, procyanidin B2, epicatechin, procyanidin C1, hyperoside, querciturone + isoquercitrin, quercitrin, pseudohypericin, hyperforin, and hypericin. The highest RSD value was observed for protopseudohypericin (compound 13) and protohypericin (compound 15), with values of 37.29% and 45.01%, respectively, followed by I3 II8 biapigenin (compound 12) and rutin (compound 7), with RSD values of 27.52% and 15.33%. Table 4 also shows the data for the percentage based on the combined peak areas of protopseudohypericin and pseudohypericin (compounds 13+14) as well as protohypericin and hypericin (compounds 15+18), which results in a significant reduction of the observed variation for these compounds based on the calculated RSD values.

4. Discussion

The flowers and flower buds of H. perforatum contain the highest concentrations of hypericin and hyperforin [21,62,63,64,65], making them the preferred targets for metabolite analysis in comparisons between individual plants. The abundance of various floral phenolics fluctuates with the stage of flower development [16]. Therefore, a reliable procedure for comparative metabolite analysis in individual plants requires the collection of flowers at the same developmental stage.
In this study, two sampling and processing approaches (Supplementary Figure S1) were evaluated for comparative flower metabolite analysis in individual plants. The “single-flower” procedure involved the collection and direct processing of a single flower sample without prior weighing. The second approach, referred to as the “bulk-flower” method, entailed harvesting multiple flowers at the same developmental stage from a given plant, grinding them, and processing a fixed, pre-weighed amount of the resulting bulk sample. The ideal developmental stage for sampling should (i) be associated with elevated levels of target metabolites, (ii) be easily identifiable by morphological traits, and (iii) exhibit minimal variation in individual flower weight, thereby reducing variability in the “single-flower” approach due to differences in sample mass.
Although the “bulk-flower” extraction method yielded highly reproducible results for analyzing the accumulation of target phenolics in H. perforatum flowers, its practical application in large-scale comparative analyses is limited. The process is time consuming, requiring milling and weighing of bulk flower samples, and demands a sufficient number of flowers at a specific developmental stage from each plant, a particularly challenging task when dealing with large populations. Conversely, this extraction method tolerates variations in plant material-to-solvent ratios. Combined with the observed relatively low variation in the weight of stage 4 flowers collected from either the same plant or different genotypes, this presents an opportunity for high-throughput extraction. This streamlined “single-flower” procedure involves collecting and freezing a single flower in a plastic tube, followed by ball milling in the same tube and direct extraction using a fixed volume of solvent, eliminating the need for weighing.
The composition and accumulation of metabolites in H. perforatum flowers and other organs have been extensively studied [22,66]. With few exceptions [67], most analyses use dried plant material, which facilitates measurements on a “per dry weight” basis and supports herb quality evaluation. However, this approach is less suitable for precise comparative analysis across a large number of individual plants, where precision is critical for population characterization, genetic resource assessment, and breeding programs. Drying introduces variability due to the photo- and thermo-sensitivity of several metabolites [68] and complicates the timely collection of samples to minimize diurnal variation. It also hinders the efficient processing of large sample batches. Furthermore, bulk samples typically contain flowers and other organs at various developmental stages, limiting reproducibility due to organ- and stage-specific metabolite profiles.
To address these limitations, we tested two extraction procedures that share a common workflow: collecting flowers at the same developmental stage, rapid freezing in liquid nitrogen, cryogenic ball milling at low temperature, and allowing for long-term storage before processing. The two differ in sampling strategy and offer complementary advantages. The “bulk-flower” method involves collecting 50–60 stage 4 flowers per plant, immediately freezing them, milling them to a fine powder, and extracting metabolites using the developed protocol. In contrast, the “single-flower” method entails collecting individual stage 4 flowers (in several replicates) per plant, placing each in an Eppendorf tube, and following the same freezing, milling, and extraction steps.
Our findings showed that while the “single-flower” method (Table 3) had slightly but significantly lower reproducibility (p < 0.001) compared to the “bulk-flower” approach (Tables S1 and S2), it still exhibited low intra-plant variability. The relative standard deviation (RSD) for 12 out of 16 identified compounds was below 15%, indicating limited variation among single flowers from the same genotype. However, elevated RSD values were observed for protopseudohypericin and protohypericin (compounds 13 and 15), exceeding 20% on average and reaching 34.92% and 36.51%, respectively, in genotype 139_28. Table 3 also presents combined data for the protoforms and their stable counterparts, pseudohypericin and hypericin (compounds 13+14 and 15+18). When protoform peak areas were summed with their corresponding stable forms, the RSD values decreased significantly and fell below 15% (Table 3).
The protoforms protopseudohypericin and protohypericin are known to be highly sensitive to light, rapidly converting to pseudohypericin and hypericin upon sunlight exposure [69]. The increased variation observed for these compounds may be attributed to differential light exposure among individual flowers in the field. Similar variability in the levels of protohypericins was previously reported in fresh buds, flowers, and leaves exposed to sunlight [69]. A common practice in H. perforatum extract analysis involves light exposure of extracts to promote the conversion of protoforms to stable forms, thereby improving consistency in naphthodianthrone quantification [70]. However, this also induces compositional changes, including hyperforin degradation, which necessitates the analysis of both light-exposed and unexposed samples for accurate profiling [68]. Alternatively, Baugh et al. proposed a single assay based on mathematical models that estimates hypericin composition from protoform levels, avoiding light exposure and preserving hyperforin from oxidative degradation [68].
Our study demonstrated that combining the peak areas of protoforms with their stable counterparts significantly reduces variability, and we recommend this approach, particularly when using the “single-flower” method in comparative metabolomic analyses. While the “bulk-flower” method offers lower variability, it also requires larger flower quantities and slower sample processing. The choice between methods should depend on the specific research objectives. Both approaches are suitable for comparative analysis of metabolite composition in individual plants.
Given that secondary metabolite accumulation reflects the plant’s biosynthetic network activity, microscale extraction and comparative analysis of relative compound levels can be valuable for various research applications, including biodiversity assessment [30], QTL mapping, and the identification of loci linked to specific compound accumulation [71,72]. However, the variability introduced by sample weighing or direct extraction from individual flowers limits the use of these methods for precise quantification of compound abundance based solely on peak area measurements. This represents the primary limitation of the proposed methods for accurate quantification.
Using the “single-flower” approach, we also compared metabolite variation among plants derived via apomixis and observed significantly lower variation compared to a group comprising three different genotypes (Table 4). This finding underscores the benefit of working with genetically identical material, such as that obtained via apomixis, which yields more homogeneous compound profiles compared to genetically diverse plants.

5. Conclusions

This study presents two ultrasound-assisted microscale extraction procedures for isolating bioactive compounds from fresh H. perforatum flowers, both of which are suitable for comparative metabolomics in population studies of this species. The “bulk-flower” procedure offers slightly higher reproducibility but is less practical for large-scale sampling due to the substantial quantity of floral material required from each plant. In contrast, the “single-flower” procedure enables high-throughput processing while preserving a high degree of reproducibility. It requires only a few flowers at the target developmental stage to be collected from each plant, making it well suited for the comparative analysis of large plant sets.
Combining the peak areas of proto- and stable forms of pseudohypericin and hypericin further enhances the reproducibility of these compounds’ quantification. Application of the “single-flower” procedure to flower extracts from individual apomictic plants resulted in significantly lower variability compared to a small group of plants representing distinct genotypes. This finding underscores the benefit of cultivating genetically identical plants produced through apomixis for generating metabolically uniform material.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app15137334/s1, Table S1: Mean value in percent (±SD) and RSD values (%), reflecting the variation between 5 different w/v ratios used for the extraction of a bulk sample of flowers at stage 4 from line 205; Table S2: Mean value (±SD) in % and RSD values (%), reflecting the variation between 6 different durations used for the extraction of a bulk sample of flowers at stage 4 and w/v ratio of 1/15 from a single plant of line 205; Figure S1: Flow diagram of “bulk-flower” and “single-flower” procedures.

Author Contributions

Conceptualization, I.A.; methodology, I.A., K.R., M.R. and L.G.; software, K.R.; formal analysis, M.R., M.A., P.G. and T.Z.; resources, M.R. and I.A.; data curation, K.R.; writing—original draft preparation, M.R.; writing—review and editing, K.R. and I.A.; visualization, K.R.; project administration, M.R.; funding acquisition, M.R. and I.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Bulgarian National Science Fund, grant KP-06-M66/2, as well as by the Centre of Competence “Sustainable Utilization of Bio-resources and Waste of Medicinal and Aromatic Plants for Innovative Bioactive Products” (BIORESOURCES BG) project BG16RFPR002-1.014-0001, funded by the Program “Research, Innovation and Digitization for Smart Transformation” 2021–2027, co-funded by the EU.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included within the article and the Supplementary Materials.

Acknowledgments

The authors would like to thank Rumyana Velcheva and Sonya Ivanova (AgroBioInstitute, Sofia, Bulgaria) for the excellent technical assistance.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
SSRSimple sequence repeat
LC-MSLiquid chromatography–mass spectrometry
DADDiode array detector
QTOFQuadrupole time of flight
ESIElectrospray ionization
TICTotal ion current
RSDRelative standard deviation
UV-VisUltraviolet–visible

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Figure 1. Stages of H. perforatum flower development used in the experiments.
Figure 1. Stages of H. perforatum flower development used in the experiments.
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Figure 2. LC/DAD/QTOF chromatogram of an extract from stage 4 flowers. (a) ESI-TIC in negative mode, (b) UV/Vis trace at 270 nm, and (c) UV/Vis trace at 590 nm. Compound numbering corresponds to Table 2.
Figure 2. LC/DAD/QTOF chromatogram of an extract from stage 4 flowers. (a) ESI-TIC in negative mode, (b) UV/Vis trace at 270 nm, and (c) UV/Vis trace at 590 nm. Compound numbering corresponds to Table 2.
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Table 1. Mean fresh weight (FW, mg) and relative standard deviation (RSD, %) of single flowers at 5 stages of flower development based on data from 3 plants representing different genotypes. Means that share a letter do not differ significantly at p < 0.05.
Table 1. Mean fresh weight (FW, mg) and relative standard deviation (RSD, %) of single flowers at 5 stages of flower development based on data from 3 plants representing different genotypes. Means that share a letter do not differ significantly at p < 0.05.
Flower StagePlant 3_29Plant 90_44Plant 139_28Across Genotypes
Mean FW RSD Mean FW RSD Mean FW RSD Mean FW RSD
1st15.144.8323.2412.0519.043.8619.14 ± 4.05 a21.16
2nd21.794.6641.9214.5834.167.9332.62 ± 10.15 a31.13
3rd47.362.3653.631.2452.586.0251.19 ± 3.36 b6.57
4th62.275.9666.9113.9065.234.3064.80 ± 2.35 b3.63
5th43.484.3248.922.4360.084.3150.83 ± 8.46 b16.65
Table 2. Compounds identified in the methanol extract of Hypericum perforatum after LC-DAD-QTOF analysis.
Table 2. Compounds identified in the methanol extract of Hypericum perforatum after LC-DAD-QTOF analysis.
CompoundtR
min		IdentityTrivial NameUV λ Max
nm		[M−H]−
m/z		ΔppmMS2 FragmentsReference
1 *5.1caffeoylquinic acidchlorogenic acid218, 300, 325353.08851.96191.06, 179.03, 135.05[50,51]
27.3procyanidin B1-279577.135831.17425.09, 407.08, 289.07, 125.02[52,53]
3 *8.8catechin-278289.07211.16245.08, 205.05, 125.02[54]
4 *10.2procyanidin B2-280577.13570.94425.09, 407.08, 289.07, 125.02[52,53]
5 *11.2epicatechin-279289.07200.82245.08, 205.05, 125.02[54]
6 *11.9procyanidin C1-280865.19870.19695.14, 577.13, 407.08, 287.06, 125.02[55]
7 *14.8quercetin-3-O-rutinosiderutin257, 358609.14721.79300.03, 271.02, 255.03, 179.00, 151.00[54]
8 *15.2quercetin-3-O-galactosidehyperoside255, 355463.08922.15300.03, 271.02, 255.03, 179.00, 151.00[54]
9 **15.4quercetin-3-O-glucoronidequerciturone255, 355477.06831.75301.03, 179.00, 151.00[56]
10 **15.5quercetin-3-O-glucosideisoquercitrin255, 355463.08891.5300.03, 271.03, 255.03[57,58]
11 *16.9quercetin-3-O-rhamnosidequercitrin255, 345447.09411.81300.03, 271.02, 255.03, 179.00, 151.00[58]
12 *24.9I3, II8-biapigeninamentoflavone268, 330537.08341.26443.04, 385.07, 151.00[53]
13 *43.0protopseudohypericin--521.0875−0.59477.10, 423.09[59]
14 *47.1pseudohypericin-592519.07220.08487.05, 475.08, 503.04, 449.07, 421.07[59]
15 *52.1protohypericin--505.09391.99461.10, 407.09[60]
1654.0furohyperforin--551.3733−1.63411.25, 482.30, 329.18[61]
17 *54.7hyperforin-223, 272535.3789−0.72466.11, 398.24, 383.22, 315.16[61]
18 *55.54hypericin-592503.07730.12459.09, 487.04, 443.07, 405.08[61]
* Compounds detected applying UV/Vis detector. ** Compounds detected as a collective peak applying UV/Vis detector.
Table 3. Mean values ± standard deviations (SDs) as well as the relative standard deviation (RSD) for the abundance of the identified compounds presented as a percentage of the sum of peak areas of the same compounds among three different genotypes (3_29, 90_44, and 139_28). Mean RSD values for each compound were calculated across the three genotypes. The numbering of compounds corresponds to Table 2 and Figure 2. Means sharing a letter along each horizontal line do not vary significantly at p < 0.05.
Table 3. Mean values ± standard deviations (SDs) as well as the relative standard deviation (RSD) for the abundance of the identified compounds presented as a percentage of the sum of peak areas of the same compounds among three different genotypes (3_29, 90_44, and 139_28). Mean RSD values for each compound were calculated across the three genotypes. The numbering of compounds corresponds to Table 2 and Figure 2. Means sharing a letter along each horizontal line do not vary significantly at p < 0.05.
3_2990_44139_28Mean RSD (%)
per Compound
CompoundMean (%)RSDMean (%)RSDMean (%)RSD
13.18 ± 0.40 b12.701.82 ± 0.2 a11.032.08 ± 0.08 a3.719.15 ± 4.78
34.15 ± 0.28 a6.831.45 ± 0.08 b5.532.81 ± 0.19 c6.866.41 ± 0.76
41.16 ± 0.0.14 a12.101.42 ± 0.04 b3.151.17 ± 0.06 a4.956.73 ± 4.73
53.88 ± 0.35 a3.095.34 ± 0.3 b5.694.51 ± 0.23 c5.014.60 ± 1.35
61.04 ± 0.03 a3.130.82 ± 0.01 b0.900.93 ± 0.05 c5.753.26 ± 2.43
713.37 ± 0.90 a6.730.08 ± 0.07 b7.834.78 ± 0.38 c7.997.52 ± 0.69
813.24 ± 0.75 a5.6721.14 ± 1.73 b8.2514.54 ± 0.85 a5.826.58 ± 1.45
9+108.26 ± 0.91 a11.0314.80 ± 0.68 b4.6210.52 ± 0.85 c8.057.90 ± 3.21
115.41 ± 0.58 a10.695.90 ± 0.44 a7.625.55 ± 0.65 a11.7210.01 ± 2.13
1211.26 ± 1.74 b15.457.78± 0.67 a8.629.68 ± 1.48 ab15.2713.11 ± 3.89
130.97 ± 0.10 a10.370.56 ± 0.13 a22.970.86 ± 0.3 a34.9222.75 ± 12.28
146.60 ± 0.71 a10.818.17 ± 0.89 a10.987.38 ± 0.66 a8.9410.24 ± 1.13
150.41 ± 0.05 ab11.340.55 ±0.15 b27.060.29 ± 0.1 a36.5124.97 ± 12.71
1723.38 ± 3.04 a12.9921.94 ± 2.94 a13.5330.93 ± 2.42 b7.8311.45 ± 3.15
183.68 ± 0.50 a13.658.21 ± 0.94 b11.533.98 ± 0.25 a6.1710.45 ± 3.86
Mean RSD per plant 9.77 ± 3.79 9.95 ± 7.01 11.30 ± 10.3310.34 ± 6.08
13+14 *7.58 ± 0.81 a10.748.74 ± 0.86 a9.807.38 ± 0.66 a8.949.66
15+18 *4.10 ± 0.55 a13.358.76 ± 0.92 b10.528.24 ± 0.53 b6.389.33
* Mean values ± SDs and RSDs for the collective detection of peaks corresponding to compounds 13 and 14 as well as 15 and 18.
Table 4. Summary of the compositions of single-flower extracts from 19 H. perforatum plants from the apomictic line Hp93 and 3 plants representing 3 different genotypes (3_29, 90_44, and 139_28). Mean values represent the mean for the percentage of the respective compound calculated as a percentage of the sum of peak areas of the 16 identified compounds. We present the minimum (MIN), maximum (MAX), and relative standard deviation (RSD), as well as percentage of the minimum and maximum from the mean.
Table 4. Summary of the compositions of single-flower extracts from 19 H. perforatum plants from the apomictic line Hp93 and 3 plants representing 3 different genotypes (3_29, 90_44, and 139_28). Mean values represent the mean for the percentage of the respective compound calculated as a percentage of the sum of peak areas of the 16 identified compounds. We present the minimum (MIN), maximum (MAX), and relative standard deviation (RSD), as well as percentage of the minimum and maximum from the mean.
Compound Apomictic Line Hp93Three Independent Genotypes
MEANRSDMINMAXMIN/MEANMAX/MEANMEANRSDMINMAXMIN/MEANMAX/MEAN
12.068.011.792.3486.67113.552.3630.491.823.1877.22134.64
31.388.081.191.6486.81119.042.8048.071.454.1551.77147.91
41.778.801.482.2083.71124.711.2511.771.161.4292.63113.57
55.009.134.556.1790.95123.494.5816.023.885.3484.83116.75
61.5311.831.271.9882.78129.550.9312.190.821.0487.82112.20
70.1215.330.090.1680.06136.356.08110.850.0813.371.33219.95
815.488.6013.8018.3389.15118.3916.3126.0013.2421.1481.18129.66
9+1016.047.8413.8917.9886.61112.1011.2029.678.2614.8073.82132.23
116.3412.635.348.2984.25130.715.624.515.415.9096.23105.00
1211.4027.526.5916.3257.82143.139.5718.197.7811.2681.29117.62
130.3837.290.210.6355.22165.870.8026.820.560.9770.10126.06
144.5410.723.765.9882.88131.547.3910.636.608.1789.40109.85
150.5045.010.260.8651.68172.700.4231.600.290.5568.59131.42
1727.397.6821.0429.7676.81108.6525.4218.9921.9430.9386.32121.68
186.1513.115.228.2284.82133.595.2947.943.688.2169.59155.60
13+14 *4.9211.744.086.5582.79133.098.197.097.588.7492.58106.71
15+18 *6.6514.115.498.9882.61135.135.7146.404.108.7671.78153.55
* Mean values ± SD and RSD for the collective detection of peaks corresponding to compounds 13 and 14 as well as 15 and 18.
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Rusanova, M.; Rusanov, K.; Alekova, M.; Georgieva, L.; Georgieva, P.; Zagorcheva, T.; Atanassov, I. A Simple High-Throughput Procedure for Microscale Extraction of Bioactive Compounds from the Flowers of Saint John’s Wort (Hypericum perforatum L.). Appl. Sci. 2025, 15, 7334. https://doi.org/10.3390/app15137334

AMA Style

Rusanova M, Rusanov K, Alekova M, Georgieva L, Georgieva P, Zagorcheva T, Atanassov I. A Simple High-Throughput Procedure for Microscale Extraction of Bioactive Compounds from the Flowers of Saint John’s Wort (Hypericum perforatum L.). Applied Sciences. 2025; 15(13):7334. https://doi.org/10.3390/app15137334

Chicago/Turabian Style

Rusanova, Mila, Krasimir Rusanov, Marina Alekova, Liliya Georgieva, Pavlina Georgieva, Tzvetelina Zagorcheva, and Ivan Atanassov. 2025. "A Simple High-Throughput Procedure for Microscale Extraction of Bioactive Compounds from the Flowers of Saint John’s Wort (Hypericum perforatum L.)" Applied Sciences 15, no. 13: 7334. https://doi.org/10.3390/app15137334

APA Style

Rusanova, M., Rusanov, K., Alekova, M., Georgieva, L., Georgieva, P., Zagorcheva, T., & Atanassov, I. (2025). A Simple High-Throughput Procedure for Microscale Extraction of Bioactive Compounds from the Flowers of Saint John’s Wort (Hypericum perforatum L.). Applied Sciences, 15(13), 7334. https://doi.org/10.3390/app15137334

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