Chlorinated paraffins (CP) are complex mixtures of polychlorinated n-alkanes with 10 to 30 carbon atoms and variable chlorine contents (30–70%). CP are categorized by their carbon chain length as short- (SCCP; C₁₀–C₁₃), medium- (MCCP; C₁₄–C₁₇), and long-chain (LCCP; C₁₈–C₃₀) CP and they are widely used for technical applications, e.g., they are added to plasticizers, paints, and flame retardants. CP are known to be persistent, and their bioaccumulation depends on the carbon chain length and the degree of chlorination. Because of their high toxicity, SCCP were classified as persistent organic pollutants (POP) according to the Stockholm Convention in 2017. MCCP and LCCP have similar chemical properties as SCCP, however, there have been only few studies about their toxicity and bioaccumulation, and their metabolism and distribution in the environment up to now, why they also pose a very high risk to humans. Due to their chemical structure, the degradation of CP in the environment is negligible, while accumulation in lipophilic tissues is very likely and high quantities might occur in lipids. CP were already detected in vegetable oils, milk, and dairy products, as well as in human milk fat and additionally in food supplements with high fat content. The analysis of CP is a very challenging task because CP are complex mixtures of polychlorinated n-alkanes, comprising thousands of congeners. The separation of these complex mixtures of unknown isomers and congeners is even not possible by gas chromatography (GC). Therefore, the presented screening method for CP uses the pSPE concept that quantifies CP as the sum. Based on the cost-effective and efficient HPTLC technique, pSPE guarantees reliable results for many samples simultaneously in a short time [1].

The determination of CP by applying the pSPE concept was successfully developed for a rapid and selective screening of CP as the sum. After sulfuric acid treatment and liquid-liquid partition into n-hexane, pSPE was performed on silica gel plates employing a twofold development. The analytes, which were focused in a single target zone, were determined by a densitometric absorption scan after derivatization with o-tolidine showing the total CP content, and the amounts were calculated as the sum by means of a reference CP. A comparison of the results obtained for the total CP in dietary supplements by pSPE with visual detection (pSPE–VIS) and by GC with highresolution mass spectrometry (GC–HRMS) proved the pSPE approach as reliable tool for screening purposes.

For method development, technical CP mixtures (SCCP with chlorine contents of 51.5%, 55.5%, and 63% and MCCP with chlorine contents of 42%, 52%, and 57%) are diluted in n-hexane to a concentration of 25 ng/μL. For the determination of the limit of decision and quantitation, recovery experiments in vegetable oils, and the analysis of dietary supplements, the reference CP (MCCP with a chlorine content of 52%) is diluted to concentrations of 0.075–0.8 ng/μL, 0.75–10 ng/μL, and 0.25–10 ng/μL, respectively. The internal standard (ISTD) 4,4-DDT is prepared at a concentration of 400 μg/mL (stock solution) and used at a final concentration of 6 ng/μL in all samples and standards. [1] For GC–HRMS measurements, SCCP calibration standards with chlorine contents of 51.5%, 53.5%, 55.5%, 59.25%, and 63% and MCCP standards with chlorine contents of 42%, 47%, 52%, 54.5%, and 57% are prepared by respective dilutions of technical CP mixtures to a concentration of 10 ng/μL. The ISTD α-PDHCH and ε-HCH are added at concentrations of 0.075 and 0.05 ng/μL to all samples and standards. [1]

Vegetable oil is treated according to Coelhan [2] with somemodifications. After the addition of 15 μL of the ISTD 4,4-DDT stock solution to 250 mg of vegetable oil, 500 μL of n-hexane and 2.5 mL of concentrated sulfuric acid are added and the mixture is shaken for 3 min at 2200 rpm prior to centrifugation for 30 min. The n-hexane phase is separated, and the extraction is repeated with 500 μL of n-hexane under the same conditions. Both n-hexane extracts are combined and used for pSPE [1]. Oil-based dietary supplement capsules (250 mg of oil) are extracted by the same procedure as described above for the vegetable oil (following the procedure of the sulfuric acid treatment of the sample solution in n-hexane) including the addition of 7.5 μL of the ISTD α-PDHCH prior to the extraction (for GC–HRMS analysis). Furthermore, the combined n-hexane extracts (≈1 mL) are shaken with 2 mL of sulfuric acid for 4 min prior to centrifugation for 5 min. After separation of the n-hexane phase, the sulfuric acid treatment is repeated under the same conditions. A 300 μL aliquot of the n-hexane extract is used for pSPE and a 400 μL aliquot of the n-hexane extract including 2 μL of the ISTD ε-HCH is used for GC–HRMS. [1]

Contact: PD Dr. habil. Claudia Oellig, Department of Food Chemistry and Analytical Chemistry (170a), Institute of Food Chemistry, University of Hohenheim, 70599 Stuttgart, claudia.oellig[at]uni-hohenheim.de

The idea of developing a substance database for natural products arose from a previous collaboration on HPTLC of flavonoids and phenolic acids, between CAMAG and the ZHAW(Zurich University of Applied Sciences, Wädenswil). The project investigated the chromatographic behavior of about 72 substances. In a pilot phase in collaboration with the company Extrasynthese we worked on two smaller projects on iridoids and coumarins to practically implement the database concept.

The HPTLC Association Substance Database is a systematic collection of data, easily accessible electronically from the website of the HPTLC Association [1]. It contains retention and spectral data of representative substances from different classes, analyzed with different developing solvents and derivatization reagents.

With the substance database, HPTLC laboratories have a free and convenient tool to help with identification of zones in a chromatogram. The user can compare R_F values, and colors prior to and after derivatization, and UV spectra of unknown zones with those of the references. As the collection will be regularly expanded with substances not limited to constituents of herbal drugs, different fields of application can benefit from the HPTLC Association substance database. Iridoids and coumarins are described in this article.

Contact: Dr. Tiên Do, CAMAG Laboratory, Sonnenmattstrasse 11, 4132 Muttenz, Switzerland, tien.do[at]camag.com

It is estimated that there are around 24,000 varieties of cocoa and more than 30,000 varieties of coffee in the world [1, 2]. Only a few varieties have been evaluated for their quality aiming at specialty and aromatic products, which is an important trait for securing markets, especially for smallholders in small producing countries. During the fermentation and drying processes, aroma precursors present in the green beans are transformed into flavour compounds determining the quality of commercial beans. The chemical analysis of non-volatile compounds is, therefore, an essential step for the selection of varieties with desired quality traits. Alkaloids (theobromine and caffeine) and polyphenols (catechins, proanthocyanidins and anthocyanins) are contributing to the cocoa flavour. Caffeine, trigonelline and chlorogenic acids are known to produce astringent and sour taste and bitterness in coffee but very few varieties have been compared in controlled conditions. Sucrose is also an aroma precursor in coffee and depends on the variety. The objectives of this study were: i) to develop a protocol for the quantification of non-volatile compounds in cocoa and coffee, ii) to analyze and compare cocoa and coffee varieties from diverse geographical origins cultivated in controlled conditions and harvested at full maturity.

Standard stock solutions of theobromine, trigonelline, caffeine, ideain-3-O-galactoside, cyanidin- 3-O-arabinoside, (–)-epicatechin, (–)-catechin, chlorogenic acid (CGA), neochlorogenic acid (NCGA or 5-O-caffeoylquinic acid), 3,4-, 3,5-, 4,5- dicaffeoylquinic acids, and sucrose are prepared at 1.0 mg/mL with methanol. Standard solutions are prepared at different concentration levels with methanol and stored at 4 °C.

Absorbance measurement at 275 nm and 330 nm prior to derivatization and for sucrose at 520 nm after derivatization with TLC Scanner 4 and winCATS, slit dimension 8.00 mm x 0.20 mm, scanning speed 50 mm/s.

Editor’s Note: A slit length of 5.00 mm is usually used for a band length of 8.00 mm.

For repeatability accuracy assessment, linear ranges were computed using the least squares method. Repeatability was confirmed by applying four repetitions of each standard at five different concentration levels (0.1, 0.2, 0.3, 0.4, 0.5 μg/μL) and the variance among repetitions was expressed as the repeatability standard deviation (%RSD) [3]. Peak area measurements were compared to individual standards and corresponding values were quantified in mg/g dry weight [3]. The repeatability of the HPTLC measurements was assessed for each individual standard and the calibration plots were linear for all analytical standards with all R² > 0.99 (P = 0.01). For each compound, %RSD values were low (< 3.5%) indicating that the HPTLC measurements are accurate enough to be used for quantification.

Multivariate analysis of non-volatile compounds (via peak areas obtained by scanning densitometry) in 137 cocoa varieties clearly discriminates the different groups of varieties (Amenolado, Criollo, Forastero, Trinitario). Theobromine was the most important compound in all accessions analyzed, followed by caffeine, epicatechin/catechin, cyanidin, and ideain. Proanthocyanidin B2 was also important and four other proanthocyanidins were minor compounds [3]. The 108 varieties of coffee analyzed were clearly differentiated based on their caffeine content which is significantly correlated with chlorogenic acids. Arabica varieties present low caffeine. Sucrose, trigonelline, caffeine, and eight chlorogenic acids were detected and quantified based on the R_F values of the standards and by matching their UV spectra with those of the samples [3]. Principal component analysis (PCA) of the coffee varieties shows that 73.2% of the total variation is explained by axis 1 and 2. C. arabica are characterized by low alkaloids and CGAs contents and are mostly located on the left side of axis 1, while C. canephora characterized with high alkaloids and CGAs contents are located on the right side of axis 1.C. arabica with < 11 mg/g of caffeine are on the far left end of axis 2 and C. canephora with > 18 mg/g of caffeine are on the far right end of axis 2. Further results and details are available at [3].

HPTLC is a cost-efficient technique when applied to both qualitative and quantitative assessment of chemical constituents in green cocoa and coffee beans. Compared to other chromatographic techniques, HPTLC presents the outcome as an image of the separated non-volatile compounds on a single plate, detected by UV light. This visible outcome and the simplicity of the technique could allow breeders to run the chromatographic procedure described in this study on hundreds or thousands of varieties and breeding lines. The analysis time for a plate with 20 tracks is comparatively short (approximately 38–42 minutes) and diverse genotypes can be analyzed side by side on the plate, making HPTLC the method of choice for rapid chemometric evaluation of cocoa and coffee varieties and detection of exceptional individuals.

[1] Laliberté, B. et al. (2012) https://agritrop.cirad.fr/568442/1/document_568442.pdf
[2] Bramel, P. et al. (2017) https://worldcoffeeresearch.org/media/documents/Coffee_ Strategy_Low_Res.pdf
[3] Lebot, V. et al. Gen Res Crop Evol (2020) 67: 895–911

Contact: Dr. Vincent Lebot, UMR AGAP, CIRAD, P.O. Box 946, Port-Vila, Republic of Vanuatu, lebot[at]vanuatu.com.vu

The research group “Nanosensors and Bioanalytical Systems” consists of researchers from the Spanish National Center for Scientific Research (CSIC, at the Instituto de Carboquímica) and the University of Zaragoza. One of the group’s areas of interest is the use of HPTLC-based techniques for analysis of complex mixtures mostly coming from petroleum conversion, biofuels and lipidomics. In addition to the inherent advantages of HPTLC (flexibility, complete sample detection, etc.), its coupling to any MS instrument through an elution-based interface opens up an unexplored range of analytical possibilities [1].

Fabry disease is a severe genetic disease of lysosomal deposition produced by malfunction of an enzyme (α-galactosidase A), which results in a gradual accumulation of Gb₃, among other metabolites, in body fluids.

A number of Gb₃-related molecular species in the plasma of Fabry patients were detected at very low concentrations and demonstrated to be biomarkers, using reversed-phase LC coupled to mass spectrometry. HPTLC has generally not been considered as adequate for detailed characterization of a complex sample of lipids. This perception is not correct since currently HPTLC-MS is a methodology to get more information online in less time from complex biological matrices. HPTLC separation reduces mass spectral complexity, which, in turn, allows for a comprehensive examination of samples, even if incompletely resolved separations are obtained.

The objective of this work is to assess whether an HPTLC densitometry-MS approach using the TLC-MS Interface 2 is adequate to determine Fabry biomarkers related to Gb₃ in human plasma.

A separation of a plasma extract into sphingolipid classes, followed by densitometry and MS coupling provides a simple but powerful approach for the detailed structural elucidation of sphingolipids present in human plasma, allowing their recognition by their m/z, and confirmation by their collision induced dissociation MS/MS data, and/or APCI fragmentation.

The TLC-MS Interface 2 is suitable for the analysis of sphingolipids and provides a rapid, precise, and targeted characterization of selected bands on the plate. In our approach we use ESI-MS and APCI-MS. Intensities of ESI(⁺)-MS ions can be related to the concentration of species.

Plasma samples from a Fabry’s patient and a healthy control are obtained from the Institute of Health Sciences (Zaragoza, Spain) after approval of the Ethical Committee of Aragon (CEICA, Spain). Informed consent was obtained from the human subjects.

The investigated Gb3 are untargeted species and no standards are available. Solutions of standards of lyso-Gb₃, Gb₃, lactosylceramide (LacCer), glucosylceramide (GlcCer) which represent the main sphingolipid families are prepared at a final concentration of 0.1 μg/μL each in dichloromethane – methanol 1:1 (v/v).

Identification and semi-quantification of 19 untargeted molecular species of globotriaosyl-ceramides (Gb₃) in extracts from a Fabry’s plasma patient and a healthy control was performed by HPTLC densitometry-MS. The species found were: five isoforms of saturated Gb₃, seven isoforms of methylated Gb₃, and seven species with three unsaturations (that of sphingosine plus two additional unsaturations). Twelve of these species were previously reported as biomarkers of Fabry’s lysosomal disorder using a LC-MS-based method, and the other seven are structurally similar, closely related to them [2].

Saturated Gb₃ isoforms come from the ESI(⁺)-MS analysis of the peak at 27.7 mm of Fabry extract (and 27.9 mm in the case of control). This is the migration distance of the Gb₃ standard. Ions are mostly detected as sodium adducts. The most preponderant isoform is d18:1;C16:0 (m/z 1046.8 [M+Na]⁺). Confirmation of identity of this most abundant ion [C₅₂H₉₇NO₁₈Na]⁺ is done by ESI-MS/MS by loss of a hexose group, giving a product ion at m/z 885.2.

Methylated and three-unsaturated Gb₃ species were both found in HPTLC-UV peaks at 14.9 (Fabry) and 14.2 mm (control). They should have a priori corresponded to preponderant sphingomyelin (SM) species. The obtained HPTLC-ESI(⁺)-MS showed intensities of 10⁵ arbitrary units (a.u.), and high S/N ratio for SM species. However, we found that some Gb₃-related species are present in low concentration and can co-migrate together with the above SM species. Intensities for these Gb₃ species are 10⁴ a.u. (arbitrary units). The zone between m/z 1000-1300 displays ESI ions that matched with either methylated Gb₃-related isoforms as [M+Na]⁺, or Gb₃-related isoforms or analogues with three unsaturations, as [M+H]⁺. The presence of these structures found by ESI-MS was verified by an analysis of lowmolecular ion fragments obtained using HPTLC-APCI(⁺)- MS [2]. This experiment was performed in duplicate and similar spectra were obtained in each case.

Ion intensities are related to concentration of Gb₃ species for several reasons summarized elsewhere [2]. Saturated Gb₃ in Fabry’s plasma were in higher concentration than in control sample in repeated experiments. ESI-MS profiles for methylated and unsaturated Gb₃ species were qualitatively similar for Fabry and control samples although relative distribution of ions is different.

HPTLC-MS has been proven to be a simple but powerful approach for the detailed structural elucidation of sphingolipids present in human plasma. The potential of HPTLC-MS for lipidomic research in general has been summarized elsewhere [1].

[1] V.L. Cebolla et al. J Liq Chromatogr & Rel Technol (2021) https://doi.org/10.1080/10826076.2020.1866600
[2] C. Jarne et al. J Chromatogr A 1638 (2021) 461895

Contact: Dr. Vicente L. Cebolla, Instituto de Carboquímica, CSIC, Miguel Luesma Castán, 4. 50018 Zaragoza, Spain, vcebolla[at]icb.csic.es

One research topic at the department of Biochemistry and Ecotoxicology of the German BfG (Federal Institute of Hydrology) is the development and improvement of effect-based methods for the rapid and cost-effective screening of environmental samples. Researchers of the Life Sciences Institute at the Hebrew University of Jerusalem focus on the genetic engineering of whole-cell biosensors, the development of novel bioreporters and the directed evolution process for the improvement of sensor performance. The collaboration between these two working groups is part of the German-Israeli cooperation in the water technology research project „Tracking Effects of Environmental organic micro-pollutants in the Subsurface“ (TREES), aiming at broadening the application of bioassays performed in combination with HPTLC.

Endocrine disrupting compounds that enter the aquatic environment, e.g., via wastewater treatment plant (WWTP) effluents, can affect the natural hormonal activity of freshwater organisms and thus pose a potential ecological threat even at low concentrations [1]. Prominent endocrine disrupting compounds bind to estrogen (ER) and androgen (AR) receptors, and thus trigger estrogenic and androgenic activity. These modes of action are detected with genetically engineered yeast cells, emitting a detectable fluorescence signal upon exposure to compounds exerting hormonal activity.

Following studies performed with Arxula adeninivorans yeast strains [2], the authors show here the parallel biological detection of estrogenicity and androgenicity in HPTLC-separated sample components using newly generated biosensors based on Saccharomyces cerevisiae. These biosensors express human estrogen and androgen receptors, co-transfected with respective reporting elements that produce either the red fluorescent protein mRuby2 in response to an estrogenic stimulus (ER-Ruby) or the blue fluorescent protein mTagBFP2 in response to an androgenic stimulus (AR-BFP).

The advantage of using HPTLC lies in its biocompatibility, which allows the combination of sample fractionation with biological effect detection directly on HPTLC plates. Several samples can be screened for estrogenicity and androgenicity on parallel tracks simultaneously, thus reducing time and effort. Its matrix robustness allows the assessment of samples with complex and heavy matrices such as WWTP influents.

The aim of the presented study was to generate yeast-based fluorescent biosensors for the parallel detection of estrogenicity and androgenicity. In contrast to yeast-based biosensors using the expression of LacZ as reporting element, which requires the disruption of the cell wall and the addition of an external substrate [3], the presented biosensors allow a fast signal detection in living cells.

The general functionality of the individual yeast biosensors was assessed in 96-well plates and in combination with HPTLC, using either individual reference compounds or their mixtures [4].

Then, the combination of the two yeast biosensors as co-culture was investigated, using three dilutions of an estrogen mix of E1, E2 and E3 and an androgen mix of testosterone and DHT. The estrogen mix was applied on tracks 1–3, the androgen mix on tracks 4–6, and both mixtures were applied on tracks 7–9. Ethanol served as control on track 10. A distinction of the different hormonal activities in the same test can be made by changing the excitation wavelength for scanning. The repeatability was determined by calculating the standard error of minimum three replicates performed on different days, using separate aliquots of the cell suspension.

Dose-effect relationships of the estrogenic and androgenic components in the mixtures were detected. The ER-Ruby biosensor detects only estrogenic compounds (red), even in the presence of androgenic compounds, highlighting the specificity of this biosensor. A slight decrease in the signal intensity at λex = 525 nm was detected, when estrogenic and androgenic model compounds were applied in a mixture. This suggests a weaker expression of the Ruby protein under these conditions.

Regarding the AR-BFP strain, the signal intensity of androgenic compounds (blue) remains unchanged in the presence of estrogenic compounds. However, some interference at the hRF values specific for E1 and E3 is observed in case of high concentrations of the reference compounds. These signals were also detectable when only the ER Ruby biosensor was applied [4]. Possibly, high concentrations of the Ruby protein lead to detectable excitation of Ruby at λ_ex = 396 nm. On the other hand, the used lamps have a low output at 396 nm, which is as well very close to the cut-off filter at 400 nm. In any case, the optical system has to be refined by optimized filters used for the scanning, which might help avoiding this artefact in the future.

An example of the yeast-based biosensor application for a simultaneous screening for androgenicity and estrogenicity using influent samples from a municipal WWTP is shown below. Matrix components are observable below the focusing line and are thus well separated from active compounds. Both influent samples showed estrogenicity and androgenicity. Due to the similar structures and physico-chemical properties of estrogens and androgens, a separation of the two compound classes by HPTLC is challenging [2]. However, the multi-parallel effect assessment in combination with HPTLC allows the detection of the different effects even if the active compounds are not fully separated. In the presented WWTP influents, two strong estrogenic and androgenic signals overlap at hRF values of 75 and 61, indicating the possible presence of E1 (hR_F = 76), E2 (hR_F = 60) and DHT (hR_F = 61). For the androgenic band at hR_F = 75, no candidate compound can be assigned. At some positions, signals from the ER-Ruby strain are visible between the tracks. Due to the leakiness of promoters, each reporter strain produces a background signal, which might be detectable especially at positions where cell densities are higher due to an inhomogeneous application of cells. However, an automated spraying device (Derivatizer) was used resulting in an even application of the cells. A further possibility is a signal broadening that can happen when silicaplates are used.

In conclusion, the presented study showed a proof of principle of the parallel detection of estrogenic and androgenic effects using a fluorescent yeast-based biosensor co-culture.

[1] A.P.A. Da Silva et al. Aquat. Sci. Technol (2018) 6: 35-51.
[2] A. Chamas et al. Sci. Total Environ. (2017) 605–606:507–513.
[3] A. Schoenborn et al. J Chromatogr A (2017) 1530:185–91.
[4] L. Moscovici et al. Biosensors (2020) 10(11): 169.

Contact:

Carolin Riegraf, Federal Institute of Hydrology, Am Mainzer Tor 1, 56068 Koblenz, Germany. Present address: Swiss Centre for Applied Ecotoxicology, Überlandstrasse 133, 8600 Dübendorf, Switzerland, carolin.riegraf[at]oekotoxzentrum.ch

The authors acknowledge Marina Ohlig and Ramona Pfänder also involved in the TREESproject funded by the Federal Ministry of Education and Research (BMBF) Germany (Grant No. 02WIL1387), and the Ministry of Science, Technology and Space (MOST) Israel (Grant No. WT1402/2560).

Quality control of herbal drugs, extracts and products is a challenging task because it requires proper identification and determination of content of active compounds or markers. Because monographs of the pharmacopoeias typically require independent methods for these tasks, the cost of analysis may be quite high. Reference materials are a major contribution to the cost. Quantified Reference Extracts (qRE) may be considered as suitable reference material in this context, reducing the need for stocking several separate chemical reference substances.

The goal of this work was to elaborate examples for using qRE for identification of plant materials and quantification of markers.

Contact:

Dr.Ophélie Fadel, of[at]chromacim.com
Dr. Debora Frommenwiler, debora.frommenwiler[at]camag.com
Dr. Daniel Jean, daniel.jean[at]insuveg.com
Dr. René De Vaumas, rdv[at]extrasynthese.com