Development of a selective methodology for methylmercury quantification and evaluation of its accumulation in hippocampus

Abstract

Methylmercury (MeHg⁺) is a highly toxic compound with significant neurotoxic effects, necessitating precise and reliable quantification methods for its assessment in biological tissues. In this study, we developed and optimized a methodology combining Microwave-Assisted Extraction (MAE), derivatization by phenylation, and preconcentration through Liquid Phase Microextraction (LPME), coupled with Gas Chromatography-Pyrolysis-Atomic Fluorescence Spectrometry (GC-PYRO-AFS) for the selective quantification of MeHg⁺ in mouse brain tissue. The optimized method demonstrated high sensitivity and reproducibility, enabling the accurate detection of MeHg⁺ at trace levels without significant matrix effects. This methodological advancement is particularly important in the field of toxicology, as it addresses the limitations of traditional techniques by reducing analysis time and cost while improving accuracy. The ability to precisely quantify MeHg⁺ concentrations in biological tissues facilitates the study of toxicokinetic behaviors, the proposal of distribution mechanisms, and the evaluation of toxicological impacts, ultimately contributing to the development of biomarkers for human health risk assessment.

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Introduction

Monomethylmercury (CH₃Hg⁺; MeHg⁺) is an organic Hg species of significant toxicological interest due to its ability to bioaccumulate in the tissues of organisms, causing toxicity at both cellular and systemic levels.^1–5 The central nervous system (CNS) is particularly vulnerable to MeHg⁺ exposure, with documented neurotoxic effects that have been widely investigated.^6–11

These studies show alterations in various brain areas, including the hippocampus.^12–15 This area of the brain is associated with cognition, learning, and the initial generation and storage of new memories.^16–19 Alterations in synaptic function and/or loss of synapses and neurons in the hippocampus have been linked to neurodegenerative diseases such as Parkinson's^20,21 and Alzheimer's disease.^22–25 Although these neurodegenerative diseases have multifactorial causes, some studies have considered exposure to MeHg⁺ as a potential risk factor.^10,26–32 Therefore, understanding the exposure dose and its accumulation in the brain is crucial for elucidating the mechanisms of MeHg⁺ neurotoxicity.

The neurotoxic effects of MeHg⁺ in the brain and its relationship with exposure dose have been studied both in cell culture^33–35 and in mice.^36–39 It has been established that the implemented methods for the analysis of MeHg⁺ must be selective and also demonstrate a high sensitivity that permits the detection of trace levels of this contaminant in tissues that are hard to access, such as the brain.

Traditional techniques for the quantification of methylmercury (MeHg⁺) in biological tissues often face significant limitations, including time-consuming sample preparation, high costs, and challenges in achieving accurate measurements due to matrix effects.^40,41 These limitations can hinder the ability to conduct precise toxicological assessments, which are crucial for understanding the toxicokinetic behavior of MeHg⁺ and its potential health impacts.⁴² Therefore, developing new methodologies that overcome these challenges is essential for advancing the field of toxicology, enabling more reliable quantification of MeHg⁺ in complex biological matrices, and supporting the proposal of distribution mechanisms and the identification of relevant biomarkers.⁴³

Nevertheless, a significant limitation in current methods for estimating MeHg⁺ content in the brain is that they are often time-consuming and complex. These methods frequently rely on mass balance calculations based on the total Hg content in the matrix rather than providing direct quantification of MeHg⁺.^44,45 Additionally, they often estimate MeHg⁺ content as equivalent to total Hg,^{38,39,46–48} overlooking the critical fact that this organic Hg species can be demethylated and converted into inorganic Hg (Hg²⁺). This oversight can lead to inaccurate assessments of MeHg⁺ levels and its associated neurotoxicity.

On the other hand, some selective analytical techniques exist that provide direct evidence of MeHg⁺ concentration in the matrix, but these methods are often limited by their high cost, the complexity of the procedures, and the need for sophisticated analytical equipment, such as gas chromatography-inductively coupled plasma-mass spectrometry (GC-ICP-MS),⁴⁹ isotope dilution in combination with GC-ICP-MS,⁵⁰ and liquid chromatography-inductively coupled plasma-mass spectrometry (LC-ICP-MS).⁵¹

For these reasons, there is a critical need for a methodology that allows for the selective and accurate determination of MeHg⁺ in the mouse brain. Such a methodology should not only provide precise results but also be user-friendly for the analyst, overcoming the complexity and high demands of current techniques. Additionally, it must be robust, reproducible, and sensitive, particularly when dealing with small samples where accuracy is crucial. Therefore, the development of our methodology is justified, as it is designed to address these exact requirements. It ensures that all stages, from sample treatment to the quantitative determination of the analyte, are performed with maximum efficiency and reliability, ultimately providing the best possible results in a more accessible and easier.

Given the above, the aim of this investigation was to develop and optimise a robust analytical methodology to directly quantify MeHg⁺ in mouse brain tissue exposed to distinct doses of MeHg⁺. Furthermore, the direct quantification of MeHg⁺ was determined in the hippocampus due to its relevance in the neuropathological process, providing direct evidence of its presence in this brain structure. Prior to the quantification of MeHg⁺ both in the brain and hippocampus, the samples were digested in alkaline medium and aliquots of extracts were derivatized to permit the preconcentration of MeHg⁺ in an organic medium.

In this line, alkaline digestion was performed to enable the extraction of MeHg⁺ from the matrix.^52,53 Our search for derivatization reagents for MeHg⁺, applicable to mouse brain and hippocampus biological samples, led us to select sodium tetraphenylborate (NaBPh₄) and phenylboronic acid (PhB(OH)₂) as the derivatization reagents.^54–56 These reagents were selected because they are easily accessible. Besides, the sample digestion, derivatization, and preconcentration stages were optimized through different experimental designs (DoE) to incorporate and study several factors simultaneously involved in each stage, thus obtaining the best experimental conditions.

Experimental

Reagents and stock standard solution

A stock standard solution of Hg²⁺ (1000 mg L⁻¹, TraceCERT grade) and methylmercury chloride (Analytical Standard grade) were obtained from Merck. The methylmercury chloride was dissolved in Milli-Q water to prepare a standard solution, which was quantified using a Direct Mercury Analyzer (DMA-80, Milestone). All reagents and solutions were stored at 4 °C to ensure stability.

Dichloroethane (C₂H₄Cl₂, ≥99.5%, GC grade, EMPLURA®), dichloromethane (CH₂Cl₂, ≥99.8%, GC grade, SupraSolv), tetrachloroethylene (C₂Cl₄, ≥99%, GC grade, EMPLURA®), glacial acetic acid (CH₃COOH, ≥99.7%, ACS grade), sodium hydroxide (NaOH, ≥99%, EMSURE®), sodium tetraphenylborate (NaBPh₄, ≥99.5%, ACS grade), phenylboronic acid (PhB(OH)₂, 95%), isoflurane (99.9%), hydrochloric acid (HCl, 37%, ACS grade), and nitric acid (HNO₃, 65–67%, puriss. p.a. grade) were all procured from Merck.

Helium gas (≥99.999%) was employed as the carrier gas for GC. Nitrogen gas (≥99.999%) and argon gas (≥99.999%) were initially utilized as carrier gases in the PYRO-AFS system, with argon being the exclusive carrier gas in subsequent stages. Compressed air (≥99.999%) was used as the carrier gas in the DMA-80 system.

Method validation, including both the reference method for total Hg analysis (DMA-80, Milestone) and the optimized methodology, was conducted using the certified reference material ERM CE-464 (Tuna Fish), containing 5.5 mg MeHg per kg.

All glassware was meticulously cleaned using hydrochloric acid (37%, ACS grade) and nitric acid (65–67%, puriss. p.a. grade) to prevent contamination.

Animals and treatment

Two-month-old female C57BL/6 mice (17–23 g) were obtained from the Animal House Facility at the Faculty of Science, Universidad de Valparaíso. The animals were housed in polycarbonate cages (373 × 190 × 138 mm) in a temperature-controlled room (20 ± 1 °C) with a 12 hour light-dark cycle, and were provided food and water ad libitum. Animal maintenance and experimental procedures were conducted in accordance with the National Institute of Health (USA) guidelines for the use of experimental animals, with approval from the Institutional Animal Ethics Committee of the Universidad de Valparaíso.

The 36 mice used in this study underwent an acclimation period of 4 days before being randomly divided into three groups: control group (n = 12), 0.5 mg L⁻¹ MeHg⁺ group (n = 12), and 5 mg L⁻¹ MeHg⁺ group (n = 12). The chosen concentrations were within a range that would not adversely affect the animals' health or alter their drinking behavior.^37,38,57 The two exposure groups received methylmercury chloride (CH₃HgCl) in filtered drinking water at the specified concentrations for 30 days, with the water being refreshed daily. The control group received only filtered water during the same period. Throughout the exposure period, the animals were monitored, and no signs of toxicity were observed.

Following the 30 day exposure period, the animals were anesthetized with isoflurane and sacrificed by cervical dislocation, followed by decapitation. The brains were promptly extracted and stored at −18 °C until analysis. Eighteen brains (n = 6 per group) were allocated for the determination of total Hg and MeHg⁺ using DMA-80 and GC-Pyro-AFS, respectively. The remaining eighteen brains were used to determine total Hg and MeHg⁺ concentrations in the hippocampus. The hippocampus was dissected following the method of Jaszczyk et al.⁵⁸ which is optimized for molecular studies requiring contaminant-free tissues while preserving anatomical and molecular integrity.⁵⁸ For hippocampal analysis, tissue samples from each group were pooled (n = 6 per group) for combined analysis.

Mercury ingestion calculation

To estimate the total mercury intake, it was assumed that each mouse consumed approximately 3 mL of water daily. Over the 30 day exposure period, this corresponds to a total consumption of about 540 mL per group (n = 6). For the group exposed to 0.5 mg L⁻¹ of MeHg⁺, the estimated total mercury intake was approximately 270 mg per group, with each mouse receiving around 45 mg of mercury. Similarly, for the group exposed to 5 mg L⁻¹ of MeHg⁺, the total mercury intake was approximately 2700 mg per group, with each mouse receiving around 450 mg of MeHg⁺. These calculations provide a quantitative assessment of the mercury dose administered to the animals, ensuring that the exposure levels are consistent with the intended experimental design.^59,60

Total mercury analysis

Total mercury in the samples was analyzed using a direct mercury analyzer (DMA-80, Milestone), which operates with a combustion dual cell detector, gold amalgamation, and atomic absorption. Hippocampus and brain samples were placed in nickel boats, weighed on an analytical balance (AUX220, Shimadzu), and then loaded into the autosampler tray. Calibration of the instrument was validated using the certified reference material ERM CE-464 (Tuna Fish). Each tissue analysis included a certified reference material, a method blank, and a duplicate sample to ensure accuracy and precision.

GC-PYRO-AFS instrumentation

The separation and detection of MeHg⁺ were conducted using a GC-PYRO-AFS system. Gas chromatographic separation was performed on a TRACE 1300 GC (Thermo Scientific), where SLB-5MS and SLB-35 MS columns were evaluated during the optimization process. Ultimately, the SLB-5MS column was selected for further analysis. The outlet of the GC column was connected to the pyrolysis system via a T-connector (1/16′), with argon (Ar) serving as the carrier gas. The other end of the pyrolysis system was coupled to the atomic fluorescence detection system. The pyrolysis system was maintained at a working temperature of 800 °C. Detection was achieved using atomic fluorescence spectroscopy, with a Millennium System (P.S. Analytical).

Derivatization reagents and LPME preconcentration factor (PF)

The derivatization of MeHg⁺ was evaluated using two reagents: sodium tetraphenylborate (NaBPh₄) and phenylboronic acid (PhB(OH)₂). A known amount of MeHg⁺ was added to a 15 mL centrifuge tube containing Milli-Q water, NaOH, and acetic acid (CH₃COOH), forming a buffer with a total concentration of 0.4 M and a pH of 4.5. The tube was sealed and manually stirred before adding 0.2 mL of dichloroethane (C₂H₄Cl₂) and the derivatization reagent at a final concentration of 0.3% w/v (n = 6). The tube was then sealed again and stirred at 700 rpm for 15 minutes using an orbital shaker (RS-OS-10, Phoenix). After stirring, the solution was centrifuged at 5000 rpm for 1 minute using a Hettich Universal 320R centrifuge. The resulting organic phase was separated and stored in a microcentrifuge tube at −18 °C until further analysis by GC-PYRO-AFS and DMA-80.

The preconcentration factor (PF) was determined using synthetic samples as the ratio of the concentration of MeHg⁺ in the organic phase of derivatized samples to the concentration of MeHg⁺ in the aqueous phase of non-derivatized samples. This was performed (n = 3) using 0.2 mL of C₂H₄Cl₂, CH₂Cl₂, or C₂Cl₄ as the organic solvent, along with an aqueous solution of 0.3% w/v NaBPh₄ and acetate buffer (0.4 M, pH 4.5). The organic phase was then separated and stored in an Eppendorf tube at −18 °C until analysis by DMA-80. The preconcentration factor was calculated using the following equation:

PF = [MeHg⁺]_org/[MeHg⁺]_ac

where, [MeHg⁺]_org: concentration of MeHg⁺ in the C₂H₄Cl₂, CH₂Cl₂ or C₂Cl₄ organic phase. [MeHg⁺]_ac: concentration of not derivatized MeHg⁺ in synthetic samples prepared in buffer acetate.

Screening and optimization of the digestion, derivatization and preconcentration procedure

The microwave digestion procedure was optimized using a Central Composite Design (CCD). In this approach, power and digestion time were refined through 12 factorial experiments and 6 central points, using CRM ERM CE-464 (tuna fish) as the sample matrix (α = 0.05).

The screening and optimization of the MeHg⁺ derivatization step and LPME preconcentration were assessed using a Plackett–Burman design for the initial screening, followed by a Box–Behnken design for further optimization, both with a 95% confidence level. Variables evaluated during the screening phase included the type of organic solvent, buffer pH, buffer concentration, percentage of derivatization reagent, agitation time, agitation speed, and LPME temperature. Both experimental designs (DoE) were carried out with 18 experiments using synthetic MeHg⁺ samples.

Statistical analysis and the development of experimental designs were conducted using Minitab® 21 statistical software.⁶¹ All DoEs were randomized to minimize the impact of uncontrollable factors on the response.

Evaluation of MeHg⁺ degradation using a focused single-mode microwave system

The recovery of MeHg⁺ during the extraction step was assessed to evaluate its potential degradation during microwave digestion. This evaluation was conducted by statistically comparing the chromatographic areas of MeHg⁺ in digested versus undigested synthetic samples. The digestion was performed using a focused single-mode microwave system (Discover, CEM), where MeHg⁺ was digested in a solution of 0.5% w/v NaOH in Milli-Q water for 6.8 minutes at 85 W (n = 4). These conditions were selected based on similar digestion protocols reported in previous studies.⁶² Following digestion, 1 mL of each digested synthetic sample was derivatized using the procedure previously described, with NaBPh₄ as the derivatization reagent and C₂H₄Cl₂ as the organic solvent. The resulting organic phases were separated and stored in microcentrifuge tubes at −18 °C. All samples were subsequently analyzed by GC-PYRO-AFS.

Determination of MeHg⁺ in the hippocampus and mouse brain

Hippocampus and brain samples were digested using a focused single-mode microwave system (Discover, CEM) with an alkaline digestion method, employing 0.5% w/v NaOH in Milli-Q water. The analysis was performed under the optimized conditions for the complete methodology, which includes microwave digestion, LPME, and GC-PYRO-AFS. Additionally, total Hg in the samples was analyzed using a DMA-80 direct mercury analyzer.

Statistical analysis

For the initial selection stage, a Plackett–Burman design of experiments (DOE) was employed to identify and reduce the number of significant factors. The statistical significance of variables such as agitation time, derivatizing agent percentage, pH, buffer concentration, temperature, mechanical shaker revolutions per minute, and organic solvent was evaluated based on the signal area of MeHg⁺ measured via GC-PYRO-AFS. Once the most relevant factors were identified, a response surface methodology (RSM) utilizing a Box–Behnken design was conducted to establish the optimal extraction conditions. All statistical analyses for optimization were performed using Minitab® 21 statistical software.⁶¹

A one-way Brown–Forsythe ANOVA test with Welch's correction was used to compare total Hg and MeHg⁺ across different analysis groups. This test is suitable for experiments involving three or more groups with unpaired measurements, assuming a Gaussian distribution. Welch's correction was specifically applied to account for unequal variances among the groups.

For two-group comparisons, such as the assessment of matrix effects, an unpaired t-test with Welch's correction was employed. The normality of the data was assessed using the Shapiro–Wilk test. All analyses were conducted with a 95% confidence level using GraphPad Prism® 8 software.

OriginPro® 8 software was used to create the calibration curve graph and perform the associated linear regression analysis.

Results and discussion

Optimization of the chromatographic parameters of the hyphenated GC-PYRO-AFS system

The chromatographic parameters of the hyphenated GC-PYRO-AFS system were optimized to ensure reproducible, robust, sensitive, selective, and quantitative detection of MeHg⁺, specifically for application in mouse hippocampus and brain samples.

Initially, the optimal conditions for the carrier gas in the PYRO-AFS system were evaluated. Synthetic MeHg⁺ samples were prepared and analyzed using the GC-PYRO-AFS system, employing either N₂ or Ar as carrier gases. The use of Ar as the carrier gas resulted in a significantly higher mean peak area of 30 ± 2 mV min⁻¹ with an RSD of 8.00%, compared to N₂, which produced a much lower mean peak area of 1.4 ± 0.2 mV min⁻¹ and a higher RSD of 14.80%. These findings are consistent with existing literature on the analysis of excited Hg⁰, where it has been demonstrated that N₂ induces a more pronounced cross-quenching effect compared to Ar, likely due to increased Hg–N₂ interactions under higher carrier gas pressure, which in turn diminishes Hg⁰ fluorescence.⁶³ As a result, Ar was determined to be the optimal carrier gas for the PYRO-AFS system, enhancing both the sensitivity and reproducibility of MeHg⁺ detection in complex biological matrices.

The carrier gas pressure was subsequently optimized to maximize signal area while achieving an optimal asymmetry factor and minimizing tailing.⁶⁴ Based on these criteria, the carrier gas pressure was set at 15 psi. The optimized operational conditions for the GC-PYRO-AFS system are detailed in Table 1.

Table 1 GC-PYRO-AFS optimized operational conditions

GC parameters	Condition
Oven program	50/300 °C to 60 °C min^−1
Carrier gas pressure	He 15 psi
Pyrolysis oven temperature	800 °C
GC injection mode	Splitless
GC injector temperature	270 °C
Injection volume	4 μL
GC column	(5%-Phenyl)-methylpolysiloxane (SLB-5MS 30 m × 0.25 mm × 0.25 μm)

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Derivatizing reagent and preconcentration factor

The LPME step was optimized by initially evaluating the derivatizing reagent. Synthetic MeHg⁺ samples containing 9.9 ng of MeHg⁺ were derivatized with either NaBPh₄ or PhB(OH)₂ and analyzed using DMA-80 and GC-PYRO-AFS. The results indicated that the preconcentration factor determined by DMA-80 was higher with NaBPh₄ (34 ± 3) compared to PhB(OH)₂ (29 ± 1), with both values showing statistically significant differences (p-value < 0.05). These results were corroborated by the chromatographic signals obtained via GC-PYRO-AFS, where the mean peak area for MeHg⁺ using NaBPh₄ (6.1 ± 0.3 mV min⁻¹) was significantly higher than that obtained with PhB(OH)₂ (4.9 ± 0.3 mV min⁻¹) (n = 5, p < 0.05). The comparison between these reagents is crucial because they play a key role in the derivatization process, which involves the transfer of MeHg⁺ from the aqueous phase to the organic phase during LPME.^52,65,66 The differences between these derivatizing reagents are well-documented in the literature.^67,68 Based on these findings, NaBPh₄ was selected as the derivatization reagent for subsequent experiments.

Following the determination of the optimal derivatization reagent, the ideal solvent for LPME was evaluated. Three solvents—dichloromethane (CH₂Cl₂), dichloroethane (C₂H₄Cl₂), and tetrachloroethylene (C₂Cl₄)—were compared based on their preconcentration factors. Synthetic MeHg⁺ samples were derivatized with NaBPh₄ and extracted using the evaluated organic solvents, with both the organic and aqueous phases analyzed by DMA-80. The preconcentration factors followed the order: CH₂Cl₂ > C₂H₄Cl₂ > C₂Cl₄ (Fig. 1). Statistical analysis revealed significant differences between CH₂Cl₂ and C₂H₄Cl₂ (p-value = 0.0067) and between CH₂Cl₂ and C₂Cl₄ (p-value = 0.0035), with no significant differences observed between C₂Cl₄ and C₂H₄Cl₂. However, CH₂Cl₂ exhibited higher dispersion compared to the other solvents. This effect could be attributed to differences in boiling points and solvent volatilization during agitation in the LPME step, leading to reduced reproducibility and potential errors in calculating the preconcentration factor. The higher variability observed with CH₂Cl₂ suggests that its use could lead to inconsistent results, potentially compromising the precision of MeHg⁺ quantification in different samples. This increased dispersion could be particularly problematic when analyzing trace levels of MeHg⁺, where small variations in solvent behavior may lead to significant deviations in measured concentrations. As a result, CH₂Cl₂ and C₂H₄Cl₂ were selected for further screening studies to determine the most suitable solvent for the method, with careful consideration of their impact on the reproducibility and accuracy of the LPME process.

Fig. 1 Comparison of preconcentration factors of MeHg⁺ LPME, obtained with different organic solvents. (a) Dichloromethane, (b) dichloroethane, (c) tetrachloroethylene (n = 3 each solvent). Significance parameter ** p-value < 0.01.

Evaluation of MeHg⁺ degradation using a focused single-mode microwave system

The Microwave-Assisted Extraction (MAE) procedure, despite being considered a mild digestion process, has the potential to degrade MeHg⁺ during the irradiation phase. Therefore, prior to optimizing the extraction procedure using DoE, it was essential to assess the potential degradation of MeHg⁺. This was achieved by comparing undigested samples with those exposed to the MAE procedure under extreme conditions—specifically, extended exposure time (10 minutes) and high power (75 W). Synthetic samples containing 9.4 ng of MeHg⁺ were prepared and analyzed under non-optimized conditions using GC-PYRO-AFS. The chromatographic areas for the digested and undigested samples were 5.7 ± 0.5 and 5.7 ± 0.2 mV min⁻¹, respectively. Statistical analysis via Analysis of Variance (ANOVA) indicated that the MAE procedure under these conditions does not cause significant degradation of MeHg⁺ (p-value = 0.807, α = 0.05). These findings align with those reported by Abrankó et al., who similarly observed no significant degradation of MeHg⁺ when using this mild digestion methodology.⁶²

To ensure the reliability of MAE in complex matrices, the absence of significant degradation under extreme conditions is crucial. This is particularly important for the accurate quantification of MeHg⁺ in biological matrices, where maintaining analyte integrity is essential for accurate results. The robustness of the MAE method under these conditions confirms its suitability for routine analysis, ensuring the extraction process does not compromise the quantification of MeHg⁺ in trace-level studies.

Screening and optimization of the digestion, derivatization and LPME procedure

Screening of the derivatization and LPME process. A Plackett–Burman experimental design was employed to identify the statistically significant variables influencing the derivatization and LPME processes, with a focus on optimizing the chromatographic signal of MeHg⁺. This study included the most relevant independent variables that could be experimentally controlled. For the derivatization step, variables such as derivatization reagent concentration, pH, and buffer concentration were considered. For the LPME process, variables including agitation time, agitation speed, temperature, and the type of organic solvent were evaluated (Table 2). The results generated a linear regression model that demonstrated a significant correlation between the independent variables and the response of interest (MeHg⁺ peak area), with the model proving statistically valid (α = 0.05; p-value = 0.015; R² = 80.96).

Table 2 Factors and conditions of the Plackett–Burman design for derivatization and LPME process

Factor	Condition
Agitation time (min)	5–20
% of derivatization reagent	0.1–0.3
pH	4–5
Buffer concentration (M)	0.4–1
Temperature (°C)	20–35
Agitation speed (rpm)	100–700
Solvent	CH2Cl2–C2H4Cl2

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Additionally, the statistical significance of the independent variables' effects on the response was determined through an analysis of variance (ANOVA) of the regression, with the results visualized using a Pareto chart (Fig. 2). The analysis revealed that the type of organic solvent (p-value = 0.033) and pH (p-value = 0.001) were statistically significant, as both factors exceeded the calculated significance threshold (dotted line), indicating a substantial impact on the MeHg⁺ signal. The chart also depicted the nature of each factor's (positive or negative) on the response. Dichloroethane (C₂H₄Cl₂) was identified as the solvent that generated higher peak areas, whereas increasing the pH had a negative effect, reducing the MeHg⁺ signal. The Pareto chart also displayed the variables with non-significant effects, distinguishing between those with positive (grey) and negative (white) impacts. To optimize the derivatization and LPME processes, the condition range of each non-significant factor was adjusted in subsequent experiments to enhance the MeHg⁺ signal.

Fig. 2 Pareto chart of the variables considered in the Placket Burman experimental design for the screening of derivatization and LPME process (α = 0.05). Positive effect: grey bars. Negative effect: white bars.

Optimization of the derivatization and LPME process. The optimization of the derivatization and LPME processes was conducted using a Box–Behnken design, incorporating the statistically significant variable identified during the screening phase (pH value) along with two additional factors—agitation speed and agitation time. These factors were selected based on their statistical significance and to meet the requirement of the Box–Behnken design, which necessitates a minimum of three variables for effective modeling. Synthetic MeHg⁺ samples were prepared using dichloroethane (C₂H₄Cl₂) as the organic solvent. The experimental design included 12 factorial points and 6 central points, as outlined in Table 3.

Table 3 Factors and conditions of the Box–Behnken optimization model for the process of derivatization and LPME

Factors	Level
	−1	1
pH	3.5	4.5
Agitation speed (rpm)	400	800
Agitation time (min)	12.5	25

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In relation to our results, the model demonstrated a significant relationship between the factors and the response (p-value = 0.011; R² = 86.71). The statistical analysis confirmed that pH (B) retained its statistical significance and continued to exert a negative effect on the MeHg⁺ response (p-value = 0.001). Additionally, the interaction effect between agitation speed (A) and agitation time (C) was found to be significant, with a negative impact on the response. Specifically, an increase in the intensity of A generate a decrease in the effect of C (p-value = 0.033) (Fig. 3).

Fig. 3 Pareto chart of the variables considered in the Box–Behnken design for the optimization of derivatization and LPME process (α = 0.05). Positive effect: grey bars. Negative effect: white bars.

The effect of the main factors and their interactions on the MeHg⁺. Signal, as predicted by quadratic modeling using the Box–Behnken design, enables the mathematical optimization of the process while maintaining a limited range of the independent variables. This limitation in the range of variables was necessary because it was not feasible to increase the solution's pH above 5.0 using the acetate buffer, as would have been required in a Central Composite Design. The change in the response is illustrated by the response surface plots of pH vs. agitation time, pH vs. agitation speed, and agitation speed vs. agitation time (S. Fig. 1). The optimization revealed that the highest MeHg⁺ signal area is achieved at a pH of 3.5, an agitation time of 25 minutes, and an agitation speed of 400 rpm, which are the optimal conditions for the derivatization and LPME processes to maximize the response.

Optimization of the microwave assisted extraction (MAE). The microwave-assisted extraction (MAE) process was optimized using an experimental design focused on the most relevant variables, specifically applied power and extraction time, through a Central Composite Design (CCD). This fractional factorial design estimates the effects of the main factors and their interactions by employing a quadratic surface model, which incorporates axial points to account for the curvature of the response during optimization via a second-order polynomial equation. The ranges for each variable were established based on the properties of the target molecule^63,64 (Table 4).

Table 4 Central composite design for the MAE process. Axial points at level −α and α (α = 1.41421)

Factors	Conditions
	−α	−1	0	1	α
Microwave power (W)	14.64	25	50	75	85.35
Extraction time (min)	1.17	2	4	6	6.80

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The CCD model, which included a second-order term, was constructed with 4 axial points and 5 central points to estimate the variance of the experimental error. The results of the CCD yielded an R² value of 80.37 and a p-value of 0.020, indicating a statistically significant correlation between the independent and dependent variables (α = 0.05). Additionally, the statistical analysis of the CCD revealed that the main factor, extraction time, was highly significant (p-value = 0.006). Moreover, the quadratic term for microwave power also showed significance (p-value = 0.025) (S. Fig. 2). The statistical significance of a quadratic term (AA) suggests that the response exhibits a notable curvature within the selected range for this factor. A response surface model (RSM) was developed to describe the behavior of the response across the tested range of independent variables, allowing for the selection of optimal conditions to achieve the highest MeHg⁺ signal. In this case, the optimal conditions were found at the extreme points of the applied range, specifically 85 W of microwave power and 6.8 minutes of exposure time, with an optimal desirability factor (D = 1). This desirability factor indicates that the effects of the evaluated variables are not antagonistic and work synergistically to maximize the response, a conclusion further supported by the surface plot graph (Fig. 4).

Fig. 4 Surface response plot for the optimization of MAE process by a central composite design.

After the optimization of the derivatization-LPME and MAE processes, the optimal conditions for each variable were established and are presented in Table 5.

Table 5 Optimal conditions for the microwave assisted extraction, derivatization and LPME

Extraction MAE	Condition
Microwave power (W)	85
Extraction time (min)	6.8
NaOH (% w/v)	0.5
Derivatization and LPME
Agitation time (min)	25
Derivative reagent (%)	0.3
pH	3.5
Buffer concentration (M)	0.4
Temperature (°C)	20
Agitation speed (rpm)	400
Solvent	C2H4Cl2

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Determination of analytical figures of merit and validation of GC-PYRO-AFS

The figures of merit calculated for the optimized methodology include the linearity limit (LOL), detection limit (LOD), and quantification limit (LOQ). The LOD and LOQ were determined using the formulas 3 × SD/slope and 10 × SD/slope, respectively, where SD corresponds to the standard deviation of blank signals from ten replicates. These values were calculated according to the general definitions provided by Eurachem, Demirkaya-Miloglu et al., and Ershadi et al.^69–71 For the determination of the LOL, simple calibration curves were constructed, and an analysis of variance (ANOVA) test demonstrated a significant correlation (p-value < 0.0001) between the MeHg⁺ concentration and the area signal over a working range of 0.5 to 100 ng of MeHg⁺. The linearity of the calibration curve was assessed through linear regression, resulting in a correlation coefficient (r²) of 0.9999 and a linear equation given by y = 1.517x − 1.41. The calibration curve was then used to determine the LOD and LOQ, which were found to be 0.0045 and 0.113 μg L⁻¹, respectively. Other parameters determined using C₂H₄Cl₂, were preconcentration factor 35 ± 2, and extraction percentage 100 ± 6%.

The results indicate that the sensitivity of the method is comparable to that reported in the literature, but with notable advantages in terms of speed and cost-effectiveness. While traditional methods often require labor-intensive sample preparation and the use of costly reagents, the optimized GC-PYRO-AFS system offers a streamlined process that reduces both time and expense, without compromising analytical performance. This reduction in time and cost is particularly advantageous for large-scale studies or routine analysis, where efficiency is crucial. The ability to achieve similar or better detection limits with a simpler and more cost-effective method could significantly influence the adoption of this methodology in future research and various applications. Moreover, the ease of implementation of this optimized method in laboratories with limited resources further highlights its potential for widespread use, particularly in regions where access to sophisticated analytical equipment and reagents may be constrained.

The LOD can be expressed as either a relative or absolute limit. The relative detection limit is defined as the minimum detectable analyte concentration (expressed in concentration units), while the absolute detection limit is defined as the minimum detectable analyte amount (expressed in mass units).⁷² In our study, the relative detection limit was 0.0045 μg L⁻¹, while the absolute detection limit was 0.4 pg MeHg⁺ as Hg per injection volume. This distinction is important because LOD is reported in the literature in one or both formats. For instance, in studies using GC-PYRO-AFS systems for the determination of MeHg⁺ in various matrices, authors such as Gibičar et al., Ebdon et al., and Berzas Nevado et al. reported the LOD as an absolute detection limit, with values in the picogram range (0.22 pg and 1.8 pg MeHg⁺ as Hg, respectively).^73–75 However, the enhanced sensitivity achieved by these authors contrasts with the time-consuming and labor-intensive sample preparation required for extracting MeHg⁺ and organomercurial compounds from certified reference materials (CRMs) and biological samples,^73,74 which involves the use of highly volatile organic solvents like dichloromethane and hexane,^74,75 and/or derivatizing agents recently discontinued in our country, such as sodium tetrapropylborate (NaBPr₄)⁷³ and sodium tetraethylborate (NaBEt₄).⁷⁵ Other authors, such as Mao et al., reported the LOD as a relative detection limit, obtaining a value of 0.03 ng L⁻¹ for MeHg⁺ and EtHg⁺ through an aqueous phenylation process with NaBPh₄, coupled to a purge-and-trap GC-PYRO-AFS system. However, the signals they obtained for both analytes were highly susceptible to parameters such as salt concentration in the aqueous medium, temperature, flow rate, and purge time, with a minimum purge time of 45 minutes being required.⁷⁶

Given these considerations, our LOD is comparable to those reported in the literature. Our methodology is also faster, less time-consuming, and allows for straightforward sample processing at a lower cost, with NaBPh₄ as a commercially accessible derivatizing agent compared to NaBPr₄ and NaBEt₄.

The matrix effect was also evaluated during the quantification of MeHg⁺ in mice brains. The analysis indicates that the sample matrix does not significantly affect the sensitivity of the method, ensuring accurate quantification with 95% confidence (p-value = 0.5279).

Regarding validation, the hyphenated GC-PYRO-AFS system, along with the MAE, derivatization, and LPME processes under optimal conditions, was validated through a statistical comparison at a 95% confidence level between the certified reference material ERM CE-464 (tuna fish) reported MeHg⁺ concentration (5.5 mg MeHg⁺ per kg) and the concentration obtained through GC-PYRO-AFS analysis (5.7 ± 0.3 mg MeHg⁺ per kg).

Selectivity of the hyphenated GC-PYRO-AFS system

After optimizing the chromatographic parameters and method procedures, the selectivity of the hyphenated GC-PYRO-AFS system was evaluated. Samples containing 10 ng of MeHg⁺, 10 ng of Hg²⁺, and blank samples were analyzed using the GC-PYRO-AFS system under identical conditions. The Hg²⁺ sample exhibited a low signal, similar to that of the blank, in contrast to the intense signal observed for the MeHg⁺ sample (Fig. 5). These results confirm that the method is selective for MeHg⁺ under the optimized conditions of the GC-PYRO-AFS system.

Fig. 5 Chromatogram of (a) 10 ng of MeHg⁺ (solid line) and control (dotted line); (b) 10 ng Hg²⁺ (solid line) and control (dotted line), under optimal operating conditions (n = 3 each group).

Biological application in mouse quantification and its importance in toxicology

Evaluation of MeHg⁺ concentration in mice brain samples. After optimizing the methodology, we proceeded to determine the total Hg and MeHg⁺ content in the hippocampus and whole brain of mice from both the control group and the groups exposed to 0.5 and 5 mg MeHg⁺ per L for 30 days. Significant differences were observed in the concentrations of total Hg (HgT) and MeHg⁺ between the treated groups and the control group, as well as within the same exposure group (Fig. 6). The comparisons between all groups were statistically evaluated using a one-way Brown–Forsythe ANOVA test with Welch's correction. A p-value of less than 0.0001 for each comparison indicates that all evaluated groups are statistically different. These results are consistent with findings from similar experiments reported in the literature, where mice were exposed to the same concentrations of MeHg⁺ used in this study.⁷⁷

Fig. 6 Total Hg and MeHg⁺ concentrations, along with their statistical significance, categorized by specific exposure conditions (measured in wet weight). Control group: white bars; 0.5 mg MeHg⁺ per L: grey bars; 5 mg MeHg⁺ per L: black and white bars.

The results of MeHg⁺ accumulation in the brain correlate with the dose administered to each evaluated group. Additionally, the concentration of MeHg⁺ in the brain exhibits consistent ratios between groups. Specifically, the control and 0.5 mg MeHg⁺ per L group show a ratio of 22, while the 0.5 and 5 mg MeHg⁺ per L groups demonstrate a ratio of 25.

In the studies, it was observed that the hippocampus accumulates a higher amount of MeHg⁺ compared to the whole brain (p-value < 0.001 in all cases). This finding is significant because it indicates that the hippocampus may be more susceptible to the accumulation of this neurotoxin. Additionally, it was found that a portion of the mercury accumulated in these tissues is not in the form of MeHg⁺, and this difference is statistically significant (p-value < 0.0001 in all cases). This suggests that, in addition to MeHg⁺, other forms of mercury might be present, or that MeHg⁺ could be undergoing transformations within the tissue, which has important implications for understanding the mechanisms of toxicity and the risks associated with MeHg⁺ exposure. Therefore, the development of precise and specialized analytical techniques is essential for identifying and quantifying these different forms of mercury, which will allow for a better assessment of toxicological risk and the formulation of more effective mitigation strategies.

The development of new methodologies that are not only precise but also time and cost-efficient allows for improved detection and quantification of these compounds in various biological matrices. These methodologies enable the exact determination of toxicant concentrations in different tissues, which in turn helps propose detailed distribution mechanisms within the organism. Understanding these mechanisms is essential for linking toxicant distribution to observed toxicological effects, aiding in risk assessment and biomarker identification.

Regarding the entry and accumulation of MeHg⁺ in the brain, it is noted that while the precise molecular mechanism of distribution through the body remains unknown, the highly lipophilic MeHg⁺ can diffuse through cell membranes without the need for carrier proteins.⁷⁸ It can also be transported across cell membranes due to the formation of thiol complexes, which enhance its water solubility and facilitate its circulation in the bloodstream. For instance, MeHg⁺ traverses the endothelial cells of the blood–brain barrier (BBB) conjugated with L-Cys via the L-type amino acid transporter 1 (LAT1) system.⁷⁹ It has also been documented that exposure to MeHg⁺ can induce BBB damage due to the upregulation of genes that promote barrier leakage, a phenomenon predominantly observed in astrocytes.⁸⁰ Similar effects have been noted in human brain microvascular endothelial cells, where MeHg⁺ exposure increased VEGF expression, leading to barrier leakage that could facilitate the entry of MeHg⁺ and other harmful substances.⁷⁸

Given the complexities of MeHg⁺ transport and accumulation, the development of these studies is crucial for the future identification of biomarkers that can help detect these species in humans as accurately as possible.

Conclusions

The results obtained in this study demonstrate the successful development and optimization of a methodology that utilizes microwave-assisted extraction, derivatization by phenylation, and preconcentration through liquid phase microextraction for the selective quantification of MeHg⁺. The optimized methodology was effectively applied to the determination of MeHg⁺ in mice brain, enabling its quantification at trace levels without significant matrix effects. This methodology allowed for the determination of the accumulated concentration of MeHg⁺ in the mice brain, which correlated with the dose to which the mice were exposed through drinking water. Furthermore, it was observed that MeHg accumulates more extensively in the hippocampus compared to the entire brain, and that only a fraction of the total mercury remains as MeHg following ingestion.

The high costs and complexities traditionally associated with MeHg⁺ quantification, often requiring mass balance calculations and the use of sophisticated equipment, have been effectively addressed by the methodology developed in this study. By integrating microwave-assisted extraction, phenylation derivatization, and preconcentration through liquid phase microextraction (LPME), this approach eliminates the need for mass balance assessments while offering a selective and sensitive quantification of MeHg⁺. The optimized methodology is not only cost-effective but also adaptable for use across a variety of matrices, enabling widespread application in both biological and environmental samples. This advancement permits for more accessible and accurate MeHg⁺ analysis, making it feasible to monitor exposure and accumulation in diverse contexts with minimal sample preparation and lower operational costs.

Moreover, the developed methodology not only enhances efficiency but also holds the potential to significantly improve the accuracy of MeHg⁺ quantification, which is crucial for toxicological risk assessment and biomarker development.

In the future, this selective quantification method for MeHg⁺ could be instrumental in establishing novel biological markers related to MeHg⁺ exposure, particularly in tissues that accumulate MeHg⁺ at trace levels, utilizing a small sample volume (0.25 g wet weight).

Abbreviations

CNS	Central nervous system
DMA	Direct mercury analyzer
DoE	Design of experiments
EHC	Enterohepatic circulation
GC-PYRO-AFS	Gas chromatography with pyrolysis oven and atomic fluorescence spectroscopy
LOD	Limit of detection
LOL	Linearity limit
LOQ	Limit of quantification
LPME	Liquid-phase microextraction
MAE	Microwave-assisted extraction
MeHg^+	Monomethylmercury
NaBPh4	Sodium tetraphenylborate
PhB(OH)2	Phenylboronic acid
RSD	Relative standard deviation
VEGF	Vascular endothelial growth factor

Data availability

The data supporting this article have been included as part of the ESI.†

Author contributions

Resources: Gabriela Lobos, Marcelo Verdugo. Supervision: Marcelo Verdugo. Project administration: Marcelo Verdugo. Conceptualization: Marcelo Verdugo. Writing – original draft: Marcelo Verdugo, Ferdinand Ávila, Jhoel Ruiz, Constanza Vásquez, Gabriela Lobos. Investigation: Marcelo Verdugo, Ferdinand Ávila, Jhoel Ruiz, Constanza Vásquez. Formal analysis: Marcelo Verdugo. Validation: Marcelo Verdugo, Nicole Roldán. Visualization: Marcelo Verdugo, Ferdinand Ávila, Jhoel Ruiz, Constanza Vásquez, Nicole Roldán. Writing – review & editing: Marcelo Verdugo, Ferdinand Ávila, Jhoel Ruiz, Constanza Vásquez, Nicole Roldán, M. Gabriela Lobos, Álvaro O. Ardiles.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This study was supported by ANID Fondecyt Iniciación 11190689 from the Government of Chile.

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Footnote

† Electronic supplementary information (ESI) available: The DoE experimental data and graphics. See DOI: https://doi.org/10.1039/d3ja00413a

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