Advancing microplastic detection in zebrafish with micro computed tomography: A novel approach to revealing microplastic distribution in organisms

Advancing microplastic detection in zebrafish with micro computed tomography: A novel approach to revealing microplastic distribution in organisms Vikt´ oria Parobkov´ a a , Luk´ aˇ s Maleˇ cek a , Marek Zemek a , Gabriela Kalˇ cíkov´ a b,c,* , Michaela Vykypˇ elov´ a f , Marcela Buchtov´ a b,d,e , Ondˇ rej Adamovský f , Tom´ aˇ s Zikmund a,b,* , Jozef Kaiser a,b a Central European Institute of Technology, Brno University of Technology, Purkyˇ nova 656/123, Brno 61200, Czech Republic b Faculty of Mechanical Engineering, Brno University of Technology, Technick´ a 2896/2, Brno 61669, Czech Republic c Faculty of Chemistry and Chemical Technology, University of Ljubljana, Veˇ cna pot 113, Ljubljana 1000, Slovenia d Department of Experimental Biology, Faculty of Science, Masaryk University, Brno 625 00, Czech Republic e Laboratory of Molecular Morphogenesis, Institute of Animal Physiology and Genetics, Czech Academy of Sciences, Brno 602 00, Czech Republic f RECETOX, Faculty of Science, Masaryk University, Kotl´ aˇ rsk´ a 2, Brno 611 37, Czech Republic HIGHLIGHTS GRAPHICAL ABSTRACT •The novel methodology for detection of microplastics in zebrafish. •MicroCT enabled 3D, non-invasive visualization of microplastics. •Spatial distribution of microplastics in the gastrointestinal tract was observed. •The proposed methodology detected polyethylene microplastics as small as 30 µm. ARTICLE INFO Keywords: Microplastics X-ray Computed Tomography Environment Plastic Pollution Imaging ABSTRACT The analysis of microplastics with current spectroscopic and pyrolytic methods is reaching its limits, especially with regard to detailed spatial distribution in biological tissues. This limitation hampers a comprehensive understanding of the effects of microplastics on organisms. Therefore, there is a pressing need to expand the analytical approaches to study microplastics in biota. In this context, the aim of this study was to test the applicability of non-destructive 3D imaging using X-ray micro-computed tomography (microCT) for the detection of microplastics in fish. Zebrafish (Danio rerio) were gavaged with polyethylene spherical microplastics (30–110 μ m) and the distribution of microplastics in the gut was investigated using microCT. The results showed * Corresponding authors at: Faculty of Mechanical Engineering, Brno University of Technology, Technick´ a 2896/2, Brno 61669, Czech Republic. E-mail addresses: [email protected] (G. Kalˇ cíkov´ a), [email protected] (T. Zikmund). Contents lists available at ScienceDirect Journal of Hazardous Materials journal homepage: www.elsevier.com/locate/jhazmat https://doi.org/10.1016/j.jhazmat.2025.137442 Received 21 November 2024; Received in revised form 10 January 2025; Accepted 28 January 2025 Journal of Hazardous Materials 488 (2025) 137442 Available online 28 January 2025 0304-3894/© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). that the particle size distribution determined by microCT closely matched the data from conventional laser diffraction analysis. In addition, microCT was able to detect microplastics in spiked fish tissue and provide precise localization data by tracing particles of known type and shape. MicroCT offers a novel approach for tracking microplastics in organisms and enables accurate sizing without compromising the integrity of the tissue under investigation. It therefore represents a valuable addition to spectroscopic methods, which are widely used for the detection of microplastics based on their chemical composition but do not provide data on their spatial distribution. 1. Introduction The plastic pollution is currently considered a worldwide problem due to its widespread distribution and persistence in the environment. In addition to the macroplastic pollution, microplastics (MPs, plastic fragments from 1 to 1000 μ m [1]) have become a serious issue due to their negative impacts on the environment [2,3] and possibly also human health [4–6]. Over the last decade, several studies have been conducted on how MPs interact with animal tissues and affect various organisms [7]. The results indicate that they can enter the organism primarily through inhalation and/or ingestion [8]. In biological samples polyethylene (PE) is the most frequently detected MP, followed by polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET) [9–11]. MPs can be found in various shapes, including fragments, fibres, beads, and films [10]. The presence of MPs in organisms can lead to severe negative effects - structural damage to the intestine, liver, and both the excretory and reproductive systems, oxidative stress, alterations in immune-related gene expression, and modifications in antioxidant status [4,12–19]. Moreover, latest research indicates that understanding the distribution of MPs in the organism is crucial for the evaluation of their health effects and accumulation in organs [6,20,21]. Therefore, to link the effects of MPs on the organism, the evidence of MPs distribution in specific tissues is essential. Several new approaches for a reliable MPs detection in biological tissues have been developed lately. Conventionally, two types of detection methods are used; pyrolysis and spectroscopy [22]. In case of pyrolytic methods, the sample is heated and the MPs are degraded into simpler organic compounds [23]. The volatile pyrolysis products are separated by gas chromatography and detected by mass spectrometry. In this approach, the results are given as a mass of polymer detected in the tissue, but the sizes and number of MPs cannot be evaluated. Moreover, studies revealed that pyrolytic methods can lead to false detection of MPs in tissue due to interference caused by molecules like fatty acid-type lipids, within the tissue, in the quantification of MPs [24]. Spectroscopic methods, e.g., Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR), can reliably detect specific particles so the information on their sizes and number is given. However, these methods face significant challenges, with the main drawback being the sample preparation requirements, such as digestion or sectioning, which carry a high risk of particle loss and contamination. These issues can lead to misleading results [25–27]. Consequently, the local distribution of particles within the analysed tissue cannot be determined due to a destructive sample preparation, precluding the assessment of a health risk posed by the accumulation of MPs in different tissue regions [28]. Moreover, the issue of a secondary contamination during sample preparation remains unresolved, as most published studies lack proper controls to assess the cross-contamination [29]. This study presents the first exploration of microCT as a method for detecting MPs in zebrafish. MicroCT offers non-destructive imaging with a high spatial resolution, enabling the 3D analysis of MP distribution and migration within a selected organism. While this method provides many benefits, analysing MPs in biological samples using microCT has not been widely applied or introduced in practice yet. Only a few studies utilized microCT for MP detection, primarily focusing on MPs in sediments [30–32]. In contrast to conventional MP detection methods, microCT does not require destructive sample preparation, allowing for a direct identification of MPs inside tissues without the risk of secondary contamination or loss of the MP’s spatial distribution. However, challenges may arise while using this method, namely the difficulty in detecting particles that are near or smaller than the voxel size, making them potentially undetectable in the volumetric data. The size of detectable particles is closely tied to the voxel size, where voxel size closer to few micrometre (or even submicron where feasible) allow for the visualization of fine details, including smaller particles. However, finer voxel size also necessitates a smaller field of view, requiring sample to be of limited dimensions. Therefore, trade-off between the resolution and sample size is necessary. To address this, we employed two different CT setups in this study for two sample types – whole zebrafish and their extracted guts. The first setup offered a larger field of view, suitable for scanning of the entire abdominal region in zebrafish samples. The second setup, optimized for scanning of the extracted zebrafish guts, provided a higher resolution by reducing the field of view, allowing for a potentially lower minimum detectable particle size. To explore the potential of microCT, MPs made of PE were selected for the optimization, as PE is a polymer produced in a high quantity and PE MPs are often found in the environment [11]. In general, the low attenuation of polymeric materials to X-rays needs to be addressed as it may lead to a reduced image contrast. Therefore, we assessed whether iodine staining enhanced or hindered MP detection. To investigate this, we tested the direct detection of PE MPs in stained biological samples using the optimized zebrafish and guts setups. Consequently, this study highlights the potential of microCT for detecting MPs in zebrafish samples and lays the foundation for further research to optimize this technique for human samples. 2. Material and methods 2.1. Microplastics MPs used in this study were spherical PE particles with a size range from 45 μ m to 75 μ m purchased from Cospheric LLC (USA). Their size was also evaluated by a particle size analyser (S3500 Bluewave laser diffraction analyser, Microtrac, Germany). MPs were placed in the dry unit, and the measurement was repeated three times, revealing that the actual size distribution of the purchased batch ranged from 30 μ m to 110 μ m. The primary reason for selecting spherical particles was that their uniform shape and size enable a reliable comparison of measurements obtained with a conventional laser diffraction analyser and microCT. 2.2. Preparation of reference samples Initially, the microCT measurements for MP detection using two different microCT setups was optimized. MP concentration of 0.7 % (w/ v), providing a very dense abundance of MPs, was prepared in agarose gel which was chosen for its similarity to soft tissue in X-ray attenuation aspects [33], facilitating quick and practical feasibility verification (Fig. 1 A), and scanned based on the requirements for two setups used further: Zebrafish (Fig. 1 B) and Guts Setups (Fig. 1 C) (set ups are further explained in Section 2.4). V. Parobkov´ a et al. Journal of Hazardous Materials 488 (2025) 137442 2 2.3. Preparation of model samples In order to confirm the applicability of the methodology for complex biological samples, we used the adult zebrafish (Danio rerio) as model organisms and the gavage approach. Zebrafish are among the most widely used model organisms in biology and ecotoxicology, offering an excellent platform for various studies. They are frequently employed to investigate the effects of MPs on organisms [34]. Using microCT in such research could provide valuable insights into how MPs migrate within the zebrafish digestive system, whether they accumulate, and, if so, the specific locations of accumulation. Intragastric gavage was performed on two adult zebrafish. Initially, an amount of 2 mg of PE MPs in 100 µL ethanol was given to the fish gastrointestinal tract by ultrafine pipette tips. Specifically, to ensure smooth passage of the microplastics, the tip of the pipette was slightly cut to enlarge the hole. The zebrafish were euthanized in a tricaine solution (0.3 mg/mL) to ensure precise delivery of the solution. The pipette tip was carefully inserted into the esophagus, avoiding injury to the fish. The solution was dispensed slowly to ensure the microspheres entered the gastrointestinal tract. Note, that to optimize and ensure the feasibility of developing a novel microCT methodology for detecting MPs and evaluating their distribution in the gut, we intentionally used a concentration higher than environmentally relevant levels. Then, the two fish were euthanized by overdosing by Tricaine Methanesulfonate (MS222, Merck). Two fish were dissected, and the whole dissected intestine was placed into a histological cassette, the other two fish were processed intact. All samples (whole fish or their dissected guts) were placed in 4 % formaldehyde in PBS and stored in a fridge before further processing. This research, using zebrafish, was approved by the Ethical Committee of the Czech Ministry of Education, Youth and Sports under approval number MSMT-12088/2024–4. Prior to the microCT measurement, whole fish and extracted guts were stained in 1 % iodine (17570–30250, Penta) in 90 % methanol (21210–11000, Penta) for 12 hours at 4◦C. To eliminate tissue shrinking, the samples were passed through an ethanol dehydration series - 30 %, 50 %, 70 %, 80 % and 90 % - each for 2 hours. Subsequently, based on the preparation of the reference samples (Fig. 1 B, C), zebrafish were placed into a 1.5 ml Eppendorf tube while the extracted guts were set into a 3 mm diameter Kapton tube for the microCT measurement. Both tubes were then filled with 1 % agarose gel (P045, Top-BIO) to ensure stability during acquisition. 2.4. MicroCT measurements of microplastics Two CT setups were optimized for different sample types. First, the Zebrafish Setup was developed to visualize the entire gastrointestinal tract (GIT) of prepared zebrafish, allowing for an accurate analysis of MPs migration and accumulation within the intact sample. Second, the Guts Setup with a reduced field of view was designed to analyse extracted guts. This setup is also suitable for a future use in examining small samples or dissected organs with a rather high detail. The data were acquired using the Nano3DX developed by Rigaku, Japan. This device is equipped with a high-power X-ray source (1200 W) with a rotating anode. Scanning parameters for both setups are listed in Table 1. Consequently, a filtered back projection was used to reconstruct the acquired projections. To mitigate deficiencies, standard algorithms for ring artifact reduction and determination of the center of rotation shift were applied during the reconstruction process. 2.5. Segmentation of microplastics The reconstructed data were processed and analysed using a VG Studio Max 2023.4 software (Volume Graphics GmbH, Germany) (Fig. 2 A). The same segmentation workflow was used for both reference and model samples. The particle segmentation was conducted using the Paint and Segment module, which utilizes a neural network for segmentation. Two label groups were created: 1. PE MPs and 2. Background. Areas from several slices were manually segmented using a Fig. 1. Preparation of reference samples: (A) PE MPs were immersed in agarose gel, (B) transferred into a 1.5 ml PCR tube, chosen to fit within the field of view for the Zebrafish Setup, enabling a voxel size of 4.1 µm. (C) Additionally, the mixture was placed into a 3 mm diameter Kapton tube, fitting the field of view within the Guts Setup. The highlighted fields of view were then scanned using microCT. Table 1 Scanning parameters for CT setups. Values annotated with asterisk were modified for reference samples. Setup Zebrafish Guts Voxel Size [µm] 4.2 2.1 Field of view [mm] 7.13 ×5.4 3.56 ×2.7 Lens L2160 L1080 Target Copper * Molybdenum Copper * Molybdenum Voltage [keV] 40 * 50 40 * 50 Current [mA] 30 * 24 30 * 24 Filter 0.1 mm Aluminium filter 0.1 mm Aluminium filter Exposure time [ms] 9 * 5 7 * 5 Number of Projections 800 800 Binning 2 2 V. Parobkov´ a et al. Journal of Hazardous Materials 488 (2025) 137442 3 paintbrush tool to identify PE MPs and annotate the remaining areas as a background. An automatic segmentation was then refined by incorporating these manually segmented regions into the classification process (Fig. 2 B). Moreover, the guts were manually segmented in model samples to enhance the visualization of the spatial distribution of retrieved MPs in 3D renderings and to prevent the detection of MPs outside the gut region. By intersecting the manually segmented gut region with the detected particles, MPs located outside the gut were excluded as outliers. Next, the diameter of the segmented particles was determined by employing the Porosity/Inclusion module (Fig. 2 C). The ’Only threshold’ algorithm was executed with a pre-segmented region from the Paint and Segment module. Within the settings, the deviation was modified to ensure that the whole region of segmented particles is overlapped in preview of this algorithm to ensure a valid diameter calculation of all the detected particles from the pre-segmented data. Additionally, ’Inclusion’ was set as the indication type and a probability threshold was set between 1 and 1.5 to eliminate false detections, such as noise annotated as particles in Paint and Segment module. Eventually, the size distribution of the processed results was plotted using MATLAB (R2022b, MathWorks), while the segmentation results, along with the size annotations of the segmented particles, were visualized through a 3D render in VG Studio (Fig. 2 D). This visualization offered a clear perspective of the analysed gut volume. 2.6. Quality assessment of the microCT data The intensity histograms were extracted using VG Studio to estimate the contrast between the surrounding area and PE MPs. For reference samples, the surrounding area’s intensities were obtained from the central region of the dataset, while for model samples, they were derived from within the guts, excluding regions containing PE MPs in both cases. A second histogram was generated to represent the intensity distribution of the particles in the same regions used for the surrounding area calculations. The peak intensities from both histograms were then identified, and the differences between these peak values were calculated to estimate the contrast between the agarose gel or surrounding tissue and the particles for both experimental setups. These regions were further used for subsequent calculations. The Contrast-to-Noise Ratio (CNR) assessed the contrast between PE MPs and the background, normalized by the noise level in the background. The CNR was calculated using the formula [35]: CNR =|xPE MPs −xBCG| µBCG Fig. 2. The process of segmentation and analysis of MPs. For visualisation purposes, the model zebrafish sample acquired using the Zebrafish Setup was utilized. The flow included: (A) data acquisition and rendering, (B) MP segmentation, (C) diameter quantification, and (D) 3D rendering of segmented particles color-labeled based on the determined diameter. V. Parobkov´ a et al. Journal of Hazardous Materials 488 (2025) 137442 4 Where xPE MPs and xBCG are the mean gray values within PE MPs and background regions. µBCG stands for the standard deviation of the background which defines the surrounding regions. Rather high CNR values indicated a good visibility of the PE MPs against the background. 2.7. Statistical analyses Statistical analyses were utilized to assess the similarity between the samples and the results from particle size analyser. Gaussian distributions were fitted to the data from each sample, which were first normalized to a range of 0–1 and then interpolated to ensure a smooth representation. A two-sample t-test was used to compare the means of each sample against the mean of the particle size distribution, with the null hypothesis stating that there was no significant difference between the means. An F-test was employed to compare the variances of each sample against the variance of the particle size distribution, with the null hypothesis asserting that there was no significant difference between the variances. Both tests were conducted using a significance level of α =0.05 and were performed using MATLAB. 2.8. Morphological analysis of particles The percentage of MP abundance near the intestinal villi was calculated by dividing the number of MPs located close to the villi by the total number of segmented particles. A mask of the internal gut region was first created to classify MPs based on their proximity to the villi or the centre of the gut. This mask was then morphologically eroded, meaning we shrank it to form a more compact shape, roughly 100 µm away from the villi endings. MPs within this eroded region were categorized as "close to the centre," while the remaining particles outside the eroded mask were considered "close to the villi." The abundance of MPs in different regions of the intestines was quantified by dividing the segmented gut into three distinct sections: anterior, middle, and posterior. The region before the first turn was designated as the anterior, the section following the last turn as the posterior, with the middle region located in between. These regions were manually segmented, and the number of PE MPs within each section was determined by extracting particles from the original segmentation for each region individually. To further assess the MP distribution along the length of the colon, a polyline was drawn in VG Studio. This tool allowed us to measure each individual MP’s distance from the gut’s beginning. We drew a polyline from each particle’s position to the gut’s start. Additionally, to calculate the total length of the gut, we used the polyline tool to trace a line from the beginning to the end of the gut. 3. Results 3.1. Preliminary visualization of microplastics With a focus only on reference samples, PE MPs ranging from 30 to 110 µm were embedded in agarose gel and visualized using microCT. Two different scanning setups were tested (Zebrafish and Guts), varying in scanning parameters and required sample dimensions. The larger sample, generated in a PCR tube and scanned with the Zebrafish Setup yielded a voxel size of 4.2 µm (Fig. 3A, A‘), while the smaller sample, prepared in a 3 mm diameter Kapton tube and measured using the Guts Setup, resulted in a voxel size of 2.1 µm (Fig. 3B, B‘). Since microCT visualizes materials based on their densities, the PE MPs appeared as darker voxels in the dataset due to their lower density compared to the agarose gel. This contrast enabled the detection and segmentation of MPs in obtained data. Next, the MP segmentation was successfully completed using automatic tools. However, some MPs were not included in the segmentation (Fig. 4A, B). By analysing selected 0.6 mm³ areas within the scanned data and comparing manual and automated segmentation, the results indicated that the Zebrafish Setup had an average of 22.5 % missed particles out of the total particles counted, all of which were below 40 µm in size. Although, using the same comparison approach for the Guts setup, 100 % of particles were detected. Fig. 3. MicroCT data acquired using the (A) Zebrafish and (B) Guts Setups. PE MPs submerged in the agarose gel appeared as dark regions, visible as black spots in the data. A ′ and B ′ provided a closer view. Selected MPs are highlighted with yellow arrows to indicate their location in the dataset. V. Parobkov´ a et al. Journal of Hazardous Materials 488 (2025) 137442 5 3.2. Verification of particle size To assess the accuracy of the microCT detection of PE MPs, a comparison of the results from the reference samples with a particle size analyser was performed. The detected particles in the microCT data were quantified, and their size distribution was plotted and compared with the reference size distribution of the PE MP batch obtained from a particle size analyser before gavaging. This comparison validated the detection accuracy of both microCT setups, as described below. The entire size range was successfully detected using both CT setups, as illustrated in Fig. 6A, B. The minimum size detected remained consistent across both setups and the particle size analyser. However, particles larger than 110 µm were observed only in the Zebrafish setup, which was likely caused by clustering. The particles were prone to cluster formation when they were within 31 µm from each other (Fig. 5), which led to challenges in distinguishing their boundaries through the automated segmentation due to minimal or no detectable reduction in the intensity between adjacent particles. In this case, the Guts Setup helped to separate the clustered particles, resulting in the decrease of abundance over 110 μ m. Statistical tests were conducted to evaluate differences between the size distribution functions from microCT and the particle size analyser Fig. 4. (A) Detected MPs in the data acquired using the Zebrafish setup. (B) Segmented particles are highlighted with blue circles, while particles not included in the segmentation are indicated by red arrows. Fig. 5. Examples of segmented particles which tended to form clusters due to their proximity (left). Particles were segmented manually (blue circles, right), allowing for the tolerance of their boundaries. Distances between neighbouring particles were measured to determine how close were the particles when they started to cluster. V. Parobkov´ a et al. Journal of Hazardous Materials 488 (2025) 137442 6 (Fig. 6C, D). The two-sample t-test compared means, and the F-test examined variances of Gaussian distributions fitted to the data from each setup and the particle size analyser. The Zebrafish Setup data (mean: 64, SD: 16) showed no significant difference in means (p =0.2) but a significant difference in variances (p =0.01) compared to the particle size analyser (mean: 61, SD: 11). Thus, while the average values were similar, the spread of the Zebrafish Setup data was significantly different from that of the distribution measured by the particle size analyser. In contrast, the Guts Setup data (mean: 62, SD: 10) exhibited no significant differences in either means (p =0.5) or variances (p =0.3), highlighting their closer resemblance to the results of the particle size analyser. 3.3. Detection of microplastics in model tissues Both setups from the previously described experiments on reference samples, with the same sample preparation steps, were utilized for the acquisition of model samples spiked with PE MPs: zebrafish and dissected guts (Fig. 7A, B). Differently from the previous experiment, staining was incorporated to enable the visualisation of the soft tissue and to consequently enhance the contrast between the tissue and MPs (Fig. 7C, C’, D, D’). To assess the effect of staining on the acquired data, CNR and the Fig. 6. Particle size distribution analysis comparing microCT and particle size analyser measurement – Zebrafish and Guts. CT measurements were conducted on reference samples using two different CT setups. (A) Normalized size distribution for the Zebrafish Setup, with the microCT data represented as a histogram and the particle size distribution overlaid as a curve. (B) The corresponding size distribution for the Guts Setup follows the exact representation. (C, D) Fitted Gaussian distributions comparing microCT (blue) and particle size analyser (red) data for the Zebrafish and Guts Setups, respectively. (E) Results of the particle size analyser with particle diameters’ frequency (bars) and cumulative number distribution (black line). V. Parobkov´ a et al. Journal of Hazardous Materials 488 (2025) 137442 7 distances between intensity histogram peaks derived from MP regions and surrounding tissue (agarose gel in the case of reference samples) were calculated and summarized in Table 2. Overall, CNR increased after staining, and peak distances were slightly broader for stained zebrafish and gut samples, indicating enhanced contrast between the particles and the surrounding area. Consequently, the segmentation of MPs was successfully performed using the AI module for both types of the data acquired with different CT setups. To avoid introducing noise into the segmentation, care was taken to filter out particles smaller than those in the actual size distribution obtained from the particle size analyser (30 µm). Afterwards, the filtered results were analysed to retrieve the diameters of the detected particles. In each sample, the guts and the particles were visualized using 3D rendering (Fig. 8A, B). Additionally, the particles were colour-coded based on their diameter, and the clipping plane was applied from the frontal view 3D render to visualise the inside regions (Fig. 8A’, B’). 3.4. Morphological and size analysis of microplastics Understanding where MPs accumulate could serve as a crucial parameter for correlating their locations with specific diseases. Therefore, the number of particles detected near the villi region where they could accumulate over time and potentially harm the organism, was analysed. Additionally, the distance the particle travelled through the gut was calculated to reveal its migration and the MP abundance was calculated in three intestinal regions divided into anterior, middle and posterior. The ratio of the particles localized near the villi compared to those closer to the center of the gut revealed that only 25 % of the particles were located in the central area, while the majority accumulated near the villi, where they were more likely to attach and aggregate. The majority of MPs were found in the anterior part, followed by the middle and posterior regions of guts, with 78 %, 18 %, and 4 % observed, respectively. Additionally, the passage of MPs through the gut was tracked by measuring the distance from the beginning of the gut to the particle’s location. For instance, the positions of two randomly selected particles were calculated to determine their locations within the colon. The first particle was found at the end of the ascending colon, at 36 % of the total gut length, while the second particle was located at 8 % of the total length, near the beginning of the ascending colon (Fig. 9A). It is important to note that the experiments were conducted on model zebrafish that were manually spiked with MPs. Therefore, the results in this section primarily highlight the potential of microCT for MP Fig. 7. Visualisation of (A) zebrafish and (B) guts scanned with microCT using two selected setups – Zebrafish and Guts. The cross-sections display the location of inserted particles within the zebrafish samples (C – transverse plane, C’ – sagittal plane) and dissected guts (D – transverse plane, D’ – sagittal plane). Blue arrows indicate the location of MPs (visible as black spots), while yellow brackets highlight the position of the guts. Table 2 Quality measurements for the selected sample data and experimental setups. Zebrafish Setup Guts Setup CNR [dB] Inter-peak distance [gray values] CNR [dB] Inter-peak distance [gray values] Agar 1.5 2273 0.1 3015 Model sample 2.6 3488 1.34 3041 V. Parobkov´ a et al. Journal of Hazardous Materials 488 (2025) 137442 8 detection and the possibilities of the data analysis rather than representing natural biological conditions. 4. Discussion Despite its many advantages, microCT has not yet been widely used in MP research, which typically relies on conventional spectroscopic and pyrolytic methods. Unlike traditional approaches, microCT allows for a non-destructive analysis and offers the capability to visualize internal structures in three dimensions. To date, successful applications of microCT in MP detection have been limited to geological studies [30]. This study introduced microCT as a novel method for detecting MPs in biological samples. Using tissue staining techniques, internal structures and MPs were visualized, enabling the investigation of MP accumulation within organs without invasive procedures. To assess the feasibility of microCT in this research area, initial experiments were conducted on reference samples, where biological tissue was replaced with agarose gel to simplify the design and MPs were successfully visualized. In the further experiment, MPs were visualized in the whole fish (Fish setup) and in the intestine (Guts setup). The entire size range of MPs was successfully detected with both optimized CT setups, and detection accuracy was validated by comparing these results to particle size analyser, showing decreased accuracy using Zebrafish setup. The Guts setup, in particular, demonstrated no statistical difference from the results obtained by conventional particle size analyser. However, achieving the voxel size used in the Guts setup requires a smaller sample, necessitating a trade-off between sample size and desired detection detail when applying microCT for MP detection in biological samples. Following the successful validation of microCT, experiments were extended to model zebrafish samples. Here, the staining applied to both the zebrafish and gut samples to enhance soft tissue visualization improved the MP detection quality. Fig. 8. 3D renderings of the segmented PE MPs in (A) zebrafish and (B) gut samples. The gut samples were segmented for an improved visualization. (A’, B’) Frontal views and (A’’, B’’) transverse views were captured, with a clipping plane added to reveal the interior of the samples. The clipping plane was positioned along the midline of the sample and adjusted according to the selected view. V. Parobkov´ a et al. Journal of Hazardous Materials 488 (2025) 137442 9