# Metal Contaminants in Fish: Blood as a Potential Non-lethal Monitoring Tool

Laurine Gouthier, Lisa Jacquin, Jules Giraud, Séverine Jean, Sophia V. Hansson

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Laurine Gouthier, Lisa Jacquin, Jules Giraud, Séverine Jean, Sophia V. Hansson. Metal Contaminants in Fish: Blood as a Potential Non-lethal Monitoring Tool. Bulletin of Environmental Contamination and Toxicology, 2023, 111 (1), pp.12. 10.1007/s00128-023-03762-0. hal-04256184

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# Metal contaminants in fish: blood as a potential non-lethal monitoring tool

Laurine Gouthier¹,²*, Lisa Jacquin²,³, Jules Giraud¹,⁴, Séverine Jean¹, Sophia V. Hansson¹,⁴

¹Laboratoire écologie fonctionnelle et environnement, CNRS/INP/Université de Toulouse, Avenue de l'Agrobiopole, 31326 Castanet Tolosan CEDEX, France

²Laboratoire Evolution & Diversité Biologique, UMR5174, Université Toulouse III - Paul Sabatier, CNRS/UPS/IRD, 118 route de Narbonne, 31062 Toulouse CEDEX 9, France

³Institut Universitaire de France IUF, Paris, France

⁴LabEx DRIIHM (OHM Haut-Vicdessos), Laboratoire écologie fonctionnelle et environnement, Avenue de l'Agrobiopole, 31326 Castanet Tolosan CEDEX, France

Corresponding author. Tel: +33 (0)6 77 17 87 31. E-mail address: laurine.gouthier@univ-tlse3.fr

## Abstract

The use of fish to monitor metal contamination is well established, but existing studies often focus on internal tissues that require the sacrifice of organisms. Developing non-lethal methods is thus a scientific challenge to enable large scale biomonitoring of wildlife health. We explored blood as a potential non-lethal monitoring tool for metal contamination in brown trout (*Salmo trutta fario*) as a model species. First, we investigated differences in metal contamination loads (i.e., Cr, Cu, Se, Zn, As, Cd, Pb and Sb) in different blood components (whole blood, red blood cells and plasma). Whole blood was reliable to measure most metals, implying that blood centrifugation is not necessary, thus minimizing sample preparation time. Second, we measured the within individual distribution of metals across tissues (whole blood, muscle, liver, bile, kidney and gonads) to test if blood could be a reliable monitoring tool compared to other tissues. Results show that the whole blood was reliable compared to muscle and bile to measure the levels of metals such as Cr, Cu, Se, Zn, Cd and Pb. This study opens the possibility for future ecotoxicological studies in fish to use blood instead of internal tissues to quantify some metals, thus reducing the negative impacts of biomonitoring on wildlife.

## Keywords

Metal contamination, Fish Blood, brown trout, non-lethal, monitoring tool

## INTRODUCTION

Environmental pollution is one of the major anthropogenic change affecting ecosystems as they impact the atmosphere, soils and water bodies, and can potentially threaten the health of living organisms (Ali et al. 2019). Metals and metalloids like arsenic (As), cadmium (Cd), lead (Pb) and antimony (Sb) are naturally occurring, but anthropogenic activities contribute in large part to their mobilisation in the environment through for example industrial emission, agriculture, forestry, or mining during which they can reach toxic levels (Méndez-Rodríguez and Alvarez-Castañeda 2016; Hansson et al. 2017; Camizuli et al. 2018). Metals are often persistent (millennial-scale) environmental contaminants, especially in soil and sediment, and can also be accumulated in food chains with potential adverse effects on wildlife and human health (Hansson et al. 2019, 2020; Ali and Khan 2019).
Bioaccumulation of metals in riverine ecosystems is well documented, highlighting harmful effects on physiology and health of aquatic organisms and ultimately on population dynamics and persistence. For example, following metal contamination exposure, fish can display toxicological effects such as haematological disturbances (Haux and Larsson 1982; Schmitt et al. 2005), nervous system perturbation (Baatrup 1991) and developmental alterations affecting their growth rates and survival (Kennedy et al. 2000; Monna et al. 2011; Sfakianakis et al. 2015; Petitjean et al. 2020), sometimes leading to a decline in fish populations (Luo et al. 2014). Fish are key components of aquatic ecosystems and, as their populations are regularly monitored, they are ideal bioindicators for monitoring metal contamination over large geographical areas and across trophic levels (Cenci 2001; Brumbaugh et al. 2005). In addition, fish are of high cultural, commercial and conservation interest, and metal contamination of wild and farmed fish for human consumption is a major human health concern (Maurya et al. 2019; Ali and Khan 2019).

However, the use of fish as a reliable bioindicator of environmental pollution is hindered by technical limitations that needs to be addressed. Indeed, few studies have investigated the within individual distribution of metals across fish organs and tissues simultaneously, so that the assessment of metal concentration in fish is often limited to specific tissues according to the study goals. For instance, if the studied goal concerns human health, edible muscle tissue is often sampled and investigated, while if the goal is to assess contamination risk for aquatic wildlife, whole-body or liver are often the priority tissues investigated (e.g., Brumbaugh et al. 2005; Maurya et al. 2019). Although useful and often reliable, the use of internal tissues displays two clear disadvantages: the time spent on dissection to collect the targeted organs and the sacrifice of individuals, which limits the possibility of large-scale biomonitoring, especially on endangered species or on species of economic value. Hence, there is a need to develop less-invasive and non-lethal monitoring tools for metal contamination in fish and more broadly on vertebrates, to limit the impacts of biomonitoring on wildlife and expand the geographical and taxonomic range of future ecotoxicological studies.

Fish blood has already been used in some previous studies on metals, but mostly to evaluate Pb exposure (e.g., Hodson et al. 1977; Haux and Larsson 1982; Schmitt et al. 1993; Nakagawa et al. 1995) and seldom for other metals (Schmitt et al. 2005, 2009; Brumbaugh et al. 2005; Sturrock et al. 2013; Hansson et al. 2020). Due to its quick turnover, it has been argued that blood could be a good indicator for recent contaminant exposure, making it a complementary tool to other tissues which are used to detect more long-term exposure (Brumbaugh et al. 2005; Hansson et al. 2020). The use of blood as a non-lethal detection method would make it possible to implement large scale studies and to investigate the inter-individual variability by sampling a higher number of individuals. It would also enable to monitor the temporal variability of metal contamination in the same individual, while also allowing the use of endangered species that have conservation or economic value. Blood could thus be a relevant, non-lethal and less-invasive monitoring tool for metal contamination in a wide variety of species that would reduce the impact of biomonitoring efforts on wild populations while improving our understanding of inter- and intrapopulation variability in metal contamination. However, empirical validation of this technique is still lacking.

In addition, previous studies on metal traceability in vertebrate blood have often used only one component of the blood; plasma (Sturrock et al., 2013) or red blood cells, i.e., erythrocytes (e.g. Fedonenko et al., 2016). To our knowledge, few if any has previously compared metal contamination loads in the different blood components (i.e., whole blood vs red blood cells vs plasma) and to other internal tissues.

The aim of this study is therefore to assess the use of blood as a potential non-lethal sampling method to monitor metal contamination in freshwater fish populations. To reach this aim, our first objective was to investigate the potential differences in metal level in different
components of the blood, i.e., whole blood (WB), red blood cells (C, hereafter called ‘blood cells’) and plasma (P). Our second objective was then to measure the within individual distribution (i.e., blood, muscle, liver, bile, kidney and gonads) to test if blood could be a reliable indicator of metal contamination levels compared to other tissues. For this study, we used the brown trout, *Salmo trutta*, as model organism as it is widely used as a sentinel species to monitor metal contamination in aquatic ecosystem (Foata et al. 2009; Monna et al. 2011; Camizuli et al. 2014; Hansson et al. 2019, 2020). In addition, brown trout is an iconic species of cultural, economic and conservation value, that has been shown to bioaccumulate metals with possible consequences forecosystems and human health (Foata et al. 2009; Monna et al. 2011).

# MATERIALS AND METHODS

## Study site and element sampling

To limit potential confounding effects of environmental variation of metal content between individuals, we purchased and sampled six captive-bred brown trout from a local hatchery in Ariège (France). Indeed, all individuals were bred in same physical and chemical conditions and fed with the same diet. We focused our study on metals and metalloids commonly dispersed and found in the environment to explore blood as a reliable tool to monitor metal contamination in aquatic ecosystems. The selected elements were chromium (Cr), copper (Cu), selenium (Se), zinc (Zn), arsenic (As), cadmium (Cd), lead (Pb) and antimony (Sb) and the selection was based on their relatively common inclusion in previous studies on freshwater species (Canli et al. 1997; Kennedy et al. 2000; Brumbaugh et al. 2005; Foata et al. 2009; Hansson et al. 2019, 2020). Some of these elements, such as Cr, Cu, Se or Zn, are essential in trace amounts for growth and development, and/or have been shown to counterbalance the toxicity of certain toxic metals (e.g., Cuvin-Aralar and Furness 1991; Khan et al. 2017). But if the level of these elements reach high values, they can instead turn toxic (Yılmaz et al. 2010; Foley et al. 2021). The other elements, i.e. As, Cd, Pb or Sb, are all known for their toxicity even in trace amounts in fish as they hold no known biological functions (Foata et al. 2009; Foley et al. 2021).

## Sample preparation

Once purchased, the fish were transported back to the laboratory alive where they were sedated using benzocaine (50 mg L⁻¹). Approximately 3-5 mL of blood was collected from the cardinal vein using a sterile needle and syringe. Half of the sample was centrifugated and separated into two different tubes of 1.5 mL for blood cells (C) and plasma (P). The other uncentrifuged part of blood was transferred in a third 1.5 mL tube as whole blood (WB). All the three blood components were then immediately frozen (−20 °C). After blood collection, all fish were subjected to an overdose of anesthetic solution (120 mg.L⁻¹ benzocaine) and sacrificed in accordance with local guidelines and regulations. Length and weight of each fish was recorded. We then performed dissection to collect muscle, liver, kidney, bile and gonads and all collected tissues were placed in individual polyethylene zip-lock bags and stored frozen (−20 °C) until further analysis.

Prior to wet-chemistry preparations and analysis, all samples (blood components and other tissues) were freeze dried during 24-48 h at -44°C and 0.05 mbar using a Christ Alpha 1-2 freeze drier and then manually homogenized into a powder using an acid cleaned agate mortar. Dried samples were then placed in falcon tubes and stored under dark, dry and cool conditions until further processing.

## Digestion and ICP-MS analysis
Three to 70 mg of dried blood samples (WB, C and P) were placed in acid (2% HNO₃) cleaned digitubes and digested following a protocol adapted from Brumbaugh et al. (2005), i.e., to each tube containing the dried blood, 1.0 mL of concentrated, sub-boiling distilled HNO₃ (Fisher Chemical, Nitric Acid HNO₃, Optima Grade, 67-69%) was added. After 1 hour, the tubes were sealed with airtight caps and placed on a hotplate at 90°C for 45 min. Digestions were then cooled down to room temperature and after adding 0.2 mL of high-purity 30% (v/v) H₂O₂ they were resealed and returned to the hotplate for 45 min. Finally, after cooling for at least 15 min, a mother solution (MS) was made by adding mQ H₂O up to the 10 mL mark on each tube. Prior to the ICP-MS analysis, the samples were diluted in a second step by transferring a 2.5 mL aliquot of the MS to a clean 15 mL Falcon tube and then diluted by adding mQ H₂O up to the 10 mL mark.

Digestion of the other tissues (muscle, liver, kidney, bile, gonads) were performed by adding 150 mg of dried and grinded tissue material to 50 mL acid cleaned digitubes to which 3mL of concentrated HNO₃ were added. The tubes were then sealed with airtight caps and placed on a hot plate at 90°C overnight (~12 hours) before being transferred to 50mL Falcon tubes and diluted with mQ H₂O to reach a mother solution of 45mL. Prior to ICP-MS analysis, the MS were further diluted by transferring a 5 mL aliquot to a 15 mL falcon tube to which mQ H₂O were added up to the 10 mL mark.

For quality assurance and accuracy, blanks and certified reference materials (i.e. DOLT-4, DORM-4) were included with every 10 samples and processed the same way as the fish blood and tissues. All blanks were >10x lower than the average measured concentration of each element and all elements, for both CRMs, fell within the certified ranges (recovery in % using DORM-4 for blood and tissue and DOLT-4 for tissues was: Cr: 73.3, 77.7, NA, Cu: 91.0, 95.4, 89.6, Se: 115.8, 101.5, 105.8, Zn: 97.1, 93.3, 87.4, As: 85.5, 90.4, 88.5, Cd: 87.1, 94.5, 95.1, respectively) with the exception of Pb using DORM-4 for blood and tissues (DORM-4 – blood: 40.5, DORM-4 – tissues: 65.3, DOLT-4 – tissues: 102.9) which is deemed to be caused by our digestion procedure here, i.e., using a leachate digestion (based on HNO₃) rather than a full digestion (based on e.g., HF). All sample preparations were performed under clean laboratory conditions (Clean room class 10000, ISO 7) using acid cleaned lab ware.

All metal concentration measurements (i.e., Cr, Cu, Se, Zn, As, Cd, Pb and Sb) were performed using Triple Quadrupole Inductively Coupled Plasma Mass Spectrometer (Thermoscientific, iCap TQ-ICP-MS) at the ICP-MS platform of the Observatoire Midi-Pyrenees, Toulouse, France.

## Statistical analyses

All the subsequent statistical analyses were run using the R software (V 3.6.3). Statistical differences in metal concentrations (i.e., Cr, Cu, Se, Zn, As, Cd, Pb and Sb) between (i) different blood components (i.e., WB vs C vs P) and (ii) blood and other tissue (i.e., WB vs muscle, liver, kidney, bile, and gonads) were tested using the non-parametric Skillings-Mack test. This test is a generalization of the statistic used in Friedman's ANOVA method and is the most adequate test to analyse small (n < 30), paired and balanced incomplete block design samples (Skillings and Mack 1981; Chatfield and Mander 2009). We built a model for each metal independently including the tissue as explicative variable and metal concentration as response variable. The individual variable was included as the blocking variable to take into account the paired sample tissues belonging to the same individual. For the analysis of blood components (WB, C, P), the blood of one individual could not be centrifuged so that sample size may vary slightly across tests. When the Skillings-Mack test was significative, we performed the post-hoc Conover-Iman all-pairs comparison test with an FDR adjustment p-values method to take into account multiple comparisons (Conover and Iman, 1979).

# RESULTS AND DISCUSSION
191 **Blood component metal concentration**

192 Our first goal was to evaluate which blood component (Whole Blood, Blood Cells, or Plasma)
193 would be the most relevant for metal measurements in fish. The ranking of each element in
194 decreasing order of concentration in the whole blood was: Zn>Cu>Se>As>Pb>Cr>Cd>Sb; in
195 blood cells: Zn>Se>Cu>As>Pb>Cr>Cd>Sb; and in plasma: Zn>Cu>As>Se>Cr>Pb>Sb>Cd.
196 The Skilling-Mack test revealed that there were significant differences in metal concentration
197 between blood components (i.e., WB vs C vs P) for Cr, Cu, Zn, As and Cd, but not for Se, Pb
198 and Sb (Table 1, Fig 1). Detailed results on each metal are discussed below. Further, we also
199 include information on the whole blood/plasma and whole blood/red blood cells ratios for
200 each individual element as an attempt to facilitate the translation of metal concentrations in
201 one blood component versus another (Table 2).

202 *Chromium*. Cr was less concentrated in blood cells compared to whole blood and plasma, but
203 there were no significant differences between whole blood and plasma (Table 1, Fig 1). This
204 result support our assumption that whole blood may be sufficient instead of blood cells and
205 plasma to quantify chromium (Cr) content in fish blood.

206 *Copper and Zinc*. According to the post-hoc Conover-Iman test, Cu and Zn contents were
207 significantly lower in blood cells and higher in plasma compared to whole blood (Table 1, Fig
208 1). These differences are similar to those observed in other studies (e.g., Bettger et al. 1987;
209 Sturrock et al. 2013). It could be due to the fact that these metals can bind to proteins in the
210 plasma (Fletcher and Fletcher 1980), but it has also been proven that a part of copper and zinc
211 can bind to some erythrocytes (Sturrock et al. 2013). Therefore, the whole blood component
212 seems to be a good compromise to take into account Cu and Zn present both in plasma
213 proteins and in erythrocytes.

214 *Arsenic*. The post-hoc Conover-Iman test revealed that the As level was significantly lower in
215 blood cells and higher in plasma compared to whole blood (Table 1, Fig 1). This observation
216 is the opposite pattern to that observed in Oladimeji et al. (1979) where the arsenic content in
217 blood cells was higher than in the plasma, although this is possibly related to their use of a
218 different study species (i.e., rainbow trout, *Oncorhynchus mykiss*). Further studies on arsenic
219 in blood from brown trout are therefore needed to fully support the relevance of whole blood
220 as a good marker for arsenic concentration instead of blood cells and plasma.

221 *Selenium, Lead, Antimony and Cadmium*. There were no significant differences in Se, Pb, and
222 Sb level between the three blood components (WB vs. C vs. P) and only marginally for Cd
223 between WB and C with more Cd in the whole blood. This implies that further studies could
224 use whole blood samples directly for tracing these elements in fish blood, without the need to
225 first centrifugate the blood, thus minimizing sample preparation time.

226 In summary, whole blood yielded reliable results and could thus be used instead of blood cells
227 or plasma for Cr, Cu, Zn, Se, Pb, Sb and Cd, but not As. Based on these results, we will focus
228 only on the WB component throughout the remainder of this discussion.

229 **Blood vs. tissue metal concentration**

230 Our second goal was to test the reliability of the blood as an indicator of metal level compared
231 to other tissues in fish. Here, we again also include information on the whole blood/tissue
232 ratios for each individual element as an attempt to facilitate the translation of metal
233 concentrations in whole blood versus other tissues (Table 2). The range of each metal level
234 from the most concentrated to the lowest was ordered as follows: in muscle:
235 Zn>As>Cu>Sb>Se>Cr>Pb>Cd; in liver: Cu>Zn>Se>As>Cd>Cr>Pb>Sb; in bile:
Zn>Cu>As>Se>Sb>Cr>Pb>Cd; in kidney: Zn>Cu>Se>As>Cd>Cr>Pb>Sb and in gonads: Zn>Cu>Se>As>Cd>Cr>Sb>Pb. This shows that two essential elements, Zn and Cu, were predominant in fish body with concentrations ranging from 15.7 to 756.9 µg.g⁻¹ and from 1.4 to 1124.5 µg.g⁻¹, respectively, which is consistent with previous studies in fish (e.g., Akbulut and Akbulut 2010; Ahmed et al. 2016).

There were significant differences in metal concentration between blood and other tissues for all elements, except for Pb content that displayed no significant differences between tissues (Table 2, Fig 2). Compared to the whole blood, levels of Cr, Cu, Se, Zn, Cd, and Pb in the muscle, which is, along with liver, the most frequently used tissue in the literature were either non significantly (for Cr, Cu, Cd and Pb) or much lower than the blood (4.7 and 3.6 times lower for Se and Zn, respectively). This important result suggests that blood could be a reliable indicator of metal contamination, at least for Cr, Cu, Se, Zn, Cd, and Pb.

However, for As, muscle contained a higher As levels compared to the blood, i.e. 9.3 times higher, with a mean of 7.1 µg.g⁻¹ and in the range of 3.9 – 13.4 µg.g⁻¹. These values were similar to those found by Shah et al. (2009), with a range of 2.12–15.2 µg.g⁻¹, and by Jankong et al. (2007), with a mean of 13.1 and 22.2 µg.g⁻¹ in two sites, both conducted in/near contaminated areas. Although the As concentrations found in our samples were not extremely high, this observation may indicate that fish were potentially contaminated by arsenic. This is consistent with previous studies showing that farmed fish can show higher levels of As than wild fish, which could be due to As content of the feed (mixture of sea fish meal) given to the farmed fish (Svobodová et al. 2002; Harkabusová et al. 2009). Furthermore, specific bioaccumulation in muscle may explain the higher arsenic content in muscle compared to blood. Indeed, Penrose (1975) shows a 5.5 times higher content of As muscle than in blood after 24h of oral injection of arsenic in brown trout (*Salmo trutta*). Due to its quick turnover, blood could accumulate less arsenic than the muscle, making muscle more reliable for measuring arsenic concentration in brown trout than blood.

Our results also showed that blood could be reliably used to monitor Se and Zn levels in brown trout instead of bile as Se and Zn levels were either non-significantly different (e.g., Se) or lower (e.g., 1.2 for Zn) in the bile compared to the blood. However, blood was less reliable to measure antimony levels (Sb). Indeed, bile i concentrated the highest Sb level compared to the blood, i.e., 786 times higher. This huge difference could be due the rapid biliary excretion of antimony (Sb), which would explain its low level in the blood (with on average 0.0003 µg.g⁻¹) and the high levelin the bile (with on average 0.24 µg.g⁻¹) as suggested by Coelho et al. (2014). As the study of Coelho et al., (2014) was carried out in rats, further analyses on trout are needed to assess if the underpinning mechanism is similar in fish species.

In addition, we found relatively high level of Cu and Se in the liver, with 187- and 2.9-times higher levels compared to the blood, respectively. These observations are consistent with other studies (e.g., Papagiannis et al. 2004; Zubcov et al. 2012; Khan et al. 2017) showing that the liver contains specific detoxification proteins called metallothioneins, which bind Cu and Se (Kaegi and Schaeffer 1988). These metallothioneins are also contained in the kidney (Olsson et al. 1988), which could explain the elevated content found for Cr, Se and Cd in kidney compared to the whole blood (2.8, 8.1 and 312.3 times higher, respectively). These findings are in accordance with others studies conducted on fish species (Hilton et al. 1980; Olsson et al. 1988; Ooi and Law 1989; Luo et al. 2014; Aslam and Yousafzai 2017). Therefore, in our study, blood seems to be less concentrated in copper, chromium, selenium and cadmium (Cu, Cr, Se, Cd) than the liver and kidney.
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We also found that gonads contained the highest Zn level, i.e., 5.6 times higher than blood. This is consistent with the fact that in fish species, zinc accumulates mostly in the gonads as this element is highly linked to reproductive processes during oocyte production (Bedwal and Bahuguna 1994; Thompson et al. 2002; Zubcov et al. 2012). Thus, sex and season are important factors to take into account when comparing Zn level in gonads (Zubcov et al. 2012).

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Finally, and in agreement with previous studies on Pb in various tissues of fish (e.g., Brumbaugh et al. 2005; Ahmed et al. 2016; Hansson et al. 2019, 2020), our study shows no significative difference in Pb between blood and other organs. This indicates that blood could be a reliable indicator to trace lead contamination rather than other tissues in brown trout and other fish species.

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It should be noted though that blood chemistry can be subject to considerable changes related to the nutritional status and the fed vs fasting state (e.g., Congleton and Wagner 2006). Here, this study was performed on fish from a hatchery (which are fed on average once a day) and as the time between collection and sacrifice (~24 hrs) were similar to all studied individuals, the fed vs fasting state, including the subsequent impact on blood chemistry, should be considered equal across all samples. However, in the wild it is more difficult to know the nutritional status of individuals. Future studies should therefore aim to gain a better understanding of the temporal variation in blood (metal) chemistry following feeding by comparing, e.g., the temporal variation of metal contamination in whole blood relative to plasma and red blood cells, while also considering the fed vs fasting state.

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## Conclusion

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This study investigated the potential of blood as a promising, and potentially non-lethal, biomonitoring tool for metal contamination in freshwater fish. First, our results revealed that whole blood was a reliable tissue compared to red blood cells or plasma to quantify Cu, Zn, Cr, Se, Cd, Pb and Sb, with the exception of As. This implies that there is no need to centrifuge the blood to measure these metals, which reduces the sample preparation time. Second, this study showed that whole blood was a reliable indicator to measure relative Pb contamination compared to all other tissues. In addition, whole blood gave comparable concentration results as muscle for Cr, Cu, Se, Zn and Cd (again, with the exception of As). Finally, blood seems to be a reliable monitoring tool to trace Se and Zn instead of bile. However, some trout tissues seem to concentrate higher levels of some metals compared to blood; bile for Sb; liver and/or kidney for Cu, Se, Cr and Cd; gonads for Zn. This study thus reveals that blood clearly has the potential to reliably monitor metals, at least compared to the classically sampled muscle and bile tissues. Interestingly, this potentially non-lethal method could open the possibility for large scale sampling of a high number of wild fish populations to allow extensive comparative field studies along gradients of pollution. Subsequently, and more generally, future ecotoxicological studies on vertebrates could directly use a small amount of blood instead of internal tissues and organs (e.g., bile or muscle tissue), thus reducing negative impacts on biotic health and ecosystems.

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## Acknowledgements

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The authors wish to thank Annie Perrault for help with dissections, Marie-Jo Tavella for help in the clean room and the ICP-MS platform at OMP for help with the ICP-MS analysis. This project was made possible thanks to the generous support by the AXA Research Fund Postdoctoral fellowship (project 14-AXA-PDOC-030); the Prestige/Campus France, co-funded by Marie Curie; Postdoctoral fellowship (project PRESTIGE-2014-1-0037); the Labex
330 DRIIHM OHM Vicdessos (project CABAC); the Université Fédérale Midi-Pyrénées and the
331 Région Occitanie (projects POLLPAT-pilote and POLLPAT) and the ANR JCJC
332 (MULTIPAT ANR-21-CE34-0018-01). L. Jacquin is a Junior member of the Institut
333 Universitaire de France. EDB is part of the Labratoire d'Excellence Labex TULIP.

334 Author contribution

335 Sophia V. Hansson designed the study; S.V. Hansson and S. Jean performed the dissections;
336 J. Giraud performed all sample preparation, digestions and ICP-MS analysis under the
337 supervision of S.V. Hansson; L. Gouthier performed statistical analyses and graphical work
338 with inputs from S.V. Hansson and L. Jacquin; all authors participated in the interpretation of
339 results; L. Gouthier wrote the manuscript under the guidance of S.V. Hansson, L. Jacquin.

340
341 Ethics
342 The authors declare that collection and sacrifice of all fish included in this study was
343 performed in accordance to local guidelines and regulations, i.e., authorized by Direction
344 Départementale des Territoires (DDT) de l'Ariège, with inputs from either the Fédération de
345 Pêche de l'Ariège or the Association Agréée de Pêche et de Protection des Milieux
346 Aquatiques (AAPPMA), and the service départemental de l'Office Français de la Biodiversité
347 (OFB).
348

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511

512 FIGURES

513 Table 1 Results of the Skilling-Mack and the post-hoc Conover-Iman test with ‘FDR’ adjustment
514 method between the blood components and between the whole blood and all other tissues, for each
515 metal independently (respectively, n = 5, n = 6). The significance levels were written as: *** p <
516 0.001, ** p < 0.01, * p < 0.05

<table><thead><tr><th></th><th>Cr</th><th>Cu</th><th>Se</th><th>Zn</th><th>As</th><th>Cd</th><th>Pb</th><th>Sb</th></tr></thead><tbody><tr><td colspan="9"><strong>Blood component</strong></td></tr><tr><td colspan="9"><em>Skilling-Mack test</em></td></tr><tr><td>P-value</td><td><strong>0.024</strong> **</td><td><strong>0.012</strong> **</td><td>0.05</td><td><strong>0.012</strong> **</td><td><strong>0.012</strong> **</td><td><strong>0.03</strong> **</td><td>0.144</td><td>0.277</td></tr><tr><td colspan="9"><em>Conover-Iman test</em></td></tr><tr><td>WB – C</td><td><strong>0.004</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td>NA</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>&lt; 0.001</strong> ***</td><td>0.064</td><td>NA</td><td>NA</td></tr><tr><td>WB – P</td><td>0.068</td><td><strong>&lt; 0.001</strong> ***</td><td>NA</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>&lt; 0.001</strong> ***</td><td>0.113</td><td>NA</td><td>NA</td></tr><tr><td>P – C</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>&lt; 0.001</strong> ***</td><td>NA</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>&lt; 0.001</strong> ***</td><td>0.438</td><td>NA</td><td>NA</td></tr><tr><td colspan="9"><strong>Blood and tissues</strong></td></tr><tr><td colspan="9"><em>Skilling-Mack test</em></td></tr><tr><td>P-value</td><td><strong>0.002</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>&lt; 0.001</strong> ***</td><td>0.05</td><td><strong>0.003</strong> **</td></tr><tr><td colspan="9"><em>Conover-Iman test</em></td></tr><tr><td>Muscle – WB</td><td>0.077</td><td>0.506</td><td><strong>0.015</strong> **</td><td><strong>0.022</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td>0.0624</td><td>NA</td><td><strong>0.030</strong> **</td></tr><tr><td>Liver – WB</td><td><strong>0.003</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>0.002</strong> **</td><td><strong>0.013</strong> **</td><td><strong>0.003</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td>NA</td><td><strong>0.002</strong> **</td></tr><tr><td>Bile – WB</td><td><strong>0.001</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td>0.842</td><td><strong>0.041</strong> **</td><td><strong>0.009</strong> **</td><td><strong>0.008</strong> **</td><td>NA</td><td><strong>&lt; 0.001</strong> ***</td></tr><tr><td>Kidney – WB</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>0.004</strong> **</td><td><strong>0.002</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>0.017</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td>NA</td><td><strong>0.002</strong> **</td></tr><tr><td>Gonads – WB</td><td><strong>0.001</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>0.009</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td><strong>0.004</strong> **</td><td><strong>&lt; 0.001</strong> ***</td><td>NA</td><td><strong>&lt; 0.001</strong> ***</td></tr></tbody></table>

517

518 Table 2 Measures of the central tendency and variance (mean ± SD) of the whole blood/plasma, whole
519 blood/red blood cells and whole blood/tissues.

<table><thead><tr><th></th><th>Cr</th><th>Cu</th><th>Se</th><th>Zn</th><th>As</th><th>Cd</th><th>Pb</th><th>Sb</th></tr></thead><tbody><tr><td colspan="9"><strong>Blood component</strong></td></tr><tr><td>WB/P</td><td>0.712 ± 0.526</td><td>0.374 ± 0.055</td><td>1.459 ± 0.460</td><td>0.438 ± 0.069</td><td>0.410 ± 0.039</td><td>1.288 ± 0.179</td><td>3.855 ± 3.285</td><td>0.697 ± 0.530</td></tr><tr><td>WB/C</td><td>2.487 ± 0.555</td><td>1.814 ± 0.633</td><td>0.978 ± 0.173</td><td>1.567 ± 0.312</td><td>1.463 ± 0.257</td><td>1.250 ± 0.161</td><td>0.913 ± 0.272</td><td>1.558 ± 0.683</td></tr><tr><td colspan="9"><strong>Blood and tissues</strong></td></tr><tr><td>WB/Muscle</td><td>0.476 ± 0.118</td><td>1.385 ± 0.507</td><td>4.744 ± 1.030</td><td>3.742 ± 0.887</td><td>0.114 ± 0.029</td><td>2.066 ± 0.573</td><td>1.966 ± 2.748</td><td>0.059 ± 0.048</td></tr><tr><td>WB/Liver</td><td>0.201 ± 0.071</td><td>0.006 ± 0.002</td><td>0.374 ± 0.131</td><td>0.795 ± 0.128</td><td>0.280 ± 0.087</td><td>0.018 ± 0.010</td><td>0.831 ± 0.381</td><td>0.031 ± 0.017</td></tr><tr><td>WB/Bile</td><td>0.257 ± 0.187</td><td>0.166 ± 0.125</td><td>1.414 ± 0.342</td><td>1.335 ± 0.736</td><td>0.354 ± 0.138</td><td>0.147 ± 0.054</td><td>0.645 ± 0.984</td><td>0.009 ± 0.009</td></tr><tr><td>WB/Kidney</td><td>0.134 ± 0.041</td><td>0.393 ± 0.083</td><td>0.371 ± 0.107</td><td>0.728 ± 0.146</td><td>0.417 ± 0.104</td><td>0.003 ± 0.001</td><td>0.446 ± 0.281</td><td>0.030 ± 0.018</td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>**Fig. 1** Boxplot showing metal concentrations (µg.g⁻¹, dw) in each blood components; Whole Blood (WB), Blood Cells (C) and Plasma (P). Logarithm transformation log(y+1) were performed on metal concentrations. The letters above the boxplots correspond to significant differences in pairwise comparisons between blood component according to the post-hoc Conover-Iman test. P-values were obtained with the Skilling-Mack test, with the statistic χ² and the degrees of freedom (df) associated. (***p < 0.001, **p < 0.01, *p < 0.05)</figcaption></figure>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

532

**Fig. 2** Boxplot showing metal concentrations (µg.g⁻¹, dw) in Whole Blood (WB) compared to the other tissues (i.e., muscle, liver, bile, kidney and gonads). Logarithm transformation log(y+1) were performed on metal concentrations. Dotted-line represent median of whole blood. The letters above the boxplots correspond to significant differences in pairwise comparisons between blood component and other tissues according to the post-hoc Conover-Iman test. P-values were obtained with the Skilling-Mack test, with the statistic χ² and the degrees of freedom (df) associated. (***p < 0.001, **p < 0.01, *p < 0.05)