# Computerized predictive approaches of genotoxicity and mutagenesis in 3D Hepoid of normal and transformed human hepatocytes

Sophie Rose, Frederic Ezan, Ludovic Huot, Thierry Pécot, Medjda Bellamri, Robert Turesky, Fabrice Nesslany, Anne Platel, Sophie Langouët

► To cite this version:

Sophie Rose, Frederic Ezan, Ludovic Huot, Thierry Pécot, Medjda Bellamri, et al. Computerized predictive approaches of genotoxicity and mutagenesis in 3D Hepoid of normal and transformed human hepatocytes. Toxicology, 2025, 517, pp.154242. 10.1016/j.tox.2025.154242. hal-05238092

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# Computerized predictive approaches of genotoxicity and mutagenesis in 3D Hepoid of normal and transformed human hepatocytes

Sophie Rose<sup>a</sup>, Frédéric Ezan<sup>a</sup>, Ludovic Huot<sup>c</sup>, Thierry Pécot<sup>b</sup>, Medjda Bellamri<sup>d</sup>, Robert Turesky<sup>d, e</sup>, Fabrice Nesslany<sup>c</sup>, Anne Platel<sup>c</sup>, Sophie Langouët<sup>a, *</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

### Keywords:

Human liver organoids

Genotoxicity

Mutagenesis

Heterocyclic Aromatic Amines

Environmental contaminants

Primary human hepatocytes

HepaRG

## ABSTRACT

The low specificity of standard *in vitro* genotoxicity tests in mammalian cells continues to necessitate animal experimentation, raising ethical concerns that conflict with the principles of the 3Rs (Replacement, Reduction, and Refinement). This underscores the urgent need for reliable alternative *in vitro* assays. Given the liver's critical role in xenobiotic detoxification and bioactivation, we developed an advanced 3D *in vitro* model of human hepatocytes that supports the proliferation and long-term differentiation of primary human hepatocytes and HepaRG cells. Using a range of methodologies, including the γH2AX assay, the comet assay, the micronucleus test, and transcriptomic analysis, we investigated DNA damage induced by well-known genotoxic carcinogens such as methylmethane sulfonate (MMS), mitomycin C (MMC), colchicine, vinblastine, 1,2-dimethylhydrazine (DMH), and aflatoxin B₁ (AFB₁), as well as non-genotoxic carcinogens, including di-2-ethylhexyl phthalate (DEHP) and methylcarbamate. Our data demonstrate the reliability of the 3D human hepatocyte Hepoid model in replicating *in vivo* results. We effectively discriminated between genotoxic and non-genotoxic carcinogenic molecules using complementary methods analyzed with advanced computational approaches. We further studied the *in vitro* genotoxic potential of four main Heterocyclic Aromatic Amines (HAAs) including 2-amino-9H-pyrido[2,3-b]indole (AαC), 2-amino-3-methylimidazo[4,5-f]quinoline (IQ), 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), which are food-borne and environmental contaminants suspected to contribute to hepatocellular carcinoma. Our results validate the Hepoid model as a reliable *in vitro* system for assessing the genotoxic and mutagenic risks of chemicals and confirm the ability of HAAs to induce DNA damage in a highly differentiated human liver model.

## 1. Introduction

Many food and environmental compounds can induce genotoxicity, leading to primary DNA damage, chromosomal aberrations, and gene mutations. This genotoxicity can induce carcinogenesis by increasing genomic instability (Sieber et al., 2003). Therefore, genotoxicity testing is a crucial step in assessing potential health risks. Regulatory agencies require evaluation of a compound's carcinogenic potential through genotoxicity tests. These tests typically begin with *in vitro* bacterial and mammalian cell-based assays, that often yield so-called "false positive" or "false negative" results, necessitating further validation through animal testing. The low specificity of these assays is a growing concern,

particularly in light of Directive 2010/63/EU, which promotes the development of reliable alternative *in vitro* methods.

The liver plays a key role in the detoxification of xenobiotics and the bioactivation of various pro-carcinogenic agents, and it is the main target organ for chemicals and drugs. Considering the poor correlation between rodents and humans, particularly in xenobiotic metabolism (Guengerich, 1997; Nauwelaers et al., 2011; Turesky et al., 1998), human liver cell-based assays play an essential role in the safety assessment of chemicals and drugs, providing a rapid, simple, and cost-effective approach to minimizing the use of animals, in full compliance with the 3Rs rule. The standard methods validated so far for *in vitro* genotoxicity assessment have relied on cell lines grown in 2D

<footnote><sup>a</sup> Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes 35000, France</footnote>

<footnote><sup>b</sup> Rennes University, SFR Biosp (UMS 3480-US 018), Rennes, France</footnote>

<footnote><sup>c</sup> Univ. Lille, CHU Lille, Institut Pasteur de Lille, ULR 4483, IMPECS-IMPact de l'Environnement Chimique sur la Santé, Lille F-59000, France</footnote>

<footnote><sup>d</sup> Masonic Cancer Center and Department of Medicinal Chemistry, University of Minnesota, Minneapolis, MN 55455, United States</footnote>

<footnote>* Correspondence to: UMR Inserm 1085, IRSET, 9 avenue du Pr Léon Bernard, Rennes 35000, France.
E-mail addresses: sophie.langouet@univ-rennes.fr, sophie.langouet@inserm.fr (S. Langouët).</footnote>
monolayer cultures. Yet, it is now well established that 3D liver models, which promote cell-cell interactions, tissue-like structures, and polarity, better mimic human in vivo exposure (Bell et al., 2016; Bomo et al., 2016; Higuchi et al., 2016; Murayama et al., 2015). While primary human hepatocytes (PHH) are the gold standard in vitro model for toxicity and drug metabolism studies (Chen et al., 2021; Gómez-Lechón et al., 2014; Oleaga et al., 2021), the well-characterized HepaRG cell line has been widely used for many years (Aninat et al., 2006; Gripon et al., 2002). When cultured in 3D, HepaRG cells display a phenotype closely resembling that of freshly isolated hepatocytes in terms of the expression of phase I and II enzymes, transporters, and nuclear factors (Leite et al., 2012; Ott et al., 2017; Ramaiahgari et al., 2017). Numerous studies have successfully used HepaRG cells to assess genotoxicity in 2D (Dumont et al., 2010; Jossé et al., 2012; Le Hégarat et al., 2014; Quesnot et al., 2016) and 3D (Ashraf et al., 2019; Mandon et al., 2019; Mueller et al., 2014; Seo et al., 2022, 2023) cultures. We have previously described two innovative 3D models of PHH and HepaRG cells embedded in a collagen matrix: the Hepoid-PHH and Hepoid-HepaRG models (Rose et al., 2021, 2022). With precise encapsulation in a well-defined matrix, the Hepoid structures are characterized as small and hollow spheroids formed by a single layer of cells with a polarized acini organization. This unique architecture exhibits distinct liver features, including cellular proliferation simultaneous with high levels of liver-specific functions and xenobiotic metabolism enzymes' expression and activity, which emerge after several days of culture and persist for at least 4 weeks. The longevity of this system allows for both chronic exposures and observation of cellular morphological changes that cannot be seen in primary hepatocytes cultivated in 2D. We have also demonstrated that the Hepoid model is well suited to acute and chronic toxicity studies as well as pathophysiological assays. However, despite their long-term survival, high differentiation levels, and relevance for studying inter-individual susceptibility to carcinogenic compounds, no genotoxicity studies have yet been conducted on 3D-cultured PHH, which are crucial for validating this model.

Phosphorylation of the H2AX histone (γH2AX) and/or the comet assay are commonly used endpoints to assess primary DNA damage, while the micronucleus (MN) assay detects chromosomal aberrations. In recent years, much effort has been devoted to automating these genotoxicity assays, providing promising data using 2D and 3D culture models (Quesnot et al., 2016; Seo et al., 2022; Wilson et al., 2021). To optimize the main standard genotoxicity tests for the Hepoid model, we have developed machine-learning models based on the open-source Fiji software and devised a computational approach to automate the quantification of γH2AX and micronuclei.

Using these approaches combined with a transcriptomic analysis by RNA-seq based on the toxicogenomic signature ('TGx') to identify DNA damage-inducing agents (DDI) in cultured human cells described by Buick et al (Buick et al., 2020). we tested and compared the genotoxicity of a panel of well-known chemicals both in vivo (rodents) and in vitro (Hepoid models). To cover a wide range of hepatotoxic effects, we selected six potent genotoxic carcinogens: the two direct-acting clasto-genic methyl methanesulfonate (MMS) and mitomycin C (MMC), the aneugenic agents colchicine and vinblastine, the DNA methylating agent 1,2-dimethylhydrazine (DMH) and the hepatocarcinogen mycotoxin Aflatoxin B₁ (AFB₁). We also selected two non-genotoxic hepatocarcinogens: the endocrine disruptor di-2-ethylhexyl-phthalate (DEHP) and methyl carbamate, and the non-hepatocarcinogen antibiotic ethionamide as negative controls. Primary and transformed human Hepoid models were set up after short- and long-term exposures.

The genotoxic hepatocarcinogens MMS, MMC, colchicine, vinblastine, DMH, and AFB₁ induced γH2AX and/or tested positively in the comet and micronuclei assays. While, DEHP, methylcarbamate, and ethionamide showed no effects in those three assays. The transcriptomic analysis complemented these results and confirmed the model's reliability. Finally, estimates of the genotoxicity and mutagenicity of contaminants of concern, such as heterocyclic aromatic amines (HAAs),

were carried out and completed by the quantifying of derived DNA adducts, key biomarkers of the exposome.

## 2. Materiel and methods

### 2.1. Chemicals

Unless mentioned otherwise, chemicals were purchased from Sigma Aldrich. All chemicals were of the highest purity available. For in vitro treatments, all compounds were dissolved in DMSO with a final concentration of 0.1 % DMSO in the culture medium except for DMH and DEHP, which were dissolved directly in the culture medium. The pH of the DMH solution was adjusted to 7.0 with NaOH. For in vivo assays, methylcarbamate and DMH were dissolved in sterile water, whereas ethionamide and DEHP were dissolved in corn oil.

## 3. In vivo assays

### 3.1. Animal model

Animal procedures followed the European directive 2010/63/EU for protecting animals used for scientific purposes and obtained Ethical Committee on Animal Experimentation (CEEA 75) approval.

French legislation regarding the use of animals in research has implemented in full EU Directive 2010/63/EU, hence the attached authorization proves that all animal experimentations was performed in full compliance with EU Directive 2010/63/EU.

Experiments were conducted with rodent models previously shown to be susceptible to many of the genotoxic agents studied. The OECD recommends rodents for comet and micronucleus assays. Young adult male OFA Sprague-Dawley rats (Charles River France, Saint-Germain-sur-l'Arbresle, France) weighing approximately 200 g and 5-10 week old were used. Groups of 5 rats were used for each dose of the compound tested, as well as for negative and positive controls (when necessary).

### 3.2. Animal treatment

Rats were treated three times at 24-hour-intervals with the test compounds by oral route (gavage) using a volume of 10 ml/kg body weight. All administrations were performed at approximately the same time each day, between 9:00 and 9:45 AM, in order to minimize variability due to diurnal fluctuations in metabolism. The maximum dose used was the maximum recommended dose (i.e., 2000 mg/kg), the highest dose compatible with the toxic activity, or the highest dose compatible with the solubility of the compound. For tests with non-genotoxic compounds, a positive control group was treated orally with methyl methanesulfonate (MMS) (100 mg/kg body weight (b.w)/day for two consecutive days in sterile water). The control groups received the same volume (10 ml/kg) of vehicle, either sterile water or corn oil, administered at the same time as the treated groups.

Two to six hours after the last treatment, the rats were euthanized, and cells from the selected target organs, i.e., bone marrow for the micronucleus test and liver for the comet assay, were isolated.

### 3.3. In vivo comet assay

The in vivo comet assay was performed with isolated liver cells under alkaline conditions (pH>13) according to previously described protocols (Platel et al., 2020; Tice et al., 2000; Witte et al., 2007) and in compliance with the OECD test guideline No. 489 (OECD Test Guideline, 2016). Randomly selected cells (n = 750) per group (i.e., 50 cells per slide, three slides per animal, five animals per group) were analyzed for DNA fragmentation scoring using the Comet Assay IV Image Analysis System, version 4.11 (Perceptive Instruments Ltd, Suffolk, United Kingdom). DNA damage was expressed as the percentage of DNA in the tail.
### 3.4. *In vivo* micronucleus test

The *in vivo* micronucleus test was performed using the bone marrow of treated mice in compliance with the OECD test guideline No. 474 (OECD Test Guideline No, 2016). The protocol has been described (Platel et al., 2020). Two slides per animal were prepared. For the determination of genotoxicity, slides were blind-coded and scored for polychromatic erythrocytes (PCE) (2000 PCE per slide, i.e., 4000 PCE per animal) having one or more Howell-Jolly bodies (micronucleated polychromatic erythrocytes, MNPCE) by microscopy. For the determination of cytotoxicity, the polychromatic/normochromatic erythrocyte ratio (PCE/NCE) was determined from the microscopic examination of at least 500 erythrocytes per slide (i.e., 1000 erythrocytes per animal).

## 4. In vitro assays

### 4.1. Cell culture and treatment

HepaRG cells were routinely propagated as described previously (Gripon et al., 2002). William's E medium supplemented with 5 µg/ml insulin, 2 mM glutamine, 10% Fetal Bovine Serum (FBS), 100 U/ml penicillin, 100 mg/ml streptomycin, and $5 \times 10^{-5}$M hydrocortisone hemisuccinate was renewed every 2-3 days, and cells were cultured until passage 18.

Fresh or cryopreserved primary human hepatocytes (PHH) were used. Freshly isolated PHH were obtained from patients undergoing liver resection through the Centre de Ressources Biologiques (CRB) Santé de Rennes (CHRU Pontchaillou, Rennes, France http://www.crbsante-rennes.com). This study was approved by the Inserm Ethical Review Committee (October 8, 2013-IRB00003888). Samples were obtained from the non-tumoral part of the biopsy, certified histologically normal, and isolated by a two-step collagenase perfusion procedure as described previously (Guguen-Guillouzo et al., 1982). The research protocol was conducted under French legal guidelines and the local institutional ethics committee. Written informed consent was obtained from all donors. Cryopreserved PHH were obtained from Biopredic International (Rennes, France) and Cytes Biotechnologies (Barcelona, Spain).

**Collagen 3D culture.** 3D cultures were prepared as described previously (Rose et al., 2021, 2022). In brief, type I collagen was diluted in the culture medium, and the pH was adjusted to 7.4 before addition of the cells at a concentration of $5 \times 10^5$ cells/ml. The cells and collagen mixture was distributed into multi-well plates and incubated at 37°C with 5% CO$_2$. After polymerization of the collagen matrix, an equal volume of medium was added. Hepoid-HepaRG were cultured in the medium described above, whereas Hepoid-PHH were maintained in William's medium supplemented with 1 g/l bovine serum albumin, 2 mM glutamine, 5 µg/ml bovine insulin, 100 U/ml penicillin, 100 µg/ml streptomycin, 10% FBS, 1.08 µM hydrocortisone hemisuccinate, 1X ITS (Recombinant insulin, transferrin, sodium selenite) (Sigma-Aldrich), 10 ng/ml rhHGF (BioLegend) and 50 ng/ml rhEGF (PeproTech). The medium was renewed every 48-72 h. This method of growing PHH is protected by an international patent (EP2018030560320180516/WO2019219828).

**Treatment.** 24-hour treatments of Hepoids were performed in FBS-free medium, whereas FBS was maintained in the culture medium for longer treatments to ensure cell viability. The time of treatment for each experiment is specified in the legend to each figure. To perform genotoxicity assays, Hepoids were extracted from the gels by digestion with Liberase (10 µg/ml, Roche) for 30 min at 37°C and incubated with Trypsin/ EDTA 0.05% (Gibco) for 6 min at 37°C.

For genotoxicity assays, in the absence of cytotoxicity or solubility issues, the maximum concentration used was 2 mg/ml or 10 mM, whichever is lower than those recommended by the OECD guidance for genetic toxicology testing (OECD 2017).

### 4.2. Toxicity assessment

Viability was assessed by measuring the cellular ATP content using the CellTiter-Glo 3D Assay (Promega) according to the manufacturer's instructions. Compounds were considered hepatotoxic when viability was reduced by at least 20% compared to the control.

### 4.3. Comet assay

We previously reported a detailed protocol for performing the comet assay in Hepoids (Rose et al., 2022). Briefly, isolated cell pellets extracted from 1 well of a 48-well plate were re-suspended in 70 µl of PBS. 30 µl of this suspension was mixed with 300 µl of LMP (low melting point) agarose maintained at 37°C and 30 µl of the mixture was deposited in a well of a CometSlide 20 wells (Trevigen). The slides were incubated overnight at 4°C in a lysis solution (Trevigen) supplemented with 10% DMSO. DNA was then allowed to unwind for 40 min in electrophoresis buffer (0.3 M NaOH, 1 mM EDTA, pH 13) before electrophoretic migration (30 min, 21 V) at 4°C. The slides were bathed twice in neutralizing solution (0.4 M Tris-HCl, pH 7.5) for 5 min and in 70% ethanol for 5 min, and dried at 37°C for at least 2 h before staining with SYBR Gold 1X (Invitrogen). The slides were blind-coded and scored using a fluorescence microscope Leica DMR equipped with a CCD-200E video camera. At least 100 nuclei per condition were acquired and analyzed using the Comet Assay IV software (v4.2) (Perceptive Instruments). The extent of DNA damage in individual cells was evaluated by measuring the percentage of DNA in the tail.

### 4.4. γH2AX quantitation

Isolated cell pellets were re-suspended in FBS-containing medium. After 5–6 h of 2D adhesion, cells were fixed in 4% paraformaldehyde for 5 min. Following fixation, cells were rinsed with PBS, permeabilized with 0.1% Triton X-100 for 5 min, and blocked with 1% BSA for 1 h. The primary antibody P-γH2AX (#2577, 1:400, Cell Signaling) was then incubated overnight at 4°C. After rinsing, the signal was revealed with goat anti-rabbit secondary antibody (711–175–152, 1:250, Jackson ImmunoResearch) and DAPI (1:1000) for 1 h. Ten images per well were acquired on a Cellomics HCS Reader. Nuclei were segmented using the deep learning-based approach Stardist (Schmidt et al., 2018), with the versatile fluorescence pre-trained model applied to the DAPI channel with a probability threshold equal to 0.3. Annotater (Pécot et al., 2022) was subsequently employed to identify γH2AX-positive cells by training a logistic regression classifier on the intensity of the γH2AX channel measured in segmented nuclei.

### 4.5. Micronucleus assay

Isolated cell pellets were re-suspended in medium supplemented with rhEGF (50 ng/ml, Peprotech). After 72 h of recovery, an osmotic shock (25% water, 5 min) was conducted before fixation in 4% paraformaldehyde for 5 min, followed by staining with WGA (Wheat Germ Agglutinin, 5 µg/ml) and DAPI (0.5 µg/ml) to visualize the cytoplasmic membranes and nuclei, respectively. A custom Fiji/ ImageJ (Schindelin et al., 2012) macro was implemented to automatically identify mono-, bi-, and tri-nucleated cells and compute the number of micronuclei associated with mononucleated cells. More specifically, nuclei were first segmented with Stardist (Schmidt et al., 2018) trained with annotated data from Greenwald et al (Greenwald et al., 2022), and Pécot et al (Pécot et al., 2022, 2021), and applied to the DAPI channel with a threshold equal to 0.01. Nuclei with an area less than 100 pixels were filtered out, and nuclei were located on the image border. Cells were then segmented with the deep learning-based method Cellpose (Stringer et al., 2021) with the pre-trained model cyto2 applied to the channel corresponding to cytoplasmic membranes with a cell size equal to 100 and a threshold equal to -4. Cells with an area less than 1000 pixels
were filtered out, as well as cells located on the image border. Mono-, bi- and tri-nucleated cells were identified by counting the number of nuclei segmented with Stardist in cells segmented with Cellpose. Micronuclei were detected with the Laplacian of Gaussian as implemented in Trackmate (Tinevez et al., 2017) with a radius of 2 pixels and a quality of 10. Finally, the number of micronuclei in mono-nucleated cells was computed. Results were visually inspected and manually corrected when needed. Per TG487 OECD test, micronuclei were scored in 2000 mononucleated cells per condition, and viability after recovery was > 60% compared to the negative control.

## 4.6. Transcriptomic analysis

**RNA-seq librarys preparation, sequencing, and data analysis:** Transcriptomic analysis by RNA-seq was carried out on three independent biological groups with an 8 h treatment of differentiated HepaRG spheroids, with control (0.1 % DMSO), AFB₁ (6 µM), and AC (10 µM). RNA was extracted using the Nucleospin RNA kit (Macherey Nagel) followed by DNAse treatment according to the manufacturer's instructions. The quality of isolated RNA was assessed by UV spectrophotometry (Nanodrop, Thermo Fisher), RIN (>8.6) with the Bioanalyzer 1200 (Agilent Technologies), and quantified by fluorimetry (Qubit, Thermo Fisher). According to the manufacturer's protocol, libraries were prepared from 200 ng of total RNA using the QIAseq stranded mRNA library kit (Qiagen). All libraries were controlled by Bioanalyzer 1200 (Agilent Technologies) and quantified by quantitative PCR (KAPA Library Quantification Kit for Illumina platforms, Kapa-Biosystems). Libraries were normalized and pooled equimolarly before sequencing on a NovaSeq sequencer (Illumina) in 2 × 150 bp mode. Obtained reads were aligned to the GRCh37 human genome using STAR (Tinevez et al., 2017) (v2.7.6a) and quantified with Salmon (Patro et al., 2017) (v1.5.2) in the nf-core RNA-seq pipeline (Ewels et al., 2020) (v3.6) to generate gene counts. Differential gene expression analysis was performed with EdgeR (Robinson et al., 2010) (v3.34.1) in Sartools (Varet et al., 2016) (v1.7.4) to generate log2 (Fold Changes) and adjusted p-values (Benjamini Hochberg correction method).

**Comparison of the TGx-DDI biomarker profiles in 2D and 3D models:** The toxicogenomics (TGx) expression of the 64 genes used to identify DDI agents were compared in (i) 2D HepaRG cells after a 3-day repeat exposure with AFB₁ at 2.5 µM based on standardized data from the publication of ($author1$ et al.,) (Buick et al., 2020), and (ii) Hepoid-HepaRG after an 8h-treatment with AFB₁ at 6 µM.

## 4.7. DNA adducts measurement

DNA (1.7–4 µg) was spiked with ¹³C₁₀-dG-C8-4-ABP, ¹³C₁₀-dG-C8-AαC ¹³C₁₀-dG-C8-PhIP, dG-d₃-C8-MeIQx, ¹³C₁₀-dG-C8-IQ at a level of 1 adduct per 10⁷ nucleotides and digested with a cocktail of nucleases in 5 mM Bis-Tris-HCl buffer (pH 7.1) (Nauwelaërs et al., 2013). DNA adducts were measured by UPLC-ESI/MS³ employing a Velos ion trap MS interfaced with a Dionex Ultimate 3000 LC equipped with an EASY-Spray ion source (Thermo Fisher, San Jose, CA). A Thermo Acclaim PepMap trap cartridge RP C18 (0.3 mm × 5 mm, 5 µm particle size, 100 Å) and, a Michrom Magic C18 AQ column (0.1 mm × 150 mm, 3 µm particle size). The LC mobile phases were (A) 0.01 % formic acid and (B) 95 % acetonitrile containing 0.01 % formic acid. The DNA digest was injected onto the trapping column and washed with mobile phase A for 6 min at a 12 µl/min flow rate. Then, the adducts were back-flushed onto the Magic C18 AQ column at a flow rate of 0.6 µl/min. A linear gradient of 1 – 99 % B over 10 min was applied for the separation, followed by 3 min of washing at 99 % B. Analyses were conducted in the positive ionization mode. Optimized instrument tuning parameters were as follows: capillary temperature, 270°C; source spray voltage, 2.5 kV; S-Lems RF levels, 63%; injection time, 10 ms, activation Q, 0.35, and a normalized collision energy, 25 and 40 for MS² and MS³ respectively. The ions monitored at the MS3 scan stage were as follows: dG-C8-4-ABP

m/z 435.1 > 319.1 > 249.1, 277.1, 302.1, ¹³C₁₀-dG-C8-4-APB m/z 445.1 > 324.1 > 252.1, 281.1, 307.1, dG-C8-AαC m/z 449.1 > 333.1 > 316.1, 291.1, 324.1, ¹³C₁₀-dG-C8-AαC m/z 459.1 > 338.1 > 321.1, 295.1, 329.1, dG-C8-PhIP m/z 490.1 > 374.1 > 250.1, 304.1, 329.1, 357.1, ¹³C₁₀-dG-C8-PhIP m/z 500.1 > 379.1 > 251.1, 307.1, 333.1, 362.1, dG-C8-MeIQx m/z 479.1 > 363.1 > 346.1, 320.1, d₃-dG-C8-MeIQx m/z 482.1 > 366.1 > 349.1, 323.1, dG-C8-IQ m/z 464.1 > 348.1 > 303.1, 224.1, 331.1, 278.1, ¹³C₁₀-dG-C8-IQ m/z 474.2 > 353.1 > 307.1, 225.1, 336.1, 281.1. External calibration curves were constructed for quantification (Goodenough et al., 2007).

## 4.8. Statistical analysis

Results are expressed as the mean ± SD. Statistical analyses were carried out using GraphPad Prism (10.0.2 version). A Shapiro-Wilk normality test was performed to evaluate the Gaussian distribution of each dataset. In the case of a normal distribution, data were analyzed by one-way or two-way ANOVA followed by a Dunnett post hoc test to assess differences between specific conditions. Data were analyzed using Kruskal-Wallis test followed by a Dunn's correction for datasets that did not follow a normal distribution. Differences were considered significant when *p* < 0.05, **p** < 0.01, ***p** < 0.001, ****p** < 0.0001. All results were from at least 3 biological replicates. The Mann-Whitney U-test was used for the *in vivo* comet assay, and the micronucleus test for the frequency of MNPCE. The Student's t-test was used for the statistical comparison of the PCE/NCE ratio (micronucleus test).

# 5. Results

## 5.1. DNA strand breaks detected by γH2AX immunostaining and comet assay in Hepoid-HepaRG

Our previous results have demonstrated that Hepoid-HepaRG cells rapidly differentiates into hepatocyte-like cells exhibiting liver-specific functions within a few days of culture. It maintains these functions for at least 4 weeks, showing its relevance for genotoxicity studies (Rose et al., 2021).

This study examined a broad panel of DNA-damaging compounds in Hepoid-HepaRG based on their well-known genotoxicity properties in animals. The measurement of DNA damage was automated, accelerating data analysis. We first determined the compounds' toxicity after a 24-hour treatment to exclude cytotoxicity as a confounding factor. For γH2AX assays, we defined the maximal dose as that which induced 20 % cytotoxicity. In cases where no toxicity was observed, the maximal dose was set at 10 mM (for DMH) or 2 mg/ml (for DEHP, i.e., 5 mM) to comply with regulatory requirements (Supplemental Figure S1).

One approach to detecting primary DNA damage is by quantifying the phosphorylation of histone H2AX (γH2AX), a marker of DNA double-strand breaks (Mah et al., 2010; Rogakou et al., 1998). To facilitate immunostaining and imaging, Hepoid-HepaRG were extracted from the collagen matrix, plated in 2D after treatment, and a deep learning-based approach was used to automate the quantification of γH2AX positive cells (Fig. 1A). As illustrated in Fig. 1B, γH2AX (red) staining in 2D-plated Hepoid-HepaRG obtained after a 24-hour treatment with several compounds was observed. We used a set of well-known reactive and non-reactive DNA carcinogens to validate the approach in the Hepoid-HepaRG model. Four DNA-reactive carcinogens induced a statistically significant increase in γH2AX positive cells: AFB₁ at 6 M (1.8-fold), MMC at 1.5 and 3 µM (4.4- and 10.6-fold, respectively). DMH and MMS were positive in a dose-dependent manner from the lowest concentrations tested (2.5 mM and 125 M, respectively) with the following fold-increases: 1.5, 2.5, and 2.3 for DMH, 3.5, 5.6, and 8.4 for MMS (Fig. 1C). As expected, no change in the number of γH2AX-positive cells was observed after treatment with the aneugens vinblastine and colchicine or after treatment with the non-DNA reactive compounds DEHP, ethionamide, and methylcarbamate. These initial results are
A

<figure><img src="image_3.png" /></figure>

Use of a deep learning-based approach (Fiji)

Step 1 : Segmentation of images (Stardist)

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

C

Genotoxic hepatocarcinogens

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

MMS

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

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

Vinblastine

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

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

Colchicine

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

B

<figure><img src="image_11.png" /></figure>

Step 2 : Machine learning (Annotater)

<figure><img src="image_12.png" /></figure>

Generation of a 'trained' classifier capable of
discriminating + and - cells for a marker

Non genotoxic hepatocarcinogens

<figure><img src="image_13.png" /></figure>

Concentration (mM)

Methylcarbamate

<figure><img src="image_14.png" /></figure>

Non hepatocarcinogens

<figure><img src="image_15.png" /></figure>

Ethionamide

**Fig. 1.** Automatic quantification of γH2Ax positive cells after acute exposure using the Hepoid-HepaRG models. A. Schematic representation of the *in vitro* experiment and description of the deep learning-based approach used to automatically quantify γH2Ax positive cells. B. Images of Hepoid-HepaRG cells plated in 2D and stained to visualize nuclei (blue) and gH2Ax (red). C. Quantification of γH2Ax positive cells (%) following a 24h-treatment with well-known genotoxic and no-genotoxic carcinogens, or no hepatocarcinogens (mean ± SD (n ≥ 3), \*p ≤ 0.05, \*\*p ≤ 0.01, \*\*\*p ≤ 0.001, \*\*\*\*p ≤ 0.0001, one-way ANOVA test followed by Dunnett's post hoc correction). A non-parametric Kruskal-Wallis test was also performed to compare all groups and significant p-values emerged for MMC, DMH and MMS (0.0012, 0.0002, and 0.0004, respectively).

encouraging for using the Hepoid-HepaRG model to discriminate be-
tween different classes of molecules.

We investigated the effect of the same set of compounds using the alkaline comet assay as a second DNA damage endpoint. We first determined their effects *in vivo* by treating rats (Fig. 2A). We found a statistically significant increase in DNA damage after acute treatment with DMH at 10 and 20 mg/kg b.w/day (19.10 and 22.17 % DNA in the tail, corresponding to 113.6- and 131.8-fold increases, respectively). Whereas DEHP, methylcarbamate, and ethionamide did not induce DNA damage at any of the doses tested, while the induction was highly significant with the positive control MMS. In Hepoid-HepaRG, DNA damage increased by 2.6-fold with AFB₁ at 6 µM and by 3.1 and 4.6-fold with DMH at 5 and 10 mM, respectively. MMS induced DNA damage in a dose-dependent manner (7.3, 14.2, and 20.7-fold increases, at 90, 225 and 450 µM, respectively), after 24 h of exposure. As expected, taking into account their mode of action (interstrand cross-linking), the DNA-

reactive carcinogens vinblastine, colchicine, and MMC did not induce detectable DNA damage using this assay, similar to the non-DNA reactive compounds ethionamide and methylcarbamate (Fig. 2B). Surprisingly, a slight increase was however detected after DEHP treatment.

Furthermore, to investigate chronic effects, we tested the genotox-
icity of the pro-mutagens AFB₁ and DMH and the non-DNA reactive
compounds DEHP, ethionamide, and methylcarbamate after 14 days of
exposure (Fig. 2C). After determining the treatment concentrations
resulting in no more than 20 % toxicity, our results demonstrate that the
effects were similar to those seen after 24 h exposures, with concen-
trations 3.3–30 times lower, except for methylcarbamate, which was not
toxic at 10 mM even after 14 days of treatment. These results confirm
the relevance of the model for acute and long-term genotoxicity studies.
Effects were seen at low concentrations, which are representative of
actual chronic human exposure.
<figure><img src="image_16.png" /><figcaption>**Fig. 2.** Performance of the comet assay using the Hepoid-HepaRG model to predict genotoxicity after acute or chronic treatment. A. Quantification of DNA damage measured *in vivo* after a 24h-treatment. B. Quantification of DNA damage measured in Hepoid-HepaRG cells *in vitro* following a 24h-treatment or C. 14 days of repeated exposure. DNA damages are expressed as the mean median % DNA in tail. Data represent mean ± SD of independently repeated experiments (n ≥ 3). For *in vitro* datasets, significant differences compared to the control were determined by the Kruskal-Wallis test followed by a Dunn's correction (*p* ≤ 0.05, **p** ≤ 0.01, ***p* ≤ 0.001, ****p ≤ 0.001). For *in vivo* data, significant differences compared to the control were determined by the Mann-Whitney test (*p* ≤ 0.05, **p** ≤ 0.01).</figcaption></figure>

## 5.2. The Hepoid-PHH model successfully detects DNA strand breaks assessed by the comet assay and underlines interindividual variations

In addition to the Hepoid-HepaRG model, we also studied the Hepoid-PHH model, as the two models complement each other (Rose et al., 2021, 2022). Although the two models share unique features of long-term differentiation and proliferation, the Hepoid-PHH model also offers the opportunity to study inter-individual variations in the response to xenobiotics linked, for example, to cytochromes P450 (CYP) expression polymorphisms (Guengerich et al., 1999; Ingelman-Sundberg et al., 2007) or N-acetyltransferases (NAT) genetic polymorphisms (Hein, 2002). Given the high and stable level of hepatic differentiation of the Hepoid-PHH model, it is crucial to explore whether inter-individual variations in enzyme activities can be identified in response to carcinogenic compounds. For this purpose, and considering the limited availability of primary human hepatocytes (PHH), we conducted comet assays on cells obtained from different donors with varying disease etiologies (Table 1). The cells were exposed to

DNA-reactive carcinogens and non-DNA-reactive compounds for 24 h (Fig. 3). Significant DNA damage was observed in cells treated with AFB₁ and DMH, though there was considerable heterogeneity between donors (Fig. 3A). In contrast, no DNA damage was detected in cells from different donors treated with non-DNA-reactive compounds.

AFB₁ treatment caused higher DNA damage in PHHs than in HepaRG, this was seen at the highest AFB₁ concentrations and even at the lowest concentrations in PHH-1 and PHH-5 samples. Specifically, DNA percentage in the tail reached 12.6% and 14.4% after treatment with 6 µM AFB₁, representing a 3.7- and 4.2-fold increase, respectively, compared to the Hepoid-HepaRG model exposed to the same concentration. Similarly, DMH treatment at 10 mM of samples PHH-2 and PHH-4 caused a 23.3% and 16.4% increase in DNA damage, corresponding to a 3.9- and 2.7-fold increase, respectively, compared to the Hepoid-HepaRG model.

It should be noted that the PHH-1 and PHH-2 samples exhibited distinct responses to these compounds. PHH-1 responded strongly to AFB₁ but weakly to DMH, while the opposite was the case for PHH-2. This demonstrates that the 3D culture of PHH in the Hepoid model maintains the inter-individual variability in metabolic activity. Additionally, the level of DNA damage observed in primary human hepatocytes (PHH) samples was notably higher than in the HepaRG cells, indicating probably a more efficient bioactivation of pro-mutagenic xenobiotics into genotoxic compounds. The mean results from the different donors clearly distinguish carcinogenic from non-carcinogenic compounds (Fig. 3B), with the higher doses of AFB₁ and DMH (i.e., 6 µM and 10 mM, respectively) showing significant induction of DNA damage.

<table><caption>**Table 1**
Clinical characteristics of primary human hepatocytes (PHH) samples.</caption><thead><tr><td>Case</td><td>Age</td><td>Gender</td><td>Liver pathology</td><td>Smoker</td><td>Diabetic</td></tr></thead><tbody><tr><td>PHH 1</td><td>50</td><td>F</td><td>Liver metastasis</td><td>No</td><td>N/A</td></tr><tr><td>PHH 2</td><td>58</td><td>F</td><td>None</td><td>Yes</td><td>N/A</td></tr><tr><td>PHH 3</td><td>50</td><td>F</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>PHH 4</td><td>72</td><td>F</td><td>Liver metastasis</td><td>No</td><td>Yes</td></tr><tr><td>PHH 5</td><td>79</td><td>F</td><td>Cholangiocarcinoma</td><td>No</td><td>No</td></tr><tr><td>PHH 6</td><td>74</td><td>F</td><td>Cholangiocarcinoma</td><td>No</td><td>No</td></tr></tbody></table>

N/A: not available
<figure><img src="image_17.png" /><figcaption>**Fig. 3.** Inter-individual variability to genotoxicity, evaluated by the comet assay, following acute exposure in the Hepoid-PHH model. Individual levels of DNA damage induction measured in Hepoid-PHH samples from different donors (see Table 1) following 24 h of treatment with well-known genotoxic (AFB1, DMH - red), or non-genotoxic, carcinogens (DEHP, methylcarbamate - green), or non- hepatocarcinogens (ethionamide - green). DNA damages are expressed as the mean median % DNA in tail. For comparison, the dotted line represents the % of tail DNA measured in Hepoid-HepaRG cells exposed at the maximal concentration. B. Mean levels of DNA damage induction in Hepoid-PHH cells following 24 h of treatment. DNA damages are expressed as the mean median % DNA in the tail (mean ± SD (n ≥ 3), *p* ≤ 0.05, **p** ≤ 0.01, Kruskal-Wallis test followed by a Dunn's correction).</figcaption></figure>

### 5.3. Chromosomal aberrations efficiently and reliably quantified by the micronucleus assay Hepoid-HepaRG

We also performed the micronucleus (MN) assay to investigate chromosomal aberrations. We first performed in vivo assays using well-known compounds (Table 2). As expected, the two non-genotoxic carcinogenic compounds (i.e., DEHP and methylcarbamate) and the hepatotoxic non-carcinogenic compound (i.e., ethionamide) did not induce a significant increase in the frequency of MNPCE (Micro-Nucleated PolyChromatic Erythrocytes) when compared to their corresponding negative controls. In contrast, the positive control MMS induced a statistically significant increase in micronuclei. No bone marrow cytotoxicity was observed. The genotoxic carcinogenic compound (i.e., DMH) did induce a statistically significant increase in MNPCE frequency, with 5.7 MNPCE/1000 cells at the two highest doses tested (10 and 20 mg/kg b.w./day) compared to 0.9 MNPCE/1000 cells for the negative control. Importantly, DMH did not cause any cytotoxicity.

Our previous work described a method to perform the MN assay using the Hepoid-HepaRG model based on manual micronuclei scoring

(i.e., microscopic analysis of micronuclei frequencies) (Rose et al., 2021). While this method yielded valuable results, it was limited by low throughput and required labor-intensive, time-consuming analysis. We developed an innovative computational image analysis approach to overcome these limitations. This analysis was performed following an experimental protocol detailed in Fig. 4A. Briefly, following drug treatment, spheroids were extracted from the matrix and plated in 2D culture in 96-well plates, followed by a 72-hour recovery period with EGF to stimulate proliferation. The automatic analysis was then performed using a self-developed macro that combined precise cell segmentation with quantitation of micronuclei inside mononuclear cells (Fig. 4B).

According to OECD Test Guideline 474 for the *in vitro* mammalian cell MN test, MN should be scored in cells undergoing mitosis during or after exposure to the test chemical (OECD Test Guideline No, 2016). We monitored cell growth to ensure the proliferative capacity of 2D-plated Hepoid-HepaRG cells, confirming that the negative control underwent approximately 1.5–2 cell population doublings (Fig. 4C).

As calculating the relative population doubling (RPD) was not feasible under our experimental conditions, we instead estimated cell
<table><caption>**Table 2**
Micronuclei frequency and cytotoxicity of well-known genotoxic and non-genotoxic compounds tested *in vivo*.</caption><thead><tr><th>Compounds</th><th>Dose<br>[(mg/kg b.w./day)x3]</th><th>MNPCE/1000 cells</th><th>Fold increase MNPCE/1000 cells<sup>a</sup></th><th>PCE/NCE</th></tr></thead><tbody><tr><td colspan="5"><strong>Genotoxic carcinogens</strong></td></tr><tr><td rowspan="3">DMH</td><td>0</td><td>0.9+/- 0.2</td><td>-</td><td>2.0+/- 0.8</td></tr><tr><td>5</td><td>1.4+/- 0.4</td><td>1.4+/- 0.3</td><td>3.4+/-0.9</td></tr><tr><td>10</td><td>2.6+/- 1.1**</td><td>2.9+/- 1.5**</td><td>2.1+/-0.6*</td></tr><tr><td>20</td><td>5.7+/- 0.8**</td><td>6.3+/- 0.9**</td><td>1.9+/-0.4</td></tr><tr><td colspan="5"><strong>Non genotoxic hepatocarcinogen</strong></td></tr><tr><td rowspan="5">DEHP</td><td>0</td><td>0.9+/- 0.2</td><td>-</td><td>0.8+/- 0.1</td></tr><tr><td>500</td><td>0.9+/- 0.7</td><td>1.0+/- 0.8</td><td>0.8+/-0.2</td></tr><tr><td>1000</td><td>1.3+/- 0.3</td><td>1.4+/- 0.3</td><td>0.7+/-0.1</td></tr><tr><td>2000</td><td>1.1+/- 0.8</td><td>1.4+/- 1.0</td><td>0.8+/-0.1</td></tr><tr><td>Positive control</td><td>13.6+/- 4.9**</td><td>16.0+/- 5.9**</td><td>0.6+/-0.3</td></tr><tr><td rowspan="4">Methylcarbamate</td><td>0</td><td>0.6+/- 0.4</td><td>-</td><td>0.8+/- 0.1</td></tr><tr><td>500</td><td>1.2+/- 0.8</td><td>1.3+/- 0.9</td><td>0.8+/-0.3</td></tr><tr><td>1000</td><td>1.1+/- 1.0</td><td>1.5+/- 0.9</td><td>0.9+/-0.3</td></tr><tr><td>Positive control</td><td>17.5+/- 1.3**</td><td>19.2+/- 1.5**</td><td>0.6+/-0.1</td></tr><tr><td colspan="5"><strong>Non hepatocarcinogen</strong></td></tr><tr><td rowspan="5">Ethionamide</td><td>0</td><td>1.0+/- 0.3</td><td>-</td><td>1.7+/- 0.5</td></tr><tr><td>125</td><td>0.9+/- 0.9</td><td>1.3+/- 0.9</td><td>2.0+/-0.7</td></tr><tr><td>250</td><td>0.7+/- 0.6</td><td>0.8+/- 0.7</td><td>1.7+/-0.9</td></tr><tr><td>500</td><td>0.8+/- 0.4</td><td>1.0+/- 0.5</td><td>1.1+/-0.5</td></tr><tr><td>Positive control</td><td>27.8+/- 7.7**</td><td>31+/- 9.9**</td><td>0.7+/-0.1**</td></tr></tbody></table>

MNPCE: MicroNucleated PolyChromatic Erythrocytes

Positive control: MMS [(100 mg/kg b.w./day)x2]

* p < 0.05, **p < 0.01 (Mann-Whitney U-test)

<sup>a</sup> Fold increases of chemical-induced MN over the vehicle control

proliferation by comparing the ATP content after incubation in a medium supplemented with 50 ng/ml recombinant human EGF (rhEGF) on days 1, 2, and 3 post-seeding. ATP levels increased by 1.2 +/- 0.0, 1.7 +/- 0.3, and 2.0 +/- 0.2-fold after 1-, 2-, and 3-day of rhEGF stimulation, respectively.

The cytotoxicity of the compounds was determined by calculating the percentage of relative survival (compared to control) using an ATP assay in 2D plated Hepoid-HepaRG spheroids following 3 days of rhEGF stimulation (Table 3). The concentrations that resulted in less than 60 % relative survival were excluded to avoid excessive cytotoxicity for MN evaluation.

The DNA-reactive carcinogen compounds tested induced a significant increase in MN frequency (Table 2). Five out of six carcinogens (MMS, MMC, colchicine, vinblastine, AFB₁) caused significant MN induction (3.1–4.7-fold increases) starting from the intermediate concentration. In contrast, only the highest concentration of DMH (10 mM) produced a significant result (1.8-fold increase). As expected, none of the DNA-reactive compounds (DEHP, methylcarbamate, ethionamide) induced MN significantly in the Hepoid-HepaRG model (Table 3). These results are consistent with *in vivo* findings, where, as expected, only DMH was able to substantially induce MN after an acute treatment.

### 5.4. Transcriptomic analysis reveals the high capacity of the Hepoid-HepaRG model to respond to DNA damage

To further validate the Hepoid-HepaRG model, an RNA-seq transcriptomic analysis was conducted. HepaRG spheroids were treated with the mutagenic, genotoxic compound AFB₁ at 6 µM for 8 h. The 64 genes comprising the TGx-DDI transcriptomic biomarker were specifically selected for analysis. The fold change in expression levels of these 64 genes from Hepoid-HepaRG were compared with those obtained from 2D HepaRG cells treated with 2.5 µM AFB₁ for 3 days, using normalized data from ($author1$ et al.,) (Buick et al., 2020). Fig. 5 shows a high degree of similarity between the expression profiles of the 64 genes, further confirming that the Hepoid-HepaRG model is a reliable tool for genotoxicity studies.

### 5.5. Application to HAAs

We used the validated tests described above to study the genotoxicity of several HAAs suspected of being carcinogenic in humans: 2-amino-9H-pyrido[2,3-b]indole (AαC), 2-amino-3-methylimidazo[4,5-f]quino-line (IQ), 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) (IARC, 1986, 1993). ATP assay performed on cells from the Hepoid-HepaRG and Hepoid-PHH models treated with the selected HAAs for 24 h showed no cytotoxic effect at 20 µM (Figure S-2). Quantitation of γH2AX and micronuclei assay performed on Hepoid-HepaRG revealed no significant changes for any of the four compounds (Fig. 6A and Table 3).

However, the comet assay detected a significant dose-dependent increase in DNA damage after treatment with AαC and IQ at 10 and 20 µM 2.6- and 3.8-fold for AαC, 3.4- and 4.2-fold for IQ, respectively) (Fig. 6B). Interestingly, the effect of these 2 compounds in the Hepoid-PHH model highlighted strong inter-individual variability in the response to these procarcinogens (Fig. 6D).

For AαC, PHH-2, PHH-4, and, to a lesser extent, PHH-6 samples showed a dose-dependent increase in DNA damage, unlike PHH-3. Damage in PHH-2 and PHH-4 was more significant than that observed in Hepoid-HepaRG from 5 µM, reaching 3.8- and 2.9-fold at 20 µM, respectively. After IQ treatment, PHH-3 and PHH-5 samples showed no DNA damage, while an increase was noted for PHH-2 and PHH-6, reaching 3.9- and 2-fold at 20 µM and slightly exceeding the levels observed in Hepoid-HepaRG by 1.8- and 1.5-fold, respectively. For PhIP and MeIQx, three PHH samples were studied, none of which showed major DNA damage in line with the results observed in Hepoid-HepaRG. Due to large standard deviations (Fig. 6E), treatment with AαC and IQ did not appear to induce consistent results in Hepoid-PHH. However, treatment with MeIQx of pooled PHH samples from three donors showed significant DNA damage at 5 and 10 µM (p = 0.07 at 20 µM), a response not observed in Hepoid-HepaRG (Fig. 6E). Additionally, we examined the ability of HAAs to form MN in Hepoid-HepaRG; no significant results were obtained for any of the four HAAs tested (Table 4).

To further investigate HAA-induced DNA alterations, we measured DNA adducts derived from HAAs in Hepoids (Figs. 6C and 6F). The highest adduct levels were observed after AαC treatment, which formed significantly higher levels of dG-C8 adducts in the Hepoid-HepaRG
<figure><img src="image_18.png" /><figcaption>**Fig. 4.** Automated detection and quantification of micronuclei based on a computational approach in the Hepoid-HepaRG model. A. Schematic representation of the *in vitro* experiment. B. Illustration of the input images obtained after staining with Dapi (nuclei, blue) and WGA (cellular membranes, green) and of output images obtained with the computational approach used to automatically detect and quantify micronuclei in mononucleated cells following 24 h treatment and 72 h recovery in presence of EGF. In the absence of cytochalasin, only micronuclei present in mononucleated cells were counted (indicated by red arrows). C. Illustration and quantification of the proliferation of the 2D-plated Hepoid-HepaRG cells during the 72 h recovery in presence of EGF (mean ± SD (n ≥ 3), **p** ≤ 0.01, ***p** ≤ 0.001, one-way ANOVA test followed by Dunnett's post hoc correction).</figcaption></figure>

model compared to 2D cultures (4.3-fold increase) (Fig. 6C). These findings were confirmed in one of the two studied PHH donors, with a 1.23-fold increase compared to 2D cultures (Fig. 6F).

## 6. Discussion

With the development of a 3D spheroid model that robustly expresses human liver metabolic enzyme activities, we analyzed three complementary genotoxic endpoints: DNA strand breaks (via γH2AX quantification and comet assay), chromosomal aberrations (loss or damage using micronucleus assays), and covalent DNA adduct formation.

For this purpose, we selected both primary and transformed human hepatocytes based on their ability to proliferate and differentiate, with the goal to establish an innovative and reliable approach for preclinical drug safety assessment. Our results demonstrate that the selected approaches are complementary. Indeed, MMC, a well-known carcinogen, causes DNA breakage visualized by γH2AX labeling. As expected, given its ability to induce interstrand cross-links in DNA, MMC effects remain undetectable by the comet assay under our conditions, while its ability to alter DNA in the Hepoid micronucleus test aligns perfectly with *in vivo* results. Similarly, colchicine, a known aneugen, led to negative results with both γH2AX labeling and the comet assay. This finding is consistent

with several studies using the comet assay *in vivo* (Sasaki et al., 1997; Uno et al., 2015) and can be explained by the fact that aneugens cause abnormalities in chromosome number without inducing DNA strand breaks. These two examples clearly illustrate the need to adopt multiple approaches to assess a test compound's genotoxicity and mutagenicity, thereby improving the evaluation of compounds with different modes of action.

Furthermore, we observed higher variability in comet assay performed in Hepoid-PHH compared to Hepoid-HepaRG with the two genotoxic hepatocarcinogens AFB₁ and DMH. This study highlights the effectiveness and robustness of the Hepoid-PHH model for distinguishing different classes of molecules and studying inter-individual sensitivity to food-borne and environmental pollutants. These promising results represent the first genotoxicity studies conducted on PHH grown in a 3D model. Additional human cases and complementary experiments would be required to confirm the relationship between the DNA damage described and a specific phenotype or genotype.

We are however well aware that the number of compounds tested can appear limited in the present study and needs to be increased in the future. By increasing the number of compounds tested, we will be able to fine-tune sensitivity/specificity and potential positive and negative values.
<table><caption>**Table 3**
Micronuclei frequency and cytotoxicity of well-known genotoxic and non-genotoxic compounds tested in Hepoid-HepaRG.</caption><thead><tr><th>Compounds</th><th>Concentration (mM)</th><th>Relative survival (%)<sup>a</sup></th><th>MNMNC (%)</th><th>Fold increase MNMNC<sup>b</sup></th></tr></thead><tbody><tr><td colspan="5"><strong>DNA reactive carcinogens Direct-acting genotoxic agents</strong></td></tr><tr><td rowspan="4">MMS</td><td>0</td><td>100</td><td>2.3+/- 0.5</td><td>-</td></tr><tr><td>125</td><td>103.6+/-12.6</td><td>5.1+/- 0.6</td><td>2.2+/- 0.3</td></tr><tr><td>250</td><td>85.3+/-15.2</td><td>7.0+/- 1.4*</td><td>3.1+/- 0.6**</td></tr><tr><td>500</td><td>62.1+/-4.2</td><td>11.2+/- 2.0**</td><td>4.9+/- 0.9**</td></tr><tr><td rowspan="4">MMC</td><td>0</td><td>100</td><td>2.3+/- 0.5</td><td>-</td></tr><tr><td>0,5</td><td>80.8+/- 5.9</td><td>8.2+/- 1.5</td><td>3.6+/- 0.7</td></tr><tr><td>1</td><td>79.5+/- 14.8</td><td>10.7+/- 2.9*</td><td>4.7+/- 1.3*</td></tr><tr><td>2</td><td>64.5+/- 2.2</td><td>15.1+/- 5.7**</td><td>6.6+/- 2.5**</td></tr><tr><td colspan="5"><strong>Aneugens</strong></td></tr><tr><td rowspan="4">Colchicine</td><td>0</td><td>100</td><td>2.3+/- 0.5</td><td>-</td></tr><tr><td>1</td><td>82.1+/- 8.5</td><td>5.5+/-1.9</td><td>2.4+/-0.8</td></tr><tr><td>2</td><td>82.9+/- 13.7</td><td>8.4+/-2.6**</td><td>3.7+/-1.1*</td></tr><tr><td>4</td><td>69.3+/- 2.2</td><td>10.4+/-2.7***</td><td>4.6+/-1.2***</td></tr><tr><td rowspan="4">Vinblastine</td><td>0</td><td>100</td><td>2.3+/- 0.5</td><td>-</td></tr><tr><td>0.5</td><td>101.6+/-25.7</td><td>5.4+/- 1.6</td><td>2.4+/- 0.7</td></tr><tr><td>1</td><td>86.3+/- 10.7</td><td>7.3+/- 0.8*</td><td>3.2+/- 0.4*</td></tr><tr><td>2</td><td>66.9+/- 8.6</td><td>9.8+/- 1.7**</td><td>4.3+/- 0.8**</td></tr><tr><td colspan="5"><strong>Genotoxic hepatocarcinogens</strong></td></tr><tr><td rowspan="4">AFB<sub>1</sub></td><td>0</td><td>100</td><td>2.3+/- 0.5</td><td>-</td></tr><tr><td>0.5</td><td>105.6+/- 16.2</td><td>3.6+/- 1.0</td><td>1.6+/- 0.5</td></tr><tr><td>1</td><td>103.4+/- 9.9</td><td>7.7+/- 2.8*</td><td>3.4+/- 1.2*</td></tr><tr><td>2</td><td>93.2+/- 20.5</td><td>9.2+/- 3.8*</td><td>4.1+/- 1.7*</td></tr><tr><td rowspan="4">DMH</td><td>0</td><td>100</td><td>2.3+/- 0.5</td><td>-</td></tr><tr><td>2500</td><td>118.5+/- 20.4</td><td>3.5+/- 1.2</td><td>1.2+/- 0.8</td></tr><tr><td>5000</td><td>110.5+/- 21.0</td><td>3.4+/- 0.4</td><td>1.5+/- 0.2</td></tr><tr><td>10000</td><td>104.8+/- 24.9</td><td>4.2+/- 1.1**</td><td>1.8+/- 0.5**</td></tr><tr><td colspan="5"><strong>Non genotoxic hepatocarcinogens</strong></td></tr><tr><td rowspan="4">DEHP</td><td>0</td><td>100</td><td>2.3+/- 0.5</td><td>-</td></tr><tr><td>1250</td><td>92.0+/- 5.2</td><td>2.3+/- 0.5</td><td>1.0+/- 0.2</td></tr><tr><td>2500</td><td>108.6+/- 10.4</td><td>2.3+/- 0.9</td><td>1.0+/- 0.4</td></tr><tr><td>5000</td><td>105.6+/- 19.3</td><td>2.5+/- 0.7</td><td>1.1+/- 0.3</td></tr><tr><td rowspan="4">Methylcarbamate</td><td>0</td><td>100</td><td>2.3+/- 0.5</td><td>-</td></tr><tr><td>2500</td><td>102+/- 12.3</td><td>2.5+/- 0.8</td><td>1.1+/- 0.4</td></tr><tr><td>5000</td><td>93.8+/- 10.9</td><td>2.5+/- 0.8</td><td>1.1+/- 0.3</td></tr><tr><td>10000</td><td>97.0+/- 7.6</td><td>2.6+/- 0.6</td><td>1.1+/- 0.3</td></tr><tr><td colspan="5"><strong>Non hepatocarcinogen</strong></td></tr><tr><td rowspan="4">Ethionamide</td><td>0</td><td>100</td><td>2.3+/- 0.5</td><td>-</td></tr><tr><td>0.75</td><td>108.9+/- 5.9</td><td>2.3+/- 0.5</td><td>1.0+/- 0.2</td></tr><tr><td>1,5</td><td>100.9+/- 5.1</td><td>2.0+/- 0.5</td><td>0.9+/- 0.2</td></tr><tr><td>3</td><td>103.3+/- 5.9</td><td>2.6+/- 0.6</td><td>1.1+/- 0.3</td></tr></tbody></table>

MNMNC: MicroNuclei in MonoNucleated Cells

* p < 0.05, ** p < 0.01, *** p < 0.001 (Kruskal-Wallis test followed by a Dunn's correction)

a Survival relative compared to the vehicle (0.1 % DMSO)

b Fold increases of chemical-induced MN over the vehicle

The genotoxicity and mutagenesis analyses demonstrated the relevance of the Hepoid models, HepaRG and PHH, to sensitively and efficiently distinguishing carcinogenic from non-carcinogenic compounds after acute and long-term treatment. Interestingly, these results align

with *in vivo* data and validate the novel methodologies developed to assess genotoxicity and mutagenesis. In this study, we utilized machine learning models based on the Fiji open-source software and designed a computational approach to automate γH2AX and micronucleus

<figure><img src="image_19.png" /><figcaption>**Fig. 5.** Comparison of the TGx-DDI biomarker profiles in 2D HepaRG cells and 3D Hepoid-HepaRG models treated with AFB₁. The TGx expression of the 64 genes used to identify DDI agents were compared in 2D HepaRG cells following a 3-day repeat exposure with AFB₁ at 2.5 µM (data from Buick et al. Genes and Environment, 2020), and in Hepoid-HepaRG cells following an 8h-treatment with AFB₁ at 6 µM.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>**Fig. 6.** Evaluation of the genotoxic potency of HAAs using the Hepoid-HepaRG and Hepoid-PHH models. A. Quantification of gH2Ax positive cells following a 24h-treatment of Hepoid-HepaRG cells and B. Level of DNA damage induction measured following a 24h-treatment of Hepoid-HepaRG measured by the comet assay (mean ± SD (n ≥ 3), \*p < 0.05, Kruskal Wallis test followed by a Dunn's correction). C. HAA-DNA adduct formation in 2D differentiated HepaRG cells or in Hepoid-HepaRG cells following a 24h-treatment at 10 mM (mean ± SD (n = 3), \*p < 0.05, Student's t-test). D. Individual levels of DNA damage induction measured following a 24h-treatment of Hepoid-PHH cells from various donors. DNA damage is expressed as the mean % tail DNA intensity. For comparison, the dotted line represents the % of tail DNA measured in Hepoid-HepaRG cells exposed at the maximal concentration. E. Mean levels of DNA damage induction in Hepoid-PHH cells from various donors following a 24h-treatment with HAAs (± SD (n ≥ 3), \*p < 0.05, Kruskal Wallis test followed by a Dunn's correction). F. HAA-DNA adduct formation in 2D cultured-PHH or in Hepoid-PHH cells following a 24h-treatment at 10 mM (n = 2).</figcaption></figure>

quantification. These user-friendly approaches are applicable to cells grown in both 2D and 3D, significantly reducing analysis time and ensuring reliable results by minimizing human bias.

The relevance of studying genotoxicity and mutagenesis in primary human hepatocytes and HepaRG aligns closely with the 3Rs recommendations, which aim to reduce animal use. Even today, while *in vivo* tests remain necessary in some instances, they raise concerns regarding species differences and ethical considerations. In particular, the complete replacement of animal models has not yet been achieved in pharmaceutical studies for endpoints such as mutagenesis. However, our work proves that highly differentiated liver spheroids can generate robust and reliable results.

Hepoid models, whether assembled from HepaRG cells or primary

human hepatocytes, exhibit similar properties in terms of organization into a single layer of polarized cells forming hollow spheroids (Rose et al., 2021, 2022). This distinctive architecture differentiates Hepoids from other 3D culture models, which are typically generated using low attachment plates, hanging drops or bioreactors (Bell et al., 2016; Mueller et al., 2014; Tostões et al., 2012). Unlike Hepoid models, these alternative approaches often result in loosely structured cell clusters, sometimes with a necrotic core due to insufficient oxygenation. A recent study analyzed AFB₁-derived DNA adducts in human organoids. In contrast to Hepoid models, lack of structural organization in these organoids forced the authors to use extremely high concentrations (150 µM) to detect DNA damage in large cellular aggregates (Garcia et al., 2024).
<table><caption>**Table 4**
Micronuclei frequency and cytotoxicity of the principal HAAs in Hepoid-HepaRG.</caption><thead><tr><th>Compounds</th><th>Concentration (mM)</th><th>Relative survival (%)<sup>a</sup></th><th>MNMNC (%)</th><th>Fold increase MNMNC<sup>b</sup></th></tr></thead><tbody><tr><td rowspan="4">AαC</td><td>0</td><td>100</td><td>2.3 +/- 0.5</td><td>-</td></tr><tr><td>5</td><td>88.2 +/- 14.1</td><td>2.7 +/- 0.8</td><td>1.1</td></tr><tr><td>10</td><td>104.4 +/- 12.5</td><td>1.9 +/- 0.9</td><td>0.7</td></tr><tr><td>20</td><td>114.2 +/- 23.4</td><td>2.0 +/- 0.6</td><td>0.8</td></tr><tr><td rowspan="4">IQ</td><td>0</td><td>100</td><td>2.3 +/- 0.5</td><td>-</td></tr><tr><td>5</td><td>130.9 +/- 17.3</td><td>2.0 +/- 0</td><td>0.8</td></tr><tr><td>10</td><td>134.13.4</td><td>2.5 +/- 0.2</td><td>1.0</td></tr><tr><td>20</td><td>110.7 +/- 19.6</td><td>2.1 +/- 0.3</td><td>0.8</td></tr><tr><td rowspan="4">PhIP</td><td>0</td><td>100</td><td>2.3 +/- 0.5</td><td>-</td></tr><tr><td>5</td><td>129.4 +/- 19.6</td><td>2.0 +/- 0.1</td><td>0.8</td></tr><tr><td>10</td><td>96.8 +/- 11.6</td><td>2.3 +/- 0.3</td><td>0.9</td></tr><tr><td>20</td><td>91.9 +/- 9.7</td><td>2.2 +/- 0.3</td><td>0.9</td></tr><tr><td rowspan="4">MeIQx</td><td>0</td><td>100</td><td>2.3 +/- 0.5</td><td>-</td></tr><tr><td>5</td><td>92.4 +/- 14.4</td><td>2.9 +/- 1.0</td><td>1.1</td></tr><tr><td>10</td><td>106.9 +/- 11.3</td><td>2.2 +/- 0.9</td><td>0.9</td></tr><tr><td>20</td><td>113.0 +/- 12.1</td><td>2.5 +/- 0.3</td><td>1.0</td></tr></tbody></table>

MNMNC: MicroNuclei in MonoNucleated Cells

a Survival relative to the vehicle (0.1 % DMSO)

b Fold increases of chemical-induced MN over the vehicle

While DNA alterations significantly impact hepatocytes in the development of HCC, we recognize the importance of incorporating non-parenchymal cells (NPCs) in the model to enhance its utility in accurately predicting drug toxicity and genotoxicity. It has been well-established that the co-culture of NPCs with hepatocytes further improves hepatic differentiation, particularly in terms of glycogen storage, albumin synthesis capabilities, and the maintenance of xenobiotic metabolism enzyme expression (Baze et al., 2018; Leite et al., 2016; Rubiano et al., 2021).

Building on this knowledge, we are improving the Hepoid model by co-culturing hepatocytes with stellate, inflammatory, and sinusoidal endothelial cells. This enhanced complexity will allow us to assess the role of non-parenchymal cells in DNA alterations and further improve the physiological relevance of our system. Furthermore, flow-based microfluidic or liver-on-chip systems that maintain *in vivo*-like flow rates of cellular interactions within the microenvironment can efficiently remove metabolites and functional products. Such systems have been shown to increase cell-to-cell communications, enhance hepatocyte differentiation and polarity, and establish oxygen gradients that mimic liver zonation (Danoy et al., 2020; Rennert et al., 2015). Recently, Kopp et al. have shown that liver-on-a-chip models are reliable for genotoxicity and mutagenesis studies (Kopp et al., 2024). These authors developed a liver-on-a-chip model in which TK6 lymphoblastoid cells were co-cultured in a separate compartment with differentiated human hepatocytes. In their model, primary hepatocytes and HepaRG cells served solely as metabolic activators rather than as direct targets of genotoxic stress, in contrast to the approach taken in our study. Our next development phase will focus on adapting the advanced Hepoid organoid models to microfluidic platforms, integrating the methodologies established in this study to further improve predictive genotoxicity assessments.

With our system, we have characterized the genotoxicity and mutagenicity induced by HAAs, well-known food and environmental contaminants of concern for humans for which there is a lack of human epidemiological data. The HAAs AαC, IQ, MeIQx, and PhIP are formed in food, tobacco smoke, and diesel exhaust and are suspected to play a role in the occurrence of hepatocellular carcinoma and other cancers (Sugimura et al., 2004; Turesky, 2002). The International Agency for Research on Cancer (IARC) has classified these compounds as either probable (2A) or possibly carcinogens (2B) to humans. Our previously published data have shown that some HAAs can form DNA adducts in 2D-cultured human hepatocytes at higher levels than in rat hepatocytes (Nauwelaers et al., 2011; Turesky et al., 1998; Langouët et al., 2001). Thus, inter-species variations in xenobiotic metabolism capabilities

reinforce the need for efficient characterization in human *in vitro* models so that novel and relevant biomarkers of exposure and effect can be defined. The mutagenicity of HAAs has been demonstrated in bacteria (Sugimura et al., 2004), but very few data exist on their genotoxic potential in human cells (Nauwelaers et al., 2011; Nauwelaërs et al., 2013). Moreover, given that metabolic activation is a major contributor to their hepatic genotoxicity, the high level of xenobiotic metabolism enzyme expression and activity within the Hepoid model make it particularly valuable, as previously described (Rose et al., 2021, 2022).

We have therefore demonstrated, using PHH or HepaRG cells, that Hepoids can detect HAA-induced genotoxicity. Although γH2AX quantification gave negative results using HepaRG cells, the comet assay measured significant DNA strand breaks after acute treatment with AαC at 20 µM and IQ at 10 and 20 µM. However, in accordance with previously published data on PhIP and IQ (Le Hegarat et al., 2010), none of the HAAs studied caused chromosomal aberrations, meaning that HAA-induced alterations are effectively repaired before mitosis or trigger cell apoptosis. The comet assay performed on 3D-cultured PHH confirmed these findings, with high inter-individual variability in susceptibility to AαC and IQ. Notably, cells from donors sensitive to these compounds showed much higher levels of DNA breaks than those measured in HepaRG-treated cells presumably because of higher activity of xenobiotic metabolism enzymes.

Furthermore, the Hepoid-PHH model revealed a significant effect of MeIQx at 5 and 10 µM that was not detected with the Hepoid-HepaRG model, reflecting a greater sensitivity of the latter. Most importantly, we demonstrated that consistent with previously published data (Nauwelaers et al., 2011; Nauwelaërs et al., 2013; Bellamri et al., 2016), AαC-derived dG-C8 DNA adduct formation was greater than that derived from MeIQx, IQ, and PhIP, and with a 3.39- to 3.87-fold increase in Hepoid-PHH compared to Hepoid-HepaRG. This increase could be linked to higher basal CYP1A2 functional activity, which is 2 fold higher in Hepoid-PHH compared to Hepoid-HepaRG cells. This highlights the power of the Hepoid model for genotoxicity and mutagenesis evaluation as well as to explore associated mechanisms (Rose et al., 2021, 2022). Overall, by using two 3D models of the human liver, this study showed that some HAAs were able to induce significant DNA damage after a 24-h treatment at concentrations not exceeding 20 µM. This is particularly interesting since previously published genotoxicity studies showed no effect or detected effects only after treatment with high concentrations, unrepresentative of actual individual exposure (Dumont et al., 2010; Seo et al., 2022; Pezdirc et al., 2013).

For the first time, our work demonstrates that some HAAs can induce DNA damage in human hepatocytes at physiologically relevant
concentrations. Additionally, our transcriptomic study carried out after an 8 h treatment with AαC (10 µM) identified 353 genes deregulated with an adjusted p-value < 0.05 (Supplemental Figure S3). The expression of phase I metabolic enzymes, including CYP1A2, CYP1A1, and CYP1B1, was found to be among the most upregulated, consistent with results obtained from the 2D culture of HepaRG cells (Dumont et al., 2010) and HepG2 cells (Pezdirc et al., 2013), both treated with HAAs.

In summary, we have developed a robust *in vitro* liver-based organoid-model on defined collagen scaffolds that fully supports genotoxicity and mutagenesis studies due to its capacity for proliferation. By forming polarized and organized hollow spheroids, the Hepoid model mimics the microarchitecture of the human liver and exhibits key liver functions with stable metabolic activity. This model represents a significant advance in the field of liver carcinogenesis while adhering to the 3Rs principles to limit animal experimentation (Replacement, Reduction, and Refinement). At a time when New Approach Methodologies (NAMs) are critically needed to address public health issues related to environmental contaminants and the exposome, our innovative *in vitro* human liver 3D model should prove to be a valuable tool to improve the hazard characterization and risk assessment of many chemicals of concern.

## 6.1. Financial support

Studies were funded in part by grants to Sophie Rose, including SATT Ouest Valorisation and with financial support from ITMO Cancer of AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé/ National Alliance for Life Sciences & Health) within the framework of the Cancer Plan. Inserm and University of Rennes also contributed to the financing of this work as well as the Ligue contre le cancer Grand Ouest. The Turesky laboratory gratefully acknowledges the support of the Masonic Chair in Cancer Causation at the University of Minnesota.

## CRediT authorship contribution statement

**Sophie Rose:** Writing – original draft, Data curation, Conceptualization. **Frédéric Ezan:** Methodology, Formal analysis, Data curation. **Ludovic Huot:** Methodology, Investigation, Formal analysis. **Thierry Pécot:** Software, Methodology. **Bellamri Madjda:** Methodology, Investigation, Formal analysis. **Turesky Robert J:** Supervision, Conceptualization. **Fabrice Nesslany:** Validation, Supervision, Methodology, Formal analysis, Conceptualization. **Anne Platel:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.tox.2025.154242.

## Data availability

Data will be made available on request.

## References

Aninat, C., et al., 2006. Expression of cytochromes P450, conjugating enzymes and nuclear receptors in human hepatoma HepaRG cells. Drug Metab. Dispos. Biol. Fate Chem. 34, 75–83.

Ashraf, M.N., Asghar, M.W., Rong, Y., Doschak, M.R., Kiang, T.K.L., 2019. Advanced In Vitro HepaRG Culture Systems for Xenobiotic Metabolism and Toxicity Characterization. Eur. J. Drug Metab. Pharm. 44, 437–458.

Baze, A., et al., 2018. Three-Dimensional Spheroid Primary Human Hepatocytes in Monoculture and Co-culture with Non-parenchymal Cells. Tissue Eng. Part C. Methods 24, 534–545.

Bell, C.C., et al., 2016. Characterization of primary human hepatocyte spheroids as a model system for drug-induced liver injury, liver function and disease. Sci. Rep. 6, 25187.

Bellamri, M., Le Hegarat, L., Turesky, R.J., Langouët, S., 2016. Metabolism of the Tobacco Carcinogen 2-Amino-9H-pyrido[2,3-b]indole (AαC) in Primary Human Hepatocytes. Chem. Res. Toxicol. https://doi.org/10.1021/acs.chemrestox.6b00394.

Bomo, J., et al., 2016. Increasing 3D Matrix Rigidity Strengthens Proliferation and Spheroid Development of Human Liver Cells in a Constant Growth Factor Environment. J. Cell. Biochem 117, 708–720.

Buick, J.K., et al., 2020. Flow cytometric micronucleus assay and TGx-DDI transcriptomic biomarker analysis of ten genotoxic and non-genotoxic chemicals in human HepaRG™ cells. Genes Environ. Off. J. Jpn. Environ. Mutagen Soc. 42, 5.

Chen, Y., et al., 2021. Assessment of long-term functional maintenance of primary human hepatocytes to predict drug-induced hepatotoxicity in vitro. Arch. Toxicol. 95, 2431–2442.

Danoy, M., et al., 2020. Characterization of liver zonation-like transcriptomic patterns in HLCs derived from hiPSCs in a microfluidic biochip environment. Biotechnol. Prog. 36, e3013.

Dumont, J., et al., 2010. Differential toxicity of heterocyclic aromatic amines and their mixture in metabolically competent HepaRG cells. Toxicol. Appl. Pharm. 245, 256–263.

Ewels, P.A., et al., 2020. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278.

Garcia, Caipa, A. L., et al., 2024. Tissue Organoid Cultures Metabolize Dietary Carcinogens Proficiently and Are Effective Models for DNA Adduct Formation. Chem. Res. Toxicol. 37, 234–247.

Gómez-Lechón, M.J., Tolosa, L., Conde, I., Donato, M.T., 2014. Competency of different cell models to predict human hepatotoxic drugs. Expert Opin. Drug Metab. Toxicol. 10, 1553–1568.

Goodenough, A.K., Schut, H.A.J., Turesky, R.J., 2007. Novel LC-ESI/MS/MS(n) method for the characterization and quantification of 2'-deoxyguanosine adducts of the dietary carcinogen 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine by 2-D linear quadrupole ion trap mass spectrometry. Chem. Res. Toxicol. 20, 263–276.

Greenwald, N.F., et al., 2022. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40, 555–565.

Gripon, P., et al., 2002. Infection of a human hepatoma cell line by hepatitis B virus. Proc. Natl. Acad. Sci. U. S. A. 99, 15655–15660.

Guengerich, F.P., 1997. Comparisons of catalytic selectivity of cytochrome P450 subfamily enzymes from different species. Chem. Biol. Inter. 106, 161–182.

Guengerich, F.P., Parikh, A., Turesky, R.J., Josephy, P.D., 1999. Inter-individual differences in the metabolism of environmental toxicants: cytochrome P450 1A2 as a prototype. Mutat. Res. 428, 115–124.

Le Hegarat, L., et al., 2014. Performance of comet and micronucleus assays in metabolic competent HepaRG cells to predict in vivo genotoxicity. Toxicol. Sci. Off. J. Soc. Toxicol. 138, 300–309.

Le Hegarat, L., et al., 2010. Assessment of the genotoxic potential of indirect chemical mutagens in HepaRG cells by the comet and the cytokinesis-block micronucleus assays. Mutagenesis 25, 555–560.

Hein, D.W., 2002. Molecular genetics and function of NAT1 and NAT2: role in aromatic amine metabolism and carcinogenesis, 506–507 Mutat. Res. 65–77.

Higuchi, Y., et al., 2016. Functional polymer-dependent 3D culture accelerates the differentiation of HepaRG cells into mature hepatocytes. Hepatol. Res. Off. J. Jpn. Soc. Hepatol. 46, 1045–1057.

IARC, 1986. Furocamarins and Ultraviolet Radiation. Some Nat. Occur. Synth. Food Compon. 40.

IARC, 1993. Some Naturally Occurring Substances: Food Items and Constituents. Heterocycl. Aromat. Amines Mycotoxins 56.

Ingelman-Sundberg, M., Sim, S.C., Gomez, A., Rodriguez-Antona, C., 2007. Influence of cytochrome P450 polymorphisms on drug therapies: pharmacogenetic, pharmacopeigenetic and clinical aspects. Pharmacol. Ther. 116, 496–526.

Jossé, R., Rogue, A., Lorge, E., Guillouzo, A., 2012. An adaptation of the human HepaRG cells to the in vitro micronucleus assay. Mutagenesis 27, 295–304.

Kopp, B., et al., 2024. Liver-on-chip model and application in predictive genotoxicity and mutagenicity of drugs. Mutat. Res. Genet. Toxicol. Environ. Mutagen 896, 503762.

Langouët, S., et al., 2001. Metabolism of 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline in human hepatocytes: 2-amino-3-methylimidazo[4,5-f]quinoxaline-8-carboxylic acid is a major detoxification pathway catalyzed by cytochrome P450 1A2. Chem. Res. Toxicol. 14, 211–221.

Leite, S.B., et al., 2012. Three-dimensional HepaRG model as an attractive tool for toxicity testing. Toxicol. Sci. Off. J. Soc. Toxicol. 130, 106–116.

Leite, S.B., et al., 2016. Novel human hepatic organoid model enables testing of drug-induced liver fibrosis in vitro. Biomaterials 78, 1–10.
Mah, L.-J., El-Osta, A., Karagiannis, T.C., 2010. gammaH2AX: a sensitive molecular marker of DNA damage and repair. Leukemia 24, 679-686.

Mandon, M., Huet, S., Dubreil, E., Fessard, V., Le Hégarat, L., 2019. Three-dimensional HepaRG spheroids as a liver model to study human genotoxicity in vitro with the single cell gel electrophoresis assay. Sci. Rep. 9, 10548.

Mueller, D., Krämer, L., Hoffmann, E., Klein, S., Noor, F., 2014. 3D organotypic HepaRG cultures as in vitro model for acute and repeated dose toxicity studies. Toxicol. Vitr. 28, 104-112.

Murayama, N., Usui, T., Slawny, N., Chesné, C., Yamazaki, H., 2015. Human HepaRG cells can be cultured in hanging-drop plates for cytochrome P450 induction and function assays. Drug Metab. Lett. 9, 3-7.

Nauwelaers, G., et al., 2011. DNA Adduct Formation of 4-Aminobiphenyl and Heterocyclic Aromatic Amines in Human Hepatocytes. Chem. Res. Toxicol. 24, 913-925.

Nauwelaërs, G., Bellamri, M., Fessard, V., Turesky, R.J., Langouët, S., 2013. DNA adducts of the tobacco carcinogens 2-amino-9H-pyrido[2,3-b]indole and 4-aminobiphenyl are formed at environmental exposure levels and persist in human hepatocytes. Chem. Res. Toxicol. 26, 1367-1377.

OECD Test Guideline No. 489: In Vivo Mammalian Alkaline Comet Assay. (OECD, 2016). doi:10.1787/9789264264885-en.

OECD Test Guideline No. 474: Mammalian Erythrocyte Micronucleus Test. (OECD, 2016). doi:10.1787/9789264264762-en.

Oleaga, C., et al., 2021. A functional long-term 2D serum-free human hepatic in vitro system for drug evaluation. Biotechnol. Prog. 37, e3069.

Ott, L.M., Ramachandran, K., Stehno-Bittel, L., 2017. An Automated Multiplexed Hepatotoxicity and CYP Induction Assay Using HepaRG Cells in 2D and 3D. *SLAS Discov.* Adv. Life Sci. R. D. 22, 614-625.

Pécot, T., Alekseyenko, A., Wallace, K., 2021. A deep learning segmentation strategy that minimizes the amount of manually annotated images. F1000Research 10, 256.

Pécot, T., Cuitiño, M.C., Johnson, R.H., Timmers, C., Leone, G., 2022. Deep learning tools and modeling to estimate the temporal expression of cell cycle proteins from 2D still images. PLoS Comput. Biol. 18, e1009949.

Pezdirc, M., Zegura, B., Filipić, M., 2013. Genotoxicity and induction of DNA damage responsive genes by food-borne heterocyclic aromatic amines in human hepatoma HepG2 cells. Food Chem. Toxicol. 59, 386-394.

Platel, A., et al., 2020. Study of in vitro and in vivo genotoxic effects of air pollution fine (PM2.5-0.18) and quasi-ultrafine (PM0.18) particles on lung models. Sci. Total Environ. 711, 134666.

Quesnot, N., et al., 2016. Evaluation of genotoxicity using automated detection of γH2AX in metabolically competent HepaRG cells. Mutagenesis 31, 43-50.

Ramaiahgari, S.C., et al., 2017. Three-Dimensional (3D) HepaRG Spheroid Model With Physiologically Relevant Xenobiotic Metabolism Competence and Hepatocyte Functionality for Liver Toxicity Screening. Toxicol. Sci. Off. J. Soc. Toxicol. 160, 189-190.

Rennert, K., et al., 2015. A microfluidically perfused three dimensional human liver model. Biomaterials 71, 119-131.

Robinson, M.D., McCarthy, D.J., Smyth, G.K., 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinforma. Oxf. Engl. 26, 139-140.

Rogakou, E.P., Pilch, D.R., Orr, A.H., Ivanova, V.S., Bonner, W.M., 1998. DNA double-stranded breaks induce histone H2AX phosphorylation on serine 139. J. Biol. Chem. 273, 5858-5868.

Rose, S., et al., 2021. Generation of proliferating human adult hepatocytes using optimized 3D culture conditions. Sci. Rep. 11, 515.

Rose, S., et al., 2022. DMSO-free highly differentiated HepaRG spheroids for chronic toxicity, liver functions and genotoxicity studies. Arch. Toxicol. 96, 243-258.

Rubiano, A., et al., 2021. Characterizing the reproducibility in using a liver microphysiological system for assaying drug toxicity, metabolism, and accumulation. Clin. Transl. Sci. 14, 1049-1061.

Sasaki, Y.F., Nishidate, E., Izumiyama, F., Matsusaka, N., Tsuda, S., 1997. Simple detection of chemical mutagens by the alkaline single-cell gel electrophoresis (Comet) assay in multiple mouse organs (liver, lung, spleen, kidney, and bone marrow). Mutat. Res. 391, 215-231.

Schmidt, U., Weigert, M., Broaddus, C., Myers, G., 2018. Cell Detection with Star-Convex Polygons. in, 11071. Springer Nature, pp. 265-273.

Seo, J.-E., et al., 2022. Evaluation of an in vitro three-dimensional HepaRG spheroid model for genotoxicity testing using the high-throughput CometChip platform. ALTEX 39, 583-604.

Seo, J.-E., et al., 2023. High-throughput micronucleus assay using three-dimensional HepaRG spheroids for in vitro genotoxicity testing. Arch. Toxicol. 97, 1163-1175.

Sieber, O.M., Heinimann, K., Tomlinson, I.P.M., 2003. Genomic instability—the engine of tumorigenesis? Nat. Rev. Cancer 3, 701-708.

Stringer, C., Wang, T., Michaelos, M., Pachitariu, M., 2021. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100-106.

Sugimura, T., Wakabayashi, K., Nakagama, H., Nagao, M., 2004. Heterocyclic amines: Mutagens/carcinogens produced during cooking of meat and fish. Cancer Sci. 95, 290-299.

Tice, R.R., et al., 2000. Single cell gel/comet assay: guidelines for in vitro and in vivo genetic toxicology testing. Environ. Mol. Mutagen 35, 206-221.

Tinevez, J.-Y., et al., 2017. TrackMate: An open and extensible platform for single-particle tracking. Methods San. Diego Calif. 115, 80-90.

Tostões, R.M., et al., 2012. Human liver cell spheroids in extended perfusion bioreactor culture for repeated-dose drug testing. Hepatol. Baltim. Md 55, 1227-1236.

Turesky, R.J., et al., 1998. Activation of heterocyclic aromatic amines by rat and human liver microsomes and by purified rat and human cytochrome P450 1A2. Chem. Res. Toxicol. 11, 925-936.

Turesky, R.J., 2002. Heterocyclic aromatic amine metabolism, DNA adduct formation, mutagenesis, and carcinogenesis. Drug Metab. Rev. 34, 625-650.

Uno, Y., Kojima, H., Hayashi, M., 2015. The JaCVAM-organized international validation study of the in vivo rodent alkaline comet assay. Mutat. Res. Genet. Toxicol. Environ. Mutagen 786-788.

Varet, H., Brillet-Guéguen, L., Coppée, J.-Y., Dillies, M.-A., 2016. SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data. PloS One 11, e0157022.

Wilson, A., et al., 2021. Transforming early pharmaceutical assessment of genotoxicity: applying statistical learning to a high throughput, multi end point in vitro micronucleus assay. Sci. Rep. 11, 2535.

Witte, I., Plappert, U., de Wall, H., Hartmann, A., 2007. Genetic toxicity assessment: employing the best science for human safety evaluation part III: the comet assay as an alternative to in vitro clastogenicity tests for early drug candidate selection. Toxicol. Sci. Off. J. Soc. Toxicol. 97, 21-26.