Multi-lineage hepatic organoids reveal toxic exosome mediated indirect hepatotoxicity

Domain: chemistry
Published: 2026-02-19
Editorial outcome: major_revision

Abstract
Indirect hepatotoxicity remains a major challenge in drug development because of limited evaluation tools and mechanistic insight. Here, we construct three-dimensional multi-lineage hepatic organoids comprising five liver cell types derived from human embryonic stem cells, and in a screen of 58 hepatotoxic drugs, identify imipramine as an inducer of indirect hepatotoxicity. Imipramine specifically engages tyrosine kinase receptor B expressed by non-parenchymal hepatic stellate cells, activating the p53/hnRNPA2B1/DGCR8 pathway, which drives selective enrichment of microRNA-34a-3p in hepatic stellate cell–derived exosomes, thereby converting them into toxic exosomes. These toxic exosomes transfer microRNA-34a-3p to hepatocytes, where it disrupts cellular homeostasis by downregulating the anti-apoptotic protein XIAP, thereby activating caspase 3 and inducing apoptosis. Consistently, imipramine long-term gavage in vivo triggers hepatic stellate cell apoptosis and toxic exosome–mediated hepatocyte injury without direct hepatocyte toxicity. Our findings establish a biomimetic organoid platform for precision drug testing and highlight the central role of intercellular communication in drug-induced liver injury.

Introduction
Drug-induced liver injury (DILI) is common in clinical practice and can lead to drug accumulation, toxic reactions, undesired efficacy, increasing morbidity and mortality rates. Therefore, assessing the hepatotoxicity of a drug is critical in drug development.

DILI is usually divided into three categories: direct hepatotoxicity, idiosyncratic hepatotoxicity (iDILI), and indirect hepatotoxicity. Direct hepatotoxicity results from the inherent toxicity of the drug itself on hepatocytes (HEPs) (e.g., acetaminophen), which is dose-dependent. iDILI is unpredictable, dose-independent, and influenced by factors such as genetics and immune response. For instance, diclofenac causes severe liver injury only in a subset of patients. In contrast, indirect hepatotoxicity is a type of liver injury caused by drugs, which occur through more complex mechanisms. Rather than the direct toxic effect of the drug on HEPs, indirect hepatotoxicity is caused by drug-induced side effects, such as immune activation or immune dysregulation. For example, antipsychotic drugs (e.g., risperidone and haloperidol) and immune checkpoint inhibitors (e.g., anti-PD-1/anti-PD-L1, nivolumab) may indirectly cause liver injury by triggering liver inflammation, while tumor necrosis factor antagonists (e.g., infliximab) may cause an imbalance in immune regulation, further exacerbating liver injury.

Current model systems for evaluating hepatotoxicity mainly include in vivo animal models, in vitro 2D and 3D HEPs cultures. Animal models can capture complex biological effects, but are costly and prone to interspecies variability. 2D cultures are suitable for high-throughput screening but fail to adequately replicate the structure and functions of the liver. In recent years, liver organoids technology has significantly improved the accuracy of liver function simulation and hepatotoxicity prediction. A variety of cell sources, including embryonic stem cells, induced pluripotent stem cells (iPSCs), mouse and human Lgr5+ liver stem cells, and primary HEPs, have been used to generate liver organoids. These models have demonstrated key hepatic functions, such as bile acid synthesis, cytochrome P450 activity, and drug metabolism, making them increasingly valuable in drug safety testing and disease modeling. In vitro, Akbari et al. developed organoids using EpCAM-positive cells for studying metabolic disorders like urea cycle defects. Hans Clevers’ group demonstrated the long-term culture capability of adult stem cell organoids, while Koike et al. constructed multi-organ organoids that modeled the liver, gallbladder, and pancreas. Rie et al. further developed a liver organoid model composed of three cell types that can induce fatty degeneration and inflammation, providing a precise platform for pathological mechanism research and drug screening. In vivo, iPSCs-derived vascularized human liver organoids were successfully transplanted into mice and exhibited liver function. Yang et al. constructed 3D bio-printed liver organoids, which not only restored liver function but also significantly prolonged the survival of mice with liver failure. These advancements underscore the potential of liver organoids for drug screening and disease modeling.

Recent work has further demonstrated that both stem cell-derived hepatocyte-like cells (HLCs) and multicellular liver organoids are effective platforms for assessing DILI. Studies using HLCs have shown their ability to model a range of hepatotoxic responses, including idiosyncratic injury mechanisms that are often difficult to reproduce in conventional systems. Liver organoids derived from human pluripotent stem cells have been shown to recapitulate complex liver injury phenotypes, such as steatosis and fibrosis, and have proven useful for evaluating both toxicity and anti-fibrotic therapeutic responses. Notably, a 2025 study highlighted the importance of incorporating non-parenchymal liver cells—such as Kupffer cells and hepatic stellate cells (HSCs)—into organoid systems to better capture inflammatory and fibrogenic responses under toxic stress. Collectively, these advances underscore the growing utility of stem cell-based liver models for predicting human-specific hepatotoxicity, especially in the context of complex or indirect injury mechanisms.

Nevertheless, despite these advancements, challenges remain. For example, most organoids did not incorporate key non-parenchymal cells (e.g., Kupffer cells KCs, endothelial cells ECs, Cholangiocytes CHOs and HSCs), hindering the accurate replication of the intricate microenvironment of the liver. In addition, current in vitro liver organoid-based hepatotoxicity testing often focuses on the direct effects of drugs on HEPs, neglecting the intercellular communication and interactions that are crucial in indirect hepatotoxicity. In this complex network, extracellular vesicles, particularly exosomes, play a critical role as mediators of intercellular signaling. Exosomes containing miRNAs and proteins secreted by donor cells can significantly influence the gene expression and function of recipient cells. Recent studies have shown that miRNA-transferring exosomes can cause cellular damage to surrounding cells by forming tumor microenvironment, regulating inflammatory responses, and initiating cell death.

In this study, we developed a biomimetic three-dimensional multi-lineage hepatic organoids (3D MLHOs) comprising five major hepatic cell types in the liver. Through drug library screening, we identified compounds with indirect hepatotoxic potential and explored exosome-mediated hepatocellular injury involving miRNAs. This work enhances the understanding of drug-induced indirect hepatotoxicity and facilitates the prediction and assessment of hepatotoxicity in the early stages of drug development. Furthermore, our findings identify an exosome-based signaling axis, tyrosine kinase receptor B (TRKB)–microRNA-34a-3p (miR-34)–XIAP, that mediates HEPs apoptosis in response to certain drugs.

Results
Generation of five hepatic cell types and 3D MLHOs from hESCs
Human embryonic stem cells (hESCs) were differentiated into major hepatic cell types through a stepwise protocol mimicking liver development. For endoderm-derived lineages, differentiation proceeded through definitive endoderm (DE) formation, hepatoblast (HB) development, and subsequent maturation into either HEPs or CHOs. Treatment with Oncostatin M (OSM), HEPs growth factor (HGF), and dexamethasone (Dex) induced hepatic maturation, as indicated by the upregulation of ALB, CYP3A4, AFP, and HNF4α in HEPs. Meanwhile, the pluripotency marker OCT4 was downregulated during differentiation. Functional bile canaliculi formation and biliary efflux activity were evident in HEPs at day 25 to 35. CHOs induction was achieved using HGF, epidermal growth factor (EGF), and transforming growth factor-β1 (TGFβ1), resulting in elevated expression of CK19, SOX9, AFP, and HNF6, with peak levels on day 25. For mesoderm-derived lineages, cells were first induced into mesoderm with upregulation of CD31, CD34, LYVE1, FLI1, and CDH5. HSCs were generated by subsequent exposure to fibroblast growth factors (FGF1, FGF3), retinol (RE), and palmitic acid (PA), which promoted the expression of PDGFR-α, Desmin, ACTA2, and Vimentin. To further confirm HSCs lineage fidelity, we evaluated both molecular and functional markers. Flow cytometric analysis showed strong surface expression of NGFR (55.3%) and PDGFR-β (63.1%) two well-established HSCs-specific markers. In addition, functional validation was performed by assessing vitamin A accumulation, a defining feature of quiescent HSCs. As shown in autofluorescence imaging, abundant vitamin A-positive cytoplasmic vesicles were observed, supporting the acquisition of functional HSCs identity. KCs were induced through cytokine-based protocols and exhibited characteristic marker expression, including CD68, CD163, CD11b, and CD14. Differentiation of all five hepatic lineages was further validated by morphology and immunofluorescence staining. Flow cytometry confirmed the marker profiles of each lineage: HEPs expressed ALB (65.9%) and CYP3A4 (52.6%); CHOs expressed CK19 (67.9%) and SOX9 (69.2%); ECs expressed CD31 (73.9%) and LYVE1 (57.2%) and KCs expressed CD163 (67.8%) and CD68 (54.1%). Each cell type was subsequently purified by fluorescence-activated cell sorting (FACS) and assembled into 3D MLHOs using PEG-modified low-attachment plates at liver-mimetic ratios (60% HEPs, 10% each of HSCs, ECs, KCs, and CHOs; total 3.0 × 10^4 cells per well). After 2 days, cells aggregated into compact spheroids and maintained tissue integrity for over 1 month. The spheroid size remained consistent throughout the 35-day culture, with no detectable surface disruption. Immunofluorescence analysis revealed tubular structures expressing CK19 and CD31, surrounded by dispersed KCs, suggesting bile duct and vascular formation. By day 35, the 3D MLHOs preserved an near-spherical geometry, evidenced by an aspect ratio of 0.87 ± 0.10 and a uniform diameter of 914.49 ± 62 μm, consistent with the stable spheroid dimensions observed over time. Despite this morphological steadiness, the 3D MLHOs exhibited modest net proliferation under defined long-term, passage-free conditions, increasing from 3.0 × 10^4 cells at day 0 to (4.9 ± 0.08) × 10^4 at day 30 and (5.0 ± 0.07) × 10^4 at day 35, after which proliferation approached a plateau. Flow cytometry at day 30 showed stable cell-type composition, with HEPs comprising 53.9%, closely matching the initial seeding ratio. Albumin secretion and cell viability were maintained throughout the culture period, indicating functional and structural stability of the 3D MLHOs.

3D MLHOs recapitulate biomimetic hepatic functions
To assess the hepatic functionality and biomimetic features of the 3D MLHOs, we performed a systematic characterization using 2D HEPs and 3D HEPs as controls. The two HEPs-only systems provided baseline and 3D-structural comparisons, enabling us to determine the extent to which 3D MLHOs recapitulate physiological liver functions. We first compared their gene expression profiles with those of human liver tissue using bulk RNA sequencing. Clustering analysis results showed that among the three liver models, the gene expression pattern of 3D MLHOs was closer to that of human liver tissue. Quantitative correlation analysis indicated that among the in vitro models, the 3D MLHOs have the highest correlation with human liver tissue at 0.8, followed by 3D HEPs at 0.71, while 2D HEPs have a correlation of only 0.33. To assess hepatic functional maturation, we performed GO-based gene set enrichment analysis across 2D HEPs, 3D HEPs, 3D MLHOs, and human liver. The bulk analysis revealed a clear hierarchy of liver-related programs—human liver > MLHOs > 3D HEPs > 2D HEPs—with strongest enrichment in canonical hepatic functions, including mitochondrial translation and gene expression, cytoplasmic translation/protein secretion, cellular responses to nutrient metabolism, and PI3K/AKT signaling. In parallel, structural and microenvironmental pathways—extracellular matrix (ECM) organization, extracellular structure/adhesion, adherens junction organization, WNT and TGFβ signaling—were markedly upregulated in 3D MLHOs and human liver but weak or absent in 2D HEPs, consistent with the requirement for a multi-lineage, 3D architecture. To determine cellular origins, we projected these GO terms onto our 3D MLHOs single-cell atlas, which assigned HEPs-dominant pathways to HEPs clusters and mapped ECM/adhesion and immune processes to HSCs/ECs and KCs populations, respectively. A cell-type-weighted single-cell GSEA further recapitulated the same hierarchy across conditions and confirmed that multi-lineage-dependent pathways are selectively restored in 3D MLHOs, thereby linking the bulk enrichments to their specific cellular sources. Subsequently, to assess the metabolic functions, we measured the basal activities of CYP3A4, CYP1A2, and CYP2C9. It was found that the 3D MLHOs retained consistent CYP3A4, CYP1A2, and CYP2C9 activities during the culture period. Compared to human 2D HEPs, the activities of CYP3A4, CYP1A2, and CYP2C9 in 3D MLHOs were approximately six times higher, and about 1.5 times higher than those in 3D HEPs. Furthermore, we measured liver functions, including albumin secretion, urea synthesis, lipid synthesis, and glycogen synthesis. Albumin secretion in the 3D MLHOs remained stable starting from day 7 and reached the highest level at 6 μg/day/10^6 cells, whereas albumin secretion in 2D HEPs and 3D HEPs remained constant at 1 μg/day/10^6 cells and 4–5 μg/day/10^6 cells, respectively. Similarly, the capacities for urea synthesis, lipid metabolism, and glycogen synthesis in the 3D MLHOs were significantly higher than those in the 3D HEPs and 2D HEPs, demonstrating superior and stable liver functions within 3D MLHOs.

We further investigated the response of 3D MLHOs to inflammatory stimuli by treating them with 10 μg/ml lipopolysaccharide (LPS) for 24 h. Secretion of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α was enhanced. However, the response towards inflammatory stimulation was significantly higher in 3D MLHOs compared with 2D and 3D HEPs. To verify that the elevated levels of proinflammatory cytokines in 3D HEPs were due to an intrinsic HEPs inflammatory response rather than nonspecific cytotoxic effects or immune cell contamination, we performed confirmatory experiments. First, stimulation with 1 μg/mL (LPS-L) and 15 μg/mL (LPS-H) LPS induced a dose-dependent increase in TNF-α secretion, whereas co-treatment with TNF-α neutralizing antibodies (LPS-H+nAbs) significantly reduced TNF-α levels. Second, flow cytometric analysis of Annexin V and PI staining showed no significant differences in apoptotic cell percentages across all conditions. These results indicate that 3D HEPs spheroids mount a specific, LPS-dependent inflammatory response independent of cytotoxicity or immune cell contamination. In conclusion, 3D MLHOs exhibited biomimetic transcriptomic profile and depicted stronger hepatic functions compared to 2D HEPs or 3D HEPs.

Screening using 3D MLHOs identified drugs with indirect hepatotoxicity
To evaluate the effectiveness of 3D MLHOs in predicting the hepatotoxicity of drug candidates, we selected 58 drugs used in clinical practice. The library comprised 24 drugs not associated with clinical liver injury and 34 compounds directly linked to human hepatotoxicity events. The selected DILI positive compounds were shown to induce various liver injury patterns. Notably, the doses of DILI-negative and DILI-positive drugs were selected to be within similar therapeutic ranges. To emulate clinically relevant systemic exposure, each compound was tested at 1× human clinical Cmax under continuous 14-day exposure with medium renewal every 3 days. On day 14, cell viability was quantified using the CellTiter-Glo ATP assay. Hepatotoxicity was predefined as viability <80% of control with statistical significance (p < 0.05). Among the 34 known DILI-positive compounds, 28 were identified as hepatotoxic under the screening with 3D MLHOs, resulting in a sensitivity of 82.4%. Among the 24 DILI-negative compounds, only 6 were incorrectly predicted as hepatotoxic, yielding a specificity and positive predictive value of 75%. We identified 7 hepatotoxic drugs using the 3D MLHOs, which did not significantly affect the cell viability in 3D HEPs spheroids. Among these, Imipramine (IMP) exhibited the most pronounced toxic effects within 3D MLHOs but not in 3D HEPs.

IMP dose–response across models. Under the same 14-day paradigm, IMP produced sigmoidal viability curves in 3D HEPs and 3D MLHOs (log-spaced). Four-parameter logistic fits yielded TC50 (95% CI) values that were lower in 3D MLHOs than in 3D HEPs, consistent with greater apparent potency in the multi-lineage context. GW4869, an inhibitor of neutral sphingomyelinase that is widely used to block exosome production, lowered particle counts and partially rescued organoid viability (right-shift and increased TC50), whereas its effect in 3D HEPs was limited, aligning with exosome-mediated, cell-interaction-dependent mechanisms. IMP is a tricyclic antidepressant commonly used to treat depression and is widely applied in clinical practice. Studies have shown that IMP may cause DILI, which usually manifests as cholestatic or mixed liver injury. In severe cases, it may cause hepatocellular necrosis or cholestatic jaundice, or even liver failure. The mechanism of hepatotoxicity of IMP is not yet fully understood, and may be related to the toxicity of its metabolites and immune-mediated reactions. The latest studies have shown that IMP may induce liver inflammation by activating the NLRP3 inflammasome, and increase oxidative stress and lipid peroxidation, aggravating hepatocellular damage. Therefore, the potential hepatotoxicity of IMP has increasingly drawn attention, making it a meaningful candidate for further exploration on its indirect hepatotoxicity. To determine whether the hepatotoxic effects of IMP involve inflammatory responses, we measured the secretion levels of key proinflammatory cytokines, including IL-1β, IL-6, and TNF-α, in 3D MLHOs following IMP exposure. 3D MLHOs were treated with IMP (10× Cmax, 0.87 μM) for 24 h, and cytokine concentrations in the culture supernatant were quantified using ELISA. Compared to the DMSO control, IMP treatment did not lead to a significant increase in the secretion of IL-1β, IL-6, or TNF-α. In contrast, treatment with 10 μg/mL LPS resulted in a robust upregulation of all three cytokines, confirming the responsiveness and sensitivity of the organoid model to inflammatory stimuli. These findings suggest that IMP does not elicit a classical inflammatory response in 3D MLHOs under the tested conditions. These findings indicate that the hepatotoxicity observed with IMP in our 3D MLHOs is unlikely to be mediated via classical inflammatory pathways. Instead, they support a noninflammatory, indirect mechanism signaling.

Indirect hepatotoxicity of IMP is mediated by HSCs-dependent way
To investigate how IMP indirectly induces HEPs damage, we first analyzed single-cell RNA sequencing data of the human and mouse liver published in public database. The results revealed that NTRK2 (TRKB), previously reported as a direct binding target of IMP, is highly enriched in HSCs compared to other liver-resident cell types, while its expression in ECs and HEPs was relatively low. These findings were further validated by qPCR and Western blot analysis, confirming high TRKB expression at both mRNA and protein levels, particularly in HSCs. TRKB, a neurotrophic factor receptor, is abundant in the central nervous system, supporting neuron survival, development, and function, also it plays a significant role in liver function. To assess its functional role in HSC-mediated hepatotoxicity, we treated HSCs with IMP at concentrations comparable to human plasma levels and compared responses between wild-type (WT) and TRKB-knockdown (KD) HSCs. Flow cytometry showed that IMP induced substantial apoptosis in WT HSCs, whereas TRKB-KD HSCs exhibited minimal apoptotic changes. Consistently, Western blot analysis demonstrated strong activation of caspase 3 in WT HSCs upon IMP exposure, which was absent in TRKB-KD cells. We further confirmed the results using IMP-treated HSCs, ECs, HEPs, and combinations of the two non-parenchymal cells with HEPs. The results showed significant apoptosis in HSCs and ECs under IMP treatment, but not in HEPs. In co-cultured cytotoxicity test, the combination of HEPs and HSCs showed the most severe damage. After treating HSCs and ECs with IMP for three days, we collected the supernatant and used it to treat HEPs for an additional 3 days. Flow cytometry analysis showed that the conditioned medium caused significant apoptosis in HEPs, which indicated higher indirect hepatic cytotoxicity of conditioned medium from HSCs than those from ECs. Western blot analysis further confirmed that HEPs treated with conditioned medium from HSCs showed higher activation level of caspase 3 compared to those treated with the medium from ECs. To mechanistically define the role of TRKB in this indirect pathway, we applied scRank, a computational method that simulates drug–target perturbations in single-cell resolution. The results ranked HSCs as the most responsive population to TRKB perturbation under IMP exposure, supporting their role as a primary effector cell type. In addition, in silico TRKB-knockout simulations demonstrated a substantial reduction in intercellular signaling output from HSCs, particularly toward HEPs and immune cells. This suggests that TRKB expression is essential for maintaining the ligand–receptor communication network that facilitates HSC-driven hepatotoxicity. Taken together, these data demonstrate that TRKB signaling in HSCs is required for the generation and propagation of indirect hepatotoxic signals, which lead to the activation of apoptotic pathways within HEPs.

IMP triggers toxic exosome secretion from HSCs to apoptosis of HEPs
Exosomes, as major carriers of miRNAs and other biomolecules, mediate intercellular communication, were hypothesized to participate in the induction of indirect hepatotoxicity of IMP. To determine whether the HEPs damage induced by IMP-treated HSCs is related to exosomes, we first isolated the exosomes from the supernatant of HSCs treated with IMP (0.87 μM) for 3 days. The size distribution of the exosomes was approximately 130–140 nm, with a quantity of 4.5 × 10^9 particles/mL. Transmission electron microscopy showed no morphological differences between exosomes from the control and IMP-treated groups, with diameters consistently falling within the 130–140 nm range. Western blot analysis verified that the exosomes isolated from both the control and IMP-treated groups expressed CD9 and TSG101, which are recognized exosomal markers. To evaluate whether IMP influences exosome biogenesis or composition in a dose-dependent manner, we expanded our analysis to include exosomes from HSCs treated with increasing concentrations of IMP (1×, 5×, and 10× Cmax). Nanoparticle tracking analysis (NTA) revealed no significant differences in total exosome concentration or average vesicle diameter across the dose range, suggesting that IMP does not globally alter exosome release.

To determine whether the toxic exosomes (toxic-EXOs) derived from HSCs affect HEPs apoptosis, we labeled the HSCs-derived exosomes with the red fluorescent dye Dil and observed the uptake of Dil in HEPs. Previous studies have shown that the CD9 antibody can directly interact with exosomes. Exosomes from HSCs culture medium under IMP treatment, either pre-incubated with CD9 antibody or not, were then co-incubated for 16 h. It was found that both toxic and normal exosomes were efficiently taken up by HEPs, and the addition of the CD9 antibody significantly reduced the uptake of exosomes by HEPs. This result was also confirmed by flow cytometry analysis. Furthermore, HEPs that took up toxic-EXOs exhibited significant apoptosis compared to those took up control exosomes, and the CD9 antibody could partially protect HEPs from the damage caused by toxic-EXOs.

miR-34–XIAP axis mediates exosome-dependent hepatocyte apoptosis
We observed that there was no significant difference in the diameter and quantity of exosomes between the control group and the IMP (0.87 μM)-treated group of HSCs. Additionally, the expression of the apoptosis-inhibiting gene XIAP was significantly down-regulated, while the pro-apoptotic gene caspase 3 was significantly upregulated within HEPs treated with toxic-EXOs. Therefore, the toxicity might not be caused by the quantity and size of the exosomes but possibly due to specific substances contained within the exosomes. Since the downregulation of XIAP is closely related to miRNAs, we hypothesized that the toxic-EXOs might contain high abundance of apoptosis-regulatory miRNAs. To identify which miRNA plays a dominant role in the exosomes, the TargetScan 8.0 database was first used to predict and analyze the miRNAs related to XIAP, and 2402 miRNAs that may target and inhibit XIAP mRNA were initially obtained. Further, based on the screening criteria of context++ score percentile ≥95% and context++ score ≤–0.2, 34 candidate miRNAs with high potential activity were screened out. Subsequently, these 34 miRNAs were cross-compared with 6 miRNAs known to be related to cell apoptosis, miR-34 and miR-200b-3p were identified as potential key regulatory factors. To validate this prediction, miRNAs were subsequently extracted from purified toxic-EXOs. The results showed that miR-34 was significantly upregulated in exosomes secreted by IMP-treated HSCs, with a clear dose-dependent enrichment trend. At 1× Cmax, IMP significantly increased exosome miR-34 in 3D MLHOs, whereas no significant change was observed in 3D HEPs; co-treatment with GW4869 reduced miR-34 toward baseline and lowered particle counts. With the TC50 right-shift upon GW4869, these results indicate that cargo remodeling- rather than enhanced vesicle biogenesis—underlies IMP’s indirect hepatotoxicity. In terms of functional validation, we further confirmed the direct targeting relationship between miR-34 and XIAP using a dual luciferase reporter gene assay. The results showed that miR-34 could significantly inhibit the luciferase activity containing the wild-type XIAP 3′UTR, and when its binding site was mutated, the inhibitory effect was significantly relieved, indicating high binding specificity. Compared to the control group, the expression of p53, which promotes miR-34 production, was significantly upregulated in the IMP-treated HSCs, and this process is dependent on the IMP surface receptor TRKB. In addition, among the 58 hepatotoxic compounds screened in this study, 7 (including IMP) were predicted to have potential indirect hepatotoxic effects. Diclofenac and fluconazole were selected as negative drug controls. The results showed that neither of these two drugs caused changes in the expression levels of miR-34 or XIAP and p53, further highlighting the high specificity of the miR-34/XIAP regulatory axis induced by IMP. Furthermore, proteins associated with miRNA synthesis and transport, DGCR8 and hnRNPA2B1, were also significantly upregulated. However, the expression of genes related to exosome synthesis and secretion, RAB27a, RAB27b, and SIRT1, did not change significantly, indicating that the quantity of exosome synthesis and secretion did not increase upon IMP treatment, which was consistent with the previous conclusion. Next, to explore the role of p53 in this process, we over-expressed p53 in HSCs. A significant upregulation of miR-34 was detected in both HSCs and their derived exosomes. After incubating HEPs with these exosomes for one day, severe apoptosis and notable downregulation of apoptosis inhibitor XIAP was detected. Similarly, p53 knock-down resulted in a significant downregulation of miR-34 in both HSCs and their derived exosomes, and HEPs treated with these exosomes did not undergo apoptosis nor showed significant changes in the expression level of XIAP.

In vivo IMP treatment induces HSCs but not direct HEPs apoptosis
To evaluate whether IMP-induced indirect hepatotoxicity in mice is consistent with in vitro experiments, we treated animals with saline or a moderate dose of IMP (5 mg/kg) and isolated HSCs and parenchymal HEPs at day 8, 16, and 35 to analyze cell apoptosis. The purity of isolated HSCs was verified to be greater than 98% through fluorescence microscopy and flow cytometry. Notably, HSCs exhibited mild apoptosis by day 8, whereas HEPs remained undamaged. By day 16 and 35, HSCs apoptosis was significantly enhanced, and the apoptosis level of HEPs also markedly increased, peaking at day 35. Mechanistically, we found that expression of p53 and miR-34 in mice HSCs increased significantly from day 8 and continued to rise through day 35. In contrast, miR-34 levels in HEPs only began to elevate at day 16 and peaked at day 35, while p53 levels in HEPs remained unchanged, suggesting that HEP damage may not result from direct IMP toxicity but rather from intercellular signaling mediated by HSC-derived factors. To further assess the severity and systemic impact of liver injury, we quantified serum ALT and AST levels on day 35. Both enzymes were significantly elevated in the IMP-treated mice compared to Saline controls, confirming hepatocellular injury at the biochemical level. Histological analysis using cleaved caspase 3 immunohistochemistry and TUNEL staining revealed a pronounced increase in apoptotic HEPs in the IMP group, consistent with active cell death in vivo. H&E stainings showed architectural disruption and HEPs disorganization, further corroborating liver injury. Although no mortality was observed during the 35-day treatment period, the combination of elevated liver enzymes and apoptotic markers indicates sublethal but pathophysiologically relevant hepatic stress. To verify the mechanism underlying these observations, we performed a series of targeted interventions. siRNA-mediated TRKB knockdown, whose efficiency was confirmed at the protein and mRNA levels, inhibition of exosome biogenesis using GW4869, and blocking of miR-34 function via antagomiR-34 all significantly reduced ALT/AST levels, cleaved caspase 3+ area, and TUNEL+ HEPs counts compared to the WT IMP group. These results confirm that the observed hepatotoxicity is dependent on exosome secretion and miR-34 activity downstream of TRKB signaling in HSCs. To further support this hypothesis, we performed multicolor immunofluorescence staining of liver sections to visualize the spatial dynamics of exosome distribution. In Saline-treated control livers, exosomal markers (CD9, green) were diffusely distributed, with no aggregation around Desmin+ HSCs (pink) or ALB+ HEPs (red), and nuclei (DAPI, blue) appeared normal, consistent with homeostatic intercellular communication. In contrast, liver sections from IMP-treated mice at day 35 revealed marked CD9 accumulation around Desmin+ HSCs and ALB+ HEPs. Merged images and high-magnification insets demonstrated nuclear condensation and cytoplasmic ALB aggregation in HEPs adjacent to CD9+/Desmin+ clusters, indicating localized apoptosis. These spatial patterns support a model in which IMP induces the pathological transformation of HSC-derived exosomes (toxic-EXOs), which act in a paracrine fashion to promote HEPs apoptosis. Together, these data demonstrate that IMP induces indirect hepatotoxicity by damaging HSCs and triggering exosome-mediated apoptotic signaling toward HEPs, rather than via direct cytotoxic or inflammatory mechanisms. Taken together, using 3D MLHOs, we reveal a previously unrecognized molecular mechanism of indirect hepatotoxicity, in which IMP specifically engages the membrane receptor TRKB expressed by HSCs and, via p53/hnRNPA2B1/DGCR8 signaling, drives selective miR-34 enrichment in HSCs-derived exosomes, converting them into toxic-EXOs that promote non-cell-autonomous HEPs apoptosis through intercellular communication.

Methods
Ethics statement
All experimental procedures were conducted in compliance with relevant ethical regulations and institutional guidelines. All animal experiments were approved by the Institutional Animal Care and Use Committee of Tsinghua University (approval number: THU-LARC-2025-008) and were performed in accordance with national and institutional guidelines for animal welfare. Human embryonic stem cell experiments were conducted using an established, commercially available H1 hESC line (WA01, WiCell Research Institute). No new derivation of human embryos or gametes was performed in this study, and therefore, no additional ethical approval was required.

Cell culture and differentiation
The human embryonic stem cell line H1 (WA01) was obtained from the WiCell Research Institute and maintained under feeder-free conditions on Matrigel-coated plates in mTeSR1 medium. Cells were passaged using Accutase to obtain single-cell suspensions and seeded onto plates coated with Matrigel diluted 1:100 in knockout DMEM. During passaging, 10 μM Y-27632 (ROCK inhibitor) was added to enhance cell survival. Cells were monitored daily for morphology and growth. For endothelial differentiation, hESCs were seeded at 1.2 × 10^5 cells per well in Matrigel-coated 12-well plates in mTeSR1 medium. The differentiation into mesoderm cells was as follows: on the first day, the medium was switched to AATS medium containing 5 ng/mL BMP4; on the second and third days, the medium was changed to AATS medium containing 5 ng/mL BMP4 and 10 μM CHIR99021. On the third day, the cells were dissociated into single cells using Accutase and seeded at a density of 220,000 cells per well in a new Matrigel-coated plate, using AATS medium supplemented with 10 ng/mL VEGF, 20 ng/mL bFGF, and 0.4% B27 supplement, and cultured for 3 days. To further mature the endothelial cells, 5 μM SB431542 was added to the differentiation medium for 6 days. The differentiation of hESCs into hepatocytes consisted of five stages, starting when the cells reached 90% confluence. In the first stage (3 days), hESCs were differentiated into definitive endoderm cells using RPMI1640 medium supplemented with 100 ng/mL Activin A, 3 μM CHIR99021, 1% GlutaMax, and 1% B27 supplement; for the following 2 days, the medium was switched to RPMI1640 containing 100 ng/mL Activin A, 5 ng/mL bFGF, 1% GlutaMax, and 1% B27 supplement. In the second stage (4 days), definitive endoderm cells were differentiated into hepatic endoderm cells using a medium composed of 75% IMDM, 25% Ham’s F12, 0.5% N2 supplement, 1% B27 supplement, 0.05% BSA, and 50 mg/mL ascorbic acid, supplemented with 100 ng/mL Activin A and 5 ng/mL bFGF. In the third stage (7 days), hepatic endoderm cells were differentiated into hepatoblasts using low glucose DMEM supplemented with 1% B27 supplement, 0.1% BSA, 50 mg/mL ascorbic acid, 40 ng/mL bFGF, and 50 ng/mL BMP4. In the fourth stage (6 days), hepatoblasts were differentiated into immature hepatocytes using low glucose DMEM supplemented with 1% B27 supplement, 0.1% BSA, 50 mg/mL ascorbic acid, 20 ng/mL OSM, 25 ng/mL HGF, and 0.1 μM dexamethasone. In the fifth stage (7 days), the immature hepatocytes were matured into hepatocytes using high glucose DMEM supplemented with 1% B27 supplement, 1% BSA, 50 mg/mL ascorbic acid, 20 ng/mL OSM, 25 ng/mL HGF, and 0.1 μM dexamethasone. For differentiation of hESCs into cholangiocytes, during the fifth stage of hepatocyte differentiation, the medium was switched to high glucose DMEM supplemented with 1% B27 supplement, 0.1% BSA, 50 mg/mL ascorbic acid, 50 ng/mL EGF, 25 ng/mL HGF, and 5 ng/mL TGFβ1 for 7 days. The differentiation of hESCs into Kupffer cells followed: from day 1 to day 3, the process was identical to the differentiation into endothelial cells, resulting in mesoderm cells. From day 4 to day 5, mesoderm cells were differentiated into hematopoietic endothelium in RPMI1640 medium containing 2% B27 supplement, 50 ng/mL VEGF, and 10 ng/mL bFGF, and replated to reach 90–100% confluence within 48 h. From days 6–10, hematopoietic endothelium was differentiated into hematopoietic progenitor cells using RPMI1640 medium supplemented with 2% B27, 50 ng/mL VEGF, 10 ng/mL bFGF, and 10 μM SB431542. From day 10 to day 15, free-floating hematopoietic progenitor cells were transferred to a new plate and induced to differentiate into monocytes in RPMI1640 medium containing 2% B27, 10 ng/mL IL-3, and 50 ng/mL M-CSF. From day 15 to day 20, monocytes were matured into Kupffer cells in RPMI1640 medium containing 2% B27 and 25 ng/mL M-CSF. For differentiation of hESCs into stellate cells, hESCs were seeded into a 12-well plate and, upon reaching 50% confluence, initiated the differentiation process. The basal medium consisted of 60% low glucose DMEM, 40% MCDB-201, 0.25% linoleic acid-BSA, 0.25% insulin-transferrin-selenium, 0.1 mM L-ascorbic acid, 2.5 μM dexamethasone, and 50 μM β-mercaptoethanol. The medium was changed every 48 h, with growth factors added as follows: 20 ng/mL BMP4 from day 0 to day 4, 20 ng/mL FGF1 and 20 ng/mL FGF3 from day 4 to day 8, and 5 μM retinoic acid and 100 μM palmitic acid from day 6 to day 12.

Preparation of microarray low-adsorption 48-well plates
Mix the PDMS monomer and crosslinker at a ratio of 10:1, then pour the mixture into a mold and vacuum it to remove air bubbles. Cure the mixture overnight at 65 °C. After curing, demold and immerse in 75% ethanol and wash it overnight with deionized water. Next, modify the PDMS surface by applying propylene glycol methyl ether acetate (PEGMEA) to each well. Add a crosslinking reaction solution containing 10% PEGMEA, 0.5% benzyl alcohol, and 0.5 mM sodium periodate to each well. Use a UV light source to perform the crosslinking process for 4 h. After crosslinking, rinse with deionized water to remove any unreacted colloid, then wash on a shaker for one day with water, followed by sterilization with 75% ethanol. Subsequently, treat the plates under UV light for 3 h to ensure complete evaporation of the ethanol. The resulting PEGMEA-modified low-adsorption PDMS well plates are now ready for organoid culture.

Cell viability assay
Cell viability is assessed by quantifying ATP using the CellTiter-Glo Luminescent Cell Viability Assay Kit. For a 96-well plate, 100 μL solution is used per well. Prepare control wells containing culture medium without cells to obtain background luminescence values. Add the test compound to the experimental wells and incubate according to the specified protocol. Equilibrate the plate and its contents at room temperature for approximately 30 min. Add an equal volume of CellTiter-Glo® Reagent to the cell culture medium in each well. For example, for a 96-well plate, add 100 μL of reagent to 100 μL of medium containing cells. Mix the solution using an orbital shaker for 2 min to induce cell lysis. Incubate the plate at room temperature for 10 min to stabilize the luminescent signal. Measure the luminescence using a PerkinElmer EnSight multimode plate reader.

RNA isolation and reverse transcription quantitative PCR (RT-qPCR)
Total RNA was extracted using TRIzol (Invitrogen) according to the manufacturer’s instructions. RNA was converted to cDNA using the Hiscript II qRT SuperMix Kit. Briefly, the reverse transcription reaction was performed in a total volume of 10 μL, containing 1 μg total RNA, 5 μL of 2× Hiscript II qRT SuperMix buffer, and RNase-free water to make up the volume to 10 μL. Quantitative PCR was conducted using AceQ qPCR SYBR Green Master Mix according to the manufacturer’s instructions on a Bio-Rad CFX96 real-time system. All experiments were performed in 3 or 6 replicates as indicated figure legends. The detailed sequences of the primers used for amplification are shown in Supplementary Data 3.

Immunofluorescence staining
Cells and organoids were fixed with 4% paraformaldehyde (PFA) for 20 min at room temperature to ensure structural preservation, followed by three washes with phosphate-buffered saline (PBS) to remove residual fixative. For permeabilization, a 0.5% Triton X-100 solution in PBS was applied for 15 min to facilitate access of the antibody to intracellular targets. Blocking was performed with 5% bovine serum albumin (BSA) in PBS for 1 h to minimize nonspecific antibody binding. Primary antibodies, diluted in the blocking solution, were incubated with the samples overnight at 4 °C. Following several washes to remove unbound antibodies, cells were incubated with appropriate secondary antibodies as specified for the primary antibodies. Cell nuclei were stained with Hoechst 33342 for 15 min at room temperature. Images were captured using a Nikon A1 HD25 confocal microscope, and the fluorescence intensity of organoids and 2D cells was analyzed using NIS-elements software version 5.2.

Flow cytometry
For flow cytometry, organoids containing five cell types (or other indicated combinations) were dissociated into single-cell suspensions by digestion with Accutase and collagenase I at 37 °C for 5–30 min (depending on organoid size and composition). Cells were resuspended in FACS wash buffer (PBS supplemented with 5% fetal calf serum (FCS) and 2.5 mM EDTA), incubated with the indicated fluorophore-conjugated antibodies for 30 min at 4 °C, washed, and resuspended in 0.5% bovine serum albumin (BSA) in PBS for acquisition. Data were acquired on a BD LSR Fortessa flow cytometer (BD Biosciences). Compensation was performed using single-color compensation controls. For gating, debris was excluded based on forward- and side-scatter (FSC/SSC), followed by singlet discrimination using FSC-A versus FSC-H. Positive and negative populations were defined relative to unstained controls. For apoptosis assays, Annexin V-FITC/propidium iodide (PI) quadrants were set using unstained and single-stained controls. Flow cytometry data were analyzed using FlowJo software (v10.8.1).

Liver function assay
To assess the liver-specific functions of the organoids, urea levels in the organoid culture media were quantified using a urea assay kit. After replacing the culture medium with fresh medium and culturing for 24 h, the supernatant was collected to measure albumin levels using an albumin detection kit (enzyme-linked immunosorbent assay). Consistent with the growth kinetics described in the main text, each mature organoid contained (4.9 ± 0.08) × 10^4 total cells by day 30, each well contained four mature day-30 3D MLHOs, corresponding to: 4 × 4.9 × 10^4 total cells/3D MLHOs = 1.96 × 10^5 total cells per well. Flow cytometry at day 30 HEPs = 53.9% of total cells. Thus, the HEPs count per well is: 1.96 × 10^5 total cells per well × 0.539 = 1.06 × 10^5 HEPs. Accordingly, ~1.1 × 10^5 HEPs per well was used as the normalization basis for all quantitative functional readouts, including albumin secretion and urea production. The measured albumin concentrations (ng/mL) were normalized to micrograms per day per million hepatocytes (μg/day/10^6 cells) to ensure comparability across experiments. Subsequently, 10 μg/ml LPS was added to the organoids and incubated for 24 h. After this treatment, the inflammatory cytokines IL-6, IL1β, and TNFα were measured using their respective ELISA kits. To evaluate cytochrome P450 enzyme activity, fresh culture medium was added, and CYP3A4 activity was assessed using the P450-Glo™ CYP3A4 assay kit. Additionally, CYP1A2 and CYP2C9 levels were detected using their specific ELISA kits. Next, organoids were sonicated using reagents provided by the kit, the supernatant was collected, and glycogen content was measured using a glycogen assay kit, while triglyceride levels were detected using a triglyceride assay kit. All procedures were strictly performed according to the manufacturer’s instructions to ensure accuracy and reproducibility of the results.

miRNA extraction and reverse transcription quantitative PCR (RT-qPCR)
In this study, miRNA levels were quantified by stem-loop RT-qPCR. miRNA was extracted from samples using the miRNeasy Serum/Plasma Kit (Qiagen, 217184) according to the manufacturer’s instructions, and reverse transcription was performed with the TaqMan MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific, 4366596). U6 small nuclear RNA was used as an endogenous internal reference. In addition, a synthetic Caenorhabditis elegans miRNA (cel-miR-54) was included as an exogenous spike-in control to monitor RNA extraction and RT-qPCR efficiency. The primers used for the miRNA RT-qPCR reactions are listed in Supplementary Data 3.

IMP dose-response and TC50 estimation
IMP was prepared as a 10 mM DMSO stock and diluted freshly (final DMSO ≤ 0.1%). 3D HEPs and 3D MLHOs were exposed for 14 days to a log-spaced series of IMP concentrations (0, 0.03×, 0.1×, 0.3×, 1×, 3×, 10× Cmax), with medium renewed every 3 days. Viability was read on day 14 by CellTiter-Glo and expressed as % of control. Curves were fitted by a constrained four-parameter logistic model (top = 100, bottom ≥ 0) in GraphPad Prism 9 to obtain TC50 and 95% CI.

Apoptosis assay
According to the manufacturer’s instructions (Beyotime, C1062L), apoptosis was measured using the FITC-Annexin V/PI apoptosis detection kit. Cells were collected and resuspended in the binding buffer provided by the manufacturer. Subsequently, the cells were incubated with Annexin V for 10 min at room temperature, followed by the addition of 5 µl propidium iodide (PI) staining before analysis. Apoptosis was analyzed using a BD Biosciences LSR Fortessa cell analyzer.

Exosome isolation
The exosome isolation method from cell supernatant is as follows: after treating HSCs with DMSO or IMP (0.87 μM), the cell supernatant samples were centrifuged at 2500 × g for 10 min. Then, 100 μL of the supernatant was mixed with ExoQuick solution and incubated at 4 °C for 30 min, followed by centrifugation at 1500 × g for 30 min. After the initial precipitation of exosomes with ExoQuick solution, the exosomes were resuspended and precipitated three times (1500 g, 5 min each) until a beige or white precipitate was visible at the bottom of the container. The final precipitate was resuspended in 100 µL of PBS buffer and stored at 4 °C for up to 7 days or at −80 °C for long-term storage.

Transmission electron microscopy
For negative staining, isolated exosomes were washed in PBS twice. Briefly, exosomes were mounted on copper grids, fixed by 2.5% glutaraldehyde in cold PBS for 10 min to stabilize the immunoreaction. Exosomes were washed twice in PBS, contrasted by 2% uranyl-oxalate solution at pH 7 for 15 min, embedded by methyl cellulose-UA for 10 min on ice. Images of exosome samples were recorded with an Olympus SIS Veleta CCD camera at a voltage of 80 kV.

Nanoparticle tracking analysis (NanoSightTM)
The number and size of exosomes isolated were measured by nanoparticle tracking analysis (NTA) using a NanoSight NS300 system (NanoSight, Amesbury, UK). The instrument was calibrated with 100 nm polystyrene beads (Thermo-Fisher Scientific, Fremont, CA) before analysis. NTA software was used to measure the concentration of nanoparticles (particles/mL) and the size distribution in nm. The batch process included in the NTA software was used to integrate the three technical measurements of each sample. The mean vesicle size as well as the concentration of each preparation were obtained from analysis and corrected by the dilution factor when required. Each sample was measured at least 3 times.

Hepatocytes and hepatic stellate cells isolation and culture
After perfusion, the mice liver is removed and gently minced with the tip of a syringe. The tissue is then incubated in 15 ml of high-glucose DMEM medium containing 2 mg/ml DNase I, 0.34 mg/ml collagenase IV, and 0.5 mg/ml protease E at 37 °C for 25 min, with gentle up-side down shaking every 5 min. The resulting cell suspension is filtered through a 100 μm sterile cell strainer and centrifuged at 580 × g for 10 min at 4 °C. The supernatant, containing the non-parenchymal cell layer, is collected. The pelleted cells are washed twice with D-Hanks solution and centrifuged at 45 × g for 2 min, then resuspended. Hepatocyte suspensions are seeded at a density of 1 × 10^6/ml in six-well plates pre-coated with type I collagen (Corning) and cultured in the medium at 37 °C in a 5% CO2 environment. The medium is changed 4–8 h later, and then every 2 days thereafter, or proceed directly to the next experiment. To collect the supernatant rich in non-parenchymal cells, it is centrifuged at 580 × g for 10 min at 4 °C. The pellet is resuspended in 32 ml GBSS-B and mixed with 16 ml Nycodenz. Slowly add 2 ml GBSS-B to the mixture, then centrifuge at 1380 × g for 17 min at 4 °C. After centrifugation, hepatocytes form a pellet at the bottom of the tube, while HSCs form a white ring in the middle layer. Carefully collect the middle layer containing HSCs, wash with GBSS-B buffer, and re-centrifuge at 580 × g for 10 min at 4 °C. Discard the supernatant and resuspend the HSCs in HSC culture medium. Seed HSCs at a density of 5 × 10^5/ml in culture dishes. Culture medium is changed after 24 h, and then every other day thereafter, or proceed directly to the next experiment.

Animal treatment and tissue analysis
Male C57BL/6J mice (8 weeks old) were housed under specific pathogen-free conditions, with six mice per cage and ad libitum access to food and water. The animal facility was maintained at 22–24 °C with 40–60% humidity under a 12 h light/12 h dark cycle (light on at 06:00 and off at 18:00). All animal procedures were approved by the Institutional Animal Care and Use Committee of Tsinghua University (approval number: THU-LARC-2025-008) and were conducted in accordance with institutional and national guidelines for animal welfare.

Mice were randomly assigned to saline or IMP treatment groups. IMP was administered by oral gavage at a dose of 5 mg/kg every other day for 35 consecutive days, while control mice received equal volumes of saline. At days 8, 16, and 35, mice were euthanized by carbon dioxide (CO2) asphyxiation, followed by cervical dislocation as a secondary method to ensure death, in accordance with institutional IACUC guidelines. Livers were immediately harvested for HEPs and HSCs isolation using in situ collagenase perfusion, followed by differential centrifugation and Nycodenz density gradient separation. Cell purity was assessed by fluorescence microscopy and Desmin-based flow cytometry, with HSCs purity exceeding 98%.

Apoptosis was evaluated using Annexin V/propidium iodide (PI) staining, TUNEL assays, and immunohistochemical detection of cleaved caspase-3. Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were measured on day 35 using commercial enzymatic assay kits to assess liver injury. For molecular analyses, total RNA was extracted from purified cells and subjected to RT–qPCR to quantify p53 and miR-34a expression, normalized to GAPDH and U6, respectively. Liver histology was examined by hematoxylin and eosin (H&E) staining.

Multicolor immunofluorescence staining was performed to visualize spatial interactions among Desmin+ HSCs, ALB+ HEPs, and CD9+ exosomes, with DAPI used for nuclear counterstaining. Images were acquired using confocal microscopy and analyzed for signal quantification.

To investigate the mechanistic involvement of the TRKB–miR-34a–exosome axis, additional cohorts of mice received TRKB-targeting siRNA (5 mg/kg, tail vein injection, every 4 days), GW4869 (2.5 mg/kg, intraperitoneal injection, every 3 days), or antagomiR-34a-3p (10 mg/kg, tail vein injection, once weekly). These interventions were initiated 3 days prior to IMP administration and continued throughout the gavage period. At study endpoints, serum biomarkers, histological markers of apoptosis, and immunostaining results were quantified to evaluate therapeutic efficacy.

Drug treatment and concentration selection
For compound screening, a panel of 58 drugs with known or suspected associations with indirect DILI was selected. The goal was not to determine precise cytotoxic potency (e.g., TC50), but rather to evaluate the capability of the organoid platform to detect hepatotoxic signatures under physiologically relevant or exaggerated exposure conditions. All chemical compounds used in this study were purchased from Selleck Chemicals (Houston, TX, USA). Compounds were dissolved according to the manufacturer’s instructions, typically in DMSO, and further diluted in serum-free medium to achieve the desired final test concentrations. The final DMSO concentration did not exceed 0.5% (v/v) in any experimental condition. To guide dose selection, we referenced the reported maximum plasma concentrations (Cmax) in humans for each compound, sourced from published pharmacokinetic data. Test concentrations were generally set at or above the reported Cmax to mimic clinically relevant or supra-therapeutic exposures. This Cmax-based approach has been widely adopted in prior hepatotoxicity modeling studies and is particularly suited for evaluating the translational relevance of in vitro liver models. Exposure was carried out over a 14-day period, with medium renewed every 72 h to simulate repeated dosing regimens used in preclinical studies. Details of Cmax values, test concentrations, and compound categories are summarized in Supplementary Data 1. Unless otherwise stated, experiments were repeated at least three times independently, and vehicle controls were included for each drug.

Single-cell transcriptomic analysis
To elucidate the hepatic cellular targets of IMP, we conducted an integrative analysis of publicly available single-cell RNA sequencing (scRNA-seq) datasets from both human and mouse liver tissues. Human liver scRNA-seq data were obtained from the Gene Expression Omnibus (GEO) database (accession numbers: GSE136103: GSM4041150-GSM4041159). Mouse liver data were retrieved under the GEO accession number GSE171993.

Raw count matrices were preprocessed using the Seurat package (v4.4.0) in R. Following standard quality control procedures and data normalization, highly variable genes were selected. Datasets were integrated via canonical correlation analysis (CCA) to account for batch effects. Dimensionality reduction was performed using principal component analysis (PCA), and clustering and visualization were performed using t-distributed stochastic neighbor embedding (t-SNE) to map high-dimensional single-cell transcriptomic data into two-dimensional space. Cell type annotation was carried out based on the expression of canonical cell-specific markers.

To predict cell-type-specific responses to TRKB inhibition, we employed the scRank framework, a computational model designed to infer drug perturbation effects at single-cell resolution. This approach calculates response scores by integrating target gene expression levels, gene network topology, and downstream transcriptional responses. Based on these analyses, HSCs were identified as the most responsive cell population to TRKB-targeted perturbation under IMP exposure.

To further explore the intercellular consequences of TRKB inhibition in HSCs, we utilized the si-scRNA signaling inference model. In silico TRKB knockout simulations were performed in HSCs to assess alterations in intercellular communication. Changes in ligand-receptor signaling pathways were analyzed to predict downstream effects on neighboring hepatic cell types. This analysis revealed a potential TRKB–miR-34a–XIAP-caspase signaling axis involved in exosome-mediated hepatocyte apoptosis.

Definition of hepatotoxicity threshold
Hepatotoxicity was defined as a statistically significant reduction in cell viability to below 80% of vehicle (DMSO) control levels, as measured by an ATP-based luminescent viability assay. A two-sided t-test (p < 0.05) was used to determine significance. The 80% threshold was selected based on empirical analysis of control-normalized response distributions across our compound panel. This cut-off was chosen to ensure sufficient sensitivity for early toxic signals while maintaining high specificity. Viability outcomes for all tested compounds are summarized in Supplementary Data 2.

Biliary efflux assay
The cells were incubated for 10 min in a maintenance medium supplemented with 10 μg/mL FDA (Sigma-Aldrich, F7378). After changing to a fresh maintenance medium, the cells were incubated to excrete fluorescein for 20 or 30 min. Then, after moving the cells to fresh maintenance medium, the cells were incubated for a further 30 min to allow excretion to occur. Images were captured with a Nikon A1 HD25 confocal microscope.

Dual luciferase reporter assays
To generate vector PGL3-XIAP (WT-3′UTR, 781, 1140) or PGL3-XIAP (MUT-3′UTR, miR-34 binding site, a template gene was synthesized (Beijing Xianghong Biotechnology Co., Ltd.), and a DNA fragment was inserted into the pGL3-basic vector through the XbaI and FseI sites, and confirmed by sequencing. HSCs were transfected with 500 ng of luciferase reporter plasmid and miR-34 mimics. Cells were grown in 24-well plate. After 48 h, cells were collected and luciferase activity was evaluated by the Dual-Luciferase Reporter Assay System (RG089S, Dual-Lumi™ II), All of procedures were executed in strict adherence to the manufacturer’s instructions.

Preparation of 3D MLHOs
To construct 3D MLHOs in vitro, we differentiated human hESCs into mature HEPs, CHOs, ECs, HSCs, and KCs, and digested them into single cells using Accutase. The dissociated cells were resuspended in an extracellular matrix, maintaining proportions that closely reflect the natural cellular composition of the human liver, resulting in organoids primarily composed of HEPs. The matrix consisted of 10 mg/mL Matrigel and 3.35 mg/mL type I collagen, mixed with organoid growth medium. The organoid growth medium was a mixture of endothelial cell differentiation medium and HEPs medium, including equal parts of AATS medium and HEPs basal medium, supplemented with 20 ng/mL OSM, 50 ng/mL M-CSF, 50 ng/mL VEGF, 20 ng/mL bFGF, 40 ng/mL HGF, and 20 ng/mL EGF. The organoid cell suspension was seeded into 48-well PDMS plates at a density of 30,000 cells per well, with 3 μL of suspension per well. The plates were incubated at 37 °C for approximately 45 min to promote gelation. After gelation, organoid growth medium supplemented with 10 μM Y-27632 was added to each well, and fresh organoid growth medium was replaced daily. The organoids were cultured at 37 °C in a controlled environment of 5% CO2 and 95% air. After 3 days of cultivation, the organoids formed dense tissue spheroids, suitable for further analysis and research.

Quantification and statistical analysis
All statistical analyses were performed using GraphPad Prism 9.0. Data distribution was assessed using normality testing in Prism. For comparisons between two groups, two-sided unpaired t-tests were used for normally distributed data; otherwise, nonparametric tests were applied. For comparisons among three or more groups, one-way ANOVA was used for a single variable and two-way ANOVA for two variables; where applicable, multiple-comparison corrections were applied as specified in the corresponding figure legends. All statistical tests were unpaired. Data are presented as mean ± SD unless otherwise stated, and exact P values are provided on the figures. No statistical method was used to predetermine sample size. Sample sizes were chosen based on prior experience with similar organoid and in vivo experiments, and on pilot experiments yielding reproducible effects; the exact n and the definition of replicates are reported in each figure legend. No data were excluded from the analyses. Animal experiments were randomized for group allocation; in vitro experiments were not randomized. For histological quantification, investigators were blinded to group allocation during outcome assessment; for other experiments, investigators were not blinded to allocation during experiments and outcome assessment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript introduces a novel multicellular liver organoid model to explore the indirect hepatotoxicity mechanisms of drugs, particularly imipramine (IMP). The 3D multi-lineage liver organoid comprises five key hepatic cell types, enabling the authors to demonstrate that IMP induces indirect hepatotoxicity through hepatic stellate cells (HSCs), which release toxic exosomes enriched with miR-34. The study describes HSC-mediated apoptotic effects on hepatocytes via the p53/miR-34/XIAP/Caspase-3 pathway. These findings are further supported by in vivo experiments that corroborate the in vitro data, providing an innovative platform for drug toxicity studies.\nDespite the manuscript’s promising premise, it falls short in several critical areas. There is a lack of experimental rigor and inadequate attention to essential controls. For instance, functional measurements of the organoid system are not normalized adequately, making interpretation difficult. The central claim regarding IMP-induced toxicity relies heavily on ATP assays comparing organoid and hepatocyte aggregates. However, these experiments lack thorough assessments of potential confounding factors such as differences in drug penetration, oxygen availability, cell number, and cellular composition. ATP levels alone do not sufficiently represent hepatocyte damage and should be supplemented with more specific damage markers, such as CK18f and serum ALT/AST levels. The dose-response relationships in this model system also require further characterization and comparison with primary hepatocyte cultures.\nThe manuscript is also hindered by the scarcity of technical details, limiting the ability to evaluate the validity of the findings. Overall, the experimental design and analysis fall below the high standards expected for Nature Communications.\nSignificant revisions are expected to address the lack of experimental rigor, clarify data interpretation, and validate key claims.\nMajor concerns:\n1. Characterization of each lineage and organoids\nThe manuscript lacks detailed characterization of individual cell lineages and the organoid system as a whole. Marker profiles and functional properties of each lineage are not well-documented. The RNA-seq analysis is particularly concerning because the comparison with primary tissue lacks justification. The differences in tissue composition between the organoids and liver tissue are not addressed, raising questions about the validity of these comparisons. A gene set enrichment analysis (e.g., GO terms) for the clusters shown in Figure 2a would enhance the functional interpretation of the organoid system.\nThe reported high albumin secretion levels (“6 μg/day/10⁶ cells”) are not well-supported, as the manuscript does not explain how hepatocyte counts were determined. Including actual (non-normalized) values and clarifying the experimental conditions, such as culture wells and medium composition, would improve the transparency of this data.\nThe response to LPS stimulation raises additional concerns. The elevation of TNF-α in hepatocytes suggests potential contamination with monocytes or macrophages, particularly in the 3D hepatocyte cultures. The authors should use neutralizing antibodies and test lower LPS doses to confirm immune activation rather than non-specific cell damage.\nLastly, while HSCs are highlighted as a key component, their lineage fidelity is not adequately assessed. The authors should evaluate markers such as LRAT, NGFR, and GFAP, along with Vitamin A accumulation, using methods like those described by Koui et al. and Vallverdú et al.\n2. Drug assay design\nThe drug assays lack sufficient detail. The authors do not disclose drug concentrations or provide references for their selection. Standard dose-response experiments should be conducted for a robust evaluation to determine TC₅₀ values. This is especially important given the novelty of the organoid model, which may differ significantly from traditional systems in drug sensitivity and response.\n3. Validation of the proposed mechanism\nThe manuscript’s focus on exosomes as the mediator of IMP-induced toxicity is introduced abruptly, with minimal discussion of alternative mechanisms. The authors should test additional control drugs to demonstrate the specificity of the observed effects. The central hypothesis that miR-34 in Toxic-EXOs represses XIAP expression is critical but lacks direct causality evidence. Bioinformatics-based miRNA binding predictions (e.g., TargetScan, miRDB) or luciferase reporter assays targeting XIAP’s 3' UTR should be employed to validate this mechanism.\n4. Interpretation of animal study data\nThe in vivo data suggest significant apoptosis at day 35; however, additional markers such as serum ALT/AST levels and histological analyses (e.g., cleaved caspase-3 or TUNEL staining) are needed to quantify the severity of liver damage. Survival analyses would also enhance the relevance of the findings.\nAdditional concerns:\n1. Western blot data: Protein size information is missing from all Western blot figures, making it challenging to interpret results. Molecular weight markers and labeled band sizes should be included.\n2. Figure clarifications: In Figure 6a, the curved arrow may be misinterpreted. Revising its design to better connect with Figure 6c would improve clarity.\n3. Missing and incorrect citations: The manuscript does not cite several state-of-the-art stem cell-derived hepatocyte and organoid models for drug toxicology studies, undermining its contextual positioning.\n4. Immune response: While the study focuses on HSC-HEP interactions, it would be worthwhile to investigate whether IMP induces immune responses (e.g., IL-1β, IL-6, TNF-α).", "reviewer_2": "The study entitled “multi-lineage hepatic organoid reveals toxic exosome mediated indirect hepatotoxicity” presents a very interesting concept. They have tried to develop a hepatic organoid using functional five different types of cells and investigated the hepatotoxic effects of multiple drugs. Proposing an indirect mechanism of DILLI which is mediated by hepatic stellate cells derived exosomes which are toxic exosomes are transfer mir-34 which induces hepatocyte damage.\nWhile the idea, the mechanism proposed is very much out of the box and fascinating but throughout the manuscript there are several concerns regarding data presentation, clarity and inconsistency all through. The major concerns are below:\n1. The concept is interesting, but the authors could not convince about the hepatic organoid model per se that how well we can utilise this model in clinical settings and its translational relevance. How this model could be utilised for other drug development or for better patient management.\n2. The authors are unable to reassure the novelty of proposed multi-lineage model and its functional relevance for other than drug toxicity studies.\n3. Since, one of the novelties of the manuscript was organoid model, but it is utmost important to validate its functional capabilities, such as to assess its ability to replicate other liver pathologies such as steatosis, fibrosis, regeneration etc.\n4. There is no long-term stability data of the organoid which might be required for the reproducibility and batch to batch variations.\n5. There are multiple inconsistencies all through the manuscript, which was extremely difficulty to follow the study such as:\na. “Secretion of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α (Fig. 2g-i)....”, mentioned in the results is not coinciding with the respective the respective figure.\nb. There is not mention about the fig. 2 j-l anywhere in the result section\nc. The results for Fig 4.h-i are not coincides with the data, in context ‘to wild type (WT) and TRKB-knockdown (KD) HSCs.’\nd. Fig 4.j&k are not available in the fig, but mentioned in the results.\n6. In the study they screened 58 drugs, data shown in supplementary but did not mention the concentrations of all drugs which were screened nor any appropriate range has been shown in the results.\n7. There are many results/ conclusions which are not supported with appropriate data, how to TRKb expression in HSC directly support its conclusion.\n8. Has the predictive performance of the drugs been studied? It is not clear in the results, as this will make the basis of whole study and extremely data to be carefully investigated. What about the predictive score of DILI-positive drugs- ‘dantrolene, dihydralazine, disulfiram, flutamide, lisinopril, methotrexate’ that predicted as “no DILI” ? and it has not been mentioned in results. There is no discussion hepatoxicity of the doses and the clinical relevance in humans.\n9. It has been mentioned in the study “identified 7 hepatotoxic drugs using the 3D liver organoids, which did not significantly affect the cell viability in 3D HEPs spheroids.’ But, there is no such data on the same, differentiating the effect of the drugs on organoids or spheroids, hence selection of imipramine is not clear. The base on which whole study was based seems to be weak.\n10. The minor lacunae which exist in the manuscript, specially the methodology is not well explained such as must provide the name of public databases used from which the single cell-RNA sequencing data has been taken for analysis.\n11. So far, this study is proposing the indirect hepatotoxicity of imipramine mediated by HSC-derived Toxic-EXOs causes activation of caspase-3 in hepatocytes. Accordingly, a simple experiment would have been performed to study the effect of imipramine, Toxic-EXOs and miR-34 in HSC-knock out mice model to study in vivo.", "reviewer_3": "Summary\nThis manuscript presents a novel multi-lineage liver organoid model derived from human embryonic stem cells, comprising five major liver cell types (hepatocytes, stellate cells, endothelial cells, Kupffer cells, and cholangiocytes). The authors demonstrate this model's high physiological relevance compared to traditional hepatocyte cultures through enhanced cytochrome P450 activities, improved albumin production, and robust inflammatory responses. The work's most significant contribution is elucidating a previously unknown mechanism of indirect hepatotoxicity for imipramine (IMP), showing that drug-induced liver injury can occur through interactions between hepatic stellate cells and hepatocytes via toxic exosomes containing miR-34. This release is triggered by IMP binding to TRKB receptors on stellate cells, ultimately leading to suppression of XIAP and downstream apoptotic pathways in hepatocytes. This was confirmed with in vivo rodent models, however there is a disconnect between the rarity of IMP human DILI and the apparent strong effect of IMP in the rodent model, which is curious.\nValidity\nThe findings appear largely valid, supported by appropriate controls and complementary methods. The authors convincingly demonstrate that IMP primarily affects stellate cells through TRKB receptor binding and that knockdown of TRKB ameliorates IMP's toxic effects, establishing causality. The in vivo validation provides additional support for the proposed mechanism. However, some questions remain about the necessity of all five cell types in the model, as co-culture of hepatocytes and stellate cells alone seemed to elicit similar injury patterns. Additionally, while the authors show that TRKB expression is crucial, the exosome quantification between DMSO and IMP treatments would benefit from dose-response analysis, as IMP has been reported to suppress exosome production in other systems.\nThe utility of the 3D liver organoid platform as described is questionable because they rely only on 3D organoid viability as the readout for drug induced liver injury which is overly simplistic and likely not a reliable predictor of DILI. Additionally, the variability of the negative control drugs was significant at an average of ~90% with a 20% standard deviation. The threshold for DILI risk looks to be drawn at a threshold (~80%) that maximizes their sensitivity/specificity and should be drawn at a statistically determined cutoff that would likely be much lower resulting in lower sensitivity/specificity. The authors did not describe how they arrived at this threshold, (perhaps it was based on the vehicle controls that were not shown?), but an arbitrary cutoff that maximizes their sensitivity/specificity is not acceptable and should be revised or explained to ensure robustness. They did not comment on the reproducibility of the effects and sensitivity/specificity in independent biological replicates.\nSignificance\nThis work represents an advancement in the technical capabilities for studying liver toxicity and mechanistic understanding of drug-induced liver injury. The multi-lineage organoid model addresses important limitations of existing hepatocyte-only systems in detecting indirect hepatotoxicity. While miR-34 has been previously identified as a biomarker for DILI and associated with mitochondrial dysfunction and oxidative stress, the specific mechanism described here—via direct binding of IMP to TRKB leading to suppression of XIAP and downstream apoptosis—is novel and important. The findings have clear implications for drug development and safety assessment, potentially leading to improved screening methods for detecting drugs with indirect hepatotoxic potential, but the platform robustness should be addressed.\nData and methodology\nThe methodology for creating and characterizing the organoid model is generally well-described and appears reproducible. The authors provide comprehensive validation of the model's functionality through multiple complementary approaches. However, certain methodological limitations exist. The authors did not describe whether the liver organoids were capable of biliary efflux, though they commented on the appearance of bile canaliculi without visualizing them with fluorescent bile acids for confirmation. Similarly, they speculated about vascularization without supporting data. The characterization of endothelial cells was limited and did not determine whether they express specific markers of liver sinusoidal endothelial cells that exhibit fenestra. The drug screening methodology relied solely on detection of reductions in total viability, whereas other signatures of DILI (lipid accumulation, HSC activation, mitochondrial toxicity, biliary efflux inhibition) could have been evaluated to provide a more comprehensive assessment.\nAnalytical approach\nThe analytical approach is generally rigorous, with appropriate controls and multiple methods used to validate key findings. The use of knockdown experiments to establish the role of TRKB and p53 in the proposed mechanism strengthens the causal relationships. The in vitro findings are complemented by in vivo validation, adding considerable weight to the conclusions. However, the focus on a single drug (imipramine) limits the broader applicability of the findings. Additionally, the sensitivity/specificity analysis for DILI risk was based only on reductions in viability, which represents a limited view of the complex phenotypic manifestations of liver injury.\nSuggested improvements\n1. Perform a more statically robust identification of positivity of DILI risk in the drug screening platform.\n2. Expand drug screening to include a broader range of compounds known to cause indirect hepatotoxicity\n3. Validate findings using human liver samples from cases of imipramine-induced liver injury\n4. Evaluate additional signatures of DILI beyond viability (lipid accumulation, HSC activation, mitochondrial toxicity)\n5. Characterize liver-specific maturation of endothelial cells into proper liver sinusoidal endothelial cells with fenestra\n6. Visualize bile canaliculi with fluorescent bile acids to confirm their presence and functionality\n7. Determine the necessity of endothelial cells, Kupffer cells, and cholangiocytes in detecting indirect hepatotoxicity\n8. Perform dose-response analysis of IMP's effects on exosome production/release\n9. More explicitly address the model's limitations in the discussion\n10. Explore the potential therapeutic implications of targeting this pathway\nClarity and context\nThe manuscript generally presents the findings clearly and places them in the appropriate scientific context. However, it would benefit from more explicit discussion of the limitations of the model system, particularly regarding how it recapitulates the relative proportions of different cell types found in the human liver and the absence of a bile duct system and circulation. The authors should also more thoroughly discuss how their findings relate to previous work on miR-34 in liver pathology and apoptosis, and more clearly articulate the potential therapeutic implications of their findings.\nReferences\nWhile the manuscript review does not specifically critique the reference list, it would be beneficial for the authors to include references to previous work on miR-34 in liver pathology and apoptosis to better contextualize their findings. They should also cite literature on imipramine-induced liver injury in clinical settings to strengthen the clinical relevance of their model system. Additionally, references supporting their methodological choices for organoid development and validation would enhance the manuscript's foundation. The authors should also reference previous studies on exosome-mediated intercellular communication in liver pathology and DILI to place their novel mechanism in the appropriate scientific context."}, "author_response": "Dear Editor,\nWe would like to thank the reviewers for their positive and constructive commen\nts on our paper (manuscript NCOMMS-24-78567-T) We have addressed all comments\nand made the requested changes in the revised manuscript. At this moment, we provide\na rebuttal letter with a point-to-point response to their comments. We believe our\nmanuscript has been greatly improved with better scientific rigor and translational\npotential. Your re-consideration of our work to be published in your prestigious\njournal will be tremendously appreciated.\nBest\nYanan Du\nComments from Reviewer #1: ... [Response text omitted for brevity as per instructions to parse structure]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have made substantial efforts to address the first-round concerns and have strengthened the study both experimentally and analytically. The use of a human pluripotent stem cell–derived 3D multi-lineage liver organoid model to delineate indirect hepatotoxicity via HSC-derived exosomes remains novel and potentially impactful. However, several critical aspects still require further clarification, validation, or presentation improvements before the manuscript can be deemed suitable for publication. These points are outlined below.\n1. Functional annotation and composition of organoids\nPlease provide correlative analyses linking the enriched pathways with specific cell types, as inferred from organoid composition. Consider single-cell level deconvolution or cell-type-weighted GSEA to support that hepatocyte-dominant functions are primarily derived from HEPs within the organoid rather than global averaging.\n2. Normalization of quantitative data\nThe normalization basis of “110,000 hepatocytes per well” for functional assays (e.g., albumin secretion) lacks justification in the main text. Please clearly state in the Methods how this hepatocyte count was derived in main manuscript section. Also clarify the starting cellular ratios used to construct the 3D organoids, ideally with actual numerical seeding data. This is especially important for data normalization and interpretation across experiments.\n3. Cytokine induction upon LPS/IMP exposure\nThere remains ambiguity in interpreting TNF-α secretion. In Supplementary Fig. 3b, LPS-induced TNF production is attributed to hepatocytes (“measured from HEPs”), yet the main text does not specify which cell types were exposed. Clarify explicitly in the main text whether LPS and IMP were administered to isolated hepatocytes, 3D hepatocyte spheroids, or multi-lineage organoids. This clarification is critical, as TNF-α production in non-immune cells like hepatocytes is context-dependent and could be confounded by other cell types in the organoid system.\n4. Clarity of drug screening table (Table 3)\nAlthough drug concentrations and Cmax references were added in the supplementary tables, Table 3 (referred to in text) lacks sufficient annotation.\nProvide a clearer explanation of Table 3 in the main text, including how test concentrations were chosen relative to Cmax and how toxicity thresholds were applied.\n5. IMP dose-response and TC₅₀ data missing\nAs IMP is central to the study:\n• The absence of dose-response curves (e.g., for miRNA induction or cytotoxicity) remains a weakness.\n• Please include IMP dose-response experiments and calculate TC₅₀ values in both HEP-only and organoid contexts. This is essential for contextualizing the strength and specificity of IMP effects, especially in miRNA and exosome regulation.\nOther Comments\n• Supplementary Fig. 4: There is a mismatch between cytokine labels (e.g., IL-6, IL-1β) in the figure panels and the corresponding legend. Please revise for consistency.\n• The authors added marker panels in the supplementary data, which is appreciated.\n• The Introduction is improved with citations to stem cell-derived hepatocyte/organoid models and DILI evaluation, adding helpful context.\n• It is commendable that cytokine response to IMP was shown to be minimal in organoids, supporting specificity of the exosomal mechanism.", "reviewer_2": "I appreciate the authors’ thorough and constructive responses to the comments. All the major and minor concerns have been adequately addressed, and the revised manuscript has improved significantly. I have no further comments and support its publication in its current form."}, "author_response": "Dear Reviewer,\nWe sincerely thank you for your continued evaluation of our manuscript\n(NCOMMS-24-78567-A). Your detailed and thoughtful comments have been\ninvaluable in further strengthening the study. In this second revision, we have\nprioritized all remaining concerns, conducted additional experiments and analyses,\nexpanded methodological details, and refined key interpretations. Below we provide a\npoint-by-point response, with all changes highlighted in dark green in the revised\nmanuscript and supplementary information. We hope the revision now meets the\nstandard for publication in Nature Communications."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have made significant improvements and have addressed most of my prior concerns. I appreciate the thoughtful revisions. Below are a few remaining minor issues that should be addressed to further improve clarity and completeness:\n1. Supplementary Figure 3: The panel callouts appear to be incorrect. Please revise to ensure all figure references match the appropriate panels.\n2. GW4869 Introduction: The authors now introduce the use of GW4869 much earlier in the manuscript than in the previous version. This change is acceptable, but it would benefit from a brief explanation upon first mention to clarify its rationale and relevance to the study.\n3. Supplementary Figure 8b, c: The Y-axis is labeled “miR-54,” which is likely a typographical error. Please confirm and correct this, as it may confuse readers.\n4. Supplementary Figure 10: On page 20, the callout for panels \"k\" and \"l\" is missing. More importantly, there appears to be no actual panel “l” in Supplementary Figure 10, despite the reference to mRNA expression changes. Please review and revise the figure and text accordingly.\n5. Missing Citations: Two important and highly cited studies on DILI organoid screening are absent from the references nor discussed. Please consider including the following:PMID: 36738840, PMID: 33039464, and discuss relevance to the current works."}, "author_response": "Dear Reviewer,\nWe thank the Editor and Reviewers for their thoughtful and constructive\ncomments on our manuscript (NCOMMS-24-78567-B). We have carefully revised the\nmanuscript in light of all suggestions, improving figure annotation, methodological\nclarity, literature context, and the description of the mechanistic findings. We believe\nthese changes have strengthened the work and address all points raised. Below, we\nrespond to each comment point by point."}]

Decision letter