Oxidative stress and GPX2 control pancreatic vs. non-pancreatic cell fate in human endoderm

Domain: biology
Published: 2026-01-03
Editorial outcome: major_revision

Abstract
Cell fate decisions in human endoderm development are tightly regulated, yet the role of metabolic products remains elusive. The endodermal posterior foregut gives rise to pancreas, liver, and intestine. Here, we identify Glutathione Peroxidase 2 as a critical regulator of human posterior foregut differentiation, revealing oxidative stress as a key determinant of pancreatic versus non-pancreatic cell fate. Cells lacking Glutathione Peroxidase 2 under pancreas-promoting conditions differentiate also into hepatic-like progenitors. Through bulk and single-cell transcriptomics, chromatin accessibility profiling, and functional studies, we reveal that Glutathione Peroxidase 2 orchestrates lineage commitment by regulating key transcription factors, leading to emergence of multilineage liver and intestinal progenitors. Mechanistically, Glutathione Peroxidase 2 deficiency triggers extracellular matrix remodeling, activating bone morphogenetic protein signaling and skewing differentiation from the pancreatic lineage. Manipulating oxidative stress recapitulates or rescues Glutathione Peroxidase 2 loss effects, establishing oxidative stress as a gatekeeper of pancreatic fate. Controlling oxidative stress during in vitro differentiation could advance regenerative medicine applications.

Introduction
Organogenesis relies on the precise specification of progenitor cell populations into distinct tissues. The developmental trajectories of the pancreas, liver, and duodenum exemplify the intricate lineage relationships and inherent cellular plasticity characterizing this process. Evidence from human genetic syndromes substantiates the concept of plasticity, given the congenital malformations manifesting as multi-organ phenotypes, affecting these related organs1. Alterations in cellular plasticity can cause aberrant differentiation and potentially increase susceptibility to malignant transformation.

Recent advances in single-cell transcriptomic analysis have revealed the dynamic nature of multipotent progenitors throughout foregut development. Experimental manipulations of developmental signals in mouse embryos and, importantly, directed differentiation of human pluripotent stem cells (hPSCs) in vitro, have demonstrated the significant plasticity within foregut endoderm territory, enabling alterations in cell fate2,3. Comprehensive CRISPR-Cas9 genomic screens and single-cell transcriptomics have elucidated the transcriptional programs governing lineage commitment and plasticity restriction during hPSC-derived endoderm differentiation4–7. However, despite extensive investigations into transcriptional networks, signaling pathways, and cellular interactions, the precise molecular mechanisms, including the contribution of metabolism and free radicals, driving lineage diversification from foregut endoderm, particularly in hPSC differentiation, remain in completely characterized.

The broad expression of pioneering transcription factors, such as GATA Binding Protein 4 (GATA4), GATA Binding Protein 6 (GATA6), and Forkhead Box A2 (FOXA2), across endoderm domains suggests the involvement of additional mechanisms in lineage segregation. Comprehensive analyses of single-cell RNA-seq datasets across developmental stages have revealed dynamical alterations in genes associated with oxidative phosphorylation, oxidative stress response, glutathione metabolism, and lipid peroxidation at critical lineage bifurcation points8,9. This observation prompted our investigation into the role of cellular metabolic status, with particular emphasis on oxidative stress during human endoderm specification.

Oxidative stress emerges from a disruption in the equilibrium between reactive oxygen species (ROS) production and cellular antioxidant defense mechanisms. ROS, encompassing free radicals and peroxides, are natural byproducts of aerobic metabolic processes. Historically, ROS were characterized as deleterious agents contributing to cellular damage, aging, and pathogenesis10. Contemporary research has nuanced this perspective by recognizing the critical regulatory roles of physiological ROS levels in developmental processes, cell signaling, and cellular homeostasis. Emerging evidence demonstrates the multifaceted involvement of ROS in diverse developmental contexts. In Drosophila, ROS orchestrate meiotic progression and early embryonic divisions11,12. Similarly, in developing sea urchin embryos, ROS modulate Nodal gradient activation, secondary axis establishment, and ectoderm differentiation13,14. Zebrafish studies further illuminate ROS functionality, with hydrogen peroxide (H2O2) mediating neural development through regulation of the Engrailed 2 gradient15.

The endoderm, situated within the developing embryo and generating the metabolically active organs, is highly susceptible to ROS fluctuations. Studies in mice have shown that aberrant ROS levels, caused by antioxidant enzyme SOD1 deficiency, lead to abnormalities in gut tube development16. Despite accumulating evidence of ROS developmental significance, the mechanistic functions of ROS in human endoderm development, particularly in the context of hPSC differentiation into clinically relevant cell types, remain largely unexplored.

To address this gap, we investigated the impact of oxidative stress on pancreatic versus non-pancreatic cell fate decisions during in vitro human endoderm differentiation. By examining key oxidative stress response components, we specifically targeted selenoprotein Glutathione Peroxidase 2 (GPX2): an antioxidant enzyme critical for H2O2 neutralization and characterized by heightened expression during endoderm specification. Our findings reveal an unexpected role for GPX2 in modulating pancreatic and hepatic cell fate decisions and provide insights into the intricate interplay between oxidative stress and cell fate determination during hPSC differentiation into endoderm derivatives, with emphasis on pancreatic endocrine cell development.

Results
Oxidative stress and GPX2 in developing β cells
To investigate the role of oxidative stress in β-cell development, we utilized an hPSC pancreatic differentiation using our well-established differentiation protocol17,18. Differentiation efficiency was assessed across different stages using immunofluorescence (IF) and quantitative imaging or flow cytometry (FC). We consistently obtained 90–98% of SRY-box Transcription Factor 17 (SOX17)+ and FOXA2+ definitive endoderm (DE) cells at day 4, 75% of cells expressing a posterior foregut (PFG) marker Hematopoietically Expressed Homeobox (HHEX) at day 6, 80–90% pancreatic and duodenal homeobox 1 (PDX1)+ and SRY-Box Transcription Factor 9 (SOX9)+ pancreatic endoderm (PEN), 68–80% pancreatic progenitors (PP) co-expressing PDX1 and NK6 Homeobox 1 (NKX6-1) at day 12, 65% of Chromogranin A (CHGA)+ and PDX1+ endocrine progenitors (EP) and 30–50% β cells co-expressing C-peptide (C-PEP) and NKX6-1 (Fig. 1A, Supplementary Fig. 1A, B). We then quantified the ROS levels utilizing a fluorescence-based sensor, H2DCFDA, at seven timepoints corresponding to the consecutive developmental stages, which revealed a progressive increase in ROS levels throughout the differentiation (Fig. 1B). At the SC-β cell stage, ROS levels were nearly three times higher than at the initial hPSC stage. Consistent with these in vitro observations, the expression of genes associated with the oxidative stress response pathway increased in developing human fetal pancreas from 4 to 11 post-conception weeks (PCW) (Supplementary Fig. 1C). This significant increase in ROS levels suggested a potential role for oxidative stress in β cell development.

Cellular responses to oxidative stress are primarily mediated by antioxidant enzymes from the superoxide dismutase (SOD), peroxiredoxin (PRDX), and glutathione peroxidase (GPX) families (Fig. 1C). SOD converts superoxide radicals (O2•-) into oxygen (O2) and hydrogen peroxide (H2O2). Subsequently, GPX neutralizes H2O2 to H2O and O2 through glutathione oxidation. PRDX also neutralizes H2O2 to H2O via a cysteine-dependent oxidation-reduction reaction. Catalase (CAT) is another antioxidant enzyme responsible for H2O2 degradation to H2O and O2. We analyzed the mRNA expression of GPX family enzymes (GPX1-8), PRDX family members (PRDX1-6), and SOD family members (SOD1-3) across the hPSC, DE, and PP stages using our RNA-seq data (Fig. 1D). Expression levels of all antioxidative enzyme transcripts remained relatively stable between the hPSC and DE stages. However, the expression of GPX2 and SOD3 increased significantly at the PP stage, contrasting with the unchanged expression of other enzymes. CAT transcripts were not detected in our RNA-seq data.

To investigate the effect of oxidative stress on pancreatic differentiation, we focused on GPX2, as it functions downstream of SOD. Single-cell analysis of mouse gastrulation and early organogenesis19 revealed remarkably high Gpx2 expression in DE and gut at embryonic days (E) E6.5-8.5 (Fig. 1E). Additionally, Gpx2 expression was prominent in bipotent progenitors (BP) and Neurogenin 3 (Ngn3)-expressing early endocrine progenitors (EP-Ngn3) in the E14.5 pancreas20 at the time point of extensive morphogenesis and cell adhesion remodeling (Fig. 1F). Given that DE contributes to the development of multiple organs within gut tube, and BPs differentiate into either endocrine or ductal cells, while Ngn3+ EPs give rise to pancreatic endocrine cells, these findings suggest a potential role for GPX2 in the determination of endoderm or pancreatic progenitor cell fate.

Next, we examined the GPX2 expression during human pancreatic development. In the fetal pancreas at 10 and 13 PCW, GPX2 protein was expressed in pancreatic cells, including PDX1+ cells (Fig. 1G). Transcriptomic analysis of the human fetal pancreas further revealed that GPX2 mRNA is detectable even at the earlier stages, peaking at 6 PCW (Fig. 1H). Finally, we examined GPX2 protein expression during hPSC differentiation into pancreatic β cells. GPX2 expression was first detected at the PFG stage and increased progressively through the PEN, PP, and EP stages, but declined at the SC-β cell stage (Fig. 1I). Collectively, these findings suggest a potential role for GPX2 in early pancreatic development but not in mature β cells.

GPX2 deletion alters endoderm in vitro differentiation
To directly assess the GPX2 role in β cell differentiation, we generated a homozygous GPX2 loss-of-function model in hPSCs by deleting the entire GPX2 gene (genomic coordinates, chr14:64,939,301-64,942,926, Hg38) (Fig. 2A) using CRISPR/Cas9 and a set of three single guide RNAs (sgRNAs). Successful GPX2 knockout (GPX2 KO) in selected clonal hPSC lines was confirmed by PCR and Sanger sequencing, and the protein level by western blot (WB) (Fig. 2A, Supplementary Fig. 2A). No significant differences were observed between WT and GPX2 KO hPSCs in terms of colony morphology (Supplementary Fig. 2B), proliferation rates (Supplementary Fig. 2C), and pluripotency marker POU Class 5 Homeobox 1 (OCT3/4) protein expression (Supplementary Fig. 2D).

Compared to WT, GPX2 KO PEN cells exhibited increased cytoplasmic stress, as measured by H2DCFDA, while no changes in mitochondrial stress levels were detected by MitoSOX staining (Fig. 2B). This suggests that GPX2 deficiency leads to specific cellular stress responses during endoderm differentiation.

Next, we evaluated the differentiation of GPX2 KO hPSCstowards β cells in a 3D culture format. DE markers, SOX1721 and FOXA222 displayed comparable expression levels in GPX2 KO compared with WT cells (Fig. 2C, Supplementary Fig. 2F). Differences in gene expression between WT and GPX2 KO cells became apparent at the PFG stage, coinciding with the onset of GPX2 expression. IF staining and quantification demonstrated increased expression levels of SOX17 by 30%, Caudal Type Homeobox 2 (CDX2) by 77%, and Hepatocyte Nuclear Factor 4 Alpha (HNF4A) by 22%, while HHEX expression decreased by 30% in PFG GPX2 KO cells (Fig. 2D). At the PFG stage, these marker genes, all key regulators of gut development, play pivotal roles in lineage specification. SOX17 is crucial for gut endoderm morphogenesis23, CDX2 is essential for initiating intestinal development24,25, HNF4A controls hepatic and pancreatic progenitor specification26, and HHEX is a key regulator of liver development and a gatekeeper of pancreatic lineage specification7,27. Additionally, WT and GPX2 KO PFG cells co-expressing SOX17 and HNF4A (Fig. 2F) and SOX17 and HHEX (Supplementary Fig. 2E) were identified.

At the PEN stage, expression levels of PDX1, CDX2, and SOX9 were increased by 66%, 20%, and 61%, respectively, and HHEX levels remained lower by 24% in GPX2 KO cells compared with WT (Fig. 2E), similar trend was observed in the second clonal GPX2 KO cell line (Supplementary Fig. 2G). PDX1 governs pancreas development28 and is also expressed in duodenum29 and SOX9 regulates pancreatic and liver progenitor development30. Moreover, at both stages, PFG and PEN, we observed the expression of the liver cell marker, Fibrinogen Beta Chain (FGB), in a fraction of HNF4A+ GPX2 KO cells (Fig. 2F, G). Interestingly, the PEN HNF4A+/FGB+ cells did not co-express PDX1, which suggests liver-like cell fate specification. Further, co-expression of HNF4A and FGB proteins in WT and KO PFG spheroids was confirmed by FC. Although expressed at low levels overall, FGB expression in KO cells was approximately two-fold higher compared with WT (Fig. 2H, Supplementary Fig. 2H, I). A similar result was obtained at the PEN stage, with 18% of FGB+ GPX2 KO cells co-expressing HNF4A compared with 8% FGB+/HNF4A+ WT cells (Fig. 2H, Supplementary Fig. 2J).

These findings strongly suggest that GPX2 plays a crucial role in directing endoderm differentiation towards a pancreatic lineage, and its absence results in the parallel adoption of alternative liver-like and intestinal-like fates. However, a subset of cells also expressed PDX1, suggesting they adopted a pancreatic progenitor fate in GPX2 absence.

GPX2 deficiency disrupts posterior foregut patterning
To comprehensively characterize the effects of GPX2 loss during pancreatic differentiation, we performed a set of RNA sequencing experiments (Fig. 3A). Bulk RNA sequencing of cells at day 6 of differentiation, corresponding to the PFG stage, showed a clear separation between WT and KO cells in the first principal component, capturing 57.2% of the variation (Fig. 3B, Supplementary Fig. 3A). Among the top differentially expressed genes (DEGs), we observed the upregulation of CDX2 and HNF4A transcripts and downregulation of HHEX (Fig. 3C, Supplementary Data 1), corroborating the IF results. Additionally, we identified the upregulation of anterior DE and midgut markers, including Insulin-Like Growth Factor Binding Protein 5 (IGFBP5) and S100 Calcium Binding Protein A13 (S100A13)31. We also noted downregulation of the DE patterning gene Orthodenticle Homeobox 2 (OTX2) and increased expression of Transcription Factor AP-2 Gamma (TFAP2C), which is upregulated in OTX2-deficient human DE32. Concomitantly with the downregulation of the anterior marker OTX2, we found the upregulation of the posterior marker CDX233, suggesting altered temporal patterning in GPX2 KO PFG. We also observed increased Bone Morphogenetic Protein 6 (BMP6) expression in KO PFG34,35, suggesting a pro-liver-like bias in GPX2 KO foregut endoderm. Together, these results demonstrate the dysregulation of multiple genes critical for foregut development and patterning.

We next investigated how the putative PFG patterning alterations, induced by the identified transcriptomic changes in GPX2 KO, influence cell fate acquisition following pancreatic progenitor induction. To this end, we analyzed WT and KO cells at day 8 of differentiation, 24h after directed pancreatic fate induction by a combination of Retinoic Acid (RA) and Protein Kinase C (PKC) pathways activation along with BMP and Sonic hedgehog (SHH) pathway inhibition, using single-cell combinatorial indexing RNA sequencing (scRNA-seq) (Fig. 3D, Supplementary Fig. 3B, C). The top variable genes within the dataset were also differentially expressed between GPX2 KO and WT cells (Supplementary Fig. 4A, B). Using Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction, we observed separation between WT and GPX2 KO cells (Fig. 3D). Due to the low detection of peri-pancreatic endoderm markers PDX1 and SOX9 (Supplementary Fig. 4C) in early PEN, likely due to scRNA-seq sensitivity, we identified four PEN clusters based on other known markers, such as multipotent pancreatic progenitor markers One Cut Homeobox 2 (ONECUT2), SRY-Box Transcription Factor 11 (SOX11), ZFP36 Zinc Finger Protein Like 1 (ZFP36L1), and ZFP36 Zinc Finger Protein Like 2 (ZFP36L2)36, as well as CD24 Molecule (CD24)37 (Fig. 3E, Supplementary Fig. 4D, F). Among these populations, the PEN_3 and PEN_4 clusters had the highest expression of these early pancreatic markers, suggesting they were the most mature pancreatic progenitors at this stage. We also identified two posterior foregut (PFG_1 and PFG_2) clusters marked by GATA4 and PR/SET Domain 1 (PRDM1), and three smaller clusters referred to as non-pancreatic progenitors (NPP), marked by Thymocyte Selection Associated High Mobility Group Box (TOX), Sodium Voltage-Gated Channel Alpha Subunit 7 (SCN7A), and Phosphodiesterase 3A (PDE3A), respectively (Fig. 3E). Interestingly, a distinct cluster was marked by the IGFBP5 gene (Fig. 3E), previously identified as upregulated in GPX2 KO cells at day 6 (Fig. 3C), which might indicate intestinal cell commitment38–40.

For each of the clusters, we identified highly enriched genes as putative novel markers of these PFG subpopulations (Supplementary Fig. 4E, Supplementary Data 2), together with genes that were deregulated between WT and KO genotypes, irrespective of cell type (Supplementary Fig. 4G, Supplementary Data 2). Interestingly, we found that the HHEX transcript, which was downregulated in GPX2 KO cells at the protein level (Fig. 2D, E), was more abundant in PEN clusters enriched in WT cells (Supplementary Fig. 4F). Similarly, transcripts of HNF4A, which were enriched in KO cells (Fig. 2D), were less abundant in WT-enriched PEN clusters (Supplementary Fig. 4F). Therefore, scRNA-seq-based results corroborated the differential expression of proteins at the PEN stage.

Importantly, 65% of KO cells belonged to PFG clusters, compared to less than 30% of WT cells. Conversely, 41% of WT cells versus only 5% of GPX2 KO cells belonged to the PEN_3 and PEN_4 clusters (Fig. 3F, Supplementary Fig. 4H), which represent the most progressed pancreatic progenitors. Based on these results, we concluded that GPX2 KO cells exhibited a reduced response to forced pancreatic fate induction compared to WT cells.

To investigate the mechanisms underlying the observed cell fate bias, we analyzed pathway deregulation. For that, we used the pathway activity analysis tool PROGENy41, which calculates pathway activity scores based on pathway regulons: a set of genes whose expression is influenced by pathway activation or inhibition. Greater changes in the expression of pathway-responsive genes result in a higher pathway activity score. This analysis revealed a downregulation of the hypoxia regulon and upregulation of the TGF-β regulon in GPX2 KO cells as the most significantly deregulated pathways (Fig. 3G, Supplementary Data 2). Importantly, the relative activities of these regulons were consistently deregulated across most PFG and PEN clusters (Fig. 3H), suggesting a genotype-specific effect rather than cluster frequency bias. Trajectory analysis of WT and KO cells transitioning from PFG to PEN stages along pseudotime (Fig. 3I, J, Supplementary Fig. 4I) revealed that Molecular Signal Database (MSigDB) terms such as “pancreas beta cells”, “hypoxia”, “reactive oxygen species” and “fatty acid metabolism” were enriched in WT cells during transcriptomic progression to PEN stages (Fig. 3K). This led us to investigate whether GPX2 loss-induced deregulation of oxidative stress response pathways contribute to the delayed pancreatic fate acquisition in GPX2 KO cells.

To further explore the role of oxidative stress, we treated WT and KO cells with varying concentrations of hydrogen peroxide (H2O2; 0.01–10 µM) to induce oxidative stress, and selenium (Se; 1–1000 pM) to reduce it. As controls, we treated WT cells with 100 pM Se and KO cells with 10 nM H2O2. H2O2 at concentrations exceeding 1 mM was lethal to the cells. Following treatment at the DE and PFG stages, PDX1 protein expression was analyzed 3 days later (Fig. 3L). Treatment with 0.01 µM H2O2 significantly elevated PDX1 expression in WT cells, mimicking the levels observed in GPX2 KO cells. Conversely, 100 pM Se treatment decreased PDX1 expression in KO cells (Fig. 3M, N) to levels comparable to untreated WT cells. PDX1 expression was not changed in the applied control conditions. We observed similar effects of H2O2 and Se on HNF4A expression (Supplementary Fig. 4J).

Next, we assessed collagen fiber integrity. WT and Se-treated KO cells exhibited shortened and disorganized collagen fibers, while KO and H2O2-treated WT cells presented more abundant and better-organized fiber structures (Fig. 3M). Furthermore, we observed the accumulation of lipid droplets in GPX2 KO and H2O2-treated WT cells (Fig. 3O). This lipid accumulation is in line with the presence of oxidative stress in these cells42,43. Therefore, H2O2-induced oxidative stress recapitulates the developmental bias of GPX2 KO, and Se treatment rescues this phenotype by restoring the expression of pancreatic and liver markers in KO cells. Together, our findings imply a critical role for oxidative stress in modulating developmental trajectories.

GPX2-deficient cells show propensity for hepatic-like lineage
We next examined the potential of GPX2-deficient cells to differentiate towards β cells. Surprisingly, at the SC-β cell stage (day 21), we did not observe significant changes in the protein levels of key β cell markers between WT and GPX2 KO cells, including PDX1, CHGA44, NKX6-145, and C-PEP46 (Fig. 4A–C, Supplementary Fig. 5A). WT and KO cells co-expressed NKX6-1 and C-PEP (Fig. 4B), as expected in β cells, with similar frequency. Thus, KO cells differentiated into β cells as efficiently as WT cells, despite the alterations observed during PFG development. However, we observed altered frequencies of lineage markers for other populations than β cells, e.g., an increased number of GCG+ α cells (Supplementary Fig. 5B, C). Moreover, we detected elevated expression of liver markers, i.e., HNF4A and α-fetoprotein (AFP)26,47,48 (Fig. 4A, C), in day 21 KO cells, confirming a higher propensity of KO cells to adopt a liver-like progenitor fate. In GPX2 KO cells, co-staining for C-PEP and FGB, as well as PDX1 and AFP (Fig. 4B), revealed that liver markers were expressed in cells lacking endocrine markers, suggesting the endocrine and hepatic-like specification occurring in parallel. Together, our data suggest that under strong pro-pancreatic conditions during in vitro differentiation, GPX2 KO cells differentiate into two distinct lineages: endocrine-like and multilineage progenitors, including liver-like progenitors. Consistently, quantification at the SC-β stages showed that FGB levels reached nearly 14% in GPX2 KO cells compared to only ~1% in WT, and AFP protein levels were also significantly increased in KO cells relative to WT counterparts (Supplementary Fig. 5D, E). Consistent findings were also observed in the second clonal GPX2 knockout cell line (Supplementary Fig. 5F, G).

To uncover and analyze the cellular landscape of WT and GPX2 KO cells at the SC-β cell stage, we performed scRNA-seq on differentiation day 21. A total of 6128 WT and 5610 KO cells at the SC-β cell stage were sequenced using 10x Chromium platform (Supplementary Fig. 6A–C). Following cell filtering and quality control, the unsupervised analysis identified 11 distinct clusters, visualized using UMAP (Supplementary Fig. 6D, E). Cell identities within each cluster were assigned based on the expression of lineage marker genes specific to each cluster (Supplementary Fig. 6D, F). Next, we excluded mesenchymal and proliferating cells from further analysis to focus on pancreatic and hepatic-like cell differentiation. After re-clustering, we retained 5515 cells for WT and 4648 cells for GPX2 KO, grouped into 10 clusters (Fig. 4D–F). Endocrine cells from both genotypes grouped, forming SC-β, δ, and multihormonal (MH) cell clusters, with similar proportions of cells in each cluster (Fig. 4F). However, in KO samples, the endocrine cell cluster (EC) contained 10% more cells compared to WT. In contrast, the EP cluster constituted 22% of WT but only 4% of KO cells (Fig. 4F). Analysis of canonical endocrine markers in the SC-β, MH, EP, EC, and δ cell clusters confirmed the identity of each cluster while showing no significant differences in these marker expression levels between genotypes (Fig. 4G). At the same time, however, we identified upregulated and downregulated genes in GPX2 KO cells compared to WT within each cluster (Fig. 4H, Supplementary Data 3), suggesting that while GPX2-deficient endocrine cells largely resemble WT, some transcriptional differences exist.

At day 21 of differentiation, we still observe a significant number of progenitor cells, a common occurrence in β-cell differentiation protocols49. Pancreatic progenitor cells (PP1 and PP2 clusters) constituted nearly 60% of WT populations but less than 2% in KO cells (Fig. 4F). Interestingly, in GPX2 KO samples, three distinct clusters emerged: multilineage progenitors 1 (MLP1, 22%), multilineage progenitors 2 (MLP2, 15%), and multilineage (ML, 23%), which collectively comprised less than 1% in WT cells. Given these pronounced differences in cluster composition between WT and KO (Supplementary Fig. 6G), we performed pseudotime trajectory analysis using Monocle350. In WT cells, differentiation trajectories progressed towards endocrine cell fates, though a substantial proportion of cells remained as various pancreatic progenitors (Fig. 4I, J). In KO cells, however, we observed bifurcation into two distinct differentiation trajectories: endocrine and multilineage cell fates (Fig. 4K, L). Liver-like and intestine-like progenitor markers were highly expressed in the MLP2 and ML clusters (Fig. 4M). We noted the increased expression of Serine Peptidase Inhibitor Kazal Type 1 (SPINK1) in MLP1, Serpin Family A Member 1 (SERPINA1), HNF4A, AFP, Apolipoprotein A1 (APOA1), and Apolipoprotein B (APOB) in MLP2; HNF4A, APOA1, AFP and HNF1 Homeobox B (HNF1B) were also highly expressed in ML (Fig. 4M). The mRNAs of hepatic endoderm markers, SOX17 and T-Box Transcription Factor 3 (TBX3)51,52, were also enriched in the ML cluster (Fig. 4M). We did not detect any albumin mRNA in our samples, which might reflect the immature, progenitor state of the cells or the insufficient sequencing depth. Furthermore, we scored each cell for expression of human liver-enriched genes6 and found that KO cells were enriched for these genes compared to WT cells, with the emphasis on MLP2, ML, and surprisingly, endocrine cell clusters (Fig. 4N). Interestingly, analysis of a scRNA-seq dataset from developing human liver (PCW 5-6) reveals that among the PRDX, GPX, and SOD gene families, GPX2 is the only transcript expressed at a substantially reduced level in liver cells (Supplementary Fig. 6H)9. These results further supported the emergence of alternative differentiation fates upon GPX2 deletion, even under pro-pancreatic signals.

Oxidative stress in liver vs. pancreas cell fate decisions
To isolate the intrinsic differentiation bias of GPX2 KO cells, we differentiated hPSCs to DE and then cultured the cells as spheroids in basal medium, lacking endocrine lineage-specific signals (Fig. 5A). By day 8, KO cells exhibited significantly elevated protein levels of liver markers: FGB (3-fold), AFP (1.5-fold), and HNF4A (2.5-fold), as well as the intestinal marker CDX2 (3.5-fold) (Fig. 5B–D). Co-staining for HNF4A, a marker of early liver development, with AFP and FGB, associated with liver maturation9,26 confirmed liver-like progenitor formation in KO, with all AFP+ or FGB+ cells also co-expressing HNF4A (Fig. 5C).

To investigate GPX2 suppressive role in liver-like fate acquisition, we generated doxycycline-inducible GPX2 overexpression (OE) hPSC line on HUES8 and H1 background (Supplementary Fig. 7A–D). 24-h treatment of GPX2 OE hPSCs with 1 µg/mL doxycycline resulted in 3-fold increase in GPX2 expression (Supplementary Fig. 7B). Inducing GPX2 OE at DE stage, followed by spontaneous differentiation, led to an 80% decrease in AFP and FGB protein expression compared to doxycycline-treated WT cells at day 8 (Fig. 5E, F, Supplementary Fig. 7D). Consequently, overexpression of GPX2 blocks liver-like fate, an effect opposite to GPX2 loss.

We then explored the correlation between differentiation bias and oxidative stress levels. We determined that GPX2 protein expression emerges at day 6 of pancreatic differentiation (Fig. 1I). As GPX2 acts as an antioxidant, we assumed that its expression may be triggered by increased oxidative stress, which led us to induce oxidative stress shortly before GPX2 expression starts. Treatment of WT cells with 10 nM H2O2 increased (by >60%) HNF4A expression, and KO cells with 100 pM Se decreased (by 30%) HNF4A levels in KO cells (Fig. 5G, H). Therefore, H2O2 treatment of WT cells during spontaneous DE differentiation recapitulates the cell fate bias observed in GPX2-deficient PEN, suggesting that H2O2 induced oxidative stress impacts the cell fate decisions regardless of the in vitro differentiation approach.

Bulk RNA-seq on WT, WT treated with H2O2 (WT+H2O2), and GPX2 KO spontaneously differentiated DE cells revealed distinct transcriptional profiles among all samples with KO and WT+H2O2 clustering more closely to each other than to WT (Supplementary Fig. 7E). KO cells, exhibited significant upregulation of liver-associated genes, including Cytochrome P450 Family 2 Subfamily E Member 1 (CYP2E1), Hepatocyte Growth Factor (HGF), extracellular matrix (ECM) components, including Collagen Type VII Alpha 1 Chain (COL7A1), Laminin Subunit Alpha 4 (LAMA4), and TGF-β pathway, including Nodal Growth Differentiation Factor (NODAL) and Bone Morphogenetic Protein 8a (BMP8A). Interestingly, WT+H2O2 cells exhibited similar transcriptional changes (Supplementary Fig. 7F, G). Focusing on liver-associated genes, we identified overlapping upregulated genes in GPX2 KO cells and WT+H2O2 cells compared to WT (Fig. 5I, Supplementary Data 4). Gene Set Enrichment Analysis (GSEA) corroborated these findings, revealing enrichment of liver- or oxidative stress-associated terms, particularly cholesterol homeostasis, in both KO and WT+H2O2 cells (Fig. 5J) and enrichment of the ROS pathway in KO cells (Supplementary Fig. 7H).

Next, we performed an Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) of WT and GPX2 KO cells differentiated spontaneously until day 8 (Supplementary Fig. 7J–N). This revealed significant changes in chromatin accessibility at liver genes or regulatory sequences, including APOA1, CYP2E1, FGB, Fibrinogen Gamma Chain (FGG), and Translocase of Outer Mitochondrial Membrane 40 (TOMM40) (Fig. 5K), consistent with the RNA-seq data. Motif enrichment analysis in KO cells showed enrichment of motifs for transcription factors involved in TGF-β signaling activation, such as JUN (AP-1) and SMAD Family Member 3 (SMAD3)53, as well as Forkhead Box P3 (FOXP3), which is crucial for TGF-β-mediated regulatory T cell differentiation and immune tolerance54. Additionally, a motif for T-Box Transcription Factor 15 (TBX15) was identified in KO cells (Fig. 5L, Supplementary Data 5). In mice, Tbx3, a related T-Box binding transcription factor, plays a crucial role in cell-fate determination and liver organogenesis51 and TBX3-deficient hPSCs demonstrate reduced hepatocyte differentiation and an increased pancreatic progenitor differentiation55.

We also investigated whether KO and WT+H2O2 cells exhibit an increased propensity for spontaneous intestinal differentiation. Analysis of intestine-associated gene expression in RNA-seq data indicated that KO cells exhibit higher expression levels of a subset of intestine marker genes, such as CDX2, Telomerase Reverse Transcriptase (TERT)56, Doublecortin Like Kinase 1 (DCLK1)57, and HOP Homeobox (HOPX)58. Here, we have also observed in WT+H2O2 cells increased intestinal marker expression, resembling the changes in KO cells (Supplementary Fig. 7I), and similarly to liver-related genes. Moreover, we found that GPX2 KO cells display increased chromatin accessibility at the SRY-Box Transcription Factor 2 (SOX2) and DCLK1 loci compared to WT cells (Supplementary Fig. 7N). Together, these results suggest that, compared to WT cells, KO and WT+H2O2 cells exhibit an enhanced capacity for intestinal differentiation. However, based on the number of intestine- and liver-related changes in RNA-seq and ATAC-seq data, the liver-like progenitor characteristics appear to be preferred.

GPX2 deficiency causes extracellular matrix alterations
We next investigated how changes in ROS metabolism influence cell fate determination. An analysis of scRNA-seq data from cells at PEN and SC-β cell stages revealed upregulation of ECM-associated genes in KO cells. These included Laminin Subunit Alpha 1 (LAMA1), Fibronectin 1 (FN1), and Laminin Subunit Beta 1 (LAMB1), as well as ECM signaling regulators, including Cadherin 1 (CDH1), Cadherin 2 (CDH2), and Glypican 3 (GPC3) (Fig. 6A–D). Consistent with these findings, a comprehensive analysis of the core matrisome (which includes ECM proteins, ECM-associated proteins, growth factors, and ECM-modifying enzymes59) at the SC-β cell stages showed increased gene set score in the ML, MLP1, and MLP2 clusters of KO cells. In WT cells, a similar increase in expression of core matrisome genes was observed in the PP2 cluster, albeit at a lower level. The ML cluster exhibited the highest RNA levels of matrisome components compared to both KO endocrine cells and WT PP cells (Fig. 6E). To further investigate changes in ECM, we analyzed RNA-seq data from spontaneously differentiated WT, KO, and WT+H2O2 cells, focusing on ECM-associated marker genes. This revealed their upregulation in KO and WT+H2O2 cells (Fig. 6F, Supplementary Data 4). Additionally, changes in chromatin accessibility were observed for ECM markers, including Laminin Subunit Alpha 5 (LAMA5) and Fibulin 2 (FN1), among others in KO cells (Fig. 6G). Furthermore, immunostaining confirmed increased expression of LAMA1 alongside HNF4A in KO compared to WT cells (Fig. 6H). We also examined other ECM proteins, specifically LAMA5 and COL4A, and observed striking changes in their composition and formation of dense clusters specifically in GPX2-deficient cells (Fig. 6H). In summary, our results indicate substantial changes in ECM composition in KO cells, which may influence the direction of endoderm differentiation.

BMP activation drives hepatic bias in GPX2-deficient PFG
To elucidate the molecular basis of the PFG differentiation bias observed in KO cells, we leveraged RNA-seq and scRNA-seq datasets to analyze differential signaling pathway activity across key developmental stages: PFG, PEN, and SC-β cell. Functional annotation of the upregulated genes in ML cells using Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed enrichment in terms associated with liver function, including coagulation cascades60,61, PPAR signaling pathway62, protein digestion and absorption, platelet activation63, and the metabolism of cholesterol and glutathione64–66. Notably, the TGF-β signaling pathway term, encompassing TGF-β and BMP signaling67,68, emerged as one of the most significantly enriched pathways (Fig. 7A, Supplementary Data 3). PROGENy analysis revealed the significant upregulation of TGF-β and WNT pathway-responsive genes in KO cells, particularly in ML cluster (Fig. 7B, C, Supplementary Data 3). This is particularly relevant because the interplay between TGF-β, BMP, and WNT signaling pathways critically regulates liver-pancreas lineage decisions35. Analysis of transcription factor regulons69 indicated elevated regulatory activity of SMAD9 (also known as SMAD8), SMAD5, and SMAD1, critical for BMP signaling (Supplementary Fig. 8A, Supplementary Data 3). Increased activity of the TGF-β regulon was observed already at the PEN stage (Fig. 3G, H). Spontaneously differentiated GPX2 KO cells also exhibited higher expression of TGF-β-related genes compared to WT cells (Fig. 7D), and increased activity of the SMAD1 and SMAD9 regulons (Supplementary Fig. 8B). Finally, H2O2-treated WT cells displayed similar gene expression alterations, recapitulating the KO phenotype (Fig. 7D, Supplementary Fig. 8B, Supplementary Data 4). ATAC-seq data revealed changes in chromatin accessibility in a cohort of TGF-β regulatory genes, including SMAD1, Bone Morphogenetic Protein Receptor Type 2 (BMPR2), Left-Right Determination Factor 1 (LEFTY1)70, NODAL, and Activin Membrane Bound Inhibitor (BAMBI)71 (Fig. 7E, Supplementary Fig. 8C). Changes were also observed in TGF-β/ACTIVIN/NODAL negative regulators (Left-Right Determination Factor 2, LEFTY2 and TGFB Induced Factor Homeobox 1 and 2, TGIF1/2) and BMP signaling enhancers/targets (Repulsive Guidance Molecule BMP Co-Receptor A, RGMA, Repulsive Guidance Molecule BMP Co-Receptor B, RGMB, Inhibitor of DNA Binding 1, ID1, Inhibitor of DNA Binding 2, ID2, and Inhibitor of DNA Binding 3, ID3) (Supplementary Fig. 8C). Increased expression of ID1, ID2, and ID3 was observed in the ML cluster of KO cells at the SC-β stage (Supplementary Fig. 8C). Finally, we have also identified changes at the loci of Fibrillin-1 (FBN1)72 and Thrombospondin Type 1 Domain Containing 4 (THSD4, ADAMTSL-6)73, which encode ECM proteins that contribute to ECM structures regulating latent TGF-β availability (Supplementary Fig. 8C). These together suggest that upon ECM remodeling, latent TGF-β complexes are released, leading to TGF-β activation in KO cells.

The TGF-β signaling family, with its TGF-β/ACTIVIN/NODAL and BMP branches72,74, uses distinct ligands to activate SMAD2/375 or SMAD1/5/976 signaling. IF staining revealed nuclear localization of phosphorylated SMAD1/5 (p-SMAD1/5) in FGB+ KO cells differentiated spontaneously and in KO cells at SC-β stage (Fig. 7F, I), indicating active BMP signaling. While total SMAD2/3 protein levels were elevated in KO cells, no nuclear localization was observed, suggesting inactive TGF-β signaling (Fig. 7G, H). No significant differences in SMAD4 expression were observed (Supplementary Fig. 8D). Increased BMP activity in KO cells likely results in preferential p-SMAD1/5/8 binding to SMAD4, depleting SMAD4 availability, and suppressing TGF-β activation77. To determine whether TGF-β or BMP signaling modulation recapitulates the KO phenotype, we treated WT DE cells with activin A (AA) and/or BMP4 to activate the TGF-β and BMP pathways, respectively. Conversely, KO DE cells were treated with Alk5iII and LDN193189, inhibiting the TGF-β and BMP pathways, respectively (Fig. 7J). BMP4 treatment of WT DE resulted in increased HNF4A and FGB expression. Combined inhibition of TGF-β and BMP signaling with LDN193189 and Alk5iII in KO cells suppressed these markers, reducing their levels below those in WT cells (Fig. 7K, L). Intriguingly, treatment of WT cells with BMP4 and AA, or AA alone, decreased HNF4A expression compared to untreated WT cells, reaching levels comparable to KO cells treated with inhibitors (Supplementary Fig. 8E). LDN193189 or Alk5iII alone in KO cells resulted in similar HNF4A levels as combined inhibition (Supplementary Fig. 8E). These findings suggest that the BMP pathway drives the KO cells towards liver-like lineages. In summary, GPX2 loss in PFG lead to increased oxidative stress, remodeling of ECM composition, allowing BMP release from its latent complex, and enhancing its activity. This ultimately increases the liver-like identity gene expression in PFG, biasing it towards hepatic-like fate (Fig. 7M).

Methods
Ethics statement
All experiments with human embryonic stem cells were performed in accordance with the relevant guidelines and regulations of the Local Bioethics Committee of the Medical University of Poznan, Poland. The hESC lines used in this study are HUES8 (sex: male, NIH registry 0021), derived by the Harvard Stem Cell Institute and Dr. Douglas A. Melton laboratory, and H1 (sex: male, NIH registry 0043), derived by the University of Wisconsin and Dr. James Thomson laboratory. The manuscript reports on expression studies with human fetal pancreas. Tissue was obtained under IRB approval IRB-3097 to M.B., and was processed at Baylor College of Medicine, Houston, TX, USA.

Cell culture
hPSCs used in this study included human H1 (derived by Dr. James Thomson, University of Wisconsin), HUES8 (derived by Dr. Doug Melton at Harvard University), and iCas9-HUES8 (a doxycycline-inducible Cas9-expressing HUES8 line generated by Dr. Danwei Huangfu, Memorial Sloan Kettering Cancer Center) cell lines. The hPSCs were cultured in Stem Flex medium (Thermo Fisher Scientific, Netherlands) on Geltrex-coated plates (Thermo Fisher Scientific, Netherlands) at 37°C in a 5% CO2 atmosphere. Cells were passaged every 3–4 days using PBS-EDTA and routinely tested for mycoplasma via PCR assay, with all tests returning negative. All experiments with human embryonic stem cells were performed in accordance with the relevant guidelines and regulations of the Local Bioethics Committee of the Medical University in Poznan, Poland.

GPX2 gene deletion in hPSCs
Three different sgRNAs targeting the GPX2 gene were designed using Benchling software and produced in-house, according to the protocol previously published18. Shortly, a PCR product consisting of T7 RNA polymerase promoter, target-specific sgRNA, and common sgRNA sequence was used for in vitro transcription to produce sgRNA for lipofection. The sequences of sgRNA are listed in Supplementary Table 1. HUES8-iCas9 cells at the confluence of 70%, treated previously with doxycycline for 24h, were transfected using Lipofectamine RNAiMax (Thermo Fisher Scientific, Netherlands) and cultured for 1 day. Next, cells were seeded at a single-cell density on a 60mm culture dish coated with Geltrex and cultured for 7 days. Single colonies were transferred to a 96-well plate. The PCR was performed on genomic DNA isolated from individual colonies with the primers flanking the GPX2 gene sequence (GPX2_F: 5′-TGATGGGGCAAGTAGGAGTC-3′, GPX2_R: 5′-CCACACCTGCCC TTTATTGG-3′). The GPX2 KO hPSC lines were established by selecting homozygous clones with desired mutations, identified based on amplified DNA electrophoresis and Sanger sequencing.

Pancreatic differentiation of hPSCs in 3D culture
For in vitro pancreatic differentiation in 3D culture, we employed a previously established protocol98 with modifications17,18. hPSCs were dissociated into single-cell suspensions using TrypLE Select (Thermo Fisher Scientific, Netherlands) and plated at a density of 3.5 × 106 cells/well in E8 medium (Thermo Fisher Scientific, Netherlands) supplemented with 10 µM Y-27632 (Peprotech, USA) on low-adhesion, non-treated 6-well plates (Eppendorf, Germany) placed on an orbital shaker at 37°C, 110 rpm, under a 5% CO2 atmosphere. The medium was replaced the next day with E8 without Y-27632. On the subsequent days spheroids were washed in DMEM/F12 medium (Corning, USA) and the medium was changed, as follows: Basal medium: S1 – MCDB131 (Corning, USA), 1% Glutamax (Thermo Fisher Scientific, Netherlands) + 1% Pen/Strep (Thermo Fisher Scientific, Netherlands), 2.44 mM glucose (Merck Millipore, USA), 29 mM sodium bicarbonate (Merck Millipore, USA), 2% BSA (Capricorn Scientific, USA), 2 µl/100 mL ITS-X (Thermo Fisher Scientific, Netherlands). S2 – MCDB131 (Corning, USA), 1% Glutamax (Thermo Fisher Scientific, Netherlands) + 1% Pen/Strep (Thermo Fisher Scientific, Netherlands), 2.44 mM glucose (Merck Millipore, USA), 13 mM sodium bicarbonate (Merck Millipore, USA), 2% BSA (Capricorn Scientific, USA), 2 µl/100 mL ITS-X (Thermo Fisher Scientific, Netherlands). S3 – MCDB131 (Corning, USA), 1% Glutamax (Thermo Fisher Scientific, Netherlands) + 1% Pen/Strep (Thermo Fisher Scientific, Netherlands), 2.44 mM glucose (Merck Millipore, USA), 13 mM sodium bicarbonate (Merck Millipore, USA), 2% BSA (Capricorn Scientific, USA), 500 µl/100 mL ITS-X (Thermo Fisher Scientific, Netherlands). S4 – MCDB131 (Corning, USA), 1% Glutamax (Thermo Fisher Scientific, Netherlands) + 1% Pen/Strep (Thermo Fisher Scientific, Netherlands), 2.44 mM glucose (Merck Millipore, USA), 29 mM sodium bicarbonate (Merck Millipore, USA), 2% BSA (Capricorn Scientific, USA), 500 µl/100 mL ITS-X (Thermo Fisher Scientific, Netherlands), 5 µg/mL heparin sulfate (Sigma Aldrich, USA), S5 – MCDB131 (Corning, USA), 1% Glutamax (Thermo Fisher Scientific, Netherlands) + 1% Pen/Strep (Thermo Fisher Scientific, Netherlands), 2.44 mM glucose (Merck Millipore, USA), 2% BSA (Capricorn Scientific, USA), 260 nM zinc sulfate (Merck Millipore, USA), 5 µg/mL heparin sulfate (Sigma Aldrich, USA), 1x Trace Elements A (Corning, USA), 1x Trace Elements B (Corning, USA). Day 1: S1 + 3 µM CHIR99021 (Peprotech, USA) + 100 ng/mL Activin A (Peprotech, USA) + 250 µM vitamin C (Sigma Aldrich, USA); Days 2-3: S1 + Activin A; Days 4-6: S2 + 50 ng/mL KGF (Peprotech, USA) + 1.25 µM IWP2 (Selleckchem, USA) + 250 µM vitamin C; Day 7: S3 + 50 ng/mL KGF + 2 µM retinoid acid (Peprotech, USA) + 500 nM PdBu (Tocris, United Kingdom) + 250 nM SANT-1 (Tocris, United Kingdom) + 200 nM LDN193189 (Peprotech, USA) + 250 µM vitamin C + 10 µM Y-27632; Days 8-12: S3 + 5 ng/mL Activin A + 50 ng/mL KGF + 100 nM retinoid acid + 250 nM SANT-1 + 1.25 µM IWP2 + 100 ng/mL EGF (Peprotech, USA) + 10 mM nicotinamide (Sigma Aldrich, USA) + 250 µM vitamin C; Days 13-19: S4 + 100 nM retinoic acid (Peprotech, USA) + 250 nM SANT-1 (Tocris, United Kingdom) + 1 µM LY-411575 (Adooq Bioscience, USA) + 1 µM T3 (Sigma Aldrich, USA) + 10 µM Alk5iII (Adooq Bioscience, USA) + 20 ng/mL betacellulin (Peprotech, USA) + 250 µM vitamin C; Day 20-22: S5 without additional components. Experiments were conducted on at least two independent hPSC clonal lines. To induce oxidative stress, H2O2 (Sigma-Aldrich, USA) in the final concentration range from 10 nM to 10 µM was added to the medium once on the 5th day of differentiation of WT cells. To estimate the effect of selenium, sodium selenite (Sigma-Aldrich, USA) in the concentration from 1 pM to 1 nM was supplemented to the medium for 3 days starting from the 5th day of differentiation.

Pancreatic hPSC differentiation in 2D culture
For in vitro pancreatic differentiation in 2D culture, we employed a protocol established previously98 with modifications17,18. hPSCs were dissociated into single-cell suspensions using TrypLE Select (Thermo Fisher Scientific, Netherlands) and plated at a density of 2 × 106 cells/well in Stem Flex medium (Thermo Fisher Scientific, Netherlands) supplemented with 10 µM Y-27632 (Peprotech, USA) on Geltrex-coated 6-well plates (Eppendorf, Germany) under a 5% CO2 atmosphere. On the subsequent days cells were washed in DMEM/F12 medium (Corning, USA) and the medium was changed, as follows: Basal medium: S1 – MCDB131 (Corning, USA), 1% Glutamax (Thermo Fisher Scientific, Netherlands) + 1% Pen/Strep (Thermo Fisher Scientific, Netherlands), 8.88 mM glucose (Merck Millipore, USA), 14 mM sodium bicarbonate (Merck Millipore, USA) and 0.1% BSA (Capricorn Scientific, USA). S2 – MCDB131 (Corning, USA), 1% Glutamax (Thermo Fisher Scientific, Netherlands) + 1% Pen/Strep (Thermo Fisher Scientific, Netherlands), 4.44 mM glucose (Merck Millipore, USA), 14 mM sodium bicarbonate (Merck Millipore, USA) and 0.1% BSA (Capricorn Scientific, USA). S3 – MCDB131 (Corning, USA), 1% Glutamax (Thermo Fisher Scientific, Netherlands) + 1% Pen/Strep (Thermo Fisher Scientific, Netherlands), 2.44 mM glucose (Merck Millipore, USA), 20.8 mM sodium bicarbonate (Merck Millipore, USA), 2% BSA (Capricorn Scientific, USA) and 500 µl/100 mL ITS-X (Thermo Fisher Scientific, Netherlands). Day 1: S1 + 3 µM CHIR99021 (Peprotech, USA) + 100 ng/mL Activin A (Peprotech, USA) (Sigma Aldrich, USA); Days 2-4: S1 + Activin A; Days 5-6: S2 + 50 ng/mL KGF (Peprotech, USA) + 1.25 µM IWP2 (Selleckchem, USA) + 250 µM vitamin C; Day 7: S3 + 50 ng/mL KGF + 2 µM retinoid acid (Peprotech, USA) + 1 µM PdBu (Tocris, United Kingdom) + 250 nM SANT-1 (Tocris, United Kingdom) + 200 nM LDN193189 (Peprotech, USA) + 250 µM vitamin C. To induce oxidative stress, H2O2 (Sigma-Aldrich, USA) in the final concentration range from 10 nM to 10 µM was added to the medium once on the 5th day of differentiation of WT cells. To check the effect of selenium, sodium selenite (Sigma-Aldrich, USA) in the concentration from 1 pM to 1 nM was supplemented to the medium for 3 days starting from the 5th day of differentiation. WT cells treated with 100 pM Se and KO cells treated with 10 nM H2O2 were used as controls in this experiment.

Spontaneous differentiation of hPSC-derived endoderm
hPSCs were differentiated to DE as described above. Starting from day 4, cells were maintained in S2 basal medium for 3 days, followed by S3 basal medium for 1 day. Spheroids were harvested on day 8 of differentiation. To evaluate the effects of oxidative stress modifications, we supplemented the basal medium with 10 µM H2O2 (Sigma-Aldrich, USA) on day 5, or with 100 pM sodium selenite (Sigma-Aldrich, USA) from day 5 to 8. TGF-β and BMP pathways were modified by the medium supplementation with 10 ng/mL Activin A, 25 µg/mL BMP4 (Peprotech, USA) in differentiation day 4 and 3 ng/mL BMP4 in differentiation days 5–8, 200 nM LDN193189 (Peprotech, USA) and 10 µM Alk5iII (RepSox) (Adooq Bioscience, USA) in different combinations.

Western blot
Cells were lysed in the buffer composed of 60 mM Tris (Bio-Shop, Canada), 2% SDS (Bio-Shop, Canada), 10% sucrose (Bio-Shop, Canada), and 1% protease inhibitor cocktail (Sigma Aldrich, USA), followed by sonication for 15s in 8 cycles with 1 min pause between cycles and centrifugation at 12,000 x g for 10 min. Protein concentration was determined using a DS-11 spectrophotometer (DeNovix, USA). 30 µg of cell lysates in Bolt LDS buffer (Thermo Fisher Scientific, Netherlands) were incubated for 5 min at 95°C and then loaded on a 12% SDS-PAGE gel. Electrophoresis was carried out at 75 V for the first 15 min and 125 V for an additional 1 h, with a constant current intensity of 30 mA. Proteins were transferred onto the PVDF (Thermo Fisher Scientific, Netherlands) membrane using the BioRad Trans-Blot Turbo Transfer System for 10 min. Membranes were blocked with 5% non-fat dry milk or 1–3% BSA in TBS-Tween 20 followed by overnight incubation with a primary antibody. On the next day, membranes were probed with HRP-conjugated secondary antibodies. All antibodies are listed in Supplementary Table 2. Western blot results were visualized using the enhanced chemiluminescent visualization (ECL) system (Thermo Scientific Pierce) or SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific, Netherlands). Signal detection was conducted using the G:Box System (Syngene, India). Western blots were quantified in ImageJ software.

Flow cytometry analysis of oxidative stress
The hPSCs and spheroids were dissociated into a single-cell suspension by incubation in TrypLE Select (Thermo Fisher Scientific, Netherlands) for 5 min with shaking, followed by PBS wash. Subsequently, live cells were stained with 0.5 µM H2DCFDA (Invitrogen, Netherlands) to assess ROS level or with 1 µM MitoSOX Mitochondrial Superoxide Indicators (Invitrogen, Netherlands) to detect mitochondrial ROS level. FC analysis was performed using Agilent NovoCyte (Agilent Technologies, USA), acquiring at least 5000 events per sample. Data analysis, including gating, quantification, and generation of density plots/histograms, was performed using the NovoFast program (Agilent Technologies, USA).

Single-cell RNA sequencing
Spheroids at the PEN and SC-β cell stages, both WT and KO, were dissociated into single cells using 1% trypsin in PBS at 37°C. scRNA-seq for the PEN stage was done using a combinatorial indexing method from SCALE Biosciences (ScaleBio, USA). Briefly, approximately 250,000 cells of WT PEN and approximately 500,000 cells of KO PEN cells were washed once with ice-cold PBS, centrifuged, and resuspended in 500 µL PBS. Cells were fixed by vortexing with simultaneous addition of Complete Cell Fixation Solution (Fixation Reagent 1, ice-cold 100% methanol, Stanlab, Poland) with DEPC, followed by 15 min incubation on ice and then washed with WRS Buffer (Scale Biosciences) and resuspended in WRS Buffer. Libraries for single-cell RNA sequencing of PEN cells were conducted with the ScaleBio Single Cell RNA Sequencing Kit according to the manufacturer’s protocol. Libraries for the SC-β cell stage scRNA-seq were prepared using the 10x Genomics kit following the manufacturer’s protocol. In this case, the single-cell transcriptome was tagged with a UMI barcode using a droplet-based method in the Chromium system (10x Genomics, USA). For both library preparation methods, the quality and quantity of cDNA libraries were assessed using an Agilent TapeStation with High Sensitivity D1000 ScreenTape (Agilent Technologies, USA). Libraries were paired-end sequenced with a depth of 10,000 (ScaleBio) or 40,000 (10x Genomics) reads per cell. 2500 and 4042 cells from the PEN stage and 6128 and 5610 cells from the SC-β cell stage were sequenced for WT and KO, respectively. Initial processing of acquired data was performed using a manufacturer’s pipeline, i.e., NextFlow pipeline for ScaleBio and Cell Ranger for 10x Genomics, the latter, including WT and KO data aggregation with the “cellranger aggr” algorithm. Further data processing analysis for both platforms, including clustering, visualization, marker identification, differential expression analysis, and pathway enrichment were performed using the Seurat v5.0.399, DElegate v1.2.1100, escape v2.5.5101, decoupleR v 2.1469, and UCell v2.12102 packages in RStudio. Enriched pathways and processes among markers for each cluster and DEGs were identified via the Kyoto Encyclopedia of Genes and Genomes (KEGG), Biological Processes (BP), Wiki Pathway, CollecTRI103, and PROGENy41 databases. The functional enrichment analysis was performed in GeneCodis, Enrichr104, or Appyter105 interactive platforms. A pseudotime trajectory was performed using Monocle3106. The significance cutoff value (adj. p-value < 0.05) was utilized in functional enrichment analysis. Human fetal pancreas PCW 7-11 (OMIX001616)36 and PCW 12-14 (GSE197064)107, datasets were imported into Seurat, followed by sub-clustering of the epithelial lineages and further analyses in Seurat as described above. Mouse E6.5-8.5 scRNA-seq data19 was analyzed using Bioconductor MouseGastrulationData package in R. Mouse E14.5 pancreas scRNA-seq dataset was generated in our previous work20 (GEO GSM2689399).

Immunofluorescence staining
Human pancreas sections were processed at Baylor College of Medicine, Houston, TX (USA) with IRB H-3097 approval granted to Dr. Malgorzata Borowiak. Donor identities were encrypted, and the data were analyzed anonymously. The human 10.6- and 13-week fetal pancreas samples were fixed in 4% paraformaldehyde/PBS for 4h, washed with PBS, soaked in 30% sucrose, and embedded in Tissue Tek. Sections (12 µm-thick) were cut onto Superfrost Plus-coated glass slides and stored at -80°C.

Cell monolayers were fixed with 4% paraformaldehyde/PBS for 15 min at room temperature and washed with PBS. Fixed cells were permeabilized by 0.5% Triton X-100 (BioShop, Canada) in PBS for 15 min, blocked for 45 min in 3% BSA (BioShop, Canada) + 0.1% Tween-20 (BioShop, Canada) in PBS, and then incubated overnight at 4°C with primary antibodies diluted in 5% normal donkey serum (NDS, Jackson Immunoresearch, UK). The next day, cells were washed three times in 0.1% Tween 20 in PBS and incubated with secondary antibodies conjugated with Alexa Fluor 488, TRITC, or Alexa Fluor 647 (diluted 1:400 in 5% NDS) for 2h at room temperature. The excess secondary antibody was removed by two washes in 0.1% Tween 20 in PBS, and the samples were incubated with DAPI (Sigma Aldrich, USA) as a counterstain. To stain lipids, 1 µg/mL Nile Red (AAT Bioquest, USA) was added to the medium, and cells were incubated at 37°C for 15 min, then washed with PBS and fixed.

3D spheroids were fixed with 4% paraformaldehyde/PBS for 45 min at 4°C, washed once with 0.1% Tween-20 in PBS, and suspended in wash buffer (SWB: 2% BSA, 0.1% Triton X-100, 0.025% SDS, 1x PBS). Fixed spheroids were blocked in SWB for 45 min at 4°C and incubated overnight with a primary antibody at 4°C on an orbital shaker. The next day, the spheroids were washed 3 times with SWB solution for 1h each at 4°C and incubated with secondary antibodies conjugated with Alexa Fluor 488, TRITC, or Alexa Fluor 647 diluted 1:400 and DAPI (1:10,000) in SWB, overnight at 4°C on an orbital shaker. On the next day, the excess of the secondary antibody was removed by 2 washes in 0.1% Tween-20 in PBS, first rapid, second for 1h at 4°C on an orbital shaker. For imaging, spheroids were kept in PBS on a high-content imaging glass-bottom 96-well plate (Corning, USA). The primary and secondary antibodies used in the study are listed in Supplementary Table 3. Representative images shown in Figures and Supplementary information were chosen from at least 3 biological repeats for each condition and 3 photograpies per repeat, or as stated in the corresponding quantitative analysis.

Microscopy imaging
Images were obtained with an epifluorescence, confocal, or light sheet microscope. Brightfield images were taken with a Leica DM IL-Led (Leica, Germany) microscope with N Plan Fluor 4x/0.12, N Plan Fluor 10x/0.30, N Plan Fluor 20x/0.40, and N Plan Fluor 40x/0.60 lenses and a JENOPTIK GRYPHAX series ProgRes camera (JENOPTIK, Germany). Fluorescence images were obtained with confocal microscope Nikon A1Rsi (Nikon, Germany) microscope with Plan Plan Apo 10x/0.45 DIC N1, Plan Apo VC 20x/0.75 DIC N2, Apo 40x/1.25 WI λSDICN2, Plan Apo VC 60x/1.4 Oil DIC N2 and Plan Apo VC 100x/1.4 Oil DIC N2 lenses and with Nikon NIS Elements AR 5.21.01 64-bit software (Nikon, Germany) or with confocal microscope LEICA Stellaris 8 (Leica Camera, Germany) with HC PL APO 20x/0.75 CS2 Air, HC PL IR APO 40x/1.10 W CORR, PL APO 63x/1.20 W CORR CS2 or PL APO 63x/1.20 W CORR CS2 lenses and with Leica LAS X Office 1.4.7 64-bit software (Leica Camera, Germany) or with lightsheet fluorescence microscopy (LSFM) ZEISS Lightsheet 7 (Zeiss, Germany) with W Plan-APO 20x/1,0 DIC lens, in water chamber and with ZEN Black 3.1 software for imaging and ZEN Blue 3.1 software for data analysis (Zeiss, Germany).

Doxycycline-inducible GPX2 overexpression in hPSCs
GPX2 cDNA was amplified from total WT PEN cDNA with primers consisting of GPX2 coding sequence, FLAG sequence at the 3′ end, and adapters to the plasmid. Primers consisted of the following sequences: F_GPX2_OE: 5′-CTTTAAAGGAACCAATTCAGccaccATGGCTTT CATTGCCAAGTCCTTC-3′, R_GPX2_OE: 5′-AAGCTGGGTCTAGATATCTTActtatcgtcgtcatccttg taatcacgTATGGCAACTTTAAGGA-3′. Primers were designed using the Benchling Assembly Wizard tool. GPX2 cDNA was amplified via PCR using Q5 polymerase (NEB, USA) following the manufacturer’s instructions, with two-step PCR cycling conditions. The cycling conditions were: 98°C for 30s, followed by 15 cycles of 98°C for 15s, 63°C for 20s, 72°C for 90s, 25 cycles of 98°C for 15s, 72°C for 15s, 72°C for 90s, followed by final extension at 72°C for 5 min. Monarch DNA Gel Extraction Kit (NEB, USA) was used for the resulting GPX2 PCR product purification. Gateway pENTR 1A plasmid (Invitrogen, Netherlands) was digested with the restriction enzymes SalI and XhoI (Thermo Fisher Scientific, Netherlands). To ligate the GPX2 PCR product with vector NEBuilder HiFi DNA Assembly Cloning Kit (NEB, USA) was used. The resulting Gateway pENTR 1A plasmid containing the GPX2 cDNA sequence was subjected to an LR recombination reaction with plasmid PB-TA-ERP2108 (gift from Dr. Knut Woltjen, Addgene, USA, 80477), leading to the acquisition of the final plasmid (pENTR-hGPX2-FLAG plasmid) for establishing doxycycline-inducible GPX2 overexpression in HUES8-iCas9 and H1 hPSCs. Next, HUES8-iCas9 and H1 hPSCs were transfected with the pENTR-hGPX2-FLAG plasmid and pCMV-hyPB plasmid109 (gift from Dr. Allan Bradley), carrying the transposase enzyme using the Lipofectamine 3000 kit (Thermo Fisher Scientific, Netherlands), following manufacturer’s instructions. Finally, cells were treated with 1 µg/mL puromycin to select positively transformed cells OE hPSCs (GPX2 OE). To induce GPX2 overexpression, cells were treated with 1 µg/mL doxycycline for 24h.

Flow cytometry
Cells were dissociated into a single-cell suspension using TrypLE Select (Thermo Fisher Scientific, Netherlands). Fixation was carried out by incubating the cells with 4% paraformaldehyde in PBS containing 0.1% saponin for 45 min at 4°C, followed by washing with PBS supplemented with 0.1% saponin and 1% BSA (SBP). The cells were then resuspended and incubated overnight at 4°C on a roller with primary antibodies diluted in SBP. On the following day, cells were washed twice with SBP and incubated for 1h at room temperature with secondary antibodies (Alexa Fluor 488, or Alexa Fluor 647; Jackson ImmunoResearch, UK) diluted 1:5,000–1:10,000 in SBP. After removing unbound secondary antibodies by a final PBS wash, cells were resuspended in PBS and immediately subjected to FC. Data were acquired using a NovoCyte Flow Cytometer (Agilent Technologies, USA). FC data processing, including gating, quantification, and generation of density plots or histograms, was performed using NovoExpress v1.6.2 (Agilent Technologies, USA). Details of the primary and secondary antibodies are provided in Supplementary Table 4.

Bulk RNA sequencing
Total RNA was extracted using the TRIzol Reagent (Thermo Fisher Scientific, Netherlands) following the phenol/chloroform method. The RNA was dissolved in RNase-free water and treated with DNase I (Thermo Fisher Scientific, Netherlands). Its concentration and quality were assessed spectrophotometrically by measuring A260/230 and A260/280 ratios using a DS-11 spectrophotometer (DeNovix, USA). We used 500 ng of total RNA isolated from WT and KO cells at PFG stage and day 8 of spontaneous differentiation, treated with H2O2, to prepare libraries (TruSeq RNA Library Prep Kit v2) according to the manufacturer’s protocol. Libraries were quantified with a Qubit fluorometer (TFS), and their quality was assessed with an Agilent High Sensitivity DNA Kit (Agilent Technologies). Libraries were sequenced with an Illumina HiScan SQ sequencer. RNA-seq raw paired-end reads were trimmed with fastp and aligned with the Ensembl GRCh38 human genome with STAR (v2.7), and counts were obtained with featureCounts v1.6. The raw RNA-seq sequence reads were analyzed via the interactive iDEP 9.6 (integrated differential expression and pathway analysis) platform (http://bioinformatics.sdstate.edu/idep96/). Differentially expressed genes (DEGs) between WT and KO, WT+H2O2 and WT or KO and WT+H2O2 cells were identified and normalized with the DESeq2 or limma-voom package. DEGs significantly (p-value ≤ 0.05) upregulated with a logFC ≥ 0.5 or downregulated with a logFC ≤ -0.5 in any of the sequenced samples were selected for further analysis. The analyzed datasets can also be found in Supplementary Data 1–4. The cluster analysis was performed with the Genesis software (http://genome.tugraz.at/genesisclient/genesisclient_description.shtml). Volcano plots were generated with GraphPad Prism 8 software or in Python using Seaborn and Matplotlib packages for genes with normalized reads, with -log10 adj p-values on the y-axis and log2 FC values on the x-axis. Genes with -log10 adj p ≥ 1 and log2 FC ≥ 0.5 or log2 FC ≤ -0.5 were significantly differentially expressed. The GeneCodis, EnrichR, decoupleR, and Appyters interactive platforms were used for functional enrichment analysis. The cutoff value for significant BP, KEGG, and Wiki pathways was an adjusted p-value ≤ 0.05. Dot plots of representative genes from selected KEGGs, BPs, and Wiki Pathways were plotted in R using the ggplot package. Gene set enrichment analysis (GSEA) was performed with GSEA v4.1.0 software (http://software.broadinstitute.org/gsea/msigdb/index.jsp). GSEA identifies functional enrichment by comparing genes with predefined gene sets. A gene set is a set of genes with similar localizations, pathways, functions, or other features. The input data for the GSEA were normalized counts for the GPX2 KO vs. WT or for WT+H2O2 vs. WT gene sets and a reference gene set based on the Molecular Signatures Database set: h.all.v7.4.symbols.gmt (Hallmarks). The permutation number was set to 1000. The enrichment gene sets with a p-value ≤ 0.05 and a false discovery rate (FDR) ≤ 0.25 in the GSEA were considered to exhibit statistically significant differences. We used the default parameters of the GSEA software. An enrichment map was used to visualize the GSEA results. The enrichment score (ES) and FDR values were used to sort hallmark pathway-enriched gene sets.

ATAC-sequencing
The ATAC libraries were generated as described previously18, following the Omni-ATAC protocol. Shortly, Transposase (Tn5) with assembled Illumina-compatible primers was produced by the Proteomics and Biochemistry Platform of the Andalusian Center for Developmental Biology (CABD, UPO/CSIC/JA) following a published protocol. The WT and KO cells at day 8 of spontaneous differentiation were dispersed to single cells using TrypLE Select (Thermo Fisher Scientific, Netherlands), counted, spun down at 500xg for 5 min at 4°C and resuspended at a concentration of 5 × 104 cells/50 µL per biological replicate in cold ATAC-RSB (10 mM Tris-HCl (Bio-Shop, Canada) pH = 7.4, 3 mM MgCl2 (Millipore, Germany), 10 mM NaCl (Bio-Shop, Canada), containing 0.1% (v/v) Tween-20 (Roche Diagnostics, Germany), 0.1% (v/v) NP-40 (Biosciences, USA)). For nuclei extraction, the cells were incubated with 0.1% (v/v) digitonin (Promega, USA) for 3 min at 4°C and centrifuged for 10 min at 500xg at 4°C with 1 mL ATAC-RSB, containing 0.1% (v/v) Tween-20. Nuclei were prepared for the transposition reaction following the previously described protocol110. The transposed fragments were purified using the Zymo DNA Clean & Concentrator (Zymo Research, USA), eluted in 20 µL of H2O, and stored at -20°C. Barcoded adapters were added to the transposed fragments via PCR using the NEB Next Ultra II Q5 2x Master Mix (New England Biolabs, USA), following the manufacturer’s protocol. All samples were amplified for 10 cycles, then purified using a double-sided AMPure XP bead cleanup (Beckman Coulter, USA) and eluted in 20 µL of H2O. DNA concentration was measured with the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Netherlands), and sample quality was assessed using the TapeStation HS D1000 Kit (Agilent Technologies, USA). Sequencing was performed on the NovaSeq X platform with 150 bp paired-end reads. The nf-core/atacseq bioinformatics pipeline (version 2.1.2, https://nf-core/atacseq) was used for ATAC-seq data analysis. After adapter trimming with Trim Galore and quality assessment with FastQC, reads were mapped to the human genome (hg38) using Bowtie 2 with default paired-end settings. Non-nuclear reads and unmapped paired reads were discarded, and duplicate reads were removed using Picard Mark Duplicates. Further filtering to exclude unmapped regions, mitochondrial DNA, and blacklisted regions was performed with SAMtools. Broad peak calling was conducted using MACS2, while peak annotation (annotatePeaks.pl) and motif enrichment analysis (findMotifsGenome.pl) were performed with HOMER. Normalized bigWig files, scaled to 1 million mapped reads, were generated using BEDTools. Differential accessibility analysis was conducted with DESeq2, and the IGV platform was used to visualize bigWig tracks, peaks, and differential sites. The analyzed dataset can also be found in Supplementary Data 5.

Live-cell imaging and analysis
The IncuCyte live-cell imaging system (Sartorius, Germany) was used to monitor living cells. hPSCs were seeded at a density of 1.5 × 10⁴ cells/cm² on 24-well plates coated with Geltrex and cultured in the IncuCyte for up to 6 days at 37°C in a 5% CO2 atmosphere. Photomicrographs were captured every 2h, and real-time confluence measurements were performed using the IncuCyte Base Analysis Software (Sartorius, Germany). Cell growth was assessed by analyzing changes in the percentage of the image area occupied by cells (confluence) over time.

Statistics and reproducibility
All graphs were generated using GraphPad Prism v8.4.2 for Windows (GraphPad Software, Boston, Massachusetts, USA). Statistical analyses were conducted using unpaired two-tailed Student’s t-tests or one-way ANOVA for multiple comparisons. Data are presented as means ± 95% confidence intervals (CIs) unless otherwise stated. No data was excluded from the analyses. Quantifications were performed independently by two investigators. At least 3 independent biological replicates were performed for each experiment.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
All data generated or analyzed during this study are available. Processed data and all datasets necessary to interpret the results are included in the published article and its Supplementary Information. Raw RNA-seq, scRNA-seq, and ATAC-Seq data generated during the study have been deposited in the NCBI GEO database under accession number GSE291135. Source data are provided as a Source Data file. Raw image files are available from the corresponding author upon a reasonable request. The authors declare that cell lines are available for the research community upon request from the corresponding author. Other published datasets we used in this study could be obtained from: E-MTAB-696719, OMIX00161636, GSE197064107, GSM268939920, E-MTAB-8210 and E-MTAB-71899. Source data are provided with this paper.

Code availability
R code generated for RNA-seq and scRNA-seq data analysis are available at GitHub (https://github.com/WJSzlachcic/BorowiakLab_2025_GPX2/).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of the NCOMSS-25-32853-T entitled “GPX2 and oxidative stress as gatekeepers of pancreatic cell fate” by J. Szpotkowska et al. The overall goal of this is to assess the role that oxidative stress has on human pancreatic differentiation in the context of a human pluripotent stem cell (hPSC) stepwise differentiation protocol. Two mediators of the oxidative stress pathway, Gpx2 and SOD, are upregulated during human fetal pancreas development. GPX2 protein is expressed from the posterior foregut (PFG) stage through the late EP stages of their SC-B differentiation protocol, suggesting a potential role for this gene in pancreatic differentiation. Intriguingly, a comparison of Gpx2 KO hPSC to WT hPSC that have each been treated with their SC-B protocol, demonstrate that in GPX2 KO cells, differentiation is altered from WT cells beginning at the PFG stage. This alteration includes upregulated expression of the intestinal marker Cdx2 and the liver marker HNF4alpha, while Hhex and Pdx1 are significantly downregulated. Remarkably, after 21 days of stepwise differentiation toward a pancreatic beta cell fate, Gpx2 loss of function cells are capable of contributing to the pancreatic beta cell population as efficiently as control cells. Single cell transcriptional analysis of WT and Gpx-/- 21 Day cultures reveals that while the differentiated beta cell populations display a similar transcriptional profile and found at a similar frequency, the less differentiated progenitor states found in WT cultures are dramatically reduced and are replaced by several populations of novel “multilineage” progenitors. The authors also demonstrat that, Gpx-/- hPSC derived DE, cultured in a basal environment and allowed to differentiate on their own for 8D under excessive oxidative stress conditions (WT + peroxide or Gpx2-/-) upregulates pro-liver and pro-intestinal markers. The paper suggests that oxidative stress is required for appropriate hPSC differentiation and that Gpx2 is in part responsible for mitigating the oxidative stress response required during hPSC derived posterior foregut differentiation.\n\nMy major critiques of the paper are as\n1) This paper difficult to follow, the hypothesis tested difficult to pin down and the significance of the conclusions drawn dampened by existing literature (some of which is outlined in 2) or by weakly supportive or poorly justified experiments.\n2) While the role of oxidative stress on pancreatic differentiation has not been previously identified, the idea that the strict control of oxidative stress is critical for fate aquisition in a developmental context (Timme-Laragy et al, 2017; PMID: 2897759) or in disease been explored in depth in a variety of contexts. Strict control of oxidative stress is known to be altered in various gut diseases and in a variety of different developmental context, including intestine development.\n3) In particular it is difficult to determine the relevance of making any conclusions on stages past the PFG stage because their biological relevance of the conclusions from results that are produced after the primary defect at the PFG stage has occurred, are difficult to determine and question the biological/experimental relevance.\n4) Finally, I am particularly flummoxed by the concluding experiments (summarized in Figures 6, 7). While it may be true that ECM and BMP signaling are upregulated in Gpx2 deficient DE differentiation, no direct evidence links these two alterations to one another (as outlined in 7M).\n\nWhat follows is non-exhaustive list of more minor that focus more on the paper up to the end of Figure 4. I have more numerous concerns with the conclusions made and the data produced that was used to produce the last 3 figures but these concerns are only briefly touched on in this review.\n1) How many independent cell lines were used throughout this work. Was only a single WT and mutant hPSC line used?\n2) Figure 1 establishes a potential unique role of Gpx2 in development of the human pancreas in vivo and in vitro by defining the expression window of the transcript or the protein. However inconsistencies in these data are not explored for example: GPX2 protein is abundantly detected in week 10 and 13 fetal pancreas while abundant transcripts are detected in published data sets at week 6 of fetal pancreas, but is severely downregulated after week 9.\n3) A concern is the difference between the two WT biological replicates used in the bulk RNA seq experiments in Fig 2 and how these inconsistencies exacerbate/complicate the trajectory of Gpx2 deficient cells under similar conditions.\n4) In general the methods used to assess oxidative stress nor their significance/caveats are highlighted in this proposal (Fig 1B, 2B, 3K).\n5) Figure 2 demonstrates that Gpx2 loss effects the initial DE  PFG stage upregulating HNF4, CDX2 and Pdx1 while downregulating Hhex compared with WT hES. It is curious that Pdx expression is upregulated in these cultures and this expression is termed precocious because later in differentiation, these cultures appear to have less Pdx expression than controls.\n6) The use of “altered spatial patterning” in PFG is confusing and inaccurate. If individual spheroids demonstrate a reproducible “pattern” in WT cultures this might be correct but the images shown demonstrate that individual spheroids exhibit wide-spread cell to cell heterogeneity thus making conclusions from scRNA seq about patterning is not accurate. Temporal patterns might be a more accurate term but “spatial patterning” is certainly not correct, given the data presented.\n7) A concern for the PEN (pancreatic endoderm) experiments outlined in Figure 3 includes the difference between the two WT replicates in the PC analysis at the PFG stage.\n8) Figure 3K is inadequately described. The plots depicting cell program use is hard to read.\n9) Is Bmp6 normally upregulated by the endoderm during intestine or liver development? BMPs are typically added to the media to induce PFG to induce hepatic fate. This suggests that the bias in these cultures is due to aberrant gene expression caused by oxidative stress conditions rather than a normal developmental process.\n10) Fig 3L how was the period of oxidative stress induction chosen?\n11) Fig 3M- it is unclear what groups the p values provided are comparing. Important controls that are missing include the addition of 0.01 uM peroxide to KO cultures and the addition of 100pM selenium to WT cultures.\n12) While the images shown in figure 3N highlight the points outlined in the paper in the absence of quantitation, they lack significance.\n13) Why does the DAPI staining of the spheroids in Fig 3N, representing PEN, look so different from the cultures in 4A, representing SC-B spheroids? 3N looks tubular and 4A looks circular? They all seem to be taken at a similar magnification.\n14) There is an apparent hepatic bias to the Gpx2 KO (versus WT) hP cells during pancreas-induction. In addition, a subset of intestinal and bile duct gene programs are also upregulated. Are these each represented in one of the 3 distinct GPX KO ML populations present after pancreatic differentiation in Figure 4? Or are these ML populations entirely multilineage?\n15) There is no direct evidence to support their other conclusion that ECM alterations drive BMP expression that in turn promotes hepatic fates.\n16) Oxidative stress is a well-known inducer of fibrosis (excess ECM)- could the changes noted in the ECM be the result of a fibrotic response?\n17) The authors demonstrate that in the absence of Gpx2 hPSC will turn on a number of hepatic genes. This cell population is termed “hepatic progenitors”. How close are hPSC induced hepatic progenitors or in vivo examined hepatic progenitors to the hepatic progenitors produced by Gpx2 deficiency herein?\n18) The conclusion that “ECM remodeling emerges as a key driver of lineage plasticity” is not well-supported by the data presented and appears mainly correlative.\n19) The connection between ECM production, TGFb superfamily signaling and hepatic fate has not been established.", "reviewer_2": "In this study, Szpotkowska et al. investigated the role of GPX2 in human pancreatic differentiation and revealed oxidative stress as a key determinant of pancreatic versus non-pancreatic cell fate. The authors demonstrated that GPX2-deficient cells performed increasing hepatic differentiation within a pancreatic differentiation system, highlighting the critical role of GPX2 in guiding lineage commitment during progenitor differentiation. Mechanistically, they showed that loss of GPX2 induces extracellular matrix (ECM) remodeling, which in turn activates BMP signaling and diverts differentiation away from the pancreatic lineage. Furthermore, manipulating ROS levels, either by induction or alleviation, was sufficient to mimic or rescue the phenotypes associated with GPX2 deficiency. Overall, the data are comprehensive, clearly presented, and logically organized, offering novel insights into the regulation of endodermal differentiation through redox signaling. In addition, the single-cell dataset provide rich information for the pancreatic differentiation field for future exploration. However, several key issues need to be addressed. Here are the detailed comments:\nMajor comments:\n1. In Figure 1I, the Western blot control is inconsistent. GAPDH expression in SC-β cells is much lower than in other samples, which may compromise the conclusion that SC-β cells do not express GPX2. More uniform loading or replicate blots would strengthen this claim.\n2. In Figures 2F and 2G, although some HNF4A⁺/FGB⁺ cells are detected, the number of positive cells appears very low. Quantitative comparison using FACS is recommended to more accurately assess the differences between WT and KO cells.\n3. In Figure 3N, it is wired that WT PEN cells show no PDX1 expression, especially since PDX1 was clearly detected in Figure 2E. This inconsistency raises concerns about the stability and efficiency of the differentiation system, and warrants further explanation or validation.\n4. In Figures 4A and 4B, the authors report an increase in AFP⁺ and FGB⁺ liver progenitors in KO cells. However, the immunostaining shows very few AFP⁺ cells. Quantitative methods such as FACS or image-based quantification should be used to confirm these differences between WT and KO.\n5. From Figures 4A-B, GPX2 KO does not appear to affect the proportion of pancreatic β-like cells. However, the functional capacity of SC-β cells remains unclear. Does GPX2 loss influence β cell function, such as insulin content, mitochondrial morphology and number, or glucose-stimulated insulin secretion (GSIS)? Functional validation is recommended.\nMinor comments:\n1. In Figure 1A, it is suggested to use double marker combinations to define cell stages more clearly—for example, DE (SOX17/FOXA2), PP (PDX1/NKX6.1), EP (CHGA/NKX2.2). Alternatively, FACS can be used to show stage-specific differentiation efficiency.\n2. Cite PMCID: PMC9293889, which also discusses pancreatic versus hepatic fate decisions at the progenitor stage.\n3. In Figure 6G and Figure 7E, some of the displayed loci show minimal or unclear differences in chromatin accessibility. If possible, the authors should highlight more pronounced differential peaks or adjust the visualization (e.g., by changing scale, zooming in, or using a different normalization method) to better emphasize significant changes. Alternatively, a brief explanation for why these loci were selected (e.g., biological relevance despite modest changes) would be helpful.\n4. In the figure legends, specify whether bar plots represent mean ± SD or mean ± SEM to improve clarity.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "This is a technically rigorous study that identifies GPX2, a redox-sensitive glutathione peroxidase, as a key regulator of human endodermal lineage decisions during pancreatic differentiation from human pluripotent stem cells (hPSCs). The authors demonstrate that GPX2 deficiency skews differentiation toward hepatic and intestinal fates, even under pro-pancreatic conditions, by altering oxidative stress levels and disrupting the cellular microenvironment, particularly ECM composition and BMP signaling. The work is conceptually novel and contributes important mechanistic insight into how redox balance regulates human cell fate determination, with potential implications for regenerative medicine and disease modeling.\nMajor concerns:\n1. The manuscript could more clearly discuss whether the liver- and intestine-like cells represent mature or immature progenitor states. This is particularly relevant given the absence of ALB transcripts and the enrichment of multilineage progenitor markers.\n2. Figures summarizing scRNA-seq cluster proportions, pseudotime trajectories, and pathway enrichment could benefit from additional quantitative clarity. Including statistical comparisons between WT and KO proportions across clusters would enhance interpretability.\nMinor concerns.\n1. Clarify in the abstract that the knockout of GPX2 leads to a bias in lineage commitment rather than a complete block in pancreatic differentiation.\n2. Improve consistency in gene nomenclature and italicization throughout the manuscript.\n3. Confirm whether the observed increase in α cells (GCG⁺) in KO samples is statistically significant and discuss its implications."}, "author_response": "We thank the Reviewers for their time and valuable input on our manuscript. We are delighted that Reviewers 2 and 4 find our work exciting and suitable for publication in Nature Communications. We appreciate all suggestions and hope Reviewer 1 will be satisfied with our additions and revisions. We have addressed all your comments; please find the schedule of our responses below, highlighted in blue.\n[... Author rebuttal text ...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed many of the concerns listed in the previous review.\nSeveral concerns remain and should be addressed.\n1) I still have reservations about the cell lines used. The authors originally omitted any information on which cell lines they used. The authors now state that the cell lines used for the KO experiments were produced from the HUES8 parental line (the methods state that the Cas9-HuES8 line was used to generate the knockout) and the over expression lines produced in the H1 and HUES8 background. Were the experiments with the KO cells performed with a single clone or were they mainly from 1 of the 2 KO clones? Which cell line was used as the control for the KO experiments? There are methods describing the creation and use of the DOX inducible HUES8 cell line but no mention of how the H1 over-expressing cell line was created. This information should be provided in the methods or in the body of the paper. Because my original concerns were not addressed; please provide details on the exact cell lines (and KO clone) used to generate the data in Figs 2-5.\n2) The title of the paper seems misleading. \"Gpx2 and oxidative stress as gatekeepers of pancreatic fate\" specifically the term gatekeeper is ambiguous/non-scientific and seems to suggest that oxidative stress levels prevent pancreatic differentiation.\n3) The new text states that 0.1--> 10 uM H202 is used to treat WT cells however the graph in the figure 3 (old 3M) that WT cells treated with 0.01 uM demonstrate an elevated level of Pdx1.", "reviewer_2": "The authors have added more data and provided further evidences. Overall, the revised manuscript has been improved.\nHowever, there are still some questions which need to be addressed:\n1. For Western blotting experiments, all samples for comparison should be performed and presented in a single gel rather than in a separated manner.\n2. The FACS results should be quantified and statistically evaluated.\n3. There is no GSIS result in the rebuttal Fig. 12. Additionally, the results of functional evaluation of β cells are important and should be added to the manuscript.\n4. GPX2 KO notably affected the cell-fate choices during pancreatic differentiation, but at the SC-β cell stage (day 21) the authors did not observe significant changes in β cell differentiation efficiency and function between WT and GPX2 KO cells. These observations are confusing, and need more explanation and discussion.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "Most of this reviewer’s comments have been addressed."}, "author_response": "Thank you for your valuable feedback and for giving us the opportunity to address your concerns more thoroughly. We sincerely apologize for not providing sufficient detail about the cell lines in our original submission.\n[... Author rebuttal text ...]"}, {"round": 3, "reviewer_comments": {"reviewer_1": "My concerns with the manuscript have been resolved with the revisions.", "reviewer_2": "The authors have addressed our comments.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter