# A ONECUT1 regulatory, non-coding region in pancreatic development and diabetes

Sarah Merz, Valérie Senée, Anne Philippi, Franz Oswald, Mina Shaigan, Marita Führer, Cosima Drewes, Chantal Allgöwer, Rupert Öllinger, Martin Heni, et al.

► To cite this version:

Sarah Merz, Valérie Senée, Anne Philippi, Franz Oswald, Mina Shaigan, et al. A ONECUT1 regulatory, non-coding region in pancreatic development and diabetes. Cell Reports, 2024, 43 (11), pp.114853. 10.1016/j.celrep.2024.114853. hal-04909960

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Distributed under a Creative Commons Attribution 4.0 International License
**Cell Reports**

# A ONECUT1 regulatory, non-coding region in pancreatic development and diabetes

## Graphical abstract

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

## Authors

Sarah Merz, Valérie Senée, Anne Philippi, ..., Cécile Julier, Alexander Kleger, Sandra Heller

## Correspondence

cecile.julier@inserm.fr (C.J.),
alexander.kleger@uni-ulm.de (A.K.),
sandra.heller@uni-ulm.de (S.H.)

## In brief

Merz et al. identify a disease-causing deletion upstream of ONECUT1. They uncover a cis-acting enhancer that regulates ONECUT1 expression during pancreatic development, harboring a low-frequency variant associated with type 2 diabetes. Additionally, they provide clinical relevance by experimentally testing in vitro therapeutic options for affected patients.

## Highlights

* Compound heterozygous mutations in ONECUT1 and a non-coding region cause neonatal diabetes

* Heterozygous deletion of the non-coding enhancer confirms cis-regulation of ONECUT1 expression

* Changes in chromatin looping promote ONECUT1 enhancer activity during pancreatic development

* ONECUT1 coding or enhancer mutations affect β cell performance, counteracted by T2D drugs

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

# A ONECUT1 regulatory, non-coding region in pancreatic development and diabetes

Sarah Merz,¹ Valérie Senée,²,¹⁶ Anne Philippi,²,¹⁶ Franz Oswald,³ Mina Shaigan,⁴ Marita Führer,⁵ Cosima Drewes,⁶

Chantal Allgöwer,¹ Rupert Öllinger,⁷ Martin Heni,⁸,⁹ Anne Boland,¹⁰ Jean-François Deleuze,¹⁰ Franziska Birkhofer,¹

Eduardo G. Gusmao,¹¹ Martin Wagner,³ Meike Hohwieler,¹ Markus Breunig,¹ Roland Rad,⁷ Reiner Siebert,⁶

David Alexander Christian Messerer,⁵,¹² Ivan G. Costa,⁴ Fernando Alvarez,¹³ Cécile Julier,²,¹⁷,* Alexander Kleger,¹,¹⁴,¹⁵,¹⁷,*
and Sandra Heller¹,¹⁷,¹⁸,*

¹Institute of Molecular Oncology and Stem Cell Biology, Ulm University Hospital, Ulm, Germany

²Université Paris Cité, Institut Cochin, INSERM U1016, CNRS UMR 8104, Paris, France

³Department of Internal Medicine 1, Ulm University Hospital, Ulm, Germany

⁴Institute for Computational Genomics, RWTH Aachen University Medical School, Aachen, Germany

⁵Institute for Clinical Transfusion Medicine and Immunogenetics, German Red Cross Blood Transfusion Service Baden-Württemberg-Hessen and University Hospital Ulm, Ulm, Germany

⁶Institute of Human Genetics, Ulm University & Ulm University Medical Center, Ulm, Germany

⁷Institute of Molecular Oncology and Functional Genomics, Center for Translational Cancer Research and Department of Medicine II, School of Medicine, Technical University of Munich, Munich, Germany

⁸Division of Endocrinology and Diabetology, Department of Internal Medicine 1, Ulm University Hospital, Ulm, Germany

⁹Institute for Clinical Chemistry and Pathobiochemistry, Department for Diagnostic Laboratory Medicine, University Hospital Tübingen, Tübingen, Germany

¹⁰Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, France

¹¹Centre of Informatics, Federal University of Pernambuco, Recife, Brazil

¹²Institute for Transfusion Medicine, University Hospital Ulm, Ulm, Germany

¹³Division of Gastroenterology, Hepatology & Nutrition, CHU Sainte-Justine, University of Montreal, Montreal, QC, Canada

¹⁴Division of Interdisciplinary Pancreatology, Department of Internal Medicine 1, Ulm University Hospital, Ulm, Germany

¹⁵Core Facility Organoids, Ulm University, Ulm, Germany

¹⁶These authors contributed equally

¹⁷These authors contributed equally

¹⁸Lead contact

*Correspondence: cecile.julier@inserm.fr (C.J.), alexander.kleger@uni-ulm.de (A.K.), sandra.heller@uni-ulm.de (S.H.)

https://doi.org/10.1016/j.celrep.2024.114853

## SUMMARY

In a patient with permanent neonatal syndromic diabetes clinically similar to cases with ONECUT1 biallelic mutations, we identified a disease-causing deletion located upstream of ONECUT1. Through genetic, genomic, and functional studies, we identified a crucial regulatory region acting as an enhancer of ONECUT1 specifically during pancreatic development. This enhancer region contains a low-frequency variant showing a strong association with type 2 diabetes and other glycemic traits, thus extending the contribution of this region to common forms of diabetes. Clinical relevance is provided by experimentally tailored therapy options for patients carrying ONECUT1 coding or regulatory mutations.

## INTRODUCTION

Diabetes mellitus has become a global major health burden. The disease is characterized by considerable clinical and etiological diversity. However, the current diabetes classification does not fully capture the disease's heterogeneity.¹ Besides common forms of diabetes (type 1 diabetes [T1D] and T2D) having a multi-factorial etiology with a polygenic background,² a subset of diabetes cases may result from single-gene mutations. So far, numerous genes whose mutations cause monogenic diabetes forms have been identified and functionally characterized, revealing various mechanisms that disrupt β cell integrity.³ Interestingly, common variants in several of these monogenic dia-

betes genes have also been associated with a polygenic predisposition to diabetes. This genetic overlap in common polygenic and rare monogenic diabetes forms suggests that disease mechanisms and biological pathways are partly shared between different forms of diabetes.⁴⁻¹² However, more than 90% of single-nucleotide polymorphisms (SNPs) associated with diabetes identified through genome-wide association studies (GWASs) are situated outside coding regions of genes and can influence cellular function and the development of pancreatic islets.¹³⁻¹⁷ Hence, dysfunctional gene regulatory elements, like promoters, enhancers, silencers, insulators, or transcribed regulatory sequences, such as long non-coding RNAs (lncRNAs), could explain part of the missing heritability of T2D.¹⁸ Indeed, such

<figure><img src="image_4.png" /></figure>
SNPs display a notable frequency in pancreatic islet-enhancer clusters, highlighting the relevance of islet-specific gene regulation in the pathophysiology of T2D.<sup>13,19,20</sup> Nevertheless, comprehensive studies on regulatory elements driving metabolic disorders are relatively rare, but more such elements are being discovered.<sup>21-24</sup> Understanding mutations in genes and their regulatory regions causing severe monogenic diabetes can thus illuminate the path toward a more personalized diabetes medicine.<sup>19</sup>

Recently, we identified ONECUT1 coding mutations as the cause of a monogenic, recessively inherited syndromic form of diabetes with neonatal or very early onset. Additionally, rare coding variants of ONECUT1 define a subgroup of patients with early-onset T2D and a favorable response to treatment. On the other hand, common regulatory variants near ONECUT1 are associated with multifactorial T2D. Functional studies based on human pluripotent stem cell differentiations demonstrate that ONECUT1 plays a crucial role in pancreatic progenitor (PP) formation and endocrine development, affecting transcriptional regulation and epigenetic processes across various forms of diabetes.<sup>7,12</sup>

Here, we studied a child with neonatal syndromic diabetes and a family history of T2D whose clinical presentation was very similar to cases with biallelic ONECUT1 truncating mutations. Genetic and genomic studies of the patient and his family followed by functional assays in genome-edited human embryonic stem cells (hESCs) forming stem cell-derived islets (SC-islets) identified a ONECUT1 regulatory element, characterized the disease-causing mechanism, and experimentally underpinned the clinical relevance of our findings.

## RESULTS AND DISCUSSION

### A boy with neonatal syndromic diabetes presenting characteristic features of ONECUT1 biallelic mutations

We studied a patient born to non-consanguineous parents with neonatal syndromic diabetes, associated with intrauterine growth retardation and pancreatic and hepatic manifestations. The patient's weight at birth was <5th percentile and height was <1st percentile. Diabetes was diagnosed at 9 days, requiring insulin thereafter. He also had exocrine pancreas insufficiency (very low fecal chymotrypsin), treated by pancreatic enzymes. He had failure to thrive and growth retardation (height and weight <1st percentile). Ultrasound showed a very hypoplastic pancreas and the absence of gall bladder, similar to neonatal diabetes patients carrying biallelic ONECUT1 mutations<sup>7,25</sup> (e.g., Figures 1A and 1B). In addition, the patient had bilateral syndactyly of the 2nd and 3rd toes of the feet. At 13 years old, the patient presented with severe gastro-intestinal bleeding. He exhibited cirrhosis, portal hypertension, and bleeding from esophageal varices. Clinically, the patient exhibited hepatomegaly, splenomegaly, and signs of chronic hepatopathy. Imaging conducted at 13 years of age revealed a nodular liver, splenomegaly (14 cm long), nephrocalcinosis, and severe pancreatic hypoplasia and gall bladder agenesis. Laboratory tests showed aminotransferases at 3 times the normal value and gamma glutamyl-transferase at 7 times the normal values, while levels of albumin and the international

normalized ratio (INR) remained within normal ranges. A liver biopsy revealed the presence of cirrhosis without inflammation, along with neoductular proliferation, suggesting that the liver disease originated from abnormal development of the bile ducts. A kidney biopsy revealed a partial atrophy of renal tubules, interstitial fibrosis, and cystic glomerulopathy. During follow-up, the patient developed ascites and recurrent variceal bleeding despite sclerotherapy. Subsequently, a transjugular intrahepatic portosystemic shunt (TIPS) was inserted, providing stability for several years. The patient died at age 23 during anesthesia while undergoing revision of the obstructed TIPS.

### Compound heterozygosity for a frameshift mutation in ONECUT1 and a deletion in a non-coding region 612 kb upstream of ONECUT1

Using Sanger sequencing of ONECUT1 exons in this patient and his parents, we identified a heterozygous frameshift mutation in ONECUT1 (ONECUT1-p.A206Tfs58X) in the patient. This mutation results in a truncated protein lacking the CUT and HOMEO-BOX (HOX) domains and was inherited from his mother (Figure 1C). The family history of adult-onset diabetes on the maternal side suggests the involvement of this protein-truncating variant in adult-onset diabetes, in line with recent findings from us and others.<sup>7,12,25</sup> Considering the severe neonatal presentation of the patient (comparable with the biallelic ONECUT1 mutation phenotype), we performed high-density SNP array genotyping, whole-genome sequencing, and confirmatory Sanger sequencing in the patient and his parents and identified a deletion of 108 kb (chr15.hg19:g.53694760_53802787del) located 612 kb upstream of ONECUT1, transmitted by the father (Figures 1C and 1D). This non-coding region contains an IncRNA gene (AC066614.1; further labeled IncRNA-AC) as well as binding peaks for ONECUT1, FOXA1/2, GATA6, NKX6.1, and PDX1 (Figure 1D), supporting its putative role in ONECUT1 regulation.

### Characterization of the ONECUT1 regulatory non-coding region during pancreatic development using human pluripotent stem cells

Thus, we hypothesized that the IncRNA-AC, which is mostly localized in the nucleus and resembles the ONECUT1 expression pattern during pancreatic differentiation (Figures 1E and 1F), mechanistically contributes to the glycemic phenotype of the patient. To explore this in greater detail, we deleted the patient-specific 108 kb region (referred to as Pat1-del, heterozygous and homozygous) in hESCs and assessed their subsequent differentiation into PPs. While the earlier stages of pancreatic development remained unaltered (Figures S1A-S1C), the PP stage was strongly impaired in both homozygous Pat1-del and ONECUT1 knockouts (KOs), as shown by reduced expression of the PP markers glycoprotein-2 (GP2) (Figures 2A and 2B) and NKX6.1/PDX1 (Figure 2C). Additionally, RNA sequencing (RNA-seq) revealed a loss of pancreatic endocrine lineage and functional endocrine genes upon gene set enrichment analysis (GSEA) (Figures 2D, S1D, and S1E). Of note, the patient's homozygous deletion (Pat1-del⁻/⁻) almost completely suppressed ONECUT1 expression at both RNA and protein levels, indicating the strong regulation of ONECUT1 transcription through this genomic region (Figures 2E, 2F, and S1F). Notably, WDR72,
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<figure><img src="image_6.png" /><figcaption>Figure 1. A patient with compound heterozygous mutations in *ONECUT1* and a non-coding region upstream of *ONECUT1* presenting with severe neonatal diabetes alike *ONECUT1* biallelic mutations</figcaption></figure>

(A) Pedigree describing a neonatal syndromic diabetes patient (patient 1) and family history of adult-onset diabetes.

(B) The clinical presentation of patient 1 resembles previously reported cases with biallelic *ONECUT1* mutations, here compared with a neonatal diabetes patient carrying a homozygous *ONECUT1* truncating mutation (patient 2).⁷

(C) Schematic genotype of patient 1 and consequences of the frameshift mutation on the *ONECUT1* coding region. Created with BioRender.

(D) Integrative genomics viewer plot of the region deleted in patient 1 (Pat1-del) with corresponding chromatin immunoprecipitation sequencing (ChIP-seq) peaks of pancreatic transcription factors, histone marks, and ATAC peaks from pancreatic progenitor (PP) stage of WT and *ONECUT1*-deleted hESCs.⁷ The annotated lncRNA is marked (lncRNA-AC; AC066614.1).

(E) Heatmap showing qPCR results of *lncRNA-AC* and *ONECUT1* mRNA during pancreatic differentiation. Ct values were normalized to HMBS, and $2^{-\Delta\Delta Ct}$ values were normalized to pancreatic endoderm (PE) and depicted as mean. n = 3-4 independent differentiations; samples of schematic stepwise pancreatic differentiation of hESCs to definitive endoderm (DE), gut tube endoderm (GTE), PE, PP, and endocrine progenitor (EP) cells and mature endocrine cells (β) are shown as symbols (created with BioRender).

(F) Subcellular localization of *lncRNA-AC* at PP stage is determined by subcellular fractionation of cytosolic and nuclear RNA and analyzed by qPCR. Ct values were normalized to *lncRNA-AC* expression in total RNA fraction. n = 4 independent differentiations. An unpaired t test was performed. Bar graphs represent mean ± SEM.

which is located near the 108 kb deleted region, is not expressed during pancreatic differentiation and remains unaffected by the KO of *ONECUT1* or the Pat1-del region. In humans, biallelic *WDR72* mutations are known to cause amelogenesis imperfecta (defect in enamel formation) and distal renal tubular acidosis,²⁶,²⁷ and diabetes has not been reported in these patients. Consequently, we did not pursue further investigation of this gene.

lncRNAs have already been described to operate in concert with transcription factors to regulate the transcriptional network in pancreatic β cells.²⁹ To investigate a possible role of the lncRNA-AC transcript as a regulator of *ONECUT1* transcription, we utilized a GapmeR-based approach to knock down *lncRNA-AC* (Figures 2G, S1G, and S1H). The decrease in *lncRNA-AC* levels did not affect differentiation efficiency, suggesting no direct role of the *lncRNA-AC* transcript (Figures 2H and S1I). Instead, we hypothesized that an enhancer activity within the

deleted non-coding region of the patient, as indicated by pancreatic transcription factor binding and histone modifications (Figure 1D), explains the drastic phenotype. Previous research indicates that neighboring genes of an lncRNA locus often interact through various mechanisms, such as RNA splice sites. This interaction can affect the function and evolution of these loci, regulating both coding and non-coding genes.³⁰

## Heterozygous deletion of the non-coding region demonstrates *ONECUT1* regulation in *cis*

To probe this hypothesis, we heterozygously deleted this 108 kb region in heterozygous *ONECUT1* KO hESCs (Figure 3A). Nanopore sequencing confirmed either the *cis*- (clone 1) or trans- (clone 3) orientation of the *ONECUT1* gene and its upstream regulatory element (Figure S2). Among the four generated clones, two exhibited complete loss of *ONECUT1* expression at the RNA and protein levels (Figures 3B and 3C) and disruption of
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<figure><img src="image_13.png" /><figcaption>Figure 2. Characterization of the ONECUT1 regulatory non-coding region during pancreatic development using human pluripotent stem cells</figcaption></figure>

(A) Flow cytometry of the pancreatic progenitor (PP) marker glycoprotein 2 (GP2) across indicated genotypes. *n* = 5–7 independent differentiations; Bar graphs represent mean ± SEM. Ordinary one-way ANOVA was performed.

(B) Representative immunofluorescence (IF) images of GP2 (red) at PP stage, performed on cytospins. Counterstaining with DAPI was performed. Scale bars represent 50 µm.

(C) Flow cytometry staining for the key PP markers NKX6.1 and PDX1 at PP stage. Indolactam V was omitted from culture medium to better detect genotypical changes. *n* = 3 independent differentiations. Bar graphs depict mean ± SEM. Ordinary one-way ANOVA was performed.

(D) Gene set enrichment analysis shows loss of an endocrine lineage gene set²⁸ in Pat1-del PPs.

(E) Relative ONECUT1 expression in WT, ONECUT1 KO, and Pat1-del cells at PP stage based on qPCR. Ct values were normalized to HMBS and depicted as 2⁻ΔΔCt. *n* = 3–4 independent differentiations. Ordinary one-way ANOVA was performed comparing the mean of each column to HUES8 wild-type control. For the HUES8 wild-type control, the same values were used as in Figure 3B, as this experiment was performed in one run with the same control line.

(legend continued on next page)
PP formation (Figures 3D-3F). The other two clones resembled a phenotype similar to single allelic deletions of either ONECUT1 or the upstream-located region (Figures 3D-3F). Global transcriptome analysis similarly separated the genotypes (Figure 3G). GSEA revealed a loss of pancreatic endocrine lineage and functional endocrine genes in the clone harboring a trans-oriented deletion of ONECUT1 and Pat1-del (Figure 3H), in line with a subsequent disruption in the formation of endocrine progenitor cells (Figures 3I, S3A, and S3B). Thus, we provide compelling genetic evidence that pinpoints a cis-regulatory element within a non-coding genomic region that acts as a transcriptional enhancer during pancreas development. Hence, the combined loss of ONECUT1 and partial deletion of the enhancer region in a compound heterozygous status (trans phase) reproduces the severe phenotype observed in patient 1, which results from the strongly impaired PP formation capacity in ONECUT1⁻/⁺ Pat1-del⁻/⁺ trans clones (Figures 1A-1C and 3B-3H).

## Differential chromatin looping and hypomethylation promotes ONECUT1 enhancer activity during pancreas development

We selected four regions (luci 1-4) from this non-coding element based on pancreatic transcription factor-binding patterns (Figure 1D) and tested their minimal enhancer activity using luciferase assays. Interestingly, the fragment most enriched for ONECUT1 binding peaks displayed elevated reporter activity exclusively in the minus orientation (luci 2), which was even enhanced by ONECUT1 protein expression (Figures 4A and 4B). On the contrary, the plus-oriented fragment 2 (luci 2) exhibited repressive effects, as did fragments 1 and 3 (Figure 4A). Another fragment (luci 4), located further upstream of the 108 kb region deleted in patient 1, also comprised pancreatic transcription factor peaks and showed similar induction patterns to luci 2 (Figures 4A and 4B), indicating at least two genomic sites with enhancer activity upstream of ONECUT1. Using nanopore-based 5-methylcytosine (5mC) detection, we identified differential hypomethylation from hESCs to the PP stage in part of the intron of ONECUT1 (Figure S4A). Additionally, we observed differential hypomethylation in the identified enhancer region (including luci 2; Figures 4C and S4B), which was associated with pancreatic transcription factor binding (Figure 1D) and luciferase reporter activity (Figure 4A), indicating that hypermethylation keeps this enhancer inactive in non-pancreatic lineages.

Assuming a differentially formed chromatin loop between the regulatory element and the ONECUT1 promoter, we compared stage-specific Hi-C genomic sequencing data from hESCs differentiated to the pancreatic lineage. Here, we identified pro-

gressive chromatin changes during pancreatic differentiation reaching significance at the PP stage (Figures 4D-4F).

We identified a chromatin loop forming from the extrusion process that combines topologically associating domains (TAD)1 and TAD2, structurally linking the ONECUT1 promoter with the regulatory element in PPs. We also observed the left boundary of TAD3 shifting to another position within the adjacent WDR72 locus, seen as a structural loss in the ESC stage and supported by relocated CTCF binding (Figure 4D). Collectively, these findings suggest the establishment of a new chromatin loop in PP cells along the respective TAD2. Intriguingly, the boundary between TAD2 and TAD3 coincides with the regulatory element located within the patient 1-specific 108 kb region (Figures 4D-4F). The emergence of this loop at the PP stage indicates a stronger interaction between the ONECUT1 promoter and the regulatory element, supporting the proposed mechanism of a distal cis-acting enhancer regulating ONECUT1 expression during pancreatic development. In conclusion, deletion of this element impairs ONECUT1 and represents a disease-causing allele to ONECUT1-recessive neonatal diabetes.

Interestingly, in an independent CRISPR interference screen, this functional ONECUT1 enhancer element was identified to play an important role in human pancreatic differentiation, further confirming and strengthening our results presented within this study.³²

## A low-frequency variant in the enhancer region is associated with common T2D

To further explore the relevance of our finding to common T2D, we investigated public data from HugeAMP. We identified a low-frequency variant, rs528350911 (chr15.hg19:53747228-C-G, minor allele frequency [MAF] = 0.0035 in gnomAD, all populations) located within the putative enhancer region (precisely within luci 2) associated with increased risk of T2D and other glycemic traits, including random glucose ($p = 2.6 \times 10^{-8}$, beta = 0.081) and T2D adjusted for body mass index (T2DadjBMI; $5.9 \times 10^{-7}$, odds ratio [OR] = 1.31) (Table 1). This variant is located at the binding sites of several pancreas-specific transcription factors such as PDX1, NKX6.1, and ONECUT1 (Figure 4G). Of note, the strength of diabetes association at this low-frequency variant is in the same order of magnitude as the strongest T2D association signal over the whole genome (e.g., OR = 1.34 for T2D and T2DAdjBMI at the TCF7L2 lead SNP rs7903146 compared to ORs = 1.38 and 1.31, respectively, for rs528350911, HugeAMP), emphasizing the significance of this association. The detected association of this variant with T2D and other glycemic traits independent of BMI underscores our findings of a pancreatic phenotype that ultimately impairs insulin secretion and argues against a mechanism dependent on insulin sensitivity.

(F) Representative immunohistochemistry staining of paraffin-embedded PPs display ONECUT1 expression in indicated genotypes. Scale bars represent 50 µm.

(G) Schematic representation of the GapmeR-based knockdown method. Partly created with BioRender.

(H) GapmeR-based IncRNA-AC knockdown during differentiation and subsequent illustration of chromogranin A (CHGA)+ cells (red) via IF on cytospins and DAPI for nuclei staining. Scale bars represent 50 µm. Pat1-del homozygous knockout was used as an additional control. The respective quantification was performed on three independent differentiations with 10 field of views each using CellProfiler. CHGA+ structures were normalized to DAPI. Mean ± SEM. Ordinary one-way ANOVA comparing each column to every other column was performed.

Significance in (A), (C), (E) and (H) is displayed as follows: *$p < 0.05$, **$p < 0.01$, ***$p < 0.001$, and ****$p < 0.0001$.

See also Figure S1.
Mutations in ONECUT1 or its regulatory region impair islet composition and β cell functionality but can be rescued by glucose-lowering drugs used in T2D

Next, we investigated the mutant SC-islet upon further differen-
tiation of the PPs (Figure S5A) to the more mature endocrine
stage 7<sup>33</sup>: unlike the high expression of ONECUT1 in PPs,
ONECUT1 expression is almost completely downregulated
upon entering the endocrine lineage (Figure S5B). Analysis of
the SC-islet composition displayed the reduced formation of β
cells in both heterozygous and homozygous Pat1-del hESCs
as well as in the ONECUT1<sup>-/+</sup> Pat1-del<sup>-/+</sup> compound heterozy-
gous clones (Figures 5A and 5B), while the number of glucagon+
α cells remained unchanged (Figures 5B and 5C). Similarly, the
percentage of pancreatic polypeptide+ or somatostatin+ γ and
δ cells remained unaffected (Figures S5C and S5D). The overall
similar chromogranin A and vimentin levels per SC-islet
excluded an aberrant loss of lineage identity (Figure S6A). Trans-
mission electron microscopy shows disturbed polarization,
elevated levels of immature granules, and a decreased overall
presence of electron-dense granules in the SC-islets carrying
either a homozygous ONECUT1 KO or homozygous Pat1-del
(Figure 5D). It is worth noting that the distinctive halo artifact of
insulin granules is less prominent in our SC-islets, a feature we
have previously observed in human primary islets.<sup>34</sup> This
discrepancy is likely due to the specific methodological process-
ing pipeline utilized.<sup>34</sup>

Next, we probed SC-islet functionality using glucose-stimulated insulin secretion (GSIS) assays and subsequent drug treatment (Figure 5E): GSIS demonstrated perturbed insulin secretion of the remaining β cells in ONECUT1 KO and ONECUT1<sup>-/+</sup> Pat1-del<sup>-/+</sup> compound heterozygous lines (Figures 5F, S6B, and S6C). It is noteworthy that single or compound cis heterozygous clones also exhibited defective insulin secretion during GSIS (Figure 5E).

Finally, our goal was to evaluate potential rescue strategies
that could improve impaired β cell function in various genotypes
affecting both the coding and regulatory, non-coding regions of
the ONECUT1 gene. Treatment of SC-islets with the glucagon-
like peptide-1 (GLP-1) receptor agonist exendin-4 stimulated in-

sulin secretion in all genotypes, with homozygous ONECUT1 KO
mutants exhibiting even better responses than wild-type (WT)
counterparts (Figures 5G, S6B, and S6C). Subsequent tests
with the sulfonylurea tolbutamide yielded consistent responses
across all genotypes (Figures 5H, S6B, and S6C). Thus, despite
the lower number of β cells and the diminished glucose respon-
siveness of carriers of ONECUT1 variants (coding or regulatory),
their β cells still respond to glucose-lowering drugs. This indi-
cates the potential of GLP-1 receptor agonists or sulfonylureas
as optimized treatment options for patients carrying mutations
in ONECUT1 coding and/or regulatory regions (Figures 5G, 5H,
S6B, and S6C).

Conclusion

In summary, our findings reveal the pathomechanism leading to neonatal diabetes in a patient with a compound heterozygous ONECUT1 frameshift mutation and a deletion affecting key regulatory elements. This discovery highlights essential ONECUT1 regulatory regions involved in both monogenic diabetes and common T2D, emphasizing the transcription factor's role in endocrine pancreas development. Variation in this locus may contribute to the specific T2D subtype characterized by low insulin secretion.¹,³⁵

Limitations of the study

The model system employed in this study successfully replicated the pancreatic phenotype observed in the patient but is not designed to target other clinical symptoms. The process of endocrine differentiation using hESCs is lengthy, spanning up to 7 weeks, and involves numerous factors and variables, leading to significant variability in the SC-β cells produced. Additionally, the experiments were conducted using a single hESC line and thus did not explore the extent of genetic variation in the region.

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be
directed to and will be fulfilled by the lead contact, Sandra Heller (sandra.
heller@uni-ulm.de).

column to HUES8 wild-type control. For the HUES8 wild-type control, the same values are used as in Figure 2E, as this experiment was performed in one run with the same control line.

(C) Representative immunohistochemistry staining of paraffin-embedded PPs shows ONECUT1 expression in wild-type and ONECUT1<sup>-/+</sup> *Pat1-del<sup>-/+</sup>* (cis and trans) cell lines. Scale bars represent 50 µm.

(D) Percentage of NKX6.1+ and PDX1+ cells of ONECUT1<sup>-/+</sup> *Pat1-del<sup>-/+</sup>* in *trans* or *cis* was measured with flow cytometry at PP stage. Differentiation was performed without Indolactam V to better detect genotypical changes. Bar graphs depict mean ± SEM of *n* = 3 independent differentiations. Ordinary one-way ANOVA comparing the means of every column to the means of every other column was performed.

(E) Percentage of glycoprotein 2 (GP2)-expressing cells determined by flow cytometry. Bar graphs depict mean ± SEM for *n* = 5 independent differentiations. Ordinary one-way ANOVA was performed comparing the mean of each column to the mean of every other column.

(F) Representative images of GP2 (red) immunofluorescence (IF) staining at PP stage on cytospins are shown. Counterstaining with DAPI was performed. Scale bars represent 50 µm.

(G) Principal-component analysis (PCA) of whole transcriptomes of indicated genotypes at PP stage. n = 3–4 independent experiments. A subset of the genotypes (HUES8, ONECUT1<sup>-/+</sup>, ONECUT1<sup>-/-</sup>, *Pat1-del<sup>-/+</sup>*<sup>+</sup>, and *Pat1-del<sup>-/-</sup>*<sup>-</sup>) is also shown in Figure S1D, as this experiment was performed in one run with the same control lines.

(H) Gene set enrichment analysis shows loss of an endocrine lineage gene set²⁸ and functional endocrine signature³¹ in trans-deleted PPs.

(I) Percentage of immature β cells at endocrine progenitor (EP) stage measured via flow cytometry (NKX6.1+/CPEP+/GCG−) was normalized to HUES8 wild type for n = 3 independent differentiations. Bar graphs represent mean ± SEM. Ordinary one-way ANOVA was performed by comparing the mean of each column to the mean of HUES8 wild-type control.

Significance in (B), (D), (E), and (I) is displayed as follows: *p* < 0.05, **p** < 0.01, ***p** < 0.001, and ****p** < 0.0001.

See also Figures S2 and S3.
A

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D

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B

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C

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E

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F

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G

<figure><img src="image_21.png" /></figure>

(legend on next page)
<table><caption>**Table 1. Glycemic traits associated with an SNP (rs528350911) mapping to the identified enhancer region**</caption><thead><tr><th>Trait</th><th>p value</th><th>OR</th><th>Beta</th><th>N</th></tr></thead><tbody><tr><td>Random glucose</td><td>2.56 × 10<sup>-8</sup></td><td>–</td><td>0.081</td><td>400,775</td></tr><tr><td>T2D adjusted for BMI</td><td>5.89 × 10<sup>-7</sup></td><td>1.31</td><td>–</td><td>175,915</td></tr><tr><td>HbA1c</td><td>5.20 × 10<sup>-5</sup></td><td>–</td><td>0.051</td><td>438,069</td></tr><tr><td>T2D</td><td>0.00057</td><td>1.38</td><td>–</td><td>183,738</td></tr></tbody></table>

Association values and effect size (OR and beta) are shown for the minor allele of SNP rs528350911 (chr15.hg19:53747228-C-G), G allele (MAF = 0.0035, gnomAD all populations). Data are extracted from HugeAMP. OR, odds ratio; N, size of the cohort (multi-ancestry).

## Materials availability

All unique/stable reagents generated in this study are available from the lead contact with a completed materials transfer agreement.

## Data and code availability

* RNA-seq data and Nanopore sequencing data generated in this study have been deposited at GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. DOIs are listed in the key resources table. This paper analyzes existing, publicly available data. Accession numbers for these datasets are listed in the key resources table. All data reported in this paper will be shared by the lead contact upon request.

* This paper does not report original code. Codes and packages used in this paper are listed in the key resources table.

* Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

## ACKNOWLEDGMENTS

We thank Marc Nicolino and Kevin Perge from the Hospices Civils de Lyon and Cochin Institute for helpful discussions. We thank the patient and his family for participating in this study. We thank Katrin Jochmann, Ralf Köhntop, Katrin Köhn, Ulrike Mayr-Beyrle, and Franziska Ott for their excellent technical support. Further, we thank all IMOS lab members for the valuable discussions. We acknowledge assistance from the Core Facility for Electron Microscopy at Ulm University. Main funding is provided by the DFG grant HE 7277/3-1 to S.H.

S.M. and S.H. are also supported by the Medical Scientist Program at Ulm University awarded to S.H. This project was also funded by the Agence Nationale de la Recherche, France (grant no. ANR-18-CE92-0031), the European Foundation for the Study of Diabetes/Juvenile Diabetes Research Foundation (USA)/Novo Nordisk (Denmark), the Assistance Publique-Hôpitaux de Paris Programme Hospitalier de Recherche Clinique (project DIAGENE), and France Génomique (project DIAPED) to C.J. A.K. is funded via the DFG Heisenberg-Program (KL 2544/6-1) and is a speaker of the EKFS Clinician Scientist Program "POTENTIAL." The CNRGH sequencing platform was supported by the France Génomique National infrastructure, funded as part of the Investissements d'Avenir program managed by the Agence Nationale de la Recherche, France (contract ANR-10-INBS-09). C.D. is a scholar of the DFG-funded SFB1074 (project B10N). The graphical abstract and schematics were partly created in BioRender (Kleger, A. [2024] biorender.com/c86x315).

## AUTHOR CONTRIBUTIONS

Conceptualization, S.M., C.J., S.H., and A.K.; methodology, S.M., C.J., S.H., and A.K.; validation, S.M. and S.H.; formal analysis, S.M., V.S., A.P., M.S., C.D., C.A., E.G.G., M.W., M. Heni, M. Hohwieler, and M.B.; investigation, S.M., V.S., A.P., F.O., I.G.C., M.F., R.O., A.B., J.-F.D., F.B., and F.A.; resources, R.R., R.S., D.A.C.M., I.G.C., C.J., S.H., and A.K.; writing – original draft, S.M., C.J., S.H., and A.K.; writing – review & editing, all authors; visualization, S.M. and S.H.; supervision, C.J., S.H., and A.K.; funding acquisition, C.J., S.H., and A.K.

## DECLARATION OF INTERESTS

The authors declare no competing interests.

## STAR★METHODS

Detailed methods are provided in the online version of this paper and include the following:

* KEY RESOURCES TABLE

* EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

* Human participants

* Cell lines

* METHOD DETAILS

* ONECUT1 mutation screening by Sanger sequencing

* Genomic studies for patient's deletion identification, with Sanger confirmation

* CRISPR-Cas9-based genome editing in hESC

<caption>**Figure 4. Differential chromatin looping and hypomethylation promotes ONECUT1 enhancer activity during pancreas development**</caption>

(A) Luciferase assays measuring enhancer activity of four distinct regions (luci 1–4) within and close to the 108 kb Pat1-del region. *n* = 8 independent experiments. Relative light units (RLUs) of each sample were normalized to empty control. The same values for the empty control were used in both graphs. Ordinary one-way ANOVA was performed comparing the mean of each column to the mean of empty control. Bar graphs depict mean ± SEM.

(B) Schematic illustration of the + or – orientation of the putative enhancers and physical occupation by ONECUT1 (left). Proposed chromatin looping mechanism, bringing the ONECUT1 promoter and enhancer regions into close proximity (right). Created with BioRender.

(C) DNA methylation in human embryonic stem cells (hESCs) and pancreatic progenitors (PPs) around the pancreatic enhancer determined via Nanopore sequencing. Boxed area (light blue) marks the enhancer region (including luci 2). Color code gives amount of methylated cytosines (red) as compared to unmethylated cytosines (blue).

(D) Identification of structural chromatin changes (Hi-C data analysis) between ESC stage (H9) and PP/definitive endoderm (DE) cells. Lost and gained structures are highlighted in blue and red squares, respectively. A resolution of 100 kb has been used for feature extraction. The regions of interest were enlarged to a resolution of 5 kb. Highly dissimilar regions identified by CHESS between ESCs and PPs or DE cells. Integrative genomics viewer (IGV) plot shows ATAC-seq signals (blue) at the PP and hESC stages. CTCF motif matching on significant peaks is shown in magenta.

(E) Hi-C data analysis identifying structural changes between hESCs and PPs is further depicted in a log2 fold change map. Lost and gained structures are highlighted in blue and red, respectively.

(F) The average log2 fold change of gained loop structure in its TAD (chr15:53065000_53765000) and for stripe loss in the 100 kb region (chr15:53700000_53800000) are displayed.

(G) IGV plot of stage-specific ATAC-seq, histone marks, and ChIP-seq of pancreatic transcription factors is shown at selected luci sites for gut tube (GT) and PP stage. An SNP, rs528350911, is located within the identified enhancer region (luci 2).

Significance in (A) and (F) is displayed as follows: *p* < 0.05, **p* < 0.01, ***p* < 0.001, and ****p* < 0.0001.

See also Figure S4.
<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /><figcaption>Figure 5. Mutations in *ONECUT1* or its regulatory region impair islet composition and *β* cell functionality but can be rescued by glucose-lowering drugs used in T2D</figcaption></figure>

(A) Percentage of β cells (insulin [INS]+/glucagon [GCG] −) in stem cell-derived islets (SC-islets) upon differentiation to stage 7 as measured by flow cytometry for *n* = 3–4 independent experiments. Ordinary one-way ANOVA was performed comparing the mean of each column to the mean of every other column. Bar graphs depict mean ± SEM.

(B) Percentage of α cells (INS−/GCG+) in SC-islets was measured with flow cytometry for *n* = 3–4 independent experiments. Ordinary one-way ANOVA with comparison of each column with every other column was performed. Bar graphs depict mean ± SEM.

(legend continued on next page)
o PCR for clone selection

o Pancreas differentiation of hESCs

o Flow cytometry

o Immunofluorescence staining using cytospin

o Immunofluorescence staining on paraffin sections

o Immunohistochemistry staining on paraffin sections

o Western blot

o RNA isolation, reverse transcription, qPCR

o RNA fractionation for subcellular localization analysis

o RNA-seq

o Gene set enrichment analysis

o GapmeR-based lncRNA knockdown

o Nanopore-sequencing

o ONT analyze phasing and methylation

o Luciferase assay

o Hi-C

o Transmission electron microscopy

o Insulin secretion

● QUANTIFICATION AND STATISTICAL ANALYSIS

SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2024.114853.

Received: June 17, 2024

Revised: August 25, 2024

Accepted: September 24, 2024

Published: October 19, 2024

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54. Hogrebe, N.J., Maxwell, K.G., Augsornworawat, P., and Millman, J.R. (2021). Generation of insulin-producing pancreatic β cells from multiple human stem cell lines. Nat. Protoc. 16, 4109-4143.
## STAR★METHODS

### KEY RESOURCES TABLE

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td><strong>Antibodies</strong></td><td></td><td></td></tr><tr><td>Alexa Fluor 488 donkey-anti-mouse IgG</td><td>ThermoFisher Scientific</td><td>A-21202; RRID: AB_141607</td></tr><tr><td>Alexa Fluor 568 donkey-anti-mouse IgG</td><td>ThermoFisher Scientific</td><td>A10037; RRID: AB_11180865</td></tr><tr><td>Alexa Fluor 647 donkey-anti-mouse IgG</td><td>ThermoFisher Scientific</td><td>A-31571; RRID: AB_162542</td></tr><tr><td>Anti-SSEA4, PE-conjugated</td><td>BD bioscience</td><td>560128; RRID: AB_1645533</td></tr><tr><td>Anti-TRA-1-60, FITC-conjugated</td><td>BD bioscience</td><td>560380; RRID: AB_1645492</td></tr><tr><td>Anti-GP2</td><td>MBL international</td><td>D277-3; RRID: AB_10598500</td></tr><tr><td>Anti-NKX6.1</td><td>DSHB</td><td>F55A12; RRID: 65193</td></tr><tr><td>Anti-PDX1</td><td>R&amp;D</td><td>AF2419</td></tr><tr><td>Anti-SOX17</td><td>R&amp;D</td><td>AF1924</td></tr><tr><td>Anti-GATA6</td><td>Cell Signaling</td><td>5851; RRID: AB_10705521</td></tr><tr><td>Anti-GCG</td><td>Sigma</td><td>G2654; RRID: AB_259852</td></tr><tr><td>Anti-CPEP</td><td>Cell Signaling</td><td>4593; RRID: AB_10691857</td></tr><tr><td>Anti-INS, AF647-conjugated</td><td>Cell Signaling</td><td>9008; RRID: AB_2687822</td></tr><tr><td>Alexa Fluor 488 donkey-anti-goat IgG</td><td>ThermoFisher Scientific</td><td>A-11055; RRID: AB_2534102</td></tr><tr><td>Alexa Fluor 568 donkey-anti-goat IgG</td><td>ThermoFisher Scientific</td><td>A-11057; RRID: AB_2534104</td></tr><tr><td>Alexa Fluor 647 donkey-anti-goat IgG</td><td>ThermoFisher Scientific</td><td>A-21447; RRID: AB_2535864</td></tr><tr><td>Alexa Fluor 488 donkey-anti-rabbit IgG</td><td>ThermoFisher Scientific</td><td>A-21206; RRID: AB_2535792</td></tr><tr><td>Alexa Fluor 568 donkey-anti-rabbit IgG</td><td>ThermoFisher Scientific</td><td>A10042; RRID: AB_2534017</td></tr><tr><td>Alexa Fluor 647 donkey-anti-rabbit IgG</td><td>ThermoFisher Scientific</td><td>A-31573; RRID: AB_2536183</td></tr><tr><td>Anti-ONECUT1 B-1</td><td>Santa Cruz</td><td>sc-376308; RRID:AB_10988385</td></tr><tr><td>anti-β-Actin</td><td>Sigma</td><td>A5316; RRID: AB_476743</td></tr><tr><td>anti-mouse-HPR</td><td>GE healthcare/Cytiva</td><td>NA931; RRID: AB_772210</td></tr><tr><td>anti-rabbit-HPR</td><td>GE healthcare/Cytiva</td><td>NA934; RRID: AB_772206</td></tr><tr><td>Anti-INS</td><td>abcam</td><td>ab181547; RRID: AB_2716761</td></tr><tr><td>Anti-CHGA</td><td>DAKO</td><td>M0869</td></tr><tr><td>Anti-VIM</td><td>Cell Signaling</td><td>5741S</td></tr><tr><td>Anti-PPY</td><td>Proteintech</td><td>15493-1-AP; RRID: AB_2878145</td></tr><tr><td>Anti-SST (G-10)</td><td>Santa Cruz</td><td>sc-55565; RRID: AB_831726</td></tr><tr><td><strong>Chemicals, peptides, and recombinant proteins</strong></td><td></td><td></td></tr><tr><td>Matrigel, hESC-qualified</td><td>Corning</td><td>354277</td></tr><tr><td>mTeSR Plus</td><td>STEMCELL technologies</td><td>100-0276</td></tr><tr><td>Penicillin-streptomycin</td><td>Sigma-Aldrich</td><td>P4333</td></tr><tr><td>TrypLE Select</td><td>Gibco</td><td>A1217701</td></tr><tr><td>ROCK inhibitor Y-27632</td><td>STEMCELL technologies</td><td>72307</td></tr><tr><td>Alt-R™ S.p. HiFi Cas9 Nuclease C3</td><td>IDT</td><td>1081060</td></tr><tr><td>CloneR</td><td>STEMCELL technologies</td><td>0588</td></tr><tr><td>Phire Tissue Direct PCR Master Mix</td><td>ThermoFisher Scientific</td><td>F170L</td></tr><tr><td>Matrigel, growth-factor reduced, Ultimatrix</td><td>Cultrex</td><td>BME001</td></tr><tr><td>Matrigel, growth-factor reduced</td><td>Corning</td><td>354230</td></tr><tr><td>Latrunculin A</td><td>Biomol</td><td>Cay10010630</td></tr><tr><td>Anti-Adherence Rinsing Solution</td><td>STEMCELL technologies</td><td>07010</td></tr><tr><td>Exendin-4</td><td>MedChem Express</td><td>HY-13443</td></tr><tr><td>Tolbutamide</td><td>MedChem Express</td><td>HY-B0401</td></tr></tbody></table>

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<table><thead><tr><td>REAGENT or RESOURCE</td><td>SOURCE</td><td>IDENTIFIER</td></tr></thead><tbody><tr><td>Panexin</td><td>PAN-biotech</td><td>P04-96950</td></tr><tr><td>Normal Donkey Serum (Histoprime)</td><td>Biozol</td><td>LIN-END9010</td></tr><tr><td>Fluoromount-G</td><td>Southern Biotech</td><td>0100-01</td></tr><tr><td>Maxima RT polymerase</td><td>Thermo Fisher</td><td>EP0742</td></tr><tr><td>Lipofectamine 2000 Transfection Reagent</td><td>ThermoFisher Scientific</td><td>11668019</td></tr></tbody></table>

Critical commercial assays

<table><tbody><tr><td>P3 Primary Cell 4D-Nucleofector X Kit</td><td>Lonza</td><td>V4XP-3032</td></tr><tr><td>insulin enzyme-linked immunosorbent assay</td><td>Mercodia</td><td>10-1113-10</td></tr><tr><td>FlexAble CoraLite Plus 647 Antibody Labeling Kit</td><td>Proteintech</td><td>KFA003</td></tr><tr><td>SuperSignal West Dura Kit</td><td>ThermoFisher Scientific</td><td>34075</td></tr><tr><td>GeneJET RNA Purification Kit</td><td>ThermoFisher Scientific</td><td>K0732</td></tr><tr><td>Scriptase RT cDNA Synthesis Kit</td><td>Genaxxon</td><td>SM3134.0500</td></tr><tr><td>iScript cDNA Synthesis Kit</td><td>Bio-Rad</td><td>1708891</td></tr><tr><td>GreenMasterMix No ROX</td><td>Genaxxon</td><td>M3023.0500</td></tr><tr><td>RNA Subcellular Isolation Kit</td><td>ActiveMotif</td><td>25501</td></tr><tr><td>NEB Ultra II FS kit</td><td>NEB</td><td>E7430</td></tr><tr><td>Dual-Luciferase® Reporter Assay System</td><td>Promega</td><td>E1910</td></tr><tr><td>Promega Wizard Genomic DNA Purification Kit</td><td>Promega</td><td>A1120</td></tr><tr><td>Quant-ITTM dsDNA Assay kit</td><td>ThermoFisher Scientific</td><td>Q33232</td></tr><tr><td>Ligation sequencing DNA V14 kit</td><td>Oxford Nanopore Technologies</td><td>SQK-LSK114</td></tr><tr><td>MinION R10.4.1 flow cell</td><td>Oxford Nanopore Technologies</td><td>FLO-MIN114</td></tr><tr><td>Flow Cell wash kit (nuclease-based)</td><td>Oxford Nanopore Technologies</td><td>EXP-WSH004</td></tr></tbody></table>

Deposited data

<table><tbody><tr><td>HIC data</td><td>Lyu et al.<sup>36</sup></td><td>GEO: GSE210524</td></tr><tr><td>ChIP-seq, bulk RNAseq, ATAC seq data</td><td>Heller et al.<sup>12</sup></td><td>GEO: GSE167606</td></tr><tr><td>RNAseq</td><td>This paper</td><td>GEO: GSE275368</td></tr><tr><td>Nanopore seq</td><td>This paper</td><td>GEO: GSE275369</td></tr></tbody></table>

Experimental models: Cell lines

<table><tbody><tr><td>HUES8</td><td>Harvard University</td><td>RRID: CVCL_B207</td></tr><tr><td>HUES8-ONECUT1 heterozygous knockout line</td><td>Philippi et al.<sup>7</sup></td><td>N/A</td></tr><tr><td>HUES8-ONECUT1 homozygous knockout line</td><td>Philippi et al.<sup>7</sup></td><td>N/A</td></tr><tr><td>HeLa (CCL-2)</td><td>ATTC</td><td>RRID: CVCL_0030</td></tr><tr><td>HUES8-Pat1-homozygous knockout line (clone 1)</td><td>This paper</td><td>N/A</td></tr><tr><td>HUES8-Pat1-homozygous knockout line (clone 2)</td><td>This paper</td><td>N/A</td></tr><tr><td>HUES8-Pat1-heterozygous knockout line (clone 1)</td><td>This paper</td><td>N/A</td></tr><tr><td>HUES8-Pat1-heterozygous knockout line (clone 2)</td><td>This paper</td><td>N/A</td></tr><tr><td>HUES8-ONECUT1-het/Pat1-het knockout line (clone 1, cis)</td><td>This paper</td><td>N/A</td></tr><tr><td>HUES8-ONECUT1-het/Pat1-het knockout line (clone 2, cis)</td><td>This paper</td><td>N/A</td></tr><tr><td>HUES8-ONECUT1-het/Pat1-het knockout line (clone 3, trans)</td><td>This paper</td><td>N/A</td></tr><tr><td>HUES8-ONECUT1-het/Pat1-het knockout line (clone 4, trans)</td><td>This paper</td><td>N/A</td></tr></tbody></table>

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Continued

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3"><strong>Oligonucleotides</strong></td></tr><tr><td>Sanger sequencing 108 kb deletion in patient 1, see Table S1</td><td>This paper</td><td>N/A</td></tr><tr><td>sgRNAs 108kb deletion; see Table S2</td><td>This paper, Synthego</td><td>N/A</td></tr><tr><td>Primer Pat1-del verification for cell line generation (screening primer), see Table S1</td><td>This paper, Biomers</td><td>N/A</td></tr><tr><td>IncRNA-AC qPCR primers; see Table S1</td><td>This paper, Biomers</td><td>N/A</td></tr><tr><td>Hs_HMBS_1_SG</td><td>Qiagen</td><td>QT00014462</td></tr><tr><td>Hs_ONECUT1_1_SG</td><td>Qiagen</td><td>QT00219352</td></tr><tr><td>Antisense LNA GapmeRs; see Table S3</td><td>This paper (Qiagen)</td><td>N/A</td></tr><tr><td colspan="3"><strong>Recombinant DNA</strong></td></tr><tr><td>pUC-IncAC_site-1 (luci 1)</td><td>This paper, Azenta Life Sciences</td><td>N/A</td></tr><tr><td>pUC-IncAC_site-1 (luci 2)</td><td>This paper, Azenta Life Sciences</td><td>N/A</td></tr><tr><td>pUC-IncAC_site-1 (luci 3)</td><td>This paper, Azenta Life Sciences</td><td>N/A</td></tr><tr><td>pUC-IncAC_site-1 (luci 4)</td><td>This paper, Azenta Life Sciences</td><td>N/A</td></tr><tr><td>pGL3 Enhancer Vector</td><td>Promega</td><td>E1751 (U47297) RRID: Addgene_212938</td></tr><tr><td>ONECUT1-wildtype-expression plasmid</td><td>Philippi et al.<sup>7</sup></td><td>N/A</td></tr><tr><td colspan="3"><strong>Software and algorithms</strong></td></tr><tr><td>CRISPOR</td><td>Concordet and Haeussler<sup>37</sup></td><td>http://crispor.tefor.net; RRID: SCR_015935</td></tr><tr><td>Integrative Genomic Viewer (IGV), v2.17.0</td><td>Robinson et al.<sup>38</sup></td><td>https://www.igv.org; RRID:SCR_011793</td></tr><tr><td>FlowJo software, v10.10.0</td><td>BD</td><td>https://www.flowjo.com; RRID: SCR_008520</td></tr><tr><td>Cell Profiler 4</td><td>Stirling et al.<sup>39</sup></td><td>https://cellprofiler.org; RRID: SCR_007358</td></tr><tr><td>Fiji software</td><td>Schindelin et al.<sup>40</sup></td><td>https://lmageJ.net/sofsoftw/fiji/; RRID: SCR_002285</td></tr><tr><td>QuPath, v0.5.0</td><td>Bankhead et al.<sup>41</sup></td><td>https://qupath.readthedocs.io/en/stable/index.html; RRID: SCR_018257</td></tr><tr><td>Drop-seq pipeline, v1.0</td><td>Broad Institute</td><td>https://github.com/broadinstitute/Drop-seq; RRID: SCR_018142</td></tr><tr><td>DESeq2 v1.38.3</td><td>Bioconductor</td><td>https://bioconductor.org/packages/release/bioc/html/DESeq2.html; RRID: SCR_015687</td></tr><tr><td>GSEA v4.3.3<sup>42,43</sup></td><td>UC San Diego and Broad Institute</td><td>https://www.gsea-msigdb.org; RRID:SCR_003199</td></tr><tr><td>Antisense LNA GapmeR Custom Builder online tool</td><td>Qiagen</td><td>https://geneglobe.qiagen.com/us/customize/rna-silencing</td></tr><tr><td>Hiclift</td><td>Wang et al.<sup>44</sup></td><td>https://github.com/XiaoTaoWang/HiCLift</td></tr><tr><td>CHESS</td><td>Galan et al.<sup>45</sup></td><td>https://github.com/vaquerizaslab/chess</td></tr><tr><td>Knight-Ruiz matrix balancing</td><td>Knight and Rui<sup>46</sup></td><td>https://github.com/deeptools/Knight-Ruiz-Matrix-balancing-algorithm</td></tr><tr><td>MinKNOW software version 23.07.12</td><td>Oxford Nanopore Technologies</td><td>N/A</td></tr><tr><td>Integrated Guppy version 7.1.4</td><td>Oxford Nanopore Technologies</td><td>N/A</td></tr><tr><td>Epi2Me human variation workflow, v1.9.0</td><td>Oxford Nanopore Technologies</td><td>https://github.com/epi2me-labs/wf-human-variation; RRID: SCR_025280</td></tr><tr><td>Prism v10.0</td><td>GraphPad</td><td>https://www.graphpad.com; RRID: SCR_002798</td></tr><tr><td>BioRender</td><td>biorender.com</td><td>BioRender.com/c86x315</td></tr><tr><td colspan="3"><strong>Other</strong></td></tr><tr><td>AggreWell 400 microwell culture plates</td><td>STEMCELL technologies</td><td>34460</td></tr><tr><td>g-TUBEs</td><td>Covaris</td><td>520079</td></tr></tbody></table>
# EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

## Human participants

### Patient with neonatal syndromic diabetes and his family (patient 1)

The index patient (patient 1, male) and his parents are described in the results section, in Figures 1A and 1B, and below. The study was explained to the patient and his parents, who gave their written consent. Blood samples were collected from the patient and his parents and DNA extraction was performed using standard procedures. The ethics committee of Sainte-Justine Hospital (Montreal) gave ethical approval for this study.

Information on the parents and family: At the time of the patient's study at 13 years of age, both parents were 39 years old and had no reported history of diabetes or treatment for this condition. However, their diabetes status could not be specifically investigated. The maternal grandfather, aged 62, had T2D, though the age at diabetes onset was not known.

## Cell lines

### Stem cell culture

The hESC culture and pancreatic differentiation were authorized by the Robert Koch Institute within the "79. und 193. Genehmigung nach dem Stammzellgesetz, AZ3.04.02/0084 and AZ 3.04.02/0190". hESC line HUES8 (Harvard University, RRID: CVCL_B207, male) was cultured in Matrigel-coated (hESC-qualified) plates in mTeSR Plus medium supplemented with 1% penicillin-streptomycin (P/S) at 37°C, 5% CO₂, 5% O₂, and humidified atmosphere. The medium was changed every other day. Cells were split at 80-95% confluence with TrypLE Select and seeded on Matrigel-coated plates in mTeSR Plus with P/S and 10 µM ROCK inhibitor Y-27632 as previously described.⁴⁷

# METHOD DETAILS

## ONECUT1 mutation screening by Sanger sequencing

Sanger sequencing of ONECUT1 coding exons was performed in the patient and his two parents as described in.⁷

## Genomic studies for patient's deletion identification, with Sanger confirmation

High-density SNP genotyping and WGS were done at the Center National de Recherche en Génomique Humaine (CNRGH), CEA.

DNA of the patient and his parents was genotyped using the Illumina Infinium Global Screening Array Multi-Disease v3.0 (GSA-MDv3.0) microarray. Genotyping call rate was greater than 99% (99.24, 99.68, 99.73 respectively), with a total of 712,875 SNPs homogenously distributed along the genome. We used pedstat from Merlin package to detect the 108 kb deleted region.

Whole genome sequencing was performed in the patient and his parents using an Illumina NovaSeq6000 platform. DNA was prepared using Illumina TruSeq DNA PCR-Free library preparation kits, according to the manufacturer's instructions. An average sequencing depth of 30x was obtained for each sample. Alignment was done using BWA and samtools packages with GRCh37+decoy of human genome reference. BAM file was analyzed and pairbase limits of the deletion was determined using Interactive Genome Viewer.

The 108 kb deletion boundaries were confirmed by Sanger sequencing using primers flanking the deletion, amplifying a 594 bp PCR product in case of deleted allele. PCR amplification of family members using these primers also confirmed the paternal transmission of the deletion in the family (father and child: amplified; mother: not amplified).

## CRISPR-Cas9-based genome editing in hESC

In this study, the heterozygous and homozygous ONECUT1 hESC (HUES8) knockout lines from Philippi et al. were used.⁷ The patient-specific 108 kb deletion was induced in both HUES8 wildtype and heterozygous ONECUT1 knockout lines using CRISPR-Cas9 gene editing technology. Two crRNAs were designed using the open-access online designing tool CRISPOR³⁷ to create two double-strand breaks flanking the region of the Pat1-deletion, resulting in a complete knockout of 108 kb. Modified single guide RNAs (sgRNAs) combining tracrRNA and the respective crRNA were obtained from Synthego. Both sgRNAs and Alt-R S.p. HiFi Cas9 Nuclease C3 (IDT, 1081060) were introduced into HUES8 or heterozygous ONECUT1 cell lines via nucleofection using a 4D Nucleofector and the P3 Primary Cell 4D-Nucleofector X Kit following the manufacturer's instructions. In brief, 200,000 cells were harvested at 60-70% confluency, and resuspended in 16.4 µL P3 solution and 3.6 µL P3 supplement. An RNP complex, consisting of 12 pmol Cas9 and 72 pmol of each sgRNA, was formed during a 10-min incubation at room temperature. RNP complex was added to the cell suspension and transferred to one well of a 16-well Nucleocuvette Strip and pulsed with the nucleofection code CB-150. 80 µL mTeSR Plus medium with 10 µM ROCK inhibitor was added and transfected cells were incubated for 5 min at 37°C and subsequently seeded on a Matrigel-coated 48-well. After 48 h cultivation at 37°C, cells were seeded at low density to obtain single cell-derived colonies within 8-10 days. The mTeSR Plus medium was supplemented with 1x CloneR to enhance cell survival. Colonies were manually isolated for expansion and genotyping of the gene-edited clones.

## PCR for clone selection

Genotyping of the transfected single cell-derived clones was performed using Phire Hot Start II DNA Polymerase without prior DNA isolation. Each 20 µL reaction consisted of 10 µL 2x Phire Tissue Direct PCR Master Mix, 125 nM forward primer, 125 nM reverse
primer, 1 µL of the respective cell suspension and nuclease-free H₂O. Three primer combinations were used for screening: (1) screening primer 1 and screening primer 2 flanking the 5'-edited region of the deletion, generating a PCR product of 849 bp only if the cell line is either wildtype or heterozygous. (2) Screening primer 3 and screening primer 4 flanking the 3'-edited region, generating a 1180 bp product if the clonal cell line is either wildtype or heterozygous. (3) Screening primer 1 and screening primer 4, producing a 1184 bp product only if the 108 kb deletion was successfully introduced, whether heterozygous or homozygous. The respective genotypes were verified by Sanger sequencing (Eurofins Genomics).

## Pancreas differentiation of hESCs

### Differentiation to pancreatic progenitors

Differentiation of hESCs to pancreatic progenitors was performed as previously reported.⁴⁷ In brief, cells were seeded on growth-factor reduced (GFR) Matrigel-coated plates (Cultrex UltiMatrix or Matrigel Corning) in mTeSR Plus medium with 10 µM ROCK inhibitor. Differentiation was initiated after 24 h at a confluency of 70-95% by adding day 0 medium. The differentiating cells were maintained at 37°C, with 5% CO₂, and the differentiation medium containing stage-specific cytokines and growth factors was replenished daily. Cells were harvested and analyzed at distinct stages: definitive endoderm (DE; day 3), gut tube endoderm (GTE; day 6), pancreatic endoderm (PE; day 9) and pancreatic progenitor (PP; day 13).

For flow cytometry analysis of NKX6.1 and PDX1 expression on day 13, the protein kinase C activator indolacam V was excluded from the differentiation medium. This adjustment was made to reduce differentiation efficiency, thereby enhancing the detection of genotype-specific differences.

### Differentiation to endocrine progenitors

Following the differentiation of hESC into pancreatic progenitors as outlined above, the cells were subsequently guided toward pancreatic endocrine progenitors using a previously established monolayer culture method.⁴⁷,⁴⁸ Briefly, at day 13 of differentiation, medium change was performed with stage 5 monolayer medium supplemented with 1 µM Latrunculin A. After 24 h, medium was replaced with stage 5 monolayer medium deprived of Latrunculin A. Daily medium changes were maintained until reaching the endocrine progenitor stage (day 20), when cells were harvested for downstream analysis.

### Differentiation to stem cell-derived islets (SC-islets)

To generate mature and functional SC-islets, PPs underwent further differentiation as previously described.³³,⁴⁹ Initially, PPs were dissociated with TrypLE for 9 min at 37°C. The reaction was stopped with DMEM, followed by centrifugation at 180 x g for 5 min. After removing the supernatant, the cell pellet was resuspended in stage 5 suspension medium supplemented with 10 µM ROCK inhibitor. Subsequently, cells were counted and 5 mL cell suspension of 1.2x10⁶ cells/ml (6x10⁶ cells per well) was seeded into one well of an AggreWell 400 microwell culture plate pretreated with Anti-Adherence Rinsing Solution according to the manufacturer's recommendations. AggreWell plates were then centrifuged at 100 x g for 3 min and subsequently incubated at 37°C to facilitate sphere formation. After 24 h, 2 mL of stage 5 medium with 10 µM ROCK inhibitor was added carefully. The following 2 days, 4 mL medium change was performed. Afterward, spheres of each AggreWell were transferred to individual wells of a 6-well suspension plate, and medium was switched to 4 mL of stage 6 suspension medium. Spheres were then cultured on an orbital shaker at 100 rpm, with daily medium changes of stage 6 suspension medium for a period of 7 days. Subsequently, SC-islets were cultured for over 3 weeks in stage 7 medium to promote maturation, with medium changes performed every other day. For downstream analysis, SC-islets of either 3 independent differentiations or of 2 independent differentiations with replicates of 2 independent wells were analyzed.

## Flow cytometry

### Surface marker staining

Cells were harvested, washed with PBS, followed by incubation in 300 µL flow cytometry buffer (FC buffer) containing 10% Panexin in PBS for 30 min on ice. Next, cells were centrifuged at 1000 x g, resuspended in 50 µL primary antibody diluted in FC buffer, and incubated overnight at 4°C. Afterward, the cell pellet was washed twice with FC buffer, then resuspended in 50 µL Alexa Fluor secondary antibodies diluted 1:500 in FC buffer, and incubated for 90 min on ice. If the primary antibody was directly coupled to a fluorochrome, this step was omitted. The cells were washed twice with FC buffer, resuspended in 300 µL FC buffer and measured using Attune NxT flow cytometer (Thermo Fisher).

The following primary antibodies were used: anti-SSEA4 1:10 (PE-conjugated) and anti-TRA-1-60 1:10 (FITC-conjugated) for hESC stage, and anti-GP2 1:5000 for PP stage.

### Intracellular marker staining

Intracellular flow cytometry staining was performed as previously described.⁴⁷,⁵⁰ The following primary antibodies were used: anti-NKX6.1 (1 µg/mL), anti-PDX1 (1:250), anti-SOX17 (1:500), anti-GATA6 (1:500), anti-GCG (1:500), anti-CPEP (1:100), anti-INS-647 (1:80, AF647-conjugated). Alexa Fluor 488-, 568-, or 647-conjugated secondary antibodies were used in 1:500 dilution.

For endocrine progenitor staining, cells were additionally incubated in NKX6.1-647 conjugated antibody (1:150) for additional 90 min on ice. Conjugation of NKX6.1 antibody was performed with FlexAble CoraLite Plus 647 Antibody Labeling Kit according to the manufacturer's protocol. Analysis of FC measurements was performed with FlowJo software.
## Immunofluorescence staining using cytospin

Cells were harvested and dissociated with TrypLE, fixed in 4% PFA/100 mM sucrose for 25 min on ice and stored in PBS until further processing. 80 µL cell suspension was centrifuged onto SuperFrost Plus Glass slides with a Cytospin Centrifuge for 3 min at 600 rcf. Cells were circled with a hydrophobic pen and subjected to blocking and permeabilization in blocking buffer (5% normal donkey serum (NDS), 0.1% Triton X-, PBS) for 30 min at room temperature. After removal of the liquid, cells were incubated in primary antibody diluted in blocking solution at 4°C overnight. Cells were washed three times with wash solution (2% NDS, 0.1% Triton X-, PBS) for 5 min and incubated subsequently in secondary antibodies (Alexa Fluor 488- or 568-conjugated secondary antibodies) diluted 1:500 in blocking buffer along with 500 ng/mL DAPI for 90 min at room temperature. Cells were washed three times with wash buffer for 5 min. For GP2 (1:1000) surface staining, blocking and wash buffer was replaced by 10% Panexin in PBS, while the staining procedure remained unchanged. Finally, cells were mounted with Fluoromount-G and imaged using a Zeiss Axioscope2 microscope (Carl Zeiss). Images were quantified using Cell Profiler.<sup>39</sup>

## Immunofluorescence staining on paraffin sections

SC-islets were fixed in 4% PFA/100 mM sucrose overnight at 4°C. Spheres were pre-embedded in 2% agarose and underwent standard histological procedures. This included automated serial dehydration, followed by paraffin embedding and sectioning (4 µm). Immunofluorescence staining was performed as previously described.<sup>50</sup> The following primary antibodies were applied: anti-INS (1:5000, abcam), anti-GCG (1:1000), anti-CHGA (1:200), anti-VIM (1:500), anti-PPY (1:500), anti-SST (1:300). Alexa Fluor secondary antibodies were used. Slides were imaged with Zeiss Axioscope2 microscope. Image adjustments including cropping and brightness/contrast enhancements were carried out using Fiji software.<sup>40</sup> Quantification was performed utilizing QuPath.<sup>41</sup>

## Immunohistochemistry staining on paraffin sections

Sections of paraffin-embedded PPs and SC-islets were stained with anti-ONECUT1 antibody (1:100) after heat-mediated antigen retrieval (citrate buffer, pH 6). Immunohistochemistry staining was performed as previously described.<sup>50</sup>

## Western blot

Western blot for ONECUT1 and β-Actin was performed on PP samples as previously described.<sup>7</sup> In brief, 40 µg protein was separated on a 10% SDS-PAGE and transferred on a PVDF membrane using a semidry transfer system (Bio-Rad). The following antibodies were used: anti-ONECUT1 (1:1000), anti-β-Actin (1:5000), anti-mouse- and anti-rabbit-horseradish peroxidase (HRP). HRP detection was performed using SuperSignal West Dura Kit and Chemiluminescence Imaging Fusion Solo X system (VILBER Lourmat). Western blot bands were quantified using Fiji software.<sup>40</sup>

## RNA isolation, reverse transcription, qPCR

RNA was isolated from PP cells using GeneJET RNA Purification Kit following the manufacturer's protocol. Isolated RNA was either used for bulk RNA sequencing or for reverse transcription and qPCR. Reverse transcription was performed with Scriptase RT cDNA Synthesis kit (Genaxxon) or iScript cDNA Synthesis Kit (Bio-Rad). qPCR was done with GreenMasterMix No ROX with Rotor-Gene Q (Qiagen).

The following primers were used: IncRNA-AC qPCR forward primer, IncRNA-AC qPCR reverse primer, Hs_HMBS_1_SG, Hs_ONECUT1_1_SG.

## RNA fractionation for subcellular localization analysis

Cells were harvested for RNA extraction. Half of the sample was further processed for total RNA isolation as described before. RNA isolation of the remaining cell portion was conducted using the RNA Subcellular Isolation Kit according to the protocol provided by the supplier, which facilitates the separation of nuclear and cytosolic RNA. Equal amounts of cytosolic, nuclear, and total RNA were used for reverse transcription and qPCR as described. Ct values of cytosolic and nuclear fractions were normalized to the IncRNA-AC Ct value of the respective total RNA sample.

## RNA-seq

For bulk-sequencing of poly(A)-RNA, library preparation was conducted as described previously.<sup>51</sup> Barcoded cDNA of each sample was generated with a Maxima RT polymerase using oligo-dT primers containing barcodes, unique molecular identifiers (UMIs) and an adapter. 5' ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adapter. NEB Ultra II FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated, and 3'-end-fragments were finally amplified using primers using Illumina P5 and P7 overhangs. Sequencing of the library was performed on a NextSeq 500 (Illumina) with 61 cycles for the cDNA in read1 and 19 cycles for the barcodes and UMIs in read2. Data processing utilized the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables.<sup>52</sup> Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to GENCODE v38.
DESeq2 was utilized to normalize raw counts, and a filtering step was applied to retain genes with a minimum count of 10 across at least 3 samples. Subsequently, a variance stabilizing transformation was performed for downstream analyses, including principal component analysis (PCA). Differential expression analysis between genotypes was conducted using the "result" function, employing a significance threshold of *p* < 0.05.

## Gene set enrichment analysis

For gene set enrichment analysis (GSEA), the GSEA desktop software<sup>42,43</sup> was employed, utilizing the GSEAPreranked tool. In this analysis, differentially expressed genes were ranked based on the negative logarithm (base 10) of their *p*-values multiplied by the sign of the corresponding log2 fold change. GSEA was performed using specific endocrine lineage gene sets.<sup>28,31</sup>

## GapmeR-based lncRNA knockdown

GapmeR sequences were designed with the Antisense locked nucleic acid (LNA) GapmeR Custom Builder online tool (Qiagen). Two GapmeRs from top-ranked sequences were selected.

LncRNA-AC knockdown was performed in HUES8 wildtype cells that were differentiated into endocrine progenitors in 48-well format as described above. Lipofection was performed on days 9, 11, 13, 15, 17, and 19 of differentiation, with cell harvesting performed on day 20 for downstream analysis. The transfection protocol involved mixing 25 µL DMEM and 0.5 µL Lipofectamine2000 added to a second tube containing 25 µL DMEM with 45 pmol GapmeR-1 (AC1), GapmeR-2 (AC2) or negative control GapmeR. Transfection mix was incubated for 15 min at room temperature. Meanwhile differentiation medium was changed. Lipofection mix was added dropwise to the wells. Subsequent medium changes were performed not earlier than 24 h after lipofection. A fluorescein amidite (FAM)-labeled negative control LNA GapmeR was used to monitor transfection efficiency and to exclude general effects based on LNA GapmeR transfection.

## Nanopore-sequencing

For methylation analysis and variant phasing, nanopore sequencing with adaptive sampling and 5hmC and 5mC methylation calling was conducted. For this purpose, high molecular weight (HMW) DNA was extracted using the Promega Wizard Genomic DNA Purification Kit for cultured cells, according to the manufacturer's manual. The DNA was dissolved overnight at 4°C and quantified by using the Quant-IT dsDNA Assay kit. Approximately 2.5-4.5 µg of the extracted DNA was sheared using g-TUBEs and used as input for nanopore library preparation with the ligation sequencing DNA V14 kit according to the manufacturer's protocol. Each library was loaded on a MinION R10.4.1 flow cell and sequenced with a MinION Mk1B device for up to 72 h. Throughout the sequencing process, the flow cells were reloaded 1-2 times after a nuclease flush with the same library according to the manufacturer's instructions to enhance sequencing yield. For adaptive sampling a more than 800 kb region (chr15.hg19: 52999842_53878937) with the "fast modified basecalling for 5hmC and 5mC in CG context" basecall model of the MinKNOW software and integrated Guppy was set.

## ONT analyze phasing and methylation

The WT HUES8, WT PP, ONECUT1 -/+ Pat1-del -/+ cis (clone 1), and trans (clone 3) cell lines were subjected to analysis using unaligned BAM files. The Epi2Me human variation workflow was executed to generate DNA methylation data, structural variant data, and phased aligned BAM files (based on the GRCh38 reference genome) for the genomic region spanning coordinates chr15.hg38:52707645_53586740. A minimum coverage threshold of 1 was applied. Additionally, the BAM files underwent manual inspection using the Integrative Genomic Viewer.<sup>38</sup>

## Luciferase assay

Luciferase assay was performed as previously described.<sup>7</sup> Enhancer sequences, containing an EcoRV restriction sites at both ends, were synthesized and inserted into a pUC vector backbone by Azenta Life Sciences. These sequences were cloned into the pGL3-enhancer vector after Smal digestion. Due to blunt end ligation, the insertion of the sequence could occur in either the minus or plus orientation. Confirmation of the correct sequence and orientation was achieved through a control digest. For reporter gene assays HeLa cells (2 x 10<sup>5</sup> cells) were seeded in 48-well plate 16 h prior to transfection with Lipofectamine 2000. Transfection included 250 ng of the enhancer plasmid along with or without a respective expression plasmid (ONECUT1<sup>7</sup>) at varying concentrations (50 ng or 100 ng). Cells were lysed after 24 h, and luciferase activity was measured in 10 µL cleared lysate using a Centro LB960 luminometer (Berthold Technology) and the luciferase assays system (Promega).

## Hi-C

Here, we used the Hi-C data from the regulation of cohesin loop extrusion, which underlies changes in enhancer-promoter interactions during pancreatic cell differentiation.<sup>36</sup> Hi-C files were obtained from GSE210524. Subsequently, we converted the coordinates from hg38 to hg19 to align with our previous sequencing data<sup>12</sup> using Hiclift.<sup>44</sup> We detected structural differences between stem cell (H9 cell line), DE and PP using CHESS.<sup>45</sup> Before, we normalized the data with Knight-Ruiz matrix balancing.<sup>46</sup> CHESS was applied to 5-kb resolution Hi-C matrices using windows of 1 mb and a step size of 1 kb to produce similarity scores for pairwise Hi-C comparisons between ESC and the differentiation stages PP and DE.
## Transmission electron microscopy

SC-islets were fixed overnight at 4°C in 4% paraformaldehyde/100 mM sucrose and stored in PBS. Sample preparation for transmission electron microscopy (TEM) was carried out by the Central Facility for Electron Microscopy at Ulm University following the protocol as previously reported.⁵³ The islets were fixed in 0.1 M phosphate buffer (pH 7.3) with 2.5% glutaraldehyde and 1% sucrose. The samples were post-fixed in 2% aqueous OsO₄ and dehydrated in a graded series of isopropanol. The samples were then block-stained with 1% uranyl acetate in ethanol and embedded in Epon. Ultrathin sections (80 nm) were cut with an ultramicrotome equipped with a diamond knife, transferred to copper TEM grids and post-stained with 0.3% lead citrate for 1 min. Imaging was performed with a JEOL TEM 1400.

## Insulin secretion

Insulin secretion assay was performed in a 24-well plate, utilizing approximately 20–30 carefully selected spheres. Spheres were rinsed twice with freshly prepared KRB buffer⁵⁴ and incubated in 200 µL low glucose (LG) condition (KRB +1 mM glucose) for 90 min at 37°C on an orbital shaker. Supernatant was discarded, spheres were washed twice with KRB buffer and sequentially incubated for 30 min in the following conditions: LG condition; high glucose (HG) condition (KRB +20 mM glucose); HG + 50 ng/mL extendin-4; LG condition, LG + 30 mM KCl. For stimulation with tolbutamide, the spheres were equilibrated for 90 min in LG condition, following 30 min stimulation with LG condition and 30 min LG + 400 µM tolbutamide. Subsequently, the supernatants were collected, and the secreted insulin content was quantified using an insulin enzyme-linked immunosorbent assay (ELISA) according to the manufacturer's protocol. Samples were diluted 1:5. The spheres were dissociated and counted using a CASY cell counter (OLS) to normalize the secreted insulin levels to the respective cell numbers.

## QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analysis was performed with GraphPad prism software. For comparison of two groups unpaired t test (two-tailed) was performed. Analysis of several groups was performed with ordinary one-way ANOVA. Bar graphs depict mean ± standard error of the mean (SEM). Significance was displayed as follows: * *p* < 0.05; ** *p* < 0.01; *** *p* < 0.001; **** *p* < 0.0001. Statistical details of experiments can be found in the respective figures and figure legends.