Epigenetic remodelling licences adult cholangiocytes for organoid formation and liver regeneration

ABSTRACT Following severe or chronic liver injury, adult ductal cells (cholangiocytes) contribute to regeneration by restoring both hepatocytes and cholangiocytes. We recently showed that

ductal cells clonally expand as self-renewing liver organoids that retain their differentiation capacity into both hepatocytes and ductal cells. However, the molecular mechanisms by which

adult ductal-committed cells acquire cellular plasticity, initiate organoids and regenerate the damaged tissue remain largely unknown. Here, we describe that ductal cells undergo a

transient, genome-wide, remodelling of their transcriptome and epigenome during organoid initiation and in vivo following tissue damage. TET1-mediated hydroxymethylation licences

differentiated ductal cells to initiate organoids and activate the regenerative programme through the transcriptional regulation of stem-cell genes and regenerative pathways including the

YAP–Hippo signalling. Our results argue in favour of the remodelling of genomic methylome/hydroxymethylome landscapes as a general mechanism by which differentiated cells exit a committed

state in response to tissue damage. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS Access through your

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SIMILAR CONTENT BEING VIEWED BY OTHERS BIPOTENT TRANSITIONAL LIVER PROGENITOR CELLS CONTRIBUTE TO LIVER REGENERATION Article Open access 13 March 2023 MULTI-ZONAL LIVER ORGANOIDS FROM HUMAN

PLURIPOTENT STEM CELLS Article 16 April 2025 DNA METHYLATION MAINTENANCE AT THE P53 LOCUS INITIATES BILIARY-MEDIATED LIVER REGENERATION Article Open access 29 March 2022 DATA AVAILABILITY

The RNA, ChIP, DamID, WGBS and RRHP sequencing data that support the findings of this study have been deposited in the Gene Expression Omnibus under the accession code GSE123133. All other

data supporting the findings of this study are available from the corresponding author on reasonable request. CODE AVAILABILITY All codes used are available on request. REFERENCES * Choi, T.

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references ACKNOWLEDGEMENTS M.H. is a Wellcome Trust Sir Henry Dale Fellow and is jointly funded by the Wellcome Trust and the Royal Society (grant no. 104151/Z/14/Z). A.H.B. was funded by

the Wellcome Trust Senior Investigator Award 103792 and a Royal Society Darwin Trust Research Professorship. S.J.F. is supported by a Medical Research Council (MRC) grant (grant no.

MR/P016839/1). L.A. was supported by a Marie Skłodowska-Curie Postdoctoral fellowship (grant no.702585-EPILIPRO-H2020-MSCA-IF-2015) and a NC3Rs grant awarded to M.H. (grant no.

NC/R001162/1). M.A.M. was supported by a MRC doctoral training grant (grant no. MR/K50127X/1). L.C.-E. was jointly funded by a Wellcome Trust Four-Year PhD Studentship with the Stem Cell

Biology and Medicine Programme and a Wellcome Cambridge Trust Scholarship. J.v.d.A. was supported by a EMBO Long-term Fellowship (grant no. ALTF 1600_2014) and Wellcome Trust Postdoctoral

Training Fellowship for Clinicians (grant no. 105839). F.A. was supported by an ERC advanced research grant to M.Z.-G. M.Z.-G. is a Wellcome Trust Senior Research Fellow. This work was

partially funded by a H2020 LSMF4LIFE (grant no. ECH2020-668350) awarded to M.H., a ERC advanced grant to M.Z.G., a Wellcome Trust Senior Investigator Award awarded to E.A.M. (grant nos

104640/Z/14/Z and 092096/Z/10/Z) and a Cancer Research Programme Grant awarded to E.A.M. (grant nos C13474/A18583 and C6946/A14492). G.V. would like to thank Wolfson College at the

University of Cambridge and the Genetics Society, London for financial help. The authors acknowledge core funding to the Gurdon Institute from the Wellcome Trust (grant no. 092096) and CRUK

(grant no. C6946/A14492). The authors thank R. Krautz and W. Sanseverino for their advice on bioinformatic analyses, R. Arnes-Benito and A. A. Malcom for assistance with histological and

immunostaining analyses, W. Reik and J. Silva for sharing TET1 plasmids, R. Butler for developing macro scripts, K. Harnish and C. Bradshaw of the Gurdon Institute’s genomic and

bioinformatic facility for high-throughput sequencing, the Gurdon Institute facilities for assistance with imaging, animal care and bioinformatics analysis, A. Riddell and M. Paramor

(Cambridge Stem Cell Institute) for assistance with FACS sorting and library preparation, respectively, the CRUK CI genomic facility for sequencing of the WGBS and RRHP libraries and M.

Keighren (MRC Human Genetics Unit, University of Edinburgh) for technical support. M.H. would like to thank “Life Science Editors” for assistance during manuscript preparation and B. Simons

and H. Clevers for their critical comments on the manuscript. AUTHOR INFORMATION Author notes * These authors contributed equally: Luigi Aloia, Mikel Alexander McKie, Grégoire Vernaz.

AUTHORS AND AFFILIATIONS * The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, UK Luigi Aloia, Mikel Alexander McKie, Grégoire Vernaz, Lucía Cordero-Espinoza, Jelle

van den Ameele, Berta Font-Cunill, German Belenguer, Andrea H. Brand, Eric A. Miska & Meritxell Huch * Department of Physiology, Development and Neuroscience, University of Cambridge,

Cambridge, UK Luigi Aloia, Mikel Alexander McKie, Lucía Cordero-Espinoza, Jelle van den Ameele, Francesco Antonica, Berta Font-Cunill, Andrea H. Brand, Magdalena Zernicka-Goetz & 

Meritxell Huch * Wellcome Trust - Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, UK Luigi Aloia, Mikel Alexander McKie, Lucía Cordero-Espinoza, Berta

Font-Cunill & Meritxell Huch * Department of Genetics, University of Cambridge, Cambridge, UK Grégoire Vernaz & Eric A. Miska * Wellcome Sanger Institute, Hinxton, UK Grégoire Vernaz

 & Eric A. Miska * MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK Niya Aleksieva, Alexander Raven & Stuart J. Forbes * Sequentia Biotech SL, Barcelona,

Spain Riccardo Aiese Cigliano * Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany German Belenguer & Meritxell Huch * Division of Biomedical and Life

Sciences, Faculty of Health and Medicine, Lancaster University, Bailrigg, Lancaster, UK Richard L. Mort * Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA Magdalena

Zernicka-Goetz Authors * Luigi Aloia View author publications You can also search for this author inPubMed Google Scholar * Mikel Alexander McKie View author publications You can also

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publications You can also search for this author inPubMed Google Scholar * Magdalena Zernicka-Goetz View author publications You can also search for this author inPubMed Google Scholar *

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Google Scholar * Meritxell Huch View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS M.H. and L.A. conceived and designed the project and

interpreted the results. L.A., M.A.M., L.C.-E., G.B., G.V., N.A., J.v.d.A., A.R. and M.H. designed and performed experiments and interpreted results. L.A. designed and performed the in vitro

experiments involving molecular biology techniques and organoid cultures, M.A.M. designed and performed the in vivo experiments and related stainings, L.C.-E. performed the

hydroxymethylation and EdU stainings, G.B. performed the experiments with small molecule inhibitors. G.V. and E.A.M. prepared and analysed the WGBS and RRHP libraries, analysed the RNA

sequencing and interpreted the corresponding bioinformatic analyses. N.A., A.R. and S.J.F. performed experiments with the β1-integrin model and interpreted the results of the p21 models.

J.v.d.A. and A.H.B. performed the DamID-seq experiments. B.F.-C. helped with the in vivo analyses. R.A.C. helped with the bioinformatics analyses. R.L.M. provided the _R26Fucci2a_ line. F.A.

and M.Z.-G. performed the live imaging of ductal cells. L.A. and M.H. wrote the manuscript. All of the authors commented on the manuscript. CORRESPONDING AUTHOR Correspondence to Meritxell

Huch. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to

jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA EXTENDED DATA FIG. 1 NON-PROLIFERATIVE EPCAM+ DUCTAL CELLS INITIATE ORGANOID CULTURES. A, EpCAM+ ductal

cells were isolated from WT livers by FACS using a sequential gating strategy as follows: cells were gated for FSC and SSC and subsequently singlets were gated using FSC/Pulse width. Then,

cells were negatively selected for PE/Cy7 (to exclude CD11b+, CD31+ and CD45+ cells) and positively selected for APC (EpCAM+) to obtain CD11b−/CD31−/CD45−/EpCAM+ ductal cells (EpCAM+ cells).

These cells give rise to proliferative organoids with ~15% efficiency. Representative bright field pictures of 500 EpCAM+ and EpCAM− cells 6 days after seeding. Graph represents mean ± SD

of n = 3 independent experiments. B, RT-qPCR analysis of gene expression of the proliferation marker _mKi67_ (left) and stem-cell (_Lgr5_) and ductal (_Epcam_ and _Sox9_) markers (right) at

the indicated time points after seeding. Graphs represent the mean of n = 3 independent experiments. _p_-value obtained using Student’s two tailed t-test upon comparison to t = 0 h. *, p 

< 0.05; ***, p < 0.001. C, Proliferation analysis. EdU (10 μM) was incorporated to sorted EpCAM+ ductal cells at different intervals after seeding (0 h, 24 h and 48 h, arrows) and

evaluated by immunofluorescence analysis 24 h after each incorporation. Representative images are shown. Scale bar, 10 μm. Graph represents the percentage of EdU + cells. Results are

expressed as mean ± SD cells from n = 3 independent experiments. Student’s two tailed t-test statistical analyses were performed versus t = 24 h. *, p < 0.05; **, p < 0.01; ***, p <

 0.001. Source data EXTENDED DATA FIG. 2 TRANSCRIPTIONAL CHANGES IN DUCTAL CELLS _IN VITRO_ DURING LIVER ORGANOID FORMATION AND _IN VIVO_ UPON DAMAGE. A-E, RNA-seq analysis of ductal cells

isolated from adult livers (0 h) and at different time points after culture. For DE genes, a pairwise approach with Wald test was performed on each gene using Sleuth. FDR < 0.1 was

selected as threshold. A, Graphs represent the number of significantly DE genes for each comparison. B, Hierarchical clustering analysis of epigenetic regulators found DE (383 out of 698

published in ref. 37), in at least one comparison. Heatmap represents averaged TPM values scaled per gene. Results are presented as the averaged gene expression of the biological replicates.

n, number of replicates. C-E, RNA-seq analysis of ductal cells isolated from adult livers (0 h) and at day 3 and day 5 after liver damage (2 mice were assessed per time point). C, The

heatmap shows the 1552 DE genes at least in one comparison (TPM > 5, FDR < 0.1, |b| > 0.58). Clustering analysis identified 5 different clusters (Clusters 1-5) according to the

expression profile (Cluster 1 n = 835; Cluster 2 n = 185; Cluster 3 n = 503; Cluster 4 n = 20; Cluster 5 n = 9). Number of genes in each cluster is indicated in brackets. Results are

presented as average of the at least 3 biological replicates. D, Graph represents the number of significant DE genes in the different comparisons. E, GO and statistical analyses of the 3

main clusters identified in C were performed using DAVID 6.8. Source data EXTENDED DATA FIG. 3 TET1 CATALYTIC ACTIVITY IS REQUIRED FOR LIVER ORGANOID FORMATION AND MAINTENANCE. A, _Tet1_ and

_Lgr5_ mRNA levels (n = 3 mice). Student’s two-tailed t-test statistical analyses were performed versus undamaged. B, _Tet1_ mRNA levels (24 h after transfection) and organoid formation

efficiency 10 days after Tet1 siRNA knock-down using 4 independent Tet1 siRNAs. Data is presented as percentage relative to siCtrl. Graph indicates mean ± SD of n = 3 independent

experiments. Student’s two-tailed t-test statistical analyses were performed versus mock-transfected cells. C, Scheme of the two different _Tet1_ alleles used. D, _Tet1_ mRNA levels in _WT_,

_Tet1__hy/+_ and _Tet1__hy/hy_ and _Tet1_ conditional knock-out (cKO) organoids presented as mean ± SD of n = 3 experiments. E, Representative Western blot image showing TET1 protein levels

in _WT_, _Tet1__hy/+_ and _Tet1__hy/hy_ organoids (3 independent experiments). F, Organoid formation efficiency from FACS-sorted EpCAM+ cells derived from _RosaCre__ERT2_ x _Tet1_ _flx/flx_

livers treated with 5 μM hydroxytamoxifen (mean ± SD of n = 3 independent experiments). Student’s two-tailed t-test statistical analyses were performed versus non-induced control. G, Whole

mount immunofluorescence staining of 5hmC (green) on _WT_, _Tet1__hy/hy_, _hypo-OE_ and _hypo-OE__cat.mut_. organoids. Representative images are shown (2 experiments). Scale bar, 50 μm. H,

Graph represents organoid size at the indicated passages (mean ± SD of n = 3 independent experiments). Student’s two tailed t-test statistical analyses were performed versus WT. I, Growth

curves. J, Organoid formation efficiency at the indicated passage expressed as a percentage of organoids. Graphs represent mean ± SD of n = 3 independent experiments. Student’s two tailed

t-test statistical analyses were performed versus WT. K, Representative confocal images of Cleaved Caspase 3 whole mount immunostaining on _WT_, _Tet1__hy/hy_, _hypo-OE_ and

_hypo-OE__cat.mut_. organoids (2 independent experiments). Scale bar, 25 μm. Source data EXTENDED DATA FIG. 4 WGBS OF DUCTAL CELLS UNCOVERS A GLOBAL EPIGENETIC REMODELLING OF THE DNA

METHYLOME UPON TISSUE DAMAGE. A, Number of WGBS unique mapped reads in the different biological replicates. B, Bisulfite conversion rate. C-H, WGBS analyses were performed in merged

biological replicates per time point (n = 2). Only CpG sites with ≥3 reads were further analysed. C, CpG counts in merged biological replicates per time point. D, Genome-wide Spearman’s

correlation score at the time points analysed shows dynamic CpG modifications. E, Functional localization of DMRs. DMRs were called if the difference in cytosine modification between samples

was ≥25% with a p-value of <0.05, using DSS software. F, Violin plot of the DMR length distribution (in base pairs) identified in the n = 2 biological replicates. Lines and numbers,

median. G, Density plot indicating the difference in mCpG levels for loss/gain DMRs for each comparison. H, Venn diagram showing the overlap between TET1 targets (see Fig. 5) that are

transcriptionally up-regulated and genes showing either loss (left) or gain (right) of mCpG at the TSS according to the WGBS analyses. Hierarchical clustering analyses of the overlapping

genes are presented as heatmaps of TPMs scaled per gene (Z-score). Source data EXTENDED DATA FIG. 5 5HMC LEVELS INCREASE IN DUCTAL CELLS _IN VITRO_ AND _IN VIVO_ UPON DAMAGE. A-C, EpCAM+

ductal cells sorted from DDC-treated WT livers (A), β1 integrin mutant mice fed with normal chow (undamaged) or DDC (B) or WT undamaged livers and grown in organoid culture conditions (C).

5hmC fluorescence intensity was normalized to DAPI. Data are presented as violin plots of the ratio 5hmC/DAPI. Each dot represents the median value of cells counted/mouse. Global median

levels are shown in red. A, 353 cells from n = 4 undamaged mice, 231 cells from n = 5 mice after 3 days of DDC, and 392 cells from n = 5 mice at DDC d5; B, 138 cells from undamaged, 119

cells at day 1, 247 at day 7 and 125 at day 14 after returning the mice to normal chow (recovery) pooled from 2 livers isolated independently from 2 mice were analysed; C, 2500 (0 h), 900

(24 h) and 2000 (48 h) cells from n = 3 independent experiments were analysed. _p_-values were calculated using pairwise comparisons with Wilcoxon rank sum test. A, d3 versus d0 p = 1 × 

10−13; d5 versus d0 p < 2.2 × 10−16. C, 0 h versus 24 h p < 2.2 × 10−16; 48 h versus 0 h p < 2.2 × 10−16. Scale bar, 10 μm. D, All 5hmC sites identified by RRHP. E, Number of genes

associated to TSS showing differential 5hmC levels. The number of CpG sites (n) with unique gain of hydroxymethylation is shown. F, Graphs represent distribution of percentage of mCpG

identified by WGBS in CGI outside TSS (n = 32673) using the average of the 2 independent samples (violin plots, black lines median, left) and number of 5hmC counts (median ± IQR) in CGI

outside TSS (n = 25579) (right). G, GO and statistical analyses of the clusters identified in Fig. 4j (Cluster 2 n = 347; Cluster 3 n = 1659; Cluster 4 n = 1424; Cluster 6 n = 140) were

performed using DAVID 6.8. Heatmap shows the expression profile of the 84 overlapping genes and is presented as averaged Z score of the 2 biological replicates. Source data EXTENDED DATA

FIG. 6 TET1 REGULATES ACTIVELY TRANSCRIBED GENES IN LIVER ORGANOIDS. A-D, DamID-sequencing was performed in EpCAM+ sorted ductal cells derived from already established liver organoids (3

independent experiments). Only TET1-Dam peaks identified in all 3 experiments were considered for further analyses. A, Scheme of DamID-seq protocol. B, Heatmaps showing TET1 peaks identified

by DamID-seq (left panels) and H3K4me3 peaks identified by ChIP-seq (right panels). Heatmaps are centred in the middle of the peak (0) and show a genomic window of ± 10 kb. Top heatmaps

represent common peaks between TET1 and H3K4me3 (2848 peaks) while bottom heatmaps represent TET1-specific peaks (2254 peaks). C, Pie-chart indicates the percentage of genomic distribution

of TET1-Dam peaks. D, GO and statistical analyses of biological processes among TET1-Dam targets in liver organoids were performed using DAVID 6.8. n, number of genes. E, 5hmC and 5mC levels

determined by MeDIP and hMeDIP followed by qPCR on the indicated genomic region surrounding _Lgr5_ TSS in _WT_ (black), _Tet1__hypo/hypo_ (blue) and _hypo-OE_ (red) organoids. Graphs

represent mean of n = 3 independent experiments. Student’s two tailed was performed comparing samples to WT. *, p < 0.05; ** = p < 0.01 F, TET1 ChIP-qPCR at _Lgr5_ TSS (left panel) and

_Lgr5_ mRNA levels (right panel) in _WT_, _Tet1__hypo/hypo_ and _hypo-OE_ organoids. Graphs represent mean ± SD of n = 3 independent experiments. Student’s two tailed t-test statistical

analyses were performed versus WT. **, p < 0.01 G, Sorted EpCAM+ cells from WT livers were cultured in organoid culture conditions and harvested for DNA, chromatin and mRNA expression

analyses at the indicated time points. Graphs represent mean of n = 3 independent experiments. Student’s two tailed t-test analyses were performed versus t = 0 h *, p < 0.05; ** = p < 

0.01; *** = p < 0.001. Source data EXTENDED DATA FIG. 7 TREATMENT WITH RAPAMYCIN IMPAIRS ORGANOID FORMATION. A, EpCAM+ ductal cells freshly isolated from the undamaged liver were treated

at 0-18hrs or 18-48hrs with the indicated small molecule inhibitors. Organoid formation was quantified at day 6. Graph represents organoid formation efficiency and indicates mean ± SD of n =

 3 independent experiments. Statistical analyses were performed with two-ways ANOVA with Bonferroni’s multiple compared test (versus DMSO control group). DMSO control quantifications are

shown in Fig. 6f. Representative pictures of organoids treated with the inhibitors at 18-48hrs are shown. Source data EXTENDED DATA FIG. 8 TET1 HYPOMORPHIC MICE EXHIBIT IMPAIRED DUCTAL

REGENERATION UPON DAMAGE. A, Graph represents mean ± SD of mouse weight of _WT_ (n = 21 mice)_, Tet1__hy/+_ (n = 13 mice) and _Tet1__hy/hy_ (n = 27 mice) littermates. Student’s two tailed

t-test statistical analyses were performed. B, Relative mouse weight of _WT_ (n = 5), _Tet1__hy/+_ (n = 1) and _Tet1__hy/hy_ (n = 5) mice. C, Representative H&E stainings (3 experiments)

of intestines from 50 week old _WT_ and _Tet1__hy/hy_ mice. Scale bar, 100μm. D, Representative H&E stainings (3 experiments) of small intestine from 10 week old _WT_ and _Tet1__hy/hy_

mice treated with DDC for 5 days. Scale bar, 100μm. E-F, Box-and-whisker plots showing median and IQR of proliferating ductal cells (OPN+/Ki67+) during recovery (n = 3 _WT_ and n = 4

_Tet1__hy/hy_ mice) (E) or total ductal cells (OPN+) at the different time points indicated (F) (Undamaged, n = 3 _WT_ and n = 3 _Tet1__hy/hy_ mice; DDC, n = 7 _WT_ and n = 6 _Tet1__hy/hy_

mice; Recovery, n = 3 _WT_ and n = 4 _Tet1__hy/hy_ mice). Grey dots, outliers from a single counted FOV defined as > 1.5 IQR above or below the median. Red squares, median level

corresponding to each independent mice. _p_-values obtained by two-sided Kolmogorov-Smirnov test. G, Population distribution of the total number of ductal cells (OPN+) Dashed lines show

median values obtained from 55 FOV for _WT_ (3 mice) and 56 FOV for _Tet1__hy/hy_ (3mice) at day 0 (undamaged) and 110 FOV for _WT_ (3 mice) and 153 FOV for _Tet1__hy/hy_ (4 mice) at day 12

(recovery). H, PCK immunohistochemistry (3 experiments) from _WT_ (left) and _Tet1__hy/hy_ (right) undamaged or in recovery after DDC (day 12) livers. Nucleus, Haematoxylin. Scale bar, 100 

μm. I, _Lgr5_ and _Tet1_ mRNA levels, TET1 ChIP and hMedIP on _Lgr5_ TSS were analysed in undamaged and DDC treated livers. Graphs represent mean ± SD of values obtained from n = 3

independent biological replicates (dot). _p-_value was calculated using Student’s two-tailed t-test. Source data EXTENDED DATA FIG. 9 DUCTAL SPECIFIC TET1 CONDITIONAL DELETION IMPAIRS

CHOLANGIOCYTE-MEDIATED LIVER REGENERATION. A, Schematic of the _Prom1Cre__ERT2__/Rosa__lslZsGreen__/Tet1__flx/flx_ mouse model. B, Representative immunofluorescence analysis (OPN+ red,

ZsGreen+, green) of _Prom1__∆Tet1/ZsGreen_ and _Prom1__Tet1WT/ZsGreen_ upon tamoxifen treatment and injection of AAV8-TBG p21 (2 mice per genotype). Nucleus, Hoechst. Scale bar, 50 μm C,

Representative immunofluorescence analysis of livers from _Prom1__Tet1WT/ZsGreen_ mice injected with AAV8-TBG p21 not receiving tamoxifen treatment (2 mice per genotype). Scale bar, 100 μm.

D, _Tet1_ expression in EpCAM+/ZsGreen+ ductal cells isolated by FACS from _Prom1__∆Tet1/ZsGreen_ (n = 4) or _Prom1__Tet/ZsGreen_ (n = 4) livers derived from mice treated for 3-cycles of DDC

and collected 12 days after damage. Graph represents mean ± SD of _Tet1_ expression expressed as a fold change compared to Prom1_T__et1__WT_/_ZsGreen_. Student’s two tailed t-test

statistical analyses were performed. ***, p < 0.001. E, Representative pictures of P21 immunohistochemistry analyses. Scale bar, 200 μm. F, Weight curves of mice undergoing AAV8-TBG-p21

injection followed by DDC treatment (mean ± 95%CI). G, TET1 ChIP-qPCR analyses on target genes in ZsGreen+/EpCAM+ ductal cells isolated from _Prom1__Tet1WT/ZsGreen_ DDC-treated livers for 5

days. Cells isolated from 3 mice littermates were pooled used for each independent experiment (n = 2). ND, not detected. H, Graph represents mean ± SD of mRNA expression of _Tet1_ and

selected target genes (fold change versus WT undamaged) in EpCAM+ ductal cells isolated from undamaged (n = 2 per genotype) or day 5 DDC-treated livers (n = 3 per genotype) derived from

_Prom1__TET1WT/ZsGreen_ (grey) or _Prom1__∆Tet1/ZsGreen_ (blue) mice. Statistical analysis was performed using Student’s two-tailed t-test compared to the _Prom1__TET1WT/ZsGreen_ value at

the corresponding time point. Source data SUPPLEMENTARY INFORMATION REPORTING SUMMARY SUPPLEMENTARY TABLE 1 List of mouse models used. SUPPLEMENTARY VIDEO 1 Time lapse movie of EpCAM+ ductal

cells FACS-sorted from undamaged R26Fucci2a mouse livers which were embedded in matrigel and grown in organoid culture conditions for 72 h. SUPPLEMENTARY DATASET 1 RNA-sequencing data.

SUPPLEMENTARY DATASET 2 WGBS and RRHP data. SUPPLEMENTARY DATASET 3 TET1-DamID sequencing and H3K4me3 ChIP-sequencing data. SUPPLEMENTARY DATASET 4 List of DE genes in vivo and merge with

TET1 targets, WGBS and RRHP. SUPPLEMENTARY DATASET 5 List of antibodies, primers and siRNA sequence. SOURCE DATA SOURCE DATA FIG. 1 Statistical Source Data Figure 1 SOURCE DATA FIG. 2

Statistical Source Data Figure 2 SOURCE DATA FIG. 3 Statistical Source Data Figure 3 SOURCE DATA FIG. 3 Unprocessed Blots Figure 3 SOURCE DATA FIG. 4 Statistical Source Data Figure 4 SOURCE

DATA FIG. 5 Statistical Source Data Figure 5 SOURCE DATA FIG. 6 Statistical Source Data Figure 6 SOURCE DATA FIG. 7 Statistical Source Data Figure 7 SOURCE DATA FIG. 8 Statistical Source

Data Figure 8 SOURCE DATA EXTENDED DATA FIG. 1 Statistical Source Data Extended Data Figure 1 SOURCE DATA EXTENDED DATA FIG. 2 Statistical Source Data Extended Data Figure 2 SOURCE DATA

EXTENDED DATA FIG. 3 Statistical Source Data Extended Data Figure 3 SOURCE DATA EXTENDED DATA FIG. 3 Unprocessed Blots Exteded Data Figure 3 SOURCE DATA EXTENDED DATA FIG. 4 Statistical

Source Data Extended Data Figure 4 SOURCE DATA EXTENDED DATA FIG. 5 Statistical Source Data Extended Data Figure 5 SOURCE DATA EXTENDED DATA FIG. 6 Statistical Source Data Extended Data

Figure 6 SOURCE DATA EXTENDED DATA FIG. 7 Statistical Source Data Extended Data Figure 7 SOURCE DATA EXTENDED DATA FIG. 8 Statistical Source Data Extended Data Figure 8 SOURCE DATA EXTENDED

DATA FIG. 9 Statistical Source Data Extended Data Figure 9 RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Aloia, L., McKie, M.A., Vernaz, G. _et al._

Epigenetic remodelling licences adult cholangiocytes for organoid formation and liver regeneration. _Nat Cell Biol_ 21, 1321–1333 (2019). https://doi.org/10.1038/s41556-019-0402-6 Download

citation * Received: 05 April 2019 * Accepted: 11 September 2019 * Published: 04 November 2019 * Issue Date: November 2019 * DOI: https://doi.org/10.1038/s41556-019-0402-6 SHARE THIS ARTICLE

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