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CRISPR screening uncovers a central requirement for HHEX in pancreatic lineage commitment and plasticity restriction

Nature Cell Biology volume 24, pages 1064–1076 (2022)Cite this article

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Abstract

The pancreas and liver arise from a common pool of progenitors. However, the underlying mechanisms that drive their lineage diversification from the foregut endoderm are not fully understood. To tackle this question, we undertook a multifactorial approach that integrated human pluripotent-stem-cell-guided differentiation, genome-scale CRISPR–Cas9 screening, single-cell analysis, genomics and proteomics. We discovered that HHEX, a transcription factor (TF) widely recognized as a key regulator of liver development, acts as a gatekeeper of pancreatic lineage specification. HHEX deletion impaired pancreatic commitment and unleashed an unexpected degree of cellular plasticity towards the liver and duodenum fates. Mechanistically, HHEX cooperates with the pioneer TFs FOXA1, FOXA2 and GATA4, shared by both pancreas and liver differentiation programmes, to promote pancreas commitment, and this cooperation restrains the shared TFs from activating alternative lineages. These findings provide a generalizable model for how gatekeeper TFs like HHEX orchestrate lineage commitment and plasticity restriction in broad developmental contexts.

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Fig. 1: CRISPR–Cas9 screens identify regulators of pancreatic differentiation.
Fig. 2: HHEX expression in pancreas development.
Fig. 3: Deletion of HHEX impairs human pancreatic differentiation.
Fig. 4: HHEX deletion results in induction of a liver-like transcriptional programme.
Fig. 5: HHEX KO cells acquire a duodenum-like cell state following inhibition of liver differentiation.
Fig. 6: scRNA-seq reveals differentiation trajectories of WT and HHEX KO cells.
Fig. 7: HHEX cooperates with FOXA2 to safeguard pancreatic differentiation.
Fig. 8: HHEX protects FOXA2 binding to pancreatic regulatory regions.

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Data availability

Sequencing data are available at the GEO under accession code GSE181480. Previously published data that were re-analysed here are available at the GEO under accession codes GSE136689 and GSE86225. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

We thank H. Lickert, K. Yang, S. S. Kim, J. Kazakov and N. Zhang for assisting with additional experiments not included in the manuscript; Z. Bao, S. Chen and L. Studer for insightful discussions; N. Verma and F. C. Pan for critical reading and editing of the manuscript; and M. Ray and B. Jarvis for technical support. This study was supported in part by grants to D.H. from DoD PRMRP (W81XWH-20-1-0298), the NIH (R01DK096239, U01HG012051) and the American Diabetes Association (1-19-IBS-125); grants to C.V.W. from the NIH (UC4DK104211, UC4DK108120) and the Leona M. and Harry B. Helmsley Charitable Trust; grants to J.S.O. and D.H. from JDRF (3-SRA-2021-1060-S-B) and DoD PRMRP (W81XWH-20-1-0670); and the University of Wisconsin-Madison, Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation. S. Stransky and S. Sidoli were supported by the Leukemia Research Foundation (Hollis Brownstein New Investigator Research Grant), AFAR (Sagol Network GerOmics award), Deerfield (Xseed award) and the NIH Office of the Director (1S10OD030286-01). This study was also supported by a MSKCC Cancer Center Support Grant from the NIH (P30CA008748), a postdoctoral fellowship from a NYSTEM training grant (DOH01-TRAIN3-2015-2016-00006 to D.Y.), NIH T32 Training Grants (T32HD060600 to G.D. and T32GM008539 to S.J.K. and B.P.R.), and a Frank Lappin Horsfall Jr Fellowship (to G.D.).

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  1. Gary Dixon

    Present address: Institute for Neurodegenerative Diseases, Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA

Authors and Affiliations

  1. Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Dapeng Yang, Hyunwoo Cho, Abhijit Shukla, Renhe Luo, Gary Dixon, Samuel J. Kaplan, Qing V. Li, Zengrong Zhu, Bess P. Rosen, Julian Pulecio, Zhong-Dong Shi & Danwei Huangfu

  2. Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Zakieh Tayyebi & Christina S. Leslie

  3. Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medical College, New York, NY, USA

    Zakieh Tayyebi, Gary Dixon, Samuel J. Kaplan, Jiwoon Park & Bess P. Rosen

  4. Louis V. Gerstner Jr Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, USA

    Renhe Luo & Qing V. Li

  5. Vanderbilt University Program in Developmental Biology and Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA

    Valeria Ursu & Christopher V. Wright

  6. Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA

    Stephanie Stransky & Simone Sidoli

  7. Department of Surgery, Division of Transplantation, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA

    Daniel M. Tremmel, Sara D. Sackett & Jon S. Odorico

  8. Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Richard Koche

  9. Division of Gastroenterology and Hepatology, Department of Medicine, Weill Medical College of Cornell University, New York, NY, USA

    Jiwoon Park, Yaron Bram & Robert E. Schwartz

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  1. Dapeng Yang
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Contributions

D.Y. and D.H. designed experiments, analysed and interpreted results. D.Y. performed most of the experiments. C.S.L. supervised and H.C., Z.T. and R.K. performed the computational analyses. A.S., Q.V.L., R.L. and Z.-D.S. assisted with the screens. R.L., G.D., B.P.R., S. Stransky and S. Sidoli assisted with scRNA-seq, ChIP-seq and ChIP–MS experiments. C.V.W. and J.S.O. supervised and V.U., D.M.T. and S.D.S. performed immunostaining on human and mouse fetal pancreas tissues. G.D., S.J.K., Z.Z. and B.P.R. assisted with the generation of hESCs lines. J. Park, J. Pulecio, Y.B. and R.E.S. assisted with additional experiments. D.Y. and D.H. wrote the manuscript. All authors provided editorial advice.

Corresponding authors

Correspondence to Christina S. Leslie or Danwei Huangfu.

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Extended data

Extended Data Fig. 1 Analysis of HHEX expression and examination of HHEX KO cells through flow cytometry and RNA-seq.

a, Bar plots for HHEX, PDX1, NKX6-1, and AFP expression in human CS12-14 stages dorsal pancreatic bud (DP), hepatobiliary primordium (HBP), and hepatic cords (HC) based on a published study38. n = 2 independent experiments. The y-axis represents the expression levels RPKM. b, Box plots for Hhex expression in Pdx1 cells sorted from ventral and dorsal pancreatic buds in E9.5 and E10.5 Pdx1-GFP mouse embryos based on a published study43. The y-axis of the box plots represents the expression levels (log2(RPKM + 1)). In each boxplot, the rectangle shows the inter-quartile range (IQR), with the bottom and top hinges representing the 25 and 75 percentiles, respectively. The middle line represents the median. The whiskers extend to the most extreme value within 1.5*IQR above or below the hinges. E9.5 dorsal Pdx1+ cells, n = 31; E10.5 dorsal Pdx1+ cells, n = 27; E9.5 ventral Pdx1+ cells, n = 44; E10.5 ventral Pdx1+ cells, n = 59. E9.5 and E10.5 data are generated from two and three independent mice, respectively. c,d, Flow cytometry gating strategy for HHEX expression. The SSC-A/FSC-A gate identifies cells based on the size and granularity. The FSC-H/FSC-W and SSC-H/SSC-W gates identify single cells. Live-dead staining distinguishes live cells from dead cells (c). HHEX KO cells were used as negative control for WT HHEX expression at the PP1 stage (d). e,f, Flow cytometry analysis of SOX17 and CXCR4 expression at the DE stage (e) and HNF1B expression at the GT stage (f). g,h, Quantification of flow cytometry analysis for SOX17, CXCR4 expression at the DE stage (g) and HNF1B expression at the GT stage (h). n = 3 independent experiments and data are presented as mean ± s.d. i,j, Flow cytometry analysis (i) and quantification (j) of cleaved caspase-3 (C-CSP3) expression at the DE, GT and PP1 stage. n = 3 independent experiments and data are presented as mean ± s.d. k, PCA based on PeakNorm normalization for all WT and KO samples during pancreatic differentiation. Two independent experiments were performed at each stage. Statistical analysis of g, h and j was performed using one-way ANOVA followed by Dunnett multiple comparisons test versus WT control.

Source data

Extended Data Fig. 2 Chromatin accessibility and transcriptional changes upon HHEX deletion.

a,b, Bar graph and volcano plot showing the number (a) and adjusted P value distribution (b) of differential peaks in KO cells compared to WT. Differential ATAC peaks were identified by DESeq2 using default parameters. FDR < 0.05 are counted as one significant peak. Less accessible peaks in KO are marked in blue and more accessible peaks in KO in orange. The number of differential peaks is indicated. c,d, TF motif enrichment in cluster I (c) and II (d) regions. One-sided hypergeometric test was used to compare the enrichment of proportions of TF motifs for each cluster (foreground ratio) versus those for total atlas (background ratio). The horizontal axis shows the binomial Z-score, representing the number of standard deviations between the observed count of each cluster peaks containing a TF motif and the expected count based on the background ratio. The P values are provided in Supplementary Table 3. e, IGV tracks (average of two independent experiments) show chromatin accessibility at representative liver genes loci identified in cluster III. Scale bar, 5 kb. f, Top 7 mouse phenotypes associated with the regulatory regions identified in cluster III. The term of mouse phenotypes was selected based on the rank of binomial test and cut-offs of region fold enrichment >1.4 and observed regions >80. The P values are indicated. g,h, Flow cytometry analysis (g) and quantification (h) of HNF4A+ cells at DE, GT, and PP1 stages. Each symbol represents one independent experiment (n = 4 independent experiments) and data are presented as mean ± s.d. Statistical analysis was performed using unpaired two-tailed Student’s t-test. Data shown in a-f are from two independent experiments.

Source data

Extended Data Fig. 3 The effects of inhibiting liver differentiation on WT and HHEX KO cells.

a,b, Flow cytometry analysis of AFP, PDX1 and CDX2 expression at the PP1 and PP2 stage WT/KO cells using differentiation Condition 1 (a) and Condition 2 (b). c, Quantification of flow cytometry analysis of AFP+, PDX1+ and CDX2 + cells at the PP1 and PP2 stages in both conditions. Each symbol represents one independent experiment (n = 8 independent experiments, except for CDX2 staining, where n = 4 independent experiments) and data are presented as mean ± s.d. Statistical analysis was performed using two-way ANOVA followed by multiple comparisons with Tukey correction. d, Immunostaining images for PDX1, AFP and CDX2 expression at the PP2 stage WT/KO cells using differentiation Condition 2. Images shown represent three independent experiments. Scale bar, 50 μm.

Source data

Extended Data Fig. 4 Investigation of differentiation trajectories in WT and HHEX KO cells through scRNA-seq analysis.

a, UMAP visualization of all Seurat clusters from experiment 2, shown with distinct colors. Clusters 1-15 were annotated as in Fig. 6b. b, UMAP visualization of the integrated data from all samples of both experiments at the DE, GT, PP1, and PP2 stages. The overlapping embedding of DE cells shows that the batch effect was removed. c, WT and KO lineages visualized by forced-directed layouts of the integrated data from the DE, GT, PP1, and PP2 stages. Cells at DE and GT stages are shown here. Data shown in a-c represent one independent experiment.

Extended Data Fig. 5 HHEX and FOXA2 ChIP-MS and ChIP-seq analysis.

a, Venn diagram showing significantly enriched proteins at the GT and PP1 stages for HHEX ChIP-MS. b, GREAT Gene Ontology showing the top 7 biological processes associated with the FOXA2-down regions with the P values indicated. The terms were selected based on the rank from the binomial test and cut-offs of region fold enrichment >1.5 and observed regions >80. c, IGV tracks (average of two independent experiments) showing chromatin accessibility and TFs binding activities at the PDX1 (left) and SOX9 (right) loci in WT and KO cells. Tracks are generated from two independent experiments. Scale bars are indicated. Regions that showed significantly decreased FOXA2 binding upon HHEX deletion are indicated by dashed boxes. d, MA plot showing differential FOXA2 binding (blue) at the PP2 stage following HHEX deletion. The number of significantly increased and decreased FOXA2 binding sites is indicated. e, TF motifs enriched in regions with differential FOXA2 binding upon HHEX deletion. Regions with significantly increased/decreased FOXA2 binding were compared with the FOXA2 ChIP-seq atlas to examine TF motif enrichments using one-sided KS test. The KS test effect size is shown on the y axis, and the proportion of peaks associated with the TF motif is plotted on the x axis. The size of each circle represents the odds ratio, which was defined as the frequency of a given TF motif in regions with increased or decreased FOXA2 binding divided by its frequency in the FOXA2 ChIP-seq atlas. TF motifs with a KS test effect size ≥ 0.1 (indicated by the dashed lines) and odds ratio ≥ 1.2 are shown. f, Volcano plot showing enriched proteins (purple) for FOXA2 ChIP-MS at the PP2 stage in WT versus KO cells. Dotted lines indicate the fold change and significance cut-offs (|log2(FC)| > 1, –log2(P) > 4.32). CDX2 (log2 (FC) = –2.01, P = 0.067) is also indicated. Data shown in a-e are from two independent experiments, and data shown in f represent three independent experiments.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Table 1

MAGeCK analysis of the DE and PP1 screens. Sheet 1, results from MAGeCK analysis of the DE and PP1 screens. Data were analysed with MAGeCK 0.5.9.4 default RRA parameters. Sheet 2, DE screen gRNA raw counts. Sheet 3, PP1 screen gRNA raw counts.

Supplementary Table 2

Results from transcriptome analysis. Sheet 1, all normalized counts. Sheet 2, significant hits at the DE stage. Sheet 3, significant hits at the GT stage. Sheet 4, significant hits at the PP1 stage. A two-tailed Wald test was used for statistical significance and a Benjamini–Hochberg procedure was used to correct for multiple hypothesis testing.

Supplementary Table 3

Results from chromatin-accessibility analysis. Sheet 1, differential peak analysis in KO cells versus WT cells at the GT and PP1 stage. Differential peaks were identified by DESeq2 using default parameters. Sheets 2–4, clusters I, II and III enriched peaks information. Differential peaks were identified by DESeq2 using default parameters, and adjusted P value cut-off is 0.01. Sheets 5–7, hypergeometric test analysis of clusters I, II and III. One-sided hypergeometric test was used to compare the enrichment of proportions of TF motifs for each cluster (foreground ratio) versus those for total atlas (background ratio). Multiple testing correction used Bonferroni. The horizontal axis shows the binomial Z-score, representing the number of standard deviations between the observed count of each cluster peaks containing a TF motif and the expected count based on the background ratio. Analysis is from two independent experiments. BG, background; FG, foreground.

Supplementary Table 4

Results from scRNA-seq analysis. Sheet 1, sample information. Sheet 2, cell meta-data. Sheet 3, differential expression analysis for the annotated PP1/PP2 group cells. Sheet 4, mapping data from mouse endoderm-derived cells to human PP1 and PP2 populations. Differential expression tests, comparing log-normalized gene expression values of each group to the rest, were performed by MAST, and P values were adjusted with Bonferroni multiple testing correction.

Supplementary Table 5

Results from HHEX and FOXA2 ChIP–MS analysis. Sheet 1, HHEX ChIP–MS analysis. HHEX KO cells stained with HHEX antibody was used as negative control. Sheet 2, FOXA2 ChIP–MS analysis on PP1 WT cells. Sheet 3, FOXA2 ChIP–MS analysis on PP1 WT versus KO cells. Sheet 4, FOXA2 ChIP–MS analysis on PP2 WT versus KO cells. Cells stained with IgG was used as negative control for FOXA2 ChIP-MS analysis. Two-tailed heteroscedastic t-test was used for all MS experiments. Sheets 5–7, raw data of HHEX and FOXA2 ChIP–MS experiments.

Supplementary Table 6

Results from FOXA2 and HHEX ChIP-seq analysis. Sheet 1, PP1 FOXA2 atlas analysis. Sheet 2, Identification of FOXA2 and HHEX co-bind ATAC peaks at the GT and PP1 stages. Sheet 3, PP2 FOXA2 atlas analysis. To show co-binding of FOXA2 and HHEX at ATAC-seq peaks, ChIP-seq signal was quantified on the same ATAC-seq-based atlas. Differential ChIP-seq analysis using the default parameters of DESeq2.

Supplementary Table 7

List of gRNA target sequences and primer sequences for PCR genotyping and RT–qPCR.

Supplementary Table 8

List of antibodies.

Source data

Source Data Fig. 3

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Unprocessed western blots.

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Source Data Extended Data Fig. 1

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Yang, D., Cho, H., Tayyebi, Z. et al. CRISPR screening uncovers a central requirement for HHEX in pancreatic lineage commitment and plasticity restriction. Nat Cell Biol 24, 1064–1076 (2022). https://doi.org/10.1038/s41556-022-00946-4

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