1. Stem Cells and Regenerative Medicine

Generation of functional hepatocytes by forward programming with nuclear receptors

  1. Rute A Tomaz
  2. Ekaterini D Zacharis
  3. Fabian Bachinger
  4. Annabelle Wurmser
  5. Daniel Yamamoto
  6. Sandra Petrus-Reurer
  7. Carola M Morell
  8. Dominika Dziedzicka
  9. Brandon T Wesley
  10. Imbisaat Geti
  11. Charis-Patricia Segeritz
  12. Miguel C de Brito
  13. Mariya Chhatriwala
  14. Daniel Ortmann
  15. Kourosh Saeb-Parsy
  16. Ludovic Vallier  Is a corresponding author
  1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, United Kingdom
  2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, United Kingdom
  3. Wellcome Sanger Institute, Wellcome Genome Campus, United Kingdom
https://doi.org/10.7554/eLife.71591
Share this article
Cite this article
  1. Rute A Tomaz
  2. Ekaterini D Zacharis
  3. Fabian Bachinger
  4. Annabelle Wurmser
  5. Daniel Yamamoto
  6. Sandra Petrus-Reurer
  7. Carola M Morell
  8. Dominika Dziedzicka
  9. Brandon T Wesley
  10. Imbisaat Geti
  11. Charis-Patricia Segeritz
  12. Miguel C de Brito
  13. Mariya Chhatriwala
  14. Daniel Ortmann
  15. Kourosh Saeb-Parsy
  16. Ludovic Vallier
(2022)
Generation of functional hepatocytes by forward programming with nuclear receptors
eLife 11:e71591.
https://doi.org/10.7554/eLife.71591
  2. Download BibTeX
  3. Download .RIS

Abstract

Production of large quantities of hepatocytes remains a major challenge for a number of clinical applications in the biomedical field. Directed differentiation of human pluripotent stem cells (hPSCs) into hepatocyte-like cells (HLCs) provides an advantageous solution and a number of protocols have been developed for this purpose. However, these methods usually follow different steps of liver development in vitro, which is time consuming and requires complex culture conditions. In addition, HLCs lack the full repertoire of functionalities characterising primary hepatocytes. Here, we explore the interest of forward programming to generate hepatocytes from hPSCs and to bypass these limitations. This approach relies on the overexpression of three hepatocyte nuclear factors (HNF1A, HNF6, and FOXA3) in combination with different nuclear receptors expressed in the adult liver using the OPTi-OX platform. Forward programming allows for the rapid production of hepatocytes (FoP-Heps) with functional characteristics using a simplified process. We also uncovered that the overexpression of nuclear receptors such as RORc can enhance specific functionalities of FoP-Heps thereby validating its role in lipid/glucose metabolism. Together, our results show that forward programming could offer a versatile alternative to direct differentiation for generating hepatocytes in vitro.

Editor's evaluation

The work by Vallier and colleagues programmes ESCs and IPSCs towards hepatocyte fate by using a combination of hepatocyte transcription factors. Based on informatic analyses comparing adult hepatocytes with hepatocyte-like cells differentiated with soluble factors they conclude that the inclusion of RORc is important for added maturity of the forward programmed cells. The challenge is a very important one as we still don't have good in vitro hepatocyte generation.

https://doi.org/10.7554/eLife.71591.sa0

Introduction

Hepatocytes are the main cell type of the liver, comprising 80% of its volume and performing a vast array of vital functions including lipid metabolism, storage of macronutrients, secretion of plasma proteins, and xenobiotic detoxification (Gordillo et al., 2015; Si-Tayeb et al., 2010; Trefts et al., 2017). Diseases affecting these functions are life-threatening and end-stage forms require liver transplantation. However, only a limited number of patients can benefit from this therapy due to scarcity of donors and the side effects of immunosuppression. Cell-based therapy using primary hepatocytes has already been found to be an attractive therapeutic alternative to whole organ transplants (Dhawan et al., 2020). However, primary human hepatocytes (PHHs) are in short supply as they can only be obtained from suboptimal livers unsuitable for transplantation. Furthermore, they display a short life, absence of proliferation, and rapid loss of functionality in vitro (Mitry et al., 2002). Similarly, the development of new platforms for drug development and toxicology screens is greatly affected by the lack of robust sources of hepatocytes. For all these reasons, alternative sources of hepatocytes are urgently needed. Producing hepatocytes from human pluripotent stem cells (hPSCs) using directed differentiation protocols has been shown to be an advantageous alternative to PHHs (Palakkan et al., 2017; Szkolnicka and Hay, 2016). These protocols commonly follow key stages of liver development in vitro and allow the production of hepatocyte-like cells (HLCs) which exhibit key hepatic functions including Albumin secretion, lipid metabolism, glycogen storage, and urea cycle activity. However, HLCs systematically present an immature/foetal-like phenotype lacking the full repertoire of functions of mature hepatocytes (Baxter et al., 2015; Grandy et al., 2019; Yiangou et al., 2018). The development of fully functional hepatocytes in vitro is challenging due to the lack of detailed knowledge concerning the molecular mechanisms driving functional maturation in vivo. Maturation is a process that occurs progressively , and mimicking this timeline and the associated combination of metabolic changes, exposure to oxygen, nutrition, and microbiome constitutes a major challenge for direct differentiation protocols (Chen et al., 2011). As an alternative, overexpression of transcription factors (TFs) has been explored as a way to improve functionality of in vitro generated hepatocytes (Boon et al., 2020; Nakamori et al., 2016; Zhao et al., 2013). Moreover, transdifferentiation of somatic cells into liver cells has been achieved by overexpression of liver-enriched transcription factors (LETFs) in mouse and human fibroblasts (Rombaut et al., 2021). Importantly, these LETFs comprise the HNF1, HNF3 (FOXA), HNF4, and HNF6 (ONECUT) families all of which play key roles in coordinating liver development (Gordillo et al., 2015; Lau et al., 2018; Schrem et al., 2002). However, direct cell conversion from somatic cell types has a low efficiency/yield due to the strong epigenetic restrictions present in fully differentiated cells. Furthermore, somatic cells have restricted capacity of proliferation which limits large-scale production of hepatocytes without the use of oncogenic manipulation (Du et al., 2014; Huang et al., 2014). Forward programming by direct overexpression of TFs in hPSCs could bypass these limitations. Indeed, the epigenetic state of hPSCs is more permissive to direct cellular conversion, while their capacity of proliferation is far more superior to somatic cells. Accordingly, this approach has been successfully used to generate neurons, skeletal myocytes, and oligodendrocytes by taking advantage of the OPTi-OX system (Pawlowski et al., 2017). Here, we decided to exploit the same platform to produce hepatocytes by forward programming. We first tested different combinations of LETFs and identified a cocktail of three factors sufficient to drive the conversion into hepatocytes. We then performed transcriptomic and epigenetic comparisons between HLCs and PHHs to identify additional TFs which could further increase the functional maturation of hepatocytes. This comparison revealed that a number of nuclear receptors are expressed in adult hepatocytes and thus are likely to be inducers of functionality and maturation in vivo. A selection of these factors was combined with LETFs and we identified that the 4TFs HNF1A-HNF6-FOXA3-RORc were the most efficient cocktail to generate hepatocytes by forward programming (FoP-Heps) displaying features of mature hepatocytes including CYP3A4 activity, protein secretion, and hepatotoxic response. Thus, forward programming offers an alternative to direct differentiation, bypassing the need for complex culture conditions and lengthy timelines. Moreover, FoP-Heps display a level of functionally relevant for regenerative medicine, as well as disease modelling or drug screening.

Results

LETFs allow forward programming into cells with hepatocyte identity

The first step to develop a forward programming method consists in identifying a cocktail of TFs which can recreate the transcriptional network characterising the target cell type. However, this step is challenging for hepatocytes as liver development is not initiated by a single and specific master regulator, and the factors driving functional maturation of hepatocytes remain to be fully uncovered. To bypass these limitations, we decided to focus on the LETFs which are known to control the induction of the hepatic program during foetal development and have been tested in somatic cell conversion (Rombaut et al., 2021). The coding sequence of four LETFs (HNF4A, HNF1A, HNF6, and FOXA3) was cloned into the OPTi-OX system (Figure 1A) and the resulting inducible cassette was targeted into the AAVS1 gene safe harbour (Bertero et al., 2016; Pawlowski et al., 2017). After selection, individual sublines were picked, expanded, and genotyped before further characterisation. Addition of doxycycline (dox) for 24 hr was sufficient to induce homogenous and robust expression of each LETF in the selected human embryonic stem cells (hESCs) (Figure 1B and C) confirming the efficacy of the OPTi-OX system in inducing transgene expression. Importantly, this induction was not associated with differentiation into liver cells (data not shown) suggesting that LETFs alone are not sufficient to impose an hepatocytic identity. Thus, we decided to screen culture conditions which could sustain both the survival and differentiation of hepatocytes (data not shown) and found that after the initial 24 hr in E6 medium, the cells acquired a hepatocyte-like morphology when cultured in Hepatozyme complete medium for 14 days (Figure 1D). Interestingly, the resulting cells expressed hepatocyte markers such as Albumin (ALB), Alpha-1 Antitrypsin (A1AT or SERPINA1), and Alpha-Fetoprotein (AFP) (Figure 1E) and displayed CYP3A4 activity levels comparable to HLCs generated by direct differentiation, albeit several fold lower than PHHs (Figure 1F). Next, we asked whether all the four LETFs were necessary to achieve this hepatocyte-like phenotype. For that, we removed each factor to generate hESCs sublines expressing combinations of three factors (Figure 1—figure supplement 1A). Robust and homogeneous expression at the protein level was again confirmed after 24 hr of dox induction (Figure 1—figure supplement 1B). Induction of each combination of three LETFs in culture conditions identified above showed that HNF1A, HNF6, or FOXA3 were necessary to generate cells expressing hepatocytes markers such as ALB, albeit with heterogeneity at the protein level (Figure 1G, Figure 1—figure supplement 2A,B). HNF4A overexpression seemed to be dispensable as cells generated by overexpression of the three remaining LETFs (HNF1A, HNF6, FOXA3) acquired a cobblestone-like morphology, and expressed high levels of ALB, SERPINA1, and AFP (Figure 1G, H and I, Figure 1—figure supplement 2). Strikingly, hepatocytes generated using these 3TFs (3TF FoP-Heps) achieved the highest levels of CYP3A4 activity suggesting overexpression of HNF4A itself is unnecessary to acquire the hepatocyte characteristics screened, as its expression might be induced by one of the three LETFs (Figure 1J). Altogether, these results showed that overexpression of HNF1A, HNF6, and FOXA3 is sufficient to forward program hPSCs towards HLCs.

Figure 1 with 2 supplements see all
Download asset Open asset
Forward programming of human pluripotent stem cells (hPSCs) into hepatocytes with four and three liver-enriched transcription factors (LETFs).

(A) Schematic representation of the two sequentially targeted loci. The human ROSA26 was targeted with a constitutively expressed reverse tetracycline transactivator (rtTA). The AAVS1 locus was targeted with the four LETFs HNF1A, HNF6, FOXA3, and HNF4A downstream of a TET-responsive element (TET). (B) mRNA induction levels of the four factors in targeted human embryonic stem cells (hESCs) (Targ) relative to untargeted (Untarg) hESCs stimulated with doxycycline (dox) for 24 hr (n=3). Data is shown relative to the untargeted control. (C) Immunofluorescence staining of the four LETFs in targeted and untargeted hESCs after 24 hr of inducible overexpression (iOX) with dox confirming transgene induction. Nuclei were counterstained with DAPI (blue). Scale bar, 200µm. (D) Schematic representation of the iOX culture conditions for forward programming. Phase contrast images of hESCs targeted with the four LETFs after 10 and 15 days of forward programming. Scale bar, 200µm. (E) mRNA levels of hepatocyte markers (ALB, SERPINA1, and AFP) in hESCs targeted with the four LETFs after 10 and 15 days of forward programming. Untargeted hESCs treated with the same protocol as in (D) were used as control (n=4). Statistical difference was calculated with unpaired t-test against untargeted. (F) CYP3A4 activity levels normalised per cell number (millions) in untargeted and targeted hESCs with the four LETFs after 15 days of forward programming (n=5) . Statistical difference between targeted and untargeted cells was calculated with unpaired t-test. (G,H,I) mRNA levels of hepatocyte markers (ALB, SERPINA1, and AFP) in hESCs targeted with the four LETFs and with combinations of three LETFs (n=4). The factor removed from each construct is indicated. Expression levels were determined after 10, 15, 20, and 25 days of forward programming. Statistical differences were calculated with one-way ANOVA, corrected for multiple comparisons compared to four LETFs. All mRNA levels were normalised to the average of two housekeeping genes (PBGD and RPLP0). (J) CYP3A4 activity levels normalised per cell number (millions) in hESCs targeted with the four LETFs and combinations of three LETFs after 10, 15, 20, and 25 days of forward programming (n=3–5). Statistical differences were calculated with one-way ANOVA, corrected for multiple comparisons compared to four LETFs. In all plots, bars represent mean with SD, and individual datapoints are shown for all biological replicates. Hepatocyte-like cells (HLCs) generated by direct differentiation and primary human hepatocytes (PHHs) where plotted as controls for all CYP3A4 activity and expression data. Significant p-values are shown at each comparison and indicated as *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Figure 1—source data 1

Individual measurements and statistical tests related to Figure 1.

https://cdn.elifesciences.org/articles/71591/elife-71591-fig1-data1-v1.xlsx

HLCs generated by direct differentiation lack the expression of specific nuclear receptors

Following these encouraging results, we aimed to increase the functionality of 3TF FoP-Heps by adding TFs which could play a role in promoting hepatic maturation and functionality. However, identifying these factors proved to be challenging as there is little information about the mechanisms driving functional maturation of hepatocytes, especially following birth when adult hepatic functions are established. To bypass this limitation, we decided to compare the transcriptome profile of adult PHHs to the transcriptome of HLCs generated from hPSCs by direct differentiation. Indeed, HLCs represent a foetal state which has been broadly characterised (Baxter et al., 2015), while 3TFs FoP-Heps are likely to be less relevant for natural development. For this comparison, we used a state-of-the-art protocol (Hannan et al., 2013; Touboul et al., 2010) which has been used for modelling liver disease (Rashid et al., 2010; Segeritz et al., 2018) and as proof of concept for cell-based therapy applications (Yusa et al., 2011). This protocol starts by the production of endoderm cells expressing SOX17, followed by the specification of foregut expressing HHEX, after which cells transit through a hepatoblast-like state marked by TBX3 (Figure 2—figure supplement 1A). Interestingly, LETFs were expressed during this differentiation protocol at levels comparable to PHHs (Figure 2—figure supplement 1B,C) confirming that these first steps follow a natural path of development. The resulting progenitors undergo a final stage of differentiation into HLCs expressing functional markers such as ALB and SERPINA1 (Figure 2A, Figure 1—figure supplement 1A,D). Despite displaying key hepatic functions (Baxter et al., 2015; Grandy et al., 2019; Yiangou et al., 2018), HLCs represent a ‘foetal’ state as shown by the expression of AFP (Figure 2—figure supplement 1A,D) or by the limited activity/expression of CYP3A4, CYP2A6, or CYP2C9 (Figure 1B, Figure 2—figure supplement 1E). RNA-sequencing (RNA-seq) performed on HLCs generated from either human induced pluripotent stem cells (hiPSCs) or hESCs, and from freshly harvested PHHs (fPHHs) or cultured in vitro as monolayer (pPHHs) reinforced these observations. Principal component analysis (PCA) of the most variable 500 genes showed a clear distinction between the three cell types, with HLCs clustering in-between undifferentiated hiPSCs and PHHs confirming their intermediate state of differentiation (PC1: 52%, Figure 2C). In order to further explore the differences between HLCs and PHHs, we combined differential gene expression (DGE) and gene ontology (GO) analyses to identify genes and biological functions specific to each cell type (Figure 2D). Genes uniquely expressed in PHHs (cluster 1) were associated with adult liver functions such as response to xenobiotic and xenobiotic metabolism, inflammatory response, and complement activation (Figure 2E). Genes expressed in both HLCs and PHHs (Heps; cluster 3) were involved in liver development, fatty acid metabolism, or broad cellular functions (Figure 2F). Of note, genes specifically upregulated in HLCs (cluster 2) were associated with extracellular matrix organisation and varied developmental functions which could originate from their in vitro environment (Figure 2—figure supplement 1F). We then decided to focus specifically on TFs using a previously curated list (Lambert et al., 2018; Figure 2G and H) and identified 36 TFs highly expressed in PHHs vs. HLCs (p<0.05, log2 fold change >2). Interestingly, reactome pathway analysis grouped these TFs into two main pathways: the NFI family and a cohort of eight nuclear receptors (Figure 2H). Nuclear receptors are known to be involved in key liver functions including the metabolism of lipids and glucose, bile acid clearance, xenobiotic sensing, and regeneration (Rudraiah et al., 2016). Thus, we hypothesise that nuclear receptors could be the most promising candidates to improve hepatocyte functionality in our culture system. Taken together these observations show that the immature state of HLCs is associated with the absence of several nuclear receptors thereby suggesting that these factors could be necessary to drive functional maturation of hepatocytes.

Figure 2 with 1 supplement see all
Download asset Open asset
Hepatocyte-like cells (HLCs) and primary human hepatocytes (PHHs) display transcriptomic differences associated with their state of maturation.

(A) Immunofluorescence staining of Albumin (yellow) and HNF4A (red) in HLCs differentiated for 30 days. Nuclei were counterstained with DAPI (blue). Scale bar, 100 µm. (B) CYP3A4 activity levels normalised per cell number (millions) in HLCs differentiated for 30 days (n=6) and PHHs (n=4). Bars represent mean with SD, and individual datapoints represent the different biological replicates. Statistical difference was calculated with unpaired t-test. (C) Principal component analysis (PCA) of undifferentiated human induced pluripotent stem cells (hiPSCs), HLCs derived from human embryonic stem cell (hESC) (hESC_HLCs) and hiPSC (hiPSC_HLCs), freshly harvested PHHs (fPHHs), or plated PHHs (pPHHs). (D) Heatmap showing the proportion of genes differentially expressed in each cell type (cluster 1 – PHHs, cluster 2 – HLCs, cluster 4 – hiPSCs) as well as in Heps (HLCs and PHHs) against undifferentiated hiPSCs (cluster 3). (E, F) Dotplot showing the top 15 hits on gene ontology enrichment analysis on genes associated to cluster 1 and cluster 3 as shown in (D). The size of each dot represents number of genes associated to each term and the colours represents the adjusted p-value. (G) Heatmap showing the differential gene expression of transcription factors between PHHs (fresh or plated) and HLCs (hESC and hiPSC derived). (H) Reactome pathway enrichment analysis on transcription factors identified in (G). Differential gene expression was calculated with log2(fold change) higher than 2 and adjusted p-value <0.05. Hierarchical clustering on samples was generated by Euclidean distance.

Figure 2—source data 1

Individual measurements and statistical tests related to Figure 2 and corresponding supplements.

https://cdn.elifesciences.org/articles/71591/elife-71591-fig2-data1-v1.xlsx
Figure 2—source data 2

List of genes differentially expressed in the four clusters in Figure 2D.

https://cdn.elifesciences.org/articles/71591/elife-71591-fig2-data2-v1.xlsx

Epigenetic characterisation of HLCs suggests a role for nuclear receptors RORc, AR, and ERα

To further refine the list of nuclear receptors identified by our transcriptomic analyses, we decided to compare the epigenetic landscape of HLCs vs. PHHs. Indeed, we hypothesised that nuclear receptors binding regulatory regions in PHHs could have a key function in maturation and thus we aimed to identify the most important factors by screening underlying motifs in such regions. Chromatin immunoprecipitation-sequencing (ChIP-seq) was performed on histone marks including H3K27ac (active regulatory regions), H3K4me1 (active or primed regulatory regions), and H3K27me3 (silenced genes) (Creyghton et al., 2010; Wang et al., 2015). These marks were profiled in HLCs derived from both hiPSCs and hESCs, and PHHs while undifferentiated hiPSCs were used as control. As expected, PCAs showed a marked divergence between the epigenetic profile of HLCs and hiPSCs independently of the mark analysed (Figure 3A). Interestingly, HLCs and PHHs clustered in close proximity suggesting that these cell types share an important part of their epigenetic profile despite their transcriptomic differences. The profiles of H3K27ac and H3K27me3 showed the highest variance between HLCs/PHHs and hiPSCs, confirming the importance of these marks for establishing cellular identity (Figure 3A). We then performed differential peak calling on H3K27ac to identify regulatory regions uniquely enriched and active in PHHs vs. HLCs (‘PHH-specific’), and vice versa (‘HLC-specific’) (Figure 3—figure supplement 1A). We also profiled H3K4me1 and H3K27me3 in either PHH or HLC-specific regions. This analysis revealed that H3K4me1 was absent at ‘PHH-specific’ regions in HLCs and seems to be broadly replaced by spread of H3K27me3 deposition instead (Figure 3B). Interestingly, discrete portions of regulatory regions lacked H3K27ac in genes downregulated in HLCs (see for example CYP3A4 and UGT1A, Figure 3C, Figure 3—figure supplement 1B). Ontology of genes associated with each set of regions highlighted several adult liver metabolic processes in the ‘PHH-specific’ set such as steroid, lipid, and xenobiotic metabolism, and range of neural functions for ‘HLC-unique’ regions (Figure 3—figure supplement 1C), in agreement with the transcriptomic analyses. Overall, these results suggested that a subset of genes involved in adult liver functions lack H3K4me1 priming as well as full H3K27ac deposition in HLCs. These regions lacking H3K27ac in HLCs appeared to display repressive marks such as H3K27me3. In addition, HLCs displayed active histone marks in regions including genes which are not associated with liver differentiation confirming that cells generated from hPSCs also present an epigenetic signature specific to their in vitro state (Figure 3—figure supplement 1C). Taken together these observations suggested that HLCs and PHHs broadly share the same epigenetic identity when compared to hiPSCs. However, the activation of a limited and specific set of regulatory regions is missing in HLCs, which could explain the absence of expression of adult liver genes and lack of functional maturation. To identify the nuclear receptors potentially involved in regulating these regions, we performed motif enrichment analysis in the ‘PHH-specific’ regions marked by H3K27ac. Interestingly, this analysis identified a significant enrichment for the androgen (AR) and estrogen (ERα) response elements, as well as RORc motifs (Figure 3D), which were among the top differentially expressed nuclear receptors in our transcriptomic analyses. We then decided to further investigate the importance of these nuclear receptors throughout development using mouse RNA-seq datasets obtained at different stages of liver organogenesis E12.5, E16.5, P0, 8-week and 10-week adults (Figure 3—figure supplement 1D). Interestingly, the expression of these three nuclear receptors was found to be upregulated specifically in the adult liver (Figure 3—figure supplement 1E,F). Altogether, these observations suggested that the nuclear receptors AR, ERα, and RORc could play a role in establishing or maintaining a transcriptional network characterising mature hepatocytes.

Figure 3 with 1 supplement see all
Download asset Open asset
Epigenetic status of regulatory regions differs between states of maturation in hepatocyte-like cells (HLCs) and primary human hepatocytes (PHHs).

(A) Principal component analysis (PCA) of the global enrichment profile of H3K27ac, H3K4me1, and H3K27me3 across two replicates of undifferentiated human induced pluripotent stem cells (hiPSCs), human embryonic stem cell (hESC), and hiPSC-derived HLCs, and PHHs. Average scores were computed for genomic regions of 1000 bp for the entire genome. (B) Average density plots and heatmaps showing enrichment levels for H3K27ac, H3K4me1, and H3K27me3 within a 10 kb window centred at H3K27ac PHH-unique (blue) or HLC-unique (green) regions. Scales are adjusted to maximum peak intensity for each dataset. (C) Enrichment profiles of H3K27ac, H3K4me1, and H3K27me3 across the UGT1A locus. Profiles are shown for one replicate of undifferentiated hiPSCs, hESC, and hiPSC-derived HLCs, and PHHs. Red bars represent PHH-unique H3K27ac peaks. (D) Nuclear receptor motifs identified as overrepresented binding sites at H3K27ac PHH-unique regions.

Figure 3—source data 1

Peak annotation results for primary human hepatocyte (PHH)-unique H3K27ac regions.

https://cdn.elifesciences.org/articles/71591/elife-71591-fig3-data1-v1.xlsx
Figure 3—source data 2

Peak annotation results for hepatocyte-like cell (HLC)-unique H3K27ac regions.

https://cdn.elifesciences.org/articles/71591/elife-71591-fig3-data2-v1.xlsx
Figure 3—source data 3

Motif enrichment results for primary human hepatocyte (PHH)-unique H3K27ac regions.

https://cdn.elifesciences.org/articles/71591/elife-71591-fig3-data3-v1.xlsx

Overexpression of RORc increases the functionality of hepatocytes generated by forward programming

We next tested the capacity of RORc (RORγ), AR, and ERα (ESR1), to further improve the functionality of FoP-Heps generated using three LETFS. For that, we generated hESC lines inducible for the expression of HNF1A, HNF6, and FOXA3 (3TFs) in combination with each of the nuclear receptors identified above (Figure 4—figure supplement 1A). The homogeneous induction of the 4TFs was validated using immunostaining. Interestingly, these analyses showed that the overexpressed nuclear receptors were located in the nucleus, and in the case of AR in both cytoplasm and nucleus (Figure 4—figure supplement 1B,C,D). We then induced forward programming using the culture conditions identified above, up to 30 days, and observed the production of polyploid cells with a cobblestone morphology (Figure 4A). The hepatocytic identity of these cells was confirmed by the expression of Albumin, SERPINA1/A1AT, and AFP in all lines (Figure 4B, C, D and E, Figure 4—figure supplement 1E). RORc overexpression resulted in a higher number of cells expressing Albumin, which were still heterogeneous across the cell population, potentially representing different subpopulations of HLCs (Figure 4B). Moreover, these cells also secreted higher levels of Albumin, although the mRNA expression of this marker was not significantly upregulated (Figure 4C and D). In addition, these cells tend to secret lower levels of AFP, although the lower mRNA levels identified were not statistically significant when compared to 3TFs alone (Figure 4D and E, Figure 4—figure supplement 1E). Notably, CYP3A4 activity levels were significantly higher in cells generated in the presence of RORc, as compared with cells forward programmed with only 3TFs (Figure 4F). We next tested whether stimulation with exogenous ligands specific for each nuclear receptor could further induce functional maturation as measured by CYP3A4 activity (desmosterol for RORc, β-estradiol for ERα, and testosterone for AR). Interestingly, only β-estradiol treatment resulted in a three fold increase in CYP3A4 activity, whereas testosterone treatment significantly decreased CYP3A4 activity and desmosterol had no effect (Figure 4G). This increase was not observed with 3TFs Fop-Heps thereby suggesting the effect of these ligands was linked to the overexpression of their receptor.

Figure 4 with 2 supplements see all
Download asset Open asset
Forward programming of human embryonic stem cells (hESCs) into hepatocytes with nuclear receptors.

(A) Phase contrast images and (B) immunofluorescence staining for Albumin (yellow) and (C) A1AT (green) in hESCs forward programmed for 20 days with 3TFs alone or in combination with the nuclear receptors RORc, ERɑ, and AR. Nuclei were counterstained with DAPI (blue). Scale bars, 200 µm. (D) mRNA levels of hepatocyte markers (ALB, SERPINA1, and AFP) in FoP-Heps generated with 3TFs alone or in combination with nuclear receptors for 20 and 30 days (n=4). Expression data was normalised to the average of two housekeeping genes (PBGD and RPLP0). (E) Protein secretion levels of Albumin, A1AT, and AFP in hESC-derived FoP-Heps generated with 3TFs alone or in combination with nuclear receptors for 20 days (n=4). Data was normalised per total cell number (millions). (F) CYP3A4 activity levels normalised per cell number (millions) in FoP-Heps targeted with 3TFs with or without nuclear receptors, after 20 and 30 days of forward programming (n=3–6). Statistical differences were calculated with one-way ANOVA, corrected for multiple comparisons compared to 3TFs (day 20). (G) CYP3A4 fold induction levels in FoP-Heps treated with 100 nM of the ligands as indicated from day 2. Data is normalised to untreated control at day 20 of forward programming (n=3). Statistical differences were calculated with paired t-test. In all plots, bars represent mean with SD, and individual datapoints are shown for all biological replicates. Hepatocyte-like cells (HLCs) generated by direct differentiation and primary human hepatocytes (PHHs) where plotted as controls for CYP3A4 activity and expression data. Significant p-values are shown at each comparison and indicated as *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Figure 4—source data 1

Individual measurements and statistical tests related to Figure 4 and supplements.

https://cdn.elifesciences.org/articles/71591/elife-71591-fig4-data1-v1.xlsx

RORc-generated FoP-Heps appeared to have the highest level of functionality and thus, we decided to validate the potential of this combination of factors in an alternative pluripotent stem cell line. We generated Opti-OX hiPSC with the 3TFs or the 3TFs + RORc FoP system, validated the upregulation of these factors after 24 hr of dox treatment (Figure 4—figure supplement 2A) and then induced differentiation following the protocol established above. FoP-Heps derived from hiPSC also displayed cobblestone morphology (Figure 4—figure supplement 2B) and expressed Albumin, AFP, and SERPINA1/A1AT (Figure 4—figure supplement 2C,D,E,F,G). Concordantly, a higher number of Albumin positive cells were detected at day 20, as well as higher levels of secreted Albumin and transcript, when RORc was overexpressed (Figure 4—figure supplement 2C,F,G). In particular, ALB transcript further increased by day 30, suggesting that this cell background might require additional time to achieve maturation. In addition, the presence of RORc significantly increased basal CYP3A4 activity thereby confirming the positive effect of this factor on functional maturity of FoP-Heps (Figure 4—figure supplement 2H). Overall, these results showed that overexpression of specific nuclear receptors was compatible with the generation of FoP-hepatocytes. In particular, overexpression of RORc could improve the functionality of hepatocytes generated by overexpression of three LETFs confirming the role of this nuclear receptor in hepatocyte maturation.

4TF FoP-Heps display functional characteristics in vitro

Next, we sought to further characterise the functionality of the 4TF (HNF1A, HNF6, FOXA3, and RORc) FoP-Heps derived from either hESC (eFoP-Heps) or hiPSCs (iFoP-Heps) in comparison with HLCs generated by the direct differentiation and PHHs. CYP3A4 activity was significantly higher in 4TF FoP-Heps forward programmed after 20 days than those achieved by HLCs after 30 days of directed differentiation (Figure 5A). In addition, we analysed the expression of markers associated with hepatic metabolic functions such as phase I (cytochrome P450 enzymes) and phase II (UGTs) biotransformation, gluconeogenesis (G6PC and PCK1), and lipid (PPARα, PPARγ, FASN, and APOA1) and bile acid (NR1H4) metabolism. FoP-Heps expressed a range of these functional markers confirming the acquisition of hepatic functionality (Figure 5B and C, Figure 5—figure supplement 1A). Overall, the levels of expression achieved by forward programming were equivalent to those achieved by direct differentiation with the exception of gluconeogenesis genes which were increased in FoP-Heps (Figure 5C). Interestingly, expression of gluconeogenesis and lipid metabolism genes was comparable between FoP-Heps and PHHs. However, induction of cytochrome P450 genes remains challenging, indicating that the acquisition of this specific hepatic function could need further refinement of our protocol (Figure 5B,). Of note, in FoP-Heps, the 4TFs remained expressed at physiological levels at the end of our protocol (Figure 5B, Figure 5—figure supplement 1B). Next, we sought to further characterise the transcriptome of day 20 4TF FoP-Heps by RNA-seq by comparing the transcriptome of eFoP-Heps with undifferentiated hiPSCs, HLCs (derived from direct differentiation of hESC and hiPSCs), adult PHHs, as well as foetal liver cells. As suggested by the marker expression pattern identified by qPCR, the transcriptome of eFoP-Heps closely clustered with HLCs derived from both hESCs and hiPSCs (Figure 5D). Interestingly, both eFoP-Heps and HLCs clustered separately from foetal liver cells, indicating that despite these cells not acquiring a fully mature phenotype, these also don’t fully resemble a foetal liver stage. We further interrogated the ontology of genes differentially expressed between eFoP and the different groups of samples (Figure 5—figure supplement 2). As expected, genes that gain expression in eFoP-Heps compared to undifferentiated hiPSCs are strongly associated with several liver functions such as hormone metabolism, lipid localisation and transport, blood coagulation (Figure 5E), further confirming that cells generated by forward programming acquire a hepatocyte identity. Indeed, eFoP-Heps, as well as hiPSC-derived HLCs, expressed a range of adult liver genes that were found to be highly expressed in adult PHHs as identified in Figure 2, Figure 2—source data 2, including genes that were not expressed in the foetal stage (Figure 5F). In addition to expressing mature hepatocyte markers, both eFoP-Heps and iFoP-Heps were also able to uptake low-density lipoprotein (LDL) from the culture medium confirming their capacity to transport lipids (Figure 6A). In order to further explore their capacity to metabolise lipids, FoP-Heps were grown in 3D for 5 (D20) or 10 (D30) days as we recently observed that 3D culture conditions facilitate lipid accumulation in HLCs (Carola M Morell, personal communication, Tilson et al., 2021). We first confirmed that FoP-Heps grown in 3D retained the expression of hepatocyte markers (Figure 6B). Interestingly, SERPINA1 or UGT1A6 expression increased in these conditions suggesting an increase in functional maturation promoted by 3D (Figure 6B). We then tested the capacity of both eFoP and iFoP-Heps to respond to fatty acids by treating these cells with both oleic acid (OA) and palmitic acid (PA) which are known to induce steatosis and lipotoxicity, respectively (Ricchi et al., 2009). In line with their known effect on hepatocytes, OA treatment induced a strong accumulation of lipids as shown by BODIPY staining (Figure 6C) while PA treatment induced a reduction in cell viability consistent with lipotoxicity (Figure 6D). Thus, FoP-Heps appear to react to fatty acids similarly to their primary counterpart. Finally, we explored the interest of FoP-Heps for modelling the hepatotoxic effect of paracetamol/acetaminophen (APAP). For that, Fop-Heps were grown in the presence of an APAP dose known to induce liver failure. This treatment resulted in up to 50% reduction in cell viability (Figure 6E) suggesting that FoP-Heps could be used for cytotoxic studies. In summary, these results showed that 4TF FoP-Heps derived from either hESC or hiPSCs display characteristics of functional hepatocytes such as expression of genes involved in drug, lipid, glucose and bile acid metabolism, capacity to uptake LDL and fatty acids from the culture medium, as well as response to hepatotoxic factors, demonstrating their potential interest for modelling liver disease in vitro and toxicology screening.

Figure 5 with 2 supplements see all
Download asset Open asset
4TF FoP-Heps are transcriptionally equivalent to hepatocyte-like cells (HLCs).

(A) CYP3A4 activity levels in eFoP-Heps at day 20 (n=6) and day 30 (n=6), iFoP-Heps at day 0 (n=6) and day 30 (n=3), against direct differentiation HLCs (n=6) and primary human hepatocytes (PHHs) (n=4). Statistical differences were tested with one-way ANOVA, corrected for multiple comparisons, between eFoP-Heps group and HLCs. (Statistical test of HLC vs. PHHs can be found in Figure 2B) (B) mRNA levels of phase I (CYP3A4, CYP2A6, and CYP2C8) and phase II (UGT1A6) biotransformation enzymes in 4TF FoP-Heps, HLCs, and PHHs (n=4). (C) mRNA level of ALB, gluconeogenesis (G6PC and PCK1), and lipid (PPARγ) metabolism in FoP-Heps, HLCs, and PHHs (n=4). Statistical differences were calculated with one-way ANOVA, corrected for multiple comparisons, and all samples compared to HLCs. p-Values are indicated as *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. (D) Principal component analysis (PCA) of undifferentiated human induced pluripotent stem cells (hiPSCs), HLCs derived from human embryonic stem cell (hESC) (hESC_HLCs) and hiPSC (hiPSC_HLCs), freshly harvested PHHs (fPHHs) or plated PHHs (pPHHs), foetal livers (FL), and 4TF hESC-derived FoP-Heps (eFoP_Heps). (E) Dotplot showing the top 15 hits on gene ontology enrichment analysis on genes associated to genes differentially expressed between eFoP-Heps and undifferentiated hiPSCs. The size of each dot represents number of genes associated to each term and the colours represents the adjusted p-values. (F) Heatmap showing expression of genes associated with adult hepatocyte functions found to be expressed in hiPSC-derived HLCs (hiPSC_HLCs) and 4TF hESC-derived FoP-Heps (eFoP_Heps), as compared to undifferentiated hiPSCs. Two clusters were separated as genes expressed (bottom) and not expressed (top) in foetal liver samples (Foetal_Liver).

Figure 5—source data 1

Individual measurements and statistical tests related to Figure 5 and corresponding supplements.

https://cdn.elifesciences.org/articles/71591/elife-71591-fig5-data1-v1.xlsx
Figure 6
Download asset Open asset
RORc promotes functionality of 4TF FoP-Heps.

(A) Immunofluorescence staining for LDL in FoP-Heps at day 20 of forward programming. Scale bars, 200 µm. Nuclei were counterstained with DAPI (blue). (B) Comparison of mRNA levels of SERPINA1, ALB, AFP, UGT1A6, G6PC, and APOA1 in FoP-Heps cultured in 2D and 3D for up to 20 or 30 days of forward programming (n=4). Statistical difference between 2D and 3D were calculated with unpaired t-test. All expression data was normalised to the average of two housekeeping genes (PBGD and RPLP0). In all plots, bars represent mean with SD, and individual datapoints are shown for all biological replicates. p-Values are indicated as *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. (C) BODIPY staining of FoP-Heps cultured in 3D from day 20 of forward programming and treated with fatty acids (oleic acid [OA], palmitic acid [PA], or BSA [Ctr]) as indicated for 7 days. Scale bars, 200 µm. Nuclei were counterstained with DAPI (blue). (D) Cell viability in FoP-Heps treated with the fatty acids as indicated, normalised against FoP-Heps treated with BSA as control (n=4). (E) Cell viability in FoP-Heps treated with 25 mM of acetaminophen (APAP) for 48 hr in 3D cultures, normalised against untreated FoP-Heps (n=4). Significant differences were determined with paired t-test. In all plots, bars represent mean with SD, and individual datapoints are shown for all biological replicates. p-Values are indicated as *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Figure 6—source data 1

Individual measurements and statistical tests related to Figure 6.

https://cdn.elifesciences.org/articles/71591/elife-71591-fig6-data1-v1.xlsx

Discussion

In this study, we have established a method to forward program hPSCs into hepatocytes by taking advantage of the OPTi-OX platform (Pawlowski et al., 2017). The success of this approach depends on the selection of TFs, combining factors controlling early liver development and regulators of adult hepatic functions. Nonetheless, most forward programming methods rely on a master regulator to convert hPSCs into a specific cell type. As an example, neurons and muscle cells can be generated by the simple overexpression of NGN2 and MYOD, respectively (Pawlowski et al., 2017). Our results show that production of hepatocytes requires a more complex process involving three TFs but also a culture media supporting primary hepatocytes. Furthermore, our best LETFs combination did not include HNF4A, which is known to be a key regulator of hepatocyte function in the adult liver. On the contrary, removing HNF4A significantly improved the identity of the hepatocytes generated. Similar observations were recently reported for the direct reprogramming of human umbilical vein endothelial cells into bipotent hepatocyte progenitor cells where HNF4A was found to be detrimental (Inada et al., 2020). HNF4A is essential not only in adult liver but also during development, especially in the establishment of the liver bud (Gordillo et al., 2015). Thus, HNF4A might also have a role in preserving foetal liver cells such as hepatoblasts and its overexpression during forward programming could block the acquisition of an adult hepatocytic identity. This example illustrates the challenges to identify factors which are uniquely expressed in the adult liver.

Importantly, FoP-Heps generated by LETF overexpression acquired an hepatocytic identity with reduced adult functions, suggesting that this cocktail of TFs might only convert hiPSCs into foetal-like cells. Thus, we decided to add factors which could direct functional maturation of the liver. This latest category of factors was identified by performing a transcriptomic and epigenetic comparison of PHHs and HLC generated by direct differentiation. The focus on HLCs was based on their well-characterised foetal state and also the broad experience with the cells. These analyses identified a subset of nuclear receptors that were exclusively expressed in the adult liver thereby confirming the relevance of our approach. Of particular interest, RORc, ERα, and AR were identified as key candidates for controlling functional maturation in hepatocytes. Importantly, nuclear receptors are well known to control diverse liver functions including lipid and glucose homeostasis, bile acid clearance, xenobiotic sensing, and regeneration (Rudraiah et al., 2016). Both steroid hormonal receptors ERα and AR have been shown to have roles in the regulation of energy homeostasis in the liver (Shen and Shi, 2021). Moreover, ERα is involved in cholesterol clearance (Zhu et al., 2018) and has also been associated with liver regeneration (Kao et al., 2018) and bilirubin metabolism through CYP2A6 (Kao et al., 2017). RORc is a nuclear receptor expressed in peripheral tissues including liver, muscle, and adipose tissue and has been proposed to function as an intermediary between the circadian clock and glucose/lipid metabolism (Cook et al., 2015). Moreover, RORy-deficient mice exhibit insulin sensitivity and reduced expression of gluconeogenesis, lipid metabolic markers, and a subset of phase I enzymes involved in bile acid synthesis and phase II enzymes (Kang et al., 2007; Takeda et al., 2014a; Takeda et al., 2014b). Based on these previous reports, we propose that the overexpression of RORc and other nuclear receptors could improve specific functions in FoP-Heps by activating a subset of target genes in the hepatic context induced by the LETFs overexpression. Importantly, hepatocyte functionality is spatially different across the liver lobule, being influenced by the gradient of oxygen, nutrients, and signalling (Trefts et al., 2017). This hepatic zonation drives different metabolic processes in regards to glucose, lipids, iron, or even xenobiotics, which are under the control of different transcriptomic programs (Halpern et al., 2017). Thus, we expect that different combinations of nuclear factors in the background induced by LETFs overexpression could enable the production of hepatocytes with a distinct repertoire of functions.

FoP-Heps generated with the overexpression of the 4TFs (HNF1A, HNF6, FOXA3, and RORc) displayed functional features of adult hepatocytes including Albumin and A1AT secretion, basal CYP3A4 activity, expression of phase I/phase II enzymes, gluconeogenesis and lipid metabolism markers, capacity to uptake LDL and fatty acids, as well as response to toxic compounds. Nonetheless, CYP3A4 expression remains limited and this gene remains difficult to induce in vitro. Thus, additional TFs could be necessary to generate FoP-Heps exhibiting the full spectrum of functional activities displayed by PHHs. Similarly, culture conditions could be further improved to support key hepatic functions. Indeed, the basal medium used in our protocol does not prevent dedifferentiation of PHHs and thus might not be compatible with the production of fully functional cells by forward programming. Nonetheless, the forward programming method established here presents several advantages over conventional directed differentiation protocols. This is a robust two-step method which bypasses the need for multi-step differentiations which are often associated with batch-to-batch variability. Furthermore, forward programming is faster, generating functional cells in 20 days, as opposed to 30–35 days for direct differentiation. Finally, the yield of cells seems favourable and compatible with large-scale production. Indeed, we observed that forward programming was associated with a six- to eight fold increase in cell number during differentiation while the yield of direct differentiation is lower (data not shown). Moreover, the phenotype achieved is stable even in the absence of dox which allows this method to be applicable in cell therapy and drug discovery (data not shown).

Taken together, our results describe the first method for generating hepatocytes using forward programming. This approach represents the first step towards the high-throughput and large-scale production of specialised hepatocytes displaying a spectrum of functions relevant for different applications in disease modelling and drug screening.

Materials and methods

hPSC culture

Request a detailed protocol

The human ESC H9 (WiCell) and IPSC A1ATDR/R (Yusa et al., 2011) lines were used in this project. Human iPSC line was derived as previously described, under approval by the regional research ethics committee (REC 08/H0311/201). Both hPSCs were cultured on vitronectin XFTM (10 μg/ml, StemCell Technologies)-coated plates and in Essential 8 (E8) chemically defined medium consisting of DMEM/F12 (Gibco), L-ascorbic acid 2-phosphate (1%), insulin-transferrin-selenium solution (2%, Life Technologies), sodium bicarbonate (0.7%), and Penicillin/Streptomycin (1%), freshly supplemented with TGFβ (10 ng/ml, R&D) and FGF2 (12 ng/ml, Qkine) (Chen et al., 2011). For routine dissociation, cells were incubated with 0.5 μM EDTA (Thermo Fisher Scientific) for 3 min at 37°C seeded in small clumps. Cells were maintained at 37°C in 20% O2, 5% CO2 and medium was replenished every 24 hr.

Authentication of hPSCs was achieved by confirming the expression of pluripotency genes. Cells were routinely confirmed to be mycoplasma free using broth and PCR-based assays. The cell lines are not on the list of commonly misidentified cell lines (International Cell Line Authentication Committee).

Gene targeting

Request a detailed protocol

Inducible hESC and hiPSC lines were generated using the OPTi-OX system as previously described (Bertero et al., 2016; Pawlowski et al., 2017). Briefly, two gene safe harbours were targeted (GSH). The hROSA26 locus was targeted with a constitutively expressed transactivator (rtTA) and the AAVS1 locus with the transgenes of interest under a TET-responsive element (TRE). Different combinations of TFs and/or nuclear receptors as stated throughout the manuscript were cloned. Template cDNA sequences were obtained either from Dharmacon: HNF6 (MHS6278-213244170), HNF1A (MHS6278-202857902), RORy (MHS6278-202800991), and ESR1 (MHS6278- 211691051); or amplified from human primary liver cDNA: HNF4A, FOXA3, and AR. Sequences were amplified using the KAPA HiFi HotStart ReadyMix (Roche). The primers used to amplify and clone the sequences into the backbone vector contained upstream and downstream overhangs in order to generate a GSG (Gly-Ser-Gly) linker and a different 2A peptide as listed in Table 1. The different vectors were constructed by Gibson Assembly (New England Biolabs) using a 1:3 pmol ratio of vector to insert. For targeting, hPSCs were dissociated into single cells with StemPro accutase (Thermo Fisher) for 5 min, and 1 million cells were transfected with 2 μg of donor vector and 2 μg of each AAVS1 ZFN expression plasmids using the P3 Primary Cell 4D-Nucleofector X Kit (Lonza). Cells were seeded in E8 medium supplemented with 10 µM ROCK Inhibitor Y-27632 (Selleckchem). After 5–7 days, colonies were selected with 1 μg/ml puromycin (Sigma-Aldrich) for at least 2 days, after which they were individually picked and genotyped as previously described (Bertero et al., 2016; Pawlowski et al., 2017).

Table 1
Sequences of primers used for cloning.
GenePrimerSequence 5'–3' (GSG linker sequence underlined)
HNF1AStart_FCAC TTT TGT CTT ATA CTT ACT AGT GCC ACC ATG GTT TCT AAA CTG AGC CAG CTG CAG
HNF1AP2A_RTTC CAC GTC TCC TGC TTG CTT TAA CAG AGA GAA GTT CGT GGC TCC GGA GCC CTG GGA GGA AGA GGC CAT CTG G
HNF4AE2A_FTAT GCT CTC TTG AAA TTG GCT GGA GAT GTT GAG AGC AAC CCT GGA CCT GTC AGC GTG AAC GCG CCC CT
HNF4AStop_RAGA GGA TCC CCG GGT ACC GAG CTC GAA TTC CTA GAT AAC TTC CTG CTT GGT GAT GGT CG
HNF4AP2A_FTCT CTG TTA AAG CAA GCA GGA GAC GTG GAA GAA AAC CCC GGT CCT GTC AGC GTG AAC GCG CCC CT
HNF4AT2A_RCTC CTC CAC GTC ACC GCA TGT TAG AAG ACT TCC TCT GCC CTC TCC GGA GCC GAT AAC TTC CTG CTT GGT GAT GGT CG
HNF4AStart_FCAC TTT TGT CTT ATA CTT ACT AGT GCC ACC ATG GTC AGC GTG AAC GCG CCC
HNF4AP2A_RTTC CAC GTC TCC TGC TTG CTT TAA CAG AGA GAA GTT CGT GGC TCC GGA GCC GAT AAC TTC CTG CTT GGT GAT GGT CG
FOXA3T2A_FAGT CTT CTA ACA TGC GGT GAC GTG GAG GAG AAT CCC GGC CCT CTG GGC TCA GTG AAG ATG GAG GC
FOXA3E2A_RCTC AAC ATC TCC AGC CAA TTT CAA GAG AGC ATA ATT AGT ACA CTG TCC GGA GCC GGA TGC ATT AAG CAA AGA GCG GGA ATA G
FOXA3P2A_FTCT CTG TTA AAG CAA GCA GGA GAC GTG GAA GAA AAC CCC GGT CCT CTG GGC TCA GTG AAG ATG GAG GC
FOXA3T2A_RCTC CTC CAC GTC ACC GCA TGT TAG AAG ACT TCC TCT GCC CTC TCC GGA GCC GGA TGC ATT AAG CAA AGA GCG GGA ATA G
FOXA3T2A_FAGT CTT CTA ACA TGC GGT GAC GTG GAG GAG AAT CCC GGC CCT CTG GGC TCA GTG AAG ATG GAG GC
FOXA3Stop_RAGA GGA TCC CCG GGT ACC GAG CTC GAA TTC CTA GGA TGC ATT AAG CAA AGA GCG GGA ATA G
HNF6P2A_FTCT CTG TTA AAG CAA GCA GGA GAC GTG GAA GAA AAC CCC GGT CCT AAC GCG CAG CTG ACC ATG GAA GC
HNF6T2A_RCTC CTC CAC GTC ACC GCA TGT TAG AAG ACT TCC TCT GCC CTC TCC GGA GCC TGC TTT GGT ACA AGT GCT TGA TGA AGA AGA T
HNF6T2A_FAGT CTT CTA ACA TGC GGT GAC GTG GAG GAG AAT CCC GGC CCT AAC GCG CAG CTG ACC ATG GAA GC
HNF6Stop_RAGA GGA TCC CCG GGT ACC GAG CTC GAA TTC CTA TGC TTT GGT ACA AGT GCT TGA TGA AGA AGA T
RORyE2A_FTAT GCT CTC TTG AAA TTG GCT GGA GAT GTT GAG AGC AAC CCT GGA CCT GAC AGG GCC CCA CAG AGA CAG
RORyStop_RAGA GGA TCC CCG GGT ACC GAG CTC GAA TTC CTA CTT GGA CAG CCC CAC AGG TGA C
ESR1E2A_FTAT GCT CTC TTG AAA TTG GCT GGA GAT GTT GAG AGC AAC CCT GGA CCT ACC ATG ACC CTC CAC ACC AAA GCA
ESR1Stop_RAGA GGA TCC CCG GGT ACC GAG CTC GAA TTC CTA GAC CGT GGC AGG GAA ACC CTC
ARE2A_FTAT GCT CTC TTG AAA TTG GCT GGA GAT GTT GAG AGC AAC CCT GGA CCT GAA GTG CAG TTA GGG CTG GGA AG
ARStop_RAGA GGA TCC CCG GGT ACC GAG CTC GAA TTC CTA CTG GGT GTG GAA ATA GAT GGG CTT G

Hepatocyte direct differentiation

Request a detailed protocol

hPSCs were dissociated into single cells following incubation with StemPro Accutase (Thermo Fisher) for 5 min at 37°C and seeded at a density of 50,000 cells/cm2 in E8 medium supplemented with 10 µM ROCK Inhibitor Y-27632 (Selleckchem). Hepatocytes were differentiated 48 hr after seeding, as previously reported (Hannan et al., 2013) with minor modifications. Following endoderm differentiation, anterior foregut specification was achieved with RPMI-B27 differentiation media supplemented with 50 ng/ml Activin A (R&D) for 5 days. Cells at the foregut stage were further differentiated into hepatocytes with Hepatozyme complete medium: HepatoZYME-SFM (Thermo Fisher) supplemented with 2 mM L-glutamine (Thermo Fisher), 1% penicillin-streptomycin (Thermo Fisher), 2% non-essential amino acids (Thermo Fisher), 2% chemically defined lipids (Thermo Fisher), 14 μg/ml of insulin (Roche), 30 μg/ml of transferrin (Roche), 50 ng/ml hepatocyte growth factor (R&D), and 20 ng/ml oncostatin M (R&D), for up to 27 days.

Forward programming into hepatocytes

Request a detailed protocol

hPSCs were dissociated into single cells following incubation with StemPro Accutase (Thermo Fisher) for 5 min at 37°C and seeded at a density of 40–50,000 cells/cm2 in E8 medium supplemented with 10 µM ROCK Inhibitor Y-27632 (Selleckchem). E8 medium was replenished the following day. Following 48 hr, initial induction of the transgenes was achieved by incubation in E6 medium (E8 without growth factors) supplemented with 1 mg/ml dox for 24 hr. Cells were then maintained in Hepatozyme complete medium supplemented with 1 mg/ml dox for the remaining duration of the protocol. Medium was replenished every day for the next 4 days, and every other day here after. For specific experiments, cell lines were treated with 100 nM of desmosterol, testosterone, or β-estradiol (E2) from day 2 of forward programming. All ligands were purchased from Sigma-Aldrich and reconstituted in ethanol. For 3D cultures, forward programmed cells were embedded in Matrigel Growth Factor Reduced Basement Membrane Matrix, Phenol Red-free (Corning) at day 15 or day 20 and cultured for 5 or 10 days, respectively. Cells were dissociated with Hank’s based cell dissociation buffer (Gibco) for 20 min at 37°C, resuspended in Matrigel and seeded in 40–50 μl domes in Hepatozyme complete medium supplemented with 1 mg/ml dox.

Primary liver samples

Request a detailed protocol

Fresh primary hepatocytes used for RNA-seq were obtained as previously reported (Segeritz et al., 2018). Primary plated hepatocytes from four donors (three males and one female) were purchased from Biopredic International (Rennes, France), meeting the manufacturer’s quality control requirements. Cells were maintained in short-term monolayer cultures in William’s E (Gibco) supplemented with 1% glutamine (Gibco), 1% penicillin-streptomycin (Gibco), 700 nM insulin (Sigma-Aldrich), and 50 μM hydrocortisone (Sigma). Functional assays such as CYP3A4 activity measurement were performed in Hepatozyme complete medium within 48 hr of receipt. Bulk foetal tissue was obtained from patients undergoing elective terminations up to the third trimester, under approval by the regional research ethics committee (REC- 96/085). The tissue was lysed and RNA harvested as indicated below.

CYP3A4 assay

Request a detailed protocol

Measurement of CYP3A4 enzymatic activity was performed using the P450 Glo kit (Promega). Cells were incubated with 1:1000 luciferin-IPA in Hepatozyme complete for 1 hr at 37°C. Supernatant was mixed with detection reagent in a 1:1 ratio and incubated at room temperature for 20 min in Greiner white 96-well microplates (Sigma-Aldrich). Luminescence was measured in triplicate on a GloMax plate reader. Hepatozyme complete medium was used as background control. Relative light units were normalised for background, volume and average total number of cells obtained after differentiation.

LDL uptake assay

Request a detailed protocol

LDL uptake capacity was measured with the LDL Uptake Assay Kit (Abcam). Cells were incubated with 1:100 human LDL conjugated to DyLight 550 in Hepatozyme complete medium for 3 hr at 37°C. Cells were then washed and fixed with 4% PFA for 20 min at 4°C.

Fatty acid treatments

Request a detailed protocol

Forward programmed cells were embedded in 3D from day 20 and cultured for 7 days in Hepatozyme complete medium supplemented with either BSA (control), or OA (0.25 mM) or PA (0.25 mM) conjugated with BSA. Intracellular lipid accumulation was detected by incubating cells with 1 µl/ml Bodipy (Thermo Fisher) for 30 min, followed by DAPI (Hoechst) diluted 1:10,000 in PBS for 30 min and imaged on a Zeiss LSM 700 confocal microscope.

APAP toxicity

Request a detailed protocol

The hepatotoxicity of APAP was tested by incubating forward programmed cells cultured in 3D from day 15 in Hepatozyme complete medium supplemented with 25 mM APAP (R&D) for 48 hr (day 18 to day 20) after which cell viability was determined.

Cell viability

Request a detailed protocol

Cell viability was determined by incubating cells with 1:10 Presto Blue reagent (Invitrogen) in Hepatozyme complete medium at 37°C for 4 hr. Fluorescence was measured using the EnVision plate reader with an excitation emission of 560 nm/590 nm.

RT-qPCR

Request a detailed protocol

RNA was extracted from either cells or tissues using GenElute Mammalian Total RNA Miniprep Kit (Sigma-Aldrich) according to the manufacturer’s instructions; 500 ng of RNA were reverse transcribed into cDNA using Random Primers and SuperScript II (Invitrogen) according to the manufacturer’s instructions. qPCR was performed using the KAPA SYBR FAST qPCR Kit low-ROX (Sigma-Aldrich) with 200 nM of forward and reverse primers (Sigma-Aldrich; primers listed in Table 2) on a QuantStudio 5 (Applied Biosystems). qPCRs were performed in technical duplicates and normalised to the average of two housekeeping genes (RPLP0 and PBGD) using the 2–ΔCt method.

Table 2
Sequences of primers used for qPCR.
GeneForwardReverse
AFPTGCGGCCTCTTCCAGAAACTTAATGTCAGCCGCTCCCTCG
ALBCCTTTGGCACAATGAAGTGGGTAACCCAGCAGTCAGCCATTTCACCATAG
APOA1AGACAGCGGCAGAGACTATGCCAGTTGTCAAGGAGCTTTAGG
CYP2A6CAGCACTTCCTGAATGAGAGGTGACTGGGAGGACTTGAGGC
CYP2C8CATTACTGACTTCCGTGCTACATCTCCTGCACAAATTCGTTTTCC
CYP2C9GCCGGCATGGAGCTGTTTTTATGCCAGGCCATCTGCTCTTCTT
CYP3A4TGTGCCTGAGAACACCAGAGGTGGTGGAAATAGTCCCGTG
FASNGCAAGCTGAAGGACCTGTCTAATCTGGGTTGATGCCTCCG
FOXA3TGGGCTCAGTGAAGATGGAGGGGGATAGGGAGAGCTTAGAG
G6PCGTGTCCGTGATCGCAGACCGACGAGGTTGAGCCAGTCTC
HHEXGCCCTTTTACATCGAGGACAAGGGCGAACATTGAGAGCTA
HNF1ATGGCCATGGACACGTACAGGCTGCTTGAGGGTACTTCTG
HNF4ACATGGCCAAGATTGACAACCTTTCCCATATGTTCCTGCATCAG
HNF6GTGTTGCCTCTATCCTTCCCATCGCTCCGCTTAGCAGCAT
NANOGCATGAGTGTGGATCCAGCTTGCCTGAATAAGCAGATCCATGG
NR1H4ACTGAACTCACCCCAGATCAATGGTTGCCATTTCCGTCAAA
PBGDGGAGCCATGTCTGGTAACGGCCACGCGAATCACTCTCATCT
PCK1ACACAGTGCCCATCCCCAAAGGTGCGACCTTTCATGCACC
POU5F1AGTGAGAGGCAACCTGGAGAACACTCGGACCACATCCTTC
PPARaCCCTCCTCGGTGACTTATCCCGGTCGCACTTGTCATACAC
PPARyGAGCCTGCATCTCCACCTTATAGAAACCCTTGCATCCTTCACA
RORyCTACGGCAGCCCCAGTTTGCTGGCATGTCTCCCTGTA
RPLP0GGCGTCCTCGTGGAAGTGACGCCTTGCGCATCATGGTGTT
SERPINA1CCACCGCCATCTTCTTCCTGCCTGAGAGCTTCAGGGGTGCCTCCTCTG
SOX17CGCACGGAATTTGAACAGTAGGA TCAGGGACCTGTCACAC
TBX3TGGAGCCCGAAGAAGAGGTGTTCGCCTTCCCGACTTGGTA
UGT1A1TGATCCCAGTGGATGGCAGCCAACGAGGCGTCAGGTGCTA
UGT1A6GGAGCCCTGTGATTTGGAGAGTGACCCCGGTCACTGAGAACC

Immunofluorescence staining

Request a detailed protocol

Cells in monolayer were fixed in 4% PFA for 20 min at 4°C and blocked for 30 min in 10% donkey serum (Bio-Rad) and 0.1% Triton X-100 (Sigma-Aldrich). Fixed cells were incubated with primary antibodies listed in Table 3 in 1% donkey serum and 0,01% Triton X-100 overnight at 4 °C. Following washing, cells were incubated with Alexa Fluor 488-, 568-, or 647-conjugated secondary antibodies (Life Technologies) for 1 hr at room temperature diluted in 1% donkey serum and 0.01% Triton X-100. For nuclei visualisation, cells were incubated with adding DAPI/Hoechst 33258 (bis-Benzimide H, Sigma-Aldrich) diluted 1:10,000 in PBS for 10 min at room temperature. Cells were imaged either on a Zeiss Axiovert 200M or on a Zeiss LSM 700 confocal microscope.

Table 3
Lisf of primary antibodies.
ProteinSupplierCatalog numberHostConcentration
AlbuminBethyl LaboratoriesA80-229Agoat1:100
Alpha-1 AntitrypsinDakoA0012rabbit1:100
Alpha-FetoproteinDakoA0008rabbit1:100
HNF4AAbcamab92378rabbit1:100
HNF1ASanta cruzsc-135939mouse1:50
HNF6Santa cruzsc-13050rabbit1:100
FOXA3Santa cruzsc-166703mouse1:50
RORcAbcamab221359rabbit1:100
ERɑAbcamab32063rabbit1:100
ARAbcamab108341rabbit1:100

Secreted protein quantification

Request a detailed protocol

Albumin, Alpha-Fetoprotein, and Alpha-1-Antitrypsin were measured in the cell culture supernatant of monolayer cultures, which were replenished with fresh Hepatozyme complete medium 24 hr prior to collection. Concentrations were detected by ELISA (performed by core biomedical assay laboratory, Cambridge University Hospitals) and normalised to cell number.

RNA-seq analyses

Request a detailed protocol

RNA-seq datasets were generated for undifferentiated hiPSCs (n=3), hESC-derived HLCs (n=2), hiPSC-derived HLCs (n=6), fPHHs (n=3), commercially purchased PHHs (pPHHs, n=2), bulk foetal liver samples (FL, n=3), and hESC-derived 4TF FoP-Heps (eFoP, n=3). RNA was extracted from either cells or tissues using GenElute Mammalian Total RNA Miniprep Kit (Sigma-Aldrich) according to the manufacturer’s instruction. Poly-A library preparation and sequencing were performed by Cambridge Genomic Services (hESC_HLCs; pPHHs), the Wellcome Trust Sanger Institute (hiPSCs, hiPSC_HLCs, fPHHs), and the Cambridge Stem Cell Institute and CRUK (FL, eFoP). Quality of reads was assessed with FastQC. For consistency, fastq reads were split into single-end reads and trimmed to the same length (40 bp) using cutadapt version 2.10. Single-end fastq files were mapped and quantified using salmon version 1.2.1 with the following parameters: -l A, -GCbias, -posbias, -validatemappings (Patro et al., 2017). The index used was pre-built from the human GRCh38 cDNA reference sequence from Ensembl (refgenomes.databio.org). DGE was calculated using DESeq2 (Love et al., 2014), with the following parameters: padj > 0.05, basemean > 100 and log2 fold change >2 or <–2 between groups as depicted in each figure. GO enrichment was calculated with the clusterProfiler package (Yu et al., 2012). Pathway analysis on significantly misregulated TFs was assessed using ReactomePA (Yu and He, 2016). Mouse liver poly-A plus RNA-seq was downloaded from ENCODE (ENCODE Project Consortium, 2012). Single-end fastq reads were trimmed in both replicates from each dataset to 70 bp using cutadapt version 2.10. Fastq were mapped and quantified using salmon version 1.2.1 with the following parameters: -l A, -Gbias, -seqbias, -validatemappings using a pre-build mm10 cDNA reference genome. DeSeq2 was used to generate all plots for visualisation.

Chromatin immunoprecipitation

Request a detailed protocol

ChIP was performed as previously reported (Brown et al., 2011). Briefly, chromatin was crosslinked with 1% formaldehyde (Sigma-Aldrich) for 10 min at room temperature and quenched with 0.125 M glycine (Sigma-Aldrich). Cells and nuclei were subsequently lysed and chromatin was sonicated to fragment DNA to about 200–500 bp on a Bioruptor Pico sonication device (Diagenode). Sonicated chromatin was pre-cleared with same-host IgG and protein G Dynabeads (Thermo Fisher), 100 µg of cleared chromatin (protein) was incubated with 2 µg of the following antibodies overnight at 4°C: H3K27ac (Abcam, ab4729), H3K4me1 (Abcam, ab8895), H3K27me3 (Active Motif, 39155), and H3K4me3 (Merk, 05-745R), after which complexes captured with 30 µl of protein G Dynabeads (Thermo Fisher). Complexes were washed, RNAse A (Thermo Fisher) and Proteinase K (Sigma-Aldrich) treated, and DNA was purified by phenol-chloroform extraction and precipitated with GlycoBlue (Thermo Fisher), sodium acetate (Thermo Fisher), and ethanol (Sigma-Aldrich). A sonicated chromatin sample (1%) was also collected as input for normalisation and 10 ng of DNA were used for ChIP-seq library preparations.

ChIP-seq analyses

Request a detailed protocol

Library preparation and sequencing and alignment were performed by the Wellcome Trust Sanger Institute DNA Sequencing Facility (Hinxton, UK). Sequencing was performed on an Illumina HiSeq v4 to obtain paired-end reads with 75 bp length. ChIP-seq reads were mapped to human genome assembly GRCh38 with bwa. Aligned data in BAM format was sorted and indexed with samtools. Coverage files were generated using deeptools bamCoverage, with a bin size of 10 bp and normalised as RPKM for visualisation in IGV and heatmap representation with deeptools. In order to plot PCA, average scores were calculated over 1000 bp bins. For peak calling, BAM files were converted to SAM and peaks called using homer (Heinz et al., 2010). Both replicates were used for peak calling against input with disabled local filtering invoking the following flags for H3K27ac: -region -L 0. In order to identify regulatory regions specifically active in PHH or HLCs, differentially bound peaks were determined using PHH datasets as target against all HLCs datasets as background, and vice versa, with a fold enrichment over background of 4. A list of genes annotated for each peak dataset can be found in, Figure 3—source data 1 and 2. For motif enrichment, peak calling was performed on nucleosome-free regions by invoking the flags -L 1 -nfr, in order to determine the ‘dips’ within H3K27ac-rich regions. These sets of regions were overlapped with the differentially bound peaks as above, in order to perform PHH or HLC-specific motif enrichment. Peak annotation and GO enrichment were determined with the clusterProfiler R package (Yu et al., 2012). Undifferentiated hiPSCs ChIP-seq reads aligned to the same genome assembly were downloaded from ENCODE (ENCODE Project Consortium, 2012) and treated as above.

Statistical analysis

Request a detailed protocol

Statistical analyses were conducted using GraphPad 9.0.0 and specific tests are indicated in the figure legends. For each figure, sample size n indicates the number of independent experiments or biological replicates and individual values are represented for every graph. Testing between groups was performed with at least n≥3 independent experiments and p-value groups are indicated within the figure where significant.

Data availability

RNA-seq datasets used in this study are accessible on Array Express under the accession number E-MTAB-10634 and E-MTAB-11852. In addition, 3 of the hiPSC_HLCs data sets have been previously deposited with the accession number E-MTAB-6781 (Segeritz et al., 2018). Mus musculus C57BL/6 liver embryo RNA-seq datasets were obtained from the ENCODE database (Nakamori et al., 2016) (https://www.encodeproject.org/) with the following accession numbers: ENCSR216KLZ (E12.5 liver), ENCSR826HIQ (E16.5 liver), ENCSR096STK (P0 liver), ENCSR000BYS (8 weeks mixed sex adult liver) and ENCSR216KLZ (10 weeks adult liver). ChIP-seq datasets generated in this study have been deposited on Array Express with the accession number E-MTAB-10637, and publicly available datasets for hiPSCs were used from the ENCODE database with the following accession numbers: ENCSR729ENO (H3K27ac), ENCSR249YGG (H3K4me1), ENCSR386RIJ (H3K27me3), ENCSR657DYL (H3K4me3) and ENCSR773IYZ (input). All data generated or analysed during this study is included in the manuscript, supporting files, and source data files.

The following data sets were generated
The following previously published data sets were used
    1. Segeritz CP
    2. Rashid ST
    3. de Brito MC
    4. Serra MP
    5. Ordonez A
    6. Morell CM
    7. Kaserman JE
    8. Madrigal P
    9. Hannan NRF
    10. Gatto L
    11. Tan L
    12. Wilson AA
    13. Lilley K
    14. Marciniak SJ
    15. Gooptu B
    16. Lomas DA
    17. Vallier L
    (2018) ArrayExpress
    ID E-MTAB-6781. RNA-seq of hepatocytes obtained through step-wise differentiation of hIPSCs from a patient with A1AT deficiency and its point mutation-corrected isogenic hIPSC line. Comparison to primary hepatocytes from a healthy donor and an A1AT-deficient patient.
    https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6781

References

Decision letter

  1. Matthew A Quinn
    Reviewing Editor; Wake Forest School of Medicine, United States
  2. Mone Zaidi
    Senior Editor; Icahn School of Medicine at Mount Sinai, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "Generation of functional hepatocytes by forward programming with nuclear receptors" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Mone Zaidi as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

The primary revision that is essential is testing the functionality of the induced hepatocytes, specifically, compared to classically induced hepatocytes.

Reviewer #1 (Recommendations for the authors):

Directed (classical) differentiation methods to obtain iPSC-derived HLC are time-consuming and lack the full repertoire of functionalities of hepatocytes. This manuscript by Tomaz et al. reports a new method to generate Hepatic Like Cells (HLC) from iPSCs and ESCs. The authors identify a combination of four factors (HNF1A, HNF6, FOXA3, and RORc) that when overexpressed in iPSCs or ESCs induce their programming to HLC. After evaluating functionality, the authors conclude that the forward programmed cells show increased functionality when compared to classic iPSC-derived HLCs. Though the manuscript is well written, involves a large amount of work, and confers a great advance to the field of regenerative medicine, there are several important points to be addressed:

1. Differentiation stability and long-term functionality of iPSC-derived cells are crucial when considering any therapeutic use. In this work, the forward programmed HLCs seem to decline in activity markers (CYP3A4, ALB) over time (see Figure 1 and Fig 4F). This questions whether the programming effect of those 4 factors is reversible. The functionality of 4F forward programmed cells should be assessed beyond day 30 and the functionality should be compared with classical direct programmed HLCs at that timepoint.

2. Are the 4F forward programmed cells functional in vivo?

3. The importance of the findings in this work very much depends on how do the forward programmed hepatic cells (eFoPs and iFoPS) compare to the classic direct reprogrammed HLC (HLCs) and the primary hepatocytes (PHHs). Therefore, those controls (HLCs and PHHs) should be included in all the experiments. i.e Figure 1D-J; Figure 4A-G, Suppl Fig6 should contain HLC and mature hepatocytes as controls.

4. How do the transcriptome of the 3F (HNF1A, HNF6, FOXA2) forward programmed cells and 4F (HNF1A, HNF6, FOXA2, RORc) forward programmed cells compare to the HLCs and PHHs? How does it compare to Foetal hepatocytes?

5. Figure 2 shows there are plenty of pathways differentially activated in PHH when compared to HLCs, which is reflected in plenty of genes including key transcription factors being differentially expressed (Fig.2G). It is not clear why the authors focus their interest exclusively on nuclear receptors. This should be discussed.

6. Figure 5 is redundant to Figure 4. Since this is just another iPSC line, it should either be merged with figure 5 or included as a supplementary figure.

7. Quantification of IF should be done for all the images presented.

8. The statistical analyses are not clear, labelling for p-values should be consistent (either value or *, but consistent in all the graphs). It is unclear at times what groups have been compared in each graph. Comparisons should be clearly indicated (line indicating the 2 groups compared as in Figure 2B). HLC and PHHs should be always included as controls in all the graphs.

9. Fig6a - eFOPS and iFOPS day 30 should be included

10. Fig6B - CYP3A4 gene and ALB should be included here.

Reviewer #2 (Recommendations for the authors):

The major strength of the manuscript is the platform on which it is built, namely the system for forward programming and the in house strength of the Vallier group in hepatocyte differentiation. The discovery that three transcription factors, HNF1A, HNF6 and FOXA3, can induce forward programming towards the hepatocyte fate is notable. Equally notable is that HNF4A was dispensable raising questions on the precise phase of HNF4A action during normal development and maturation of hepatocytes. This is interesting and maps to similar questions during in vivo development.

In the informatic analyses I did not quite follow the logic of scrutinising variance by restricting the data to the 500 most variable genes rather than an unbiased scrutiny of the whole dataset. Similarly, the conclusion about H3K27ac modifications being more discriminatory than the H3K27me3 differences was unclear to me. As suggested in my recommendations, I don't think this really matters-the main rationale for studying H3K27ac modifications could be made clearer; namely to arrive at motifs in active regulatory element with potential binding factors that influence hepatocyte maturation. That alone provides a strong case.

While the initial findings on the three TFs are very interesting, I am less convinced about the additional role of RORc from the data presented. Some results between ESC and IPSC derived cells were discordant, differences in effect were apparent between the two later time-points of culture, and some statistical analyses seemed insignificant but accompanied by positive text statements. The main 'take home' message for me was that the resultant cells possess hepatocyte-like qualities but still fall far short of primary adult hepatocytes in terms of their primary function for xenobiotic metabolism. This is not really criticism as that goal is a massive one that will require multiple stepwise breakthroughs. In this regard, while I feel that the Results text needs moderation and perhaps reconsideration in places, the Discussion is very nicely balanced.

I have made my comments in order, rather than priority.

1. Introduction: 'after birth when it takes nearly 12 months for the liver to become functional'; this should be changed. The liver is unequivocally functional from early embryogenesis. No liver equals embryonic lethality.

2. In figure 1G-J the '-' sign gets lost on the x-axis. I suggest 'no' would be clearer.

3. Line 127: I think this should be changed. Measuring mRNA for ALB, SERPINA1, AFP and luciferase based CYP3A4 activity is useful screening but not assessing functional characteristics of hepatocytes in anything like their totality. HNF4A is proven to be a fundamental regulator of the hepatocyte phenotype. While it might be dispensable for some aspects, and may lower the handful of parameters selected, that does not equate to 'functional characteristics of hepatocytes'. I also think the conclusion (line 129) should be forward programming 'towards' rather than 'into' based on the very limited screening analyses undertaken to this point.

4. Figure 1, supplement 2B: it is striking how heterogeneous the albumin detection is (compared to A1AT or AFP)? Compared to the DAPI it is in only a fairly small minority of the cells. I would comment on this. It is not a criticism; we notice the same in vivo. We tend to treat 'hepatocytes' as one population with the potential the varying degrees of differentiation when actually I think they represent quite different subpopulations over and above what we know about zonation.

5. Line 137, I would describe this as 'following birth'. 'Adult' function is not established in the 'neonatal' period (by definition).

6. Line 141: I am not sure what is intended by 'are likely to be less relevant for natural development'?

7. Line 158: I don't understand the logic of scrutinising variance by restricting the choice to the 500 most variable genes. Isn't the point to scrutinise variance within the whole dataset? Restriction to the 500 most variable means the downstream analyses and individual GO terms end up based on remarkably few genes (which should be compiled as supplementary tables). What do the data look like without this arbitrary cut-off? Why 500?

8. Line 192-5. Why is H3K27ac providing the 'strongest distinction' (not a particularly scientific phrase? Variance?) and 'most informative'. There looks to be as much segregation of the PHH replicates on the H3K27me3 plot? I.e. cell identity is as much determined by what is actively repressed as what is actively 'switched on'.

9. Fig. 3 supplement 1C. I am not sure the terms are particularly 'developmental'; more seem to relate to neural function?

10. Fig. 3C right hands labels are cropped; I am not sure what message these panels are trying to convey? The text also becomes hard to follow in line 208-the H3K27ac regions can also harbour K27me3 marks? Line 211-212: I don't see evidence to support the statement, 'taken together these observations suggested that HLCs and PHHs broadly share the same epigenetic identity'-compared to what? In line 214, an association is taken as proof of causality. It potentially explains why these genes are not expressed. This whole section could do with reconsideration. I would favour a re-write with a far simpler focus: we wanted to identify the regulatory regions responsible for the differential gene expression so we could identify the underlying motifs and nuclear receptors/TFs that might bind to them.

11. Nuclear receptors for sex steroids: were the PHH samples from males or females (given the presence of AR and ER motifs)? Do the authors have a comment on the detection of HNF4A (dispensable) but not HNF1A, HNF6 or FOXA3 in the heatmap in Fig 3 supplement 1E? And might the upregulation of AR and ER in mice at 8 and 10 weeks simply reflect post-pubertal status rather than hepatocyte maturity?

12. Figure 4, supplement 1B: I disagree with the authors a little here. AR clearly has a cytoplasmic distribution in addition to potential nuclear detection (it is hard to say given the potential for piled up cells and signal overlying the nucleus that is not actually nuclear; in any case the nature of the AR panel is obviously different compared to the other nuclear receptors). This implies strongly that its ligand, either testosterone or dihydrotestosterone, is absent from the conditions in which case one wouldn't anticipate much functional effect even if AR was important in hepatocytes. Have the authors tried adding the relevant sex steroid to the culture conditions and repeating the immunocytochemistry? For AR it would induce comprehensive nuclear localisation. I disagree about the statement about potentially bypassing the need for ligand. The conformational change induced by ligand binding is highly relevant to DNA binding and gene activation. Just because the relevant receptor may (or may not) be in the nucleus doesn't allow confident statements on activity.

13. Consistency of the data and interpretation / risk of over-interpretation. RORc on albumin. I think the statement made in line 241-2, needs toning down. There isn't a statistical difference? Day 30 ALB mRNA levels are directly equivalent between 3TFs and 3TFs+RORc (Fig. 4D). Arguably (presuming gain settings are the same in panel B), while there are the same number of cells with robust detection of albumin there are more cells with faint detection in the presence of RORc. Likewise, I don't think much can be said about AFP levels being lower given the overlap in range and lack of statistical significance.

14. Consistency of the data and interpretation / risk of over-interpretation. Developing the second cell line: in the text the authors say that RORc overexpression increased expression of AFP and SERPINA1-this is not evident at Day 30 for transcript (D) or protein (E) and is non-significant for albumin for all except the day 20 mRNA analysis. Presumably day 30 is the most mature stage of culture (Fig . 5D and E)? Moreover, in the IPSC analysis RORc is quoted as decreasing AFP transcript detection (Fig. 4D, albeit non-significant), whereas in the second line studied, AFP transcript detection is actually increased by RORc (Figure 5D)-this discrepancy needs explaining as it might legitimately raise concerns that the detected differences are not meaningful.

15. How do the authors explain the lack of effect from the ligand for RORc (Fig. 4G)?

16. Figure 6A. Rather than being a side-by-side analysis, the CYP3A4 analysis (Fig. 6A) looks like it re-uses the preceding eFoP (Fig. 4A) and iFoP data (Fig. 5F) (the position of the balls and the SD bars look the same?). However, the HLC v PHH data look different from those in Figure 2B when the P value for their difference was only 0.0190. Here, P is 0.0002 for eFoP v. HLC? Given the massively higher activity in PHH, this suggests the HLCs in Figure 6 are suboptimal compared to those in Figure 2. For robustness, these comparative experiments ought to be done side-by-side. For me, the main message is that I agree with the authors that the considerable 'ask' remains unmet for CYP expression compared to PHH.

Reviewer #3 (Recommendations for the authors):

Valier's lab is one of the pioneering labs in endoderm differentiation and generation of hepatocyte- and cholangiocyte-like cells from human pluripotent stem cells (hPSCs) and has always performed and published high-quality research. This paper is no different in that respect. The authors worked meticulously and systematically to find the right combination of transcription factors involved in the liver development for forward programming of hPSCs. Comparing the transcriptome profile of primary human hepatocytes (PHHs) and hepatocyte-like cells (HLCs), they identified a number of nuclear receptors known to play a role in liver metabolic activity. To further improve the functionality of forward programmed HLCs, the authors tested the induction of three nuclear receptors in combination with three liver-enriched transcription factors. Notably, overexpression of RORc was found to reduced alpha-fetoprotein secretion as a foetal marker and improved Albumin protein production and CYP3A4 metabolic activity.

The conclusions of this paper are supported mainly by data, as forward programming can be considered a less complicated and more cost-effective alternative methodology than stepwise differentiation of hPSCs into HLCs using recombinant growth factors. However, it would be helpful to clarify the following points to draw a better conclusion:

1) Since the study aimed to generate HLCs with improved functionalities, comparing the level of HNF6, FOXA3 and HNF1 between generated forward programmed HLCs (FoP-Heps) and PHHs, considering that doxycycline (dox) induction was maintained throughout the differentiation.

2) The authors indicated that culture medium supplemented with 1mg/ml dox for the protocol duration (page 19, line 443). If tested, it would be helpful to share the consequence of dox exclusion after cells acquired hepatic phenotype. Would the FoP-Heps dedifferentiate and lose hepatic phenotype earlier than usual? If yes, then it would be helpful to elaborate on the potential implication for using the FoP-Heps in disease modelling and drug screening in the discussion.

3) While gene expression analysis for both FoP-Heps and HLCs were included for some genes (Figure 6 and Figure 6 Supplement 1), comparison of key hepatic functions such as secretion of ALB, AFP and Alpha1-Antitrypsin and CYP3A4 metabolic functionality are missing. The addition of these data would help to make a direct comparison between HLCs and FoP-Heps functionality.

4) It is unclear how long 2D-cultured FoP-Heps maintain their hepatic phenotype and function in culture.

The forward programming is an intriguing idea, and I would like to congratulate the authors for performing and presenting the data at such a high standard which is not surprising considering the high calibre of the Principal Investigator.

https://doi.org/10.7554/eLife.71591.sa1

Author response

Essential revisions:

The primary revision that is essential is testing the functionality of the induced hepatocytes, specifically, compared to classically induced hepatocytes.

Reviewer #1 (Recommendations for the authors):

Directed (classical) differentiation methods to obtain iPSC-derived HLC are time-consuming and lack the full repertoire of functionalities of hepatocytes. This manuscript by Tomaz et al. reports a new method to generate Hepatic Like Cells (HLC) from iPSCs and ESCs. The authors identify a combination of four factors (HNF1A, HNF6, FOXA3, and RORc) that when overexpressed in iPSCs or ESCs induce their programming to HLC. After evaluating functionality, the authors conclude that the forward programmed cells show increased functionality when compared to classic iPSC-derived HLCs. Though the manuscript is well written, involves a large amount of work, and confers a great advance to the field of regenerative medicine, there are several important points to be addressed:

1. Differentiation stability and long-term functionality of iPSC-derived cells are crucial when considering any therapeutic use. In this work, the forward programmed HLCs seem to decline in activity markers (CYP3A4, ALB) over time (see Figure 1 and Fig 4F). This questions whether the programming effect of those 4 factors is reversible. The functionality of 4F forward programmed cells should be assessed beyond day 30 and the functionality should be compared with classical direct programmed HLCs at that timepoint.

We acknowledge that stability and long-term functionality are key requirements that need to be met by stem-cell derived cell types in order to be applicable in therapy. Indeed FoP-Heps display higher levels of CYP3A4 activity at day 20 and these appear to reduce by day 30. However, hepatocyte marker expression in FoP-Heps did not appear to decreased from day 20 to day 30, apart from AFP (Figure 4D). We have previously characterised direct differentiation HLCs after long term cultures and found that conventional 2D culture systems were not compatible with maintaining HLCs functionality beyond day 45, resulting in cell detachment, potentiality due to lack of an appropriate matrix. Instead, it was found that 3D systems, such as the collagen-based RATF system, were appropriate to preserve an hepatic phenotype and further increase functionality up to 75 days (Gieseck et al, 2014).

In the current study, we have developed a forward programming method to generate hepatocytes using conventional 2D cultures which we believe limits their applicability and longterm functionality. We have differentiated FoP-Heps up to day 40 and observed that these still retain CYP3A4 activity, as well as HLCs (Author response image 1), but the cell attachment tends to deteriorate with longer times in culture (not shown). Here, we aimed to develop a versatile and faster alternative to generate hepatocytes in vitro and have chosen to characterised the generated cells in their optimal time frame (day 20). Nevertheless, we agree with the reviewer that should this method be further validated for therapeutic use, improvements should be made to extend their lifespan and usability, potentially adapting extra cellular matrix to meet the requirements for proper hepatocyte attachment and/or 3D system.

Author response image 1
Download asset Open asset
CYP3A4 Activity in long-term cultures.

hiPSCs expressing the 4TF combination were induced to differentiate for up to 40 days, with CYP3A4 activity measure at days 20, 30 and 40 (left panel). HLCs where differentiated by directed differentiation up to 40 days, with CYP3A4 activity measured at days 30, 35 and 40 (right panel). Data is represented as fold induction over day 20 (iFoP) or day 30 (HLCs).

2. Are the 4F forward programmed cells functional in vivo?

Assessing in vivo functionality is key to define the potential of forward programmed cells. Indeed, we have deployed our resources into addressed this question during the course of the revision by performing in vivo injections of eFoPs and iFoPs using a healthy mouse model. Initially, we performed injections in kidney capsule but this approach showed no hAlbumin detection on mice serum by up to 4 weeks (Author response image 2). Subsequently, we performed injections directly into the liver capsule which was found to provide a more appropriate niche, as hAlbumin expression could indeed be detected in mice serum at 3 or 4 weeks post-injection (Author response image 2), although at variable levels. However, hAlbumin was not detected consistently in all mice injected highlighting that the use of a healthy mouse model was inappropriate for sufficient and robust integration of the forward programmed cells into the liver. Using a liver injury model would provide a more suitable environment, however the time frame required to establish these models or to establish appropriate collaborations would fall beyond the appropriate time for this revision, and thus we propose to pursue these experiments as a follow-up study with suitable mouse models.

Author response image 2
Download asset Open asset
Human Albumin secretion in mice sera.

eFoP (hESC-derived) or iFoP (hIPSC-derived) cells differentiated up to 20 days were dissociated into small clusters, and 1 million cells mixed with growth-factor reduced Matrigel and injected (per mouse) into the kidney or liver capsules of healthy mice. Blood samples were collected at the timepoints indicated post-injection.

3. The importance of the findings in this work very much depends on how do the forward programmed hepatic cells (eFoPs and iFoPS) compare to the classic direct reprogrammed HLC (HLCs) and the primary hepatocytes (PHHs). Therefore, those controls (HLCs and PHHs) should be included in all the experiments. i.e Figure 1D-J; Figure 4A-G, Suppl Fig6 should contain HLC and mature hepatocytes as controls.

Data for the controls HLCs and PHHs has now been included in every figure for comparison where relevant, in the revised version of this manuscript.

4. How do the transcriptome of the 3F (HNF1A, HNF6, FOXA2) forward programmed cells and 4F (HNF1A, HNF6, FOXA2, RORc) forward programmed cells compare to the HLCs and PHHs? How does it compare to Foetal hepatocytes?

We have performed RNA-sequencing analysis on newly generated datasets of eFoP (4TFs) and foetal liver samples, in order to characterise what we identified as cells generated with the best combination of TFs identified in our study (4TFs). This data has been incorporated into a new Figure 5 and Figure 5 – supplement 2. From these analysis, eFoP cells seem to be distinct from foetal liver cells, as well as adult PHHs. eFoP were found to cluster in close proximity to HLCs derived from hESC and hiPSCs, and thus reinforce these to be an equivalent cell population generated with an alternative method. Interestingly, eFoP express adult liver genes similarly to HLCs, including genes not yet expressed in foetal liver cells.

5. Figure 2 shows there are plenty of pathways differentially activated in PHH when compared to HLCs, which is reflected in plenty of genes including key transcription factors being differentially expressed (Fig.2G). It is not clear why the authors focus their interest exclusively on nuclear receptors. This should be discussed.

Indeed, several pathways are lacking activation in HLCs when compared to PHHs and our differential gene expression analysis identified 36 TFs highly expressed in PHHs vs HLCs (p < 0.05, log2 fold change > 2). It is possible that many of the TFs identified in this study play a key role in hepatocyte maturation. However, it is known that nuclear receptors are involved in key liver functions including the metabolism of lipid and glucose levels, bile acid clearance, xenobiotic sensing and regeneration (Rudraiah et al., 2016). Given that the approach that we used to test the role of TFs in hepatocyte maturation involved gene targeting, generation of cell lines, and differentiation, all of which are lengthy processes, we decided to narrow down the list of candidate TFs for our study to nuclear receptors, since based on literature there was a high chance that these would could improve hepatocyte metabolism. The choice for nuclear receptors has been discussed in the results section in the revised version of this manuscript (line 174).

6. Figure 5 is redundant to Figure 4. Since this is just another iPSC line, it should either be merged with figure 5 or included as a supplementary figure.

Figures 5 has been relabelled as Figure 4 – figure supplement 2. In the revised version of this manuscript.

7. Quantification of IF should be done for all the images presented.

The authors agree that quantification is essential, especially for comparisons among cell lines overexpressing different TF combinations. Indeed we provide quantifiable data in qPCR and ELISA for all the same markers shown in IF as we believe these to be more robust and reliable methods for quantification and not dependent of staining/image quality or field of imaging.

8. The statistical analyses are not clear, labelling for p-values should be consistent (either value or *, but consistent in all the graphs). It is unclear at times what groups have been compared in each graph. Comparisons should be clearly indicated (line indicating the 2 groups compared as in Figure 2B). HLC and PHHs should be always included as controls in all the graphs.

For consistency, all figures have been amended to identify the groups compared in each test for which a significant p-value was obtained. P-value groups for all figures have been defined as: *p < 0.05, **p < 0.01, ***p< 0.001, ****p< 0.0001. HLCs and PHHs have been included as controls in every graph.

9. Fig6a - eFOPS and iFOPS day 30 should be included

CYP3A4 activity measurements taken on day 30 have been included for eFoP and iFoP in the new Figure 5A (previously named 6A), in the revised version of this manuscript.

10. Fig6B - CYP3A4 gene and ALB should be included here.

Data for CYP3A4 and Albumin has now been included in the new Figure 5B and C (previously named 6A and 6B), respectively, in the revised version of this manuscript.

Reviewer #2 (Recommendations for the authors):

The major strength of the manuscript is the platform on which it is built, namely the system for forward programming and the in house strength of the Vallier group in hepatocyte differentiation. The discovery that three transcription factors, HNF1A, HNF6 and FOXA3, can induce forward programming towards the hepatocyte fate is notable. Equally notable is that HNF4A was dispensable raising questions on the precise phase of HNF4A action during normal development and maturation of hepatocytes. This is interesting and maps to similar questions during in vivo development.

In the informatic analyses I did not quite follow the logic of scrutinising variance by restricting the data to the 500 most variable genes rather than an unbiased scrutiny of the whole dataset. Similarly, the conclusion about H3K27ac modifications being more discriminatory than the H3K27me3 differences was unclear to me. As suggested in my recommendations, I don't think this really matters-the main rationale for studying H3K27ac modifications could be made clearer; namely to arrive at motifs in active regulatory element with potential binding factors that influence hepatocyte maturation. That alone provides a strong case.

While the initial findings on the three TFs are very interesting, I am less convinced about the additional role of RORc from the data presented. Some results between ESC and IPSC derived cells were discordant, differences in effect were apparent between the two later time-points of culture, and some statistical analyses seemed insignificant but accompanied by positive text statements. The main 'take home' message for me was that the resultant cells possess hepatocyte-like qualities but still fall far short of primary adult hepatocytes in terms of their primary function for xenobiotic metabolism. This is not really criticism as that goal is a massive one that will require multiple stepwise breakthroughs. In this regard, while I feel that the Results text needs moderation and perhaps reconsideration in places, the Discussion is very nicely balanced.

I have made my comments in order, rather than priority.

1. Introduction: 'after birth when it takes nearly 12 months for the liver to become functional'; this should be changed. The liver is unequivocally functional from early embryogenesis. No liver equals embryonic lethality.

The sentence had been modified accordingly in the revised version of this manuscript (line 57).

2. In figure 1G-J the '-' sign gets lost on the x-axis. I suggest 'no' would be clearer.

The axis on Fig.1G-J have been modified accordingly in the revised version of this manuscript as well as every figure where “-“ was previously used. .

3. Line 127: I think this should be changed. Measuring mRNA for ALB, SERPINA1, AFP and luciferase based CYP3A4 activity is useful screening but not assessing functional characteristics of hepatocytes in anything like their totality. HNF4A is proven to be a fundamental regulator of the hepatocyte phenotype. While it might be dispensable for some aspects, and may lower the handful of parameters selected, that does not equate to 'functional characteristics of hepatocytes'. I also think the conclusion (line 129) should be forward programming 'towards' rather than 'into' based on the very limited screening analyses undertaken to this point.

The text has been modified accordingly in the revised version of this manuscript (line 127 and line 127).

4. Figure 1, supplement 2B: it is striking how heterogeneous the albumin detection is (compared to A1AT or AFP)? Compared to the DAPI it is in only a fairly small minority of the cells. I would comment on this. It is not a criticism; we notice the same in vivo. We tend to treat 'hepatocytes' as one population with the potential the varying degrees of differentiation when actually I think they represent quite different subpopulations over and above what we know about zonation.

We acknowledge that indeed there is heterogeneity in the Albumin levels which can be detected with all TF combinations, and could relate to subpopulations. We have commented on the heterogeneity displayed by Albumin expression in the revised version of this manuscript (line 121 and line 244).

5. Line 137, I would describe this as 'following birth'. 'Adult' function is not established in the 'neonatal' period (by definition).

The text has been modified accordingly in the revised version of this manuscript (line 137).

6. Line 141: I am not sure what is intended by 'are likely to be less relevant for natural development'?

FoP-heps are not differentiated by mimicking different steps of liver development, like the process for directed differentiationto generate HLCs. Moreover, HLCs have been previously been reported to represent a foetal stage (Baxter et al., 2015). Thus, we assumed HLCs from directed differentiation would serve as natural “immature” state of hepatocytes as control for the RNA-seq comparison of immature vs mature hepatocytes.

7. Line 158: I don't understand the logic of scrutinising variance by restricting the choice to the 500 most variable genes. Isn't the point to scrutinise variance within the whole dataset? Restriction to the 500 most variable means the downstream analyses and individual GO terms end up based on remarkably few genes (which should be compiled as supplementary tables). What do the data look like without this arbitrary cut-off? Why 500?

We appreciate the reviewer’s remark on the choice of 500 most variable genes for a principal component analysis. This is a default parameter of the PCA function, and it is used to focus the PCA plot on the most variable genes across different samples, which will most likely relate to the differential expression identified between sample groups. We have performed PCA using 1000, 5000 and 20.000 genes and did not observe any differences in the clustering of the samples (Author response image 3), thus we assume that the top 500 most variable genes sufficiently captures the differences and similarities between samples.

Author response image 3
Download asset Open asset
PCA plot.

PCA of undifferentiated hiPSCs, HLCs derived from hESC (hESC_HLCs) and hiPSC (hiPSC_HLCs), freshly harvested PHHs (fPHHs) or plated PHHs (pPHHs), where generated with the top 1000 (left panel), 5000 (middle panel) and 20.000 (right panel) most variable genes.

8. Line 192-5. Why is H3K27ac providing the 'strongest distinction' (not a particularly scientific phrase? Variance?) and 'most informative'. There looks to be as much segregation of the PHH replicates on the H3K27me3 plot? I.e. cell identity is as much determined by what is actively repressed as what is actively 'switched on'.

The text has been modified accordingly in the revised version of this manuscript (line 195).

9. Fig. 3 supplement 1C. I am not sure the terms are particularly 'developmental'; more seem to relate to neural function?

The text has been modified accordingly in the revised version of this manuscript (line 204).

10. Fig. 3C right hands labels are cropped; I am not sure what message these panels are trying to convey? The text also becomes hard to follow in line 208-the H3K27ac regions can also harbour K27me3 marks? Line 211-212: I don't see evidence to support the statement, 'taken together these observations suggested that HLCs and PHHs broadly share the same epigenetic identity'-compared to what? In line 214, an association is taken as proof of causality. It potentially explains why these genes are not expressed. This whole section could do with reconsideration. I would favour a re-write with a far simpler focus: we wanted to identify the regulatory regions responsible for the differential gene expression so we could identify the underlying motifs and nuclear receptors/TFs that might bind to them.

Figure 3C has been modified to have visible panels which show the levels of all histone modifications across the UGT1A locus. The text has been clarified to convey the aim of the epigenetic analysis, as suggested by the reviewer (line 187, 215, 217).

11. Nuclear receptors for sex steroids: were the PHH samples from males or females (given the presence of AR and ER motifs)? Do the authors have a comment on the detection of HNF4A (dispensable) but not HNF1A, HNF6 or FOXA3 in the heatmap in Fig 3 supplement 1E? And might the upregulation of AR and ER in mice at 8 and 10 weeks simply reflect post-pubertal status rather than hepatocyte maturity?

We appreciate the reviewer’s questions on nuclear receptors for sex steroids. Both proteins have been reported to be expressed in both genders in rodents and humans. In addition, their expression is age-dependent in rodents, being expressed at higher levels post-puberty (Shen and Shi, 2015). Indeed, we cannot establish causality between expression or AR and ERa and hepatic maturation, but the observation that these are specifically expressed in adult / postpubertal livers, versus foetal stage livers, was interesting as it reiterates that these markers seem to be “adult-specific”, as suggested by our PHH /HLCs RNA-seq comparisons. The PHH samples used in the ChIP-sequencing analyses were derived from a male donor. In our PHH qPCR expression data which is limited to 3 male and 1 female donor, we could not identify a clear difference in expression levels of these markers associated with the gender of the donor (Author response image 4).

The identification of HNF4A specifically in mouse liver RNA-seq was due to this heatmap specifically representing transcription factors that are categorised as nuclear receptors, and hence FOXA3, HNF6, HNF1A would not be expected to appear in this analysis in particular as they are not categorised as such.

Author response image 4
Download asset Open asset
Expression of nuclear receptors in PHHs.

mRNA levels of AR and ESR1 (ERa) genes in primary human hepatocytes from 4 donors including 1 female (F) and 3 males (M1, M2, M3). Data were normalised to the average of 2 housekeeping genes (PBGD and RPLP0).

12. Figure 4, supplement 1B: I disagree with the authors a little here. AR clearly has a cytoplasmic distribution in addition to potential nuclear detection (it is hard to say given the potential for piled up cells and signal overlying the nucleus that is not actually nuclear; in any case the nature of the AR panel is obviously different compared to the other nuclear receptors). This implies strongly that its ligand, either testosterone or dihydrotestosterone, is absent from the conditions in which case one wouldn't anticipate much functional effect even if AR was important in hepatocytes. Have the authors tried adding the relevant sex steroid to the culture conditions and repeating the immunocytochemistry? For AR it would induce comprehensive nuclear localisation. I disagree about the statement about potentially bypassing the need for ligand. The conformational change induced by ligand binding is highly relevant to DNA binding and gene activation. Just because the relevant receptor may (or may not) be in the nucleus doesn't allow confident statements on activity.

We agree with the reviewer’s comment that we cannot assume that the overexpression system used bypasses the need for a ligand. Indeed, we acknowledge that the pattern of staining of AR is clearly different from the other nuclear receptors RORc and ERa which are uniquely nuclear. Adding the ligand to the culture conditions would potentially induce comprehensive translocation to the nucleus. However, from the immunofluorescence results it appears that AR is present in both nucleus and cytoplasm in our overexpressing cell line without ligand, and thus we would expect to have some level transcriptional activity. We present here a different biological replicate of this staining with a closer field that better represents the dual localisation of this protein in the cytoplasm and nucleus (Author response image 5). We have modified the text to remove statements regarding activity related to AR overexpression (line 238).

Author response image 5
Download asset Open asset
Cellular localisation of AR.

Immunofluorescence staining of HNF1A an AR in hESCs targeted with 3TFs+AR after 24h of iOX with dox. Scale bar, 100µm.

13. Consistency of the data and interpretation / risk of over-interpretation. RORc on albumin. I think the statement made in line 241-2, needs toning down. There isn't a statistical difference? Day 30 ALB mRNA levels are directly equivalent between 3TFs and 3TFs+RORc (Fig. 4D). Arguably (presuming gain settings are the same in panel B), while there are the same number of cells with robust detection of albumin there are more cells with faint detection in the presence of RORc. Likewise, I don't think much can be said about AFP levels being lower given the overlap in range and lack of statistical significance.

We agree with the reviewer that the effect in Albumin and AFP is not statistically significant and instead it shows simply is a trend in the secretion of these two proteins, not supported by changes in transcript levels. We have modified the text to describe this data accurately (line 246).

14. Consistency of the data and interpretation / risk of over-interpretation. Developing the second cell line: in the text the authors say that RORc overexpression increased expression of AFP and SERPINA1-this is not evident at Day 30 for transcript (D) or protein (E) and is non-significant for albumin for all except the day 20 mRNA analysis. Presumably day 30 is the most mature stage of culture (Fig . 5D and E)? Moreover, in the IPSC analysis RORc is quoted as decreasing AFP transcript detection (Fig. 4D, albeit non-significant), whereas in the second line studied, AFP transcript detection is actually increased by RORc (Figure 5D)-this discrepancy needs explaining as it might legitimately raise concerns that the detected differences are not meaningful.

We have modified the text to describe this data accurately. Indeed, for the hiPSC background, it appears that cells continue to induce ALB expression by day 30, suggesting they require additional time for maturation (line 268).

15. How do the authors explain the lack of effect from the ligand for RORc (Fig. 4G)?

Intermediates of the cholesterol biosynthetic pathway have been found to bind to and enhance the transcriptional activity of RORy (RORc), and these have been proposed to act as endogenous ligands. In particular, the cholesterol precursor desmosterol has been reported as one of most effective ligands and thus it has been selected as a candidate ligand in this study. However, most studies investigating the potential of endogenous ligands have focused on the activity of RORyt, an isoform which is specifically expressed in a range of immune cells including T helper cells (Th7). Indeed, the effect of sterols in the differentiation of Th7, via transcriptional activation of RORyt, has been widely studied, but it is unclear whether the same sterols have the same effect on the RORy isoform expressed in the liver. In addition, it is possible that the FoP-heps activate to some extend the cholesterol biosynthetic pathway and provide endogenous ligands that activate the transcriptional activity of RORc.

16. Figure 6A. Rather than being a side-by-side analysis, the CYP3A4 analysis (Fig. 6A) looks like it re-uses the preceding eFoP (Fig. 4A) and iFoP data (Fig. 5F) (the position of the balls and the SD bars look the same?). However, the HLC v PHH data look different from those in Figure 2B when the P value for their difference was only 0.0190. Here, P is 0.0002 for eFoP v. HLC? Given the massively higher activity in PHH, this suggests the HLCs in Figure 6 are suboptimal compared to those in Figure 2. For robustness, these comparative experiments ought to be done side-by-side. For me, the main message is that I agree with the authors that the considerable 'ask' remains unmet for CYP expression compared to PHH.

The HLC and PHH datasets used (including both CYP3A4 data and expression data) are the same throughout the figures and are now included in every figure for comparison as suggested. Previously, HLC vs PHH CYP3A4 data was presented as log scale when compared alone in Figure 2 and linear scale when compared with FoP in Figure 6. We have now modified the figures so that all data is presented as linear scale and the reader can best identified when the same data is presented in multiple Figures.

Figure 6 (now renamed as Figure 5) aims to compare data from 4TF lines from both backgrounds (eFoP and iFoP) with controls and reused data from 4TF experiments in Figure 4 and 5 (now renamed as Figure 4 and supplement). We agree with the reviewer that ideally all experiments in different figures should be performed “side-by-side”, but given the multiple cell lines and replicates used throughout this study for 20/30-day long protocols we found this to be technically challenging. Thus, we have performed side-by-side experiments only in the case where different TF combinations where screened for the same cell line background: 3TF combinations in Figure 1G-J, 4TF combinations in Figure D-F, and 3TF vs 4TF in Figure 4supplement 2 (previously Figure 5).

We indicate in the source data every time the same dataset is used as control in multiple figures.

Reviewer #3 (Recommendations for the authors):

Valier's lab is one of the pioneering labs in endoderm differentiation and generation of hepatocyte- and cholangiocyte-like cells from human pluripotent stem cells (hPSCs) and has always performed and published high-quality research. This paper is no different in that respect. The authors worked meticulously and systematically to find the right combination of transcription factors involved in the liver development for forward programming of hPSCs. Comparing the transcriptome profile of primary human hepatocytes (PHHs) and hepatocyte-like cells (HLCs), they identified a number of nuclear receptors known to play a role in liver metabolic activity. To further improve the functionality of forward programmed HLCs, the authors tested the induction of three nuclear receptors in combination with three liver-enriched transcription factors. Notably, overexpression of RORc was found to reduced alpha-fetoprotein secretion as a foetal marker and improved Albumin protein production and CYP3A4 metabolic activity.

The conclusions of this paper are supported mainly by data, as forward programming can be considered a less complicated and more cost-effective alternative methodology than stepwise differentiation of hPSCs into HLCs using recombinant growth factors. However, it would be helpful to clarify the following points to draw a better conclusion:

1) Since the study aimed to generate HLCs with improved functionalities, comparing the level of HNF6, FOXA3 and HNF1 between generated forward programmed HLCs (FoP-Heps) and PHHs, considering that doxycycline (dox) induction was maintained throughout the differentiation.

We have included the expression data for HNF1A, FOXA3 and HNF6 in the new Figure 5 – supplement 1.

2) The authors indicated that culture medium supplemented with 1mg/ml dox for the protocol duration (page 19, line 443). If tested, it would be helpful to share the consequence of dox exclusion after cells acquired hepatic phenotype. Would the FoP-Heps dedifferentiate and lose hepatic phenotype earlier than usual? If yes, then it would be helpful to elaborate on the potential implication for using the FoP-Heps in disease modelling and drug screening in the discussion.

We appreciate the reviewer’s comment and agree it would be important to validate whether doxycycline is required for the whole duration of the differentiation or only required for the initial induction of transcription factors. We have indeed performed the 20-day forward programming protocol with dox removal from day 10 and day 15 onwards, and have observed no difference in cell morphology (not shown), as well as CYP3A4 activity and hepatocyte marker expression (Author response image 6). This suggests that actually dox is only necessary for initial induction of the transgenes and to initiate cell commitment, which will likely lead to the expression of the endogenous transcription factors. We consider this finding very relevant for the applicability of this method into clinic as it shows the phenotype is stable even in the absence of long-term doxycycline treatment. Therefore, we have included this information in the discussion section (line 404) and we propose that a further refinement of the current forward programming protocol should be presented a follow-up study.

Author response image 6
Download asset Open asset
Dox removal during forward programming.

hiPSCs overexpressing the 4TF combination were induced to differentiate for 20 days. Dox was removed from 10 and from day 15. iFoP-Heps where assessed for CYP3A4 activity levels (left panel). Expression levels of ALB, SERPINA1 and CYP2A6 were also quantified and normalised to the average of 2 housekeeping genes (PBGD and RPLP0). Data is represented as fold induction over control (continuous dox over 20 days).

3) While gene expression analysis for both FoP-Heps and HLCs were included for some genes (Figure 6 and Figure 6 Supplement 1), comparison of key hepatic functions such as secretion of ALB, AFP and Alpha1-Antitrypsin and CYP3A4 metabolic functionality are missing. The addition of these data would help to make a direct comparison between HLCs and FoP-Heps functionality.

We agree with the reviewer that including HLC and PHH data as controls throughout the manuscript is helpful for direct comparison. Indeed, we have now included the datasets for CYP3A4 activity on HLCs and PHH with every FoP-Heps dataset. In addition, we have included the expression data for HLCs and PHH as controls in every graph with FoP-Heps data, including quantification of expression of Alb, AFP and SERPINA1. We indicate in the source data every time the same dataset is used as control in multiple figures.

4) It is unclear how long 2D-cultured FoP-Heps maintain their hepatic phenotype and function in culture.

We appreciate the reviewer’s question about long-term stability. The forward programming method in this study relies on conventional 2D cultures which we believe limits their applicability and long-term functionality, which we have experienced with direct differentiation, possibly due to lack of an appropriate matrix to sustain long-term culture.

We have differentiated FoP-Heps up to day 40 and observed that these retain CYP3A4 activity, (Author response image 1 and Author response image 7), but the cell attachment deteriorates with longer times in culture (not shown). Here, we aimed to develop a versatile and faster alternative to generate hepatocytes in vitro and have chosen to characterised the generated cells in their optimal time frame (day 20). Nevertheless, we agree with the reviewer that should this method be further validated for therapy, improvements should be made to extend their lifespan and usability, possibly with appropriate liver ECM and/or using 3D systems. We propose that a further refinement of the current forward programming protocol should be presented a followup study

Author response image 7
Download asset Open asset
CYP3A4 Activity in long term cultures.

hiPSCs expressing the 4TF combination were induced to differentiate for up to 40 days, with CYP3A4 activity measured at days 20, 30 and 40.

https://doi.org/10.7554/eLife.71591.sa2

Article and author information

Author details

  1. Rute A Tomaz

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Conceptualization, Software, Formal analysis, Validation, Investigation, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9377-1431

  2. Ekaterini D Zacharis

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared

  3. Fabian Bachinger

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Validation, Investigation
    Competing interests
    is a PhD student sponsored by bit.bio

  4. Annabelle Wurmser

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared

  5. Daniel Yamamoto

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Software, Formal analysis
    Competing interests
    No competing interests declared

  6. Sandra Petrus-Reurer

    Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  7. Carola M Morell

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared

  8. Dominika Dziedzicka

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared

  9. Brandon T Wesley

    Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Resources
    Competing interests
    No competing interests declared

  10. Imbisaat Geti

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Resources, Generation of RNA-seq dataset
    Competing interests
    No competing interests declared

  11. Charis-Patricia Segeritz

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Resources, Generation of RNA-seq dataset
    Competing interests
    No competing interests declared

  12. Miguel C de Brito

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Resources, Generation of RNA-seq dataset
    Competing interests
    No competing interests declared

  13. Mariya Chhatriwala

    Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Resources, Generation of RNA-seq dataset
    Competing interests
    No competing interests declared

  14. Daniel Ortmann

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Methodology
    Competing interests
    No competing interests declared

  15. Kourosh Saeb-Parsy

    Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    Contribution
    Supervision
    Competing interests
    No competing interests declared

  16. Ludovic Vallier

    1. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
    2. Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, United Kingdom
    3. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
    Contribution
    Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing
    For correspondence
    lv225@cam.ac.uk
    Competing interests
    is a founder and shareholder of DefiniGEN, Aculive Therapeutics and Billitech
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3848-2602

Funding

European Research Council (New-Chol)

  • Rute A Tomaz
  • Ludovic Vallier

UK Regenerative Medicine Platform

  • Rute A Tomaz
  • Rute A Tomaz

Wellcome - MRC Cambridge Stem Cell Institute, University of Cambridge

  • Annabelle Wurmser
  • Annabelle Wurmser
  • Annabelle Wurmser

Gates Cambridge Trust (PhD Studentship)

  • Brandon T Wesley

Chan Zuckerberg Initiative

  • Carola M Morell
  • Carola M Morell

bit.bio (PhD Studentship)

  • Fabian Bachinger

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by funding from the European Research Council Grant New-Chol, the UK Regenerative Medicine Platform, and a core support grant from the Wellcome MRC – Cambridge Stem Cell Institute. We thank Anna Osnato and Pedro Madrigal for bioinformatics support, and Stephanie Brown for technical advice. We acknowledge the Wellcome Trust Sanger Institute sequencing platform, the ENCODE Consortium, and the ENCODE production laboratories in generating the particular datasets used in this manuscript.

Senior Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Reviewing Editor

  1. Matthew A Quinn, Wake Forest School of Medicine, United States

Publication history

  1. Received: June 24, 2021
  2. Preprint posted: June 26, 2022 (view preprint)
  3. Accepted: July 25, 2022
  4. Version of Record published: August 12, 2022 (version 1)

Copyright

© 2022, Tomaz et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,780
    Page views
  • 336
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Rute A Tomaz
  2. Ekaterini D Zacharis
  3. Fabian Bachinger
  4. Annabelle Wurmser
  5. Daniel Yamamoto
  6. Sandra Petrus-Reurer
  7. Carola M Morell
  8. Dominika Dziedzicka
  9. Brandon T Wesley
  10. Imbisaat Geti
  11. Charis-Patricia Segeritz
  12. Miguel C de Brito
  13. Mariya Chhatriwala
  14. Daniel Ortmann
  15. Kourosh Saeb-Parsy
  16. Ludovic Vallier
(2022)
Generation of functional hepatocytes by forward programming with nuclear receptors
eLife 11:e71591.
https://doi.org/10.7554/eLife.71591

Categories and tags

Research organism

Further reading

    1. Cell Biology
    2. Stem Cells and Regenerative Medicine

    Atf3 defines a population of pulmonary endothelial cells essential for lung regeneration

    Terren K Niethamer, Lillian I Levin ... Edward E Morrisey
    Research Article

    Following acute injury, the capillary vascular bed in the lung must be repaired to reestablish gas exchange with the external environment. Little is known about the transcriptional and signaling factors that drive pulmonary endothelial cell (EC) proliferation and subsequent regeneration of pulmonary capillaries, as well as their response to stress. Here, we show that the transcription factor Atf3 is essential for the regenerative response of the mouse pulmonary endothelium after influenza infection. Atf3 expression defines a subpopulation of capillary ECs enriched in genes involved in endothelial development, differentiation, and migration. During lung alveolar regeneration, this EC population expands and increases the expression of genes involved in angiogenesis, blood vessel development, and cellular response to stress. Importantly, endothelial cell-specific loss of Atf3 results in defective alveolar regeneration, in part through increased apoptosis and decreased proliferation in the endothelium. This leads to the general loss of alveolar endothelium and persistent morphological changes to the alveolar niche, including an emphysema-like phenotype with enlarged alveolar airspaces lined with regions that lack vascular investment. Taken together, these data implicate Atf3 as an essential component of the vascular response to acute lung injury that is required for successful lung alveolar regeneration.

    1. Stem Cells and Regenerative Medicine

    Hippo signaling impairs alveolar epithelial regeneration in pulmonary fibrosis

    Rachel Warren, Handeng Lyu ... Stijn P De Langhe
    Research Article Updated

    Idiopathic pulmonary fibrosis (IPF) consists of fibrotic alveolar remodeling and progressive loss of pulmonary function. Genetic and experimental evidence indicates that chronic alveolar injury and failure to properly repair the respiratory epithelium are intrinsic to IPF pathogenesis. Loss of alveolar type 2 (AT2) stem cells or mutations that either impair their self-renewal and/or impair their differentiation into AT1 cells can serve as a trigger of pulmonary fibrosis. Recent reports indicate increased YAP activity in respiratory epithelial cells in IPF lungs. Individual IPF epithelial cells with aberrant YAP activation in bronchiolized regions frequently co-express AT1, AT2, conducting airway selective markers and even mesenchymal or EMT markers, demonstrating ‘indeterminate’ states of differentiation and suggesting that aberrant YAP signaling might promote pulmonary fibrosis. Yet, Yap and Taz have recently also been shown to be important for AT1 cell maintenance and alveolar epithelial regeneration after Streptococcus pneumoniae-induced injury. To investigate how epithelial Yap/Taz might promote pulmonary fibrosis or drive alveolar epithelial regeneration, we inactivated the Hippo pathway in AT2 stem cells resulting in increased nuclear Yap/Taz, and found that this promotes their alveolar regenerative capacity and reduces pulmonary fibrosis following bleomycin injury by pushing them along the AT1 cell lineage. Vice versa, inactivation of both Yap1 and Wwtr1 (encoding Taz) or Wwtr1 alone in AT2 cell stem cells impaired alveolar epithelial regeneration and resulted in increased pulmonary fibrosis upon bleomycin injury. Interestingly, the inactivation of only Yap1 in AT2 stem cells promoted alveolar epithelial regeneration and reduced pulmonary fibrosis. Together, these data suggest that epithelial Yap promotes, and epithelial Taz reduces pulmonary fibrosis suggesting that targeting Yap but not Taz-mediated transcription might help promote AT1 cell regeneration and treat pulmonary fibrosis.

    1. Cell Biology
    2. Stem Cells and Regenerative Medicine

    Single-cell transcriptomic atlas of lung microvascular regeneration after targeted endothelial cell ablation

    Rafael Soares Godoy, Nicholas D Cober ... Duncan J Stewart
    Research Article Updated

    We sought to define the mechanism underlying lung microvascular regeneration in a model of severe acute lung injury (ALI) induced by selective lung endothelial cell ablation. Intratracheal instillation of DT in transgenic mice expressing human diphtheria toxin (DT) receptor targeted to ECs resulted in ablation of >70% of lung ECs, producing severe ALI with near complete resolution by 7 days. Using single-cell RNA sequencing, eight distinct endothelial clusters were resolved, including alveolar aerocytes (aCap) ECs expressing apelin at baseline and general capillary (gCap) ECs expressing the apelin receptor. At 3 days post-injury, a novel gCap EC population emerged characterized by de novo expression of apelin, together with the stem cell marker, protein C receptor. These stem-like cells transitioned at 5 days to proliferative endothelial progenitor-like cells, expressing apelin receptor together with the pro-proliferative transcription factor, Foxm1, and were responsible for the rapid replenishment of all depleted EC populations by 7 days post-injury. Treatment with an apelin receptor antagonist prevented ALI resolution and resulted in excessive mortality, consistent with a central role for apelin signaling in EC regeneration and microvascular repair. The lung has a remarkable capacity for microvasculature EC regeneration which is orchestrated by newly emergent apelin-expressing gCap endothelial stem-like cells that give rise to highly proliferative, apelin receptor-positive endothelial progenitors responsible for the regeneration of the lung microvasculature.