A single‐cell transcriptomic analysis reveals precise pathways and regulatory mechanisms underlying hepatoblast differentiation

Yang, Li; Wang, Weihua; Qiu, Wei-Lin; Guo, Zhen; Bi, Erfei; Xu, Cheng‐Ran

doi:10.1002/hep.29353

Cited by 152 publications

(205 citation statements)

References 46 publications

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“…Single‐cell RT–qPCR analysis revealed that tdTomato‐expressing cells co‐expressed the hepatocyte marker genes Alb , Afp, and Dlk1 (Fig EV5F and G). Single‐cell RNA sequencing and PCA showed that these cells clustered with hepatocytes we identified in our previous study (Fig EV5H). Therefore, the genetic tracing study demonstrated that the Pdx1‐expressing cells in the ventral region retained the potential to develop into hepatic cells, but these cells have almost no contribution to the liver mass.…”

Section: Resultssupporting

confidence: 65%

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Single‐cell transcriptomic analyses reveal distinct dorsal/ventral pancreatic programs

Qiu

Zhang

et al. 2018

EMBO Reports

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The pancreas of vertebrates is separately derived from both the dorsal and ventral endodermal domains. However, the difference between these two programs has been unclear. Here, using a pancreatic determination gene, , driven GFP transgenic mouse strain, we identified Pdx1-GFP highly expressing cells (Pdx1) and Pdx1-GFP lowly expressing cells (Pdx1) in both embryonic dorsal Pdx1-expressing region (DPR) and ventral Pdx1-expressing region (VPR). We analyzed the transcriptomes of single Pdx1 and Pdx1 cells from the DPR and VPR. In the VPR, Pdx1 cells have an intermediate progenitor identity and can generate hepatoblasts, extrahepatobiliary cells, and Pdx1 pancreatic progenitor cells. In the DPR, Pdx1 cells are directly specified as pancreatic progenitors, whereas Pdx1 cells are precocious endocrine cells. Therefore, our study defines distinct road maps for dorsal and ventral pancreatic progenitor specification. The findings provide guidance for optimization of current β-cell induction protocols by following the dorsal pancreatic specification program.

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Section: Resultssupporting

confidence: 65%

“…Cells were sorted from five E15.5 Pdx1‐Cre ; Rosa26‐tdTomato liver tissue. 1–5 represent five individual tdTomato + cells. HPCA plot of single‐cell transcriptomes from tdTomato + cells and embryonic hepatobiliary cells from previous study . Each dot represents a single cell.…”

Section: Resultsmentioning

confidence: 99%

Single‐cell transcriptomic analyses reveal distinct dorsal/ventral pancreatic programs

Qiu

Zhang

et al. 2018

EMBO Reports

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“…4F). The MHCs-to-HCCs transition occurs immediately after embryonic 272 day 12.5 (E12.5), which is consistent with the results of the original experimental observation [22]. some genes were also discovered as the "dark" genes, which were non-differential in gene expression, 289 but sensitive to SGE scores.…”

supporting

confidence: 86%

“…As the red curve shown in Fig. 2D, for MHCs-to-HCCs data, the drastic increase of average SGE 201 appeared from E11.5 to E12.5 and reaches its peak at E12.5, after which hepatoblast-to-hepatocyte and 202 cholangiocytes transition occurs [22]. Moreover, the median values of the red box plot of SGE score The SGE method has been applied to mESCs-to-MPs data, which is obtained from an experiment 207 of a retinoic acid (RA)-driven differentiation of pluripotent mouse embryonic stem cells (mESCs) to 208 lineage commitment [23].…”

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confidence: 88%

Predicting cell fate commitment of embryonic differentiation by single-cell graph entropy

Zhong

Han

Zhang

et al. 2020

Preprint

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16Cell fate commitment occurs during early embryonic development, that is, the embryonic 17 differentiation sometimes undergoes a critical phase transition or "tipping point" of cell fate 18 commitment, at which there is a drastic or qualitative shift of the cell populations. In this study, we 19 presented a novel computational approach, the single-cell graph entropy (SGE), to explore the gene-20 gene associations among cell populations based on single-cell RNA sequencing (scRNA-seq) data. 21 Specifically, by transforming the sparse and fluctuating gene expression data to the stable local network 22 entropy, the SGE score quantitatively characterizes the criticality of gene regulatory networks among 23 cell populations, and thus can be employed to predict the tipping point of cell fate or lineage 24 commitment at the single cell level. The proposed SGE method was applied to five scRNA-seq datasets. 25 For all these datasets of embryonic differentiation, SGE effectively captures the signal of the 26 impending cell fate transitions, which cannot be detected by gene expressions. Some "dark" genes that 27 are non-differential but sensitive to SGE values were revealed. The successful identification of critical 28 transition for all five datasets demonstrates the effectiveness of our method in analyzing scRNA-seq 29 data from a network perspective, and the potential of SGE to track the dynamics of cell differentiation. 30 31 gene; cell fate commitment. 33 34 35Predicting cell-fate commitment by SGE 2 This is a provisional file, not the final typeset article 36 1. Introduction 37 Complex systems may switch abruptly to a contrasting state through a critical transition [1]. In recent 38 years, detecting critical transitions for general systems, such as ecosystems systems [2-3], climates 39 systems [4-5], financial systems [6,7], and epidemic model [8][9], has drawn more and more attentions. 40In biomedical fields, the rapid growth of single-cell datasets has shed new light on the complex 41 mechanisms of cellular heterogeneity. In these single-cell experiments, the cell fate commitment 42 represents a critical state transition or "tipping point" at which complex systems undergo a qualitative 43 shift. Characterizing and predicting such critical transition is crucial for patient-specific disease 44 modeling and drug testing [10]. Recent studies provided a plethora of statistical quantities such as 45 variance, correlation coefficient, and coordination of gene expression, to detect a cell fate transition of 46 embryonic differentiation [10,11]. However, these statistical quantities mainly focused on the analyses 47 at the gene expression level, while single-cell RNA sequencing (scRNA-seq) may offer more 48 information of an insight into the cell-specific network systems. In contrast to gene expression, cell-49 specific network is a stable form against the time and condition [12], and thus reliably characterize the 50 biological processes such as cell fate commitment. Such a network system is viewed as a nonlinear 5...

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“…Moreover, Bone hepatoblasts (Yanai, Tatsumi et al, 2008); expression of hepatic Bmp4 was increased in 160 L DKO mice compared with L WT mice ( Fig 2E). Hepatoblasts maturate to cholangiocytes 161 through activation of ERK pathway (Yang, Wang et al, 2017) and BA can increase 162 proliferation by ERK activation through FXR/FGF15/FGR4 pathway (Li & Chiang, 2015).…”

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confidence: 99%

Hepatic JNK-mediated bile acid homeostasis regulates liver cancer through PPARα

Manieri

Esteban-Lafuente²,

Rodríguez³

et al. 2019

Preprint

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43cJun NH 2 -terminal kinase (JNK) inhibition has been suggested as a potential treatment for 44 insulin resistance and steatosis through activation of the transcription factor PPAR. 45However, the long-term consequences have not been evaluated. We found that hepatic 46 JNK deficiency alters bile acid and cholesterol metabolism, resulting in hepatic expression 47 of FGF15 and activation of ERK in cholangiocytes, which ultimately promotes their 48 proliferation. Genetic inactivation of PPARα identifies PPARα hyperactivation as the 49 molecular mechanism for these deleterious effects. Our analysis indicates that hepatic 50 PPARα activation is oncogenic: PPARα deficiency protects mice against carcinogen-51 induced hepatocellular carcinoma under high fat diet (HFD) condition. These surprising 52 results urge the re-consideration of using JNK inhibitors or PPAR agonists for the 53 treatment of metabolic syndrome. 54 55 Keywords: Cell damage / cholangiocarcinoma / hepatocellular carcinoma / JNK / 56 PPARalpha 57 58 59 60 101 Kolodziejczyk et al., 2018). These contrasting potential functions of BAs prompted us to 102 examine whether lack of JNK in hepatocytes, and the resulting hyperactivation of PPAR, 103 could alter BA homeostasis with subsequent deleterious effects. Elucidation of the 104 contribution of JNK and PPAR to liver carcinogenesis may help the development of 105 effective treatments against this malignancy.106 107 108 109 Elisa Manieri et al., Page 6 6 RESULTS 110 Hepatic JNK-deficiency alters bile acid homeostasis 111 We have previously shown that hepatic JNK deficiency results in the activation of the 112 nuclear transcription factor PPAR and protection against diet-induced insulin resistance 113 and steatosis (Vernia et al., 2014). The activation of PPAR caused altered BA 114 metabolism (Li & Chiang, 2009). We therefore examined BA in hepatic JNK deficient mice 115 (L DKO ) and control mice (L WT ) at 6 months of age. We found that total BA concentration in 116 the blood of L DKO mice were significantly increased compared with L WT mice ( Fig 1A). The 117 increase in circulating BA concentration is consistent with the possibility that L DKO mice 118 may suffer from cholestasis. 119The analysis of bile collected from the gallbladder of L DKO and L WT mice revealed 120 significantly increased amounts of BA relative to the amount of cholesterol and 121 phosphatidylcholine (PC) ( Fig 1B). Hepatic expression of genes related to hepatic PC 122 synthesis (Scd2, Chpt1, and Chkb) or hepatocyte-mediated transport of PC (Abcb4 and 123 Atp8b1) and BA (Abc11 and Slc10a1) was markedly increased in L DKO mice 124 ( Supplementary Fig EV1A, B). Similarly, increased expression of genes related to 125 cholesterol synthesis (Hmgcs1, Hmgcr) and BA synthesis (Baat, Cyp8b1 and Cyp27a) was 126 detected in L DKO mice ( Fig 1C). These data are consistent with altered biosynthesis and 127 secretion of both cholesterol and BA through PPAR activation. 129Hepatic JNK-deficiency causes cholestasis and liver damage 130 It is esta...

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A single‐cell transcriptomic analysis reveals precise pathways and regulatory mechanisms underlying hepatoblast differentiation

Cited by 152 publications

References 46 publications

Single‐cell transcriptomic analyses reveal distinct dorsal/ventral pancreatic programs

Single‐cell transcriptomic analyses reveal distinct dorsal/ventral pancreatic programs

Predicting cell fate commitment of embryonic differentiation by single-cell graph entropy

Hepatic JNK-mediated bile acid homeostasis regulates liver cancer through PPARα

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