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Background: Mesenchymal cells, including hepatic stellate cells (HSCs),
fibroblasts (FBs), myofibroblasts (MFBs), and vascular smooth muscle cells
(VSMCs), are the main cells that affect liver fibrosis and play crucial roles in
maintaining tissue homeostasis. The dynamic evolution of mesenchymal cells is
very important but remains to be explored for researching the reversible
mechanism of hepatic fibrosis and its evolution mechanism of hepatic fibrosis to
cirrhosis. Methods: Here, we analysed the transcriptomes of more than
50,000 human single cells from three cirrhotic and three healthy liver tissue
samples and the mouse hepatic mesenchymal cells of two healthy and two fibrotic
livers to reconstruct the evolutionary trajectory of hepatic mesenchymal cells
from a healthy to a cirrhotic state, and a subsequent integrative analysis of
bulk RNA sequencing (RNA-seq) data of HSCs from quiescent to active (using
transforming growth factor
Liver fibrosis is the formation of a fibrous scar caused by the accumulation of extracellular matrix (ECM) proteins, mainly cross-linked collagen types I and III, that replace damaged normal tissue in most chronic liver diseases [1]. Advanced liver fibrosis can lead to cirrhosis and even hepatocellular carcinoma. Cirrhosis is widespread worldwide and has various causes such as obesity, high alcohol consumption, non-alcoholic fatty liver disease (NAFLD), autoimmune diseases, hepatitis B or C infection, and cholestatic diseases [2]. Liver disease accounts for approximately 2 million deaths worldwide each year, 1 million from complications of cirrhosis, and is now the 11th most common cause of death worldwide [3].
The liver consists of approximately 80% hepatocytes and 20% non-parenchymal
cells (NPCs). Liver fibrosis involves complex interactions between multiple
lineages of NPCs, which include immune, endothelial, and mesenchymal cells
spatially located in scar areas called fibrotic niches [4]. Various populations
of mesenchymal cells, such as hepatic stellate cells (HSCs), fibroblasts (FBs),
myofibroblasts (MFBs), and vascular smooth muscle cells (VSMCs) contribute to
fibrosis after liver injury by producing ECM proteins [5]. The major
mesenchymal cells populating the liver are HSCs (6%) [6]. In a healthy liver,
HSCs are quiescent (qHSCs) and pericyte-like cells, which reside in the space of
Disse between parenchymal and endothelial cells, express neural (LRAT,
GFAP, SYNM, SYP) and lipogenic (PPARG,
ADIPOR1) genes, BAMBI, NGFR, CEBPB,
CEBPA [1, 7, 8, 9]. Following chronic liver injury, qHSCs are continuously
activated (aHSCs), express myogenic markers (ACTA2, MEF2C), and
transdifferentiate into MFBs expressing COL3A1, PDGFRB,
COL1A1, TIMP1, TGFB1, FAP, acquiring
contractile, proinflammatory, and fibrogenic properties [1, 7, 10, 11]. HSCs
produce a wide range of cytokines and chemokines [10]. One of the
well-characterised cytokines and the main contributor for liver fibrosis is
transforming growth factor
Single-cell spatial transcriptome analysis showed that, in addition to HSCs,
portal fibroblasts are (COL15A1
In this study, we used scRNA-seq to investigate the origin of qHSCs, aHSCs, and
MFBs during liver cirrhosis and whether there is a process of MFB reversal to
iHSC. According to the pathological mechanism of liver fibrosis, we used
TGF-
The scRNA-seq data of three human cirrhotic tissue samples and three healthy
liver tissue samples, two mouse healthy liver mesenchymal cell samples and two
fibrotic (following chronic CCl
The R package Seurat v4.1.0 (Satija lab, New York, NY, USA) [21] was used to process human
liver scRNA-seq data (GSE136103). Genes expressed in fewer than three cells in a
sample, cells that expressed fewer than 300 genes, and more than 6000 genes, and
cells with mitochondrial gene content
We focused on mesenchymal cells, therefore, we extracted subpopulations of mesenchymal cells (cluster 9 and 15) from healthy and cirrhotic samples. Because we were more concerned with the dynamic evolution of mesenchymal cells in cirrhosis, we used the FindClusters function to sub-cluster these mesenchymal cells from healthy and cirrhotic liver separately. Dimensionality reduction analysis was performed using the RunPCA function, and sub-clustering was based on the nine most important key components. The resolution was set to 0.9 (healthy) and 1.2 (cirrhotic). After subclustering, healthy and cirrhotic mesenchymal cells were visualised with t-distributed stochastic neighbor embedding (t-SNE) using the nine most important PCs.
Mouse hepatic mesenchymal cells from two healthy and two fibrotic liver
(GSE137720) were analysed using Seurat. Low quality cells (
The scRNA-seq data of mouse Lin-negative cells of bilio-vascular tree
(GSE163777) was analysed in a similar manner. Low-quality cells (
After obtaining cell clusters, the FindAllMarkers function was used to search
for differentially expressed genes in the clusters. We set the log fold-change of
the average expression between the two clusters (avg_logFC)
The differentiation trajectory of the mesenchymal subpopulations was constructed
using the R package Monocle v2.24.1 (Cole Trapnell’s lab, Seattle, WA, USA) [23]. The
newCellDataSetFirst function was used to build a Monocle object. We used
dispersionTable (mean_expression
We verified the Monocle trajectory and its directionality using the velocyto Python package v 0.17.17 (La Manno Lab, Stockholm, Sweden) [24] to estimate the cell velocity from the spliced and unspliced mRNA content. We used velocyto to convert the cellranger result files (output directory) into loom files and merged the loom files of all samples. The merged loom files were used as input for scvelo v0.2.4 (Volker Bergen, Munich, Germany) Python pipeline [25]. The calculation of RNA velocity values for each gene in each cell and embedding of the RNA velocity vector in t-SNE were performed using scvelo and finally visualised with Python v3.8 (Python Software Foundation, Delaware, DE, USA).
The R package CellChat v1.5.0 (Department of Mathematics, University of California, Irvine, CA, USA) [26] was used for cell communication analysis, and the Seurat object was exported as the input to CellChat. After the construction of the CellChat object using the createCellChat function, the ligand-receptor interaction database CellChatDB.human (or CellChatDB.mouse) was set up. The computeCommunProb function was used to calculate the communication probability with default parameters and infer the CellChat network. Function filterCommunication was used to filter groups with fewer than 10 cells. The function subset Communication was then used to save the prediction results of cell communication in the form of a data frame. The computeCommunProbPathway function was used to summarise the communication probability of all ligand-receptor interactions related to each signalling pathway, to calculate the communication probability at the signal pathway level. The integrated cell communication network was evaluated using the aggregateNet function by calculating the number of links or summarising the communication probability. The results of cell-cell communication were displayed using netVisual heatmap function.
The Python package pySCENIC v0.12.0 (VIB Center for Brain Disease Research, Laboratory of Computational Biology, Leuven, Belgium) [27] was used for transcription factor analysis. First, the gene expression matrix was exported from R and saved as “csv” files, and then the “csv” file was converted into a loom file in Python. The pyscenic grn, pyscenic cistarget, and pyscenic AUCell functions were run sequentially for transcription factor analysis and the results were visualised.
The human hepatic stellate cell line (LX-2) was provided without Mycoplasma
contamination from Professor Wang
Qingqing’s research group at Zhejiang University School of Medicine and was
verified as LX-2 cells by the STR technology of Wuhan Punosei Life Technology. To
maintain the cells in the same state before dosing, LX-2 cells in the logarithmic
growth phase were starved for 24 h (the medium did not contain foetal bovine
serum or penicillin/streptomycin). TGF-
The stimulate, control, and recovery period samples were repeated three times, and RNA extraction, quality control, library construction, and RNA-seq were performed on nine samples. Total RNA was extracted using TRIzol reagents, RNA integrity was assessed using 1% agar-gel, and RNA concentration and purity were quantitatively and qualitatively analysed using a nanoophotometer spectrophotometre. RNA volume was 3 µg/ sample, the RNA-seq library was constructed using NEBNext UltraTM RNA Library Prep Kit for Illumina, and index codes were added to the attribute sequence of each sample. Following the manufacturer’s instructions, we used the TruSeq PE Cluster Kit v3-cBot-HS (Illumina, CA, USA) , to cluster samples using index codes on the cBot Cluster Generation System. After clustering, we sequenced the library using the Illumina platform and obtained 150 bp paried-end sequences.
First, FastQC was used to evaluate Illumina reads. The Fastp software was used to discard low-quality reads. Then, we downloaded the human reference genome (GRCh38) from the Ensembl database and used HISAT2 software for read alignment. Finally, the gene levels were quantitatively analysed using the featureCounts tool to obtain the final standardised matrix. The expression level of each gene was quantified as normalised fragments per kilobase of transcript per million mapped reads (FPKM).
Unless otherwise stated, statistical analyses were performed using Graphpad
Prism v8.0 (GraphPad Software, San Diego, CA, USA). A paired t-test was used to compare the
differences between the two groups. Differences were considered statistically
significant at p
A graphical overview of the study design is shown in Fig. 1A. To examine the
heterogeneity of hepatic NPCs, we analysed the hepatic NPC scRNA-seq data
obtained from the GEO database (GSE136103). Hepatic NPCs were isolated from three
healthy and three cirrhotic human livers. NAFLD was the cause of liver fibrosis
in one female patient, and alcohol was the cause of liver fibrosis in the other
two male patients (Supplementary Fig. 1A). Leucocytes
(CD45
Single-cell transcriptomic profile of human liver. (A) Graphic overview of the study design. (B) Clustering 52,669 cells from three healthy and three cirrhotic human livers. (C) The 11 lineages of 52,669 human liver cells, inferred from the expression of marker genes. Right, annotation by injury condition. ILC, innate lymphoid cell; MP, mononuclear phagocyte; pDC, plasmacytoid dendritic cell. (D) UMAP plots of conserved hepatic cell markers. CD79A, CD79a Molecule; MKI67, Marker Of Proliferation Ki-67; VWF, Von Willebrand Factor; ALB, Albumin; KRT18, Keratin 18; PRF1, Perforin 1; TPSAB1, Tryptase Alpha/Beta 1; COL3A1, Collagen Type III Alpha 1 Chain; ACTA2, Actin Alpha 2, Smooth Muscle; TAGLN, Transgelin; PDGFRB, Platelet Derived Growth Factor Receptor Beta; C1QB, Complement C1q B Chain; FCRL5, Fc Receptor Like 5; TRAC, T Cell Receptor Alpha Constant. (E) Bar plot representing the relative contribution of cells from each donor (three healthy livers, three cirrhosis livers) for each cell type.
We focused on mesenchymal cells, and we isolated clusters 9 and 15 (2918 mesenchymal cells) for further analysis. In healthy liver, mesenchymal cells sub-clustered into eight distinct mesenchymal subpopulations (Fig. 2A). We identified the cells in clusters 1, 2, 3, and 7 as VSMCs that displayed high expression levels of MYH11, PLN, CNN1, and RCAN2. Cells in clusters 0, 4, and 5 were classified as qHSCs based on their high expression levels of markers PPARG and NGFR [1]. Finally, cells in cluster 6 were annotated as FBs because of their high expression levels of markers DPT, GGT5, and MFAP4 (Fig. 2B, Supplementary Table 2). Each cluster was assigned a unique name based on the expression of the predominant marker (Supplementary Fig. 3A).
Pseudotime analysis revealed the differentiation trajectories of healthy mesenchymal cells. (A) Mesenchymal cells clustered into eight subpopulations in human healthy livers. FB-c6-GGT5, the marker gene of fibroblasts from cluster 6 is GGT5. VSMC, vascular smooth muscle cell; FB, fibroblast; HSC, hepatic stellate cell; tSNE, t-distributed Stochastic Neighbor Embedding. (B) Dot plots showing the expression of mesenchymal cellular marker genes in healthy human livers. (C) A differentiation trajectory of healthy human liver mesenchymal cells inferred by Monocle 2 (pseudotime along differentiation trajectory in inset). (D) Gene expression dynamics along the trajectory of healthy hepatic mesenchymal subtypes. (E) RNA velocity of mesenchymal subtypes in healthy livers, pseudotime (right) detected (purple to yellow). (F) Heatmap showing the TF activity of mesenchymal subtypes in healthy livers. (G) The relative expression of mesenchymal cellular marker genes and transcription factors (TFs) along the differentiation trajectory in healthy livers. (H) Schematic diagram illustrating the molecular mechanisms of mesenchymal cell differentiation in healthy human livers. (I) Dot plot showing the significance and average expression of ligand-receptor interactions between FB and other cell types in healthy human livers.
To determine the evolutionary trajectory of hepatic mesenchymal cells in healthy livers, we used Monocle 2 for trajectory analysis. This trajectory indicated that FBs differentiated into VSMCs and qHSCs (Fig. 2C). To provide further insights into FB differentiation, we identified three sets of differentially expressed genes (DEGs) along the trajectory within branch 1 (Fig. 2D). The first set, consisting of VSMC markers (ACTA2, TAGLN, PLN, and TPM2), increased towards the end of trajectory. Meanwhile, the second set, consisting of HLA-DRA, ANK3 and PCDH9, were highly expressed in FB and showed an upward trend in the early developmental trajectory. The third set of genes (PDE3A, PLA2G5, and CTNNA3) were highly expressed in HSC and showed an upward trend in the late developmental trajectory (Supplementary Fig. 3B).
To validate the developmental trajectory of Monocle 2, we utilized the scVelo pipeline to compute RNA velocity values for each gene in each cell. The analysis predicted that HSCs and VSMCs arose from FBs (Fig. 2E), which is consistent with Monocle2’s results (Fig. 2C). Moreover, VSMC-c2-GBP2, VSMC-c3-SEMA4A, and HSC-c4-IGFBP5 exhibited faster velocities in the late pseudotime than FB-c6-GGT5.
FB differentiation is regulated by a complex network of transcription factors (TFs) that function in conjunction with one another. We used PySCENIC to explore the top five activities in the TF regulatory network. We observed that NFATC2 and NR1H4 displayed high expression and activity levels in the FB regulatory network (Fig. 2F). However, transcriptomics-based TF exploration provides indirect results. As pseudotime increased, the expression of NFATC2, NR1H4, and ZEB2 gradually decreased, along with the expression of FBs-specific genes (IGFBP3, MFAP4, DPT, GGT5, LUM), and the FBs ultimately differentiated into qHSCs and VSMCs (Fig. 2G,H, Supplementary Fig. 3B). During the differentiation of FBs into qHSCs, the qHSC marker genes RGS5 and PLAT was upregulated. Regarding the differentiation of FBs into VSMC, the expression of ACTA2, TPM2, PLN, and MYH11 were upregulated. Finally, CellChat was used to analyse unbiased interactions of mesenchymal subpopulations with ligands and receptors [26]. CellChat revealed that FB significantly interacted with HSC and VSMC, particularly through the ligand-receptor pair GAS6-AXL (Fig. 2I).
In human cirrhotic livers, mesenchymal cells were sub-clustered into 10 subpopulations (Fig. 3A) based on the expression patterns of marker genes (Fig. 3B, Supplementary Table 3). Cells in cluster 0 and 6 express high levels of MYH11, ACTA2, TAGLN, and CNN1, and were identified as VSMCs. Cells in cluster 1, 5, and 7 expressed high levels of activation markers TAGLN, ACTA2, VIM, A2M, CD36, IGFBP5, PDGFRB, and quiescent marker PPARG, showing characteristics common to both qHSC and MFBs [29], and were annotated as HSCs. Cells in cluster 2, 4, 8, and 9 expressed high levels of PDGFRA, VIM, COL3A1, and COL1A1, were identified as MFBs. The cells in cluster 3 were annotated as mesothelial cells expressing high levels of SLPI and KRT19 (Fig. 3B). Each cluster was renamed based on the specific marker gene expressed (Supplementary Fig. 3C).
Pseudotime analysis revealed the differentiation trajectories of mesenchymal cells in cirrhotic liver. (A) Human cirrhotic hepatic mesenchymal cells clustered into 10 subpopulations. MFB, myofibroblast. (B) Dot plots showing the expression of mesenchymal cellular marker genes in cirrhotic human livers. (C) A differentiation trajectory of cirrhotic human liver mesenchymal cells inferred by monocle. (D) Gene expression dynamics along the trajectory of cirrhotic liver mesenchymal subtypes. (E) RNA velocity of mesenchymal subtypes in cirrhotic livers. (F) Heatmap showing the TFs activity of mesenchymal cells in cirrhotic livers. (G) The relative expression of mesenchymal cellular marker genes and TFs along the trajectory in cirrhotic livers. (H) Schematic illustrating molecular mechanisms of mesenchymal subpopulations differentiation in cirrhotic human livers. (I) Dot plot showing the significance and average expression of ligand-receptor interactions between HSC and MFB in cirrhotic livers.
Monocle 2 indicated that VSMC-c0-MYH11 and VSMC-c6-ADRA1A were at the beginning of the trajectory, and HSC-c7-PDGFRB was located at the end stage of branch 1. MFB-c8-IGFBP3, MFB-C9-PDGFRA, MFB-c4-CRABP2, and Meso-c3-SLPI were concentrated at the two terminal stages of branch 2 (Fig. 3C). The top 50 genes related to developmental trajectory in branch 1 were clustered into three groups (Fig. 3D). Genes in cluster 1 were highly expressed during the early stage of trajectory development, such as MYH11 and COX4I2, which were highly expressed in VSMCs. Genes in cluster 2 (S100A6, TIMP1, COL1A1, COL3A1, COL1A2, LUM) were highly expressed in MFBs; it has been reported that S100A6 is highly upregulated on activated MFBs [16]. Genes in cluster 3 showed high expression levels in the transitional stage of the developmental trajectory, such as PDGFRB, CD36, MEF2C, and SOX5, which were highly expressed in HSCs (Fig. 3D).
The RNA velocity results are mostly consistent with those of the Monocle, indicating that VSMC-c0-MYH11 and VSMC-c6-ADRA1A were in an early developmental trajectory and tended to differentiate into HSC-c5-RGS5 cells (Fig. 3E). A small proportion of HSC-c1-CD36 and most HSC-C7-PDGFRBs were in the late stages of development, whereas some cells tended to develop into MFB-c2-LUM and MFB-c8-IGFBP3. Surprisingly, MFB-C9-PDGFRA showed a tendency to transform into HSC-C7-PDGFRB (Fig. 3E); we hypothesised that, as liver damage was interrupted, MFBs underwent apoptosis or reverted to the iHSCs state.
To further determine the primary regulators of mesenchymal subpopulations, we evaluated the top six activities of the TF regulatory network using pySCENIC (Fig. 3F). During VSMC differentiation into HSCs, the expression of the VSMCs marker genes MYH11, ACTA2 and TAGLN decreased gradually, whereas the expression of HSC-specific genes (PDGFRB, IGFBP5, CD36, RGS5, A2M) increased gradually, and the expression of HSC TFs (SOX5, MEF2C, TCF7L2) also increased (Fig. 3G,H, Supplementary Fig. 3C). During the transformation of HSCs to MFBs, the expression of MFB-specific genes (COL1A1, COL3A1, COL1A2, LUM, PDGFRA, TIMP1) and TFs (NR1H4, SOX6, PBX1, ZNF23) increased, whereas the expression of PDGFRB, RGS5, JUNB, and MEF2C was gradually downregulated and almost undetectable in MFBs. Furthermore, we investigated the cell-cell communication mechanisms of HSCs and MFBs. The results indicated that all HSC subtypes, except HSC-c7-PDGFRB, had strong interactions with MFBs through the adhesive ligand-receptor pairs MIF-(CD74 + CD44), MDK-LRP1, and NGF-NGFR (Fig. 3I).
Next, we analysed scRNA-seq (GSE137720) data of mouse livers, including two
healthy livers and two livers with fibrosis resulting from chronic
CCl
Dynamic evolution trajectory of mouse liver mesenchymal cells is similar to that of humans. (A,B) t-SNE visualisation of mesenchymal cells clustered into nine and 10 subpopulations in mouse healthy and fibrotic livers, respectively. (C,D) Dot plots showing the expression of mesenchymal cellular marker genes in healthy and cirrhotic mouse livers. (E) Trajectory of mouse healthy hepatic mesenchymal cells inferred by Monocle 2. (F,I) Gene expression dynamics along the healthy and fibrotic mouse hepatic mesenchymal cells trajectory. (G,J) RNA velocity of mesenchymal subtypes in healthy and fibrotic livers, pseudotime (right) detected (purple to yellow). (H) A differentiation trajectory of mouse fibrotic liver mesenchymal cells.
In healthy mouse livers, Monocle 2 analysis suggested a differentiation
trajectory from FBs to VSMCs and HSCs, which is similar to that in humans (Fig. 2C) but with a simpler trajectory, with each cell type distributed more centrally
in three branches (Fig. 4E). Furthermore, this trajectory is consistent with the
previously reported results of trajectory analysis of mouse Lin-negative cell in
biliary tree fragments (Supplementary Fig. 5A–G, Supplementary Table 6)
[14]. Pi16, Col15a1, Cd34, Thy1,
Clec3b, Fbln2 are highly expressed in FB (Supplementary Table
7), and Pi16
In mouse fibrotic liver, Monocle trajectory analysis indicated that HSCs and MFBs originated form VSMCs (Fig. 4H), which is consistent with pseudotime analysis results from human hepatic mesenchymal cells with cirrhosis (Fig. 3C,E). The first 50 key genes related to cell trajectory were divided into three clusters. Genes in cluster 1 (Pth1r, Stat1, Vipr1 and Eng) showed high expression mainly in the final stage of the trajectory, genes in cluster 2 (Dpt, Lama2, Cd34, Clec3b, Pi16 and Thy1) were highly expressed in the transitional stage of trajectory, and genes in cluster 3 (Des, Tagln, Rgs4) were highly expressed in the early trajectory (Fig. 4I). The results of RNA velocity analysis in mouse fibrotic liver (Fig. 4J) are consistent with the Monocle results (Fig. 4H). Interestingly, MFB-c0-Pdgfra showed a tendency to differentiated into HSC-c1-Pdgfrb, which is consistent with the RNA velocity of human hepatic mesenchymal cells with cirrhosis (Fig. 3E). LAMA2, CCBE1, PDGFRA and NAALADL2, which were highly expressed in the MFBs of human liver, were also highly expressed in the MFBs of the mouse liver (Supplementary Fig. 6A,B). PDGFRB, ARHGAP42, ASAP1, and GUCY1A2, which were highly expressed in human HSCs, were also highly expressed in mouse HSCs (Supplementary Fig. 6C,D). Therefore, we suggest that MFBs and HSCs in mouse fibrotic liver are similar subpopulations as MFBs and HSCs in human cirrhotic livers.
Finally, we examined the molecular mechanisms underlying mesenchymal cell differentiation in the mouse liver. In healthy mouse livers, FBs TFs Zeb1 showed higher activity during the transformation stage of FBs to VSMCs and qHSCs, the expressions of FBs marker genes (Cd34, Pi16) and TFs (Zeb, Jund) continuously decreased, and the expressions of VSMCs marker genes (Acta2, Myh11, Tagln) and TFs (Mef2c, Klf2) gradually increased (Fig. 5A,B). During FBs differentiation into qHSCs, the expressions of Acta2, Myh11 and TFs Mef2c gradually decreased, whereas the expression of qHSCs marker genes (Ngfr, Lrat and Hgf), qHSC-related TFs (Sox5, Nr1h4, Hand2 and Cebpb) were gradually increased (Fig. 5B,C, Supplementary Fig. 7A). Cell-cell communication analysis showed that FBs interacted with HSCs and VSMCs through the Angptl1-(Itga1 + Itgb1) ligand-receptor pair (Supplementary Fig. 7B).
Molecular mechanism of the differentiation of mesenchymal subpopulations in the mouse liver. (A) Heatmap showing the TF activity of mesenchymal cells in healthy mouse livers. (B) Relative expression of mesenchymal cellular marker genes and TFs along differentiation trajectory in healthy mouse livers. (C) Schematic diagram illustrating the molecular mechanism of the differentiation of mesenchymal subpopulations in healthy mouse livers. (D) Heatmap showing the TF activity of mesenchymal cells in mouse fibrotic livers. (E) Relative expression of mesenchymal cellular marker genes and TFs along the trajectory in mouse fibrotic livers. (F) Schematic diagram illustrating the molecular mechanism of differentiation of mesenchymal subpopulations in mouse fibrotic livers.
In mouse chronic fibrosis liver, pySCENIC combined trajectory analysis showed that, at the stage of trans-formation from VSMCs to MFBs and aHSCs, the expression of VSMCs TFs (Mef2c, Thra, Gata4), markers Myh11 and Tagln continuously decreased, the expression of Tagln was the lowest when differentiation to MFBs, and it gradually increased when differentiation into aHSCs (Fig. 5D–F, Supplementary Fig. 7C). In the differentiation from VSMCs to MFBs, the expressions of the MFBs markers Col1a1, Clec3b, and TF Zeb1 was upregulated, and then downregulated with the differentiation of VSMCs to aHSCs. In the differentiation from VSMCs to aHSCs, the expression of aHSCs marker genes Hgf and Pth1r was upregulated, and the expression of the TF Irf1 and Junb gradually decreased from the beginning of VSMCs differentiation to the lowest level at the MFBs stage, and then gradually increased at the differentiation stage of aHSCs (Fig. 5D–F, Supplementary Fig. 7C). Among the ligand-receptor pairs in which VSMCs interacted with MFBs and HSCs, Fgf1-Fgfr2 was the most significant, followed by Ngf-Ngfr; Gas6-Axl also significant (Supplementary Fig. 7D).
To examine the translatability of the core genes in scRNA-seq result, we used
TGF-
MFB exhibited a reversal to HSC following removal of hepatic
injury stimulation. (A) Schematic diagram of the obtention of a human liver
fibrosis model using TGF-
In LX-2 cells, after exposure to TGF-
After stimulation removal, qHSC-specific genes were upregulated in the recovery stage, and fibrogenic genes were downregulated (Fig. 6B,C). This implies that MFB transdifferentiates into iHSC, which further supports the results of the reversal of MFB to iHSCs in human cirrhosis and mouse liver fibrosis in the pseudotime analysis (Figs. 3E,4J).
In this study, we performed scRNA-seq to determine the differentiation trajectory of mesenchymal cells from healthy and fibrotic/cirrhotic livers, and utilized pySCENIC and CellChat to investigate the molecular mechanism underlying the trajectory. Finally, analysis of Bulk RNA-seq data from HSCs confirmed key genes and transcription factors in process of HSC activation to MFB and MFB reversal to iHSC.
In healthy human livers, a pesudotime trajectory showned that FBs differentiated into VSMCs and qHSCs with downregulation of FB-specific genes (IGFBP3, MFAP4, LUM, DPT, GGT5) and TFs (NFATC2, NR1H4, ZEB2), and the upregulation of HSC-specific genes (RGS5 and PLAT) and VSMC-specific genes (ACTA2, MYH11, TPM2, and PLN) (Fig. 2C–H, Supplementary Fig. 3B). In healthy mouse livers, FBs differentiated into VSMCs with upregulation of VSMC-specific genes (Acta2, Tagln, Myh11) and downregulation of TF Zeb1. In contrast, FBs differentiated into qHSCs with upregulation of TFs (Sox5, Nr1h4, Hand2, and Cebpb) and HSC-specific genes (Hgf and Lrat). Finally, FB-specific genes (Cd34 and Pi16) were not expressed (Fig. 5B–C). The scRNA-seq analysis of healthy mouse Lin-negative cells of bilio-vascular tree fragments (Supplementary Fig. 5A–G) showed that HSCs and VSMCs differentiated from FBs [14], which is consistent with our findings. In healthy human and mouse livers, the expression of TFs NR1H4 and the member of the ZEB families (ZEB1, ZEB2) changed significantly with the differentiation of FBs into qHSCs and VSMCs, and these may be the core TFs driving differentiation.
In the damaged liver, aHSCs, portal vein FBs, and bone marrow-derived MFBs are
the major collagen-producing cells [31]. Cell fate mapping and deep phenotyping
have demonstrated in experimental models of liver fibrosis that aHSCs and
activated portal FBs comprise
Fibrotic models are important tools for studying the cellular and molecular
mechanisms of liver fibrosis and for developing specific anti-fibrosis therapies.
We used a previous constructed human liver fibrosis model (LX-2 treated with
TGF-
Taken together, by performing single-cell and bulk transcriptome analyses on human and mouse hepatic mesenchymal cells, we elucidates the essential genes and regulatory factors involved in mesenchymal cell differentiation trajectories during liver fibrosis. We found that the differentiation trajectory of mouse hepatic mesenchymal cells was similar to that of humans, but there were also differences. Furthermore, our findings suggest promising targets for the treatment of liver fibrosis and provide valuable insights into the molecular mechanisms underlying its onset and progression.
CCl
The single-cell RNA sequencing (scRNA-seq) data of human liver cells, mouse mesenchymal cells, and mouse Lin-negative cells of the bilio-vascular tree fragments were downloaded from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/, GSE136103, GSE137720, GSE163777) database. Raw data were obtained from the NCBI Sequence Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra, SRP218975, SRP222529, SRP299106) database. All data generated or analysed in this study are included in this article. Scripts describing the main steps of the analysis and other relevant data are available from the corresponding authors upon reasonable request.
HZ, LZ, ZH, and JW conceived and designed this study; XD drafted the manuscript; YM and YW performed the experiments; XD, MW, and HC made substantial contributions to the analysis, acquisition, and interpretation of data. All authors have participated sufficiently in the work to take public responsibility for appropriate portions of the content and agreed to be accountable for all aspects of the work in ensuring that questions related to its accuracy or integrity. All authors read and approved the final manuscript. All authors contributed to editorial changes in the manuscript.
Not applicable.
We would like to thank Editage (https://www.editage.cn) for English language editing and Dr. Rongli Fan, Dr. Zichen Wei, Dr. Xinlei Huang for their comments and helpful suggestions.
This research was funded by Key Research and Development Program of Zhejiang Province (No. 2020C03057).
The authors declare no conflict of interest.
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