SCODiA Report by MLBI Lab

Single-Cell Transcriptomic Analysis Reveals Cellular Reprogramming and Dysregulated Communication in Human Liver Cirrhosis

This report details a single-cell RNA sequencing analysis of human liver tissue, comparing healthy controls with cirrhotic conditions. Key findings include dramatic shifts in cellular composition, particularly a reduction in hepatocytes and an expansion of immune and fibrogenic cells. Intercellular communication is profoundly altered, characterized by increased pro-inflammatory, pro-fibrotic, and pro-angiogenic signaling. Furthermore, specific cell types exhibit distinct transcriptional reprogramming, pointing to dysregulated metabolic, immune, and proliferative pathways that drive disease progression.

Contents

  1. Dataset overview
  2. UMAP Visualization of Single-Cell Liver Data Across Conditions and Cell Types
  3. Major Cell Type Score UMAP Visualization and Annotation Assessment
  4. Marker Expression Dot Plot for Cell Subsets
  5. Cell Type Population Analysis in Healthy and Cirrhotic Liver
  6. Population Dynamics of T cell Subsets in Cirrhotic vs. Healthy Liver
  7. Alterations in Liver T Cell Subset Proportions in Cirrhosis
  8. Macrophage Subset Population Analysis in Cirrhosis
  9. Macrophage Subset Population Shifts in Cirrhosis
  10. Cell-Cell Interaction Patterns in Liver Cirrhosis and Healthy Conditions
  11. Condition-Specific Cell-Cell Interaction Patterns in Liver Cirrhosis
  12. Cholangiocyte Condition-Specific Surfaceome Markers in Liver Cirrhosis
  13. Macrophage Condition-Specific Surfaceome Markers in Liver Cirrhosis
  14. Hepatic Stellate Cell Condition-Specific Surfaceome Markers in Cirrhosis
  15. T cell CD4+ Condition-Specific Surfaceome Markers in Liver Cirrhosis
  16. Gene Ontology (GSA) Analysis of Cholangiocytes in Liver Cirrhosis
  17. Gene Set Enrichment Analysis of Cell Type-Specific Pathways in Cirrhosis
  18. Discussion
  19. Query List

0. Dataset overview

Dataset Summary

Cell Type Annotations:

Inferred Biological Context

1. UMAP Visualization of Single-Cell Liver Data Across Conditions and Cell Types

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) plots, visualizing the global transcriptional landscape of single-cell RNA-sequencing data from human liver tissue. The UMAPs are colored by various metadata attributes: condition (healthy vs. cirrhosis), sample, celltype_major, celltype_minor, and celltype_subset. The primary objective is to inspect the overall data structure, assess the quality of cell type annotations, identify potential batch effects, and observe the distribution of cells from different conditions within the embedding space.

Visual Summary

Condition and Sample Distribution

The UMAP plot colored by condition shows a clear separation between healthy and cirrhosis cells, particularly in several distinct regions of the embedding. Cirrhosis cells (red) appear to form several dominant clusters that are either distinct from or largely intermingled with healthy cells (blue) in other clusters. This indicates that transcriptomic differences between the healthy and cirrhotic liver are substantial and drive the overall separation in the UMAP space.

The sample UMAP plot reveals that cells from individual samples generally mix well within their respective conditions, without strong clustering of cells purely based on the original sample identifier. While some samples show localized enrichment, the overall structure largely reflects the condition-driven separation, suggesting that major batch effects originating from individual samples are not overtly dominating the UMAP projection. The variety of colors for different samples helps confirm broad representation of cells across the UMAP space, supporting the robust nature of the embedding.

Cell Type Clustering and Annotation Quality

The UMAPs colored by celltype_major, celltype_minor, and celltype_subset demonstrate a high degree of coherence and separation for most cell populations.

Biological Interpretation

The observed separation of cirrhosis and healthy cells in the UMAP suggests significant transcriptomic reprogramming occurring in the liver microenvironment during cirrhosis. This differential clustering is likely driven by condition-specific gene expression changes impacting multiple cell types and their proportions.

The clear and distinct clustering of most celltype_major, celltype_minor, and celltype_subset annotations indicates that the cell type assignments are robust and biologically meaningful. This strong cellular identity separation provides a solid foundation for downstream analyses, such as differential gene expression or cell-cell interaction studies, ensuring that comparisons are made between genuinely distinct cell populations.

For instance, the prevalence of certain cell types in the "cirrhosis-dominant" regions of the UMAP (e.g., potentially enriched immune or stromal cell clusters, or altered liver epithelial populations) might point to their involvement in disease pathology. Conversely, "healthy-dominant" clusters would represent populations or states more characteristic of a non-diseased liver. The granularity of the celltype_subset annotations allows for the investigation of specific immune cell activation states or dysfunctional parenchymal/stromal cells in the context of cirrhosis. For example, specific macrophage polarization states (M1, M2 subtypes) or T cell exhaustion (T cell (CD8 Exhausted)) could be differentially enriched in cirrhotic samples, aligning with known immune responses in chronic liver disease (e.g., PubMed search for "liver cirrhosis immune cell infiltration").

Annotation Notes

The UMAP plots provide strong evidence for high-quality cell type annotations across major, minor, and subset levels. The distinct clustering for most cell populations validates the accuracy of the cell assignments, which is crucial for reliable interpretation of biological processes. The lack of severe sample-specific batch effects dominating the overall structure is also a positive indicator for the quality and generalizability of the dataset. These robust annotations and dataset structure are well-suited for subsequent in-depth analyses, such as identifying differentially expressed genes within specific cell types or investigating cell-cell communication changes in cirrhosis.

2. Major Cell Type Score UMAP Visualization and Annotation Assessment

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the distribution of HiCAT major cell type scores across the UMAP embedding for individual cell types, alongside the final assigned celltype_major labels. The purpose is to assess the quality and distinctness of the major cell type annotations derived from the single-cell RNA-seq data. Each plot displays the UMAP embedding, with cells colored according to their score for a specific major cell type, where warmer colors (yellow) indicate higher scores and cooler colors (purple) indicate lower scores. The final plot shows the UMAP colored by the assigned celltype_major to serve as a reference.

Visual Summary

The UMAP plots clearly demonstrate distinct clustering of cells corresponding to high scores for each major cell type.

Biological Interpretation

The robust and distinct segregation of cells based on their major cell type scores, as observed on the UMAP embedding, provides strong confidence in the quality and accuracy of the cell type annotations. The clear spatial separation of clusters for T cells, Myeloid cells, B cells, Plasma cells, Liver Epithelial cells, Endothelial cells, Stromal cells, and Mast cells suggests that each major cell type possesses a unique and well-defined transcriptional signature. This distinctness is crucial for downstream analyses, such as differential gene expression or cell-cell interaction studies, ensuring that biological findings are attributed to correctly identified cell populations. The pattern indicates that the initial clustering and subsequent major cell type assignment successfully captured the underlying biological heterogeneity of the liver tissue.

Annotation Notes

The visualization of major cell type scores on the UMAP largely confirms the quality of the cell type annotations. The strong correspondence between high-score regions for individual cell types and their final assigned clusters in the celltype_major plot indicates that:

  1. High Annotation Confidence: The major cell types are well-separated and distinctly identifiable based on their gene expression profiles.
  2. Robust Clustering: The UMAP embedding effectively organizes cells into biologically meaningful groups, which are then accurately captured by the cell type scoring and annotation process.
  3. Minimal Ambiguity: There is minimal overlap in high scores for different major cell types across the UMAP, suggesting low ambiguity in assignments at this resolution.

This level of robust annotation is essential for subsequent in-depth analyses, particularly when investigating disease-specific changes in cirrhosis, as it ensures that observed molecular alterations are genuinely associated with specific cellular compartments.

3. Marker Expression Dot Plot for Cell Subsets

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a marker expression dot plot generated for various celltype_subset populations from human liver single-cell RNA-seq data. The plot visualizes the expression patterns of key marker genes across these cell subsets, providing an essential check for the quality and specificity of cell type annotations. The size of each dot corresponds to the fraction of cells within a given subset expressing a particular gene, while the color intensity indicates the mean expression level of that gene. Genes are grouped by expected cell type association, with red boxes highlighting clusters of specific markers. The primary goal of this analysis is to confirm the identity and validate the annotations of the identified celltype_subset populations based on their canonical marker gene expression.

Visual Summary

The dot plot effectively displays differential gene expression across 44 distinct celltype_subset populations. Key observations include:

Biological Interpretation

The marker gene expression patterns observed in the dot plot strongly validate the assigned celltype_subset annotations and provide robust biological support for the identification of specific cell populations within the liver tissue.

Immune Cell Lineages:

Non-Immune Liver Resident Cells:

Annotation Notes

The comprehensive marker expression patterns observed across all celltype_subset populations provide strong evidence for the accuracy and quality of the cell type annotations in the AnnData object. The clear segregation of distinct marker profiles for each cell subset, coupled with the consistency of these markers with established biological knowledge, validates the cellular identities at a high resolution. This analysis confirms that the celltype_subset annotations are well-supported by the underlying gene expression data, laying a solid foundation for further downstream biological investigations into liver health and disease, such as cirrhosis, which is the inferred disease context for this dataset.

4. Cell Type Population Analysis in Healthy and Cirrhotic Liver

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Analysis Overview

This stacked bar plot visualizes the relative abundance of minor cell types across individual samples, separated by disease condition (cirrhosis vs. healthy). Samples are further stratified by their CD45 expression status, indicated by _cd45- (likely non-hematopoietic cells) and _cd45+ (likely hematopoietic immune cells), providing insights into compositional shifts within distinct cellular compartments of the liver.

Visual Summary

The plot reveals significant alterations in liver cell type composition between healthy and cirrhotic conditions, particularly when distinguishing between CD45- and CD45+ cell fractions.

Healthy Liver Samples:

Cirrhotic Liver Samples:

Biological Interpretation

The observed shifts in cell populations are highly consistent with the known pathophysiology of liver cirrhosis:

Clinical or Translational Implications

These findings have several potential clinical and translational implications:

5. Population Dynamics of T cell Subsets in Cirrhotic vs. Healthy Liver

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Analysis Overview

This analysis presents a population bar plot visualizing the relative proportions of various T cell subsets, NK cells, and Innate Lymphoid Cells (ILCs) within the major 'T cell' population across individual samples from both healthy and cirrhotic human liver tissues. Each bar represents a single sample, and the stacked segments illustrate the percentage contribution of each specific cell type (e.g., T cell (Cytotoxic), NK cell, T cell (Treg), ILCs). The primary goal is to identify shifts in immune cell composition associated with cirrhosis.

Visual Summary

The stacked bar plots display the relative proportions of T cell subsets, NK cells, and ILCs for each sample, grouped by condition (cirrhosis vs. healthy).

Biological Interpretation

The observed shifts in immune cell populations within the liver parenchyma provide insights into the immune dysregulation characteristic of cirrhosis.

Clinical or Translational Implications

The changes in T cell subsets and NK cell populations have potential clinical implications for understanding disease progression and identifying therapeutic targets in cirrhosis.

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References

  1. NK cell function in liver disease:
  1. Role of NK cells in fibrosis:
  1. T cell exhaustion:
  1. Immunotherapy in liver disease:
  1. Immune surveillance and HCC:

6. Alterations in Liver T Cell Subset Proportions in Cirrhosis

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Analysis Overview

This analysis presents box plots illustrating the proportions of specific T cell subsets within the total T cell population, comparing healthy liver tissue to cirrhotic liver tissue. The plot_box_for_celltype_population_with_signif_difference tool was used to identify and visualize subsets showing statistically significant differences (p < 0.1) between conditions. These results provide insights into the T cell immune landscape shifts occurring in the liver during cirrhosis.

Visual Summary

The visualizations reveal distinct and statistically significant changes in the proportions of several T cell subsets in cirrhosis compared to healthy controls:

In summary, cirrhotic livers exhibit an expansion of CD4 Memory, Th22, Regulatory T cells, and CD4 Naive T cells, alongside a reduction in Cytotoxic T cell proportions.

Biological Interpretation

The observed shifts in T cell subset populations in cirrhotic livers indicate a profound dysregulation of the local immune environment, consistent with the chronic inflammatory and fibrotic processes characteristic of cirrhosis.

  1. Increased CD4 Memory, Th22, Th17, and CD4 Naive T cells:
  1. Increased Regulatory T cells (Tregs): The significant expansion of Tregs is a common feature in chronic inflammatory conditions and cancer, including cirrhosis. Tregs suppress immune responses to maintain tolerance and limit inflammation. While they may attempt to dampen excessive inflammation in cirrhosis, an overabundance can also contribute to immune evasion, impair anti-viral or anti-tumor immunity, and potentially promote persistent tissue damage and fibrosis by dampening beneficial immune responses [3].
  2. Decreased Cytotoxic T cells (T_Cyto): The significant reduction in the proportion of Cytotoxic T cells (primarily CD8+ T cells) in cirrhotic livers is a critical finding. These cells are essential for clearing virally infected cells and tumor cells. Their decrease could imply immune exhaustion, functional impairment, or preferential sequestration elsewhere, potentially leading to impaired anti-viral immunity (e.g., in viral hepatitis-induced cirrhosis) and reduced immune surveillance against hepatocellular carcinoma (HCC), a major complication of cirrhosis [4]. This reduction might contribute to the immune tolerant state often seen in advanced liver disease.

Collectively, these findings suggest a complex immune milieu in cirrhosis characterized by chronic activation (memory T cells), potentially both protective and pathological cytokine responses (Th22, Th17), an attempt at immune suppression (Tregs), and a concerning deficit in effector functions (Cytotoxic T cells).

Clinical or Translational Implications

The observed shifts in T cell populations have important clinical implications for understanding disease progression and potential therapeutic strategies in cirrhosis:

These population-level changes underscore the need for further functional characterization of these T cell subsets to fully elucidate their roles in the pathogenesis of human liver cirrhosis.

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References:

  1. IL-22 in liver disease:

PubMed Search: IL-22 liver cirrhosis fibrosis

  1. Th17 cells in liver fibrosis:

PubMed Search: Th17 liver fibrosis cirrhosis

  1. Regulatory T cells in liver cirrhosis:

PubMed Search: regulatory T cells liver cirrhosis

  1. Cytotoxic T cells in cirrhosis and HCC:

PubMed Search: cytotoxic T cells liver cirrhosis HCC

7. Macrophage Subset Population Analysis in Cirrhosis

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Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of various macrophage subsets, specifically Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), and Macrophage (M2D), across individual liver samples from both healthy individuals and patients with cirrhosis. The stacked bars for each sample represent the percentage contribution of each macrophage subset to the total macrophage population in that sample.

Visual Summary

The visualization clearly displays distinct patterns in macrophage subset composition between healthy and cirrhotic liver samples.

Biological Interpretation

The observed shift in macrophage subset populations from healthy to cirrhotic liver tissues has significant biological implications for understanding liver disease progression.

Clinical or Translational Implications

The findings highlight the potential role of macrophage subset dysregulation in the pathogenesis of liver cirrhosis and suggest several clinical and translational avenues.

8. Macrophage Subset Population Shifts in Cirrhosis

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Analysis Overview

This analysis investigates the proportional changes of macrophage subsets within the liver, comparing healthy individuals to those with cirrhosis. Specifically, the proportions of Macrophage (M1) and Macrophage (M2A) subsets were quantified across conditions, and statistical significance was assessed using a p-value cutoff of 0.1. The results reveal statistically significant shifts in the relative abundance of these crucial immune cell populations in the context of cirrhosis.

Visual Summary

The box plots illustrate the celltype proportion (as a percentage) for two macrophage subsets, Mac (M1) and Mac (M2A), across healthy and cirrhotic liver samples.

Macrophage (M1) Population:

Macrophage (M2A) Population:

Biological Interpretation

Macrophages are critical immune cells in the liver, where they exist as resident Kupffer cells and monocyte-derived macrophages. Their polarization into distinct functional phenotypes, such as M1 (pro-inflammatory) and M2 (anti-inflammatory/pro-resolving/pro-fibrotic), plays a pivotal role in liver homeostasis, injury, and repair.

Taken together, these findings suggest a significant reprogramming of the macrophage compartment in the cirrhotic liver, characterized by a shift towards a more pro-inflammatory (M1-skewed) and potentially less reparative (reduced M2A) phenotype. This imbalance in macrophage polarization is consistent with the chronic inflammation and ongoing tissue remodeling observed in liver cirrhosis, where macrophage activity significantly influences disease progression [2].

Clinical or Translational Implications

The distinct shifts in M1 and M2A macrophage proportions observed in cirrhosis could have several clinical and translational implications:

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References:

[1] Macrophage polarization in liver disease (PubMed search): https://pubmed.ncbi.nlm.nih.gov/?term=macrophage+polarization+liver+cirrhosis

[2] Role of macrophages in liver fibrosis (PubMed search): https://pubmed.ncbi.nlm.nih.gov/?term=macrophage+liver+fibrosis

9. Cell-Cell Interaction Patterns in Liver Cirrhosis and Healthy Conditions

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Analysis Overview

This analysis utilizes CellPhoneDB to investigate cell-cell interaction (CCI) patterns in human liver tissue, comparing healthy individuals with those affected by cirrhosis. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between different cell types, with a focus on identifying the top 80 interactions per condition based on significance. These dot plots display the statistical significance (-log10(p-value), represented by dot size) and the mean expression level (log2(mean), represented by dot color) of predicted ligand-receptor pairs for each interacting cell pair. The reference condition for comparison in downstream analyses is 'healthy', establishing cirrhosis as the primary disease context.

Visual Summary

The provided dot plots illustrate distinct cell-cell communication landscapes between healthy and cirrhotic liver conditions.

CCI for Cirrhosis

Prominent Ligand-Receptor Pairs: Several axes stand out as highly active

CCI for Healthy

Biological Interpretation

The differential cell-cell interaction patterns observed between healthy and cirrhotic livers highlight the profound changes in the tissue microenvironment during disease progression.

  1. Inflammation and Immune Cell Activation in Cirrhosis: The dramatic increase in interactions involving T cells, NK cells, and Macrophages, coupled with prominent TNF-TNFRSF and CXCL12-CXCR4 signaling, underscores a highly active inflammatory and immune response in cirrhosis.
  1. Extracellular Matrix Remodeling and Fibrosis: The robust presence of interactions involving SPP1, FN1, and COL4A with various integrins in cirrhosis is a direct signature of active fibrogenesis.
  1. Vascular Remodeling and Angiogenesis: The increased VEGFA interactions with its receptors (FLT1, VEGFR2) in cirrhotic liver point towards significant angiogenesis. Neovascularization is a common feature in chronic liver diseases, contributing to increased intrahepatic resistance and portal hypertension PubMed search: VEGFA liver cirrhosis angiogenesis.
  2. Macrophages as Central Regulators: Macrophages appear as key hubs of interaction in cirrhosis, engaging with multiple cell types through diverse pro-inflammatory and pro-fibrotic pathways. This highlights their pivotal role in orchestrating the pathological microenvironment.
  3. Cholangiocyte Involvement in Disease: Interactions of Cholangiocytes with Macrophages and Endothelial cells, coupled with pro-fibrotic signaling (e.g., SPP1-integrin), suggest their active participation in bile duct injury, proliferation, and contribution to liver fibrosis.
  4. Loss of Homeostatic Regulation: The reduced number of interactions in healthy tissue, with some involving molecules like Annexin A1 and Prostaglandin E2, suggests that homeostatic and immunoregulatory pathways are critical for maintaining liver health. These pathways appear to be overwhelmed or suppressed in the cirrhotic state.

Clinical or Translational Implications

The differential cell-cell interaction landscape provides critical insights for understanding cirrhosis pathogenesis and identifying potential therapeutic avenues.

  1. Therapeutic Target Prioritization: Ligand-receptor pairs that are highly active and specific to cirrhosis, such as SPP1-integrin axes, CXCL12-CXCR4, TNF-TNFRSF, and VEGFA-VEGFR pathways, represent promising therapeutic targets. Interventions aimed at blocking these specific interactions could attenuate inflammation, reduce fibrosis, and normalize vascular architecture in cirrhotic patients. For example, blocking SPP1 or its integrin receptors could directly impact fibrotic progression PubMed search: SPP1 inhibitor liver fibrosis therapy.
  2. Biomarker Discovery: The identified cell-cell interaction signatures could serve as novel biomarkers for disease progression, severity, or response to therapy. Monitoring the expression levels of key ligands or receptors involved in these pathological interactions might offer non-invasive ways to assess disease status.
  3. Understanding Disease Heterogeneity: The detailed mapping of cell-cell interactions can help dissect the complex interplay between different cell types in cirrhosis, potentially revealing specific cellular communication networks that drive distinct disease trajectories or patient subgroups.
  4. Experimental Validation: The identified crucial interactions provide a strong basis for further experimental validation in in vitro and in vivo models of liver cirrhosis, aiming to confirm their functional roles and therapeutic potential.

10. Condition-Specific Cell-Cell Interaction Patterns in Liver Cirrhosis

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Analysis Overview

This analysis identifies cell-cell interactions (CCIs) that significantly differ between healthy and cirrhotic liver conditions, focusing on major tissue cells, immune cells, and stromal cells. The dot plot visualizes the scaled log strength and significance (-log10(p-value)) of specific ligand-receptor interactions between defined cell types across individual samples for both conditions. A t-test with an alternative='greater' setting was applied to detect interactions that are significantly stronger in one condition compared to the other. The goal is to uncover CCIs that characterize the pathological state of cirrhosis and those associated with the healthy liver environment, providing insights into disease mechanisms and potential therapeutic targets.

Visual Summary

The dot plot clearly differentiates cell-cell interaction patterns between cirrhotic and healthy liver samples.

The max_n_items_per_group=60 parameter ensures that the top 60 most significantly differential interactions for each condition are displayed, providing a focused view of the most impactful changes.

Biological Interpretation

The observed condition-specific cell-cell interactions provide critical biological insights into the progression of liver cirrhosis. The upregulation of specific interactions in cirrhosis highlights key cellular processes driving inflammation, fibrosis, and immune dysregulation. Conversely, the reduction of certain interactions in cirrhosis may reflect a loss of normal tissue homeostasis.

Key Interactions Upregulated in Cirrhosis:

  1. FN1-integrin / COL-integrin complexes (e.g., FN1_integrin_a1b1_complex -- Hepatic stellate cell | Hepatic stellate cell; COL1A2_integrin_a1b1_complex -- Hepatic stellate cell | Hepatic stellate cell; FN1_integrin_a3b1_complex -- Endo | Cholangiocyte)
  1. SPP1-integrin complexes (e.g., SPP1_integrin_a4b1_complex -- Mac | Mac; SPP1_integrin_aVb5_complex -- Mac | Mac)
  1. HLA-G-LILRB2 (e.g., HLA-G_LILRB2 -- Hepatic stellate cell | T cell)
  1. TNF-TNFRSF1B (e.g., TNF_TNFRSF1B -- T cell | Hepatic stellate cell)
  1. CCL23-CCR1 (e.g., CCL23_CCR1 -- Cholangiocyte | Mac)

Key Interactions Enriched in Healthy Liver (or Reduced in Cirrhosis):

  1. VCAM1-integrin_a4b1_complex -- Endo | T cell CD4+
  1. ProstaglandinD2_by_AKR1C3_PTGDR -- Mac | Mac

Clinical or Translational Implications

The differential CCI landscape between healthy and cirrhotic livers offers promising avenues for therapeutic intervention and biomarker discovery.

11. Cholangiocyte Condition-Specific Surfaceome Markers in Liver Cirrhosis

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Analysis Overview

This analysis identifies surfaceome markers specifically expressed on cholangiocytes in liver tissue, comparing cirrhotic conditions against healthy controls. The markers were selected using a one-vs-rest comparison approach, with specified cutoffs for expression percentage score (nz_pct_score) and fold change (nz_pct FC). The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing the gene (dot size) for each marker across different samples clustered by condition (cirrhosis vs. healthy). This focused approach aims to pinpoint surface proteins that characterize cholangiocyte states in cirrhosis, offering insights into disease mechanisms and potential therapeutic avenues.

Visual Summary

The dot plot prominently displays surfaceome markers for Cholangiocytes, grouped by individual samples under "cirrhosis" (top) and "healthy" (bottom) conditions.

Biological Interpretation

Cholangiocytes are crucial epithelial cells lining the bile ducts in the liver. In cirrhosis, these cells undergo significant pathological changes, contributing to disease progression through altered proliferation, inflammation, and fibrosis. The observed changes in surfaceome markers reflect these dynamic cellular responses.

Markers Upregulated in Cirrhosis-Associated Cholangiocytes:

Several surface markers show prominent upregulation in cirrhotic cholangiocytes, suggesting their involvement in disease pathogenesis:

Cell Adhesion and Matrix Remodeling

Growth Factor Signaling and Proliferation

Immune Modulation and Inflammation

Transport and Metabolism

Other Notable Markers

Markers Relatively Higher in Healthy-Associated Cholangiocytes:

The visualization suggests a minor set of genes that show relatively higher expression in some healthy samples. For instance, MYADM (Myeloid Associated Differentiation Marker), SERINC5 (Serine Incorporator 5), and C5orf15 show some presence in healthy cholangiocytes. While less prominent in the plot, these could represent markers of quiescent or non-activated cholangiocyte states, or genes involved in normal, homeostatic functions that are suppressed or diluted in the diseased context. Further analysis with an unbiased selection focused on healthy-specific markers would be beneficial to fully characterize their roles.

Clinical or Translational Implications

The identification of these cholangiocyte-specific surfaceome markers provides valuable insights for diagnostic, prognostic, and therapeutic strategies in liver cirrhosis.

Therapeutic Targets

Further experimental validation in relevant models of liver disease and patient samples would be essential to confirm the functional significance of these markers and explore their therapeutic potential.

12. Macrophage Condition-Specific Surfaceome Markers in Liver Cirrhosis

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Analysis Overview

This analysis identifies surfaceome markers differentially expressed in macrophages isolated from liver samples, comparing 'cirrhosis' and 'healthy' conditions. A dot plot visualization displays the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) for individual samples within each condition. The markers were selected using a one-vs-rest comparison strategy, identifying genes with significant differential expression and prevalence. This approach aims to pinpoint specific surface proteins that characterize macrophage phenotypes associated with either healthy liver homeostasis or the pathological state of cirrhosis.

Visual Summary

The dot plot clearly segregates samples into 'cirrhosis' and 'healthy' groups, highlighting distinct patterns of surfaceome marker expression.

Biological Interpretation

The distinct surfaceome marker profiles observed in macrophages from cirrhotic versus healthy livers indicate a profound shift in macrophage identity and function during disease progression. These markers are crucial for cell-cell interactions, immune signaling, and cellular metabolism, all of which are dysregulated in liver cirrhosis.

Markers Upregulated in Cirrhosis Macrophages:

These markers often signify pro-inflammatory, pro-fibrotic, or altered immune-regulatory functions, consistent with the pathological environment of cirrhosis.

Markers Upregulated in Healthy Macrophages:

These markers typically reflect homeostatic functions, anti-inflammatory, or tolerogenic roles of resident macrophages.

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers provides valuable insights into the pathophysiology of liver cirrhosis and offers potential avenues for therapeutic intervention and biomarker discovery.

Therapeutic Targets for Cirrhosis:

Biomarkers for Disease Progression or Response to Therapy:

Diagnostic and Stratification Tools:

Immunomodulatory Strategies:

Potential Therapeutic Agents:

References:

[1] Basigin (CD147) in Liver Disease: GeneCards entry https://www.genecards.org/cgi-bin/carddisp.pl?gene=BSG

[2] FCGR2A (CD32a) function: UniProt entry https://www.uniprot.org/uniprot/P12318

[3] Role of C3a and C3aR1 in inflammation: PubMed search "C3AR1 inflammation liver cirrhosis" https://pubmed.ncbi.nlm.nih.gov/?term=C3AR1+inflammation+liver+cirrhosis

[4] AXL in liver fibrosis and inflammation: PubMed search "AXL receptor liver fibrosis" https://pubmed.ncbi.nlm.nih.gov/?term=AXL+receptor+liver+fibrosis

[5] GPR65 (TDAG8) and pH sensing in immunity: GeneCards entry https://www.genecards.org/cgi-bin/carddisp.pl?gene=GPR65

[6] CD39 in purinergic signaling and inflammation: PubMed search "CD39 ENTPD1 liver inflammation" https://pubmed.ncbi.nlm.nih.gov/?term=CD39+ENTPD1+liver+inflammation

[7] PILRA function: UniProt entry https://www.uniprot.org/uniprot/Q9NVE5

[8] LAIR1 in immune regulation: PubMed search "LAIR1 immune tolerance" https://pubmed.ncbi.nlm.nih.gov/?term=LAIR1+immune+tolerance

[9] SIGIRR as a negative regulator of TLR signaling: GeneCards entry https://www.genecards.org/cgi-bin/carddisp.pl?gene=SIGIRR

[10] SORL1 function: UniProt entry https://www.uniprot.org/uniprot/Q92625

13. Hepatic Stellate Cell Condition-Specific Surfaceome Markers in Cirrhosis

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Analysis Overview

This analysis identifies condition-specific surfaceome markers in Hepatic stellate cells (HSCs) from single-cell RNA sequencing data comparing cirrhotic and healthy liver tissues. Surfaceome markers are particularly relevant for understanding cell-surface interactions and for their potential as therapeutic targets due to their accessibility. The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) across individual samples, grouped by cirrhosis and healthy conditions. The analysis utilized a one-vs-rest comparison strategy to identify markers specifically enriched in each condition, with specific nz_pct_score and nz_pct_FC cutoffs for marker selection.

Visual Summary

The dot plot clearly delineates two distinct sets of surfaceome markers: those predominantly expressed in HSCs from cirrhotic samples (left panel) and those enriched in HSCs from healthy samples (right panel).

Biological Interpretation

Hepatic stellate cells (HSCs) are the primary collagen-producing cells in liver fibrosis, which is a key component of cirrhosis. In healthy liver, HSCs are quiescent and store vitamin A. Upon liver injury, they activate, proliferate, migrate, and transdifferentiate into myofibroblast-like cells, driving extracellular matrix (ECM) deposition and leading to fibrosis. The identified surfaceome markers reflect distinct activation states and functional roles of HSCs in health versus cirrhosis.

Markers Upregulated in Cirrhotic Hepatic Stellate Cells

  1. PDGFRA (Platelet-Derived Growth Factor Receptor Alpha):
  1. ENG (Endoglin, CD105):
  1. THY1 (Thy-1 cell surface antigen, CD90):
  1. CD44:
  1. MHC Class II molecules (HLA-DRB1, HLA-DRA, HLA-DPA1, HLA-DPB1):
  1. CD74 (MHC Class II invariant chain):
  1. ITGA1 (Integrin Alpha-1):

Markers Upregulated in Healthy Hepatic Stellate Cells

  1. CD9:
  1. EDNRA (Endothelin Receptor Type A):
  1. NTRK2 (Neurotrophic Receptor Tyrosine Kinase 2, TRKB):

Clinical or Translational Implications

The identification of surfaceome markers differentially expressed on Hepatic stellate cells in cirrhosis offers significant clinical and translational opportunities, particularly for therapeutic targeting and diagnostic purposes.

Therapeutic Targets for Cirrhosis

Diagnostic Biomarkers and Monitoring

The differentially expressed surfaceome markers, particularly those highly expressed in cirrhotic HSCs, could serve as non-invasive biomarkers for diagnosing cirrhosis, staging fibrosis severity, or monitoring therapeutic responses. Their cell-surface location makes them accessible for detection through techniques like flow cytometry (if cells are isolated) or potentially through imaging modalities targeting these surface proteins in vivo.

Considerations for Drug Development

Targeting surfaceome markers offers advantages for drug development, as antibodies or small molecules can directly interact with these accessible proteins. However, specificity to activated HSCs (over healthy cells or other cell types) will be crucial to minimize off-target effects. Further validation in preclinical models and clinical studies is essential to confirm efficacy and safety.

14. T cell CD4+ Condition-Specific Surfaceome Markers in Liver Cirrhosis

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Analysis Overview

This analysis investigates the expression of surfaceome markers on CD4+ T cells in liver tissue from individuals with cirrhosis compared to healthy controls. A dot plot visualization was generated using a one-vs-rest comparison, identifying markers that show differential expression patterns between the two conditions. The analysis focused on surfaceome markers to highlight cell-surface proteins that are readily accessible and often implicated in cell-cell interactions, signaling, and as potential therapeutic targets.

Visual Summary

The dot plot displays the mean expression level (color intensity, ranging from white for low to dark red for high) and the fraction of cells expressing the marker (dot size, from small for low to large for high) for eleven surfaceome markers (CD53, CD7, GYPC, SIT1, CD27, BST2, FLT3LG, SIGIRR, LMAN2, CCR7, RNF167) across multiple healthy and cirrhotic liver samples.

Key observations include:

Biological Interpretation

The observed differential expression of surfaceome markers on CD4+ T cells provides insights into their altered state and function within the cirrhotic liver.

Indicators of T cell Activation and Memory (CD53, CD7, CD27):

Modulators of Immune Responses (SIT1, BST2, SIGIRR):

T Cell Homing and Trafficking (CCR7):

Cellular Communication (FLT3LG):

Clinical or Translational Implications

The differential expression of these surfaceome markers identifies potential candidates for therapeutic intervention and biomarkers for disease progression in liver cirrhosis.

Therapeutic Targets for Immune Modulation:

Biomarkers for Disease Activity/Prognosis:

Considerations for Cell Trafficking:

15. Gene Ontology (GSA) Analysis of Cholangiocytes in Liver Cirrhosis

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates Gene Ontology (GO) enrichment in cholangiocytes from human liver tissue, comparing conditions of cirrhosis versus healthy, and healthy versus cirrhosis. The results are based on genes found to be significantly upregulated in each comparison, providing insights into the biological processes and pathways characteristic of cholangiocytes in different disease states. The plots display the top enriched GO terms ranked by their statistical significance (-log(p-val) and -log(q-val)).

Visual Summary

The provided bar plots illustrate the top enriched Gene Ontology (GO) terms for cholangiocytes.

The first plot, "GSA for Cholangiocyte: cirrhosis_vs_others," shows pathways upregulated in cholangiocytes from cirrhotic livers compared to other conditions (primarily healthy). Key observations include:

The second plot, "GSA for Cholangiocyte: healthy_vs_others," highlights pathways upregulated in cholangiocytes from healthy livers compared to those in cirrhosis. Key observations include:

Biological Interpretation

Cholangiocytes in Cirrhosis: Responding to Chronic Stress

Cholangiocytes in cirrhotic livers exhibit a robust upregulation of pathways related to protein synthesis and degradation machinery (Ribosome, Protein processing in endoplasmic reticulum, Proteasome, Lysosome). This suggests a significant cellular response to chronic injury and stress, possibly involving increased protein turnover for repair, adaptation, or coping with misfolded proteins, a common feature in chronic liver diseases. The enrichment of Oxidative phosphorylation and other metabolic pathways (Citrate cycle, Pyruvate metabolism) indicates a profound shift in energy metabolism, potentially reflecting increased metabolic demand or mitochondrial dysfunction in response to the cirrhotic microenvironment. The appearance of "Non-alcoholic fatty liver disease" is directly relevant to liver pathology, suggesting shared molecular underpinnings or consequences. The frequent enrichment of pathways associated with neurodegenerative diseases (e.g., Parkinson, Huntington, Alzheimer) is intriguing; these diseases often involve protein aggregation, mitochondrial dysfunction, and cellular stress, suggesting that cholangiocytes in cirrhosis may be undergoing similar fundamental stress responses, rather than directly implying neurological pathology within the cholangiocytes themselves. Furthermore, the presence of "Tight junction" pathways suggests altered regulation of barrier function, which is critical for bile duct integrity.

Cholangiocytes in Healthy Liver: Maintaining Homeostasis and Responsiveness

In contrast, cholangiocytes from healthy livers show a significant upregulation of immune and inflammatory signaling pathways (TNF, MAPK, IL-17, NF-kappa B signaling pathways). This indicates that healthy cholangiocytes are actively involved in immune surveillance and maintaining a finely tuned inflammatory balance within the liver, essential for protecting against pathogens and tissue damage. The strong enrichment of Focal adhesion and Tight junction pathways underscores their critical role in maintaining structural integrity, cell-cell communication, and mechanical sensing, which are fundamental for normal bile duct function. Pathways like mTOR signaling highlight active metabolic regulation and cellular growth processes in a healthy, homeostatic state. The presence of infection-related pathways may reflect the cholangiocytes' role as a first line of defense against ascending infections from the gut or as part of the liver's broader immune ecosystem.

Comparison and Context

The stark differences in enriched pathways between cirrhotic and healthy cholangiocytes highlight a clear shift in cellular priorities and function. In cirrhosis, cholangiocytes appear to be in a state of stress response, attempting to manage protein homeostasis and adapt their metabolism to a hostile environment. In health, they are primarily engaged in maintaining structural integrity, active immune signaling, and metabolic regulation. This shift underscores the pathological changes occurring in cholangiocytes during cirrhosis, moving from homeostatic maintenance to stress adaptation.

Clinical or Translational Implications

  1. Biomarker Discovery: Upregulated genes contributing to pathways like "Ribosome," "Oxidative phosphorylation," and "Protein processing in endoplasmic reticulum" in cirrhotic cholangiocytes could represent novel biomarkers for disease progression or severity in cirrhosis, particularly reflecting cholangiocyte stress or dysfunction. These could potentially be detected in bile or tissue biopsies.
  2. Therapeutic Targets: Targeting specific pathways found to be dysregulated in cirrhotic cholangiocytes, such as those involved in protein quality control (e.g., ER stress, proteasomal activity) or mitochondrial function, could offer therapeutic avenues to mitigate cholangiocyte injury, improve bile duct integrity, and slow the progression of liver fibrosis in cirrhosis. For example, modulating ER stress responses has shown promise in various fibrotic diseases PubMed Search: ER stress fibrosis.
  3. Understanding Pathogenesis: A deeper understanding of these pathway shifts provides mechanistic insights into how cholangiocytes contribute to the pathogenesis of cirrhosis, including their role in cholestasis and fibrosis. Identifying factors that drive the altered metabolic state or protein handling could reveal key drivers of disease.
  4. Drug Repurposing: The enrichment of pathways associated with neurodegenerative diseases, which share common cellular stress mechanisms, might suggest potential for repurposing drugs that target these fundamental cellular processes to treat cholangiocyte pathology in cirrhosis.

16. Gene Set Enrichment Analysis of Cell Type-Specific Pathways in Cirrhosis

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for various cell types in human liver single-cell RNA-seq data, comparing two conditions: healthy and cirrhosis. The dot plot visualizes the enrichment of 120 selected gene sets (pathways) across different cell types. For each cell type, GSEA was performed to identify pathways enriched or depleted in that specific cell type relative to all other cell types *within the same condition* (i.e., celltype: cirrhosis_vs_others compares B cells in cirrhosis against all other cell types in cirrhosis). The size of the dot corresponds to the statistical significance (-log(p-value)), and the color indicates the Normalized Enrichment Score (NES), with red for positive enrichment (upregulation) and blue for negative enrichment (downregulation).

Visual Summary

The dot plot reveals widespread and complex alterations in pathway enrichment across different cell types in the cirrhotic liver compared to the healthy liver.

Biological Interpretation

The GSEA results provide a detailed molecular landscape of cellular dysfunction and adaptation in the cirrhotic liver.

  1. Exacerbated Inflammation and Immune Dysfunction: The pervasive upregulation of immune signaling pathways across various immune cells signifies a state of chronic inflammation in cirrhosis. Macrophages, Dendritic cells, and T cells adopt a highly activated and inflammatory phenotype, characterized by enhanced cytokine production, antigen presentation, and phagocytic activity. This sustained inflammatory environment contributes to liver damage and fibrosis progression [PubMed search: chronic inflammation liver cirrhosis].
  2. Fibrogenesis driven by HSC Activation: Hepatic stellate cells (HSCs) are central to liver fibrosis. Their strong enrichment in "ECM-receptor interaction", "Focal adhesion", "TGF-beta signaling pathway", and "PI3K-Akt signaling pathway" in cirrhosis reflects their activation and transition into myofibroblast-like cells, leading to excessive extracellular matrix deposition and scarring [GeneCards: HSC].
  3. Metabolic Reprogramming: The observed shifts in metabolic pathways across cell types suggest a profound metabolic reprogramming in cirrhosis. The negative enrichment for NAFLD pathways might indicate that cells in advanced cirrhosis have transitioned beyond the specific metabolic dysregulations associated with early fatty liver disease, or that these pathways are no longer uniquely distinctive compared to other cell types in the cirrhotic milieu.
  4. Oncogenic Potential: The consistent enrichment of "Cell cycle" and "Pathways in cancer" across multiple cell types, including Hepatic stellate cells, Cholangiocytes, and Endothelial cells, underscores the high risk of developing hepatocellular carcinoma (HCC) or cholangiocarcinoma (CCA) in cirrhotic patients. These pathways indicate increased cellular proliferation and potentially dysregulated growth control.
  5. Immune Evasion and Exhaustion: The activation of "PD-L1 expression and PD-1 checkpoint pathway" in immune cells (Macrophages, T cells) suggests a mechanism of immune exhaustion or suppression. This could contribute to the liver's inability to clear chronic infections or mount effective anti-tumor responses, perpetuating inflammation and promoting disease progression [UniProt: PD-1, PD-L1].
  6. Vascular Remodeling and Cholangiopathy: Endothelial cells show enrichment in pathways related to vascular integrity and remodeling, consistent with the development of portal hypertension. Cholangiocytes exhibit enrichment in pathways related to bile duct biology and cancer, indicating their active involvement in cholestasis and potential malignant transformation.

Clinical or Translational Implications

The comprehensive GSEA results offer several insights with clinical relevance:

17. Discussion

The comprehensive single-cell analysis of human liver tissue provides a high-resolution view of the profound cellular and molecular changes occurring in cirrhosis. Initial UMAP visualizations clearly demonstrate a global transcriptomic separation between healthy and cirrhotic samples, indicative of widespread cellular and molecular reprogramming. This separation is supported by robust cell type annotations across major, minor, and subset levels, ensuring the reliability of downstream analyses.

Population analyses reveal a hallmark of cirrhosis: a drastic reduction in functional hepatocytes, replaced by an expansion of fibrogenic cells such as Hepatic stellate cells and Fibroblasts, alongside increased Cholangiocytes, signifying ongoing liver damage, repair, and ductular reaction. The immune compartment undergoes a significant remodeling, with a prominent increase in macrophages, T cells, and NK cells. Specifically, macrophages show a statistically significant shift towards a pro-inflammatory M1 phenotype, with a concomitant decrease in the reparative M2A subset. Within T cell populations, there is a complex dysregulation characterized by an increase in CD4 Memory T cells, Th22, Regulatory T cells (Tregs), and CD4 Naive T cells, while crucial Cytotoxic T cells are significantly reduced. This suggests a chronic inflammatory milieu with concurrent attempts at immune suppression and an impaired capacity for effector functions, potentially contributing to immune evasion and increased risk of hepatocellular carcinoma (HCC).

Cell-cell interaction (CCI) analyses underscore a highly active and pathological microenvironment in cirrhosis. The cirrhotic liver exhibits a dramatic upregulation of interactions involving fibrogenic pathways, such as various integrins binding to extracellular matrix components like SPP1 (Osteopontin), Fibronectin (FN1), and Collagen (COL). Pro-inflammatory axes like TNF-TNFRSF and CXCL12-CXCR4 are also highly active, largely mediated by macrophages, endothelial cells, and hepatic stellate cells. Increased VEGFA-VEGFR interactions point to active angiogenesis, contributing to portal hypertension. In contrast, healthy liver tissue shows a more quiescent communication network, with some homeostatic interactions (e.g., ANXA1-FPR, ProstaglandinD2) appearing suppressed in cirrhosis, indicating a loss of protective regulatory mechanisms. The involvement of HLA-G-LILRB2 in cirrhosis-specific interactions further suggests an active role of immune suppression, potentially by hepatic stellate cells on T cells.

Surfaceome marker expression analyses provide molecular details for specific cell types. Cirrhotic cholangiocytes upregulate markers like Syndecan-1 (SDC1), ALCAM, various Tetraspanins (TSPANs), and strikingly, the growth factor receptors ERBB2 and ERBB3, along with MHC Class II molecules (HLA-DRB1), indicative of increased proliferation, altered adhesion, and antigen-presenting capabilities. Macrophages in cirrhosis show elevated expression of pro-inflammatory markers such as Basigin (BSG/CD147), FCGR2A, C3AR1, and AXL, while homeostatic markers like PILRA, LAIR1, and SIGIRR are relatively higher in healthy macrophages. Activated Hepatic stellate cells in cirrhosis upregulate key fibrogenic receptors including PDGFRA, Endoglin (ENG/CD105), THY1 (CD90), CD44, and Integrin Alpha-1 (ITGA1), and also express MHC Class II molecules, confirming their activated, profibrotic, and immune-modulatory phenotype. CD4+ T cells in cirrhosis show increased expression of activation/memory markers (CD53, CD7, CD27) and immune modulators (SIT1, BST2, SIGIRR), coupled with reduced CCR7 expression, suggesting their retention and chronic activation within the inflamed liver.

Gene Ontology (GSA) analysis of cholangiocytes in cirrhosis highlights pathways related to protein synthesis, processing, and degradation (e.g., Ribosome, ER stress, Proteasome, Lysosome), alongside metabolic reprogramming (Oxidative phosphorylation, TCA cycle), indicating a cellular stress response. In contrast, healthy cholangiocytes are enriched in immune signaling (TNF, MAPK, NF-kappa B) and structural maintenance (Focal adhesion, Tight junction) pathways. The broader Gene Set Enrichment Analysis (GSEA) reinforces these findings, showing pervasive immune activation, fibrosis, ECM remodeling, and cell proliferation pathways across multiple cell types in cirrhosis. Notably, the 'PD-L1 expression and PD-1 checkpoint pathway' is enriched in macrophages, dendritic cells, and T cells in cirrhosis, further supporting immune exhaustion/tolerance. The presence of 'Pathways in cancer' across several cell types underscores the oncogenic risk in the cirrhotic microenvironment. Overall, the analyses reveal a highly pathogenic cellular ecosystem characterized by chronic inflammation, active fibrogenesis, immune dysregulation, and a heightened proliferative drive, all contributing to the progressive nature of liver cirrhosis and its complications.

Hypotheses:

  1. The sustained M1 macrophage polarization and decreased M2A macrophage population directly contribute to chronic inflammation and hinder tissue repair in cirrhotic livers.
  2. Dysregulated cell-cell interactions, particularly the upregulation of SPP1-integrin and PDGFRA signaling, are central drivers of hepatic stellate cell activation and subsequent liver fibrosis in cirrhosis.
  3. The observed expansion of regulatory T cells (Tregs) and reduction of cytotoxic T cells contribute to an immune-tolerant microenvironment, impairing anti-viral and anti-tumor immunity in cirrhosis, thereby increasing the risk of HCC.
  4. Cholangiocyte proliferation, driven by ERBB2/ERBB3 signaling, contributes to ductular reaction and biliary fibrosis, further exacerbating liver injury in cirrhosis.
  5. The loss of homeostatic macrophage surface markers like PILRA and LAIR1 in cirrhosis reflects a compromised ability of macrophages to maintain immune quiescence, contributing to persistent inflammation.

Potential therapeutic targets:

  1. PDGFRA (Platelet-Derived Growth Factor Receptor Alpha): PDGFRA is strongly upregulated on activated Hepatic stellate cells (HSCs) in cirrhosis. Its activation by PDGF ligands is a critical driver of HSC proliferation, migration, and activation, making it a central pathway in fibrogenesis. Evidence: Analysis shows PDGFRA is significantly upregulated in cirrhotic HSCs compared to healthy. CCI analysis also highlights pro-fibrotic signaling involving HSCs. Literature confirms PDGF-PDGFRA signaling's role in liver fibrosis. Validation: Preclinical models of liver fibrosis can be treated with PDGFRA inhibitors (e.g., imatinib, nintedanib) to assess reductions in HSC activation, ECM deposition, and overall fibrosis. In vitro studies with primary HSCs can confirm proliferation and activation inhibition.
  2. SPP1 (Osteopontin) and its Integrin Receptors (e.g., αVβ3, αVβ5): SPP1 is a pleiotropic cytokine upregulated in liver injury and cirrhosis, interacting with various integrins on macrophages and HSCs. This interaction promotes inflammation, immune cell recruitment, cell survival, and fibrosis by activating pro-inflammatory and pro-fibrotic phenotypes. Evidence: CCI analysis shows prominent SPP1-integrin interactions in cirrhosis, involving macrophages and HSCs, and are largely absent in healthy samples. Literature supports SPP1's role in inflammation and fibrosis. Validation: Blocking SPP1 or its cognate integrin receptors using neutralizing antibodies or small molecule inhibitors in cirrhotic animal models could reduce inflammation, HSC activation, and fibrosis. In vitro, perturbing this axis can evaluate macrophage polarization and fibrotic gene expression.
  3. ERBB2 (HER2) and ERBB3 (HER3) on Cholangiocytes: ERBB2 and ERBB3 are receptor tyrosine kinases involved in cell proliferation, survival, and differentiation. Their significant upregulation in cirrhotic cholangiocytes suggests enhanced growth factor responsiveness and pathological proliferation (ductular reaction), contributing to biliary fibrosis. Evidence: Cholangiocyte surfaceome marker analysis shows significant upregulation of ERBB2 and ERBB3 in cirrhotic samples. GSEA also indicates enrichment of 'Cell cycle' and 'Pathways in cancer' in cholangiocytes in cirrhosis. Validation: Investigate the effect of HER2/HER3 inhibitors (e.g., trastuzumab, lapatinib) on cholangiocyte proliferation and fibrogenic activity in vitro and in relevant in vivo models of cholestatic liver injury leading to cirrhosis.
  4. BSG (CD147) on Macrophages: CD147 is a transmembrane protein that induces matrix metalloproteinases (MMPs), promoting fibrosis by stimulating myofibroblast activation and extracellular matrix remodeling. Its upregulation in cirrhotic macrophages suggests a role in disease progression. Evidence: Macrophage surfaceome marker analysis identifies BSG as significantly upregulated in cirrhotic macrophages. Validation: Employ neutralizing antibodies or small molecule inhibitors against CD147 in macrophage cultures and liver fibrosis models to assess reductions in MMP production, HSC activation, and fibrosis severity.
  5. C3AR1 on Macrophages: C3AR1 is the receptor for the pro-inflammatory complement component C3a. Its upregulation on macrophages in cirrhosis indicates heightened sensitivity to complement activation products, driving inflammatory responses that contribute to liver injury and fibrosis. Evidence: Macrophage surfaceome marker analysis shows C3AR1 is significantly upregulated in cirrhotic macrophages. GSEA indicates 'Complement and coagulation cascades' enrichment in cirrhosis. Validation: Evaluate the impact of C3AR1 antagonists in animal models of cirrhosis on macrophage activation, inflammatory cytokine production, and fibrosis progression.
  6. TNF-TNFRSF1B signaling pathway: TNF is a potent pro-inflammatory cytokine that, via TNFRSF1B (TNF-R2), activates pathways leading to inflammation, cell survival, and fibrosis. This axis promotes hepatic stellate cell activation and proliferation in cirrhosis. Evidence: CCI analysis shows significant upregulation of TNF-TNFRSF1B interactions, particularly between T cells and Hepatic stellate cells, in cirrhosis. GSEA also highlights 'TNF signaling pathway' enrichment in multiple immune cells and HSCs in cirrhosis. Validation: Investigate selective TNFRSF1B inhibitors (if available, distinct from broad anti-TNF agents) in localized liver fibrosis models to reduce inflammation and HSC activation without systemic immunosuppression. Assess the impact on liver injury and fibrosis.
  7. HLA-G-LILRB2 (ILT4) pathway: The HLA-G-LILRB2 axis promotes immune suppression. Its upregulation in cirrhosis, involving interactions between hepatic stellate cells and T cells, could dampen effective anti-fibrotic or anti-tumor immune responses, contributing to immune evasion and fibrosis progression or HCC risk. Evidence: CCI analysis identifies HLA-G-LILRB2 as a significantly upregulated interaction in cirrhosis, involving HSCs and T cells. Validation: Blocking LILRB2 or inhibiting HLA-G expression in in vitro co-culture systems (HSCs with T cells) can assess restoration of T cell effector function or anti-fibrotic immunity. In vivo models with targeted modulators could evaluate effects on fibrosis and immune surveillance.

Follow-up validation ideas:

  1. Perform flow cytometry or mass cytometry on liver biopsies from healthy and cirrhotic patients to validate the altered proportions of T cell and macrophage subsets, specifically M1/M2A macrophages, Tregs, and Cytotoxic T cells, and their surface marker expression (e.g., CD147, C3AR1, PILRA, LAIR1).
  2. Conduct ex vivo co-culture experiments using primary human hepatic stellate cells, macrophages, and cholangiocytes. Perturb key interaction pathways identified (e.g., blocking SPP1, PDGFRA, ERBB2/3, or integrins with neutralizing antibodies or small molecule inhibitors) and assess their effects on cell activation, proliferation, migration, and ECM production.
  3. Utilize spatial transcriptomics or multiplexed immunostaining on liver sections to localize and quantify cells expressing key surface markers (e.g., PDGFRA, ENG, CD147, ERBB2, HLA-DRB1) and specific ligand-receptor interactions identified in CCI analysis within the fibrotic lesion versus non-fibrotic areas.
  4. Generate in vivo models of liver cirrhosis (e.g., CCl4, bile duct ligation) and test the therapeutic efficacy of targeted interventions (e.g., anti-PDGFRA antibodies, integrin inhibitors, AXL inhibitors, anti-C3AR1) on fibrosis progression, inflammation, and cellular composition using histological, biochemical, and single-cell RNA-seq endpoints.
  5. Investigate the functional consequences of altered T cell subset proportions by performing T cell functional assays (e.g., cytotoxicity assays, cytokine production) on sorted T cells from cirrhotic livers, and evaluate the impact of modulating SIT1 or SIGIRR expression on T cell activation and exhaustion.
  6. Perform proteomics on circulating extracellular vesicles or liver biopsies to assess the translational potential of identified surfaceome markers (e.g., SDC1, ERBB2, CD147, PDGFRA) as non-invasive biomarkers for disease severity or treatment response.

Limitations:

This report is based on single-cell RNA sequencing data, which provides a snapshot of gene expression and does not directly measure protein levels or cellular function. Inferences regarding cell-cell interactions and pathway activities are based on transcriptional data and require further experimental validation. The specific etiology and stage of cirrhosis were not provided, which could introduce heterogeneity in findings. Causality cannot be directly inferred from observational transcriptomic changes. While the data suggests potential therapeutic targets, their efficacy and safety in a clinical setting remain to be rigorously tested.

18. Query List

  1. Show and save UMAPs for condition, sample, major cell type, minor cell type, and celltype_subset in two columns.
  2. Show major cell type scores on UMAP and save the result.
  3. Show and save a marker expression dot plot for celltype_subset. SET target_cell = None and var_group_rotation = 45. Leave all other arguments at their default values.
  4. Show and save a population bar plot of minor cell types.
  5. Show and save a population bar plot of T cell subsets.
  6. Show and save box plots of T cell subset populations with statistically significant differences between conditions, if any. Choose ncols based on the total number of panels.
  7. Show and save a population bar plot of macrophage subsets.
  8. Show and save box plots of macrophage subset populations with statistically significant differences between conditions, if any. Choose ncols based on the total number of panels.
  9. Show and save cell-cell interaction patterns. Select at most 80 cell-cell interactions per group.
  10. Find cell-cell interactions involving major tissue cells, immune cells and stromal cells that differ significantly between conditions, show them as a dot plot, and save the result. Set max_n_items_per_group to 60. For each interaction identified under each condition, using the provided dataset context, describe the associated signaling pathways and mechanisms and, where relevant, its potential as a therapeutic target and related therapeutic agents.
  11. Show marker expressions for Cholangiocyte in a dot plot, and save the result. Use only surfaceome markers, with at most 50 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways and biological mechanisms within the tissue and, where relevant, its potential as a therapeutic target for Cirrhosis and related therapeutic agents.
  12. Show marker expressions for Macrophage in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways and biological mechanisms within the tissue and, where relevant, its potential as a therapeutic target for Cirrhosis and related therapeutic agents.
  13. Show marker expressions for Hepatic stellate cell in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways and biological mechanisms within the tissue and, where relevant, its potential as a therapeutic target for Cirrhosis and related therapeutic agents.
  14. Show marker expressions for T cell CD4+ in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways and biological mechanisms within the tissue and, where relevant, its potential as a therapeutic target for Cirrhosis and related therapeutic agents.
  15. Show Gene Ontology (GSA) analysis results for Cholangiocyte as a bar plot, and save the result.
  16. Show Gene Set Enrichment Analysis results as a dot plot, and save it. Set the color map to RdBu_r, n_pws_to_show to 120, and target_cells to None.
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