SCODiA Report by MLBI Lab

Single-Cell Landscape of Progressive Non-alcoholic Fatty Liver Disease and NASH Cirrhosis Reveals Distinct Cellular and Molecular Remodeling

This single-cell analysis reveals a profound shift in cellular composition, cell-cell communication, and pathway activity during the progression of NAFLD to NASH cirrhosis and end-stage disease. Key findings include a decrease in hepatocyte proportions, an expansion of fibrogenic and inflammatory cell populations like hepatic stellate cells and macrophages, and the activation of pro-fibrotic and pro-inflammatory signaling pathways. These changes highlight a distinct immune and metabolic dysregulation driving advanced liver pathology.

Contents

  1. Dataset overview
  2. UMAP Visualization of Liver Single-Cell RNA-seq Data
  3. Major Cell Type Score UMAP Visualization and Annotation Review
  4. Hepatocyte Condition-Specific Surface Marker Analysis
  5. Liver Minor Cell Type Population Analysis Across NAFLD Progression
  6. T 세포 및 관련 면역 세포 아형의 간 질환별 변화 분석
  7. Changes in Lymphocyte Subset Proportions Across Liver Disease Conditions
  8. Macrophage Subset Population Dynamics in Liver Disease Progression
  9. Macrophage (M2B) Population Dynamics in Liver Disease Progression
  10. Healthy Liver Cell-Cell Interaction Landscape
  11. Condition-Specific Cell-Cell Interaction Patterns in Liver Immune and Stromal Cells
  12. Macrophage Condition-Specific Surfaceome Markers in Liver Disease
  13. Sub-population Specific Surfaceome Markers for T cell CD4+ in NASH Cirrhosis
  14. Gene Set Enrichment Analysis (GSEA) of Liver Cell Types Across Disease Conditions
  15. Discussion
  16. Query List

0. Dataset overview

Dataset Summary

데이터 종류: SCODA로 처리된 단일 세포 RNA-seq 데이터 (AnnData 형식)

세포 및 유전자 수: 99,640개 세포와 29,269개 유전자

종: 인간 (human)

조직: 간 (Liver)

조건: nash_cirrhosis, nafld, healthy, end_stage_nafld

주요 사전 계산된 분석 결과

1. UMAP Visualization of Liver Single-Cell RNA-seq Data

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[Analysis Visualization Results]...

Analysis Overview

This analysis provides a UMAP (Uniform Manifold Approximation and Projection) visualization of single-cell RNA-seq data from human liver tissue, encompassing nearly 100,000 cells and over 29,000 genes. The UMAPs are colored by various metadata features: condition, sample, celltype_major, celltype_minor, and celltype_subset. This visualization is crucial for understanding the overall data structure, assessing cell type annotation quality, identifying potential batch effects, and observing how different conditions or samples distribute across the cellular landscape.

Visual Summary

The UMAP plots display the high-dimensional gene expression data in a two-dimensional space, where cells with similar expression profiles cluster together.

Condition

The condition UMAP shows clear separation between disease states. Healthy cells (light orange) primarily form distinct clusters, suggesting a unique transcriptional profile compared to diseased states. Cells from "end_stage_nafld" (dark red) and "nash_cirrhosis" (dark purple) largely overlap and form several prominent, interconnected clusters, indicating shared transcriptional signatures and potentially similar cellular compositions or states in advanced liver disease. "NAFLD" cells (light green) show more heterogeneity, partially overlapping with healthy clusters but also extending into regions associated with more severe disease, reflecting its intermediate position in disease progression.

Sample

The sample UMAP displays a large number of individual samples across the dataset. While some areas show enrichment for specific samples (indicated by distinct color patches), there is a good degree of intermixing of cells from different samples throughout many of the UMAP clusters. This suggests that the data integration process has been largely effective in mitigating major batch effects, allowing biological variability (like cell type or disease state) to drive the clustering more than sample-specific technical variations. However, some minor localized sample-specific clusters might warrant further investigation.

Celltype_major

The celltype_major UMAP reveals well-defined and distinct clusters for the broad cell types in the liver. "Liver Epithelial cell" (light orange), primarily hepatocytes, forms the largest and most prominent cluster, consistent with their high abundance in the liver. "Endothelial cell" (red-orange) and "Myeloid cell" (light green) also form substantial, relatively separated clusters. "T cell" (light blue), "B cell" (dark red), and "Stromal cell" (teal) occupy smaller but distinct regions. The "unassigned" cells (dark purple) appear to be somewhat scattered but also form a small, distinct cluster, suggesting a population whose identity couldn't be confidently assigned at this major level.

Celltype_minor

The celltype_minor UMAP provides a finer resolution of cell types, largely maintaining the structure seen in celltype_major. Hepatocytes (light orange) remain the dominant population. Within the broad "Endothelial cell" region, there is still a contiguous cluster, suggesting a general endothelial identity. "Macrophage" (light green) and "T cell CD4+" (light blue) form distinct, well-separated clusters, indicating successful sub-classification of immune cells. Other minor populations like "Hepatic stellate cell" (orange), "Plasma cell" (teal), "NK cell" (green), "DC" (red), and "ILC" (yellow) also form discernible clusters, validating the sub-clustering. The "unassigned" cells (dark purple) again form a noticeable, somewhat diffuse cluster, often situated between other defined populations.

Celltype_subset

The celltype_subset UMAP presents the highest resolution of cell types. This level further refines the populations seen in celltype_minor. For instance, "Hepatocyte" (light orange) is still dominant. Within immune cell clusters, specific T cell subsets like "T cell (Th1)", "T cell (Th17)", "T cell (Treg)", and "T cell (Naive)" (various shades of green/blue) are discernible, as are different macrophage populations (e.g., "Macrophage (M1)", "Macrophage (M2a, M2b, M2c, M2d)" - shades of yellow/light green). B cell subsets like "B cell (Memory)", "B cell (Follicular)", and "Breg" (darker reds) also show some distinction. The clear separation of these fine-grained cell types on the UMAP indicates robust and meaningful sub-clustering. The "unassigned" population (dark purple) at this level is still present, often forming small, potentially heterogeneous groups or being interspersed among other cells.

Biological Interpretation

The UMAP visualizations provide several key biological insights into the liver disease progression:

  1. Disease Progression Signature: The distinct clustering of healthy cells versus advanced disease states (end-stage NAFLD and NASH cirrhosis) highlights a profound shift in cellular transcriptional profiles associated with progressive liver disease. The intermediate position and heterogeneity of NAFLD cells suggest a continuum of pathological changes. This aligns with the understanding that NAFLD progresses through stages of steatosis, inflammation (NASH), fibrosis, and ultimately cirrhosis [PubMed search: NAFLD NASH progression].
  2. Cell Type Specificity: The robust clustering and clear separation of major, minor, and subset cell types confirm the successful identification and annotation of diverse cell populations within the human liver. The large "Hepatocyte" population, as expected, dominates the landscape, while the presence of various immune cells (T cells, B cells, Macrophages, NK cells, ILCs), endothelial cells, and stromal cells (hepatic stellate cells) reflects the complex multicellular environment of the liver and its immune surveillance functions.
  3. Immune Cell Heterogeneity in Disease: The detailed celltype_subset UMAP demonstrates the fine-grained diversity of immune cells, including different macrophage polarization states (M1, M2 subtypes) and T helper cell subsets (Th1, Th2, Th17, Treg). This granular resolution is critical for understanding the specific roles these immune cell subsets play in the inflammation, fibrosis, and immune dysregulation characteristic of NAFLD and NASH [GeneCards: CD68 (macrophage marker), CD3D (T-cell marker)]. Changes in the proportions or activation states of these specific immune cell subsets are often central to liver disease pathogenesis.
  4. Stromal Cell Dynamics: The clear identification of "Hepatic stellate cells" as a distinct population is important, as these cells are central mediators of liver fibrosis in chronic liver diseases like NASH [UniProt: P99999 (collagen type I alpha 1)].
  5. Potential for Further Discovery: The presence of "unassigned" cells, even at the celltype_subset level, suggests either populations that could not be clearly matched to known markers or potentially novel/rare cell states that warrant further investigation. If these unassigned cells form coherent clusters, they might represent unique transitional states or previously uncharacterized cell types relevant to liver biology or pathology.

Annotation Notes

The UMAPs demonstrate high-quality cell type annotation, as evidenced by the distinct and biologically meaningful separation of cell populations across all levels (major, minor, subset). The relatively low level of sample-specific clustering on the sample UMAP indicates effective data integration, enhancing confidence that the observed cell type and condition-specific patterns are driven by biological differences rather than technical artifacts. However, the persistent "unassigned" populations across cell type levels suggest areas where further refinement of annotation or identification of novel cell states could be explored.

2. Major Cell Type Score UMAP Visualization and Annotation Review

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the transcriptional scores for major cell types on a UMAP embedding, alongside the final celltype_major annotations. The primary goal is to assess the spatial organization of different cell populations and validate the quality and consistency of the major cell type assignments within the single-cell RNA-seq data from human liver. This helps confirm that cells assigned to a specific major cell type indeed exhibit high transcriptional similarity to that cell type's signature.

Visual Summary

The UMAP embedding displays distinct clusters representing various cell populations present in the human liver. The individual plots showing HiCAT_major_score for each cell type highlight regions with high transcriptional similarity to that specific cell type's signature (indicated by yellow/green coloration).

Biological Interpretation

Annotation Notes

The visualization of major cell type scores on the UMAP provides strong evidence for the quality and clarity of the major cell type annotations. The distinct clustering and high confidence scores across most major cell populations suggest accurate assignment. The 'unassigned' cluster is relatively small, indicating that a large proportion of cells have been successfully classified. The presence of a Mast cell score plot without a corresponding celltype_major category indicates an area for potential future refinement, possibly by integrating such rare populations into more granular annotations.

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References:

  1. Liver Cell Diversity: Review articles on liver cell biology and single-cell atlas projects.
  1. Mast Cells in Liver: Information on mast cells in liver pathology.

3. Hepatocyte Condition-Specific Surface Marker Analysis

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the expression of condition-specific markers within the Hepatocyte cell population. The plot_markers_and_expression_dot tool was used with target_cell='Hepatocyte', focusing on genes identified as markers for this specific cell type. The analysis was configured to find surfaceome_only markers, meaning the genes displayed encode proteins primarily located on the cell surface.

The dot plot displays:

Visual Summary

The dot plot reveals clear condition-specific expression patterns of surface markers within various Hepatocyte sub-clusters, primarily distinguishing advanced disease stages from earlier stages or healthy conditions.

  1. End-stage NAFLD-Specific Hepatocyte Markers: A prominent group of genes (enclosed in the top red box) including LEPR, NRG1, SLC38A2, EFNA1, FAT1, FGFR2, AGTR1, SLC51A, OSMR, ATP2B4, SLC22A15, IFNGR1, CPD, BACE2, CNTNAP3B, VNN3, ADAM19 shows significantly high expression and prevalence in a distinct set of Hepatocyte sub-clusters (e.g., I-SITTD1, F-SITTD3, E-SITTE9, H-SITTD12) almost exclusively within the end_stage_nafld condition. Their expression is minimal or absent in healthy, nafld, and nash_cirrhosis conditions for these specific sub-clusters.
  2. NASH Cirrhosis-Specific Hepatocyte Markers: Another cluster of genes (enclosed in the bottom red box) such as IL15RA, SLC38A1, PKD1L3, SLC6A13, NCAM2, MME, CDH23, SLC10A1 displays strong enrichment in a different, larger set of Hepatocyte sub-clusters (e.g., E-SITTD9, SITTD1, D-SITTA7, E-SITTG5) primarily in the nash_cirrhosis condition. Some of these markers also show moderate expression in the nafld condition in certain sub-clusters, suggesting a continuum of expression during disease progression.
  3. Healthy/NAFLD Markers: Within this selected set of markers, there are no clearly defined genes that are specifically upregulated in the healthy or nafld conditions compared to the advanced disease states. This suggests that the identified markers are predominantly associated with the pathological changes occurring in end-stage NAFLD and NASH cirrhosis.
  4. Hepatocyte Sub-cluster Heterogeneity: The y-axis shows considerable heterogeneity among Hepatocyte sub-clusters in terms of marker expression patterns. The bar chart on the right indicates varying cell numbers per sub-cluster, with some (e.g., D-SITTA7, E-SITTD9) being quite numerous.

Biological Interpretation

The analysis highlights the emergence of distinct Hepatocyte states characterized by unique surface marker expression profiles during the progression of non-alcoholic fatty liver disease (NAFLD) to advanced stages like end-stage NAFLD and NASH cirrhosis. The fact that these are surfaceome_only markers is particularly important for their functional and translational relevance.

  1. End-stage NAFLD-Associated Hepatocyte Phenotypes: The cluster of genes specific to end-stage NAFLD suggests a profound reprogramming of Hepatocytes.
  1. NASH Cirrhosis-Associated Hepatocyte Phenotypes: The markers enriched in NASH cirrhosis reveal another distinct set of pathological adaptations.

Clinical or Translational Implications

The identification of condition-specific surface markers within Hepatocyte sub-clusters has significant clinical and translational potential.

  1. Diagnostic and Prognostic Biomarkers: The distinct expression profiles of surface markers could serve as highly specific diagnostic or prognostic indicators for stratifying NAFLD patients into different disease stages, particularly distinguishing end-stage NAFLD and NASH cirrhosis. These surface markers are more accessible for detection in circulating cells, exosomes, or biopsy samples.
  2. Therapeutic Targets: Since these markers are surface proteins, they represent excellent candidates for targeted therapeutic interventions.
  1. Understanding Disease Pathogenesis: These markers provide deeper insights into the specific molecular mechanisms driving disease progression in Hepatocytes at different stages of NAFLD/NASH. This knowledge can guide the development of stage-specific therapies.
  2. Drug Development: The identified surface markers can inform rational drug design, focusing on developing antibodies, receptor antagonists/agonists, or small molecules that specifically target these proteins in diseased Hepatocytes, potentially minimizing off-target effects.
  3. Experimental Validation: The findings warrant further experimental validation in preclinical models and human studies to confirm the functional relevance of these markers in NAFLD progression and their utility as therapeutic targets.

4. Liver Minor Cell Type Population Analysis Across NAFLD Progression

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents the relative proportions of minor cell types within individual liver samples, categorized by their disease condition: healthy, NAFLD, NASH cirrhosis, and end-stage NAFLD. The goal is to observe shifts in cellular composition across the progression of non-alcoholic fatty liver disease (NAFLD) to more severe stages like non-alcoholic steatohepatitis (NASH) and cirrhosis.

Visual Summary

The stacked bar plots illustrate the percentage distribution of 11 minor cell types across numerous samples for each of the four conditions.

Biological Interpretation

The observed shifts in minor cell type populations provide strong biological insights into the progression of NAFLD to advanced liver disease.

Clinical or Translational Implications

The distinct cellular signatures observed across disease stages have important clinical and translational implications:

References

  1. Hepatocyte Loss and Regeneration: PubMed Search: "Hepatocyte death liver disease regeneration" https://pubmed.ncbi.nlm.nih.gov/?term=Hepatocyte+death+liver+disease+regeneration
  2. Hepatic Stellate Cells in Fibrosis: Kisseleva, T., & Brenner, D. A. (2012). Hepatic stellate cells and the pathogenesis of fibrosis. *Gastroenterology, 142*(6), 1251-1262. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218520/
  3. Endothelial Cells and Liver Disease: PubMed Search: "Liver sinusoidal endothelial cells NAFLD cirrhosis" https://pubmed.ncbi.nlm.nih.gov/?term=Liver+sinusoidal+endothelial+cells+NAFLD+cirrhosis
  4. Macrophages in NAFLD/NASH: Tacke, F. (2017). Targeting hepatic macrophages to treat liver diseases. *Journal of Hepatology, 66*(6), 1300-1312. https://www.jhep-reports.eu/article/S2589-5559(17)30065-X/fulltext30065-X/fulltext)
  5. T cells in NAFLD/NASH: Long, Q., et al. (2020). Immune cell heterogeneity in non-alcoholic fatty liver disease. *Frontiers in Immunology, 11*, 593717. https://www.frontiersin.org/articles/10.3389/fimmu.2020.593717/full
  6. Therapeutic Strategies for Liver Fibrosis: Trautwein, C., et al. (2015). Hepatic fibrosis: from basic science to new therapies. *Journal of Hepatology, 62*(1 Suppl), S116-S129. https://www.journal-of-hepatology.eu/article/S0168-8278(15)00155-8/fulltext00155-8/fulltext)

5. T 세포 및 관련 면역 세포 아형의 간 질환별 변화 분석

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[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 사용하여 간 조직 내 T 세포 및 관련 Innate Lymphoid Cell(ILC) 아형의 개체군 비율을 다양한 간 질환 조건(healthy, nafld, end_stage_nafld, nash_cirrhosis)에서 비교한 결과를 시각화합니다. 특히 'T cell'로 분류된 주요 세포 집단 내의 하위 아형(subset) 구성 변화를 샘플별로 정량화하여 보여줍니다. 이 분석은 간 질환 진행에 따른 면역 환경의 재편성을 이해하는 데 중요한 통찰력을 제공합니다.

Visual Summary

제공된 막대 그래프는 각 조건(end_stage_nafld, healthy, nafld, nash_cirrhosis) 내 개별 샘플에서 T 세포 및 관련 ILC 아형의 상대적 비율을 시각화합니다.

Biological Interpretation

간은 복잡한 면역 미세환경을 가지고 있으며, T 세포와 ILC는 면역 항상성 유지 및 염증/손상 반응에 중요한 역할을 합니다. 비알코올성 지방간 질환(NAFLD)은 단순 지방간에서 비알코올성 지방간염(NASH), 섬유증, 간경변증, 간세포암으로 진행될 수 있는 스펙트럼 질환입니다. NASH는 지방증, 염증, 간세포 풍선을 특징으로 하며, 간경변증은 진행성 간 섬유화를 나타냅니다.

NASH Cirrhosis에서의 T 세포 아형 변화:

Clinical or Translational Implications

6. Changes in Lymphocyte Subset Proportions Across Liver Disease Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportions of various lymphocyte subsets, including T cell subsets, NK cells, and Innate Lymphoid Cells (ILCs), within the liver across different disease conditions: NASH cirrhosis, end-stage NAFLD, NAFLD, and healthy controls. The boxplots visualize the celltype proportion for each subset, with significant differences (p-value $\le$ 0.1) highlighted between conditions. This allows for an understanding of how immune cell composition shifts with the progression of non-alcoholic fatty liver disease (NAFLD) and its more severe forms, Non-alcoholic Steatohepatitis (NASH) and cirrhosis.

Visual Summary

The boxplots reveal distinct patterns in the proportions of different lymphocyte subsets across the four liver conditions:

Biological Interpretation

The observed shifts in lymphocyte subset proportions provide valuable insights into the immune landscape of progressive liver disease:

Clinical or Translational Implications

These findings highlight specific lymphocyte subsets as potential biomarkers for the progression and severity of NAFLD, NASH, and cirrhosis:

7. Macrophage Subset Population Dynamics in Liver Disease Progression

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the relative proportions of different macrophage subsets (M1, M2A, M2B, M2C, M2D) within the total macrophage population across individual liver samples from healthy donors and patients with varying stages of non-alcoholic fatty liver disease (NAFLD) progression, specifically NAFLD, NASH-cirrhosis, and end-stage NAFLD. The stacked bar plots allow for a direct comparison of macrophage polarization states across conditions and among individual samples.

Visual Summary

The stacked bar plots illustrate distinct shifts in macrophage subset compositions across the disease spectrum:

In summary, there is a progressive increase in the proportion of pro-inflammatory M1 macrophages as liver disease advances from healthy to NAFLD, NASH cirrhosis, and especially to end-stage NAFLD. Macrophage (M2A) maintains a significant presence throughout disease progression.

Biological Interpretation

Macrophages, including tissue-resident Kupffer cells and monocyte-derived macrophages, play a pivotal role in the initiation and progression of non-alcoholic fatty liver disease (NAFLD) and its more severe form, non-alcoholic steatohepatitis (NASH), which can lead to cirrhosis and liver failure. The observed shifts in macrophage subsets provide critical insights into the underlying immune responses:

This dynamic polarization of macrophages, with a distinct shift towards M1-like populations in advanced disease stages, highlights their central role in propagating liver inflammation and fibrosis in NAFLD/NASH.

Clinical or Translational Implications

The differential distribution of macrophage subsets across liver disease stages holds several potential clinical and translational implications:

References

  1. M1 Macrophages in Inflammation: PubMed Search for "M1 macrophage inflammation liver disease" https://pubmed.ncbi.nlm.nih.gov/?term=M1+macrophage+inflammation+liver+disease
  2. M2 Macrophages and Fibrosis: PubMed Search for "M2 macrophage fibrosis liver" https://pubmed.ncbi.nlm.nih.gov/?term=M2+macrophage+fibrosis+liver
  3. Therapeutic Targeting of Macrophages in NAFLD: PubMed Search for "macrophage targeting NAFLD NASH therapy" https://pubmed.ncbi.nlm.nih.gov/?term=macrophage+targeting+NAFLD+NASH+therapy

8. Macrophage (M2B) Population Dynamics in Liver Disease Progression

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Analysis Overview

This analysis investigates the proportion of Macrophage (M2B) cells within the total cell population across various liver conditions: healthy, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH) cirrhosis, and end-stage NAFLD. The goal is to identify how the abundance of this specific macrophage subset changes with disease progression, highlighting statistically significant differences.

Visual Summary

The boxplot illustrates the distribution of Macrophage (M2B) cell proportions for each condition.

Statistical Significance:

Biological Interpretation

Macrophages are critical immune cells in the liver, contributing to homeostasis, inflammation, and fibrosis. M2 macrophages, including the M2B subtype, are generally associated with anti-inflammatory responses, tissue repair, and the promotion of fibrosis in chronic liver diseases.

The observed increase in Macrophage (M2B) proportions in conditions like NASH cirrhosis and, most notably, end-stage NAFLD, suggests a potential role for this specific macrophage subtype in the advanced stages of non-alcoholic fatty liver disease progression.

Clinical or Translational Implications

The findings suggest that the proportion of Macrophage (M2B) cells could serve as a potential biomarker for disease severity or progression in NAFLD/NASH, particularly in distinguishing advanced stages like end-stage NAFLD from earlier stages or healthy liver.

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References:

[1] Macrophages in non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH).

PubMed Search

[2] Macrophage polarization: an updated view.

PubMed Search

[3] Macrophages as therapeutic targets in liver diseases.

PubMed Search

9. Healthy Liver Cell-Cell Interaction Landscape

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the top 80 predicted cell-cell interactions (CCI) within the healthy liver microenvironment, focusing on interactions involving Hepatocytes and Endothelial cells. The dot plot displays ligand-receptor pairs (x-axis) and the interacting cell pairs (y-axis). The size of each dot represents the statistical significance (-log10(p-value)), with larger dots indicating more significant interactions (smaller p-value). The color intensity of the dot indicates the average expression level (log2(mean)) of the interacting ligand-receptor pair across the respective cell populations, with brighter colors (yellow/green) corresponding to higher expression.

Visual Summary

The visualization highlights a complex network of cell-cell interactions crucial for maintaining healthy liver function. We observe significant interactions across all four queried cell-pair categories: Hepatocyte-Hepatocyte, Hepatocyte-Endothelial cell, Endothelial cell-Hepatocyte, and Endothelial cell-Endothelial cell.

Key observations include:

Biological Interpretation

The observed CCI landscape in the healthy liver reflects the intricate cellular crosstalk required for its complex functions, including metabolism, detoxification, and regeneration.

  1. Liver Architecture and Adhesion: The high prevalence of integrin, cadherin, and nectin interactions underscores the importance of robust cell-cell and cell-ECM adhesion for maintaining the structural integrity of the liver parenchyma and sinusoidal endothelium. Integrins mediate cell attachment to the ECM (e.g., collagen, fibronectin, vitronectin), crucial for hepatocyte function and survival, and also facilitate bidirectional signaling. Cadherins and nectins are key for homotypic cell adhesion, ensuring tight junctions and structural stability within hepatocyte plates and endothelial linings.
  2. Metabolic Regulation and Communication: The significant number of interactions involving steroid hormones and cholesterol metabolites (e.g., DHEA-sulfate, Dihydrotestosterone, cholesterol) highlights the liver's central role in steroidogenesis, lipid metabolism, and detoxification. These interactions suggest direct communication pathways between hepatocytes and, to a lesser extent, with endothelial cells, to coordinate these metabolic processes within the hepatic lobule.
  3. Vascular Homeostasis and Angiogenesis: VEGFA signaling is critical for endothelial cell survival, proliferation, and maintaining the unique fenestrated structure of liver sinusoids. Its activity in both endothelial-endothelial and hepatocyte-endothelial interactions suggests a dynamic interplay in maintaining vascular integrity and potentially initiating repair or regeneration in response to normal physiological cues. Notch signaling, particularly DLL4-NOTCH, in endothelial cells plays a vital role in regulating vascular branching and arterial-venous differentiation within the liver vasculature.
  4. Growth and Regeneration: Interactions involving IGF1-IGF1R and BMP6 pathways are essential for cell proliferation, differentiation, and tissue repair. IGF1, largely produced by hepatocytes, acts as a potent growth factor. BMP6 is involved in iron homeostasis and liver regeneration [NCBI]. Their presence indicates a constant readiness for tissue maintenance and repair mechanisms even in a healthy state.

Clinical or Translational Implications

Understanding the baseline cell-cell interaction network in the healthy liver is critical for identifying dysregulated pathways in liver diseases such as NASH, NAFLD, and cirrhosis.

10. Condition-Specific Cell-Cell Interaction Patterns in Liver Immune and Stromal Cells

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates statistically significant differences in cell-cell interactions (CCIs) among major immune cells (NK cell, ILC, Macrophage, Plasma cell, T cell CD4+, B cell, Dendritic cell) and hepatic stellate cells across various liver disease conditions (healthy, nafld, nash_cirrhosis, end_stage_nafld). Using CellPhoneDB, the plot_dot_for_cci_with_signif_difference tool was employed to visualize interactions that are significantly stronger in one condition compared to others, aiming to identify condition-specific communication patterns relevant to disease progression in the liver.

Visual Summary

The dot plot displays a complex landscape of cell-cell interactions, with distinct patterns emerging across the different liver conditions and individual samples.

Clusters of High Activity

Biological Interpretation

The observed patterns provide crucial insights into the evolving cellular microenvironment during liver disease progression:

The overall shift towards increased and diversified cell-cell interactions in end_stage_nafld and nash_cirrhosis compared to healthy and nafld highlights the dynamic and progressive nature of intercellular communication in liver disease pathophysiology.

Clinical or Translational Implications

The identification of significantly altered cell-cell interactions in advanced liver disease stages (end_stage_nafld, nash_cirrhosis) holds several clinical and translational implications:

11. Macrophage Condition-Specific Surfaceome Markers in Liver Disease

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes surfaceome markers that are specifically enriched in liver macrophages across different conditions: healthy, nafld (Nonalcoholic Fatty Liver Disease), nash_cirrhosis (Nonalcoholic Steatohepatitis with cirrhosis), and end_stage_nafld (End-stage NAFLD). By focusing on surfaceome markers, this analysis highlights potential cell-surface molecules that mediate cell-cell interactions, signaling, and could serve as diagnostic biomarkers or therapeutic targets. The plot_markers_and_expression_dot tool was used, configured to find up to 50 surfaceome markers per condition and to remove markers common in 3 or more groups, thereby emphasizing condition-specificity.

Visual Summary

The dot plot effectively displays the expression patterns of identified macrophage surfaceome markers across the four liver disease conditions.

Condition-Specific Marker Expression:

Biological Interpretation

The observed condition-specific surfaceome marker profiles provide insights into the functional adaptation and distinct phenotypes of liver macrophages during different stages of NAFLD progression to cirrhosis.

Macrophage Activation and Damage in End-stage NAFLD:

Homeostatic and Early Disease Macrophage States (Healthy and NAFLD):

Advanced Inflammatory and Metabolic Remodeling in NASH Cirrhosis:

Clinical or Translational Implications

The identification of condition-specific surfaceome markers on macrophages holds significant clinical and translational potential:

12. Sub-population Specific Surfaceome Markers for T cell CD4+ in NASH Cirrhosis

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers for T cell CD4+ cells. The provided dot plot visualizes surfaceome markers that distinguish three distinct sub-populations (labeled SITTC3, B-SITTB8, and SIGAA4) within the T cell CD4+ compartment, specifically observed under the nash_cirrhosis condition. This approach helps in understanding the functional heterogeneity of T cell CD4+ cells in the context of advanced liver disease.

Visual Summary

The dot plot displays the expression patterns of various surfaceome genes across the three identified T cell CD4+ sub-populations: SITTC3, B-SITTB8, and SIGAA4, within the nash_cirrhosis cohort.

Dot Characteristics:

Key Observations per Sub-population:

Biological Interpretation

The identification of these three distinct T cell CD4+ sub-populations within the nash_cirrhosis liver highlights significant functional heterogeneity in the immune response to advanced liver disease. Their unique surfaceome profiles suggest specialized roles in the disease microenvironment.

The distinct profiles of these T cell CD4+ sub-populations suggest that the CD4+ T cell compartment in NASH cirrhosis is not monolithic but comprises functionally diverse subsets that likely contribute differentially to disease progression or resolution.

Clinical or Translational Implications

The identification of these sub-population-specific surfaceome markers offers significant clinical and translational potential, particularly for characterizing and potentially targeting T cell CD4+ subsets in NASH cirrhosis.

Therapeutic Targets:

13. Gene Set Enrichment Analysis (GSEA) of Liver Cell Types Across Disease Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes Gene Set Enrichment Analysis (GSEA) to identify biological pathways that are significantly enriched or depleted in various liver cell types (B cell, Endothelial cell, Hepatic stellate cell, Hepatocyte, ILC, Macrophage, NK cell, Plasma cell, T cell CD4+) when comparing specific disease conditions (NAFLD, NASH cirrhosis, end-stage NAFLD) against a reference (the average of all other conditions). The dot plot visualizes the Normalized Enrichment Score (NES) and the statistical significance (-log(p-value)) for the top 80 enriched pathways, offering insights into condition-associated biology and potential cell-state shifts within the liver microenvironment.

Visual Summary

The dot plot displays pathways on the y-axis and specific cell type-condition comparisons on the x-axis. The color of each dot indicates the Normalized Enrichment Score (NES), where red signifies positive enrichment (pathway genes generally upregulated) and blue signifies negative enrichment (pathway genes generally downregulated). The size of the dot reflects the statistical significance (-log(p-value)), with larger dots indicating greater significance.

Key observations include:

Biological Interpretation

The GSEA results provide a detailed molecular landscape of liver pathophysiology across NAFLD progression.

  1. Metabolic Dysregulation in Hepatocytes: The consistent downregulation of Glycolysis / Gluconeogenesis and PPAR signaling in advanced NAFLD/NASH cirrhosis hepatocytes points to a severe impairment of core metabolic functions. PPARs are crucial for lipid metabolism and glucose homeostasis, and their suppression exacerbates steatosis and metabolic dysfunction. Conversely, the upregulation of these pathways in healthy hepatocytes (vs. others) underscores their importance in a functional liver. The upregulation of p53 signaling indicates cellular stress, potentially from lipotoxicity, oxidative stress, or DNA damage, which can lead to cell cycle arrest or apoptosis, contributing to liver injury and inflammation in advanced disease.
  1. Inflammation and Immune Activation: In macrophages, the upregulation of Phagosome, Cytokine-cytokine receptor interaction, and IL-17 signaling pathways signifies a highly inflammatory and phagocytically active state. This is consistent with the accumulation of pro-inflammatory macrophages (e.g., M1-like) in NAFLD/NASH, contributing to chronic inflammation and fibrosis. The enrichment of bacterial infection pathways suggests increased response to gut-derived bacterial products due to dysbiosis and increased gut permeability, a known contributor to liver inflammation and progression in chronic liver diseases. Similarly, upregulation of cytokine and IL-17 signaling in T cells further supports a robust inflammatory T-cell response in NASH cirrhosis.
  1. Fibrogenesis and HSC Activation: The strong upregulation of Wnt signaling pathway in Hepatic Stellate Cells (HSCs) in NASH cirrhosis and end-stage NAFLD is a critical finding, as Wnt signaling is a key driver of HSC activation, proliferation, and extracellular matrix production, leading to liver fibrosis. The upregulation of mTOR signaling in HSCs in these conditions, contrasting with its downregulation in hepatocytes, might indicate distinct roles in cell survival and proliferation specific to the fibrogenic process.
  1. Endothelial Dysfunction: Endothelial cell activation, indicated by upregulated Cytokine-cytokine receptor interaction, plays a role in hepatic microcirculation changes, increased vascular permeability, and recruitment of immune cells, contributing to disease progression.
  2. Viral and Oncogenic Signals: Intermittent enrichment of viral infection pathways (e.g., Hepatitis B, Human cytomegalovirus) in immune cells (ILC, Macrophage, Plasma cell, NK cell) in NASH cirrhosis could suggest co-infections or a general heightened antiviral state that could exacerbate liver injury. The appearance of cancer-related pathways across various cell types in advanced disease conditions highlights the established link between chronic liver inflammation, fibrosis, and increased risk of hepatocellular carcinoma.

Clinical or Translational Implications

The distinct pathway signatures identified in specific cell types offer valuable insights for clinical applications:

Therapeutic Targets:

14. Discussion

The comprehensive single-cell analysis of human liver tissue across healthy, NAFLD, NASH cirrhosis, and end-stage NAFLD conditions reveals a dynamic and progressive cellular and molecular remodeling landscape. A primary observation is the significant reduction in hepatocyte proportions in advanced disease states (NASH cirrhosis and end-stage NAFLD), reflecting ongoing liver injury, cell death, and impaired regeneration (Section 4). Concurrently, there is a pronounced expansion of non-parenchymal cells, notably hepatic stellate cells and endothelial cells, consistent with increased fibrogenesis and angiogenesis characteristic of progressive liver disease (Section 4).

Immune cell populations undergo substantial shifts. Macrophages, a critical player in liver inflammation and fibrosis, exhibit a progressive increase in pro-inflammatory M1-like macrophages from healthy to end-stage NAFLD, while M2A macrophages remain abundant. M2B macrophages also show a significant increase in end-stage NAFLD, indicating their potential role in advanced fibrotic processes (Section 7, 8). The T cell compartment is also extensively reshaped in NASH cirrhosis, with significant increases in Th17 cells, ILC3(-), T_Naive cells, and ILC1s, alongside a concerning reduction in NK cell proportions (Section 5, 6). This collectively points to a heightened pro-inflammatory environment and potentially compromised innate immune surveillance.

Cell-cell interaction (CCI) analysis further illuminates the complexity of disease progression. While healthy liver exhibits a balanced network of integrin-mediated adhesion, metabolic signaling, and growth factor communication (Section 9), advanced disease stages (end-stage NAFLD and NASH cirrhosis) show a highly perturbed and activated CCI landscape (Section 10). Key pro-fibrotic and pro-inflammatory interactions become robustly upregulated, including hepatic stellate cell interactions with integrin-collagen complexes, TGFB1-TGFbeta_receptor1, SPP1-integrin/ADGRE5, CXCL12-CXCR4, and JAG1-NOTCH1. These interactions underscore the intense fibrogenic and inflammatory crosstalk driving advanced liver disease.

Gene Set Enrichment Analysis (GSEA) provides molecular detail, revealing significant metabolic reprogramming in hepatocytes, with downregulation of Glycolysis/Gluconeogenesis and PPAR signaling, coupled with upregulation of p53 signaling in advanced disease, indicative of metabolic collapse and cellular stress (Section 13). Macrophages show strong upregulation of phagosome, cytokine, and IL-17 signaling, along with bacterial infection pathways, reflecting their activated inflammatory and phagocytic roles. Hepatic stellate cells exhibit robust activation of Wnt signaling, a critical pathway for fibrosis, along with apoptosis and mTOR signaling. CD4+ T cells also show activated Th1/Th2 differentiation, cytokine, and IL-17 signaling pathways in NASH cirrhosis, further supporting a vigorous inflammatory immune response (Section 13).

Analysis of condition-specific surfaceome markers reveals distinct cell-type specific phenotypes. Hepatocytes in end-stage NAFLD upregulate LEPR and IFNGR1, while those in NASH cirrhosis express SLC10A1, IL15RA, NCAM2, and CDH23, suggesting metabolic and adhesion dysregulation (Section 3). Macrophages in end-stage NAFLD show high expression of CD163L1 and CD83, indicative of activated M2-like and inflammatory states, respectively. In NASH cirrhosis macrophages, SLCO1B1/B3 and CADM1 are prominent, hinting at altered metabolic transport and adhesion (Section 11). Notably, a distinct CD4+ T cell subset (SIGAA4) in NASH cirrhosis strongly expresses TGFBR3, KDR (VEGFR2), and STAB1, pointing to a direct involvement in pro-fibrotic and pro-angiogenic processes, a finding that may differ from generalized T cell responses and highlights specific functional subsets (Section 12). These granular findings collectively portray a liver microenvironment driven by chronic inflammation, progressive fibrosis, and metabolic failure, with distinct cellular players and molecular pathways orchestrating disease advancement.

Hypotheses:

  1. Progressive hepatocyte loss and metabolic dysfunction in advanced NAFLD/NASH are driven by sustained p53 activation and suppressed PPAR signaling.
  2. The sustained shift towards pro-inflammatory M1 macrophages, along with increased Th17 and ILC1 populations, creates a persistent inflammatory milieu that accelerates liver fibrosis in NASH cirrhosis.
  3. Activated Hepatic Stellate Cells, through heightened Wnt signaling and strong integrin-ECM interactions, are central mediators of the extensive fibrogenesis observed in end-stage NAFLD and NASH cirrhosis.
  4. Distinct CD4+ T cell subsets, particularly those expressing high TGFBR3 and KDR, play specific pro-fibrotic and pro-angiogenic roles, contributing to the progression of NASH cirrhosis.
  5. Dysregulated bile acid metabolism and altered drug transport, as evidenced by macrophage SLCO1B1/B3 upregulation, contribute to chronic inflammation and tissue remodeling in NASH cirrhosis.

Potential therapeutic targets:

  1. Wnt Signaling Pathway (in Hepatic Stellate Cells): The Wnt signaling pathway is strongly upregulated in Hepatic Stellate Cells (HSCs) in NASH cirrhosis and end-stage NAFLD. This pathway is a critical driver of HSC activation, proliferation, and extracellular matrix production, which are central to liver fibrosis. Evidence: GSEA analysis shows prominent upregulation of 'Wnt signaling pathway' in Hepatic stellate cells in 'nash_cirrhosis_vs_others' and 'end_stage_nafld_vs_others' (Section 13). Validation: Test specific Wnt pathway inhibitors (e.g., inhibitors of β-catenin or frizzled receptors) in *in vitro* activated HSCs and *in vivo* fibrotic liver models to assess their ability to reduce fibrosis and HSC activation.
  2. TGF-β Signaling (via TGFBR3 on CD4+ T cells and TGFB1-TGFbeta_receptor1 CCI): TGF-β signaling is a potent pro-fibrotic pathway. Both direct cell-cell interactions involving TGFB1-TGFbeta_receptor1 and high expression of TGFBR3 on a specific CD4+ T cell subset (SIGAA4) are highly active in advanced liver disease, suggesting multifaceted contributions to fibrosis. Evidence: CCI analysis shows robust 'TGFB1-TGFbeta_receptor1' interactions, particularly between Hepatic stellate cells and other cells, elevated in 'end_stage_nafld' and 'nash_cirrhosis' (Section 10). A distinct CD4+ T cell sub-population (SIGAA4) in 'nash_cirrhosis' shows high expression of 'TGFBR3' (Section 12). Validation: Develop antibodies or small molecule inhibitors against TGFBR3 or TGF-beta receptor 1. Evaluate their efficacy in reducing T cell-mediated pro-fibrotic responses and overall liver fibrosis in preclinical models.
  3. IL-17 Signaling Pathway (in Macrophages and CD4+ T cells): IL-17 signaling is consistently and strongly upregulated in both macrophages and CD4+ T cells in NASH cirrhosis and end-stage NAFLD, indicating its central role in driving chronic inflammation, which is a hallmark of NASH and critical for fibrosis progression. Evidence: GSEA analysis shows upregulation of 'IL-17 signaling pathway' in Macrophages and T cell CD4+ from 'nash_cirrhosis_vs_others' and 'end_stage_nafld_vs_others' (Section 13). Validation: Use anti-IL-17 antibodies or IL-17 receptor antagonists in NAFLD/NASH animal models to assess their impact on inflammatory markers, immune cell infiltration, and fibrosis progression.
  4. Macrophage CD83: CD83 is an activation marker specifically upregulated in macrophages in end-stage NAFLD, suggesting an intensely activated, potentially detrimental macrophage state in the most advanced disease stage. Targeting activated macrophages could mitigate chronic inflammation and liver damage. Evidence: 'CD83' is identified as a prominent macrophage surfaceome marker, showing high mean expression and prevalence almost exclusively in the 'end_stage_nafld' condition (Section 11). Validation: Investigate the functional role of CD83 on macrophages in NAFLD progression using *in vitro* gene knockdown or blocking antibodies, followed by *in vivo* studies in advanced fibrotic models.
  5. Hepatocyte LEPR and SLC10A1: LEPR indicates altered inflammatory and metabolic regulation in end-stage NAFLD hepatocytes. SLC10A1 (NTCP) reflects dysregulated bile acid homeostasis in NASH cirrhosis hepatocytes. Modulating these could address metabolic dysfunction and cholestasis in diseased hepatocytes. Evidence: 'LEPR' is a prominent surfaceome marker showing significantly high expression in end-stage NAFLD hepatocytes (Section 3). 'SLC10A1' shows strong enrichment in NASH cirrhosis hepatocytes (Section 3). Validation: Develop specific agonists or antagonists for LEPR or SLC10A1. Test their effects on hepatocyte function, lipid metabolism, bile acid transport, and inflammation in relevant *in vitro* and *in vivo* models of NAFLD/NASH.

Follow-up validation ideas:

  1. Quantify specific macrophage (M1, M2B, CD163L1+, CD83+), T cell (Th17, ILC1, NK, TGFBR3+, KDR+ CD4+ T cells) and HSC (activated markers) populations in liver biopsies from NAFLD/NASH patients versus healthy controls using flow cytometry or immunostaining.
  2. Map the localization and expression levels of key surface markers (e.g., LEPR, SLC10A1, CD163L1, TGFBR3, KDR, STAB1) and CCI ligand-receptor pairs (e.g., TGFB1-TGFbeta_receptor1, SPP1-integrin) to specific cell types and their microenvironmental niches within diseased liver tissue sections using spatial transcriptomics or proteomics.
  3. Use primary liver cell co-culture models (hepatocytes, HSCs, macrophages, T cells) to functionally validate identified CCIs (e.g., TGFB1-TGFbeta_receptor1, CXCL12-CXCR4) by blocking one component and observing downstream effects on inflammation, fibrosis, or metabolic function.
  4. Employ mouse models of NAFLD/NASH to study the causal roles of highly enriched pathways (e.g., Wnt signaling in HSCs, p53 in hepatocytes, IL-17 signaling in macrophages/T cells) or specific cell surface markers by genetic knockout/overexpression or targeted pharmacological inhibition.
  5. Confirm gene and protein expression levels of key markers (e.g., LEPR, SLC10A1, CD163L1, TGFBR3, KDR) in sorted cell populations from patient samples using targeted qPCR and Western blot to validate single-cell RNA-seq findings at the protein level.

Limitations:

This report provides a snapshot of gene expression and cellular composition, and while suggestive of disease mechanisms, it does not directly infer causality. The interpretation of findings relies on existing biological knowledge of gene and pathway functions, which may require further contextualization in liver disease. Some 'unassigned' cell populations remain, potentially harboring rare yet significant cell states. Furthermore, the use of a p-value cutoff of 0.1 for highlighting significance in some population analyses indicates borderline findings that warrant careful consideration and further validation. Experimental validation is crucial to confirm these findings and establish their functional relevance in disease progression.

15. Query List

  1. Show UMAPs including condition, sample, major cell type, minor cell type, and celltype_subset in 2 columns and save it.
  2. Show major cell type scores on UMAP and save it.
  3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
  4. Show a population bar plot for minor cell types and save it.
  5. Show a subset population barplot for T cells and save it.
  6. Show boxplots for T cell subset populations, highlighting significant differences between conditions, and save it. Set ncols appropriately based on the total number of panels.
  7. Show a subset population barplot for macrophages and save it.
  8. Show boxplots for macrophage subset populations, highlighting significant differences between conditions, and save it. Set ncols appropriately based on the total number of panels.
  9. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
  10. Find and show dot plots of statistically significant differences in cell-cell interactions among major immune and stromal cells by condition, and save it. Set max_n_items_per_group = 25.
  11. Extract condition-specific surfaceome markers for Macrophage and show a dot plot, saving it. Include up to 50 surfaceome markers per condition.
  12. Extract condition-specific surfaceome markers for T cell CD4+ and show a dot plot, saving it. Include up to 50 surfaceome markers per condition.
  13. Show a dot plot of Gene Set Enrichment Analysis results for B cell, Endothelial cell, Hepatic stellate cell, Hepatocyte, ILC, Macrophage, NK cell, Plasma cell, T cell CD4+ and save it. Use the RdBu_r color map and set n_pws_to_show = 80.
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