SCODiA Report by MLBI Lab

Single-Cell Analysis of Liver Tissue Reveals Cell-Type Specific Alterations and Communication Dynamics in Non-alcoholic Fatty Liver Disease Progression

This report details single-cell RNA sequencing analysis of human liver tissue across different stages of Non-alcoholic fatty liver disease (NAFLD), including NAFLD, NASH cirrhosis, and end-stage NAFLD, compared to healthy controls. Key findings include significant shifts in cellular composition, particularly the loss of hepatocytes and the infiltration and activation of diverse immune and stromal cell populations. Macrophages undergo a distinct pro-inflammatory and pro-fibrotic polarization, while hepatic stellate cells exhibit enhanced fibrogenic activation and cell-cell interactions, all contributing to the escalating inflammation and fibrosis characteristic of advanced NAFLD.

Contents

0. Dataset overview
1. UMAP Visualization of Cell Populations Across Conditions and Cell Types in NAFLD Progression
2. Major Cell Type Score Visualization on UMAP
3. Marker Expression Dot Plot for Cell Subtypes in Human Liver
4. Liver Minor Cell Type Population Analysis Across NAFLD Progression Stages
5. T Cell and Innate Lymphoid Cell Subpopulation Dynamics Across NAFLD Progression
6. T Cell and Innate Lymphoid Cell Subset Population Shifts in NAFLD Progression
7. Macrophage Subset Population Dynamics Across NAFLD Progression
8. Macrophage Subset Population Dynamics Across NAFLD/NASH Progression
9. Cell-Cell Interaction Patterns Across NAFLD Progression Stages
10. Condition-Specific Cell-Cell Interaction Patterns in NAFLD Progression
11. Hepatocyte Surfaceome Marker Expression in NAFLD Progression
12. Macrophage Condition-Specific Surfaceome Markers in NAFLD Progression
13. Hepatic Stellate Cell Condition-Specific Surfaceome Markers in NAFLD Progression
14. Gene Ontology (GSA) Analysis of Hepatocytes Across NAFLD/NASH Progression
15. Gene Set Enrichment Analysis (GSEA) of Liver Cell Types Across NAFLD Progression
16. Discussion
17. Query List

0. Dataset overview

Dataset Summary

• Data Type: Single-cell RNA-seq data (AnnData format).
• Dimensions: Contains 69,730 cells and 29,269 genes.
• Species: Human.
• Tissue: Liver.
• Observed Columns (Cell Metadata): Includes 'sample', 'condition', 'sample_title', 'tissue', 'disease_status', 'patient_id', 'gender', 'saf_score', 'age', 'accession', 'sample_ext', 'celltype_major', 'celltype_minor', 'celltype_subset', and 'cluster'.
• Variable Columns (Gene Metadata): Includes 'variable_genes'.
• Conditions: 'nash_cirrhosis', 'nafld', 'healthy', 'end_stage_nafld'.
• Major Cell Types: 'Liver Epithelial cell', 'unassigned', 'Endothelial cell', 'T cell', 'Myeloid cell', 'Stromal cell', 'B cell'.
• Minor Cell Types: 'Hepatocyte', 'unassigned', 'Endothelial cell', 'NK cell', 'T cell CD4+', 'ILC', 'Macrophage', 'Hepatic stellate cell', 'Plasma cell', 'B cell', 'Fibroblast', 'Dendritic cell'.
• Subset Cell Types: Includes various specific subtypes of the major/minor cell types, such as 'Hepatocyte', 'T cell (Tfh)', 'Macrophage (M2A)', 'Endothelial tip cell', 'B cell (MZ)', etc.
• Tumor Identification: Not requested/enabled for this dataset.
• CNV Estimates: Not available in this dataset.
• Reference Condition: 'healthy' is used for DEG_vs_ref, GSEA_vs_ref, and GSA_vs_ref_up analyses.

Precomputed Results

• Cell-Cell Interaction (CCI): Results available per condition ('uns['CCI']') and per sample ('uns['CCI_sample']'), including 'gene_pair', 'cell_pair', 'gene_A', 'gene_B', 'cell_A', 'cell_B', 'pval', 'mean'.
• Differential Expression Genes (DEG): Results for each 'celltype_minor' comparing one condition vs. rest ('uns['DEG']') and vs. reference ('uns['DEG_vs_ref']'), including 'log2_FC', 'pval', 'pval_adj', 'gene', 'Rp', etc.
• Gene Set Enrichment Analysis (GSEA): Results for each 'celltype_minor' comparing one condition vs. rest ('uns['GSEA']') and vs. reference ('uns['GSEA_vs_ref']'), including 'Name', 'ES', 'NES', 'NOM p-val', 'FDR q-val', 'Lead_genes', etc.
• Gene Ontology (GO)/Gene Set Analysis (GSA): Results for each 'celltype_minor' comparing one condition vs. rest ('uns['GSA_up']') and vs. reference ('uns['GSA_vs_ref_up']'), including 'pval', 'pval_adj', 'genes', 'overlap', 'pw_size', 'Term', etc.

Inferred Biological Context

• The dataset focuses on Non-alcoholic fatty liver disease (NAFLD) and its complications, including Non-alcoholic steatohepatitis (NASH) and cirrhosis, based on the provided 'conditions' ('nash_cirrhosis', 'nafld', 'healthy', 'end_stage_nafld') and 'disease_status' metadata for human liver tissue.

1. UMAP Visualization of Cell Populations Across Conditions and Cell Types in NAFLD Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis presents five UMAP (Uniform Manifold Approximation and Projection) plots, visualizing the distribution of 69,730 single cells from human liver tissue. Each plot is colored by a different metadata annotation: condition, sample, celltype_major, celltype_minor, and celltype_subset. These visualizations provide an overview of the dataset's structure, cell type clustering, and the distribution of cells from different disease conditions and samples within the learned embedding space. The primary goal is to assess the quality of cell type annotations, identify potential batch effects, and observe how different disease states manifest in the cellular landscape.

Visual Summary

Condition

The UMAP colored by condition reveals a clear separation of cells based on disease status.

• Healthy cells (light orange) are largely confined to a distinct cluster, suggesting a unique transcriptional state for healthy liver cells.
• NAFLD (light green) and nash_cirrhosis (dark blue) cells show considerable overlap and intermingling, indicating a continuum or shared transcriptional profiles between these progressive disease stages, but they also occupy regions distinct from healthy cells.
• End_stage_nafld (dark red) cells also show overlap with NAFLD/NASH clusters but appear to be enriched in specific, smaller clusters, potentially representing highly diseased or unique cell states.
• Overall, the embedding successfully segregates healthy tissue from diseased tissue, with varying degrees of overlap among the disease conditions.

Sample

The UMAP colored by sample shows a generally good mixing of samples within broader condition-specific regions, especially for the more abundant cell populations.

• While some local clustering of individual samples can be observed, there isn't a dominant pattern where entire clusters are exclusively composed of cells from a single sample across the entire embedding. This suggests that major batch effects influencing global clustering are not overtly present, which is crucial for robust comparisons across conditions.
• Different samples from the same conditions tend to overlap, supporting that biological variation rather than technical noise primarily drives the main structure of the UMAP.

Celltype_major

The UMAP colored by celltype_major demonstrates excellent segregation of major cell types.

• Liver Epithelial cells (Hepatocytes, light orange) form the largest and most prominent cluster, consistent with their abundance in the liver.
• Myeloid cells (light yellow), T cells (light teal), Stromal cells (light green), and Endothelial cells (red) each form distinct, well-separated clusters, indicating clear transcriptional identities.
• B cells (dark red) form smaller, more diffuse clusters, as expected for their lower abundance.
• A small population of unassigned cells (dark blue) is visible, appearing largely clustered together or intermingled with some major cell types, warranting further investigation.

Celltype_minor

The UMAP colored by celltype_minor refines the celltype_major separation, showing more granular distinct populations.

• Hepatocytes (light orange) remain the dominant cluster.
• Endothelial cells (red), Hepatic stellate cells (peach), Macrophages (light green), and various T cell populations (e.g., T CD4+, blue) are clearly delineated.
• Dendritic cells (DC) and Fibroblasts (Fib), which are subsets of Myeloid and Stromal cells respectively, emerge as distinct clusters.
• The unassigned population (dark blue) is still present and exhibits similar distribution as in the major cell type map.

Celltype_subset

The UMAP colored by celltype_subset provides the highest resolution of cell identities.

• Subpopulations within major and minor cell types are resolved, such as various Macrophage subsets (Mac_M1, Mac_M2a, M2b, M2c, M2d), diverse T cell subsets (T_Naive, Tfh, Th1, Th2, Th9, Th17, Th22, Treg), and different B cell types (BMZ, Bf, Breg, Memory, Plasma cell).
• Even specialized endothelial cells like Endothelial tip cell and Lymphatic Endothelial cell are discernible as distinct, albeit smaller, clusters.
• The granular detail in this plot confirms the robust and comprehensive annotation strategy applied to the single-cell data. The unassigned cells (dark purple) remain visible, largely forming their own small cluster or diffuse regions, suggesting they may represent cells that do not fit neatly into predefined subsets or are rare populations.

Biological Interpretation

The UMAP visualizations collectively provide a powerful overview of the cellular landscape in NAFLD progression within the liver.

• Disease-associated Cellular Remodeling: The clear separation of healthy cells from diseased cells in the condition UMAP suggests significant transcriptional and cellular composition changes occur during NAFLD development. The overlap between NAFLD, nash_cirrhosis, and end_stage_nafld highlights the progressive and overlapping nature of these liver pathologies, implying shared underlying molecular mechanisms that intensify with disease progression.
• Specific Cell Type Involvement: By overlaying the condition and cell type UMAPs mentally, it is evident that certain cell types are enriched or altered in diseased states. For instance, the large Hepatocyte cluster shows regions strongly colored by nafld and nash_cirrhosis, suggesting significant transcriptional reprogramming of hepatocytes during disease.
• Immune Cell Activation and Infiltration: Immune cell populations, including various Macrophage and T cell subsets, form distinct clusters. Their spatial relationship within the UMAP, particularly in regions enriched for diseased conditions, might indicate their active role in inflammation and fibrosis characteristic of NASH and cirrhosis. Different macrophage polarization states (M1, M2a-d) and T helper cell subsets (Th1, Th2, Th17, Th22) are known to play diverse roles in immune responses and tissue remodeling in liver disease, and their distinct clustering here supports their specific involvement.
• Fibrogenic Cell States: Hepatic stellate cells and Fibroblasts, crucial players in liver fibrosis, are clearly identified. Their location in the embedding, especially relative to advanced disease conditions, could highlight their activation and contribution to extracellular matrix deposition in nash_cirrhosis.
• Annotation Quality: The highly resolved and largely distinct clusters for celltype_major, celltype_minor, and celltype_subset indicate a high quality of cell type annotation. This robust annotation is critical for downstream differential expression and pathway analyses. The presence of 'unassigned' cells, while small, could represent either cells that couldn't be confidently classified with existing markers or potentially novel, rare cell types that warrant further investigation.

Annotation Notes

• The UMAP projections demonstrate a high degree of confidence in the assigned cell type labels, as evidenced by the clear segregation of distinct cell populations at major, minor, and subset levels.
• The successful integration of multiple samples across different conditions, without obvious global batch effects, strengthens the validity of biological interpretations drawn from this dataset.
• The unassigned cells, while not forming a single tight cluster, are consistently observed across different levels of annotation. Future analyses could aim to characterize these cells further to ensure a complete understanding of the liver cellular landscape in NAFLD. This could involve re-clustering these cells or looking for unique marker genes.

2. Major Cell Type Score Visualization on UMAP

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the distribution of major cell type scores across the UMAP embedding, alongside the final celltype_major annotations. The "HiCAT_major_score" plots display the confidence score for each major cell type (T cell, B cell, Myeloid cell, Mast cell, Endothelial cell, Stromal cell, Liver Epithelial cell) across all cells in the dataset. The final celltype_major plot shows the assigned cell type for each cell based on these scores and potentially other clustering information. This visualization helps to assess the quality and distinctness of the identified cell populations.

Visual Summary

The UMAP embedding reveals a complex yet structured landscape of cell populations, with several distinct clusters and interconnected regions.

• HiCAT_major_score: T cell: High scores (yellow/green) are concentrated in a specific cluster towards the upper right of the UMAP, clearly separating from other cell populations. This region corresponds to the T cell cluster in the celltype_major annotation.
• HiCAT_major_score: B cell: A smaller, distinct cluster with high B cell scores is visible, slightly below and to the right of the T cell cluster, indicating a well-defined B cell population. This also aligns with the B cell annotation.
• HiCAT_major_score: Myeloid cell: Cells with high myeloid scores form a large, somewhat diffuse cluster, primarily located in the central and upper-left regions of the UMAP. This broadly matches the Myeloid cell annotation, suggesting heterogeneity within this major group.
• HiCAT_major_score: Mast cell: Mast cell scores are relatively low across the UMAP, with only a very small, faintly highlighted region, suggesting a minor population or less distinct transcriptional profile compared to other major cell types. The celltype_major annotation does not explicitly list "Mast cell" as a major category, which implies they might be sub-grouped under "Myeloid cell" or represent a very rare population.
• HiCAT_major_score: Endothelial cell: A prominent cluster with high endothelial cell scores is observed in the middle-right region of the UMAP, forming a distinct branch-like structure. This corresponds well with the Endo (Endothelial cell) annotation.
• HiCAT_major_score: Stromal cell: High stromal cell scores are concentrated in a cluster located towards the far right of the UMAP, forming another distinct branch. This region aligns perfectly with the Stromal cell annotation.
• HiCAT_major_score: Liver Epithelial cell: A very large and broad region in the lower-left and central parts of the UMAP shows high scores for liver epithelial cells. This expansive region encompasses the Liver Epithelial cell annotation, reflecting the dominant presence of hepatocytes (a major subtype of liver epithelial cells) in liver tissue.
• celltype_major: The final celltype_major plot confirms the distinct clustering observed in the individual score plots. Each major cell type is assigned a unique color and forms coherent, largely non-overlapping clusters. The "unassigned" category is also visible, indicating cells that did not strongly match any major cell type profile.

Biological Interpretation

The UMAP visualization of major cell type scores and their corresponding annotations demonstrates a robust and well-resolved separation of distinct cellular populations within the human liver. This clear segregation is crucial for understanding the cellular heterogeneity in the context of Non-alcoholic fatty liver disease (NAFLD) and its progression.

• Cellular Heterogeneity: The distinct clusters for Liver Epithelial cells (predominantly hepatocytes), Endothelial cells, Stromal cells, Myeloid cells, T cells, and B cells highlight the complex cellular composition of the liver. This heterogeneity is fundamental to liver function and its response to pathological stimuli like those encountered in NAFLD.
• Reliable Annotation: The strong alignment between high confidence scores for a particular major cell type and the distinct clustering in the UMAP, as well as the final celltype_major annotation, suggests that the cell type identification is reliable. This is critical for downstream analyses, such as differential gene expression or cell-cell interaction studies, ensuring that biological signals are attributed to the correct cell populations.
• Relevance to NAFLD: In the context of NAFLD, accurate identification of these major cell types is paramount. For example:
  • Liver Epithelial cells (Hepatocytes) are central to NAFLD pathology, undergoing steatosis, inflammation, and potential cell death.
  • Myeloid cells (Macrophages/Kupffer cells) play key roles in initiating and perpetuating inflammation and fibrosis in NASH.
  • Stromal cells (Hepatic stellate cells) are critical mediators of liver fibrosis progression in NAFLD to cirrhosis.
  • Endothelial cells contribute to vascular changes and immune cell trafficking in the diseased liver.
  • T cells and B cells are involved in adaptive immune responses that contribute to chronic inflammation in NASH.

The clear demarcation of these populations provides a solid foundation for investigating cell-type-specific changes in gene expression and cell-cell communication across different NAFLD stages.

Annotation Notes

The consistency between the major cell type scores and the final celltype_major annotation confirms the quality of the cell type assignment. The distinct clustering for most major cell types indicates robust identification. The presence of an "unassigned" category is expected and allows for exclusion of cells with ambiguous profiles from specific cell type analyses or for further investigation of potentially novel or transitional cell states. The generally low scores for "Mast cell" and its absence as a primary celltype_major category suggest it might be a rare cell type or subsumed under a broader category like Myeloid cells within this dataset's annotation scheme, which is a reasonable approach for lower abundance cell types.

3. Marker Expression Dot Plot for Cell Subtypes in Human Liver

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a marker expression dot plot for various celltype_subset populations identified in human liver single-cell RNA-seq data. The purpose of this visualization is to assess the specificity and average expression levels of key marker genes within each designated cell subtype, thereby validating the quality and distinctness of the cell annotations. Markers were selected based on their differential expression characteristics (including non-zero percentage score, fold change, and adjusted p-value) and were restricted to surfaceome genes to enhance biological relevance for cell identification. The plot displays the mean expression of each marker gene (color intensity) and the fraction of cells expressing the gene (dot size) within each cell subtype.

Visual Summary

The dot plot effectively illustrates the expression patterns of a curated set of marker genes across 25 distinct celltype_subset populations.

• Diagonal Specificity: A prominent feature is the concentration of highly expressed and broadly detected markers along the diagonal of the plot (highlighted by red boxes), indicating strong specificity of most selected markers for their respective cell subtypes. This suggests that the identified celltype_subset populations are largely well-defined by unique transcriptional profiles.
• Expression and Prevalence: The color intensity (red scale) indicates the mean expression level, with darker red signifying higher expression. The size of the dot corresponds to the fraction of cells within that group expressing the gene, with larger dots representing higher prevalence. For specific cell types, markers generally show both high expression and high prevalence.
• Clustering of Cell Types: Related cell types often show similar marker expression profiles or share certain markers. For example, the B cell subtypes (B cell (Breg), B cell (MZ), Plasma cell) express common B cell lineage markers like POU2F2, EBF1, and TCF3, but also exhibit distinct markers differentiating them. Similarly, macrophage subtypes (Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C)) show some shared macrophage markers while also displaying genes associated with their specific polarization states.
• Major Cell Populations: Hepatocytes, as the most abundant cell type (48046 cells), show a distinct and broad set of highly expressed markers, as expected for the primary functional cells of the liver. Immune cell populations such as T cells, B cells, NK cells, and Macrophages also show clear, specific marker profiles.

Biological Interpretation

The marker expression patterns observed are largely consistent with established knowledge of these cell types in the liver and broader immunology, supporting the quality of the celltype_subset annotations.

• Hepatocytes: Highly express liver-specific metabolic and functional genes such as *ALB* (Albumin), *APOB* (Apolipoprotein B), *CPS1* (Carbamoyl-phosphate synthase 1), *HNF4A* (Hepatocyte Nuclear Factor 4 Alpha), *TTR* (Transthyretin), *G6PC* (Glucose-6-phosphatase catalytic subunit), and cytochrome P450 enzymes like *CYP2E1* and *CYP3A4*. These markers confirm their role in protein synthesis, lipid metabolism, glucose homeostasis, and detoxification GeneCards: ALB.

Endothelial Cells

• Endothelial tip cell: Characterized by *ANGPT2* (Angiopoietin 2) and *DLL4* (Delta Like Canonical Notch Ligand 4), crucial for angiogenesis and vascular sprouting PubMed: Angiopoietin-2 in angiogenesis.
• Lymphatic Endothelial cell: Identified by *PROX1* (Prospero Homeobox 1), a master regulator of lymphatic endothelial cell fate GeneCards: PROX1.

• Hepatic Stellate Cells: Show strong expression of genes associated with extracellular matrix production and fibrosis, such as *DCN* (Decorin), *COL1A1* (Collagen Type I Alpha 1 Chain), *COL3A1* (Collagen Type III Alpha 1 Chain), and *PDGFRA* (Platelet-Derived Growth Factor Receptor Alpha) PubMed: Hepatic stellate cell markers.

Macrophages

• Macrophage (M1): Express markers like *CD69* (early activation marker), *IFNGR1* (Interferon Gamma Receptor 1), *STAT1* (Signal Transducer And Activator Of Transcription 1), and *CLEC7A* (Dectin-1), consistent with pro-inflammatory M1 polarization.
• Macrophage (M2A, M2B, M2C, M2D): Express markers often associated with M2-like phenotypes, such as *ARG1* (Arginase 1) and *PPARG* (Peroxisome Proliferator Activated Receptor Gamma), involved in tissue repair and anti-inflammatory responses.

B Cells

• B cell (Breg) and B cell (MZ): Share common B cell transcription factors like *POU2F2*, *EBF1*, and *TCF3*. *IGHD* (Immunoglobulin Heavy Constant Delta) is prominent in B cell (MZ), reflecting naive B cell status. *FCRL3* (Fc Receptor Like 3) is observed in B cell (Breg) and Plasma cells, consistent with regulatory B cell functions GeneCards: FCRL3.
• Plasma cell: Characterized by markers like *XBP1* (X-Box Binding Protein 1), *MZB1* (Marginal Zone B And B1 Cell Specific Protein), and *CD84*, indicative of their role in antibody production.

T Cells

• T cell (Naive): Express *SELL* (CD62L) and *LEF1* (Lymphoid Enhancer-Binding Factor 1), characteristic of naive T cells.
• T cell (Tfh): Express *BCL6* (B Cell Lymphoma 6 Protein) and *CD40LG* (CD40 Ligand), key for T follicular helper cell function in germinal centers GeneCards: BCL6.
• T cell (Th1): Show markers such as *STAT1*, *IFNGR1*, *IL18R1*, and *IL12RB2*, consistent with Th1-type immune responses.
• T cell (Th17): Express *RORA* (RAR Related Orphan Receptor A) and *IL21R* (Interleukin 21 Receptor), involved in Th17 differentiation and function.
• T cell (Th22): Identified by *BATF* (Basic Leucine Zipper ATF-Like Transcription Factor) and *TNFRSF1A* (TNF Receptor Superfamily Member 1A), suggesting a Th22 phenotype.

Innate Lymphoid Cells (ILCs) and NK cells

• NK cell: Express NK-specific cytotoxic markers such as *KLRD1* (CD94), *KLRK1* (NKG2D), *NCAM1* (CD56), and *NKG7*.
• ILC1: Identified by *KLRG1* (Killer Cell Lectin Like Receptor G1), consistent with ILC1 characteristics.
• ILC2: Express *GATA3* (GATA Binding Protein 3) and *CD44*, typical of ILC2 lineage.
• ILCreg and LTI: These are less defined, but express genes like *RORC* and *IL21R* related to RORγt-dependent ILC subsets (ILC3-like) PubMed: ILC subsets.

Annotation Notes

The dot plot provides strong evidence supporting the distinctness and biological fidelity of the celltype_subset annotations within this dataset. The clear separation of marker expression across most cell types, with highly specific markers aligning with known biological roles, suggests that the cell populations have been accurately identified and characterized.

• Robust Cell Identity: For major cell types like Hepatocytes, Hepatic stellate cells, and the primary immune cell lineages (T, B, NK, Macrophages), the chosen markers demonstrate excellent specificity and expression, reinforcing confidence in their annotation.
• Subtype Differentiation: The ability to distinguish between closely related subtypes, such as different B cell or T cell subsets and macrophage polarization states, using specific marker panels is a key strength of this analysis, indicating fine-grained and reliable annotation.
• Potential Overlaps: While largely distinct, some minor overlaps in marker expression may exist between very closely related cell types (e.g., within macrophage or T cell subsets). However, the overall pattern suggests that these overlaps are not extensive enough to compromise the distinct identity of each celltype_subset.
• Future Validation: Although the current markers provide strong support, further functional validation or integration with other omics data could provide even deeper insights into the specific states and roles of these cell subtypes, particularly for less well-characterized populations like ILCreg or LTI.

4. Liver Minor Cell Type Population Analysis Across NAFLD Progression Stages

[Analysis Visualization Results]...

Analysis Overview

This analysis provides a population bar plot illustrating the relative proportions of different minor cell types within individual samples, grouped by various conditions related to Non-alcoholic fatty liver disease (NAFLD) progression: 'healthy', 'nafld', 'nash_cirrhosis', and 'end_stage_nafld'. Each bar represents a single sample, and the stacked segments within each bar show the percentage contribution of each identified minor cell type. This visualization allows for a direct comparison of cellular composition changes as the liver disease progresses.

Visual Summary

The bar plot reveals distinct shifts in the cellular landscape of the liver across different disease stages.

• Hepatocytes (light yellow) consistently represent the largest cell population in healthy livers. Their relative proportion generally decreases with disease progression, particularly noticeable in 'nash_cirrhosis' and 'end_stage_nafld' samples, where some samples show a substantial reduction in hepatocyte percentage.
• Non-parenchymal cells collectively show an increasing trend in their relative proportions from 'healthy' to 'end_stage_nafld'.
  • Immune cells: Macrophages (light green), T cell CD4+ (teal), Dendritic cells (red), and to a lesser extent, B cells (maroon) appear to increase in frequency as the disease advances, becoming more prominent in 'nash_cirrhosis' and 'end_stage_nafld'.
  • Stellate cells and Fibroblasts: Hepatic stellate cells (light orange) and Fibroblasts (coral) show a notable increase in their relative proportions in 'nash_cirrhosis' and 'end_stage_nafld' conditions, consistent with fibrotic changes.
  • Endothelial cells (orange) also exhibit an apparent increase in their relative proportion in more advanced disease stages.
• The 'unassigned' cell population (dark blue) varies across samples but appears to be more prevalent in some samples from 'nash_cirrhosis' and 'end_stage_nafld' compared to 'healthy' or 'nafld'. This could represent cells that are difficult to classify due to disease-induced transcriptional changes or rare cell types not fully captured by the annotation.
• Considerable inter-sample variability is observed within each disease condition, particularly in 'nash_cirrhosis' and 'end_stage_nafld', suggesting heterogeneity in disease manifestation among patients.

Biological Interpretation

The observed shifts in minor cell type populations align well with the known pathological progression of NAFLD, Non-alcoholic steatohepatitis (NASH), and cirrhosis.

• Hepatocyte Loss and Dysfunction: The relative decrease in hepatocytes in advanced disease stages ('nash_cirrhosis', 'end_stage_nafld') reflects hepatocyte injury, death, and replacement by fibrotic tissue, a hallmark of severe liver disease and cirrhosis.
• Inflammatory Cell Infiltration: The increased proportions of macrophages, T cells, and dendritic cells in 'nash_cirrhosis' and 'end_stage_nafld' underscore the chronic inflammatory state characteristic of NASH. Macrophages, particularly liver-resident Kupffer cells and recruited monocytes, play a critical role in initiating and perpetuating inflammation and fibrosis in NAFLD/NASH [PubMed Search: NAFLD macrophage]. T cells contribute to adaptive immune responses, further driving tissue damage and remodeling.
• Fibrosis Development: The expansion of Hepatic stellate cells and Fibroblasts is a direct indicator of liver fibrosis. Activated hepatic stellate cells are the primary collagen-producing cells in the liver, leading to extracellular matrix accumulation that characterizes fibrosis and cirrhosis [GeneCards: HSC]. This increase is a key driver of liver stiffness and impaired function in NASH progression.
• Vascular Remodeling: The increased proportion of endothelial cells may reflect angiogenesis and vascular remodeling that occurs in chronic liver disease and cirrhosis, influencing blood flow and oxygenation.

Clinical or Translational Implications

The distinctive changes in liver cell composition across NAFLD progression stages have several clinical and translational implications:

• Disease Staging and Prognosis: The relative proportions of specific cell types, particularly the decline in hepatocytes and the increase in fibrogenic and inflammatory cells, could serve as robust indicators for disease staging and severity. These population shifts might help stratify patients and predict progression to more advanced stages like cirrhosis or end-stage liver disease.
• Biomarker Discovery: The identified cell population changes could lead to the discovery of novel cell-based biomarkers. For instance, circulating levels or specific markers of activated hepatic stellate cells or specific immune cell subsets might correlate with disease severity.
• Therapeutic Target Identification: Understanding the dynamic changes in cell populations highlights specific cell types (e.g., activated hepatic stellate cells, pathogenic macrophages, certain T cell subsets) that are significantly expanded during disease progression. These cells represent potential therapeutic targets for interventions aimed at halting or reversing fibrosis and inflammation in NASH and cirrhosis.
• Refining Cell-Based Therapies: For future cell-based therapies, understanding the endogenous cellular landscape and its perturbation in disease is critical. For instance, maintaining hepatocyte viability or promoting their regeneration is crucial, while simultaneously addressing the pathogenic contributions of other cell types.

5. T Cell and Innate Lymphoid Cell Subpopulation Dynamics Across NAFLD Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot visualizing the proportional distribution of various T cell subsets and related innate lymphoid cells (ILCs), categorized by celltype_subset, within the broad 'T cell' major cell type. The data is stratified by individual samples (sample) and grouped by disease conditions: healthy, nafld, nash_cirrhosis, and end_stage_nafld. This allows for a detailed assessment of how the composition of these immune cell populations shifts during the progression of Non-alcoholic fatty liver disease (NAFLD) to its advanced stages.

Visual Summary

The stacked bar plots illustrate distinct shifts in the proportions of T cell and ILC subsets across the different conditions, with notable heterogeneity among individual samples within each group.

• Healthy Condition: Samples from healthy individuals are predominantly characterized by a high proportion of ILCreg cells and Lymphoid Tissue Inducer (LTI) cells. NK cells and T cell (Naive) are also present in varying but generally lower proportions. ILC1, ILC2, and ILC3 subsets (NCR+ and NCR-) are present but typically represent a smaller fraction of the total "T cell" compartment. Effector T cell subsets like Th1, Th2, Th17, Th22, Th9, Tfh, and Treg are largely minimal or absent.
• NAFLD Condition: The cell type composition in NAFLD samples generally resembles that of healthy controls, with ILCreg and LTI remaining dominant. However, some samples show a slight increase in ILC1 and ILC3 (NCR-) populations compared to healthy. T cell (Naive) maintains a notable presence.
• NASH Cirrhosis Condition: This stage shows a more pronounced departure from the healthy and early NAFLD profiles. There is a visible increase in the proportions of ILC1, ILC2, ILC3 (NCR+), and ILC3 (NCR-) in several samples. The presence of effector T cell subsets, including T cell (Th1), T cell (Th17), T cell (Th2), T cell (Th22), T cell (Th9), and T cell (Treg), becomes more evident, though their proportions still vary significantly across samples. ILCreg and LTI generally show a relative decrease in proportion compared to earlier stages, suggesting a shift from tissue-resident/regulatory ILCs to more inflammatory/effector ILCs and T cells.
• End-stage NAFLD Condition: This stage exhibits the most significant alterations in T cell and ILC subset distribution. In particular, ILC1 cells show a striking increase, becoming the dominant population in several end_stage_nafld samples. Other ILCs (ILC2, ILC3) also appear elevated in some samples. Effector T cell subsets (Th1, Th2, Th17) are consistently more represented than in healthy or NAFLD stages, suggesting persistent immune activation. T cell (Treg) also shows a more consistent presence across samples at this stage, possibly indicating an attempt at immune regulation or chronic activation of these cells.

Biological Interpretation

The observed shifts in T cell and ILC subset populations provide critical insights into the immune landscape during NAFLD progression.

• Dysregulation of ILCs in Liver Disease: The progressive increase in ILC1, ILC2, and ILC3 subsets, particularly in NASH cirrhosis and end-stage NAFLD, indicates their active involvement in disease pathogenesis.
  • ILC1s: Their substantial increase in advanced NAFLD stages suggests a prominent role in driving type 1 inflammation, which is characteristic of NASH and contributes to hepatocyte damage and fibrosis. ILC1s, known for producing IFN-γ, can exacerbate inflammation and immune responses in chronic liver injury [PubMed Search: ILC1 liver fibrosis NAFLD].
  • ILC2s: An increase in ILC2s could point towards a type 2 inflammatory response, which can be involved in both pro-fibrotic processes and tissue repair depending on the context in the liver [PubMed Search: ILC2 NAFLD liver fibrosis].
  • ILC3s: Changes in ILC3s, particularly NCR+ and NCR- subsets, might reflect alterations in gut-liver axis communication and immune responses to microbiota, which are known to contribute to NAFLD progression [PubMed Search: ILC3 gut liver axis NAFLD].
• Activation of Adaptive T Cell Responses: The emergence and increased proportions of effector T cell subsets (Th1, Th2, Th17, Th22, Th9) in NASH cirrhosis and end-stage NAFLD indicate a robust and diverse adaptive immune response in the diseased liver.
  • Th1 and Th17 cells: These subsets are well-known for their pro-inflammatory roles. Their expansion in advanced NAFLD stages suggests a contribution to chronic liver inflammation, hepatocyte injury, and the progression to fibrosis [PubMed Search: Th1 Th17 NAFLD NASH].
  • Treg cells: The more consistent presence of T cell (Treg) in later stages might represent a compensatory mechanism to control excessive inflammation, or these cells may become dysfunctional in the chronic inflammatory microenvironment of the cirrhotic liver, failing to suppress disease progression [PubMed Search: Regulatory T cells liver fibrosis].
• Transition from Homeostasis to Pathological Inflammation: The shift from a predominant ILCreg/LTI profile in healthy and early NAFLD to an enriched ILC1 and effector T cell profile in NASH and end-stage NAFLD signifies a critical transition from immune homeostasis to a state of chronic, active inflammation and immune-mediated tissue damage.

Clinical or Translational Implications

The distinct alterations in T cell and ILC subset compositions across NAFLD progression have several clinical and translational implications:

• Biomarker Discovery: The changing proportions of specific ILCs (e.g., ILC1) and effector T cells (e.g., Th17, Th1) could serve as potential non-invasive biomarkers for diagnosing NASH, assessing disease severity, or predicting the risk of progression to cirrhosis in NAFLD patients.
• Therapeutic Targets: Identifying the specific immune cell subsets that are significantly expanded and activated in advanced NAFLD stages (e.g., ILC1, Th17) provides novel targets for immunomodulatory therapies aimed at mitigating liver inflammation and fibrosis. Strategies to inhibit pro-inflammatory ILCs or T cell subsets, or to restore the function of regulatory immune cells, could offer new therapeutic avenues for NASH.
• Monitoring Treatment Response: Tracking the dynamics of these immune cell populations in response to therapeutic interventions could provide valuable insights into drug efficacy and patient response, allowing for personalized treatment strategies in NAFLD and NASH.

6. T Cell and Innate Lymphoid Cell Subset Population Shifts in NAFLD Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional changes of specific T cell and related innate lymphoid cell (ILC) subsets across different stages of Non-alcoholic fatty liver disease (NAFLD): healthy, end-stage NAFLD, NAFLD, and NASH cirrhosis. Box plots illustrate the distribution of cell type proportions for each condition, with statistical significance (p-value < 0.1) indicated for pairwise comparisons. The goal is to identify immune cell populations that are significantly altered during NAFLD progression, providing insights into the immune landscape of these liver pathologies.

Visual Summary

The box plots reveal distinct patterns of change in T cell and ILC subset proportions across the different conditions, with several statistically significant differences (p < 0.1):

• T_Naive Cells: Show significantly increased proportions in both NAFLD (p=0.000689 vs. healthy; p=0.0489 vs. end_stage_nafld) and NASH cirrhosis (p=0.00993 vs. healthy; p=0.0772 vs. end_stage_nafld) compared to healthy and end-stage NAFLD.
• Th17 Cells: Exhibit a significant increase in NAFLD (p=0.029 vs. healthy; p=0.000697 vs. end_stage_nafld) and NASH cirrhosis (p=0.0195 vs. healthy; p=0.0829 vs. end_stage_nafld) compared to healthy and end-stage NAFLD.
• Th1 Cells: Are significantly elevated in NASH cirrhosis compared to healthy (p=0.0338) and end-stage NAFLD (p=0.0013).
• Th2 Cells: Also demonstrate a significant increase in NASH cirrhosis compared to healthy (p=0.0099) and end-stage NAFLD (p=0.061).
• Th22 Cells: Display significantly higher proportions in NAFLD (p=0.0679 vs. healthy) and NASH cirrhosis (p=0.0327 vs. healthy) compared to healthy individuals.
• Treg Cells: Show a significant increase only in NAFLD compared to healthy (p=0.0292), but not in end-stage NAFLD or NASH cirrhosis.
• ILC3(+) Cells: Are significantly more abundant in NAFLD (p=0.0222 vs. healthy; p=0.0586 vs. end_stage_nafld) and NASH cirrhosis (p=0.0209 vs. healthy) compared to healthy or end-stage NAFLD.
• ILCreg Cells: Notably, show a significant decrease in NASH cirrhosis compared to NAFLD (p=0.0484).

For most T cell subsets (T_Naive, Th1, Th2, Th17, Th22) and ILC3(+), the lowest proportions are observed in healthy and end-stage NAFLD samples, with a general trend of increasing proportions in NAFLD and particularly in NASH cirrhosis. 'end_stage_nafld' often appears similar to 'healthy' or shows lower proportions than 'NAFLD' or 'NASH cirrhosis' for these subsets.

Biological Interpretation

The observed shifts in T cell and ILC subset populations underscore a dynamic and dysregulated immune response during the progression of NAFLD to more advanced stages like NASH and cirrhosis.

• Activation of Adaptive Immunity: The consistent increase in T_Naive, Th1, Th2, Th17, and Th22 cell proportions in NAFLD and NASH cirrhosis suggests a robust activation and expansion of adaptive immune responses within the liver.
  • Pro-inflammatory T cells (Th1, Th17): Increased Th1 and Th17 cells indicate a significant pro-inflammatory environment. Th1 cells are critical for cell-mediated immunity and can promote hepatocyte damage and fibrosis. Th17 cells, known producers of IL-17, are key drivers of chronic inflammation and are strongly implicated in liver fibrosis in NASH.
  • Th2 and Th22 Cells: Elevated Th2 cells, traditionally associated with humoral immunity and allergic responses, can also contribute to fibrogenesis. Th22 cells, which produce IL-22, have complex roles in tissue repair and inflammation, and their increase could reflect an attempt at tissue protection or contribute to chronic inflammatory processes.

Dysregulation of Immune Homeostasis:

• Treg Cells: The transient increase in Treg cells in NAFLD compared to healthy, but not in end-stage NAFLD or NASH cirrhosis, suggests an initial compensatory immunosuppressive response that may fail as the disease progresses. A lack of sustained Treg expansion in advanced stages could contribute to uncontrolled inflammation and fibrosis.
• ILCreg Cells: The significant reduction of ILCreg cells in NASH cirrhosis compared to NAFLD is a critical finding. ILCreg cells are considered regulatory innate lymphoid cells that help maintain immune tolerance. Their decrease in advanced disease suggests a loss of innate immune regulation, further exacerbating inflammation and tissue damage.

• Involvement of Innate Lymphoid Cells: The increase in ILC3(+) cells in NAFLD and NASH cirrhosis indicates active involvement of innate lymphoid cells in the disease pathology. ILC3s play a role in host defense and inflammation, and their expansion could contribute to the overall inflammatory burden in the liver.
• Distinct Role of End-stage NAFLD: The 'end_stage_nafld' condition often showing T cell proportions similar to 'healthy' or lower than 'NAFLD' and 'NASH cirrhosis' for several subsets is intriguing. This could imply a different immunological phenotype in end-stage NAFLD compared to active NASH cirrhosis, possibly related to immune exhaustion or a distinct stage of immune modulation.

Clinical or Translational Implications

The observed immune cell population shifts provide valuable insights for understanding NAFLD pathogenesis and identifying potential therapeutic targets.

• Biomarkers of Disease Progression: The significant changes in specific T cell (Th1, Th2, Th17, Th22) and ILC (ILCreg, ILC3(+)) subsets could serve as valuable biomarkers for assessing disease severity, monitoring progression from NAFLD to NASH and cirrhosis, or predicting treatment response.
• Targeted Immunomodulation: The prevalence of pro-inflammatory T cell subsets (Th1, Th17) in advanced NAFLD stages suggests that immunomodulatory therapies targeting these pathways (e.g., IL-17 inhibitors) could be beneficial in reducing liver inflammation and fibrosis.
• Restoring Immune Regulation: The failure of Treg cells to sustain their expansion and the decrease of ILCreg cells in NASH cirrhosis highlight the potential for therapeutic strategies aimed at restoring or bolstering immune regulatory mechanisms. Enhancing Treg or ILCreg function could help re-establish immune tolerance and mitigate chronic inflammation in the liver.
• Understanding Disease Heterogeneity: The distinct immune profiles across different conditions, including the unique pattern in 'end_stage_nafld', emphasize the immunological heterogeneity of NAFLD progression. This necessitates a nuanced approach to diagnosis and treatment, potentially leading to personalized immunotherapeutic strategies based on a patient's specific immune landscape.

7. Macrophage Subset Population Dynamics Across NAFLD Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of different macrophage subsets (Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), Macrophage (M2D)) within the 'Macrophage' celltype_minor category. The data is stratified by individual samples and grouped by four conditions: 'healthy', 'nafld', 'end_stage_nafld', and 'nash_cirrhosis'. This visualization provides insights into how the macrophage immune landscape changes during the progression of non-alcoholic fatty liver disease (NAFLD) to more severe stages like NASH and cirrhosis.

Visual Summary

The stacked bar plots display the percentage contribution of each macrophage subset to the total macrophage population per sample.

• Macrophage (M1) (Burgundy): The proportion of M1 macrophages is relatively low in healthy samples but shows a noticeable increase in NAFLD, becoming substantially more dominant in 'end_stage_nafld' and 'nash_cirrhosis' conditions. In these advanced disease stages, M1 often represents the largest individual macrophage subset, reflecting a prominent pro-inflammatory state.
• Macrophage (M2A) (Coral/Orange): M2A macrophages are present across all conditions. While they represent a considerable fraction in healthy and NAFLD livers, their relative proportion can vary. In advanced disease, despite the significant rise in M1, M2A still constitutes a substantial part of the macrophage population, potentially indicating ongoing tissue remodeling and fibrotic responses.
• Macrophage (M2B) (Light Orange): This subset generally maintains a low proportion across all conditions and samples, not showing a dramatic shift in response to disease progression.
• Macrophage (M2C) (Light Yellow): In healthy liver samples, Macrophage (M2C) is the predominant subset, often comprising the majority of the macrophage population. As the disease progresses through 'nafld' to 'end_stage_nafld' and 'nash_cirrhosis', there is a marked and consistent decrease in the proportion of M2C macrophages, indicating a substantial shift away from this specific phenotype.
• Macrophage (M2D) (Teal): This subset is consistently observed at very low levels across all conditions and samples, suggesting a minor role or rare presence in this dataset.

In summary, the most striking observation is a significant shift in macrophage polarization from a predominantly M2C state in healthy livers towards a pronounced M1 and M2A dominance in advanced NAFLD and NASH cirrhosis.

Biological Interpretation

The observed shifts in macrophage subsets provide critical biological insights into NAFLD progression:

1. Healthy Liver Homeostasis: In healthy liver tissue, the high prevalence of Macrophage (M2C) suggests a role in maintaining tissue homeostasis, resolving inflammation, and potentially facilitating tissue repair. M2C macrophages are generally associated with anti-inflammatory and regulatory functions.
2. NAFLD/NASH Progression and Inflammation: As NAFLD progresses to more severe stages like NASH and cirrhosis, there is a clear increase in the proportion of Macrophage (M1) and Macrophage (M2A) subsets.
   • M1 Macrophages: M1 macrophages are characterized by their pro-inflammatory phenotype, secreting cytokines such as TNF-α, IL-1β, and IL-6. Their significant increase in 'end_stage_nafld' and 'nash_cirrhosis' strongly indicates an intensified inflammatory microenvironment, which is a hallmark of NASH and a driver of hepatocyte injury and disease progression. PubMed search: M1 macrophages in NAFLD NASH
   • M2A Macrophages: M2A macrophages are often associated with wound healing, tissue repair, and pro-fibrotic responses, producing factors like TGF-β and arginase-1. Their sustained presence, even as M1 increases, suggests their involvement in the ongoing fibrotic process that characterizes NAFLD progression to cirrhosis. PubMed search: M2A macrophages liver fibrosis
3. Loss of Regulatory Macrophages: The dramatic decrease in Macrophage (M2C) populations in diseased states is a critical finding. This reduction suggests a failure in the liver's ability to maintain an anti-inflammatory and tissue-resolving environment, potentially exacerbating chronic inflammation and fibrosis. M2C macrophages are known for their immunosuppressive and pro-resolving functions.

This dynamic shift from a homeostatic M2C-dominated state to a pro-inflammatory M1/pro-fibrotic M2A-dominated state is consistent with the known pathophysiology of NAFLD and NASH, where chronic inflammation and dysregulated immune responses drive disease progression and liver damage.

Clinical or Translational Implications

The findings have several potential clinical and translational implications:

• Biomarker for Disease Severity: The altered proportions of macrophage subsets, particularly the ratio of M1 to M2C macrophages, could serve as a valuable biomarker for assessing disease severity and progression in NAFLD patients. Monitoring these shifts might help identify patients at higher risk of progressing to NASH or cirrhosis.
• Therapeutic Targets: The observed dominance of pro-inflammatory M1 macrophages in advanced disease stages highlights them as potential therapeutic targets. Strategies aimed at modulating macrophage polarization (e.g., inhibiting M1 activation or promoting M2C differentiation) could be beneficial in ameliorating liver inflammation and fibrosis in NAFLD/NASH.
• Understanding Pathogenesis: These results deepen our understanding of the immune cell dynamics in NAFLD. By identifying the specific macrophage subsets involved at different disease stages, future research can focus on dissecting their precise roles and developing targeted interventions to reverse unfavorable immune environments.

8. Macrophage Subset Population Dynamics Across NAFLD/NASH Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis presents box plots visualizing the celltype proportion of specific macrophage subsets – Mac (M2B), Mac (M2D), and Mac (M1) – across different liver conditions: nash_cirrhosis, nafld, end_stage_nafld, and healthy. Statistical significance between conditions is indicated by p-values, with a cutoff of 0.1 applied for identifying significant differences. This allows for an investigation into how the composition of macrophage populations shifts during the progression of Non-alcoholic fatty liver disease (NAFLD) to Non-alcoholic steatohepatitis (NASH) and cirrhosis.

Visual Summary

The box plots illustrate the distribution of macrophage subset proportions, with individual data points overlaid, enabling an assessment of population changes and variability within each condition.

Mac (M2B):

• The proportion of Mac (M2B) cells is significantly lower in the end_stage_nafld condition compared to the healthy control (p=0.00974).
• Conversely, nash_cirrhosis shows a significantly higher proportion of Mac (M2B) cells compared to end_stage_nafld (p=0.0745).
• No statistically significant differences were observed between nash_cirrhosis and healthy (p=0.159), nash_cirrhosis and nafld (p=0.751), or nafld and healthy (p=0.484).

Mac (M2D):

• Mac (M2D) cell proportions are significantly elevated in both nash_cirrhosis (p=0.0824) and nafld (p=0.0318) conditions when compared to healthy individuals.
• Notably, nafld exhibits a significantly higher proportion of Mac (M2D) cells than nash_cirrhosis (p=0.0426), suggesting a peak in this subtype during the earlier NAFLD stage.
• No significant difference was found between end_stage_nafld and healthy (p=0.422) or nash_cirrhosis and end_stage_nafld (p=0.791).

Mac (M1):

• The proportion of pro-inflammatory Mac (M1) cells is significantly higher in nash_cirrhosis compared to nafld (p=0.038).
• No other pairwise comparisons involving Mac (M1) showed statistically significant differences (nash_cirrhosis vs. healthy: p=0.294; nafld vs. healthy: p=0.708; end_stage_nafld vs. healthy: p=0.188; nash_cirrhosis vs. end_stage_nafld: p=0.688).

Biological Interpretation

These findings highlight dynamic shifts in macrophage subset populations within the liver during the progression of NAFLD to NASH and eventual end-stage disease. Macrophages are pivotal immune cells in the liver, known to contribute to both inflammation and resolution/fibrosis.

1. M1 Macrophages and Inflammation in NASH: The significant increase of pro-inflammatory M1 macrophages in nash_cirrhosis compared to nafld is consistent with the established role of M1 macrophages in driving inflammation in NASH. NASH, by definition, involves steatosis with inflammation and hepatocellular injury, which can progress to fibrosis and cirrhosis. The elevated M1 proportion in nash_cirrhosis likely reflects the active inflammatory state associated with advanced stages of the disease. PubMed search: M1 macrophages NASH inflammation
2. M2D Macrophages in Early Disease Progression (NAFLD): The most pronounced increase in M2D macrophages is observed in the nafld stage, significantly higher than both healthy and nash_cirrhosis conditions. M2D macrophages are a less classically defined M2 subtype, often associated with immune suppression, angiogenesis, and tissue remodeling, and sometimes with tumor progression. Their peak presence in nafld suggests a complex role potentially related to initial adaptive responses to steatosis or early fibrosis. The subsequent decrease from nafld to nash_cirrhosis might indicate a shift in macrophage polarization or a dysregulation of these specific subsets as the disease progresses from simple steatosis to overt inflammation and fibrosis.
3. M2B Macrophages in Late-Stage Disease: The observed decrease in M2B macrophages in end_stage_nafld compared to healthy, coupled with a higher proportion in nash_cirrhosis relative to end_stage_nafld, indicates a disruption of this macrophage subset in very advanced disease. M2B macrophages are known for their role in immune regulation, antigen presentation, and production of both pro- and anti-inflammatory cytokines. Their reduction in end-stage disease might reflect impaired immune homeostatic mechanisms or a shift towards less regulated inflammatory or fibrotic responses that characterize end-stage liver disease.

Clinical or Translational Implications

The distinct patterns of macrophage subset proportions across different stages of NAFLD/NASH offer valuable insights for disease monitoring and potential therapeutic strategies:

• Biomarker Potential: The significant shifts in Mac (M1), Mac (M2D), and Mac (M2B) proportions could serve as potential biomarkers for distinguishing disease stages or predicting progression, particularly the elevation of M1 in nash_cirrhosis and M2D in nafld.
• Targeted Therapies: Understanding the specific roles of these macrophage subsets in each disease stage could inform targeted immunomodulatory therapies. For instance, strategies aimed at reducing M1 activity or modulating M2D function might be considered. For example, interventions to restore M2B populations or their regulatory functions could be explored for patients with end-stage NAFLD. PubMed search: Macrophage polarization NAFLD therapy
• Disease Heterogeneity: The variability observed within conditions, especially in the nafld and nash_cirrhosis groups, underscores the heterogeneity of macrophage responses in patients and the complex interplay of immune cells in liver pathology. This highlights the need for personalized approaches in managing NAFLD progression.

9. Cell-Cell Interaction Patterns Across NAFLD Progression Stages

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using CellPhoneDB across various stages of Non-alcoholic fatty liver disease (NAFLD), including healthy, NAFLD, NASH cirrhosis, and end-stage NAFLD. The dot plots visualize the top 80 significant ligand-receptor interactions for specific cell type pairs: Hepatocyte-Hepatocyte, Hepatocyte-Endothelial cell (Endo), Endo-Hepatocyte, and Endo-Endo. The size of each dot represents the statistical significance (-log10(p-value)), and the color indicates the interaction strength (log2(mean expression)). This provides insights into how cellular communication changes during disease progression, highlighting potential drivers of pathology.

Visual Summary

The provided dot plots illustrate distinct cell-cell communication landscapes across the different liver conditions:

• Healthy Liver: Characterized by a robust and diverse set of interactions, particularly strong integrin-extracellular matrix (ECM) interactions (e.g., various COLxx_integrin_a1b1_complex, FN1_integrin_avb1_complex) among Hepatocytes and between Hepatocytes and Endothelial cells. Bone Morphogenetic Protein (BMP) signaling (e.g., BMP6_BMPR1A_AVR2B) and Cadherin (CDH1) adhesion are also prominent, suggesting stable tissue architecture and homeostatic signaling. Vascular Endothelial Growth Factor A (VEGFA) signaling is present in endothelial cells.
• NAFLD Liver: Shows many similarities to the healthy state, indicating that core homeostatic interactions are largely maintained. However, subtle shifts in interaction strengths or significance may be present for specific pairs. Integrin and BMP signaling remain strong.
• NASH Cirrhosis Liver: Exhibits a notable increase in inflammatory and profibrotic signaling. Interactions involving Tumor Necrosis Factor (TNF) family members (e.g., TNFSF14_TNFRSF14) and Transforming Growth Factor Beta (TGFB) family members (e.g., TGFB2_TGFBR1_complex) become more prominent across Hepatocyte-Endothelial and Endothelial-Endothelial pairs. While integrin interactions persist, their specific roles might be altered in the context of increasing fibrosis.
• End-Stage NAFLD Liver: Represents the most severely altered communication landscape.
  • The emergence of 'unassigned' cell types as interaction partners, particularly 'unassigned|unassigned' and 'unassigned|Hepatocyte', is a critical observation, possibly indicating significant cellular dysregulation, stress, or the presence of novel, poorly characterized cell states in advanced disease.
  • Inflammatory and Fibrotic Pathways Intensify: TGFB-related interactions (e.g., TGFB1_TGFBR1_complex, TGFB2_TGFBR1_complex) and TNF-related interactions (e.g., TNFSF10_TNFRSF10B) show increased prevalence and strength across various cell pairs, especially involving Endothelial cells.
  • ECM Remodeling and Angiogenesis: Integrin-ECM interactions continue to be significant. VEGFA and Fibroblast Growth Factor (FGF) signaling (e.g., FGF9_FGFR3) are also observed, suggesting ongoing angiogenesis and tissue remodeling.
  • Metabolic Dysregulation Signatures: Interactions related to cholesterol and steroid metabolism (e.g., DHEA/Sulfate by SULT2A1, Dihydrotestosterone-AR, Cholesterol by LXR) are consistently observed in Hepatocyte-Hepatocyte interactions, reflecting the metabolic core of NAFLD pathogenesis.

Biological Interpretation

The observed changes in cell-cell interactions provide a molecular narrative for NAFLD progression.

• Maintenance of Liver Homeostasis (Healthy/Early NAFLD): In healthy and early NAFLD stages, strong integrin-ECM interactions and BMP signaling highlight the critical role of cell adhesion, structural integrity, and growth factor-mediated maintenance of liver parenchyma. These interactions are essential for normal liver function, including metabolic regulation and regeneration.
• Transition to Inflammation and Fibrosis (NASH Cirrhosis): The increased prominence of TNF and TGFB signaling in NASH cirrhosis is a hallmark of disease progression.
  • TGFB signaling is a well-established master regulator of fibrosis, promoting the activation of hepatic stellate cells (HSCs, though not directly shown as interacting partners in these specific cell pairs, Endothelial cells and Hepatocytes themselves can contribute to fibrogenic processes or respond to TGFB). Increased TGFB communication suggests enhanced collagen production and ECM remodeling, leading to scar tissue formation.
  • TNF signaling drives inflammation, hepatocyte injury, and apoptosis, contributing to the "steatohepatitis" aspect of NASH. (GeneCards: TNF)
• Advanced Disease and Dysregulation (End-Stage NAFLD): The amplified inflammatory and fibrotic signals in end-stage NAFLD underscore the chronic and severe nature of the disease. The appearance of 'unassigned' cell types engaging in interactions suggests a profound cellular reprogramming or damage, where cells may lose their canonical identities or exhibit novel pathological communication patterns. These unassigned cells might represent highly stressed, senescent, or partially differentiated cells that emerge in severe liver injury.
  • The continued presence of metabolic interactions (e.g., DHEA, Cholesterol) emphasizes that even in advanced stages, the underlying metabolic dysregulation remains a key component, further influencing cellular behavior.
  • VEGFA signaling (GeneCards: VEGFA) and FGF signaling, consistently active, likely contribute to pathological angiogenesis in the fibrotic liver, which can exacerbate portal hypertension and disease progression.

Clinical or Translational Implications

The analysis of cell-cell interaction patterns offers valuable insights for therapeutic development and disease management in NAFLD.

• Therapeutic Targets for Fibrosis and Inflammation: The heightened activity of TGFB and TNF signaling pathways in advanced NAFLD stages (NASH cirrhosis, End-stage NAFLD) positions their constituent ligand-receptor pairs as prime therapeutic targets. Modulating these interactions could directly counteract fibrosis (e.g., by inhibiting TGFBR1) or reduce inflammation and hepatocyte injury (e.g., by targeting TNFRSF10B or TNFSF14). [PubMed Search: "TGFB inhibition liver fibrosis"]
• Metabolic Crosstalk as a Target: The persistence of interactions related to cholesterol and steroid metabolism in hepatocytes throughout disease progression suggests that targeting these metabolic pathways, possibly through specific receptors like PXR, PPARA, RORA, or enzymes like SULT2A1, could complement anti-fibrotic strategies by addressing the root metabolic cause of NAFLD. [PubMed Search: "PPAR agonists NAFLD"]
• Angiogenesis Modulation: The consistent presence of VEGFA signaling in endothelial cells suggests its ongoing role in angiogenesis. In the context of liver fibrosis, pathological angiogenesis contributes to disease progression. Targeting VEGFA or its receptors could be a strategy to normalize liver vasculature and reduce fibrotic remodeling.
• Understanding 'Unassigned' Cell Biology: The significant interactions involving 'unassigned' cells in end-stage NAFLD highlight a critical area for further research. Identifying the nature of these cells and their unique communication patterns could reveal novel pathological mechanisms and potential therapeutic vulnerabilities specific to severe liver disease.
• Biomarker Discovery: Specific ligand-receptor pairs that show significant changes in strength or presence across disease stages could serve as non-invasive biomarkers for disease progression or response to therapy. For instance, increased soluble forms of TGFB receptors or TNF family members might correlate with advanced disease.

These findings suggest that a multi-pronged therapeutic approach, simultaneously addressing inflammation, fibrosis, and metabolic dysregulation by targeting key cell-cell communication pathways, may be most effective in managing NAFLD progression.

10. Condition-Specific Cell-Cell Interaction Patterns in NAFLD Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interaction (CCI) patterns within human liver tissue, comparing healthy individuals to those with Non-alcoholic fatty liver disease (NAFLD), Non-alcoholic steatohepatitis (NASH) with cirrhosis, and end-stage NAFLD. The dot plot visualizes the strength and significance of interactions between major liver cell types (Liver Epithelial cell, Hepatocyte), immune cells (T cell, Myeloid cell, Macrophage), and stromal cells (Stromal cell, Hepatic stellate cell, Fibroblast). The analysis used the CellPhoneDB method to identify ligand-receptor interactions, and significant differences between conditions were highlighted.

The dot size in the plot represents the -log10(p-value) of the interaction, indicating its statistical significance, while the color intensity (red scale) indicates the scaled log strength of the interaction, reflecting the overall expression levels of the ligand and receptor. Only the top 60 most significant interactions were selected for each group for display, providing a focused view on key communication axes.

Visual Summary

The dot plot reveals distinct patterns of cell-cell communication across the different liver conditions:

1. Healthy Liver: In the 'healthy' condition, cell-cell interactions generally appear less numerous, weaker (lighter red), and less significant (smaller dots) compared to the diseased states. This suggests a more quiescent and homeostatic cellular environment with fewer activated signaling pathways.
2. NAFLD Progression (NAFLD to End-Stage NAFLD/NASH Cirrhosis): There is a clear and progressive increase in the number, strength, and significance of cell-cell interactions as the disease advances from 'nafld' to 'end_stage_nafld' and 'nash_cirrhosis'. These advanced disease states exhibit a dense array of large, dark red dots, indicating robust and highly significant intercellular communication.
3. Dominant Interactions in Disease:
   • Extracellular Matrix (ECM) Interactions: A striking feature in 'end_stage_nafld' and 'nash_cirrhosis' is the prominence of interactions involving collagens (e.g., COL1A1, COL4A1, COL4A2, COL4A5, COL10A1, COL18A1) and fibronectin (FN1) with various integrin complexes (e.g., integrin_a1b1_complex, integrin_aVb1_complex, integrin_aVb5_complex). These interactions are particularly strong between Hepatic stellate cells and Hepatocytes, and between Macrophages and Hepatic stellate cells.
   • Pro-fibrotic Signaling: Ligand-receptor pairs associated with fibrosis, such as TGFB1_TGFbeta_receptor1, show increased strength and significance, especially between Macrophages and Hepatic stellate cells, and Macrophages and Hepatocytes, in the diseased conditions.
   • Immune-Mediated Interactions: Interactions involving immune cells, such as Macrophages, show enhanced communication with Hepatocytes and Hepatic stellate cells. For instance, SPP1_integrin_aVb1_complex (Macrophage-Hepatocyte and Hepatic stellate cell-Macrophage) and GAS6_AXL (Macrophage-Hepatocyte) are notably active in disease. CXCL12_CXCR4 (Hepatic stellate cell-Macrophage) also displays increased activity.
   • Adhesion and Chemotaxis: Interactions involving CD44 (CD44_MRC1 between Macrophage and Hepatocyte) and other adhesion molecules are pronounced in disease.
   • Other notable interactions include those involving BMP5_BMPR1A_AVR2B (Hepatic stellate cell-Hepatocyte), PTPRC_MRC1 (Macrophage-ILC), and various complement system components or metabolic signaling molecules.

Biological Interpretation

The observed shifts in cell-cell interactions highlight key pathological processes driving NAFLD progression to advanced liver disease:

1. Fibrogenesis and ECM Remodeling: The strong and numerous interactions between collagen/fibronectin and integrins, particularly involving Hepatic stellate cells and Hepatocytes, underscore the critical role of ECM remodeling and cellular adhesion in liver fibrosis. Hepatic stellate cells (HSCs) are the primary collagen-producing cells in the liver, and their activation and interaction with hepatocytes and the ECM are central to fibrosis progression. Integrins on HSCs bind to ECM components, mediating cell adhesion, migration, and activation, which perpetuates fibrotic signaling [1].
2. Inflammation and Immune Cell Infiltration: The upregulation of interactions involving Macrophages (e.g., SPP1_integrin_aVb1_complex, GAS6_AXL, CD44_MRC1, CXCL12_CXCR4) with Hepatocytes and Hepatic stellate cells signifies heightened inflammatory activity. Macrophages, especially liver-resident Kupffer cells and recruited macrophages, are key players in NASH-associated inflammation, secreting pro-inflammatory cytokines and chemokines, and interacting with other cell types to drive disease progression. For example, SPP1 (Osteopontin) is a pro-inflammatory cytokine that can promote fibrosis and inflammation in the liver via integrin signaling [2]. The CXCL12-CXCR4 axis is known to mediate immune cell trafficking and contribute to liver fibrosis by recruiting macrophages and other inflammatory cells to sites of injury [3].
3. Hepatocyte Stress and Dysfunction: The extensive communication between Hepatocytes and other cell types (HSCs, Macrophages) reflects sustained hepatocyte injury and attempts at regeneration or adaptation. These interactions often involve pathways that can either protect or further damage hepatocytes, depending on the context. For instance, Macrophage-Hepatocyte interactions can modulate steatosis and inflammation within hepatocytes.
4. TGF-beta Signaling Activation: The increased significance of TGFB1_TGFbeta_receptor1 interactions is a hallmark of fibrosis in various organs, including the liver. TGF-β is the most potent pro-fibrogenic cytokine, directly activating HSCs and promoting ECM production. Its elevated activity indicates robust fibrotic signaling loops are active in advanced NAFLD stages [4].
5. Role of ILCs: The increased interactions involving ILCs (e.g., PTPRC_MRC1 -- Macrophage -- ILC) suggest their involvement in modulating immune responses during NAFLD progression. ILCs are diverse innate immune cells that contribute to tissue homeostasis, inflammation, and fibrosis, depending on their subtype and context.

Clinical or Translational Implications

The identified condition-specific cell-cell interactions offer promising avenues for therapeutic intervention in NAFLD and NASH:

1. Targeting Integrin-ECM Interactions: Given the pervasive involvement of integrin complexes (e.g., αvβ1, α1β1, αvβ5) interacting with collagens and fibronectin, particularly on Hepatic stellate cells, inhibitors of specific integrins could attenuate fibrotic progression. For example, small molecule inhibitors targeting αv integrins are under investigation for fibrosis [5].
2. Modulating TGF-beta Signaling: The strong activation of TGFB1_TGFbeta_receptor1 signaling suggests that direct inhibition of the TGF-β pathway or its downstream effectors could be highly effective in reducing liver fibrosis. Several anti-TGF-β strategies, including antibodies and receptor kinase inhibitors, are in various stages of development for fibrotic diseases [6].
3. Disrupting Macrophage-Mediated Pathogenic Signaling: Blocking key macrophage interactions, such as SPP1-integrin or CXCL12-CXCR4 axes, could mitigate inflammation and fibrosis.
   • SPP1 (Osteopontin): Given its role in inflammation and fibrosis via integrins, anti-SPP1 therapies or integrin inhibitors could be beneficial [2].
   • CXCL12-CXCR4 axis: Inhibitors of CXCR4 are already used in other diseases (e.g., cancer) and could be repurposed or investigated for their potential in NAFLD/NASH by reducing immune cell recruitment and HSC activation [7].
4. Anti-inflammatory Strategies Targeting Immune Cell Cross-talk: Given the increased macrophage-hepatocyte interactions, therapies aimed at re-polarizing macrophages from pro-inflammatory to anti-inflammatory phenotypes or inhibiting their recruitment could be beneficial [8].

By selectively targeting these upregulated and pathogenic cell-cell communication pathways, it may be possible to halt or reverse the progression of NAFLD to advanced fibrosis and cirrhosis.

---

References:

1. Integrins in fibrosis: Search PubMed: integrin fibrosis liver [PubMed Search]
2. SPP1 (Osteopontin) in liver disease: GeneCards for SPP1 [GeneCards] or Search PubMed: SPP1 osteopontin liver fibrosis [PubMed Search]
3. CXCL12-CXCR4 in liver fibrosis: Search PubMed: CXCL12 CXCR4 liver fibrosis [PubMed Search]
4. TGF-beta in liver fibrosis: Search PubMed: TGFB1 liver fibrosis [PubMed Search]
5. Integrin inhibitors for fibrosis: Search PubMed: integrin inhibitors fibrosis [PubMed Search]
6. Anti-TGF-beta therapies: Search PubMed: anti-TGF-beta therapy fibrosis [PubMed Search]
7. CXCR4 inhibitors: Search PubMed: CXCR4 inhibitors clinical [PubMed Search]
8. Macrophage polarization liver disease: Search PubMed: macrophage polarization liver fibrosis [PubMed Search]

11. Hepatocyte Surfaceome Marker Expression in NAFLD Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify differentially expressed surfaceome markers within Hepatocytes across various stages of Non-alcoholic fatty liver disease (NAFLD): nafld, nash_cirrhosis, and end_stage_nafld, compared to healthy controls. The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) for each condition. The selected markers are restricted to surfaceome proteins, which are particularly relevant for cell-cell interactions, signaling, and potential therapeutic targeting.

Visual Summary

The dot plot reveals distinct patterns of surfaceome marker expression in Hepatocytes associated with different disease stages:

• healthy vs. Disease States: A notable cluster of markers (e.g., in rows G-SITTB7 to D-SITTE5) shows high expression and prevalence specifically in healthy hepatocytes. These markers are generally downregulated or show reduced prevalence in nafld, nash_cirrhosis, and end_stage_nafld conditions, indicating a loss of normal hepatocyte function or identity as the disease progresses. Key examples include several SLC (solute carrier) family transporters.
• end_stage_nafld Enrichment: A prominent group of markers (e.g., in rows E-SITTE9 to H-SITTC12) exhibits very high expression levels and prevalence predominantly in end_stage_nafld hepatocytes. Many of these markers are also upregulated in nash_cirrhosis, but generally with lower intensity and cell fraction compared to end_stage_nafld, suggesting a progressive increase in their expression with disease severity. These include genes associated with fibrogenesis, inflammation, and altered lipid metabolism.
• nafld and nash_cirrhosis Overlap: While end_stage_nafld shows the most pronounced changes, nafld and nash_cirrhosis also present distinct marker profiles, often reflecting intermediate stages of disease progression. Some markers show increased expression from nafld to nash_cirrhosis and then to end_stage_nafld, highlighting a continuum of pathological changes. Conversely, markers abundant in healthy are often progressively diminished in these stages.

Biological Interpretation

The identified surfaceome markers in Hepatocytes provide insights into the molecular mechanisms driving NAFLD progression and suggest potential targets for therapeutic intervention.

Markers Down-regulated in Disease (Enriched in Healthy Hepatocytes)

SLCO1B1 (Organic Anion Transporting Polypeptide 1B1):

• Pathways/Mechanisms: SLCO1B1 is a crucial hepatocyte-specific uptake transporter for a wide array of endogenous compounds (e.g., bile acids, bilirubin) and xenobiotics (e.g., statins, rifampicin) from the blood into the liver. It is essential for drug detoxification and bile acid homeostasis. [GeneCards: SLCO1B1: GeneCards]
• Role in NAFLD: Its significant downregulation in diseased conditions suggests impaired hepatocyte metabolic function and reduced capacity for drug and bile acid clearance, potentially contributing to liver injury and altered pharmacokinetics of NAFLD treatments. [PubMed search: OATP1B1 NAFLD: PubMed Search]
• Therapeutic Target Potential: Restoring SLCO1B1 function or considering its altered expression when dosing drugs could improve liver detoxification and optimize therapeutic outcomes.

SLC2A1 (Glucose Transporter Type 1, GLUT1):

• Pathways/Mechanisms: SLC2A1 facilitates basal glucose uptake into cells, providing substrate for glycolysis and energy production. In healthy hepatocytes, it plays a role in glucose sensing and metabolism. [GeneCards: SLC2A1: GeneCards]
• Role in NAFLD: The observed reduction in end_stage_nafld may reflect a shift in glucose metabolism characteristic of severe liver disease, where hepatocytes undergo significant metabolic reprogramming. [PubMed search: GLUT1 NAFLD: PubMed Search]
• Therapeutic Target Potential: Understanding and potentially modulating glucose transporter activity could help in addressing the metabolic dysregulation central to NAFLD.

Markers Up-regulated in Disease (Especially in NASH/Cirrhosis and End-Stage NAFLD)

LEPR (Leptin Receptor):

• Pathways/Mechanisms: LEPR is the receptor for leptin, a hormone primarily produced by adipocytes, involved in regulating energy balance, appetite, and metabolism. In the context of liver disease, leptin signaling (via JAK/STAT pathway) can drive inflammation and fibrogenesis. [GeneCards: LEPR: GeneCards]
• Role in NAFLD: Hyperleptinemia and leptin resistance are common in obesity and NAFLD. Elevated leptin, signaling through LEPR on hepatocytes and hepatic stellate cells, promotes steatosis, inflammation, and fibrosis in the liver. [PubMed search: Leptin Receptor NAFLD fibrosis: PubMed Search]
• Therapeutic Target Potential: Blocking excessive LEPR signaling could be a strategy to mitigate inflammation and fibrosis in advanced NAFLD/NASH.
• Related Therapeutic Agents: Leptin antagonists or signaling inhibitors are under investigation for their anti-fibrotic potential.

ITGAV (Integrin Alpha V):

• Pathways/Mechanisms: ITGAV forms heterodimers with various beta integrins (e.g., ITGB1), acting as a cell surface receptor for extracellular matrix (ECM) proteins. It is crucial for cell adhesion, migration, and, importantly, activation of latent TGF-β, a master regulator of fibrosis. [GeneCards: ITGAV: GeneCards]
• Role in NAFLD: Upregulation of ITGAV in end_stage_nafld and nash_cirrhosis indicates its involvement in the extensive tissue remodeling and fibrogenesis characteristic of these severe stages. Its role in TGF-β activation directly contributes to the progression of hepatic fibrosis. [PubMed search: ITGAV NAFLD fibrosis: PubMed Search]
• Therapeutic Target Potential: Inhibiting αV integrins could represent a broad anti-fibrotic strategy by disrupting pathological cell-ECM interactions and blocking TGF-β activation.

OSMR (Oncostatin M Receptor):

• Pathways/Mechanisms: OSMR is the receptor for Oncostatin M (OSM), a cytokine from the IL-6 family. Its activation, often via JAK/STAT pathways, is involved in inflammation, tissue repair, and fibrosis. [GeneCards: OSMR: GeneCards]
• Role in NAFLD: Elevated OSM and OSMR signaling contribute to liver inflammation and fibrosis. OSMR activation can stimulate collagen production by hepatic stellate cells and induce inflammatory responses in hepatocytes, exacerbating liver damage. [PubMed search: OSMR NAFLD fibrosis: PubMed Search]
• Therapeutic Target Potential: Targeting OSMR signaling could offer a therapeutic avenue to attenuate both inflammation and fibrosis in progressive NAFLD/NASH.
• Related Therapeutic Agents: Inhibitors or antibodies against OSM or OSMR are being explored for various fibrotic diseases.

IL11RA (Interleukin 11 Receptor Alpha):

• Pathways/Mechanisms: IL11RA is the receptor subunit for Interleukin 11 (IL-11), another cytokine of the IL-6 family. IL-11/IL11RA signaling is a potent driver of fibrosis in multiple organs, activating cellular pathways that lead to increased ECM production. [GeneCards: IL11RA: GeneCards]
• Role in NAFLD: Strong upregulation of IL11RA in nash_cirrhosis and end_stage_nafld highlights the critical role of IL-11 signaling in promoting hepatic fibrosis by activating fibroblasts and stellate cells. [PubMed search: IL11RA NAFLD fibrosis: PubMed Search]
• Therapeutic Target Potential: Inhibition of IL11RA or IL-11 itself is a highly promising anti-fibrotic strategy currently under preclinical and early clinical investigation.

ABCB1 (ATP Binding Cassette Subfamily B Member 1, P-glycoprotein/MDR1):

• Pathways/Mechanisms: ABCB1 is an ATP-dependent efflux pump that transports a broad range of substrates, including many drugs and toxins, out of cells. It is a key player in multi-drug resistance and cellular detoxification. [GeneCards: ABCB1: GeneCards]
• Role in NAFLD: Upregulation in severe disease (end_stage_nafld, nash_cirrhosis) could reflect an adaptive response to increased cellular stress and accumulation of toxic metabolites in diseased hepatocytes, or it might impact the efficacy of medications used to treat NAFLD. [PubMed search: ABCB1 NAFLD: PubMed Search]
• Therapeutic Target Potential: Modulating ABCB1 activity could optimize drug delivery and efficacy in NAFLD patients by altering intracellular concentrations of therapeutic agents in hepatocytes.

NPC1L1 (Niemann-Pick C1-Like 1):

• Pathways/Mechanisms: NPC1L1 is a cholesterol absorption protein found on the apical membrane of intestinal enterocytes and the canalicular membrane of hepatocytes. It mediates cholesterol uptake from the gut and reabsorption in the liver. [GeneCards: NPC1L1: GeneCards]
• Role in NAFLD: Hepatic cholesterol accumulation is a hallmark of NAFLD/NASH. Upregulation of NPC1L1 in nash_cirrhosis and end_stage_nafld could contribute to increased cholesterol uptake by hepatocytes, exacerbating lipotoxicity and inflammation. [PubMed search: NPC1L1 NAFLD: PubMed Search]
• Therapeutic Target Potential: Inhibition of NPC1L1 can reduce hepatic cholesterol content, ameliorating steatosis and improving liver function.
• Related Therapeutic Agents: Ezetimibe, an FDA-approved NPC1L1 inhibitor, is used for hypercholesterolemia and has shown promise in NAFLD management.

SLC40A1 (Ferroportin-1):

• Pathways/Mechanisms: SLC40A1 is the only known mammalian iron exporter. It plays a critical role in regulating systemic and cellular iron homeostasis by mediating iron efflux from iron-storing cells, including hepatocytes. [GeneCards: SLC40A1: GeneCards]
• Role in NAFLD: Iron overload is a significant aggravator of NAFLD/NASH, promoting oxidative stress, inflammation, and fibrosis. Dysregulation of SLC40A1 can lead to iron accumulation in hepatocytes, contributing to disease progression. Its upregulation in nash_cirrhosis and end_stage_nafld might reflect a response to severe iron dysregulation. [PubMed search: SLC40A1 NAFLD iron: PubMed Search]
• Therapeutic Target Potential: Modulating SLC40A1 activity or other iron-regulating pathways could be a therapeutic strategy to reduce hepatic iron accumulation and mitigate iron-induced liver damage in NAFLD.

MME (Membrane Metallo-Endopeptidase, Neprilysin):

• Pathways/Mechanisms: MME is a cell surface metalloendopeptidase that degrades various biologically active peptides, including natriuretic peptides. It is involved in processes like blood pressure regulation and inflammation. [GeneCards: MME: GeneCards]
• Role in NAFLD: MME shows prominent expression in nafld and nash_cirrhosis but less in end_stage_nafld and healthy. This pattern suggests a potential role in early-to-mid stage disease, possibly by modulating peptide signaling that influences hepatic metabolism or inflammation before advanced remodeling. [PubMed search: MME NAFLD: PubMed Search]
• Therapeutic Target Potential: Modulating MME activity could impact peptide-mediated signaling pathways relevant to liver metabolism or inflammation, especially in earlier stages of NAFLD progression.

Clinical or Translational Implications

This analysis identifies several hepatocyte surfaceome markers with significant differential expression during NAFLD progression, offering potential for clinical translation:

• Biomarkers for Disease Staging and Progression: The distinct expression patterns of genes like SLCO1B1 (downregulated in disease) and LEPR, ITGAV, OSMR, IL11RA, NPC1L1, and SLC40A1 (upregulated in disease) could serve as novel non-invasive or biopsy-based biomarkers for distinguishing healthy liver from various NAFLD stages and monitoring disease progression to cirrhosis. Surfaceome markers are particularly amenable to detection by advanced imaging techniques or liquid biopsy.
• Therapeutic Targets for NAFLD and its Complications: The upregulation of key pro-fibrotic signaling receptors (LEPR, ITGAV, OSMR, IL11RA) in advanced disease stages highlights critical pathways to target for anti-fibrotic therapies. Similarly, NPC1L1 and SLC40A1 present targets for managing lipid and iron overload, respectively, which are central to NAFLD pathogenesis.
• Optimizing Drug Treatment Strategies: Altered expression of transporters like SLCO1B1 and ABCB1 in diseased hepatocytes suggests that drug pharmacokinetics and efficacy could be significantly impacted in NAFLD patients. This information is crucial for optimizing dosing regimens and predicting drug-drug interactions, potentially leading to more personalized treatment approaches.
• Further Validation: These findings warrant further validation through functional studies in in vitro models (e.g., primary human hepatocytes, organoids) and in vivo models of NAFLD, as well as confirmation in larger patient cohorts using immunohistochemistry or other spatially resolved proteomics. Investigating the specific cell types expressing these markers within the complex liver microenvironment would also be crucial for comprehensive understanding.

12. Macrophage Condition-Specific Surfaceome Markers in NAFLD Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes the expression of surfaceome markers in macrophages across different stages of Non-alcoholic fatty liver disease (NAFLD) progression: healthy, NAFLD, end-stage NAFLD, and NASH cirrhosis. The dot plot displays individual samples on the y-axis, grouped by condition, and specific surfaceome genes on the x-axis. Dot size represents the fraction of cells within a sample expressing a given marker, while dot color intensity indicates the mean expression level. The markers were selected based on differential expression analysis comparing each disease condition against the 'healthy' reference, focusing on genes with a significant non-zero percentage score and fold change. The goal is to highlight macrophage markers that are characteristic of specific disease stages, interpret their biological roles, and discuss their potential as therapeutic targets for NAFLD and its complications.

Visual Summary

The dot plot reveals distinct patterns of macrophage surfaceome marker expression across the different liver conditions:

• Advanced Disease Upregulation: A prominent group of markers shows high expression (dark red, large dots) in macrophages from samples categorized as end_stage_nafld and nash_cirrhosis. These markers are generally expressed at much lower levels (lighter colors, smaller dots) or are absent in healthy and, to a lesser extent, in nafld samples. This pattern strongly suggests a specific macrophage activation state or population shift associated with advanced liver pathology.
  • Examples include MSR1, CD163L1, HLA-DRB1, CD74, HAVCR2 (TIM-3), TLR4, ALCAM, CSF1R, and MRC2, which are notably upregulated in end_stage_nafld and nash_cirrhosis conditions across many individual samples within those groups.
• Healthy vs. Disease Distinction: Macrophages in healthy liver samples generally exhibit lower and less widespread expression of many markers, particularly those highly upregulated in advanced disease. Some markers show constitutive expression across all conditions, indicating baseline macrophage functions, but the differential patterns are striking.
• Intermediate Stages: The nafld condition often shows an intermediate expression pattern, with some markers beginning to show increased expression compared to healthy, but typically not reaching the high levels observed in end_stage_nafld or nash_cirrhosis. This aligns with the progressive nature of NAFLD.
• Sample Heterogeneity: Within each condition, there is some variability in marker expression across individual samples (y-axis labels like SITTH12, SITTA2, etc.), indicating patient-specific differences or differing disease severity even within the same broad condition category. However, the overall trends per condition are clear.

Biological Interpretation

The observed differential expression of surfaceome markers on macrophages highlights their dynamic roles in NAFLD progression, encompassing immune activation, inflammation, lipid metabolism, and extracellular matrix remodeling. Below, we detail key markers prominently upregulated in advanced NAFLD/NASH and their biological significance.

Macrophage Markers Upregulated in Advanced NAFLD/NASH:

1. MSR1 (Macrophage Scavenger Receptor 1 / CD204)
   • Signaling & Mechanisms: MSR1 is a pattern recognition receptor critical for the uptake of modified lipoproteins (e.g., oxidized LDL) and apoptotic cells. Its activation contributes to foam cell formation in macrophages, a hallmark of steatohepatitis. MSR1 signaling can induce both pro-inflammatory (e.g., via DAMP recognition) and anti-inflammatory responses, depending on the context and ligands.
   • Liver Relevance: High expression of MSR1 in macrophages from end_stage_nafld and nash_cirrhosis suggests an active role in lipid accumulation, sterile inflammation, and the clearance of cellular debris in the severely damaged liver. This contributes to the perpetuation of liver injury and fibrosis.
   • Therapeutic Potential: Inhibiting MSR1 could reduce lipid overload in macrophages and dampen downstream inflammatory pathways, thereby mitigating liver inflammation and preventing the progression to fibrosis.
     • *Therapeutic Agents:* Research into scavenger receptor inhibitors for atherosclerosis may offer transferable strategies for NAFLD/NASH.
2. CD163L1 (CD163 molecule like 1)
   • Signaling & Mechanisms: CD163 is a well-known marker for M2-like macrophages, implicated in the clearance of hemoglobin-haptoglobin complexes and anti-inflammatory/pro-resolving functions. CD163L1 is related. High levels often reflect chronic inflammation and tissue repair, indicating a regulatory or potentially pro-fibrotic macrophage phenotype, rather than a purely pro-inflammatory one.
   • Liver Relevance: Upregulation of CD163L1 in advanced disease stages (end_stage_nafld, nash_cirrhosis) suggests a shift towards a reparative/pro-fibrotic macrophage phenotype in the liver. These macrophages actively participate in debris clearance and contribute to extracellular matrix remodeling and scar formation, key processes in liver fibrosis.
   • Therapeutic Potential: Modulating CD163L1/CD163 function could influence macrophage polarization, potentially shifting them away from pro-fibrotic activities.
     • *Therapeutic Agents:* Strategies aimed at repolarizing macrophages through surface receptor modulation are under investigation for fibrotic diseases. (See: PubMed search: CD163 NAFLD fibrosis macrophage )
3. HLA-DRB1 and CD74 (MHC Class II pathway)
   • Signaling & Mechanisms: HLA-DRB1 is an MHC class II gene critical for presenting exogenous antigens to CD4+ T cells, initiating adaptive immune responses. CD74 (Invariant chain) guides MHC class II molecules to the appropriate compartments for peptide loading. High expression signifies active antigen presentation.
   • Liver Relevance: The marked upregulation of these genes in end_stage_nafld and nash_cirrhosis indicates a robust activation of antigen-presenting functions by hepatic macrophages. This suggests ongoing adaptive immune responses against liver-derived antigens, which can perpetuate chronic inflammation, hepatocyte damage, and contribute to fibrosis in advanced liver disease.
   • Therapeutic Potential: While essential for immune surveillance, excessive or aberrant antigen presentation can drive chronic inflammation. Modulating this pathway could dampen pathological adaptive immune responses without broadly immunosuppressing the patient.
4. HAVCR2 (Hepatitis A virus cellular receptor 2 / TIM-3)
   • Signaling & Mechanisms: HAVCR2, also known as TIM-3, is an immune checkpoint molecule found on various immune cells, including macrophages. It typically delivers inhibitory signals upon ligand binding (e.g., galectin-9), limiting immune responses. However, on macrophages, TIM-3 signaling can also promote pro-inflammatory or pro-fibrotic responses depending on the context.
   • Liver Relevance: High TIM-3 expression on macrophages in end_stage_nafld and nash_cirrhosis suggests its involvement in shaping the hepatic immune microenvironment. It may contribute to immune dysfunction and chronic inflammation, potentially by regulating macrophage phenotype towards pro-fibrotic states in the context of persistent liver injury.
   • Therapeutic Potential: Modulating TIM-3 signaling on macrophages could reprogram their functions to be less pro-inflammatory or pro-fibrotic. The dual role of TIM-3 (inhibitory vs. pro-inflammatory/pro-fibrotic) requires careful consideration for therapeutic intervention in NAFLD/NASH. (See: UniProt: HAVCR2 )
5. TLR4 (Toll-like Receptor 4)
   • Signaling & Mechanisms: TLR4 is a central innate immune receptor recognizing bacterial lipopolysaccharide (LPS) and endogenous danger-associated molecular patterns (DAMPs) released during tissue damage. Its activation triggers potent pro-inflammatory signaling pathways (MyD88- and TRIF-dependent), leading to the production of cytokines and chemokines.
   • Liver Relevance: The strong upregulation of TLR4 in end_stage_nafld and nash_cirrhosis highlights its critical role in driving inflammation. Increased gut permeability in NAFLD/NASH allows LPS translocation to the liver, activating TLR4 on Kupffer cells and recruited macrophages. Additionally, saturated fatty acids and DAMPs from damaged hepatocytes can activate TLR4, perpetuating sterile inflammation and liver injury.
   • Therapeutic Potential: Targeting TLR4 signaling is a highly attractive strategy to reduce inflammation in NAFLD/NASH. Inhibiting this receptor could interrupt a key pathway driving disease progression.
     • *Therapeutic Agents:* Several TLR4 antagonists are under development for inflammatory conditions, and some may hold promise for NAFLD/NASH. (See: PubMed search: TLR4 inhibitor NAFLD )
6. ALCAM (Activated Leukocyte Cell Adhesion Molecule / CD166)
   • Signaling & Mechanisms: ALCAM is a cell adhesion molecule involved in homophilic and heterophilic interactions, facilitating cell-cell adhesion, immune cell trafficking, and tissue organization. It is important for lymphocyte-APC interactions and leukocyte extravasation.
   • Liver Relevance: Upregulation of ALCAM on hepatic macrophages in advanced NAFLD/NASH likely promotes the recruitment and retention of immune cells within the liver, intensifying inflammation. It may also influence interactions between macrophages and other liver cell types, potentially contributing to fibrogenesis by facilitating cell-cell contact and signaling in the damaged liver microenvironment.
   • Therapeutic Potential: Disrupting ALCAM-mediated adhesion could reduce pathogenic immune cell infiltration and potentially mitigate inflammation and fibrosis in the liver.
7. CSF1R (Colony Stimulating Factor 1 Receptor / CD115)
   • Signaling & Mechanisms: CSF1R is a tyrosine kinase receptor vital for the survival, proliferation, and differentiation of monocytes and macrophages, activated by CSF1 and IL-34. Its downstream signaling pathways (e.g., MAPK, PI3K/Akt) are crucial for macrophage homeostasis.
   • Liver Relevance: High CSF1R expression in macrophages from end_stage_nafld and nash_cirrhosis indicates a significant dependence of these macrophage populations on CSF1R signaling. This pathway drives the recruitment, proliferation, and survival of pro-inflammatory and pro-fibrotic macrophages in the chronically injured liver, acting as a master regulator of macrophage accumulation.
   • Therapeutic Potential: CSF1R inhibition is a promising strategy to deplete pathogenic macrophages or reprogram their functions in fibrotic diseases, including liver fibrosis.
     • *Therapeutic Agents:* CSF1R inhibitors (e.g., pexidartinib) are in clinical use or advanced clinical trials for various conditions, showing potential for reducing macrophage-driven pathology. (See: GeneCards: CSF1R, PubMed search: CSF1R inhibitor NAFLD fibrosis )
8. MRC2 (Mannose Receptor C-Type 2 / CD280)
   • Signaling & Mechanisms: MRC2 is an endocytic receptor expressed by macrophages and fibroblasts, involved in the clearance of collagen and other extracellular matrix (ECM) components. It contributes to ECM remodeling and can interact with uPAR.
   • Liver Relevance: Upregulation of MRC2 in macrophages during end_stage_nafld and nash_cirrhosis may reflect increased phagocytic activity necessary for clearing damaged cells and ECM debris in the fibrotic liver. While it contributes to ECM turnover, its role in a sustained inflammatory and fibrotic environment could also be complex, potentially influencing the balance between ECM degradation and deposition.
   • Therapeutic Potential: Modulating MRC2 activity could influence the dynamic processes of ECM turnover in liver fibrosis. Strategies to enhance beneficial ECM clearance or inhibit any pro-fibrotic signaling associated with MRC2 could be explored. (See: UniProt: MRC2 )

Clinical or Translational Implications

The identification of these macrophage surfaceome markers significantly advanced NAFLD/NASH provides critical insights for therapeutic development:

• Biomarker Potential: The differential expression patterns suggest that these markers could serve as diagnostic or prognostic biomarkers for assessing disease severity and progression, particularly distinguishing advanced fibrotic stages (end_stage_nafld, nash_cirrhosis) from earlier stages or healthy liver.
• Targeted Therapies: Several identified markers (e.g., TLR4, CSF1R, TIM-3, MSR1) represent cell surface receptors that are amenable to targeted therapeutic intervention using small molecules or antibodies.
  • Anti-inflammatory and Anti-fibrotic Strategies: Targeting TLR4 could reduce the influx of pro-inflammatory signals. Inhibiting CSF1R could decrease pathogenic macrophage populations and their pro-fibrotic activities. Modulating TIM-3 and MSR1 could reprogram macrophage function to be less detrimental in the context of chronic liver injury.
  • Precision Medicine: Understanding the specific macrophage phenotypes (as indicated by these surface markers) prevalent at different disease stages could enable the development of more precise therapies that target the most pathological macrophage populations, minimizing off-target effects.
• Experimental Validation: These findings warrant further experimental validation in preclinical models and human biopsy studies to confirm the functional significance of these markers in NAFLD/NASH pathogenesis and their responsiveness to potential therapeutic agents.

13. Hepatic Stellate Cell Condition-Specific Surfaceome Markers in NAFLD Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Hepatic stellate cells (HSCs) across different stages of Non-alcoholic fatty liver disease (NAFLD) progression: healthy, nafld, nash_cirrhosis, and end_stage_nafld. The dot plot visualizes the expression patterns of these markers, selected based on differential expression (using DEG_vs_ref against 'healthy') and surfaceome annotation, with a maximum of 40 markers per condition. The goal is to understand the changing surface landscape of HSCs during NAFLD progression, identify key signaling pathways involved, and explore potential therapeutic targets.

Visual Summary

The dot plot effectively illustrates the mean expression level (color intensity, from light to dark red) and the fraction of cells expressing a marker (dot size, from small to large) for Hepatic stellate cells across four conditions.

• Distinct Condition-Specific Profiles: The plot clearly shows different clusters of markers characteristic of each condition.
  • end_stage_nafld: A prominent set of markers (e.g., LRP1, OSMR, IL6ST, CACNA1C) shows high expression and prevalence specifically in the end_stage_nafld condition (top red box).
  • healthy: A cluster of markers (e.g., CDH19, CRIM1, ADGRE5, GPM6A, ESR1) is highly expressed and prevalent in healthy HSCs but largely absent or very low in all disease states (middle red box). This suggests their role in maintaining HSC quiescence.
  • nafld and nash_cirrhosis: These conditions share some upregulated markers (e.g., PDGFRB, ADRA1A, EDNRB, SLC2A7, SLC01B3, SERINC5), indicating common fibrogenic pathways, though with varying intensities and prevalence. Some markers appear to increase progressively from nafld to nash_cirrhosis and end_stage_nafld.
• Progressive Changes: Several markers show a gradient of expression and/or prevalence that increases from healthy through nafld and nash_cirrhosis to end_stage_nafld, suggesting their involvement in disease progression and increasing fibrotic activity. Conversely, markers specific to the healthy state diminish as disease progresses.
• Marker Abundance: The bar chart on the right indicates the mean expression in the group for each marker, reinforcing the visual impression of differential expression.

Biological Interpretation

Hepatic stellate cells (HSCs) are central to liver fibrosis, transitioning from a quiescent to an activated myofibroblast-like state in response to chronic liver injury, including that caused by NAFLD/NASH. The identified surfaceome markers reflect critical changes in HSC phenotype and function during disease progression.

Markers Associated with Quiescent/Healthy HSCs

Markers highly expressed in healthy HSCs, such as CDH19, CRIM1, ADGRE5, GPM6A, ESR1, and LGALS1, likely play roles in maintaining HSC quiescence and normal liver homeostasis.

• CDH19 (Cadherin-19): A cell adhesion molecule, its presence in healthy HSCs suggests a role in maintaining cell-cell contacts or cell-matrix interactions that keep HSCs in a quiescent state. Loss of such adhesion molecules can be associated with cell activation and migration. Its downregulation in disease states could facilitate HSC activation and migration to injury sites. GeneCards: CDH19
• CRIM1 (Cysteine-rich transmembrane BMP regulator 1): Involved in regulating BMP (Bone Morphogenetic Protein) signaling, which plays diverse roles in liver development, regeneration, and disease. In quiescent HSCs, CRIM1 might help maintain a balanced microenvironment or suppress profibrogenic BMP pathways.
• ADGRE5 (Adhesion G Protein-Coupled Receptor E5 / CD97): An adhesion GPCR with roles in cell adhesion, migration, and immune regulation. Its high expression in healthy HSCs suggests it might contribute to maintaining the quiescent phenotype or regulate HSC interactions with other liver cells under normal conditions.

Markers Associated with NAFLD/NASH Progression and Fibrosis

Several markers are upregulated in NAFLD, nash_cirrhosis, and end-stage NAFLD, indicating their involvement in HSC activation and fibrogenesis.

• PDGFRB (Platelet-Derived Growth Factor Receptor Beta): Highly expressed in diseased states. PDGF-BB is a well-established potent mitogen and chemoattractant for HSCs, driving their proliferation and activation. Activation of PDGFRβ signaling is a hallmark of activated HSCs and a key pathway in liver fibrosis. PubMed: PDGF-BB, PDGFRβ, and Liver Fibrosis
• EDNRB (Endothelin Receptor Type B): Endothelin-1 (ET-1) is a potent vasoconstrictor and profibrogenic peptide. Binding to EDNRB on HSCs can promote their contraction, activation, and extracellular matrix production, contributing to increased portal pressure and fibrosis. GeneCards: EDNRB
• ADRA1A (Alpha-1A Adrenergic Receptor): Adrenergic signaling via ADRA1A on HSCs can induce their contraction and potentially contribute to fibrosis and intrahepatic vasoconstriction in liver disease.
• LRP1 (LDL Receptor Related Protein 1): Upregulated in end-stage NAFLD. LRP1 is a multifunctional receptor involved in ECM degradation, TGF-β activation, and cellular signaling. In activated HSCs, LRP1 can regulate collagen synthesis and degradation, contributing to ECM remodeling. UniProt: LRP1
• OSMR (Oncostatin M Receptor) and IL6ST (GP130): These receptors, which are part of the IL-6 family receptor complex, are highly expressed in end-stage NAFLD HSCs. Oncostatin M (OSM) signaling through OSMR/GP130 activates JAK-STAT pathways, promoting inflammation, proliferation, and fibrogenesis in HSCs. PubMed: Oncostatin M liver fibrosis
• FGFR1 (Fibroblast Growth Factor Receptor 1): FGF signaling, mediated by FGFR1, can promote HSC proliferation, survival, and fibrogenic activity, playing a role in the progression of liver fibrosis.
• SLC2A7 (GLUT7): A glucose transporter, its upregulation suggests altered metabolic activity in activated HSCs, possibly related to increased energy demands for ECM synthesis.

Potential as Therapeutic Targets for NAFLD and its Complications

The identified surfaceome markers represent attractive therapeutic targets due to their accessibility and specific roles in HSC activation and fibrogenesis.

• PDGFRB: Given its central role in HSC proliferation and activation, inhibition of PDGFRβ signaling is a well-studied antifibrotic strategy. Tyrosine kinase inhibitors (TKIs) like imatinib, sorafenib, or nilotinib, which target PDGFRβ, have been investigated in preclinical models of liver fibrosis. PubMed: PDGFRbeta inhibitor liver fibrosis
• EDNRB: Endothelin receptor antagonists (ERAs) such as bosentan, ambrisentan, or macitentan are approved for pulmonary hypertension and have shown antifibrotic potential in preclinical liver fibrosis models by reducing portal hypertension and HSC activation. Targeting EDNRB specifically might offer a more refined approach.
• OSMR/IL6ST: Inhibiting OSM/GP130 signaling, potentially through antibodies against OSM or its receptors, or JAK inhibitors, could mitigate inflammatory and fibrogenic responses in activated HSCs. Preclinical studies have shown promise for JAK inhibitors in liver fibrosis.
• FGFR1: Inhibitors targeting FGFR1 signaling could be explored to suppress HSC proliferation and fibrogenesis. Various FGFR inhibitors are in clinical development for cancer, and their repurposing for fibrotic diseases warrants investigation.
• Markers of Quiescence (e.g., CDH19, ADGRE5): Upregulating or restoring the function of these "healthy" markers could be a strategy to induce reversion of activated HSCs to a quiescent state or prevent their activation. This approach might involve agonists or gene therapies designed to enhance their expression.

Clinical or Translational Implications

The differential expression of surfaceome markers on Hepatic stellate cells across NAFLD progression offers several clinical and translational opportunities:

• Biomarker Discovery: The identified markers could serve as non-invasive biomarkers for diagnosing NAFLD stages, assessing fibrosis severity, or monitoring therapeutic response. For example, specific markers highly upregulated in nash_cirrhosis or end_stage_nafld could indicate advanced disease.
• Targeted Therapies: The surface localization of these markers makes them highly accessible for antibody-based therapies, small molecule inhibitors, or even cell-targeted drug delivery systems. Targeting receptors like PDGFRβ, EDNRB, or OSMR/IL6ST could specifically modulate HSC activity, reducing fibrosis progression while minimizing off-target effects. This is particularly relevant for NAFLD, where effective antifibrotic therapies are urgently needed.
• Understanding Disease Mechanisms: The shift in surfaceome profile from healthy to diseased states provides deeper insights into the molecular mechanisms driving HSC activation and fibrogenesis in NAFLD. This understanding can inform the development of novel therapeutic strategies. For instance, the metabolic shift suggested by SLC2A7 upregulation could point to metabolic reprogramming as a therapeutic avenue.
• Combination Therapies: Targeting multiple pathways simultaneously, perhaps through a combination of existing drugs or novel agents against these HSC surface markers, might offer a more comprehensive and effective approach to treating advanced NAFLD and cirrhosis.

14. Gene Ontology (GSA) Analysis of Hepatocytes Across NAFLD/NASH Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GO) enrichment results, referred to as Gene Set Analysis (GSA), specifically for Hepatocytes. The GSA was performed by comparing gene expression in Hepatocytes from different conditions (NAFLD, NASH cirrhosis, end-stage NAFLD) against a healthy reference, and also healthy Hepatocytes against all diseased conditions combined. The results highlight biological pathways and processes that are significantly up-regulated (based on up-regulated differentially expressed genes, DEGs) in each compared condition. These findings provide insights into the functional changes occurring in Hepatocytes as non-alcoholic fatty liver disease (NAFLD) progresses through its various stages.

Visual Summary

The provided visualizations consist of four bar plots, each representing GSA results for Hepatocytes under different comparative conditions. Each plot displays the top enriched GO terms (pathways/processes) on the y-axis, ranked by their statistical significance (-log(p-val) on the left x-axis, and -log(q-val) on the right x-axis).

1. GSA for Hepatocyte: end_stage_nafld_vs_healthy: Shows pathways up-regulated in Hepatocytes from end-stage NAFLD patients compared to healthy individuals. Prominent terms include lipid metabolism, cellular senescence, apoptosis, focal adhesion, and various signaling pathways.
2. GSA for Hepatocyte: healthy_vs_others: Illustrates pathways up-regulated in healthy Hepatocytes compared to a pooled group of all diseased conditions (NAFLD, NASH cirrhosis, end-stage NAFLD). This highlights functions potentially suppressed or dysregulated in disease. Key terms include protein processing, complement and coagulation cascades, and certain metabolic pathways.
3. GSA for Hepatocyte: nafld_vs_healthy: Details pathways up-regulated in Hepatocytes from NAFLD patients compared to healthy. This plot is heavily dominated by various metabolic processes, particularly fatty acid degradation, PPAR signaling, and drug metabolism.
4. GSA for Hepatocyte: nash_cirrhosis_vs_healthy: Presents pathways up-regulated in Hepatocytes from NASH cirrhosis patients compared to healthy. Similar to NAFLD, metabolic pathways are strong, but also includes terms related to pyruvate metabolism, thermogenesis, and steroid biosynthesis.

Across the progression from NAFLD to NASH cirrhosis and end-stage disease, there's a clear shift in the most significantly perturbed pathways. Initial metabolic dysregulation in NAFLD gives way to increasing cellular stress, death, and tissue remodeling pathways in more advanced stages, while healthy hepatocytes demonstrate robust protein homeostasis and specific metabolic functions.

Biological Interpretation

Hepatocyte Changes in Early to Advanced NAFLD (nafld_vs_healthy and nash_cirrhosis_vs_healthy)

Hepatocytes in NAFLD and NASH cirrhosis exhibit profound metabolic reprogramming:

• Lipid Metabolism Dysregulation: A hallmark of NAFLD, pathways like "Fatty acid degradation," "Peroxisome," "PPAR signaling pathway," "Cholesterol metabolism," "Glycerolipid metabolism," and "Primary bile acid biosynthesis" are consistently up-regulated. This reflects the liver's attempt to process excessive lipids, leading to oxidative stress and lipotoxicity. [PubMed search: NAFLD lipid metabolism pathway]
• Amino Acid and Carbohydrate Metabolism Alterations: Terms such as "Glyoxylate and dicarboxylate metabolism," "Glycine, serine and threonine metabolism," "Valine, leucine and isoleucine degradation," "Pyruvate metabolism," and "Glycolysis / Gluconeogenesis" indicate shifts in energy substrate utilization and amino acid catabolism, crucial for maintaining cellular energy balance under stress.
• Drug Metabolism and Xenobiotic Processing: "Drug metabolism" and "Metabolism of xenobiotics by cytochrome P450" are highly enriched. This suggests altered detoxification capacities in diseased hepatocytes, potentially increasing susceptibility to drug-induced liver injury or environmental toxins.
• Bile Acid Homeostasis: "Bile secretion" and "Primary bile acid biosynthesis" are up-regulated in NAFLD, indicating dysregulation of bile acid synthesis and transport, which can contribute to cholestasis and progression of liver injury.
• Energy Regulation: "AMPK signaling pathway" is a key sensor of cellular energy status. Its dysregulation in NAFLD/NASH points to impaired energy homeostasis. "Thermogenesis" is also notably up-regulated in NASH cirrhosis, suggesting altered mitochondrial function or energy expenditure.

Severe Pathological Changes in End-Stage NAFLD (end_stage_nafld_vs_healthy)

In end-stage NAFLD, hepatocytes show a severe state of cellular stress, damage, and adaptation:

• Cellular Stress and Death: "Cellular senescence," "Apoptosis," and "Autophagy" are highly up-regulated, indicating widespread cellular damage and activation of programmed cell death and degradation pathways. This is characteristic of chronic liver injury leading to fibrosis and cirrhosis.
• Tissue Remodeling and Fibrosis: Pathways like "Focal adhesion," "Adherens junction," and "Tight junction" are enriched. These terms relate to cell-matrix interactions and cell-cell junctions, suggesting significant alterations in hepatocyte adhesion, morphology, and contribution to extracellular matrix remodeling that underlies fibrosis. [PubMed search: liver fibrosis cell adhesion pathways]
• Inflammatory and Immune Responses: Terms such as "Bacterial invasion of epithelial cells," "Salmonella infection," and "NOD-like receptor signaling pathway" suggest that end-stage hepatocytes actively engage in or respond to inflammatory signals, potentially including pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs) that drive sterile inflammation in the liver.
• Cancer-Related Pathways: "Pathways in cancer" and "MAPK signaling pathway" are activated, which can be linked to chronic inflammation, regeneration attempts, and increased risk of hepatocellular carcinoma in cirrhotic livers.

Defining Healthy Hepatocyte Function (healthy_vs_others)

When comparing healthy hepatocytes against all diseased conditions, certain pathways are up-regulated in the healthy state, implying these functions are potentially suppressed or dysregulated in disease:

• Protein Homeostasis: "Protein processing in endoplasmic reticulum" and "Protein export" are prominent in healthy hepatocytes. This indicates a robust capacity for protein synthesis, folding, and secretion, which is vital for liver function but often compromised in chronic liver disease.
• Baseline Metabolic and Immune Regulation: "Lysine degradation," "Sphingolipid signaling pathway," "Complement and coagulation cascades," and "TNF signaling pathway" appear. These suggest that healthy hepatocytes maintain specific metabolic balances and a controlled inflammatory response that becomes perturbed in NAFLD/NASH.
• Detoxification Capacity: The enrichment of "Chemical carcinogenesis" in healthy hepatocytes might reflect a maintained capacity for xenobiotic metabolism and detoxification, which could be overwhelmed or dysregulated as disease progresses.

Clinical or Translational Implications

The GSA results for Hepatocytes across NAFLD/NASH progression highlight several potential areas for clinical focus and therapeutic development:

• Targeting Metabolic Reprogramming: The consistent dysregulation of lipid, amino acid, and carbohydrate metabolism pathways (e.g., PPAR signaling, fatty acid degradation) reinforces these as primary targets for NAFLD interventions. New therapies could aim to restore normal metabolic flux and reduce lipotoxicity.
• Addressing Cellular Stress and Death: In advanced disease, pathways related to cellular senescence, apoptosis, and autophagy are prominent. Developing treatments that mitigate these cellular demise pathways could be crucial for preventing progression to cirrhosis and liver failure. [PubMed search: cellular senescence liver disease]
• Modulating Fibrosis: Alterations in cell adhesion and junction pathways in end-stage disease point to the involvement of hepatocytes in matrix remodeling. Therapies targeting these cellular interactions could help reduce fibrosis progression.
• Drug Safety in Liver Disease: The significant changes in "Drug metabolism" and "Metabolism of xenobiotics by cytochrome P450" across all disease stages have critical implications for drug prescribing and dosing in NAFLD/NASH patients, as drug efficacy and toxicity profiles may be altered.
• Understanding Disease Susceptibility: The up-regulation of infection-related pathways in late-stage disease suggests a potential altered immune response or increased susceptibility to infections, warranting careful clinical management.

15. Gene Set Enrichment Analysis (GSEA) of Liver Cell Types Across NAFLD Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results visualized as a dot plot. It compares gene expression rankings in various liver cell types (Endothelial cell, Hepatic stellate cell, Hepatocyte, ILC, Macrophage, NK cell, Plasma cell) under different Non-alcoholic fatty liver disease (NAFLD) conditions (nafld, nash_cirrhosis, end_stage_nafld) against a healthy reference state. Additionally, comparisons of 'healthy' cells against 'others' (all disease conditions combined) are included. The plot shows 120 gene sets (pathways) with their Normalized Enrichment Score (NES) indicated by color (red for upregulation, blue for downregulation) and significance by dot size (-log10(p-value)).

Visual Summary

The dot plot effectively illustrates condition- and cell-type-specific pathway enrichments.

• Color Scale (NES): A gradient from blue (NES ~ -2.5, downregulated) to red (NES ~ 2.5, upregulated) indicates the direction and strength of pathway enrichment. Intense red dots signify pathways highly activated or upregulated in a given cell type and condition compared to healthy, while intense blue dots indicate significant downregulation.
• Dot Size (-log(P)): Larger dots represent more statistically significant enrichment (smaller p-value), with sizes corresponding to -log(P) values from 6 to 30. This highlights pathways with the strongest evidence of differential activity.
• Widespread Dysregulation: Many pathways show significant enrichment (large, colored dots) across multiple cell types and disease conditions, particularly in nash_cirrhosis and end_stage_nafld.
• Cell-type Specificity: Distinct patterns of pathway enrichment are observed for different cell types, reflecting their specialized roles in liver pathology.
• Progression-Related Changes: As the disease progresses from nafld to nash_cirrhosis and end_stage_nafld, there is a general trend towards more pronounced and widespread pathway dysregulation, often with an increase in both NES magnitude and statistical significance.
• Healthy vs. Others: The 'healthy_vs_others' columns provide a contrasting view, showing pathways characteristic of healthy cells, often in opposition to the trends observed in disease conditions.

Biological Interpretation

The GSEA results provide strong biological insights into the molecular mechanisms underpinning NAFLD progression:

Hepatocytes: The Core of Metabolic Dysfunction

• Metabolic Reprogramming: In Hepatocytes, pathways central to NAFLD are highly enriched: "Non-alcoholic fatty liver disease", "Fatty acid degradation", "Fatty acid biosynthesis", "Cholesterol metabolism", and "Insulin resistance" are consistently and strongly upregulated across all disease stages (nafld, nash_cirrhosis, end_stage_nafld vs. healthy). This reflects the core pathology of lipid accumulation, altered glucose and lipid handling, and reduced insulin sensitivity.
• Cellular Stress and Dysfunction: "Protein processing in endoplasmic reticulum" is upregulated, indicative of ER stress which is a key driver in NASH progression. "HIF-1 signaling pathway" upregulation suggests a hypoxic or pseudo-hypoxic environment, often associated with metabolic stress. "Apoptosis" pathway shows increasing upregulation in advanced stages (nash_cirrhosis, end_stage_nafld), highlighting increased hepatocyte cell death.
• Energy Metabolism: "Oxidative phosphorylation" is consistently downregulated in diseased Hepatocytes, contrasting with its strong upregulation in healthy Hepatocytes (seen in healthy_vs_others), suggesting mitochondrial dysfunction.
• Cancer Risk: The "Hepatocellular carcinoma" pathway is markedly upregulated in Hepatocytes during nash_cirrhosis and end_stage_nafld, underscoring the increased risk of liver cancer in advanced liver disease.

Hepatic Stellate Cells (HSCs): Drivers of Fibrosis

• Fibrogenic Activation: HSCs, the primary fibrogenic cells in the liver, show significant upregulation of "ECM-receptor interaction" and "Focal adhesion" pathways in all disease conditions. These pathways are crucial for cell-extracellular matrix interactions and cellular mechanosensing, central to HSC activation and fibrogenesis. This activation becomes more prominent in nash_cirrhosis and end_stage_nafld.
• Inflammation and Stress: "TNF signaling pathway", "HIF-1 signaling pathway", "Protein processing in endoplasmic reticulum", and "Apoptosis" are also upregulated in diseased HSCs, indicating that these cells are actively involved in inflammatory processes and experience stress and cell death.

Macrophages: Orchestrators of Inflammation

• Immune Activation: Macrophages exhibit strong upregulation of inflammatory and immune response pathways, including "Antigen processing and presentation", "Cytosolic DNA-sensing pathway", "RIG-I-like receptor signaling pathway", "TNF signaling pathway", and "MAPK signaling pathway" across all disease stages. This points to active innate immune responses and a pro-inflammatory (e.g., M1-like) phenotype, crucial for NASH pathogenesis.
• Cell Migration: "Leukocyte transendothelial migration" is notably upregulated in advanced disease stages (nash_cirrhosis, end_stage_nafld), indicating increased immune cell infiltration into the liver.

Endothelial Cells: Vascular Remodeling and Inflammation

• Angiogenesis and Endothelial Dysfunction: Endothelial cells show consistent upregulation of "HIF-1 signaling pathway", "ECM-receptor interaction", and "Focal adhesion" in disease, suggesting active vascular remodeling, angiogenesis, and altered interactions with the ECM.
• Inflammatory Response: Similar to macrophages, endothelial cells also exhibit upregulation of "TNF signaling pathway", "MAPK signaling pathway", and "Leukocyte transendothelial migration" in advanced disease, contributing to liver inflammation and recruitment of immune cells.

ILCs, NK Cells, and Plasma Cells: Modulators of Immunity

• ILCs and NK Cells: These innate immune cells show more variable, but still significant, upregulation of certain immune pathways (e.g., "Antigen processing and presentation", "TNF signaling pathway") in advanced disease, suggesting their involvement in the evolving immune landscape of chronic liver injury.
• Plasma Cells: While less broadly enriched, plasma cells show upregulation of "B cell receptor signaling pathway" in nash_cirrhosis, indicating an activated humoral immune response, potentially contributing to chronic inflammation.

Clinical or Translational Implications

The comprehensive GSEA across cell types and disease stages highlights several clinically relevant aspects:

• Biomarker Identification: The consistently dysregulated pathways and their associated genes, particularly in Hepatocytes and HSCs, could serve as targets for developing non-invasive biomarkers for early NAFLD/NASH detection and monitoring disease progression. For instance, markers related to lipid metabolism, ER stress, and fibrosis.
• Therapeutic Targets: The identified pathways provide potential therapeutic targets. Inhibiting key pathways like "TNF signaling pathway" (in macrophages, HSCs, endothelial cells) could mitigate inflammation, while targeting "ECM-receptor interaction" or "Focal adhesion" (in HSCs) could reduce fibrosis. Modulating "Insulin resistance" and core metabolic pathways in Hepatocytes remains crucial for NAFLD treatment.
• Precision Medicine: The cell-type-specific nature of pathway dysregulation suggests that therapeutic strategies might need to be tailored to target specific cell populations or their distinct molecular vulnerabilities to achieve optimal outcomes in NAFLD/NASH.
• Understanding Disease Progression: The progressive enrichment of pathways like "Apoptosis" and "Hepatocellular carcinoma" with disease advancement underscores the importance of early intervention to prevent these severe complications.

The detailed breakdown of pathway activity by cell type and disease condition offers a rich resource for understanding NAFLD pathogenesis and for guiding future research and therapeutic development.

16. Discussion

The comprehensive single-cell analysis of human liver tissue across NAFLD progression stages reveals a complex and dynamic landscape of cellular remodeling, immune dysregulation, and altered intercellular communication. UMAP visualizations and population analyses clearly demonstrate a progressive departure from healthy liver homeostasis, marked by a relative decrease in hepatocytes and a concomitant increase in various immune and stromal cell populations as the disease advances to NASH cirrhosis and end-stage NAFLD. This shift underscores the central role of hepatocyte injury and replacement by fibrotic and inflammatory cells in disease progression.

Hepatocytes, the primary functional cells of the liver, exhibit profound metabolic reprogramming. Gene Ontology (GSA) and Gene Set Enrichment Analysis (GSEA) consistently highlight upregulation of lipid metabolism, fatty acid degradation, and cholesterol metabolism in diseased hepatocytes, reflecting lipotoxicity and metabolic stress. Notably, pathways related to ER stress, apoptosis, and cellular senescence are increasingly prominent in end-stage NAFLD hepatocytes, signifying widespread cellular damage. Conversely, healthy hepatocytes maintain robust protein homeostasis and specific metabolic functions that appear compromised in disease. The upregulation of the 'Hepatocellular carcinoma' pathway in advanced stages also underscores the increased cancer risk.

Immune cells, particularly macrophages and T cells, are key orchestrators of inflammation. Macrophage population analysis reveals a critical shift from a predominantly homeostatic M2C phenotype in healthy livers towards a pro-inflammatory M1 and pro-fibrotic M2A dominance in advanced NAFLD/NASH. Surfaceome marker analysis further supports this, showing significant upregulation of MSR1, TLR4, CSF1R, and HAVCR2 (TIM-3) on macrophages in diseased states, indicative of heightened lipid uptake, innate immune activation, and proliferation of pathogenic macrophage subsets. Similarly, T cell and Innate Lymphoid Cell (ILC) analysis demonstrates a progressive increase in ILC1 and effector T cell subsets (Th1, Th17, Th2, Th22) in NASH cirrhosis and end-stage NAFLD, coupled with a decrease in regulatory ILCreg cells. This suggests a robust, yet dysregulated, adaptive and innate immune response driving chronic inflammation and immune-mediated liver damage.

Hepatic stellate cells (HSCs), the main drivers of fibrosis, undergo significant activation. Surfaceome analysis of HSCs shows downregulation of quiescent markers (e.g., CDH19) and marked upregulation of activation markers such as PDGFRB, EDNRB, OSMR, IL6ST, and FGFR1 in diseased conditions. These receptors mediate crucial pro-fibrotic signaling pathways, leading to HSC proliferation, contraction, and extracellular matrix production. The extensive involvement of these cells in ECM remodeling is corroborated by GSEA findings.

Cell-cell interaction (CCI) analyses further elucidate the intricate crosstalk driving pathology. A striking increase in the number, strength, and significance of interactions is observed as NAFLD progresses. Notably, strong integrin-extracellular matrix (ECM) interactions, as well as pro-fibrotic (TGFB1-TGFbeta_receptor1, PDGF) and pro-inflammatory (TNF, SPP1-integrin, CXCL12-CXCR4) signaling pathways, become dominant between hepatocytes, HSCs, and macrophages in advanced disease. The emergence of interactions involving 'unassigned' cell types in end-stage NAFLD suggests significant cellular dysregulation or the presence of novel pathological cell states. This sustained and intensified intercellular communication forms a vicious cycle, perpetuating inflammation, hepatocyte injury, and fibrogenesis.

In summary, this study provides a high-resolution view of the cellular and molecular changes during NAFLD progression. The data highlights a coordinated, cell-type-specific dysregulation of metabolic, inflammatory, and fibrogenic pathways, driven by complex intercellular communication. These findings underscore the multifactorial nature of NAFLD pathogenesis and offer a foundation for identifying novel diagnostic biomarkers and targeted therapeutic strategies that address the specific contributions of distinct cell populations at different disease stages.

Hypotheses:

1. Progressive activation of hepatic stellate cells (HSCs), driven by paracrine signaling (e.g., PDGF, ET-1, OSM) from injured hepatocytes and activated macrophages, is a central mechanism for fibrosis in advanced NAFLD/NASH.
2. Macrophage polarization shifts from anti-inflammatory (M2C) to pro-inflammatory (M1) and pro-fibrotic (M2A/M2D) phenotypes, mediated by TLR4 and CSF1R signaling, exacerbate liver inflammation and injury in NAFLD progression.
3. Dysregulation of innate (ILC1, ILC2, ILC3) and adaptive (Th1, Th17) immune cell subsets, coupled with a decrease in regulatory immune cells (Treg, ILCreg), contributes to chronic inflammation and immune-mediated liver damage in NASH cirrhosis.
4. Metabolic reprogramming and ER stress in hepatocytes, characterized by altered lipid and glucose metabolism, directly fuel hepatocyte lipotoxicity and trigger inflammatory and fibrotic responses in the liver microenvironment.
5. Increased and altered cell-cell communication involving integrin-ECM, TGF-β, and TNF pathways between hepatocytes, HSCs, and macrophages forms a positive feedback loop that accelerates inflammation and fibrosis in advanced NAFLD.

Potential therapeutic targets:

1. PDGFRB (Platelet-Derived Growth Factor Receptor Beta): PDGFRB is highly expressed in activated hepatic stellate cells (HSCs) across diseased conditions, and its signaling is a well-established potent mitogen and chemoattractant for HSCs, driving their proliferation and activation, which is central to liver fibrosis. Evidence: Analysis shows PDGFRB is highly expressed in diseased HSCs (nafld, nash_cirrhosis, end_stage_nafld). Activation of PDGFRβ signaling is a hallmark of activated HSCs and a key pathway in liver fibrosis. Validation: Inhibition of PDGFRβ signaling using tyrosine kinase inhibitors (e.g., imatinib, sorafenib, nilotinib) has been investigated in preclinical models of liver fibrosis, showing antifibrotic potential. Clinical trials for similar fibrotic conditions are ongoing.
2. TLR4 (Toll-like Receptor 4): TLR4 is strongly upregulated in macrophages from advanced NAFLD/NASH, acting as a central innate immune receptor that recognizes bacterial LPS and endogenous DAMPs, triggering potent pro-inflammatory signaling pathways. Evidence: High TLR4 expression in macrophages from end_stage_nafld and nash_cirrhosis samples highlights its critical role in driving inflammation, perpetuated by increased gut permeability and activation by LPS/DAMPs in the diseased liver. Validation: Developing and testing TLR4 antagonists in preclinical models of NAFLD/NASH to reduce inflammation. Clinical trials for TLR4 antagonists in other inflammatory conditions may offer insights for repurposing.
3. IL11RA (Interleukin 11 Receptor Alpha): IL11RA is strongly upregulated in hepatocytes in nash_cirrhosis and end_stage_nafld. IL-11/IL11RA signaling is a potent driver of fibrosis in multiple organs, activating pathways that lead to increased extracellular matrix production. Evidence: Analysis shows strong upregulation of IL11RA in hepatocytes from nash_cirrhosis and end_stage_nafld patients, indicating a critical role for IL-11 signaling in promoting hepatic fibrosis by activating fibroblasts and stellate cells. Validation: Preclinical and early clinical investigations are underway for inhibitors of IL11RA or IL-11 itself as highly promising anti-fibrotic strategies.
4. NPC1L1 (Niemann-Pick C1-Like 1): NPC1L1 is upregulated in hepatocytes during nash_cirrhosis and end_stage_nafld, contributing to increased hepatic cholesterol uptake, which exacerbates lipotoxicity and inflammation, a hallmark of NAFLD/NASH. Evidence: High NPC1L1 expression in hepatocytes from nash_cirrhosis and end_stage_nafld conditions contributes to hepatic cholesterol accumulation, a central pathology of NAFLD. Validation: Ezetimibe, an FDA-approved NPC1L1 inhibitor for hypercholesterolemia, has shown promise in reducing hepatic cholesterol content and ameliorating steatosis in NAFLD management. Further clinical evaluation in NAFLD/NASH is warranted.
5. TGFbeta_receptor1 (Transforming Growth Factor Beta Receptor 1): The TGFB1-TGFbeta_receptor1 pathway shows strong activation in advanced NAFLD, particularly in macrophage-hepatic stellate cell interactions, as TGF-β is a master regulator of liver fibrosis. Evidence: Cell-cell interaction analysis shows increased strength and significance of TGFB1-TGFbeta_receptor1 interactions in end_stage_nafld and nash_cirrhosis, promoting extracellular matrix production and HSC activation. Validation: Several anti-TGF-β strategies, including antibodies targeting TGF-β ligands or small molecule inhibitors of TGFbeta_receptor1 (e.g., galunisertib), are in various stages of clinical development for fibrotic diseases and can be applied to NAFLD.

Follow-up validation ideas:

1. Perform flow cytometry or immunostaining on NAFLD patient liver biopsies to validate the observed changes in T cell, ILC, and macrophage subset populations (e.g., ILC1, M1 macrophages, Th17 cells) and surface marker expression (e.g., MSR1, TLR4 on macrophages; PDGFRB, OSMR on HSCs).
2. Utilize spatial transcriptomics or proteomics to map the precise location and interactions of activated HSCs, specific macrophage subsets, and stressed hepatocytes within the fibrotic liver architecture, confirming cell-cell communication hubs identified by CellPhoneDB.
3. Conduct in vitro perturbation assays using primary human hepatocytes, HSCs, or liver macrophage cell lines to test the functional impact of inhibiting identified receptors (e.g., PDGFRβ, TLR4, OSMR, ITGAV) on cell activation, proliferation, extracellular matrix production, and inflammatory cytokine secretion.
4. Administer specific inhibitors (e.g., CSF1R inhibitors, TGF-β receptor kinase inhibitors, integrin αV antagonists) in diet-induced NAFLD/NASH preclinical models to evaluate their efficacy in reducing liver inflammation, steatosis, and fibrosis.
5. Perform co-culture experiments with hepatocytes, HSCs, and macrophages to validate the functional consequences of specific ligand-receptor pairs (e.g., TGFB1-TGFbeta_receptor1, SPP1-integrin_aVb1_complex, CXCL12-CXCR4) on cell activation and signaling in a controlled setting.
6. Characterize the 'unassigned' cell population in end-stage NAFLD using re-clustering or focused marker gene analysis to identify their specific phenotype and functional contribution to advanced liver disease.

Limitations:

This report is based on single-cell RNA sequencing data, which provides transcriptomic snapshots of cellular states and inferred cell-cell interactions. Functional validation of these findings through experimental assays (e.g., in vitro perturbation, in vivo models) is crucial to establish causality. The 'unassigned' cell populations in advanced disease stages warrant further characterization to fully understand their biological significance. Observed inter-sample variability within conditions suggests patient heterogeneity in disease manifestation. Finally, while therapeutic targets are identified based on strong evidence, clinical efficacy and safety require rigorous clinical trials.

17. 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 (e.g., Liver Epithelial cell, Hepatocyte), immune cells (e.g., T cell, Macrophage), and stromal cells (e.g., Hepatic stellate cell, Fibroblast) 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 Hepatocyte 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 Liver tissue and, where relevant, its potential as a therapeutic target for Non-alcoholic fatty liver disease (NAFLD) and its complications 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 Liver tissue and, where relevant, its potential as a therapeutic target for Non-alcoholic fatty liver disease (NAFLD) and its complications 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 Liver tissue and, where relevant, its potential as a therapeutic target for Non-alcoholic fatty liver disease (NAFLD) and its complications and related therapeutic agents.
14. Show Gene Ontology (GSA) analysis results for Hepatocyte as a bar plot, and save the result.
15. 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.