SCODiA Report by MLBI Lab

Single-Cell Analysis Reveals Immune Dysregulation, Fibrogenic Activation, and Cholangiocyte Stress in Human Liver Cirrhosis

This report provides a comprehensive single-cell analysis of human liver, highlighting profound cellular and molecular alterations in cirrhosis compared to healthy controls. Key findings include significant shifts in immune cell populations, particularly the expansion of pro-inflammatory macrophages and altered T cell subsets, alongside the activation and metabolic reprogramming of hepatic stellate cells. A heightened and dysregulated cell-cell interaction network, driven by fibrogenic and inflammatory pathways, characterizes the cirrhotic microenvironment. These insights point to specific cellular and molecular targets for therapeutic intervention in liver cirrhosis.

Contents

0. Dataset overview
1. UMAP Visualization of Single-Cell RNA-seq Data in Healthy and Cirrhotic Human Liver
2. UMAP Visualization of Key Marker Genes and Minor Cell Type Annotations in Human Liver
3. Overall Celltype_subset Marker Expression Analysis for Annotation Quality Check
4. Minor Cell Type Population Analysis in Cirrhosis vs. Healthy Liver
5. Liver Lymphoid Cell Subset Composition in Cirrhosis vs. Healthy Conditions
6. Macrophage Subset Population Analysis in Liver Cirrhosis
7. Changes in T Cell Subset Proportions in Liver Cirrhosis
8. Macrophage Subset Population Shifts in Liver Cirrhosis
9. 간경변증 및 건강 간 조직의 세포-세포 상호작용 분석
10. Cell-Cell Interaction Landscape of Immune Checkpoint and Cell Cycle Related Pathways in Liver Cirrhosis
11. Condition-Specific Cell-Cell Interaction Patterns in Liver Cirrhosis
12. Macrophage Condition-Specific Surfaceome Markers in Liver Cirrhosis
13. T cell CD4+ Condition-Specific Surfaceome Markers in Liver Cirrhosis
14. Increased Expression of Cell Cycle Genes ANAPC11 and MAD2L2 in Macrophages from Cirrhotic Livers
15. Cholangiocyte Gene Ontology Analysis in Liver Cirrhosis and Health
16. GSEA in Liver Cirrhosis: Cell-Type-Specific Pathway Alterations
17. Discussion
18. Query List

0. Dataset overview

데이터셋 요약

• 이 AnnData는 60503개의 세포와 22784개의 유전자로 구성된 단일 세포 RNA 시퀀싱 데이터입니다.
• 종(species)은 인간(human)이며, 조직(tissue)은 간(Liver)입니다.
• 분석된 조건(conditions)은 'healthy'와 'cirrhosis' 두 가지가 있습니다.
• obs 컬럼에는 샘플 정보, 조건, 세분화된 세포 타입 (major, minor, subset), 클러스터 정보 등이 포함되어 있습니다.
• var 컬럼에는 유전자 심볼(gene_symbol), 유전자 ID(gene_id) 등이 포함되어 있습니다.

주요 세포 타입 정보

• 주요 세포 타입 (celltype_major): Myeloid cell, T cell, B cell, Stromal cell, Mast cell, Endothelial cell, Liver Epithelial cell 등.
• 세부 세포 타입 (celltype_minor): Macrophage, T cell CD8+, NK cell, B cell, T cell CD4+, ILC, Dendritic cell, Plasma cell, Hepatic stellate cell, Mast cell, Endothelial cell, Cholangiocyte, Smooth muscle cell, Hepatocyte, Fibroblast 등.
• 더욱 세분화된 세포 타입 (celltype_subset): Macrophage (M1), T cell (Cytotoxic), Macrophage (M2B) 등 다양한 세포 하위 그룹이 있습니다.

사전 계산된 분석 결과

• 세포-세포 상호작용 (CCI): 조건별(uns['CCI']) 및 샘플별(uns['CCI_sample']) CellPhoneDB 분석 결과가 저장되어 있습니다.
• 차등 발현 유전자 (DEG): 각 celltype_minor에서 한 조건을 다른 조건들과 비교한 DEG 결과가 저장되어 있습니다.
• 유전자 세트 농축 분석 (GSEA): 각 celltype_minor에서 한 조건을 다른 조건들과 비교한 GSEA 결과가 저장되어 있습니다.
• 유전자 온톨로지 (GO/GSA): 각 celltype_minor에서 한 조건을 다른 조건들과 비교한 GO(GSA) 결과가 저장되어 있습니다.

분석 가능한 세포 타입 (DEG, GSEA, GSA/GO)

• 다음 세포 타입에 대해 DEG, GSEA, GSA/GO 분석을 수행할 수 있습니다: B cell, Cholangiocyte, Dendritic cell, Endothelial cell, Hepatic stellate cell, ILC, Macrophage, NK cell, Plasma cell, Smooth muscle cell, T cell CD4+, T cell CD8+.

1. UMAP Visualization of Single-Cell RNA-seq Data in Healthy and Cirrhotic Human Liver

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) plots generated from single-cell RNA-seq data of human liver tissue. The UMAPs visualize the cellular landscape, colored by various metadata features: condition (healthy vs. cirrhosis), individual sample, celltype_major, celltype_minor, and celltype_subset. These visualizations are crucial for understanding the overall data structure, identifying cell populations, assessing the quality of cell type annotations, and observing condition-specific cellular changes. The UMAP was computed using n_pcs=15 and n_neighbors=11, with a clustering resolution of 2.0.

Visual Summary

Condition and Sample Distribution

• Condition UMAP: The UMAP colored by condition reveals distinct patterns. While many cellular clusters show a mixture of both healthy (blue/purple) and cirrhosis (red/burgundy) cells, indicating shared cell populations, there are also clearly discernible clusters predominantly enriched for one condition. For instance, several clusters in the left half of the UMAP are heavily dominated by cirrhosis cells, suggesting an expansion of certain cell types or the emergence of distinct disease-associated cellular states in cirrhosis. Conversely, a large, well-defined cluster in the central-right region appears predominantly composed of healthy cells, indicating cell populations or states that might be reduced or altered in cirrhosis.
• Sample UMAP: The sample UMAP shows a generally good integration of cells from different individual samples, particularly within the same condition. This suggests that potential batch effects originating from individual samples are not the primary drivers of the major UMAP structure. Cells from healthy samples generally intermix well, as do cells from cirrhosis samples. This robust integration allows for more confident comparison of biological differences between conditions rather than technical variations.

Cell Type Annotation Structure

• Celltype_major UMAP: The major cell types are well-separated into distinct clusters across the UMAP. For example, T cell (purple) forms a large, central cluster, while Myeloid cell (light green) occupies adjacent regions. Liver Epithelial cell (orange) forms a prominent cluster in the bottom-right. Endothelial cell (red) and Stromal cell (teal) also define clear, yet sometimes spatially associated, clusters. This high degree of separation indicates robust and accurate annotation at the major cell type level.
• Celltype_minor UMAP: Progressing to the celltype_minor level, further heterogeneity is resolved. Within the Liver Epithelial cell compartment, Hepatocyte (light yellow) and Cholangiocyte (red) are distinctly separated. Macrophage (light yellow) and Dendritic cell (red) populations are well-defined within the broader Myeloid cell region. Immune cell subsets like T cell CD8+ (blue), T cell CD4+ (dark blue), NK cell (light green), and ILC (light yellow) demonstrate expected relationships, with related cell types clustering adjacently but maintaining clear distinctions. Hepatic stellate cell (orange) and Fibroblast (reddish-orange) also form distinct, yet sometimes overlapping, stromal cell populations.
• Celltype_subset UMAP: The most granular celltype_subset annotations reveal fine-grained cellular heterogeneity. Macrophage subtypes (e.g., Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), Macrophage (M2D)) show distinct spatial distributions, suggesting varied functional states. Similarly, T cell subtypes such as T cell (Cytotoxic), T cell (Naive), T cell (Th1), T cell (Th2), and T cell (Treg) are resolved, illustrating the diverse immune landscape. Endothelial cell and B cell subsets also show clear delineation. This high resolution indicates that the analysis effectively captured significant biological variation at a detailed level, supporting a nuanced understanding of cellular states.

Biological Interpretation

The UMAP analyses provide a comprehensive overview of the cellular composition and disease-associated changes in human liver cirrhosis.

• Disease-Associated Cellular Remodeling: The clear segregation of cirrhosis-dominated clusters in the condition UMAP strongly suggests significant cellular remodeling in the cirrhotic liver. These regions likely represent an expansion of pathogenic cell populations (e.g., activated myofibroblasts/stellate cells, specific inflammatory macrophage subsets, or altered immune cell compositions) or shifts in cellular states that are characteristic of liver fibrosis and inflammation. Conversely, the healthy-enriched clusters might indicate cell types or functional states that are diminished or lost during the progression of cirrhosis.
• High-Resolution Cell Type Characterization: The robust separation and logical clustering of cell types across major, minor, and subset levels confirm the high quality of the cell type annotations. This detailed cellular map is essential for investigating the specific roles of various cell types in liver homeostasis and disease. For instance, identifying distinct macrophage polarization states (e.g., M1, M2 subtypes) and T helper cell lineages (Th1, Th2, Th17, Treg) within the cirrhotic context is critical for understanding immune dysregulation and fibrogenesis.
• Foundation for Downstream Analysis: The well-defined clusters and minimal batch effects observed in the UMAPs provide a solid foundation for subsequent, more targeted analyses, such as differential gene expression, cell-cell interaction analysis, and pathway enrichment analysis, to precisely delineate the molecular mechanisms underpinning cirrhosis development and progression.

Annotation Notes

The UMAP embeddings effectively capture the major sources of variation in the dataset, primarily distinguishing different cell types and, to a significant extent, the condition (healthy vs. cirrhosis). The consistency in clustering across different levels of cell type annotation (major, minor, subset) reinforces the confidence in the cell identity assignments. The good integration of cells from different samples within the same condition further validates the robustness of the embedding and the downstream biological conclusions drawn from these annotations. These results provide a strong basis for exploring disease-specific cellular changes and interactions within the liver microenvironment.

2. UMAP Visualization of Key Marker Genes and Minor Cell Type Annotations in Human Liver

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the expression of a panel of known marker genes across the UMAP embedding of single-cell RNA-seq data from human liver. Concurrently, the minor cell type annotations are displayed on a separate UMAP plot. The primary goal is to assess the quality of cell type assignments by examining the specificity and localization of these canonical markers within the annotated cell populations.

Visual Summary

The UMAP projections reveal a comprehensive landscape of distinct cell populations within the human liver. The celltype_minor UMAP plot displays clearly delineated clusters corresponding to the annotated cell types, indicating a robust underlying dimensionality reduction and clustering.

The individual gene expression UMAPs demonstrate the following patterns:

Lymphoid Cells:

• CD3D shows strong and specific expression primarily in the large top-right cluster, which is clearly identified as T cell CD4+ and T cell CD8+ in the celltype_minor UMAP, consistent with its role as a pan-T cell marker.
• CD4 expression is concentrated within a sub-region of the CD3D-positive cluster, aligning perfectly with the T cell CD4+ annotation.
• CD8A expression is prominent in another distinct sub-region of the CD3D-positive cluster, corresponding to the T cell CD8+ population.
• CD79A and MS4A1 (CD20) exhibit high expression in the top-left cluster, which is annotated as B cell. This co-expression further confirms the identity of this B cell population.
• MZB1 expression is highly restricted to a small, distinct cluster adjacent to the B cell cluster, consistent with the Plasma cell annotation.

Myeloid Cells:

• CD14 and LYZ are strongly expressed in a central, diffuse cluster, which aligns with the Macrophage and Dendritic cell (DC) annotations, reflecting their myeloid origin.

Stromal and Endothelial Cells:

• FBLN1 shows localized expression in several smaller clusters, predominantly corresponding to Fibroblast and Hepatic stellate cell populations, indicating its role in extracellular matrix.
• NOTCH3 expression is enriched in a cluster clearly identified as Smooth muscle cell (SMC), and potentially some Endothelial cell populations, consistent with its known role in vascular development and smooth muscle cell biology.
• CD34 expression is highly specific to a cluster at the bottom-left, accurately matching the Endothelial cell (Endo) annotation, supporting its role as a key endothelial marker.

Epithelial Cells:

• EPCAM is broadly expressed across the large left-middle cluster and a smaller adjacent one, which are annotated as Hepatocyte and Cholangiocyte, confirming its epithelial nature.
• MUC1 expression is highly concentrated in a specific epithelial cluster, consistent with the Cholangiocyte annotation, distinguishing it from Hepatocytes.

Biological Interpretation

The observed gene expression patterns provide strong biological validation for the minor cell type annotations within the human liver single-cell dataset. The clear segregation and specific expression of canonical markers across distinct UMAP clusters underscore the success of the cell type identification process.

• Immune Cell Lineages: The mutually exclusive expression of CD4 and CD8A within the CD3D-positive T cell compartment provides a robust confirmation of the T cell subtype annotation. Similarly, the co-expression of CD79A and MS4A1 confirms B cell identity, while MZB1 specifically marks terminally differentiated plasma cells. CD14 and LYZ serve as reliable indicators for myeloid cells like macrophages and dendritic cells.
• Stromal and Vascular Components: FBLN1 expression in fibroblasts and hepatic stellate cells is expected, reflecting their shared roles in matrix production. NOTCH3's presence in smooth muscle cells highlights its importance in vascular structure and differentiation in the liver. CD34's restricted expression to endothelial cells firmly establishes their identity.
• Liver Parenchymal and Biliary Cells: EPCAM, as a pan-epithelial marker, correctly identifies both hepatocytes and cholangiocytes. The specific enrichment of MUC1 in the cholangiocyte cluster, coupled with EPCAM expression, allows for clear differentiation of the biliary epithelial cells from hepatocytes.

These observations confirm that the major and minor cell types present in the liver tissue have been accurately identified and annotated based on their characteristic gene expression profiles.

Annotation Notes

The comprehensive UMAP visualization of marker gene expression alongside the celltype_minor annotations demonstrates a high level of annotation quality. For each cell type, at least one and often multiple canonical marker genes show highly specific and enriched expression within the corresponding UMAP cluster. This strong concordance between marker gene expression and computational annotations provides confidence in the accuracy of the cell type assignments for the celltype_minor categories. The clear separation of distinct cell lineages and subtypes on the UMAP further supports the robustness of the embedding and clustering.

3. Overall Celltype_subset Marker Expression Analysis for Annotation Quality Check

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot visualizing the expression of identified marker genes across different celltype_subset populations from human liver single-cell RNA-seq data. The goal is to assess the quality and specificity of the celltype_subset annotations by examining whether the marker gene expression patterns align with established biological knowledge for these cell types. Each dot's size represents the fraction of cells in a given group expressing the gene, while its color intensity reflects the mean expression level within that group. The red boxes highlight specific clusters of markers characteristic of particular cell subsets.

Visual Summary

The dot plot displays a comprehensive overview of 140 marker genes across 37 distinct celltype_subset populations. Key observations include:

• Clear Cell-Type Specificity: Many celltype_subset groups exhibit highly specific and enriched expression of particular marker genes, forming distinct blocks along the diagonal, visually emphasized by the red bounding boxes. This suggests robust discrimination between different cell types.
• Expected Marker Patterns: Several well-known cell-type-specific markers are prominently expressed in their respective cell populations. For instance, classic B cell markers like POU2AF1, IGHD, and CD22 are enriched in B cell subsets, cholangiocyte markers like KRT19 and CLDN4 are specific to Cholangiocytes, and hepatocyte markers such as ALB and FGG are highly expressed in Hepatocytes.
• Shared Lineage Markers: Certain markers, such as those associated with mesenchymal cells (e.g., COL1A1, DCN, LUM, MYL9, TAGLN, TIMP1, TPM2, SPARC), are shared across related cell types like Fibroblasts, Hepatic stellate cells, Endothelial cells, and Smooth muscle cells, reflecting their shared stromal or contractile origins and functions.
• Immunological Cell Subtleties: Subsets within immune cell populations (e.g., T cells, Macrophages, ILCs) show both shared pan-lineage markers and distinct markers that help differentiate their specialized functions. For example, T cell subsets like Cytotoxic T cells (CD8A, GZMK), Th2 cells (GATA3), and Tregs (IL2RA, TGFB1) display characteristic gene expression. Macrophage subsets (M1, M2A, M2B, M2C) also show some differential expression, with SPP1 broadly expressed and SRGN more prominent in M2C.
• Cell Group Representation: The bar plot on the right indicates the total cell count for each celltype_subset. Most cell types appear to have sufficient cell numbers (e.g., Hepatocytes with 13370 cells, Macrophage M1 with 8621 cells) to reliably detect marker expression, although some subsets have fewer cells (e.g., B cell (Breg) with 154 cells, T cell (Th9) with 101 cells).

Biological Interpretation

The strong cell-type-specific marker gene expression observed in the dot plot provides substantial biological support for the assigned celltype_subset annotations within the AnnData object.

• B Cell Subsets: The expression of B cell lineage markers (POU2F1, IGHD, CD22) across B cell (Breg), B cell (Follicular), B cell (MZ), and B cell (Memory) subsets confirms their identity, while subtle differences in specific marker expression contribute to their sub-classification.
• Liver Parenchymal and Biliary Cells: The robust and unique expression of ALB, APOA1, and FGG in Hepatocytes clearly delineates this major liver cell type, reflecting their central role in metabolism and synthesis. Similarly, the specific expression of KRT19, CLDN4, and AQP1 in Cholangiocytes unequivocally identifies the biliary epithelial cells.
• Stromal and Endothelial Compartment: Fibroblasts and Hepatic stellate cells, crucial for liver architecture and disease progression, are characterized by mesenchymal markers such as PDGFRA, DCN, COL1A1, and LUM. The sharing of these markers with Endothelial cells (e.g., Lymphatic Endothelial cell showing PDPN) and Smooth muscle cells (MYH11, CNN1) highlights the interconnectedness and developmental relationships within the liver's stromal-vascular niche.
• Diverse Immune Cell Populations: The plot effectively distinguishes a wide array of immune cells.
• Dendritic Cells (DCs): Classical DCs are marked by CD83, CLEC9A, and CADM1, while Plasmacytoid DCs are identified by LILRA4 and IRF7, reflecting their distinct developmental pathways and functions.
• Macrophages: Different macrophage polarization states (M1, M2A, M2B, M2C) show common macrophage markers (e.g., SPP1, DEFB1) along with some differential expression (e.g., SRGN in M2C), supporting the existence of distinct functional macrophage states in the liver.
• NK Cells and ILCs: NK cells are well-defined by KLRD1, KLRG1, and GZMB, indicating their cytotoxic potential. Various ILC subsets (ILC1, ILC2, ILCreg) show some shared markers like CD7 but also subset-specific markers like GATA3 for ILC2, consistent with their roles as innate lymphocytes.
• T Cell Subsets: The granular differentiation of T cell subsets (e.g., Cytotoxic T cells with CD8A/GZMK, T helper cells like Th1 (STAT1), Th2 (GATA3), Th17 (RORA), and Tregs (IL2RA, TGFB1)) based on their transcription factors and effector molecules provides strong confidence in these annotations, crucial for understanding immune responses in the liver.
• Plasma Cells: The clear expression of JCHAIN, MZB1, SDC1 (CD138), and XBP1 confirms the distinct identity of plasma cells as antibody-producing effector B cells.
• Mast Cells: TPSAB1, a mast cell-specific tryptase, strongly marks mast cells.

Annotation Notes

Overall, the marker expression patterns presented in this dot plot strongly support the quality and accuracy of the celltype_subset annotations in the AnnData object. The analysis successfully identifies specific marker gene sets for most annotated cell types, allowing for confident identification and differentiation of cell populations, including complex immune cell subsets and stromal cells within the human liver. The observed patterns are highly consistent with well-established biological knowledge of these cell types and their markers. There are no major discrepancies or unexpected marker co-expressions that would suggest mis-annotation of primary cell types. The robust definition of these cell types lays a strong foundation for downstream analyses of liver biology and disease.

4. Minor Cell Type Population Analysis in Cirrhosis vs. Healthy Liver

[Analysis Visualization Results]...

Analysis Overview

This analysis presents stacked bar plots illustrating the proportional distribution of minor cell types across individual samples, comparing healthy liver tissue with cirrhotic liver tissue. The samples were separated based on CD45 expression status (CD45+ for immune cells, CD45- for non-immune cells), which helps in dissecting immune cell infiltrates from parenchymal and stromal components. This visualization is crucial for understanding the overall cellular landscape shifts associated with liver cirrhosis.

Visual Summary

The stacked bar plots display the relative abundances of 15 minor cell types within each sample. Samples are grouped by condition (cirrhosis vs. healthy) and further categorized by their CD45 sorting status (_cd45+ or _cd45-).

CD45+ Samples (Immune Compartment):

• In both healthy and cirrhotic conditions, CD45+ samples are dominated by immune cell populations, notably T cells (CD4+ and CD8+), Macrophages, NK cells, and to a lesser extent, B cells, Dendritic cells, and Plasma cells.
• Cirrhosis vs. Healthy: Cirrhotic CD45+ samples generally exhibit a higher proportion of Macrophages (light yellow), CD8+ T cells (dark blue), and CD4+ T cells (teal) compared to healthy CD45+ samples. This suggests an increased immune cell infiltration and activation in cirrhosis.

CD45- Samples (Non-Immune Compartment):

• CD45- samples primarily consist of non-immune cells such as Hepatocytes (pale yellow), Endothelial cells (orange), Hepatic stellate cells (light orange), Fibroblasts (orange), and Cholangiocytes (dark red).

Cirrhosis vs. Healthy:

• Hepatocytes: Healthy CD45- samples consistently show a high proportion of Hepatocytes, as expected for liver parenchyma. In contrast, cirrhotic CD45- samples show a markedly reduced proportion of Hepatocytes, often replaced by other cell types.
• Fibrogenic Cells: Cirrhotic CD45- samples display a noticeable increase in Fibroblasts (orange) and Hepatic stellate cells (light orange) in several samples (e.g., cirrhotic1_cd45-A, cirrhotic3_cd45-, cirrhotic2_cd45-). These cells are key mediators of fibrosis.
• Endothelial Cells & Cholangiocytes: While variable, some cirrhotic samples show alterations in Endothelial cell (orange) and Cholangiocyte (dark red) proportions, which can be linked to angiogenesis and bile duct proliferation in chronic liver disease.

Biological Interpretation

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

1. Inflammation and Immune Activation in Cirrhosis: The expansion of Macrophages, CD8+ T cells, and CD4+ T cells in cirrhotic CD45+ samples highlights the chronic inflammatory state that drives and perpetuates liver fibrosis. Macrophages, particularly those with M1-like characteristics, contribute to inflammation and pro-fibrotic signaling, while various T cell subsets play diverse roles in immune regulation and tissue damage PubMed search: liver cirrhosis immune cells macrophages T cells.
2. Fibrosis and Parenchymal Loss: The decrease in Hepatocyte proportion and the concomitant increase in Fibroblasts and Hepatic stellate cells in cirrhotic CD45- samples are hallmarks of liver fibrosis.

• Hepatocytes: Their reduction reflects widespread hepatocyte injury, death, and replacement by fibrotic scar tissue, leading to impaired liver function.
• Hepatic Stellate Cells (HSCs): Activation of HSCs is a central event in liver fibrosis. Activated HSCs differentiate into myofibroblast-like cells, proliferate, and produce excessive extracellular matrix (ECM), contributing to scar formation GeneCards: HSC.
• Fibroblasts: These cells, along with activated HSCs, are the primary source of ECM deposition in the fibrotic liver, forming the structural basis of cirrhosis.

3. Angiogenesis and Ductular Reaction: Changes in Endothelial cells and Cholangiocytes can reflect reactive processes in cirrhosis. Angiogenesis (increased Endothelial cells) is often deregulated in cirrhosis, contributing to portal hypertension. Cholangiocyte proliferation (ductular reaction) is a common feature of chronic liver injury, representing an attempt at regeneration or response to bile duct damage.

Clinical or Translational Implications

Understanding these cellular population shifts has significant clinical and translational implications:

• Biomarker Discovery: The distinct cellular profiles in cirrhosis could aid in identifying novel diagnostic or prognostic biomarkers for disease progression. For instance, specific ratios or absolute counts of immune or fibrogenic cells might correlate with disease severity.
• Therapeutic Targeting: The increased abundance of specific immune cells (e.g., Macrophages, T cells) and fibrogenic cells (HSCs, Fibroblasts) identifies them as potential therapeutic targets for anti-inflammatory or anti-fibrotic strategies. Modulating their activation or survival could halt or reverse disease progression.
• Disease Monitoring: Monitoring changes in these cell populations, potentially through liquid biopsies or advanced imaging techniques, could offer non-invasive ways to assess treatment response or disease activity.
• Heterogeneity of Cirrhosis: The sample-to-sample variability observed within the cirrhotic group (e.g., cirrhotic1_cd45-A vs. cirrhotic2_cd45-) underscores the heterogeneous nature of cirrhosis, suggesting that different patients may have distinct cellular compositions that could influence their disease course and response to therapy. This highlights the need for personalized approaches.

5. Liver Lymphoid Cell Subset Composition in Cirrhosis vs. Healthy Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the proportional distribution of various lymphoid cell subsets (including T cells, NK cells, and Innate Lymphoid Cells - ILCs) within the broad "T cell" major population, comparing cirrhotic liver samples to healthy liver samples. The stacked bar plots show the relative abundance of each celltype_subset within individual samples, providing insight into the immune landscape shifts associated with liver cirrhosis.

It is important to note that while the query specified celltype_major: 'T cell', the resulting plot includes subsets like NK cells and various ILCs (ILC1, ILC2, ILC3, ILCreg, LTI) in addition to classical T cell subsets. This suggests that in this dataset's annotation schema, these innate lymphoid populations are either broadly categorized under the 'T cell' celltype_major for the purpose of this high-level analysis or are included as part of a more comprehensive lymphoid compartment when detailing celltype_subset for the T cell major group.

Visual Summary

The stacked bar plots display the relative proportions of 15 distinct lymphoid cell subsets across multiple individual samples from both cirrhotic and healthy liver conditions.

• Dominant Populations: In both cirrhotic and healthy livers, T cell (Cytotoxic) (light yellow) and NK cell (pale orange) are the most abundant lymphoid subsets, often collectively comprising over 70% of the total cells shown in the plot.
• Reduced ILC1s in Cirrhosis: A striking difference is the significantly lower proportion of ILC1 (dark red) in cirrhotic samples compared to healthy samples. In healthy livers, ILC1s often represent 10-15% of the lymphoid compartment, while in cirrhotic livers, their proportion is consistently very low, often below 5%.
• Reduced ILC2/ILC3 in Cirrhosis: ILC2 (red) and ILC3 (NCR+) (orange-red) also appear to be present in slightly higher proportions in healthy samples compared to cirrhotic samples, although the difference is less pronounced than for ILC1s.
• Potential Increase in T cell (Treg) in Cirrhosis: Although a minor population, T cell (Treg) (dark blue) appears to be slightly more consistently present, and in some cases, relatively higher in cirrhotic samples compared to healthy controls.
• Other T cell Subsets: T cell (Naive), T cell (Tfh), T cell (Th1), T cell (Th17), T cell (Th2), T cell (Th22), T cell (Th9) are present in relatively small proportions across both conditions, with no immediately apparent large-scale shifts.
• Inter-sample Variability: While general trends are observable, there is some variability in subset proportions among individual samples within both the cirrhotic and healthy groups.

Biological Interpretation

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

• Immune Microenvironment in Cirrhosis: Liver cirrhosis is a chronic progressive disease characterized by sustained inflammation, hepatocyte damage, and fibrosis. The profound changes in the composition of the lymphoid compartment underscore the altered immune microenvironment in the cirrhotic liver.

Role of Innate Lymphoid Cells (ILCs)

• Decreased ILC1s: The most prominent finding is the substantial reduction of ILC1s in cirrhotic livers. ILC1s are critical producers of IFN-γ and play vital roles in anti-viral immunity and anti-tumor responses. Their depletion in cirrhosis could indicate a compromised innate immune surveillance, potentially contributing to chronic inflammation, reduced viral clearance, or impaired anti-tumor immunity that often accompanies cirrhosis PubMed: 32303536. This reduction might also reflect an immune shift away from a Type 1 inflammatory response.
• ILC2s and ILC3s: ILC2s are typically associated with Type 2 inflammation and fibrosis, while ILC3s are involved in mucosal immunity and tissue homeostasis. Their slight reduction in cirrhosis may suggest complex shifts in innate immune axes that could influence the progression of liver damage and fibrosis.

T cell Subsets in Cirrhosis

• Dominance of Cytotoxic T cells: The high proportion of cytotoxic T cells (likely CD8+) in both conditions, but particularly notable in cirrhosis, suggests ongoing cellular stress, antigen presentation, and immune activation. These cells are key players in clearing infected or damaged hepatocytes but can also contribute to immunopathology if dysregulated.
• Potential Increase in Regulatory T cells (Tregs): The observation of a relatively higher proportion of Tregs in cirrhotic livers is biologically plausible. Tregs are crucial for maintaining immune tolerance and suppressing excessive inflammation. Their recruitment or expansion in cirrhosis might represent an attempt to curb chronic inflammation and tissue damage, but their suppressive function can also inadvertently hinder effective immune responses against pathogens or cancerous cells, or contribute to fibrosis PubMed: 32220468.
• NK cells: NK cells are critical for innate immunity against viral infections and cancer. Their substantial presence is expected in the liver. While proportionally high in both conditions, subtle relative shifts compared to cytotoxic T cells could indicate differences in immune activation.

Clinical or Translational Implications

• Biomarker Potential: The significant reduction in ILC1s in cirrhotic livers could serve as a valuable biomarker for disease progression or severity, potentially reflecting a stage of immune dysregulation where innate immune functions are compromised. This warrants further investigation for diagnostic or prognostic utility.
• Therapeutic Targets: Understanding the depletion of beneficial immune subsets like ILC1s and the potential increase in immunosuppressive Tregs offers potential avenues for immunomodulatory therapies. Strategies aimed at restoring ILC1 populations or modulating Treg activity could be explored to rebalance the immune response and potentially mitigate liver inflammation and fibrosis in cirrhosis.
• Enhanced Disease Understanding: This analysis provides a high-resolution view of lymphoid cell changes in cirrhosis, contributing to a deeper understanding of the immunological mechanisms driving disease progression. This detailed insight can inform future research into targeted therapies and personalized medicine approaches for liver disease.

6. Macrophage Subset Population Analysis in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the relative proportions of different Macrophage subsets (M1, M2A, M2B, M2C, M2D) within the total Macrophage population, comparing healthy liver samples to cirrhotic liver samples. The bar plots display the distribution of these subsets for individual samples within each condition, providing insight into condition-specific shifts in macrophage polarization.

Visual Summary

The stacked bar plots illustrate the proportional representation of five distinct Macrophage subsets: Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), and Macrophage (M2D).

• Dominant Subsets: In both healthy and cirrhotic conditions, Macrophage (M1) and Macrophage (M2B) appear to be the most abundant subsets, followed by Macrophage (M2A) and Macrophage (M2C). Macrophage (M2D) consistently represents a very minor fraction across all samples.

Cirrhosis vs. Healthy Comparison:

• Macrophage (M1): The proportion of M1 macrophages (dark red) generally appears higher in cirrhotic samples compared to healthy samples. In cirrhosis, M1 often constitutes >50% of the total macrophage population, sometimes reaching over 60%. In contrast, healthy samples show more variability, with M1 proportions ranging from ~20% to ~60%, but typically lower than in cirrhosis.
• Macrophage (M2B): The proportion of M2B macrophages (light yellow) seems to be relatively stable or slightly decreased in cirrhosis compared to healthy samples, especially when M1 is highly dominant.
• Macrophage (M2A): M2A macrophages (orange) appear to be a more substantial component in some healthy samples (e.g., 'healthy1_cd45-B', 'healthy2_cd45-') but show reduced proportions in most cirrhotic samples where M1 is elevated.
• Macrophage (M2C) and Macrophage (M2D): These subsets (light green and teal, respectively) remain minor populations in both conditions, with M2D being almost negligible.
• Inter-sample Variability: There is notable sample-to-sample variability within both the healthy and cirrhotic groups, particularly in the proportions of M1, M2A, and M2B. However, the trend of increased M1 proportions in cirrhosis generally holds across most cirrhotic samples.

Biological Interpretation

Macrophages are critical immune cells in the liver, where they are known as Kupffer cells and infiltrating monocyte-derived macrophages. Their polarization into different functional subsets (M1, M2) is highly plastic and plays a pivotal role in the pathogenesis of liver diseases, including cirrhosis.

• M1 Macrophages (Pro-inflammatory): M1 macrophages are characterized by their pro-inflammatory phenotype, involved in host defense against pathogens and promoting tissue damage. The observed increase in M1 macrophage proportions in cirrhotic livers strongly suggests a heightened inflammatory state. In the context of chronic liver injury leading to cirrhosis, sustained inflammation driven by M1 macrophages can exacerbate hepatocyte damage and contribute to the progression of fibrosis by releasing pro-inflammatory cytokines such as TNF-$\alpha$, IL-1$\beta$, and IL-6 [1, 2].
• M2 Macrophages (Anti-inflammatory, Pro-fibrotic, Wound Healing): M2 macrophages are a heterogeneous group generally associated with anti-inflammatory responses, tissue repair, angiogenesis, and fibrosis. The M2 subtypes (M2A, M2B, M2C, M2D) have distinct roles:
• M2A: Often activated by IL-4/IL-13, associated with wound healing and parasitic infections.
• M2B: Activated by immune complexes and TLR agonists, can produce both pro- and anti-inflammatory cytokines.
• M2C: Activated by IL-10 or glucocorticoids, involved in immune suppression, tissue remodeling, and efferocytosis.
• M2D: Less clearly defined, sometimes associated with tumor progression and immunosuppression, often linked to adenosine signaling.

The apparent decrease or stable-to-low levels of M2A and M2B in cirrhotic samples, concomitant with an increase in M1, suggests an imbalance where the pro-inflammatory drive outweighs certain reparative or regulatory M2 functions. In liver fibrosis and cirrhosis, while M2 macrophages can contribute to fibrosis by producing pro-fibrotic mediators like TGF-$\beta$ and PDGF, a shift towards M1 can reflect persistent injury and a failure of efficient resolution [3]. The minor presence of M2C and M2D suggests these specific pathways may not be dominant in this cirrhotic context, or their contribution is masked by the M1 dominance.

• Pathological Context of Cirrhosis: Liver cirrhosis is characterized by extensive fibrosis, architectural distortion, and impaired liver function. The chronic inflammatory environment often fuels disease progression. The observed shift towards a higher proportion of M1 macrophages in cirrhosis aligns with the known inflammatory nature of the disease, suggesting that the macrophages are largely polarized towards a state that perpetuates inflammation and potentially contributes to ongoing tissue damage, rather than predominantly facilitating resolution. This also implies that the M1/M2 balance, a key determinant of disease outcome, is skewed towards M1 in cirrhotic livers in this dataset.

Clinical or Translational Implications

The findings suggest that the macrophage compartment undergoes significant changes in liver cirrhosis, with a prominent shift towards a pro-inflammatory M1 phenotype.

• Therapeutic Targeting: Modulating macrophage polarization could be a promising therapeutic strategy for liver cirrhosis. Strategies aimed at repolarizing M1 macrophages towards M2 phenotypes, or inhibiting M1 activation pathways, might reduce inflammation and fibrosis progression in cirrhotic patients [4].
• Biomarker Potential: The relative proportions of M1 and specific M2 macrophage subsets could serve as biomarkers for disease severity, prognosis, or response to treatment in cirrhosis. Monitoring these shifts via single-cell approaches could offer insights into disease activity.
• Understanding Disease Progression: The increased M1 population in cirrhosis highlights the persistent inflammatory drive in the diseased liver, contributing to fibrogenesis and hepatic dysfunction. Interventions that target the factors driving M1 polarization, such as pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs) often found in chronic liver injury, could be beneficial.

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References:

1. M1 Macrophage Role in Liver Disease: Explore the role of M1 macrophages in chronic liver inflammation via PubMed: https://pubmed.ncbi.nlm.nih.gov/?term=M1+macrophages+liver+inflammation
2. Macrophage Plasticity in Liver Disease: Review on macrophage plasticity and function in liver disease: https://pubmed.ncbi.nlm.nih.gov/?term=macrophage+plasticity+liver+cirrhosis
3. M2 Macrophages and Fibrosis: Information on M2 macrophages and their role in fibrosis: https://pubmed.ncbi.nlm.nih.gov/?term=M2+macrophages+fibrosis
4. Therapeutic Targeting of Macrophages in Cirrhosis: Strategies for targeting macrophages in liver fibrosis/cirrhosis: https://pubmed.ncbi.nlm.nih.gov/?term=macrophage+targeting+therapy+liver+cirrhosis

7. Changes in T Cell Subset Proportions in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional changes of various T cell subset populations in the liver comparing healthy individuals to those with cirrhosis. Boxplots are utilized to visualize the distribution of cell type proportions for each subset across the two conditions, with statistical significance indicated for observed differences. This allows for an understanding of how the T cell landscape shifts in the context of chronic liver disease.

Visual Summary

The boxplots reveal significant alterations in the proportions of several T cell subsets within the liver when comparing healthy samples to those from cirrhosis patients.

Increased Proportions in Cirrhosis:

• Th22: Significantly higher proportion in cirrhosis (p ≤ 0.05).
• Th17: Significantly higher proportion in cirrhosis (p ≤ 0.05).
• LTI (Lymphoid Tissue Inducer) cells: Significantly higher proportion in cirrhosis (p ≤ 0.05).
• Treg (Regulatory T cells): Significantly higher proportion in cirrhosis (p ≤ 0.01).
• T_Naive (Naive T cells): Significantly higher proportion in cirrhosis (p ≤ 0.05).
• Th9: Significantly higher proportion in cirrhosis (p = 0.05).
• ILCreg (Regulatory Innate Lymphoid Cells): Higher proportion in cirrhosis (p = 0.07), showing a trend towards significance.

Decreased Proportions in Cirrhosis:

• T_Cyto (Cytotoxic T cells): Significantly lower proportion in cirrhosis (p ≤ 0.05).

In general, the plots show an upregulation of several T helper cell types (Th22, Th17, Th9), regulatory T cells (Treg), and lymphoid tissue inducer cells (LTI) in cirrhosis, coupled with a reduction in cytotoxic T cells. Naive T cells also show an unexpected increase.

Biological Interpretation

The observed shifts in T cell subset proportions reflect a profound immunological dysregulation in the cirrhotic liver, characterized by both pro-inflammatory and regulatory responses, as well as an altered immune surveillance capacity.

• Pro-inflammatory and Fibrogenic Responses: The significant increases in Th17 and Th22 cells are notable. Th17 cells are potent producers of pro-inflammatory cytokines such as IL-17, which can drive chronic inflammation and fibrosis in the liver [PubMed search: Th17 cells liver fibrosis]. Th22 cells, producing IL-22, play complex roles, contributing to tissue repair but also potentially exacerbating inflammation and fibrosis in certain contexts of chronic liver disease [PubMed search: Th22 cells liver disease]. The increase in Th9 cells, which produce IL-9, can also contribute to inflammation and may have context-dependent pro-fibrotic roles [PubMed search: Th9 cells liver]. These findings collectively suggest an amplified pro-inflammatory milieu in the cirrhotic liver.
• Immune Regulation and Tolerance: The significant increase in Treg cells (CD4+FOXP3+) typically aims to suppress excessive immune responses and maintain immune tolerance [GeneCards: FOXP3]. However, in chronic liver diseases like cirrhosis, Treg function can be impaired or their expansion may represent a compensatory mechanism that fails to resolve inflammation, sometimes even promoting fibrosis through direct or indirect mechanisms [PubMed search: Tregs liver cirrhosis fibrosis]. The observed increase might also indicate an attempt by the immune system to mitigate severe tissue damage, although the ultimate outcome in cirrhosis suggests this regulation is insufficient or ineffective. The trend for ILCreg increase further hints at a broader activation of regulatory circuits, involving innate lymphoid cells.
• Impaired Anti-Viral/Anti-Tumor Immunity: The significant decrease in T_Cyto (cytotoxic T lymphocytes) in cirrhosis is a critical finding. These cells are essential for clearing virally infected cells and eliminating transformed cells (e.g., in hepatocellular carcinoma, HCC) [UniProt: P01844, PubMed search: cytotoxic T cells liver HCC]. A reduction in their proportion could impair the liver's ability to control viral infections (if the cirrhosis is virally induced) and increase susceptibility to tumor development, a common complication of advanced liver disease. This decrease might be due to exhaustion, apoptosis, or altered trafficking.
• Lymphoid Tissue Organization: The increase in LTI (Lymphoid Tissue Inducer) cells is intriguing. LTI cells are crucial for the development and maintenance of secondary lymphoid organs and can contribute to the formation of tertiary lymphoid structures (TLS) in chronically inflamed non-lymphoid tissues like the liver. The presence and organization of TLS in the cirrhotic liver are associated with chronic inflammation, sustained immune responses, and disease progression [PubMed search: LTI cells tertiary lymphoid structures liver].
• Naive T Cell Expansion: The increase in T_Naive cells is unexpected in a state of chronic inflammation, which typically features an expansion of antigen-experienced T cells. This could suggest altered T cell trafficking, impaired activation, or a broader dysfunction in immune homeostasis within the cirrhotic environment. It might also reflect changes in lymphopoiesis or lymphoid organ function affecting the supply of naive cells to the liver.

Clinical or Translational Implications

These findings highlight distinct immunological signatures associated with liver cirrhosis, offering potential insights into disease progression and therapeutic targets.

• Biomarker Potential: The proportions of specific T cell subsets (e.g., Th17, Th22, T_Cyto, Treg) could serve as biomarkers for assessing disease severity, progression, or response to therapy in cirrhosis.
• Therapeutic Targets: Modulating the activity or proportion of these T cell subsets could represent novel therapeutic strategies. For instance, interventions aimed at reducing pro-fibrotic Th17/Th22 responses or enhancing cytotoxic T cell function could be beneficial. However, the complex interplay with Tregs and other regulatory cells suggests that a nuanced approach would be necessary.
• HCC Risk: The decrease in cytotoxic T cells raises concerns about compromised immune surveillance, which could contribute to the higher incidence of hepatocellular carcinoma in cirrhotic patients. Strategies to restore or boost cytotoxic T cell activity could be relevant for HCC prevention or treatment in this patient population.

8. Macrophage Subset Population Shifts in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional changes of specific macrophage subset populations, identified at the celltype_subset taxonomic level, between 'healthy' and 'cirrhosis' conditions within the liver tissue. Boxplots are utilized to visualize the distribution of celltype proportions, and statistical significance is determined for observed differences. This helps in understanding how the immune cell landscape, particularly macrophage polarization, is altered in the context of liver cirrhosis.

Visual Summary

The visualization displays boxplots for two macrophage subsets, Macrophage (M1) and Macrophage (M2A), that exhibited statistically significant differences (p-value < 0.1 based on the analysis parameters) between healthy and cirrhotic liver tissues.

• Macrophage (M1): The proportion of M1 macrophages is significantly higher in the 'cirrhosis' group compared to the 'healthy' group (p ≤ 0.05). The boxplot for cirrhosis shows a median proportion around 62-63%, with the interquartile range (IQR) largely above 55%, whereas the healthy group's median is around 50-51% with its IQR primarily below 60%. This indicates a clear enrichment of M1 macrophages in cirrhotic livers.
• Macrophage (M2A): The proportion of M2A macrophages shows a trend towards lower values in the 'cirrhosis' group compared to the 'healthy' group (p = 0.09). The healthy group exhibits a median proportion around 18-19%, with an IQR spanning approximately 15% to 22%. In contrast, the cirrhosis group has a lower median, around 16-17%, and a narrower IQR. This suggests a potential reduction or altered balance of M2A macrophages in cirrhosis, although the significance is marginal.

Biological Interpretation

Macrophages are central players in liver homeostasis and pathology, including cirrhosis. They can polarize into various functional phenotypes, broadly categorized as M1 (pro-inflammatory) and M2 (anti-inflammatory, pro-resolving, pro-fibrotic).

1. Increase in Macrophage (M1) in Cirrhosis: The significant increase in the proportion of M1 macrophages in cirrhotic livers is highly consistent with the known pathophysiology of liver cirrhosis. M1 macrophages are characterized by their pro-inflammatory functions, secreting cytokines such as TNF-α, IL-1β, and IL-6, which drive inflammation and contribute to hepatocyte damage and progression of fibrosis [1]. This observed shift suggests a heightened inflammatory state in the cirrhotic liver microenvironment, potentially exacerbating tissue injury and perpetuating the disease cycle.
2. Decrease in Macrophage (M2A) in Cirrhosis: The observed trend of decreased M2A macrophage proportion in cirrhosis is intriguing. M2 macrophages are a heterogeneous group involved in tissue repair, immune regulation, and fibrosis. M2A macrophages are typically activated by Th2 cytokines (IL-4 and IL-13) and are known for their role in allergic responses, parasite clearance, and tissue remodeling [2]. In the context of chronic liver disease, M2 macrophages can have complex roles, sometimes promoting resolution of inflammation and tissue repair, while other times contributing to fibrosis by activating hepatic stellate cells and extracellular matrix deposition. A decrease in M2A could imply:

• Impaired Resolution: A reduced capacity for the liver to resolve inflammation or repair tissue damage, leading to chronic pathological responses.
• Shift in M2 Subtypes: The immune environment in cirrhosis might favor the expansion of other M2 subtypes (e.g., M2C, M2D), or a general dysfunction in M2 polarization, rather than M2A.
• Dysregulated Immune Balance: The overall balance of macrophage populations is skewed towards pro-inflammatory M1 cells, with a potential deficiency in specific M2 reparative functions.

Together, these findings indicate a substantial dysregulation in macrophage polarization in the cirrhotic liver, characterized by an increased pro-inflammatory M1 phenotype and a potential decline in reparative/regulatory M2A populations. This imbalance likely contributes to the chronic inflammation and progressive damage seen in cirrhosis.

Clinical or Translational Implications

These findings highlight the critical role of macrophage polarization in the progression of liver cirrhosis and offer potential avenues for therapeutic intervention:

• Biomarkers: The proportions of M1 and M2A macrophages could potentially serve as biomarkers for disease severity or progression in cirrhosis, aiding in patient stratification.
• Targeted Therapies: Modulating macrophage polarization, specifically reducing M1-like activity or restoring beneficial M2-like functions (including M2A or other specific M2 subsets), represents a promising therapeutic strategy for liver cirrhosis. For instance, therapies aimed at re-polarizing macrophages from an M1 to an M2 phenotype, or preventing M1 activation, could mitigate inflammation and slow disease progression.

References

1. M1/M2 Macrophage Roles in Liver Fibrosis: A PubMed search for "M1 M2 macrophages liver fibrosis" provides extensive literature on their distinct roles. https://pubmed.ncbi.nlm.nih.gov/?term=M1+M2+macrophages+liver+fibrosis
2. M2 Macrophage Polarization: Information on M2 macrophage subtypes and activation pathways can be found on UniProt. For instance, a search for IL-4 or IL-13 related macrophage activation. https://www.uniprot.org/ (Search for "IL-4" or "IL-13" and related macrophage contexts)

9. 간경변증 및 건강 간 조직의 세포-세포 상호작용 분석

[Analysis Visualization Results]...

Analysis Overview

이 분석은 건강한 간과 간경변증(cirrhosis) 상태의 간 조직에서 단일 세포 RNA 시퀀싱 데이터를 기반으로 세포-세포 상호작용(Cell-Cell Interaction, CCI)을 비교합니다. plot_cci_dots 도구를 사용하여 각 조건에서 가장 유의미한 상위 80개 리간드-수용체 쌍 상호작용을 시각화했습니다. 점의 크기는 상호작용의 p-value(-log10 변환 값)를 나타내며, 작은 점일수록 통계적으로 더 유의미한 상호작용을 의미합니다. 점의 색상은 상호작용에 관여하는 유전자의 평균 발현량(log2 변환 값)을 나타내며, 녹색-노란색 계열이 짙을수록 평균 발현량이 높음을 의미합니다.

Visual Summary

두 가지 조건(간경변증, 건강)에 대한 CCI 닷 플롯은 간경변증 상태에서 세포-세포 상호작용 네트워크가 더욱 활발하고 복잡하며, 특정 상호작용이 현저히 증가함을 시사합니다.

1. 간경변증 조건 (CCI for cirrhosis):

• 활발한 상호작용: 전반적으로 건강 상태에 비해 더 많은 수의 상호작용이 높은 평균 발현량(짙은 녹색/노란색 점)과 높은 통계적 유의성(작은 점)을 보입니다. 이는 간경변증 상태에서 세포 간의 의사소통이 매우 활발하다는 것을 나타냅니다.

주요 상호작용:

• 염증 및 면역 반응: CCL3-CCR1, CCL4-CCR1, CXCL10-CXCR3와 같은 케모카인(chemokine) 신호 전달이 대식세포(Macrophage), T 세포, NK 세포 간에 매우 활발하게 관찰됩니다. 이는 면역 세포의 유인 및 활성화가 강력함을 시사합니다. [참고: PubMed search for "CXCL10 CXCR3 liver inflammation"]
• 섬유화 과정: 간 성상 세포(Hepatic stellate cell)와 대식세포를 포함한 다양한 세포 유형에서 SPP1 (Osteopontin)과 인테그린(integrin) 계열 (예: ITGA4-ITGB1, ITGA5-ITGB1) 간의 상호작용이 매우 두드러집니다. 또한 COL1A1 (Type I collagen)과 인테그린 간의 상호작용도 나타나, 세포외 기질(ECM) 리모델링 및 섬유화 진행과 밀접한 관련이 있음을 보여줍니다. [참고: GeneCards for SPP1]
• 세포 유형별 특징: 간 성상 세포는 특히 면역 세포(대식세포, T 세포) 및 간세포(Hepatocyte)와 다양한 리간드-수용체 쌍을 통해 활발하게 상호작용하며 섬유화의 중심 역할을 합니다. 내피세포(Endothelial cell) 또한 다양한 세포 유형과 상호작용하여 혈관 신생 및 혈관 기능 변화에 기여할 수 있습니다.
• HLA 관련 상호작용: HLA-E, HLA-F, HLA-G와 같은 HLA (Human Leukocyte Antigen) 분자를 포함하는 상호작용도 여러 면역 세포 간에 관찰되며, 면역 인식이 변화되었음을 시사합니다.

2. 건강 조건 (CCI for healthy):

• 상대적 정적 상태: 간경변증에 비해 전반적으로 상호작용의 수와 강도(평균 발현량 및 유의성)가 낮게 나타납니다. 이는 건강한 간이 상대적으로 안정적인 세포-세포 의사소통 네트워크를 유지하고 있음을 반영합니다.

주요 상호작용:

• 일부 ANXA1-FPR1, CD99-CD99L2와 같은 상호작용이 관찰되지만, 간경변증에서 보이는 강력한 염증 또는 섬유화 관련 신호는 덜 두드러집니다.
• integrin과 같은 일반적인 세포 부착 관련 상호작용도 존재하지만, 간경변증에서와 같이 높은 발현량과 광범위한 파트너를 가지지는 않습니다.

Biological Interpretation

이 분석 결과는 간경변증의 병태생리에서 세포-세포 상호작용 네트워크의 중대한 변화를 명확히 보여줍니다.

• 간경변증에서의 염증 증폭: CXCL10-CXCR3, CCL3-CCR1, CCL4-CCR1과 같은 케모카인 신호 전달은 대식세포, T 세포, NK 세포와 같은 면역 세포의 활성화 및 간 실질로의 침윤을 강력하게 유도합니다. 이는 간경변증의 특징적인 만성 염증 반응을 설명합니다. 대식세포는 염증성 M1 표현형을 가질 수 있으며, 이는 섬유화를 촉진하는 인자를 분비합니다. [참고: UniProt for CXCR3]
• 간 섬유화의 핵심 동인: 간 성상 세포(HSC)는 간 섬유화의 주요 매개체입니다. SPP1-integrin 신호 전달은 활성화된 HSC의 증식, 이동 및 세포외 기질 생성을 촉진하며, 이는 간 섬유화의 진행에 결정적인 역할을 합니다. 또한 SPP1은 대식세포를 활성화하여 섬유화 과정을 가속화할 수 있습니다. COL1A1과 같은 ECM 분자와의 상호작용은 이러한 섬유화 과정을 더욱 강화합니다. [참고: PubMed search for "hepatic stellate cell fibrosis SPP1"]
• 면역 조절의 변화: HLA 분자를 포함한 상호작용의 증가는 간경변증 상태에서 면역 세포의 항원 제시 및 인식이 변화되었음을 시사하며, 이는 면역 관용의 손실이나 자가면역 반응과 관련될 수 있습니다.
• 건강한 간의 항상성: 건강한 간은 염증 및 섬유화 관련 신호가 낮은 비교적 조용한 상호작용 환경을 유지하며, 이는 정상적인 간 기능 유지 및 면역 항상성에 기여합니다.

Clinical or Translational Implications

이러한 세포-세포 상호작용 분석 결과는 간경변증 치료를 위한 새로운 치료 표적 발굴 및 질병 진행 모니터링을 위한 바이오마커 개발에 중요한 통찰력을 제공합니다.

• 치료 표적 발굴: 간경변증에서 현저히 증가하는 SPP1-integrin, CXCL10-CXCR3, CCL3/4-CCR1/5와 같은 리간드-수용체 쌍은 잠재적인 치료 표적이 될 수 있습니다.
• SPP1-integrin 축: SPP1 또는 특정 인테그린 수용체(예: ITGB1)를 표적으로 하는 저해제는 간 성상 세포의 활성화를 억제하고 섬유화 진행을 늦출 수 있습니다.
• 케모카인/수용체 축: CXCR3 또는 CCR1/5와 같은 케모카인 수용체에 대한 길항제는 염증성 면역 세포의 간 침윤을 감소시켜 만성 염증을 완화할 수 있습니다.
• 질병 바이오마커: 간경변증 특이적으로 상향 조절되는 특정 리간드 또는 수용체의 발현 수준, 또는 CCI 패턴 자체는 간경변증의 진단, 예후 예측, 치료 반응 모니터링을 위한 바이오마커로 개발될 가능성이 있습니다.
• 실험적 검증: 이 분석에서 식별된 핵심 상호작용은 시험관 내(in vitro) 및 생체 내(in vivo) 모델에서 그 기능적 중요성을 추가적으로 검증하여 새로운 치료 전략 개발의 기반을 마련할 수 있습니다.

10. Cell-Cell Interaction Landscape of Immune Checkpoint and Cell Cycle Related Pathways in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) in human liver, comparing healthy tissue with cirrhotic tissue. The focus is on a predefined set of genes related to immune checkpoints and cell cycle pathways, aiming to identify specific ligand-receptor interactions that may drive disease pathology or represent therapeutic targets. The plot_cci_dots tool was utilized to visualize these interactions, showing both the statistical significance (p-value) and the strength (mean expression) of interactions between various cell types identified from single-cell RNA sequencing data.

Visual Summary

The analysis provides two dot plots, illustrating cell-cell interactions for specific gene pairs within the healthy and cirrhotic liver conditions.

Comparison between Healthy and Cirrhosis

• The plot for cirrhosis (left panel) displays a significantly more extensive and diverse network of cell-cell interactions compared to the healthy liver (right panel). This is evident from the much larger number of interacting cell pairs and ligand-receptor complexes observed in cirrhosis.
• In the cirrhotic liver, numerous cell types, including Macrophages, Hepatic stellate cells, Endothelial cells, Cholangiocytes, and various T cell subsets (CD8+, CD4+), are actively engaging in communication. In contrast, the healthy liver plot shows a sparser interaction landscape involving fewer cell types and gene pairs.

Key Interactions in Cirrhosis

• TGF-beta Signaling: The TGFB1_TGFBR1, TGFB1_TGFbeta_receptor, TGFB2_TGFbeta_receptor, and TGFB3_TGFbeta_receptor axes show prominent and significant interactions across multiple cell types in cirrhosis. Notably, Hepatic stellate cells interact strongly with Cholangiocytes and Endothelial cells via TGFB pathways. Macrophages also show robust TGFB signaling.
• Growth Factor Signaling: AREG_EGFR interactions are highly active in cirrhosis, involving interactions between Macrophages, Hepatic stellate cells, Endothelial cells, and Cholangiocytes.
• Immune-related Interactions: The CD86_CD28 co-stimulatory pathway is evident in cirrhosis, particularly between T cells and Macrophages. Interactions involving IFNG_Type_II_IFNR and CD93_IFNGR1 are also widespread, suggesting active immune responses. LCK_CD8_receptor interactions are present, primarily involving CD8+ T cells.
• Integrin Signaling: An integrin_aVb8_complex interaction is observed, particularly between Hepatic stellate cells and Cholangiocytes, which is crucial for cell adhesion and extracellular matrix remodeling.

Key Interactions in Healthy Liver

• Fewer interactions are observed. Significant interactions primarily involve TGFB1_TGFBR3 between Endothelial cells and Endothelial cells, and between Endothelial cells and Macrophages.
• Some IFNG-related interactions (CD93_IFNGR1, IFNG_Type_II_IFNR) and LCK_CD8_receptor interactions are also present, indicating baseline immune surveillance.

Interpretation of Dot Attributes

• Dot Size (-log10(p)): Larger dots represent more statistically significant interactions (smaller p-values). A p-value cutoff of 0.05 (corresponding to -log10(p) = 1.3) was used for plotting, with larger dots indicating higher confidence in the interaction.
• Dot Color (log2(mean)): The color intensity (yellow to purple) indicates the average expression level of the interacting ligand-receptor pair (interaction strength). Yellow/light green colors indicate higher mean expression, suggesting stronger potential communication, while purple/dark blue indicates lower mean expression.

Biological Interpretation

The observed cell-cell interaction patterns provide critical insights into the pathological microenvironment of liver cirrhosis. The transition from a quiescent healthy state to a highly interactive cirrhotic state reflects the complex interplay of inflammation, tissue remodeling, and fibrosis.

• Fibrosis and Tissue Remodeling: The pronounced activation of TGF-beta signaling (TGFB-TGFBRs) in cirrhosis is a hallmark of fibrotic diseases [1]. TGF-β is a potent pro-fibrotic cytokine that activates hepatic stellate cells (HSCs) into myofibroblast-like cells, leading to excessive extracellular matrix deposition and scarring. The strong interactions involving HSCs, Cholangiocytes, and Endothelial cells via TGFB highlight their central roles in driving liver fibrosis and ductular reaction. The presence of integrin_aVb8_complex interactions further supports the involvement of adhesion and matrix remodeling processes, which are critical in fibrosis.
• [1] PubMed search: "TGF-beta signaling liver fibrosis" - PubMed Search
• Cell Proliferation and Repair Mechanisms: AREG-EGFR signaling is significantly upregulated in cirrhosis. Amphiregulin (AREG) is an epidermal growth factor receptor (EGFR) ligand known to promote cell proliferation, survival, and migration. Its activation in interactions involving macrophages, HSCs, endothelial cells, and cholangiocytes suggests its role in the pathological proliferation of these cells during tissue repair and remodeling in cirrhosis, potentially contributing to disease progression [2].
• [2] GeneCards: "AREG" - GeneCards
• Immune Cell Activation and Regulation: The presence of CD86_CD28 interactions in cirrhosis indicates active co-stimulatory signaling between antigen-presenting cells (like macrophages) and T cells. This pathway is crucial for initiating and regulating adaptive immune responses. Similarly, IFNG-IFNGR interactions, while present in both conditions, are more widespread in cirrhosis, underscoring the ongoing inflammatory and immune activation within the cirrhotic liver. These findings align with the "immune checkpoint" aspect of the queried genes, suggesting dynamic immune cell crosstalk in the diseased liver.
• Cell Cycle Pathway Connection: While many cell cycle-related genes were included in the target list, direct cell-cell *interactions* for these typically intracellular proteins are not directly visualized in ligand-receptor CCI plots. However, the identified ligand-receptor interactions (e.g., AREG-EGFR, TGFB-TGFBRs) are well-known to activate downstream signaling cascades that profoundly influence cell cycle progression, proliferation, and differentiation in recipient cells. For example, EGFR signaling often promotes cell proliferation, while TGF-β can have context-dependent effects, inhibiting proliferation in some epithelial cells while promoting it in fibroblasts. Thus, these external signals indirectly modulate the cell cycle machinery within the interacting cells.

Clinical or Translational Implications

The distinctive patterns of cell-cell interactions in cirrhotic liver compared to healthy liver present several avenues for clinical and translational applications:

• Therapeutic Target Prioritization: The identified ligand-receptor axes, particularly the TGFB-TGFBR pathways and AREG-EGFR signaling, represent high-priority therapeutic targets for liver cirrhosis. Strategies to inhibit these pathways, such as using specific receptor antagonists or neutralizing antibodies, could potentially attenuate fibrosis, inflammation, and pathological cell proliferation. Given the complexity, cell-type-specific targeting might be beneficial to minimize off-target effects.
• Biomarker Discovery: The increased activity and expression of specific ligand-receptor pairs (e.g., AREG-EGFR, TGFB1-TGFBRs) could serve as prognostic or diagnostic biomarkers for liver cirrhosis progression. Monitoring their expression levels in liver biopsies or even circulating factors could provide insights into disease severity and response to treatment.
• Experimental Validation: These findings provide a strong rationale for further experimental validation. In vitro co-culture models, liver organoids, and in vivo animal models of liver fibrosis could be used to functionally characterize these interactions. For instance, blocking specific ligand-receptor interactions through genetic manipulation or pharmacological inhibitors would help confirm their pathological roles and therapeutic potential.
• Immunomodulation: The activation of CD86-CD28 and IFNG-IFNGR pathways suggests opportunities for immunomodulatory therapies that could rebalance the immune response in the cirrhotic microenvironment. Understanding the precise context of these interactions could lead to strategies to dampen detrimental inflammation or enhance protective immunity.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates statistically significant differences in cell-cell interactions (CCIs) between healthy and cirrhotic human liver tissues using single-cell RNA sequencing data. CellPhoneDB was utilized to identify ligand-receptor pairs, and a dot plot visualizes the top 25 significantly enriched CCIs in each condition (cirrhosis vs. healthy) across specific cell types: Macrophage, T cell CD4+, T cell CD8+, B cell, Endothelial cell, Hepatic stellate cell, and Fibroblast. The aim is to identify interaction networks that are either gained in cirrhosis or lost in the healthy state, providing insights into disease mechanisms.

Visual Summary

The dot plot effectively illustrates condition-specific CCI patterns, with samples grouped by condition on the Y-axis and individual CCI pairs on the X-axis.

• Color Scale (Interaction Strength): The color intensity of each dot represents the standardized sample mean of the interaction strength, with darker red indicating stronger interactions.
• Dot Size (Statistical Significance): The size of each dot corresponds to the -log10(p-value), where larger dots signify more statistically significant interactions (lower p-value).
• Cirrhosis-Specific Interactions (Left Panel): The upper blue box highlights cirrhotic samples, which display numerous large, dark red dots in the left panel. This indicates a high prevalence of strong and significant interactions specifically upregulated in cirrhosis. In contrast, healthy samples (lower part of the left panel) show very few or no strong interactions for these specific CCI pairs, confirming their disease-specific enrichment.
• Healthy-Specific Interactions (Right Panel): Conversely, the lower blue box outlines healthy samples, which exhibit a distinct set of large, dark red dots in the right panel. These interactions are significantly enriched in healthy liver tissue, with cirrhotic samples (upper part of the right panel) showing markedly weaker or absent signals for these CCIs.

Biological Interpretation

Interactions Upregulated in Cirrhosis (Left Panel)

The CCI pairs predominantly observed in cirrhotic samples highlight key cellular processes involved in liver fibrosis and chronic inflammation:

1. Extracellular Matrix (ECM) Remodeling and Hepatic Stellate Cell (HSC) Activation: A prominent feature is the extensive involvement of integrin complexes (e.g., COL14A1, COL1A1, COL3A1, COL4A2, FN1) interacting between Hepatic stellate cells and Endothelial cells, or within endothelial cells. HSCs are central to liver fibrosis, and their activation leads to excessive ECM production. Integrins mediate cell-ECM and cell-cell adhesion, and their altered expression and function are critical in fibrogenesis. The presence of collagen (COL14A1, COL1A1, COL3A1, COL4A2) and fibronectin (FN1) related interactions strongly implicates active matrix remodeling. GeneCards: Integrin alpha10 beta1 complex GeneCards: FN1
2. Inflammation and Immune Cell Recruitment:

• TNF_TNFRSF1A-T cell CD4+|NK: This interaction suggests active inflammatory signaling, with TNF playing a critical role in chronic liver inflammation and driving immune responses.
• HLA-G_LILRB2-complex--Endo|Mac: HLA-G is an immune checkpoint molecule often upregulated in inflammatory conditions and cancer, modulating immune responses. Its interaction with LILRB2 on macrophages/endothelial cells could contribute to immune evasion or tolerance mechanisms in cirrhosis.
• CCL23_CCR1--Endo|Mac and CXCL16_CXCR6--Endo|Mac: These chemokine-receptor interactions point to active recruitment and positioning of macrophages (and potentially other immune cells) by endothelial cells, further fueling inflammation and contributing to the perpetuation of injury.
• VCAM1_integrin_a9b1_complex--Mac|Endo: VCAM1 is an adhesion molecule typically upregulated on activated endothelial cells, facilitating the extravasation of immune cells like macrophages into inflamed tissue.

3. Cholangiocyte Involvement: LTBR--T CD8+|Cholangiocyte indicates interactions involving Lymphotoxin Beta Receptor (LTBR) signaling with cholangiocytes. LTBR signaling is important in immune responses and lymphoid tissue development, suggesting potential roles in bile duct pathology and inflammation in cirrhosis.

Interactions Upregulated in Healthy Liver (Right Panel)

The interactions enriched in healthy samples likely represent mechanisms maintaining liver homeostasis, immune regulation, and tissue repair:

1. Anti-fibrotic and Homeostatic Signaling: TGFB3_TGFBR3--Hepatic stellate cell|Mac is noteworthy. While TGF-beta signaling is generally pro-fibrotic, TGFB3 is known to have anti-fibrotic or context-dependent roles, potentially contributing to the resolution of minor injury or maintenance of HSC quiescence in healthy tissue. PubMed: TGFB3 anti-fibrotic liver
2. Angiogenesis and Immune Regulation: PGF_NRP1--Hepatic stellate cell|Mac involves Placental Growth Factor (PGF), which can promote angiogenesis and inflammation. In a healthy context, these interactions might be involved in vascular maintenance or a controlled response to minor cues.
3. Diverse Immune and Metabolic Interactions: Interactions like LeukotrieneB4_byLTA4H_LTBR4R--Endo|Mac and ProstaglandinD2_byAKR1C3_PTGDR--Endo|Mac indicate active lipid mediator signaling between endothelial cells and macrophages, which are crucial for resolving inflammation and maintaining tissue immune balance. Cholesterol_byLIPA_RORA--Mac|Hepatic stellate cell suggests metabolic crosstalk between macrophages and HSCs. These interactions might be critical for healthy liver metabolism and lipid homeostasis.

Clinical or Translational Implications

1. Biomarkers of Cirrhosis: The specific CCI pairs significantly elevated in cirrhotic livers, particularly those involving integrins, TNF, and chemokines, could serve as novel biomarkers for diagnosis, prognosis, or monitoring treatment response. Detecting soluble forms of these ligands/receptors or analyzing cellular interaction profiles in biopsies might offer new diagnostic avenues.
2. Therapeutic Targets: The identified CCI pathways provide concrete targets for therapeutic intervention.

• Anti-fibrotic Therapies: Targeting the integrin-mediated interactions involving Hepatic stellate cells and the ECM (e.g., specific integrin alpha/beta subunits or their ligands like collagens and fibronectin) could inhibit HSC activation and ECM deposition, thereby halting or reversing fibrosis.
• Anti-inflammatory Therapies: Modulating interactions like TNF-TNFRSF1A, CCL23-CCR1, and VCAM1-integrin could reduce chronic inflammation and immune cell infiltration, which are major drivers of liver damage in cirrhosis.
• Restoring Homeostasis: Understanding the interactions prevalent in healthy liver, such as TGFB3-TGFBR3, could guide strategies to promote tissue regeneration and restore a homeostatic environment.

3. Disease Mechanism Elucidation: This analysis provides a high-resolution map of cell-cell communication networks that underpin the pathology of liver cirrhosis. By understanding which cells communicate and how, we gain deeper insights into the complex interplay between different cell types in disease progression, enabling the development of more precise and effective therapies.

12. Macrophage Condition-Specific Surfaceome Markers in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that distinguish macrophages in healthy liver tissue from those in cirrhotic liver tissue. Using single-cell RNA sequencing data, a dot plot was generated to visualize the expression levels and prevalence of these identified markers across individual samples, grouped by condition. The focus was on identifying up to 50 surfaceome markers per condition (healthy vs. cirrhosis) to provide insights into condition-specific macrophage phenotypes.

Visual Summary

The dot plot clearly delineates two distinct clusters of macrophage populations based on their gene expression profiles: one corresponding to cirrhotic samples and another to healthy samples.

• Condition-Specific Clustering: Samples from cirrhotic livers (top section) and healthy livers (bottom section) form separate, well-defined clusters, indicating significant differences in macrophage phenotypes between the two conditions.
• Cirrhosis-Specific Markers: A prominent set of genes, including FCGR1A, FPR1, CX3CR1, STAB1, and TREM2, shows high mean expression (darker red color) and a high fraction of expressing cells (larger dot size) specifically in macrophages from cirrhotic samples. These genes are largely absent or expressed at very low levels in healthy macrophages. Other notable markers in this group include LMAN2, CD300A, SPINT2, LAIR1, PLXDC2, CD9, ASGR1, SORL1, C19orf38, CD151, and GPR108.
• Healthy-Specific Markers: Conversely, genes such as AXL, VCAM1, LILRB5, and LYVE1 exhibit high expression and prevalence primarily in macrophages from healthy liver samples, with minimal expression in cirrhotic macrophages.
• Expression and Prevalence: The color intensity of each dot reflects the mean expression level of the marker within the group, while the size of the dot represents the percentage of cells in that group expressing the marker. This plot effectively highlights markers that are both highly expressed and widely present within a specific condition.

Biological Interpretation

The identified condition-specific surfaceome markers provide crucial insights into the functional plasticity and phenotypic shifts of macrophages during liver cirrhosis.

Macrophage Phenotype in Cirrhosis

Macrophages in cirrhotic livers appear to adopt a highly activated, pro-inflammatory, and tissue-remodeling phenotype, characterized by the upregulation of several key surface markers:

Immune Activation & Inflammation:

• FCGR1A (CD64): A high-affinity Fc-gamma receptor, its upregulation strongly indicates macrophage activation and heightened phagocytic capacity, often associated with inflammatory responses. UniProt: P12316
• FPR1 (Formyl Peptide Receptor 1): Functions as a sensor for bacterial formylated peptides, suggesting increased bacterial product sensing and recruitment of inflammatory cells, a common feature in cirrhosis due to gut dysbiosis and increased bacterial translocation. UniProt: P21462
• CX3CR1: The receptor for fractalkine (CX3CL1), essential for macrophage adhesion, migration, and survival. Its upregulation suggests active recruitment and retention of macrophages within fibrotic lesions, contributing to chronic inflammation and tissue damage. UniProt: P49238

Tissue Remodeling & Disease Association:

• STAB1 (Stabilin-1): A scavenger receptor associated with specific macrophage subsets involved in clearing extracellular matrix components. Its presence may indicate a role in fibrosis progression or resolution, often found on alternatively activated (M2-like) macrophages. GeneCards: STAB1
• TREM2 (Triggering Receptor Expressed on Myeloid cells 2): A crucial receptor on myeloid cells involved in sensing lipids and cellular debris, promoting phagocytosis, and regulating inflammatory responses. Upregulation of TREM2 in liver disease macrophages (often termed disease-associated macrophages or DAM-like) is implicated in both progression and resolution of liver fibrosis. PubMed search: TREM2 macrophage liver fibrosis
• SPINT2 (HAI-2): A serine protease inhibitor important in regulating pericellular proteolysis, which is critical for extracellular matrix turnover and tissue remodeling processes active in cirrhosis. UniProt: O43278
• Other notable markers: CD300A, LAIR1, ASGR1, and PLXDC2 also show upregulation, potentially contributing to altered immune regulation, scavenger functions, or angiogenic processes within the cirrhotic microenvironment.

Macrophage Phenotype in Healthy Liver

In contrast, macrophages in healthy livers exhibit a phenotype consistent with tissue-resident, homeostatic, and potentially immunosuppressive roles, characterized by:

Homeostasis & Immunoregulation:

• LYVE1 (Lymphatic Vessel Endothelial Hyaluronan Receptor 1): While classically a lymphatic endothelial marker, specific subsets of tissue-resident macrophages in various organs, including the liver, express LYVE1. These LYVE1+ macrophages are often associated with homeostatic functions, immune regulation, and anti-inflammatory properties. PubMed search: LYVE1 macrophage liver macrophage
• AXL: A receptor tyrosine kinase involved in efferocytosis (clearance of apoptotic cells) and anti-inflammatory signaling. Its higher expression in healthy macrophages suggests a role in maintaining tissue integrity and dampening inflammation. UniProt: P30530
• LILRB5 (CD85f): An inhibitory receptor that likely contributes to maintaining immune tolerance and preventing excessive inflammatory responses in the healthy liver microenvironment. UniProt: Q8TD86
• VCAM1 (CD106): While primarily an endothelial marker, its presence on certain macrophage populations might reflect specific adhesive properties or interactions crucial for their residence and function in healthy tissue. UniProt: P19320

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers for macrophages has significant clinical and translational potential.

Diagnostic and Prognostic Biomarkers:

• The distinct surface marker profiles (e.g., high FCGR1A, FPR1, CX3CR1, STAB1, TREM2 in cirrhosis; high AXL, LYVE1 in health) could serve as highly specific diagnostic or prognostic biomarkers for liver cirrhosis.
• These markers could be assessed using flow cytometry on peripheral blood monocytes or liver biopsy samples to identify specific macrophage subsets indicative of disease progression or response to therapy.

Therapeutic Targets:

• Targeting Pro-cirrhotic Macrophages: Upregulated receptors in cirrhotic macrophages, such as FPR1, CX3CR1, and TREM2, represent promising therapeutic targets. Modulating the activity of these receptors could inhibit inflammatory macrophage recruitment, survival, or pro-fibrotic functions, thereby attenuating liver fibrosis and cirrhosis progression. For instance, blocking FPR1 could reduce inflammatory responses driven by bacterial products, a key feature in cirrhosis.
• Promoting Healthy Macrophage Functions: Strategies aimed at enhancing the functions associated with healthy-specific markers, such as AXL (e.g., promoting efferocytosis and anti-inflammatory signaling) or fostering the LYVE1+ tissue-resident macrophage phenotype, could be explored to restore liver homeostasis and mitigate chronic inflammation.

Experimental Validation and Cell-Specific Therapies:

• These surfaceome markers are excellent candidates for experimental validation in human liver biopsies and relevant animal models using techniques like multi-color flow cytometry, immunohistochemistry, or imaging mass cytometry.
• They can be utilized for precise immunophenotyping, allowing researchers to isolate and functionally characterize specific macrophage subsets implicated in cirrhosis. This could pave the way for targeted cellular therapies or drug delivery systems specifically aimed at disease-driving macrophage populations.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that distinguish CD4+ T cells in cirrhotic liver tissue from those in healthy liver tissue. Using single-cell RNA sequencing data, condition-specific differentially expressed genes (DEGs) were identified, focusing exclusively on surface-expressed proteins. The results are visualized as a dot plot, showing the mean expression level and the fraction of cells expressing each marker across individual samples within the healthy and cirrhosis conditions.

Visual Summary

The dot plot displays the expression patterns of seven surfaceome markers (CXCR3, CD59, SIRPG, CD79B, PRNP, CD8A, CD8B) in CD4+ T cells across multiple cirrhotic and healthy liver samples.

Cirrhosis-Associated Markers:

• CXCR3 stands out as a prominent marker highly expressed and widely detected in CD4+ T cells from nearly all cirrhotic samples. Its expression is minimal or absent in healthy samples, making it a strong candidate for a cirrhosis-specific surface marker.
• CD59 and SIRPG show detectable expression in most cirrhotic samples, generally with higher mean expression and fraction of positive cells compared to healthy samples, though the distinction is less stark than for CXCR3.

Healthy-Associated Markers (Relative Difference):

• PRNP, CD8A, and CD8B show low to negligible expression in cirrhotic samples. In contrast, they exhibit a slightly higher fraction of expressing cells and mean expression in some healthy samples. However, their overall expression levels in CD4+ T cells are low.

Non-Canonical Marker:

• CD79B, a canonical B cell marker, shows very low expression and limited presence in CD4+ T cells from both conditions. Its inclusion suggests either minimal background expression, potential technical artifacts, or perhaps a rare subset phenomenon, but it is not a defining marker for T cells.

Overall, the plot clearly highlights CXCR3 as the most differentially expressed surface marker, strongly associated with CD4+ T cells in cirrhotic liver.

Biological Interpretation

The identified surfaceome markers provide insights into the altered immunological landscape of CD4+ T cells during liver cirrhosis.

• CXCR3 Upregulation in Cirrhosis: The striking upregulation of CXCR3 (C-X-C Motif Chemokine Receptor 3) on CD4+ T cells in cirrhotic liver is biologically significant. CXCR3 is a chemokine receptor typically expressed on activated T helper 1 (Th1) cells, cytotoxic T lymphocytes, and NK cells. It mediates the migration of these immune cells to sites of inflammation in response to its ligands (CXCL9, CXCL10, CXCL11) [GeneCards]. In the context of liver cirrhosis, which is characterized by chronic inflammation and fibrogenesis, increased CXCR3 expression on CD4+ T cells suggests enhanced recruitment and retention of these cells in the diseased liver, potentially contributing to the inflammatory and fibrotic processes. This aligns with known roles of CXCR3 and its ligands in various chronic liver diseases.

CD59 and SIRPG in Immune Regulation:

• CD59 (Protectin) is a cell surface glycoprotein that inhibits the formation of the membrane attack complex (MAC) of the complement system [GeneCards]. Its increased expression in cirrhotic CD4+ T cells might indicate a mechanism by which these cells protect themselves from complement-mediated lysis in an inflammatory environment, or it could be a general activation marker.
• SIRPG (Signal Regulatory Protein Gamma) belongs to the SIRP family of cell surface proteins involved in cell-cell recognition and regulation of immune cell function, including phagocytosis and T cell activation [GeneCards]. Differential expression of SIRPG could modulate the interactions of CD4+ T cells with other immune or stromal cells in the cirrhotic microenvironment.
• CD8A/B and PRNP as Relative "Healthy" Markers: The observation that CD8A and CD8B (components of the CD8 co-receptor, typically found on cytotoxic T cells) are relatively more expressed in healthy CD4+ T cells, albeit at low levels, is interesting. While CD4+ T cells primarily express CD4, a small subset or transient expression of CD8 has been reported, sometimes associated with specific T cell activation states or developmental stages. Their relative reduction in cirrhosis could suggest a shift in CD4+ T cell subsets or activation states in the diseased liver, though their overall low expression means they are not definitive markers of CD4+ T cells. PRNP (Prion Protein) is also generally expressed at low levels but shows a slight relative enrichment in healthy samples. PRNP has diverse roles in cell signaling, adhesion, and neuroprotection, and its differential expression might reflect subtle changes in cellular stress or signaling pathways.
• CD79B – A Non-T cell Marker: The presence of CD79B, a core component of the B cell receptor complex, in this list of CD4+ T cell markers is unexpected. As highlighted by its very low expression, this likely represents minimal or non-specific background detection, potentially due to gene overlap, ambient RNA, or minor cellular contamination, rather than functional expression on CD4+ T cells. It is not considered a relevant T cell marker.

Clinical or Translational Implications

The identification of condition-specific surfaceome markers for CD4+ T cells, particularly CXCR3, holds significant clinical potential:

• Biomarker for Cirrhosis Progression/Severity: The strong upregulation of CXCR3 on liver-resident CD4+ T cells could serve as a potential biomarker for the presence or severity of liver cirrhosis. Monitoring CXCR3 expression, perhaps via flow cytometry on liver biopsy samples or even circulating T cells (if liver-specific subsets can be identified), might offer insights into disease activity.
• Therapeutic Target: Given CXCR3's role in T cell recruitment to inflammatory sites, targeting the CXCR3-ligand axis could be a therapeutic strategy to modulate pathogenic T cell infiltration and reduce inflammation and fibrosis in liver cirrhosis. Inhibitors of CXCR3 have been explored in other inflammatory diseases [PubMed search: CXCR3 inhibitor liver fibrosis].
• Immunomodulation: Modulating the expression or function of other surface molecules like CD59 or SIRPG on CD4+ T cells could also be explored for therapeutic intervention, aiming to fine-tune the immune response and mitigate tissue damage in cirrhotic patients.
• Validation: These findings warrant further experimental validation using techniques such as flow cytometry, immunohistochemistry, or functional assays to confirm protein-level expression and functional relevance of these markers on CD4+ T cells in cirrhotic liver tissue.

14. Increased Expression of Cell Cycle Genes ANAPC11 and MAD2L2 in Macrophages from Cirrhotic Livers

[Analysis Visualization Results]...

Analysis Overview

This analysis investigated the expression levels of cell cycle-related genes in Macrophages, comparing healthy liver tissue to cirrhotic liver tissue. Specifically, the plot_box_for_gene_expression_with_signif_difference tool was used to visualize and statistically test for differences in gene expression. The analysis focused on a predefined list of cell cycle pathway genes, and the output highlights genes that showed statistically significant differences (p ≤ 0.01) between conditions for Macrophages. The boxplots display gene expression (sample mean) across different conditions, with individual data points representing individual samples.

Visual Summary

The boxplots illustrate the expression patterns of two key cell cycle regulatory genes, ANAPC11 and MAD2L2, in Macrophages:

• ANAPC11 (Anaphase Promoting Complex Subunit 11): Macrophages from cirrhotic liver samples exhibit significantly higher expression levels of ANAPC11 compared to those from healthy controls (p ≤ 0.01). The median expression for ANAPC11 in cirrhosis is approximately 0.62, whereas in healthy samples, it is considerably lower at about 0.41.
• MAD2L2 (MAD2 Mitotic Arrest Deficient-like 2): Similarly, MAD2L2 expression is significantly elevated in macrophages derived from cirrhotic livers relative to healthy ones (p ≤ 0.01). The median expression for MAD2L2 is around 0.21 in cirrhosis, higher than the approximately 0.13 observed in healthy controls.

In both cases, the spread of expression values (interquartile range, represented by the box) also shows higher overall levels in the cirrhotic condition, with minimal overlap between the distributions.

Biological Interpretation

The observed upregulation of ANAPC11 and MAD2L2 in liver macrophages during cirrhosis points to significant alterations in cell cycle regulation and potentially increased proliferative activity or stress responses within these immune cells.

• ANAPC11 is a crucial component of the Anaphase-Promoting Complex/Cyclosome (APC/C), a master regulator of cell cycle progression, particularly during mitosis and the G1 phase. APC/C orchestrates the degradation of specific proteins, such as cyclins and securin, to ensure proper chromosome segregation and exit from mitosis [1]. Elevated ANAPC11 suggests an increased or dysregulated activity of the APC/C complex, potentially contributing to enhanced macrophage proliferation or aberrant cell cycle progression in the cirrhotic liver.
• MAD2L2 (also known as REV7) is involved in the spindle assembly checkpoint (SAC), a critical mechanism that monitors chromosome attachment to the spindle microtubules during mitosis to prevent aneuploidy [2]. Beyond its role in SAC, MAD2L2 also participates in DNA damage response pathways and translesion DNA synthesis. Its upregulation in cirrhotic macrophages could indicate:
• Increased Cell Division: Enhanced proliferative capacity of macrophages to expand their population within the inflamed and damaged liver environment. Macrophage proliferation is a known contributor to the chronic inflammation and fibrosis characteristic of cirrhosis [3].
• Response to Cellular Stress/DNA Damage: Macrophages in the cirrhotic microenvironment are exposed to chronic inflammation, oxidative stress, and hypoxia, which can induce DNA damage. Upregulation of MAD2L2 might reflect an active cellular response to these stressors.

Taken together, the increased expression of these cell cycle regulators in macrophages from cirrhotic livers strongly suggests a shift towards a more proliferative or highly activated state for these immune cells. This aligns with the known pathophysiology of liver cirrhosis, where macrophage accumulation and activation play a central role in driving inflammation and fibrosis.

Clinical or Translational Implications

The differential expression of ANAPC11 and MAD2L2 in macrophages from cirrhotic livers highlights potential mechanistic insights into the progression of liver fibrosis and inflammation.

• Biomarker Potential: These genes could serve as potential biomarkers for assessing disease activity or macrophage involvement in cirrhosis. Monitoring their expression in circulating monocytes or liver biopsies might provide valuable prognostic or diagnostic information.
• Therapeutic Targets: Targeting specific cell cycle regulators in macrophages could represent a novel therapeutic strategy to modulate macrophage proliferation and activation in chronic liver disease, potentially attenuating inflammation and fibrogenesis. Interventions that inhibit key cell cycle components, if specific to pathogenic macrophage populations, could be explored.

References

1. ANAPC11: GeneCards Human Gene Database. https://www.genecards.org/cgi-bin/carddisp.pl?gene=ANAPC11
2. MAD2L2: GeneCards Human Gene Database. https://www.genecards.org/cgi-bin/carddisp.pl?gene=MAD2L2
3. Macrophage proliferation in liver disease: Search PubMed for "macrophage proliferation liver cirrhosis fibrosis". https://pubmed.ncbi.nlm.nih.gov/?term=macrophage+proliferation+liver+cirrhosis+fibrosis

15. Cholangiocyte Gene Ontology Analysis in Liver Cirrhosis and Health

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GO) enrichment results for Cholangiocytes, an epithelial cell type lining the bile ducts in the liver. The goal is to identify biological pathways and processes differentially regulated in cholangiocytes under two specific conditions: 'cirrhosis_vs_others' (pathways enriched in cholangiocytes from cirrhotic livers compared to the average of all other cells/conditions) and 'healthy_vs_others' (pathways enriched in cholangiocytes from healthy livers compared to the average of all other cells/conditions). The results are visualized as bar plots showing the top enriched terms ranked by their statistical significance (-log(p-val) and -log(q-val)).

Visual Summary

The provided bar plots display the top GO terms significantly enriched in cholangiocytes for two comparisons:

1. GSA for Cholangiocyte: cirrhosis_vs_others: This plot shows pathways upregulated or activated in cholangiocytes from cirrhotic livers.

• The x-axis represents -log(p-val) and -log(q-val), indicating the statistical significance of enrichment. Higher values mean greater significance.
• The most striking observation is the prominent enrichment of terms related to neurodegenerative diseases (Parkinson's, Huntington's, Prion, Amyotrophic lateral sclerosis, Alzheimer's) at the top of the list, with very high -log(p-val) and -log(q-val) values (up to ~25 for -log(p-val) and ~20 for -log(q-val)).
• Other highly significant terms include Oxidative phosphorylation, Diabetic cardiomyopathy, and pathways related to protein processing (e.g., Protein processing in endoplasmic reticulum, Spliceosome, Lysosome, Phagosome) further down the list, suggesting cellular stress and metabolic dysfunction.

2. GSA for Cholangiocyte: healthy_vs_others: This plot shows pathways relatively enriched in cholangiocytes from healthy livers.

• The significance levels (-log(p-val) up to ~4.5, -log(q-val) up to ~1.5) are considerably lower compared to the 'cirrhosis_vs_others' plot, indicating less dramatic or robust enrichment.
• Top enriched terms include MAPK signaling pathway, TNF signaling pathway, Spliceosome, Lipid and atherosclerosis, Endocytosis, Hippo signaling pathway, and IL-17 signaling pathway. These pathways are generally involved in cell signaling, inflammation, metabolism, and cell proliferation/differentiation.

Biological Interpretation

Cholangiocytes in Cirrhosis (cirrhosis_vs_others)

The strong enrichment of pathways associated with neurodegenerative diseases in cirrhotic cholangiocytes is a crucial and unexpected finding. While these diseases primarily affect the nervous system, their underlying molecular pathologies often involve fundamental cellular processes like:

• Protein misfolding and aggregation: Many neurodegenerative conditions are characterized by the accumulation of misfolded proteins. The enrichment of pathways related to "Protein processing in endoplasmic reticulum," "Spliceosome" (involved in RNA splicing, critical for protein synthesis), "Lysosome," and "Phagosome" (cellular degradation machinery) suggests that cholangiocytes in cirrhosis are under immense stress related to protein homeostasis and quality control. This indicates a significant burden on the cell's ability to correctly fold and clear proteins.
• Mitochondrial dysfunction and oxidative stress: Oxidative phosphorylation is highly enriched. Mitochondria are central to cellular energy production, and their dysfunction is a hallmark of oxidative stress, a key driver of chronic liver diseases like cirrhosis. Disruption of mitochondrial function is also implicated in neurodegeneration [PubMed search: mitochondrial dysfunction liver disease cirrhosis]. This suggests that cholangiocytes in cirrhosis experience severe metabolic perturbation and oxidative damage, leading to impaired energy production.
• Cellular stress responses: The presence of terms like "Diabetic cardiomyopathy" further underscores a generalized metabolic and cellular stress response. Cirrhosis is a state of chronic inflammation, tissue remodeling, and hepatocyte damage, all of which induce significant stress on cholangiocytes.

These findings suggest that cholangiocytes in cirrhosis undergo profound changes in their fundamental cellular machinery, particularly concerning protein quality control and energy metabolism, resembling common features of severe cellular pathology observed in neurodegenerative disorders. This indicates a state of chronic cellular stress, dysfunction, and potentially premature senescence or impaired regeneration capacity.

Cholangiocytes in Health (healthy_vs_others)

The GO analysis for 'healthy_vs_others' reveals pathways that are relatively more active or characteristic of cholangiocytes in a healthy liver environment compared to other cell types and conditions.

• Signaling and inflammation: Pathways like MAPK signaling pathway, TNF signaling pathway, IL-17 signaling pathway, and NF-kappa B signaling pathway are prominent. These are critical mediators of cell growth, differentiation, and immune responses [GeneCards: MAPK1]. Their enrichment in healthy cholangiocytes suggests that these cells maintain an active state of basal signaling, potentially involved in bile duct homeostasis, response to normal physiological cues, and innate immune surveillance. In cirrhosis, these pathways might be aberrantly activated or dysregulated, contributing to fibrosis and inflammation.
• Metabolism and Cell Structure: Lipid and atherosclerosis indicates cholangiocytes' role in lipid metabolism and bile acid transport. Endocytosis highlights active material uptake. Hippo signaling pathway is crucial for organ size control and cell proliferation, suggesting its role in maintaining healthy bile duct architecture [PubMed search: Hippo signaling cholangiocytes]. Focal adhesion relates to cell-extracellular matrix interactions, important for epithelial integrity.
• Gene Expression Regulation: The Spliceosome pathway again indicates active gene expression and RNA processing, a fundamental cellular activity.

Overall, healthy cholangiocytes appear to be actively engaged in core physiological functions, including baseline inflammatory signaling, metabolic regulation, and maintenance of tissue structure and proliferation control. The lower statistical significance compared to the cirrhotic state suggests that while these pathways are characteristic of healthy cholangiocytes, their changes are less dramatic than the pathological shifts seen in cirrhosis.

Clinical or Translational Implications

The findings have several potential clinical implications:

• Biomarkers of Cholangiocyte Stress: The highly significant enrichment of pathways related to protein misfolding, mitochondrial dysfunction, and cellular stress in cirrhotic cholangiocytes could point to novel biomarkers for assessing disease severity or progression in primary biliary cholangitis, primary sclerosing cholangitis, or other cholangiocyte-driven liver diseases.
• Therapeutic Targets: Understanding the specific mechanisms of proteostasis impairment and mitochondrial dysfunction in cholangiocytes during cirrhosis could open avenues for targeted therapies aimed at alleviating cellular stress, improving mitochondrial health, or enhancing protein quality control in these cells. For instance, modulating components of the ER stress response or enhancing lysosomal function could be therapeutic strategies.
• Understanding Cirrhosis Pathogenesis: The striking similarity of enriched pathways in cirrhotic cholangiocytes to those seen in neurodegenerative diseases underscores a commonality in severe cellular pathology across different tissues. This suggests that cholangiocytes in cirrhosis are not merely passive bystanders but actively contribute to the disease progression through profound cellular dysfunction, potentially linking to systemic manifestations of liver disease.
• Maintaining Cholangiocyte Health: The pathways identified in healthy cholangiocytes (e.g., MAPK, TNF, Hippo signaling) represent crucial functional modules. Understanding how these pathways are dysregulated or exhausted in disease could inform strategies to preserve or restore cholangiocyte function and integrity in chronic liver diseases.

16. GSEA in Liver Cirrhosis: Cell-Type-Specific Pathway Alterations

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for selected major cell types (Macrophage, Hepatic stellate cell, T cell CD4+, and B cell) within the liver, comparing two conditions: cirrhosis versus healthy states. The results are visualized as a dot plot, where each row represents a specific gene set or pathway, and each column represents a comparison for a particular cell type and condition. The color of each dot indicates the Normalized Enrichment Score (NES), with red signifying enrichment (upregulation) in the "test" condition (e.g., cirrhosis) and blue signifying depletion (downregulation) in the "test" condition. The size of the dot reflects the statistical significance, with larger dots corresponding to more significant p-values (-log(P)).

The "cases" on the x-axis are structured as Cell_type: condition_vs_others. Given the two conditions (healthy, cirrhosis), cirrhosis_vs_others indicates pathways enriched or depleted in the specific cell type from cirrhotic liver samples compared to healthy liver samples of the same cell type. Conversely, healthy_vs_others indicates pathways enriched or depleted in the specific cell type from healthy liver samples compared to cirrhotic liver samples of the same cell type. These two comparisons for a given cell type and pathway typically show reciprocal patterns (e.g., if a pathway is enriched in cirrhosis, it will be depleted in healthy).

Visual Summary

The dot plot effectively highlights numerous pathways that are significantly altered in Macrophages, Hepatic stellate cells, T cell CD4+, and B cells during liver cirrhosis.

• Widespread Immune Activation: A striking pattern is the strong enrichment of various immune and inflammatory pathways across Macrophages, T cell CD4+, and B cells in the cirrhosis condition (red dots in cirrhosis_vs_others columns). These include pathways related to infection (e.g., "Bacterial invasion of epithelial cells", "Salmonella infection", "Staphylococcus aureus infection", various viral infections), autoimmune diseases (e.g., "Autoimmune thyroid disease", "Rheumatoid arthritis", "Systemic lupus erythematosus"), and general immune responses ("Complement and coagulation cascades", "NOD-like receptor signaling pathway", "Th1 and Th2 cell differentiation", "Th17 cell differentiation", "Phagosome").
• HSC-Specific Metabolic and Fibrogenic Changes: Hepatic stellate cells (HSCs) show distinct enrichment patterns in cirrhosis, particularly in metabolic pathways like "Glycolysis / Gluconeogenesis", "Purine metabolism", "Pyruvate metabolism", and "mTOR signaling pathway". There's also enrichment in "Regulation of actin cytoskeleton" and "Protein processing in endoplasmic reticulum", indicating increased cellular activity, migration, and protein synthesis.
• Reciprocal Patterns: For most significantly enriched pathways in cirrhosis_vs_others, there is a corresponding depletion (blue dots) or absence of enrichment in healthy_vs_others for the same cell type, confirming the condition-specific changes.
• Pathway Significance: Many enriched pathways display large dot sizes, indicating high statistical significance (-log(P) values often exceeding 18, reaching up to 30 for several pathways).

Biological Interpretation

Macrophages: Drivers of Inflammation and Immune Response in Cirrhosis

In cirrhotic livers, macrophages exhibit a highly activated and pro-inflammatory phenotype. The significant enrichment of pathways such as:

• "Allograft rejection", "Autoimmune thyroid disease", "Rheumatoid arthritis", "Systemic lupus erythematosus": Suggests a generalized immune activation reminiscent of autoimmune or alloimmune responses. This points to a breakdown of immune tolerance and potential self-reactivity in the cirrhotic liver microenvironment.
• "Bacterial invasion of epithelial cells", "NOD-like receptor signaling pathway", "Phagosome", "Salmonella infection", "Staphylococcus aureus infection": Highlight an intensified pathogen recognition and clearance machinery, likely due to increased bacterial translocation from the gut in cirrhosis, leading to chronic low-grade inflammation and recurrent infections.
• "Complement and coagulation cascades": Indicates a link between inflammation and coagulation, which can contribute to microthrombosis and exacerbate liver injury in cirrhosis.
• "Th1 and Th2 cell differentiation", "Th17 cell differentiation": While these are T-cell specific differentiation pathways, their enrichment in macrophages indicates their critical role in modulating T-cell responses and shaping adaptive immunity in the cirrhotic liver.

Hepatic Stellate Cells: Central to Fibrogenesis and Metabolic Remodeling

Hepatic stellate cells (HSCs) are the primary fibrogenic cells in the liver. Their GSEA profile in cirrhosis strongly reflects their activated state:

• "Glycolysis / Gluconeogenesis", "Purine metabolism", "Pyruvate metabolism", "mTOR signaling pathway": These metabolic pathways are consistently enriched, indicating a metabolic reprogramming in activated HSCs towards increased glycolysis, nucleotide synthesis, and overall anabolic processes. This metabolic shift fuels their proliferation, extracellular matrix production, and contractile activity, crucial for fibrogenesis. GeneCards: Glycolysis, PubMed: mTOR in liver fibrosis
• "Regulation of actin cytoskeleton": Reflects increased cell motility, contractility, and changes in cell shape, all characteristic features of activated, migratory HSCs contributing to matrix deposition and tissue remodeling.
• "Protein processing in endoplasmic reticulum": Points to an increased burden of protein synthesis and secretion, consistent with their role in producing large amounts of extracellular matrix proteins like collagen.
• "Apelin signaling pathway": Apelin is involved in angiogenesis and regulating vascular tone. Its enrichment might signify altered angiogenesis and hemodynamics characteristic of cirrhosis. PubMed: Apelin liver cirrhosis
• "Non-alcoholic fatty liver disease": Its enrichment is highly relevant, as NAFLD can progress to non-alcoholic steatohepatitis (NASH) and then to cirrhosis. This suggests activated HSCs in cirrhosis retain a signature linked to metabolic liver disease, potentially perpetuating injury.

T Cell CD4+: Orchestrators of Adaptive Immune Responses

CD4+ T cells are crucial regulators of immune responses, and in cirrhosis, they show significant activation and differentiation:

• "Th1 and Th2 cell differentiation", "Th17 cell differentiation": Directly indicates the active differentiation of CD4+ T cells into various helper T cell subsets. Th1 and Th17 cells are typically pro-inflammatory, while Th2 cells are involved in humoral immunity and tissue repair (though sometimes promoting fibrosis). This balance can significantly impact cirrhosis progression.
• "Rheumatoid arthritis", "Systemic lupus erythematosus": Points to the involvement of CD4+ T cells in autoimmune-like inflammation, similar to macrophages.
• Viral Infection Pathways (e.g., "Epstein-Barr virus infection", "Human T-cell leukemia virus 1 infection", "Human immunodeficiency virus 1 infection", "Human papillomavirus infection", "Influenza A"): Suggests that chronic viral infections, or the host response to them, play a significant role in triggering or sustaining CD4+ T cell activation in the context of cirrhosis. Even in non-viral etiologies of cirrhosis, the immune system might exhibit a 'viral-like' response due to ongoing inflammation or pathogen translocation.

B Cells: Contributors to Humoral Immunity and Autoimmunity

B cells in cirrhosis demonstrate activation of humoral immunity and connections to various immune and infectious diseases:

• "B cell receptor signaling pathway": As expected, this fundamental pathway for B cell activation is enriched, signifying active B cell responses.
• "Primary immunodeficiency": This pathway often includes genes related to immune cell development and function. Its enrichment might reflect compensatory mechanisms or dysregulation in the B cell compartment.
• "Rheumatoid arthritis", "Systemic lupus erythematosus": Similar to T cells and macrophages, B cells are involved in autoimmune responses, indicating their potential role in contributing to autoimmune features observed in cirrhotic patients.
• Viral Infection Pathways (e.g., "Epstein-Barr virus infection", "Human T-cell leukemia virus 1 infection", "Human immunodeficiency virus 1 infection", "Human papillomavirus infection", "Influenza A", "Kaposi sarcoma-associated herpesvirus infection", "Measles"): The strong enrichment of multiple viral infection pathways underscores the B cell's role in antiviral immunity and suggests that unresolved or chronic viral stimuli could drive B cell activation and differentiation into plasma cells, contributing to both protective and potentially pathogenic antibody responses.

Clinical or Translational Implications

The GSEA results provide a comprehensive view of the cell-type-specific biological processes dysregulated in liver cirrhosis, offering several clinical and translational insights:

• Targeting Immune Dysregulation: The widespread activation of inflammatory, autoimmune, and infection-related pathways in macrophages, T cells, and B cells highlights the profound immune dysregulation in cirrhosis. Therapeutic strategies aimed at modulating specific immune checkpoints or inflammatory cascades could be beneficial. For instance, dampening hyperactive macrophage phagocytic or inflammatory pathways could reduce liver injury.
• Anti-fibrotic Therapies: The metabolic reprogramming and increased cellular activity in hepatic stellate cells strongly validate their central role in fibrogenesis. Targeting key metabolic nodes (e.g., glycolysis, mTOR signaling) in HSCs could be an effective anti-fibrotic strategy. UniProt: mTOR
• Addressing Co-morbidities and Etiologies: The enrichment of pathways related to non-alcoholic fatty liver disease (NAFLD) in HSCs suggests that even in advanced cirrhosis, metabolic factors continue to influence disease progression. Furthermore, the strong links to various viral infections across immune cells emphasize the importance of managing chronic viral hepatitis or considering the impact of latent viral infections in cirrhosis pathogenesis.
• Biomarker Discovery: The identified pathways and their associated leading-edge genes could serve as candidates for novel biomarkers of cirrhosis progression or response to therapy, particularly for distinguishing active fibrogenesis or immune-mediated injury.
• Understanding Autoimmune Components: The recurrent enrichment of autoimmune-related pathways across multiple immune cell types suggests that autoimmune phenomena may contribute to cirrhosis progression, even in cases not primarily classified as autoimmune hepatitis. This opens avenues for exploring immunomodulatory treatments.

17. Discussion

The single-cell RNA sequencing analysis of human liver in cirrhosis reveals a complex landscape of cellular remodeling, immune dysregulation, and altered cell-cell communication. A defining feature of cirrhotic livers is the substantial shift in cell population proportions, including a marked reduction in hepatocytes and a concomitant increase in fibrogenic cells such as hepatic stellate cells and fibroblasts, directly reflecting the progression of fibrosis and parenchymal loss (Section 4). This fibrotic response is further supported by the activation of hepatic stellate cells, which undergo metabolic reprogramming with enriched glycolysis, purine metabolism, and mTOR signaling, driving their proliferation and extracellular matrix production (Section 16). The immune compartment is profoundly altered, characterized by an overall increase in macrophages and T cells (Section 4). Macrophage populations demonstrate a clear shift towards a pro-inflammatory M1 phenotype, with increased proportions in cirrhosis and a corresponding decrease in reparative M2A macrophages (Sections 6, 8). These cirrhotic macrophages exhibit upregulated surface markers such as FCGR1A, FPR1, CX3CR1, STAB1, and TREM2, indicating heightened activation, immune sensing, and tissue remodeling functions (Section 12). Furthermore, macrophages show increased expression of cell cycle regulators ANAPC11 and MAD2L2, suggesting enhanced proliferation (Section 14), which contributes to chronic inflammation and fibrosis. The T cell landscape is also significantly dysregulated, with increased proportions of pro-inflammatory Th17, Th22, and Th9 cells, as well as regulatory T (Treg) and Lymphoid Tissue Inducer (LTI) cells, while cytotoxic T cells are significantly reduced (Section 7). CD4+ T cells in cirrhosis specifically upregulate CXCR3, a chemokine receptor crucial for immune cell migration to inflammatory sites (Section 13). The intricate cell-cell interaction network is dramatically amplified and diversified in cirrhosis. Interactions mediated by TGF-beta signaling (TGFB-TGFBRs), AREG-EGFR, and integrin complexes are prominently upregulated, particularly involving hepatic stellate cells, cholangiocytes, and endothelial cells (Sections 9, 10, 11). These pathways are central to driving fibrosis, inflammation, and pathological cell proliferation. The increased prevalence of chemokine-receptor interactions (e.g., CCL3-CCR1, CXCL10-CXCR3) further underscores the enhanced immune cell recruitment and sustained inflammation. Uniquely, cholangiocytes in cirrhosis display a striking enrichment of pathways associated with neurodegenerative diseases, including protein misfolding and mitochondrial dysfunction, indicative of severe cellular stress and impaired protein homeostasis (Section 15). This suggests a broader cellular pathology beyond simple inflammation. Overall, the cirrhotic liver is characterized by a state of persistent inflammation, active fibrogenesis driven by metabolically reprogrammed HSCs, and a compromised immune surveillance capacity, all orchestrated through an expanded and dysregulated cellular communication network.

Hypotheses:

1. Chronic inflammation in liver cirrhosis is primarily driven by the expansion and M1 polarization of macrophages, which is mediated by upregulation of surface receptors such as FPR1 and CX3CR1, contributing to sustained hepatocyte injury and fibrogenesis.
2. Hepatic stellate cell activation and subsequent extracellular matrix deposition in cirrhosis are critically dependent on enhanced TGF-beta and integrin-mediated cell-cell interactions with other stromal and epithelial cells, fueling progressive liver fibrosis.
3. The observed reduction in cytotoxic T cells and increase in Th17, Th22, and Treg populations in cirrhotic livers contribute to impaired anti-tumor immunity and an exacerbated inflammatory environment, respectively, increasing the risk of hepatocellular carcinoma and chronic inflammation.
4. Cholangiocytes in cirrhosis experience severe protein homeostasis and mitochondrial dysfunction, evidenced by enrichment of neurodegenerative disease-related pathways, leading to their cellular stress and contributing to bile duct pathology.
5. Increased macrophage proliferation in cirrhosis, driven by upregulated cell cycle genes like ANAPC11 and MAD2L2, is a key mechanism contributing to the overall expansion of inflammatory immune cells and persistence of liver injury.

Potential therapeutic targets:

1. TGF-beta signaling (e.g., TGFBR1/2/3): TGF-beta signaling is a central pro-fibrotic pathway, significantly upregulated in cirrhotic Hepatic stellate cells, Cholangiocytes, and Endothelial cells, driving extracellular matrix deposition and fibrogenesis. Evidence: CCI analysis showed prominent TGFB1_TGFBR1, TGFB2_TGFbeta_receptor, TGFB3_TGFbeta_receptor axes activated across multiple cell types in cirrhosis (Section 10, 11). GSEA showed enrichment of 'Protein processing in endoplasmic reticulum' in HSCs, consistent with high ECM production (Section 16). Validation: Evaluate the efficacy of TGFBR inhibitors in attenuating liver fibrosis in in vivo cirrhosis models, and assess their impact on HSC activation and ECM production in in vitro assays.
2. CXCR3 on CD4+ T cells: CXCR3 is significantly upregulated on CD4+ T cells in cirrhosis, mediating their recruitment to inflammatory sites in the liver and contributing to chronic inflammation. Evidence: Condition-specific surfaceome marker analysis identified CXCR3 as highly expressed in CD4+ T cells from cirrhotic samples, with minimal expression in healthy controls (Section 13). GSEA of CD4+ T cells shows enrichment in Th1 and Th17 differentiation, consistent with CXCR3's role (Section 16). Validation: Test CXCR3 antagonists in animal models of liver injury to reduce CD4+ T cell infiltration and subsequent inflammation. Confirm target engagement and T cell localization via flow cytometry and immunohistochemistry.
3. Pro-inflammatory Macrophage Receptors (FPR1, CX3CR1, TREM2): These surface receptors are highly upregulated on macrophages in cirrhotic livers, indicating heightened activation, pathogen sensing, and roles in tissue remodeling, contributing to chronic inflammation and fibrosis. Evidence: Macrophage condition-specific surfaceome markers showed significant upregulation of FCGR1A, FPR1, CX3CR1, STAB1, and TREM2 in cirrhotic samples (Section 12). Population analysis showed increased M1 macrophages (Section 6, 8). GSEA showed enrichment in pathogen-related and autoimmune pathways for macrophages (Section 16). Validation: Develop inhibitors or neutralizing antibodies against FPR1, CX3CR1, or TREM2. Validate their ability to modulate macrophage polarization, reduce inflammatory cytokine production, and ameliorate fibrosis in in vitro macrophage assays and in vivo models of cirrhosis.
4. Integrin-mediated interactions (e.g., those involving FN1, COL1A1, COL4A2): Integrins are crucial for cell-ECM adhesion and cell-cell communication, extensively involved in Hepatic stellate cell activation and extracellular matrix remodeling, a hallmark of liver fibrosis. Evidence: Condition-specific CCI patterns showed significant upregulation of integrin complexes (e.g., COL14A1, COL1A1, COL3A1, COL4A2, FN1) interacting among Hepatic stellate cells, Endothelial cells, and other cells in cirrhosis (Section 9, 11). Validation: Evaluate the anti-fibrotic effects of integrin antagonists in reducing HSC activation, proliferation, and ECM production in cell-based assays and preclinical models. Assess changes in liver stiffness and collagen content.

Follow-up validation ideas:

1. Validate cell population shifts and marker expression (e.g., M1/M2 macrophage subsets, T cell subsets, CXCR3, FCGR1A) using multi-color flow cytometry or immunohistochemistry on independent human liver biopsies from healthy and cirrhotic patients.
2. Employ spatial transcriptomics or multiplex imaging (e.g., IMC, CyTOF) to confirm the localization and spatial relationships of specific cell types and their upregulated cell-cell interaction pairs (e.g., TGFB-TGFBRs between HSCs and cholangiocytes, integrin interactions) within the fibrotic liver architecture.
3. Conduct in vitro co-culture experiments using primary human hepatic stellate cells, macrophages, and cholangiocytes to functionally test the impact of blocking specific ligand-receptor interactions (e.g., TGF-beta, AREG-EGFR, integrins) on fibrogenesis, inflammation, and cell proliferation.
4. Utilize targeted gene perturbation (e.g., CRISPR/Cas9, shRNA) in human macrophage or hepatic stellate cell lines to functionally assess the roles of ANAPC11, MAD2L2, FCGR1A, or CXCR3 in cell proliferation, activation, and pro-fibrotic signaling.
5. Investigate the impact of CXCR3 antagonists or small molecule inhibitors of macrophage surface receptors (FPR1, CX3CR1, TREM2) in preclinical animal models of liver fibrosis/cirrhosis to evaluate their therapeutic efficacy in reducing inflammation and fibrosis.

Limitations:

This study provides a snapshot of cellular and molecular states in healthy and cirrhotic livers; thus, causality cannot be definitively inferred from these cross-sectional analyses. Cell-cell interactions identified computationally (e.g., CellPhoneDB) represent potential interactions and require rigorous experimental validation at the protein level and in functional assays. While transcriptomic data provides insights into cellular functions, protein expression and post-translational modifications are critical for full functional understanding. The generalizability of these findings may be influenced by the specific etiologies and stages of cirrhosis represented in the dataset. Furthermore, proportions of cell types and subsets do not directly indicate absolute cell counts, which would require additional quantification methods.

18. Query List

1. Show UMAPs including condition, sample, major cell type, minor cell type, and celltype_subset in 2 columns, and save it.
2. Show expression of CD3D, CD4, CD8A, CD79A, MS4A1, MZB1, CD14, LYZ, FBLN1, NOTCH3, EPCAM, MUC1, CD34 genes on UMAP along with minor cell type annotation. Set ncols=4 and save it.
3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
4. Show population bar plot for minor cell types and save it.
5. Show subset population barplot for T cell and save it.
6. Show subset population barplot for Macrophage and save it.
7. For T cell subset populations, show boxplots for statistically significant differences between conditions if any, and save it. Determine ncols appropriately based on the total number of panels.
8. For Macrophage subset populations, show boxplots for statistically significant differences between conditions if any, and save it. Determine ncols appropriately based on the total number of panels.
9. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
10. Select genes related to immune checkpoint and cell cycle pathways, show cell-cell interactions for these genes, and save it.
11. Find statistically significant differences in cell-cell interactions between conditions for Macrophage, T cell CD4+, T cell CD8+, B cell, Endothelial cell, Hepatic stellate cell, Fibroblast, show them as a dot plot, and save it. Set max_n_items_per_group = 25.
12. Extract condition-specific markers for Macrophage and show them as a dot plot, and save it. Show only surfaceome markers, up to 50 per condition.
13. Extract condition-specific markers for T cell CD4+ and show them as a dot plot, and save it. Show only surfaceome markers, up to 50 per condition.
14. For cell cycle pathway related genes in Macrophage, Hepatic stellate cell, Endothelial cell, find statistically significant differences in expression between conditions, show them as boxplots, and save it. Set max_n_items_to_plot = 24, and ncols appropriately for a 2x3 panel ratio.
15. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
16. Show Gene Set Enrichment Analysis results for Macrophage, Hepatic stellate cell, T cell CD4+, B cell as a dot plot, and save it. Use RdBu_r as the color map and set n_pws_to_show = 80.