SCODiA Report by MLBI Lab

Single-Cell Atlas of Liver Cirrhosis Reveals Profound Cellular Remodeling, Dysregulated Communication, and Distinct Immune Signatures

Liver cirrhosis is characterized by extensive cellular remodeling, marked by significant shifts in cell type proportions, altered intercellular communication networks, and distinct immune cell phenotypes. Our single-cell analysis highlights the expansion of pro-fibrotic stromal cells and pro-inflammatory macrophages, alongside a complex dysregulation of T cell subsets. These changes collectively drive chronic inflammation, progressive fibrosis, and impaired liver function, offering critical insights into disease pathogenesis and potential therapeutic avenues.

Contents

0. Dataset overview
1. UMAP Visualization of Liver Single-Cell Transcriptome by Condition, Sample, and Cell Types
2. Major Cell Type Score Analysis on UMAP
3. Celltype_subset Marker Expression Overview in Human Liver Single-Cell RNA-seq Data
4. Liver Cell Population Shifts in Cirrhosis: A Single-Cell Analysis
5. 간 섬유증에서 T 세포 아형 및 선천 림프구 집단의 변화 분석
6. T cell Subset Population Shifts in Liver Cirrhosis
7. Macrophage Subset Population Shifts in Liver Cirrhosis
8. Macrophage Subset Population Shifts in Liver Cirrhosis
9. Condition-Specific Cell-Cell Interaction Patterns in Liver Cirrhosis
10. Condition-Specific Cell-Cell Interaction Patterns in Liver Cirrhosis
11. Macrophage Condition-Specific Surfaceome Markers in Liver Cirrhosis
12. T cell CD4+ Condition-Specific Surfaceome Markers in Liver Cirrhosis
13. Gene Set Enrichment Analysis of Liver Cell Types in Cirrhosis
14. 간경변증 담관세포의 유전자 온톨로지 (GSA) 분석
15. Discussion
16. Query List

0. Dataset overview

데이터셋 요약

• 이 데이터는 60503개의 세포와 22784개의 유전자로 구성된 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터입니다.
• 데이터는 인간 간 조직에서 유래했으며, healthy(건강) 및 cirrhosis(간경변) 두 가지 조건으로 구분됩니다.
• 세포 주석은 celltype_major, celltype_minor, celltype_subset의 세 가지 계층적 수준으로 제공됩니다.
• celltype_major에는 Myeloid cell, T cell, B cell, Stromal cell, Liver Epithelial cell, Mast cell, Endothelial cell 등이 포함됩니다.
• celltype_minor에는 Macrophage, T cell CD4+, T cell CD8+, Hepatocyte, Cholangiocyte 등이 포함됩니다.
• celltype_subset에는 Macrophage (M1), T cell (Tfh), B cell (Follicular), Hepatic stellate cell 등 더 세분화된 세포 타입이 포함됩니다.

다음과 같은 사전 계산된 분석 결과가 포함되어 있습니다

uns['CCI']: 조건별 세포-세포 상호작용(CellPhoneDB) 결과

uns['CCI_sample']: 샘플별 세포-세포 상호작용(CellPhoneDB) 결과

• uns['DEG']: 각 celltype_minor에 대해 한 조건을 다른 조건들과 비교한 차등 발현 유전자(DEG) 결과
• uns['GSEA']: 각 celltype_minor에 대해 한 조건을 다른 조건들과 비교한 유전자 세트 농축 분석(GSEA) 결과
• uns['GSA_up']: 각 celltype_minor에 대해 한 조건을 다른 조건들과 비교한 유전자 온톨로지(GO/GSA) 결과

1. UMAP Visualization of Liver Single-Cell Transcriptome by Condition, Sample, and Cell Types

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) plots of single-cell RNA sequencing data from human liver tissue. UMAP is a dimensionality reduction technique used to visualize high-dimensional data, such as gene expression, in a 2D or 3D space, preserving the global and local structure of the data. Each point on the UMAP represents a single cell, and cells that are transcriptionally similar are located closer together. The UMAPs are colored by different metadata attributes: condition (healthy vs. cirrhosis), individual sample, celltype_major, celltype_minor, and celltype_subset to provide an overview of the dataset structure, cell type distribution, and condition-specific patterns.

Visual Summary

Condition UMAP

The UMAP colored by condition shows a clear separation between cells from 'healthy' and 'cirrhosis' conditions. While some clusters appear to be predominantly 'healthy' (e.g., the large cluster in the lower-left, colored blue), and others predominantly 'cirrhosis' (e.g., the large cluster in the upper-middle and top-right, colored maroon), there are also regions where cells from both conditions are intermixed. This suggests that while there are condition-specific cellular states and populations, some cell types or states are shared or exhibit a continuous spectrum of changes across conditions. The overall shape of the UMAP is defined by major cell populations, which are then differentially populated or transcriptionally altered in the disease state.

Sample UMAP

The UMAP colored by sample shows good mixing of cells from different samples within each condition. For example, cells from 'cirrhotic1_cd45+' and 'cirrhotic2_cd45+' samples generally intersperse with other cirrhotic samples within the 'cirrhosis'-dominant regions. Similarly, 'healthy1_cd45+' cells mix well with other healthy samples in the 'healthy'-dominant regions. This indicates that potential batch effects originating from individual samples are largely minimized, and the observed differences are more likely driven by biological variation rather than technical artifacts.

Celltype_major UMAP

This plot reveals distinct clusters for the major cell types.

• T cells (light blue/cyan) form a large, relatively cohesive cluster in the lower-left, which largely overlaps with the 'healthy' predominant region in the condition UMAP.
• Myeloid cells (light green/yellow-green) are prominent and distributed across several clusters, showing significant presence in both healthy and cirrhotic areas.
• Liver Epithelial cells (orange) form a distinct cluster towards the bottom-right, likely representing hepatocytes and cholangiocytes.
• Stromal cells (dark green) occupy a smaller, but distinct, cluster, often adjacent to epithelial cells.
• B cells (maroon), Endothelial cells (red), ILC (light orange), and Mast cells (yellow) form smaller, more diffuse clusters, often intermixed with other immune populations.
• Unassigned cells (purple) are present in smaller numbers, mostly at the periphery or between major clusters, suggesting they might be rare cell types or cells with ambiguous transcriptional profiles.

Celltype_minor UMAP

The celltype_minor UMAP refines the major cell type annotations, showing more granular populations.

• Within the T cell cluster, distinct sub-clusters for T cell CD4+ and T cell CD8+ are visible, though somewhat intermixed, particularly in the larger aggregate.
• Macrophage (Mac) cells (light yellow), a key myeloid subpopulation, form prominent clusters, often overlapping with the 'cirrhosis'-rich areas, indicating their potential role in disease.
• Hepatocytes (light yellow-orange) form a very distinct, tight cluster, primarily in the lower-right, suggesting a relatively homogenous transcriptional state among these cells.
• Hepatic stellate cells (Fib, orange), Cholangiocytes (red), Dendritic cells (DC, dark red), Endothelial cells (Endo, red-orange), NK cells (light green), Plasma cells (cyan), and B cells (maroon) are also resolved into specific, though sometimes overlapping, clusters.

Celltype_subset UMAP

This UMAP provides the highest resolution of cell types, revealing fine substructures within the minor cell type clusters.

• Within the Macrophage population, distinct subsets like Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), and Macrophage (M2D) are resolved. These subsets show some spatial separation, indicating different activation states or functions that might be differentially involved in healthy vs. cirrhotic liver.
• T cell subsets like T cell (Cytotoxic), T cell (Tfh), T cell (Naive), T cell (Treg), T cell (Th1), T cell (Th17), etc., are also visible, further highlighting the diversity of T cell responses.
• Specific B cell subsets (e.g., B cell (Memory), B cell (Follicular), B cell (MZ), B cell (Breg), Plasma cell) and Endothelial cell subsets (Endothelial tip cell, Lymphatic Endothelial cell) are also discernible.
• The tight cluster of Hepatocytes remains largely homogenous at this level.

Biological Interpretation

The UMAP visualizations provide crucial insights into the cellular landscape of the human liver in healthy and cirrhotic conditions:

1. Condition-Specific Cellular Shifts: The clear, albeit incomplete, separation of 'healthy' and 'cirrhosis' cells in the condition UMAP suggests significant cellular and transcriptional reprogramming in liver cirrhosis. This likely reflects changes in cell type proportions, activation states, and gene expression profiles.

• Immune Cell Infiltration/Activation: The 'cirrhosis' predominant regions appear to be enriched with various immune cells, particularly Macrophages (Mac_M1, M2A, M2B, M2C, M2D subsets), Dendritic cells (cDC, iDC, pDC), and specific T cell subsets (e.g., Cytotoxic T cells, T helper subsets like Th1, Th17). This aligns with the known inflammatory and immune activation processes in chronic liver disease and fibrosis progression. PubMed search: Macrophages liver cirrhosis
• Resident Liver Cell Changes: While Hepatocytes form a distinct, large cluster, their distribution across the condition UMAP may reveal subtle transcriptional differences in cirrhotic livers not immediately apparent here, or shifts in their relative abundance. Hepatic stellate cells (Fibroblast, SMC) and Endothelial cells are crucial in fibrosis progression and angiogenesis, respectively, and their specific subsets may show altered states in cirrhosis.

2. Robust Cell Type Annotation: The distinct clustering of celltype_major, celltype_minor, and celltype_subset on the UMAPs confirms that the cell type annotations are well-defined and biologically meaningful. Each cell type occupies a coherent space, indicating that cells assigned to the same type share similar transcriptional profiles. This enhances confidence in downstream differential expression and pathway analyses.
3. Absence of Major Batch Effects: The excellent intermixing of cells from different samples within each condition on the sample UMAP is a strong indicator that the data integration and preprocessing steps effectively mitigated technical variation. This ensures that observed biological differences are genuine and not artifacts of experimental or sequencing batch variations.
4. Complexity of Liver Myeloid and Lymphoid Cells: The highly fragmented and diverse clusters for Myeloid cells (especially Macrophages and DCs) and T cells (with numerous Th subsets) underscore the intricate immune microenvironment of the liver, particularly in disease. The distinct macrophage subsets (M1, M2A-D) suggest a spectrum of macrophage polarization states, which are critically involved in inflammation, tissue repair, and fibrosis in the liver. PubMed search: Macrophage polarization liver fibrosis

Annotation Notes

• The consistent and distinct clustering for major, minor, and subset cell types strongly supports the quality of the cell type annotations. Cells within each annotated group generally cluster together, validating the biological coherence of the assigned identities.
• The presence of a small 'unassigned' population suggests either rare cell types not captured by the current annotation strategy or cells with ambiguous transcriptional profiles that did not clearly align with known cell markers. Their scattered distribution suggests they are not a single coherent population.
• The clear separation of healthy and cirrhotic cells on the UMAP, coupled with good sample mixing, indicates that the embedding structure effectively captures condition-specific biological variability while minimizing technical noise. This provides a strong foundation for further investigations into disease mechanisms.

2. Major Cell Type Score Analysis on UMAP

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the major cell type scores across the UMAP embedding, alongside the final celltype_major assignments. For each major cell type (T cell, B cell, Myeloid cell, Mast cell, Endothelial cell, Stromal cell, Liver Epithelial cell), a score is calculated for every cell, representing the likelihood or strength of its belonging to that specific cell type based on its gene expression profile. These scores are then projected onto the UMAP, where warmer colors (yellow/green) indicate higher scores. The final plot shows the assigned celltype_major labels for direct comparison. This helps to validate the quality and distinctness of the cell type annotations.

Visual Summary

The UMAP plots clearly illustrate the spatial distribution of major cell types and their corresponding scores:

• Overall Embedding Structure: The UMAP displays several distinct clusters, suggesting diverse cell populations within the liver tissue.
• T cell: Cells with high "HiCAT_major_score: T cell" are predominantly concentrated in the large cluster located on the left side of the UMAP, which aligns perfectly with the T cell annotation in the celltype_major plot.
• B cell: High "HiCAT_major_score: B cell" values are observed in a distinct cluster towards the top-middle, corresponding to the B cell region in the celltype_major plot.
• Myeloid cell: The "HiCAT_major_score: Myeloid cell" shows strong enrichment in a large cluster on the right-center, matching the Myeloid cell annotation.
• Mast cell: A smaller, well-defined cluster at the bottom-left shows high "HiCAT_major_score: Mast cell", consistent with its assigned label.
• Endothelial cell: Cells in the top-right region exhibit high "HiCAT_major_score: Endothelial cell", coinciding with the Endothelial cell cluster.
• Stromal cell: A distinct cluster on the middle-right shows high "HiCAT_major_score: Stromal cell", matching the Stromal cell annotation.
• Liver Epithelial cell: High "HiCAT_major_score: Liver Epithelial cell" values are concentrated in the cluster at the bottom-right, corresponding to the Liver Epithelial cell annotation.
• Unassigned cells: In the celltype_major plot, a small cluster of "unassigned" cells is visible. These cells do not exhibit strong scores for any of the explicitly plotted major cell types, indicating their distinct or ambiguous nature.

In general, each major cell type score plot shows a concentrated area of high scores that spatially overlaps with the corresponding annotated cluster in the celltype_major plot. The boundaries between high-scoring regions for different cell types appear well-defined, with minimal overlap of high scores across distinct cell type clusters.

Biological Interpretation

The strong concordance between the HiCAT major type scores and the celltype_major annotations provides robust biological validation for the cell type assignments in this single-cell RNA-seq dataset of human liver.

• Robust Cell Type Identity: The clear and distinct clustering of high scores for each major cell type demonstrates that these populations possess unique and well-defined transcriptional signatures. This indicates that the chosen marker genes or gene programs for defining these major cell types are effective in discriminating them within the complex liver microenvironment.
• Annotation Quality: The visual alignment confirms the high quality of the celltype_major annotations. Cells annotated as a particular major type consistently show high scores for that type, reinforcing confidence in the classification. This is crucial for downstream analyses, ensuring that differential expression, cell-cell interaction, or pathway analyses are performed on accurately grouped cell populations.
• Liver Cell Composition: The presence and distribution of various major cell types, including immune cells (T, B, Myeloid, Mast), structural cells (Endothelial, Stromal), and parenchymal cells (Liver Epithelial cell), reflect the known cellular heterogeneity of the liver. The distinct clustering implies that these cells maintain their specific identities even under the conditions studied (healthy and cirrhosis), although their proportions or functional states might differ across conditions (which is not directly shown in this particular plot but supported by the overall data context).
• Unassigned Population: The "unassigned" cluster merits further investigation. While it does not strongly align with any of the defined major cell type scores, it might represent rare cell populations, progenitor cells, or cells in transient states that do not fit conventional definitions. Alternatively, these could be cells of lower quality or technical artifacts, which should be assessed further.

Annotation Notes

This analysis primarily serves as a validation of the existing cell type annotations (celltype_major). The high consistency between the derived cell type scores and the assigned labels indicates that the celltype_major classification is well-supported by the underlying gene expression data. This strengthens the foundation for subsequent analyses, ensuring that cell type-specific findings are based on accurately identified populations. Further investigation into the "unassigned" cells using minor or subset cell type scoring or de novo clustering could refine their identity.

3. Celltype_subset Marker Expression Overview in Human Liver Single-Cell RNA-seq Data

[Analysis Visualization Results]...

Analysis Overview

This analysis provides an overview of marker gene expression across various celltype_subset populations identified in the human liver single-cell RNA-seq dataset. The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing a given gene (dot size) for key surface marker genes (due to surfaceome_only=True parameter) within each celltype_subset. The primary goal of this visualization is to assess the quality and specificity of existing cell type annotations by examining whether expected marker genes are highly and uniquely expressed in their corresponding cell populations.

Visual Summary

The dot plot effectively displays the expression profiles of marker genes across 37 distinct celltype_subset populations. Key observations include:

• Distinct Expression Patterns: The visualization shows clear diagonal patterns, with most cell types exhibiting strong, specific expression of a set of marker genes, primarily confined to their own row. This "diagonal" banding is highlighted by the red boxes, indicating good specificity of the identified markers for their respective cell type subsets.

Quantitative Metrics:

• Mean Expression (Color): Darker red dots indicate higher mean expression levels of a gene within a cell type subset, suggesting strong transcriptional activity for that marker.
• Fraction of Cells (Size): Larger dots signify that a higher percentage of cells within a subset express the marker gene, indicating widespread presence of the marker within the population.
• Cell Type Abundance: The bar plot on the right displays the total number of cells assigned to each celltype_subset. Hepatocytes represent the largest population (13,413 cells), as expected in liver tissue. Other abundant populations include Macrophage (M1) (5,790 cells) and Mast cells (8,409 cells), suggesting their significant presence in the liver microenvironment.
• Marker Gene Grouping: Marker genes are grouped horizontally based on the celltype_subset they are associated with, enhancing readability and facilitating identification of specific markers for each group.

Biological Interpretation

The marker gene expression patterns largely validate the celltype_subset annotations, demonstrating distinct identities for most populations within the human liver.

• B cells: Subsets like B cell (Follicular), B cell (MZ), B cell (Memory), and B cell (Breg) show specific expression of established B cell markers such as POU2F2 (OCT2), IGHD, and CD22. SPIB, a B-cell specific transcription factor, is also observed, supporting their annotation GeneCards: POU2F2, GeneCards: CD22.
• Cholangiocytes: This epithelial cell type is characterized by canonical markers like KRT7, KRT19, EPCAM, and CLDN4, along with SPP1 and TACSTD2, confirming their identity GeneCards: KRT7, GeneCards: EPCAM.
• Dendritic Cells (DC): Classical DCs are marked by CLEC9A and XCR1 (typical for cDC1), while Plasmacytoid DCs are identified by IRF7 and TCF4, indicating distinct DC subtypes GeneCards: IRF7.
• Stromal Cells (Endothelial, Fibroblast, Hepatic Stellate Cell): These cell types show some expected overlap in expression of extracellular matrix (ECM) components and mesenchymal markers, such as DCN, COL1A1, FBLN1, LUM, and TAGLN. This is biologically plausible, as hepatic stellate cells are liver-resident pericytes with fibroblast-like properties, especially upon activation. DLL4 is a specific marker for endothelial cells GeneCards: DLL4. The high expression of TAGLN in Fibroblasts and Hepatic stellate cells suggests a potential myofibroblastic phenotype, which is relevant in liver disease contexts like cirrhosis GeneCards: TAGLN.
• Hepatocytes: As the main parenchymal cells of the liver, hepatocytes exhibit a unique and robust expression profile of genes involved in liver-specific metabolic functions, including APOA1, APOB, ASGR1, TTR, and PAH. This pattern strongly confirms their identity GeneCards: ASGR1.
• Innate Lymphoid Cells (ILC): Subsets of ILCs (ILC1, ILC2, ILC3) express general lymphocyte markers like CD69, CD7, IL2RG, and CD44. GATA3, a key transcription factor, is notably expressed in ILC2, consistent with its role in ILC2 development and function, similar to Th2 cells GeneCards: GATA3.
• Macrophages: Macrophage subsets (M1, M2A, M2B, M2C, M2D) display markers like CD36, STAT1, CLEC7A, and MSR1. The strong expression of STAT1 in M1 macrophages aligns with their pro-inflammatory polarization GeneCards: STAT1.
• Mast Cells: Mast cells are clearly identified by the expression of canonical tryptases, TPSAB1 and TPSB2, and SRGN GeneCards: TPSAB1. Their high abundance in the dataset is notable.
• NK Cells: KLRD1 (CD94), GZMK, and IFNG are expressed, supporting the NK cell annotation GeneCards: KLRD1.
• Plasma Cells: These antibody-secreting cells show high expression of specific markers such as XBP1, JCHAIN, SDC1 (CD138), and TNFRSF17 (BCMA), which are critical for their differentiation and function GeneCards: SDC1, GeneCards: TNFRSF17.
• Smooth Muscle Cells: Classic smooth muscle contractile proteins and regulatory genes including TAGLN, MYL9, TPM2, CALD1, MYH11, and CNN1 validate this cell type GeneCards: CALD1.
• T Cells: Various T cell subsets (Cytotoxic, Tfh, Th1, Th17, Th2, Th22, Treg) show appropriate marker expression. CD8A is specific to Cytotoxic T cells. CD40LG is seen in Tfh cells. Th1 cells express STAT1 and CXCR3. Th2 cells show GATA3. While canonical transcription factors like FOXP3 for Treg were not plotted (likely due to the surfaceome_only filter), TGFB1 and TNFRSF18 (GITR) are indicative of Treg activity GeneCards: CD8A, GeneCards: CD40LG, GeneCards: TGFB1.

Annotation Notes

The overall high specificity and strong expression of known marker genes within their respective celltype_subset populations provide robust support for the current cell type annotations. The clear separation of most cell types based on their marker profiles indicates a well-resolved single-cell atlas.

• Strength of Annotation: For most celltype_subset groups, the marker genes displayed are highly characteristic and widely accepted in the literature, strongly affirming the assigned identities. This is particularly evident for Hepatocytes, Plasma cells, Mast cells, and Cholangiocytes.
• Expected Overlaps: The observed gene expression overlaps among certain stromal and mesenchymal cells (e.g., Endothelial cells, Fibroblasts, Hepatic stellate cells) are biologically expected given their developmental origins and functional plasticity, especially in the context of liver tissue which is prone to fibrotic changes. This does not necessarily suggest mis-annotation but rather reflects biological relatedness or a continuum of cellular states.
• Surfaceome Focus: The surfaceome_only parameter limited the analysis to surface markers. While this is beneficial for identifying cell types based on membrane proteins, it might exclude some critical intracellular markers (e.g., transcription factors like FOXP3 for Tregs, RORC for Th17 cells) that could further solidify certain subset annotations. However, the chosen surface markers and their expression patterns are still highly informative and generally sufficient for validation.

4. Liver Cell Population Shifts in Cirrhosis: A Single-Cell Analysis

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes single-cell RNA sequencing data from human liver tissue to compare the cellular composition between healthy and cirrhotic conditions. The plot_celltype_population tool was used to visualize the relative proportions of minor cell types across individual samples, grouped by condition (healthy vs. cirrhosis). The goal is to identify significant shifts in cell type abundance associated with liver cirrhosis, providing insights into disease pathology.

Visual Summary

The stacked bar plots display the proportional representation of various minor cell types within each liver sample, categorized by 'cirrhosis' and 'healthy' conditions. Each bar represents a single sample, and the colored segments within each bar denote the relative percentage of a specific cell type.

Key observations from the visualization include:

• Increased Cholangiocytes in Cirrhosis: Several cirrhotic samples, particularly those with a cd45- enrichment (e.g., cirrhotic1_cd45-A, cirrhotic1_cd45-B, cirrhotic3_cd45-), show a marked expansion of Cholangiocytes (red bars), which are generally less prominent in healthy samples.
• Elevated Stromal Cells in Cirrhosis: Hepatic stellate cells (orange-yellow) and Fibroblasts (orange) appear to be more abundant in many cirrhotic samples compared to healthy controls, especially within the cd45- fractions. Endothelial cells (orange) also show an apparent increase in some cirrhotic samples.
• Immune Cell Infiltration: Macrophages (light yellow) show a generally higher proportion across cirrhotic samples, particularly in cd45+ enriched fractions. Dendritic cells (red-orange) also appear elevated in some cirrhotic samples. T cells (CD4+ - teal, CD8+ - dark blue) are major components of the cd45+ fractions in both healthy and cirrhotic samples, but their relative distribution might shift or other immune cells expand in cirrhosis.
• Reduced Hepatocytes in Cirrhosis: In the cd45- enriched fractions of cirrhotic samples where stromal and cholangiocyte populations are expanded, Hepatocytes (yellow) appear to be relatively less abundant compared to their higher proportions in healthy cd45- samples.
• Sample Heterogeneity: There is notable heterogeneity in cell type proportions even within samples of the same condition, particularly in the cirrhotic group, which might reflect different stages or etiologies of cirrhosis or specific tissue sampling.

Biological Interpretation

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

• Ductular Reaction and Cholangiocyte Proliferation: The significant increase in Cholangiocytes in cirrhotic samples indicates a robust ductular reaction, a hallmark of chronic liver injury and fibrosis. This reaction involves the proliferation of bile duct cells and their progenitors, often in response to chronic damage and impaired bile flow. This contributes to the fibrotic scar and can be a source of pro-fibrogenic signals.
• Fibrogenesis and Stromal Cell Activation: The increased proportions of Hepatic stellate cells and Fibroblasts are direct evidence of active fibrogenesis. Hepatic stellate cells are the primary collagen-producing cells in the liver; upon activation during injury, they transform into myofibroblast-like cells that drive extracellular matrix deposition, leading to fibrosis and ultimately cirrhosis [GeneCards: HSC, Fibroblast].
• Chronic Inflammation and Immune Cell Recruitment: The expansion of Macrophages and Dendritic cells in cirrhotic samples reflects ongoing inflammation. Liver macrophages (Kupffer cells and monocyte-derived macrophages) play a central role in both initiating and resolving liver injury, but in chronic disease, they often contribute to pro-fibrogenic and pro-inflammatory responses [PubMed: Liver Macrophages in Disease].
• Parenchymal Loss and Liver Dysfunction: The relative decrease in Hepatocytes, the main functional cells of the liver, in cirrhotic samples is expected. As fibrosis progresses, hepatocytes are replaced by scar tissue, leading to impaired liver function. The "unassigned" category also shows some variability, which may reflect uncharacterized cell states or technical aspects.

Clinical or Translational Implications

These findings highlight the profound cellular remodeling that occurs in cirrhotic liver. Understanding these population shifts has several clinical and translational implications:

• Biomarkers of Disease Progression: The relative proportions of specific cell types, such as Cholangiocytes, Hepatic stellate cells, and Macrophages, could serve as potential diagnostic or prognostic biomarkers for assessing the severity and progression of liver fibrosis and cirrhosis.
• Therapeutic Targets: Cells driving fibrosis (Hepatic stellate cells, Fibroblasts) and inflammation (Macrophages, Dendritic cells) represent critical therapeutic targets. Strategies aimed at inhibiting their activation, proliferation, or pro-fibrogenic functions could potentially halt or reverse disease progression [PubMed: Anti-fibrotic strategies]. Targeting the ductular reaction (Cholangiocytes) could also be a relevant approach.
• Understanding Disease Heterogeneity: The observed sample-to-sample variability within the cirrhotic group emphasizes the heterogeneous nature of cirrhosis, which may influence treatment responses and clinical outcomes. Single-cell analysis provides a valuable tool to dissect this heterogeneity and identify specific cellular drivers in individual patients.

5. 간 섬유증에서 T 세포 아형 및 선천 림프구 집단의 변화 분석

[Analysis Visualization Results]...

Analysis Overview

제공된 막대 그래프는 간경변증(cirrhosis)과 건강(healthy) 두 가지 조건에서 "T cell" (T 세포) 주요 집단 내 하위 아형 및 기타 림프구 집단의 상대적 비율을 시료별로 비교하여 보여줍니다. 여기에는 다양한 T 세포 아형(예: Cytotoxic T cell, Naive T cell, Tfh, Th1, Th17, Th2, Th22, Th9, Treg)뿐만 아니라 자연 살해(NK) 세포 및 선천 림프구(ILC1, ILC2, ILC3 (NCR+), ILC3 (NCR-), ILCreg, LTI)도 포함됩니다. 이 분석은 간 질환 상태에 따른 면역 세포 구성의 변화를 파악하는 데 중요한 통찰력을 제공합니다.

Visual Summary

1. 전반적인 구성: 간경변증 및 건강한 간 조직 모두에서 T cell (Cytotoxic)과 T cell (Naive)이 T 세포 주요 집단 내에서 가장 큰 비율을 차지하는 것으로 보입니다.
2. ILC1 및 ILC2의 감소: 건강한 시료에서는 ILC1 (진한 빨간색) 및 ILC2 (빨간색) 집단이 일부 시료에서 상대적으로 눈에 띄게 높은 비율을 차지하지만, 간경변증 시료에서는 이들 집단의 비율이 현저히 낮은 것으로 관찰됩니다. 특히 ILC1의 감소가 두드러집니다.
3. Treg 세포의 일관된 존재: T cell (Treg) (짙은 파란색)은 두 조건 모두에서 낮은 비율로 꾸준히 존재하며, 이는 면역 조절 역할이 지속되고 있음을 시사합니다.
4. Cytotoxic T cell의 우세: 간경변증 시료에서 T cell (Cytotoxic)의 비율이 건강한 시료에 비해 전반적으로 높거나 유사한 수준으로 유지되는 경향이 있습니다. 이는 만성 염증이나 세포 손상이 지속될 때 나타날 수 있는 반응입니다.
5. 다양한 Th 세포 아형: Tfh, Th1, Th17, Th2, Th22, Th9 등 다양한 헬퍼 T 세포 아형은 두 조건 모두에서 상대적으로 낮은 비율을 차지하며, 시료 간에 일부 변동성을 보입니다.
6. 시료 간 변동성: 특히 건강한 시료에서는 ILC1 및 ILC2의 비율이 시료에 따라 큰 차이를 보이는 등, 개별 시료 간에 T 세포 하위 집단의 구성에 상당한 변동성이 존재합니다.

Biological Interpretation

이러한 관찰 결과는 간경변증에서 간 내 면역 환경의 중요한 변화를 시사합니다.

• ILC1 및 ILC2의 감소: ILC1은 주로 IFN-γ를 분비하여 항바이러스 및 항종양 면역 반응에 관여하며, Th1과 유사한 기능을 합니다 GeneCards: ILC1. ILC2는 IL-5, IL-13과 같은 Th2 사이토카인을 분비하여 항기생충 면역, 알레르기 및 조직 복구/섬유화에 기여하는 것으로 알려져 있습니다 GeneCards: ILC2. 간경변증에서 이들 ILC 집단의 감소는 선천 면역 감시 기능의 저하 또는 질병 진행에 따른 ILC의 고갈이나 이동을 의미할 수 있습니다. 특히 간경변증과 같은 만성 간 질환 환경에서 ILC 집단의 역동적인 변화는 간 염증 및 섬유화의 병태생리에 중요한 영향을 미 미칠 수 있습니다.
• Cytotoxic T cell의 지속적인 존재: Cytotoxic T cell (CD8+ T 세포)은 감염된 세포나 손상된 세포를 직접 제거하는 역할을 합니다. 간경변증 시료에서 이들 세포의 높은 비율은 간염 바이러스 감염, 자가면역 반응 또는 지속적인 간세포 손상으로 인한 항원 제시의 결과일 수 있습니다 PubMed search: CD8 T cells liver cirrhosis. 이는 간경변증에서 만성적인 염증 및 조직 재형성 과정에 기여할 가능성이 있습니다.
• Treg 세포의 역할: T cell (Treg)은 면역 관용을 유지하고 과도한 염증 반응을 억제하는 중요한 역할을 합니다 GeneCards: FOXP3. 간경변증과 같은 만성 염증 상태에서 Tregs의 존재는 간 손상과 염증을 제한하려는 면역 체계의 시도로 해석될 수 있습니다. 그러나 간 섬유화를 촉진하는 면역 반응을 억제할 수도 있어 그 역할은 복합적입니다.
• 간 면역 미세환경의 변화: 이러한 림프구 하위 집단의 변화는 간경변증 동안 간 내 면역 미세환경이 건강한 상태와는 다르게 재구성됨을 명확히 보여줍니다. 이는 간 염증, 섬유화 및 재생 과정에 관여하는 복잡한 면역 상호작용의 증거입니다.

Clinical or Translational Implications

이러한 T 세포 아형 및 선천 림프구 집단의 변화는 간경변증의 병태생리를 이해하고 잠재적인 치료 전략을 개발하는 데 중요한 의미를 가집니다.

• 질병 바이오마커로서의 가능성: 간경변증 환자에서 ILC1 및 ILC2의 감소는 질병 진행의 바이오마커가 될 수 있으며, 이들 세포의 비율 변화를 추적하여 질병의 중증도나 예후를 예측하는 데 활용될 수 있습니다.
• 면역 조절 치료의 목표: Cytotoxic T cell의 지속적인 활성화는 간 손상을 악화시킬 수 있으므로, 이들의 활성을 조절하는 것이 치료적 접근법이 될 수 있습니다. 또한, Treg 세포의 기능을 조절하여 간 섬유화를 억제하거나 염증을 완화하는 전략도 고려해 볼 수 있습니다.
• ILC 기반 치료의 잠재력: ILC1 및 ILC2의 감소가 간경변증의 병리학적 변화에 기여한다면, 이들 ILC 집단을 회복시키거나 기능을 강화하는 치료법(예: 특정 사이토카인 투여)이 새로운 치료 옵션으로 연구될 수 있습니다.
• 맞춤형 치료 전략: 간경변증 환자의 T 세포 및 ILC 구성의 개인별 차이는 환자 맞춤형 치료 전략을 설계하는 데 중요한 정보를 제공할 수 있습니다.

6. T cell Subset Population Shifts in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional changes in various T cell subsets within the liver tissue, comparing individuals with cirrhosis to healthy controls. The box plots display the proportion of each T cell subset (Th1, LTI, T_Cyto, Treg, Th17, T_Naive, Th22) across the two conditions, with statistical significance indicated by p-values. The goal is to identify T cell populations that are significantly altered in cirrhosis, providing insights into the immune landscape of the diseased liver.

Visual Summary

The box plots reveal statistically significant differences in the proportions of several T cell subsets when comparing cirrhotic liver tissue to healthy liver tissue.

Increased in Cirrhosis (vs. Healthy)

• Th1 cells: Show a trend towards higher proportions in cirrhosis (p = 0.06).
• LTI cells: Also show a trend towards higher proportions in cirrhosis (p = 0.06).
• Cytotoxic T cells (T_Cyto): Significantly increased proportion in cirrhosis (p ≤ 0.05). The median proportion in cirrhosis is around 25%, compared to approximately 45% in healthy, suggesting a *higher* proportion in healthy which is counter-intuitive. Let me re-examine the T_Cyto plot. Ah, the box for cirrhosis is lower (median ~25%) than for healthy (median ~45-50%). The p-value indicates a significant difference. So, *lower* proportion of T_Cyto in cirrhosis relative to healthy, contrary to my initial reading of the box position. Let me re-evaluate this carefully.
• For T_Cyto: Median for cirrhosis is ~25%, median for healthy is ~45-50%. The box for healthy is *higher* than for cirrhosis. The p-value is <= 0.05. This means Cytotoxic T cells are *significantly lower* in cirrhosis compared to healthy.
• Regulatory T cells (Treg): Significantly increased proportion in cirrhosis (p ≤ 0.01). The median proportion in cirrhosis is around 1.8%, compared to approximately 0.5% in healthy.
• Th17 cells: Significantly increased proportion in cirrhosis (p ≤ 0.01). The median proportion in cirrhosis is around 2.0%, compared to approximately 0.7% in healthy.
• Th22 cells: Significantly increased proportion in cirrhosis (p ≤ 0.05). The median proportion in cirrhosis is around 1.2%, compared to approximately 0.4% in healthy.

Decreased in Cirrhosis (vs. Healthy)

• Naive T cells (T_Naive): Significantly decreased proportion in cirrhosis (p ≤ 0.05). The median proportion in cirrhosis is around 12%, compared to approximately 8% in healthy. My reading of the box is inverse again. Let me be careful. The box for cirrhosis is *higher* (median ~12-15%) than for healthy (median ~8-10%). So, *Naive T cells are significantly higher in cirrhosis* (p ≤ 0.05).

It seems I misread the direction of the box plots initially. Let me re-evaluate based on the visual medians.

Revised Visual Summary:

• T_Cyto: Proportion is lower in cirrhosis (median ~25%) compared to healthy (median ~45-50%), with p ≤ 0.05.
• Treg: Proportion is higher in cirrhosis (median ~1.8%) compared to healthy (median ~0.5%), with p ≤ 0.01.
• Th17: Proportion is higher in cirrhosis (median ~2.0%) compared to healthy (median ~0.7%), with p ≤ 0.01.
• Th22: Proportion is higher in cirrhosis (median ~1.2%) compared to healthy (median ~0.4%), with p ≤ 0.05.
• T_Naive: Proportion is higher in cirrhosis (median ~12-15%) compared to healthy (median ~8-10%), with p ≤ 0.05.
• Th1: Proportion is higher in cirrhosis (median ~2.5%) compared to healthy (median ~1.0%), with p = 0.06 (borderline significant, but within the pval_cutoff of 0.1).
• LTI: Proportion is higher in cirrhosis (median ~7.5%) compared to healthy (median ~4.0%), with p = 0.06 (borderline significant, but within the pval_cutoff of 0.1).

In summary, for the selected T cell subsets, all subsets shown (Th1, LTI, Treg, Th17, Th22, T_Naive) appear to be proportionally *increased* in the cirrhotic liver compared to healthy liver, with the exception of Cytotoxic T cells (T_Cyto) which are *decreased* in cirrhosis.

Biological Interpretation

The observed shifts in T cell subset proportions in liver cirrhosis suggest a profoundly altered immune microenvironment, indicative of chronic inflammation, tissue damage, and dysregulated immune responses.

1. Increased Pro-inflammatory and Regulatory T Cells:

• Th1, Th17, Th22 (increased in cirrhosis): The elevation of these effector T cell populations (Th1, Th17, Th22) strongly indicates an active, pro-inflammatory state in the cirrhotic liver.
• Th1 cells primarily produce IFN-γ and are critical for cell-mediated immunity, often implicated in chronic inflammation and autoimmune responses PubMed Search: Th1 cells liver cirrhosis.
• Th17 cells produce IL-17 and IL-22 and are key players in inflammatory and autoimmune diseases, and contribute to fibrosis in various organs, including the liver GeneCards: IL17A. Their increase suggests a strong inflammatory axis driving liver damage and fibrosis.
• Th22 cells produce IL-22, which can have both protective (tissue repair) and pathogenic (pro-fibrotic) roles in chronic liver disease depending on the context PubMed Search: Th22 cells liver fibrosis. Their increase could reflect ongoing tissue damage and repair attempts, which can become dysregulated and contribute to fibrosis.
• Treg cells (increased in cirrhosis): The significant increase in regulatory T cells (Treg) is often seen in chronic inflammatory conditions and cancer. Tregs suppress immune responses and maintain immune tolerance. In cirrhosis, this increase might represent a compensatory mechanism to control excessive inflammation, but it could also contribute to immune evasion by pathogens or hepatocellular carcinoma (HCC) cells, or hinder the clearance of damaged hepatocytes GeneCards: FOXP3.

2. Increased Lymphoid Tissue Inducer (LTI) cells (increased in cirrhosis): LTI cells are crucial for the development of secondary lymphoid organs and can contribute to the formation of ectopic lymphoid structures in chronically inflamed tissues. Their increase in cirrhotic liver might indicate ongoing neo-lymphogenesis, which can exacerbate chronic inflammation and contribute to disease progression PubMed Search: LTi cells liver inflammation.
3. Increased Naive T cells (increased in cirrhosis): An unexpected finding is the higher proportion of Naive T cells in cirrhotic liver. Typically, chronic inflammation leads to the activation and differentiation of naive T cells, thereby decreasing their proportion. This observation might suggest an altered recruitment pattern of T cells into the cirrhotic liver or a specific subpopulation of naive T cells expanding in this environment, which warrants further investigation.
4. Decreased Cytotoxic T cells (T_Cyto) (decreased in cirrhosis): The significant decrease in cytotoxic T cells (predominantly CD8+ T cells) in cirrhotic livers is notable. Cytotoxic T cells are vital for clearing virus-infected cells and tumor cells. A reduction could imply impaired anti-viral or anti-tumor immunity, potentially increasing susceptibility to viral re-activation or contributing to the development and progression of hepatocellular carcinoma, which is a common complication of cirrhosis PubMed Search: CD8 T cells liver cirrhosis HCC. This reduction could also be a result of chronic exhaustion or displacement by other immune cell types.

Collectively, these shifts depict a complex immune environment in the cirrhotic liver characterized by pervasive inflammation (Th1, Th17, Th22, LTI), attempts at immune regulation (Treg), and potentially compromised cellular immunity (decreased T_Cyto). The increase in naive T cells is a peculiar observation that might hint at specific immune dynamics in this chronic disease state.

Clinical or Translational Implications

The distinct alterations in T cell subset proportions offer potential clinical and translational implications for liver cirrhosis:

• Biomarker Potential: The proportions of specific T cell subsets, particularly Treg, Th17, Th22, and T_Cyto, could serve as diagnostic or prognostic biomarkers for the severity or progression of liver cirrhosis. Monitoring these populations might help stratify patients.
• Therapeutic Targets: The overrepresentation of pro-inflammatory T cell subsets (Th1, Th17, Th22) suggests that immunomodulatory therapies targeting these pathways could potentially mitigate liver inflammation and fibrosis. Conversely, strategies to restore or enhance cytotoxic T cell function might improve anti-viral or anti-tumor immunity in cirrhotic patients.
• Understanding Pathogenesis: The observed immune shifts deepen our understanding of the immunopathogenesis of cirrhosis. For instance, the imbalance between effector T cells and reduced cytotoxic T cells might explain chronic viral persistence or increased risk of HCC in these patients. Further studies are needed to determine if the increased naive T cells are functionally distinct or indicative of specific immune evasion mechanisms.
• HCC Surveillance: Given the decreased cytotoxic T cells, strategies to boost anti-tumor immunity, perhaps through checkpoint blockade or adoptive cell therapies, might be particularly relevant for preventing or treating HCC in cirrhotic patients.

7. Macrophage Subset Population Shifts in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the relative proportions of various macrophage subsets (Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), Macrophage (M2D)) within the liver tissue, comparing individual samples from healthy and cirrhotic conditions. The plot provides a stacked bar chart for each sample, showing the percentage contribution of each macrophage subset to the total macrophage population. This helps to identify shifts in macrophage polarization states associated with liver cirrhosis.

Visual Summary

The stacked bar plots clearly illustrate distinct differences in macrophage subset distribution between healthy and cirrhotic liver samples.

• Dominance of M1 Macrophages in Cirrhosis: In samples from cirrhotic livers, the Macrophage (M1) subset (dark red) is predominantly abundant, consistently accounting for over 50% of the total macrophage population in most samples, frequently reaching 60-70%.
• Reduced M2 Subsets in Cirrhosis: Conversely, the relative proportions of M2 macrophage subsets (M2A, M2B, M2C, M2D) are noticeably diminished in cirrhotic samples compared to healthy controls. Notably, Macrophage (M2D) (teal) appears to be nearly absent or present in very small proportions across most cirrhotic samples.
• More Diverse Macrophage Landscape in Healthy Liver: Healthy liver samples exhibit a more heterogeneous and less M1-dominant macrophage composition. While M1 macrophages are present, their proportions are generally lower than in cirrhosis, often below 50% and sometimes as low as ~25%.
• Higher M2 Subsets in Healthy Liver: In healthy samples, M2 macrophage subsets, particularly M2A (orange), M2B (light yellow), and M2D (teal), contribute more significantly to the overall macrophage pool in some individuals, suggesting a more balanced or alternative activation state. There is also greater variability in the distribution of M2 subtypes among healthy individuals.

Biological Interpretation

Macrophages are critical immune cells in the liver, playing multifaceted roles in maintaining homeostasis, initiating immune responses, and contributing to tissue repair and fibrosis. Liver macrophages, including resident Kupffer cells and monocyte-derived macrophages, are highly plastic and can polarize into different functional states, traditionally categorized as M1 (pro-inflammatory) and M2 (anti-inflammatory, pro-resolving, or pro-fibrotic) phenotypes. The M2 spectrum is further divided into M2A, M2B, M2C, and M2D, each with distinct functional characteristics.

The observed shift towards an M1-dominant macrophage phenotype in cirrhotic livers is highly significant. M1 macrophages are typically characterized by their pro-inflammatory functions, including the production of inflammatory cytokines (e.g., TNF-α, IL-6, IL-1β) and reactive oxygen/nitrogen species. This M1 polarization is often associated with tissue damage and persistent inflammation, which are hallmarks of chronic liver diseases like cirrhosis [PubMed search: M1 macrophages liver cirrhosis].

The concomitant reduction in the relative abundance of M2 subsets, particularly M2D, in cirrhosis suggests a diminished capacity for immune resolution, tissue repair, and anti-inflammatory responses. M2 macrophages are generally associated with promoting tissue remodeling, fibrosis, angiogenesis, and immune suppression. For instance:

• M2A macrophages are often induced by IL-4/IL-13 and are involved in allergic responses and anti-helminth immunity, as well as promoting fibrosis and tissue repair.
• M2B macrophages are induced by immune complexes and LPS, contributing to both pro- and anti-inflammatory responses, and are involved in B cell activation.
• M2C macrophages are induced by IL-10 or TGF-β and are linked to immune regulation, tissue remodeling, and fibrosis.
• M2D macrophages, also known as tumor-associated macrophages (TAMs) or regulatory macrophages, are associated with angiogenesis, immune suppression, and tumor progression, and their reduction in cirrhosis might indicate a shift away from certain immunoregulatory or pro-angiogenic functions.

The pronounced shift towards M1 macrophages in cirrhosis likely contributes to the chronic inflammatory state that drives hepatocellular damage and the progression of fibrosis, eventually leading to end-stage liver disease [GeneCards: TNF-α, IL-6]. This suggests that the immune microenvironment in cirrhotic livers is skewed towards inflammation and away from resolution, potentially perpetuating the disease process.

Clinical or Translational Implications

The findings highlight the critical role of macrophage polarization in the pathogenesis of liver cirrhosis and suggest potential therapeutic avenues:

• Biomarker Potential: The M1/M2 ratio, or specifically the high prevalence of M1 macrophages, could serve as a valuable biomarker for monitoring disease progression or severity in patients with chronic liver disease, potentially even predicting the risk of decompensation or liver failure.
• Therapeutic Targeting: Modulating macrophage polarization could represent a promising therapeutic strategy for cirrhosis. Shifting the balance from pro-inflammatory M1 to pro-resolving/anti-fibrotic M2-like phenotypes, or directly inhibiting M1 activation, might mitigate inflammation and halt or reverse fibrotic progression [PubMed search: macrophage polarization liver fibrosis therapy].
• Drug Development: Future drug development could focus on agents that specifically influence macrophage polarization in the liver, for example, by blocking M1-inducing signals or promoting M2-inducing pathways. This precision immunomodulation could offer novel approaches for managing cirrhosis where current treatments largely address symptoms rather than underlying pathology.

8. Macrophage Subset Population Shifts in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates statistically significant differences in the proportions of macrophage subset populations (specifically Mac (M1) and Mac (M2A)) between healthy and cirrhosis conditions in liver tissue, as identified from single-cell RNA sequencing data. The comparison aims to reveal how the immune landscape, particularly macrophage polarization, shifts in the context of liver disease.

Visual Summary

The box plots display the cell type proportion of two macrophage subsets, Mac (M1) and Mac (M2A), across healthy and cirrhotic liver samples.

• Mac (M1) Proportion: The proportion of Mac (M1) cells is significantly higher in cirrhotic samples compared to healthy samples (p ≤ 0.05). The median proportion for Mac (M1) in healthy livers is around 50-55%, while in cirrhotic livers, it rises to approximately 65%. This indicates an expansion or enrichment of M1-like macrophages in diseased tissue.
• Mac (M2A) Proportion: Conversely, the proportion of Mac (M2A) cells shows a trend towards being lower in cirrhotic samples compared to healthy samples (p = 0.08). The median proportion for Mac (M2A) in healthy livers is around 16-17%, which slightly decreases in cirrhotic livers to about 15%. This suggests a potential reduction or shift away from M2A-like macrophages in cirrhosis.

Biological Interpretation

Macrophages are critical immune cells in the liver, where they are known as Kupffer cells or monocyte-derived macrophages, playing diverse roles in homeostasis, immunity, and disease. Their polarization into distinct functional subsets, such as M1 (pro-inflammatory) and M2 (anti-inflammatory/pro-resolving), is crucial for maintaining liver health and responding to injury.

• Increased Mac (M1) in Cirrhosis: The significant increase in Mac (M1) cells in cirrhosis is consistent with the known inflammatory nature of this disease. M1 macrophages are typically activated by pro-inflammatory stimuli (e.g., LPS, IFN-γ) and produce inflammatory cytokines (e.g., TNF-α, IL-6, IL-1β) and reactive oxygen species, contributing to tissue damage and fibrosis progression in chronic liver diseases. Their accumulation suggests a persistent pro-inflammatory environment in cirrhotic livers [1].
• Decreased Mac (M2A) in Cirrhosis: The observed trend of decreased Mac (M2A) cells in cirrhosis is also noteworthy. M2A macrophages are generally associated with Th2 responses, wound healing, tissue repair, and the resolution of inflammation, often producing anti-inflammatory cytokines (e.g., IL-10, TGF-β) and growth factors that promote tissue remodeling. A reduction in this subset might imply an impaired ability of the liver to resolve inflammation and repair tissue damage effectively, potentially exacerbating fibrosis and liver dysfunction [2].
• Macrophage Polarization Shift: The overall picture suggests a pathological shift in macrophage polarization within the liver microenvironment during cirrhosis, moving towards a more pro-inflammatory (M1-dominant) and less reparative (M2A-deficient) state. This imbalance likely contributes to the chronic inflammation, fibrogenesis, and impaired regeneration characteristic of liver cirrhosis [3].

Clinical or Translational Implications

The distinct alterations in macrophage subset proportions between healthy and cirrhotic livers have several clinical and translational implications:

• Biomarkers for Disease Progression: The ratio or absolute proportions of M1 and M2A macrophages could potentially serve as biomarkers for the diagnosis, staging, or prognosis of liver cirrhosis. Monitoring these cellular shifts might offer insights into disease activity and response to therapy.
• Therapeutic Targets: Modulating macrophage polarization represents a promising therapeutic strategy for liver cirrhosis. Interventions aimed at reducing M1-like activity or enhancing M2A-like functions could potentially mitigate inflammation, halt fibrosis progression, and promote tissue repair [4].
• Immunomodulation in Liver Disease: Understanding the specific triggers and pathways driving this macrophage polarization shift in cirrhosis could lead to the development of novel immunomodulatory drugs. For example, therapies that re-polarize macrophages from an M1 to an M2 phenotype, or that selectively deplete pathogenic M1 populations, could offer new avenues for treatment.

References

1. Macrophages in Liver Fibrosis: A review on the roles of macrophages in liver fibrosis and their potential as therapeutic targets. [PubMed Search: "macrophages liver fibrosis M1" PubMed Search]
2. M2 Macrophages and Liver Regeneration/Repair: Information on the role of M2 macrophages in tissue repair and resolution of inflammation. [PubMed Search: "M2 macrophages liver repair" PubMed Search]
3. Immune Cell Landscape in Liver Cirrhosis: Overview of immune cells involved in liver cirrhosis pathophysiology. [PubMed Search: "immune cells liver cirrhosis" PubMed Search]
4. Targeting Macrophage Polarization in Liver Disease: Research on therapeutic strategies involving macrophage polarization. [PubMed Search: "macrophage polarization therapy liver fibrosis" PubMed Search]

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

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interaction (CCI) patterns by comparing cirrhotic and healthy human liver samples using single-cell RNA sequencing data. The plot_dot_for_cci_with_signif_difference tool was used to visualize the most significant and differentially regulated CCIs for each condition, highlighting both the strength of interaction (dot color) and statistical significance (dot size). The goal is to identify distinct communication networks that characterize the disease state versus healthy liver tissue.

Visual Summary

The dot plot effectively illustrates clear distinctions in cell-cell communication landscapes between healthy and cirrhotic liver samples.

• Condition-Specific Clustering: The plot is horizontally divided into two major sections, corresponding to CCIs predominantly observed in 'cirrhosis' (left side) and those predominant in 'healthy' samples (right side). This indicates a dramatic shift in intercellular communication depending on the disease state.
• Cirrhosis-Specific Interactions: In cirrhotic samples (top rows), a large number of interactions are highly active (dark red dots) and statistically significant (large dots). These interactions mostly involve extracellular matrix (ECM) components like various collagens (COL1A1, COL3A1, COL4A1, COL6A3, COL12A1, COL18A1) and fibronectin (FN1), interacting with various integrin complexes (integrin_a1b1_complex, integrin_a10b1_complex, etc.). The primary cellular players in these interactions are Hepatic stellate cells and Endothelial cells. Additionally, interactions involving immune cells (e.g., Macrophage, T cell CD8+) with Endothelial cells and Hepatic stellate cells through adhesion molecules like ICAM1 and immune modulators like HLA-G and TNF family receptors are prominently featured.
• Healthy-Specific Interactions: In contrast, healthy samples (bottom rows) exhibit a distinct set of active CCIs, which are generally absent or weak in cirrhotic samples. These interactions often involve Prostaglandin E2 (ProstaglandinE2_byPTGES2_PTGER4) signaling between various cell types, including Hepatic stellate cell, Macrophage, Plasma cell, Endothelial cell, T cell CD8+, and Cholangiocyte. Other notable interactions involve APOA1_ABCA1 (Plasma cell|Hepatic stellate cell), SPN_SIGLEC1 (various Macrophage interactions), CD40LG_CD40 (T cell CD4+|Hepatic stellate cell), and IGF2_IGF2R (Endothelial cell|Hepatic stellate cell).
• Sample Heterogeneity: While general patterns are clear, some heterogeneity exists within samples of the same condition, indicating individual variations in disease progression or physiological state.

Biological Interpretation

The observed condition-specific CCI patterns provide critical insights into the molecular mechanisms driving liver cirrhosis and maintaining liver homeostasis.

Cirrhosis-Associated Communication Networks

1. Extracellular Matrix Remodeling and Fibrosis: The most striking feature in cirrhosis is the profound upregulation of interactions centered around the extracellular matrix. Hepatic stellate cells (HSCs), crucial mediators of liver fibrosis, show extensive communication with Endothelial cells via numerous collagen-integrin and fibronectin-integrin pairs. This signifies heightened ECM production and deposition, a hallmark of liver fibrosis and cirrhosis. Integrins are essential for HSC activation and their attachment to and remodeling of the fibrotic matrix.
2. Inflammation and Immune Cell Recruitment: Elevated interactions involving ICAM1 (Endothelial cell with Macrophage) point to increased leukocyte-endothelial adhesion, facilitating immune cell infiltration and perpetuating chronic inflammation in the cirrhotic liver. Furthermore, TNF family receptor signaling (e.g., TNF_TNFRSF1A/B) and LTB_LTBR interactions underscore the involvement of pro-inflammatory pathways. The presence of HLA-G_LILRB interactions suggests potential immune evasion or modulation, often observed in chronic inflammatory conditions.
3. Cross-talk between Liver Parenchyma and Immune Cells: The broad range of interacting cell types, including Macrophages, T cells, Endothelial cells, and Hepatic stellate cells, highlights a complex interplay among immune, stromal, and vascular compartments in driving cirrhosis pathogenesis.

Healthy Liver Homeostatic Communication Networks

1. Anti-inflammatory and Pro-resolving Signaling: The prominence of Prostaglandin E2 (PGE2) signaling (ProstaglandinE2_byPTGES2_PTGER4 interactions) in healthy livers suggests a crucial role for this lipid mediator in maintaining liver homeostasis. PGE2 is known for its anti-inflammatory and anti-fibrotic properties, often acting to resolve inflammation and modulate HSC activity, which appears to be compromised in cirrhosis PubMed: PGE2 liver fibrosis.
2. Metabolic and Regenerative Support: Interactions such as APOA1_ABCA1 (involved in cholesterol efflux and lipid metabolism) and IGF2_IGF2R (a growth factor important for liver development and regeneration) indicate healthy metabolic and regenerative functions. Dysregulation of lipid metabolism is a common feature of liver disease.
3. Immune Surveillance and Regulation: SPN_SIGLEC1 interactions, often involving macrophages, and CD40LG_CD40 interactions, critical for adaptive immune responses, suggest active immune surveillance and regulatory functions in the healthy liver.

Clinical or Translational Implications

The differential CCI patterns identified hold significant clinical and translational potential for liver cirrhosis:

• Diagnostic and Prognostic Biomarkers: The distinct sets of CCIs highly active in cirrhosis, particularly those involving ECM components and integrins from Hepatic stellate cells and Endothelial cells, could serve as novel biomarkers for early diagnosis, assessment of fibrosis stage, and prediction of disease progression.

Therapeutic Targets:

• Anti-fibrotic Therapies: Inhibiting key integrin-collagen/fibronectin interactions or modulating HSC activation pathways could be a promising anti-fibrotic strategy. Targeting specific integrin complexes or their upstream regulators might reduce ECM deposition and reverse fibrosis. GeneCards: Integrin alpha 1
• Immunomodulation: The elevated ICAM1, TNF, and LTB signaling in cirrhosis suggests that targeting these inflammatory pathways could mitigate disease progression. Conversely, enhancing PGE2 signaling, which is diminished in cirrhosis, could offer a pro-resolving and anti-fibrotic therapeutic avenue.
• Cell-Type Specific Interventions: The identification of specific cell pairs involved (e.g., Hepatic stellate cell-Endothelial cell, Macrophage-Endothelial cell) allows for the development of highly targeted therapies that selectively modulate communication between key pathogenic cell types.

These findings underscore the importance of intercellular communication networks in liver health and disease, providing a foundation for developing new diagnostic tools and therapeutic strategies for liver cirrhosis.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) between healthy and cirrhotic liver conditions. The dot plot visualizes these differences, focusing on key immune cells (Macrophage, T cell CD4+, T cell CD8+, B cell, Dendritic cell, NK cell) and stromal cells (Hepatic stellate cell, Fibroblast, Endothelial cell). The strength of interaction is indicated by color intensity (standardized sample mean), and statistical significance by dot size (-log10(p-value)). Interactions are grouped by condition (cirrhosis vs. healthy) to highlight condition-specific patterns.

Visual Summary

The dot plot clearly segregates CCIs into two distinct patterns: those enriched in cirrhotic samples (left side of the plot) and those enriched in healthy samples (right side of the plot).

Cirrhosis-Enriched Interactions:

• A prominent cluster of strong and highly significant interactions (dark red, large dots) is observed across most cirrhotic samples (e.g., cirrhotic1_cd45+ to cirrhotic5_cd45+). These interactions are largely absent or very weak in healthy samples.
• These interactions frequently involve extracellular matrix (ECM) components and their receptors, such as various collagen (COL) complexes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL14A1) and fibronectin (FN1) interacting with integrins (e.g., integrin_a1b1_complex, integrin_a5b1_complex, integrin_a10b1_complex, integrin_a6b1_complex, integrin_a3b1_complex, integrin_a4b1_complex, integrin_a7b1_complex). These interactions mainly occur between Endothelial cells, Hepatic stellate cells, and Macrophages.
• Other significant interactions include LAMC1-integrin_a6b1_complex, HLA-G-LILRB2, CCL21-CCR7, and ILCAM1-SPN, primarily involving Endothelial cells, Hepatic stellate cells, Macrophages, and T cells.
• The interactions are strongly present in both CD45+ (immune cell-enriched) and CD45- (non-immune cell-enriched) fractions of cirrhotic samples, suggesting a broad impact across different cell compartments.

Healthy-Enriched Interactions:

• Another distinct cluster of strong and significant interactions (dark red, large dots) is observed in healthy samples (e.g., healthy1_cd45- to healthy5_cd45+), with minimal activity in cirrhotic samples.
• These interactions include pathways like TGFB3-TGFBR3, TYROBP-CD44, VCAM1-integrin_a9b1_complex, CXCL16-CXCR6, CCL4-CCR5, SEMA6A-PlexinA2_complex1, CD320-JAML, SPN-SIGLEC1, CD48-CD244, PTGES2-PTGER4 (Prostaglandin E2 signaling), IGF2-IGF2R, LeukotrieneC4_byLTC4S-CYSLTR1, LipoxinA4_byALOX5-CYSLTR1, ICAM1-integrin_aMb2_complex, PGF-NRP1, CSF1-CSF1R, and CD55-ADGRE5.
• These interactions predominantly involve Macrophages, Endothelial cells, T cells (CD4+, CD8+), and Hepatic stellate cells.
• Notably, some of these interactions (e.g., PTGES2-PTGER4, LeukotrieneC4, LipoxinA4) are particularly strong in CD45- fractions of healthy samples, suggesting active communication between non-immune cells and immune cells, or among non-immune cells.

Biological Interpretation

The observed differences in CCI patterns reflect fundamental changes in the liver microenvironment during cirrhosis compared to health.

Cirrhotic Liver Microenvironment: Fibrosis and Inflammation:

• The overwhelming enrichment of ECM-integrin interactions (e.g., FN1-integrin, various COL-integrin complexes) in cirrhosis is a hallmark of liver fibrosis. Hepatic stellate cells (HSCs), when activated, are the primary collagen-producing cells, and their interactions with other stromal cells (Endothelial cells) and immune cells (Macrophages) through ECM components are critical drivers of disease progression. These interactions promote myofibroblast differentiation, ECM deposition, and tissue stiffening characteristic of cirrhosis. PubMed: Hepatic stellate cell fibrosis integrin ECM
• CCL21-CCR7 signaling, involving Endothelial cells and T cells, indicates altered chemokine-mediated immune cell trafficking, potentially contributing to chronic inflammation and lymphocyte recruitment within the cirrhotic liver. GeneCards: CCL21
• HLA-G-LILRB2 is an immune checkpoint interaction often associated with immune evasion or modulation, suggesting altered immune surveillance or regulatory mechanisms in cirrhosis. GeneCards: HLA-G

Healthy Liver Microenvironment: Homeostasis and Immune Surveillance:

• The healthy liver displays interactions crucial for maintaining tissue homeostasis, immune regulation, and basal immune surveillance.
• TGFB3-TGFBR3 signaling is critical for tissue repair, immune tolerance, and regulating fibrotic responses, suggesting its role in maintaining a balanced microenvironment in the healthy state. GeneCards: TGFB3
• The presence of PTGES2-PTGER4 (Prostaglandin E2) and lipid mediators like Leukotriene C4 and Lipoxin A4 suggests active immunomodulatory and inflammation-resolving pathways. Prostaglandin E2 is known for its anti-inflammatory effects and immune regulation. PubMed: Prostaglandin E2 liver inflammation Lipoxin A4 is a pro-resolving lipid mediator that actively downregulates inflammation.
• CXCL16-CXCR6 and CCL4-CCR5 represent chemokine axes involved in T cell and macrophage recruitment and activation, vital for immune surveillance and response to minor challenges in the healthy liver. GeneCards: CXCL16
• CSF1-CSF1R signaling, prominently involving Macrophages, is essential for macrophage differentiation, survival, and function, indicating healthy macrophage populations contributing to tissue maintenance. GeneCards: CSF1

Clinical or Translational Implications

• Disease Biomarkers: The distinct CCI signatures could serve as valuable biomarkers for diagnosing liver cirrhosis, staging disease severity, or monitoring therapeutic responses. Detecting elevated ECM-integrin interactions could indicate active fibrogenesis.

Therapeutic Targets:

• In cirrhosis, targeting specific ECM-integrin axes (e.g., blocking key integrin subunits or their ligands like FN1 or collagens) could be a promising anti-fibrotic strategy. Modulating the HLA-G-LILRB2 axis might offer novel immunotherapeutic approaches to reshape the immune landscape in cirrhotic livers.
• Understanding and potentially enhancing the homeostatic and anti-inflammatory CCIs observed in healthy livers (e.g., increasing Prostaglandin E2 or Lipoxin A4 signaling, or strengthening TGFB3-TGFBR3 interactions) could offer strategies to prevent disease progression or promote liver regeneration.
• Pathogenic Mechanisms: This analysis highlights the fundamental shift in cellular communication that underlies liver cirrhosis, emphasizing the critical interplay between immune cells (Macrophages, T cells) and stromal cells (HSCs, Endothelial cells) in driving fibrogenesis and inflammation.

11. Macrophage Condition-Specific Surfaceome Markers in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Macrophage cells from human liver, comparing cirrhotic and healthy conditions. The dot plot visualizes the expression of up to 50 surface markers for each condition, showing both the mean expression level (color intensity) and the fraction of cells expressing the gene (dot size) across different samples within each condition. These markers are crucial for understanding the distinct functional states of macrophages in healthy versus diseased liver.

Visual Summary

The dot plot displays a clear segregation of macrophage surface marker expression patterns between cirrhotic and healthy liver samples.

• Condition Grouping: Samples are distinctly clustered into "cirrhotic" and "healthy" groups on the y-axis, allowing for easy comparison of marker expression specific to each condition.
• Cirrhosis-Specific Markers: A prominent set of genes (highlighted by the upper red box) shows high mean expression and high cell prevalence exclusively in the "cirrhotic" macrophage samples. Key markers include LMAN2, CD300A, FCGR1A, FPR1, SPINT2, LAR1, HM13, SLC3A2, PLXDC2, CD9, SIGIRR, ASGR1, SORL1, ENG, GPR34, CX3CR1, and STAB1. These genes show minimal to no expression in healthy samples.
• Healthy-Specific Markers: Conversely, a distinct cluster of genes (highlighted by the lower red box) demonstrates elevated expression primarily in "healthy" macrophage samples. These markers include AXL, VCAM1, and LILRB5. They exhibit very low or absent expression in cirrhotic samples.
• Expression and Prevalence: The color intensity gradient from light red to dark red indicates increasing mean expression of a marker within a sample group, while the dot size reflects the proportion of cells in that group expressing the marker. For instance, markers like FCGR1A, FPR1, and ENG show both high expression and high prevalence in cirrhotic macrophages. AXL, VCAM1, and LILRB5 show high expression and prevalence in healthy macrophages.
• Cell Counts: Bar plots on the right indicate the number of macrophage cells contributed by each sample, providing context for the robustness of marker detection in each group. Most samples have sufficient cell numbers to support the observed patterns.

Biological Interpretation

The differential expression of these surfaceome markers provides significant biological insights into macrophage phenotypes and their roles in liver health and disease.

Macrophages in Cirrhosis: Pro-inflammatory, Pro-fibrotic, and Dysfunctional Phenotypes

The markers upregulated in cirrhotic macrophages suggest a phenotype characterized by increased inflammation, altered immune regulation, and pro-fibrotic activities, consistent with the pathogenesis of liver cirrhosis.

Immune Activation and Inflammation

• FCGR1A (CD64): A high-affinity IgG Fc receptor, its upregulation indicates macrophage activation, often in response to immune complexes or inflammatory stimuli, suggesting a more pro-inflammatory state in cirrhosis UniProt: P12316.
• FPR1: Formyl peptide receptor 1 is critical for chemotaxis and recruitment of immune cells, pointing to persistent inflammatory cell infiltration in the cirrhotic liver UniProt: P25091.
• CX3CR1: A chemokine receptor important for macrophage migration and adhesion. Its altered expression could reflect changes in the dynamics of Kupffer cells and monocyte-derived macrophages within the diseased liver UniProt: P49238.
• SIGIRR (IL1R8): An inhibitory receptor for IL-1R/TLR signaling. Its upregulation might represent a compensatory mechanism attempting to dampen excessive inflammation or indicate a state of immune exhaustion/dysregulation in chronic disease UniProt: Q6UWZ5.

Pro-fibrotic and Tissue Remodeling Roles

• ENG (CD105): Endoglin is a co-receptor for TGF-beta, a central cytokine in fibrogenesis. Its strong expression on macrophages in cirrhosis strongly suggests their involvement in pro-fibrotic pathways and angiogenesis within the diseased liver UniProt: P34120.
• SPINT2: A serine protease inhibitor, likely involved in regulating protease activity crucial for extracellular matrix remodeling and tissue repair/fibrosis in the cirrhotic microenvironment UniProt: O43292.
• LAR1 (PTPRF): A receptor protein tyrosine phosphatase involved in cell adhesion and signaling, possibly mediating macrophage interactions with the fibrotic matrix UniProt: P10586.

Altered Metabolism and Phagocytosis

• SLC3A2 (CD98): A component of an amino acid transporter, suggesting increased metabolic activity or proliferative capacity of macrophages in cirrhosis UniProt: P08195.
• STAB1 (Stabilin-1): A scavenger receptor. While often associated with anti-inflammatory M2 macrophages, its role in fibrotic liver can be complex, involving clearance of cellular debris and matrix components UniProt: Q9Y5C1.
• ASGR1: Asialoglycoprotein receptor 1, typically on hepatocytes, but its presence on macrophages could indicate altered recognition or clearance functions related to hepatocyte damage in cirrhosis UniProt: P07306.

Macrophages in Healthy Liver: Homeostatic and Resolving Phenotypes

The markers upregulated in healthy macrophages suggest a homeostatic and potentially resolving phenotype, crucial for maintaining liver health.

Efferocytosis and Anti-inflammatory Functions

• AXL: Receptor tyrosine kinase AXL is a key player in efferocytosis (clearance of apoptotic cells) and can promote anti-inflammatory signaling. Its high expression in healthy macrophages indicates a role in maintaining tissue integrity and resolving minor cellular damage without escalating inflammation UniProt: P30530.

Immune Regulation and Adhesion

• VCAM1 (CD106): Vascular cell adhesion molecule 1, although often on endothelial cells, its expression on resident macrophages can play a role in their interaction with other immune cells and maintaining their niche UniProt: P19320.
• LILRB5: Leukocyte immunoglobulin-like receptor B5 is an inhibitory receptor, suggesting a role in fine-tuning immune responses and maintaining immune tolerance in the healthy liver environment UniProt: Q8NHS4.

Clinical or Translational Implications

The identified condition-specific surfaceome markers have significant clinical and translational potential.

• Diagnostic and Prognostic Biomarkers: The distinct expression patterns of surface markers like FCGR1A, ENG, CX3CR1, and AXL could serve as novel diagnostic or prognostic biomarkers for liver cirrhosis. Flow cytometric analysis of circulating or liver-resident macrophages for these markers could help assess disease stage or progression.
• Therapeutic Targets: Several markers present on cirrhotic macrophages represent promising therapeutic targets.
• ENG (CD105), being a TGF-beta co-receptor, could be targeted to inhibit pro-fibrotic signaling in macrophages, potentially slowing or reversing fibrosis.
• FCGR1A (CD64), indicating macrophage activation, could be targeted to modulate inflammatory responses in cirrhosis.
• Inhibiting key pro-inflammatory chemokine receptors like FPR1 or CX3CR1 could reduce macrophage recruitment and activation in the diseased liver.
• Conversely, strategies to enhance AXL signaling in macrophages or promote a "healthy-like" macrophage phenotype might foster efferocytosis and inflammation resolution, contributing to tissue repair.
• Cellular Immunotherapy and Drug Delivery: These surface markers offer opportunities for targeted drug delivery to specific macrophage subsets in the liver. For instance, nanoparticles or antibody-drug conjugates engineered to bind to cirrhosis-specific markers (e.g., ENG, FCGR1A) could selectively deliver anti-fibrotic or anti-inflammatory agents to pathogenic macrophages, minimizing off-target effects.
• Understanding Disease Mechanisms: Further functional studies on these markers will deepen our understanding of macrophage heterogeneity and their precise contributions to liver homeostasis and the progression of cirrhosis, paving the way for more targeted interventions.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that are differentially expressed in CD4+ T cells between healthy and cirrhotic liver conditions. Using single-cell RNA sequencing data from human liver tissue, the plot_markers_and_expression_dot tool was employed to compare gene expression profiles within the 'T cell CD4+' minor cell type across various samples. The analysis specifically focused on surfaceome genes, identifying up to 50 markers per condition based on criteria including expression score, p-value, and fold change. The resulting dot plot visualizes the mean expression (color intensity) and the fraction of cells expressing each marker (dot size) for selected genes across individual samples, clustered by condition.

Visual Summary

The dot plot effectively highlights condition-specific patterns of surfaceome gene expression within CD4+ T cells from liver tissue. Samples clearly cluster by condition: "cirrhosis" samples show a distinct expression profile compared to "healthy" samples.

1. Cirrhosis-Associated Markers:

• CXCR3 shows high mean expression and high prevalence across nearly all cirrhotic CD4+ T cell samples, with minimal to no expression in healthy samples.
• SIRPG also displays notably elevated expression and prevalence in most cirrhotic samples, absent or very low in healthy controls.
• CD59 exhibits higher expression and prevalence in several cirrhotic samples, particularly cirrhotic5_cd45+ and cirrhotic1_cd45-B, relative to healthy samples where it is largely absent.
• TGOLN2 and PRNP show some level of differential expression, with slightly higher prevalence and expression in certain cirrhotic samples, though less striking than CXCR3 or SIRPG.
• CD79B shows very low and sparse expression in a few cirrhotic samples, suggesting a less consistent or robust signal.

2. Healthy-Associated Markers:

• A striking observation is the consistent and relatively high mean expression and prevalence of CD8A and CD8B in CD4+ T cells from healthy liver samples. These markers are almost entirely absent or at negligible levels in CD4+ T cells from cirrhotic samples.

3. Sample-Level Variation: Within each condition, there is some variability in marker expression and prevalence across individual samples (e.g., cirrhotic5_cd45+ often shows stronger marker expression than other cirrhotic samples for genes like CXCR3, TGOLN2, CD59, and SIRPG). The bar plot on the right indicates the total number of CD4+ T cells in each sample group.

Biological Interpretation

The identified surfaceome markers provide insights into the phenotypic alterations of CD4+ T cells in the context of liver cirrhosis.

1. Pro-Inflammatory and Migratory Phenotype in Cirrhosis:

• The strong upregulation of CXCR3 in cirrhotic CD4+ T cells suggests an enhanced migratory capacity towards inflammatory chemokines (CXCL9, CXCL10, CXCL11) typically produced in the inflamed liver microenvironment. This indicates that CD4+ T cells in cirrhotic livers are likely activated and recruited to sites of inflammation and tissue damage https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386866/.
• SIRPG (Signal Regulatory Protein Gamma) plays a role in cell-cell interactions and modulating immune responses. Its upregulation could signify altered T cell activation states or interactions with antigen-presenting cells in the cirrhotic liver.
• CD59 acts as a complement regulatory protein. Its increased expression might represent a protective mechanism of T cells against complement-mediated injury in the inflammatory milieu of cirrhosis, or it could be associated with cellular stress responses.

2. Unique CD4+ T Cell Subpopulation in Healthy Liver:

• The consistent expression of CD8A and CD8B within the CD4+ T cell population from healthy livers, and their near absence in cirrhosis, is a highly significant and unexpected finding. While CD8A/B are canonical markers for cytotoxic CD8+ T cells, their co-expression with CD4 suggests the presence of a distinct "CD4+CD8+ double-positive" T cell subset in the healthy human liver. Such populations have been described in various tissues, including the liver, often exhibiting regulatory, memory, or even cytotoxic functions https://pubmed.ncbi.nlm.nih.gov/30678687/. The loss of these cells or a phenotypic switch in cirrhosis suggests that these unique CD4+CD8+ T cells might play a crucial role in maintaining liver homeostasis, and their disappearance could contribute to disease progression. This finding warrants further investigation to confirm the cellular identity and functional relevance of this subset.

3. Ambiguous Markers:

• The low and sporadic expression of CD79B, a key component of the B cell receptor, in some cirrhotic T cell CD4+ samples is unusual. While rare aberrant expression cannot be entirely ruled out, this signal should be interpreted cautiously and may suggest minor B cell contamination within the 'T cell CD4+' population or a very unique, potentially stressed, T cell phenotype.

Clinical or Translational Implications

1. Biomarkers for Disease Progression and Therapeutic Response:

• The differential expression of surface markers like CXCR3 and SIRPG on CD4+ T cells could serve as valuable biomarkers for assessing the severity or progression of liver cirrhosis. Monitoring their expression levels could also provide insights into the efficacy of anti-inflammatory or immunomodulatory therapies.
• The presence of CD8A/B on CD4+ T cells in healthy individuals, and their reduction in cirrhosis, offers a unique opportunity to investigate this specific T cell subset as a potential diagnostic or prognostic marker for liver health.

2. Potential Therapeutic Targets:

• CXCR3 signaling plays a critical role in T cell recruitment to inflamed tissues. Targeting CXCR3 or its ligands could be a strategy to modulate pathogenic T cell infiltration in the cirrhotic liver, potentially mitigating inflammation and fibrosis.
• Further elucidating the function of the CD4+CD8+ T cell subset in the healthy liver could open avenues for cell-based therapies or interventions aimed at restoring these protective populations in cirrhotic patients.

3. Need for Validation:

• Given the unexpected co-expression of CD8A/B (and to a lesser extent CD79B) on CD4+ T cells, rigorous experimental validation is crucial. Techniques such as multiparameter flow cytometry, immunohistochemistry, or spatial transcriptomics on purified cell populations would be essential to confirm these findings and definitively characterize the identity and functional roles of these distinct T cell subsets in both healthy and cirrhotic liver microenvironments. This would ensure the accuracy of cell annotations and the biological relevance of the identified markers.

13. Gene Set Enrichment Analysis of Liver Cell Types in Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for key liver cell types (Macrophage, T cell CD4+, T cell CD8+, B cell, Endothelial cell, Hepatic stellate cell) comparing cirrhotic liver tissue to healthy liver tissue. The dot plot visualizes enriched pathways, where the dot size corresponds to the significance (-log10(p-value)) and the color indicates the Normalized Enrichment Score (NES): red signifies pathways upregulated in the 'test' condition (cirrhosis or healthy, as specified in the column header), while blue indicates downregulation.

Visual Summary

The dot plot effectively illustrates distinct and shared pathway enrichments across the selected cell types and conditions.

• Widespread Metabolic Shift: A consistent pattern observed across almost all analyzed cell types in cirrhosis (columns ending in cirrhosis_vs_others) is the strong upregulation of pathways related to protein synthesis (e.g., "Ribosome", "Ribosome biogenesis in eukaryotes", "Protein processing in endoplasmic reticulum", "RNA transport", "Spliceosome") and "Glycolysis / Gluconeogenesis" (red dots). Conversely, "Oxidative phosphorylation", "Adipocytokine signaling pathway", "AMPK signaling pathway", and "Fatty acid degradation" generally show downregulation (blue dots) in cirrhotic conditions across many cell types.
• Inflammation and Immune Response: Pathways associated with inflammation and immune activation, such as "NF-kappa B signaling pathway", "Cytokine-cytokine receptor interaction", and various "Viral infection" pathways, are consistently upregulated in immune cells (Macrophage, T cells, B cells) and also significantly in Endothelial cells and Hepatic stellate cells in cirrhosis.

Cell-Type Specific Signatures

• Macrophage: Prominent upregulation of immune-related and phagocytic pathways like "Phagosome" and "Fc gamma R-mediated phagocytosis".
• T cells (CD4+ and CD8+): Show activation of T cell receptor signaling, various cytokine signaling pathways (e.g., IL-17 signaling), and strong antiviral responses.
• Endothelial cells: Significant enrichment in pathways related to angiogenesis ("VEGF signaling pathway"), cell adhesion ("Focal adhesion", "Cell adhesion molecules (CAMs)"), and inflammation.
• Hepatic stellate cells: Display a strong signature of activation and fibrogenesis, with marked upregulation of "TGF-beta signaling pathway", "PI3K-Akt signaling pathway", "Hippo signaling pathway", "Focal adhesion", and "ECM-receptor interaction".

Biological Interpretation

The GSEA results provide clear biological insights into the cellular and molecular changes occurring in the liver during cirrhosis.

1. Metabolic Reprogramming towards Glycolysis: The pervasive upregulation of glycolysis and downregulation of oxidative phosphorylation, fatty acid degradation, and AMPK signaling across multiple cell types (macrophages, T cells, endothelial cells, HSCs) suggests a metabolic shift akin to the "Warburg effect." This adaptation often supports rapid proliferation and biosynthetic demands in diseased tissues, including chronic inflammation and fibrosis. This metabolic shift implies an energy state that favors biomass production over efficient ATP generation, contributing to disease progression. PubMed search: liver cirrhosis metabolic reprogramming glycolysis
2. Chronic Inflammation and Immune Activation:

• Immune Cells (Macrophages, T cells, B cells): These cells exhibit hyper-activated immune responses characterized by elevated NF-kappa B signaling, cytokine-cytokine receptor interactions, and robust responses to various infections (viral, bacterial). Macrophages, in particular, show enhanced phagocytic functions. This reflects the chronic inflammatory environment in the cirrhotic liver, where immune cells are constantly activated, potentially contributing to ongoing tissue damage and fibrogenesis rather than effective resolution.
• Non-immune Cells (Endothelial cells, HSCs): These cells also show upregulation of inflammatory pathways, indicating their active participation in the inflammatory cascade within the cirrhotic microenvironment.

3. Fibrogenesis and Angiogenesis driven by Hepatic Stellate Cells and Endothelial Cells:

• Hepatic Stellate Cells (HSCs) are central to liver fibrosis. Their strong enrichment in pro-fibrotic pathways like TGF-beta, PI3K-Akt, and Hippo signaling, along with pathways for ECM-receptor interaction and focal adhesion, confirms their activation into myofibroblast-like cells that produce excessive extracellular matrix, a hallmark of cirrhosis. Increased protein synthesis pathways support their enhanced biosynthetic activity. GeneCards: TGFB1
• Endothelial cells show activation of VEGF signaling and pathways related to cell adhesion and actin cytoskeleton reorganization, indicating active angiogenesis and vascular remodeling, which are crucial features of portal hypertension and altered liver architecture in cirrhosis. PubMed search: liver cirrhosis angiogenesis

4. Integrated Cellular Responses: The coordinated upregulation of protein synthesis machinery (Ribosome, ER processing) across nearly all cell types highlights a general increase in cellular activity and biosynthesis, whether for immune effector functions, ECM production, or cellular repair/dysfunction. The broad activation of 'viral infection' pathways in various cell types might indicate a general antiviral state in the cirrhotic liver, possibly due to viral etiology (e.g., Hepatitis C, B) or non-viral etiologies that mimic viral infection, triggering innate immune responses.

Clinical or Translational Implications

• Therapeutic Targets for Fibrosis: The clear activation of TGF-beta, PI3K-Akt, and Hippo signaling pathways in Hepatic Stellate Cells offers promising therapeutic targets for anti-fibrotic strategies in cirrhosis. Inhibitors of these pathways could potentially halt or reverse the progression of fibrosis.
• Modulating Inflammation: The pervasive activation of NF-kappa B and cytokine signaling pathways suggests that broad anti-inflammatory or immunomodulatory approaches could be beneficial in managing the chronic inflammation driving cirrhosis. However, specific targeting of pro-fibrotic inflammatory loops would be critical to avoid immunosuppression.
• Metabolic Interventions: The widespread metabolic shift to glycolysis points to a potential vulnerability that could be exploited therapeutically. Targeting key enzymes in glycolysis or restoring oxidative phosphorylation might reprogram cellular metabolism to a healthier state, potentially mitigating disease progression.
• Angiogenesis Control: The activation of VEGF signaling in endothelial cells indicates that anti-angiogenic therapies, often used in cancer, might have a role in managing portal hypertension and aberrant vascularization in cirrhosis, although careful consideration of potential side effects is warranted.
• Biomarker Discovery: The identified enriched pathways and their associated genes could serve as sources for novel diagnostic or prognostic biomarkers for cirrhosis, reflecting the underlying cellular pathologies.

14. 간경변증 담관세포의 유전자 온톨로지 (GSA) 분석

[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 사용하여 간경변증(cirrhosis)과 건강한(healthy) 상태에서 담관세포(Cholangiocyte)의 유전자 온톨로지(Gene Ontology, GSA) 결과를 비교한 것입니다. GSA는 특정 세포 유형에서 각 조건에 따라 유의하게 상향 조절되는 유전자 그룹(경로 또는 생물학적 과정)을 식별합니다. 여기서는 cirrhosis_vs_others 및 healthy_vs_others 두 가지 비교에 대한 결과가 막대 그래프로 시각화되어 있습니다. 이는 각 조건에서 담관세포의 특징적인 생물학적 변화를 이해하는 데 도움이 됩니다.

Visual Summary

제공된 두 개의 막대 그래프는 담관세포에서 각각 cirrhosis_vs_others 및 healthy_vs_others 비교에서 유의하게 상향 조절된 유전자 온톨로지(GO) 용어 및 경로를 보여줍니다. Y축은 GO 용어 및 경로 이름을 나타내고, X축은 유의성(-log(p-val) 및 -log(q-val))을 나타냅니다.

1. 담관세포 GSA_up: cirrhosis_vs_others

• cirrhosis_vs_others 비교에서 담관세포는 다양한 질병 관련 경로 및 세포 스트레스 경로가 매우 유의하게 상향 조절됨을 보여줍니다.
• 특히, Huntington disease, Parkinson disease, Prion disease, Amyotrophic lateral sclerosis (ALS), Alzheimer disease 등 여러 신경퇴행성 질환 경로가 높은 유의성으로 나타납니다.
• Oxidative phosphorylation (산화적 인산화), Protein processing in endoplasmic reticulum (소포체 내 단백질 처리), Lysosome (리소좀)과 같은 대사 및 세포 소기관 관련 경로도 두드러집니다.
• Infection (감염) 및 Inflammation (염증) 관련 경로 (예: Influenza A, Antigen processing and presentation, Epstein-Barr virus infection)도 활성화되어 있습니다.
• Ferroptosis (페로토시스)와 같은 조절된 세포 사멸 경로도 관찰됩니다.

2. 담관세포 GSA_up: healthy_vs_others

• healthy_vs_others 비교에서 담관세포는 건강한 상태를 유지하는 데 필요한 기본적인 신호 전달 및 세포 기능 관련 경로가 유의하게 상향 조절됨을 나타냅니다.
• TNF signaling pathway, MAPK signaling pathway, IL-17 signaling pathway, Hippo signaling pathway, PI3K-Akt signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway와 같은 핵심적인 신호 전달 경로가 상위권을 차지합니다.
• Focal adhesion (국소 접착), Tight junction (치밀 결합), Epithelial cell signaling (상피세포 신호)과 같이 상피 세포의 구조적 무결성 및 기능을 나타내는 경로가 활성화되어 있습니다.
• Lipid and atherosclerosis (지질 및 죽상동맥경화증), Circadian rhythm (일주기 리듬), Insulin resistance (인슐린 저항성) 등 대사 조절 관련 경로도 관찰됩니다.
• 감염 관련 경로도 일부 존재하지만, 염증보다는 면역 감시 또는 기본 방어 기전에 더 가까운 맥락으로 보입니다.

Biological Interpretation

이 분석은 간경변증에서 담관세포가 겪는 중요한 생물학적 변화를 명확하게 보여줍니다.

간경변증 상태의 담관세포 변화:

• 광범위한 세포 스트레스 및 손상: 간경변증 담관세포에서는 Protein processing in endoplasmic reticulum 및 Lysosome 경로의 활성화가 두드러지며, 이는 단백질 접힘 오류, 응집 및 손상된 세포 구성 요소 제거에 대한 높은 부담을 시사합니다. 이는 간경변증 진행과 관련된 세포 스트레스 반응의 핵심 구성 요소입니다. 참고: PubMed - ER stress in liver disease
• 대사 재프로그래밍: Oxidative phosphorylation, Pyruvate metabolism, Citrate cycle (TCA cycle), Glycolysis / Gluconeogenesis 등의 경로는 에너지 생성 및 대사 경로의 변화를 나타냅니다. 이는 염증성 환경 및 손상에 대한 반응으로 담관세포의 에너지 수요가 증가하거나 대사 방식이 변화했음을 의미할 수 있습니다. Non-alcoholic fatty liver disease (NAFLD) 경로의 활성화는 간경변증의 흔한 원인 중 하나인 지방간 질환과의 연관성을 강조합니다.
• 면역 및 염증 반응: Antigen processing and presentation 및 다양한 감염 관련 경로의 활성화는 간경변증 간에서 만성 염증 상태와 면역 시스템의 지속적인 활성화를 반영합니다. 간경변 환자는 면역 기능 장애와 감염에 대한 취약성이 높습니다.
• 예상치 못한 신경퇴행성 경로 활성화: Huntington disease, Parkinson disease, Alzheimer disease, Amyotrophic lateral sclerosis 등 여러 신경퇴행성 질환 관련 경로의 상향 조절은 흥미로운 발견입니다. 비록 이들이 중추신경계 질환이지만, 공통적으로 단백질 항상성(proteostasis)의 파괴, 미토콘드리아 기능 장애, 산화 스트레스와 같은 세포 손상 기전을 공유합니다. 이는 간경변증 담관세포가 심각한 세포 스트레스 상황에서 이러한 보편적인 손상 기전을 활성화하고 있음을 시사할 수 있습니다.
• Ferroptosis (페로토시스): 철 의존성 세포 사멸인 Ferroptosis 경로의 활성화는 간 손상 및 섬유화의 중요한 메커니즘으로 알려져 있으며, 간경변증 진행에 기여할 수 있습니다. 참고: PubMed - Ferroptosis in liver disease

건강한 상태의 담관세포 특징:

• 기본적인 세포 신호 전달 및 항상성 유지: TNF, MAPK, IL-17, Hippo, PI3K-Akt, NF-kappa B, Toll-like receptor signaling pathway 등은 세포 성장, 분화, 생존 및 면역 반응 조절에 필수적인 경로로, 건강한 담관세포가 정상적인 생리 기능을 유지하는 데 중요합니다.
• 상피세포 무결성 및 기능: Focal adhesion, Tight junction, Regulation of actin cytoskeleton 경로는 담관 상피 장벽의 유지와 세포 간 통신에 필수적인 구조적 및 기능적 역할을 강조합니다. 이는 담즙 생성 및 수송을 위한 정상적인 담관 기능을 보장합니다.
• 대사 및 생리적 조절: Lipid and atherosclerosis, Circadian rhythm, Insulin resistance 관련 경로는 간의 전반적인 대사 조절 기능과 연관된 담관세포의 역할을 반영합니다.

두 상태 간의 차이점:

건강한 담관세포가 항상성 유지와 기본적인 신호 전달에 중점을 둔다면, 간경변증 담관세포는 심각한 세포 스트레스, 대사 재구성, 염증 및 손상 반응으로 전환됨을 보여줍니다. 특히, 신경퇴행성 질환 관련 경로의 활성화는 간경변증에서 담관세포가 겪는 비정상적인 세포 기능 및 생존 메커니즘을 강조하는 새로운 관점을 제공합니다.

Clinical or Translational Implications

• 치료 표적 발굴: 간경변증 담관세포에서 상향 조절되는 Oxidative phosphorylation, ER stress 관련 경로, Ferroptosis 및 신경퇴행성 질환 관련 경로들은 간경변증 진행을 늦추거나 담관세포 기능을 개선하기 위한 잠재적인 치료 표적이 될 수 있습니다. 이러한 경로를 조절하는 약물은 간경변증 치료에 새로운 접근법을 제공할 수 있습니다.
• 질병 바이오마커: 신경퇴행성 질환 관련 경로 활성화는 간경변증의 세포 손상 메커니즘에 대한 새로운 이해를 제공하며, 이러한 경로에 관련된 특정 유전자나 대사 산물은 질병의 진행 또는 치료 반응을 모니터링하기 위한 바이오마커로 개발될 가능성이 있습니다.
• 질병 메커니즘 이해 확장: 간과 뇌 질환 사이의 예상치 못한 연결고리를 시사하는 신경퇴행성 경로의 발견은 간경변증의 전신적 영향을 이해하는 데 중요한 통찰력을 제공하며, 간경변증 환자에서 관찰되는 신경학적 합병증(예: 간성 뇌병증)의 세포 기반 메커니즘 연구에 새로운 방향을 제시할 수 있습니다. 참고: GeneCards - Huntington disease, GeneCards - Parkinson disease

15. Discussion

The single-cell analysis of human liver tissue provides a comprehensive view of the profound cellular and molecular changes distinguishing cirrhotic from healthy states. UMAP visualizations confirm significant condition-specific cellular shifts, with 'cirrhosis' clusters showing enrichment in various immune and stromal cells, indicative of active disease.

Cellular Composition and Activation Shifts: Population bar plots reveal a marked increase in Cholangiocytes, Hepatic stellate cells, Fibroblasts, Macrophages, and Dendritic cells in cirrhotic samples, direct evidence of ductular reaction, active fibrogenesis, and chronic immune infiltration. Conversely, Hepatocytes show a relative decrease, consistent with parenchymal loss. Further delving into immune subsets, macrophages in cirrhosis exhibit a striking M1-dominant phenotype, significantly increasing in proportion while M2A macrophages show a decreasing trend. This polarization towards pro-inflammatory M1 macrophages likely fuels chronic inflammation and contributes to fibrotic progression. T cell subset analysis reveals a complex picture: most subsets, including Th1, LTI, Treg, Th17, Th22, and Naive T cells, are proportionally *increased* in cirrhotic livers compared to healthy controls. This suggests an active, broad immune response coupled with attempts at immune regulation (Treg). However, a notable and unexpected finding is the *decrease* in cytotoxic T cells (T_Cyto) in cirrhosis, potentially implying impaired anti-viral or anti-tumor immunity. The increase in Naive T cells also warrants further investigation into recruitment patterns or specific local expansion.

Altered Intercellular Communication: Cell-cell interaction analysis dramatically highlights a shift from homeostatic to pathogenic communication networks. In cirrhosis, there is a profound upregulation of interactions centered on extracellular matrix (ECM) components like collagens and fibronectin with various integrin complexes, predominantly involving Hepatic stellate cells and Endothelial cells. This underscores heightened ECM production, deposition, and remodeling, which are central to liver fibrosis. Inflammatory adhesion molecules like ICAM1 also show increased interactions, facilitating immune cell infiltration. Conversely, healthy liver communication networks are characterized by distinct anti-inflammatory and pro-resolving signals, such as Prostaglandin E2 (PGE2) and Lipoxin A4 signaling, along with pathways for tissue repair (TGFB3) and robust immune surveillance (CXCL16-CXCR6, CSF1-CSF1R). The diminished presence of these pathways in cirrhosis suggests a compromised ability to resolve inflammation and repair tissue effectively.

Condition-Specific Markers and Functional Reprogramming: Analysis of surfaceome markers reveals distinct cell-type specific phenotypes. Macrophages in cirrhosis upregulate markers like FCGR1A, FPR1, ENG (CD105), and CX3CR1, consistent with a pro-inflammatory, pro-fibrotic, and migratory phenotype. In contrast, healthy macrophages express AXL, VCAM1, and LILRB5, indicative of efferocytosis, homeostatic maintenance, and immune regulation. CD4+ T cells in cirrhosis show high expression of CXCR3 and SIRPG, suggesting enhanced migratory capacity towards inflammatory cues and altered activation states. Strikingly, healthy CD4+ T cells consistently co-express CD8A and CD8B, indicating a unique CD4+CD8+ double-positive T cell subset that is largely absent in cirrhosis. This population may play a crucial role in liver homeostasis, and its loss could contribute to disease progression.

Metabolic and Pathway Dysregulation: Gene Set Enrichment Analysis (GSEA) reveals a widespread metabolic reprogramming in cirrhosis across most cell types (macrophages, T cells, endothelial cells, HSCs), characterized by increased glycolysis and protein synthesis pathways (Ribosome, ER processing), and downregulation of oxidative phosphorylation and fatty acid degradation. This metabolic shift, akin to the "Warburg effect," likely fuels the high demands of proliferation, inflammation, and ECM production. Furthermore, chronic inflammatory pathways (NF-kappa B, cytokine signaling) are broadly activated in immune and stromal cells, while HSCs show clear activation of pro-fibrotic pathways (TGF-beta, PI3K-Akt, Hippo signaling) and Endothelial cells show angiogenesis-related pathways (VEGF). Gene Ontology (GSA) for Cholangiocytes highlights extensive cellular stress in cirrhosis, with active pathways related to ER stress, lysosomal dysfunction, ferroptosis, and pathways shared with neurodegenerative diseases (Huntington, Parkinson, Alzheimer, ALS). This suggests a shared cellular pathology involving protein aggregation, mitochondrial dysfunction, and oxidative stress that impacts cholangiocyte health in cirrhosis. In healthy cholangiocytes, pathways primarily maintain epithelial integrity and basic signaling.

In summary, liver cirrhosis is characterized by a complex pathological shift involving coordinated cellular remodeling, dysregulated intercellular communication, a dominant pro-inflammatory and pro-fibrotic macrophage phenotype, distinct T cell dynamics including a decrease in cytotoxic T cells, and widespread metabolic and stress responses across multiple liver cell types. The loss of homeostatic and pro-resolving mechanisms, coupled with the activation of pathogenic pathways, underscores the chronic and progressive nature of this disease. The discovery of a unique CD4+CD8+ T cell subset in healthy liver and the unexpected activation of neurodegenerative pathways in cirrhotic cholangiocytes open new avenues for understanding and potentially targeting disease mechanisms.

Hypotheses:

1. The M1-dominant macrophage phenotype, characterized by high FCGR1A and ENG expression, drives persistent inflammation and fibrogenesis in liver cirrhosis by producing pro-inflammatory cytokines and facilitating ECM remodeling.
2. The diminished proportion of cytotoxic T cells and the loss of a unique CD4+CD8+ T cell subset in cirrhosis impair the liver's ability to clear damaged hepatocytes, combat infections, or suppress early tumor development, contributing to disease progression and HCC risk.
3. Dysregulated ECM-integrin interactions, particularly those involving fibronectin and various collagens with integrin complexes on Hepatic stellate cells and Endothelial cells, are critical for sustaining myofibroblast activation and accelerating extracellular matrix deposition in cirrhotic liver.
4. The metabolic shift towards glycolysis and away from oxidative phosphorylation across multiple liver cell types in cirrhosis is a critical adaptation that supports inflammatory and fibrotic processes, rather than a healthy energetic state.
5. Cholangiocytes in cirrhotic liver undergo significant cellular stress, evidenced by activated ER stress, lysosomal dysfunction, ferroptosis, and pathways shared with neurodegenerative diseases, contributing to the ductular reaction and overall liver injury.
6. The reduced Prostaglandin E2 and Lipoxin A4 signaling in cirrhotic livers, compared to healthy tissue, reflects a compromised ability to resolve inflammation and maintain tissue homeostasis, thereby perpetuating chronic liver damage.

Potential therapeutic targets:

1. Integrin-ECM Axis (e.g., Integrin alpha 1, 5, 10; Fibronectin, Collagens): Overwhelming evidence points to extensive ECM-integrin interactions by activated Hepatic stellate cells and Endothelial cells as key drivers of fibrosis in cirrhosis. Inhibiting these interactions could reduce ECM deposition and myofibroblast activation. Evidence: Increased FN1-integrin and COL-integrin interactions involving Hepatic stellate cells and Endothelial cells are highly significant in cirrhotic samples (Sections 9, 10). Validation: Test specific integrin antagonists or antibodies against ECM components in preclinical models of liver fibrosis (e.g., CCl4 or bile duct ligation models) to evaluate their efficacy in reducing fibrosis and improving liver function.
2. TGF-beta Signaling Pathway (e.g., TGFBR3, downstream PI3K-Akt/Hippo components; Endoglin/CD105): TGF-beta is a central pro-fibrotic cytokine. Its signaling pathways are strongly activated in Hepatic stellate cells in cirrhosis, driving ECM production. Endoglin (CD105), upregulated on cirrhotic macrophages, is a TGF-beta co-receptor, suggesting its role in mediating TGF-beta's effects on these immune cells. Evidence: GSEA shows strong upregulation of "TGF-beta signaling pathway," "PI3K-Akt signaling pathway," and "Hippo signaling pathway" in Hepatic stellate cells (Section 13). ENG (CD105) is significantly upregulated on cirrhotic macrophages (Section 11). Validation: Utilize small molecule inhibitors targeting TGF-beta receptors, PI3K/Akt, or Hippo pathway components in activated HSCs or in vivo fibrosis models. Assess the impact of anti-ENG antibodies or genetic ablation of ENG on macrophage-mediated fibrosis in relevant models.
3. M1 Macrophage Activation Markers (e.g., FCGR1A, FPR1, CX3CR1): Cirrhotic livers are dominated by pro-inflammatory M1 macrophages, contributing to chronic inflammation and tissue damage. Targeting specific surface receptors associated with this phenotype could reduce their pathogenic activity or recruitment. Evidence: FCGR1A, FPR1, and CX3CR1 are significantly upregulated surface markers on macrophages in cirrhotic samples (Section 11), indicating increased activation and chemotaxis. Validation: Develop antibodies or small molecule inhibitors against FCGR1A, FPR1, or CX3CR1 to test their ability to reduce macrophage activation, inflammatory cytokine production, and recruitment in liver inflammation/fibrosis models.
4. Prostaglandin E2 (PGE2) Signaling (e.g., PTGES2, PTGER4): PGE2 signaling, known for its anti-inflammatory and anti-fibrotic properties, is a prominent homeostatic pathway in healthy liver but is diminished in cirrhosis. Augmenting this pathway could restore beneficial regulatory functions. Evidence: PTGES2-PTGER4 interactions are significantly enriched in healthy samples but reduced in cirrhosis (Sections 9, 10), suggesting a loss of this protective mechanism. Validation: Administer PGE2 analogs or agonists targeting PTGER4 in preclinical models of liver cirrhosis to evaluate effects on inflammation, fibrosis, and liver regeneration.
5. CXCR3: CXCR3 is highly expressed on CD4+ T cells in cirrhotic livers, suggesting their recruitment to and retention within inflamed tissue. Blocking CXCR3 could mitigate pathogenic T cell infiltration and inflammation. Evidence: CXCR3 shows high mean expression and prevalence in cirrhotic CD4+ T cells but minimal expression in healthy samples (Section 12). Validation: Use CXCR3 antagonists in animal models of liver inflammation or fibrosis to assess reduction in T cell infiltration and overall disease severity.

Follow-up validation ideas:

1. Flow Cytometry/Immunohistochemistry (IHC) & Spatial Transcriptomics: Validate the proportions and localization of M1 vs. M2 macrophage subsets (using FCGR1A, ENG, AXL, STAB1 markers) and specific T cell subsets (CXCR3+ CD4+ T cells, and confirm the existence and location of CD4+CD8+ T cells) in healthy vs. cirrhotic human liver biopsies to confirm the spatial context of these cell populations.
2. In Vitro/Ex Vivo Perturbation Assays: Use small molecules or biologics to induce repolarization of cirrhotic macrophages from an M1 to an M2-like phenotype in vitro, then test their impact on HSC activation and collagen production in co-culture systems. Additionally, apply integrin-blocking antibodies or small molecule inhibitors to activated Hepatic stellate cells or Endothelial cells in 2D or 3D culture models to assess effects on ECM deposition and cell-cell interaction strength.
3. Functional T cell Assays: Isolate T cell subsets from healthy and cirrhotic patient samples and perform functional assays (e.g., cytotoxic killing assays for T_Cyto cells, proliferation assays, cytokine secretion profiles) to assess differences in immune competence and confirm the functional relevance of the observed shifts.
4. Metabolic Tracing: Perform stable isotope tracing with glucose/glutamine in isolated liver cells (e.g., macrophages, HSCs) from healthy and cirrhotic tissue to quantitatively assess glycolytic flux and oxidative phosphorylation activity, thus validating the metabolic reprogramming hypothesis.
5. Cholangiocyte Stress Response Analysis: Investigate the activation of ER stress markers (e.g., BiP, CHOP), lysosomal dysfunction (e.g., LAMP1, cathepsin activity), and markers of ferroptosis (e.g., GPX4, lipid peroxidation) in primary cirrhotic cholangiocytes. Use genetic or pharmacological interventions to target these pathways and assess their impact on cholangiocyte survival and function.
6. Validation Cohorts: Analyze the identified cell population shifts and marker gene expression in larger, independent cohorts of patients with different etiologies and stages of liver cirrhosis to confirm their diagnostic or prognostic value and improve generalizability.

Limitations:

This single-cell analysis provides a high-resolution snapshot of cellular states and interactions but is inherently correlative, limiting causal inferences. Further experimental validation, both in vitro and in vivo, is crucial to establish the functional roles and causal relationships of the identified cellular shifts, marker genes, and interaction pathways in liver cirrhosis. The surfaceome_only filter, while useful for identifying accessible therapeutic targets, may exclude critical intracellular markers or pathways. The presence of a 'CD4+CD8+ double-positive' T cell subset in healthy liver and its loss in cirrhosis requires rigorous orthogonal validation (e.g., multiparameter flow cytometry, spatial analysis) to confirm its existence and functional relevance, as well as to exclude potential technical artifacts or rare cell contamination. Similarly, the activation of neurodegenerative disease pathways in cholangiocytes, though intriguing, should be interpreted as reflecting general cellular stress and protein dyshomeostasis rather than direct brain pathology, and warrants further investigation into its specific contributions to cholangiocyte dysfunction. The observed heterogeneity within cirrhotic samples highlights the complexity of the disease, and the current sample size, while informative, may not capture the full spectrum of disease stages or etiologies.

16. Query List

1. Show UMAPs with condition, sample, major cell type, minor cell type, and celltype_subset in 2 columns and save it.
2. Show major cell type scores on UMAP and save it.
3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
4. Show population bar plot for minor cell types and save it.
5. Show subset population bar plot for T cells and save it.
6. If there are statistically significant differences between conditions in T cell subset populations, show them as box plots and save it. Determine ncols appropriately based on the total number of panels.
7. Show subset population bar plot for Macrophages and save it.
8. If there are statistically significant differences between conditions in Macrophage subset populations, show them as box plots 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. Find statistically significant differences in cell-cell interactions between conditions for key immune cells (Macrophage, T cell CD4+, T cell CD8+, B cell, Dendritic cell, NK cell) and stromal cells (Hepatic stellate cell, Fibroblast, Endothelial cell) and show them as a dot plot and save it. Set max_n_items_per_group = 25.
11. Extract condition-specific markers for Macrophage and show them as a dot plot and save it. Include only surfaceome markers, up to 50 per condition.
12. Extract condition-specific markers for T cell CD4+ and show them as a dot plot and save it. Include only surfaceome markers, up to 50 per condition.
13. Show Gene Set Enrichment Analysis results for key cell types (Macrophage, T cell CD4+, T cell CD8+, B cell, Endothelial cell, Hepatic stellate cell) as a dot plot and save it. Use color map RdBu_r and set n_pws_to_show = 80.
14. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.