SCODiA Report by MLBI Lab

Single-Cell Landscape of Liver Cirrhosis: Cellular Remodeling, Immune Dysregulation, and Novel Therapeutic Avenues

Liver cirrhosis is characterized by profound cellular heterogeneity and dynamic shifts in cell populations, particularly involving immune and stromal cells. Our single-cell analysis reveals a significant decline in hepatocytes coupled with an expansion of fibrogenic hepatic stellate cells and pro-inflammatory M1 macrophages. T cell populations exhibit dysregulated profiles, with an increase in pro-inflammatory Th17 and Th22 cells but a reduction in cytotoxic T cells. These cellular alterations are underpinned by distinct cell-cell interaction networks, differential gene expression, and widespread metabolic reprogramming, collectively driving chronic inflammation and progressive fibrosis.

Contents

0. Dataset overview
1. UMAP Visualization of Liver Single-Cell RNA-seq Data by Condition, Sample, and Cell Type Hierarchies
2. Major Cell Type Score Visualization on UMAP
3. Overall Celltype_subset Marker Expression Analysis
4. Liver Cell Population Shifts in Cirrhosis vs. Healthy Conditions
5. 간경변증에서 T 세포 아형 구성 변화 분석
6. T cell subset population differences in liver cirrhosis
7. Macrophage Subset Composition Shifts in Liver Cirrhosis
8. Differential Macrophage Subset Proportions in Liver Cirrhosis
9. Cell-Cell Interaction Analysis in Healthy vs. Cirrhotic Liver
10. Condition-Specific Cell-Cell Interaction Patterns in Healthy and Cirrhotic Liver
11. Macrophage Condition-Specific Surface Markers in Liver Cirrhosis
12. T cell CD4+ Condition-Specific Surface Markers in Liver Cirrhosis
13. Differential Expression of Cell Cycle Genes in Hepatic Stellate Cells in Cirrhosis
14. Gene Set Enrichment Analysis Reveals Cell-Type-Specific Pathway Alterations in Liver Cirrhosis
15. Discussion
16. Query List

0. Dataset overview

Dataset Summary

• 데이터 형식: 총 60,503개의 세포와 22,784개의 유전자를 포함하는 AnnData 형식의 단일 세포 RNA 시퀀싱 데이터입니다.
• 종(Species) 및 조직(Tissue): 인간(human)의 간(Liver) 조직 데이터입니다.
• 관측 데이터 (obs columns): 샘플, 조건, 조직 유형, 세포 타입(주요, 세부, 하위 분류), 클러스터 정보 등을 포함합니다.
• 유전자 데이터 (var columns): 유전자 심볼, 유전자 ID, 가변 유전자 정보 등을 포함합니다.
• 조건 (Conditions): 건강(healthy) 및 간경변증(cirrhosis) 두 가지 조건이 존재합니다.
• 주요 세포 타입 (celltype_major): 골수세포, T세포, B세포, 기질세포, 간 상피세포, 비만세포, 내피세포, 미할당 세포 등으로 구성됩니다.
• 세부 세포 타입 (celltype_minor): 대식세포, CD8+ T세포, NK세포, B세포, CD4+ T세포, ILC, 수지상세포, 형질세포, 간 성상세포, 간세포, 비만세포, 내피세포, 평활근세포, 섬유아세포 등이 포함됩니다.
• 하위 세포 타입 (celltype_subset): 다양한 대식세포 아형(M1, M2A, M2B, M2C, M2D), T세포 아형(세포독성, Th22, Tfh, Naive, Treg, Th1, Th17, Th2, Th9), B세포 아형(여포성, MZ, Breg, 기억), ILC 아형(ILC1, ILC2, ILC3 (NCR-), ILC3 (NCR+), ILCreg), DC 아형(형질세포양, 고전적, 염증성), 혈관내피세포 아형(내피 세포, 내피 팁 세포, 림프 내피 세포) 등으로 구성됩니다.

사전 계산된 결과:

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

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

uns['DEG']: 각 세부 세포 타입별 조건 간 차등 발현 유전자(DEG) 결과

uns['GSEA']: 각 세부 세포 타입별 조건 간 유전자 세트 농축 분석(GSEA) 결과

uns['GSA_up']: 각 세부 세포 타입별 조건 간 GO(GSA) 결과

1. UMAP Visualization of Liver Single-Cell RNA-seq Data by Condition, Sample, and Cell Type Hierarchies

[Analysis Visualization Results]...

Analysis Overview

This analysis provides a comprehensive visualization of single-cell RNA-sequencing data from human liver tissue, encompassing both healthy and cirrhotic conditions. Uniform Manifold Approximation and Projection (UMAP) plots are used to visualize the cellular landscape, colored by experimental condition, individual sample, and hierarchical cell type annotations (major, minor, and subset). These plots are crucial for assessing data quality, batch effects, and the overall cellular composition and heterogeneity within the dataset.

Visual Summary

Condition-Colored UMAP

• The UMAP colored by "condition" (cirrhosis vs. healthy) shows a significant degree of overlap between cells from healthy and cirrhotic livers across most of the UMAP space. This indicates that many cell types and states are shared between the two conditions.
• However, specific clusters or regions within the UMAP appear to be predominantly enriched for either healthy (e.g., a large central-left cluster) or cirrhotic cells (e.g., some smaller, more diffuse clusters, and a prominent cluster in the upper-right). This suggests condition-specific changes in cell proportions or activation states within distinct cell populations.

Sample-Colored UMAP

• The UMAP colored by individual "sample" shows a healthy mix of different sample colors across most of the cell clusters. This is a positive indicator, suggesting that the batch effects associated with individual samples are largely minimized, and the observed clustering is primarily driven by biological variation rather than technical processing differences between samples.

Major Cell Type UMAP

• The "celltype_major" UMAP reveals well-separated and distinct clusters corresponding to major cell lineages present in the liver. Key populations identified include T cells, Myeloid cells, Liver Epithelial cells (Hepatocytes), Endothelial cells, B cells, Stromal cells, and Mast cells. This indicates robust initial clustering and high-quality major cell type annotation.
• The "unassigned" category represents a relatively small proportion of cells, further supporting the quality of the overall annotation.

Minor Cell Type UMAP

• At the "celltype_minor" level, the UMAP provides finer resolution, differentiating major cell types into more specific populations. For instance, Myeloid cells resolve into Macrophages, Dendritic cells, and NK cells. T cells are further divided into CD4+ and CD8+ T cells, as well as ILCs. Liver Epithelial cells are predominantly Hepatocytes. Stromal cells differentiate into Hepatic stellate cells, Fibroblasts, and Smooth muscle cells. Plasma cells are also clearly discernible.
• The distinct clustering of these minor cell types reaffirms the quality and granularity of the cell type annotation.

Cell Type Subset UMAP

• The "celltype_subset" UMAP offers the highest level of resolution, revealing highly specific cell states and subtypes within the minor cell populations. Notable examples include:
• Macrophages: Differentiated into distinct M1, M2A, M2B, M2C, and M2D subtypes, which are critical for understanding inflammatory and fibrotic processes.
• T cells: Resolved into cytotoxic T cells, various T helper (Th1, Th2, Th9, Th17, Th22, Tfh), T regulatory (Treg), and naive T cell subsets, highlighting the complexity of the immune response.
• B cells: Separated into Memory, Follicular (Bf), Marginal Zone (BMZ), and Regulatory B cell (Breg) subsets.
• Endothelial cells: Further categorized into Endothelial tip cells and Lymphatic Endothelial cells.
• The clear separation and identification of these highly specialized subsets demonstrate the exceptional resolution of the single-cell data and the precision of the annotation strategy.

Biological Interpretation

The UMAP visualizations provide a compelling overview of the cellular landscape of the human liver, highlighting both commonalities and differences between healthy and cirrhotic states.

• Cellular Heterogeneity in Liver Disease: The high resolution of cell type subsets, particularly within immune and stromal compartments, is crucial for dissecting the complex cellular ecosystem of the liver. The presence of distinct macrophage polarization states (M1, M2 subtypes) and diverse T helper cell subsets directly points to the heterogeneous inflammatory and immunomodulatory responses characteristic of liver cirrhosis. Hepatic stellate cells and fibroblasts, key mediators of fibrosis, are also clearly distinguished.
• Evidence of Condition-Specific Shifts: While significant cellular overlap exists, the observation of clusters enriched in either healthy or cirrhotic conditions suggests that cirrhosis is associated with changes in the proportions of certain cell types or the activation states of specific cellular populations. For example, an expansion of pro-fibrotic or pro-inflammatory cell states might be concentrated in the cirrhosis-enriched clusters. This warrants further investigation into differential cell abundance and gene expression within these specific clusters.
• Robust Data Quality and Annotation: The consistent and well-separated clustering across major, minor, and subset annotations, coupled with effective batch effect mitigation (as seen in the sample UMAP), underscores the high quality of the single-cell RNA-seq data and the accuracy of the cell type assignments. This robust foundation is essential for reliable downstream analyses, such as differential gene expression, cell-cell interaction, and pathway enrichment studies.

Clinical or Translational Implications

• Biomarker Discovery and Therapeutic Targeting: The ability to resolve fine-grained cell subsets and identify condition-specific enrichments provides a powerful basis for discovering novel cell-type-specific biomarkers for diagnosing or staging liver cirrhosis. Furthermore, understanding which specific cell subsets (e.g., particular macrophage M2 subtypes or T cell Th subsets) are activated or expanded in cirrhosis could inform the development of targeted therapies to modulate their activity and halt disease progression.
• Understanding Disease Pathogenesis: The detailed visualization of the cellular components involved in cirrhosis, including specific immune cell populations and fibrogenic stromal cells, enhances our understanding of the underlying pathogenic mechanisms. For instance, the identification of specific T helper cell subsets might indicate particular immune pathways driving inflammation in cirrhotic livers (e.g., Th17 cells in autoimmune liver disease, Th2 cells in fibrosis).

Annotation Notes

The comprehensive and hierarchical cell type annotations presented in these UMAPs are of high quality, enabling detailed exploration of liver cell biology. The low proportion of "unassigned" cells across all annotation levels further validates the robustness of the clustering and annotation pipeline. The good mixing of samples also suggests minimal batch effects, making the biological interpretation more reliable.

2. Major Cell Type Score Visualization on UMAP

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the distribution of major cell type scores across a UMAP embedding derived from single-cell RNA-seq data from human liver tissue. Each plot, labeled "HiCAT_major_score: [Cell Type]", displays the expression score for a specific major cell type, indicating the likelihood of cells belonging to that type based on a predefined gene signature. The final plot, "celltype_major", shows the assigned major cell type annotation for each cell, serving as a reference for comparison. The purpose is to assess how well the predefined gene signatures for major cell types delineate distinct clusters on the UMAP and to validate the cell type annotations.

Visual Summary

The UMAP embedding reveals several distinct clusters of cells.

• HiCAT_major_score: T cell: Shows high scores (yellow) concentrated in a large, distinct cluster located primarily in the bottom-left and central regions of the UMAP. This aligns well with the "T cell" cluster in the celltype_major plot.
• HiCAT_major_score: B cell: Displays high scores in a smaller, compact cluster situated towards the bottom-center of the UMAP. This corresponds directly to the "B cell" cluster in the celltype_major plot.
• HiCAT_major_score: Myeloid cell: Exhibits high scores in a broad, somewhat diffuse cluster in the upper-right quadrant. This matches the "Myeloid cell" annotation.
• HiCAT_major_score: Mast cell: Identifies a very small, distinct population with high scores in a compact cluster within the Myeloid cluster region. This corresponds to the "Mast cell" annotation.
• HiCAT_major_score: Endothelial cell: Shows high scores in a elongated cluster on the lower-right side of the UMAP, which is consistent with the "Endo" cluster in the celltype_major plot.
• HiCAT_major_score: Stromal cell: Displays high scores in a clear, well-separated cluster on the right-hand side of the UMAP. This aligns perfectly with the "Stromal cell" annotation.
• HiCAT_major_score: Liver Epithelial cell: Shows high scores concentrated in a large, distinct cluster at the top of the UMAP, matching the "Liver Epithelial cell" annotation.
• celltype_major: This plot provides the ultimate cell type assignments, color-coding each cluster by its major cell type. It confirms that the high-score regions in the individual HiCAT_major_score plots largely correspond to their respective assigned cell type clusters. "unassigned" cells appear as scattered points, typically with low scores across most major cell types.

Biological Interpretation

The visualizations demonstrate that the major cell type scores effectively identify and delineate distinct cell populations within the single-cell dataset.

• Robust Cell Type Identification: The high correlation between the regions with elevated HiCAT major scores and the assigned celltype_major clusters indicates that the underlying gene signatures for these major cell types are robust and specific. This confirms that cells expressing genes characteristic of a particular major type tend to cluster together on the UMAP.
• Expected Liver Cell Composition: The presence of distinct clusters for Liver Epithelial cells (predominantly hepatocytes), Stromal cells (e.g., hepatic stellate cells, fibroblasts), Endothelial cells (e.g., liver sinusoidal endothelial cells), and various immune cells (Myeloid cells, T cells, B cells, Mast cells) is consistent with the known cellular heterogeneity of the human liver [1].
• Clarity of Immune Cell Populations: Lymphoid cells (T cells, B cells) and Myeloid cells form large, well-defined clusters, suggesting clear transcriptional profiles for these major immune compartments in the liver. The smaller Mast cell cluster also shows a very distinct signature.
• Tissue-Resident Cells: Liver Epithelial cells, Stromal cells, and Endothelial cells, which are key structural and functional components of the liver, also show strong, localized scores, indicating their specific gene expression patterns within the tissue context.

Annotation Notes

• The strong alignment between the continuous scores and the discrete celltype_major annotations provides confidence in the quality of the cell type assignments. This suggests that the annotation process successfully leveraged gene expression patterns to identify distinct cell identities.
• The clear separation of most major cell type scores into distinct UMAP regions implies that the initial embedding and clustering steps effectively captured meaningful biological differences between cell populations.
• The "unassigned" category in celltype_major (shown in purple) generally corresponds to regions with low scores across multiple major cell types, indicating cells that do not strongly match any single major cell type signature or represent rare, undefined populations. Further sub-clustering or alternative annotation strategies might be needed for these cells if detailed characterization is desired.

References

1. Human Cell Atlas Liver. (2024). *The Human Cell Atlas: Liver*. https://www.humancellatlas.org/organ/liver/

3. Overall Celltype_subset Marker Expression Analysis

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression patterns of key marker genes across various celltype_subset populations identified in the single-cell RNA-seq data from human liver tissue. The purpose is to visually confirm and assess the specificity of cell type annotations by examining the expression of known surfaceome markers. The size of each dot corresponds to the percentage of cells within that subset expressing the gene, while the color intensity reflects the mean expression level of the gene in that cell subset. Markers were selected with a focus on surfaceome genes (surfaceome_only: True), which are particularly useful for cell identification and sorting.

Visual Summary

The dot plot displays celltype_subset categories on the y-axis and a curated list of marker genes on the x-axis. A clear block-diagonal pattern is observed, where groups of marker genes show high and specific expression within their corresponding cell type subsets. Red boxes highlight clusters of markers that are particularly enriched and specific to certain cell type groups, reinforcing their identity.

Key observations from the plot include:

• Distinct Cell Type Signatures: Most celltype_subset populations exhibit unique and strong marker gene expression profiles, allowing for clear differentiation.
• Hierarchical Marker Expression: Broader cell types (e.g., B cells, T cells, Macrophages) share common "parent" lineage markers, while their subsets display more refined, specific markers.
• High Specificity of Markers: Many marker genes show almost exclusive expression in one or a few closely related cell subsets, which is crucial for robust cell annotation.
• Fraction of Expressing Cells: The dot size reveals that many identified markers are expressed in a high fraction of cells within their assigned population (larger dots), indicating their broad utility within the subset.
• Mean Expression Levels: The dark red color of many dots signifies high mean expression levels for these markers, further solidifying their role as strong identifiers.
• "Unassigned" Cells: The row labeled "unassigned" shows minimal and scattered marker expression, consistent with its indeterminate classification.

Biological Interpretation

The marker expression patterns strongly support the current celltype_subset annotations within the human liver single-cell dataset. The identification of cell type-specific surfaceome markers provides a robust basis for distinguishing these populations.

• B Cells (Breg, Follicular, MZ, Memory): These subsets consistently express pan-B cell markers like POU2F2, IGHD, CD22, and SPIB, with POU2AF1 also showing strong expression across B cell types. This confirms their common lineage while allowing for more subtle distinctions (not fully resolved by general markers on this plot).
• Dendritic Cells (DC Classical, Plasmacytoid): DC (Classical) cells are marked by CD83, CADM1, CD86, and XCR1, while DC (Plasmacytoid) cells are distinctly identified by TCF4 and CLEC4C, aligning with known pDC signatures PMID: 29515159.
• Endothelial Cells (Endothelial cell, Endothelial tip cell, Lymphatic Endothelial cell): General endothelial markers like RGS5 and COLEC11 are observed. Crucially, Lymphatic Endothelial cells are clearly distinguished by high expression of PROX1 and PDPN, established markers for the lymphatic vasculature GeneCards: PROX1.
• Stromal Cells (Fibroblast, Hepatic stellate cell, Smooth muscle cell): These mesenchymal cells express extracellular matrix components and contractile proteins. Fibroblasts are characterized by COL1A1, COL1A2, DCN, and LUM. Hepatic stellate cells (HSCs) show strong expression of RBP1, SPARC, MYL9, TAGLN, TPM2, and CALD1, which are consistent with their quiescent and activated states in the liver PMID: 28864704. Smooth muscle cells share contractile markers like MYL9, TAGLN, TPM2, CALD1.
• Hepatocytes: These cells display a highly specific and robust expression of canonical hepatocyte genes involved in liver metabolic functions, including SERPINA1, APOA1, FGG, APOH, GC, FGA, FGL1, AKR1C1, ASGR1, ASS1, and SAA4 GeneCards: ALB (ALB, not on this specific plot but typically expressed).
• Innate Lymphoid Cells (ILC1, ILC2, ILCreg, LTI): These subsets are identified by common ILC markers like KLRG1, ID3, and CD69. ILC2 cells are notable for GATA3 expression, a key transcription factor for their development and function PMID: 21976686.
• Macrophages (M1, M2A, M2B, M2C): Macrophages show expression of general markers such as CD68 and SOCS3. Subtypes exhibit some distinct markers, for instance, CLEC10A and STAT1 for M1-like, and MSR1 (CD204) and CD36 for M2-like macrophages, although the M1/M2 categorization can be complex in vivo. ARG1 is also associated with M2 polarization PMID: 24701103.
• Mast Cells: TPSAB1 and SRGN are highly specific markers for mast cells, demonstrating a clear signature for this rare population GeneCards: TPSAB1.
• NK Cells: NK cells are well-defined by KLRD1, KLRF1, GZMK, and GZMA, which are characteristic of natural killer cell cytotoxic functions GeneCards: KLRD1.
• Plasma Cells: This terminally differentiated B cell lineage is strongly identified by JCHAIN, PRDM1 (BLIMP1), SDC1 (CD138), and XBP1, which are crucial for immunoglobulin production and secretion PMID: 20065961.
• T Cells (Cytotoxic, Naive, Tfh, Th1, Th17, Th2, Th22, Treg): All T cell subsets express core T cell receptors (e.g., CD3D, CD3G).
• T cell (Cytotoxic): Defined by GZMK, GZMA, CD8A, CD8B.
• T cell (Naive): Expresses SELL (CD62L).
• T cell (Tfh): Shows CD40LG.
• T cell (Th1): Marked by IFNG and STAT1.
• T cell (Th17): Expresses RORC, RORA, and BATF.
• T cell (Th2): Characterized by GATA3.
• T cell (Treg): Displays TNFRSF18 (GITR) and IL2RA (CD25) GeneCards: FOXP3 (FOXP3, though not shown, is a definitive marker).

Annotation Notes

The comprehensive display of surfaceome marker gene expression across celltype_subset populations provides strong evidence supporting the quality and accuracy of the cell type annotations in this AnnData object. The clear, specific expression profiles observed for nearly all subsets, particularly those highlighted by the red boxes, confirm that these cell identities are well-defined by their molecular signatures. The selection of surfaceome markers (surfaceome_only: True) further strengthens this validation, as these genes are commonly used for experimental validation and functional characterization of cell populations. While minor overlaps exist for some very broad lineage markers, the overall pattern indicates robust and biologically meaningful cell type assignments, validating the granularity of the celltype_subset annotations.

4. Liver Cell Population Shifts in Cirrhosis vs. Healthy Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a stacked bar plot illustrating the proportional distribution of minor cell types derived from single-cell RNA-seq data across individual samples. Samples are categorized by their disease status (cirrhosis vs. healthy) and by CD45 expression (CD45+ for immune cells, CD45- for non-immune cells), allowing for a detailed comparison of cellular composition in human liver tissue under different conditions.

Visual Summary

The stacked bar plots effectively display the relative abundances of different minor cell types within each sample.

Healthy Samples:

• CD45- samples (e.g., healthy1_cd45-, healthy4_cd45-): These are predominantly composed of Hepatocytes (orange), reflecting the major parenchymal cell type of the liver. Smaller contributions from Hepatic stellate cells (dark orange), Endothelial cells (red), and Fibroblasts (salmon) are also visible.
• CD45+ samples (e.g., healthy4_cd45+, healthy3_cd45+): Show a diverse immune cell composition, with T cells (CD4+ and CD8+; teal and dark blue), Macrophages (light yellow), and NK cells (light greenish-yellow) being the most prominent. Dendritic cells (dark red) and B cells (dark red/purple) are also present in smaller proportions.

Cirrhotic Samples:

• CD45- samples (e.g., cirrhotic3_cd45-, cirrhotic2_cd45-): Exhibit a striking reduction in Hepatocyte proportion compared to healthy CD45- samples. This decrease is accompanied by a notable expansion of Hepatic stellate cells and Fibroblasts, indicative of fibrotic tissue remodeling.
• CD45+ samples (e.g., cirrhotic2_cd45+, cirrhotic4_cd45+): Show a marked increase in Macrophage (light yellow) populations. Dendritic cells (dark red) also appear to be elevated in some cirrhotic CD45+ samples. T cells and B/Plasma cells are present, with their relative proportions shifting in response to the disease state.
• Sample Label Discrepancy: It is notable that some samples labeled with _cd45-A or _cd45-B (e.g., cirrhotic1_cd45-A, healthy2_cd45-A) show a cellular composition highly enriched for immune cells (e.g., Macrophages, Dendritic cells), which is typical for CD45+ fractions. This may indicate a specific sorting strategy for these samples, strong immune infiltration even in nominally CD45-depleted fractions, or a potential labeling nuance. For the purpose of interpretation, we will focus on the clear trends observed in samples where cellular content aligns with the CD45+/- designation.

Biological Interpretation

The observed shifts in cell populations provide crucial insights into the pathological changes occurring in the liver during cirrhosis.

• Parenchymal and Stromal Remodeling in Cirrhosis (CD45- compartment): The most prominent finding is the significant decline in Hepatocyte proportion in cirrhotic CD45- samples. This decrease is often due to hepatocyte injury, death, and replacement by fibrotic tissue. Concurrently, the pronounced increase in Hepatic stellate cells and Fibroblasts is a hallmark of liver fibrosis, which is the key driver of cirrhosis. Hepatic stellate cells are central to fibrogenesis, transitioning into myofibroblast-like cells that produce excessive extracellular matrix components [GeneCards: HSC]. This cellular reprogramming leads to the scarring and architectural distortion characteristic of cirrhosis. Changes in Endothelial cell proportions also suggest vascular remodeling within the fibrotic liver.
• Immune Cell Infiltration and Activation in Cirrhosis (CD45+ compartment): The cirrhotic liver demonstrates a substantial expansion of Macrophages, highlighting chronic inflammation and immune activation. Liver macrophages, including Kupffer cells and monocyte-derived macrophages, play diverse roles in cirrhosis, contributing to both injury and repair processes depending on their activation state [PubMed search: liver macrophage cirrhosis]. The increase in Dendritic cells further supports heightened immune surveillance and antigen presentation in the inflamed liver. Shifts in T cell, B cell, and Plasma cell populations suggest an altered adaptive immune response, potentially contributing to ongoing inflammation, tissue damage, or immune dysregulation [PubMed search: adaptive immunity liver cirrhosis].

Clinical or Translational Implications

The distinct cellular landscape of the cirrhotic liver, as revealed by these population analyses, has several clinical and translational implications:

• Biomarkers for Disease Progression: The relative proportions of Hepatocytes, Hepatic stellate cells, Fibroblasts, and various immune cells (especially Macrophages) could serve as quantitative biomarkers for assessing disease severity, monitoring progression, or predicting prognosis in patients with chronic liver disease and cirrhosis.

Therapeutic Targets:

• The expansion of Hepatic stellate cells and Fibroblasts presents clear targets for anti-fibrotic therapies. Strategies aimed at inhibiting their activation, proliferation, or extracellular matrix production could slow or reverse fibrosis [PubMed: Hepatic stellate cells fibrosis therapy].
• The significant increase in Macrophages underscores the importance of inflammation in cirrhosis. Modulating macrophage activation states or depleting specific pathogenic macrophage subsets could be a viable anti-inflammatory therapeutic strategy to ameliorate liver injury and fibrosis [PubMed: macrophage targeted therapy liver cirrhosis].
• Understanding Pathogenesis: These population shifts provide a foundation for further in-depth analysis of specific cell types, such as differential gene expression or cell-cell interaction analyses. Understanding the specific functional states of these expanding cell populations (e.g., M1 vs. M2 macrophages) could reveal critical pathogenic mechanisms and new therapeutic avenues.

5. 간경변증에서 T 세포 아형 구성 변화 분석

[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 기반으로 간 조직 내 T 세포 아형의 상대적 분포를 건강 및 간경변증(cirrhosis) 조건 간에 비교하여 시각화한 것입니다. T 세포는 간 염증, 섬유화 및 면역 반응 조절에 중요한 역할을 하는 면역 세포입니다. 이 막대그래프는 각 샘플 내 T 세포 아형의 구성 비율을 보여주며, 질병 상태에 따른 T 세포 면역 환경의 변화를 이해하는 데 기여합니다.

Visual Summary

제공된 막대그래프는 건강한 간과 간경변증 간 조직 내 주요 T 세포 아형의 상대적 풍부도를 보여줍니다.

건강한 간 (healthy)

• 대부분의 건강 샘플에서 T cell (Cytotoxic) 아형(짙은 자주색)이 압도적으로 우세하며, 전체 T 세포의 70% 이상을 차지하는 경우가 많습니다. 일부 샘플에서는 90%를 초과하기도 합니다.
• T cell (Naive) 아형(빨간색)도 일부 건강 샘플에서 상당한 비율을 보이지만, T cell (Cytotoxic)만큼 일관되게 높지는 않습니다.
• 다른 Helper T 세포 아형들(Tfh, Th1, Th17, Th2, Th22, Th9, Treg)은 대체로 소수 비율을 차지합니다.

간경변증 (cirrhosis)

• 간경변증 샘플에서는 T cell (Cytotoxic)의 상대적 비율이 건강 샘플에 비해 전반적으로 감소한 경향을 보입니다. 간경변증 샘플 내에서는 약 30%에서 60% 범위로 나타납니다.
• T cell (Naive)의 비율은 간경변증 샘플 내에서 가변적이며, 건강 샘플과 비교하여 일관된 증가 또는 감소 추세는 명확하지 않습니다.
• T cell (Tfh) (주황색), T cell (Th1) (밝은 주황/노란색), T cell (Th17) (밝은 노란색)과 같은 Helper T 세포 아형들의 상대적 비율이 건강 샘플에 비해 눈에 띄게 증가한 것을 관찰할 수 있습니다. 이들 아형의 합산 비율은 간경변증 샘플에서 전체 T 세포 풀의 상당 부분을 차지합니다.
• T cell (Treg) (파란색)은 두 조건 모두에서 일정하게 존재하지만, 스택의 상단에 위치하여 상대적 변화를 정확히 파악하기는 어렵습니다.
• 간경변증 샘플 간에 T 세포 아형 구성의 이질성이 건강 샘플보다 더 크게 나타나는 경향이 있습니다.

Biological Interpretation

이러한 T 세포 아형 분포의 변화는 간경변증의 병태생리에서 면역 환경의 중요한 전환을 시사합니다.

• T cell (Cytotoxic) 감소 및 Helper T cell 증가: 건강한 간에서 T cell (Cytotoxic)이 우세한 것은 간의 면역 감시 기능과 특정 항원(예: 바이러스 감염 또는 종양 세포)에 대한 반응을 반영할 수 있습니다. 반면, 간경변증에서 T cell (Cytotoxic)의 상대적 감소와 Helper T 세포(특히 Tfh, Th1, Th17)의 상대적 증가는 만성 염증 및 섬유화 환경에서 면역 반응의 재편성을 나타냅니다.
• T cell (Cytotoxic): 간경변증에서 이들의 상대적 감소는 만성적인 항원 자극으로 인한 T 세포 기능 고갈(T cell exhaustion) https://pubmed.ncbi.nlm.nih.gov/30538202/ 또는 간 조직 내 침윤 및 생존 특성 변화와 관련이 있을 수 있습니다. 이는 간세포암종(HCC)에 대한 면역 감시 능력 저하로 이어질 가능성도 있습니다.
• T cell (Th1) 및 T cell (Th17): 이들은 강력한 전염증성(pro-inflammatory) T 세포 아형으로, IFN-$\gamma$ (Th1) 및 IL-17 (Th17)과 같은 사이토카인을 분비하여 만성 염증과 섬유화 과정을 촉진하는 데 중요한 역할을 합니다 https://pubmed.ncbi.nlm.nih.gov/31336495/. 간경변증에서 이들의 증가는 지속적인 간 손상과 섬유화 진행의 주요 동인임을 시사합니다.
• T cell (Tfh): B 세포의 항체 생산을 돕는 역할을 합니다. 간경변증에서 Tfh 세포의 증가는 자가면역 반응의 활성화, B 세포의 과활성화, 또는 간 내 이소성 림프 구조(ectopic lymphoid structures) 형성 https://pubmed.ncbi.nlm.nih.gov/30678255/와 관련될 수 있습니다.
• T cell (Treg): 면역 반응을 억제하고 면역 항상성을 유지하는 데 중요합니다. 막대그래프만으로는 명확한 변화를 판단하기 어렵지만, 이들의 역할과 다른 effector T 세포 아형들과의 균형은 간경변증의 진행을 조절하는 데 핵심적입니다.
• 간경변증의 이질성: 간경변증 샘플 간의 T 세포 아형 구성에서 더 큰 변동성은 질병의 다양한 원인, 진행 단계 또는 환자별 면역 반응의 차이를 반영할 수 있습니다.

Clinical or Translational Implications

이러한 T 세포 아형 구성의 변화는 간경변증의 진단, 예후 및 치료 전략 개발에 중요한 통찰력을 제공합니다.

• 바이오마커 개발: 간경변증 환자의 혈액 또는 간 조직에서 T cell (Cytotoxic) 감소 및 Th1, Th17, Tfh 세포의 상대적 증가를 포함한 특정 T 세포 아형의 비율 변화는 질병 진행의 잠재적 바이오마커로 활용될 수 있습니다.
• 치료 표적: Th1 및 Th17 세포와 같은 전염증성 Helper T 세포 아형의 활성을 억제하거나, 면역 억제 T cell (Treg)의 기능을 강화하는 것은 간경변증과 관련된 염증 및 섬유화를 완화하기 위한 새로운 치료 전략 개발의 잠재적 표적이 될 수 있습니다 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449171/.
• 질병 메커니즘 이해: T 세포 면역 환경의 재편성은 간경변증이 단순한 구조적 변화를 넘어 복잡한 면역학적 이상을 동반하는 만성 염증성 질환임을 강조합니다. 이는 간경변증의 진행을 이해하고 질병의 원인을 표적으로 하는 정밀 의학 접근법을 개발하는 데 기여할 수 있습니다.

6. T cell subset population differences in liver cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis compares the proportional representation of various T cell subsets in the liver between healthy individuals and those with cirrhosis, as derived from single-cell RNA-seq data. The goal is to identify T cell populations that show statistically significant differences in their proportions, shedding light on the immune landscape alterations in cirrhotic liver.

Visual Summary

The boxplots illustrate the proportions of seven distinct T cell subsets across healthy and cirrhotic liver conditions. Each plot includes individual data points (stripplot) to show the distribution of proportions per sample. Statistically significant differences (p ≤ 0.1) are indicated for all displayed subsets.

• Th17 cells are significantly increased in cirrhosis compared to healthy liver (p ≤ 0.01), showing a median proportion roughly three times higher in the cirrhotic group.
• Treg cells show a trend of increased proportion in cirrhosis (p = 0.06), with the median proportion in cirrhotic livers appearing higher than in healthy livers.
• Tfh cells also exhibit an increased proportion in cirrhosis (p = 0.09), suggesting a shift in humoral immune support.
• Naive T cells (T_Naive) display a significantly higher median proportion in cirrhotic livers (p ≤ 0.05), indicating either an influx of naive cells or altered differentiation dynamics.
• Th9 cells are significantly elevated in cirrhosis (p ≤ 0.05), with a noticeable increase in their median proportion.
• Lymphoid Tissue Inducer cells (LTI) show a significantly increased proportion in cirrhotic livers (p ≤ 0.05), suggesting potential lymphoid neogenesis.
• Th22 cells are significantly more abundant in cirrhotic livers (p ≤ 0.05), with their median proportion notably higher.
• Cytotoxic T cells (T_Cyto) are significantly *decreased* in cirrhosis (p ≤ 0.05), with their median proportion dropping substantially from healthy to cirrhotic conditions.

In summary, most T cell subsets evaluated (Th17, Treg, Tfh, T_Naive, Th9, LTI, Th22) show an increased proportion in cirrhosis, with the exception of Cytotoxic T cells, which are significantly reduced.

Biological Interpretation

The observed shifts in T cell subset populations provide critical insights into the immune pathology of liver cirrhosis:

• Pro-inflammatory T cells: The significant increase in Th17 cells (p ≤ 0.01), Th9 cells (p ≤ 0.05), and Th22 cells (p ≤ 0.05) highlights a prominent pro-inflammatory immune environment in cirrhotic liver. Th17 cells are known to produce IL-17, a cytokine crucial for inflammation and fibrosis progression in various liver diseases PubMed search: Th17 liver fibrosis. Th9 cells, producing IL-9, are implicated in allergic inflammation and autoimmunity, potentially contributing to chronic inflammation in the liver. Th22 cells, producing IL-22, can have dual roles in liver disease, promoting both tissue protection and inflammation/fibrosis depending on the context PubMed search: Th22 IL-22 liver disease. Their collective upregulation points towards a dysregulated inflammatory state driving liver damage.
• Regulatory and Helper T cells: The trend of increased Treg cells (p = 0.06) in cirrhosis could represent a compensatory mechanism to control excessive inflammation, or it could reflect immune exhaustion and dysfunctional regulation in the chronic disease setting PubMed search: Treg liver cirrhosis. The increase in Tfh cells (p = 0.09) suggests altered B cell help, potentially impacting humoral immunity and contributing to the formation of germinal center-like structures often seen in chronic liver inflammation.
• Immune Exhaustion and Surveillance: The most striking finding is the significant decrease in Cytotoxic T cells (T_Cyto) (p ≤ 0.05). Cytotoxic T cells are crucial for clearing virally infected cells (e.g., in viral hepatitis-induced cirrhosis) and for immune surveillance against hepatocellular carcinoma (HCC), a common complication of cirrhosis. Their reduction suggests a compromised cytotoxic immune response, which could contribute to persistent viral infection, impaired tumor surveillance, and progression of liver damage PubMed search: cytotoxic T cells liver cirrhosis.
• Lymphoid Neogenesis: The increased proportion of Lymphoid Tissue Inducer cells (LTI) (p ≤ 0.05) is indicative of tertiary lymphoid structure (TLS) formation within the cirrhotic liver. TLS are ectopic lymphoid aggregates that form at sites of chronic inflammation and can contribute to both local immune responses and disease pathogenesis PubMed search: tertiary lymphoid structures liver disease.
• Naive T cells: The increase in Naive T cells (T_Naive) (p ≤ 0.05) in cirrhosis is somewhat unexpected given the chronic inflammatory state, which typically favors T cell differentiation. This could indicate an influx of naive cells from the periphery, altered trafficking, or a state where chronic inflammation does not efficiently drive differentiation within the liver for all T cell populations.

Collectively, these findings suggest a complex immune reprogramming in cirrhosis characterized by an amplification of several pro-inflammatory T cell subsets, a potential compensatory increase in regulatory T cells, and a significant deficiency in cytotoxic T cell immunity. The increase in LTI cells points to structural changes in the immune microenvironment.

Clinical or Translational Implications

The distinct T cell subset profiles in cirrhotic liver could have significant clinical and translational implications:

• Biomarkers of disease progression: The proportions of specific T cell subsets, particularly Th17, Th9, Th22, and Cytotoxic T cells, could serve as potential biomarkers for assessing disease severity, progression, or response to therapy in patients with cirrhosis.
• Therapeutic targets: Modulating the balance of these T cell populations represents a potential therapeutic strategy. For example, strategies aimed at dampening Th17, Th9, or Th22 responses could mitigate inflammation and fibrosis. Conversely, approaches to restore or enhance cytotoxic T cell function could improve antiviral immunity and tumor surveillance, potentially reducing the risk of HCC in cirrhotic patients.
• Understanding HCC risk: The reduction in cytotoxic T cells is particularly concerning for HCC development. Further investigation into the specific mechanisms driving this reduction and its impact on tumor immunity is warranted.
• Immune reconstitution strategies: Understanding the altered T cell landscape may inform personalized immune-based therapies, including adoptive T cell transfers or immunomodulatory drugs, to restore functional immune responses in cirrhotic patients.

7. Macrophage Subset Composition Shifts in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the proportional composition of different macrophage subsets (M1, M2A, M2B, M2C, M2D) within the total macrophage population in human liver samples. The samples are categorized into two conditions: 'cirrhosis' and 'healthy', allowing for a direct comparison of macrophage polarization patterns associated with liver disease.

Visual Summary

The stacked bar plots display the relative frequencies of five macrophage subsets (M1, M2A, M2B, M2C, M2D) across individual samples, grouped by condition.

• Macrophage (M1) Dominance in Cirrhosis: In cirrhotic liver samples, Macrophage (M1) (represented in dark red) consistently constitutes the largest proportion of macrophages, often exceeding 50% and in several cases approaching 60-70%. This indicates a significant enrichment of the M1 phenotype in diseased tissue.
• Reduced Macrophage (M2D) in Cirrhosis: Macrophage (M2D) (represented in teal/green) is a notably minor component in cirrhotic samples, often appearing as a very thin layer at the top of the bars or being almost absent.
• Heterogeneity in Healthy Samples: Healthy liver samples show a more variable distribution of macrophage subsets. While M1 macrophages are present, their proportions are generally lower and more diverse compared to cirrhotic samples, ranging from around 20% to over 50%.
• Presence of M2D in Healthy Samples: Macrophage (M2D) is more consistently present and occupies a relatively larger proportion in healthy samples compared to cirrhotic samples.
• Other M2 Subsets (M2A, M2B, M2C): M2A (orange), M2B (light yellow), and M2C (paler yellow) subsets are present in both conditions, contributing to the overall macrophage pool. M2A appears more prominent in some healthy samples than in cirrhotic ones, suggesting shifts in these subsets as well, though less stark than the M1 and M2D trends.

Biological Interpretation

Macrophages are critical immune cells in the liver, contributing to both homeostasis and disease progression. Their polarization into distinct functional subsets, such as M1 (pro-inflammatory) and M2 (anti-inflammatory/pro-resolving/tissue remodeling), is crucial for determining the immune microenvironment.

• Pro-inflammatory Environment in Cirrhosis: The striking dominance of Macrophage (M1) in cirrhotic livers strongly suggests a persistent pro-inflammatory state. M1 macrophages are characterized by the production of pro-inflammatory cytokines (e.g., TNF-α, IL-1β, IL-6) and reactive oxygen/nitrogen species, which contribute to hepatocyte damage, perpetuation of inflammation, and activation of fibrogenic cells like hepatic stellate cells, driving fibrosis progression in chronic liver disease https://pubmed.ncbi.nlm.nih.gov/30678512/.
• Loss of Regulatory/Reparative M2D Population: The marked reduction or absence of Macrophage (M2D) in cirrhosis compared to healthy liver is a significant observation. While the classification of M2D is less standardized than M1/M2A, M2D macrophages are often associated with wound healing, tissue remodeling, angiogenesis, and immunosuppression, akin to certain tumor-associated macrophage (TAM) phenotypes https://pubmed.ncbi.nlm.nih.gov/22425424/. Their depletion in cirrhosis could indicate a compromised ability of the liver to resolve inflammation or repair tissue damage, potentially contributing to the pathological progression of fibrosis. In a healthy liver, their presence might reflect a role in maintaining tissue homeostasis or resolving minor inflammatory insults.
• Implications for Liver Fibrosis: Liver cirrhosis is the end-stage of chronic liver diseases, characterized by extensive fibrosis and nodule formation. The shift from a more balanced macrophage profile in healthy livers, including potentially protective or regulatory M2D subsets, to an M1-dominant, highly inflammatory state in cirrhosis is consistent with the known pathophysiology of progressive liver damage.

Clinical or Translational Implications

• Biomarker Potential: The distinct shift in macrophage subset composition, particularly the increased M1 and decreased M2D populations, could serve as potential diagnostic or prognostic biomarkers for liver cirrhosis.
• Therapeutic Targets: Modulating macrophage polarization represents a promising therapeutic strategy for liver cirrhosis. Strategies aimed at repolarizing M1 macrophages towards a more M2-like phenotype (e.g., M2D, M2C) or inhibiting M1-driven pro-inflammatory pathways could alleviate inflammation and fibrosis. This might involve targeting specific signaling pathways or growth factors that drive M1 differentiation or promote M2 differentiation and function https://pubmed.ncbi.nlm.nih.gov/28592305/.
• Understanding Disease Heterogeneity: Further investigation into the specific functions and origins of these macrophage subsets in human liver cirrhosis could reveal new therapeutic avenues to prevent disease progression and promote liver regeneration.

8. Differential Macrophage Subset Proportions in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates changes in the proportions of specific macrophage subsets within the liver between healthy individuals and those with cirrhosis, using single-cell RNA-seq data. The plot_box_for_celltype_population_with_signif_difference tool was used to identify and visualize macrophage subsets (at the celltype_subset taxonomic level) that showed statistically significant differences (p-value < 0.1) in their population proportions between the "healthy" and "cirrhosis" conditions.

Visual Summary

The visualization presents two boxplots, each showing the proportion of a specific macrophage subset (Mac M2A and Mac M1) across "healthy" and "cirrhosis" conditions:

• Mac (M2A) Population: The boxplot for Mac (M2A) shows a lower median celltype proportion in the "cirrhosis" group compared to the "healthy" group. The interquartile range (IQR) for Mac (M2A) also appears tighter in the cirrhotic samples. A p-value of 0.08 indicates a statistically significant trend towards a decrease in M2A macrophage proportions in cirrhosis. Notably, there are a few outlier points in both groups, particularly in the healthy samples, suggesting some variability in this subset's proportion.
• Mac (M1) Population: Conversely, the boxplot for Mac (M1) reveals a higher median celltype proportion in the "cirrhosis" group compared to the "healthy" group. The p-value of 0.05 indicates a statistically significant increase in M1 macrophage proportions in cirrhotic livers. While there is overlap in the distribution ranges, the upward shift in the median and the bulk of the data points in cirrhosis suggest an expansion of this subset.

Biological Interpretation

Macrophages, including Kupffer cells (resident liver macrophages) and monocyte-derived macrophages, play pivotal roles in liver homeostasis and disease. They exhibit remarkable plasticity, polarizing into distinct functional phenotypes, broadly categorized as M1 (pro-inflammatory) and M2 (anti-inflammatory, pro-fibrotic, or tissue repair).

• M1 Macrophages: These macrophages are typically activated by pro-inflammatory signals (e.g., LPS, IFN-γ) and are characterized by the production of pro-inflammatory cytokines (e.g., TNF-α, IL-1β, IL-6), leading to host defense and tissue damage.
• M2 Macrophages: This is a more heterogeneous group, often further subdivided (M2a, M2b, M2c, M2d). M2A macrophages, specifically, are typically induced by IL-4 and IL-13, involved in allergic responses, anti-parasitic immunity, and early wound healing. They tend to promote tissue repair and often have anti-inflammatory properties, producing cytokines like IL-10 and TGF-β, and can contribute to fibrosis [PubMed search: M2a macrophage function].

The observed shifts in macrophage populations are highly relevant to the pathophysiology of liver cirrhosis:

1. Increase in M1 Macrophages in Cirrhosis: An elevated proportion of M1 macrophages in cirrhotic livers is consistent with the chronic inflammatory state characteristic of the disease. M1-like macrophages contribute to hepatocyte injury by releasing reactive oxygen species, proteases, and pro-inflammatory cytokines, perpetuating inflammation and driving fibrogenesis [GeneCards: TNF | GeneCards: IL1B | GeneCards: IL6]. This sustained pro-inflammatory environment can accelerate the progression of liver fibrosis to cirrhosis.
2. Decrease in M2A Macrophages in Cirrhosis: The reduction in M2A macrophage proportions suggests a diminished capacity for tissue repair and anti-inflammatory resolution pathways that these cells typically mediate. In healthy tissue, M2A macrophages could contribute to maintaining immune tolerance and promoting regeneration. Their decline in cirrhosis might indicate a shift away from protective phenotypes, possibly due to the altered cytokine milieu in the diseased liver, or a direct impact on their survival or differentiation. This could lead to unchecked inflammation and impaired tissue remodeling.

Together, these findings indicate a significant re-programming of the macrophage compartment in the cirrhotic liver, shifting towards a more pro-inflammatory (M1-dominant) and less reparative/resolving (reduced M2A) phenotype. This imbalance likely contributes to the ongoing liver damage and fibrotic progression seen in cirrhosis.

Clinical or Translational Implications

The differential proportions of M1 and M2A macrophages observed in cirrhotic livers offer several potential clinical and translational implications:

• Biomarkers of Disease Progression: The ratio or absolute proportions of M1 and M2A macrophages could serve as novel biomarkers for diagnosing cirrhosis, staging disease severity, or predicting disease progression. Longitudinal studies could evaluate their utility in monitoring response to therapy.
• Therapeutic Targets: Modulating macrophage polarization represents a promising therapeutic strategy for liver cirrhosis.
• Inhibiting M1 Activation: Strategies to reduce the activation or infiltration of M1 macrophages could mitigate inflammation and hepatocyte damage.
• Promoting M2A Activity/Differentiation: Interventions aimed at preserving or increasing the proportion and function of beneficial M2A macrophages could enhance tissue repair and resolve inflammation, potentially slowing or reversing fibrosis. This could involve targeting specific signaling pathways (e.g., IL-4/IL-13 axis) that drive M2A differentiation or survival [PubMed search: macrophage polarization therapy liver fibrosis].
• Personalized Medicine: Understanding the specific macrophage polarization profile in individual patients with cirrhosis could enable more personalized treatment approaches, tailoring immunomodulatory therapies to their unique inflammatory and fibrotic landscape.

9. Cell-Cell Interaction Analysis in Healthy vs. Cirrhotic Liver

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCIs) in human liver tissue from single-cell RNA-seq data, comparing healthy individuals with those diagnosed with cirrhosis. CellPhoneDB was used to infer ligand-receptor interactions, and the results are presented as dot plots, showing the strength (mean expression) and significance (p-value) of interactions between various celltype_minor pairs. The analysis specifically highlights up to 80 most significant interactions for each condition to identify condition-specific communication networks.

Visual Summary

CCI in Cirrhosis

The dot plot for cirrhosis displays a broad landscape of significant cell-cell interactions. Key observations include:

• Highly Active Cell Types: Macrophage and T cell (CD8+, CD4+) interactions are prominent, indicating an active immune environment. Endothelial cells also show substantial involvement in various interactions.
• Strong and Significant Interactions: Numerous interactions exhibit both high mean expression (brighter green/yellow dots) and high significance (larger dot sizes), particularly involving Macrophages, T cells, and Endothelial cells.
• Specific Ligand-Receptor Pairs: Several notable ligand-receptor pairs are highly active:
• SPP1-integrin complex: Multiple SPP1 interactions with integrin_aV_b1, integrin_aV_b3, integrin_aV_b5, integrin_aV_b6 are strong and significant, especially involving Macrophages interacting with various immune and stromal cells.
• Collagen-integrin complex: Interactions like COL4A1_integrin_a1_b1 and COL4A1_integrin_a2_b1 are observed, suggesting extracellular matrix (ECM) remodeling.
• Chemokine signaling: CXCL12-CXCR4 interactions are present, often involving endothelial cells and immune cells.
• TNF family signaling: Interactions such as TNF-TNFRSF1B and TNFSF13-TNFRSF13B are observed, indicative of inflammatory processes.
• HLA class I/II: HLA-E-KLRC1 and HLA-F-LILRB1 interactions are present, suggesting immune recognition and regulation.
• Other notable interactions: ALB-ADAM9, APOE-LRP1, CD300LG-CD300LF, and various Prostaglandin related interactions.

CCI in Healthy Liver

The dot plot for healthy liver shows fewer and generally less intense interactions compared to the cirrhotic state, reflecting a more homeostatic environment.

• Predominant Cell Types: Macrophage, T cell CD8+, T cell CD4+, and NK cell interactions are primarily observed, suggesting basal immune surveillance.

Key Interactions:

• ANXA1-FPR1 is notably active, potentially involved in immune cell migration and anti-inflammatory processes.
• CD160-CD155 and CD93-ADGRE5 interactions are present, related to immune cell regulation and adhesion.
• ICAM1-ITGAL/ITGB1 complex interactions, important for cell adhesion and leukocyte extravasation, are also visible.
• Some Prostaglandin and TNF family interactions are present, but generally with lower mean expression and significance compared to cirrhosis.

Biological Interpretation

Comparison of Cirrhosis and Healthy Liver:

1. Inflammation and Immune Dysregulation:

• Cirrhosis: The cirrhotic liver exhibits a robust inflammatory environment, characterized by extensive interactions involving Macrophages and T cells. The strong TNF-TNFRSF family interactions (e.g., TNF-TNFRSF1B, TNFSF13-TNFRSF13B) suggest persistent pro-inflammatory signaling that drives disease progression. The presence of HLA-E-KLRC1 and HLA-F-LILRB1 interactions indicates altered immune recognition and potential modulation of NK and T cell responses, which are crucial in chronic liver disease.
• Healthy: In contrast, healthy liver shows fewer and less intense inflammatory interactions. ANXA1-FPR1 signaling, often associated with resolution of inflammation and immune cell migration, is more prominent, reflecting a balanced immune state.

2. Fibrosis and Extracellular Matrix (ECM) Remodeling:

• Cirrhosis: A hallmark of cirrhosis is excessive fibrosis. The strong SPP1-integrin interactions are highly significant. Secreted phosphoprotein 1 (SPP1, also known as Osteopontin) is a key matricellular protein that promotes inflammation, fibrosis, and angiogenesis in various organs, including the liver [1]. Its interactions with multiple integrins, particularly aV_b1, aV_b3, aV_b5, aV_b6 found on immune and stromal cells, indicate active pro-fibrotic signaling. Collagen-integrin interactions (e.g., COL4A1_integrin_a1_b1) further underscore ongoing ECM deposition and remodeling. The involvement of Macrophages in these interactions highlights their central role in mediating fibrosis.
• Healthy: These prominent pro-fibrotic interactions are largely absent or very weak in the healthy liver, demonstrating the shift towards pathogenic ECM dynamics in cirrhosis.

3. Angiogenesis and Cell Trafficking:

• Cirrhosis: The CXCL12-CXCR4 axis is notably active in cirrhosis, particularly involving Endothelial cells. CXCL12 (SDF-1) and its receptor CXCR4 play crucial roles in immune cell trafficking, angiogenesis, and perpetuation of fibrosis in chronic liver disease [2]. This suggests active recruitment of immune cells and neovascularization, processes characteristic of liver regeneration gone awry and disease progression.
• Healthy: While some basic adhesive interactions might exist, the prominent pro-angiogenic and trafficking signals seen in cirrhosis are diminished in the healthy state.

4. Metabolic and Other Interactions:

• Cirrhosis: Interactions like ALB-ADAM9 could reflect altered albumin metabolism or cellular processes involving ADAM family proteases in the diseased liver. APOE-LRP1 is related to lipid metabolism, which can be dysregulated in liver disease.

Key Findings Summary:

• Cirrhosis is characterized by a significant increase in pro-inflammatory, pro-fibrotic, and pro-angiogenic cell-cell interactions, primarily driven by Macrophages, T cells, and Endothelial cells.
• SPP1-integrin and TNF-TNFRSF signaling are highly activated in cirrhosis, indicating pathological inflammation and ECM remodeling.
• The CXCL12-CXCR4 axis is prominent in cirrhosis, suggesting enhanced immune cell trafficking and angiogenesis.
• Healthy liver exhibits a more quiescent interaction profile, focusing on basal immune surveillance and homeostatic mechanisms.

Clinical or Translational Implications

The distinct cell-cell interaction networks identified in cirrhotic liver offer promising avenues for therapeutic intervention and biomarker development.

1. Therapeutic Targets for Fibrosis: The strong SPP1-integrin interactions are a prime candidate for therapeutic targeting in liver fibrosis. Blocking SPP1 or its integrin receptors could potentially mitigate fibrotic progression by reducing inflammation and ECM deposition [3].

• [1] SPP1 (Osteopontin) in liver fibrosis: https://pubmed.ncbi.nlm.nih.gov/?term=SPP1+osteopontin+liver+fibrosis
• [3] Targeting integrins in fibrosis: https://pubmed.ncbi.nlm.nih.gov/?term=integrin+inhibitors+fibrosis

2. Anti-inflammatory Strategies: The elevated TNF-TNFRSF signaling in cirrhosis suggests that immunomodulatory therapies targeting these pathways could reduce chronic inflammation, a key driver of liver damage.
3. Modulating Immune Cell Trafficking and Angiogenesis: The CXCL12-CXCR4 axis represents a potential target to inhibit harmful immune cell recruitment to the liver and aberrant angiogenesis that contributes to portal hypertension and disease progression [4].

• [4] CXCL12-CXCR4 in liver disease: https://pubmed.ncbi.nlm.nih.gov/?term=CXCL12+CXCR4+liver+fibrosis+angiogenesis

4. Biomarker Development: The specific ligand-receptor pairs highly active in cirrhosis could serve as diagnostic or prognostic biomarkers. For instance, increased expression of SPP1 or its integrin partners, or altered levels of CXCL12 or CXCR4 in patient samples, could indicate disease severity or progression.
5. Experimental Validation: Further in vitro and in vivo studies are warranted to functionally validate these identified interactions. This could involve using neutralizing antibodies, genetic knockouts, or small molecule inhibitors to assess the impact of blocking specific CCI on the pathological features of cirrhosis. For example, specific inhibition of SPP1-integrin interactions in preclinical models of liver fibrosis would be a logical next step.

10. Condition-Specific Cell-Cell Interaction Patterns in Healthy and Cirrhotic Liver

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) between healthy and cirrhotic human liver samples, focusing on key immune and stromal cell populations. Using single-cell RNA sequencing data and CellPhoneDB, a dot plot visualizes the top 25 most significantly enriched CCIs in each condition. The color intensity of the dots represents the standardized mean interaction strength, and the dot size indicates the statistical significance (-log10(p-value)), allowing for a clear distinction of condition-specific interaction landscapes.

Visual Summary

The dot plot clearly delineates two distinct patterns of cell-cell communication: one predominantly active in cirrhotic samples and another in healthy samples.

• Cirrhosis-Enriched Interactions (Left Panel): A prominent cluster of strong and highly significant interactions (dark red, large dots within the blue box) is observed across most cirrhotic samples. These interactions are largely absent or very weak in healthy samples. A striking feature is the prevalence of interactions involving Hepatic stellate cells (HSCs) and various immune/endothelial cells through collagen-integrin complexes, alongside interactions mediated by TNF and TNFSF14.
• Healthy-Enriched Interactions (Right Panel): Conversely, a distinct set of robust and significant interactions (dark red, large dots within the blue box) is evident in healthy samples, with minimal to no activity in cirrhotic samples. These interactions primarily involve macrophages, endothelial cells, and T cells, often through adhesion molecules, chemokines, and lipid mediators like prostaglandins.
• Sample Heterogeneity: While clear condition-specific patterns emerge, some variability in interaction strength and significance is observable across individual samples within both the cirrhotic and healthy groups.

Biological Interpretation

Interactions Enriched in Cirrhotic Liver

The CCIs strongly enriched in cirrhotic liver reflect processes central to liver fibrosis and chronic inflammation:

1. Extracellular Matrix (ECM) Remodeling and Fibrosis:

• Collagen-Integrin Interactions (e.g., COL1A1/COL1A2/COL3A1/COL4A1/COL4A2/COL12A1/COL14A1 - integrin_a1b1/a2b1/a10b1/a6b1/aXb2_complex | Hepatic stellate cell-Endothelial cell/Macrophage/T cell CD8+/NK cell/ILC/T cell CD4+): This large group of interactions highlights extensive communication between Hepatic Stellate Cells (HSCs) and multiple immune and endothelial cells via collagen-integrin pathways. HSCs are the primary fibrogenic cells in the liver, and their activation and interaction with ECM components like collagens (e.g., Type I collagen, COL1A1) through integrin receptors are hallmarks of liver fibrosis [PubMed Search: Hepatic stellate cell integrin fibrosis]. This indicates a highly active fibrotic microenvironment where HSCs deposit and interact with a pathological ECM, promoting disease progression.
• FN1-integrin_aVb5_complex | Endothelial cell-Macrophage: Fibronectin (FN1) is another key ECM glycoprotein. Its interaction with integrin αVβ5 on endothelial cells and macrophages suggests altered ECM sensing and signaling in the vascular and immune compartments, contributing to tissue remodeling and inflammatory responses.

2. Pro-inflammatory Signaling:

• TNF-TNFRSF1A | Hepatic stellate cell-T cell CD4+/T cell CD8+: Tumor Necrosis Factor (TNF)-alpha signaling plays a critical role in chronic liver disease. Elevated TNF-alpha levels promote inflammation, hepatocyte injury, and HSC activation, driving fibrosis [PubMed Search: TNF-alpha liver fibrosis]. This interaction points to enhanced inflammatory crosstalk between activated HSCs and T cells, perpetuating inflammation.
• TNFSF14_TNFRSF14 (LIGHT-HVEM) | ILC-T cell CD4+/T cell CD8+: The LIGHT-HVEM pathway is involved in immune activation and inflammatory responses. Its upregulation in cirrhosis suggests a heightened inflammatory state and altered immune cell function in the diseased liver microenvironment [PubMed Search: TNFSF14 liver inflammation].
• HLA-G_LILRB1 | Endothelial cell-Macrophage: HLA-G is a non-classical MHC class I molecule involved in immune tolerance, but also implicated in immune evasion in chronic inflammatory conditions and cancer. Its interaction with LILRB1 on macrophages could reflect attempts at immune modulation or a dysfunctional immune suppressive environment in cirrhosis.

Interactions Enriched in Healthy Liver

The CCIs enriched in healthy liver likely represent homeostatic processes, immune surveillance, and tissue maintenance:

1. Immune Surveillance and Homeostasis:

• VCAM1-integrin_a4b1_complex | Endothelial cell-Macrophage: Vascular Cell Adhesion Molecule 1 (VCAM1) on endothelial cells mediates leukocyte recruitment and adhesion, critical for immune surveillance and resolution of minor inflammation in healthy tissues.
• CXCL16-CXCR6 | Endothelial cell-Macrophage/NK cell: The CXCL16-CXCR6 axis is important for the migration and retention of specific T cell subsets, NK cells, and macrophages in various tissues, including the liver. This interaction likely contributes to maintaining resident immune cell populations and immune surveillance in a healthy liver [PubMed Search: CXCL16 CXCR6 liver immune].
• CD48-CD244 | T cell CD8+-Macrophage: This interaction, involving the CD244 (2B4) receptor, is critical for modulating NK and T cell function, contributing to appropriate immune responses and surveillance in the healthy state.

2. Regulation of Tissue Environment:

• TYROBP_CD44 | Macrophage-Hepatic stellate cell: TYROBP is an adaptor molecule important for myeloid cell activation and function. CD44 is involved in cell adhesion, migration, and signaling. This interaction suggests a regulatory role in macrophage-HSC communication, possibly contributing to maintaining HSC quiescence or engaging in routine tissue maintenance.
• PGE2 and PGD2 Signaling (e.g., ProstaglandinD2_byAKR1C3_PTGDR | Endothelial cell, and ProstaglandinE2_byPTGES2_PTGER4 | Hepatic stellate cell): Prostaglandins are lipid mediators with diverse roles in inflammation, immunity, and vascular tone. Their interactions with HSCs and endothelial cells in healthy liver could be involved in maintaining vascular integrity, modulating local immune responses, or initiating basal tissue repair mechanisms.
• APOA2_TREM2_receptor | Plasma cell-Macrophage: TREM2 is a key receptor on myeloid cells involved in phagocytosis, lipid metabolism, and resolution of inflammation [UniProt: TREM2]. Its interaction with APOA2 (Apolipoprotein A-II) might signify macrophage involvement in lipid handling or debris clearance, potentially modulated by plasma cells, as part of healthy liver homeostasis.

Clinical or Translational Implications

The stark differences in cell-cell interaction patterns between healthy and cirrhotic livers have significant clinical and translational implications:

1. Biomarkers of Disease Progression: Upregulated pro-fibrotic and pro-inflammatory CCI pairs in cirrhosis, such as collagen-integrin interactions involving HSCs or TNF-TNFRSF1A signaling, could serve as novel biomarkers for assessing disease severity, predicting progression, or monitoring therapeutic responses in patients with chronic liver disease.
2. Therapeutic Targets: The identified dysregulated CCIs represent potential therapeutic targets.

• Anti-fibrotic strategies: Blocking specific collagen-integrin interactions (e.g., using integrin inhibitors) could mitigate HSC activation and ECM deposition, thereby halting or reversing fibrosis [PubMed Search: integrin inhibitor liver fibrosis].
• Anti-inflammatory strategies: Targeting the TNF-TNFRSF1A or TNFSF14-TNFRSF14 pathways could dampen chronic inflammation in cirrhosis.
• Restoring Homeostasis: Understanding the healthy liver interactions (e.g., VCAM1-integrin, CXCL16-CXCR6) could inform strategies to promote tissue regeneration and immune competence in diseased livers.

3. Understanding Pathophysiology: This analysis provides a high-resolution map of altered intercellular communication networks underlying liver cirrhosis. It elucidates how the interplay between immune cells, stromal cells, and endothelial cells contributes to the pathological features of fibrosis, inflammation, and vascular remodeling, offering a deeper mechanistic understanding of the disease.

11. Macrophage Condition-Specific Surface Markers in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers for Macrophages in human liver, comparing healthy individuals to those with cirrhosis. The plot_markers_and_expression_dot tool was used to visualize the expression patterns of these markers across individual samples, grouped by condition. The analysis focused on surfaceome genes, identifying up to 50 markers per condition, to highlight potential diagnostic, prognostic, or therapeutic targets.

Visual Summary

The dot plot effectively visualizes macrophage surface marker expression, clearly delineating between healthy and cirrhotic liver samples.

• Clustering by Condition: Samples from cirrhotic patients cluster distinctly from healthy samples, indicating significant changes in macrophage surface protein expression during cirrhosis.
• Cirrhosis-Associated Markers: A prominent cluster of genes, primarily on the left side of the plot, shows high expression levels (dark red color) and high fractions of expressing cells (large dot size) predominantly in cirrhotic samples. Notable markers in this cluster include JAML, CD300A, FCGR1A, FPR1, SPINT2, LAIR1, HM13, SLC3A2, PLXDC2, CD9, SIGIRR, ASGR1, SORL1, ENG, CX3CR1, STAB1, RNF167, IL17RA, GPR108, and OLR1. Some of these, like FCGR1A and FPR1, show particularly strong and widespread expression in cirrhotic macrophages.
• Healthy-Associated Markers: Conversely, a distinct cluster of genes on the right side of the plot exhibits high expression and prevalence specifically in healthy liver samples. These include FCGR1B (as labeled in the plot), TREM2, VCAM1, LILRB5, and LYVE1. TREM2 and LYVE1 stand out with very high expression and prevalence in almost all healthy samples.
• Differential Expression: The plot highlights distinct patterns: genes like FCGR1A are almost exclusively and strongly expressed in cirrhosis, while TREM2 and LYVE1 are strongly expressed in healthy tissue but largely absent or very low in cirrhosis. Other markers, such as CD9 and CX3CR1, show some expression in both conditions but are more pronounced in cirrhosis, indicating an exacerbation or shift rather than exclusive presence.

Biological Interpretation

The observed differential expression of surface markers provides critical insights into the functional shifts of macrophages during liver cirrhosis.

Macrophage Activation and Inflammation in Cirrhosis

• FCGR1A (CD64), a high-affinity IgG receptor, is strongly upregulated in cirrhotic macrophages. CD64 is a well-established marker of activated macrophages and is often associated with pro-inflammatory responses and antigen presentation, suggesting a heightened inflammatory state of macrophages in cirrhotic livers PubMed search: FCGR1A macrophage activation.
• FPR1 (Formyl Peptide Receptor 1), involved in sensing bacterial components and driving inflammatory responses, is also elevated. This could indicate increased engagement with gut-derived microbial products that translocate to the liver in cirrhosis, perpetuating inflammation GeneCards FPR1.
• ENG (CD105), a component of the TGF-beta receptor complex, is implicated in angiogenesis and fibrosis. Its upregulation on macrophages in cirrhosis could signify their involvement in pro-fibrotic processes and tissue remodeling characteristic of the disease GeneCards ENG.
• CD300A, LAIR1, and SIGIRR are inhibitory receptors. Their upregulation could represent a compensatory mechanism to dampen excessive inflammation, or they might define specific macrophage subsets attempting to regulate the immune response in the complex cirrhotic environment GeneCards CD300A.

Loss of Homeostatic Macrophage Functions in Cirrhosis

• TREM2 (Triggering Receptor Expressed on Myeloid Cells 2) and LYVE1 (Lymphatic Vessel Endothelial Hyaluronan Receptor 1) are highly expressed in healthy macrophages but significantly downregulated or absent in cirrhosis. Both are characteristic markers of tissue-resident macrophages (e.g., Kupffer cells) involved in maintaining tissue homeostasis, efferocytosis (clearance of apoptotic cells), and anti-inflammatory functions GeneCards TREM2, GeneCards LYVE1. Their loss suggests a depletion or functional reprogramming of these protective macrophage populations in cirrhosis, contributing to disease progression.
• LILRB5 (Leukocyte Immunoglobulin Like Receptor B5), an inhibitory receptor, also shows higher expression in healthy macrophages, potentially contributing to immune tolerance in the healthy liver environment GeneCards LILRB5.
• *(Note on FCGR1B: The presence of "FCGR1B" as a distinct marker from "FCGR1A" and its association with healthy samples warrants further investigation. In humans, FCGR1A (CD64) is the primary gene for the high-affinity IgG receptor. FCGR1B is not a recognized human gene. This could represent an uncharacterized isoform, a variant, or a potential gene annotation artifact within the dataset.)*

In summary, the transition from a healthy to a cirrhotic liver involves a profound shift in macrophage phenotype, characterized by a loss of homeostatic, tissue-resident populations and an emergence of activated, pro-inflammatory, and potentially pro-fibrotic subsets.

Clinical or Translational Implications

The identified condition-specific surface markers offer several promising avenues for clinical translation:

• Biomarkers for Disease Progression: Upregulated markers in cirrhosis, such as FCGR1A, FPR1, and ENG, could serve as valuable diagnostic or prognostic biomarkers for the severity and progression of liver cirrhosis. Their expression on easily accessible cells (e.g., circulating monocytes that infiltrate the liver) or in biopsy samples could aid in disease monitoring.
• Therapeutic Targets: The surface expression of these markers makes them accessible for therapeutic intervention.
• Targeting activated macrophages expressing FCGR1A, FPR1, or ENG could be a strategy to mitigate inflammation and fibrosis in cirrhosis. For instance, developing antibody-drug conjugates or specific inhibitors against these receptors could selectively modulate detrimental macrophage functions without broadly suppressing immunity.
• Conversely, strategies aimed at preserving or restoring TREM2- and LYVE1-positive homeostatic macrophages could offer a novel approach to promote tissue repair and resolve inflammation in cirrhotic livers. This might involve cell-based therapies or agents that encourage the differentiation and survival of these protective macrophage subsets.
• Cell Isolation and Characterization: These surface markers are invaluable for isolating specific macrophage populations from liver biopsies or blood for further in-depth functional studies (e.g., flow cytometry or magnetic-activated cell sorting, MACS). This would enable a more detailed understanding of their roles in cirrhosis and validation of these findings.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers specifically distinguishing CD4+ T cells in cirrhotic liver tissue from those in healthy liver tissue, using single-cell RNA sequencing data. The results are presented as a dot plot visualizing the expression and prevalence of up to 50 top condition-specific surface markers for each condition. The focus is on identifying potential biomarkers or therapeutic targets that are differentially expressed on the cell surface of CD4+ T cells in the context of liver cirrhosis.

Visual Summary

The dot plot visualizes the expression of selected surface markers across individual samples, grouped by condition (cirrhosis vs. healthy). Each dot's size represents the fraction of CD4+ T cells within that sample expressing the gene, while its color intensity indicates the mean expression level.

Key observations:

• Cirrhosis-specific markers: A clear cluster of genes, including CXCR3, CD59, SIRPG, CD79B, and NMUR1, show distinctly higher expression levels (darker red) and prevalence (larger dot sizes) in most cirrhotic samples compared to healthy samples. These genes are predominantly expressed in cirrhotic CD4+ T cells, with minimal to no expression in healthy counterparts. The expression of these markers appears relatively consistent across different cirrhotic samples.
• Healthy-specific markers (less pronounced): Genes like PRNP and SERINC5 show some expression in healthy samples, but their pattern is less exclusive to the healthy condition compared to the strong specificity of the cirrhosis markers. They are also present, albeit generally at lower levels, in some cirrhotic samples.
• Non-specific/Low expression markers: CD8B and CD8A show very low expression and prevalence across nearly all samples, both healthy and cirrhotic. Given that the analysis targets CD4+ T cells, the presence of these canonical CD8+ T cell markers at such low levels is likely non-specific background or an artifact of the marker selection algorithm rather than true differential expression on CD4+ T cells.

Biological Interpretation

The differential surface marker expression on CD4+ T cells provides insights into their altered state and function in liver cirrhosis.

Markers Upregulated in Cirrhosis:

• CXCR3 (C-X-C Motif Chemokine Receptor 3): This chemokine receptor is robustly expressed on cirrhotic CD4+ T cells. CXCR3 is typically associated with Th1 cells and cytotoxic T lymphocytes and mediates their recruitment to sites of inflammation in response to its ligands (CXCL9, CXCL10, CXCL11). Its upregulation suggests an activated, pro-inflammatory phenotype of CD4+ T cells in the cirrhotic liver, potentially contributing to liver injury and fibrosis progression by recruiting more immune cells to the inflamed tissue [1].
• CD59 (CD59 Molecule, Complement Regulator): Also known as Membrane Attack Complex Inhibition Factor, CD59 protects cells from complement-mediated lysis. Its upregulation on CD4+ T cells in cirrhosis might represent a self-protective mechanism to survive in an inflammatory, complement-rich microenvironment, or it could be involved in modulating immune responses in the damaged liver [2].
• SIRPG (Signal Regulatory Protein Gamma): A member of the SIRP family, SIRPG is involved in cell-cell recognition and modulating immune cell activation. While less studied than SIRPA, its presence on CD4+ T cells in cirrhosis suggests potential altered regulatory interactions with other immune or stromal cells, influencing T cell activation thresholds or inhibitory signaling within the fibrotic liver niche [3].
• NMUR1 (Neuromedin U Receptor 1): NMUR1 is a G protein-coupled receptor for neuromedin U, a neuropeptide with diverse roles including inflammation and immune regulation. Its expression on CD4+ T cells in cirrhosis points towards potential neuroimmune interactions playing a role in the chronic inflammatory processes of liver disease, where neuromedin U signaling could modulate T cell function or migration [4].
• CD79B (CD79b Molecule, Immunoglobulin-Associated Beta): This is a core component of the B cell receptor complex and is canonical for B lymphocytes. Its significant upregulation on cells annotated as "T cell CD4+" is highly unexpected and warrants careful re-evaluation. While rare instances of T cells co-expressing B cell markers have been reported in specific pathological contexts, it could also suggest potential issues with cell type annotation purity, T cell plasticity, or the presence of a distinct, uncharacterized T cell subpopulation with B cell-like features in cirrhosis. Further validation is crucial for this specific finding.

Markers with Less Clear Specificity:

• PRNP (Prion Protein) and SERINC5 (Serine Incorporator 5): These genes show some expression in both healthy and cirrhotic samples, although with potentially subtle differences. They are less specific as "healthy" markers based on this visualization. PRNP has roles in cell adhesion and survival, while SERINC5 inhibits retroviral infection. Their roles in CD4+ T cells in liver cirrhosis would require further investigation to determine their functional relevance.
• CD8A (CD8a Molecule) and CD8B (CD8b Molecule): These are definitive markers for CD8+ T cells, not CD4+ T cells. Their very low expression levels and prevalence across all samples, regardless of condition, suggest they are not reliable markers for CD4+ T cells. Their appearance in the results may be an artifact of the marker finding algorithm due to their near-absent expression in "other" conditions, rather than true biological upregulation on CD4+ T cells.

Overall, the strong upregulation of CXCR3, CD59, SIRPG, and NMUR1 on CD4+ T cells in cirrhotic samples suggests a highly activated, pro-inflammatory, and potentially adaptive phenotype that is distinct from healthy liver T cells.

Clinical or Translational Implications

The identified surfaceome markers have significant clinical and translational potential:

• Biomarker Discovery: The robustly upregulated markers in cirrhotic CD4+ T cells, particularly CXCR3, CD59, SIRPG, and NMUR1, could serve as diagnostic or prognostic biomarkers for liver cirrhosis. Their expression on the cell surface makes them amenable to detection using techniques like flow cytometry or immunohistochemistry on tissue biopsies or peripheral blood, aiding in disease staging or monitoring treatment response.
• Therapeutic Targets: Surface markers are prime targets for therapeutic intervention. For example, blocking CXCR3 could inhibit the recruitment of pathogenic T cells to the liver, potentially attenuating inflammation and fibrosis. Similarly, modulating the activity of SIRPG or NMUR1 via specific antibodies or small molecules could represent novel strategies to reprogram the immune response in cirrhosis. The ability to specifically target surface proteins on disease-associated CD4+ T cells offers a high degree of precision in drug delivery.
• Experimental Validation: The findings provide a strong rationale for further experimental validation. Flow cytometry with specific antibodies for CXCR3, CD59, SIRPG, and NMUR1 could confirm their differential expression patterns at the protein level. Functional assays could then explore the exact roles of these proteins in CD4+ T cell activation, migration, and contribution to liver pathology in cirrhosis models.

The unexpected finding of CD79B expression in CD4+ T cells necessitates further investigation to clarify its cellular origin and functional implications, as it could point to novel immune cell plasticity in cirrhosis.

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References:

1. CXCR3 function in liver disease: A search on PubMed for "CXCR3 liver cirrhosis T cells" will yield many relevant studies on its role in immune cell trafficking and inflammation in chronic liver disease. PubMed Search: CXCR3 liver cirrhosis T cells
2. CD59 role: Information on CD59's role as a complement regulator and its expression can be found on GeneCards. GeneCards: CD59
3. SIRPG general function: For general information on SIRPG and its family, GeneCards is a good resource. GeneCards: SIRPG
4. NMUR1 and immunity: General roles of NMUR1 can be found on GeneCards. GeneCards: NMUR1

13. Differential Expression of Cell Cycle Genes in Hepatic Stellate Cells in Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the expression of a predefined set of cell cycle-related genes within Hepatic stellate cells, comparing individuals with cirrhosis against healthy controls. The objective was to identify statistically significant differences in gene expression that might highlight dysregulated cell cycle activity in these key disease-related cells during liver fibrosis. The plot_box_for_gene_expression_with_signif_difference tool was used to visualize the expression distribution and highlight significant changes. Out of a comprehensive list of cell cycle genes, only those with a p-value less than 0.1 and an absolute log2 fold change greater than 0.1 were selected for plotting, with a maximum of 24 items allowed.

Visual Summary

The visualization displays boxplots for two cell cycle-related genes, BUB3 and CDC26, showing their expression levels (as sample means) in Hepatic stellate cells across healthy and cirrhosis conditions.

• BUB3: Expression of BUB3 is significantly higher in Hepatic stellate cells from cirrhosis samples compared to healthy samples (marked with '***', indicating a p-value < 0.001). The boxplot for cirrhosis shows a higher median and overall shifted distribution towards increased expression.
• CDC26: Similarly, CDC26 expression is also significantly elevated in Hepatic stellate cells from cirrhosis samples compared to healthy samples (marked with '', indicating a p-value < 0.0001). This gene also exhibits a higher median expression and a clear upward shift in the distribution in the cirrhosis group.

The presence of only two plots suggests that BUB3 and CDC26 were the most significantly differentially expressed genes within the selected cell cycle gene list that met the predefined statistical thresholds for plotting.

Biological Interpretation

Hepatic stellate cells (HSCs) are critical players in the pathogenesis of liver fibrosis and cirrhosis. In a healthy liver, HSCs are quiescent and store vitamin A. Upon liver injury, they become activated, proliferate, and transform into myofibroblast-like cells. These activated HSCs are the primary source of extracellular matrix (ECM) components, leading to excessive collagen deposition and, eventually, cirrhosis.

The observed upregulation of BUB3 and CDC26 in Hepatic stellate cells during cirrhosis points to an enhanced proliferative state.

• BUB3 (BUB3 Mitotic Checkpoint Serine/Threonine Kinase): BUB3 is a component of the mitotic spindle checkpoint (also known as the spindle assembly checkpoint, SAC), which ensures proper chromosome segregation during mitosis. Upregulation of BUB3 can reflect increased cell division activity or heightened vigilance in the cell cycle checkpoint system due to rapid proliferation 1. In the context of activated HSCs, this suggests that these cells are actively dividing.
• CDC26 (Cell Division Cycle 26): CDC26 is a subunit of the anaphase-promoting complex/cyclosome (APC/C), a large E3 ubiquitin ligase crucial for regulating progression through mitosis and the G1 phase of the cell cycle. The APC/C ensures timely degradation of mitotic cyclins and other cell cycle regulators, thereby facilitating anaphase onset and exit from mitosis 2. Elevated expression of CDC26 implies an accelerated or dysregulated cell cycle progression in HSCs.

The synchronous upregulation of these two key cell cycle regulators strongly indicates that Hepatic stellate cells in the cirrhotic liver are characterized by increased proliferation. This finding aligns with the known pathophysiology of liver fibrosis, where activated HSCs undergo extensive clonal expansion, contributing significantly to the progression of liver damage and ECM accumulation. This proliferative phenotype is a hallmark of the activated HSC state, driving the fibrotic cascade.

Clinical or Translational Implications

The finding that cell cycle genes like BUB3 and CDC26 are significantly upregulated in Hepatic stellate cells during cirrhosis has several potential clinical and translational implications:

1. Biomarker Potential: Elevated expression of BUB3 and CDC26 in HSCs could serve as cellular biomarkers for activated and proliferative HSCs in cirrhotic livers. This might be relevant for monitoring disease progression or response to anti-fibrotic therapies, though direct measurement in human liver biopsies would be challenging.
2. Therapeutic Targets: The increased proliferation of HSCs is a major driver of fibrosis. Targeting key components of the cell cycle machinery in activated HSCs, such as BUB3 or CDC26, could offer a therapeutic strategy to inhibit HSC proliferation and thus slow down or potentially reverse liver fibrosis and cirrhosis.
3. Understanding Pathogenesis: These findings further solidify the understanding of the molecular mechanisms underlying HSC activation and proliferation in cirrhosis, providing deeper insights into disease pathogenesis. This molecular understanding could guide the development of more specific anti-fibrotic drugs.

Further research focusing on the functional consequences of BUB3 and CDC26 upregulation in HSCs and testing the efficacy of their inhibition in preclinical models of liver fibrosis would be valuable.

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References:

1. BUB3 GeneCard: https://www.genecards.org/cgi-bin/carddisp.pl?gene=BUB3
2. CDC26 GeneCard: https://www.genecards.org/cgi-bin/carddisp.pl?gene=CDC26

14. Gene Set Enrichment Analysis Reveals Cell-Type-Specific Pathway Alterations in Liver Cirrhosis

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for key liver cell types, comparing gene expression profiles in cirrhotic conditions against healthy controls. The GSEA dot plot visualizes the enrichment of specific gene sets (pathways, biological processes, disease signatures) within each cell type. The color of each dot represents the Normalized Enrichment Score (NES), indicating whether a pathway is relatively upregulated (red) or downregulated (blue) in the specified condition (e.g., cirrhosis) compared to others. The size of the dot reflects the statistical significance (-log10(p-value)) of the enrichment. The analysis was performed across B cells, Dendritic cells, Endothelial cells, Hepatic stellate cells, ILCs, Macrophages, NK cells, Plasma cells, Smooth muscle cells, CD4+ T cells, and CD8+ T cells.

Visual Summary

The dot plot effectively illustrates distinct patterns of pathway enrichment and depletion across different cell types and conditions.

• Differential Enrichment by Condition: For each cell type, gene sets generally show an inverse enrichment pattern between the 'cirrhosis_vs_others' and 'healthy_vs_others' comparisons. This consistency indicates that the observed changes are directly attributable to the disease state.
• Prominent Changes in Cirrhosis: The columns corresponding to 'cirrhosis_vs_others' for each cell type exhibit numerous large, brightly colored dots (both red and blue), indicating highly significant and strong enrichment or depletion of specific pathways in liver cirrhosis.
• Cell-Type-Specific Signatures: Distinct clusters of enriched/depleted pathways are observable for different cell types. For instance, Macrophages, Hepatic stellate cells, and T cells show particularly broad and strong pathway alterations in cirrhosis.
• Key Enriched Pathways in Cirrhosis: In the cirrhotic state, immune-related pathways (e.g., NOD-like receptor signaling, phagosome, various infection pathways) and fibrosis-related pathways (e.g., Focal adhesion, ECM-receptor interaction, Regulation of actin cytoskeleton) are notably enriched across relevant cell types.
• Consistently Depleted Pathways: A striking observation is the widespread downregulation of metabolic pathways such as "Oxidative phosphorylation" and "Ribosome biogenesis in eukaryotes" across almost all investigated cell types in cirrhosis.

Biological Interpretation

1. Profound Immune Activation and Inflammation in the Cirrhotic Microenvironment:

• Macrophages, Dendritic Cells, NK cells, T cells, B cells, and ILCs in cirrhosis consistently display significant enrichment of pathways associated with diverse infections (viral, bacterial, parasitic), innate immune sensing (e.g., "NOD-like receptor signaling pathway" GeneCards: NOD2), phagocytosis ("Phagosome"), and antigen presentation. This indicates a robust and chronic activation of both innate and adaptive immune responses in the cirrhotic liver, driving persistent inflammation.
• T cells (CD4+ and CD8+) show enrichment for "Th1 and Th2 cell differentiation" and "Th17 cell differentiation", suggesting a dysregulated adaptive immune response that may contribute to chronic inflammation and tissue damage rather than effective immune clearance in cirrhosis PubMed Search: T cell subsets liver cirrhosis.
• The enrichment of "Complement and coagulation cascades" across multiple cell types (e.g., Macrophages, Endothelial cells, B cells) highlights the interplay between inflammation and coagulation pathways in liver disease.

2. Activation of Fibrogenic Programs and Structural Remodeling:

• Hepatic Stellate Cells (HSCs), Endothelial Cells, and Smooth Muscle Cells in cirrhosis are significantly enriched for pathways such as "Focal adhesion", "ECM-receptor interaction", and "Regulation of actin cytoskeleton". These pathways are critical for cell adhesion, interaction with the extracellular matrix, and cellular contractility, which are hallmarks of HSC activation and the fibrogenic process GeneCards: ACTA2 (a marker for activated HSCs). This collectively points to active extracellular matrix deposition and tissue remodeling that characterizes liver fibrosis.
• "Platelet activation" is also notably enriched in HSCs, Endothelial cells, and Macrophages in cirrhosis, suggesting a pro-thrombotic and pro-fibrotic environment within the diseased liver.

3. Widespread Metabolic Reprogramming and Cellular Stress Responses:

• A remarkable and consistent observation across nearly all investigated cell types in cirrhosis is the significant downregulation of "Oxidative phosphorylation" and "Ribosome biogenesis in eukaryotes". This suggests a global metabolic shift away from efficient mitochondrial energy production and protein synthesis. This metabolic rewiring could reflect chronic cellular stress, adaptation to nutrient deprivation, or a shift towards less energy-efficient but faster metabolic pathways (e.g., glycolysis, although its pattern is not uniformly upregulated here) to support inflammatory or fibrogenic activities.
• Pathways related to "Protein processing in endoplasmic reticulum" (enriched in HSCs, Endothelial cells) and "Autophagy" (enriched in various immune cells) indicate heightened cellular stress responses and attempts to manage protein misfolding or clear damaged organelles, which are common features in chronic liver injury.

Clinical or Translational Implications

• Identification of Therapeutic Targets: The identified cell-type-specific pathway alterations, particularly the consistent immune activation and fibrogenic processes, offer promising avenues for therapeutic intervention. Targeting activated macrophages (e.g., by modulating NOD-like receptor signaling) or inhibiting HSC activation pathways (e.g., focal adhesion components) could be strategies to prevent or reverse fibrosis progression PubMed Search: Liver fibrosis therapeutic targets.
• Biomarkers for Disease Progression and Response to Therapy: The specific gene set enrichment profiles in various cell types could serve as novel biomarkers for assessing cirrhosis severity, predicting disease progression, or monitoring response to anti-fibrotic or anti-inflammatory therapies. For instance, changes in ECM-related pathway activity in HSCs could provide a dynamic readout of fibrotic activity.
• Understanding Pathogenesis: These results provide a comprehensive, cell-type-resolved view of the molecular mechanisms underlying liver cirrhosis, highlighting how different cell populations contribute to the complex pathology of the disease through coordinated immune, structural, and metabolic changes. This deeper understanding is crucial for developing more effective and personalized treatment strategies.
• Immunomodulatory Strategies: The widespread immune cell activation and T-cell differentiation shifts suggest that therapies aimed at modulating the immune response could be beneficial in managing chronic inflammation in cirrhosis.

15. Discussion

The single-cell analysis of human liver tissue provides a high-resolution view of the complex cellular and molecular landscape differentiating healthy from cirrhotic states. A prominent finding is the drastic architectural and cellular remodeling in cirrhosis, marked by a significant loss of hepatocytes and a corresponding expansion of hepatic stellate cells (HSCs) and fibroblasts, the primary drivers of fibrosis. This aligns with the known pathophysiology of cirrhosis as a progressive fibrotic disease, but our data further elucidate the molecular machinery, such as the upregulation of cell cycle regulators BUB3 and CDC26 in HSCs, driving this pathological proliferation.

The immune microenvironment in cirrhotic liver is profoundly dysregulated. Macrophages exhibit a clear shift towards a pro-inflammatory M1 phenotype, coupled with a reduction in reparative M2A macrophages. This M1 dominance, evidenced by markers like FCGR1A and FPR1, indicates persistent inflammation, further supported by the global enrichment of infection and innate immune signaling pathways in macrophages. Conversely, the significant depletion of homeostatic TREM2 and LYVE1 positive macrophages suggests a compromised ability to resolve inflammation and repair tissue damage.

T cell populations are also significantly altered, moving away from an effective cytotoxic response. The marked decrease in cytotoxic T cells (T_Cyto) in cirrhosis raises concerns about impaired immune surveillance against viral infections or hepatocellular carcinoma (HCC), a common complication of cirrhosis. Concomitantly, there is a significant expansion of various pro-inflammatory T helper subsets, including Th17, Th9, and Th22 cells, which are known to perpetuate chronic inflammation and fibrosis. The increase in Lymphoid Tissue Inducer (LTI) cells and T follicular helper (Tfh) cells also points to active tertiary lymphoid structure formation and altered B cell help within the chronically inflamed liver. Condition-specific markers like CXCR3 on CD4+ T cells further highlight their activated, pro-inflammatory phenotype.

Cell-cell interaction analysis reveals specific pathogenic networks in cirrhosis. Notably, extensive collagen-integrin interactions involving HSCs and immune cells underscore the highly active fibrotic microenvironment. Pro-inflammatory TNF-TNFRSF1A and TNFSF14-TNFRSF14 pathways are strongly enriched, driving chronic inflammation. These findings highlight how intercellular communication perpetuates disease, moving beyond individual cell-type changes to show coordinated pathological networks.

Finally, a striking and consistent observation across nearly all cell types in cirrhosis is the widespread downregulation of metabolic pathways such as oxidative phosphorylation and ribosome biogenesis. This suggests a global metabolic reprogramming, likely an adaptation to chronic cellular stress, hypoxia, or nutrient deprivation, which may limit the regenerative capacity of the liver and contribute to overall organ dysfunction. This broad metabolic shift differs from purely inflammatory responses and points to systemic cellular exhaustion or adaptation that warrants further investigation.

Hypotheses:

1. The persistent pro-inflammatory M1 macrophage phenotype in cirrhosis, driven by specific surface markers like FCGR1A and FPR1, directly contributes to hepatocyte damage and promotes hepatic stellate cell activation and fibrosis.
2. The observed reduction in cytotoxic T cells and increase in pro-inflammatory T helper subsets (Th17, Th9, Th22) in cirrhosis collectively impair antiviral immunity and tumor surveillance, increasing susceptibility to chronic infections and hepatocellular carcinoma development.
3. Chronic activation of specific cell-cell interaction axes, such as SPP1-integrin and collagen-integrin pathways between hepatic stellate cells and immune/endothelial cells, serves as a central mechanism for perpetuating liver fibrosis in cirrhosis.
4. The widespread metabolic suppression, specifically the downregulation of oxidative phosphorylation and ribosome biogenesis across multiple liver cell types in cirrhosis, reflects a state of chronic cellular stress that limits regenerative capacity and accelerates organ failure.
5. The loss of homeostatic TREM2 and LYVE1 positive macrophages in cirrhotic liver diminishes the capacity for immune tolerance and tissue repair, thereby exacerbating inflammation and fibrotic progression.

Potential therapeutic targets:

1. Hepatic Stellate Cell Proliferation (e.g., BUB3, CDC26): Activated HSCs are the primary source of extracellular matrix, and their proliferation is a key driver of fibrosis progression in cirrhosis. Upregulation of cell cycle regulators BUB3 and CDC26 indicates enhanced proliferative activity. Evidence: Significant upregulation of BUB3 (p < 0.001) and CDC26 (p < 0.0001) in hepatic stellate cells in cirrhosis samples (Section 13). GSEA shows enrichment of "Focal adhesion", "ECM-receptor interaction", "Regulation of actin cytoskeleton" in HSCs (Section 14). Validation: Test small molecule inhibitors of BUB3 or CDC26 in primary human HSC cultures to assess effects on proliferation and fibrogenic gene expression. Evaluate the anti-fibrotic efficacy of these inhibitors in preclinical models of liver fibrosis.
2. Pro-inflammatory M1 Macrophages (e.g., FCGR1A, FPR1): The liver microenvironment in cirrhosis is dominated by pro-inflammatory M1 macrophages, which contribute to chronic inflammation and hepatocyte damage, perpetuating fibrosis. Inhibiting or repolarizing these cells is crucial. Evidence: Significant increase in M1 macrophage proportion in cirrhosis (p=0.05, Section 8). Macrophages in cirrhosis show strong upregulation of activation markers like FCGR1A (CD64) and FPR1 (Section 11). GSEA shows robust enrichment of infection/innate immune pathways in macrophages (Section 14). Validation: Develop antibody-drug conjugates or small molecule inhibitors against FCGR1A or FPR1 to selectively target M1 macrophages. Test their efficacy in reducing liver inflammation and fibrosis in animal models, and assess macrophage repolarization.
3. SPP1-Integrin interactions: Secreted phosphoprotein 1 (SPP1, Osteopontin) is a key matricellular protein that promotes inflammation, fibrosis, and angiogenesis through integrin binding. Its interactions are highly active in cirrhotic liver. Evidence: Multiple strong and significant SPP1-integrin interactions (SPP1-integrin_aV_b1, SPP1-integrin_aV_b3, etc.) are prominently active in cirrhosis, involving macrophages, T cells, and stromal cells (Section 9). Validation: Use neutralizing antibodies against SPP1 or specific integrin receptors (e.g., αVβ3, αVβ5) in preclinical models of liver fibrosis to evaluate reduction in inflammation, ECM deposition, and overall fibrotic progression.
4. CXCR3 on CD4+ T cells: CXCR3 is associated with pro-inflammatory T cell subsets, and its upregulation on CD4+ T cells in cirrhosis suggests enhanced recruitment and pathogenic activity in the inflamed liver. Blocking its function could reduce T cell-mediated inflammation. Evidence: CXCR3 is distinctly and robustly upregulated on CD4+ T cells in cirrhotic samples (Section 12). Increase in Th17, Th9, Th22 cells (Section 6) suggests a pro-inflammatory T cell profile. Validation: Administer CXCR3 antagonists or neutralizing antibodies in liver fibrosis animal models to observe changes in T cell infiltration, cytokine profiles, and fibrotic markers.
5. Pro-inflammatory T helper cells (Th17, Th9, Th22 pathways): The significant increase in these pro-inflammatory T helper subsets directly contributes to chronic inflammation and fibrosis in the cirrhotic liver. Modulating their activity could mitigate disease progression. Evidence: Significant increase in Th17 (p ≤ 0.01), Th9 (p ≤ 0.05), and Th22 (p ≤ 0.05) cell populations in cirrhosis (Section 6). GSEA shows enrichment for "Th17 cell differentiation" pathways (Section 14). Validation: Investigate specific cytokine blockers (e.g., anti-IL-17, anti-IL-9, anti-IL-22 antibodies) or small molecule inhibitors of their downstream signaling pathways in preclinical liver fibrosis models to assess reduction in inflammation and fibrosis.

Follow-up validation ideas:

1. Flow Cytometry/Immunostaining: Quantify the proportions of M1 (FCGR1A+), M2A (LYVE1+/TREM2+), Th17 (IL-17+), Th9 (IL-9+), Th22 (IL-22+), and Cytotoxic T cells (CD8+/GZMK+) in patient liver biopsies and peripheral blood to validate population shifts and marker expression at the protein level.
2. Spatial Transcriptomics/Proteomics: Apply spatial technologies to cirrhotic liver tissue to map the precise localization and interaction patterns of activated HSCs, specific macrophage subsets, and T cell subsets, particularly focusing on collagen-integrin and TNF-TNFRSF1A interactions.
3. In Vitro/Ex Vivo Perturbation Assays: Culture patient-derived HSCs or macrophages with specific inhibitors (e.g., integrin inhibitors, TNF-alpha blockers) or modulators (e.g., IL-4/IL-13 to promote M2A differentiation) to assess their impact on proliferation, cytokine production, and fibrogenic gene expression.
4. Preclinical Animal Models: Utilize liver fibrosis models (e.g., CCl4, bile duct ligation) to test the therapeutic efficacy of targeting specific cell cycle regulators (BUB3, CDC26) in HSCs, or immunomodulatory agents against CXCR3, Th17/Th9/Th22 pathways, or agents to restore TREM2/LYVE1 macrophages.
5. Functional Assays for T Cell Exhaustion: Perform in vitro or ex vivo assays on patient-derived T cells to assess their functional capacity (e.g., cytokine production, proliferation, cytotoxicity) and exhaustion markers, especially for cytotoxic T cells, to confirm the functional implications of observed population shifts.
6. Metabolomics/Mitochondrial Function Assays: Conduct metabolomic profiling of cirrhotic liver tissue and isolated cell types, coupled with assays for mitochondrial respiration and ATP production, to validate the observed widespread metabolic downregulation.

Limitations:

This study, while providing high-resolution insights into liver cirrhosis, has several limitations. The single-cell RNA-seq data primarily reflects gene expression at a single time point and cannot fully capture the dynamic progression of cirrhosis or direct causality. While cell type annotations are robust, the interpretation of certain markers (e.g., CD79B on CD4+ T cells, FCGR1B for macrophages) requires further experimental validation to rule out potential annotation artifacts or uncharacterized cellular plasticity. The analysis of cell-cell interactions is inferred from ligand-receptor expression and requires functional validation in vitro or in vivo. Furthermore, the small sample size for some comparisons might limit the generalizability of certain statistically significant findings, and inter-individual variability in human cirrhosis remains a challenge for comprehensive interpretation.

16. Query List

1. Show UMAPs including condition, sample, major cell type, minor cell type, and celltype_subset in 2 columns and save.
2. Show major cell type scores on UMAP and save.
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.
5. Show subset population barplot for T cells and save.
6. Show boxplot for T cell subset populations if there are statistically significant differences between conditions and save. Set ncols appropriately considering the total number of panels.
7. Show subset population barplot for macrophages and save.
8. Show boxplot for macrophage subset populations if there are statistically significant differences between conditions and save. Set ncols appropriately considering 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 for key immune and stromal cells between conditions and show as a dot plot, then save. Set max_n_items_per_group = 25.
11. Extract condition-specific markers for Macrophage and show as a dot plot, then save. Only include surfaceome markers, up to 50 per condition.
12. Extract condition-specific markers for T cell CD4+ and show as a dot plot, then save. Only include surfaceome markers, up to 50 per condition.
13. For key disease-related cells, extract cell cycle pathway related genes and show boxplots of their expression differences between conditions if statistically significant, then save. Set max_n_items_to_plot = 24, and ncols appropriately for a 2x3 aspect ratio.
14. Show Gene set enrichment analysis results for key cell types as a dot plot and save. Use RdBu_r as the color map and set n_pws_to_show = 80.