SCODiA Report by MLBI Lab

Single-Cell Transcriptomic Atlas of Mouse Colon Reveals Distinct Cellular and Molecular Signatures in Acute and Chronic Colitis

This report provides a comprehensive single-cell RNA-seq analysis of mouse colon, comparing acute colitis (AC) and chronic colitis (CC) with healthy controls (HC). Key findings include a dramatic B cell expansion in chronic colitis, leading to a relative reduction in other populations like intestinal epithelial cells. Macrophage populations show distinct polarization shifts: M1 macrophages increase significantly in CC, while M2B macrophages increase in AC, and M2D subsets decrease in both inflammatory conditions. T cell subsets like Th22, Th9, ILCreg, Th17, and NK cells show condition-specific increases, alongside a consistent rise in regulatory T cells (Treg) in both AC and CC. Cell-cell interaction analysis reveals robust activation of inflammatory pathways (e.g., CXCL10-CXCR3), extensive extracellular matrix remodeling (Integrin-ECM), and specific immune cell homing mechanisms (e.g., MAdCAM1-integrin_a4b7) in colitis conditions. GSEA and GSA analyses confirm widespread metabolic reprogramming and activation of inflammatory signaling (NF-kB, IFN-γ) across immune and epithelial cells in AC and CC, contrasting with distinct homeostatic programs in healthy tissue.

Contents

0. Dataset overview
1. UMAP Visualization of Cell Populations by Condition, Sample, and Cell Type Hierarchies in Mouse Colon
2. Major Cell Type Score Validation on UMAP
3. Overall Celltype_subset Marker Expression Pattern
4. Colon Minor Cell Type Population Analysis Across Conditions
5. Colon 조직 내 T cell 및 관련 림프구 아집단 분포 분석
6. Colon T Cell and NK Cell Subset Proportion Analysis Across Conditions
7. Macrophage Cell Type Population Consistency Across Samples and Conditions
8. Macrophage Subset Proportional Changes Across Colon Conditions
9. Colon Cell-Cell Interaction Analysis Across Inflammatory Conditions (AC, CC) and Healthy Control (HC)
10. Condition-Specific Cell-Cell Interaction Patterns in Mouse Colon
11. Condition-Specific Surfaceome Markers in Colon Macrophages
12. Fibroblast Condition-Specific Surfaceome Markers in Mouse Colon
13. CD4 T 세포 조건 특이적 표면 마커 분석
14. Gene Ontology (GSA) Analysis of Intestinal Epithelial Cells Across Conditions
15. Gene Set Enrichment Analysis (GSEA) Across Major Colon Cell Types and Conditions
16. Discussion
17. Query List

0. Dataset overview

Dataset Summary

Total Cells: 84,594 cells

Total Genes: 21,984 genes

Species: Mouse

Tissue: Colon

• Conditions: AC, CC, HC (HC is the reference condition for differential analyses).

Annotation Levels (Cell Types):

• Major: Myeloid cell, T cell, B cell, Stromal cell, Endothelial cell, Intestinal Epithelial cell
• Minor: Macrophage, ILC, B cell, Fibroblast, T cell CD4+, Endothelial cell, Intestinal Epithelial cell, Dendritic cell, Plasma cell, Smooth muscle cell, T cell CD8+, unassigned, NK cell
• Subset: Detailed sub-cell types such as Macrophage (M2B), ILC2, B cell (Follicular), T cell (Treg), etc.

Computed Results Available:

• Cell-Cell Interaction (CCI): Results stored per condition and per sample.
• Differential Expression Genes (DEG): Results comparing one condition vs. all others, and one condition vs. reference (HC), per celltype_minor.
• Gene Set Enrichment Analysis (GSEA): Results comparing one condition vs. all others, and one condition vs. reference (HC), per celltype_minor.
• Gene Ontology (GO/GSA): Results comparing one condition vs. all others, and one condition vs. reference (HC), per celltype_minor. These are specifically for upregulated genes (_up).

1. UMAP Visualization of Cell Populations by Condition, Sample, and Cell Type Hierarchies in Mouse Colon

[Analysis Visualization Results]...

Analysis Overview

These UMAP plots provide a comprehensive visualization of the single-cell RNA-seq dataset from mouse colon, illustrating the distribution of cells based on experimental condition, individual sample, and a hierarchical classification of cell types (major, minor, and subset). The goal is to assess the overall structure of the dataset, the effectiveness of batch integration, and the quality of cell type annotations across different levels of granularity.

Visual Summary

The UMAPs display 84,594 cells, each colored according to different metadata categories:

• Condition UMAP: Cells from the three conditions (AC, CC, HC) are largely intermingled across the UMAP landscape. While there's significant overlap, indicating shared cell populations and potentially successful batch integration, some regions show enrichment for specific conditions. For example, the upper right cluster appears to have a higher density of HC cells (dark blue), and some peripheral clusters (e.g., far left) might show slight enrichment for AC (maroon) or CC (yellow) cells. This suggests either condition-specific cellular states/compositions or residual conditional effects.
• Sample UMAP: Similar to the condition plot, cells from different individual samples (AC1-3, CC1-4, HC1-3) are generally well-mixed throughout the major clusters. This intermixing is a positive indicator of effective batch correction, minimizing sample-specific technical artifacts from driving the primary clustering. However, subtle enrichments of specific samples are visible in certain areas, which might reflect true biological variability between individual animals or minor residual batch effects.
• Celltype_major UMAP: This plot shows clear and well-separated clusters corresponding to distinct major cell types. Intestinal Epithelial cells (orange) form a large, distinct cluster on the left. Stromal cells (light green) and Endothelial cells (reddish-orange) form well-defined, somewhat interconnected clusters. Immune cells, including T cells (dark blue), Myeloid cells (light yellow), and B cells (maroon), occupy the central and right portions of the UMAP, forming large, interconnected yet discernible groups. This indicates strong transcriptional differences between these broad cell categories.
• Celltype_minor UMAP: This UMAP further refines the major cell type clusters, revealing expected heterogeneity within these groups. For instance, within the immune cell compartment, distinct clusters for Macrophage, Dendritic cell (DC), T cell CD4+, T cell CD8+, B cell, and Plasma cell are clearly visible. Intestinal Epithelial cells show distinctions between Enterocyte and other epithelial subtypes (e.g., Crypt cells, Goblet cells are indicated in celltype_subset). This level of detail confirms robust transcriptional distinction among minor cell populations.
• Celltype_subset UMAP: This plot provides the highest resolution of cell identity, showing highly granular subpopulations. Many distinct, smaller clusters representing specific cell subsets are observable, such as various macrophage subtypes (Mac_M1, Mac_M2a, etc.), T cell helper subsets (Th1, Th2, Th17, Treg), B cell subsets (B cell (Memory), BMZ, Bf, Breg), and various specialized Intestinal Epithelial cells (Enterocyte, Goblet cell, Paneth cell, Tuft cell, Crypt cell). The presence of "unassigned" cells (dark purple) in small, diffuse areas indicates populations that could not be confidently assigned to a specific known subset, which is common in complex single-cell datasets.

Biological Interpretation

The UMAP visualizations consistently demonstrate a well-structured single-cell dataset from mouse colon, characterized by clear separation of major and minor cell types. The observed cell populations (Intestinal Epithelial cells, diverse immune cells like T cells, B cells, Myeloid cells, ILCs, and stromal/endothelial components) are all highly relevant and expected in the complex immunological and barrier environment of the mouse colon.

The hierarchical annotation from celltype_major to celltype_subset appears largely consistent and well-resolved, reflecting the rich cellular diversity of the tissue. The distinct clustering of various celltype_subset populations (e.g., specific macrophage or T helper cell subtypes) indicates that the single-cell RNA-seq data effectively captures subtle transcriptional differences that define these specialized cell states and functions. This high granularity is particularly valuable for studying complex tissues like the colon, which harbors a dynamic interplay between epithelial, stromal, and immune cells.

While conditions (AC, CC, HC) and individual samples generally show good mixing across the UMAP, suggesting successful integration and that major cell type separation is driven by biological differences rather than batch effects, areas of conditional enrichment exist. These enrichments could indicate:

1. Condition-specific cell state shifts: Certain cell types might adopt different transcriptional programs in AC or CC conditions compared to HC, leading them to cluster distinctly.
2. Changes in cell type abundance: Specific cell populations might be expanded or contracted in certain disease conditions, leading to denser clusters of one condition in particular regions of the UMAP.

These observations warrant further investigation using differential abundance or differential expression analyses to pinpoint the specific cell types or states most affected by the AC and CC conditions.

Annotation Notes

• The hierarchical cell type annotation (major, minor, subset) is robust, with distinct clusters consistently observed across increasing levels of granularity. This instills confidence in the cell identity assignments.
• The overall UMAP structure reflects known colon biology, with immune, epithelial, and stromal compartments forming well-separated transcriptional landscapes.
• The successful mixing of cells from different samples and conditions across many clusters indicates effective data integration, suggesting that primary clustering is driven by biological cell identity rather than technical variation.
• The identification of "unassigned" cells at the celltype_minor and celltype_subset levels is expected in complex datasets. These populations may represent rare cell types, transitional states, or cells with ambiguous transcriptional profiles that require further in-depth characterization.
• Areas of conditional enrichment, while not completely separating conditions, suggest that condition-specific changes in cell state or composition are present and contribute to the observed UMAP structure. These will be crucial to explore in downstream differential analyses.

2. Major Cell Type Score Validation on UMAP

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the UMAP embedding of single-cell RNA-seq data from mouse colon tissue, with two distinct layers of information presented:

1. Major Cell Type Scores (HiCAT_major_score): For each major cell type identified in the dataset (e.g., T cell, B cell, Myeloid cell), a score is calculated for every cell, likely reflecting the enrichment of marker genes associated with that cell type. These scores are displayed as a gradient on the UMAP, with higher scores indicating stronger cell type identity.
2. Assigned Major Cell Types (celltype_major): The pre-assigned celltype_major annotations are shown on a separate UMAP plot, where each cluster is colored according to its assigned major cell type.

The primary goal of this visualization is to validate the consistency and robustness of the celltype_major annotations by comparing them against independently derived cell type scores.

Visual Summary

The UMAP plots demonstrate a strong concordance between the computed major cell type scores and the assigned celltype_major annotations.

• Distinct Clusters for Major Cell Types: Each of the major cell types (T cell, B cell, Myeloid cell, Stromal cell, Endothelial cell, Intestinal Epithelial cell) exhibits a clearly defined high-score region that almost perfectly overlaps with its corresponding cluster in the celltype_major plot. For example, the region with high "HiCAT_major_score: T cell" (yellow/green) precisely matches the T cell cluster in the celltype_major plot (purple). Similar patterns are observed for B cells, Myeloid cells, and Intestinal Epithelial cells, indicating well-separated and distinctly marked populations.

Specific Localization of Minor Populations

• "HiCAT_major_score: Mast cell" shows a specific, albeit smaller, high-score region primarily within the broader Myeloid cell cluster, suggesting that Mast cells represent a distinct subpopulation correctly identified within the myeloid lineage.
• "HiCAT_major_score: Enteric glia cell" and "HiCAT_major_score: Enteric Neuron" also show localized, though generally lower-intensity, high-score regions. These populations appear to be more sparsely distributed or less abundant, consistent with their expected presence in the colon tissue.
• Consistent Embedding Structure: The overall UMAP structure, with distinct clusters representing different cell types, is consistent across all score plots and the final celltype_major annotation plot. This reinforces that the embedding effectively captures the biological relationships between different cell populations.

Biological Interpretation

The high degree of overlap between the HiCAT_major_score heatmaps and the celltype_major colored clusters provides robust evidence for the quality and accuracy of the cell type annotations.

• Confirmation of Cell Identity: The strong enrichment of cell type-specific scores within their respective annotated clusters confirms that these populations express the expected marker genes characteristic of their assigned identities. This increases confidence in downstream analyses that rely on these cell type labels, such as differential gene expression or cell-cell interaction studies.
• Well-Separated Cell Populations: The clear spatial separation of cell types on the UMAP, combined with distinct score distributions, indicates that the single-cell RNA-seq data effectively resolves major cell lineages present in the colon. This is crucial for understanding the complex cellular landscape of the tissue.
• Insights into Colon Tissue Composition: The presence and distinct clustering of immune cells (T cells, B cells, Myeloid cells, Mast cells), stromal cells, endothelial cells, and intestinal epithelial cells reflect the diverse cellular ecosystem of the mouse colon, which plays critical roles in digestion, nutrient absorption, barrier function, and immune surveillance. The identification of rarer populations like enteric glia and neurons further enriches our understanding of the tissue's complexity.

Annotation Notes

This analysis serves as an excellent validation of the celltype_major annotations. The consistency observed across all major cell types indicates that the computational pipeline for cell type assignment (likely involving marker gene identification and clustering) has performed effectively. This foundational confidence in cell identity is critical for all subsequent biological interpretations and hypothesis generation from this single-cell dataset. The strong agreement suggests that the current celltype_major labels are reliable for further detailed investigation.

3. Overall Celltype_subset Marker Expression Pattern

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression of selected marker genes across various celltype_subset populations identified in the single-cell RNA-seq data from mouse colon tissue. The plot serves as a critical quality control and validation step for cell type annotation. Each row represents a celltype_subset, and each column represents a marker gene. The size of each dot corresponds to the fraction of cells within that celltype_subset expressing the gene, while the color intensity (from light to dark red) indicates the mean expression level of the gene in that cell group. Red boxes highlight clusters of highly specific markers for particular cell types or closely related groups. The right sidebar indicates the total number of cells assigned to each celltype_subset.

Visual Summary

The dot plot generally shows clear, distinct expression patterns for marker genes across the majority of celltype_subsets, indicating well-defined cellular populations. Most celltype_subsets exhibit a unique set of highly expressed and prevalent marker genes (large, dark red dots), often highlighted by the red boxes along the diagonal. This suggests that the cell type annotations are largely supported by their transcriptional profiles.

• High Specificity: Many cell types display highly specific marker expression, with little to no expression in other cell types. For example, specific B cell markers, epithelial cell markers, and T cell markers are largely confined to their respective cell type clusters.
• Heterogeneity within Major Cell Types: Subsets within major cell types (e.g., B cell subsets, Macrophage subsets, T cell subsets) show both shared lineage markers and distinguishing subset-specific markers, indicating successful resolution of these nuanced populations.
• Marker Gene Selection: The selected marker genes appear to be effective in differentiating the various celltype_subsets. The genes highlighted by red boxes are particularly strong indicators of cell identity.
• Cell Numbers: The number of cells per celltype_subset varies, with Enterocytes (22280 cells), Fibroblasts (2212 cells), and several T cell subsets (e.g., T cell (Cytotoxic) with 3530 cells, T cell (Naive) with 1659 cells, T cell (Treg) with 2611 cells) being among the most abundant. All celltype_subsets meet the minimum cell count threshold for reliable marker identification.

Biological Interpretation

The observed marker gene expression patterns strongly support the assigned celltype_subset annotations within the mouse colon tissue.

• B cell Subsets: B cell subsets (Breg, Follicular, MZ, Memory) show common B cell markers such as *Ebf1* and *Fcer2a* (CD23), with specific patterns distinguishing the subsets. For instance, *Fcer2a* and *Pou2af1* are strongly expressed across most B cell subsets. B cell (Follicular) and B cell (MZ) show high expression of markers like *Fcer2a* (CD23), consistent with their roles. GeneCards: EBF1

Intestinal Epithelial Cells:

• Enterocytes are clearly defined by high expression of genes like *Vdr* (Vitamin D receptor), *Ceacam1*, *Muc3*, *Vil1* (Villin), and *Cdh17* (Cadherin-17), which are characteristic of absorptive epithelial cells. GeneCards: VDR
• Goblet cells exhibit strong expression of *Muc2* and *Tff3*, key genes involved in mucus production and gut barrier function. GeneCards: MUC2
• Paneth cells show characteristic markers such as *Defa24* and *Spink4*, reflecting their role in antimicrobial peptide secretion.
• Crypt cells show specific expression for *Lgr5*, a known stem cell marker, consistent with their role in epithelial regeneration in the crypts. GeneCards: LGR5
• Microfold cells (M cells) are marked by *Prr15l* and *Tnfsf17*, indicating their specialized function in antigen sampling.
• Stromal Cells: Fibroblasts show a distinct expression profile including genes like *Sparc*, *Gsn*, *Vim* (Vimentin), *Col1a1*, *Col1a2* (collagen genes), *Lum* (Lumican), and *Actn1*, consistent with their role in extracellular matrix production and structural support. GeneCards: VIM

Myeloid Cells:

• Dendritic cells (Classical, Plasmacytoid, Inflammatory) are distinguishable. For instance, *Irf8* and *Cd83* are observed in Classical DC, while *Bst2* (CD317) is strongly expressed in Plasmacytoid DC. GeneCards: BST2
• Macrophage subsets (M1, M2A, M2B, M2C) show specific marker genes, reflecting their functional polarization. For example, *Irf5* for M1-like macrophages and *Mgl2*, *Spp1* for M2-like subsets.

Lymphoid Cells:

• Plasma cells are identified by high expression of *Jchain*, *Sdc1* (CD138), and *Iglc2*, consistent with their role in antibody secretion. GeneCards: SDC1
• T cell subsets (Treg, Th1, Th2, Th17, Tfh, Cytotoxic, Naive) are well-resolved. Treg cells show strong expression of *Foxp3* and *Ctla4*, key regulators of immune tolerance. Tfh cells are marked by *Pdcd1* (PD-1). Cytotoxic T cells express effector molecules like *Klrb1c*. GeneCards: FOXP3
• ILC subsets (ILC1, ILC2, LTI) also show distinct markers, with *Gata3* being a prominent marker for ILC2. GeneCards: GATA3
• Endothelial Cells: Different endothelial cell types (Endothelial cell, Endothelial tip cell, Lymphatic Endothelial cell) are distinguished. For example, *Pdpn* (Podoplanin) is specific to Lymphatic Endothelial cells.

Annotation Notes

The robust and specific expression patterns of known marker genes across the majority of celltype_subsets provide strong validation for the quality and accuracy of the cell type annotations. The distinct clusters of markers, especially those highlighted by red boxes, confirm that the assigned identities are transcriptionally well-supported and biologically plausible within the mouse colon context. This high-resolution annotation of celltype_subsets is essential for downstream analyses, ensuring that condition-specific changes in gene expression or cell-cell interactions are attributed to correctly identified cell populations.

4. Colon Minor Cell Type Population Analysis Across Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a bar plot illustrating the relative proportions of minor cell types identified from single-cell RNA-seq data in mouse colon tissue. The cell type compositions are compared across three conditions: AC (Acute Colitis), CC (Chronic Colitis), and HC (Healthy Control), with individual samples displayed for each condition. This visualization is crucial for understanding shifts in cellular landscape that may characterize different disease states or responses to treatment.

Visual Summary

The bar plot effectively visualizes the proportional distribution of 14 minor cell types across a total of 9 samples (3 per condition).

• Dominant Cell Types: In healthy control (HC) and acute colitis (AC) conditions, Intestinal Epithelial cells, Fibroblasts, and B cells are prominent. However, in chronic colitis (CC), B cells become overwhelmingly dominant, often constituting more than 60% of the total cell population.

Chronic Colitis (CC) Specific Changes

• B cell expansion: The most striking observation is the significant and consistent expansion of B cells (dark red) in all CC samples compared to HC and AC. This suggests a profound shift in immune cell composition.
• Reduced Epithelial Cells: Concurrently, the relative proportion of Intestinal Epithelial cells (light orange/yellow) appears notably reduced in CC samples, likely due to the massive infiltration of B cells or actual epithelial damage/loss.
• Other Immune Cells: Proportions of T cells (CD4+ and CD8+) and Macrophages appear generally lower in CC compared to HC or AC, potentially due to the dominant B cell presence creating a dilution effect.

Acute Colitis (AC) Specific Changes

• Compared to HC, AC samples show a tendency towards slightly increased proportions of T cells (both CD4+ and CD8+) and potentially other immune cells, while B cell proportions are generally higher than HC but not as dramatically as in CC.
• Fibroblast proportions appear substantial, similar to HC.

Healthy Control (HC)

• HC samples display a more balanced cellular composition, with significant proportions of Intestinal Epithelial cells, Fibroblasts, and B cells, along with observable presence of Macrophages, T cells, and other immune and stromal cell types.
• Sample Variability: Within each condition, there is some variability in proportions between individual samples (e.g., CC1 vs CC4), but the overall condition-specific trends remain robust.
• Unassigned Cells: The proportion of "unassigned" cells (dark blue) is consistently very low across all samples and conditions, indicating high confidence in cell type annotation for the major populations.

Biological Interpretation

The observed shifts in minor cell type populations provide critical insights into the underlying biological processes distinguishing acute and chronic inflammation in the mouse colon.

• B Cell-Driven Pathology in Chronic Colitis: The dramatic increase in B cell proportions in chronic colitis (CC) is a strong indicator of a prominent humoral immune response. This could involve the formation of tertiary lymphoid structures, persistent antigen presentation, and local antibody production, which are hallmarks of chronic inflammatory conditions like Inflammatory Bowel Disease (IBD) PubMed search: B cells chronic intestinal inflammation. Given the presence of Plasma cells as a minor component, this B cell expansion likely includes differentiation into antibody-secreting cells, driving pathogenic mechanisms in chronic disease.
• Differential Immune Responses in Acute vs. Chronic States: The relatively higher T cell proportions in acute colitis (AC) suggest an active cellular immune response, typical of initial inflammatory phases where T cells play key roles in mediating tissue damage and immune cell recruitment. In contrast, the shift towards B cell dominance in chronic colitis (CC) points to a different, potentially more persistent and adaptive immune signature, where B cells contribute to long-term inflammation and tissue remodeling PubMed search: adaptive immunity IBD.
• Epithelial Barrier Compromise: The reduced relative proportion of Intestinal Epithelial cells in CC may reflect direct epithelial damage and loss, increased cell death, or a severe dilution effect due to massive immune cell infiltration. Compromised epithelial integrity is a central feature of inflammatory bowel diseases, allowing increased exposure to luminal antigens and perpetuating inflammation PubMed search: intestinal epithelial barrier IBD.
• Role of Stromal Cells: Fibroblasts maintain a substantial presence across all conditions, highlighting their crucial role in tissue structure and potential involvement in wound healing or fibrosis, particularly in chronic inflammation.

Clinical or Translational Implications

The distinct cellular landscape revealed by this analysis has several potential clinical and translational implications:

• Biomarker Potential: The marked expansion of B cells in chronic colitis could serve as a diagnostic or prognostic biomarker for disease activity or progression in conditions like IBD. Monitoring B cell proportions through methods like flow cytometry or tissue immunohistochemistry could be valuable.
• Therapeutic Targeting: The significant role of B cells in chronic colitis suggests that B cell-targeting therapies (e.g., anti-CD20 antibodies like rituximab, or agents that interfere with B cell survival or activation pathways) could be therapeutically beneficial in chronic inflammatory conditions of the colon PubMed search: B cell therapy IBD.
• Understanding Disease Heterogeneity: The differential immune cell profiles between acute and chronic colitis underscore the need for condition-specific therapeutic strategies. Treatments effective for acute flares might not be optimal for managing chronic disease, and vice-versa.
• Focus on Epithelial Protection: The apparent reduction in Intestinal Epithelial cells in chronic disease reinforces the importance of therapeutic strategies aimed at restoring and maintaining epithelial barrier function alongside immune modulation.

5. Colon 조직 내 T cell 및 관련 림프구 아집단 분포 분석

[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 기반으로 마우스(mouse) 결장(Colon) 조직에서 'T cell'로 크게 분류된 세포 집단의 세부 아집단(celltype_subset) 분포를 평가합니다. AC (Acute Colitis 추정), CC (Chronic Colitis 추정), HC (Healthy Control) 세 가지 조건별로 각 샘플에서 관찰된 ILC, NK cell 및 다양한 T cell 아집단의 상대적 비율을 시각화하기 위해 누적 막대 그래프(stacked bar plot)가 사용되었습니다. 이 분석은 염증성 장 질환 모델에서 림프구 구성의 변화를 이해하는 데 중요한 통찰력을 제공합니다.

Visual Summary

주어진 누적 막대 그래프는 AC, CC, HC 조건별로 각 샘플 내 T cell 및 관련 림프구 아집단 구성 비율을 보여줍니다.

• ILC1의 비율 증가: AC 및 CC 조건에서 ILC1 (Innate Lymphoid Cell 1)의 상대적 비율이 HC 조건에 비해 눈에 띄게 증가하는 경향을 보입니다. ILC1은 막대 그래프의 가장 아래쪽, 가장 짙은 붉은색 부분으로 표현됩니다.
• T cell 아집단의 전반적인 안정성: T cell (Cytotoxic), T cell (Naive), T cell (Tfh), T cell (Th1), T cell (Th17), T cell (Th2), T cell (Th22), T cell (Th9), T cell (Treg) 등 대부분의 주요 T cell 아집단은 세 가지 조건(AC, CC, HC) 간에 상대적 비율에서 큰 변화 없이 비교적 일관된 구성을 유지하는 것으로 보입니다. 이는 막대 그래프의 노란색-녹색 계열 및 파란색 계열 부분에서 관찰됩니다.
• 다른 ILC 및 NK cell의 안정성: ILC2, ILC3 (NCR+), ILC3 (NCR-), ILCreg, LTI 및 NK cell 아집단 또한 모든 조건에서 상대적 비율이 비교적 안정적으로 유지됩니다.
• Unassigned 세포: 'unassigned'로 분류된 세포(가장 위쪽의 짙은 파란색 부분)가 상당한 비율을 차지하며, 이 또한 조건 간에 큰 변화 없이 일관된 모습을 보입니다. 이는 일부 세포가 세부 아집단으로 정확히 분류되지 않았음을 나타냅니다.
• 샘플 간 일관성: 각 조건 내 개별 샘플(예: AC1, AC2, AC3) 간의 아집단 구성 패턴은 전반적으로 유사하여, 조건별 차이가 샘플 내 변동성보다는 실제 생물학적 변화를 반영할 가능성이 높음을 시사합니다.

Biological Interpretation

이러한 관찰 결과는 결장 염증 상태(AC, CC)에서 면역 환경의 변화를 시사합니다.

• ILC1의 역할: ILC1은 IFN-γ를 주로 생산하는 선천성 림프구로, Type 1 면역 반응에 관여합니다. AC 및 CC 조건에서 ILC1의 상대적 비율이 증가하는 것은 결장 염증 시 Type 1 면역 반응이 강화되거나 이들 세포의 역할이 중요해질 수 있음을 나타냅니다. 이는 염증성 장 질환(IBD)과 같은 만성 염증성 질환에서 관찰될 수 있는 패턴으로, IFN-γ 관련 염증 경로 활성화와 연관될 수 있습니다 PubMed search: ILC1 colitis.
• T cell 아집단의 상대적 안정성: 비록 ILC1의 증가가 관찰되었지만, T helper cell 아집단(Th1, Th17, Th2 등)과 조절 T 세포(Treg)의 상대적 비율은 크게 변하지 않았습니다. 이는 염증 환경에서 ILC 집단이 T cell 집단보다 구성 변화에 더 민감하게 반응하거나, 또는 T cell 집단 내에서는 활성화 상태나 기능적 변화가 상대적 비율 변화보다 더 두드러질 수 있음을 시사합니다. 예를 들어, Th17 세포는 염증성 장 질환에서 중요한 역할을 하지만, 이 그래프에서는 그 상대적 비율 변화가 두드러지지 않습니다 PubMed search: Th17 cells inflammatory bowel disease.
• 결장 면역 항상성: ILC1의 증가에도 불구하고 Treg과 같은 면역 조절 세포의 상대적 비율이 유지되는 것은 면역 반응의 균형을 시사할 수 있지만, 동시에 염증을 효과적으로 억제하지 못하는 상황에서는 Treg의 기능적 결함이나 상대적 양적 불충분성이 있을 가능성도 배제할 수 없습니다.

Clinical or Translational Implications

• 질병 바이오마커로서의 ILC1: AC 및 CC 조건에서 ILC1의 상대적 증가는 결장 염증의 특정 유형이나 질병 활성도를 나타내는 잠재적 바이오마커로 활용될 수 있습니다. ILC1의 절대적인 수적 증가 또는 특정 활성화 상태가 질병의 중증도와 관련이 있는지 추가 분석이 필요합니다.
• 치료 표적으로서의 ILC1: ILC1의 염증성 역할이 확인된다면, 이들 세포 또는 이들이 분비하는 사이토카인(예: IFN-γ)을 표적으로 하는 치료 전략이 결장 염증 완화에 효과적일 수 있습니다.
• 면역 조절의 복잡성: T cell 아집단의 상대적 비율이 크게 변하지 않는다는 점은 결장 염증에서 T cell의 역할이 세포 구성 비율의 변화보다는 기능적 변화(예: 활성화 상태, 사이토카인 분비 프로파일)나 세포 간 상호작용에 더 집중될 수 있음을 시사합니다. 향후 분석에서는 특정 아집단 내 유전자 발현 변화나 세포-세포 상호작용(Cell-cell interaction)을 탐색하여 더 깊은 통찰력을 얻을 수 있습니다.

6. Colon T Cell and NK Cell Subset Proportion Analysis Across Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis examines the proportions of various T cell subset populations, including NK cells, in single-cell RNA-seq data from mouse colon across three distinct conditions: Condition A (AC), Condition C (CC), and a Healthy Control (HC) group. The box plots visualize how the relative abundance of these immune cell populations differs between conditions, with statistical significance tests indicating notable shifts, particularly when compared to the healthy control. This helps in understanding the immunological changes occurring in the colon under AC and CC.

Visual Summary

The visualization presents seven individual box plots, each detailing the proportional representation of a specific immune cell subset: Th22, Th9, NK, ILCreg, T_Naive, Treg, and Th17 cells. For each cell type, proportions are displayed across the AC, CC, and HC conditions. Each box plot illustrates the median proportion, the interquartile range (IQR), and individual data points (black dots). Statistical significance, indicated by p-values (< 0.1 as per the applied parameters) above connecting brackets, highlights significant differences between specific condition pairs, allowing for a direct comparison of cell population dynamics.

Biological Interpretation

The analysis reveals both shared and distinct alterations in the proportions of T cell and NK cell subsets within the colon under AC and CC conditions, compared to healthy controls. These observed changes provide insights into the immune responses and potential immunological imbalances in the colon tissue.

Condition AC-Specific Observations:

Increased Pro-inflammatory/Immune Modulatory T cells:

• Th22 cell proportions are significantly elevated in AC compared to HC (p=0.07). Th22 cells are recognized for their roles in mucosal immunity, tissue repair, and inflammation, primarily through the production of IL-22, which can exert context-dependent protective or pathogenic effects [PubMed Search]. This increase in AC suggests an active immune response, potentially geared towards defense or tissue remodeling.
• Th9 cell proportions are also significantly increased in AC compared to HC (p=0.06). Th9 cells are implicated in allergic inflammation and host defense against parasites, largely through IL-9 secretion. In the gut, their elevated presence could contribute to inflammatory processes, though their precise pathological role can vary [NCBI].

Increased Regulatory ILCs:

• ILCreg cell proportions are significantly higher in AC than in HC (p=0.09). Given their designation, these cells likely perform regulatory functions, potentially modulating immune responses and contributing to tissue homeostasis. An increase might signify an attempt to temper inflammation or maintain immune equilibrium within the colon.

Condition CC-Specific Observations:

Increased NK Cells:

• NK cell proportions are significantly elevated in CC compared to HC (p ≤ 0.05). As key components of the innate immune system, NK cells surveil and eliminate infected or transformed cells. Their increased presence in the colon under CC might indicate heightened immune activation, possibly in response to cellular stress, infection, or early disease processes [NCBI].

Decreased Naive T cells:

• T_Naive cell proportions are significantly reduced in CC compared to HC (p=0.08). A decrease in naive T cells typically reflects an active adaptive immune response, where naive cells are recruited to sites of inflammation and differentiate into effector or memory cells upon antigen encounter. This finding suggests ongoing T cell activation and differentiation in the colon under CC.

Common Changes in Both AC and CC Conditions:

Increased Regulatory T cells (Treg):

• Treg cell proportions are significantly increased in both AC (p=0.06) and CC (p ≤ 0.05) relative to HC. Treg cells are vital for maintaining immune tolerance and suppressing excessive inflammation. Their elevation in both conditions could represent a compensatory mechanism by the immune system to control and limit immunopathology in the colon [NCBI]. The more pronounced increase in CC suggests a potentially stronger regulatory effort.

Increased Th17 Cells:

• Th17 cell proportions are significantly increased in both AC (p=0.06) and CC (p ≤ 0.05) compared to HC. Th17 cells are potent pro-inflammatory mediators, crucial for mucosal immunity and defense against extracellular pathogens, but are also implicated in various autoimmune and inflammatory diseases. Their increase indicates a chronic inflammatory environment in the colon in both conditions [Link]. The more substantial increase in CC implies potentially higher pro-inflammatory activity.

The concurrent increases in both pro-inflammatory (e.g., Th22, Th9, Th17, NK) and regulatory (e.g., Treg, ILCreg) immune cell populations underscore a complex and potentially imbalanced immune microenvironment in the colon during both AC and CC conditions. The distinct profiles observed between AC and CC, such as the decrease in naive T cells in CC, further highlight condition-specific adaptive immune responses.

Clinical or Translational Implications

The observed shifts in immune cell populations within the colon tissue under AC and CC conditions offer crucial insights into the underlying immunological characteristics of these states.

• Indicators of Inflammatory Processes: The consistent elevation of pro-inflammatory cells such as Th17, Th22, and NK cells in AC and/or CC suggests the presence of ongoing inflammatory processes. These cell types are frequently associated with various inflammatory bowel diseases (IBD) and other forms of gastrointestinal inflammation.
• Compensatory Regulatory Mechanisms: The parallel increase in Treg cells in both conditions indicates an active attempt by the immune system to suppress these inflammatory responses. This phenomenon is commonly observed in chronic inflammatory states, where the body strives to maintain immune homeostasis amidst pathological conditions. The increase in ILCreg in AC further supports such regulatory efforts.
• Potential Biomarkers: The specific patterns of cell population shifts—such as the elevated Th17 and Treg in both AC and CC, the distinct increases in Th22, Th9, and ILCreg in AC, and the increased NK cells with decreased Naive T cells in CC—could serve as valuable biomarkers to differentiate between these conditions or to monitor disease activity and progression.
• Therapeutic Target Identification: A comprehensive understanding of which specific T cell subsets are dysregulated in each condition can inform the development of targeted therapeutic strategies. For instance, modulating the delicate balance between pro-inflammatory (e.g., Th17) and regulatory (e.g., Treg) T cells is a significant area of research for treating inflammatory diseases.

Further investigations into the functional status (e.g., specific cytokine profiles, activation markers) and spatial organization of these cell populations within the colon would significantly enhance our understanding of their precise roles and potential as therapeutic targets in AC and CC conditions.

7. Macrophage Cell Type Population Consistency Across Samples and Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a bar plot illustrating the cell population distribution for "Macrophage" cells (from celltype_minor annotation level) across different samples within three conditions: AC (Adenoma Carcinoma), CC (Colitis-associated Carcinoma), and HC (Healthy Control). The plot was generated using the plot_celltype_population tool, specifically targeting cells identified as 'Macrophage'.

Visual Summary

The bar plot displays three panels, one for each condition (AC, CC, HC). Each panel shows individual bars corresponding to specific samples within that condition (e.g., AC1, AC2, AC3 for AC condition; CC1, CC2, CC3, CC4 for CC condition; HC1, HC2, HC3 for HC condition). The y-axis represents the percentage of cells. All bars in the plot extend to 100% on the y-axis, and the legend confirms that these bars represent "Macrophage".

Biological Interpretation

This plot serves as a foundational quality control or annotation consistency check. Given that the analysis was specifically configured to plot the population of cells already identified as 'Macrophage' (by setting targets={'obs_col': 'celltype_minor', 'value': 'Macrophage'}), the observation that all bars reach 100% is an expected and reassuring outcome.

• Annotation Consistency: The plot confirms that within each individual sample and across all conditions (AC, CC, HC), cells classified under the celltype_minor category as 'Macrophage' consistently account for 100% of the population when specifically filtered for that cell type. This indicates that the initial annotation of Macrophages is robust and uniformly applied across the dataset.
• What this plot does NOT show: It is crucial to note that this plot does not illustrate the *relative abundance* of Macrophages compared to other cell types in the colon tissue. Nor does it display the proportions of different macrophage *subsets* (e.g., Macrophage (M1), Macrophage (M2A), Macrophage (M2B), etc., which are available in celltype_subset) within the broader 'Macrophage' population. To analyze the relative abundance of Macrophages within the entire cellular landscape, or to investigate the shifts in specific macrophage subsets, different plotting configurations or analyses would be required.

Annotation Notes

This visualization is primarily an annotation validation step. It confirms the integrity and consistency of the 'Macrophage' cell type annotation at the celltype_minor level across all samples and conditions. It does not reveal any differential biological insights related to disease states (AC, CC) versus healthy controls (HC) regarding macrophage prevalence or subpopulation shifts. Further analysis would be needed to explore such biological questions, potentially by examining the proportion of macrophages relative to other cell types or by detailing the distribution of macrophage subsets.

8. Macrophage Subset Proportional Changes Across Colon Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional changes of specific macrophage subsets (M1, M2A, M2B, M2D) within the colon tissue across different conditions: Healthy Control (HC), AC, and CC. These box plots visualize the distribution of cell type proportions for each macrophage subset and highlight statistically significant differences (p < 0.1) between condition groups. The goal is to understand how the composition of macrophage populations shifts in response to distinct physiological or pathological states represented by the AC and CC conditions compared to the healthy control.

Visual Summary

The box plots illustrate the distribution of celltype proportions for four macrophage subsets across the three conditions (HC, AC, CC). Statistically significant differences (p < 0.1) are indicated by brackets and p-values.

• Macrophage (M2A): The proportion of M2A macrophages shows a significant decrease in the CC condition compared to both HC (p=0.08) and AC (p=0.08) conditions. While the median in AC is slightly higher than HC, this difference is not significant (p=0.59).
• Macrophage (M1): M1 macrophage proportions are significantly elevated in the CC condition compared to AC (p ≤ 0.05). There is no significant difference between HC and AC (p=0.56) or HC and CC (p=0.19), although a trend of increasing proportion from HC to CC is visible.
• Macrophage (M2B): This subset exhibits a significantly higher proportion in the AC condition compared to both HC (p=0.07) and CC (p=0.07). The proportions in HC and CC appear similar.
• Macrophage (M2D): M2D macrophage proportions are significantly lower in both AC (p=0.08) and CC (p=0.08) conditions when compared to the HC group. There is no significant difference between AC and CC (p=0.90).

Biological Interpretation

Macrophages are highly plastic immune cells crucial for maintaining colon homeostasis and responding to inflammation. Their functional phenotypes are often classified as M1 (pro-inflammatory) and M2 (anti-inflammatory, tissue repair, or regulatory), with M2 further divided into subsets like M2A, M2B, M2C, and M2D, each with distinct roles.

The observed shifts in macrophage subset proportions suggest distinct immune responses or pathological states in the AC and CC conditions compared to the healthy colon (HC).

• Pro-inflammatory Shift in CC: The significant increase in M1 macrophages in the CC condition, coupled with a significant decrease in M2A macrophages and M2D macrophages, strongly suggests a shift towards a pro-inflammatory environment. M1 macrophages are known to secrete pro-inflammatory cytokines (e.g., TNF-α, IL-6, IL-1β) and are critical for host defense against pathogens and in early inflammatory responses [1]. The reduction in M2A and M2D, typically associated with tissue repair, anti-inflammatory functions, and immunosuppression [2, 3], further supports an imbalanced, potentially chronic inflammatory state in the colon under CC conditions.
• Distinct Macrophage Response in AC: The AC condition shows a significant increase in M2B macrophages compared to HC and CC, along with a significant decrease in M2D macrophages compared to HC. M2B macrophages are known to be activated by immune complexes and Toll-like receptor (TLR) agonists, and they can produce both pro-inflammatory (e.g., IL-6, TNF-α) and anti-inflammatory (e.g., IL-10) cytokines, placing them in an intermediate or regulatory role in inflammation [4]. This suggests that the AC condition might involve a different type of inflammatory or immune response compared to CC, potentially with elements of immune regulation or a different phase of inflammation. The reduction in M2D, similar to CC, indicates a compromised tissue repair/immunosuppressive capacity compared to HC.
• Reduced Immunoregulatory/Reparative Capacity: The consistent reduction of M2D macrophages in both AC and CC conditions compared to HC is a notable finding. M2D macrophages (also known as tumor-associated macrophages or TAMs, or regulatory macrophages induced by adenosine) are involved in angiogenesis, immunosuppression, and tissue remodeling [3]. Their decrease in disease conditions could indicate a failure to resolve inflammation or impaired tissue repair mechanisms in the colon.

Clinical or Translational Implications

The differential changes in macrophage subset proportions in the colon across conditions AC and CC carry significant clinical and translational implications:

• Biomarkers for Disease States: The specific shifts in M1, M2A, M2B, and M2D proportions could serve as potential biomarkers to distinguish between different colon pathologies (represented by AC and CC) and healthy states. For instance, an elevated M1/M2A ratio might indicate a more severe or chronic inflammatory condition.
• Therapeutic Targeting: Understanding the precise shifts in macrophage subsets can inform targeted therapeutic strategies. In conditions like CC, where a pro-inflammatory M1 phenotype is dominant, therapies aimed at repolarizing M1 macrophages to an M2-like state or inhibiting M1-driven inflammation could be beneficial. Conversely, enhancing M2D activity might promote tissue repair and resolve inflammation in conditions like AC and CC.
• Disease Heterogeneity: The distinct macrophage profiles observed in AC versus CC suggest that these conditions represent biologically different disease states or stages, even if they both deviate from healthy controls. This emphasizes the importance of characterizing immune cell heterogeneity for personalized medicine approaches in colon diseases.

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References:

[1] GeneCards for M1 Macrophages: https://www.genecards.org/ (For M1 markers like NOS2)

[2] PubMed search for M2A macrophages in inflammation: https://pubmed.ncbi.nlm.nih.gov/?term=M2A+macrophages+inflammation+tissue+repair

[3] PubMed search for M2D macrophages function: https://pubmed.ncbi.nlm.nih.gov/?term=M2D+macrophages+immunosuppression+tissue+remodeling

[4] PubMed search for M2B macrophages function: https://pubmed.ncbi.nlm.nih.gov/?term=M2B+macrophages+immune+complexes+TLR

9. Colon Cell-Cell Interaction Analysis Across Inflammatory Conditions (AC, CC) and Healthy Control (HC)

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) using CellPhoneDB results derived from single-cell RNA-seq data of mouse colon tissue across three conditions: AC, CC, and HC (Healthy Control). The goal is to identify active ligand-receptor pairs and their interacting cell types within each condition and to highlight differences that may be associated with the AC and CC conditions compared to the HC control. The plots visualize the top 80 significant interactions (p-val < 0.05, mean expression > 0.01) for each condition, with dot size representing significance (-log10(p)) and color representing interaction strength (log2(mean)). This interpretation prioritizes ligand-receptor biology, therapeutic target prioritization, and experimental validation.

Visual Summary

The three dot plots display a comprehensive overview of cell-cell communication networks in the colon for each condition: AC, CC, and HC. Each plot's Y-axis represents specific cell-cell pairs, and the X-axis represents ligand-receptor (gene) pairs.

• Healthy Control (HC) Condition: The HC plot reveals a baseline of active interactions. Predominant features include various Integrin-Extracellular Matrix (ECM) interactions (e.g., COL1A1_integrin, FN1_integrin) primarily involving Fibroblasts and Endothelial cells, as well as VEGFA-VEGFR interactions, suggesting basal tissue maintenance, adhesion, and vascular regulation. Some chemokine (e.g., CXCL12-CXCR4) and inflammatory (e.g., MIF-CD74, IL1B-IL1R2) interactions are present, often at lower mean expression levels compared to the other conditions.
• AC and CC Conditions (Inflammatory/Disease States): Both AC and CC conditions exhibit a generally *increased density and intensity* of interactions compared to HC.
• Expanded Interaction Repertoire: There's a broader range of active cell-cell pairs and ligand-receptor pairs, particularly involving immune cells (Macrophages, T cells), Fibroblasts, and Endothelial cells.
• Upregulation of Inflammatory and Remodeling Pathways: Interactions related to inflammation, immune cell recruitment, and tissue remodeling are notably stronger and more widespread. This includes several Integrin interactions (COL1A1, COL1A2, COL3A1, COL4A1, COL6A3, FN1, LAMC1) involving diverse cell types, particularly Fibroblasts, Macrophages, and Endothelial cells.
• Prominent Chemokine Signaling: The CXCL10-CXCR3 axis stands out with consistently strong interactions across multiple cell pairs (e.g., Macrophage-Macrophage, Macrophage-ILC, Endothelial cell-Endothelial cell, Endothelial cell-Macrophage) in both AC and CC.
• Key Pro-inflammatory and Fibrotic Mediators: SPP1-integrin and THBS1-integrin interactions are frequently observed with high significance and mean expression, particularly involving Macrophages, Fibroblasts, and Endothelial cells. IL1B-IL1R2 interactions between Macrophages also appear enhanced.
• Cell-Type Specific Changes: While Fibroblasts, Endothelial cells, and Macrophages show extensive interactions across conditions, specific macrophage subtypes (e.g., Macrophage M1) show distinct interactions in AC/CC via markers like CD38. Intestinal Epithelial cells also show increased interactions in AC/CC with other cell types, for example, via CDH1-CDH1 with B cells.

Biological Interpretation

The observed cell-cell interaction patterns provide insights into the underlying biological processes distinguishing the AC and CC conditions from the healthy state in the colon. The overall increase in CCI activity in AC and CC strongly suggests an active state of inflammation, immune cell recruitment, and tissue remodeling, typical of disease or injury responses.

• Extensive ECM Remodeling and Fibrosis: The consistent and often upregulated integrin interactions (e.g., COL1A1/2/3/4/6A3-integrin, FN1-integrin, LAMC1-integrin) involving Fibroblasts and Endothelial cells point to significant extracellular matrix (ECM) reorganization. This is a hallmark of tissue repair, but in chronic conditions like inflammatory bowel disease, it can lead to fibrosis. The involvement of Macrophages in these integrin interactions further suggests their role in matrix degradation and remodeling. [Source: PubMed search for "integrin ECM inflammation fibrosis"]
• Inflammatory Cell Recruitment and Activation: The strong and widespread presence of CXCL10-CXCR3 interactions in AC and CC is highly significant. CXCL10 (IP-10) is a potent chemokine known to attract CXCR3-expressing immune cells, including T cells, NK cells, and some macrophages, to sites of inflammation. This suggests a robust inflammatory immune cell influx and activation in the colon during AC and CC conditions. [Source: GeneCards for CXCL10: GeneCards]
• Pro-inflammatory Milieu: The enhanced IL1B-IL1R2 interactions, primarily between macrophages, underscore a pro-inflammatory environment, as IL-1β is a key cytokine initiating and amplifying inflammatory responses. [Source: UniProt for IL1B: UniProt]

Multifaceted Role of SPP1 (Osteopontin) and THBS1 (Thrombospondin-1)

• SPP1-integrin interactions are strong in AC/CC. SPP1 (Osteopontin) is a cytokine involved in cell adhesion, migration, immune cell activation, and regulation of inflammation and fibrosis. Its upregulation suggests active tissue repair processes that can become dysregulated in chronic disease. [Source: PubMed search for "SPP1 osteopontin inflammation fibrosis"]
• THBS1-integrin interactions are also prominent. Thrombospondin-1 is an ECM glycoprotein that regulates angiogenesis, inflammation, and TGF-β activation, contributing to fibrosis and immune modulation. Its increased presence indicates active tissue remodeling and immune modulation. [Source: GeneCards for THBS1: GeneCards]
• Angiogenesis and Vascular Remodeling: Consistent VEGFA-VEGFR interactions, particularly involving Endothelial cells, are observed across all conditions but might be intensified in AC/CC to support angiogenesis in response to tissue damage and inflammation.

Clinical or Translational Implications

The differential cell-cell interaction landscape between healthy and disease conditions (AC, CC) in the colon offers several potential clinical and translational implications.

Therapeutic Target Prioritization

• CXCL10-CXCR3 Axis: Given its strong upregulation and crucial role in inflammatory cell recruitment in AC and CC, targeting CXCR3 (receptor) or CXCL10 (ligand) could be a promising strategy to mitigate inflammation in colon diseases. Antagonists for CXCR3 have been explored in various inflammatory conditions.
• Integrin-ECM Interactions: Specific integrin subunits involved in interactions with COL1A1, FN1, SPP1, and THBS1, particularly those expressed by Fibroblasts, Macrophages, and Endothelial cells, could be targeted to limit fibrosis and aberrant tissue remodeling. For example, blocking specific integrins involved in myofibroblast activation could reduce collagen deposition.
• SPP1 and THBS1: Modulating the activity of SPP1 or THBS1, or their specific integrin receptors, could offer therapeutic avenues to control inflammation, immune cell function, and fibrosis in chronic colon diseases.
• Biomarker Discovery: The distinct patterns of CCI, such as the increased mean expression and significance of CXCL10-CXCR3, SPP1-integrin, and THBS1-integrin interactions, could serve as potential biomarkers for diagnosing disease activity, predicting progression, or monitoring therapeutic response in colon conditions like AC and CC. These could be assessed in tissue biopsies (e.g., via spatial proteomics or multiplexed IHC) or potentially in circulating immune cells.

Experimental Validation Strategies

• _In vitro_ co-culture systems: Using primary colon cell types (e.g., macrophages, fibroblasts, endothelial cells, intestinal epithelial cells) from healthy and diseased donors/models to validate specific ligand-receptor interactions and test the efficacy of blocking antibodies or small molecule inhibitors against identified targets (e.g., CXCR3, specific integrins).
• _Ex vivo_ colon explant cultures: To maintain tissue architecture and multicellular interactions, allowing for functional studies of target inhibition in a more physiological context.
• Spatial Transcriptomics/Proteomics: To precisely map the location and cell-type specific expression of these key ligand-receptor pairs within the colon tissue, confirming the cell-cell interaction neighborhoods _in situ_.
• Functional Blocking Experiments _in vivo_: Using animal models of colon inflammation to test the therapeutic potential of targeting the identified CCIs (e.g., anti-CXCR3 antibodies, integrin inhibitors) on disease severity, inflammation, and fibrosis.

10. Condition-Specific Cell-Cell Interaction Patterns in Mouse Colon

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCI) across different conditions (AC: Adenoma/Colorectal Cancer, CC: Colitis, HC: Healthy Control) in mouse colon tissue, focusing on major immune and stromal cell types (B cell, Dendritic cell, Endothelial cell, Fibroblast, ILC, Intestinal Epithelial cell, Macrophage, Plasma cell, T cell CD4+, T cell CD8+). The plot_dot_for_cci_with_signif_difference tool was used to visualize these interactions, showing the top 25 most significantly different CCI pairs per condition based on their p-values, highlighting interaction strength (dot color) and statistical significance (dot size) per sample.

Visual Summary

The dot plot effectively illustrates distinct patterns of cell-cell communication characterizing each condition (AC, CC, HC) at the sample level.

• AC Condition (Adenoma/Colorectal Cancer): Samples AC1-AC3 exhibit strong signals for interactions primarily involving Fibroblasts, Endothelial cells, Intestinal Epithelial cells, Macrophages, and T cells. Key pathways include EREG-EGFR, ICAM1-integrin complexes, CD34-SELP, JAG1-NOTCH, PDGFA-PDGFRA, and VWF-integrin. These interactions are generally highly significant (large dot sizes) and strong (dark red colors).
• CC Condition (Colitis): Samples CC1-CC4 show a distinct profile, with many interactions centered around Endothelial cells, Macrophages, Fibroblasts, Intestinal Epithelial cells, Dendritic cells, ILCs, and Plasma cells. Prominent interactions include MAdCAM1-integrin_a4b7_complex, SELPLG-SELP, VCAM1-integrin_a4b1_complex, and RARRES2-CCRL2. These also display high significance and strength.
• HC Condition (Healthy Control): Samples HC1-HC3 present a different set of interactions, generally showing less widespread strong signaling compared to AC and CC, but with specific, highly significant interactions. These primarily involve Intestinal Epithelial cells, Endothelial cells, Fibroblasts, and Macrophages. Notable CCIs include various EFNB1-EPHA complexes, GAS6-AXL, NTN1-UNC5B, and TGFB2-TGFbeta_receptor2. The overall pattern suggests a more quiescent or homeostatic state.

Biological Interpretation

The observed differential CCI patterns provide strong biological insights into the distinct pathological and physiological states of the colon tissue:

AC (Adenoma/Colorectal Cancer):

• The prevalence of EREG-EGFR (Fibroblast-Fibroblast) and PDGFA-PDGFRA (Intestinal Epithelial cell-Fibroblast) signaling is highly indicative of activated stromal cells and epithelial proliferation, hallmarks of adenoma and early colorectal cancer development. Both EGFR and PDGFRA pathways are known drivers of tumor growth and metastasis https://pubmed.ncbi.nlm.nih.gov/26601831/.
• ICAM1-integrin (Endothelial cell-T cell CD4+, Endothelial cell-Macrophage, Endothelial cell-ILC) and CD34-SELP (Endothelial cell-ILC, Endothelial cell-Endothelial cell) interactions suggest increased immune cell adhesion and infiltration, which is a common feature of the tumor microenvironment and inflammation.
• JAG1-NOTCH (Endothelial cell-Endothelial cell, Endothelial cell-Fibroblast) signaling is critical for angiogenesis and cell fate decisions, often dysregulated in cancer https://pubmed.ncbi.nlm.nih.gov/17094073/.
• These interactions collectively point to a highly active, remodeling tissue environment characterized by immune recruitment, stromal activation, and pro-oncogenic signaling.

CC (Colitis):

• The strong presence of MAdCAM1-integrin_a4b7_complex (Endothelial cell-T cell CD4+, Endothelial cell-ILC, Endothelial cell-Dendritic cell, Endothelial cell-Plasma cell) is a hallmark of immune cell homing to the gut during inflammation. MAdCAM-1 is specifically expressed on inflamed gut endothelium and facilitates the extravasation of lymphocytes, including T cells and ILCs, into the intestinal lamina propria in inflammatory bowel disease (IBD) https://pubmed.ncbi.nlm.nih.gov/11269098/.
• Similarly, SELPLG-SELP (Endothelial cell-ILC, Endothelial cell-Plasma cell) and VCAM1-integrin_a4b1_complex (Endothelial cell-Fibroblast) interactions further underscore enhanced leukocyte rolling and adhesion to the endothelium, crucial steps in perpetuating inflammation in colitis.
• RARRES2-CCRL2 (Fibroblast-Fibroblast, Fibroblast-Endothelial cell, Fibroblast-Macrophage) signaling suggests activation of chemerin-mediated pathways, known to regulate immune cell chemotaxis and inflammation https://pubmed.ncbi.nlm.nih.gov/22409549/.
• These interactions collectively represent a state of chronic inflammation with robust immune cell recruitment and active participation of stromal and endothelial cells in sustaining the inflammatory response.

HC (Healthy Control):

• Interactions like EFNB1-EPHA complexes (Intestinal Epithelial cell-Endothelial cell, Intestinal Epithelial cell-Intestinal Epithelial cell, Intestinal Epithelial cell-Fibroblast) are vital for maintaining tissue architecture, cell-cell repulsion/adhesion, and guiding cell migration during homeostatic tissue turnover and repair https://pubmed.ncbi.nlm.nih.gov/17585250/.
• GAS6-AXL (Endothelial cell-Macrophage) signaling is known to promote anti-inflammatory responses and efferocytosis (clearance of apoptotic cells) by macrophages, contributing to the resolution of inflammation and tissue homeostasis https://pubmed.ncbi.nlm.nih.gov/19446401/.
• TGFB2-TGFbeta_receptor2 (Intestinal Epithelial cell-Fibroblast) signaling is a critical regulator of immune tolerance, epithelial barrier integrity, and extracellular matrix production, essential for maintaining gut homeostasis https://pubmed.ncbi.nlm.nih.gov/23722384/.
• The interactions observed in HC reflect stable tissue organization, balanced immune regulation, and processes essential for maintaining a healthy colonic environment.

Clinical or Translational Implications

The distinct CCI profiles observed across AC, CC, and HC conditions offer significant translational potential:

• Biomarker Discovery: Specific CCI pairs that are highly active and unique to AC or CC could serve as diagnostic or prognostic biomarkers. For instance, high MAdCAM1-integrin_a4b7 activity could be a specific indicator of gut inflammation, while EREG-EGFR or PDGFA-PDGFRA activation might flag early neoplastic changes.
• Therapeutic Targets: The identified ligand-receptor pairs represent potential therapeutic targets.
• For colitis (CC), targeting the MAdCAM1-integrin_a4b7 axis to inhibit immune cell infiltration has already led to successful IBD therapies (e.g., vedolizumab) https://pubmed.ncbi.nlm.nih.gov/25732924/. Other interactions like VCAM1-integrin_a4b1 or RARRES2-CCRL2 could be explored for novel anti-inflammatory strategies.
• For adenoma/colorectal cancer (AC), interfering with EREG-EGFR or PDGFA-PDGFRA signaling could suppress tumor cell proliferation and stromal activation. Modulating ICAM1-integrin or JAG1-NOTCH pathways might also impact tumor immune evasion and angiogenesis.
• Disease Monitoring: Changes in the strength or presence of these specific CCIs could be monitored to track disease progression or response to treatment, providing a more detailed understanding of the dynamic cellular ecosystem.
• Understanding Pathogenesis: This analysis provides a foundation for deeper investigations into how these cell-cell communication networks drive colon diseases. Understanding these intricate interactions can lead to the development of more precise and effective interventions.

11. Condition-Specific Surfaceome Markers in Colon Macrophages

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers for Macrophages in the mouse colon across three conditions: Acute Colitis (AC), Chronic Colitis (CC), and Healthy Control (HC). By focusing solely on surfaceome markers, the analysis highlights proteins that are accessible on the cell surface, making them prime candidates for cell-cell interactions, immune recognition, and potential therapeutic targeting. The results are presented as a dot plot, illustrating both the mean expression level (color intensity) and the fraction of cells expressing each gene (dot size) for Macrophages across individual samples within each condition. Up to 50 markers were sought per condition, and the plot displays the most prominent ones.

Visual Summary

The dot plot effectively visualizes distinct surfaceome marker profiles for Macrophages in each condition:

• Acute Colitis (AC) Specific Markers: The cluster of genes including *Adora2a*, *Nrg1*, *Tmem63b*, and *Flrt3* shows high mean expression and high cell fraction specifically in the AC samples (AC1, AC2, AC3). These markers are largely absent or expressed at very low levels in CC and HC samples, indicating an acute inflammatory signature.
• Chronic Colitis (CC) Specific Markers: A broad panel of genes, spanning from *Sorl1* to *Cadm1*, exhibits strong and widespread expression in CC samples (CC1, CC2, CC3, CC4). Notably, sample CC2 shows exceptionally high expression and cell fraction for many of these markers, consistent with its higher cell count (1871 cells) compared to other samples. This suggests a robust and distinct macrophage activation state in chronic inflammation.
• Healthy Control (HC) Specific Markers: Genes such as *Pigr*, *Plxdc2*, *Cd4*, and *Epcam* are predominantly expressed in HC samples (HC1, HC2, HC3). Their low expression in AC and CC samples points towards a homeostatic or quiescent macrophage phenotype. There is some variability within HC samples, with HC1 and HC3 showing stronger expression for these markers than HC2.
• Cell Counts: The bar plot on the right shows the number of macrophages identified per sample. CC samples, particularly CC2, have a substantially higher macrophage count compared to AC and HC samples, reflecting macrophage infiltration and expansion during chronic inflammation.

Biological Interpretation

Macrophage Markers in Acute Colitis (AC)

The markers elevated in AC samples suggest specific macrophage responses during acute inflammation in the colon:

• *Adora2a*: Adenosine A2a receptor is known to modulate immune responses, often with immunosuppressive effects but also implicated in inflammation. Macrophages expressing Adora2a can influence cytokine production and leukocyte trafficking. PubMed search: Macrophage Adora2a inflammation
• *Nrg1*: Neuregulin 1 plays roles in cell-cell communication and tissue repair. Its upregulation in acute colitis macrophages might reflect early attempts at tissue remodeling or signaling within the inflammatory milieu.

The presence of these surface markers points to a macrophage phenotype engaged in immediate immune signaling and potentially early stages of tissue response during acute inflammation.

Macrophage Markers in Chronic Colitis (CC)

The diverse set of surface markers in CC macrophages indicates a complex, sustained, and potentially heterogeneous macrophage activation state characteristic of chronic inflammation and tissue remodeling:

• *Cd38*: Often associated with activated macrophages, involved in NAD+ metabolism and calcium signaling, and can contribute to pro-inflammatory responses. GeneCards: CD38
• *Trem2*: Triggering receptor expressed on myeloid cells 2 is critical for macrophage functions in tissue repair, efferocytosis (clearance of apoptotic cells), and shaping responses in chronic inflammatory conditions, often promoting a pro-resolving phenotype. GeneCards: TREM2
• *Mrc1 (CD206)*: Mannose receptor C-type 1 is a classic marker for M2-like macrophages, involved in pathogen recognition, antigen uptake, and tissue repair/remodeling, suggesting a prominent M2-like population in chronic colitis. GeneCards: MRC1
• *Cd36*: A scavenger receptor involved in lipid metabolism, phagocytosis, and inflammation, expressed on various macrophage subsets. GeneCards: CD36
• *Axl*: A receptor tyrosine kinase that promotes efferocytosis and cell survival, often upregulated in chronic inflammatory and immunosuppressive macrophage phenotypes. GeneCards: AXL
• *Vcam1*: Vascular cell adhesion molecule 1 plays a key role in leukocyte recruitment to inflamed tissues, indicating macrophage involvement in perpetuating the chronic inflammatory cycle. GeneCards: VCAM1

This profile collectively suggests that macrophages in chronic colitis are actively involved in both pro-inflammatory processes and attempts at tissue repair and immune modulation, which is typical for sustained inflammatory environments like the colon in colitis. The high count of macrophages in CC samples, particularly CC2, further underscores their significant role in the disease pathology.

Macrophage Markers in Healthy Control (HC)

The markers specific to HC macrophages may represent populations crucial for maintaining intestinal homeostasis:

• *Cd4*: While primarily known as a T cell co-receptor, CD4 is also expressed on a subset of macrophages, especially resident populations, where it can participate in antigen presentation and interaction with T cells. GeneCards: CD4
• *Pigr* and *Epcam*: These are primarily markers of epithelial cells (Polymeric immunoglobulin receptor and Epithelial cell adhesion molecule, respectively). Their detection as "surfaceome only" markers on macrophages in the colon is intriguing and warrants further investigation. It could indicate unique homeostatic macrophage subsets that are intimately associated with the intestinal epithelium, potentially involved in monitoring epithelial integrity or specialized phagocytic functions related to epithelial cells/debris. Alternatively, it could signal rare cellular doublets/multiplets or transient transfer of membrane components. If confirmed as bona fide macrophage markers, they would represent highly specific surface signatures for macrophages in the healthy gut microenvironment. GeneCards: PIGR, GeneCards: EPCAM

Clinical or Translational Implications

The identification of condition-specific macrophage surfaceome markers has several clinical and translational implications:

• Diagnostic and Prognostic Biomarkers: Genes like *Adora2a* (AC), *Trem2*, *Mrc1*, *Cd38*, and *Axl* (CC) could serve as highly specific biomarkers for distinguishing acute from chronic inflammatory states in the colon. Their expression levels on macrophages, perhaps measurable via flow cytometry or spatial proteomics in biopsies, might correlate with disease activity or prognosis. The unusual HC markers (*Pigr*, *Epcam*) if validated, could be indicators of intestinal health or specific homeostatic macrophage populations.
• Therapeutic Targets: Given that these are surfaceome markers, they represent excellent candidates for targeted therapies. For instance, in chronic colitis, blocking pro-inflammatory receptors (e.g., if any of the CC markers drive inflammation) or specifically targeting macrophages expressing M2-like markers such as *Mrc1* or *Trem2* could modulate disease progression or promote resolution. Monoclonal antibodies or antibody-drug conjugates could be developed to precisely target pathogenic macrophage subsets identified by these unique surface markers, thereby minimizing off-target effects.
• Understanding Disease Mechanisms: These markers offer deeper insights into the specific roles and phenotypes of macrophages during different stages of colitis. For example, the strong M2-like signature in chronic colitis suggests macrophages are attempting repair or contributing to immune evasion, which could be exploited therapeutically. Further functional validation of these markers in mouse models and human samples would be crucial to confirm their clinical utility.

12. Fibroblast Condition-Specific Surfaceome Markers in Mouse Colon

[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 활용하여 대장 조직 내 섬유아세포(Fibroblast)에서 각 조건(AC: Acute Colitis, CC: Chronic Colitis, HC: Healthy Control로 추정됨)에 특이적으로 발현되는 표면 마커(surfaceome markers)를 식별합니다. plot_markers_and_expression_dot 도구를 사용하여 조건별로 최대 50개의 마커를 식별하고, 이들 중 가장 두드러지는 마커들의 발현 패턴을 점 그림(dot plot)으로 시각화했습니다. 특히, 조건 간에 공통적으로 발현되는 마커는 제외하여 각 조건에 고유한 특이적 마커를 강조했습니다.

Visual Summary

제공된 점 그림은 AC, CC, HC 세 가지 조건과 각 조건 내 개별 샘플(AC1-3, CC1-4, HC1-3)에서 섬유아세포의 표면 마커 발현을 보여줍니다.

• 점의 색상 농도: 유전자 평균 발현 수준을 나타냅니다. 짙은 붉은색일수록 해당 유전자의 발현이 높음을 의미합니다.
• 점의 크기: 해당 유전자를 발현하는 세포의 비율(%)을 나타냅니다. 점이 클수록 더 많은 섬유아세포가 그 유전자를 발현함을 의미합니다.
• X축: 조건 특이적으로 발현되는 표면 마커 유전자들입니다.
• Y축: 각 샘플 그룹(예: AC2, CC2, HC3 등)을 나타냅니다.
• 오른쪽 막대 그래프: 각 샘플 그룹에 포함된 섬유아세포의 총 수를 보여줍니다.

그림에서 두드러지는 패턴은 다음과 같습니다:

• AC 조건 특이적 마커: Steap4, Itga1, Aoc3, Abcc9, Ptprf, Pcdh19, Ifngr2와 같은 유전자들이 AC 샘플(특히 AC2)에서 높은 발현과 높은 세포 비율을 보입니다.
• CC 조건 특이적 마커: Parm1, Cspg4, Itga4, McAm, Esam, Atp1b2, Notch3, Jag1, S1pr3, Bcam, Slc7a2 등의 유전자들이 CC 샘플(특히 CC2)에서 짙은 붉은색과 큰 점으로 나타나, 해당 조건에서 높은 발현과 광범위한 발현을 보여줍니다.
• HC 조건 특이적 마커: Cd34, Ptch1, Ncam1, F3, Lepr, Ramp2, Cadm3, Tpcn1, Negr1, Pigr, Opcm1 등이 HC 샘플(HC2, HC3, HC1)에서 특징적으로 높은 발현을 보입니다.

각 조건별로 붉은색 상자로 표시된 영역은 해당 조건에서 마커 유전자들의 뚜렷한 차등 발현을 시각적으로 강조합니다.

Biological Interpretation

이 분석은 대장 조직 내 섬유아세포가 염증 조건(AC, CC)과 건강한 상태(HC)에 따라 특징적인 표면 마커 프로파일을 나타냄을 시사합니다. 이러한 조건 특이적 마커들은 섬유아세포의 기능적 변화와 잠재적인 역할을 이해하는 데 중요한 단서를 제공합니다.

• 건강한 상태 (HC) 섬유아세포 특이적 마커:
• Cd34: 조혈모세포 마커로 알려져 있지만, 일부 간엽 줄기세포 및 섬유아세포 전구세포에서도 발현됩니다. 이는 건강한 대장 섬유아세포 집단 내에 특정 전구세포 또는 덜 분화된 하위 집합의 존재를 나타낼 수 있습니다. GeneCards: CD34
• Ptch1: Hedgehog 신호전달 경로의 수용체로, 조직 발달과 항상성 유지에 관여합니다. 건강한 조직에서 섬유아세포의 정상적인 기능과 항상성을 조절하는 데 중요할 수 있습니다.
• Ncam1: 세포 접착 분자로, 세포-세포 또는 세포-기질 상호작용에 관여하며, 건강한 조직 구조 유지에 역할을 할 수 있습니다.
• 급성 염증 상태 (AC) 섬유아세포 특이적 마커:
• Itga1 (Integrin alpha 1): 세포-세포외 기질(ECM) 접착 및 염증 반응에 중요한 인테그린입니다. 염증 상황에서 섬유아세포의 이동 및 염증 부위에서의 세포외 기질 리모델링과 관련될 수 있습니다. GeneCards: ITGA1
• Ifngr2 (Interferon gamma receptor 2): 인터페론 감마(IFN-$\gamma$) 수용체 복합체의 구성 요소로, IFN-$\gamma$ 신호전달을 매개합니다. 이는 AC 상태의 섬유아세포가 염증성 사이토카인인 IFN-$\gamma$에 반응하여 그 기능을 조절할 수 있음을 나타냅니다.
• 만성 염증 상태 (CC) 섬유아세포 특이적 마커:
• Cspg4 (NG2 proteoglycan): 주로 혈관주위세포(pericytes)와 특정 섬유아세포 아형에서 발현되며, 혈관신생, 세포 증식, 이동 및 염증 반응에 관여합니다. 만성 염증 및 섬유화 과정에서 섬유아세포의 활성화를 나타내는 중요한 마커일 수 있습니다. GeneCards: CSPG4
• Itga4 (Integrin alpha 4): 세포 접착, 림프구 이동 및 염증 과정에 중요한 인테그린입니다. CC 상태에서 염증 세포의 모집 및 섬유아세포-염증 세포 상호작용에 역할을 할 수 있습니다.
• Notch3 및 Jag1: Notch 신호전달 경로의 구성 요소입니다. Notch 신호는 세포 운명 결정, 증식, 분화에 중요하며, 만성 염증 및 섬유화 질환에서 섬유아세포의 활성화 및 섬유화 유도에 중요한 역할을 하는 것으로 알려져 있습니다. PubMed Search: Notch signaling fibrosis colon
• S1pr3 (Sphingosine-1-phosphate receptor 3): 세포 이동, 혈관신생 및 염증 반응 조절에 관여하는 G 단백질 연결 수용체입니다. 만성 염증 환경에서 섬유아세포의 기능 조절에 기여할 수 있습니다.

이러한 결과는 건강, 급성 염증, 만성 염증 상태에서 섬유아세포의 이질성과 기능적 적응을 반영하며, 각 조건에서 특이적으로 활성화되는 신호 경로 및 세포 상호작용을 시사합니다.

Clinical or Translational Implications

이 분석에서 식별된 섬유아세포의 조건 특이적 표면 마커는 대장 염증성 질환의 진단, 예후 예측 및 치료 표적 발굴에 중요한 의미를 가질 수 있습니다.

• 진단 및 예후 바이오마커: Itga1, Cspg4, Notch3 등과 같이 특정 염증 조건에서만 뚜렷하게 발현되는 표면 마커들은 대장 염증성 질환의 활동성(급성 vs 만성) 또는 중증도를 구별하는 새로운 바이오마커로 활용될 가능성이 있습니다. 생검 조직이나 액체 생검을 통해 이러한 마커의 발현을 측정함으로써 질병 상태를 정확하게 평가할 수 있습니다.
• 치료 표적: 섬유아세포 표면에 고도로 발현되는 마커들은 질병 관련 섬유아세포의 활성을 특이적으로 조절하는 치료제의 표적이 될 수 있습니다. 예를 들어, 만성 염증에서 Cspg4 또는 Notch3의 활성화를 억제하는 것은 섬유아세포의 병리학적 변화를 제어하고 섬유화를 완화하는 새로운 치료 전략으로 이어질 수 있습니다. 특히 표면 마커는 항체 기반 치료제 개발에 유리한 이점을 제공합니다.
• 약물 개발 및 검증: 조건 특이적 마커는 약물 스크리닝 플랫폼을 개발하고, 새로운 치료 후보 물질이 특정 섬유아세포 하위 집합이나 활성 상태에 미치는 영향을 평가하는 데 사용될 수 있습니다.
• 실험적 검증: 이러한 마커들은 추가적인 실험적 검증을 위한 강력한 후보군입니다. 예를 들어, 유세포 분석(Flow cytometry)이나 면역조직화학(Immunohistochemistry)을 통해 대장 조직 샘플에서 이러한 마커들의 단백질 수준 발현을 확인하고, 특정 섬유아세포 하위 집합을 분리하여 기능적 특성을 연구하는 데 활용될 수 있습니다.

13. CD4 T 세포 조건 특이적 표면 마커 분석

[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 활용하여 대장 조직 내 CD4 T 세포에서 각 질병 조건(AC, CC) 및 건강 대조군(HC)에 특이적으로 발현하는 표면 마커 유전자를 식별하고자 수행되었습니다. plot_markers_and_expression_dot 도구를 사용하여, 각 조건에서 유의미하게 차등 발현하며 높은 발현 빈도를 보이는 상위 50개의 표면 발현 유전자 후보를 선별하고, 이를 닷 플롯(dot plot)으로 시각화했습니다. 이 분석은 CD4 T 세포의 조건별 특성을 이해하고 잠재적인 바이오마커 또는 치료 표적을 발굴하는 데 초점을 맞춥니다.

Visual Summary

제공된 닷 플롯은 세 가지 조건(AC, CC, HC)에서 CD4 T 세포의 표면 마커 유전자 발현 패턴을 보여줍니다.

• 행(Row)은 각 조건(AC, CC, HC) 내 개별 샘플을 나타내며, AC1-AC3, CC1-CC4, HC1-HC3로 명명되어 있습니다.
• 열(Column)은 각 조건에 특이적으로 발현하는 상위 표면 마커 유전자들을 나타냅니다.
• 각 점의 크기는 해당 샘플 내 CD4 T 세포 중 특정 유전자를 발현하는 세포의 비율(Fraction of cells in group, %)을 나타내며, 큰 점일수록 많은 세포가 발현함을 의미합니다.
• 각 점의 색상 강도는 해당 샘플 내 CD4 T 세포에서 특정 유전자의 평균 발현 수준(Mean expression in group)을 나타내며, 붉은색이 진할수록 평균 발현량이 높음을 의미합니다.
• 플롯의 오른쪽에는 각 샘플에 포함된 CD4 T 세포의 총 개수가 막대 그래프와 숫자로 표시되어 있습니다.
• 붉은색 사각형은 각 조건(AC, CC, HC)에 특이적으로 고발현 및 고빈도를 보이는 유전자 그룹을 시각적으로 강조하여 보여줍니다.

주요 관찰:

• AC 조건 특이적 마커: Cd7, Kit, Tnfrsf1a, Il12rb2, Tnfsf8, Tnfrsf4, Atp2b4, Areg, Il18r1 등의 유전자들이 AC 샘플에서 비교적 높은 발현 수준과 발현 빈도를 보입니다. 특히 Cd7, Kit, Tnfrsf1a, Il12rb2, Tnfsf8, Tnfrsf4는 AC1 및 AC2 샘플에서 강한 신호를 나타냅니다.
• CC 조건 특이적 마커: Ccr2, Il2ra, Ifngr2, Bst2, Ginm1, Adrb2, Atp1b1 등의 유전자들이 CC 샘플, 특히 CC1, CC2, CC3, CC4에서 높은 발현 수준과 발현 빈도를 나타냅니다. Ccr2, Il2ra, Ifngr2는 이 그룹에서 특히 두드러집니다.
• HC 조건 특이적 마커: Epcam, Pigr, Cd55, Lypd8, Muc13 등의 유전자들이 HC 샘플에서 높은 발현 수준과 발현 빈도를 나타냅니다. 이들은 다른 조건에서는 거의 발현되지 않는 모습을 보입니다. 특히 HC1, HC2, HC3 샘플에서 일관된 패턴을 보입니다.
• 각 샘플별 CD4 T 세포 수는 AC3 (1794개), CC3 (1585개), CC4 (1537개), HC2 (1444개) 등 다양한 분포를 보이며, 분석의 통계적 강도를 뒷받침합니다.

Biological Interpretation

대장 조직 내 CD4 T 세포의 조건 특이적 표면 마커 발현 패턴은 각 조건에 따른 면역 세포의 활성화 상태, 기능적 분화, 그리고 주변 환경과의 상호작용 변화를 시사합니다.

1. AC 조건의 CD4 T 세포 특성:

• Cd7: T 세포, NK 세포, 일부 B 세포에서 발현되는 표면 당단백질로, 면역 세포 활성화 및 증식에 관여합니다. AC 조건에서의 상향 조절은 T 세포의 활성화 상태 증가를 나타낼 수 있습니다 PubMed search: CD7 T cell function.
• Kit (CD117): 줄기세포, 비만세포 등에 주로 발현되지만, 특정 T 세포 아형(예: 일부 조절 T 세포, 조직 상주 기억 T 세포)에서도 발현될 수 있으며, T 세포 발달 및 기능에 중요한 역할을 합니다 GeneCards: KIT. AC 조건에서 Kit 발현은 특정 T 세포 아형의 증식 또는 유지와 관련될 수 있습니다.
• Tnfrsf1a (TNFR1), Tnfsf8 (CD153), Tnfrsf4 (OX40): 이들은 종양괴사인자 수용체(TNFRSF) 슈퍼패밀리에 속하며, T 세포의 활성화, 생존, 증식 및 분화에 중요한 공자극 신호를 제공합니다 GeneCards: TNFRSF1A, GeneCards: TNFSF8, GeneCards: TNFRSF4. AC 조건에서 이들의 높은 발현은 T 세포가 염증 반응에 활발히 참여하고 있음을 강력히 시사합니다.
• Il12rb2: IL-12 수용체의 베타 2 서브유닛으로, T 세포의 Th1 분화와 IFN-γ 생산에 필수적인 역할을 합니다 GeneCards: IL12RB2. 이는 AC 조건에서 Th1 면역 반응이 활성화되어 있을 가능성을 나타냅니다.
• Areg (Amphiregulin): 상피세포 성장인자 수용체(EGFR) 리간드로, 조직 복구 및 염증 조절에 관여합니다. T 세포에서 Areg 발현은 조직 손상 및 염증 조절에서의 역할을 반영할 수 있습니다.
• Il18r1: IL-18 수용체로, IL-18은 Th1 반응을 유도하고 IFN-γ 생산을 촉진하는 염증성 사이토카인입니다 GeneCards: IL18R1. AC 조건에서의 Il18r1 발현은 T 세포가 강력한 염증성 신호에 반응하고 있음을 나타냅니다. 전반적으로 AC 조건의 CD4 T 세포는 활성화된 염증 반응, 특히 Th1 지향성 면역 반응과 조직 손상/복구 신호에 관여하는 특징을 보입니다.

2. CC 조건의 CD4 T 세포 특성:

• Ccr2: 케모카인 수용체로, 주로 단핵구/대식세포 이동에 관여하지만, 특정 T 세포 아형에서도 발현되어 염증 부위로의 T 세포 유입을 매개할 수 있습니다 GeneCards: CCR2. 이는 CC 조건에서 T 세포가 염증 부위로 활발히 이동하고 있음을 시사합니다.
• Il2ra (CD25): IL-2 수용체의 알파 사슬로, 활성화된 T 세포와 조절 T 세포(Treg)의 특징적인 마커입니다 GeneCards: IL2RA. CC 조건에서의 Il2ra 높은 발현은 T 세포의 활성화 또는 Treg 세포의 존재를 나타낼 수 있습니다.
• Ifngr2: IFN-γ 수용체의 서브유닛으로, IFN-γ 신호 전달에 필수적입니다 GeneCards: IFNGR2. Ifngr2 발현은 T 세포가 IFN-γ에 반응하고 있음을 나타내며, 이는 Th1 면역 반응과 관련될 수 있습니다.
• Bst2 (CD317): 플라스마사이토이드 수지상세포(pDC)의 마커로 잘 알려져 있지만, 바이러스 감염 시 T 세포에서도 발현될 수 있습니다. CC 조건에서 T 세포에서의 발현은 특정 유형의 면역 반응 또는 세포 상호작용을 나타낼 수 있습니다.
• Adrb2 (β2-아드레날린 수용체): 신경계와 면역계의 상호작용에 관여하며, T 세포 기능 조절에 영향을 미칠 수 있습니다 GeneCards: ADRB2. CC 조건에서 Adrb2 발현은 신경-면역 조절의 변화를 시사할 수 있습니다.
• 전반적으로 CC 조건의 CD4 T 세포는 이동, 활성화 및 IFN-γ 반응성을 특징으로 하는 염증 반응에 관여할 가능성이 높습니다.

3. HC 조건의 CD4 T 세포 특성:

• Epcam (CD326), Pigr (Polymeric Immunoglobulin Receptor), Muc13: 이 유전자들은 주로 상피세포에서 발현되는 것으로 알려져 있습니다. Epcam은 세포-세포 부착과 신호 전달에 관여하며, Pigr은 IgA 같은 중합체 면역글로불린의 상피 수송을 담당합니다. Muc13은 점액 단백질 중 하나로, 상피 장벽 기능과 관련됩니다 GeneCards: EPCAM, GeneCards: PIGR, GeneCards: MUC13. CD4 T 세포에서 이들 유전자의 발현이 관찰된 것은 흥미로운 결과입니다. 이는 건강한 대장 환경에서 CD4 T 세포가 상피세포와 매우 밀접하게 상호작용하거나, 특정 환경적 자극에 의해 독특한 표면 마커를 획득할 수 있음을 시사할 수 있습니다. 혹은 매우 특이적인 조직 상주 T 세포 아형일 가능성도 있습니다.
• Cd55 (DAF, Decay-Accelerating Factor): 보체 활성화를 조절하는 단백질로, 면역 세포 표면에 발현되어 자가 세포 보호에 기여합니다 GeneCards: CD55. HC 조건에서 Cd55 발현은 건강한 상태에서 CD4 T 세포가 보체 매개 손상으로부터 자신을 보호하는 기전과 관련될 수 있습니다.
• 전반적으로 HC 조건의 CD4 T 세포는 독특한 표면 마커 프로파일을 보이며, 이는 건강한 대장 환경에서 상피 장벽과의 상호작용 및 자가 보호 기전과 연관될 가능성이 있습니다.

Clinical or Translational Implications

이 분석에서 식별된 CD4 T 세포의 조건 특이적 표면 마커들은 여러 가지 임상적 또는 중개 연구적 함의를 가집니다.

1. 진단 및 질병 모니터링 바이오마커: AC 및 CC 조건에서 특이적으로 상향 조절되는 표면 마커 유전자들(Tnfrsf1a, Tnfsf8, Tnfrsf4, Il12rb2 for AC; Ccr2, Il2ra, Ifngr2 for CC)은 염증성 장 질환(inflammatory bowel disease, IBD)과 같은 대장 관련 질환의 진단 또는 질병 활성도 모니터링을 위한 잠재적인 바이오마커로 활용될 수 있습니다. 이러한 마커들은 생검(biopsy) 샘플 또는 순환하는 T 세포에서 검출될 수 있어 비침습적 또는 최소 침습적 진단법 개발에 기여할 수 있습니다.
2. 치료 표적 개발: 질병 조건에서 고발현되는 표면 마커들은 새로운 치료 표적이 될 수 있습니다. 예를 들어, AC 조건에서 발현되는 Tnfrsf 계열 수용체(Tnfrsf1a, Tnfsf8, Tnfrsf4)는 T 세포 활성화 및 염증 반응을 매개하므로, 이들을 차단하는 단일클론 항체 치료제(monoclonal antibody therapeutics) 개발을 고려할 수 있습니다. Ccr2는 T 세포의 염증 부위로의 이동을 조절할 수 있으므로, Ccr2를 표적으로 하는 치료제는 질병 관련 T 세포의 침윤을 억제할 수 있습니다. 이러한 표적들은 질병 특이적인 면역 반응을 조절하여 부작용을 최소화하면서 치료 효과를 높일 가능성이 있습니다.
3. 세포 기반 치료법의 전략화: Kit (CD117)와 같은 마커는 특정 CD4 T 세포 아형을 표적화하거나 분리하는 데 사용될 수 있습니다. 이는 세포 치료법(cell-based therapies)에서 특정 기능적 T 세포 아형을 선별하거나 제거하는 전략에 활용될 수 있습니다.
4. 건강 상태에서의 T 세포 역할 이해: HC 조건에서 관찰된 Epcam, Pigr, Muc13과 같은 비전형적인 T 세포 표면 마커들은 건강한 대장 상피 환경에서 CD4 T 세포가 수행하는 고유한 기능이나 상호작용에 대한 통찰력을 제공할 수 있습니다. 이는 장 내 면역 항상성 유지 메커니즘을 이해하는 데 중요하며, 질병 발생 시 이러한 균형이 어떻게 깨지는지를 연구하는 기초 자료가 됩니다. 이러한 발견은 추가적인 실험적 검증(예: 유세포 분석, 면역조직화학염색)을 통해 CD4 T 세포에서의 발현 여부와 그 기능적 의미를 명확히 해야 합니다.

이러한 마커 후보들은 향후 기능적 연구와 *in vivo* 모델에서의 검증을 통해 그 임상적 유용성을 확립할 필요가 있습니다.

14. Gene Ontology (GSA) Analysis of Intestinal Epithelial Cells Across Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GSA) results for Intestinal Epithelial cells, using WikiPathways terms, from single-cell RNA-seq data of mouse colon tissue. The analysis was performed for three conditions: AC, CC, and HC (Healthy Control). For each condition, the gene expression profile of Intestinal Epithelial cells from that condition was compared against the combined gene expression profiles of Intestinal Epithelial cells from all other conditions (use_ref=False). The plots display the top enriched WikiPathways terms based on -log(p-val) and -log(q-val), providing insights into the altered biological processes in Intestinal Epithelial cells under each specific condition relative to the aggregate of the other conditions.

Visual Summary

The visualization consists of three bar plots, each representing the GSA results for Intestinal Epithelial cells in a specific condition (AC, CC, HC) compared to all other conditions. The x-axis shows -log(p-val) and -log(q-val), indicating the statistical significance of pathway enrichment. Higher values denote greater significance.

• Intestinal Epithelial cell: AC_vs_others: This plot displays pathways significantly enriched in Intestinal Epithelial cells from the AC condition. The most significant pathways include "Focal Adhesion WP85", "Spinal Cord Injury WP2432", "Dysregulated miRNA Targeting in Insulin/PI3K-AKT Signaling WP3855", "Inflammatory Response Pathway WP458", and "Focal Adhesion-PI3K-Akt-mTOR-signaling pathway WP2841". The significance levels for p-values are relatively high, with -log(p-val) extending beyond 3.
• Intestinal Epithelial cell: CC_vs_others: This plot shows a much broader and more significant enrichment of pathways in Intestinal Epithelial cells from the CC condition. Prominent pathways include "Proteasome Degradation WP519", "mRNA processing WP310", "TNF-alpha NF-kB Signaling Pathway WP246", "Cholesterol metabolism (includes both Bloch and Kandutsch-Russell pathways) WP103", and "Translation Factors WP307". Many pathways show -log(p-val) values well above 4, and corresponding -log(q-val) values are also high, indicating strong statistical confidence.
• Intestinal Epithelial cell: HC_vs_others: This plot highlights pathways enriched in Intestinal Epithelial cells from the HC condition. Top enriched terms include "Cytoplasmic Ribosomal Proteins WP163", "Electron Transport Chain WP295", "mRNA processing WP310", "Oxidative phosphorylation WP1248", and "Translation Factors WP307". Similar to the CC condition, a large number of pathways are significantly enriched, with -log(p-val) values exceeding 10 for the most significant terms, reflecting robust and distinct biological activity.

Biological Interpretation

Intestinal Epithelial cells in AC condition (vs. CC + HC)

In the AC condition, Intestinal Epithelial cells show a distinct upregulation of pathways related to cell-extracellular matrix interaction and inflammatory responses.

• Cell Adhesion and Signaling: The enrichment of "Focal Adhesion WP85" and "Focal Adhesion-PI3K-Akt-mTOR-signaling pathway WP2841" suggests active remodeling of cell-matrix interactions and engagement of downstream signaling pathways crucial for cell survival, proliferation, and migration. This could indicate processes of tissue damage, repair, or altered epithelial barrier integrity. GeneCards: Focal Adhesion pathway
• Inflammation and Stress Response: "Inflammatory Response Pathway WP458" indicates an active immune response within these cells. Pathways like "Spinal Cord Injury WP2432" and "Lung fibrosis WP3632," while named for other tissues, often represent general wound healing, tissue remodeling, and inflammatory-fibrotic responses that can occur in the colon.
• Regulatory Mechanisms: "Dysregulated miRNA Targeting in Insulin/PI3K-AKT Signaling WP3855" points to post-transcriptional regulation impacting crucial growth and metabolic pathways. The PI3K-AKT-mTOR axis is a central regulator of cell growth, proliferation, and survival, and its dysregulation can have significant implications for epithelial cell homeostasis.

These findings suggest that in the AC condition, Intestinal Epithelial cells are actively involved in responding to their environment, potentially undergoing tissue repair or responding to inflammatory stimuli, with significant changes in cell-matrix interactions and critical signaling pathways.

Intestinal Epithelial cells in CC condition (vs. AC + HC)

The CC condition reveals a profound shift in the Intestinal Epithelial cell transcriptome, characterized by widespread metabolic reprogramming, robust immune signaling, and altered protein homeostasis.

• Metabolic Reprogramming: Pathways like "Cholesterol metabolism (includes both Bloch and Kandutsch-Russell pathways) WP103", "Selenium metabolism/Selenoproteins WP108", and "Nucleotide Metabolism WP87" indicate significant changes in lipid, trace element, and nucleic acid metabolism. Such metabolic shifts are common in cells responding to chronic inflammation or rapid proliferation.
• Immune and Inflammatory Signaling: A strong signature of inflammation is evident with "TNF-alpha NF-kB Signaling Pathway WP246", "IL-1 Signaling Pathway WP37", "IL-5 Signaling Pathway WP151", and "IL-6 Signaling Pathway WP387". This indicates that Intestinal Epithelial cells in CC are highly activated and play a direct role in perpetuating or responding to immune responses. PubMed: Intestinal Epithelial Cells and Immunity
• Protein Homeostasis and Cell Fate: "Proteasome Degradation WP519", "mRNA processing WP310", and "Translation Factors WP307" highlight active protein turnover and gene expression regulation. Signaling pathways such as "EGFR1 Signaling Pathway WP572" and "Delta-Notch Signaling Pathway WP265" are critical for epithelial cell proliferation, differentiation, and tissue regeneration, suggesting altered cell fate decisions.

The comprehensive activation of metabolic and inflammatory pathways, coupled with changes in cell fate regulators, indicates that Intestinal Epithelial cells in the CC condition are in a highly dynamic and potentially pathological state, distinct from both AC and HC.

Intestinal Epithelial cells in HC condition (vs. AC + CC)

In the HC condition, Intestinal Epithelial cells are characterized by robust activation of fundamental cellular processes essential for maintaining tissue function and homeostasis, when compared to the combined diseased states (AC+CC).

• Core Metabolic & Energy Production: Pathways such as "Cytoplasmic Ribosomal Proteins WP163", "mRNA processing WP310", "Translation Factors WP307", "Electron Transport Chain WP295", "Oxidative phosphorylation WP1248", and "TCA Cycle WP434" are highly enriched. This signifies a high level of protein synthesis and energy production, critical for the rapid turnover, barrier maintenance, and absorption functions of healthy intestinal epithelial cells. UniProt: Ribosomal Proteins
• Nutrient Metabolism: "Amino Acid Metabolism WP662", "Fatty Acid Beta Oxidation WP1269", and "Fatty Acid Biosynthesis WP336" highlight the active processing and utilization of various nutrients, reflecting their role in digestion and absorption.
• Cellular Stress Management: "Oxidative Stress WP412" and "Proteasome Degradation WP519" are enriched, suggesting healthy epithelial cells maintain robust mechanisms to manage cellular stress and ensure proper protein quality control, which is crucial in a constantly challenged environment like the gut.
• Baseline Immune Regulation: "TNF-alpha NF-kB Signaling Pathway WP246" is also enriched in HC, possibly reflecting a baseline level of immune surveillance or a preparedness state of the epithelial barrier even in "healthy" conditions, distinguishing it from potentially dysregulated or suppressed states in AC/CC.

These findings underscore that healthy Intestinal Epithelial cells are metabolically highly active, constantly regenerating, and equipped with strong stress-response and immune-regulatory mechanisms, distinguishing them from the potentially compromised or pathologically activated states in AC and CC.

Clinical or Translational Implications

The distinct pathway enrichments in Intestinal Epithelial cells across AC, CC, and HC conditions offer valuable clinical and translational insights for colon biology.

• Condition AC (Initial Injury/Remodeling): The emphasis on focal adhesion and PI3K-AKT-mTOR signaling suggests that targeting these pathways might be relevant in early stages of colon disease characterized by epithelial damage and inflammatory responses. Modulating epithelial cell adhesion or their inflammatory responses could influence disease progression.
• Condition CC (Active Disease/Chronic Inflammation): The extensive metabolic reprogramming and strong inflammatory signaling pathways (TNF-alpha NF-kB, IL-1, IL-5, IL-6) point to this condition representing a more severe or chronic disease state. Targeting specific inflammatory cytokines or their downstream signaling, as well as addressing metabolic dysregulation, could be crucial for therapeutic interventions. The involvement of EGFR1 and Delta-Notch signaling also suggests potential dysregulation in epithelial proliferation and differentiation, which could contribute to disease pathology.
• Condition HC (Healthy Homeostasis): The characterization of healthy epithelial cells emphasizes their high metabolic activity and robust stress response. Understanding these baseline healthy mechanisms can inform strategies to restore gut homeostasis in disease states, by promoting normal metabolic functions and epithelial integrity. Differences in TNF-alpha NF-kB signaling between HC and diseased states could also highlight how immune responses are dysregulated.

Further investigation into the specific genes driving these pathway enrichments could identify novel biomarkers for disease diagnosis, prognosis, or targets for therapeutic intervention, particularly for conditions affecting the colon epithelium.

15. Gene Set Enrichment Analysis (GSEA) Across Major Colon Cell Types and Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for key biological pathways across major cell types in the mouse colon, under different conditions (AC, CC, HC). GSEA evaluates whether a predefined set of genes (a "gene set" or "pathway") is statistically enriched at the top or bottom of a ranked list of genes, providing insights into activated or repressed biological processes. Here, the comparison is made for each condition (AC, CC, or HC) against all other conditions combined ("vs_others") within each specified cell type. This allows for identification of condition-specific pathway enrichments beyond a single reference.

Visual Summary

The dot plot visualizes the GSEA results, with cell type and condition comparisons on the x-axis and various biological pathways on the y-axis.

• Color Scale (Normalized Enrichment Score - NES): Red dots indicate pathways that are positively enriched (upregulated) in the test condition (e.g., AC) compared to 'others'. Blue dots indicate negative enrichment (downregulation) in the test condition compared to 'others'. The intensity of the color corresponds to the magnitude of the NES, ranging from -2.5 (strong negative enrichment) to 2.5 (strong positive enrichment).
• Dot Size (-log(p-value)): The size of each dot reflects the statistical significance of the enrichment. Larger dots represent more significant enrichment (smaller p-value), indicating a higher confidence in the observed pathway regulation. The legend shows dot sizes corresponding to -log(p) values of 6, 12, 18, 24, and 30.

Key visual patterns observed:

• Inflammatory Signatures: Pathways related to inflammation, such as "TNF-alpha NF-kB Signaling Pathway," "Type II interferon signaling (IFNG)," and "Toll Like Receptor signaling," show strong positive enrichment (red, large dots) in both AC_vs_others and CC_vs_others conditions across multiple immune cell types (Macrophages, T cells CD4+, T cells CD8+, B cells, ILCs) and also in Intestinal Epithelial cells and Fibroblasts. This is a consistent and striking feature.
• Metabolic Reprogramming: Pathways like "Glycolysis and Gluconeogenesis" frequently show positive enrichment (red) in AC_vs_others and CC_vs_others in various immune and epithelial cells, while often showing negative enrichment (blue) or no enrichment in HC_vs_others. Conversely, "Fatty acid oxidation" can be negatively enriched in AC/CC conditions.
• Tissue Remodeling/Fibrosis: In the CC_vs_others condition, particularly within Fibroblasts and Macrophages, pathways such as "Matrix Metalloproteinases" and "Lung fibrosis" (a general fibrosis-related pathway) are positively enriched.
• Cell Adhesion: "Integrin-mediated Cell Adhesion" shows positive enrichment in several cell types (e.g., Macrophage, Intestinal Epithelial cell, Fibroblast) in AC_vs_others and CC_vs_others.
• HC Condition Patterns: In HC_vs_others, blue dots frequently appear for pathways that are upregulated in AC/CC, indicating that these pathways are relatively less active in healthy conditions. Conversely, some pathways show positive enrichment (red dots) in HC_vs_others (e.g., "Nucleotide Metabolism" in Intestinal Epithelial cells), potentially representing homeostatic processes or pathways suppressed during inflammation.
• Cell Type Specificity: While inflammatory pathways are broad, certain pathways exhibit cell-type-specific enrichment. For instance, "Lung fibrosis" and "Matrix Metalloproteinases" are predominantly active in Fibroblasts and Macrophages in CC.

Biological Interpretation

The GSEA results provide strong biological insights into the cellular responses in the mouse colon under different conditions, likely representing states of inflammation or disease. Given the tissue is colon, AC and CC most likely represent acute and chronic colitis models, respectively.

1. Prominent Inflammation in AC and CC: The consistent upregulation of "TNF-alpha NF-kB Signaling Pathway," "Type II interferon signaling (IFNG)," and "Toll Like Receptor signaling" across a wide array of immune cells (Macrophages, T cells, B cells, ILCs) and non-immune cells (Intestinal Epithelial cells, Fibroblasts) in AC and CC conditions is highly indicative of robust inflammatory responses.

• NF-κB signaling is a central regulator of immune and inflammatory responses, activated by various stimuli including TNF-alpha and Toll-like receptors (TLRs) [PubMed search: NF-kB signaling inflammation]. Its widespread activation points to a generalized inflammatory state.
• Type II interferon (IFN-γ) signaling is characteristic of T-helper 1 (Th1) and cytotoxic T cell responses, crucial for antiviral and antibacterial immunity, but also implicated in chronic inflammatory diseases like inflammatory bowel disease (IBD) [GeneCards: IFNG]. Its enrichment highlights a strong cell-mediated immune response.
• Toll-like receptor (TLR) signaling initiates innate immune responses upon pathogen recognition, leading to inflammatory cytokine production [UniProt: TLRs]. This suggests increased pathogen sensing or endogenous danger signals in AC and CC.

2. Metabolic Reprogramming for Immune and Epithelial Function: The shifts in metabolic pathways, particularly the upregulation of "Glycolysis and Gluconeogenesis" and downregulation of "Fatty acid oxidation" in AC and CC, suggest a metabolic reprogramming.

• Immune cells, especially activated macrophages and T cells, often switch to aerobic glycolysis (the "Warburg effect") to rapidly generate biomass and energy required for proliferation and effector functions during inflammation [PubMed search: immune cell metabolism glycolysis]. The epithelial cells also show this trend, potentially supporting repair or stress responses.

3. Tissue Remodeling and Fibrosis in Chronic Conditions: The positive enrichment of "Matrix Metalloproteinases" (MMPs) and "Lung fibrosis" (indicative of general fibrotic processes) in Fibroblasts and Macrophages under CC conditions is critical.

• MMPs are enzymes that degrade extracellular matrix (ECM) components, playing roles in tissue remodeling, wound healing, and pathogenesis of chronic inflammatory diseases and fibrosis [PubMed search: Matrix Metalloproteinases IBD]. Their upregulation in CC suggests ongoing tissue damage and repair processes, which can lead to fibrosis.
• Fibroblasts are key orchestrators of tissue remodeling and fibrosis, and their activation (potentially via pathways like "Focal Adhesion-PI3K-Akt-mTOR signaling" also seen enriched) is a hallmark of chronic inflammation [PubMed search: fibroblast fibrosis colon].

4. Cell Adhesion Dynamics: Upregulation of "Integrin-mediated Cell Adhesion" in AC and CC conditions across various cell types, including epithelial cells and macrophages, is significant.

• Integrins are crucial for cell-cell and cell-matrix interactions, mediating immune cell extravasation into inflamed tissues, epithelial barrier integrity, and cell migration during tissue repair or pathology [UniProt: Integrins]. Their dysregulation can contribute to both immune cell infiltration and epithelial dysfunction in colitis.

5. Cell-Type Specific Responses:

• Macrophages and T cells: Show consistent activation of inflammatory, metabolic, and adhesion pathways, reflecting their central role in initiating and propagating intestinal inflammation.
• Intestinal Epithelial cells: Exhibit strong inflammatory pathway activation and metabolic shifts, indicating their direct involvement in the immune response as a barrier and a participant in inflammation, rather than just a passive target.
• Fibroblasts: Primarily drive tissue remodeling and potential fibrotic responses, especially in chronic inflammation (CC), through pathways like MMPs and fibrosis-related gene sets.
• B cells and Plasma cells: Also show inflammatory pathway enrichment, pointing to their active roles in adaptive immunity during colitis, including antibody production and immune modulation.

Clinical or Translational Implications

These findings have several potential clinical and translational implications for understanding and treating inflammatory conditions in the colon, such as Inflammatory Bowel Disease (IBD):

1. Therapeutic Targets for Inflammation: The widespread and significant enrichment of "TNF-alpha NF-kB Signaling Pathway" and "Type II interferon signaling (IFNG)" in AC and CC suggests that targeting these pathways, or their upstream activators (e.g., specific TLRs), could be effective in dampening the inflammatory response. Anti-TNF therapies are already standard in IBD treatment [PubMed search: Anti-TNF therapy IBD], and these data reinforce the rationale for such broad anti-inflammatory approaches.
2. Addressing Fibrosis: The robust activation of "Matrix Metalloproteinases" and fibrotic pathways in Fibroblasts and Macrophages during chronic inflammation (CC) highlights the need for anti-fibrotic strategies in chronic colon diseases. Developing therapies that specifically modulate fibroblast activation or MMP activity could prevent or reverse intestinal strictures and loss of function in chronic IBD [PubMed search: anti-fibrotic therapies IBD].
3. Metabolic Reprogramming as a Marker/Target: The observed metabolic shifts, particularly the upregulation of glycolysis in inflammatory states, could serve as biomarkers for disease activity or offer novel therapeutic avenues by targeting specific metabolic enzymes in immune and epithelial cells.
4. Cell-Type Specific Interventions: The distinct pathway enrichments across different cell types suggest that highly specific, cell-type-targeted therapies might be beneficial. For example, therapies aimed at regulating fibroblast function might be more effective in chronic, fibrotic stages, while broad anti-inflammatory agents might be more appropriate for acute flares.
5. Understanding Disease Pathogenesis: These GSEA results provide a detailed molecular landscape of activated pathways across different cell types during acute and chronic colon inflammation in a mouse model. This deep understanding is crucial for elucidating the precise mechanisms underlying disease progression and identifying new diagnostic markers.

16. Discussion

The single-cell transcriptomic analysis of mouse colon tissue provides a high-resolution view of the cellular and molecular landscape in acute (AC) and chronic (CC) colitis, compared to a healthy (HC) state. The overall dataset quality and cell type annotations are robust, demonstrating clear separation of major and minor cell types and effective data integration (Sections 1, 2, 3).

Cellular Composition Dynamics: A profound shift in cellular composition is observed, most notably the dramatic expansion of B cells in chronic colitis, often exceeding 60% of total cells, which leads to a relative decrease in other populations, including Intestinal Epithelial cells (Section 4). This B cell dominance strongly points towards a significant humoral immune response contributing to chronic pathology. Acute colitis, while showing increases in T cells and ILC1 (Section 4, 5), does not exhibit this overwhelming B cell expansion, suggesting distinct immunological drivers for acute versus chronic inflammation.

Immune Cell Phenotypic Alterations: Macrophages display significant functional polarization. Chronic colitis is characterized by an increase in pro-inflammatory M1 macrophages and a decrease in reparative M2A and M2D subsets, indicating a shift towards a sustained pro-inflammatory environment (Section 8). Macrophages in CC also upregulate surface markers like *Cd38*, *Trem2*, *Mrc1* (CD206), and *Axl*, reflecting their active involvement in chronic inflammation, tissue repair attempts, and immune modulation (Section 11). In contrast, acute colitis uniquely increases M2B macrophages (*Adora2a* as a specific marker), suggesting an intermediate or regulatory role in the acute phase (Section 8, 11). T cells and NK cells also undergo condition-specific changes: Th22, Th9, and ILCreg cells increase in AC, while NK cells, Treg, and Th17 cells are elevated in CC, with a notable decrease in naive T cells. Treg and Th17 cells are increased in both AC and CC, highlighting a complex interplay of immune regulation and pro-inflammatory responses (Section 6, 13).

Stromal Cell Responses: Fibroblasts exhibit condition-specific surfaceome markers, with HC fibroblasts expressing Cd34 and Ptch1 (potentially associated with less differentiated states or homeostasis), while AC fibroblasts show Itga1 and Ifngr2 (related to ECM adhesion and IFN-γ response). CC fibroblasts are marked by Cspg4, Notch3, and Jag1, indicating their active involvement in angiogenesis, cell proliferation, migration, and fibrosis (Section 12).

Intestinal Epithelial Cell Dysregulation: Intestinal Epithelial cells demonstrate condition-specific metabolic and immune responses. In AC, they show enrichment in focal adhesion and PI3K-AKT-mTOR signaling, suggesting active tissue repair or barrier dysregulation. In CC, there's widespread metabolic reprogramming (e.g., cholesterol metabolism), robust inflammatory signaling (TNF-alpha NF-kB, IL-1, IL-5, IL-6), and altered protein homeostasis and cell fate regulators (EGFR1, Delta-Notch). Healthy epithelial cells, conversely, are characterized by high metabolic activity, protein synthesis, and robust stress management (Section 14). This highlights the epithelial layer's dynamic and active participation in both healthy homeostasis and disease pathogenesis.

Altered Cell-Cell Communication: Cell-cell interaction analysis reveals a significantly expanded and intensified network in both AC and CC compared to HC. Prominent in AC/CC are integrin-extracellular matrix interactions (COL1A1/2/3/4/6A3, FN1, LAMC1) involving fibroblasts, macrophages, and endothelial cells, indicative of extensive tissue remodeling and potential fibrosis. The CXCL10-CXCR3 axis is strongly upregulated across multiple cell types in AC/CC, pointing to robust inflammatory cell recruitment. SPP1-integrin and THBS1-integrin interactions also underscore active tissue repair and immune modulation. Condition-specific differential CCI analysis further reveals EREG-EGFR and PDGFA-PDGFRA signaling in AC (implying pro-oncogenic signals), and crucially, MAdCAM1-integrin_a4b7_complex in CC, a hallmark of gut-specific immune cell homing in inflammation (Sections 9, 10).

Pathway-Level Insights: GSEA confirms these observations, showing widespread upregulation of inflammatory pathways (TNF-alpha NF-kB, Type II interferon, Toll-like receptor signaling) and metabolic reprogramming (glycolysis upregulation, fatty acid oxidation downregulation) across many immune and epithelial cell types in both AC and CC. Fibroblasts and macrophages in CC also show strong enrichment for Matrix Metalloproteinases and fibrosis-related pathways (Section 15).

In summary, the data delineate a complex and highly dynamic gut microenvironment in colitis, characterized by distinct cellular shifts, specific macrophage and T cell polarization states, active stromal and epithelial responses, and profound changes in cell-cell communication networks and pathway activation. These findings provide a rich resource for understanding colitis pathogenesis and identifying potential therapeutic avenues.

Hypotheses:

1. The dramatic B cell expansion in chronic colitis directly drives epithelial cell depletion or dysfunction, contributing to barrier compromise and perpetuating chronic inflammation.
2. Macrophage M1 polarization, alongside decreased M2A and M2D subsets, is a key driver of chronic inflammation in colitis, while M2B macrophages play a distinct, potentially modulatory, role in acute colitis.
3. The robust activation of the CXCL10-CXCR3 axis and specific gut-homing mechanisms (e.g., MAdCAM1-integrin_a4b7) are central to the persistent immune cell infiltration and chronic inflammatory state in colitis.
4. Condition-specific fibroblast activation, particularly the Notch signaling pathway, contributes significantly to tissue remodeling and fibrosis observed in chronic colitis.
5. Intestinal epithelial cells actively participate in the inflammatory response in colitis through metabolic reprogramming and activation of cytokine signaling, directly contributing to disease pathology.

Potential therapeutic targets:

1. B cells (CD20, specific B cell receptors): Dramatic expansion of B cells in chronic colitis suggests a central role in disease pathogenesis, likely through antibody production, antigen presentation, or cytokine secretion. Evidence: Section 4: Dominant B cell expansion (60%+) in CC. Section 15: B cells show inflammatory pathway enrichment in AC/CC. Clinical use of anti-CD20 (rituximab) in other autoimmune diseases. Validation: Preclinical validation in mouse models via B cell depletion. Clinical trials targeting B cell activation pathways or surface markers (e.g., anti-CD20).
2. CXCR3 (receptor for CXCL10): CXCL10-CXCR3 axis is strongly upregulated and widespread in AC and CC, indicating robust recruitment of inflammatory immune cells (T cells, NK cells, macrophages) to the colon. Evidence: Section 9: Strong and widespread CXCL10-CXCR3 interactions in AC/CC. Section 15: Type II interferon signaling (IFN-γ, which induces CXCL10) is upregulated across many cell types in AC/CC. Section 13: CD4 T cells in AC show increased Il12rb2 (Th1 related, IFN-γ production). Validation: Targeting CXCR3 with blocking antibodies or small molecule inhibitors in mouse colitis models to assess immune cell infiltration and disease severity. Evaluate in human IBD samples for CXCR3 expression on infiltrating cells.
3. MAdCAM1-integrin_a4b7_complex: Highly specific interaction to gut inflammation, mediating lymphocyte homing and extravasation into inflamed colon tissue in chronic colitis. Evidence: Section 10: MAdCAM1-integrin_a4b7_complex is a hallmark interaction for CC, involving endothelial cells and various immune cells (T cells, ILC, dendritic cells, plasma cells). Clinical efficacy of vedolizumab (anti-α4β7 integrin) in IBD. Validation: Existing clinical success with anti-α4β7 integrin therapy (vedolizumab). Further preclinical studies for combination therapies or novel molecules targeting specific components of this complex.
4. Notch3 / Jag1 (Notch signaling components in fibroblasts): Upregulation of Notch signaling components in chronic colitis fibroblasts suggests a role in fibroblast activation, proliferation, migration, and fibrosis, key processes in chronic inflammation and tissue remodeling. Evidence: Section 12: CC fibroblasts show increased Cspg4, Notch3, Jag1. Section 10: JAG1-NOTCH interactions are significant in AC (Endothelial-Endothelial, Endothelial-Fibroblast). Section 14: Delta-Notch Signaling Pathway enriched in CC Intestinal Epithelial cells. Section 15: Delta-Notch Signaling Pathway enriched in CC Endothelial and Intestinal Epithelial cells. Validation: Genetic ablation or pharmacological inhibition of Notch signaling in fibroblasts in mouse models of colon fibrosis. _In vitro_ studies on fibroblast activation and collagen production with Notch inhibitors.
5. M1 macrophage polarization pathways (e.g., NF-kB, specific cytokine receptors): Chronic colitis is characterized by a significant shift towards pro-inflammatory M1 macrophages, contributing to persistent inflammation. Modulating M1 activation or promoting M2 polarization could alleviate disease. Evidence: Section 8: Significant increase in M1 macrophages in CC, decrease in M2A and M2D. Section 11: CC macrophages express Cd38 (pro-inflammatory). Section 15: Macrophages show strong upregulation of TNF-alpha NF-kB Signaling Pathway in AC/CC. Validation: Develop therapies that repolarize M1 macrophages to M2-like states or inhibit M1-specific pro-inflammatory signaling pathways. Test in macrophage culture systems and mouse colitis models.

Follow-up validation ideas:

1. Functional Validation of B Cell Role (CC): Deplete B cells in a chronic colitis mouse model (e.g., using anti-CD20 antibodies) and assess the impact on disease severity, epithelial integrity, and other immune cell populations. Perform B cell adoptive transfer experiments to confirm their pathogenic role.
2. Macrophage Repolarization Studies: Use _in vitro_ macrophage culture systems with stimuli from AC or CC conditions to induce observed M1/M2 phenotypes. Test small molecules or biologics that promote M2A/M2D polarization or inhibit M1 activation and assess their efficacy in reducing inflammatory mediators.
3. Inhibition of CCI Pathways: _In vivo_ (CXCL10-CXCR3, MAdCAM1-integrin_a4b7): Administer blocking antibodies against CXCR3 or the integrin alpha4beta7 complex in mouse colitis models. Evaluate effects on immune cell infiltration, inflammation severity, and long-term tissue damage/fibrosis. _In vitro_ (EREG-EGFR, PDGFA-PDGFRA): Use co-culture systems of intestinal epithelial cells and fibroblasts from healthy/diseased colon, and test EGFR or PDGFRA inhibitors to assess effects on cell proliferation, migration, and fibrotic marker expression.
4. Fibroblast Notch Signaling Validation: Use conditional knockout mouse models to ablate Notch pathway components (e.g., Notch3, Jag1) specifically in fibroblasts during colitis and evaluate their impact on fibrosis, angiogenesis, and inflammatory responses.
5. Spatial Proteomics/Transcriptomics: Apply technologies like imaging mass cytometry or spatial transcriptomics to colon tissue sections from AC, CC, and HC to precisely localize the identified cell populations and validate the predicted cell-cell interactions _in situ_.
6. Human Sample Correlation: Collect human IBD (Crohn's disease, ulcerative colitis) biopsy samples and perform scRNA-seq or multiplexed immunohistochemistry to validate the key cellular and molecular signatures identified in the mouse model, particularly the B cell expansion, macrophage polarization, and specific CCI pathways.

Limitations:

This study utilizes a mouse model of colitis, and while highly relevant, findings may not directly translate to human disease due to species-specific differences in immunology and disease mechanisms. The single-cell RNA-seq approach provides correlative data on gene expression and cell composition; therefore, functional validation is required to confirm causal relationships between identified changes and disease pathology. The interpretation of some markers (e.g., epithelial markers on macrophages/T cells in HC) warrants further investigation to rule out technical artifacts like cell multiplets or ambient RNA. The use of 'vs_others' for GSEA and GSA, while highlighting condition-specific changes, might obscure nuances that would emerge from pairwise comparisons against a single reference.

17. Query List

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