SCODiA Report by MLBI Lab

Single-Cell Transcriptomic Analysis Reveals Condition-Specific Cellular Landscape and Intercellular Communication in Human Colon Inflammation

This report comprehensively analyzes single-cell RNA sequencing data from human colon tissue, distinguishing between Healthy, Inflamed, and Non-inflamed conditions. We uncover significant shifts in immune and stromal cell populations, with notable infiltration of B cells, Plasma cells, T cells, and Macrophages in inflamed states. Distinct cell-cell interaction networks and gene expression profiles characterize each condition, highlighting active immune responses, tissue remodeling, and metabolic adaptations. The 'Non-inflamed' condition often presents an intermediate or distinct molecular signature, suggesting subclinical activity or a state of persistent alteration, providing critical insights into the pathogenesis and potential therapeutic avenues for colonic inflammatory diseases.

Contents

  1. Dataset overview
  2. UMAP Embedding of Colon Single-Cell RNA-seq Data by Condition, Sample, and Cell Type Annotation
  3. UMAP Visualization of Key Marker Gene Expression and Cell Type Annotation in Colon Single-Cell RNA-Seq Data
  4. Celltype_subset Marker Gene Expression Dot Plot Interpretation
  5. Colon Minor Cell Type Population Analysis Across Health and Disease Conditions
  6. T Cell and Innate Lymphoid Cell Subset Composition Across Colon Tissue Conditions
  7. Macrophage Subset Population Barplot Analysis
  8. Macrophage Subset Proportion Differences Across Colon Conditions
  9. Non-inflamed Colon Cell-Cell Interaction Landscape
  10. Immune Checkpoint and Cell Cycle Gene-Associated Cell-Cell Interactions in Colon Conditions
  11. Condition-Specific Cell-Cell Interaction Patterns in Human Colon
  12. Macrophage Condition-Specific Surfaceome Markers in Colon
  13. Fibroblast Condition-Specific Surfaceome Markers in Colon
  14. T cell CD4+ Condition-Specific Surfaceome Markers in Colon
  15. Differential Expression of Cell Cycle-Related Genes in Colonic B Cells Across Conditions
  16. Gene Ontology (GSA) Analysis of Intestinal Epithelial Cells in Colon Health and Disease
  17. Colon Cell Type-Specific Gene Set Enrichment Analysis Across Inflammatory Conditions
  18. Discussion
  19. Query List

0. Dataset overview

Dataset Summary

Species: Human

Tissue: Colon

Key Precomputed Results:

1. UMAP Embedding of Colon Single-Cell RNA-seq Data by Condition, Sample, and Cell Type Annotation

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the cellular landscape of human colon tissue from single-cell RNA sequencing data using Uniform Manifold Approximation and Projection (UMAP). Cells are colored according to different metadata attributes: physiological condition (Healthy, Inflamed, Non-inflamed), individual sample, major cell type, minor cell type, and cell type subset. This provides a comprehensive overview of cell population distribution, assessment of data integration, and insights into condition-specific cellular changes and annotation quality.

Visual Summary

Condition

The UMAP colored by condition reveals distinct patterns associated with the physiological state of the colon. Cells from the 'Inflamed' condition (yellow) tend to cluster in specific regions, particularly enriched in the lower-left and central clusters, suggesting condition-specific cellular states or compositional shifts. 'Healthy' cells (dark red) are broadly distributed but also show enrichment in other distinct areas. 'Non-inflamed' cells (purple) appear more interspersed with 'Healthy' cells across various clusters, possibly reflecting a continuum or baseline state that shares characteristics with healthy tissue, or representing non-inflamed regions within patients with inflammation. This differential distribution highlights condition-associated cellular heterogeneity.

Sample

The sample UMAP demonstrates a generally good integration of data across individual samples (N7 to N661). Cells from multiple samples are largely intermixed within most clusters, indicating that batch effects have been effectively mitigated. No single sample predominantly drives a major cluster, which is crucial for robust downstream comparative analyses. While overall integration is good, some minor regions might show slight enrichment of specific samples, particularly in smaller or rare cell populations.

Celltype_major

The celltype_major UMAP shows clear and well-separated clusters corresponding to broad cell identities. For instance, Intestinal Epithelial cells (light orange) form a large, distinct cluster on the left side of the UMAP. T cells (cyan) form another prominent central cluster. B cells (dark red), Myeloid cells (light green), Stromal cells (blue), Endothelial cells (orange-red), and Mast cells (yellow) also form discernible, albeit sometimes smaller, clusters. The minimal presence of 'unassigned' cells (dark purple) and their scattered distribution suggests high confidence and comprehensive coverage in major cell type annotation.

Celltype_minor

Further resolution is observed in the celltype_minor UMAP, where major cell type clusters are refined into more specific populations. Within the T cell major cluster, T cell CD4+ (dark blue) and T cell CD8+ (light blue) subsets are resolved. Plasma cells (pink-red) emerge from the B cell cluster. Macrophages (light green) and Dendritic cells (dark red) become distinct within the Myeloid cluster. Fibroblasts (light orange) are clearly separated from other Stromal cells. Intestinal Epithelial cells largely remain as a cohesive block at this level, indicating its further subdivision into specialized epithelial cell types at the next level of annotation. This level of granularity confirms successful identification of key immune and stromal components in the colon.

Celltype_subset

The celltype_subset UMAP provides the highest level of annotation detail, further dissecting minor cell type clusters into highly specific cell states. Within the Intestinal Epithelial cell cluster, distinct populations like Enterocytes (light orange), Goblet cells (light orange), Paneth cells (light yellow), and Crypt cells (red) are clearly delineated. Various T cell subsets such as T cell (Naive) (light green), T cell (Cytotoxic) (dark blue), T cell (Th1) (light green), T cell (Th17) (cyan), T cell (Th22) (light green), and T cell (Treg) (dark blue) are also resolved, highlighting the diverse adaptive immune landscape. Myeloid cells further resolve into Macrophage (M1, M2A, M2B, M2C, M2D) subsets, showcasing macrophage polarization states. This detailed annotation confirms the ability to differentiate highly specialized cell populations relevant to colon biology and disease.

Biological Interpretation

The UMAP visualizations collectively provide a robust foundational analysis of the single-cell RNA-seq data from human colon.

  1. Comprehensive Cellular Heterogeneity: The consistent and hierarchical clustering from major to minor to subset cell types demonstrates the extensive cellular heterogeneity within the human colon, capturing both common cell types and rare, specialized populations. This rich dataset allows for deep biological investigations into the distinct functions of these cell populations in colon health and disease.
  2. Condition-Specific Cellular Dynamics: The clear separation and enrichment of 'Inflamed' cells in particular regions of the UMAP strongly suggest that inflammatory conditions in the colon induce significant shifts in cellular composition or cellular states. This implies specific cell types or activation states are either recruited, expanded, or undergo phenotypic changes during inflammation, which is a key biological insight for understanding inflammatory bowel diseases. PubMed search: "colon inflammation single cell RNA sequencing"
  3. High-Quality Cell Type Annotation: The formation of distinct and coherent clusters for each cell type at all levels of granularity (major, minor, subset) validates the quality and accuracy of the cell type annotation pipeline. The minimal presence of 'unassigned' cells across the UMAPs further strengthens the confidence in the cell identity assignments. The identification of diverse cell subsets (e.g., various T helper cell types, macrophage polarization states, specialized epithelial cells) confirms the power of this dataset for fine-grained biological discovery. GeneCards: "Enterocyte", UniProt: "IL-17A (Th17 marker)"
  4. Effective Data Integration: The intermixing of cells from different samples within most clusters indicates successful integration of the dataset, suggesting that observed cellular patterns are biological rather than technical artifacts. This is critical for drawing reliable conclusions from multi-sample single-cell studies.

Clinical or Translational Implications

The UMAP analyses lay crucial groundwork for translational research in colon health and disease:

2. UMAP Visualization of Key Marker Gene Expression and Cell Type Annotation in Colon Single-Cell RNA-Seq Data

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) plots, illustrating the global transcriptional landscape of single cells from human colon tissue. The visualization displays the expression levels of 13 canonical marker genes and the corresponding minor cell type annotations (celltype_minor). The primary objective is to validate the quality and specificity of existing cell type annotations by correlating them with the expression patterns of well-known cell-specific genes.

Visual Summary

The UMAP projection effectively organizes the 76,200 cells into distinct clusters, reflecting their underlying biological identities. The celltype_minor annotation plot (bottom right) serves as the reference for cell identity, showing major populations such as Intestinal Epithelial cells (large upper-left cluster), T cells (bottom central cluster), B cells (lower central-left), Plasma cells (bottom right), Macrophages (central-right), and Fibroblasts (upper-right).

T Cell Lineage Markers:

B Cell Lineage Markers:

Myeloid Cell Markers:

Stromal Cell Marker:

Endothelial Cell Marker:

Epithelial Cell Markers:

Endothelial/Hematopoietic Stem Cell Marker:

Biological Interpretation

The observed expression patterns of the selected marker genes provide strong validation for the celltype_minor annotations across the UMAP projection. This analysis demonstrates a high degree of concordance between transcriptional profiles and assigned cell identities, which is fundamental for reliable downstream analyses.

Annotation Notes

This visualization serves as an excellent internal control for the quality of the cell type annotation. The observed gene expression patterns are highly consistent with established biological functions of these markers, strongly reinforcing the validity of the celltype_minor assignments. The clear partitioning of cells on the UMAP by both cell type and marker gene expression indicates a well-integrated and properly annotated dataset, making it suitable for deeper investigations into colon biology and disease. While major cell types are well-defined, further detailed analysis with additional markers might be beneficial for refining annotations of smaller or less distinctly marked populations, if such granularity is required for specific research questions.

3. Celltype_subset Marker Gene Expression Dot Plot Interpretation

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[Analysis Visualization Results]...

Analysis Overview

This dot plot visualizes the expression of key marker genes across various celltype_subset populations identified in the single-cell RNA-seq data from the human colon tissue. The analysis aimed to identify surface-specific marker genes (30 per group, up to 140 total) that are highly expressed (mean expression, red color intensity) and prevalent (fraction of cells, dot size) within each cell type group. This provides an overall view of cell type identity and annotation quality based on marker gene specificity.

Visual Summary

The dot plot displays celltype_subset populations along the y-axis and their corresponding marker genes along the x-axis.

Biological Interpretation

The marker gene expression patterns largely confirm the distinct identities of many celltype_subset populations in the human colon, consistent with known biology.

Immune Cell Lineages

T cells (Cytotoxic, Th1, Th17, Th2, Th22, Treg)

Epithelial Cell Lineages

Stromal and Endothelial Cells

Annotation Notes

The dot plot generally provides strong evidence for the robust annotation and distinct identity of many celltype_subset populations within the colon tissue based on their specific marker gene expression. However, a few observations suggest areas for potential refinement or deeper investigation:

Overall, the annotation quality for most major and many minor celltype_subset populations is well-supported by this marker gene analysis. The identified ambiguities highlight specific subsets where further scrutiny of marker specificity or broader marker panels might enhance the precision of cell type assignment.

4. Colon Minor Cell Type Population Analysis Across Health and Disease Conditions

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Analysis Overview

This analysis provides a visual representation of the relative proportions of minor cell types within individual samples, grouped by their disease condition: Healthy, Inflamed, and Non-inflamed. The stacked bar plots allow for an assessment of shifts in cellular composition in the colon tissue under different conditions, offering insights into disease-associated cellular microenvironment changes. Each bar represents a single sample, with cell type proportions adding up to 100%.

Visual Summary

The bar plot effectively illustrates the cellular landscape of the colon tissue at the minor cell type resolution, highlighting differences in cell type proportions across Healthy, Inflamed, and Non-inflamed conditions.

Biological Interpretation

The observed shifts in minor cell type populations provide strong biological insights into the immune responses and tissue remodeling occurring in the colon during inflammation.

Clinical or Translational Implications

The analysis of cell type populations has several important clinical and translational implications:

References

  1. Plasma cells in IBD: Neurath, M. F. (2014). The intestinal immune system and inflammatory bowel disease. *Cell Research*, 24(5), 517–529. PubMed Search
  2. T cells in IBD: Sarra, M., et al. (2010). Th17 cells in inflammatory bowel disease: A new therapeutic target. *Digestive Diseases*, 28(3), 478–485. PubMed Search
  3. Macrophages and Dendritic cells in IBD: Cooney, R., et al. (2009). The intestinal macrophage. *Inflammatory Bowel Diseases*, 15(11), 1715–1720. PubMed Search
  4. Subclinical inflammation in IBD: Peyrin-Biroulet, L., et al. (2016). Endoscopic remission in ulcerative colitis: from definition to implications. *Gut*, 65(5), 870–880. PubMed Search

5. T Cell and Innate Lymphoid Cell Subset Composition Across Colon Tissue Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a bar plot illustrating the proportional distribution of T cell and other lymphoid cell subsets within the T cell major cell type category. The populations are displayed for individual samples, grouped by their corresponding health conditions: Healthy, Inflamed, and Non-inflamed colon tissue. This visualization allows for a direct comparison of the immune landscape changes in lymphoid populations across different physiological states.

Visual Summary

The stacked bar plots reveal distinct patterns in the composition of T cell and innate lymphoid cell (ILC) subsets across the Healthy, Inflamed, and Non-inflamed conditions:

Biological Interpretation

The observed shifts in lymphoid cell populations provide crucial insights into the immune responses within the colon under different conditions:

Clinical or Translational Implications

These findings have significant clinical implications for understanding and potentially managing inflammatory conditions in the colon:

6. Macrophage Subset Population Barplot Analysis

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[Analysis Visualization Results]...

Analysis Overview

This visualization presents a barplot depicting the population composition within the Macrophage cell type, as defined by the celltype_minor annotation. The plots are organized by condition (Healthy, Inflamed, Non-inflamed) and show individual samples within each condition. The primary purpose of this plot, given the parameters, is to confirm that the cells selected for Macrophage were indeed identified as 100% Macrophages across all samples and conditions.

Visual Summary

The barplot consists of three subplots, one for each condition: Healthy, Inflamed, and Non-inflamed. Within each subplot, multiple bars represent individual samples.

Biological Interpretation

The consistent observation of 100% for all bars under all conditions signifies that the selection process for cells annotated as Macrophage from the celltype_minor category was successful and accurate. When querying specifically for "Macrophage" cells, the output correctly shows that 100% of the cells in the resulting subset are indeed Macrophages.

This plot serves as an important sanity check, confirming the integrity of the celltype_minor annotation for Macrophages and the correct application of the subsetting operation across all samples and experimental conditions (Healthy, Inflamed, Non-inflamed). It indicates that downstream analyses focusing on Macrophages can confidently proceed, knowing that the cellular input has been correctly identified as this specific cell type.

Annotation Notes

This plot primarily functions as a verification of cell type annotation and subsetting. It does not provide information regarding:

To investigate population changes or condition-associated biology, further analyses such as comparing the overall proportion of Macrophages to other cell types across conditions would be required.

7. Macrophage Subset Proportion Differences Across Colon Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportions of specific macrophage subsets, Mac (M2D) and Mac (M2B), across different conditions (Non-inflamed, Healthy, Inflamed) in human colon single-cell RNA-seq data. Box plots are used to visualize the celltype proportions, with statistical significance between groups indicated by p-values. The goal is to identify how the relative abundance of these macrophage populations shifts in various physiological and pathological states of the colon.

Visual Summary

The box plots display the proportion of Mac (M2D) and Mac (M2B) cells as a percentage of total cells, comparing Non-inflamed, Healthy, and Inflamed colon conditions.

Mac (M2D) Proportions:

Mac (M2B) Proportions:

Biological Interpretation

Macrophages are highly plastic immune cells that play crucial roles in both maintaining tissue homeostasis and orchestrating inflammatory responses. Their polarization into distinct subsets, such as M1, M2A, M2B, M2C, and M2D, dictates their functional phenotype.

Mac (M2D) in Colon Homeostasis and Inflammation:

Mac (M2B) in Colon Inflammation:

Clinical or Translational Implications

The differential distribution of macrophage subsets like M2D and M2B across colon conditions carries significant clinical and translational implications:

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References

  1. M2 Macrophages and their polarization: A general overview of macrophage polarization states, including M2 subsets, and their roles in various biological processes. PubMed Search: "M2 macrophage polarization" Colon

8. Non-inflamed Colon Cell-Cell Interaction Landscape

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the cell-cell interaction (CCI) landscape within the colon in a "Non-inflamed" state, leveraging single-cell RNA sequencing data. CellPhoneDB was used to infer ligand-receptor interactions between different cell types, and the plot_cci_dots tool displays the most significant interactions (up to 80 pairs) based on p-value and mean expression. The goal is to understand the baseline communication networks present in a healthy or quiescent colon environment.

Visual Summary

The dot plot displays a matrix where the y-axis represents interacting cell type pairs (e.g., CellA|CellB), and the x-axis represents specific ligand-receptor gene pairs or complexes. Each dot signifies a significant cell-cell interaction.

Several patterns emerge:

Biological Interpretation

The observed cell-cell interactions in the non-inflamed colon highlight crucial pathways involved in maintaining tissue homeostasis, barrier integrity, and immune surveillance.

  1. Extracellular Matrix (ECM) Remodeling and Adhesion: The strong presence of Integrin-mediated interactions (e.g., involving Collagen, Fibronectin, Laminin) between Fibroblasts, Intestinal Epithelial cells, Endothelial cells, and various immune cells (T cells, Macrophages) underscores the critical role of cell-ECM and cell-cell adhesion. Integrins are essential for cell migration, proliferation, differentiation, and tissue organization. In the colon, these interactions are vital for maintaining the structural integrity of the gut lining and regulating cell behavior within the stromal and epithelial compartments. For instance, Fibroblast-Intestinal Epithelial cell interactions via Integrins likely contribute to crypt architecture and epithelial cell turnover. GeneCards: Integrin Family
  2. Immune Cell Migration and Homeostasis:
  1. Prostaglandin Signaling in Gut Homeostasis: Interactions involving Prostaglandin E2 (PGE2) and its receptors (PTGER2, PTGER4) are prominent. PGE2 is a lipid mediator that plays a multifaceted role in the gut, including maintaining epithelial barrier function, modulating immune responses, and regulating motility. In the non-inflamed state, it likely contributes to immune tolerance and tissue repair mechanisms. PubMed Search: Prostaglandin E2 gut homeostasis
  2. Baseline Immune Cell Cross-talk: Interactions between T cells (CD4+, CD8+) and other immune cells (Macrophages, Plasma cells), as well as with Intestinal Epithelial cells and Fibroblasts, reflect the constant immune surveillance and intricate communication necessary to maintain immune tolerance in the gut, even in the absence of overt inflammation. For example, Macrophage-T cell interactions are fundamental for antigen presentation and T cell activation/regulation.

Clinical or Translational Implications

Understanding the baseline cell-cell interaction network in the non-inflamed colon provides a crucial reference for identifying dysregulated communication in disease states, offering insights for therapeutic development and experimental validation.

  1. Reference for Disease States: This non-inflamed CCI map serves as a fundamental baseline. By comparing these interactions with those in "Inflamed" conditions (as provided in the conditions context), researchers can identify specific ligand-receptor pairs and cell-cell communication hubs that are gained or lost, amplified or suppressed during inflammation. This differential analysis can pinpoint disease-specific interaction targets.
  2. Therapeutic Target Prioritization:
  1. Experimental Validation: The identified high-confidence ligand-receptor pairs provide concrete targets for further experimental validation.

9. Immune Checkpoint and Cell Cycle Gene-Associated Cell-Cell Interactions in Colon Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) mediated by a curated set of genes related to immune checkpoints and cell cycle pathways across different colon conditions: Healthy, Inflamed, and Non-inflamed. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between various cell types, focusing on the mean interaction strength and statistical significance (p-value). The precomputed CCI results from CellPhoneDB are leveraged to understand the dynamic changes in intercellular communication within the colonic microenvironment in response to different physiological states.

Visual Summary

The provided dot plots illustrate cell-cell interactions for specific ligand-receptor pairs across Healthy, Inflamed, and Non-inflamed colon tissues.

Biological Interpretation

The analysis of cell-cell interactions mediated by immune checkpoint and related genes reveals dynamic changes in intercellular communication across different colon conditions.

  1. Healthy Colon Homeostasis: In the healthy colon, interactions are primarily driven by CD8+ T cells, involving IFNG_Type II_IFNR and the LCK_CD8_receptor module. This suggests a state of immune surveillance and homeostatic maintenance where CD8+ T cells play a central role, possibly in minor tissue maintenance or early detection of threats. IFN-gamma signaling is crucial for host defense and immune regulation [PubMed search: IFN-gamma signaling].
  2. Inflammation-driven Immune Activation: The inflamed condition is characterized by a marked increase in immune cell activation and diversification of interacting cell types and ligand-receptor pairs.
  1. Non-inflamed State: Transition or Chronic Low-grade Activity: The "Non-inflamed" condition, while not "Healthy," presents a complex profile. It retains features of activated immunity seen in the "Inflamed" state (e.g., CD86-CD28, HBEGF-EGFR) but also introduces new elements.

The absence of direct cell cycle gene (e.g., CDK1, CCNA2) interactions as ligand-receptor pairs is expected, as these are primarily intracellular signaling molecules, and the CCI analysis focuses on surface ligand-receptor pairs. The detected interactions are indeed predominantly related to immune signaling and cell fate.

Clinical or Translational Implications

The observed patterns of cell-cell interactions offer significant insights for therapeutic targeting and understanding disease mechanisms in the colon.

  1. Therapeutic Targets for Inflammatory Bowel Disease (IBD):
  1. Biomarkers for Disease State and Prognosis:
  1. Context-Specific Immune Modulation: Understanding how these ligand-receptor interactions vary across conditions allows for more precise therapeutic strategies. For instance, interventions might focus on T cell activation in acute inflammation (CD86-CD28), while therapies in "Non-inflamed" states might focus on regulating fibrotic responses (TGFB1-TGFbeta_receptor1) or promoting epithelial healing (HBEGF-EGFR).
  2. Role of Non-Immune Cells: The consistent involvement of Intestinal Epithelial cells and Fibroblasts in diverse interactions highlights that these non-immune cells are not merely passive bystanders but active participants in shaping the immune microenvironment. Targeting interactions involving these cell types could offer novel therapeutic avenues.

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify statistically significant differences in cell-cell interactions (CCIs) among major immune and stromal cells, including intestinal epithelial cells, across three distinct conditions: Healthy, Inflamed, and Non-inflamed colon tissue. The CellPhoneDB method was utilized to infer ligand-receptor interactions, and a statistical test (t-test) was applied to pinpoint CCIs demonstrating significantly greater interaction strength in one condition compared to the others. The results are presented in a dot plot, where the size of each dot corresponds to the significance of the difference (-log10(p-value)), and the color intensity reflects the standardized mean interaction strength within each sample. The analysis specifically considered interactions involving B cells, T cells, Myeloid cells (e.g., Macrophages), Mast cells, Stromal cells (e.g., Fibroblasts), Endothelial cells, and Intestinal Epithelial cells.

Visual Summary

The "Condition-specific CCI pattern" dot plot vividly illustrates unique cell-cell interaction profiles across the Healthy, Inflamed, and Non-inflamed colon tissue samples.

Biological Interpretation

The identified condition-specific CCI patterns offer crucial biological insights into the intricate cellular communications governing colon health and disease pathogenesis.

Homeostatic Interactions in Healthy Colon:

Pro-inflammatory and Fibrotic Crosstalk in Inflamed Colon:

Clinical or Translational Implications

These findings carry several significant clinical and translational implications for understanding and managing colon inflammatory conditions.

Therapeutic Target Discovery:

11. Macrophage Condition-Specific Surfaceome Markers in Colon

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that are specifically enriched in Macrophage cells from different colon conditions: 'Healthy' and 'Non-inflamed'. Using single-cell RNA sequencing data, differentially expressed genes (DEGs) were identified for Macrophage cells comparing each condition against others, focusing solely on surfaceome proteins. The results are visualized as a dot plot, where each row represents a distinct sample (identified by N prefix) and each column represents a selected surfaceome gene. The size of the dot indicates the fraction of cells in that sample expressing the gene, while the color intensity reflects the mean expression level of the gene in those cells.

Visual Summary

The dot plot clearly segregates Macrophage samples into two major groups based on their surfaceome marker expression profiles: 'Healthy' and 'Non-inflamed'.

Overall, the visualization effectively identifies two distinct transcriptional programs at the cell surface of Macrophages, strongly associated with the 'Healthy' versus 'Non-inflamed' states in the colon.

Biological Interpretation

The identified surfaceome markers provide significant biological insights into the functional states of colon Macrophages in healthy and non-inflamed conditions.

Healthy Macrophage Signature:

Non-inflamed Macrophage Signature:

In summary, 'Healthy' colon macrophages appear to be engaged in homeostatic immune surveillance and tolerance, whereas 'Non-inflamed' macrophages show a profile consistent with a subtle shift towards immune activation, enhanced pathogen recognition, cytokine responsiveness, and tissue remodeling, reflecting a deviation from pure health, perhaps in response to environmental cues or subclinical stress in the colon.

Clinical or Translational Implications

The identification of condition-specific surfaceome markers for Macrophages in the colon has several important clinical and translational implications:

12. Fibroblast Condition-Specific Surfaceome Markers in Colon

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Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers in Fibroblast cells from human colon tissue, comparing Healthy, Inflamed, and Non-inflamed conditions. The plot_markers_and_expression_dot tool was used to visualize differential gene expression, focusing exclusively on surface-expressed proteins (surfaceome markers). Markers were selected based on their differential expression and prevalence across conditions, with a maximum of 50 markers identified per condition. The dot plot illustrates the mean expression level (color intensity) and the fraction of cells expressing the gene (dot size) for each marker across different cell groups within each condition.

Visual Summary

The dot plot displays a panel of surfaceome markers identified in Fibroblast cells, grouped by their expression patterns across Healthy, Inflamed, and Non-inflamed colon conditions.

Inflamed and Non-inflamed Condition-Associated Markers:

General Observations:

Biological Interpretation

The observed surfaceome markers provide valuable insights into the phenotypic shifts of fibroblasts in the context of colon inflammation.

Clinical or Translational Implications

The identified condition-specific surfaceome markers in colon fibroblasts hold significant clinical and translational potential:

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

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Analysis Overview

This analysis identifies and visualizes condition-specific surfaceome markers in CD4+ T cells from human colon tissue, comparing Healthy, Inflamed, and Non-inflamed conditions. The results are presented as a dot plot, where dot size reflects the fraction of cells expressing a given gene, and dot color indicates the mean expression level. This approach allows for the discovery of potential biomarkers that distinguish T cell states across different pathological conditions of the colon.

Visual Summary

The dot plot displays the expression patterns of several surfaceome markers across individual samples, grouped by their clinical condition (Healthy, Inflamed, Non-inflamed).

Biological Interpretation

The observed condition-specific surfaceome markers provide critical insights into the functional states and roles of CD4+ T cells in the colon microenvironment during health and disease.

Clinical or Translational Implications

The identified condition-specific surface markers hold significant potential for clinical applications, particularly in diagnostics, prognostics, and therapeutic development for colon inflammatory conditions.

  1. Biomarkers for Disease Activity and Stratification: The differential expression of surface markers such as TIGIT, CTLA4, TNFRSF18, and TNFRSF1B in CD4+ T cells could serve as valuable biomarkers to distinguish between healthy, inflamed, and non-inflamed states in the colon. These markers could potentially be measured on immune cells from colon biopsies or even peripheral blood (if tissue-specific populations are reflected) to monitor disease activity, assess treatment response, or stratify patients based on their immune profiles.
  2. Potential Therapeutic Targets:
  1. Experimental Validation and Deeper Characterization: Further experimental validation, potentially using flow cytometry or immunohistochemistry on tissue samples, would be crucial to confirm the surface expression and cell-type specificity of these markers. Investigating the functional consequences of modulating these pathways in relevant *in vitro* and *in vivo* models of colon inflammation would elucidate their precise roles and therapeutic potential. The role of EMB in T cells, especially in the context of non-inflamed colon, merits dedicated research to uncover its function and potential as a novel marker or target.

14. Differential Expression of Cell Cycle-Related Genes in Colonic B Cells Across Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the expression of a predefined list of cell cycle pathway related genes within B cells (identified as celltype_minor: B cell) in human colon tissue. The goal was to identify genes with statistically significant expression differences when comparing 'Inflamed' and 'Non-inflamed' conditions against a 'Healthy' reference, focusing on a maximum of 24 genes for visualization. The analysis utilized single-cell RNA-seq data and performed differential expression testing with a p-value cutoff of 0.1 and a log2 Fold Change cutoff of 0.1. The visualization displays boxplots for genes that met these significance criteria, showing gene expression (sample mean) across the three conditions: Healthy, Inflamed, and Non-inflamed.

Visual Summary

The visualization presents boxplots for three specific genes: TGFB1, YWHAB, and YWHAZ. These were the only genes from the comprehensive list of cell cycle-related genes that showed statistically significant differences based on the defined cutoffs when compared to the Healthy condition in B cells.

  1. TGFB1 (Transforming Growth Factor Beta 1):
  1. YWHAB (Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein, Beta Polypeptide):
  1. YWHAZ (Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein, Zeta Polypeptide):

In summary, all three genes (TGFB1, YWHAB, YWHAZ) show a consistent pattern of upregulation in B cells from 'Inflamed' colon tissue when compared to 'Healthy' colon tissue. There were no statistically significant differences observed between the 'Healthy' and 'Non-inflamed' conditions, nor between 'Inflamed' and 'Non-inflamed' conditions for these genes based on the provided p-values.

Biological Interpretation

The observed upregulation of TGFB1, YWHAB, and YWHAZ in B cells specifically within the 'Inflamed' colon tissue suggests significant shifts in B cell biology in response to inflammation.

  1. TGFB1 Upregulation and B Cell Function in Inflammation:
  1. YWHAB and YWHAZ Upregulation and Cell Cycle/Stress Response:

The fact that only these three genes, from a broader list of cell cycle regulators, showed significant changes in B cells in the inflamed colon highlights their specific relevance in this particular immune cell type and disease context. This suggests a targeted modulation of certain regulatory rather than a widespread activation of all cell cycle machinery.

Clinical or Translational Implications

The differential expression of TGFB1, YWHAB, and YWHAZ in B cells within the inflamed colon has several potential clinical and translational implications:

15. Gene Ontology (GSA) Analysis of Intestinal Epithelial Cells in Colon Health and Disease

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the enriched Gene Ontology (GO) terms in Intestinal Epithelial cells across different colonic conditions: Healthy, Inflamed, and Non-inflamed. The Gene Set Association (GSA) analysis was performed by comparing each condition against all other cells in the dataset ('vs_others') to identify pathways that are significantly upregulated (GSA_up) in that specific condition. The results are presented as bar plots, showing the -log(p-val) and -log(q-val) for the top enriched terms. This helps us understand the distinct biological processes active in intestinal epithelial cells in varying states of colon health and disease.

Visual Summary

The provided bar plots illustrate the top enriched GO terms for Intestinal Epithelial cells in three distinct comparisons:

  1. Healthy_vs_others: This plot shows pathways relatively upregulated in healthy intestinal epithelial cells. Prominent terms include various metabolic pathways such as "Oxidative phosphorylation," "Citrate cycle (TCA cycle)," and "Valine, leucine and isoleucine degradation," alongside several neurodegenerative disease pathways like "Diabetic cardiomyopathy," "Non-alcoholic fatty liver disease," "Parkinson disease," and "Alzheimer disease." The high significance of these metabolic terms suggests a robust baseline metabolic activity in healthy epithelial cells.
  2. Inflamed_vs_others: In contrast, the inflamed state exhibits a strong enrichment for pathways indicative of cellular stress, infection response, and altered cell fate. Highly significant terms include "Protein processing in endoplasmic reticulum," "Spliceosome," "RNA transport," "Ribosome," "Ubiquitin mediated proteolysis," and pathways related to various viral and bacterial infections such as "Epstein-Barr virus infection," "Salmonella infection," "Pathogenic Escherichia coli infection," and "Coronavirus disease." Additionally, terms like "Cell Cycle," "Cellular senescence," and "Apoptosis" suggest significant cellular perturbation and turnover.
  3. Non-inflamed_vs_others: This plot shows an intermediate or distinct profile compared to the Healthy and Inflamed states. It shares some stress-related pathways with the inflamed condition, such as "Protein processing in endoplasmic reticulum," "Coronavirus disease," and "Ubiquitin mediated proteolysis." Notably, it also highlights immune-related terms like "Antigen processing and presentation," "Bacterial invasion of epithelial cells," and "Th17 cell differentiation." Pathways related to structural integrity, such as "Adherens junction," are also enriched. The overall significance of the enriched terms appears lower than in the "Inflamed" state, suggesting a milder or more heterogeneous cellular response.

Biological Interpretation

The distinct Gene Ontology profiles observed in Intestinal Epithelial cells across the Healthy, Inflamed, and Non-inflamed conditions provide crucial insights into their functional adaptations during colon disease.

In the healthy colon, intestinal epithelial cells are highly metabolically active, responsible for nutrient absorption and maintaining barrier integrity. The enrichment of pathways like "Oxidative phosphorylation," "Citrate cycle (TCA cycle)," and "Valine, leucine and isoleucine degradation" reflects their continuous high energy demands and robust metabolic machinery. The appearance of "Diabetic cardiomyopathy" and other neurodegenerative pathways might seem unusual but often indicates that the underlying metabolic or cellular stress response pathways (e.g., mitochondrial function, protein quality control, lipid metabolism) are fundamental processes across various cell types, and their dysregulation is implicated in these diseases. In healthy colon epithelial cells, these core metabolic processes are likely operating optimally [PubMed search: intestinal epithelial cell metabolism].

During inflammation, intestinal epithelial cells face significant challenges, including direct exposure to pathogens and inflammatory mediators. The strong enrichment for "Protein processing in endoplasmic reticulum," "Spliceosome," "RNA transport," "Ribosome," and "Ubiquitin mediated proteolysis" indicates a substantial activation of the cellular stress response, particularly ER stress and protein quality control mechanisms. This is a common cellular response to inflammation and infection, aiming to manage misfolded proteins and maintain cellular homeostasis under duress [PubMed search: ER stress inflammation gut].

Furthermore, the presence of numerous infection-related pathways (e.g., "Epstein-Barr virus infection," "Salmonella infection," "Pathogenic Escherichia coli infection") suggests that in the inflamed state, epithelial cells are actively involved in host defense, recognizing and responding to microbial challenges. The upregulation of "Cell Cycle," "Cellular senescence," and "Apoptosis" reflects the dynamic nature of the inflamed epithelium, where cells may be undergoing increased turnover, attempting repair, or being eliminated if damaged [PubMed search: epithelial cell apoptosis inflammation].

The "Non-inflamed" state, while not overtly inflamed, shows signs of an active but potentially subclinical immune response and cellular stress. Similar to the inflamed state, "Protein processing in endoplasmic reticulum" and certain infection pathways are enriched, suggesting ongoing cellular stress and pathogen encounter. Crucially, the enrichment of "Antigen processing and presentation" and "Bacterial invasion of epithelial cells" highlights the active role of epithelial cells in innate immunity and their capacity to interact with the immune system even in the absence of overt inflammation [PubMed search: intestinal epithelial cell innate immunity]. The involvement of "Th17 cell differentiation" further underscores this, as epithelial cells can influence T cell polarization. The enrichment of "Adherens junction" could indicate alterations in epithelial barrier integrity or ongoing repair processes, which are critical in maintaining gut health and are often compromised in early stages of gut disorders [PubMed search: adherens junction intestinal epithelial barrier]. This suggests that epithelial cells in "Non-inflamed" conditions are not truly quiescent but are actively sensing and responding to their environment, possibly acting as sentinels or attempting to restore homeostasis.

Clinical or Translational Implications

These findings have significant clinical implications for understanding inflammatory bowel diseases (IBD) and other colonic inflammatory conditions.

  1. Biomarkers of Disease Progression: The distinct pathway enrichments in inflamed vs. non-inflamed epithelial cells could provide novel insights into disease mechanisms and potential biomarkers for different stages of inflammation. For instance, the robust ER stress and extensive pathogen response in inflamed cells could serve as indicators of active disease, while antigen processing and subtle barrier alterations in non-inflamed cells might reflect a pre-inflammatory or resolving state.
  2. Therapeutic Targets: Targeting specific pathways identified in the inflamed state, such as ER stress response components or specific host-pathogen interaction pathways, could offer new therapeutic strategies to mitigate inflammation and epithelial damage. Conversely, supporting the metabolic health of epithelial cells, as observed in the healthy state, could be beneficial for maintaining gut barrier function and overall health.
  3. Understanding "Non-inflamed" Disease: The active immune and stress responses in "Non-inflamed" epithelial cells emphasize that macroscopically non-inflamed areas in disease contexts are not necessarily healthy at a cellular level. This highlights the importance of cellular-level analysis to detect subtle pathological changes that could precede overt inflammation or indicate persistent subclinical disease activity, guiding treatment decisions or disease monitoring.

16. Colon Cell Type-Specific Gene Set Enrichment Analysis Across Inflammatory Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for major cell types found in the human colon, comparing gene expression profiles across different conditions: Healthy, Inflamed, and Non-inflamed. The dot plot visualizes the Normalized Enrichment Score (NES) and statistical significance (-log(p-value)) for 80 KEGG pathways. Each column represents a comparison of a specific cell type in one condition against all other conditions combined (e.g., "B cell: Healthy_vs_others" compares B cell expression in the Healthy condition to B cell expression in the Inflamed and Non-inflamed conditions). Red dots indicate positive NES (pathway generally upregulated in the 'test' condition), while blue dots indicate negative NES (pathway generally downregulated). The size of the dot reflects the statistical significance, with larger dots indicating smaller p-values (higher -log(p-value)).

Visual Summary

The dot plot reveals widespread and distinct pathway enrichment patterns across different cell types and conditions.

Biological Interpretation

Global Inflammatory and Immune Activation

A consistent and strong signal across multiple immune cells (Macrophages, T cells CD4+, T cells CD8+, B cells, ILCs) and even resident cells (Intestinal Epithelial cells, Fibroblasts, Endothelial cells) in the 'Inflamed_vs_others' condition is the enrichment of key inflammatory and immune-related pathways.

Distinct Signatures in Non-Inflamed Diseased Tissue

The 'Non-inflamed_vs_others' comparisons reveal significant molecular perturbations even in the absence of overt inflammation, suggesting subclinical disease activity or a state of predisposition.

Cell Type-Specific Contributions to Colon Pathobiology

Clinical or Translational Implications

The distinct pathway enrichments identified through this GSEA analysis provide valuable insights for understanding the molecular mechanisms driving colon inflammation and disease progression.

References

  1. IL-17 signaling pathway in inflammatory bowel disease: https://pubmed.ncbi.nlm.nih.gov/?term=IL-17+signaling+inflammatory+bowel+disease
  2. HIF-1 signaling pathway in inflammation and hypoxia: https://pubmed.ncbi.nlm.nih.gov/?term=HIF-1+signaling+inflammation+hypoxia
  3. Chronic inflammation and colorectal cancer: https://pubmed.ncbi.nlm.nih.gov/?term=chronic+inflammation+colorectal+cancer
  4. Warburg effect in immune cells: https://pubmed.ncbi.nlm.nih.gov/?term=Warburg+effect+immune+cells

17. Discussion

The comprehensive single-cell analysis of human colon tissue across Healthy, Inflamed, and Non-inflamed conditions reveals a dynamic and complex cellular ecosystem, with profound alterations in cell populations, gene expression, and intercellular communication during inflammation.

A central finding is the significant infiltration and phenotypic shift of immune and stromal cells in both Inflamed and, to a lesser extent, Non-inflamed tissues compared to Healthy controls. Specifically, the relative proportions of B cells, Plasma cells, T cells (CD4+ and CD8+), Macrophages, and Dendritic cells are markedly increased in inflamed and non-inflamed states. Within the lymphoid compartment, Inflamed tissue is characterized by a prominent expansion of Innate Lymphoid Cell (ILC) subsets (ILC1, ILC2, ILC3 (NCR+), ILCreg), indicative of robust innate immune activation, while T regulatory cell (Treg) proportions appear relatively diminished. Macrophage subset analysis further refines this, showing a significant increase in M2B macrophages in both Non-inflamed and Inflamed conditions, suggesting their involvement in early immune dysregulation or sustained inflammatory responses, distinct from the higher M2D populations observed in Healthy tissue.

Beyond population shifts, the phenotypic characteristics of key cell types are distinctly altered. Healthy macrophages maintain an antigen presentation and immune regulatory profile (HLA-DQB2, ADORA3, HLA-G). In contrast, Non-inflamed macrophages adopt a primed state with enhanced pathogen recognition (CLEC7A), cytokine responsiveness (IL6ST), and active tissue remodeling (TGFBR1/2, ADAM17/28, CYSLTR1). Similarly, healthy fibroblasts exhibit immune modulatory functions (HLA-DPA1/DRB1/G), while inflamed and non-inflamed fibroblasts show markers of activation, increased adhesion (ITGAV, ITGA1, CDH11), growth factor signaling (FGFR1, AXL), and ECM remodeling (GPNMB), consistent with fibrotic processes. CD4+ T cells in inflamed tissue display a complex activation profile, marked by both co-stimulatory (TNFRSF18/GITR) and inhibitory (TIGIT, CTLA4) immune checkpoint receptors, alongside responsiveness to TNF (TNFRSF1B/TNFR2). This suggests a dynamic interplay of activating and dampening signals to fine-tune T cell responses in the inflammatory milieu. Notably, B cells in the inflamed colon uniquely upregulate TGFB1, YWHAB, and YWHAZ, suggesting specific regulatory or stress-response mechanisms rather than a global cell cycle activation.

Cell-cell interaction (CCI) analyses unveil condition-specific communication networks. Healthy tissue emphasizes homeostatic interactions involving T CD8+ cells (IFNG, LCK_CD8_receptor) and pro-resolving signals (ANXA1-FPR3). Inflamed tissue shows a pronounced increase in T cell co-stimulation (CD86-CD28), tissue remodeling (HBEGF-EGFR), and extensive extracellular matrix (ECM)-related interactions (e.g., COL3A1_integrin_a1b1, FN1_integrin_a3b1) predominantly between fibroblasts, indicative of active fibrogenesis. Epithelial-stromal crosstalk involving Wnt and Prostaglandin E2 (PGE2) signaling (WNT2B-FZD5_LRP5, ProstaglandinE2-PTGER4) and angiogenesis (JAG1-NOTCH4) are also hallmarks of inflammation. The Non-inflamed state shares some inflammatory signals, particularly PGE2-related interactions and sustained T cell activation markers, but crucially reveals an emergence of TGFB1-TGFbeta_receptor1 signaling, hinting at ongoing immune regulation or early fibrotic propensity distinct from acute inflammation.

Gene Ontology (GSA) and Gene Set Enrichment Analysis (GSEA) further elucidate the functional shifts. Healthy intestinal epithelial cells are highly metabolically active (oxidative phosphorylation, TCA cycle). In contrast, inflamed epithelial cells exhibit strong signs of cellular stress (ER protein processing, ribosome, ubiquitin-mediated proteolysis), active host-pathogen interaction (infection pathways), and dynamic turnover (cell cycle, apoptosis). Non-inflamed epithelial cells show an intermediate profile with active antigen processing and presentation, bacterial invasion responses, and Th17 cell differentiation, suggesting an active immunological engagement even without overt inflammation. Across major cell types, GSEA confirms widespread activation of chemokine, cytokine, IL-17, and Toll-like receptor signaling pathways in inflamed conditions. Macrophages and T cells in inflamed tissue undergo significant metabolic reprogramming towards aerobic glycolysis (Warburg effect), fueling their pro-inflammatory functions, while fibroblasts show activation of focal adhesion, HIF-1, and PI3K-Akt signaling, contributing to ECM remodeling and angiogenesis. The consistent enrichment of 'Pathways in cancer' in inflamed and non-inflamed intestinal epithelial cells and fibroblasts underscores the critical link between chronic inflammation and increased colorectal cancer risk.

Collectively, these findings paint a detailed picture of colonic inflammation, emphasizing the coordinated dysregulation across multiple cell types and their intercellular communication networks. The 'Non-inflamed' condition emerges as a critical intermediate state, not simply a return to health, but often characterized by a primed immune system, ongoing tissue remodeling, and altered homeostatic mechanisms that could predispose to or reflect persistent subclinical disease.

Hypotheses:

  1. The expansion of ILC subsets (ILC1, ILC2, ILC3 (NCR+), ILCreg) in inflamed colon directly contributes to the perpetuation of inflammation through cytokine production and host-pathogen interactions.
  2. The increase in M2B macrophages in both non-inflamed and inflamed colon represents a distinct macrophage polarization state that modulates immune responses and promotes tissue remodeling, acting as a bridge between quiescent and active inflammatory states.
  3. Aberrant activation of Integrin-mediated interactions (e.g., COL3A1_integrin_a1b1, FN1_integrin_a3b1) and Wnt/Frizzled signaling (WNT2B-FZD5_LRP5) in fibroblasts is a primary driver of fibrotic remodeling in chronic colonic inflammation.
  4. The upregulation of immune checkpoint receptors (TIGIT, CTLA4) on inflamed CD4+ T cells reflects a compensatory immune-regulatory mechanism attempting to limit excessive inflammation, rather than just an activated effector phenotype.
  5. Non-inflamed intestinal epithelial cells, despite lacking overt signs of inflammation, actively engage in antigen processing and presentation, and bacterial invasion responses, serving as sentinels that contribute to the maintenance of low-grade inflammation or predispose to future inflammatory flares.
  6. Metabolic reprogramming, characterized by increased glycolysis and reduced oxidative phosphorylation in activated macrophages and T cells, is a critical adaptive mechanism that fuels pro-inflammatory functions in the inflamed colon.

Potential therapeutic targets:

  1. M2B Macrophages / CLEC7A, CYSLTR1, OLR1 pathways: M2B macrophages are significantly increased in both Non-inflamed and Inflamed conditions, suggesting their critical role in the initiation or maintenance of colonic inflammation. Their surface markers like CLEC7A (pattern recognition), CYSLTR1 (lipid mediator response), and OLR1 (oxidative stress) indicate a primed, activated, or pro-inflammatory phenotype. Evidence: Mac (M2B) proportions are significantly higher in Non-inflamed and Inflamed conditions compared to Healthy (p ≤ 0.01). Surfaceome analysis shows distinct upregulation of CLEC7A, CYSLTR1, OLR1, IL6ST, TGFBR1/2, ADAM17 in Non-inflamed macrophages. Validation: In vivo genetic or pharmacological depletion/modulation of M2B macrophages or their specific surface receptors (e.g., anti-CLEC7A antibodies, CYSLTR1 antagonists) in experimental colitis models to assess impact on inflammation and tissue repair.
  2. Fibroblast Activation / FGFR1, AXL, Integrin-αV (ITGAV), GPNMB, CDH11: Activated fibroblasts contribute significantly to inflammation, tissue remodeling, and fibrosis in chronic inflammatory conditions. Upregulation of these surface markers indicates their active pathogenic role. Evidence: Inflamed and Non-inflamed fibroblasts show robust upregulation of FGFR1, AXL, ITGAV, GPNMB, CDH11, and other ECM-related integrins, linked to increased cell adhesion, growth factor signaling, and ECM remodeling. CCI analysis further highlights extensive ECM interactions (COL3A1_integrin_a1b1_complex--Fib|Fib, FN1_integrin_a3b1_complex--Fib|Fib) in inflamed tissue. Validation: Small molecule inhibitors targeting FGFR1 or AXL, or blocking antibodies against ITGAV or GPNMB, in ex vivo human colon tissue explants or in vivo fibrosis models to reduce collagen deposition and fibroblast activation.
  3. IL-17 Signaling Pathway: IL-17 is a key pro-inflammatory cytokine implicated in IBD pathogenesis, and its signaling pathway is broadly enriched across multiple cell types in inflamed colon. Evidence: GSEA shows strong enrichment of the IL-17 signaling pathway in T cells, Macrophages, Intestinal Epithelial cells, and Fibroblasts in inflamed tissue. ILC subsets, which can produce IL-17, are expanded in inflamed conditions. Validation: Use of existing anti-IL-17 antibodies or receptor antagonists (e.g., anti-IL-17A, anti-IL-17RA) in preclinical models of colitis to assess efficacy in reducing inflammation.
  4. T Cell Co-stimulation / CD86-CD28 axis & Immune Checkpoints (TIGIT, CTLA4, GITR): The CD86-CD28 axis is critical for T cell activation, and immune checkpoints (TIGIT, CTLA4) regulate this activity in inflamed tissue. Modulating these pathways can control excessive T cell responses. Evidence: CCI analysis shows strong CD86-CD28 interaction in inflamed conditions. Surfaceome analysis of CD4+ T cells shows upregulation of TIGIT, CTLA4, and TNFRSF18 (GITR) in inflamed states. Validation: Develop or repurpose immunomodulatory agents (e.g., CTLA4-Ig fusion protein, TIGIT agonists, GITR modulators) to dampen T cell overactivity in inflammatory models.

Follow-up validation ideas:

  1. Flow cytometry or Immunohistochemistry: Quantify the proportions of ILC subsets (ILC1, ILC2, ILC3 (NCR+), ILCreg), M2B macrophages (using CLEC7A, CYSLTR1, OLR1 markers), and Treg cells (TIGIT, CTLA4, GITR) in colon biopsies from Healthy, Non-inflamed, and Inflamed patients to validate population shifts and surface marker expression observed in scRNA-seq.
  2. Spatial Transcriptomics or Multiplex Immunofluorescence: Map the spatial distribution and co-localization of activated fibroblasts (expressing FGFR1, AXL, GPNMB) and their interacting immune cells within inflamed and non-inflamed colon tissue to confirm CCI findings and identify microenvironmental niches of fibrogenesis and inflammation.
  3. In vitro co-culture experiments: Co-culture fibroblasts with intestinal epithelial cells or immune cells, perturbing key ligand-receptor interactions like WNT2B-FZD5/LRP5 or Integrin-ECM components, to assess their impact on epithelial proliferation, barrier function, or fibroblast activation and ECM production.
  4. Organoid or ex vivo colon tissue models: Use patient-derived colon organoids or explants from different conditions to functionally validate the role of specific signaling pathways (e.g., IL-17, PI3K-Akt, HIF-1, or PGE2 via PTGER4) identified by GSEA/GSA in epithelial and immune cell responses to inflammatory stimuli.
  5. Perturbation assays in animal models of colitis: Administer inhibitors or activators targeting pathways such as FGFR1, AXL, Integrins, or modulators of M2B macrophage function in experimental colitis models (e.g., DSS-induced colitis) to evaluate their therapeutic potential in reducing inflammation and fibrosis.
  6. Metabolomic analysis: Perform targeted metabolomics on sorted macrophages and T cells from healthy and inflamed colon tissue to confirm the predicted metabolic reprogramming (e.g., increased glycolysis intermediates, reduced TCA cycle metabolites).
  7. Functional assays for B cells: Investigate the functional consequences of elevated TGFB1, YWHAB, and YWHAZ expression in inflamed B cells, focusing on IgA class switching, proliferation, and cytokine production using sorted B cells from colon tissue or in vitro stimulated B cells.

Limitations:

This study provides a comprehensive single-cell view of human colon tissue, yet it is subject to several limitations. The cross-sectional nature of the data restricts causal inference regarding observed cellular and molecular changes; longitudinal studies would be required to establish temporality. While robust cell type annotation was achieved, some macrophage and epithelial subsets, as noted in the marker gene analysis, warrant further detailed characterization with additional specific markers or functional assays to resolve subtle phenotypic differences. The inferred cell-cell interactions are computational predictions based on ligand-receptor expression; direct functional validation through experimental perturbation is essential to confirm their biological relevance. Finally, the "Non-inflamed" category represents a heterogeneous group, likely encompassing quiescent disease, subclinical inflammation, or remission states, which may mask more nuanced molecular programs relevant to disease progression or resolution. Future studies integrating spatial omics and functional assays will be crucial to overcome these limitations and fully translate the identified molecular signatures into clinical applications.

18. Query List

  1. Show UMAPs including condition, sample, major cell type, minor cell type, and celltype_subset, in 2 columns, and save it.
  2. Show expression levels of CD3D, CD4, CD8A, CD79A, MS4A1, MZB1, CD14, LYZ, FBLN1, NOTCH3, EPCAM, MUC1, CD34 genes on UMAP, along with minor cell type annotation. Use ncols=4, and save it.
  3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
  4. Show a population bar plot of minor cell types and save it.
  5. Show a subset population barplot for T cells and save it.
  6. Show a subset population barplot for Macrophages and save it.
  7. For Macrophage subset populations, find any statistically significant differences between conditions and show them as box plots. Determine ncols appropriately based on the total number of panels, and save it.
  8. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
  9. Select only genes related to immune checkpoint and cell cycle pathways, and show cell-cell interactions for these genes, and save it.
  10. Find statistically significant differences in cell-cell interactions between conditions for major immune and stromal cells, show them as a dot plot with max_n_items_per_group = 25, and save it.
  11. Extract condition-specific markers for 'Macrophage' cells, show them as a dot plot, including only surfaceome markers up to 50 per condition, and save it.
  12. Extract condition-specific markers for 'Fibroblast' cells, show them as a dot plot, including only surfaceome markers up to 50 per condition, and save it.
  13. Extract condition-specific markers for 'T cell CD4+' cells, show them as a dot plot, including only surfaceome markers up to 50 per condition, and save it.
  14. For major disease-related cell types (B cell, Endothelial cell, Fibroblast, ILC, Intestinal Epithelial cell, Macrophage, Mast cell, NK cell, Plasma cell, T cell CD4+, T cell CD8+), identify cell cycle pathway related genes with statistically significant expression differences between conditions, show them as boxplots with max_n_items_to_plot = 24, set ncols to maintain a 2x3 aspect ratio, and save it.
  15. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
  16. Show a dot plot of Gene Set Enrichment Analysis results for major cell types (B cell, Endothelial cell, Fibroblast, ILC, Intestinal Epithelial cell, Macrophage, Mast cell, NK cell, Plasma cell, T cell CD4+, T cell CD8+), use RdBu_r as the color map, n_pws_to_show = 80, and save it.
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