SCODiA Report by MLBI Lab

Single-Cell Landscape and Tumor-Immune Interactions in Colon Cancer: Unveiling Therapeutic Targets

This single-cell RNA sequencing report delineates the cellular and molecular landscape of colon cancer by comparing tumor and adjacent normal tissues. We observed a clear distinction between malignant aneuploid epithelial cells and diploid non-malignant cells, accompanied by significant remodeling of the tumor microenvironment. Key findings include increased infiltration of diverse immune and stromal cell populations, with notable shifts towards immunosuppressive T cell and macrophage phenotypes. Furthermore, distinct cell-cell interaction networks and condition-specific surfaceome markers highlight dysregulated communication pathways and potential therapeutic vulnerabilities in tumor cells, cancer-associated fibroblasts, and tumor-associated macrophages.

Contents

  1. Dataset overview
  2. UMAP Visualization of Single-Cell Transcriptomic Data by Condition, Sample, and Cell Type Annotations
  3. UMAP Visualization of Key Marker Gene Expression and Minor Cell Type Annotation
  4. Celltype Subtype Marker Expression Validation in Colon Single-Cell RNA-seq Data
  5. Copy Number Variation Analysis of Intestinal Epithelial Cells and Unassigned Cells
  6. UMAP Visualization of CNV Patterns across Cell Types, Ploidy, Condition, and Samples
  7. Colon Tissue Minor Cell Type Population Analysis
  8. T Cell Subset Population Dynamics in Colorectal Cancer
  9. Macrophage Subset Population Shifts in Colon Cancer
  10. Differential T Cell Subset Proportions in Colon Tumor vs. Adjacent Normal Tissue
  11. Macrophage (M1) Subset Proportion in Colon Tumor vs. Adjacent Normal Tissue
  12. Ploidy Population Analysis of Intestinal Epithelial and Unassigned Cells in Colon Tissue
  13. Colon Cancer Cell-Cell Interaction Landscape: Tumor vs. Adjacent Normal
  14. Adjacent Normal 및 종양 조직의 세포-세포 상호작용 분석
  15. Immune Checkpoint and Cell Cycle Related Cell-Cell Interactions in Colon Tissue
  16. Condition-Specific Cell-Cell Interaction Patterns in Colon Tissue
  17. Intestinal Epithelial Cell Condition-Specific Surfaceome Markers in Colon Tissue
  18. Macrophage Condition-Specific Surfaceome Markers in Colon Tissue
  19. Condition-Specific Surfaceome Markers in Colon Fibroblasts
  20. CD4 T Cell Surfaceome Markers in Colon Cancer Conditions
  21. Intestinal Epithelial Cells in Colon Cancer Exhibit Widespread Upregulation of Cell Cycle Genes
  22. Gene Ontology (GSA) Analysis of Intestinal Epithelial Cells in Colon Tissue
  23. Gene Set Enrichment Analysis across Major Cell Types in Colon Tissue
  24. Discussion
  25. Query List

0. Dataset overview

Dataset Summary

Precomputed Results: The dataset includes precomputed results for

1. UMAP Visualization of Single-Cell Transcriptomic Data by Condition, Sample, and Cell Type Annotations

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[Analysis Visualization Results]...

Analysis Overview

This analysis provides a comprehensive visualization of single-cell RNA-sequencing data using UMAP (Uniform Manifold Approximation and Projection) plots. These plots illustrate the overall cellular landscape of the colon tissue, colored by various metadata features: disease condition (tumor vs. adjacent normal), individual samples, major cell types, minor cell types, ploidy status, and highly detailed cell type subsets. The goal is to assess the data's overall structure, the quality of cell type annotations, and the distribution of cells across different biological and technical factors.

Visual Summary

Condition

The UMAP colored by condition shows a clear separation between cells originating from 'tumor' (indigo) and 'adjacent_normal' (maroon) tissues. A large, dense cluster on the right side of the UMAP is predominantly composed of tumor cells, while adjacent normal cells are more interspersed, often co-localizing with tumor cells in some regions, but also forming distinct smaller clusters. This indicates significant transcriptional differences between cells in the tumor microenvironment and those in healthy adjacent tissue.

Sample

The sample UMAP displays a remarkable intermixing of cells from different individual samples (C103-C173) across the entire embedding. No single sample forms isolated, large clusters that dominate the landscape. This suggests effective integration of data from multiple donors, indicating that sample-specific technical variations (batch effects) have been largely mitigated, allowing true biological heterogeneity to emerge.

Celltype Major

The celltype_major UMAP reveals well-defined clusters corresponding to broad cell lineages. 'Intestinal Epithelial cell' (orange) forms a large, central cluster, consistent with its role as the tumor origin cell type. 'T cell' (teal), 'Stromal cell' (light green), 'Myeloid cell' (yellow-green), and 'B cell' (maroon) populations also form distinct clusters, reflecting their unique transcriptional profiles and demonstrating robust major cell type annotation. 'Endothelial cell' (red) and 'Mast cell' (yellow) appear as smaller, distinct groups.

Celltype Minor

The celltype_minor UMAP provides a finer resolution of cell identities within the major groups. For example, 'T cell CD4+' (dark blue) and 'T cell CD8+' (blue) are clearly resolved within the broader T cell compartment. 'Macrophage' (yellow) and 'Fibroblast' (light orange) emerge as prominent populations, further refining the immune and stromal compartments, respectively. This demonstrates the ability to distinguish functionally distinct cell populations within the major lineages.

Ploidy_dec

The ploidy_dec UMAP highlights the distribution of aneuploid and diploid cells. 'Aneuploid' cells (maroon) are predominantly concentrated within the large cluster on the right, which largely overlaps with the 'tumor' condition and the 'Intestinal Epithelial cell' cluster. 'Diploid' cells (light yellow) are widely distributed across the entire UMAP, encompassing most immune, stromal, and some epithelial cell populations. This strong association of aneuploidy with the epithelial cell compartment within tumor regions is a key finding. A small proportion of cells are labeled as 'Unclear' (dark blue).

Celltype Subset

The celltype_subset UMAP presents the highest resolution of cell type annotation, distinguishing highly specific cell populations. Within the 'Intestinal Epithelial cell' compartment, various specialized cells like 'Enterocyte', 'Goblet cell', 'Crypt cell', 'Paneth cell', and 'Tuft cell' are identified. Immune cells are further subdivided into numerous functional subsets, including various T helper cells ('Tfh', 'Th1', 'Th2', 'Th9', 'Th17', 'Th22'), 'Treg', 'T cell (Cytotoxic)', and distinct 'Macrophage' polarization states (M1, M2A, M2B, M2C, M2D). This granular annotation offers detailed insight into cellular heterogeneity.

Biological Interpretation

The UMAP visualizations collectively provide a robust overview of the cellular composition and transcriptional states within the colon tissue, differentiating between tumor and adjacent normal conditions.

  1. Tumor-specific Cellular States: The condition UMAP clearly shows that tumor cells drive a significant portion of the transcriptional variability, forming distinct clusters. This is expected in colorectal cancer, where malignant transformation alters gene expression in tumor cells and reshapes the surrounding microenvironment.
  2. Aneuploidy as a Tumor Cell Marker: The ploidy_dec UMAP provides strong evidence for identifying malignant epithelial cells. Aneuploidy, a hallmark of cancer characterized by an abnormal number of chromosomes, is predominantly found in the 'Intestinal Epithelial cell' cluster that overlaps with the 'tumor' condition. This suggests that the large 'Intestinal Epithelial cell' cluster on the right side of the UMAP predominantly represents the cancerous epithelial cells [PubMed search: aneuploidy cancer biomarker]. Other cell types, such as immune and stromal cells, consistently maintain a diploid state, as expected for non-malignant cells.
  3. Comprehensive Cell Type Annotation: The progressive resolution from celltype_major to celltype_subset highlights the rich cellular heterogeneity of the colon and its microenvironment. Identifying specific subsets like various T cell helper populations (e.g., Th1, Th17, Treg), diverse macrophage polarization states (e.g., M1, M2 subtypes), and specialized intestinal epithelial cells (e.g., Enterocytes, Goblet cells) is crucial for understanding their specific functions and dysregulation in disease.
  4. Robust Data Integration: The lack of prominent sample-specific clustering in the sample UMAP confirms the success of data integration methods. This ensures that observed cellular differences and patterns are primarily driven by biological variation rather than technical artifacts, enhancing the reliability of subsequent analyses comparing conditions or cell types across the entire dataset.

Annotation Notes

The consistency across the UMAPs – specifically, the strong alignment between the large Intestinal Epithelial cell cluster, the tumor condition, and the Aneuploid ploidy status – provides high confidence in the quality of the cell type annotations, particularly for distinguishing malignant epithelial cells from other stromal and immune cells. The granular celltype_minor and celltype_subset annotations appear biologically meaningful and are well-segregated in the embedding space, indicating accurate assignment of cell identities based on their transcriptional profiles. The effective removal of batch effects (as seen in the sample UMAP) further validates the integrity of the embedding for downstream analyses.

2. UMAP Visualization of Key Marker Gene Expression and Minor Cell Type Annotation

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the expression of a panel of known marker genes across the single-cell RNA-seq dataset on a UMAP embedding, alongside the pre-computed celltype_minor annotations. The primary goal is to assess the quality of the cell type annotations by examining if specific marker genes show enriched expression in their expected cell populations. This helps to confirm the distinct identity and spatial separation of different cell types within the UMAP landscape.

Visual Summary

The UMAP plot displays a complex cellular landscape from human colon tissue, with multiple distinct clusters representing different cell populations. The celltype_minor annotation plot reveals well-separated clusters for major immune cell types (T cells, B cells, Macrophages, NK cells, Plasma cells, Mast cells, DCs), stromal cells (Fibroblasts, Smooth muscle cells, Endothelial cells), and Intestinal Epithelial cells (Ent.Epi).

Upon examining the expression patterns of the selected marker genes:

Immune Cell Markers:

Stromal and Epithelial Cell Markers:

Biological Interpretation

The UMAP plots clearly demonstrate that the celltype_minor annotations are well-supported by the expression patterns of established marker genes. Each marker gene exhibits highly restricted expression to its expected cell type cluster, indicating a high degree of specificity and accuracy in the cell type assignments.

Overall, the visualizations confirm that the UMAP embedding effectively separates distinct cell populations, and the celltype_minor annotations are reliable and biologically coherent based on these classic gene markers. This robust annotation forms a strong foundation for subsequent downstream analyses, such as differential gene expression or cell-cell interaction studies.

Annotation Notes

The strong concordance between known cell type-specific marker gene expression and the celltype_minor annotations provides significant confidence in the quality of the current cell type assignments. The UMAP embedding effectively resolves distinct cell populations, and the marker gene expression patterns reinforce the biological identity of each annotated cluster. This visual validation is crucial for ensuring the interpretability and reliability of any conclusions drawn from this single-cell dataset.

3. Celltype Subtype Marker Expression Validation in Colon Single-Cell RNA-seq Data

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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 dataset of human colon tissue. The purpose is to visually confirm the distinct identity of each cell subtype based on the specificity and abundance of its marker gene expression. The plot visualizes the mean expression level (color intensity) and the fraction of cells expressing the gene (dot size) within each celltype_subset. Only surfaceome-related markers are shown, which are often relevant for functional characterization or therapeutic targeting.

Visual Summary

The dot plot displays celltype_subset populations on the y-axis and marker genes on the x-axis. A clear block-diagonal pattern is observed, with distinct clusters of highly expressed (dark red color) and prevalent (large dot size) markers for most cell subtypes. Red boxes are drawn around these clusters, indicating genes that are highly specific to a particular cell type or a closely related group of cell types. This pattern suggests that the selected marker genes are effective at distinguishing the various celltype_subset populations. The legend on the right indicates the number of cells in each group, while the color bar and dot size legend explain mean expression and fraction of cells, respectively.

Biological Interpretation

The marker gene expression patterns largely confirm the distinct biological identities of the annotated celltype_subset populations, showing specific enrichment of known markers within their respective groups.

Intestinal Epithelial Cells

The diverse intestinal epithelial cell subtypes show highly specific marker expression:

Immune Cells (Lymphoid Lineage)

T cells

Immune Cells (Myeloid Lineage)

Stromal Cells

Endothelial Cells

Annotation Notes

The dot plot provides strong evidence supporting the quality and distinctness of the celltype_subset annotations within this AnnData object. The clear, specific expression patterns of known marker genes across most cell types indicate robust clustering and annotation. The selection of surfaceome-only markers further enhances the practical utility of these findings, as these markers are often amenable to validation by techniques such as flow cytometry or immunohistochemistry, and are potential candidates for cell-specific targeting. The consistency between observed gene expression and established biological knowledge reinforces confidence in the cell type assignments for downstream analyses.

4. Copy Number Variation Analysis of Intestinal Epithelial Cells and Unassigned Cells

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Analysis Overview

This analysis investigates copy number variations (CNVs) in Intestinal Epithelial cells and "unassigned" cell populations, grouped by individual samples. The primary objective is to visualize recurrent genomic amplifications and deletions and to identify potential genomic instability associated with the ploidy_dec (Aneuploid vs. Diploid) status and tumor origin. The results are presented as a heatmap showing log2(CNR) values across genomic spots for individual cells, followed by a summary heatmap and bar plot of significantly amplified cytogenetic bands and associated genes.

Visual Summary

CNV Heatmap (First Image)

The first heatmap displays log2(CNR) values across the genome for individual cell groups, colored by amplification (red) and deletion (blue).

CNV Summary Heatmap and Bar Plot (Second Image)

The second visualization provides a summary of the frequency of significant copy number alterations across cytogenetic bands for the selected samples.

Biological Interpretation

The analysis clearly reveals distinct genomic profiles within the selected Intestinal Epithelial cells and unassigned cells, providing critical insights into their biological states.

Key Oncogene Amplifications in Colorectal Cancer:

Tumor Suppressor Gene Deletion:

Annotation Notes

5. UMAP Visualization of CNV Patterns across Cell Types, Ploidy, Condition, and Samples

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Analysis Overview

This analysis presents UMAP embeddings derived from Copy Number Variation (CNV) estimates, providing a dimensionality reduction view of the single-cell RNA-seq data. The UMAP plots are colored by various metadata features: major cell type, minor cell type, ploidy status (aneuploidy/diploidy), sample condition (tumor/adjacent normal), and individual sample IDs. This visualization helps to understand how cell types, malignancy status, and experimental conditions are structured in the CNV landscape.

Visual Summary

The UMAP projections reveal distinct patterns driven by CNV information:

Cell Type Distribution (celltype_major, celltype_minor)

Ploidy Status (ploidy_dec)

Condition Distribution (condition)

Sample Distribution (sample)

Biological Interpretation

The CNV-based UMAP provides strong biological insights into the cellular composition of the colon samples, particularly in the context of cancer:

Annotation Notes

6. Colon Tissue Minor Cell Type Population Analysis

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Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 사용하여 대장 조직 내 인접 정상(adjacent_normal) 및 종양(tumor) 샘플에서 마이너 세포 유형(celltype_minor)의 상대적 분포를 시각화한 것입니다. 각 막대 그래프는 개별 샘플을 나타내며, 각 색상 세그먼트는 해당 샘플 내 특정 마이너 세포 유형의 상대적 비율을 보여줍니다. 이 분석은 대장암 발생 및 진행에 따른 미세환경 변화를 이해하는 데 중요한 초기 단계를 제공합니다.

Visual Summary

제공된 막대 그래프는 인접 정상 조직과 종양 조직 간의 마이너 세포 유형 구성에서 뚜렷한 차이를 보여줍니다.

인접 정상 조직 (adjacent_normal)

종양 조직 (tumor)

전반적으로, 종양 미세환경은 면역 세포(특히 T 세포, 대식세포, 형질세포) 및 기질 세포(특히 섬유아세포)의 침윤이 증가하고, 장 상피세포 및 평활근 세포의 상대적 비율이 변화하는 특징을 보입니다.

Biological Interpretation

이러한 세포 집단 변화는 대장암의 복잡한 생물학적 과정과 종양 미세환경(TME)의 재구성을 반영합니다.

면역 세포 침윤 증가

기질 세포 변화

이러한 세포 집단 구성의 변화는 대장암에서 종양 미세환경이 질병 진행에 중요한 역할을 하며, 종양 세포뿐만 아니라 주변 면역 및 기질 세포 간의 복잡한 상호작용이 암의 생물학을 결정한다는 것을 보여줍니다.

Clinical or Translational Implications

이러한 세포 집단 분석 결과는 대장암의 진단, 예후 예측 및 치료 전략 개발에 중요한 임상적 시사점을 제공합니다.

치료 표적 발굴

7. T Cell Subset Population Dynamics in Colorectal Cancer

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Analysis Overview

This analysis presents a stacked bar plot visualizing the relative proportions of various T cell subsets and related innate lymphoid cells (ILCs) within the 'T cell' major population across individual samples. The samples are stratified by 'adjacent_normal' and 'tumor' conditions, allowing for a direct comparison of immune cell composition shifts in the tumor microenvironment (TME) versus healthy tissue. Each bar represents a single sample, and the colored segments indicate the percentage contribution of each specific T cell/ILC subset (e.g., T cell (Cytotoxic), T cell (Treg), ILCs) to the total T cell compartment for that sample.

Visual Summary

The stacked bar plots display the proportional distribution of 17 distinct T cell and ILC subsets across multiple samples from 'adjacent_normal' and 'tumor' tissues.

Biological Interpretation

The observed shifts in T cell subset populations in the colorectal tumor microenvironment provide critical insights into the immune landscape of the disease.

  1. Immune Activation and Differentiation: The reduction in T cell (Naive) populations in tumor tissue suggests that T cells are actively recruited to the TME and undergo differentiation into various effector or regulatory phenotypes. This is a common feature of immune responses in inflammatory and cancerous environments.
  2. Treg Expansion and Immune Suppression: The most notable finding is the apparent enrichment of T cell (Treg) populations in tumor samples. Tregs are master regulators of immune tolerance, and their accumulation in the TME is a well-established mechanism by which tumors evade anti-tumor immunity. By suppressing the activity of effector T cells (like Cytotoxic T cells) and other immune cells, Tregs promote tumor growth and metastasis [1].
  3. Cytotoxic T Cell Presence: The sustained presence of T cell (Cytotoxic) cells in the tumor, despite Treg expansion, indicates an ongoing anti-tumor immune response. However, the effectiveness of these cytotoxic cells is likely dampened by the increased suppressive activity of Tregs. The balance between effector and regulatory T cells (Teff/Treg ratio) is crucial for effective anti-tumor immunity [2].
  4. Role of ILCs and Other Th Subsets: While minor, the presence and potential subtle shifts in ILCs and various T helper subsets (Th1, Th17, Th22) highlight the complexity of the immune response in colorectal cancer. For instance, ILC3s play a significant role in gut immunity and can contribute to both protective and pathogenic responses depending on the context of the TME [3]. Th1 cells are generally pro-inflammatory and anti-tumor, while Th17 cells have context-dependent roles in cancer [4].

Clinical or Translational Implications

The findings from this T cell subset analysis have several important clinical and translational implications for colorectal cancer:

  1. Prognostic Marker: An increased Treg infiltration in colorectal cancer is often associated with a poorer prognosis, as it signifies a more immunosuppressive TME [1]. This observation aligns with typical findings in cancer immunology.
  2. Immunotherapeutic Target: The enrichment of Tregs in the TME presents a potential therapeutic target. Strategies aimed at depleting Tregs, inhibiting their function, or converting them into effector T cells could enhance anti-tumor immunity and improve responses to other immunotherapies, such as checkpoint inhibitors [5].
  3. Teff/Treg Ratio as a Biomarker: Monitoring the Teff/Treg ratio in tumor tissue could serve as a valuable biomarker for predicting patient response to immunotherapy or for assessing disease progression.
  4. Developing Combination Therapies: Understanding the precise shifts in the balance of T cell subsets can inform the development of more effective combination immunotherapies that simultaneously boost effector responses and counteract immunosuppression.

References:

  1. Treg in Cancer Immune Evasion: UniProt. (n.d.). FOXP3 - Forkhead box protein P3. Retrieved from https://www.uniprot.org/uniprot/Q9BZS1 (FOXP3 is a key marker for Tregs, and its role in immune suppression in cancer is well-established.)
  2. Teff/Treg Ratio: PubMed Search: "effector T cell regulatory T cell ratio cancer prognosis" https://pubmed.ncbi.nlm.nih.gov/?term=effector+T+cell+regulatory+T+cell+ratio+cancer+prognosis
  3. ILC3 in Gut Immunity/Cancer: PubMed Search: "ILC3 colorectal cancer" https://pubmed.ncbi.nlm.nih.gov/?term=ILC3+colorectal+cancer
  4. Th17 in Cancer: PubMed Search: "Th17 cells cancer" https://pubmed.ncbi.nlm.nih.gov/?term=Th17+cells+cancer
  5. Treg-targeting Therapies: PubMed Search: "Treg depletion cancer immunotherapy" https://pubmed.ncbi.nlm.nih.gov/?term=Treg+depletion+cancer+immunotherapy

8. Macrophage Subset Population Shifts in Colon Cancer

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Analysis Overview

This analysis visualizes the relative proportions of different Macrophage subsets (M1, M2A, M2B, M2C, M2D) within individual samples, comparing 'adjacent_normal' colon tissue with 'tumor' colon tissue. This type of analysis is crucial for understanding the composition of the tumor microenvironment and how immune cell populations shift during carcinogenesis.

Visual Summary

The stacked bar plot presents the percentage composition of five distinct macrophage subsets for each sample, separated by tissue condition: 'adjacent_normal' and 'tumor'.

Condition-Specific Differences:

Biological Interpretation

Macrophages are highly plastic immune cells that play diverse roles in health and disease, including cancer. In the context of the tumor microenvironment (TME), macrophages are often referred to as Tumor-Associated Macrophages (TAMs) and can be broadly categorized into pro-inflammatory M1-like (classically activated) and anti-inflammatory/pro-tumorigenic M2-like (alternatively activated) phenotypes.

Specific M2 Subtypes:

Clinical or Translational Implications

The differential distribution of macrophage subsets between normal and tumor colon tissue carries significant clinical implications:

9. Differential T Cell Subset Proportions in Colon Tumor vs. Adjacent Normal Tissue

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Analysis Overview

This analysis investigates the proportional differences of specific T cell subset populations within colorectal tumor tissue compared to adjacent normal tissue. Boxplots are utilized to visualize the distribution of cell type proportions for T cell subsets where statistically significant differences (p-value ≤ 0.1, with some stricter cutoffs observed) were identified between the 'tumor' and 'adjacent_normal' conditions. This provides insights into how the immune cellular landscape, specifically T cell compartments, is altered in the tumor microenvironment.

Visual Summary

The boxplots illustrate the proportions of five T cell subset populations: Th22, unassigned, Th2, ILCreg, and Treg, across 'tumor' and 'adjacent_normal' conditions.

Biological Interpretation

The observed shifts in T cell subset proportions provide critical biological insights into the immune microenvironment of colon cancer:

[1] PubMed search for "IL-22 colon cancer"

[2] GeneCards entry for IL22

[4] GeneCards entry for FOXP3 (a key Treg marker)

Overall, the data points to a substantial remodeling of the T cell compartment in colon tumors, with a clear enrichment of immunosuppressive (Tregs, ILCreg) and potentially pro-tumorigenic inflammatory (Th22) populations.

Clinical or Translational Implications

These findings have significant clinical and translational implications for colon cancer:

[5] PubMed search for "Treg depletion cancer therapy"

10. Macrophage (M1) Subset Proportion in Colon Tumor vs. Adjacent Normal Tissue

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Analysis Overview

This analysis investigates the proportional abundance of the Macrophage (M1) subset population in colon tissue, comparing tumor samples against adjacent normal tissue samples. The boxplot visualization, generated using plot_box_for_celltype_population_with_signif_difference, highlights statistically significant differences in cell type proportions between these two conditions, based on a predefined p-value cutoff of 0.1.

Visual Summary

The boxplot displays the celltype proportion of Macrophage (M1) cells across "tumor" and "adjacent_normal" conditions.

Biological Interpretation

Macrophages are a critical component of the tumor microenvironment (TME) and play diverse roles in cancer progression. M1 macrophages are typically characterized by their pro-inflammatory and anti-tumorigenic functions. They are involved in pathogen clearance, antigen presentation, and secretion of inflammatory cytokines (e.g., TNF-α, IL-1β, IL-6), leading to cytotoxic effects against tumor cells [1].

The analysis shows that the proportion of M1 macrophages is significantly higher in tumor tissue compared to adjacent normal tissue (p=0.08). This observation is intriguing, as many studies report a shift towards M2-like (pro-tumor) macrophages in the TME, often associated with immune suppression and tumor growth [2]. However, the presence and activity of M1 macrophages in colorectal cancer (CRC) can vary depending on the specific tumor stage, location, and the overall immune context. A higher proportion of M1 macrophages in the tumor might suggest an ongoing inflammatory response attempting to contain the tumor, or it could reflect the complexity and heterogeneity of macrophage polarization within the TME, where different macrophage phenotypes coexist and interact [3].

The observation is consistent with the colon tissue context, where chronic inflammation is a known risk factor for CRC, and the immune landscape is often characterized by a dynamic interplay of various immune cell subsets.

Clinical or Translational Implications

The finding of a higher proportion of M1 macrophages in colon tumor tissue, while potentially indicative of an anti-tumor immune response, warrants further investigation into their functional state. If these M1 macrophages are indeed active and functional, they could contribute to a more favorable immune environment.

References

  1. M1 Macrophage Function:

PubMed Search: "M1 macrophage function cancer"

  1. M2 Macrophages in TME:

PubMed Search: "M2 macrophages tumor microenvironment"

  1. Macrophages in Colorectal Cancer:

PubMed Search: "macrophage polarization colorectal cancer"

  1. Targeting Macrophages in Cancer Therapy:

PubMed Search: "macrophage targeting cancer therapy"

11. Ploidy Population Analysis of Intestinal Epithelial and Unassigned Cells in Colon Tissue

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Analysis Overview

This analysis investigates the ploidy status (Aneuploid, Diploid, or Unclear) of a combined population of Intestinal Epithelial cells and unassigned cells across various patient samples from both adjacent normal colon tissue and tumor tissue. The goal is to understand the chromosomal stability or instability within these cell types, particularly in the context of colorectal cancer. Intestinal Epithelial cells are identified as the tumor origin cell type, making their ploidy status especially relevant to malignancy.

Visual Summary

The stacked bar plots display the proportion of Aneuploid (maroon), Diploid (orange), and Unclear (light green) cells for each sample, separated by adjacent_normal and tumor conditions. Each bar represents a specific patient sample.

  1. Adjacent Normal Tissue:
  1. Tumor Tissue:

Biological Interpretation

The observed ploidy patterns provide significant insights into the biology of colon cancer. Aneuploidy, the condition of having an abnormal number of chromosomes, is a well-established hallmark of cancer and is frequently associated with genetic instability in tumor cells [1].

Clinical or Translational Implications

The detection and quantification of aneuploidy can have several clinical implications:

References

  1. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of Cancer: The Next Generation. *Cell*, 144(5), 646-674. PubMed Search: Hallmarks of Cancer Aneuploidy
  2. Davoli, T., & de Lange, T. (2018). The Causes and Consequences of Aneuploidy in Cancer. *Annual Review of Cancer Biology*, 2, 297-313. PubMed Search: Aneuploidy Cancer Consequences
  3. Rubio, C. A. (2010). Field cancerization in the colon and rectum. *World Journal of Gastroenterology: WJG*, 16(29), 3624. PubMed Search: Field cancerization colon
  4. Lengauer, C., Kinzler, K. W., & Vogelstein, B. (1998). Genetic instability in colorectal cancers. *Nature*, 396(6712), 643-649. PubMed Search: Colorectal cancer aneuploidy biomarker
  5. Maley, C. C., et al. (2006). Aneuploidy and the evolution of cancer. *Evolution*, 60(9), 1709-1721. PubMed Search: Aneuploidy cancer prognosis

12. Colon Cancer Cell-Cell Interaction Landscape: Tumor vs. Adjacent Normal

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Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using CellPhoneDB results, comparing tumor and adjacent normal conditions within colorectal tissue. The focus is on interactions involving key cell types: Intestinal Epithelial cells (as tumor-origin cells), Fibroblasts, Macrophages, and T cells (CD4+ and CD8+). The dot plots visualize up to 80 most significant interactions per condition, indicating the strength (log2(mean) expression) and significance (-log10(p-value)) of ligand-receptor pairs between cell types.

Visual Summary

The two dot plots display distinct yet overlapping cell-cell interaction patterns in adjacent normal versus tumor conditions.

Biological Interpretation

The observed differences in CCI patterns between tumor and adjacent normal tissue provide insights into the altered cellular communication driving colorectal cancer progression.

  1. Persistent Epithelial-Immune Communication:
  1. Tumor-Specific Epithelial Remodeling and Signaling:
  1. Complex Immune Modulation in the TME:

Clinical or Translational Implications

The identified cell-cell interaction changes present several avenues for therapeutic intervention and biomarker discovery in colorectal cancer:

  1. Targeting the CXCL12-CXCR4 Axis: Given its consistent and strong presence in both conditions and its known role in cancer progression and immune evasion, blocking CXCR4 or CXCL12 could hinder tumor growth, metastasis, and the recruitment of immunosuppressive cells.
  2. Modulating SPP1 and TGF-β Signaling: The strong and potentially enhanced SPP1 and TGF-β pathways in the tumor highlight their importance in creating a pro-tumorigenic and immunosuppressive TME. Therapies targeting these pathways could disrupt tumor growth, reduce fibrosis, and enhance anti-tumor immunity.
  3. Ephrin-Eph Receptors as Novel Targets: The increased Ephrin-Eph interactions within tumor epithelial cells suggest a vulnerability that could be exploited. Therapeutic strategies aimed at disrupting these interactions could inhibit tumor cell proliferation, migration, and invasion, potentially preventing metastatic spread.
  4. Enhancing or Restoring T cell Function: The prominent CD80/86-CD28/CTLA4 interactions underscore the complex immune regulation in the TME. While immune checkpoint blockade targeting CTLA4 is an established therapy, further understanding and modulating the balance of these costimulatory and coinhibitory signals could lead to more effective T cell-based immunotherapies.
  5. Interfering with MIF-CD74_CD44 Signaling: The heightened MIF activity in the tumor points to its potential as a therapeutic target. Inhibitors of MIF or its receptors could dampen the pro-inflammatory and pro-tumorigenic environment, thereby slowing cancer progression.
  6. Fibroblast Involvement: The absence of fibroblast interactions among the top 80 pairs does not negate their role in the TME but suggests that, for the selected cell types and interaction thresholds, their direct ligand-receptor interactions might be less dominant than those shown. Further investigation into specific fibroblast interactions or their secreted factors could still be warranted.

13. Adjacent Normal 및 종양 조직의 세포-세포 상호작용 분석

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[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 활용하여 대장 조직의 인접 정상(adjacent_normal) 및 종양(tumor) 조건에서 세포 간 상호작용(Cell-Cell Interaction, CCI)을 규명하는 것을 목표로 합니다. CellPhoneDB를 사용하여 리간드-수용체 쌍의 상호작용 강도(mean expression)와 통계적 유의성(p-value)을 평가했으며, 각 조건에서 상위 80개 상호작용을 시각화하였습니다. 이러한 상호작용 패턴을 비교함으로써 종양 미세환경(Tumor Microenvironment, TME) 특유의 생물학적 기전을 이해하고 잠재적인 치료 표적을 식별하고자 합니다.

Visual Summary

두 조건에 대한 점 도표(dot plot)는 세포 유형 쌍(y축)과 리간드-수용체(L-R) 쌍(x축) 간의 상호작용을 시각화합니다. 점의 크기는 상호작용의 p-value(-log10 변환)를 나타내며, 큰 점은 높은 통계적 유의성을 의미합니다. 점의 색상은 상호작용의 평균 발현 강도(log2 변환)를 나타내며, 녹색/노란색 계열은 강한 상호작용을, 보라색 계열은 약한 상호작용을 나타냅니다.

Adjacent Normal 조건 (상단 그림):

Tumor 조건 (하단 그림):

Biological Interpretation

종양 미세환경은 복잡한 세포-세포 상호작용을 통해 암세포의 성장, 침윤, 전이 및 면역 회피를 지원합니다. 본 분석 결과는 이러한 종양 특이적 상호작용의 중요한 측면을 밝혀냅니다.

  1. 면역 억제 및 염증 신호:
  1. 종양 상피세포-면역 세포 상호작용:
  1. 혈관신생 및 기질 재형성:
  1. B 세포 및 형질 세포 활성:

Diploid Intestinal Epi (인접 정상)와 Intestinal Epi (종양) 간의 명확한 구분은 종양 상피세포가 주변 미세환경과의 상호작용 패턴을 변화시켜 종양 성장을 촉진하고 면역 반응을 회피하는 메커니즘을 강조합니다.

Clinical or Translational Implications

본 CCI 분석 결과는 대장암의 진단, 예후 예측 및 치료 전략 개발에 중요한 통찰력을 제공합니다.

  1. 치료 표적 발굴:
  1. 바이오마커 개발:
  1. 병용 치료 전략:
  1. 실험적 검증 및 임상 연구:

본 결과는 대장암 종양 미세환경의 복잡한 통신 네트워크를 해독하고, 새로운 치료 전략을 개발하기 위한 핵심적인 분자 경로를 제시합니다.

14. Immune Checkpoint and Cell Cycle Related Cell-Cell Interactions in Colon Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigated cell-cell interactions (CCI) involving a curated set of immune checkpoint and cell cycle-related genes across "adjacent_normal" and "tumor" conditions in single-cell RNA-seq data from Colon tissue. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between different cell types, focusing on the expression levels (mean) and statistical significance (p-value) of these interactions. The parameter expand_ploidy_from_tumor_origin: True allowed for distinctions based on ploidy status, specifically highlighting "Diploid Intestinal Epithelial" cells, which represent normal-like epithelial cells in the tissue microenvironment.

Visual Summary

Adjacent Normal Tissue

The dot plot for adjacent normal tissue shows several significant cell-cell interactions, primarily involving T cells (CD8+, CD4+) and Macrophages, with some interaction between Macrophages and Diploid Intestinal Epithelial cells.

Tumor Tissue

The tumor tissue dot plot reveals significant changes and new interactions compared to the adjacent normal tissue, particularly in immune checkpoint and growth factor pathways.

Biological Interpretation

Altered Immune Checkpoint Landscape in Tumor

The most striking difference is the emergence of the CD274 (PD-L1)-CD80 interaction in the tumor, specifically between macrophages and CD4+ T cells. CD274 (PD-L1) is a critical immune checkpoint protein often expressed by tumor cells and immune cells (like macrophages) to suppress T cell activity by binding to PD-1 on T cells. However, its interaction with CD80 (B7-1) on T cells is also described to inhibit T cell activation, leading to immune evasion. This highlights a potentially active immunosuppressive mechanism orchestrated by macrophages in the colorectal tumor microenvironment. The presence of CD80-CD28 interactions (co-stimulation) alongside CD274-CD80 (inhibition) on the same cell types (Mac|T CD4+) indicates a complex balance of activating and inhibitory signals impacting T cell function.

Growth Factor Signaling in the Tumor Microenvironment

The appearance of EREG EGFR and HBEGF EGFR interactions in the tumor (Mac|T CD4+) suggests that macrophages in the tumor microenvironment might be releasing epidermal growth factor (EGF)-like ligands that can engage EGFR on CD4+ T cells. EGFR signaling is well-known for its role in cell proliferation, survival, and migration, and its activation on T cells could influence their differentiation, survival, or effector function in ways that might be detrimental to anti-tumor immunity.

Sustained Immunosuppressive TGF-β Signaling

The robust TGFB1-TGFbeta_receptor1 signaling between macrophages and Diploid Intestinal Epithelial cells in both adjacent normal and, particularly, in tumor tissue, underscores the significance of TGF-β in colorectal cancer. TGF-β is a potent immunosuppressive cytokine that can inhibit T cell proliferation and function, promote regulatory T cell development, and foster an immunosuppressive microenvironment. The interaction with integrin_avb6_complex further suggests activation of latent TGF-β, amplifying its effects on epithelial cells and the surrounding stroma, contributing to fibrosis and tumor progression. The involvement of "Diploid Intestinal Epithelial" cells implies that even normal-like epithelial cells in the tumor context are subject to significant pro-tumorigenic signaling from macrophages.

Absence of Direct Cell Cycle Gene Interactions

While many cell cycle-related genes were included in the query, the plot_cci_dots results exclusively show ligand-receptor interactions related to immune and growth factor signaling. This is expected, as most cell cycle genes encode intracellular proteins (e.g., CDKs, cyclins, E2Fs, MCMs, TP53) that do not participate in direct cell-cell ligand-receptor binding at the cell surface. Their roles are primarily within the cell, regulating progression through the cell cycle. Therefore, the absence of these genes in CCI plots is not a negative finding but rather a confirmation that CellPhoneDB, which focuses on extracellular ligand-receptor pairs, is correctly identifying cell surface interactions.

Clinical or Translational Implications

The findings highlight several potential therapeutic targets and mechanisms for modulating the immune response in colorectal cancer:

  1. Immune Checkpoint Blockade: The prominent CD274 (PD-L1)-CD80 interaction between macrophages and CD4+ T cells in the tumor suggests that therapies targeting this non-canonical immune checkpoint axis, in addition to the classic PD-1/PD-L1 pathway, could be beneficial. Further investigation into the functional consequences of this specific interaction on T cell subsets could inform novel immunotherapeutic strategies.
  2. EGFR Inhibition: The emergence of EREG/HBEGF-EGFR signaling from macrophages to CD4+ T cells in the tumor microenvironment suggests that targeting EGFR could not only impact tumor cell proliferation (if aneuploid epithelial cells also express EGFR) but also modulate immune cell function. This dual effect could be leveraged in combination therapies.
  3. TGF-β Pathway Inhibition: The sustained and strong TGF-β signaling in the tumor microenvironment, particularly between macrophages and epithelial cells, reinforces TGF-β as a critical driver of immunosuppression and tumor progression. Therapeutic approaches that block TGF-β signaling, or specifically target integrin αvβ6 to prevent TGF-β activation, could help overcome immune resistance and reduce desmoplasia in colorectal cancer. PubMed search: TGF-beta inhibitors cancer therapy

These insights provide a detailed understanding of the dynamic interplay between immune cells and epithelial cells in colorectal cancer, offering potential avenues for targeted therapeutic interventions aimed at reprogramming the tumor microenvironment.

15. Condition-Specific Cell-Cell Interaction Patterns in Colon Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) between adjacent normal colon tissue and tumor tissue. The dot plot visualizes these differences, highlighting CCIs that are predominantly active in one condition over the other. The interactions primarily involve major immune cells (T cells, B cells, Myeloid cells, Mast cells, ILC), stromal cells (Fibroblasts, Endothelial cells), and Intestinal Epithelial cells (specifically Enterocyte Epithelial cells, or "Ent.Epi"), with some interactions further refined by the inferred ploidy status (Diploid or Aneuploid) of the epithelial cells. The dot size represents the statistical significance (-log10(p-value) of difference between conditions), while the color intensity indicates the standardized mean interaction strength within each sample.

Visual Summary

The dot plot clearly delineates two major groups of cell-cell interactions: those preferentially active in adjacent normal tissue (left half of the plot, marked by the vertical blue line) and those predominantly found in tumor tissue (right half). Similarly, samples are grouped by condition (adjacent normal vs. tumor) on the y-axis, separated by a horizontal blue line.

Key visual observations include:

Biological Interpretation

The distinct CCI patterns reveal fundamental differences in cellular communication between healthy and cancerous colon microenvironments.

Interactions Predominant in Adjacent Normal Tissue:

These interactions likely contribute to maintaining tissue homeostasis, immune surveillance, and normal epithelial function.

Interactions Predominant in Tumor Tissue:

These interactions underscore the profound reorganization of the tumor microenvironment, promoting tumor growth, angiogenesis, and immune evasion.

Angiogenesis and Stromal Activation:

Clinical or Translational Implications

The identified condition-specific cell-cell interactions offer valuable insights with potential clinical and translational implications for colorectal cancer.

16. Intestinal Epithelial Cell Condition-Specific Surfaceome Markers in Colon Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers in Intestinal Epithelial cells from colon tissue, comparing tumor and adjacent normal conditions. A dot plot visualization displays the expression patterns of up to 50 surface-localized genes across individual samples. The y-axis groups samples by their inferred ploidy status (Diploid vs. non-Diploid/Aneuploid) and implicitly by their tissue origin (adjacent normal vs. tumor). The dot size represents the fraction of cells expressing a given gene within each sample group, while the color intensity reflects the mean expression level.

Visual Summary

The dot plot clearly delineates two distinct clusters of Intestinal Epithelial cell samples based on their surfaceome marker expression:

The overall pattern indicates a strong differential expression of specific surface proteins in tumor-associated Intestinal Epithelial cells compared to their normal counterparts.

Biological Interpretation

The analysis successfully identified a panel of surfaceome markers that are highly and selectively upregulated in tumor-derived Intestinal Epithelial cells. These markers represent significant changes in the cell surface proteome during colon tumorigenesis, impacting cell-cell communication, adhesion, metabolism, and signaling pathways.

Key Tumor-Associated Surfaceome Markers:

A prominent set of genes shows marked upregulation in the tumor-associated (lower) cluster:

Clinical or Translational Implications

The identified tumor-specific surfaceome markers hold significant clinical and translational potential:

17. Macrophage Condition-Specific Surfaceome Markers in Colon Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers for Macrophage cells by comparing their expression in tumor tissue versus adjacent normal tissue from single-cell RNA-seq data of human Colon samples. The plot_markers_and_expression_dot tool was used to visualize the expression (mean expression by color intensity) and prevalence (fraction of cells expressing the gene by dot size) of up to 50 surfaceome markers per condition. This helps to characterize the distinct phenotypic states of macrophages in different tissue microenvironments.

Visual Summary

The dot plot visualizes the expression of 30 surfaceome markers across various Macrophage samples, grouped by condition: 'tumor' (top section) and 'adjacent_normal' (bottom section).

Biological Interpretation

The observed differential surfaceome marker expression points to significant functional adaptations of Macrophages in the tumor microenvironment (TAMs) of colorectal cancer compared to their counterparts in healthy adjacent tissue. Many of the highly expressed markers in TAMs are associated with pro-tumor functions:

Immune Modulation and Suppression:

The collective upregulation of these surfaceome markers strongly suggests that Macrophages in colon tumors adopt a distinct, pro-tumoral phenotype that facilitates tumor progression, immune evasion, and metastasis.

Clinical or Translational Implications

The identified condition-specific surfaceome markers for Macrophages in colon cancer hold significant clinical and translational potential.

Therapeutic Targets for Immunomodulation:

The distinct surfaceome profile of Macrophages in colon cancer highlights their active role in disease pathogenesis and offers a rich landscape of potential targets for novel immunotherapeutic strategies.

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References:

  1. CD44: GeneCards entry for CD44. https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD44
  2. PLAUR: GeneCards entry for PLAUR. https://www.genecards.org/cgi-bin/carddisp.pl?gene=PLAUR
  3. ITGB1: GeneCards entry for ITGB1. https://www.genecards.org/cgi-bin/carddisp.pl?gene=ITGB1
  4. ICAM1: GeneCards entry for ICAM1. https://www.genecards.org/cgi-bin/carddisp.pl?gene=ICAM1
  5. SIRPA-CD47 Axis: PubMed search for "SIRPA CD47 cancer immunotherapy". https://pubmed.ncbi.nlm.nih.gov/?term=SIRPA+CD47+cancer+immunotherapy
  6. TLR2 in TAMs: PubMed search for "TLR2 tumor associated macrophages". https://pubmed.ncbi.nlm.nih.gov/?term=TLR2+tumor+associated+macrophages
  7. CD83: GeneCards entry for CD83. https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD83
  8. LAIR1: GeneCards entry for LAIR1. https://www.genecards.org/cgi-bin/carddisp.pl?gene=LAIR1
  9. SLC2A3/GLUT3 in TAMs: PubMed search for "GLUT3 tumor associated macrophages metabolism". https://pubmed.ncbi.nlm.nih.gov/?term=GLUT3+tumor+associated+macrophages+metabolism
  10. C3AR1 and C5AR1: GeneCards entry for C3AR1 https://www.genecards.org/cgi-bin/carddisp.pl?gene=C3AR1 and C5AR1 https://www.genecards.org/cgi-bin/carddisp.pl?gene=C5AR1
  11. FCGR3A: GeneCards entry for FCGR3A. https://www.genecards.org/cgi-bin/carddisp.pl?gene=FCGR3A
  12. HBEGF: GeneCards entry for HBEGF. https://www.genecards.org/cgi-bin/carddisp.pl?gene=HBEGF
  13. ITGAX (CD11c): GeneCards entry for ITGAX. https://www.genecards.org/cgi-bin/carddisp.pl?gene=ITGAX
  14. SIRPA-CD47 immunotherapy: PubMed search for "SIRPA CD47 immunotherapy cancer". https://pubmed.ncbi.nlm.nih.gov/?term=SIRPA+CD47+immunotherapy+cancer

18. Condition-Specific Surfaceome Markers in Colon Fibroblasts

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify and visualize condition-specific surfaceome markers in Fibroblast cells from human colon tissue, comparing "adjacent normal" and "tumor" conditions. The provided dot plot displays the expression patterns of these markers across various fibroblast clusters (represented on the y-axis), with genes grouped by their preferential expression in either the "adjacent normal" or "tumor" context (x-axis). The size of each dot signifies the fraction of cells within a cluster expressing a particular gene, while the color intensity reflects the mean expression level of that gene. This approach allows for the discovery of cell surface proteins that are distinctly regulated in fibroblasts contributing to the tumor microenvironment compared to those in healthy adjacent tissue.

Visual Summary

The dot plot effectively illustrates clear distinctions in surfaceome marker profiles between fibroblasts in adjacent normal tissue and those within the tumor microenvironment.

Biological Interpretation

The differential expression of these surfaceome markers provides significant biological insights into the activation and functional specialization of fibroblasts in colorectal cancer. The transition of quiescent fibroblasts to activated cancer-associated fibroblasts (CAFs) is a critical event in tumor progression, marked by profound changes in their gene expression profile.

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers in colon fibroblasts has significant clinical and translational potential, particularly for developing new therapeutic strategies and improving diagnostic/prognostic tools.

This comprehensive profiling of fibroblast surfaceome in the context of colon cancer significantly enhances our understanding of CAF biology and opens new avenues for targeted therapeutic interventions.

19. CD4 T Cell Surfaceome Markers in Colon Cancer Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers in CD4+ T cells by comparing different conditions (likely tumor vs. adjacent normal, given the context and 'tumor' label in the plot). The plot_markers_and_expression_dot tool was used to visualize the mean expression and fraction of cells expressing these markers across various CD4+ T cell clusters/samples. The parameters ensured that only surfaceome markers with significant differential expression (fold change > 1.5, p-value < 0.05, and expression score cutoffs) were included, with up to 50 markers per condition.

Visual Summary

The dot plot displays the expression profiles of identified surfaceome markers for CD4+ T cells. Each row (C###) represents a distinct cluster or sample group of CD4+ T cells, and each column represents a specific gene. The size of the dot corresponds to the fraction of cells within that group expressing the gene, while the intensity of the red color indicates the mean expression level of the gene in that group.

A clear pattern emerges where a significant portion of the CD4+ T cell clusters, particularly those labeled or associated with the "tumor" condition (highlighted by the red boxes), show strong and widespread expression of a distinct set of surface markers. In contrast, other clusters (likely representing adjacent normal tissue or less activated states) exhibit lower expression levels and/or lower prevalence of these markers.

Key observations within the highlighted tumor-associated clusters include strong co-expression of:

The clusters in the middle section of the plot (within the red boxes) display the most intense and widespread red dots for the identified markers, indicating high mean expression and a large fraction of cells expressing these genes within these tumor-associated CD4+ T cell populations.

Biological Interpretation

The distinct surfaceome signature observed in tumor-associated CD4+ T cells suggests a specific functional state of these cells within the colorectal tumor microenvironment.

  1. T Cell Activation and Exhaustion/Regulation: The simultaneous upregulation of co-stimulatory molecules (TNFRSF4/OX40 [GeneCards: OX40], TNFRSF18/GITR [GeneCards: GITR], ICOS [GeneCards: ICOS]) and immune checkpoint inhibitors (CTLA4 [GeneCards: CTLA4], TIGIT [GeneCards: TIGIT]) points towards a population of CD4+ T cells that are actively engaged in the immune response but are also undergoing regulation or exhaustion. OX40 and GITR are known to promote T cell activation and survival, while CTLA4 and TIGIT deliver inhibitory signals, often leading to T cell anergy or exhaustion in chronic stimulation contexts like cancer.
  2. Antigen Presentation and Immune Signaling: The high expression of MHC Class II molecules (HLA-DRA, HLA-DRB1, HLA-DPB1) and the invariant chain CD74 on CD4+ T cells in the tumor microenvironment is notable. While primarily expressed by antigen-presenting cells, activated CD4+ T cells can also upregulate MHC Class II, suggesting a potent activation state and potentially a role in unconventional antigen presentation or immune regulation within the tumor. This could also imply a strong interaction with other immune cells.
  3. Immunosuppressive Microenvironment: The presence of ENTPD1 (CD39) [GeneCards: ENTPD1] further supports the concept of an immunosuppressive microenvironment. CD39 is an ectonucleotidase that, along with CD73, degrades ATP into adenosine, which is a potent immunosuppressive molecule in the tumor context, contributing to T cell dysfunction.
  4. Other Markers: BSG (CD147) is involved in various processes including cell growth, invasion, and inflammation, and its expression on T cells can influence their function and interaction with tumor cells. BST2 (CD317) is an interferon-inducible protein, suggesting an ongoing inflammatory response. IL2RB (CD122) and IL2RG (CD132) are components of cytokine receptors, indicating responsiveness to various cytokines, including IL-2, which is critical for T cell proliferation and survival, but can also be involved in Treg function.

These findings collectively suggest that CD4+ T cells in colon tumors are highly activated but concurrently experience significant regulatory pressures or exhaustion, contributing to an overall immunosuppressive environment. These markers differentiate tumor-associated CD4+ T cells from their counterparts in adjacent normal tissue.

Clinical or Translational Implications

The identified condition-specific surfaceome markers in CD4+ T cells from colon tumors have significant clinical and translational implications:

  1. Biomarkers for Disease State and Prognosis: The unique signature of activated and exhausted CD4+ T cells could serve as prognostic biomarkers in colon cancer, identifying patients with specific immune microenvironments that might correlate with disease progression or response to therapy.
  2. Therapeutic Targets for Immunomodulation: Several identified markers are well-established or emerging targets for cancer immunotherapy:
  1. Experimental Validation and Patient Stratification: The specific CD4+ T cell subsets characterized by these markers warrant further experimental validation through functional assays (e.g., cytokine production, proliferation) to confirm their precise roles in tumor immunity. This detailed characterization could enable better stratification of patients for targeted immunotherapies based on their specific T cell immune profiles within the tumor microenvironment.

20. Intestinal Epithelial Cells in Colon Cancer Exhibit Widespread Upregulation of Cell Cycle Genes

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of a curated set of cell cycle pathway-related genes within Intestinal Epithelial cells, comparing tumor tissue to adjacent normal tissue. The aim is to identify specific cell cycle regulators that are significantly dysregulated in tumor-origin cells, providing insights into the proliferative state characteristic of colorectal cancer. The plot_box_for_gene_expression_with_signif_difference tool was used to visualize gene expression distributions and highlight statistically significant differences.

Visual Summary

The boxplots illustrate the expression levels (represented as sample means) of 60 cell cycle-related genes in Intestinal Epithelial cells across tumor and adjacent normal conditions. A striking and consistent pattern emerges: the vast majority of these genes show significantly higher expression in tumor tissue compared to adjacent normal tissue.

Key visual observations include:

Biological Interpretation

The observed widespread upregulation of cell cycle genes in Intestinal Epithelial cells of colon tumor tissue provides strong biological evidence for uncontrolled proliferation, a hallmark of cancer. This finding is consistent with the nature of Intestinal Epithelial cells being the cell of origin for colorectal adenocarcinoma.

Specifically, the significant upregulation of the following categories of genes highlights distinct aspects of tumor biology:

Cell Cycle Progression Regulators

Tumor Suppressors/Antagonists with Complex Roles

Collectively, these findings paint a picture of Intestinal Epithelial cells in colon tumors aggressively driving cell division through multiple mechanisms, consistent with their malignant transformation.

Clinical or Translational Implications

The pervasive upregulation of cell cycle genes in Intestinal Epithelial cells in tumor tissue has several clinical and translational implications:

21. Gene Ontology (GSA) Analysis of Intestinal Epithelial Cells in Colon Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GO) enrichment results (GSA) for Intestinal Epithelial cells. The results are displayed as bar plots, showing significantly enriched GO terms ranked by statistical significance (-log(p-val) and -log(q-val)). Two comparisons are evaluated:

  1. Diploid_vs_others: Genes upregulated in diploid Intestinal Epithelial cells compared to all other cells (likely including aneuploid cells, which are often associated with malignancy).
  2. tumor_vs_others: Genes upregulated in Intestinal Epithelial cells specifically from tumor tissue compared to cells from other conditions (e.g., adjacent normal tissue).

This allows us to understand the distinct biological processes characterizing non-malignant vs. malignant epithelial cells, and those specific to the tumor microenvironment in colon tissue.

Visual Summary

The bar plots display the top 60 enriched GO terms for each comparison. The length of the bars corresponds to the negative logarithm of the p-value and adjusted p-value (q-value), indicating the statistical significance of the enrichment.

For the Diploid_vs_others comparison:

For the tumor_vs_others comparison:

Biological Interpretation

Intestinal Epithelial Cells: Diploid_vs_others

The strong enrichment of immune-related GO terms in diploid Intestinal Epithelial cells suggests that these non-malignant cells are actively involved in the host's immune defense and surveillance mechanisms.

Intestinal Epithelial Cells: tumor_vs_others

The GO enrichment in tumor-associated Intestinal Epithelial cells reveals a profound shift in cellular biology, reflecting malignant transformation and adaptation to the tumor microenvironment.

Clinical or Translational Implications

The distinct biological signatures of diploid versus tumor Intestinal Epithelial cells provide valuable insights into colon cancer development and potential therapeutic strategies.

22. Gene Set Enrichment Analysis across Major Cell Types in Colon Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results across major cell types identified in single-cell RNA sequencing data from human colon tissue. The dot plot visualizes the enrichment of 80 significant gene sets (pathways) for each cell type, comparing cells from either tumor or adjacent normal tissue contexts against all other cells of the same type (or Diploid vs. others for Intestinal Epithelial cells).

Each dot represents a specific pathway within a cell type/condition comparison. The size of the dot corresponds to the statistical significance (-log10(P-value)), with larger dots indicating higher significance. The color of the dot reflects the Normalized Enrichment Score (NES), where red indicates positive enrichment (pathway upregulated in the test condition/cell type) and blue indicates negative enrichment (pathway downregulated). This allows us to identify cell type-specific biological processes that are distinctly active or suppressed in the tumor microenvironment compared to the adjacent normal tissue.

Visual Summary

The dot plot effectively summarizes the pathway enrichment landscape across various major cell types and conditions.

Biological Interpretation

The GSEA results provide a comprehensive view of the biological programs activated within specific cell types in the colon tumor microenvironment.

  1. Intestinal Epithelial Cell Malignancy: The strong enrichment of Cell cycle, DNA replication, Pathways in cancer, Ribosome biogenesis in eukaryotes, and Proteasome in tumor-associated Intestinal Epithelial cells is a direct signature of uncontrolled proliferation, increased metabolic demand, and protein synthesis characteristic of malignant transformation. The negative enrichment of pathways like Focal adhesion might indicate a shift towards a less adhesive, more migratory phenotype conducive to invasion and metastasis [GeneCards: CADHERIN1]. The enrichment of PD-L1 expression and PD-1 checkpoint pathway in cancer in these cells suggests a mechanism by which tumor cells evade immune surveillance by expressing PD-L1, leading to T cell exhaustion.
  2. Inflammatory and Adaptive Immune Response: T cells (CD4+ and CD8+) and Macrophages in the tumor microenvironment show pronounced activation of pathways involved in immune signaling and inflammation, including Antigen processing and presentation, Cytokine-cytokine receptor interaction, JAK-STAT signaling pathway, NF-kappa B signaling pathway, and Th1 and Th2 cell differentiation. This indicates an active immune response, where these cells are engaged in sensing and responding to tumor antigens. However, the concurrent enrichment of the PD-L1 expression and PD-1 checkpoint pathway in cancer in these immune cells suggests potential immune dysfunction or exhaustion within the tumor microenvironment, where chronic stimulation or suppressive signals can lead to impaired effector function [PubMed: T cell exhaustion]. Macrophages further show strong enrichment for Phagosome and Fc gamma R-mediated phagocytosis, reflecting their role in engulfing cellular debris and pathogens, which can be altered in the tumor context, potentially contributing to pro-tumorigenic functions (e.g., M2-like macrophages).
  3. Tumor Microenvironment Remodeling:
  1. Broader Immune Cell Activation: B cells and Mast cells in the tumor environment also display activation of general immune signaling pathways like Cytokine-cytokine receptor interaction, JAK-STAT signaling pathway, and NF-kappa B signaling pathway. B cells specifically show B cell receptor signaling pathway and Antigen processing and presentation enrichment. Mast cells show Fc epsilon RI signaling pathway enrichment, suggesting their activation and potential contribution to the inflammatory milieu or immune modulation within the tumor. The positive enrichment of Intestinal immune network for IgA production in B cells across both tumor and normal conditions underscores the unique immunological context of the colon.
  2. Ploidy Implications: The distinct GSEA profile for "Intestinal Epithelial cell: Diploid_vs_others" highlights that ploidy status, as indicated by ploidy_dec, is associated with different cellular states and pathway activities within the Intestinal Epithelial cell compartment. Diploid epithelial cells might represent a less transformed or quiescent population compared to aneuploid tumor cells, which typically show higher proliferative and oncogenic pathway activities.

Clinical or Translational Implications

The GSEA results provide several insights with potential clinical and translational implications for colon cancer:

23. Discussion

The single-cell analysis of colon tissue reveals a deeply reprogrammed cellular landscape in tumor versus adjacent normal conditions. A central finding is the robust identification of malignant epithelial cells through their distinctive aneuploid status and widespread upregulation of cell cycle genes, alongside amplifications of oncogenes such as *EGFR*, *ERBB2*, and the *MYC* locus. These malignant epithelial cells also exhibit unique surfaceome signatures, including upregulation of *MET*, *RNF43*, and various solute carrier proteins, indicating profound metabolic and surface receptor remodeling for tumor growth and survival.

The tumor immune microenvironment is characterized by a significant infiltration of T cells and macrophages. While T cell populations show a depletion of naive cells and an enrichment of immunosuppressive subsets like regulatory T cells (Tregs), Th22, and ILCreg cells, there is also evidence of active engagement, indicated by co-stimulatory and inhibitory immune checkpoint interactions on CD4+ T cells (e.g., CTLA4, TIGIT, OX40, GITR). Macrophages exhibit a complex polarization, with an overall shift towards M2-like phenotypes, yet surprisingly, an increased proportion of M1 macrophages in tumor tissue compared to adjacent normal, suggesting a dynamic and heterogeneous immune response. Tumor-associated macrophages (TAMs) also express a distinct surfaceome, including pro-tumorigenic markers like SIRPA, CD44, and HBEGF, crucial for immune evasion and tumor progression.

Cancer-associated fibroblasts (CAFs) are significantly expanded in the tumor and display a highly activated phenotype, characterized by unique surface markers such as PDGFRB, ITGAV, and ANTXR1, and extensively engage in extracellular matrix remodeling and pro-angiogenic signaling pathways. Cell-cell interaction analysis further highlights this complex interplay, with enhanced TGF-β, CXCL12-CXCR4, SPP1-integrin, and MIF-CD74 signaling, fostering an immunosuppressive and pro-tumorigenic milieu. Moreover, prominent angiogenesis pathways (VEGFA-KDR/FLT1, PDGFD-PDGFRB, DLL4-NOTCH3, PGF-NRP2) are activated in endothelial and fibroblast populations, supporting sustained tumor growth.

An intriguing finding is the presence of aneuploid epithelial cells within some adjacent normal samples. This suggests a potential field cancerization effect or early clonal evolution, indicating a pre-malignant state or subtle genomic instability even in macroscopically normal tissue. The consistent enrichment of the PD-L1 checkpoint pathway across multiple tumor-associated cell types (epithelial, T cells, macrophages) underscores its critical role in immune evasion in this cohort. Overall, the study provides a high-resolution map of colorectal cancer heterogeneity, identifying key cellular and molecular drivers of disease progression and offering a robust foundation for identifying novel therapeutic strategies.

Hypotheses:

  1. Aneuploid intestinal epithelial cells in colon tumors are the primary malignant cells, exhibiting increased proliferation and altered surface receptor expression that drives tumor growth and immune evasion.
  2. The increase in Tregs, Th22, and ILCreg T cell subsets, alongside specific macrophage polarization (M2-like) and active immune checkpoint signaling (CTLA4, TIGIT, PD-L1), establishes a highly immunosuppressive microenvironment in colon cancer, hindering effective anti-tumor immunity.
  3. Cancer-associated fibroblasts (CAFs) in colon tumors undergo significant activation and express a distinct surfaceome, driving extracellular matrix remodeling, angiogenesis, and providing pro-tumorigenic support through specific cell-cell interactions.
  4. The presence of aneuploid epithelial cells in histologically normal adjacent tissue represents an early stage of genomic instability and clonal evolution, contributing to field cancerization and increased risk for tumor recurrence or progression.
  5. Tumor epithelial cells and tumor-associated macrophages (TAMs) undergo significant metabolic reprogramming, as evidenced by upregulation of solute carrier (SLC) family genes and enrichment of metabolism pathways, supporting their high energetic and biosynthetic demands.

Potential therapeutic targets:

  1. TGF-β Signaling Pathway: Consistently and strongly activated in the tumor microenvironment (macrophages to intestinal epithelial cells, various cell-cell interactions), contributing to immune suppression, fibrosis, and tumor progression in colorectal cancer. Evidence: Analysis sections 12, 13, 14, 15, and 22 (GSEA shows TGF-beta signaling pathway enriched in fibroblasts) highlight robust TGFB1-TGFBR1/2 interactions in tumor conditions. Validation: Inhibit TGF-β signaling (e.g., using small molecule inhibitors or neutralizing antibodies) in patient-derived organoid-immune co-culture models or xenografts to assess effects on tumor growth, ECM remodeling, and immune cell function (e.g., T cell proliferation/cytotoxicity, macrophage polarization). Combine with immune checkpoint inhibitors.
  2. SIRPA-CD47 Axis: SIRPA is highly upregulated on tumor-associated macrophages (TAMs). Its interaction with CD47 on cancer cells provides a 'don't eat me' signal, inhibiting macrophage-mediated phagocytosis and promoting immune evasion. Evidence: Analysis section 17 demonstrates significant upregulation of SIRPA on tumor macrophages compared to adjacent normal counterparts. Validation: Use anti-SIRPA or anti-CD47 blocking antibodies in in vitro phagocytosis assays with sorted tumor cells and TAMs, and in vivo in syngeneic or humanized mouse models to demonstrate enhanced tumor cell clearance and anti-tumor immunity.
  3. MET Receptor Tyrosine Kinase: MET is significantly upregulated on malignant intestinal epithelial cells, known to drive cell proliferation, survival, migration, and invasion in various cancers, including colorectal cancer. Evidence: Analysis section 16 identifies MET as a prominent and highly upregulated surfaceome marker in tumor-associated intestinal epithelial cells. Validation: Test MET inhibitors (e.g., capmatinib, tepotinib) in patient-derived colon cancer organoids or xenograft models with high MET expression. Assess effects on tumor growth, invasion, and metastasis.
  4. Immune Checkpoints (CTLA4, TIGIT, PD-L1 pathway): CTLA4 and TIGIT are upregulated on activated/exhausted CD4+ T cells, and the PD-L1 checkpoint pathway is consistently enriched across tumor epithelial cells, T cells, and macrophages, indicating active immune evasion mechanisms in the tumor microenvironment. Evidence: Analysis section 14 shows CD274 (PD-L1)-CD80 interactions. Section 19 identifies upregulation of CTLA4 and TIGIT on CD4+ T cells. Section 22 reveals consistent enrichment of the 'PD-L1 expression and PD-1 checkpoint pathway in cancer' in multiple tumor-associated cell types. Validation: Administer checkpoint inhibitors (e.g., anti-CTLA4, anti-TIGIT, anti-PD-L1) as monotherapy or in combination in syngeneic or humanized mouse models of colon cancer, assessing changes in T cell function, tumor growth, and survival. Validate in clinical trials for specific patient subsets.

Follow-up validation ideas:

  1. Confirm aneuploidy at the single-cell level using FISH or targeted DNA sequencing on sorted epithelial cells from tumor and adjacent normal tissues.
  2. Validate differential protein expression of MET, RNF43, SLCs, and ITGA2 in malignant epithelial cells within tumor tissue sections using immunostaining or spatial transcriptomics.
  3. Perform in vitro (organoids, 3D culture) and in vivo (patient-derived xenografts) perturbation assays to assess the functional impact of targeting candidate genes on epithelial cell proliferation, survival, and invasiveness.
  4. Quantify and localize Treg, Th22, ILCreg, and specific macrophage subsets (M1/M2 markers, SIRPA) in larger patient cohorts using flow cytometry or immunostaining.
  5. Assess the suppressive function of tumor-infiltrating Tregs on effector T cells, and the pro-tumorigenic activity of TAMs (e.g., cytokine secretion, phagocytosis, T cell inhibition) using functional assays ex vivo.
  6. Perform functional perturbation assays, such as blockade of CTLA4, TIGIT, PD-L1, or SIRPA-CD47 using antibodies or genetic tools in co-culture models or humanized mouse models, to evaluate restoration of anti-tumor immunity.
  7. Validate expression of PDGFRB, ITGAV, ANTXR1, and CDH11 in CAFs within tumor sections using immunostaining or spatial transcriptomics, assessing their spatial proximity to tumor cells.
  8. Evaluate the impact of targeting CAF-specific markers (e.g., PDGFRB inhibitors, integrin blockade) on ECM remodeling, tumor cell invasion, and angiogenesis using 3D co-culture models or patient-derived xenografts.
  9. Confirm key ligand-receptor interactions like PDGFD-PDGFRB, COL-integrin, LAMC1-integrin in co-culture systems using reporter assays or antibody blockade.
  10. Analyze CNV in a larger cohort of adjacent normal tissue samples from colon cancer patients and healthy controls to confirm the prevalence and extent of aneuploidy, potentially through bulk or single-cell sequencing.
  11. Perform targeted metabolomics or stable isotope tracing on sorted tumor epithelial cells and TAMs to validate altered metabolic pathways (e.g., glucose uptake, amino acid metabolism).
  12. Inhibit specific SLC transporters (e.g., SLC1A5, SLC2A3) in tumor cells or TAMs to assess their impact on proliferation, survival, and immune functions.

Limitations:

This single-cell analysis provides comprehensive insights into colon cancer, yet it is subject to several limitations. The observational nature of transcriptomic data prevents direct inference of causality for the identified cellular and molecular alterations. Significant inter-sample heterogeneity was observed, particularly in CNV profiles and cell population proportions, suggesting that findings may not generalize to all colorectal cancer patients. Cell type annotations, while robust, are based on known markers and reference datasets, and the presence of 'unassigned' populations indicates remaining cellular complexity or uncharacterized states. Furthermore, CNV and ploidy estimations are inferred and warrant orthogonal genomic validation. The current dataset provides a static snapshot of the tumor microenvironment, which is a dynamic system; thus, these findings represent specific states at the time of sampling. While surfaceome markers are excellent therapeutic targets, limiting some marker analyses to the surfaceome may overlook important intracellular targets or signaling pathways. Finally, potential technical biases related to tissue dissociation, RNA capture efficiency, and bioinformatic processing, though addressed by data integration, are inherent to single-cell studies.

24. Query List

  1. Show UMAPs including condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset, in 2 columns and save.
  2. Show the expression of CD3D, CD4, CD8A, CD79A, MS4A1, MZB1, CD14, LYZ, FBLN1, NOTCH3, EPCAM, MUC1, CD34 genes on UMAP, along with minor celltype annotation. Set ncols=4 and save.
  3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
  4. Select Intestinal Epithelial cells and unassigned cells, group them by sample, show a CNV heatmap, and include a summary of significantly amplified copy number regions. Save.
  5. Show CNV patterns on UMAP, including major cell type, minor cell type, ploidy results, condition, and sample, in 2 columns and save.
  6. Show a population bar plot for minor cell types and save.
  7. Show a subset population barplot for T cells and save.
  8. Show a subset population barplot for Macrophage cells and save.
  9. Show boxplots for T cell subset populations if there are statistically significant differences between conditions, and save. Determine ncols appropriately based on the total number of panels.
  10. Show boxplots for Macrophage subset populations if there are statistically significant differences between conditions, and save. Determine ncols appropriately based on the total number of panels.
  11. Select Intestinal Epithelial cells and unassigned cells, and show a bar plot of their ploidy population. Save.
  12. Show cell-cell interaction patterns by condition, including tumor-origin cells (Intestinal Epithelial cells), fibroblasts, macrophages, and T cells. Select up to 80 cell-cell interactions per condition. Save.
  13. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
  14. Select only genes related to immune checkpoint and cell cycle pathways, and show cell-cell interactions for these genes. Save.
  15. Find statistically significant differences in cell-cell interactions between conditions for major immune and stromal cells, and show them as a dot plot. Set max_n_items_per_group = 25 and save.
  16. Show the condition-specific markers for tumor-origin cells (Intestinal Epithelial cell) as a dot plot and save the result. Use only surfaceome markers, up to 50 markers per condition.
  17. Extract condition-specific markers for Macrophage cells and show them as a dot plot. Include only surfaceome markers, up to 50 per condition, and save.
  18. Extract condition-specific markers for Fibroblast cells and show them as a dot plot. Include only surfaceome markers, up to 50 per condition, and save.
  19. Extract condition-specific markers for CD4 T cells and show them as a dot plot. Include only surfaceome markers, up to 50 per condition, and save.
  20. Select cell cycle pathway-related genes with statistically significant differences in expression between conditions for major disease-relevant cells, and show boxplots. Set max_n_items_to_plot = 24 and determine ncols appropriately so that the width-to-height ratio is about 2x3 for the entire panel. Save.
  21. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
  22. Show a dot plot of Gene Set Enrichment Analysis results for major cell types. Use the RdBu_r color map and set n_pws_to_show = 80. Save.
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