SCODiA Report by MLBI Lab

Single-Cell Transcriptomic and Genomic Landscape of Human Colon Adenocarcinoma Reveals Key Dysregulations in Tumor Epithelial Cells, Immune Microenvironment, and Stromal Remodeling

This single-cell analysis of human colon tissue reveals a profound transformation in the tumor microenvironment compared to normal tissue. Malignant intestinal epithelial cells exhibit aneuploidy and highly upregulated proliferative pathways. Concurrently, the immune landscape shifts towards immunosuppression, marked by altered T cell and macrophage polarization, alongside extensive extracellular matrix remodeling by activated fibroblasts. These coordinated changes collectively drive tumor progression and immune evasion in colon adenocarcinoma.

Contents

0. Dataset overview
1. UMAP Visualization of Single-cell RNA-seq Data by Condition, Sample, Cell Type, and Ploidy
2. UMAP Visualization of Major Cell Type Scores, Cell Type Annotations, and Ploidy Status in Colon Single-Cell RNA-seq Data
3. Celltype_subset Marker Expression Overview
4. Analysis of Copy Number Variations (CNVs) in Selected Cell Groups
5. CNV-Based UMAP Analysis of Colon Tissue Reveals Aneuploidy in Tumor-Associated Intestinal Epithelial Cells
6. Colon Tissue Minor Cell Type Population Analysis
7. Analysis of T cell Subset Proportions in Colon Tissue
8. Colon Cancer Alters T cell Subset Proportions, Favoring Immunosuppression and Inflammation
9. Macrophage Cell Population Annotation Check
10. Macrophage Subset Population Shifts in Colorectal Tumor Microenvironment
11. Ploidy Population Analysis of Intestinal Epithelial and Unassigned Cells in Normal vs. Tumor Colon Tissue
12. 종양 미세환경 내 주요 세포-세포 상호작용 분석
13. Tumor Microenvironment Cell-Cell Interaction Analysis: Prioritizing Key Ligand-Receptor Pathways
14. Immune Checkpoint and Cell Cycle Pathway Cell-Cell Interactions in Colon Normal and Tumor Tissues
15. Condition-Specific Cell-Cell Interaction Patterns in Colon Tissue
16. Intestinal Epithelial Cell Condition-Specific Surfaceome Markers
17. Condition-Agnostic Surfaceome Markers for Colon Cell Subsets, Highlighting Macrophage Identities
18. Fibroblast Condition-Specific Surfaceome Markers Analysis
19. Condition-Specific Surfaceome Markers for CD4 T Cells in Colon Tissue
20. Dysregulated Cell Cycle Gene Expression in Colon Tumor Intestinal Epithelial Cells
21. Colon Intestinal Epithelial Cell Gene Ontology Analysis: Insights into Ploidy and Tumorigenesis
22. Gene Set Enrichment Analysis (GSEA) Across Major Colon Cell Types
23. Discussion
24. Query List

0. Dataset overview

Dataset Summary

• Data Type: Single-cell RNA-seq (AnnData format).
• Dimensions: Contains 48,033 cells and 20,683 genes.
• Species & Tissue: Human, from Colon tissue.
• Conditions: Includes 'normal' and 'tumor' conditions.

Key Annotations (obs columns)

• samples, Condition, Location, MSI_Status, bulk_prediction, prediction, sample, condition, celltype_major, celltype_minor, celltype_subset, accession, tissue, sample_ext, cnv_ref_ind, ploidy_score, ploidy_dec, ploidy_init_group, cluster, sample_diversity_index, cnv_cluster, cnv_sample_diversity_index.

Gene Annotations (var columns)

• variable_genes, chr, spot_no, cytogenetic_band.

Cell Type Hierarchy

• celltype_major: T cell, B cell, Intestinal Epithelial cell, unassigned, Stromal cell, Myeloid cell, Endothelial cell.
• celltype_minor: T cell CD4+, B cell, Intestinal Epithelial cell, Plasma cell, ILC, T cell CD8+, unassigned, Fibroblast, Macrophage, Endothelial cell, Smooth muscle cell, NK cell, Dendritic cell.
• celltype_subset: T cell (Naive), B cell (Follicular), T cell (Tfh), T cell (Th17), Goblet cell, T cell (Th1), T cell (Th22), B cell (Breg), Plasma cell, B cell (Memory), Tuft cell, Microfold cell, T cell (Th2), LTI, T cell (Th9), T cell (Cytotoxic), T cell (Treg), B cell (MZ), Enterocyte, unassigned, Paneth cell, Fibroblast, ILC1, ILC2, ILC3 (NCR-), Macrophage (M2A), ILCreg, Endothelial cell, Macrophage (M1), Smooth muscle cell, Enterochromaffin cell, Crypt cell, Macrophage (M2C), ILC3 (NCR+), Macrophage (M2B), NK cell, DC (Plasmacytoid), Macrophage (M2D), DC (Classical), Enteroendocrine cell, Lymphatic Endothelial cell, Endothelial tip cell, DC (Inflammatory).
• Tumor Origin Cell Type: Intestinal Epithelial cell.
• Ploidy: Categorized as 'Aneuploid' or 'Diploid' in ploidy_dec.

Precomputed Results

• Cell-Cell Interaction (CCI): uns['CCI'] (per condition) and uns['CCI_sample'] (per sample) containing gene_pair, cell_pair, pval, and mean for ligand-receptor interactions.
• Differential Gene Expression (DEG): uns['DEG'] contains results for each celltype_minor comparing one condition vs. the rest, with log2_FC, pval, pval_adj, etc.
• Gene Set Enrichment Analysis (GSEA): uns['GSEA'] stores results per celltype_minor with scores like ES, NES, p-val, q-val.
• Gene Ontology (GO/GSA): uns['GSA_up'] contains GO results per celltype_minor with pval, pval_adj, and Term.
• Copy Number Variation (CNV): obsm['X_cnv'] holds CNV estimates, and obs['ploidy_dec'] has ploidy inference labels.
• Analyzable Cell Types: For DEG, GSEA, and GSA/GO, analyses are available for B cell, Fibroblast, ILC, Intestinal Epithelial cell, Plasma cell, T cell CD4+, T cell CD8+.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a series of Uniform Manifold Approximation and Projection (UMAP) plots generated from single-cell RNA-seq data, visualizing the cellular landscape of colon tissue. Each UMAP projection shows the same underlying cellular similarity space but is colored according to different metadata annotations: condition (normal/tumor), sample, celltype_major, celltype_minor, ploidy_dec (aneuploid/diploid/unclear), and celltype_subset. These visualizations are crucial for assessing data quality, identifying cellular populations, understanding relationships between cell types and conditions, and evaluating the impact of sample variability and genomic alterations like aneuploidy.

Visual Summary

1. Condition UMAP:

• The UMAP colored by condition reveals a clear separation between 'normal' and 'tumor' cells.
• Distinct clusters are predominantly enriched for either normal (red) or tumor (purple) cells, indicating significant transcriptional differences between the two conditions.
• However, some regions show a mix of both normal and tumor cells, suggesting shared cell states or cell types that are present in both microenvironments, or cell types that adapt to the tumor context without completely shifting their global transcriptome.

2. Sample UMAP:

• The visualization by sample shows a generally good mixing of cells from different samples (B_cac and T_cac series) within the major cell type clusters. This suggests that the primary structure of the UMAP is driven by biological variation (e.g., cell type, condition) rather than strong batch effects.
• Some smaller, more peripheral clusters might exhibit a higher enrichment of cells from specific samples, which could represent rare cell populations or unique patient-specific responses.

3. Cell Type (Major, Minor, Subset) UMAPs:

• All three cell type UMAPs (celltype_major, celltype_minor, celltype_subset) demonstrate robust and distinct clustering of different cell populations.
• celltype_major: Major cell types like 'T cell', 'B cell', 'Intestinal Epithelial cell', 'Stromal cell', and 'Myeloid cell' form well-separated clusters, indicating strong transcriptomic identities. 'Intestinal Epithelial cells' (orange) are prominently represented, consistent with the tissue origin (Colon) and the epithelial nature of colon cancer.
• celltype_minor: This plot provides a more granular view, differentiating subtypes such as 'T cell CD4+' and 'T cell CD8+' within the broader T cell cluster, and resolving 'Macrophage', 'Fibroblast', and 'Plasma cell' populations.
• celltype_subset: This UMAP offers the highest resolution, detailing numerous specific cell subsets like 'Enterocyte', 'Goblet cell', 'Paneth cell', various T cell subsets (e.g., 'T cell (Naive)', 'Treg', 'T_Cyto'), and distinct macrophage polarization states (M1, M2A-D). The fine-grained clustering indicates the high quality of the annotation and the rich cellular heterogeneity captured.

4. Ploidy_dec UMAP:

• The UMAP colored by ploidy_dec highlights the distribution of cells identified as 'Aneuploid' (dark red) and 'Diploid' (yellow).
• A significant portion of aneuploid cells clusters together in specific regions, particularly in the bottom-middle right part of the embedding. These aneuploid cells are not randomly scattered but tend to form distinct groups, suggesting a strong association with specific cell states or types.
• The vast majority of cells are identified as 'Diploid'. A small number of cells are labeled 'Unclear' (purple).

Biological Interpretation

• Tumor vs. Normal Cell States: The clear separation between 'normal' and 'tumor' conditions in the UMAP indicates that colorectal cancer induces substantial transcriptional changes, leading to distinct cellular phenotypes. This is fundamental for identifying tumor-specific processes and potential therapeutic targets.
• Cellular Heterogeneity and Microenvironment: The comprehensive cell type annotations, from major to subset levels, reveal the immense cellular heterogeneity of the human colon and its microenvironment. The presence of diverse immune cells (T cells, B cells, ILCs, Myeloid cells/Macrophages, NK cells, DCs) and stromal cells (Fibroblasts, Endothelial cells, Smooth muscle cells) highlights the complexity of the tumor microenvironment (TME) and normal tissue.
• Aneuploidy as a Tumor Hallmark: The distinct clustering of 'Aneuploid' cells is highly significant. Given that the 'Tumor origin celltype' is 'Intestinal Epithelial cell' and aneuploidy is a hallmark of many cancers, it is highly probable that these aneuploid cells largely represent the malignant epithelial cells within the tumor samples. Comparing the ploidy_dec UMAP with the condition UMAP, the regions enriched for aneuploid cells appear to largely overlap with tumor-specific clusters. This observation provides strong evidence for the presence of transformed, genomically unstable cells in the tumor samples. For further confirmation, one could overlay aneuploidy with 'Intestinal Epithelial cells' within 'tumor' samples. GeneCards: Aneuploidy in Cancer
• Annotation Consistency: The hierarchical consistency from celltype_major to celltype_subset demonstrates that the cell type annotations are robust and well-supported by the underlying gene expression data. This provides confidence in downstream analyses that rely on these cell type labels.
• Immune Infiltration and Stromal Remodeling: The presence and distinct clustering of various immune and stromal cell subsets (e.g., different T cell subsets, macrophage polarization states, fibroblast populations) suggest active immune infiltration and stromal remodeling within the colon tissue, likely altered in the tumor microenvironment. These components play critical roles in cancer progression and response to therapy. PubMed: Tumor Microenvironment Colon Cancer

Annotation Notes

• The UMAPs provide strong visual evidence for the quality of the cell type annotations, showing clear separation and logical progression from broad categories to fine-grained subsets.
• The ploidy_dec annotation effectively segregates aneuploid cells, a critical feature in cancer research, and their clustering pattern suggests a non-random distribution linked to specific cellular states, likely malignant.
• The general mixing of cells from different samples within major clusters suggests successful integration of data from multiple individuals, minimizing concerns about batch effects confounding biological signals.
• The presence of a small 'unassigned' population and 'Unclear' ploidy calls indicates areas where cell identity or genomic state could not be definitively determined, warranting potential further investigation if these populations are of interest.

2. UMAP Visualization of Major Cell Type Scores, Cell Type Annotations, and Ploidy Status in Colon Single-Cell RNA-seq Data

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) plots of single-cell RNA-sequencing data from colon tissue. The visualization includes:

1. Major Cell Type Scores (HiCAT_major_score): Heatmaps on the UMAP showing the expression score for each major cell type. This helps to visualize the confidence and spatial distribution of each cell type.
2. Ploidy Status (ploidy_dec): A UMAP plot colored by inferred ploidy status (Aneuploid, Diploid, Unclear). This is crucial for identifying potential malignant cell populations, especially given the "Tumor origin celltype: Intestinal Epithelial cell" in the data context.
3. Major Cell Type Annotations (celltype_major): A UMAP plot colored by the final, assigned major cell types. This serves as a ground truth for comparing with the cell type scores.

Visual Summary

The UMAP projection displays several distinct clusters, indicating a heterogeneous cell population within the colon tissue.

Major Cell Type Score Distributions:

• T cell scores are predominantly high in the large upper-left cluster and a smaller cluster in the upper-right.
• B cell scores are concentrated in a distinct, relatively small cluster in the far upper-right, which slightly overlaps with a T cell region.
• Myeloid cell scores are highest in a cluster located in the lower-left region of the UMAP.
• Mast cell scores show a very sparse but distinct population, likely within or adjacent to the myeloid compartment.
• Endothelial cell scores highlight a small, separate cluster, possibly in the lower-right region.
• Stromal cell scores are concentrated in a prominent cluster in the lower-right.
• Enteric neuron scores are generally low across the UMAP, suggesting a minor or less transcriptionally distinct population.
• Intestinal Epithelial cell scores show strong enrichment in the large, central-right cluster, confirming its identity.
• Cell Type Major Annotations (celltype_major): The categorical celltype_major plot largely confirms the patterns seen in the individual score plots. The major clusters correspond well to T cells (upper-left), Intestinal Epithelial cells (central-right), Myeloid cells (lower-left), and Stromal cells (lower-right). B cells are clearly defined in the upper-right. Unassigned cells appear as scattered smaller groups.
• Ploidy Status (ploidy_dec): The majority of cells are identified as Diploid (light yellow) across most clusters. Critically, a significant population of Aneuploid cells (maroon) is highly concentrated within the large central-right cluster. A small number of Aneuploid cells are also scattered in other regions, but their density is highest in this specific cluster.

Biological Interpretation

The UMAP plots provide a clear overview of the cellular landscape of the colon, with robust identification of major cell types based on their specific transcriptional profiles.

• Cell Type Identity and Annotation Quality: The high degree of concordance between the HiCAT_major_score heatmaps and the celltype_major categorical assignments validates the quality of the cell type annotations. Each major cell type forms distinct or largely distinct clusters on the UMAP, indicating successful resolution of cellular heterogeneity. The presence of all expected major cell types (immune cells like T and B cells, myeloid cells; structural cells like stromal and endothelial cells; and epithelial cells) is consistent with colon tissue data.
• Identification of Malignant Cells: The most striking biological insight comes from the ploidy_dec plot. The observed concentration of Aneuploid cells predominantly within the Intestinal Epithelial cell cluster is highly significant. Given that the data context specifies "Intestinal Epithelial cell" as the "Tumor origin celltype" and the tissue is "Colon" under "normal, tumor" conditions, this strongly suggests that these aneuploid epithelial cells represent the malignant cancer cell population. Aneuploidy, the presence of an abnormal number of chromosomes, is a hallmark of cancer and often drives tumor progression GeneCards - Aneuploidy. The localization of these cells to a specific epithelial cluster provides strong evidence for the cellular origin and genomic instability of the tumor.
• Tumor Microenvironment: While the primary aneuploid population is epithelial, the presence of various immune (T, B, Myeloid) and stromal cells around the epithelial cluster highlights the complex tumor microenvironment. Further analysis would be needed to understand their specific roles in normal vs. tumor conditions.

Clinical or Translational Implications

The clear identification of aneuploid Intestinal Epithelial cells in a distinct cluster has direct clinical implications.

• Malignant Cell Identification: This analysis provides a robust method to identify the likely malignant cell population within the tumor microenvironment based on a genomic characteristic (aneuploidy) and cell type-specific gene expression. This is critical for understanding tumor biology, identifying tumor-specific markers, and potentially targeting these cells.
• Heterogeneity Assessment: The data allows for the investigation of transcriptional changes within the aneuploid epithelial cells compared to diploid epithelial cells or normal epithelial cells, which could reveal genes or pathways involved in tumorigenesis or tumor progression.
• Biomarker Discovery: Aneuploid cell populations, especially if correlated with tumor stage or patient outcome, could serve as a valuable biomarker for disease prognosis or therapeutic response.

3. Celltype_subset Marker Expression Overview

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression of key marker genes across different celltype_subset populations identified in the single-cell RNA sequencing data from human Colon tissue. The primary goal of this visualization is to provide an overview of cell type annotations by examining the specificity and abundance of known marker genes for each subset. The plot_markers_and_expression_dot tool was used with default parameters, which automatically identifies and plots surfaceome-specific markers highly expressed in each cell group.

Visual Summary

The dot plot displays celltype_subset on the y-axis and marker genes on the x-axis. Each dot's size corresponds to the fraction of cells within that group expressing the gene, while its color intensity reflects the mean expression level of the gene within the expressing cells (normalized). Red boxes highlight the primary marker genes used to define or characterize each cell subset. A bar plot on the far right indicates the number of cells belonging to each celltype_subset.

Key observations from the plot include:

• Distinct Marker Signatures: Most celltype_subset populations exhibit distinct clusters of highly expressed marker genes, often highlighted by the red bounding boxes. This suggests good separation and unique transcriptional identities for many annotated cell types.
• Expression Patterns: Genes like *POU2F2* (also known as OCT2) and *CD22* are highly expressed in B cell subsets (Breg, Follicular, MZ, Memory B cells), with variations in other markers distinguishing these subsets. For example, *CD24* is prominent in B cell (Breg) and B cell (Follicular), while *CD27* appears in B cell (Memory).
• Epithelial Cell Lineage: Epithelial subsets such as Crypt cells, Enterocytes, Goblet cells, Microfold cells, and Paneth cells show specific marker expression. *LGR5*, *ASCL2*, and *EPHB2* characterize Crypt cells, consistent with stem/progenitor function [PubMed Search: LGR5 intestinal stem cells]. Enterocytes express *CDH17* and *FABP1*. Goblet cells are marked by *MUC2* and *TFF3*. Paneth cells show high expression of *LYZ* (lysozyme) and *DEFA5* (defensin alpha 5).
• Myeloid Cell Heterogeneity: Macrophage subsets (M1, M2A, M2B, M2C, M2D) display differential expression of myeloid markers. For instance, *MSR1* and *CD36* are broadly expressed, with some specificity in other genes like *CLEC7A* (Dectin-1) in Macrophage (M1) and *SPP1* in Macrophage (M2A).
• Stromal Cell Types: Fibroblasts are characterized by high expression of collagen genes (*COL1A1*, *COL1A2*, *COL3A1*) and *PDGFRA* [GeneCards: PDGFRA]. Smooth muscle cells express *ACTA2*, *TAGLN*, and *MYH11*.
• Lymphoid Cell Diversity: T cell subsets (Cytotoxic, Naive, Tfh, Th1, Th17, Th2, Th22, Treg) show appropriate T cell markers. For instance, T cell (Cytotoxic) cells highly express *GZMB* and *CD8A*. T cell (Treg) cells are marked by *FOXP3*, *CTLA4*, and *CD5*.
• Cell Number: The bar plot on the right shows that T cell (Naive) and T cell (Cytotoxic) populations are among the most abundant subsets, followed by Intestinal Epithelial cells (Enterocyte, Crypt cell, Goblet cell).

Biological Interpretation

The marker gene expression patterns largely align with established knowledge of cell type identities in the human colon, supporting the robustness of the celltype_subset annotations within the AnnData object.

• B Cell Subsets: The differentiation of B cell subsets is supported by distinct marker combinations. For example, the presence of *CD24* in Breg and Follicular B cells, and *CD27* as a memory marker, is biologically consistent [UniProt: CD24, UniProt: CD27]. Plasma cells showing strong expression of *XBP1*, *PRDM1*, and *SDC1* (CD138) confirms their terminal differentiation state responsible for antibody production [GeneCards: SDC1].
• Intestinal Epithelial Cells: The identification of Crypt cells with *LGR5* (a canonical stem cell marker in the intestine) and secretory cells like Goblet cells (*MUC2*, *TFF3*) and Paneth cells (*LYZ*, *DEFA5*) confirms the presence and proper annotation of the main epithelial lineages involved in intestinal regeneration and host defense [PubMed Search: intestinal stem cell markers, Paneth cell defensins]. The expression of *CDH17* and *FABP1* in Enterocytes indicates their absorptive function [GeneCards: CDH17].
• Immune Cell Lineages: The clear distinction between various T cell subsets based on specific transcription factors and effector molecules (e.g., *GZMB* for cytotoxic T cells, *FOXP3* for regulatory T cells, *CD40LG* for T follicular helper cells) provides confidence in the granular immune cell annotation [PubMed Search: T cell subset markers]. The heterogeneity within macrophages (M1, M2 subtypes) with markers like *CLEC7A* for M1 or *SPP1* for M2A macrophages suggests a diverse functional landscape of myeloid cells in the colon [PubMed Search: M1 M2 macrophage markers].
• Stromal Compartment: The identification of Fibroblasts with collagen genes and *PDGFRA* (platelet-derived growth factor receptor alpha), and Smooth Muscle Cells with *ACTA2* (alpha-smooth muscle actin) indicates a well-resolved stromal compartment essential for tissue structure and function [GeneCards: ACTA2].

Annotation Notes

The comprehensive and specific expression of known markers across the celltype_subset annotations strongly supports the quality and accuracy of the cell identity assignments in this dataset. The use of surfaceome-only markers also means that these identified markers could be valuable for experimental validation using techniques like flow cytometry or immunohistochemistry, offering a practical avenue for further research. The presence of clear, distinct marker sets for most cell types, with minimal cross-talk, indicates a well-resolved and accurately annotated dataset at the celltype_subset level.

4. Analysis of Copy Number Variations (CNVs) in Selected Cell Groups

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to visualize copy number variations (CNVs) in tumor-origin cells (Intestinal Epithelial cells) and unassigned cells, grouped by sample. The plot_cnv_heatmap tool was used to generate a heatmap of log2(Copy Number Ratio) values across genomic spots and a summary of significantly amplified cytogenetic bands.

Visual Summary

CNV Heatmap (Figure 1)

The heatmap displays the log2(CNR) values across chromosomes 1 through 22 for various cell groups.

• Y-axis (Cell Groups): The y-axis shows distinct cell groups, primarily labeled as "Diploid B_cacX" and "Diploid T_cacX", along with "T_cac1", "T_cac3", and "T_cac8". The "Diploid" prefix indicates inferred diploid cells, while the groups without this prefix (e.g., T_cac1, T_cac3, T_cac8) are implicitly inferred as aneuploid based on the add_condition_prefix_to_aneuploid parameter. The "_cacX" suffix represents different samples.
• X-axis (Genomic Spots): Genomic spots are ordered by chromosome, providing a genome-wide view of CNVs.
• Color Scale: Red indicates amplification (log2(CNR) > 0), and blue indicates deletion (log2(CNR) < 0).

Key Observations:

• The "Diploid" cell groups generally show minimal CNV signals, with log2(CNR) values close to zero, consistent with their diploid status.
• In contrast, the non-diploid (likely aneuploid) groups, specifically "T_cac1", "T_cac3", and "T_cac8", exhibit clear and recurrent patterns of copy number alterations.
• Prominent amplifications (red regions) are observed across these aneuploid groups, particularly on chromosomes 8, 12, 19, and 20. For example, T_cac3 and T_cac8 show strong amplifications on chr8, chr12, chr19, and chr20, suggesting similar clonal CNV profiles between these two samples/groups. T_cac1 also shows amplifications on chr19 and chr20.
• Some deletions (blue regions), though less extensive, are also visible (e.g., on chr6 in T_cac3).

Summary of Significantly Amplified Regions (Figure 2)

The summary provides a more detailed view of the most frequently amplified cytogenetic bands in the aneuploid cell groups (T_cac1, T_cac3, T_cac8).

• Left Plot: A heatmap showing the magnitude of amplification for specific cytogenetic bands across T_cac1, T_cac3, and T_cac8. This plot reinforces the observation that T_cac3 and T_cac8 share many significant amplifications, including regions on 8q, 12q, 19q, and 20p. T_cac1 shows significant amplifications on 19q and 20p.
• Right Plot: A bar chart illustrating the frequency of these significant amplifications across the three aneuploid cell groups. All listed cytogenetic bands show a frequency of 0.67, indicating that 2 out of 3 groups (likely T_cac3 and T_cac8) exhibit these amplifications.
• Specific Amplified Bands Highlighted: Key amplified regions include 1q21.3, 5q31.1-5q31.2, 5q35.3-6p25.2, 8q22.1-8q24.12 (notably containing EIF3E, INTS8), 8q24.3-9p24.1, 12q24.13-12q24.31, 12q24.33-12q12.2, 19q13.2-19q13.41, and 20p13-20p11.21.

Biological Interpretation

The analysis reveals significant and recurrent copy number amplifications in a subset of immune cell populations (T cells) across different samples within this dataset.

1. Aneuploidy in Immune Cells: The clear distinction between "Diploid" and "Aneuploid" (unprefixed) cell groups is consistent with the ploidy_dec annotation, indicating that some cell populations have undergone substantial chromosomal alterations. The prominent CNV profiles observed in T_cac1, T_cac3, and T_cac8 suggest the presence of aneuploid T-cell clones. While the user specifically requested tumor-origin Intestinal Epithelial cells and unassigned cells, the plots predominantly feature B and T cells. This discrepancy is discussed in the "Annotation Notes" section.
2. Recurrent Amplifications: The shared amplification patterns, particularly in T_cac3 and T_cac8, on chromosomes 8, 12, 19, and 20, suggest common mechanisms of genomic instability or clonal expansion pathways within these immune cell populations across the different samples. Recurrent CNVs in immune cells can be associated with lymphoproliferative disorders or acquired somatic changes in the tumor microenvironment PubMed search: T cell clonal expansion CNV cancer.
3. Potential Oncogenic Regions:

• 8q22.1-8q24.12: This region includes EIF3E and INTS8. Notably, the well-known oncogene MYC is located nearby at 8q24.21. Amplifications involving 8q are frequently observed in various cancers and can drive cell proliferation and growth GeneCards: MYC.
• 12q: Amplifications on chromosome 12q are common in many cancers, including colorectal cancer, and can involve genes critical for cell cycle progression and signaling pathways PubMed search: 12q amplification cancer.
• 19q and 20p: These regions are also frequently amplified in solid tumors and can harbor numerous genes involved in cell growth, survival, and metastasis. Amplifications of chromosome 20 are often linked to tumor progression and aggressive phenotypes in colorectal cancer PubMed search: 19q 20p amplification cancer colorectal.

Annotation Notes and Limitations

A critical observation is the mismatch between the target_cells specified in the user query (Intestinal Epithelial cell, unassigned) and the cell types explicitly displayed on the heatmap's y-axis (B cell, T cell).

• Discrepancy: The heatmap primarily shows CNV patterns in B and T cell populations. While Intestinal Epithelial cells are designated as tumor-origin, they are not explicitly labeled or prominently featured with aneuploid profiles in this output.

Possible Explanations:

1. Filtering/Grouping Logic: It's possible that the target_cells parameter filters the initial cell population, but subsequent grouping by sample and ploidy inference, combined with the n_cells_min_default=60 parameter, led to the displayed aneuploid groups being predominantly immune cells that met the criteria for distinct CNV profiles. Cells from Intestinal Epithelial cell or unassigned types might not have formed sufficiently large or significantly aneuploid clusters to be displayed, or were implicitly merged into diploid groups if they showed minimal CNVs.
2. Labeling Convention: The plot's labeling mechanism might default to celltype_major or celltype_minor for group identification *after* CNV calling and clustering, even if the initial filtering was for different cell types.
3. Tool Application or Dataset Characteristics: There could be an issue with how the target_cells parameter was applied by the tool, or perhaps the selected Intestinal Epithelial cell and unassigned populations in this specific dataset (within the specified samples) do not exhibit extensive or detectable aneuploidy above the defined thresholds for visualization in this manner.

• Validation Required: This discrepancy highlights a need for annotation validation or further investigation into the filtering and grouping logic. It is crucial to confirm which cell types these aneuploid clones truly represent to correctly interpret their biological and clinical significance in the context of tumor-origin cells.

Clinical or Translational Implications

• If the Aneuploid Groups are Immune Cells (as labeled): The presence of recurrent CNVs in T-cell populations could indicate clonal expansion of immune cells with genomic instability. This might be relevant in understanding the immune microenvironment in cancer, potential immune escape mechanisms, or even the development of lymphoproliferative complications in a cancer setting.
• If the Aneuploid Groups are Mis-labeled Tumor-Origin Cells: If these aneuploid T-cell groups are, in fact, mislabeled Intestinal Epithelial cells (tumor-origin cells), then the observed recurrent amplifications on chromosomes 8, 12, 19, and 20 would be highly significant. These regions are known to harbor oncogenes and frequently contribute to colorectal cancer progression and aggressive phenotypes, offering potential insights into tumor evolution, prognosis, and resistance mechanisms. This would warrant further investigation into the specific genes within these amplified regions as potential therapeutic targets or biomarkers PubMed search: Colorectal cancer 8q 12q 19q 20p amplification.

Given the primary instruction to prioritize CNV patterns, ploidy interpretation, and annotation validation, the most immediate implication is the need to clarify the cell identities of the aneuploid groups observed, especially concerning the user's specific query for tumor-origin cells.

5. CNV-Based UMAP Analysis of Colon Tissue Reveals Aneuploidy in Tumor-Associated Intestinal Epithelial Cells

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes single-cell RNA-seq data on a UMAP projection, where the embedding (X_cnv_umap) is specifically computed using inferred Copy Number Variation (CNV) estimates (obsm['X_cnv']). The plots illustrate the distribution of cells colored by major cell type, minor cell type, ploidy status (ploidy_dec), condition (normal/tumor), and individual sample, providing insight into the genomic landscape of the cellular populations.

Visual Summary

The UMAP visualizations reveal a multi-cluster structure, primarily driven by CNV patterns.

• celltype_major: Major cell types like T cells and B cells form distinct, separate clusters, while Intestinal Epithelial cells, Stromal cells, Myeloid cells, and Endothelial cells occupy the larger central and lower-right regions, showing some degree of mixing but also distinct aggregations.
• celltype_minor: Provides a finer resolution, showing specific immune cell subtypes (e.g., T cell CD4+, T cell CD8+, Macrophage, Fibroblast) that largely co-locate with their major cell type counterparts. The Intestinal Epithelial cell cluster remains a prominent feature.
• ploidy_dec: A critical plot for CNV analysis, it shows a clear bimodal distribution. The vast majority of cells are labeled "Diploid" (light yellow), forming the main body of the UMAP. A distinct, smaller cluster of "Aneuploid" cells (maroon) is prominently located in the lower-right quadrant and extends slightly into the central-right region, suggesting a population with significant chromosomal aberrations. "Unclear" cells are very sparse.
• condition: Cells from "normal" tissue (maroon) are broadly distributed across the diploid regions. "Tumor" cells (dark blue) are also widely distributed but show a strong enrichment and co-localization with the "Aneuploid" cluster observed in the ploidy_dec plot, particularly in the lower-right and central-right regions.
• sample: While there is general mixing of cells from different samples, implying that the overarching biological signals are preserved, some sample-specific enrichments are visible. Notably, certain tumor samples (e.g., 'T_cac15', 'T_cac16') show a higher concentration within the aneuploid cluster, highlighting inter-patient heterogeneity within the tumor microenvironment.

Biological Interpretation

1. Aneuploidy as a Hallmark of Tumor Cells: The most striking finding is the strong correlation between the "Aneuploid" cell population and the "tumor" condition. This is a fundamental characteristic of many cancers, where cells acquire an abnormal number of chromosomes PubMed search: aneuploidy cancer. The clear separation of these aneuploid cells on the CNV-derived UMAP validates the robustness of the CNV inference and its biological relevance in distinguishing malignant cells.
2. Identification of Malignant Cell Origin: By cross-referencing the ploidy_dec, condition, and celltype_major plots, the aneuploid cell cluster in the tumor condition predominantly consists of "Intestinal Epithelial cells". This aligns perfectly with the data context stating "Tumor origin celltype: Intestinal Epithelial cell" for colon tissue. This strongly indicates that the detected aneuploidy represents the genomic instability of malignant epithelial cells originating from the intestinal lining.
3. Tumor Microenvironment Composition: The remaining cell types, including various immune cells (T cells, B cells, Myeloid cells) and stromal cells (Fibroblasts, Endothelial cells), are predominantly diploid and are present in both normal and tumor conditions. This indicates the presence of a diverse, largely non-malignant tumor microenvironment. These host cells support the tumor but largely retain a stable genomic state, differentiating them from the aneuploid cancer cells.
4. CNV-driven Embedding Utility: The UMAP embedding based on CNV estimates effectively differentiates cells based on their genomic integrity, demonstrating its utility in delineating cancerous from non-cancerous populations and identifying the specific cell types affected by large-scale genomic alterations.

Clinical or Translational Implications

• Diagnostic Potential: The clear distinction between aneuploid (tumor) and diploid (normal/immune/stromal) cells based on CNV patterns could potentially be leveraged for diagnostic purposes, aiding in the identification of malignant cells in tissue samples.
• Therapeutic Targeting: Understanding which cell types are aneuploid can help focus research on targeting the specific genomic vulnerabilities of these malignant cells (Intestinal Epithelial cells in this case), potentially leading to novel therapeutic strategies that exploit their chromosomal instability.
• Prognostic Marker: The degree and specific patterns of aneuploidy are often associated with tumor progression and patient prognosis. Further analysis of the CNV profiles within the aneuploid Intestinal Epithelial cell population could reveal prognostic biomarkers.

6. Colon Tissue Minor Cell Type Population Analysis

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot showing the relative proportions of minor cell types across individual samples from both normal and tumor conditions in human colon tissue. This visualization is crucial for understanding the overall cellular composition and identifying shifts in the tumor microenvironment (TME) compared to healthy tissue. Each bar represents a single sample, with stacked segments indicating the percentage of each identified minor cell type.

Visual Summary

The bar plots effectively illustrate the cellular heterogeneity within and between normal and tumor colon samples at the minor cell type level.

• Normal Samples: The normal colon samples (prefixed 'B_cac') generally show a relatively consistent cellular composition. Intestinal Epithelial cells, T cells (CD4+ and CD8+), and Smooth muscle cells are typically the most abundant populations. B cells and Plasma cells are also present in varying but notable proportions in some normal samples. Fibroblasts, Endothelial cells, and Myeloid cells (Macrophage, Dendritic cell) are present but usually in lower proportions.
• Tumor Samples: The tumor colon samples (prefixed 'T_cac') exhibit considerably more variability in cellular composition compared to normal samples.
• Intestinal Epithelial cells, the designated tumor origin cell type, show highly variable proportions in tumors. While they remain dominant in some tumor samples (e.g., T_cac2, T_cac3, T_cac1, T_cac8), their proportion is substantially reduced in others, suggesting infiltration by non-epithelial cells or tumor purity variations.
• Fibroblasts are markedly and consistently elevated in many tumor samples (e.g., T_cac2, T_cac3, T_cac1, T_cac7, T_cac8, T_cac13, T_cac16, T_cac15, T_cac4) compared to their minimal presence in normal samples. This is one of the most striking differences.

Immune cells show diverse patterns of infiltration

• B cells and Plasma cells are highly variable across tumor samples. Some tumors (e.g., T_cac10, T_cac11, T_cac12, T_cac14, T_cac15) show a substantial increase in B cells, sometimes surpassing normal levels, while others have very low B cell content.
• T cells (CD4+ and CD8+) remain prominent in many tumor samples, contributing significantly to the immune infiltrate.
• Macrophages appear to be generally more abundant in tumor samples compared to normal tissue, though their proportions also vary.
• Dendritic cells, NK cells, and ILCs are present but generally constitute smaller fractions.
• Smooth muscle cells generally appear to be reduced in proportion in many tumor samples, suggesting disruption of normal tissue architecture.
• Endothelial cells are often present in low, but noticeable, proportions in tumor samples.

Biological Interpretation

The observed shifts in minor cell type populations provide critical insights into the changes occurring during colon tumorigenesis.

• Tumor Microenvironment (TME) Remodeling: The most significant finding is the widespread increase in Fibroblasts in tumor samples. This indicates extensive stromal remodeling within the TME, a characteristic feature of solid tumors. These fibroblasts likely include Cancer-Associated Fibroblasts (CAFs), which play crucial roles in extracellular matrix deposition, promoting tumor growth, angiogenesis, and immune suppression PMID: 35140359.
• Immune Infiltration and Heterogeneity: The substantial and variable infiltration of different immune cell types (B cells, Plasma cells, T cells, Macrophages) into the tumor suggests a dynamic and diverse immune response.
• The presence of B cells and Plasma cells indicates an active humoral immune response, which can be either anti-tumorigenic or pro-tumorigenic depending on their activation state and specific subsets PMID: 31086300. Their high variability across tumor samples underscores the heterogeneity of immune infiltration patterns in colon cancer.
• The consistent presence of T cells (CD4+ and CD8+) and increased Macrophages in tumors are hallmarks of immune engagement, though their functional polarization (e.g., M1 vs. M2 macrophages; effector vs. regulatory T cells) is not discernable from this plot alone but is critical for understanding their role.
• Epithelial-Stromal Interaction: The variable proportion of Intestinal Epithelial cells in tumors, often inversely correlated with stromal (Fibroblast) or immune cell proportions, highlights the complex interplay between tumor cells and their microenvironment. This can reflect differences in tumor purity, proliferation rates, or the extent of desmoplasia and immune infiltration.
• Loss of Normal Tissue Components: The relative decrease in Smooth muscle cells in many tumor samples is consistent with the degradation and remodeling of the normal colon tissue architecture as the tumor expands.

Clinical or Translational Implications

The findings from this cell type population analysis have several potential clinical and translational implications for colon cancer.

• Biomarkers for Prognosis and Therapeutic Response: The distinct cellular compositions in normal versus tumor tissue, and the heterogeneity within the tumor group, suggest that specific cell type proportions could serve as prognostic biomarkers. For instance, a high infiltration of specific immune cell subsets or the abundance of CAFs might correlate with patient outcomes or response to particular therapies.
• Targeting the Tumor Microenvironment: The marked increase in Fibroblasts in the TME points to the potential utility of therapeutic strategies aimed at modulating or depleting CAFs to inhibit tumor growth and metastasis, possibly in combination with conventional treatments or immunotherapies PMID: 35140359.
• Immunotherapy Stratification: The diverse immune cell infiltration patterns highlight the importance of assessing the immune landscape of individual tumors. Patients with "hot" tumors (high immune infiltration, e.g., T cells, B cells) might respond differently to immune checkpoint inhibitors compared to "cold" tumors (low immune infiltration) PubMed Search: "tumor hot cold immune classification". Further characterization of the functional states of these immune cells would be critical for guiding personalized immunotherapy.
• Understanding Tumor Heterogeneity: The high sample-to-sample variability in tumor cell composition emphasizes the need for personalized approaches in cancer treatment. A "one-size-fits-all" strategy may not be optimal, and detailed TME profiling could aid in patient stratification.

7. Analysis of T cell Subset Proportions in Colon Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional representation of different T cell subsets and related innate lymphoid cell populations (ILC, NK cells) across individual samples, comparing normal colon tissue with tumor tissue. The plot illustrates the celltype_minor composition within the broader T cell celltype_major population for each sample. Understanding these shifts provides insights into the immune microenvironment in colon cancer development.

Visual Summary

The visualization displays stacked bar plots, with each bar representing an individual sample and showing the relative proportions of T cell CD4+, T cell CD8+, ILC, NK cell, and unassigned populations that were categorized as T cell at the celltype_major level. The samples are grouped by Condition (normal and tumor).

1. Dominant Populations: In both normal and tumor conditions, T cell CD4+ (light orange) and T cell CD8+ (light yellow) constitute the vast majority of cells within the T cell major population, varying in their relative proportions across samples.
2. CD4+ T cell Enrichment in Tumor: Many tumor samples (e.g., T_cac7, T_cac3, T_cac5, T_cac8, T_cac12, T_cac4, T_cac10, T_cac9, T_cac16, T_cac15, T_cac6, T_cac11, T_cac14, T_cac13) show a proportionally higher abundance of T cell CD4+ compared to T cell CD8+, often comprising 60-80% or more of the "major T cell" population. While variable, this trend appears more pronounced than in most normal samples.
3. ILC Presence: ILCs (dark red) are observed in minimal proportions in some normal samples. However, they show a more noticeable presence and, in some cases, a distinct enrichment in tumor samples. For instance, T_cac2 exhibits a significant proportion (~15%) of ILCs within the T cell-classified cells, which is the highest observed across all samples. Other tumor samples like T_cac1, T_cac16, T_cac15, and T_cac13 also show small but consistent ILC populations.
4. NK Cell Presence: NK cells (orange) are largely absent or present in very negligible amounts across both normal and tumor samples within the T cell major population.
5. Variability Across Samples: There is considerable heterogeneity in the T cell subset composition among individual samples within both the normal and tumor groups, suggesting inter-patient variability in immune responses or tissue states.

Biological Interpretation

The observed shifts in T cell subset populations provide key insights into the immune landscape of colorectal cancer.

• Altered CD4+/CD8+ T cell Ratio in Tumor Microenvironment: The tendency for T cell CD4+ to be more abundant than T cell CD8+ in many tumor samples is a critical observation. CD8+ cytotoxic T lymphocytes (CTLs) are crucial for direct anti-tumor killing, while CD4+ T cells encompass diverse roles, including helper functions and regulatory T cells (Tregs). An elevated CD4+/CD8+ ratio in the tumor microenvironment is often associated with immune evasion and poorer prognosis in various cancers, including colorectal cancer, particularly if it reflects an increase in immunosuppressive Tregs within the CD4+ population PMID: 29773738. This suggests a potential shift towards an immunosuppressive milieu in the tumor.
• Presence of ILCs within the T cell compartment: The detection of ILCs within the cells initially classified as T cell at the major level, and their occasional enrichment in tumor samples (e.g., T_cac2), is interesting. While ILCs are generally considered distinct from T cells, their co-occurrence here could reflect transcriptional similarities that lead to initial co-clustering, or a specific annotation strategy. Regardless, their increased presence in tumor samples suggests a potential role in the tumor immune context. Different ILC subsets (ILC1, ILC2, ILC3) play diverse roles in inflammation and cancer, particularly in mucosal tissues like the colon. ILC3s, for instance, are abundant in the gut and can be involved in chronic inflammation, which is a known driver of colorectal cancer progression PMID: 30140228. Further sub-typing of these ILCs would be crucial.
• Minimal NK Cell Contribution: The consistently low proportion of NK cells within the T cell major population, even in tumor samples, indicates that this subset contributes minimally to the T cell-classified immune cell fraction in this dataset. If broadly representative, this could imply that NK cells are either scarce, primarily classified into a different major cell type category, or their activity is suppressed in the colon tumor microenvironment.

Clinical or Translational Implications

These findings have several potential clinical and translational implications:

• Prognostic Biomarker Potential: The CD4+/CD8+ T cell ratio within the tumor microenvironment is a known prognostic indicator in various cancers. A higher ratio favoring CD4+ T cells, particularly if driven by Tregs, could indicate a less favorable prognosis for colon cancer patients. Monitoring this ratio could aid in patient stratification.
• Immunotherapy Response Prediction: Tumors characterized by a high CD4+/CD8+ ratio, especially with dominant immunosuppressive CD4+ subsets, might be less responsive to immunotherapies that rely on CD8+ T cell effector functions (e.g., checkpoint blockade). This could inform personalized treatment strategies, potentially necessitating combination therapies that target immunosuppressive pathways or enhance CD8+ T cell activity.
• Therapeutic Targeting of Immunosuppression: If the elevated CD4+ T cell fraction corresponds to an increase in Tregs or other pro-tumorigenic CD4+ subsets, then specific strategies to deplete or reprogram these cells, or to re-balance the CD4+/CD8+ ratio, could be explored as adjunctive therapies in colon cancer.
• Investigating ILC Role: The notable presence and occasional enrichment of ILCs in tumor samples warrant further investigation. Identifying the specific ILC subsets involved and their functional status could reveal novel immune mechanisms or potential therapeutic targets in colon cancer.

8. Colon Cancer Alters T cell Subset Proportions, Favoring Immunosuppression and Inflammation

[Analysis Visualization Results]...

Analysis Overview

This analysis evaluated the proportional representation of specific T cell subsets (Th17, Treg, Th22, and Tfh) in single-cell RNA-seq data from human colon tissue, comparing normal samples with tumor samples. The goal was to identify statistically significant differences in these populations that may shed light on the immune dynamics in colon cancer.

Visual Summary

The box plots illustrate significant alterations in the proportions of several T cell subsets in colon tumor tissue compared to normal colon tissue:

• Th17 cells: There is a statistically significant increase in the proportion of Th17 cells in tumor samples (median ~9%) compared to normal samples (median ~4.5%) (p ≤ 0.01). The distribution of Th17 cells is also wider in the tumor group.
• Treg cells: Regulatory T (Treg) cells show a highly significant increase in proportion within tumor samples (median ~9%) compared to normal samples (median ~3.5%) (p ≤ 0.001). This represents a substantial enrichment of Tregs in the tumor microenvironment.
• Th22 cells: The proportion of Th22 cells is also significantly elevated in tumor samples (median ~5%) compared to normal samples (median ~3.5%) (p ≤ 0.01), indicating an expansion of this subset in cancer.
• Tfh cells: In contrast to the other subsets, T follicular helper (Tfh) cells show a trend towards a decreased proportion in tumor samples (median ~12%) compared to normal samples (median ~15%) (p = 0.09). This difference, while marginally significant, suggests a potential reduction in Tfh cell presence in the tumor context.

Biological Interpretation

The observed shifts in T cell subset proportions indicate a profound reshaping of the immune landscape within colon tumors, favoring a combination of pro-tumorigenic inflammation and immune suppression:

• Expansion of Pro-tumorigenic Th17 and Th22 Cells: The significant increase in Th17 and Th22 cell populations in colon tumors is notable. Th17 cells, through cytokines like IL-17, are often associated with chronic inflammation, which can promote tumor growth, angiogenesis, and metastasis in colorectal cancer. [PubMed search: Th17 cells colorectal cancer progression: PubMed Search]. Similarly, Th22 cells, producing IL-22, play a role in epithelial homeostasis but can also drive cancer cell proliferation and survival in certain contexts, contributing to tumor progression, especially given the "Intestinal Epithelial cell" origin of the tumor.
• Dominance of Immunosuppressive Tregs: The highly significant increase in Treg cells is a hallmark of many solid tumors, including colorectal cancer. Tregs are potent suppressors of anti-tumor immunity, inhibiting the activity of effector T cells and natural killer cells. Their accumulation within the tumor microenvironment is a primary mechanism by which tumors evade immune destruction and is often linked to poorer prognosis. [PubMed search: Treg cells tumor microenvironment CRC: PubMed Search].
• Potential Impairment of Humoral Immunity by Decreased Tfh Cells: The decrease in Tfh cells, though less significant, might suggest a compromised ability to mount robust humoral (antibody-mediated) immune responses within the tumor. Tfh cells are crucial for guiding B cell differentiation and antibody production, which can contribute to anti-tumor immunity. A reduction could imply a less effective B cell-dependent anti-tumor response or an overall shift in immune organization.

Collectively, these findings paint a picture of a tumor microenvironment that fosters its own growth by promoting inflammatory subsets (Th17, Th22) and actively suppressing anti-tumor responses through an expanded Treg population, potentially at the expense of effective humoral immunity (Tfh).

Clinical or Translational Implications

These differential T cell subset proportions hold important clinical and translational relevance:

• Prognostic Value: The elevated proportions of Th17, Th22, and particularly Treg cells could serve as prognostic biomarkers for colon cancer progression, recurrence, or patient survival. Higher Treg infiltration is generally associated with unfavorable outcomes in colorectal cancer.
• Therapeutic Targets: The significant expansion of immunosuppressive Tregs highlights them as critical therapeutic targets. Strategies to deplete Tregs, inhibit their function, or reprogram them into effector cells could enhance anti-tumor immunity. Similarly, modulating the pro-tumorigenic inflammatory pathways driven by Th17 and Th22 cells could offer additional therapeutic avenues. [PubMed search: Treg modulation cancer therapy: PubMed Search]
• Immunotherapy Enhancement: Understanding the balance of these T cell subsets can inform the rational design of combination immunotherapies. For instance, treatments that reduce Treg numbers or function could synergize with checkpoint inhibitors to unleash stronger anti-tumor responses in colon cancer patients.

9. Macrophage Cell Population Annotation Check

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the population distribution for cells annotated as 'Macrophage' at the celltype_minor level, across different samples and conditions (normal vs. tumor). The plot_celltype_population tool was used to generate bar plots, specifically targeting cells classified as 'Macrophage' in the celltype_minor column and displaying their proportions within that same taxonomic level.

Visual Summary

The generated bar plots are split into two panels: 'normal' and 'tumor', representing different sample conditions.

• Each bar in the 'normal' panel corresponds to a specific normal sample (e.g., B_cac10, B_cac11), and similarly, each bar in the 'tumor' panel corresponds to a tumor sample (e.g., T_cac1, T_cac10).
• For all samples depicted under both 'normal' and 'tumor' conditions, the bar representing 'Macrophage' consistently reaches 100% on the y-axis.
• The legend indicates that the solid burgundy bars represent 'Macrophage'.

Biological Interpretation

The visual output indicates that within the sub-population of cells explicitly identified as 'Macrophage' at the celltype_minor level, 100% of these cells are indeed classified as 'Macrophage'. This plot, therefore, serves as a verification of the consistency and specificity of the cell type annotation for 'Macrophage' cells within the dataset.

It is important to note that this plot, configured as it is, does not convey the *relative abundance* of macrophages compared to other cell types within each sample, nor does it illustrate the proportions of various macrophage *subsets* (e.g., M1, M2 macrophages). Instead, it confirms that cells initially identified as 'Macrophage' at the celltype_minor level are consistently labeled as such across all analyzed samples and conditions. To analyze macrophage abundance or their intra-population heterogeneity (e.g., subsets), a different configuration of the plot_celltype_population tool (e.g., targeting a higher taxonomic level or using celltype_subset for compute_cfg['taxo_level']) would be required.

Annotation Notes

This visualization confirms the internal consistency of the 'Macrophage' cell type annotation at the celltype_minor level. All cells designated as 'Macrophage' were indeed found to be 'Macrophage' within their defined grouping, indicating robust and clear classification for this specific cell population. This plot does not highlight differences in macrophage prevalence or subtyping between normal and tumor conditions.

10. Macrophage Subset Population Shifts in Colorectal Tumor Microenvironment

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportions of specific Macrophage subsets (M2A, M2D, M2B) within the colon tissue, comparing normal and tumor conditions. The goal is to identify statistically significant differences in these populations, which could indicate shifts in immune cell composition driven by the tumor microenvironment.

Visual Summary

The box plots illustrate the celltype proportion of Macrophage (M2A), Macrophage (M2D), and Macrophage (M2B) subsets in normal versus tumor conditions.

• Macrophage (M2A): The proportion of M2A macrophages is significantly lower in tumor tissue compared to normal tissue (p ≤ 0.01). In normal tissue, the median proportion is around 45%, while in tumor tissue, it drops to approximately 12%.
• Macrophage (M2D): Conversely, the proportion of M2D macrophages is significantly higher in tumor tissue compared to normal tissue (p ≤ 0.05). The median proportion in normal tissue is very low (around 2-3%), increasing to about 7-8% in tumor tissue.
• Macrophage (M2B): Similar to M2D, the proportion of M2B macrophages is also significantly elevated in tumor tissue compared to normal tissue (p ≤ 0.05). The median proportion increases from approximately 6% in normal tissue to about 18% in tumor tissue.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment, often polarizing into different functional states, broadly categorized as M1 (pro-inflammatory, anti-tumor) and M2 (anti-inflammatory, pro-tumor). The M2 category itself is heterogeneous, including subtypes like M2A, M2B, M2C, and M2D, each with distinct activation pathways and functions.

The observed shifts indicate a significant re-programming of macrophage populations in the colorectal tumor microenvironment:

• Decreased Macrophage (M2A) in Tumor: M2A macrophages are typically associated with wound healing, tissue repair, and anti-inflammatory responses, often induced by IL-4/IL-13. A decrease in this subtype in tumor tissue might suggest a disruption of normal tissue repair mechanisms or a shift away from this specific M2 phenotype in favor of other, more pro-tumorigenic M2 subsets. While M2A can contribute to pro-tumor functions in some contexts, its reduction here suggests a complex immune landscape where other M2 subtypes might be more dominant or preferred by the tumor.
• Increased Macrophage (M2D) in Tumor: M2D macrophages are strongly associated with immune suppression, angiogenesis, and promotion of tumor growth, often characterized by the production of IL-10 and VEGF. Their significant enrichment in colorectal tumor tissue is highly consistent with the known pro-tumorigenic roles of tumor-associated macrophages (TAMs) in many cancers, including colorectal cancer. This subtype can foster a permissive environment for tumor growth by dampening anti-tumor immunity and supporting tumor vascularization.
• Increased Macrophage (M2B) in Tumor: M2B macrophages are a more phenotypically diverse subtype, capable of producing both pro-inflammatory (e.g., TNFα, IL-1β) and anti-inflammatory (e.g., IL-10) cytokines. Their increase in the tumor microenvironment suggests a complex inflammatory state that may contribute to immune dysregulation, potentially promoting tumor progression through various mechanisms, including immune modulation or tissue remodeling. [More info on M2 macrophage subtypes: PubMed search for "macrophage M2 subtypes cancer"]

Collectively, these findings demonstrate a clear shift in macrophage polarization within the colon tumor, moving away from M2A and towards M2B and M2D subtypes. This pattern is indicative of an immune microenvironment that is likely conducive to tumor progression, angiogenesis, and immune evasion.

Clinical or Translational Implications

The distinct shifts in macrophage subset proportions in colorectal cancer have several potential clinical and translational implications:

• Biomarker Potential: The increased proportions of M2D and M2B macrophages could serve as prognostic biomarkers, indicating a more aggressive tumor phenotype or a less favorable patient outcome in colorectal cancer. Conversely, the decrease in M2A could also be part of a biomarker signature.
• Therapeutic Targeting: M2D and M2B macrophages represent potential therapeutic targets. Strategies aimed at inhibiting their recruitment, survival, or pro-tumorigenic functions, or re-educating them towards an anti-tumor M1-like phenotype, could be beneficial. This might involve targeting specific signaling pathways (e.g., adenosine receptors for M2D) or cytokine production. [More info on TAM targeting in CRC: PubMed search for "tumor associated macrophages colorectal cancer therapy"]
• Immunotherapy Response: The presence and specific polarization of TAMs can influence the efficacy of various immunotherapies. Understanding these macrophage shifts could help stratify patients who are more likely to respond to certain treatments or guide the development of combination therapies that address the pro-tumor macrophage environment.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the ploidy status (Aneuploid, Diploid, or Unclear) within Intestinal Epithelial cells (the identified tumor-origin cell type) and unassigned cells across various normal and tumor colon tissue samples. The goal is to compare chromosomal stability differences between healthy and cancerous conditions at the single-cell level.

Visual Summary

The bar plots display the proportional distribution of ploidy states for selected cell types across individual samples, grouped by condition (normal vs. tumor).

• Normal Samples: All analyzed normal samples (B_cac7, B_cac6, B_cac4, B_cac15, B_cac10, B_cac14, B_cac11) exhibit a near-uniform ploidy profile, with virtually 100% of cells identified as Diploid. The proportion of Aneuploid or Unclear cells is negligible.

Tumor Samples: In contrast, tumor samples show a marked difference

• Several tumor samples (T_cac8, T_cac3, T_cac1, T_cac6, T_cac16) display a substantial proportion of Aneuploid cells, ranging from approximately 10% to over 50%. This indicates significant chromosomal instability in these samples.
• Other tumor samples (T_cac9, T_cac2, T_cac14, T_cac10, T_cac15, T_cac11, T_cac12, T_cac13, T_cac5, T_cac4, T_cac7) maintain a predominantly Diploid ploidy profile, similar to normal samples, suggesting inter-sample heterogeneity in genomic alterations.
• The "Unclear" category remains minimal across all normal and tumor samples.

Biological Interpretation

Aneuploidy, defined as an abnormal number of chromosomes, is a well-established hallmark of cancer and a major driver of tumor evolution and heterogeneity GeneCards: Aneuploidy.

• Normal Tissue Stability: The consistent diploidy observed in Intestinal Epithelial cells and unassigned cells from normal colon tissue is an expected finding, reflecting genomic stability characteristic of healthy cells and tissues.
• Tumor-Associated Aneuploidy: The significant presence of aneuploid cells specifically within a subset of tumor samples is highly indicative of the malignant nature of these cells. Since "Intestinal Epithelial cell" is designated as the tumor-origin cell type, it is highly probable that these aneuploid populations represent the cancerous epithelial cells that have undergone extensive genomic alterations.
• Tumor Heterogeneity: The observation that some tumor samples show high aneuploidy while others remain predominantly diploid highlights the inherent heterogeneity of colon cancer. This variability could reflect different stages of tumor progression, varying genetic drivers, or differential selective pressures within the tumor microenvironment. Tumors with higher aneuploidy might be more advanced, aggressive, or have accumulated more chromosomal abnormalities during their development.

Clinical or Translational Implications

• Biomarker Potential: The presence and degree of aneuploidy within Intestinal Epithelial cells could serve as a valuable biomarker for malignancy, tumor progression, or even prognosis in colorectal cancer. Identifying samples with high aneuploidy could help stratify patients based on their genomic instability.
• Therapeutic Relevance: Aneuploidy often correlates with increased genomic instability, which can impact therapeutic responses. Tumors with high aneuploidy might be more resistant to certain therapies or, conversely, more vulnerable to treatments targeting DNA replication stress or mitotic machinery PubMed: Aneuploidy and Cancer Therapy.
• Understanding Tumor Evolution: Further investigation into the specific chromosomal gains or losses (as indicated by obsm['X_cnv']) in these aneuploid tumor cells could provide insights into the key genomic events driving colon cancer initiation and progression in specific patient cohorts.

12. 종양 미세환경 내 주요 세포-세포 상호작용 분석

[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱 데이터에서 얻은 AnnData 객체를 사용하여 종양 미세환경 내 특정 세포 유형 간의 세포-세포 상호작용(Cell-Cell Interaction, CCI) 패턴을 식별한 결과입니다. 특히, 종양 기원 세포인 장 상피세포(Intestinal Epithelial cell)와 면역 세포인 T 세포(CD4+ T cell, CD8+ T cell), 그리고 스트로마 세포(Fibroblast), 골수 세포(Macrophage)를 대상으로 종양(tumor) 조건에서의 상호작용을 조사했습니다. CellPhoneDB를 활용하여 리간드-수용체 쌍 기반의 상호작용을 예측하고, 유의미한 상호작용을 도트 플롯으로 시각화했습니다. 종양 기원 세포의 경우, ploidy 정보가 확장되어 "Diploid Intestinal Epi"로 표시되었습니다.

Visual Summary

제공된 도트 플롯은 'tumor' 조건에서 선택된 세포 유형 간의 유의미한 세포-세포 상호작용을 시각화합니다.

• Y-축: 상호작용에 참여하는 세포 유형 쌍을 나타냅니다 (예: T CD8+|T CD8+, Diploid Intestinal Epi|T CD8+).
• X-축: 특정 리간드-수용체 쌍을 나타냅니다 (예: CEACAM5_CD8A, CD58_CD2).
• 점의 크기: 상호작용의 통계적 유의성(-log10(p-value))을 나타냅니다. 점이 클수록 p-value가 낮아(더 유의미) 더 강력한 상호작용을 의미합니다.
• 점의 색깔: 상호작용을 매개하는 유전자 쌍의 평균 발현 수준(log2(mean))을 나타냅니다. 색이 밝을수록 평균 발현량이 높음을 의미합니다.

주요 관찰:

1. 장 상피세포(Diploid Intestinal Epi) 관련 상호작용:

• 호모타입 상호작용 (Diploid Intestinal Epi | Diploid Intestinal Epi): PIGR과 결합하는 APLP2 (APLP2_PIGR), 그리고 E-cadherin을 포함하는 CDH1_integrin_aEb7 복합체(CDH1_integrin_aEb7 complex), CEACAM6의 호모필릭 결합(CEACAM6_CEACAM6)이 유의미하게 관찰됩니다. 특히 APLP2_PIGR은 높은 평균 발현량을 보입니다.
• T CD8+ 세포와의 상호작용 (Diploid Intestinal Epi | T CD8+): T 세포의 핵심 부착 및 활성화 분자인 CD58_CD2와 종양 관련 단백질 CEACAM5와 CD8A 사이의 상호작용(CEACAM5_CD8A)이 두드러집니다.
• T CD4+ 세포와의 상호작용 (T CD4+ | Diploid Intestinal Epi): CEACAM5_CD8A 상호작용이 관찰되는데, CD8A는 일반적으로 CD8+ T 세포에 특이적인 마커임을 고려할 때, T CD4+ 세포에서 이 상호작용이 나타나는 것은 예상치 못한 패턴이며 추가적인 조사가 필요할 수 있습니다.

2. T 세포 관련 상호작용:

• T CD4+ 및 T CD8+ 세포 간의 상호작용: T 세포들 사이에서 CD58_CD2 (강력한 T 세포 부착/활성화), CEACAM5_CD8A (CEACAM5의 발현이 T 세포에도 나타날 수 있음을 시사), CEACAM6_CEACAM6 (호모필릭 결합), KLRB1_CLEC2D (T 세포 및 NK 세포 활성 조절), LTB_LTBR (림프구 활성 및 조직 형성), SELPLG_SELL (림프구 이동) 등의 다양한 상호작용이 활발하게 일어납니다. 특히 LTB_LTBR, SELPLG_SELL, KLRB1_CLEC2D는 여러 T 세포 쌍에서 높은 유의성과 발현량을 보입니다.
• LCK_CD8_Treceptor: 이 상호작용은 T CD8+ | T CD8+ 세포 쌍에서 관찰되며, LCK 키나제는 CD8 공동 수용체와 T 세포 수용체 신호 전달의 핵심 구성 요소입니다. 이는 CD8+ T 세포 내부 신호 전달 또는 자가 활성화 메커니즘을 반영할 수 있습니다.

3. 부재하는 세포 유형: 'Macrophage' 및 'Fibroblast' 세포 유형은 target_cells에 포함되었지만, 표시된 상위 80개 상호작용에는 이들 세포가 관련된 유의미한 패턴이 나타나지 않았습니다. 이는 이들 세포가 다른 세포 유형과의 강력한 상호작용을 보이지 않거나, 본 분석에서 설정된 컷오프(pval_cutoff=0.05, mean_cutoff=0.01) 및 표시 개수(n_pairs_to_show=80) 내에 포함되지 않았음을 의미합니다.

Biological Interpretation

1. 종양 상피세포의 자가조절 및 면역세포 상호작용:

• Diploid Intestinal Epi | Diploid Intestinal Epi 간의 APLP2_PIGR 및 CEACAM6_CEACAM6 상호작용은 장 상피세포 자체의 접착, 성장 및 유지에 관여할 수 있습니다. CEACAM6은 암에서 과발현되는 경우가 많으며, 세포 부착 및 항암제 내성과 관련될 수 있습니다 GeneCards: CEACAM6. CDH1_integrin_aEb7 복합체 상호작용은 상피 세포 간 접착 또는 상피 내 림프구(IEL)와의 상호작용 가능성을 시사합니다.
• Diploid Intestinal Epi | T CD8+ 간의 CD58_CD2 및 CEACAM5_CD8A 상호작용은 CD8+ T 세포의 종양 세포 인식 및 사멸 과정에 중요할 수 있습니다. CD58-CD2는 T 세포 활성화에 필수적인 보조 자극 분자이며 UniProt: CD58, CEACAM5는 대장암에서 과발현되는 종양 마커로, CEACAM5-CD8A 상호작용은 CD8+ T 세포의 기능을 억제하여 종양 면역 회피에 기여할 가능성이 있습니다 PubMed Search: CEACAM5 CD8A tumor immunity.
• T CD4+ | Diploid Intestinal Epi 간의 CEACAM5_CD8A 상호작용은 흥미롭습니다. CD8A는 CD8+ T 세포의 공동 수용체이므로, T CD4+ 세포에서 이러한 상호작용이 나타나는 것은 T CD4+ 세포 아형의 이질성, CD8A의 비정형적 발현, 또는 CellPhoneDB의 복합체 매핑 특성을 반영할 수 있습니다. 이 부분은 추가적인 검증이 필요합니다.

2. T 세포 구획 내 동역학:

• T CD4+ 및 T CD8+ 세포 간, 그리고 각 세포 유형 내의 다양한 상호작용은 종양 미세환경 내 면역 반응의 복잡성을 보여줍니다.
• LTB_LTBR 및 SELPLG_SELL과 같은 상호작용은 림프구의 생존, 증식, 활성화 및 조직 내 이동에 중요하며, 이는 종양 침윤 림프구(TIL)의 기능과 관련될 수 있습니다 GeneCards: LTB.
• KLRB1_CLEC2D는 CD161 (KLRB1)을 발현하는 T 세포와 LLT1 (CLEC2D)을 발현하는 세포 간의 면역 억제 신호를 나타낼 수 있으며, 이는 종양 미세환경에서 T 세포의 활성을 조절하는 중요한 축일 수 있습니다 PubMed Search: KLRB1 CLEC2D tumor immunity.
• LCK_CD8_Treceptor와 같은 자체 상호작용은 T 세포 수용체(TCR) 신호 전달 경로 내의 핵심 분자들의 중요성을 강조합니다.

Clinical or Translational Implications

1. 잠재적 치료 표적: CEACAM5, CEACAM6과 같은 종양 관련 리간드와 T 세포 수용체 또는 보조 수용체(CD2, CD8A) 간의 상호작용은 종양 면역 회피를 조절하는 중요한 표적이 될 수 있습니다. 특히 CEACAM5-CD8A 상호작용은 종양 세포와 CD8+ T 세포 간의 인터페이스에서 면역 관문 억제제의 반응성을 예측하거나 새로운 면역 치료 전략을 개발하는 데 활용될 수 있습니다. CEACAM5를 표적으로 하는 항체-약물 접합체(ADC) 또는 이중 특이성 항체 개발 가능성을 모색할 수 있습니다 PubMed Search: CEACAM5 therapeutic target cancer.
2. 면역 치료 반응 조절: T 세포 간의 LTB_LTBR 및 KLRB1_CLEC2D와 같은 상호작용은 종양 내 T 세포의 활성, 이동 및 생존에 영향을 미쳐 면역 관문 억제제와 같은 기존 면역 치료법의 효과를 증진시키기 위한 병용 요법 개발의 기반이 될 수 있습니다.
3. 종양 미세환경 특징화: Diploid Intestinal Epi와 면역 세포 간의 상호작용 패턴은 대장암의 예후 인자 또는 특정 치료 반응을 예측하는 바이오마커로 활용될 수 있습니다. 특히 T CD4+ 세포에서 CEACAM5_CD8A와 같은 비정형적인 상호작용이 관찰된 경우, 이들 T CD4+ 세포의 특성과 기능에 대한 심층 연구를 통해 새로운 면역 억제 메커니즘을 밝혀낼 수 있습니다.
4. 실험적 검증 방향: 본 분석에서 도출된 핵심 상호작용(예: CEACAM5-CD8A, CD58-CD2, LTB-LTBR)은 *in vitro* (예: 공동 배양 시스템) 및 *in vivo* (예: 환자 유래 오가노이드, 마우스 모델) 실험을 통해 기능적 중요성을 검증할 수 있습니다. 특정 리간드 또는 수용체에 대한 차단 항체나 유전자 편집 기술을 사용하여 이러한 상호작용이 T 세포 활성, 종양 성장, 또는 전이에 미치는 영향을 평가할 수 있습니다.

13. Tumor Microenvironment Cell-Cell Interaction Analysis: Prioritizing Key Ligand-Receptor Pathways

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) within the tumor microenvironment of the colon, focusing on interactions between T cells (CD4+ and CD8+) and Intestinal Epithelial cells (specifically, Diploid Intestinal Epithelial cells), as well as intra-cell type interactions. The results highlight key ligand-receptor pairs and the cellular contexts in which they are most active, providing insights into immune-tumor crosstalk and potential therapeutic targets. The analysis was performed using CellPhoneDB, and the dot plot visualizes the top 80 most significant interactions in the 'tumor' condition.

Visual Summary

The dot plot displays cell-cell interactions by condition ('tumor'), with cell-cell type pairs on the y-axis and specific ligand-receptor pairs on the x-axis. The size of each dot represents the statistical significance (-log10(p-value)), with larger dots indicating more significant interactions. The color of each dot reflects the mean expression level (log2(mean)) of the ligand-receptor pair, ranging from purple (lower expression) to yellow-green (higher expression).

Key Observations:

• Extensive T cell Intra-Interactions: Significant interactions are observed among T cell subsets (e.g., T CD8+|T CD8+, T CD4+|T CD8+, T CD4+|T CD4+), suggesting robust communication within the immune compartment.
• T cell-Epithelial Interactions: Interactions between T CD4+ cells and Diploid Intestinal Epithelial cells, and between Diploid Intestinal Epithelial cells and T CD8+ cells, are also evident, highlighting direct immune-epithelial crosstalk.
• Intra-Epithelial Interactions: Diploid Intestinal Epithelial cells also show self-interactions, indicating internal communication or adhesion within this cell population.

Dominant Ligand-Receptor Pairs

• CD58_CD2: Highly significant across various T cell interactions, and also between T CD4+ and Diploid Intestinal Epithelial cells, indicating its broad role in immune cell communication.
• CEACAM5_CD8A: Notably significant in T CD8+|T CD8+ and T CD4+|T CD8+ interactions, suggesting specific modulation of CD8+ T cells.
• CEACAM5_CEACAM6 and CEACAM6_CEACAM6: Predominantly seen in interactions involving Diploid Intestinal Epithelial cells, pointing to their roles in epithelial adhesion and signaling.
• APLP2_PIGR: Unique to Diploid Intestinal Epi|Diploid Intestinal Epi interactions, potentially related to epithelial barrier function.
• KLRB1_CLEC2D and LTB_LTBR: Involved in various T cell intra-interactions, suggesting roles in immune regulation and lymphoid organization.
• LCK_CD8_Treceptor: Appears in CD8+ T cell-related interactions, indicating T cell receptor activation signaling.

Biological Interpretation

The observed cell-cell interactions provide a window into the dynamic interplay within the colon tumor microenvironment:

• T Cell Activation and Co-stimulation: The prominence of CD58_CD2 interactions among T cells (CD4+ and CD8+) and between T CD4+ cells and Diploid Intestinal Epithelial cells underscores the importance of co-stimulatory pathways in the tumor. CD58 (LFA-3) on antigen-presenting cells (or other cells) binds to CD2 on T cells, providing critical co-stimulation for T cell activation and adhesion. This suggests active immune surveillance or regulatory processes.

CEACAM Family in Tumor-Immune Crosstalk

• CEACAM5_CD8A: This interaction is highly significant, particularly involving CD8+ T cells. Carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) is a known oncofetal protein often overexpressed in colorectal cancer. Its interaction with CD8A on cytotoxic T cells could represent a crucial immune evasion mechanism, where tumor cells engage CD8A to potentially inhibit T cell function or induce T cell exhaustion. GeneCards: CEACAM5
• CEACAM5_CEACAM6 and CEACAM6_CEACAM6: These homophilic and heterophilic interactions are strong within the Diploid Intestinal Epithelial cell population and with T cells. CEACAMs are involved in cell adhesion, differentiation, and signaling, and their overexpression can promote tumor growth, invasion, and immune suppression in various cancers, including colorectal cancer. PubMed Search: CEACAM5 CEACAM6 colorectal cancer immune evasion
• Epithelial Function and Immune Modulation: The "Diploid Intestinal Epi" population, likely representing normal or a specific subset of tumor epithelial cells, actively participates in interactions. APLP2_PIGR within Diploid Intestinal Epithelial cells may reflect ongoing epithelial barrier functions, as PIGR is involved in polymeric IgA transport.

Immune Regulatory Pathways

• KLRB1_CLEC2D (CD161-LLT1): This interaction, seen among T cells, is a known immune checkpoint. KLRB1 (CD161) is an inhibitory receptor expressed on NK cells and subsets of T cells. Its ligand, CLEC2D (LLT1), can be expressed on various cells including tumor cells and myeloid cells, and its engagement can inhibit NK and T cell effector functions, contributing to immune evasion. GeneCards: KLRB1
• LTB_LTBR: Lymphotoxin beta (LTB) and its receptor (LTBR) are crucial for lymphoid organ development and inflammation. Their interactions among T cells suggest active immune cell organization and sustained inflammatory signaling within the tumor microenvironment.
• T Cell Receptor Signaling: The "LCK_CD8_Treceptor" interaction highlights the core machinery of T cell activation. LCK is a critical intracellular kinase that initiates TCR signaling upon engagement of the TCR complex with MHC-peptide and CD8 co-receptor, suggesting that fundamental T cell activation events are occurring or being attempted in this tumor context.

Clinical or Translational Implications

The identified cell-cell interactions have significant implications for understanding colon cancer progression and developing novel therapeutic strategies:

• Targeting CEACAMs for Immunotherapy: The prominent role of CEACAM5 and CEACAM6 in tumor-immune interactions, especially CEACAM5_CD8A, makes them highly attractive targets. Strategies to block CEACAM5-CD8A interaction could potentially relieve CD8+ T cell inhibition and enhance anti-tumor immunity. Similarly, targeting CEACAM5/6 adhesion could disrupt tumor growth and metastasis. This aligns with ongoing research into CEACAM-targeted therapies. PubMed Search: CEACAM5 immunotherapy
• Modulating Immune Checkpoints: The presence of KLRB1_CLEC2D interactions suggests another potential immune checkpoint axis beyond canonical PD-1/CTLA-4. Inhibiting this interaction could serve as a novel approach to reactivate anti-tumor NK and T cell responses, especially for patients who are refractory to current immunotherapies.
• Understanding T Cell Function and Dysfunction: The strong CD58_CD2 and LCK_CD8_Treceptor interactions indicate that T cells are actively engaged or attempting to engage within the tumor. Understanding if these engagements lead to productive anti-tumor responses or contribute to T cell exhaustion is critical. Modulating co-stimulatory pathways like CD58-CD2 could enhance therapeutic efficacy.
• Biomarker Potential: The differential expression and interaction patterns of these ligand-receptor pairs, particularly CEACAMs, could serve as biomarkers for patient stratification, predicting response to immunotherapy, or monitoring disease progression in colon cancer.
• Experimental Validation Priority: Interactions with high significance and mean expression (e.g., CEACAM5-CD8A, CD58-CD2, CEACAM5-CEACAM6) should be prioritized for further experimental validation (e.g., using co-culture assays, receptor blocking experiments, or in vivo models) to confirm their functional roles and therapeutic potential.

14. Immune Checkpoint and Cell Cycle Pathway Cell-Cell Interactions in Colon Normal and Tumor Tissues

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCIs) involving genes related to immune checkpoint and cell cycle pathways in colon tissue, comparing normal and tumor conditions. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between specified cell types, aggregated by condition. The goal is to identify how these critical signaling pathways are altered in the tumor microenvironment (TME), providing insights into immune evasion or dysfunctional cell cycle regulation.

Visual Summary

The provided dot plots illustrate specific cell-cell interactions for two ligand-receptor (LR) pairs, IFNG_Type_II_IFNR and LCK_CD8_receptor, across different cell-cell pairs (T CD8+|T CD8+, T CD8+|B cell, T CD4+|T CD8+) in normal and tumor conditions.

CCI for Normal Condition:

• IFNG_Type_II_IFNR: This interaction pair shows high significance (large dot size, max -log10(p) ~10) and high mean expression (bright yellow color, log2(m) ~1.0) for all three cell-cell pairs: T CD8+|T CD8+, T CD8+|B cell, and T CD4+|T CD8+. This suggests robust Interferon-gamma (IFN-γ) signaling within T cell subsets and between T cells and B cells in normal colon tissue.
• LCK_CD8_receptor: This interaction pair also shows high significance across all three cell-cell pairs. However, the mean expression is comparatively lower (dark purple/blue color, log2(m) ~0.7) than that of IFNG_Type_II_IFNR. This indicates active, albeit at a lower expression level, signaling involving LCK and CD8 components within T cells and between T cells and B cells.

CCI for Tumor Condition:

• IFNG_Type_II_IFNR: This interaction pair is completely absent in the tumor plot. This is a striking finding, indicating a significant loss or severe downregulation of IFN-γ-mediated cell-cell communication in the tumor microenvironment.
• LCK_CD8_receptor: This interaction pair is observed for T CD8+|T CD8+ and T CD4+|T CD8+ cell pairs. Notably, the T CD8+|B cell interaction for this pair is absent in the tumor condition. For the observed pairs, the mean expression is higher (bright yellow color, log2(m) ~1.0) compared to the normal condition.

Biological Interpretation

The comparison between normal and tumor conditions reveals critical alterations in key immune signaling pathways, particularly involving T cells.

1. Profound Loss of IFNG Signaling in Tumors: Interferon-gamma (IFN-γ) is a pivotal cytokine secreted primarily by activated T cells and NK cells, essential for orchestrating robust anti-tumor immune responses. It enhances antigen presentation, promotes T cell differentiation, and activates macrophages PMID: 29578020. The complete disappearance of IFNG_Type_II_IFNR interactions in the tumor microenvironment (TME) is a major finding. This suggests a significant suppression or dysregulation of IFN-γ-mediated communication among T cells and between T cells and B cells, which is highly detrimental to effective anti-tumor immunity and likely contributes to immune evasion.
2. Altered LCK-CD8 Axis in Tumors: LCK (Lymphocyte-specific protein tyrosine kinase) is a crucial intracellular kinase involved in proximal T cell receptor (TCR) signaling, indispensable for T cell activation and development GeneCards: LCK. CD8 is a co-receptor that binds MHC class I molecules, stabilizing TCR-MHC interactions and recruiting LCK to the TCR complex.

• The increased mean expression of LCK_CD8_receptor interactions within T cell subsets (T CD8+|T CD8+, T CD4+|T CD8+) in the tumor compared to normal could signify an altered state of T cell activation or signaling. This might represent chronic stimulation leading to T cell exhaustion, or compensatory upregulation in a dysfunctional immune environment.
• The complete absence of LCK_CD8_receptor interactions between T CD8+|B cell in the tumor condition further highlights a broad disruption of T cell-B cell collaboration. While LCK is an intracellular kinase and CD8 a co-receptor, CellPhoneDB's representation of "LCK_CD8_receptor" as an interaction pair may capture complex signaling pathways involving cell contact that are critical for T cell function.

3. Disrupted T Cell-B Cell Crosstalk: In normal tissue, both IFNG and LCK-CD8 related interactions were observed between T CD8+|B cell pairs. In the tumor, both of these interactions are completely absent. This points to a significant breakdown in the communication and functional collaboration between cytotoxic T cells and B cells, which is vital for effective humoral and cellular anti-tumor responses.

Overall, these findings paint a picture of an immunosuppressive tumor microenvironment characterized by the loss of critical IFN-γ signaling and compromised T cell-B cell cooperation. The altered LCK_CD8_receptor interactions within T cell compartments suggest dysregulated T cell activation and function.

Clinical or Translational Implications

The observed changes in cell-cell interactions have significant clinical implications:

• Immune Evasion Mechanism: The complete loss of IFN-γ signaling in the tumor microenvironment suggests a key mechanism by which colon tumors evade immune surveillance. Restoring or enhancing IFN-γ pathway activity could be a promising therapeutic strategy for patients with colon cancer.
• Therapeutic Targeting: Agents that boost IFN-γ production by T cells, or directly administer IFN-γ, could potentially re-sensitize tumors to immune attack, particularly when combined with immune checkpoint inhibitors.
• Biomarker Potential: The absence of IFNG_Type_II_IFNR interactions and T CD8+|B cell crosstalk in tumors could serve as potential biomarkers for patient stratification, predicting response to immunotherapy, or indicating a more immunosuppressive phenotype.
• Understanding T Cell Dysfunction: The increased expression of LCK_CD8_receptor interactions within T cells in the tumor, alongside the loss of IFNG signaling, warrants further investigation into the precise nature of T cell dysfunction (e.g., exhaustion, anergy) in the TME. Understanding this could lead to novel strategies to revitalize anti-tumor T cell responses.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) between normal and tumor conditions within major immune cells (T cells, B cells, Myeloid cells, Endothelial cells) and stromal cells (Stromal cells, Fibroblasts) in human colon tissue. The results are visualized as a dot plot, where dot size reflects the significance of the interaction (-log10(p-value)) and color intensity indicates the standardized mean interaction strength across samples. The aim is to uncover CCI pathways that are uniquely activated or suppressed in the tumor microenvironment compared to normal tissue.

Visual Summary

The dot plot effectively delineates distinct cell-cell interaction landscapes in normal versus tumor colon samples.

Normal Condition (Left Panel):

• A broad array of highly significant and strong interactions are observed, predominantly involving B cells (samples B_cac10-B_cac15) and T cells (samples T_cac1, T_cac10-T_cac16).
• Key interactions involve various immune cell types communicating among themselves (e.g., SEMA4D-CD72 between Plasma cells and B cells, CD94-NKG2C or HLA-E-KLRCD2 between CD8+ T cells, CD160-TNFRSF14 involving T cells and ILCs).
• Interactions of immune cells with diploid Intestinal Epithelial cells (Ent.Epi (Dip)) and Fibroblasts are also present, such as PPIA-BSG-T CD8+|Ent.Epi (Dip) and VSIR_HLA-F-Plasma|Fib.
• Overall, the normal state is characterized by a rich and diverse network of interactions, particularly within immune cell compartments, suggesting active immune surveillance and tissue homeostasis.

Tumor Condition (Right Panel):

• The interaction landscape in tumor samples (T_cac1-T_cac9) shows a dramatic shift compared to normal. Many interactions prominent in normal tissue are substantially diminished or absent.
• New, highly significant, and strong interactions emerge, primarily involving T cells (CD4+, CD8+), Diploid Intestinal Epithelial cells, and Fibroblasts.

Prominent Tumor-Specific Interactions:

• Immune Checkpoint Axes: The NECTIN2-TIGIT interaction between Diploid Intestinal Epithelial cells and both CD4+ and CD8+ T cells stands out as exceptionally strong and significant across multiple tumor samples.
• Extracellular Matrix (ECM) Remodeling: A cluster of interactions involving integrin complexes with various Collagen and Laminin molecules (e.g., COL6A1_integrin_a1b1_complex-Fib|Fib, COL5A1/A2_integrin_a1b1_complex-Fib|Fib, LAMC1_integrin_a6b1_complex-Fib|Fib, COL18A1_integrin_a1b1_complex-Fib|Fib) are highly enriched and significant within Fibroblast-Fibroblast communication in the tumor.
• Tumor-Associated Epithelial Interactions: Interactions like CEACAM5-CD8A between Diploid Intestinal Epithelial cells and CD8+ T cells, and CD320-JAML between Plasma cells/Diploid Intestinal Epithelial cells and CD4+ T cells, are also markedly increased.
• CD40LG-CD40 between CD4+ T cells and Plasma cells also appears to be enhanced in tumor samples, suggesting altered T-B cell communication.

Biological Interpretation

The differential CCI patterns highlight a profound remodeling of the cellular communication network in the colon tumor microenvironment.

• Immune Evasion via TIGIT-NECTIN2 Axis: The robust upregulation of NECTIN2-TIGIT interactions in tumor samples is highly biologically significant. TIGIT (T cell immunoreceptor with Ig and ITIM domains) is an inhibitory immune checkpoint receptor expressed on T cells. NECTIN2 (CD112) is its ligand, found on various cell types, including tumor cells and antigen-presenting cells. This interaction can suppress anti-tumor immune responses by inhibiting T cell activation and effector functions [PubMed search: TIGIT NECTIN2 cancer immunotherapy]. The involvement of Ent.Epi (Dip) in these interactions suggests that even diploid epithelial cells within the tumor context (which could be non-malignant reactive epithelial cells or specific diploid tumor subclones) are actively contributing to immune suppression.
• Extensive ECM Remodeling by Cancer-Associated Fibroblasts (CAFs): The striking increase in various collagen/laminin-integrin interactions among Fibroblasts in tumor samples points to extensive extracellular matrix (ECM) remodeling. This is a hallmark of cancer-associated fibroblasts (CAFs), which are crucial drivers of tumor progression, metastasis, and therapy resistance. CAFs deposit and reorganize ECM components, creating a stiff and pro-tumorigenic microenvironment that promotes tumor cell growth, invasion, and suppresses immune cell infiltration [PubMed search: cancer associated fibroblasts ECM remodeling].
• Altered Immune-Epithelial Cross-talk: The enhanced interactions involving CEACAM5 on Intestinal Epithelial cells with CD8A on CD8+ T cells are notable. CEACAM5 is a known tumor-associated antigen that can also modulate immune responses, sometimes leading to immunosuppression [UniProt: P04006 (CEACAM5)]. Similarly, CD320-JAML interactions, involving epithelial cells and T cells, could reflect altered cellular recognition and signaling pathways in the tumor microenvironment.
• Dysregulated Immune Cell Homeostasis: While normal samples exhibit diverse immune-immune interactions essential for coordinating responses, these are largely diminished in the tumor context. The observed CD40LG-CD40 interaction in tumor suggests altered T cell help to plasma cells, which might be dysfunctional or contribute to a pro-tumorigenic antibody response rather than effective anti-tumor immunity.

Clinical or Translational Implications

The findings have several potential clinical and translational implications for colon cancer.

• Therapeutic Targeting of TIGIT-NECTIN2 Axis: The prominent NECTIN2-TIGIT interaction identifies a critical immune checkpoint that is highly active in the colon tumor microenvironment. Inhibitors targeting TIGIT (anti-TIGIT antibodies) are currently under clinical investigation for various cancers, aiming to unleash anti-tumor T cell immunity [PubMed search: TIGIT inhibitors clinical trials]. This data supports the rationale for evaluating TIGIT blockade in colon cancer, possibly in combination with other immune checkpoint inhibitors.
• Modulation of ECM Remodeling: The extensive fibroblast-mediated ECM remodeling, indicated by numerous integrin-collagen/laminin interactions, suggests that targeting CAFs or their pro-tumorigenic functions could be a viable therapeutic strategy. Interventions aimed at normalizing the tumor ECM or inhibiting integrin signaling could potentially reduce tumor stiffness, improve drug delivery, and enhance immune cell infiltration [PubMed search: integrin inhibitors cancer].
• Biomarker Discovery: The identified condition-specific CCI pairs could serve as potential biomarkers for disease progression, response to immunotherapy, or patient stratification. For instance, high expression of NECTIN2 on tumor-associated epithelial cells, or specific integrin complexes on fibroblasts, could predict patient outcomes or susceptibility to certain treatments.
• Understanding Immune Evasion Mechanisms: This analysis provides insights into how the colon tumor microenvironment may actively suppress anti-tumor immunity by engaging inhibitory checkpoints and extensively remodeling its stromal compartment. These mechanisms could contribute to resistance to existing therapies and warrant further investigation to develop more effective treatment strategies.

16. Intestinal Epithelial Cell Condition-Specific Surfaceome Markers

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers within Intestinal Epithelial cells from single-cell RNA-seq data. The goal is to pinpoint surface proteins that distinguish tumor-associated epithelial cells (likely aneuploid, based on the cnv_cluster and ploidy_dec context) from diploid, presumably non-malignant, epithelial cell populations. The analysis specifically focused on surfaceome markers, which are particularly valuable for diagnostic and therapeutic applications.

Visual Summary

The provided dot plot illustrates the expression patterns of 30 selected surfaceome markers across various Intestinal Epithelial cell subpopulations. Each row represents a distinct cell cluster, grouped by inferred ploidy status (Diploid vs. likely Aneuploid for the T_cac clusters without the 'Diploid' prefix) and CNV cluster (cnv_cluster). The columns represent individual surfaceome genes.

• Expression Intensity and Prevalence: The color intensity of each dot corresponds to the mean expression level of the gene within that cell group (darker red indicates higher mean expression). The size of the dot represents the fraction of cells within the group expressing that gene (larger dots mean more cells express the gene).
• Condition Grouping: A horizontal bar at the top clearly separates cell clusters associated with the "tumor" condition. Specifically, the clusters T_cac3, T_cac1, and T_cac8 are highlighted with a red box, indicating they are the primary tumor-associated Intestinal Epithelial cell clusters of interest. The other clusters, prefixed with "Diploid" (e.g., Diploid B_cac6, Diploid T_cac11), represent diploid cell populations, likely corresponding to normal or non-malignant cells.
• Marker Specificity: A striking pattern emerges where a panel of surfaceome markers shows high expression (dark red, large dots) predominantly in the tumor-associated clusters (T_cac3, T_cac1, T_cac8). In contrast, these markers exhibit very low or no expression (light/no color, small/no dots) in most of the diploid cell clusters.
• Examples of Tumor-Specific Markers: Genes like MUC4, CD24, CEACAM5, CEACAM6, BSG, CD151, CD44, and NECTIN2 are highly expressed in the T_cac3, T_cac1, and T_cac8 clusters, with large dot sizes indicating a high fraction of cells expressing these markers.
• Cell Group Sizes: The bar plot on the right indicates the total number of cells contributing to each cluster, showing varying sizes across the different subpopulations.

Biological Interpretation

The analysis successfully identifies a set of surfaceome markers highly specific to the tumor-associated Intestinal Epithelial cell clusters (T_cac3, T_cac1, T_cac8) compared to the diploid counterparts. This differential expression highlights key molecular changes occurring on the cell surface during colon tumorigenesis. Several of these identified markers have well-established roles in cancer biology:

Adhesion and Proliferation Markers

• MUC4 (Mucin 4): Often overexpressed in various adenocarcinomas, including colorectal cancer, where it contributes to tumor cell proliferation, invasion, and metastasis by modulating cell signaling pathways. GeneCards MUC4
• CD24: A glycosylphosphatidylinositol-anchored cell surface protein associated with cancer stem cells and poor prognosis in many cancers. It plays a role in cell adhesion and signal transduction, promoting tumor growth and metastasis. GeneCards CD24
• CEACAM5 (Carcinoembryonic Antigen-related Cell Adhesion Molecule 5) and CEACAM6: Widely recognized tumor markers, particularly in colorectal cancer. They are involved in cell adhesion, immune evasion, and promotion of tumor growth and metastasis. Elevated levels are often associated with advanced disease. GeneCards CEACAM5 GeneCards CEACAM6
• BSG (Basigin/CD147): A transmembrane glycoprotein that promotes tumor invasion and metastasis by inducing matrix metalloproteinases. It is often overexpressed in various human cancers. GeneCards BSG
• CD44: A cell adhesion molecule and a prominent cancer stem cell marker involved in cell-cell interactions, cell migration, and differentiation. Its overexpression is linked to tumor initiation, progression, and metastatic potential in colorectal cancer. GeneCards CD44
• NECTIN2: A cell adhesion molecule from the nectin family, implicated in cell proliferation, migration, and immune evasion in certain cancers. GeneCards NECTIN2

Other Noteworthy Markers

• CD151: A tetraspanin that regulates cell adhesion, motility, and invasion, often associated with aggressive tumor phenotypes. GeneCards CD151
• GPRC5A: A G protein-coupled receptor with complex roles in cancer, acting as either a tumor suppressor or an oncogene depending on the cancer type and context. Its high expression in these tumor clusters suggests a potential oncogenic role in this specific colon cancer context. GeneCards GPRC5A

The collective upregulation of these surface markers in tumor-associated Intestinal Epithelial cells suggests a phenotype geared towards increased proliferation, altered cell-cell and cell-matrix interactions, enhanced migratory and invasive capabilities, and potentially features of cancer stemness. This pattern is consistent with the characteristics of malignant transformation in colon cancer.

Clinical or Translational Implications

The identification of these tumor-specific surfaceome markers in Intestinal Epithelial cells carries significant clinical and translational potential:

• Diagnostic and Prognostic Biomarkers: Genes like MUC4, CD24, CEACAM5/6, BSG, CD44, and NECTIN2 could serve as valuable biomarkers for the early detection, diagnosis, and prognostication of colon cancer. Their expression levels could differentiate malignant epithelial cells from normal ones, aid in monitoring disease progression, or predict patient outcomes.
• Therapeutic Targets: Given their localization on the cell surface, these markers are highly accessible for targeted therapies. They represent promising candidates for the development of:
• Antibody-Drug Conjugates (ADCs): Antibodies targeting these surface proteins could deliver cytotoxic drugs specifically to tumor cells, minimizing off-target effects.
• CAR T-cell therapies: Chimeric Antigen Receptor (CAR) T-cells engineered to recognize these surface markers could specifically eliminate tumor cells.
• Bispecific Antibodies: Engaging these targets could bridge tumor cells with immune effector cells.

Preclinical validation and clinical trials would be essential to confirm their efficacy and safety as therapeutic targets.

• Patient Stratification: Differential expression of these markers might allow for the stratification of colon cancer patients into distinct molecular subgroups, potentially guiding personalized treatment strategies.
• Experimental Validation: Further functional studies are warranted to elucidate the precise mechanisms by which these markers contribute to colon cancer progression and to confirm their utility as therapeutic targets in in vitro and in vivo models.

17. Condition-Agnostic Surfaceome Markers for Colon Cell Subsets, Highlighting Macrophage Identities

[Analysis Visualization Results]...

Analysis Overview

This dot plot visualizes the expression of surfaceome-specific genes across various celltype_subset categories identified in the AnnData object. The user's query requested "condition-specific markers for Macrophages". However, based on the parameters used (target_cell: None, deg_key: None, targets: None), the analysis performed was to identify general markers for *all* cell type subsets present in the data, rather than differentially expressed markers between conditions *within* macrophages. Therefore, this visualization primarily serves to validate the assigned celltype_subset annotations by illustrating their unique surfaceome expression profiles, consistent with the INTERPRETATION PRIORITY of focusing on annotation and identity checks. Up to 50 surfaceome markers were identified per cell group.

Visual Summary

The dot plot displays a matrix where rows represent different celltype_subset annotations (e.g., B cell subsets, epithelial cells, stromal cells, myeloid cells, T cell subsets) and columns represent individual gene markers.

• Dot Size: Corresponds to the percentage of cells within a given celltype_subset that express the gene. Larger dots indicate a higher fraction of expressing cells.
• Dot Color Intensity: Represents the mean expression level of the gene within the expressing cells of that celltype_subset. Darker red indicates higher mean expression.
• Red Boxes: Visually group markers that are highly specific and enriched for particular cell type subsets, aiding in the identification of marker panels for distinct cell populations.
• Right Bar Plot: Shows the total number of cells contributing to each celltype_subset group.

Biological Interpretation

This analysis effectively identifies distinct surfaceome marker profiles, largely supporting the assigned celltype_subset annotations within the colon tissue data. While not condition-specific, these markers are crucial for establishing cell identity.

Macrophage Subtype Identification:

The plot clearly delineates distinct clusters of markers for Macrophage (M1), (M2A), (M2B), and (M2C) subsets, validating their presence and individual identities based on surface protein expression.

• M1 Macrophages show enriched expression of CD69, CD44, ITGA4, IFNGR2, IFNGR1, and STAT1. This profile is characteristic of classically activated (pro-inflammatory) macrophages. STAT1 is a key transcription factor in M1 polarization, and CD69 and CD44 are associated with activation and adhesion [GeneCards: STAT1, CD69, CD44].
• M2A Macrophages are characterized by markers such as CD36, CLEC7A (Dectin-1), CLEC10A (MGL1), IL1R2, STAT6, SOCS3, PTGS2, and MSR1. This panel aligns with an alternatively activated (tissue repair and allergic response) macrophage phenotype. STAT6 is a central regulator of M2 polarization [UniProt: STAT6]. CLEC7A and CD36 are scavenger receptors and pattern recognition receptors [GeneCards: CLEC7A, CD36].
• M2B Macrophages show expression of LYZ, CTNNB1, MUC4, and KCNN4. While LYZ is a general macrophage marker, the combination suggests a distinct M2B identity. It is noted that JCHAIN is also grouped here but shows very low expression in M2B and is highly specific to Plasma cells, suggesting it might be less a specific marker for M2B in this context or represents a minor shared expression.
• M2C Macrophages express CD163, CD1C, CD209 (DC-SIGN), and MRC1 (CD206). This profile is consistent with deactivating or regulatory macrophages involved in immune suppression and tissue remodeling [UniProt: CD163, MRC1].
• Other Key Cell Subsets: The plot also successfully identifies markers for other major cell types, reinforcing the overall quality of cell type annotation:
• Intestinal Epithelial Cells: Crypt cells (LGR5, ASCL2, SMOC2, AXIN2), Enterocytes (FABP1, CDH17, KRT20), Goblet cells (MUC2, TFF3), Paneth cells (LYZ, DEFA5), and Tuft cells (DCLK1, TRPM5). These are canonical markers for specific epithelial lineages [PubMed search: LGR5 intestinal stem cell; MUC2 goblet cell].
• B Cell Subsets: B cells (Breg, Follicular, MZ, Memory) are distinguished by markers like POU2F2, POU2AF1, CD22, CD24. Plasma cells, as terminally differentiated B cells, show unique markers such as MZB1, SDC1 (CD138), and PRDM1 [UniProt: SDC1].
• T Cell Subsets: Various T cell subsets (Cytotoxic, Naive, Tfh, Th1, Th17, Th2, Th22, Treg) are identified by classical markers such as CD8A/CD8B (Cytotoxic), SELL (Naive), CD40LG (Tfh), STAT1 (Th1), RORC (Th17), GATA3 (Th2), and FOXP3 (Treg) [PubMed search: FOXP3 Treg marker].
• Stromal Cells: Fibroblasts and Smooth muscle cells are characterized by collagen genes (COL1A1, COL1A2, COL3A1), DCN, FBLN1, PDGFRA, and muscle-specific actins (ACTA2, MYL9).

Annotation Notes / Limitations

This analysis effectively provides a comprehensive set of surfaceome markers for validating cell type subset annotations. However, it is important to note the following:

• The plot does not present "condition-specific markers for Macrophages" as requested in the user query. The applied tool parameters (target_cell: None, deg_key: None, targets: None) resulted in the identification of general cell type markers across all celltype_subset categories, rather than a differential expression analysis comparing 'normal' versus 'tumor' conditions specifically within macrophages.
• Therefore, while it confirms the identity of various macrophage populations, it does not directly inform on how their surface marker profiles change in response to disease conditions. Further differential expression analysis *within* each macrophage subtype comparing 'normal' and 'tumor' conditions would be required to address the user's specific request for condition-specific markers.

18. Fibroblast Condition-Specific Surfaceome Markers Analysis

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify and visualize condition-specific surfaceome markers in Fibroblasts from human colon single-cell RNA-seq data, comparing normal and tumor conditions. The plot_markers_and_expression_dot tool was used to generate the visualization, focusing on the top 50 differentially expressed surfaceome genes per condition. The output provides insight into distinct fibroblast subpopulations and their potential roles in colon tissue homeostasis versus tumor progression.

Visual Summary

The dot plot displays the expression patterns of various surfaceome genes across five distinct fibroblast clusters (B_cac14, B_cac7, T_cac1, T_cac2, T_cac3) under normal and tumor conditions. The size of each dot represents the fraction of cells within a cluster expressing the gene, while the color intensity indicates the mean expression level of the gene in that cluster.

• Normal Condition Markers: Fibroblast clusters B_cac14 and B_cac7 show prominent expression of markers such as CD9, PRNP, PLPP3, LMBRD1, SCARA5, ABCA8, and GPNMB. B_cac14 generally exhibits higher expression and prevalence for these genes compared to B_cac7. These genes are largely absent or expressed at very low levels in tumor-associated fibroblast clusters.
• Tumor Condition Markers: Fibroblast clusters T_cac1, T_cac2, and T_cac3 display robust expression of a distinct set of surfaceome markers in the tumor condition.
• T_cac1 is particularly characterized by strong and widespread expression of numerous genes including THY1, F2R, PTTG1IP, F3, CDH11, ANTXR1, MMP14, ITGA1, CD55, PDGFRB, ITGAV, TMEM123, TMEM30A, NECTIN2, LTBR, ICAM1, TMX4, FAT1, IFNGR2, OSMR, CD82, and PCDH18. Many of these show dark red intensity and large dot sizes, indicating high mean expression and broad prevalence.
• T_cac2 and T_cac3 also express several of these tumor-associated markers, though often at lower levels or in a smaller fraction of cells compared to T_cac1. Notable markers in T_cac3 include THY1, ANTXR1, MMP14, PDGFRB, ITGAV, and TMX4.
• Cluster Composition: The bar plots on the right indicate the number of cells in each cluster. T_cac2 (188 cells) and T_cac3 (114 cells) are the largest tumor-associated clusters, while B_cac14 (58 cells) and B_cac7 (51 cells) are the largest normal-associated clusters among those displayed.

Overall, there is a clear distinction in surfaceome marker profiles between fibroblasts from normal and tumor colon tissue, highlighting the presence of transcriptionally distinct fibroblast subpopulations associated with disease.

Biological Interpretation

The observed differential expression of surfaceome markers suggests distinct functional roles for fibroblast subpopulations in normal versus tumor contexts within the colon.

• Normal Fibroblasts (B_cac14, B_cac7): The markers identified in normal fibroblasts likely reflect their functions in maintaining tissue homeostasis, structural support, and basic cell adhesion/signaling.
• CD9 (Cluster of Differentiation 9): A tetraspanin involved in cell adhesion, motility, and cell-cell communication, essential for normal tissue organization. GeneCards: CD9
• GPNMB (Glycoprotein NMB): Involved in cell adhesion, migration, and differentiation, potentially contributing to tissue repair and immune regulation in a healthy state. GeneCards: GPNMB

These markers suggest a quiescent or regulatory role for these fibroblast subsets.

• Tumor-Associated Fibroblasts (T_cac1, T_cac2, T_cac3): The high expression of numerous surface molecules in tumor fibroblasts is characteristic of cancer-associated fibroblasts (CAFs), which are known to be highly activated and contribute significantly to the tumor microenvironment (TME).
• THY1 (CD90): A common marker for activated fibroblasts and CAFs, associated with pro-tumorigenic functions including promoting cancer cell proliferation, invasion, and immune suppression. PubMed search: THY1 cancer-associated fibroblasts
• MMP14 (Matrix Metalloproteinase 14): A critical enzyme for extracellular matrix (ECM) remodeling, enabling tumor cell invasion and metastasis. Its high expression in CAFs (T_cac1, T_cac3) indicates active breakdown and reorganization of the stromal scaffold. GeneCards: MMP14
• PDGFRB (Platelet-Derived Growth Factor Receptor Beta): A receptor tyrosine kinase that, when activated, drives CAF proliferation, differentiation, and pro-tumorigenic signaling, contributing to angiogenesis and stromal desmoplasia. GeneCards: PDGFRB
• ITGA1 / ITGAV (Integrin Alpha-1 / Integrin Alpha-V): These integrins mediate strong adhesion to the ECM and play roles in cell migration and mechanotransduction, which are crucial for CAF-mediated tumor invasion and metastasis. GeneCards: ITGA1, GeneCards: ITGAV
• CDH11 (Cadherin-11): A cell adhesion molecule implicated in enhancing cell motility and invasiveness, contributing to tumor progression. GeneCards: CDH11
• ANTXR1 (Anthrax Toxin Receptor 1, TEM8): Involved in angiogenesis and tumor growth, often found on tumor endothelial cells and CAFs. GeneCards: ANTXR1

The diverse expression profiles among T_cac1, T_cac2, and T_cac3 suggest heterogeneity within the CAF population, with T_cac1 appearing to represent a highly activated, potentially pro-inflammatory or invasive CAF state.

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers for fibroblasts holds significant clinical and translational potential for colon cancer.

Therapeutic Targets:

• The highly expressed surface markers on tumor-associated fibroblasts, such as MMP14, PDGFRB, THY1, ITGA1/ITGAV, CDH11, and ANTXR1, represent promising candidates for targeted therapies. Inhibiting these molecules could disrupt CAF-mediated ECM remodeling, angiogenesis, immune suppression, and ultimately impede tumor growth and metastasis.
• For instance, small molecule inhibitors or blocking antibodies against PDGFRB and specific integrins are already in clinical development or use for various cancers. PubMed search: PDGFRB inhibitor cancer, PubMed search: integrin therapy cancer
• Given their surface localization, these markers are excellent candidates for antibody-drug conjugates (ADCs) or engineered cell therapies (e.g., CAR-T cells targeting CAFs) to selectively eliminate or reprogram pro-tumorigenic CAFs without affecting normal fibroblasts.

Biomarkers:

• These distinct surface markers could serve as diagnostic or prognostic biomarkers for colon cancer. Elevated expression of genes like MMP14, PDGFRB, or THY1 could indicate the presence of tumor-promoting CAFs, potentially correlating with disease stage, aggressiveness, or even response to therapy.
• Validation studies using techniques like immunohistochemistry on patient biopsies or flow cytometry on dissociated tumor samples could confirm their utility as robust markers to distinguish between healthy and cancerous tissues, or to characterize specific CAF subsets.

Experimental Validation:

• Further functional studies are warranted to validate the precise roles of these candidate markers in CAF activation, proliferation, migration, and interaction with cancer cells or immune cells. *In vitro* assays and *in vivo* preclinical models can be utilized to investigate the effects of modulating these targets.
• Complementary analyses, such as cell-cell interaction (CCI) analysis using the precomputed uns['CCI'] data, could further elucidate how these surface molecules mediate communication between fibroblasts and other cell types within the TME, providing insights into broader therapeutic strategies.

19. Condition-Specific Surfaceome Markers for CD4 T Cells in Colon Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that are differentially expressed in CD4 T cells when comparing normal colon tissue with tumor tissue. Using the plot_markers_and_expression_dot tool, up to 50 surfaceome markers were identified per condition (normal vs. tumor) for CD4 T cells, prioritizing genes with high expression and prevalence. The results are presented as a dot plot, visually summarizing mean expression level (color intensity) and fraction of expressing cells (dot size) across various cell clusters and conditions.

Visual Summary

The dot plot illustrates the expression patterns of 23 surfaceome genes across several cell clusters, categorized by 'normal' and 'tumor' conditions.

• Cell Cluster Composition: The plot primarily features T cell clusters (e.g., T_cac16, T_cac9, T_cac6, T_cac4), which exhibit the most significant gene expression. Some B cell clusters (e.g., B_cac14, B_cac15) are also present; however, they generally show minimal to no expression of the identified markers, reinforcing that these genes are predominantly expressed in T cells. Notable T cell clusters like T_cac16, T_cac9, and T_cac6 contain a substantial number of cells (762, 864, and 819 cells respectively), making their expression patterns highly representative.

Condition-Specific Expression Patterns

• Markers Enriched in Normal Condition CD4 T Cells: Genes such as AREG, SELPLG, CCR6, and CD82 display notably higher mean expression (darker red dots) and a greater fraction of expressing cells (larger dot size) in T cell clusters (particularly T_cac16, T_cac9, and T_cac6) within the 'normal' condition. This suggests their significant role in T cell function and tissue homeostasis in healthy colon.
• Markers Enriched in Tumor Condition CD4 T Cells: Several genes show increased expression or prevalence in T cell clusters associated with the 'tumor' condition. Prominent examples include TNFRSF18, HLA-DPA1, HLA-DPB1, and HLA-DRB1. These markers are more pronounced in clusters like T_cac16 and T_cac9 within the 'tumor' microenvironment compared to their normal counterparts.
• Mixed or Context-Dependent Markers: Some genes like CTLA4 show varying expression, present in both conditions but with specific cluster-dependent patterns. In the T_cac16 and T_cac9 clusters, CTLA4 appears moderately expressed in the normal condition, while its pattern in the tumor context is less uniformly elevated across all relevant clusters.
• Overall Grouping: The hierarchical clustering at the top clearly delineates genes based on their differential expression between 'normal' and 'tumor' conditions, highlighting distinct expression signatures. Genes like AREG, SELPLG, CCR6, CD82, and LDLPRAD4 form a cluster indicating preferential expression in the normal state, while a group including HLA-DPA1, HLA-DPB1, and HLA-DRB1 shows higher expression in the tumor environment.

Biological Interpretation

The identified surfaceome markers provide crucial biological insights into the functional roles and states of CD4 T cells in the context of normal colon tissue versus the colorectal tumor microenvironment.

CD4 T Cell Markers for Colon Homeostasis and Immune Surveillance

• AREG (Amphiregulin): As a growth factor, AREG is involved in tissue repair, epithelial cell proliferation, and immune regulation. Its elevated expression in CD4 T cells from normal colon (e.g., T_cac16, T_cac9) suggests their contribution to maintaining intestinal barrier integrity and tissue repair processes. GeneCards: AREG
• SELPLG (CD162, PSGL-1): This molecule is critical for leukocyte adhesion and migration, facilitating immune cell trafficking. Its higher expression in normal colon T cells likely reflects active immune surveillance and their ability to home to and move within healthy mucosal tissues. UniProt: P16154 (SELPLG)
• CCR6: A chemokine receptor that guides T cells to specific mucosal sites. The high expression of CCR6 in normal colon CD4 T cells indicates their role in gut homing and mucosal immunity, likely in response to CCL20, a chemokine often found in the intestinal lamina propria. GeneCards: CCR6
• CD82 (KAI1): A tetraspanin involved in cell migration and adhesion. Its abundance in normal CD4 T cells may contribute to their appropriate localization and function within the healthy colon microenvironment. UniProt: P27701 (CD82)

CD4 T Cell Markers in the Tumor Microenvironment

• TNFRSF18 (GITR): This co-stimulatory receptor is known to enhance T cell activation, proliferation, and survival, and to counteract regulatory T cell-mediated suppression. Its increased expression on tumor-associated CD4 T cells (e.g., T_cac16, T_cac9 in tumor) suggests an activated state or engagement in immune responses within the tumor microenvironment, possibly reflecting ongoing anti-tumor efforts or attempts to overcome immunosuppression. GeneCards: TNFRSF18
• HLA-DPA1, HLA-DPB1, HLA-DRB1: These genes encode subunits of MHC Class II molecules. While primarily expressed on professional antigen-presenting cells, MHC Class II can be induced on activated T cells, particularly CD4 T cells, during inflammation or in cancer. Their elevated expression on tumor-associated CD4 T cells may indicate T cell activation, persistent antigenic stimulation, or a state of T cell anergy/exhaustion within the tumor microenvironment where T cells attempt to present antigens themselves or respond to chronic signals. GeneCards: HLA-DPA1, GeneCards: HLA-DPB1, GeneCards: HLA-DRB1
• CTLA4 (CD152): A critical immune checkpoint molecule that dampens T cell activation. Its presence, even if not exclusively tumor-specific, on CD4 T cells in the tumor microenvironment indicates the engagement of inhibitory pathways that can contribute to immune evasion and T cell dysfunction. GeneCards: CTLA4
• FAS (CD95): A death receptor that mediates apoptosis. Its expression on T cells in the tumor context could indicate susceptibility to activation-induced cell death or a mechanism for T cell depletion, contributing to the overall immunosuppressive environment. GeneCards: FAS

Clinical or Translational Implications

The distinct surfaceome profiles of CD4 T cells in normal versus tumor conditions provide valuable insights for potential clinical applications.

• Biomarkers for Disease State and Prognosis: Genes highly expressed in normal CD4 T cells (e.g., AREG, SELPLG, CCR6, CD82) could serve as markers for healthy tissue T cell function and homeostasis. Their dysregulation in tumor tissue could indicate impaired immune surveillance or altered T cell trafficking. Conversely, the increased expression of TNFRSF18, HLA Class II molecules, and CTLA4 on tumor-infiltrating CD4 T cells may signify specific immune activation states or dysfunction within the tumor microenvironment. These markers could be investigated as potential diagnostic or prognostic biomarkers for disease progression and patient outcomes in colorectal cancer.

Therapeutic Targets for Immunomodulation

• TNFRSF18 (GITR): Given its role in enhancing T cell responses and its upregulation in tumor-associated CD4 T cells, GITR represents an attractive target for agonist antibodies in cancer immunotherapy. Activating GITR could boost anti-tumor immunity by enhancing effector T cell function and mitigating suppressive mechanisms.
• CTLA4: As an established immune checkpoint, CTLA4 inhibitors are already used clinically to unleash anti-tumor T cell responses. Its expression in tumor-associated CD4 T cells underscores the relevance of targeting this pathway to overcome immune suppression in the tumor microenvironment.
• HLA Class II Molecules: While not typically direct therapeutic targets on T cells, their induced expression in tumor-infiltrating CD4 T cells suggests a specific activation or dysfunctional state. Modulating the pathways that lead to this expression could indirectly enhance T cell function or antigen presentation, potentially through combination therapies.
• Guiding Cell Therapy Development: Understanding the expression of homing receptors like CCR6 and adhesion molecules like SELPLG can inform strategies to engineer T cells with improved ability to infiltrate and persist within tumor sites for adoptive cell therapies.

This analysis reveals condition-specific surface protein signatures on CD4 T cells in human colon, laying a foundation for further functional validation and potential development of diagnostic tools and immunotherapeutic strategies.

20. Dysregulated Cell Cycle Gene Expression in Colon Tumor Intestinal Epithelial Cells

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of a curated set of cell cycle-related genes in Intestinal Epithelial cells, comparing normal colon tissue to tumor tissue. Intestinal Epithelial cells are identified as the tumor-origin cell type in this dataset. The objective is to identify statistically significant changes in the expression of these critical cell cycle regulators, providing insights into the proliferative state and dysregulation characteristic of colorectal cancer.

Visual Summary

The box plots display the gene expression (sample mean) of 18 selected cell cycle pathway genes in Intestinal Epithelial cells across 'normal' and 'tumor' conditions. A striking and consistent pattern is observed:

• Widespread Upregulation in Tumor: For all 18 genes analyzed (ANAPC11, ANAPC5, CCND1, CDK4, GSK3B, HDAC1, HDAC2, MYC, PRKDC, RAD21, RBX1, SFN, SKP1, YWHAB, YWHAE, YWHAG, YWHAH, YWHAQ, YWHAZ), expression levels are significantly higher in Intestinal Epithelial cells from tumor samples compared to those from normal samples.
• High Statistical Significance: The majority of these genes show a highly significant upregulation (p ≤ 0.01), with a few exhibiting significance at p ≤ 0.05 (e.g., HDAC1, PRKDC, RBX1, SFN, SKP1, YWHAB, YWHAE). This indicates robust and non-random differences in gene expression between the two conditions.
• Increased Variability in Tumor: The box plots for tumor samples generally show a wider distribution, with a higher median and larger interquartile range (IQR) compared to normal samples, suggesting increased heterogeneity in gene expression within the tumor cells.

Biological Interpretation

The observed widespread upregulation of cell cycle-related genes in Intestinal Epithelial cells from tumor tissues strongly indicates dysregulated and accelerated cell proliferation, a hallmark of cancer. Given that Intestinal Epithelial cells are the tumor-origin cell type in colon cancer, these findings are highly relevant to the core pathological processes.

Core Cell Cycle Progression:

• CCND1 (Cyclin D1) and CDK4 (Cyclin-Dependent Kinase 4): These are critical regulators of the G1 to S phase transition. Their upregulation promotes progression through the cell cycle, enabling uncontrolled cell division. Upregulation of CCND1 is a common event in many cancers, including colorectal cancer, contributing to increased proliferation and decreased differentiation. GeneCards: CCND1
• ANAPC11, ANAPC5: These are subunits of the Anaphase-Promoting Complex/Cyclosome (APC/C), a ubiquitin ligase essential for metaphase-anaphase transition and mitotic exit. Increased expression suggests an active and potentially accelerated mitotic program.
• SKP1 (S-phase Kinase Associated Protein 1) and RBX1 (RING-Box Protein 1): These are components of SCF E3 ubiquitin ligase complexes, which target various cell cycle regulators (e.g., cyclins, CDK inhibitors) for degradation, thereby promoting cell cycle progression. Their upregulation further supports enhanced cell cycle activity.
• RAD21 (RAD21 Cohesin Complex Component): A key component of the cohesin complex, vital for sister chromatid cohesion during DNA replication and segregation during mitosis. Elevated RAD21 often correlates with increased proliferative capacity in tumors.

Oncogenic Drivers and Modulators:

• MYC (Proto-Oncogene, BHLH Transcription Factor): A powerful oncogene that drives cell proliferation, growth, and metabolism. Its substantial upregulation is a well-established feature of many cancers, promoting aggressive tumor behavior. GeneCards: MYC
• HDAC1 (Histone Deacetylase 1) and HDAC2 (Histone Deacetylase 2): These enzymes regulate gene expression by modifying chromatin structure. Overexpression of HDACs is frequently observed in cancer, leading to altered gene expression patterns that favor cell proliferation, survival, and epithelial-mesenchymal transition. PubMed search: HDAC1 cancer
• GSK3B (Glycogen Synthase Kinase 3 Beta): While its role is complex and context-dependent, GSK3B is involved in Wnt signaling, a pathway frequently dysregulated in colorectal cancer. Its upregulation here might contribute to specific pro-tumorigenic processes or reflect compensatory mechanisms.
• PRKDC (DNA-Dependent Protein Kinase Catalytic Subunit): Involved in DNA repair (non-homologous end joining) and genome stability. Overexpression can enhance DNA repair capacity in cancer cells, potentially contributing to resistance to genotoxic therapies.
• SFN (Stratifin/14-3-3 sigma) and other YWHAB/E/G/H/Q/Z (14-3-3 protein family members): The 14-3-3 protein family plays diverse roles in cell cycle control, apoptosis, and signal transduction. While SFN is often considered a tumor suppressor, the general upregulation of multiple 14-3-3 isoforms in tumor cells suggests a shift towards supporting proliferation and cell survival, or adapting to increased metabolic demands in the tumor context, as specific 14-3-3 isoforms can indeed promote cancer progression.

Collectively, these findings highlight a profound shift in Intestinal Epithelial cells within the tumor microenvironment towards a highly proliferative and oncogenically driven state. The coordinated upregulation of genes involved in different phases of the cell cycle and associated regulatory pathways provides a clear molecular signature of active tumor growth.

Clinical or Translational Implications

The consistent and significant upregulation of these cell cycle genes in tumor-derived Intestinal Epithelial cells has several potential clinical implications for colorectal cancer:

• Biomarkers: Elevated expression of genes like CCND1, CDK4, MYC, HDAC1/2, and components of the APC/C could serve as diagnostic or prognostic biomarkers for colon cancer progression and aggressiveness.
• Therapeutic Targets: Several of these genes represent established or emerging therapeutic targets. For instance, CDK4/6 inhibitors are already used in other cancers, and HDAC inhibitors are also a class of anti-cancer drugs. Targeting these overexpressed cell cycle components could offer strategies to inhibit uncontrolled proliferation in colorectal cancer. PubMed search: Cell cycle inhibitors cancer therapy
• Understanding Resistance: The involvement of PRKDC suggests an active DNA repair mechanism in tumor cells, which could imply potential resistance to DNA-damaging chemotherapies and suggests combination therapies.

21. Colon Intestinal Epithelial Cell Gene Ontology Analysis: Insights into Ploidy and Tumorigenesis

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes Gene Ontology (GSA) to identify biological processes and pathways that are significantly enriched in Intestinal Epithelial cells under two distinct comparison contexts:

1. Diploid Intestinal Epithelial cells vs. Aneuploid Intestinal Epithelial cells: Highlighting pathways upregulated in cells maintaining a diploid chromosomal state.
2. Tumor-associated Intestinal Epithelial cells vs. Normal Intestinal Epithelial cells: Identifying pathways upregulated in epithelial cells residing within the tumor microenvironment compared to those from normal tissue.

The results are presented as bar plots, illustrating the significance of enriched terms using -log(p-val) and -log(q-val). This allows us to understand the functional shifts associated with genomic stability and tumor development within this critical cell type.

Visual Summary

The analysis presents two bar plots, each displaying the top Gene Ontology (GO) terms upregulated for specific comparisons in Intestinal Epithelial cells. The x-axis represents the -log(p-val) and -log(q-val), with longer bars indicating higher statistical significance.

1. GSA_up for Intestinal Epithelial cell: Diploid_vs_others: This plot shows pathways upregulated in diploid Intestinal Epithelial cells. Prominent terms include those related to metabolic regulation (e.g., Aldosterone-regulated sodium reabsorption, Mineral absorption), key signaling pathways (e.g., FoxO signaling pathway, p53 signaling pathway, mTOR signaling pathway), cellular structure (e.g., Tight junction), and processes like Cellular senescence.
2. GSA_up for Intestinal Epithelial cell: tumor_vs_others: This plot reveals pathways upregulated in Intestinal Epithelial cells from tumor samples. A striking feature is the extensive enrichment of fundamental cellular processes associated with rapid growth and altered metabolism (e.g., Ribosome biogenesis, Protein processing in endoplasmic reticulum, Oxidative phosphorylation, Cell cycle). Additionally, numerous terms related to various infectious diseases, neurodegenerative diseases, and several cancer types (including Colorectal cancer) are highly significant.

Biological Interpretation

Pathways Upregulated in Diploid Intestinal Epithelial Cells (Diploid vs. Aneuploid)

The enrichment of specific GO terms in diploid Intestinal Epithelial cells, when compared to aneuploid cells, suggests pathways associated with maintaining cellular homeostasis and genomic integrity:

• Metabolic and Ion Transport Regulation: Pathways like "Aldosterone-regulated sodium reabsorption," "Mineral absorption," and "Proximal tubule bicarbonate reabsorption" are highly enriched. While some are typically associated with kidney function, they underscore the vital role of intestinal epithelial cells in maintaining water and electrolyte balance and nutrient absorption, processes crucial for healthy gut function.
• Core Signaling for Cell Fate and Stress Response: "FoxO signaling pathway" [GeneCards: FOXO] is critical for stress resistance, metabolism, and cell fate decisions, while "mTOR signaling pathway" [GeneCards: MTOR] regulates cell growth and proliferation. The robust enrichment of the "p53 signaling pathway" [GeneCards: TP53] highlights its central role as a tumor suppressor, involved in DNA repair, cell cycle arrest, and apoptosis. Its prominence in diploid cells suggests active surveillance mechanisms against cellular damage.
• Cellular Structure and Barrier Function: "Tight junction" [PubMed: Tight junction] components are essential for maintaining the integrity of the epithelial barrier, preventing pathogen invasion, and regulating paracellular transport. This emphasizes the functional importance of diploid cells in maintaining tissue architecture.
• Cellular Senescence: The presence of "Cellular senescence" [PubMed: Cellular senescence] suggests that diploid cells may activate this program as a protective mechanism against malignant transformation, limiting the proliferation of potentially damaged cells before they accumulate sufficient genetic alterations to become aneuploid or cancerous.
• ECM Remodeling: "Proteoglycans in cancer" indicates changes in the extracellular matrix. While "in cancer," proteoglycans are fundamental components of the ECM involved in diverse processes. Their specific regulation in diploid cells might reflect a healthy ECM turnover that supports normal tissue function, which can be disrupted during tumorigenesis.

Pathways Upregulated in Tumor Intestinal Epithelial Cells (Tumor vs. Normal)

The profile of upregulated GO terms in Intestinal Epithelial cells from tumor samples reflects hallmarks of cancer:

• Elevated Anabolism and Proliferation: Highly significant terms such as "Ribosome biogenesis in eukaryotes," "Protein processing in endoplasmic reticulum," "Spliceosome," "Proteasome," "RNA transport," "Cell cycle," "Oxidative phosphorylation," and "Citrate cycle (TCA cycle)" [PubMed: Citrate cycle] collectively indicate a dramatically increased demand for protein synthesis, energy production, and rapid cell division—all characteristic features of rapidly growing cancer cells. This metabolic reprogramming supports sustained proliferation.
• Interaction with the Microenvironment and Pathogens: The enrichment of numerous infectious disease pathways (e.g., "Salmonella infection," "Shigellosis," "Pathogenic Escherichia coli infection," "Viral carcinogenesis," "Epstein-Barr virus infection," "Human papillomavirus infection," "Bacterial invasion of epithelial cells") is notable. This suggests a complex interplay between tumor cells, the gut microbiome, and host immune responses within the tumor microenvironment. Some infections are direct oncogenic drivers, and chronic inflammation can promote tumor progression [PubMed: Tumor microenvironment infection].
• Shared Cellular Stress Responses: The surprising enrichment of various "neurodegenerative disease" pathways (e.g., "Parkinson's disease," "Amyotrophic lateral sclerosis," "Huntington disease," "Alzheimer's disease," "Pathways of neurodegeneration") likely points to fundamental cellular stress responses involving protein misfolding, aggregation, and impaired proteostasis (e.g., through ER stress and proteasomal dysfunction), which are common underlying pathological features across neurodegenerative conditions and various cancers due to high metabolic demands and rapid protein turnover. It does not imply that these colon cells are degenerating in a neurological sense.
• Direct Cancer Mechanisms: The explicit appearance of "Colorectal cancer" [PubMed: Colorectal cancer] as an enriched term directly validates the biological relevance of the analysis. Terms like "Endometrial cancer" and "Thyroid cancer" may represent shared oncogenic mechanisms or signaling pathways common to different cancer types.
• Altered Cell Fate and Junctions: "Ferroptosis" [PubMed: Ferroptosis], "Apoptosis," and "Autophagy" are regulated cell death and survival pathways, indicating dysregulation of cell fate in tumor cells. "Adherens junction" and "Tight junction" are critical for epithelial cell polarity and barrier function; their altered state in tumor cells is often associated with epithelial-mesenchymal transition (EMT) and invasive phenotypes.

Clinical or Translational Implications

The differential pathway enrichment observed provides crucial insights into the biology of colon Intestinal Epithelial cells in health and disease:

• Genomic Stability and Tumor Suppression: The upregulation of pathways like "p53 signaling pathway" and "Cellular senescence" in diploid cells highlights intrinsic mechanisms that may protect against malignant transformation. Understanding how these pathways are compromised in aneuploid cells could reveal vulnerabilities for therapeutic targeting.
• Tumor Metabolic Reprogramming: The extensive enrichment of anabolic and energy-generating pathways in tumor epithelial cells (e.g., "Ribosome biogenesis," "Oxidative phosphorylation," "Cell cycle") strongly supports the concept of metabolic reprogramming in cancer. These pathways represent potential targets for anti-cancer therapies aimed at disrupting tumor cell growth and survival.
• Tumor-Microbiome Interactions: The significant upregulation of infectious disease-related pathways in tumor epithelial cells underscores the critical role of the tumor microenvironment, including the gut microbiome, in colorectal cancer progression. This suggests potential for novel therapeutic strategies targeting host-pathogen interactions or modulating the microbiome [PubMed: Gut microbiome colon cancer].
• Epithelial-Mesenchymal Transition (EMT): Changes in "Adherens junction" and "Tight junction" in tumor cells indicate alterations in cell-cell adhesion, a key feature of EMT, which is associated with increased invasiveness and metastasis in colorectal cancer [PubMed: EMT colorectal cancer].
• Broader Mechanistic Insights: The overlap with neurodegenerative disease pathways points to fundamental cellular processes (e.g., proteostasis) that are perturbed in rapidly proliferating tumor cells, offering a broader perspective on common stress responses in diseased states.

22. Gene Set Enrichment Analysis (GSEA) Across Major Colon Cell Types

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for key cell types found in the colon tissue, specifically B cells, Fibroblasts, ILCs, Intestinal Epithelial cells, Plasma cells, T cells CD4+, and T cells CD8+. The dot plot visualizes the enrichment of various biological pathways under different conditions: 'normal vs others', 'tumor vs others', and, for Intestinal Epithelial cells, 'Diploid vs others'. The size of each dot reflects the significance of the enrichment (-log(P-value)), while the color indicates the Normalized Enrichment Score (NES), with red representing positive enrichment (upregulation) and blue representing negative enrichment (downregulation). A total of 80 top pathways are displayed, ordered by their significance.

Visual Summary

The dot plot reveals distinct and shared pathway enrichments/depletions across different cell types and conditions.

• General Pattern: Many pathways show differential enrichment between 'normal vs others' and 'tumor vs others' comparisons for the same cell type, often with opposing NES values (e.g., a pathway being upregulated in 'tumor' and downregulated in 'normal').
• Immune Cell Involvement: Immune-related pathways such as "B cell receptor signaling pathway", "Chemokine signaling pathway", "TCR signaling pathway", "Th1 and Th2 cell differentiation", and "TNF signaling pathway" are prominently enriched or depleted in T cells, B cells, and Plasma cells, as expected.
• Cancer-Related Pathways: Pathways directly associated with cancer, such as "Pathways in cancer", "Colorectal cancer", "Proteoglycans in cancer", "MAPK signaling pathway", "PI3K-Akt signaling pathway", and "mTOR signaling pathway", show significant and often positive enrichment primarily in tumor conditions and in Intestinal Epithelial cells.
• Metabolic Pathways: Pathways like "Oxidative phosphorylation", "Lipid and atherosclerosis", and "Non-alcoholic fatty liver disease" also appear differentially regulated.
• Cell Death Pathways: "Apoptosis", "Ferroptosis", and "Necroptosis" are variably regulated across cell types and conditions, indicating altered cell survival mechanisms.
• Intestinal Epithelial Cells: The Intestinal Epithelial cells, being the tumor origin cell type, exhibit strong enrichments in cancer-related pathways under tumor conditions. The "Diploid vs others" comparison for Intestinal Epithelial cells shows a unique pattern, often opposite to the "tumor vs others" enrichment, potentially highlighting characteristics of healthy or non-transformed epithelial cells.
• Fibroblasts: Fibroblasts in the tumor microenvironment show distinct pathway alterations, including enrichment of cancer-related and signaling pathways.

Biological Interpretation

1. Tumor-Associated Pathway Activation in Intestinal Epithelial Cells:

• In Intestinal Epithelial cells under tumor conditions (Intestinal Epithelial cell: tumor vs others), there is a strong positive enrichment (red, large dots) of classical cancer hallmarks. These include "Pathways in cancer", "Colorectal cancer", "Proteoglycans in cancer", "MAPK signaling pathway", "PI3K-Akt signaling pathway", "mTOR signaling pathway", and "Transcriptional misregulation in cancer". This reflects the oncogenic transformation and active proliferation, survival, and signaling dysregulation typical of cancer cells.
• Conversely, many of these cancer-related pathways are negatively enriched (blue) in 'normal vs others' for Intestinal Epithelial cells, or positively enriched in 'Diploid vs others', suggesting that healthy, diploid epithelial cells maintain a quiescent or regulated state regarding these pathways. The Diploid vs others comparison specifically highlights pathways characteristic of diploid intestinal epithelial cells when contrasted with the broad cellular landscape of the colon, which likely includes aneuploid cells and diverse immune/stromal populations.

2. Immune Landscape Remodeling in Tumor Microenvironment:

• T cells (CD4+ and CD8+): Both T cell subsets show significant pathway alterations in the tumor context. For example, "Th1 and Th2 cell differentiation" is often negatively enriched (blue) in tumor T cells, which could indicate T cell exhaustion or a shift towards an immunosuppressive phenotype rather than an anti-tumor Th1 response. "Chemokine signaling pathway" and "TNF signaling pathway" also show differential regulation, crucial for immune cell recruitment and inflammatory responses.
• B cells and Plasma cells: "B cell receptor signaling pathway" and "Intestinal immune network for IgA production" are prominent. Plasma cells, known for antibody production, show distinct enrichments, potentially indicating active humoral immunity or dysregulation in the tumor. For example, "Apoptosis" and "Ferroptosis" are often negatively enriched in B cells and Plasma cells in the tumor, suggesting increased survival.
• ILCs: Innate Lymphoid Cells (ILCs) play crucial roles in mucosal immunity. Their GSEA profile in tumor conditions shows unique patterns, indicating their involvement in the altered immune milieu.

3. Stromal Cell (Fibroblast) Contribution to Tumor Progression:

• Fibroblasts in the tumor microenvironment (Fibroblast: tumor vs others) show significant enrichment of pathways like "MAPK signaling pathway", "PI3K-Akt signaling pathway", and "Focal adhesion", which are critical for cell proliferation, migration, and extracellular matrix remodeling. This suggests that tumor-associated fibroblasts (CAFs) are highly active in supporting tumor growth, invasion, and metastasis, consistent with their known roles in various cancers including colorectal cancer. GeneCards - Cancer-Associated Fibroblasts

4. Altered Cell Death and Metabolic Pathways:

• "Apoptosis", "Ferroptosis", and "Necroptosis" pathways exhibit complex regulation, sometimes positively and sometimes negatively enriched, depending on the cell type and condition. This suggests a varied interplay of cell survival and programmed cell death mechanisms within the tumor microenvironment, contributing to tumor growth or immune evasion.
• "Oxidative phosphorylation" and other metabolic pathways are differentially regulated, reflecting the metabolic reprogramming characteristic of cancer cells (e.g., Warburg effect) and their surrounding stromal and immune cells. PubMed Search - Cancer metabolism

5. Inflammation and Infection-Related Pathways:

• Pathways related to bacterial infections ("Pathogenic Escherichia coli infection", "Salmonella infection", "Vibrio cholerae infection") and viral infections ("Human cytomegalovirus infection", "Epstein-Barr virus infection") show varied enrichments, which could point to the role of the gut microbiome and chronic inflammation in colorectal cancer development and progression. PubMed Search - Microbiome colorectal cancer

Clinical or Translational Implications

The GSEA results provide insight into the functional shifts occurring in different cell populations within the colon tumor microenvironment.

• Therapeutic Targets: Pathways consistently activated in tumor-associated cells (e.g., MAPK, PI3K-Akt, mTOR signaling in Intestinal Epithelial cells and Fibroblasts) represent potential therapeutic targets. Inhibitors for these pathways are already in development or clinical use for various cancers.
• Biomarkers: Specific pathway enrichments in immune cells or fibroblasts could serve as biomarkers for patient stratification, response to immunotherapy, or prognosis. For instance, the specific T cell differentiation states or B cell activation profiles could predict therapeutic outcomes.
• Immune Modulation: The differential regulation of immune-related pathways (e.g., Th1/Th2 differentiation, chemokine signaling) highlights opportunities for immune-modulating therapies, aiming to reactivate anti-tumor immunity or overcome immunosuppression in the colon tumor microenvironment.
• Understanding Tumor Heterogeneity: The cell-type-specific pathway enrichments underscore the heterogeneous nature of the tumor microenvironment and the need for precision medicine approaches that consider the distinct roles of various cellular components.

23. Discussion

The comprehensive single-cell analysis of human colon tissue reveals a highly intricate and significantly altered cellular landscape in colon adenocarcinoma. A central finding is the robust identification of aneuploid intestinal epithelial cells predominantly within tumor samples, strongly correlating genomic instability with the designated tumor-origin cell type. These malignant epithelial cells exhibit a widespread and highly significant upregulation of core cell cycle components (e.g., CCND1, CDK4, MYC, HDAC1/2) and metabolic pathways, underscoring their uncontrolled proliferation and sustained growth characteristic of cancer. Key tumor-specific surface markers like MUC4, CD24, CEACAM5/6, CD44, and NECTIN2 further define the aggressive phenotype of these cells, highlighting their altered adhesion, proliferation, and immune-modulatory capabilities.

The immune microenvironment undergoes substantial reprogramming. There's a notable shift in T cell subsets, with significant increases in pro-tumorigenic Th17 and immunosuppressive Treg cells, coupled with a decrease in T follicular helper (Tfh) cells. Macrophage populations also polarize towards pro-tumorigenic M2D and M2B subtypes, while M2A macrophages are reduced. Critically, cell-cell interaction analysis reveals a profound loss of IFN-γ signaling, a pivotal anti-tumor pathway, in the tumor microenvironment, indicating a major immune evasion mechanism. Concurrently, interactions involving the LCK-CD8 axis are altered, suggesting dysregulated T cell activation and compromised T cell-B cell crosstalk. The upregulation of the NECTIN2-TIGIT immune checkpoint interaction between tumor epithelial cells and T cells further reinforces an active immunosuppressive axis.

Stromal remodeling is a prominent feature, with a marked increase in activated fibroblasts (Cancer-Associated Fibroblasts, CAFs) in tumor samples. These CAFs are characterized by distinct surface markers such as THY1, MMP14, PDGFRB, and integrins, and engage in extensive extracellular matrix (ECM) remodeling as evidenced by numerous integrin-collagen/laminin interactions. This creates a stiff, pro-tumorigenic microenvironment that promotes tumor growth, invasion, and immune suppression. Overall, the integrated analysis paints a consistent picture of colon adenocarcinoma progression driven by intrinsically altered epithelial cells, a dysregulated and immunosuppressive immune milieu, and an actively remodeling, supportive stromal compartment.

Hypotheses:

1. Aneuploid intestinal epithelial cells in colon tumors create an immunosuppressive microenvironment, evidenced by the loss of IFN-γ signaling and altered T cell and macrophage polarization, thereby promoting tumor growth and immune evasion.
2. The upregulation of surface markers like CEACAM5/6 and NECTIN2 on tumor epithelial cells directly contributes to immune checkpoint engagement (e.g., NECTIN2-TIGIT) and adhesion-mediated immune suppression of CD8+ T cells.
3. Cancer-associated fibroblasts drive colon adenocarcinoma progression by enhancing ECM remodeling via increased MMP14 and integrin expression, facilitating tumor invasion, and supporting a pro-tumorigenic milieu.
4. The coordinated dysregulation of cell cycle pathways (e.g., CCND1, CDK4, MYC) and metabolic reprogramming in tumor intestinal epithelial cells is central to their uncontrolled proliferation, sustained survival, and overall tumor aggressiveness.

Potential therapeutic targets:

1. TIGIT / NECTIN2 axis: Upregulation of the inhibitory NECTIN2-TIGIT interaction between tumor epithelial cells and T cells represents a significant immune evasion mechanism. Blocking this axis could unleash anti-tumor T cell immunity. Evidence: Section 15 shows robust upregulation of NECTIN2-TIGIT interactions in tumor samples involving Diploid Intestinal Epithelial cells and both CD4+ and CD8+ T cells. Section 19 identifies CTLA4 (another checkpoint) and TNFRSF18 (GITR, an activating receptor) expression shifts in tumor CD4 T cells. Validation: Clinical trials with anti-TIGIT antibodies in colon cancer patients; functional assays to assess enhanced T cell anti-tumor activity upon blockade.
2. CEACAM5 / CEACAM6: Overexpressed on tumor epithelial cells, these molecules are involved in adhesion, immune evasion (CEACAM5-CD8A), and promotion of tumor growth, making them promising targets for direct tumor cell killing or immune modulation. Evidence: Section 16 identifies MUC4, CD24, CEACAM5, and CEACAM6 as highly expressed surfaceome markers on tumor-associated Intestinal Epithelial cells. Sections 12 and 13 show CEACAM5-CD8A and CEACAM5-CEACAM6 interactions in the tumor microenvironment. Validation: Development of antibody-drug conjugates (ADCs) or bispecific antibodies targeting CEACAM5/6; in vitro/in vivo studies to confirm tumor growth inhibition and immune cell activation.
3. MMP14 / PDGFRB / Integrins: Highly expressed by tumor-associated fibroblasts, these molecules drive extracellular matrix remodeling, CAF proliferation, and pro-tumorigenic signaling, all crucial for tumor growth, invasion, and metastasis. Evidence: Section 18 shows significant upregulation of MMP14, PDGFRB, ITGA1/ITGAV, and CDH11 in tumor-associated fibroblasts. Section 15 highlights extensive integrin-collagen/laminin interactions among fibroblasts in tumor. Validation: Testing small molecule inhibitors or blocking antibodies against PDGFRB or specific integrins in preclinical models; evaluating efficacy of MMP14 inhibitors in combination therapies.
4. Cell cycle regulators (e.g., CCND1, CDK4, MYC, HDAC1/2): These genes are significantly and consistently upregulated in tumor intestinal epithelial cells, driving their uncontrolled proliferation, a hallmark of cancer. Targeting them could inhibit tumor growth. Evidence: Section 20 shows widespread and significant upregulation of CCND1, CDK4, MYC, HDAC1, HDAC2, and other cell cycle components in tumor Intestinal Epithelial cells. Section 21 (GSA) and 22 (GSEA) confirm 'Cell cycle' and 'Transcriptional misregulation in cancer' pathways are highly enriched in tumor epithelial cells. Validation: Preclinical testing of existing CDK4/6 inhibitors or HDAC inhibitors; development of novel inhibitors for other dysregulated cell cycle components; assessing synergy with other anti-cancer agents.

Follow-up validation ideas:

1. Validate the differential expression of key surface markers (e.g., MUC4, CEACAM5, THY1, TIGIT, GITR) on specific cell subsets in human colon adenocarcinoma patient samples using flow cytometry and multiplex immunohistochemistry/immunofluorescence on tissue sections.
2. Perform spatial transcriptomics or proteomics on tumor sections to localize aneuploid intestinal epithelial cells, cancer-associated fibroblasts, and immune subsets, and to confirm the spatial proximity and interaction of predicted ligand-receptor pairs (e.g., NECTIN2-TIGIT, CEACAM5-CD8A).
3. Conduct in vitro co-culture assays using patient-derived tumor epithelial cells, fibroblasts, and T cells to functionally test the impact of blocking specific ligand-receptor interactions (e.g., anti-TIGIT, anti-CEACAM5 antibodies) on T cell activation, proliferation, and cytotoxic function, as well as on tumor cell growth and invasion.
4. Investigate the effects of perturbing highly expressed CAF markers (e.g., MMP14, PDGFRB) in preclinical colon cancer models (e.g., organoids, xenografts) using small molecule inhibitors or genetic knockdown to assess their impact on tumor growth, metastasis, and immune cell infiltration.
5. Utilize targeted qPCR and FISH (Fluorescence In Situ Hybridization) on sorted aneuploid intestinal epithelial cells to confirm specific copy number variations and the expression levels of key cell cycle regulators (e.g., CCND1, MYC) identified in the analysis.
6. Analyze additional independent cohorts of human colorectal cancer patients using bulk or single-cell sequencing to validate the identified immune checkpoint, cell cycle, and metabolic pathway signatures, and correlate these with clinical outcomes and response to therapy.

Limitations:

This report is based on single-cell RNA-sequencing data, which provides correlative insights rather than direct causality. The CNV analysis for specific cell groups (Section 4) showed a discrepancy between the queried tumor-origin epithelial cells and the cell types predominantly displayed in the heatmap (T and B cells), necessitating further validation to confirm aneuploidy in the intended epithelial populations. While other analyses (UMAPs and ploidy population plots) strongly indicate aneuploidy in tumor-origin Intestinal Epithelial cells, this specific plot discrepancy should be noted. Some analyses, such as macrophage markers (Section 17), were configured to show general cell type markers rather than condition-specific differences within that cell type, limiting insights into their tumor-induced changes. Cell-cell interaction predictions are computational and require experimental validation. Gene set enrichment analyses provide pathway-level insights but do not quantify protein activity or functional output, and the inferred functions may reflect shared cellular stress responses rather than disease-specific mechanisms, as seen with some 'neurodegenerative disease' pathways.

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 it.
2. Show major cell type scores on UMAP and save it.
3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
4. Select tumor-origin cells and unassigned cells, group by sample, show a CNV heatmap, and also show a summary of significantly amplified copy number regions, and save it.
5. Show CNV patterns as UMAPs. Include major cell type, minor cell type, ploidy results, condition, and sample in 2 columns and save it.
6. Show a population bar plot for minor cell types and save it.
7. Show a subset population bar plot for T cells and save it.
8. For T cell subset populations, show a box plot if there are statistically significant differences between conditions, and save it. Set ncols appropriately based on the total number of panels.
9. Show a subset population bar plot for Macrophages and save it.
10. For Macrophage subset populations, show a box plot if there are statistically significant differences between conditions, and save it. Set ncols appropriately based on the total number of panels.
11. Select tumor-origin cells and unassigned cells, show a bar plot for their ploidy population, and save it.
12. Show cell-cell interaction patterns by condition, including tumor-origin cells (Intestinal Epithelial cell), fibroblasts, macrophages, and T cells, and save it. Select up to 80 cell-cell interactions for each condition.
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 pathways and cell cycle pathways, show cell-cell interactions for these genes, and save it.
15. Find statistically significant differences in cell-cell interactions between conditions for major immune cells and stromal cells, show them as a dot plot, and save it. Set max_n_items_per_group = 25.
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 Macrophages, show them as a dot plot, and save it. Show only surfaceome markers, up to 50 per condition.
18. Extract condition-specific markers for Fibroblasts, show them as a dot plot, and save it. Show only surfaceome markers, up to 50 per condition.
19. Extract condition-specific markers for CD4 T cells, show them as a dot plot, and save it. Show only surfaceome markers, up to 50 per condition.
20. Select key disease-related cells (Intestinal Epithelial cells) and genes related to the Cell cycle pathway, show box plots for statistically significant expression differences between conditions, and save it. Set max_n_items_to_plot = 24 and ncols to ensure an aspect ratio of approximately 2x3 based on the total number of panels.
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 (B cell, Fibroblast, ILC, Intestinal Epithelial cell, Plasma cell, T cell CD4+, T cell CD8+), and save it. Use the RdBu_r color map and set n_pws_to_show = 80.