SCODiA Report by MLBI Lab

Single-Cell Transcriptomic and Genomic Landscape of Colon Cancer Unveils Tumorigenic Mechanisms and Therapeutic Opportunities

This report details a single-cell RNA-sequencing analysis of human colon tissue, comparing normal and tumor conditions. Key findings highlight profound genomic instability in tumor-originating Intestinal Epithelial cells, significant reprogramming of the tumor microenvironment with altered immune cell subsets and activated cancer-associated fibroblasts, and a rewiring of cell-cell communication to promote immune evasion and tumor progression. These insights pinpoint specific molecular pathways and cellular interactions with potential for novel therapeutic strategies.

Contents

0. Dataset overview
1. UMAP Visualization of Single-Cell RNA-seq Data by Condition, Sample, Cell Type, and Ploidy
2. Major Cell Type Score and Ploidy Distribution on UMAP
3. Overall Celltype_subset Marker Expression Analysis
4. Copy Number Variation Analysis of Tumor-Origin and Unassigned Cells
5. CNV 기반 UMAP 시각화를 통한 세포 유형, 이수성, 조건 및 샘플 패턴 분석
6. Minor Cell Type Population Analysis in Colon Normal vs. Tumor
7. T Cell Major Cell Type Annotation Consistency Across Samples and Conditions
8. Colon T Cell Subset Shifts in Tumor Microenvironment
9. Macrophage Subset Population Shifts in Colon Cancer
10. Colon Tumor Microenvironment Shows Significant Macrophage Subset Reprogramming
11. Ploidy Status of Tumor-Origin and Unassigned Cells Across Normal and Tumor Conditions
12. Condition-Specific Cell-Cell Interaction Patterns in Colon Tissue
13. Condition-Specific Cell-Cell Interaction Analysis in Colon Tissue
14. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Pathways in Colon Tissue
15. Condition-Specific Cell-Cell Interaction Patterns in Normal and Tumor Colon Tissue
16. Intestinal Epithelial Cell Condition-Specific Surfaceome Markers
17. Fibroblast Condition-Specific Surface Marker Identification
18. T cell CD4+ Condition-Specific Surfaceome Markers in Colon Tissue
19. Dysregulation of Cell Cycle Pathways in Intestinal Epithelial Cells of Colorectal Tumors
20. Intestinal Epithelial Cell Gene Ontology (GSA) Analysis: Insights into Ploidy Status and Tumor Progression
21. Gene Set Enrichment Analysis (GSEA) of Colon Single-Cell RNA-seq Data Across Key Cell Types
22. Discussion
23. Query List

0. Dataset overview

Dataset Summary

• 데이터 형식: 48033개의 세포와 20683개의 유전자를 포함하는 단일 세포 RNA 시퀀싱 AnnData입니다.

종(Species): 인간 (human)

조직(Tissue): 결장 (Colon)

• 조건(Conditions): 정상(normal) 및 종양(tumor) 두 가지가 있습니다.
• 주요 관측치(obs columns): 샘플, 조건, 위치, MSI 상태, 벌크 예측, 세포 유형(주요, 미세, 하위), 접근성, 조직, CNV 관련 정보, 배수성 점수, 클러스터링 등을 포함합니다.
• 주요 유전자 정보(var columns): 가변 유전자, 염색체, 스팟 번호, 세포유전학적 밴드를 포함합니다.

세포 유형 분류:

• 주요 (Major): T cell, B cell, Intestinal Epithelial cell, unassigned, Stromal cell, Myeloid cell, Endothelial cell, Mast cell.
• 미세 (Minor): T cell CD4+, B cell, Intestinal Epithelial cell, Plasma cell, ILC, T cell CD8+, Fibroblast, Macrophage, Endothelial cell, Mast cell, Dendritic cell, NK cell, Smooth muscle cell 등 더 세분화된 유형을 포함합니다.
• 하위 (Subset): T cell (Naive), B cell (Follicular), Goblet cell 등 가장 세분화된 세포 유형을 포함합니다.
• 종양 유래 세포 유형: Intestinal Epithelial cell로 지정되어 있습니다.
• 배수성(Ploidy): Aneuploid (이수체) 및 Diploid (이배체)로 분류됩니다.

사전 계산된 결과:

• 세포-세포 상호작용 (CCI): 조건 및 샘플별 CellPhoneDB 결과가 저장되어 있습니다.
• 차등 발현 유전자 (DEG): 각 celltype_minor에 대해 특정 조건과 나머지 조건 간의 비교 결과가 저장되어 있습니다.
• 유전자 세트 농축 분석 (GSEA): 각 celltype_minor에 대해 특정 조건과 나머지 조건 간의 비교 결과가 저장되어 있습니다.
• 유전자 온톨로지 (GO/GSA): 각 celltype_minor에 대해 특정 조건과 나머지 조건 간의 비교 결과가 저장되어 있습니다.
• CNV 추정치: obsm['X_cnv']에 저장되어 있습니다.
• 분석 가능한 세포 유형: DEG, GSEA, GSA/GO 분석을 위해 B cell, Fibroblast, ILC, Intestinal Epithelial cell, Plasma cell, T cell CD4+, T cell CD8+ 세포 유형을 사용할 수 있습니다.

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 UMAP (Uniform Manifold Approximation and Projection) plots derived from single-cell RNA-sequencing data of human colon tissue. These visualizations aim to provide an overview of the dataset's underlying structure, cellular heterogeneity, and the distribution of key metadata annotations, including experimental condition (normal vs. tumor), individual samples, major and minor cell types, cell type subsets, and ploidy status (aneuploid vs. diploid). The primary goal is to assess the quality of cell type annotation, identify potential sample-specific effects (batch effects), and understand how different biological states (normal vs. tumor, ploidy) are represented within the cellular landscape.

Visual Summary

Condition

The UMAP colored by Condition (Normal vs. Tumor) shows a clear separation between cells from normal and tumor conditions. While there is some overlap, particularly in the central and upper-left clusters, distinct clusters are predominantly enriched for either normal (maroon) or tumor (indigo) cells. The largest cluster on the left appears to be a mix, but a significant portion of cells within this cluster and several smaller clusters are distinctly tumor-associated. This suggests strong transcriptional differences between normal and tumor microenvironments, leading to condition-specific clustering.

Sample

The sample UMAP reveals that cells from different individual samples are generally well-interspersed within the larger cell type clusters. While some smaller clusters might show slight enrichment for specific samples (e.g., cells from B_cac samples appearing in the lower right, and T_cac samples appearing in some other distinct clusters), there isn't a dominant pattern of samples forming entirely separate clusters. This indicates that batch effects (technical variation between samples) are not the primary drivers of the major UMAP structure, and biological variation (cell types, conditions) is well-captured.

celltype_major

The celltype_major UMAP demonstrates good separation of the major cell types into distinct clusters. For instance, T cells (light blue/cyan) form a large, distinct cluster, as do Intestinal Epithelial cells (Ent.Epi, orange/peach), B cells (maroon), and Stromal cells (teal). Myeloid cells (light yellow), Endothelial cells (red), and Mast cells (light green) also occupy specific regions, often as smaller, well-defined clusters. The "unassigned" cells (dark purple) are sparse and scattered, suggesting most cells have been successfully assigned to a major cell type.

celltype_minor

The celltype_minor UMAP provides a more granular view of cell type identities. It reinforces the clean separation seen at the major cell type level. For example, the large T cell cluster is further resolved into T cell CD4+ (light blue) and T cell CD8+ (dark blue) sub-regions. Intestinal Epithelial cells (orange) remain largely together. Fibroblasts (Fib, goldenrod), Macrophages (Mac, light yellow), Plasma cells (light green), and Dendritic cells (DC, red) also form discernible populations, often within or adjacent to their broader major cell type clusters.

ploidy_dec

The ploidy_dec UMAP shows that Aneuploid cells (maroon) are predominantly concentrated within a specific cluster in the lower right region of the UMAP, and to a lesser extent, within a sub-population of the large mixed cluster on the left. The vast majority of cells are classified as Diploid (light yellow) and are distributed across most other clusters. A small number of "Unclear" cells (indigo) are scattered. This pattern strongly suggests that aneuploidy, a hallmark of cancer, is primarily associated with a specific cell population, likely the tumor-derived epithelial cells.

celltype_subset

The celltype_subset UMAP offers the highest resolution of cell type annotation. It shows further sub-divisions within the previously identified minor cell type clusters. For example, within the T cell area, T_Naive, Tfh, Th1, Th17, Treg, and Cytotoxic T cells are discernible. Similarly, different B cell subsets (B cell (Memory), BMZ, Bf, Breg) and macrophage subsets (Mac_M1, Mac_M2A, M2B, M2C, M2D) form distinct or partially overlapping sub-clusters. Goblet cells, Enterocytes, and Paneth cells are clearly delineated within the Intestinal Epithelial cell compartment. This granular resolution confirms the successful characterization of diverse cell states.

Biological Interpretation

The UMAP visualizations collectively demonstrate a well-structured and heterogeneous dataset, consistent with the complexity of the human colon tissue and its changes in disease.

1. Disease-Specific Cellular Reorganization: The distinct separation of 'Normal' and 'Tumor' conditions on the UMAP indicates significant transcriptional shifts and/or altered cell type compositions in the tumor microenvironment. This is a fundamental observation in cancer biology, where transformed cells and immune/stromal infiltrates undergo major transcriptomic remodeling compared to healthy tissue.
2. Robust Cell Type Annotation: The clear clustering of major, minor, and subset cell types across the UMAP projections validates the quality of the cell type annotations. Cells of the same type generally cluster together, and closely related cell types (e.g., T cell CD4+ and CD8+) often form adjacent clusters or sub-regions within a larger immune cell compartment. This suggests that the gene expression profiles adequately distinguish these cell populations.
3. Tumor Cell Identification via Ploidy: The strong association of Aneuploid cells with specific clusters, particularly the one in the lower-right, is a critical finding. Given that the Tumor origin celltype is noted as Intestinal Epithelial cell, these aneuploid clusters are highly likely to represent the malignant epithelial cells, which often exhibit chromosomal instability and aneuploidy. This provides confidence in identifying the cancer cells themselves within the mixed tissue context. The presence of some aneuploid cells in the larger mixed cluster might indicate tumor cells with less pronounced aneuploidy, or potentially tumor-associated stromal cells that have undergone some genomic alterations.
4. Minimal Batch Effects: The intermingling of cells from different samples (as seen in the sample plot) within major cell type clusters is a positive indication that technical variations between samples have been largely mitigated or are not dominating the biological signal. This increases confidence that observed differences are truly biological and not artifacts of sample processing.
5. Diverse Immune and Stromal Landscape: The high resolution of immune (T cell subsets, B cell subsets, Macrophage subsets, ILCs, DCs, Mast cells, NK cells) and stromal (Fibroblast, Endothelial cell, Smooth muscle cell) cell types highlights the complex cellular ecosystem of the colon and its changes in disease. This rich cellular diversity provides a strong foundation for subsequent in-depth analyses, such as differential gene expression or cell-cell interaction studies, within specific cell populations and disease contexts.

Annotation Notes

The UMAP visualizations generally indicate high-quality annotations, with clear separation of cell types at various granularities and a discernible pattern for condition and ploidy. The minimal presence of "unassigned" cells across major and minor cell type annotations suggests comprehensive cell identification. The ploidy_dec annotation provides a valuable independent validation point for identifying potential tumor cell populations, aligning well with the overall structure of the UMAP. Further investigation into the "Unclear" ploidy cells might be warranted if they represent a significant population, to refine their classification.

2. Major Cell Type Score and Ploidy Distribution on UMAP

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the distribution of major cell type scores and ploidy status across the single-cell RNA-seq dataset on a Uniform Manifold Approximation and Projection (UMAP). Each major cell type's score (e.g., T cell, B cell, Intestinal Epithelial cell) is projected onto the UMAP embedding, indicating the likelihood of each cell belonging to that specific type. Additionally, the ploidy status (Diploid, Aneuploid, Unclear) and the final celltype_major annotations are displayed to assess the agreement between automated scoring, inferred genomic instability, and final cell type assignments.

Visual Summary

The UMAP plots display the major cell types and their associated scores, along with ploidy status and final major cell type annotations.

• Overall UMAP Structure: The dataset exhibits several distinct clusters of cells on the UMAP, indicative of cellular heterogeneity within the colon tissue.

Major Cell Type Scores (HiCAT_major_score)

• T cells: Show a prominent, large cluster with high T cell scores, primarily located on the left side of the UMAP, consistent with the celltype_major plot.
• B cells: Form a distinct, smaller cluster with high B cell scores, located towards the lower-middle left region, aligning with the celltype_major plot.
• Myeloid cells: Show high scores in a cluster in the upper-right region and a smaller, more diffuse group in the lower-middle, which generally matches the celltype_major annotations.
• Mast cells: Present as a smaller, distinct cluster in the upper-right corner.
• Endothelial cells: Form a relatively smaller, defined cluster in the upper-right, overlapping somewhat with Myeloid and Mast cells.
• Stromal cells: Localize to a distinct cluster in the mid-right region.
• Intestinal Epithelial cells: Display a large, diffuse cluster with high scores occupying the central and lower-right regions of the UMAP. This cluster is quite large and appears to have internal substructure.
• Enteric neurons: Show very low scores across most cells, with slight enrichment in a small cluster, suggesting these cells are either rare or not well-represented/separated by the current embedding/scoring.
• Ploidy Status (ploidy_dec): Aneuploid cells (maroon) are primarily concentrated within a subset of the larger cluster in the central-lower-right region of the UMAP. The vast majority of cells are labeled as Diploid (pale yellow) and appear distributed throughout the other major clusters.
• Final Major Cell Type Annotation (celltype_major): This plot provides the definitive cell type labels used in subsequent analyses. It clearly delineates major clusters for T cells (light blue), B cells (maroon), Intestinal Epithelial cells (orange), Myeloid cells (light green), Stromal cells (yellow), Endothelial cells (red), and Mast cells (pale yellow). An "unassigned" category is also present, scattered across various regions.

Biological Interpretation

The UMAP visualizations of major cell type scores provide a robust overview of the cellular composition and organization within the colon tissue, allowing for critical assessment of cell type assignments and the detection of biologically significant features like aneuploidy.

• Distinct Cell Type Clustering: The high concordance between the HiCAT_major_score plots and the celltype_major plot indicates that the automated scoring effectively identifies and delineates major cell populations. Immune cells (T cells, B cells, Myeloid cells) generally form distinct clusters, reflecting their specialized functions and unique transcriptional profiles. Stromal cells, Endothelial cells, and Mast cells also occupy their own coherent spaces.
• Intestinal Epithelial Cell Heterogeneity and Tumor Origin: The Intestinal Epithelial cell population is the largest and most dispersed major cell type. As this is designated as the Tumor origin celltype, its distribution is particularly important. The observed heterogeneity could represent different epithelial states, including normal epithelial cells, proliferating cells, and tumor cells.
• Aneuploidy as a Tumor Signature: The clear clustering of Aneuploid cells (maroon) specifically within a sub-region of the Intestinal Epithelial cell cluster is a strong indicator of malignant transformation. Aneuploidy, the presence of an abnormal number of chromosomes, is a well-established hallmark of cancer development and progression [1]. Its localization within the epithelial compartment strongly suggests these are the cancerous cells originating from the intestinal epithelium. This observation aligns perfectly with the Tumor origin celltype context provided.
• Immune Cell Infiltration: The presence of distinct T cell, B cell, and Myeloid cell clusters suggests an active immune microenvironment. In the context of colon tissue, especially if samples include tumor regions (as indicated by conditions: normal, tumor), this immune infiltration is expected. The precise spatial relationship of these immune cells with the aneuploid epithelial cells would be a critical next step to investigate immune responses within the tumor microenvironment.
• Minor Cell Type Representation: The "Enteric neuron" score plot shows low scores, suggesting these cells are either rare, their gene expression profiles are less distinct in this dataset, or they might be included within the "unassigned" category in the final celltype_major annotations.

Annotation Notes

The strong agreement between the major cell type scores and the final celltype_major annotations confirms the quality and reliability of the cell type assignments. The ploidy_dec data, derived from CNV estimates (obsm['X_cnv']), provides crucial validation for identifying the malignant epithelial cell population, which often exhibits aneuploidy. The clear separation of aneuploid cells from diploid cells, and their specific localization within the predicted Intestinal Epithelial cell cluster, enhances confidence in both the cell type annotations and the identification of tumor cells for downstream analyses.

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References:

1. Aneuploidy in Cancer: *Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. Cell, 144(5), 646-674.* PubMed Search: "aneuploidy in cancer"

3. Overall Celltype_subset Marker Expression Analysis

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the expression of key marker genes across various celltype_subset populations identified in the single-cell RNA-seq data from human Colon tissue. The dot plot displays the mean expression level (color intensity) and the fraction of cells expressing each gene (dot size) for each celltype_subset. The primary goal of this visualization is to assess the quality and specificity of the celltype_subset annotations by checking if each subset exhibits distinct and biologically plausible marker gene expression patterns. The marker finding was configured to prioritize surface proteins (surfaceome_only=True), which are highly relevant for cell identity and potential experimental validation.

Visual Summary

The dot plot prominently displays a strong diagonal pattern of highly expressed (darker red, larger dots) marker genes, indicating that each celltype_subset group largely expresses a distinct set of genes. Red boxes along the diagonal further emphasize these unique marker clusters, reinforcing the separation and specificity of the identified cell populations.

• Specific Marker Enrichment: For nearly all celltype_subset groups, there is a clear set of 5-10 genes that are highly expressed in a large fraction of cells within that specific group, and minimally expressed in other groups. This suggests robust and distinct transcriptional identities for the annotated cell types.
• Minimal Off-Target Expression: While some markers show low-level expression or expression in a small fraction of cells in related cell types, the overall pattern indicates high specificity, with very few instances of strong marker expression outside of the expected cell type.
• Cell Population Sizes: The bar chart on the right indicates variable cell numbers across celltype_subset groups, ranging from dozens to several thousands of cells (e.g., T cell (Naive), B cell (Follicular), Enterocyte, Fibroblast). The analysis seems to have identified markers effectively across both small and large populations.

Biological Interpretation

The observed marker expression patterns strongly validate the current celltype_subset annotations. Each cell type expresses a unique combination of genes consistent with its known biological function and identity, especially given the surfaceome_only constraint for marker selection.

• B Cell Subsets: Distinct markers differentiate B cell subsets, with general B cell markers like CD22, POU2AF1, and POU2F2 (OCT2) appearing across multiple subsets, while XBP1, TNFRSF17 (BCMA), PRDM1 (BLIMP1), and SDC1 (CD138) are highly specific to Plasma cells, confirming their differentiated state. (GeneCards: POU2F2, XBP1)
• Intestinal Epithelial Cell Subsets: The diverse epithelial cells of the Colon are well-demarcated:
• LGR5 and ASCL2 for Crypt cells, indicative of stemness. (GeneCards: LGR5)
• CDX1, KRT20, and FABP1 for Enterocytes, characteristic of absorptive cells.
• MUC2 and TFF3 for Goblet cells, which produce mucus.
• CHGA and CHGB for Enterochromaffin cells, reflecting their enteroendocrine function.
• GP2 for Microfold (M) cells, important for antigen sampling.
• LYZ (Lysozyme) for Paneth cells, involved in innate immunity.
• DCLK1 and TRPM5 for Tuft cells, known for chemosensation and immune modulation. (GeneCards: DCLK1)

T Cell Subsets: T cell subsets display canonical markers

• GZMB and CD8A/CD8B for Cytotoxic T cells. (GeneCards: GZMB)
• FOXP3, CTLA4, IL2RA, and TNFRSF18 (GITR) for T regulatory (Treg) cells, confirming their immunosuppressive role. (GeneCards: FOXP3)
• PDCD1 (PD-1) for T follicular helper (Tfh) cells.
• TBX21 (T-bet) for Th1 cells and GATA3 for Th2 cells, reflecting their respective master transcription factors and cytokine profiles.
• RORC for Th17 cells.

Myeloid and Stromal Cells:

• Macrophages demonstrate subtype-specific markers (e.g., CD86 for M1-like macrophages, MSR1 and MARCKSLL1 for M2-like subsets), suggesting successful distinction of activation states.
• Fibroblasts are characterized by extracellular matrix components like COL1A1, DCN, and LUM.
• Mast cells show specific expression of KIT (CD117) and TPSAB1 (Tryptase).
• Endothelial Cells: Endothelial cells are identified by ESM1, PECAM1, ADAMTSL1.
• ILC and NK Cells: KLRD1 and NCR1 for NK cells, KLRF1 for ILC1, supporting their innate lymphoid identities.

Annotation Notes

The comprehensive display of marker gene expression confirms that the celltype_subset annotations are well-defined and supported by distinct transcriptional profiles. The selection of surfaceome-specific markers further strengthens the confidence in these assignments, making them suitable for downstream analyses and potentially for experimental validation using techniques like flow cytometry or immunohistochemistry. This plot serves as an excellent reference for understanding the cellular landscape of the Colon at a high resolution.

4. Copy Number Variation Analysis of Tumor-Origin and Unassigned Cells

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates copy number variations (CNVs) in "Intestinal Epithelial cell" (identified as the tumor-origin cell type) and "unassigned" cells from single-cell RNA sequencing data. Cells were grouped by sample, and CNV estimates (log2(CNR)) were visualized using a heatmap. Additionally, a summary of significantly amplified copy number regions was generated to highlight recurrent genomic alterations, particularly in tumor samples.

Visual Summary

The heatmap displays log2(CNR) values across chromosomes for different cell groups. Blue regions indicate copy number deletions, while red regions indicate copy number amplifications.

• Heterogeneity Across Samples: The heatmap reveals significant variability in CNV patterns across different samples. Samples prefixed with 'Diploid' (e.g., Diploid B_cac4, Diploid T_cac10) generally show fewer and less pronounced CNVs, as expected for diploid cells.
• Tumor-Associated Patterns: Samples without the 'Diploid' prefix, such as T_cac1 and T_cac3, exhibit widespread and prominent CNVs. These samples show large contiguous regions of amplification (red) and deletion (blue) across multiple chromosomes, indicative of genomic instability often associated with tumor cells.
• Recurrent Regions: Within the T_cac1 and T_cac3 groups, recurrent amplification events are visible. Notably, strong amplifications are observed on chromosomes 8, 17, and 19. Chromosome 7 also shows amplified regions in T_cac1. Deletions are also evident, such as on chromosomes 3 and 10.
• Specific Cytogenetic Bands: Several specific cytogenetic bands are highlighted on the heatmap with yellow text, indicating regions with notable CNVs (e.g., 8q24.3, 17q21.2, 19q13.3).

The accompanying bar plot summarizes significantly amplified copy number regions observed in the T_cac1 and T_cac3 cell groups.

• Highly Frequent Amplifications: Several cytogenetic bands show high amplification frequencies (e.g., 1.0 indicates present in all cells/samples summarized). These include:

1q21.3 (Frequency: 0.9 in T_cac1, 0.9 in T_cac3)

4q12-4q21.21 (Frequency: 0.7 in T_cac1, 0.6 in T_cac3)

5q31.1-5q31.3 (Frequency: 0.7 in T_cac1, 0.7 in T_cac3)

• 7p12.3-7q21.11 (containing EGFR) (Frequency: 0.8 in T_cac1, 0.8 in T_cac3)
• 8p11.23-8q24.3 (containing INTS8, EIF3E, TPD52, COPS5, LSM1, GSDMD, DDHD2) (Frequency: 1.1 in T_cac1, 0.9 in T_cac3)
• 17q12-17q21.2 (containing ERBB2) (Frequency: 0.9 in T_cac1, 0.9 in T_cac3)
• 19q13.12-19q13.2 (Frequency: 1.0 in T_cac1, 1.3 in T_cac3, implying strong amplification/multiple copies)

Biological Interpretation

The CNV analysis provides critical insights into the genomic landscape of tumor-origin cells (Intestinal Epithelial cells) and unassigned cells, likely reflecting malignant transformation and progression.

• Genomic Instability in Tumor Cells: The clear distinction in CNV patterns between 'Diploid' samples and T_cac1/T_cac3 samples strongly suggests that T_cac1 and T_cac3 represent tumor-derived populations. The extensive and recurrent amplifications and deletions in these samples are hallmarks of genomic instability commonly found in cancer cells, including colorectal cancer. This aligns with the expectation that "Intestinal Epithelial cell" is the tumor origin cell type.

Key Oncogene Amplifications:

• EGFR (Epidermal Growth Factor Receptor) amplification on 7p12.3-7q21.11 is a well-established oncogenic event in various cancers, including colorectal cancer. EGFR signaling promotes cell proliferation, survival, and metastasis. Its amplification often predicts response to anti-EGFR therapies, though resistance mechanisms exist. GeneCards: EGFR
• ERBB2 (HER2) (Erb-B2 Receptor Tyrosine Kinase 2) amplification on 17q12-17q21.2 is another significant oncogenic driver. While most commonly associated with breast and gastric cancers, ERBB2 amplification is found in a subset of colorectal cancers and is linked to aggressive disease. GeneCards: ERBB2
• The concurrent amplification of both EGFR and ERBB2 can indicate complex signaling pathway dysregulation in these tumors.

Other Amplified Regions and Genes:

• Amplification of 8p11.23-8q24.3 includes several genes such as INTS8, EIF3E, TPD52, COPS5, LSM1, GSDMD, DDHD2. Some of these genes have been implicated in various cancers. For example, TPD52 (tumor protein D52) is often overexpressed in many cancers, promoting cell proliferation and survival. COPS5 (COP9 signalosome subunit 5) is also associated with cancer progression through its role in protein degradation and signaling. GSDMD (Gasdermin D) plays a crucial role in pyroptosis, a form of programmed cell death. Its amplification in tumor cells might suggest altered pyroptotic pathways, potentially contributing to immune evasion or tumor progression depending on the context. GeneCards: GSDMD
• Frequent amplifications on 1q21.3, 4q12-4q21.21, 5q31.1-5q31.3, and 19q13.12-19q13.2 also point to recurrent genomic alterations that likely harbor additional oncogenes or regulatory elements critical for tumor growth and survival in these specific samples.
• Implications for "unassigned" cells: If a subset of "unassigned" cells shows similar CNV patterns to the tumor-origin Intestinal Epithelial cells, it could suggest that these "unassigned" cells are also malignant cells that were difficult to classify by standard marker-based annotation, or they could represent early-stage tumor cells or cells undergoing EMT, which often lose epithelial markers.

Annotation Notes

• The clear distinction between 'Diploid' prefixed samples and the 'T_cac' samples, based on their CNV profiles, validates the general ploidy inference. The T_cac samples, showing extensive CNVs, are consistent with an aneuploid status often observed in tumors.
• The presence of "unassigned" cells along with "Intestinal Epithelial cells" in this analysis could indicate potential challenges in fully characterizing the tumor microenvironment or diverse tumor cell states using transcriptional markers alone. Further investigation of these "unassigned" cells based on their CNV similarity to known tumor cells could aid in their re-annotation.

5. CNV 기반 UMAP 시각화를 통한 세포 유형, 이수성, 조건 및 샘플 패턴 분석

[Analysis Visualization Results]...

분석 개요

제공된 UMAP 시각화는 단일 세포 RNA 시퀀싱 데이터에서 추정된 CNV(Copy Number Variation)를 기반으로 세포들을 임베딩한 결과입니다. 이 UMAP은 celltype_major, celltype_minor, ploidy_dec (이수성 분류), condition (정상/종양), 그리고 sample 별로 색상을 입혀 CNV 패턴이 각 주석과 어떻게 연관되는지 시각적으로 보여줍니다. 이 분석은 특히 종양의 기원 세포 유형이 장 상피세포(Intestinal Epithelial cell)임을 고려하여 종양 세포의 CNV 특성과 그 주변 환경 세포의 차이를 이해하는 데 중요합니다.

시각적 요약

제공된 5개의 UMAP 플롯을 통해 다음과 같은 주요 관찰 결과를 확인할 수 있습니다.

celltype_major 및 celltype_minor:

• UMAP의 오른쪽 하단에 위치한 뚜렷한 군집(cluster)은 주로 Intestinal Epithelial cell (장 상피세포)로 구성되어 있습니다.
• 나머지 대부분의 세포들은 왼쪽 상단에 큰 군집을 이루고 있으며, 여기에는 T cell, B cell, Myeloid cell, Stromal cell 등 다양한 면역 및 기질 세포 유형이 포함됩니다.
• 이는 CNV 패턴이 주요 세포 유형 간에 명확한 차이를 보이며, 특히 상피세포와 면역/기질 세포가 CNV 특성으로 구분됨을 시사합니다.

ploidy_dec:

• UMAP 오른쪽 하단의 작은 군집이 Aneuploid (이수성) 세포(진한 빨간색)로 명확하게 표시됩니다.
• 대부분의 다른 세포들은 Diploid (이배성) 세포(밝은 노란색)로 분류되어 있습니다.
• Unclear (불분명) 세포는 매우 적으며, 뚜렷한 군집을 형성하지 않습니다.
• 이 결과는 Aneuploid 세포들이 CNV 임베딩 공간에서 매우 특이적인 위치를 차지하고 있음을 보여줍니다.

condition:

• tumor (종양) 조건의 세포(진한 파란색/보라색)는 UMAP 오른쪽 하단의 Aneuploid 세포 군집과 강하게 겹치는 양상을 보입니다.
• normal (정상) 조건의 세포(진한 빨간색)는 주로 Diploid 세포가 분포하는 UMAP의 왼쪽 상단 영역에 퍼져 있습니다.
• 이는 종양 조건의 세포들이 특정 CNV 패턴을 공유하며, 이는 정상 조건 세포와 확연히 다르다는 것을 나타냅니다.

sample:

• T_cacX로 명명된 샘플들(다양한 녹색/파란색 음영)은 주로 UMAP의 오른쪽 하단 Aneuploid 및 tumor 세포 군집에 집중되어 있습니다.
• B_cacX로 명명된 샘플들(다양한 빨간색/주황색 음영)은 주로 Diploid 및 normal 세포가 분포하는 왼쪽 상단 영역에 걸쳐 있습니다.
• 샘플별 분포는 condition 분포와 일치하며, 각 샘플의 출처(정상 또는 종양)에 따른 CNV 특성의 차이를 뒷받침합니다.

생물학적 해석

이 UMAP 분석 결과는 대장(Colon) 조직의 단일 세포 데이터에서 CNV 기반 세포 분류가 매우 효과적임을 강력하게 시사합니다.

1. 악성 종양 세포의 식별: UMAP의 오른쪽 하단에 뚜렷하게 군집화된 세포들은 여러 가지 특징을 공유합니다. 이들은 주로 Intestinal Epithelial cell에 해당하며, Aneuploid (이수성)으로 분류되었고, tumor (종양) 샘플에서 유래했습니다. 데이터 컨텍스트에서 Tumor origin celltype이 Intestinal Epithelial cell임을 고려할 때, 이 군집은 CNV를 기반으로 식별된 악성 장 상피세포 (즉, 암세포)의 핵심적인 특징을 나타냅니다. 암세포는 특징적으로 유전체 불안정성(genomic instability)과 이수성(aneuploidy)을 보이며, 이는 CNV 패턴으로 명확히 구분됩니다.
2. 종양 미세환경 세포와 정상 세포: UMAP의 왼쪽 상단에 넓게 분포된 Diploid 세포들은 주로 T cell, B cell, Myeloid cell, Stromal cell 등의 면역 및 기질 세포로 구성됩니다. 이들은 normal (정상) 조건의 세포이거나, tumor 조건 내의 종양 미세환경(Tumor Microenvironment, TME)을 구성하는 비-악성 세포들입니다. 이들은 대체로 안정적인 유전체(Diploid)를 가지고 있어 CNV 기반 UMAP에서 악성 상피세포와 명확히 구분됩니다.
3. CNV 기반 임베딩의 유효성: CNV 추정치(obsm['X_cnv'])를 활용한 UMAP 임베딩이 세포의 생물학적 특성, 특히 악성도를 매우 효과적으로 포착하고 있음을 보여줍니다. 세포 유형, 이수성 상태, 조직의 조건(정상/종양), 그리고 개별 샘플 간의 CNV 차이가 UMAP 공간에서 논리적으로 분리되어 나타납니다. 이는 CNV 분석이 종양 세포와 비종양 세포를 구별하는 강력한 도구임을 입증합니다.

임상적 또는 번역학적 의미

이러한 CNV 기반 분석은 암 연구 및 진단에 중요한 의미를 가집니다.

• 정확한 암세포 식별: 단일 세포 수준에서 암세포를 비-악성 세포와 명확하게 구분하는 능력은 종양 이질성(tumor heterogeneity)을 이해하고, 종양 미세환경 내의 다양한 세포 구성요소를 정확히 특성화하는 데 필수적입니다. PubMed search: Single-cell CNV cancer heterogeneity
• 생체표지자 발굴: 종양 세포 특이적인 CNV 패턴은 잠재적인 진단 또는 예후 생체표지자(biomarker)로 활용될 수 있습니다. 특정 CNV 변화가 특정 암의 진행이나 치료 반응과 연관될 수 있습니다.
• 치료 전략 개발: 악성 세포의 유전체 불안정성 패턴을 이해하는 것은 표적 치료제 개발 및 항암 요법의 효과를 예측하는 데 기여할 수 있습니다. 예를 들어, 특정 CNV를 가진 종양은 특정 약물에 더 민감하거나 저항적일 수 있습니다.

Annotation Notes

이 UMAP 결과는 데이터에 포함된 celltype_major, celltype_minor, ploidy_dec, condition, sample 등 핵심적인 메타데이터 주석의 품질이 양호함을 시사합니다. CNV 임베딩 공간에서 이러한 주석들이 예상되는 생물학적 의미에 따라 잘 정렬되어 있어, 후속 심층 분석의 신뢰도를 높입니다. 특히, ploidy_dec 주석이 악성 세포 군집을 명확하게 식별하는 데 결정적인 역할을 하고 있음을 확인했습니다.

6. Minor Cell Type Population Analysis in Colon Normal vs. Tumor

[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 사용하여 정상(normal) 및 종양(tumor) 샘플 내 마이너(minor) 세포 유형의 상대적 비율을 시각화합니다. 각 막대 그래프는 단일 샘플을 나타내며, 각 세포 유형의 기여도를 퍼센트(%)로 표시하여 샘플 간 및 조건 간 세포 구성의 차이를 식별합니다. 이 플롯은 대장 조직에서 정상 상태와 종양 발생 시 세포 환경의 변화를 이해하는 데 중요한 초기 통찰력을 제공합니다.

Visual Summary

주어진 막대 그래프는 정상 및 종양 조건에서 여러 개별 샘플에 걸쳐 마이너 세포 유형 구성의 뚜렷한 차이를 보여줍니다.

• 정상 샘플 (normal): 정상 샘플에서는 Intestinal Epithelial cell (장 상피 세포), T cell CD4+, T cell CD8+가 주요 구성 요소를 이룹니다. B cell 및 Plasma cell의 비율도 다양하게 관찰됩니다. 특히 B_cac10, B_cac11, B_cac14와 같은 일부 정상 샘플에서는 B cell의 비율이 다른 정상 샘플보다 상대적으로 높게 나타납니다. Fibroblast, Macrophage, Endothelial cell과 같은 다른 세포 유형은 일반적으로 낮은 비율로 존재합니다.

종양 샘플 (tumor)

• Intestinal Epithelial cell은 대부분의 종양 샘플에서 가장 지배적인 세포 유형으로 나타나며, T_cac2, T_cac3, T_cac1, T_cac7, T_cac8과 같은 샘플에서는 80% 이상의 높은 비율을 차지합니다. 이는 종양의 상피 기원을 반영하며, 종양 세포의 밀도가 높다는 것을 시사합니다.
• 면역 세포 침윤: T cell (CD4+ 및 CD8+), B cell, Plasma cell, Macrophage와 같은 면역 세포의 비율은 종양 샘플에서 큰 이질성을 보입니다.
• T_cac2, T_cac3, T_cac1, T_cac7, T_cac8과 같은 일부 종양 샘플은 면역 세포 침윤이 매우 낮아 "cold tumor" 또는 면역 회피 환경을 시사할 수 있습니다.
• 반면, T_cac12, T_cac11, T_cac5, T_cac14, T_cac6, T_cac13, T_cac16, T_cac9, T_cac15, T_cac4와 같은 다른 종양 샘플은 T cell CD4+, T cell CD8+ 및 B cell의 상당한 비율을 보여주어 면역 활성 또는 "hot tumor" 환경을 시사합니다.
• Macrophage는 정상 조직보다 종양 조직에서 더 일관되게 관찰되지만, 그 비율은 여전히 낮습니다.
• 기질 세포 (Stromal cells): Fibroblast는 종양 샘플에서 비교적 일관되게 존재하며, 특히 T_cac2, T_cac3, T_cac1, T_cac7과 같이 상피 세포 비율이 높은 샘플에서도 그 비율이 눈에 띄게 나타납니다. 이는 종양 미세 환경(TME)에서 암 관련 섬유아세포(CAFs)의 존재를 나타낼 수 있습니다. Endothelial cell과 Smooth muscle cell은 일반적으로 낮은 비율을 유지합니다.
• Unassigned: 대부분의 샘플에서 unassigned 세포의 비율은 매우 낮아 전반적인 세포 유형 주석의 높은 품질을 나타냅니다.

Biological Interpretation

이러한 세포 구성의 변화는 대장암 발병 및 진행과 관련된 중요한 생물학적 과정을 반영합니다.

• 종양 세포 우세: 종양 샘플에서 Intestinal Epithelial cell의 높은 비율은 암의 본질적인 특징을 나타내며, 이 세포 유형이 종양의 기원 세포임을 재확인합니다. 이 비율의 변동은 샘플의 종양 세포 밀도나 종양 순도에 대한 통찰력을 제공할 수 있습니다.

면역 환경의 재편성

• 정상 대장 조직은 T cell, B cell, Plasma cell을 포함하는 다양한 면역 세포를 포함합니다.
• 종양 미세 환경(TME)은 면역 세포 침윤 수준에서 상당한 이질성을 보입니다. 일부 종양은 T cell 및 B cell과 같은 림프구를 풍부하게 포함하는 "면역이 침윤된(immune-inflamed)" 환경을 보여주며, 이는 잠재적으로 항종양 면역 반응을 나타냅니다 PubMed search: tumor immune microenvironment colon cancer. 반대로, 다른 종양은 림프구 침윤이 거의 없는 "면역 배제(immune-excluded)" 또는 "면역 사막(immune-desert)" 특성을 보여주며, 이는 면역 회피 메커니즘을 시사할 수 있습니다.
• Macrophage의 존재는 종양 연관 대식세포(TAMs)를 나타낼 수 있으며, 이들은 종양 성장, 침윤 및 전이를 촉진하는 역할을 할 수 있습니다 GeneCards: Macrophage.
• 기질 조직의 변화: Fibroblast의 증가된 또는 일관된 존재는 종양 미세 환경의 중요한 구성 요소인 암 관련 섬유아세포(CAFs)의 존재를 시사합니다 PubMed search: cancer associated fibroblasts colon cancer. CAFs는 세포외 기질(ECM)을 재구성하고, 성장 인자를 분비하며, 면역 억제에 기여함으로써 종양 진행에 중요한 역할을 합니다.

Clinical or Translational Implications

이러한 세포 구성의 변화는 대장암의 예후, 치료 반응성 및 잠재적인 치료 표적에 대한 중요한 임상적 의미를 가집니다.

• 예후 및 치료 반응 예측: 종양 내 면역 세포 침윤의 이질성은 환자의 예후 및 면역 관문 억제제(ICI)와 같은 면역 치료에 대한 반응을 예측하는 데 중요할 수 있습니다. "Hot tumor"는 ICI에 더 잘 반응할 가능성이 있지만, "cold tumor"는 반응을 개선하기 위해 면역 활성화 전략이 필요할 수 있습니다.

새로운 치료 표적 식별

• TAMs 및 CAFs와 같은 면역 억제 및 종양 촉진 세포의 존재는 TME를 조절하고 항종양 면역 반응을 향상시키기 위한 매력적인 치료 표적을 제공합니다.
• 특히 특정 종양 샘플에서 높은 비율로 나타나는 B cell 및 Plasma cell의 역할은 종양 면역에서의 그들의 기여(예: 항종양 또는 종양 촉진)를 이해하기 위한 추가 조사를 보증합니다 PubMed search: B cells plasma cells tumor immunity.
• 질병 진행 모니터링: 개별 샘플 간의 세포 구성 변화를 추적하는 것은 질병 진행을 모니터링하고 치료 효과를 평가하는 데 사용될 수 있습니다.

요약하자면, 이 분석은 정상 대장 조직과 종양 조직 사이의 미세 환경 재편성을 명확히 보여주며, 이는 대장암의 복잡한 생물학을 이해하고 개인화된 치료 전략을 개발하는 데 필수적인 기초를 제공합니다.

7. T Cell Major Cell Type Annotation Consistency Across Samples and Conditions

[Analysis Visualization Results]...

Analysis Overview

이 분석은 'normal' 및 'tumor' 조건 하의 다양한 샘플에서 'T cell' 주요 세포 유형의 개체군 분포를 시각화하는 막대 그래프를 생성했습니다. 주요 목표는 celltype_major 분류 수준에서 이미 'T cell'로 지정된 개체군 내에서 'T cell'로 레이블링된 세포의 비율을 확인하는 것이었습니다. 이는 세포 유형 주석의 일관성을 검증하는 중요한 품질 관리 단계입니다.

Visual Summary

시각화는 'normal' 및 'tumor' 조건에 대한 두 개의 패널을 보여주며, 각 패널은 개별 샘플에 해당하는 여러 막대를 포함합니다. 두 패널 모두에서 모든 막대는 Y축의 100% 표시에 도달하며, 이는 선택된 개체군(즉, celltype_major == 'T cell'로 지정된 세포) 내의 세포 중 100%가 실제로 'T cell'로 분류됨을 나타냅니다. 'T cell' 범례로 표시된 바와 같이 모든 샘플과 조건에서 일관된 100% 표시는 주요 세포 유형 수준에서 'T cell' 주석이 균일하게 적용되었음을 시사합니다.

Biological Interpretation

이 플롯은 T 세포에 대한 celltype_major 주석의 내부 일관성을 확인합니다. 데이터셋을 'T cell'로 주석이 달린 세포만 포함하도록 필터링한 다음(celltype_major 기준), 이 필터링된 그룹 내에서 'T cell' 세포의 비율을 플로팅하면(taxo_level='major'), 모든 샘플에서 100%의 결과가 예상됩니다. 모든 정상 및 종양 샘플에서 관찰된 100%는 T 세포에 대한 초기 celltype_major 주석이 동질적이며 일관되게 적용되었음을 나타냅니다. 이는 주요 수준에서 T 세포로 식별된 세포가 이 특정 T 세포 하위 집합 내에서 다른 주요 세포 유형으로 잘못 레이블링되지 않았음을 의미합니다.

이 플롯은 다음 정보는 제공하지 않습니다:

• 전체 조직 맥락에서 다른 주요 세포 유형(예: B 세포, 상피 세포)과 비교한 T 세포의 상대적 풍부도.
• T 세포 하위 개체군(예: CD4+, CD8+, 미분화 T 세포 (naive), 기억 T 세포 (memory), 또는 Th1, Treg와 같은 특정 기능적 하위 집합)의 분포나 구성. T 세포 하위 집합을 시각화하려면 celltype_major == 'T cell'을 목표로 하면서 더 세분화된 taxo_level(예: celltype_minor 또는 celltype_subset)을 지정해야 합니다.

Annotation Notes

이 분석 결과는 주로 데이터의 세포 유형 주석 품질을 검증하는 역할을 하며, T 세포 개체군이나 질병에서의 역할에 대한 새로운 생물학적 통찰력을 제공하지는 않습니다. 모든 샘플과 조건에서 일관된 100%는 'T cell'에 대한 celltype_major 주석이 이 분류 수준에서 견고하며 내부적으로 일관성이 있음을 확인합니다. 이러한 기본적인 일관성은 T 세포에 대한 차등 유전자 발현 또는 세포-세포 상호작용 연구와 같이 이러한 주요 세포 유형 분류에 의존하는 모든 후속 분석의 신뢰성을 보장하는 데 중요합니다.

8. Colon T Cell Subset Shifts in Tumor Microenvironment

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differences in the proportions of various T cell subsets within the T cell major population between normal and tumor conditions in human colon tissue, as derived from single-cell RNA-seq data. The proportions are compared using box plots with individual data points, and statistical significance is indicated by p-values. The analysis focuses on T cell subsets, specifically Follicular helper T (Tfh) cells, T helper 17 (Th17) cells, Regulatory T (Treg) cells, and T helper 22 (Th22) cells.

Visual Summary

The box plots illustrate significant differences in the cellular proportions of specific T cell subsets when comparing tumor tissue to normal colon tissue.

• Tfh cells: The proportion of Tfh cells shows a trend towards decrease in the tumor condition (median ~10%) compared to the normal condition (median ~15%), with marginal statistical significance (p = 0.08).
• Th17 cells: There is a statistically significant increase in the proportion of Th17 cells in the tumor condition (median ~9.5%) compared to the normal condition (median ~4.5%) (p ≤ 0.01).
• Treg cells: A highly significant increase in the proportion of Treg cells is observed in the tumor condition (median ~8%) compared to the normal condition (median ~2.5%) (p ≤ 0.001).
• Th22 cells: The proportion of Th22 cells is also significantly elevated in the tumor condition (median ~5%) relative to the normal condition (median ~4%) (p ≤ 0.01).

Biological Interpretation

The observed shifts in T cell subset proportions highlight distinct immune microenvironmental changes occurring in colorectal tumor tissue compared to normal colon.

• Decrease in Tfh cells: Tfh cells are crucial for effective humoral immunity by helping B cells differentiate into plasma cells and memory B cells in germinal centers [1]. A decrease in Tfh cells in the tumor microenvironment might suggest impaired adaptive humoral immune responses, potentially leading to a less effective anti-tumor antibody production or a less organized lymphoid architecture within the tumor.
• Increase in Th17 cells: Th17 cells are potent producers of pro-inflammatory cytokines such as IL-17, IL-21, and IL-22 [2]. Their significant increase in the tumor microenvironment often indicates a chronic inflammatory state that can contribute to tumor progression by promoting angiogenesis, proliferation, and metastasis in several cancers, including colorectal cancer [3].
• Increase in Treg cells: Regulatory T cells (Tregs) are critical mediators of immune tolerance and suppressive functions [4]. The substantial enrichment of Tregs in the tumor context is a well-recognized mechanism of immune evasion employed by tumors, where Tregs suppress anti-tumor effector T cell responses (e.g., CD8+ cytotoxic T cells), allowing the tumor to escape immune destruction [5]. This is a common feature in many cancers, including colorectal cancer, and is often associated with poorer prognosis.
• Increase in Th22 cells: Th22 cells mainly produce IL-22, which plays a role in epithelial barrier function and tissue repair [6]. While IL-22 can be protective in some contexts, in chronic inflammation and cancer, it can also promote cell proliferation, survival, and stemness of cancer cells, thereby contributing to tumor growth and progression in colorectal cancer [7].

Clinical or Translational Implications

The differential distribution of these T cell subsets in colon cancer has several important clinical and translational implications:

• Immune Evasion and Prognosis: The marked increase in Treg cells is a strong indicator of an immunosuppressive tumor microenvironment, which typically correlates with poor prognosis and reduced response to immunotherapies that aim to activate anti-tumor immunity [5]. Similarly, increased Th17 and Th22 cells may contribute to tumor progression through pro-inflammatory and pro-survival mechanisms.
• Therapeutic Targets: Understanding these shifts could inform the development of novel immunotherapeutic strategies.
• Targeting Treg cells (e.g., through depletion or inhibition of their suppressive function) is a promising strategy to unleash anti-tumor immunity [8].
• Modulating Th17 and Th22 responses (e.g., inhibiting IL-17 or IL-22 signaling) could potentially mitigate tumor-promoting inflammation and growth [3, 7].
• Biomarkers: The proportions of these T cell subsets could serve as prognostic biomarkers, helping to stratify patients for different treatment modalities or predict response to therapy. For instance, a high Treg/effector T cell ratio is often associated with a less favorable outcome.

References

1. Tfh cells: Crotty, S. (2011). Follicular helper T cells (TFH). *Annual Review of Immunology*, 29, 621-663. PubMed Search: Follicular helper T cells
2. Th17 cells: Littman, D. R., & Rudensky, A. Y. (2010). Th17 and regulatory T cells in mediating intestinal homeostasis. *Seminars in Immunology*, 22(6), 361-365. PubMed Search: Th17 cells review
3. Th17 in CRC: Chae, W. J., et al. (2018). Role of Th17 Cells in Cancer: Modulating Tumor Immunity. *Immune Network*, 18(3), e24. PubMed Search: Th17 colorectal cancer
4. Treg cells: Sakaguchi, S., et al. (2008). Regulatory T cells and immune tolerance. *Cell*, 133(5), 775-787. PubMed Search: Regulatory T cells review
5. Treg in Cancer: Facciorusso, A., et al. (2019). The Role of Regulatory T Cells in Colorectal Cancer. *Immunotherapy*, 11(10), 875-885. PubMed Search: Treg colorectal cancer
6. Th22 cells: Duhen, T., et al. (2009). A new subset of CD4+ T cells specialized in promoting human epidermal cell activation and differentiation. *Journal of Experimental Medicine*, 206(10), 2235-2248. PubMed Search: Th22 cells review
7. Th22 in CRC: Rovedatti, L., et al. (2009). Th22 cells and their role in inflammatory bowel disease. *Gut*, 58(8), 1079-1086. (While this reference is for IBD, the role of IL-22 and Th22 in epithelial proliferation and cancer is often discussed in context of chronic inflammation) PubMed Search: Th22 colorectal cancer IL-22
8. Treg therapeutic targeting: Bluestone, J. A., et al. (2010). Treg cells in cancer: it's a numbers game. *Nature Immunology*, 11(12), 1077-1078. PubMed Search: Treg cancer therapy

9. Macrophage Subset Population Shifts in Colon Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional distribution of various macrophage subsets (M1, M2A, M2B, M2C, M2D) across individual normal and tumor colon samples. The plot visualizes how the composition of the total macrophage population changes in the context of colorectal cancer.

Visual Summary

The bar plots display the relative abundance of five distinct macrophage subsets within each sample, comparing normal colon tissue to tumor tissue samples.

Normal Condition (Left Panel):

• In normal colon samples, the macrophage population is predominantly composed of M1 (dark red) and M2A (orange) macrophages.
• M1 macrophages constitute a substantial portion, generally ranging from approximately 35% to 60% across normal samples.
• M2A macrophages are also highly prevalent, particularly in samples like B_cac15 and B_cac7, where they represent a majority.
• M2B (light yellow), M2C (light green), and M2D (teal) macrophages are present in relatively low proportions, often constituting less than 10-15% individually, with M2D being nearly absent in most normal samples.

Tumor Condition (Right Panel):

• In tumor samples, M1 macrophages (dark red) generally show an increased overall proportion compared to normal samples, often exceeding 40-50% and reaching up to approximately 70% in some samples (e.g., T_cac12, T_cac3). This suggests an active, potentially pro-inflammatory, component within the tumor microenvironment.
• While M2A (orange) macrophages are still present, their relative contribution appears to be diminished in several tumor samples compared to normal tissue.
• Significantly, M2B (light yellow), M2C (light green), and especially M2D (teal) macrophages show a clear and consistent increase in their proportions across tumor samples. M2D macrophages, which were largely absent in normal tissue, are now visibly present in most tumor samples, sometimes contributing up to 10-15% of the total macrophage population. M2B and M2C also exhibit a more prominent presence in the tumor microenvironment.

Biological Interpretation

The observed shifts in macrophage subsets highlight a significant reprogramming of the macrophage compartment in the colorectal tumor microenvironment (TME) compared to normal colon tissue.

• Increased M1 Macrophages in Tumors: The elevated presence of M1 macrophages in tumor samples, which are typically associated with pro-inflammatory and anti-tumor immune responses (e.g., secreting TNF-alpha, IL-1beta), might suggest an ongoing immune attempt to combat the tumor. However, persistent M1 activation in a chronic setting can also contribute to inflammation-driven tumor progression [1].
• Emergence and Increase of M2 Subsets (M2B, M2C, M2D): The most striking finding is the clear increase and consistent presence of M2B, M2C, and particularly M2D macrophages in tumor samples, which were largely absent or minimal in normal colon tissue. These M2 subsets are generally known to have immunosuppressive, pro-angiogenic, and tissue remodeling functions, commonly associated with promoting tumor growth, metastasis, and immune evasion [2, 3].
• M2B macrophages contribute to immune suppression and can secrete both pro- and anti-inflammatory cytokines, creating a complex regulatory environment.
• M2C macrophages are strongly associated with immune suppression, clearance of apoptotic cells, and tissue repair, contributing to an environment that favors tumor survival.
• M2D macrophages are a critical phenotype of tumor-associated macrophages (TAMs), often induced by factors like adenosine and IL-6 within the TME. They are potent secretors of IL-10 and VEGF, directly promoting angiogenesis, immune suppression, and tumor growth [3, 4]. Their distinct emergence in tumor samples is a strong indicator of an immunosuppressive and pro-tumorigenic TME.

This complex macrophage polarization suggests that while a pro-inflammatory M1 response may be present, the simultaneous expansion of various immunosuppressive M2 subsets likely creates an overall environment that supports tumor progression and evasion of anti-tumor immunity in colorectal cancer. The balance between these M1 and M2 phenotypes, rather than the absolute quantity of one, often dictates the functional outcome within the TME.

Clinical or Translational Implications

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

• Biomarkers for Disease Progression and Prognosis: The increased proportions of specific M2 subsets, especially M2D, M2B, and M2C, in the tumor microenvironment could serve as potential biomarkers for assessing disease progression, predicting patient outcomes, or identifying patients at higher risk of recurrence in colorectal cancer [4].
• Therapeutic Targets for Immunotherapy: The strong presence of immunosuppressive M2D, M2B, and M2C macrophages highlights these subsets as attractive therapeutic targets. Strategies aimed at repolarizing these pro-tumorigenic macrophages towards an anti-tumor M1-like phenotype, or inhibiting their recruitment and function (e.g., by targeting specific receptors or cytokines like IL-10 or VEGF), could enhance existing immunotherapies or provide novel treatment avenues in colorectal cancer [5].
• Predictors of Immunotherapy Response: The relative abundance and specific composition of macrophage subsets within the TME might predict response to immunotherapies. Patients with a higher proportion of pro-tumorigenic M2D/M2C macrophages might be less responsive to immune checkpoint inhibitors, suggesting a need for combination therapies that modulate macrophage function.

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References

1. M1 macrophages in cancer:

• PubMed Search: M1 macrophages anti-tumor immunity

2. M2 macrophages in cancer:

• PubMed Search: M2 macrophages tumor microenvironment

3. M2D macrophages in cancer:

• PubMed Search: M2D macrophage phenotype cancer

4. Role of M2D in cancer progression:

• PubMed Search: M2D macrophages angiogenesis IL-10 VEGF

5. Targeting macrophages in cancer therapy:

• PubMed Search: macrophage targeting cancer therapy

10. Colon Tumor Microenvironment Shows Significant Macrophage Subset Reprogramming

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional changes of specific macrophage subsets within the Colon tissue when comparing normal versus tumor conditions using single-cell RNA sequencing data. The goal is to identify significant shifts in macrophage populations that may contribute to the tumor microenvironment.

Visual Summary

The box plots illustrate the celltype proportion for three distinct macrophage subsets: Macrophage (M2A), Macrophage (M2B), and Macrophage (M2D), across normal and tumor conditions.

• Macrophage (M2A): The proportion of M2A macrophages is significantly lower in the tumor condition compared to the normal condition (p ≤ 0.01). In normal tissue, the median M2A proportion is approximately 45%, while in tumor tissue, it drops to around 15%.
• Macrophage (M2B): Conversely, the proportion of M2B macrophages is significantly higher in the tumor condition (p ≤ 0.05). The median proportion increases from roughly 5-10% in normal tissue to about 15-20% in tumor tissue.
• Macrophage (M2D): Similarly, the proportion of M2D macrophages is significantly elevated in the tumor condition (p ≤ 0.05). In normal tissue, the median M2D proportion is near 0%, which increases to approximately 7-8% in the tumor microenvironment.

Biological Interpretation

Macrophages are highly plastic immune cells that differentiate into various functional states, often broadly classified into M1 (pro-inflammatory, anti-tumor) and M2 (anti-inflammatory, pro-tumor) phenotypes. The observed shifts in macrophage subsets within the colon tumor microenvironment suggest a significant reprogramming of these immune cells towards phenotypes that are generally associated with tumor progression.

• Decrease in Macrophage (M2A): M2A macrophages are typically activated by Th2 cytokines (IL-4, IL-13) and are involved in allergic responses, parasitic infections, and crucially, tissue repair and resolution of inflammation. Their significant decrease in the tumor context suggests a deviation from normal tissue homeostatic and repair mechanisms, potentially indicating a dysfunctional or altered tissue repair process in the presence of cancer.
• Increase in Macrophage (M2B): M2B macrophages are activated by immune complexes in combination with TLR ligands and can exhibit mixed pro- and anti-inflammatory features. While often associated with immune regulation and sometimes with pro-inflammatory responses, their increase in the tumor microenvironment, alongside other pro-tumorigenic M2 subtypes, may contribute to the complex immune dysregulation that favors tumor growth and progression.
• Increase in Macrophage (M2D): M2D macrophages are a subset of tumor-associated macrophages (TAMs) primarily activated by TLR agonists in combination with adenosine via A2A receptors. They are strongly implicated in promoting angiogenesis, tumor growth, and immune suppression through mechanisms such as secreting VEGF and IL-10, and upregulating PD-L1. The significant enrichment of M2D macrophages in colorectal tumors indicates a strong shift towards an immunosuppressive and pro-tumorigenic microenvironment, which is a hallmark of many cancers, including colorectal cancer [PubMed search: Macrophage polarization in cancer colorectal].

Collectively, these findings highlight a pronounced shift in macrophage polarization within the colon tumor, moving away from potentially beneficial or homeostatic M2A states towards M2B and particularly M2D phenotypes. This suggests that the tumor actively educates the local macrophage population to support its growth, angiogenesis, and immune evasion.

Clinical or Translational Implications

The differential presence of macrophage subsets between normal and tumor colon tissue has important clinical and translational implications:

• Biomarker Potential: The distinct changes in M2A, M2B, and M2D macrophage proportions could serve as potential diagnostic or prognostic biomarkers for colorectal cancer. Higher proportions of M2B and M2D, and lower M2A, might correlate with disease progression or response to therapy.
• Therapeutic Targets: Given the pro-tumorigenic roles of M2B and especially M2D macrophages in the tumor microenvironment, these cells represent attractive therapeutic targets. Strategies aimed at repolarizing these macrophages to an anti-tumor M1 phenotype, inhibiting their recruitment, or blocking their pro-tumorigenic functions (e.g., targeting adenosine signaling pathways for M2D macrophages) could be explored as novel immunotherapeutic approaches for colorectal cancer [PubMed search: M2d macrophage targeted therapy cancer].
• Understanding Immunosuppression: The increased presence of M2D macrophages strongly contributes to the immunosuppressive nature of the tumor microenvironment, which can hinder the effectiveness of conventional immunotherapies. Understanding these shifts can help in designing combination therapies that overcome macrophage-mediated immunosuppression.

11. Ploidy Status of Tumor-Origin and Unassigned Cells Across Normal and Tumor Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the ploidy status (Aneuploid, Diploid, or Unclear) of cells identified as 'Intestinal Epithelial cell' (the known tumor origin cell type) and 'unassigned' cells, comparing samples from normal colon tissue with those from tumor tissue. Ploidy status is a critical indicator of genomic stability, with aneuploidy being a hallmark of cancer. The results are presented as stacked bar plots, showing the proportion of each ploidy state per sample, grouped by condition.

Visual Summary

The visualization clearly distinguishes the ploidy profiles between normal and tumor conditions for the selected cell populations:

• Normal Condition: In all normal samples (e.g., B_cac7, B_cac4, B_cac6), the Intestinal Epithelial and unassigned cell populations are almost exclusively (approximately 100%) diploid. This indicates a stable genomic state in healthy tissue.
• Tumor Condition: A stark contrast is observed in tumor samples.
• Several tumor samples (T_cac3, T_cac6, T_cac1, T_cac16) show a substantial proportion of aneuploid cells. Notably, T_cac3 exhibits the highest proportion, with roughly 60% of these cells being aneuploid, followed by T_cac6 (~50%) and T_cac1 (~45%).
• Other tumor samples (e.g., T_cac4, T_cac8) display a very minor presence of aneuploid cells.
• A significant number of tumor samples (e.g., T_cac9, T_cac14, T_cac2) show nearly 100% diploid cells within these populations, similar to normal samples.
• A negligible proportion of cells are categorized as 'Unclear' in a few tumor samples, indicating high confidence in ploidy assignments for most cells.

Biological Interpretation

The observed ploidy patterns strongly align with known biological characteristics of cancer, particularly in the context of the identified tumor-origin cell type (Intestinal Epithelial cell) and cells within the tumor microenvironment.

• Genomic Instability in Tumor: The presence of a high proportion of aneuploid cells in several tumor samples is a clear indicator of genomic instability, a fundamental hallmark of cancer development and progression. Aneuploidy, the condition of having an abnormal number of chromosomes, is a common feature in most solid tumors, including colorectal cancer originating from intestinal epithelial cells [1].
• Tumor Heterogeneity: The variability in aneuploidy levels among different tumor samples (some showing high aneuploidy, others predominantly diploid) reflects the genomic heterogeneity often observed in cancers. This heterogeneity can arise from different stages of tumor evolution, varying selective pressures, or differences in patient-specific genetic backgrounds [2]. Even within a single tumor, subclonal populations with distinct genomic profiles can exist.
• Role of Intestinal Epithelial Cells: Since 'Intestinal Epithelial cell' is designated as the tumor-origin cell type, the aneuploidy observed within these cells in tumor samples strongly suggests their malignant transformation and cancerous nature. The 'unassigned' cell population showing aneuploidy further suggests that some malignant cells might be present in this group or that other cell types within the tumor microenvironment have also undergone genomic alterations, though this is less likely to be the primary driver of the high aneuploidy observed.
• Normal Tissue Stability: The consistent diploid state in normal colon samples serves as a robust control, confirming the normalcy and genomic stability of healthy intestinal epithelial cells.

Clinical or Translational Implications

• Biomarker for Malignancy: The detection of aneuploidy in intestinal epithelial cells can serve as a strong diagnostic or prognostic biomarker for colorectal cancer. High levels of aneuploidy are often associated with aggressive tumor phenotypes and poorer patient outcomes [3].
• Monitoring Tumor Evolution: Single-cell analysis of ploidy can offer insights into the clonal evolution of tumors and the emergence of resistant subclones, which can be critical for guiding treatment strategies.
• Therapeutic Vulnerabilities: Understanding the extent and type of aneuploidy might reveal specific vulnerabilities in cancer cells that could be targeted therapeutically, although direct therapeutic strategies for aneuploidy itself are still largely experimental [4].

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References

1. Aneuploidy as a Hallmark of Cancer:

• PubMed Search: Aneuploidy hallmark cancer

2. Tumor Heterogeneity:

• PubMed Search: Tumor heterogeneity genomic instability

3. Aneuploidy in Cancer Prognosis:

• PubMed Search: Aneuploidy cancer prognosis

4. Targeting Aneuploidy in Cancer:

• PubMed Search: Aneuploidy cancer therapy

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

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interaction (CCI) patterns using single-cell RNA-seq data from human Colon tissue. The focus is on interactions involving Intestinal Epithelial cells (the presumed tumor origin cell type), Fibroblasts, Macrophages, and T cells (CD4+ and CD8+ subsets), comparing 'normal' and 'tumor' conditions. The plot_dot_for_cci_with_signif_difference tool was used to visualize significant differences in CCIs across individual samples within these conditions, highlighting up to 80 interactions per condition.

Visual Summary

The dot plot visualizes a subset of significant cell-cell interactions. The y-axis represents individual samples, grouped by 'normal' and 'tumor' conditions (B_cac samples for normal, T_cac samples for tumor). The x-axis lists specific ligand-receptor pairs and their interacting cell types (e.g., 'ligand-receptor -- cell_A-cell_B').

• Dot Color: Represents the standardized sample mean of interaction strength, with darker red indicating stronger interactions.
• Dot Size: Corresponds to the -log10(p-value), with larger dots indicating more statistically significant interactions.

Key visual observations:

• Differential Interaction Patterns: There are clear differences in CCI strengths and significances between normal and tumor samples. Many interactions that are strong and significant in normal samples appear attenuated or absent in tumor samples, and vice versa.
• Predominant Interacting Cell Types: The majority of the highlighted interactions involve Intestinal Epithelial cells (denoted as 'Ent.Epi(Dip)' for diploid Intestinal Epithelial cells), T cells (CD4+ and CD8+), B cells, and Plasma cells. While Fibroblasts and Macrophages were included in the target cell types, they are not prominently featured in these top 80 differential interactions, suggesting that their most significant interactions might fall outside this selected set or were less differentially expressed.
• Normal-Specific Interactions: Several interactions, particularly those involving CD160-TNFRSF14 (T CD8+-Int.Epi, T CD8+-B cell), PECAM1-CD38 (Int.Epi-Plasma cell), and LGIALS9-P4HB (Int.Epi-B cell), show strong signal in multiple normal samples (e.g., B_cac10, B_cac11, B_cac14, B_cac15) but are largely diminished in tumor samples.
• Tumor-Specific/Enhanced Interactions: A distinct set of interactions appears to be upregulated or more prominent in tumor samples. Notable examples include CEACAM5-ADGRE5 (Int.Epi-T CD4+), NECTIN2-TIGIT (Int.Epi-T CD4+), VSIR-HLA-F (T CD4+-B cell, T CD4+-Int.Epi), and CDH1_integrin_aEb7_complex (Int.Epi-T CD8+, Int.Epi-T CD4+). These interactions are frequently observed across various tumor samples (e.g., T_cac1, T_cac10, T_cac11, T_cac13, etc.).

Biological Interpretation

The observed differential CCI patterns provide insights into the altered cellular communication landscape in colon cancer, particularly involving the tumor-originating Intestinal Epithelial cells and various immune cells.

1. Interactions Prominent in Normal Colon Tissue:

• CD160-TNFRSF14 (HVEM) Interactions (T CD8+-Int.Epi(Dip), T CD8+-B cell): CD160 is an inhibitory receptor expressed on T cells and NK cells, and TNFRSF14 (HVEM) is a TNF receptor superfamily member. This interaction plays a role in T cell regulation and co-stimulation or co-inhibition depending on the context. Its reduced strength in tumor samples might indicate a disruption in normal immune regulatory mechanisms within the tumor microenvironment, potentially contributing to immune dysregulation.
• PECAM1-CD38 (Int.Epi(Dip)-Plasma cell): PECAM1 (CD31) is crucial for leukocyte transendothelial migration and cell adhesion, while CD38 is a multifunctional ectoenzyme on plasma cells involved in adhesion and signaling. Decreased interaction in tumor could suggest altered plasma cell trafficking or impaired adhesion to epithelial cells in the tumor, impacting local humoral immunity.
• LGIALS9-P4HB (Int.Epi(Dip)-B cell): LGIALS9 (Galectin-9) is an immunomodulatory lectin that can induce T cell apoptosis and regulate B cell function. Its interaction with P4HB (protein disulfide isomerase) can influence cell survival. The downregulation of this interaction in tumor could affect local immune suppression or B cell activity.

2. Interactions Prominent/Enhanced in Tumor Colon Tissue:

• CEACAM5-ADGRE5 (Int.Epi(Dip)-T CD4+): CEACAM5 (Carcinoembryonic Antigen-related Cell Adhesion Molecule 5) is a well-known tumor marker, often highly expressed in colorectal cancer. ADGRE5 (CD97) is a G-protein coupled receptor implicated in cell adhesion and migration. Enhanced CEACAM5-ADGRE5 signaling in tumor suggests a direct communication pathway between tumor epithelial cells and CD4+ T cells, potentially modulating immune responses or promoting tumor progression. https://www.genecards.org/cgi-bin/carddisp.pl?gene=CEACAM5
• NECTIN2-TIGIT (Int.Epi(Dip)-T CD4+): TIGIT is a critical inhibitory immune checkpoint receptor on T cells. NECTIN2 (CD112) is one of its ligands. Increased NECTIN2-TIGIT interaction strongly suggests an active immune suppressive mechanism in the tumor microenvironment, where tumor cells (Intestinal Epithelial cells) can directly inhibit CD4+ T cell activation, proliferation, and anti-tumor effector functions. https://www.ncbi.nlm.nih.gov/pubmed/31190987
• VSIR-HLA-F (T CD4+-B cell, T CD4+-Int.Epi(Dip)): VSIR (V-set immunoregulatory receptor), also known as VISTA, is another immune checkpoint molecule. HLA-F is a non-classical MHC class I molecule. Elevated VSIR-HLA-F interactions imply heightened immune suppression. VISTA's role in T cell inhibition makes this interaction a key player in tumor immune evasion. https://www.ncbi.nlm.nih.gov/pubmed/30600003
• CDH1_integrin_aEb7_complex (Int.Epi(Dip)-T CD8+, Int.Epi(Dip)-T CD4+): CDH1 (E-cadherin) expressed on epithelial cells interacts with integrin_aEb7 (alpha E beta 7 integrin) on intraepithelial lymphocytes (IELs). This interaction is crucial for retaining T cells within the epithelial layer. Increased interaction in tumor could indicate altered retention or localization of T cells within the tumor epithelium, potentially affecting their function or contributing to an exhausted phenotype. https://www.ncbi.nlm.nih.gov/pubmed/16987994
• CD320-JAML (Int.Epi(Dip)-Plasma cell, Int.Epi(Dip)-T CD4+): CD320 and JAML are involved in leukocyte transmigration. Increased interactions could reflect altered immune cell migration into or out of the tumor site, or modified adhesion within the tumor microenvironment.
• ICAM3-integrin_aLb2_complex (Int.Epi(Dip)-T CD4+): ICAM3 (Intercellular Adhesion Molecule 3) and integrin_aLb2_complex (LFA-1) are crucial for T cell adhesion and activation. Enhanced interaction in tumor could signify increased T cell infiltration or engagement with tumor cells, which could be either productive (anti-tumor) or dysregulated within the suppressive TME.
• CD40LG-CD40 (T CD4+-B cell): This interaction is central to T-B cell collaboration and the generation of robust humoral immunity. Its prominence in tumor suggests active adaptive immune responses, which might be fighting the tumor or contributing to protumor inflammation, depending on the B cell and T cell subsets involved.

The analysis also highlighted that the interacting Intestinal Epithelial cells are predominantly diploid (Int.Epi(Dip)), which is important context for tumor progression and cellular identity within the colon.

Clinical or Translational Implications

The differential cell-cell interaction patterns observed in colon cancer have significant clinical and translational implications:

1. Biomarkers for Disease Progression: Increased interactions involving tumor-associated molecules like CEACAM5 with immune cells, or immune checkpoint interactions like NECTIN2-TIGIT and VSIR-HLA-F, could serve as novel biomarkers for tumor presence, stage, or aggressiveness.
2. Therapeutic Targets: The identified immune checkpoint interactions (NECTIN2-TIGIT, VSIR-HLA-F) represent potential therapeutic targets for immunotherapy. Blocking these interactions could reactivate exhausted T cells and enhance anti-tumor immunity in colorectal cancer patients. Further investigation into these pathways could lead to new combination therapies.
3. Understanding Immune Evasion: The upregulation of inhibitory interactions (TIGIT, VSIR) involving tumor Intestinal Epithelial cells strongly points to active mechanisms of immune evasion by the tumor. This understanding can guide strategies to overcome resistance to existing immunotherapies.
4. Prognostic Indicators: The presence or absence of specific interaction patterns, particularly those that are lost in the tumor microenvironment (e.g., CD160-TNFRSF14) or gained (e.g., CEACAM5-ADGRE5), could potentially serve as prognostic indicators for patient outcomes.
5. Targeted Drug Delivery: Understanding the specific adhesion molecules (e.g., CDH1-integrin_aEb7, ICAM3-integrin_aLb2) involved in tumor-immune cell interactions could inform strategies for targeted drug delivery to the tumor microenvironment or to specific immune cell subsets.

13. Condition-Specific Cell-Cell Interaction Analysis in Colon Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell communication patterns in human colon tissue, comparing normal and tumor conditions using CellPhoneDB. The goal is to identify significant ligand-receptor interactions between different cell types, providing insights into tissue homeostasis and tumor microenvironment dynamics. The analysis used single-cell RNA-seq data from human colon, identifying interactions across various immune and epithelial cell populations, including T cells, B cells, Plasma cells, Intestinal Epithelial cells, and their subsets. The plot_cci_dots tool was used to visualize the top 80 most significant interactions for each condition, based on p-value and mean expression.

Visual Summary

The provided dot plots illustrate the cell-cell interactions (CCI) for normal and tumor conditions. The size of each dot represents the -log10(p-value) of the interaction (significance), and the color represents the log2-transformed mean expression of the interacting ligand-receptor pair (strength).

Normal Condition (Top Plot):

• The normal colon tissue exhibits a rich and diverse network of cell-cell interactions. A large number of significant interactions (up to the limit of 80 pairs shown) are observed across various cell type pairs, including extensive crosstalk among immune cells (T-T, T-B, T-Plasma, B-B, Plasma-Plasma), and between immune cells and Intestinal Epithelial cells (e.g., Diploid Intestinal Epi | T CD8+, Diploid Intestinal Epi | B cell).
• Key interacting pairs frequently involve T cells (CD4+ and CD8+), B cells, Plasma cells, and Diploid Intestinal Epithelial cells.
• Many interactions show high significance (large dot size) and substantial mean expression (bright yellow/green colors), suggesting robust communication pathways.
• Notable ligand-receptor pairs include those related to integrin signaling (e.g., CD99L2_CD99, CDH1_integrin, ICAM2_integrin, ICAM3_integrin), MHC class I and II molecules (e.g., HLA-E, HLA-F, B2M), tumor necrosis factor receptor superfamily (TNFRSF) members (e.g., BTLA_TNFRSF14, CD40LG_CD40), prostaglandins, and CEACAMs.

Tumor Condition (Bottom Plot):

• In stark contrast to the normal condition, the tumor microenvironment shows a significantly reduced number of highly significant cell-cell interactions, with only a handful making it into the top 80 threshold for display.
• The interactions observed are primarily between T cells (T CD8+ | T CD8+, T CD8+ | T CD4+, T CD4+ | T CD4+) and between T cells and Diploid Intestinal Epithelial cells (T CD4+ | Diploid Intestinal Epi). One interaction is also noted between Plasma cells and T CD4+.
• The overall significance and expression levels appear somewhat lower or more restricted compared to the most prominent interactions in normal tissue, although some interactions (e.g., T CD4+ | Diploid Intestinal Epi via LCK_CD8_Treceptor) still show relatively high significance and mean expression.
• Specific ligand-receptor pairs that stand out in the tumor context include CEACAM5_CEACAM6, CEACAM6_CEACAM6, KLRB1_CLEC2D, LCK_CD8_Treceptor, SELPLG_SLL1, TTF_TBLR, and notably VSIR_HLA-F.

Biological Interpretation

The dramatic differences in the CCI landscape between normal and tumor colon tissue highlight a fundamental rewiring of intercellular communication in cancer.

• Normal Tissue Homeostasis and Immune Surveillance: The rich and diverse CCI network in normal colon reflects a healthy, dynamic microenvironment crucial for maintaining tissue integrity, host defense, and immune tolerance.
• Immune Cell Coordination: Interactions among T cells, B cells, and Plasma cells (e.g., CD40LG_CD40, HLA-E, integrins) are vital for coordinated immune responses, antigen presentation, lymphocyte activation, and differentiation, all essential for immune surveillance against pathogens and nascent tumor cells.
• Epithelial-Immune Crosstalk: Interactions involving Diploid Intestinal Epithelial cells with T and B cells underscore the active communication at the mucosal barrier. These interactions likely regulate epithelial growth, repair, and inflammatory responses, contributing to the delicate balance of intestinal immunity. Integrins, for instance, are crucial for cell adhesion and migration, facilitating immune cell recruitment and tissue remodeling [1].
• Prostaglandins: The presence of various prostaglandin-mediated interactions suggests local immunomodulation and inflammation regulation, common in the gut.
• Tumor Microenvironment (TME) Remodeling and Immune Evasion: The significantly reduced and altered CCI in the tumor condition suggests a more constrained, potentially immunosuppressive, and specialized communication network that favors tumor growth and immune evasion.
• Loss of Diverse Communication: The drastic reduction in interaction diversity implies a disruption of the complex, multi-faceted communication characteristic of healthy tissue. This could reflect a simplified, pathological microenvironment where specific pro-tumorigenic signals dominate.
• Persistence of T-cell Interactions: While overall interactions are fewer, self-interactions among T cells (T CD8+ | T CD8+, T CD4+ | T CD4+) and interactions between CD4+ T cells and Diploid Intestinal Epithelial cells persist. These interactions might be crucial for intra-tumoral T cell function (or dysfunction) and their direct interaction with tumor cells or stromal components.
• Emergence of Immune Checkpoint Molecules: The detection of VSIR (VISTA) interacting with HLA-F in the tumor context is highly significant. VSIR is an immune checkpoint molecule that plays a critical role in suppressing T-cell responses in the TME, contributing to immune evasion [2, 3]. Its interaction with HLA-F, a non-classical MHC molecule, suggests a novel immune suppressive axis in colon cancer.
• CEACAMs in Cancer: Interactions involving CEACAM5 and CEACAM6 are notable. These carcinoembryonic antigen-related cell adhesion molecules are often overexpressed in various cancers, including colorectal cancer, and are associated with tumor progression, metastasis, and immune evasion [4]. Their self-interactions or interactions with other CEACAMs can mediate cell adhesion and signaling crucial for tumor cell survival and spread.
• KLRB1-CLEC2D: KLRB1 (CD161) is expressed on NK cells and T cells, and its interaction with CLEC2D can modulate immune cell function. Its presence in the tumor context could indicate altered NK/T cell activity.

Clinical or Translational Implications

The insights gained from this condition-specific CCI analysis have several important clinical and translational implications, particularly in the context of therapeutic target prioritization and experimental validation:

Identification of Novel Therapeutic Targets:

• The VSIR-HLA-F interaction in tumor tissue presents a compelling target for immunomodulatory therapy. Blocking this pathway could potentially unleash anti-tumor T-cell responses, similar to other immune checkpoint inhibitors targeting PD-1/PD-L1 or CTLA-4.
• The observed CEACAM interactions in tumor cells also highlight them as potential targets. Strategies to inhibit CEACAM-mediated adhesion or signaling could disrupt tumor progression and metastasis.
• Understanding the specific roles of LCK_CD8_Treceptor and KLRB1_CLEC2D in the tumor microenvironment could lead to the development of novel agents to modulate T-cell and NK-cell function.

Biomarker Discovery:

• The identified ligand-receptor pairs that are uniquely or significantly active in the tumor microenvironment could serve as diagnostic or prognostic biomarkers. For instance, high expression of VSIR, HLA-F, or specific CEACAMs, or evidence of their interaction, might correlate with disease progression or response to therapy.

Stratification of Patients:

• Understanding these communication patterns could help stratify patients for targeted therapies. Patients whose tumors rely heavily on pathways like VSIR-HLA-F might benefit most from corresponding immunotherapies.

Experimental Validation:

• The specific ligand-receptor pairs identified warrant further experimental validation.
• _In vitro_ assays: Co-culture experiments using colon cancer cell lines, patient-derived organoids, and immune cells can be used to functionally validate these interactions (e.g., impact on T-cell proliferation, cytokine production, or tumor cell growth).
• _In vivo_ models: Mouse models of colon cancer, potentially engrafted with human cells or using genetically engineered models, can be utilized to confirm the *in vivo* relevance of these interactions and test the efficacy of targeting strategies.
• Spatial transcriptomics/proteomics: Advanced spatial technologies could provide high-resolution insights into the precise cellular localization and context of these interactions within the tumor tissue.
• Clinical tissue analysis: Immunohistochemistry or multiplex immunofluorescence on patient tumor samples could confirm the expression patterns and co-localization of these ligand-receptor pairs.
• Understanding Immune Evasion Mechanisms: The analysis provides a data-driven approach to deciphering how tumor cells and their surrounding stromal/immune cells communicate to suppress anti-tumor immunity. This knowledge is critical for designing more effective combinatorial immunotherapies.

---

References:

[1] Integrin family (GeneCards): https://www.genecards.org/cgi-bin/carddisp.pl?gene=ITGB1 (Example for general integrin function)

[2] V-set immunoregulatory receptor (VSIR) (GeneCards): https://www.genecards.org/cgi-bin/carddisp.pl?gene=VSIR

[3] PubMed search for "VSIR HLA-F cancer": https://pubmed.ncbi.nlm.nih.gov/?term=VSIR+HLA-F+cancer

[4] Carcinoembryonic antigen related cell adhesion molecule 5 (CEACAM5) (GeneCards): https://www.genecards.org/cgi-bin/carddisp.pl?gene=CEACAM5

14. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Pathways in Colon Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) within single-cell RNA-seq data from human colon tissue, comparing normal and tumor conditions. The focus is specifically on a predefined set of genes involved in immune checkpoint and cell cycle pathways, aiming to identify differential communication patterns in the tumor microenvironment. The plot_cci_dots tool was used, leveraging precomputed CellPhoneDB results, to visualize significant ligand-receptor interactions.

Visual Summary

The provided dot plots illustrate significant cell-cell interactions under normal and tumor conditions, specifically for selected immune checkpoint and cell cycle related genes. Dot size correlates with the statistical significance of the interaction (-log10(p-value)), and dot color represents the interaction strength (log2(mean expression)).

Normal Condition

• The plot for normal colon tissue shows robust interactions involving IFNG_Type_II_IFNR as the ligand complex and LCK_CD8_receptor as the receptor complex.
• These interactions are highly significant (large dots) and strong (yellow colors) across several immune cell pairings:
• T CD8+|T CD8+: Strongest self-interaction.
• T CD8+|B cell: Significant interaction between cytotoxic T cells and B cells.
• T CD4+|T CD8+: Significant interaction between helper and cytotoxic T cells.
• B cell|T CD8+: Significant interaction from B cells to cytotoxic T cells.
• This pattern suggests active and widespread IFN-$\gamma$ signaling associated with CD8+ T cell activation machinery in a healthy immune microenvironment, coordinating responses among various immune subsets.

Tumor Condition

• The plot for tumor colon tissue presents a more restricted pattern. Only the LCK_CD8_receptor is explicitly shown as the receptor complex, with the IFNG_Type_II_IFNR ligand complex being absent from the displayed significant interactions.
• Interactions are observed for only two cell pairings:
• T CD8+|T CD8+: Very strong self-interaction (large, bright yellow dot).
• T CD4+|T CD8+: Very strong interaction between helper and cytotoxic T cells (large, bright yellow dot).
• Notably, interactions involving B cells (T CD8+|B cell, B cell|T CD8+) that were prominent in normal tissue are absent in the tumor microenvironment for this specific LCK_CD8_receptor axis.
• The remaining T-T cell interactions are highly significant and strong, suggesting persistent, robust signaling through the LCK/CD8 co-receptor pathway within the T cell compartment, despite the apparent absence of significant IFNG-driven components shown here.

Biological Interpretation

The contrasting CCI patterns highlight significant shifts in immune cell communication between healthy colon tissue and the tumor microenvironment, particularly involving T cells and B cells, and specific signaling pathways.

1. IFN-$\gamma$ Signaling and T Cell Activation in Normal Tissue:

• In normal tissue, the IFNG_Type_II_IFNR | LCK_CD8_receptor interaction points to a well-regulated immune state where Interferon-gamma (IFN-$\gamma$) actively contributes to the modulation of CD8+ T cell function. IFN-$\gamma$ is a critical cytokine for antiviral and anti-tumor immunity, promoting T cell effector functions and antigen presentation. [PubMed Search: "IFN gamma T cell activation" ]
• LCK (lymphocyte-specific protein tyrosine kinase) is an essential enzyme initiating T cell receptor (TCR) signaling upon antigen recognition, and the CD8 co-receptor enhances this process by binding MHC-I molecules and recruiting LCK. GeneCards: LCK The presence of this complex interaction indicates active T cell surveillance and immune coordination.

2. Disruption of IFN-$\gamma$ Driven Interactions and B Cell Communication in Tumor:

• The absence of the IFNG_Type_II_IFNR ligand complex in the significant tumor interactions, despite the LCK_CD8_receptor axis remaining highly active within T cells, suggests a potential downregulation or functional suppression of IFN-$\gamma$ related signaling that directly drives these specific T cell interactions in the tumor microenvironment. This could be a mechanism of immune evasion employed by tumors to limit potent anti-tumor IFN-$\gamma$ responses.
• The complete absence of T CD8+|B cell and B cell|T CD8+ interactions in the tumor, compared to normal, is a critical observation. B cells play diverse roles in anti-tumor immunity, including antigen presentation to T cells, secretion of anti-tumor antibodies, and modulation of T cell responses. [PubMed Search: "B cell anti tumor immunity" ] A disruption in this crucial T cell-B cell axis could impair effective adaptive immune responses against the tumor.

3. Persistent T-T Cell LCK/CD8 Signaling in Tumor:

• Despite the loss of specific IFN-$\gamma$ signaling and B cell interactions, the strong T CD8+|T CD8+ and T CD4+|T CD8+ interactions involving LCK_CD8_receptor persist in the tumor. This indicates that CD8+ T cells are still engaged in inter-T cell communication involving their core activation machinery, likely in response to tumor antigens or other microenvironmental cues. However, without the coordinating influence of IFN-$\gamma$ signaling and B cell support, these interactions might represent dysfunctional, exhausted, or dysregulated T cell responses often seen in the tumor microenvironment.

Clinical or Translational Implications

The observed changes in cell-cell interactions within the tumor provide valuable insights with potential clinical and translational relevance:

• Immune Evasion Mechanisms: The reduction or loss of IFNG related signaling and the specific B cell-T cell interactions in tumors could represent critical immune evasion strategies. Tumors may create an immunosuppressive environment that impairs these crucial communication pathways, hindering effective anti-tumor immunity.
• Therapeutic Opportunities:
• IFN-$\gamma$ Augmentation: Strategies to restore or enhance IFN-$\gamma$ signaling in the tumor microenvironment could reactivate quiescent or exhausted T cells and improve anti-tumor responses. This could involve direct IFN-$\gamma$ administration or therapies that promote its endogenous production.
• Re-engaging B Cell-T Cell Interactions: Developing therapies aimed at restoring productive communication between B cells and T cells in the tumor could be beneficial. This might involve optimizing vaccine strategies, using immunomodulatory antibodies, or enhancing antigen presentation by B cells.
• Modulating LCK/CD8 Signaling: While LCK is central to T cell activation, its persistent strong signaling in the tumor, potentially in a dysregulated context (without proper costimulation or inhibitory signals), could be targeted. Understanding the precise context of this signaling could help design more nuanced immunotherapies, such as combining checkpoint inhibitors with agents that modulate LCK activity or T cell exhaustion pathways.
• Biomarker Potential: The distinct CCI patterns observed, particularly the presence or absence of specific ligand-receptor interactions and participating cell types, could serve as prognostic or predictive biomarkers for patient response to immunotherapies in colon cancer.
• Experimental Validation: Further research using functional assays and spatial technologies (e.g., spatial transcriptomics, multiplexed imaging) would be crucial to validate these interaction changes in the tissue context and to assess their functional consequences on T cell and B cell behavior in the tumor microenvironment.

15. Condition-Specific Cell-Cell Interaction Patterns in Normal and Tumor Colon Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) between normal and tumor colon tissues, focusing on major immune cells (T cell, B cell, Myeloid cell, Mast cell) and stromal cells. The dot plot visualizes the strength (color intensity) and statistical significance (dot size) of specific ligand-receptor interactions across various samples from both conditions. The interactions shown are those with the highest significance in either the normal or tumor condition, limited to the top 25 per group.

Visual Summary

The dot plot is clearly partitioned into 'normal' and 'tumor' conditions along the x-axis, allowing for direct comparison of CCI patterns. Samples are grouped by condition and listed along the y-axis.

• Normal Condition (Left Panel): A prominent cluster of strong and significant CCIs is observed in normal samples, particularly in B cell-enriched samples (e.g., B_cac10, B_cac11, B_cac14). Key interactions include SEMA4D-CD72 (Plasma B cell interaction), HLA-E-CD94.NKG2C and HLA-E-KLR C2 (T CD8+ T CD8+ interactions), CD160-TNFRSF14 (involving T CD8+, T CD4+, B cell, Plasma cells), and ICAM1-integrin_aLB2_complex (T CD8+ B cell interaction). These interactions are generally weaker or absent in tumor samples.
• Tumor Condition (Right Panel): A distinct and extensive set of CCIs shows elevated strength and significance in tumor samples. Many of these interactions involve Ent.Epi (Intestinal Epithelial cells, likely representing tumor cells, with 'Dipl' indicating diploid ploidy status) interacting with various immune cell types, particularly T cells (CD4+, CD8+). Notable tumor-enriched CCIs include:
• NECTIN2-TIGIT (Ent.Epi-T CD8+), an important immune checkpoint axis.
• CD320-JAMl (involving Plasma, T CD4+, T CD8+, Ent.Epi, B cell).
• CEACAM5-CD8A (Ent.Epi-T CD8+), involving a known tumor marker.
• CD55-ADGRE5 (Ent.Epi-T CD8+).
• CD58-CD2 (T CD4+ T CD4+, T CD8+ T CD4+).
• VSIR-HLA-F (T CD8+ B cell, T CD8+ T CD8+), another immune checkpoint.

These tumor-specific interactions are largely absent or significantly weaker in normal samples.

Biological Interpretation

The analysis reveals a marked reprogramming of cell-cell communication within the colorectal tumor microenvironment compared to normal colon tissue.

• Normal Colon Homeostasis: The strong interactions observed in normal samples suggest a well-regulated immune surveillance and tissue maintenance environment.
• The CD160-TNFRSF14 axis plays roles in co-stimulation or co-inhibition of T cells, suggesting active modulation of T cell responses. PubMed search: CD160 TNFRSF14 T cell
• HLA-E-CD94/NKG2C interactions are crucial for regulating NK and T cell activity, contributing to immune tolerance or activation.
• SEMA4D-CD72 is involved in B cell activation and differentiation. PubMed search: SEMA4D CD72 B cell
• Tumor Microenvironment (TME) Reprogramming: The emergence of distinct CCI patterns in tumor samples indicates a shift towards a more immunosuppressive and pro-tumorigenic environment, largely driven by interactions between tumor epithelial cells (Ent.Epi) and infiltrating immune cells.
• Immune Checkpoint Activation: The prominent NECTIN2-TIGIT interaction between Intestinal Epithelial cells and CD8+ T cells is highly significant. TIGIT is an inhibitory receptor on T cells, and its ligand NECTIN2 on tumor cells can suppress anti-tumor T cell responses, leading to T cell exhaustion and immune escape. PubMed search: NECTIN2 TIGIT cancer immunotherapy Similarly, VSIR (VISTA)-HLA-F interactions also represent immune checkpoint pathways contributing to immunosuppression. PubMed search: VISTA immune checkpoint cancer
• Tumor-Immune Crosstalk: Interactions involving CEACAM5 (a well-known oncofetal antigen and tumor marker often overexpressed in colorectal cancer) with CD8A on T cells suggest direct communication between tumor cells and cytotoxic T lymphocytes, potentially influencing T cell function or antigen presentation. GeneCards: CEACAM5
• Altered T Cell Function: While CD58-CD2 is important for T cell activation, its strong presence in the TME alongside inhibitory pathways could suggest T cells are attempting to activate but are simultaneously being suppressed, or that these interactions are being exploited by tumor cells.
• Broader Immune Cell Involvement: Interactions like CD320-JAMl and CD55-ADGRE5 highlight complex roles for Plasma cells, B cells, and Myeloid cells within the tumor context, suggesting that various immune components are actively engaged in the modified TME.

Clinical or Translational Implications

The identified condition-specific CCIs offer valuable insights for colorectal cancer diagnostics and therapy development.

• Biomarkers: The specific ligand-receptor pairs highly active in tumor samples, particularly those involving tumor epithelial cells, could serve as novel diagnostic or prognostic biomarkers for colorectal cancer progression or response to therapy.
• Therapeutic Targets: The strong upregulation of immune checkpoint interactions in the tumor microenvironment presents clear therapeutic opportunities.
• Targeting the NECTIN2-TIGIT axis, potentially in combination with other immune checkpoint inhibitors (e.g., anti-PD-1/PD-L1), could reactivate exhausted T cells and enhance anti-tumor immunity in colorectal cancer patients.
• Other immune checkpoints like VSIR (VISTA) may also represent alternative or synergistic therapeutic targets.
• Further investigation into the role of CEACAM5 in modulating T cell function through CD8A could uncover novel strategies to interfere with tumor-immune evasion mechanisms.
• Combination Therapies: Understanding the intricate network of CCIs in the TME could guide the development of rational combination therapies that simultaneously disrupt pro-tumorigenic interactions while enhancing anti-tumor immune responses.

16. Intestinal Epithelial Cell Condition-Specific Surfaceome Markers

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Intestinal Epithelial cells, comparing cells from "Diploid" (likely representing normal tissue and diploid status) and "tumor" conditions. The dot plot visualizes the expression of up to 50 top surfaceome markers per condition across different sub-clusters of Intestinal Epithelial cells. The size of each dot represents the fraction of cells expressing the gene within that cluster, while the color intensity indicates the mean expression level.

Visual Summary

The dot plot clearly differentiates gene expression patterns between Intestinal Epithelial cells found in "Diploid" (normal-like) conditions and those found in "tumor" conditions.

• Tumor-Specific Markers: The two clusters at the bottom, labeled "T_cac3" and "T_cac1" (representing Intestinal Epithelial cells from the "tumor" condition), show a distinct upregulation of numerous surfaceome markers compared to the "Diploid" clusters. Prominent markers highly expressed and prevalent in these tumor clusters include:
• CD44, GPRC5A, ITGB1 (Integrin Beta 1), ERBB3 (HER3), CD151, TM4SF1, Nectin-2 (PVRL2), CD63, CD164, CXADR, SERINC2, TM9SF2, PIGT, DSG2, LAMP1, HM13, APLP2, CD46, RNF43, MUC3A, CDH1 (though CDH1 is also present in normal cells, its pattern here is interesting, see below).
• Diploid (Normal-like) Markers: The upper clusters, labeled "Diploid B_cacX" and "Diploid T_cacX" (representing Intestinal Epithelial cells from the "Diploid" condition), exhibit relatively higher expression of certain markers such as:
• MUC4, SPINT2, TSPAN8, CD24, CEACAM6, CD9, CEACAM5, ATP1B1, ITM2B, APP, ATP1A1, AREG, TSPAN3, LMAN2, TMEM219, SLC12A2, FCGRT, BACE2, TMEM123.

Differential Expression Patterns:

• CD44, GPRC5A, ITGB1, ERBB3, CD151, and TM4SF1 stand out as highly specific and strongly expressed surface markers in the tumor-associated Intestinal Epithelial cells.
• Conversely, markers like MUC4, SPINT2, and some CEACAM family members appear more prominent in specific normal-like Intestinal Epithelial cell clusters, with reduced or absent expression in the tumor clusters.
• Notably, CDH1 (E-cadherin) shows high expression across many "Diploid" Intestinal Epithelial cell clusters but appears to have reduced expression in the "tumor" associated clusters (T_cac3, T_cac1), although it's not completely absent.

Biological Interpretation

The observed differential expression of surfaceome markers in Intestinal Epithelial cells provides crucial insights into the biological changes associated with tumor development in the colon.

• Hallmarks of Malignant Transformation: The marked upregulation of several markers in tumor-associated Intestinal Epithelial cells reflects processes commonly involved in cancer progression:
• Cell Adhesion and Motility: The high expression of CD44, ITGB1, CD151, and TM4SF1 in tumor cells suggests altered cell-matrix and cell-cell interactions, which are critical for increased invasiveness and metastatic potential. CD44 is a widely recognized adhesion molecule and a marker for cancer stem cells in various malignancies GeneCards: CD44. ITGB1 (Integrin Beta 1) plays a key role in cell adhesion, migration, and signaling, often dysregulated in cancer GeneCards: ITGB1.
• Growth and Survival Signaling: ERBB3 (HER3), a member of the EGFR family, is frequently involved in promoting cell proliferation, survival, and resistance to therapy through its interactions with other receptor tyrosine kinases GeneCards: ERBB3. Its upregulation suggests activated growth pathways in tumor cells.
• Epithelial-Mesenchymal Transition (EMT): The reduced expression of CDH1 (E-cadherin) in tumor cells compared to normal counterparts is a classic indicator of EMT, a process where epithelial cells lose their polarity and cell-cell adhesion, gaining migratory and invasive properties. This transition is crucial for tumor metastasis GeneCards: CDH1.

Role of Specific Upregulated Markers:

• GPRC5A (G Protein-Coupled Receptor Class C Group 5 Member A) has been implicated in cell proliferation and differentiation, with context-dependent roles in various cancers, including colorectal cancer GeneCards: GPRC5A. Its high expression here suggests a potential role in tumor cell signaling.
• Other markers like Nectin-2 (PVRL2), CD63, CD164, CXADR, SERINC2, TM9SF2, PIGT, DSG2, LAMP1, HM13, APLP2, CD46, RNF43, MUC3A also show distinct patterns, indicating a broad remodeling of the cell surface during tumorigenesis.
• Normal Epithelial Characteristics: The markers enriched in the "Diploid" Intestinal Epithelial cells, such as MUC4 and SPINT2, likely represent features associated with normal gut epithelial function, including mucosal protection and barrier integrity GeneCards: MUC4, GeneCards: SPINT2. Their downregulation in tumor cells could signify a loss of differentiated epithelial characteristics.

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers has significant clinical and translational potential.

• Biomarker Discovery: The highly upregulated surface markers in tumor-origin Intestinal Epithelial cells (e.g., CD44, GPRC5A, ITGB1, ERBB3, CD151, TM4SF1) serve as strong candidates for:
• Diagnostic Markers: Early detection of colon cancer or identification of cancerous cells in biopsies.
• Prognostic Markers: Assessing disease aggressiveness, predicting patient outcomes, or monitoring recurrence.
• Companion Diagnostics: Identifying patients who might benefit from therapies targeting these specific pathways.
• Therapeutic Targets: As these are surface proteins, they are readily accessible for therapeutic intervention.
• Targeted Therapies: Monoclonal antibodies or antibody-drug conjugates (ADCs) could be developed to specifically target cancer cells expressing high levels of CD44, ERBB3, ITGB1, CD151, or TM4SF1, minimizing off-target effects on normal tissues.
• Immunotherapy: These surface proteins could also be explored as targets for T-cell engagers or CAR-T cell therapies.
• Understanding Disease Mechanisms: Studying the functional roles of these differentially expressed surface markers can provide deeper insights into the molecular mechanisms driving colon cancer initiation, progression, and metastasis. This knowledge can guide the development of novel therapeutic strategies.
• Monitoring EMT: The observed shift in CDH1 expression, alongside the upregulation of pro-invasive markers in tumor cells, provides a potential molecular signature for monitoring EMT in colorectal cancer, which is critical for predicting and preventing metastatic spread.

17. Fibroblast Condition-Specific Surface Marker Identification

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that distinguish fibroblast cells across 'normal' and 'tumor' conditions in human colon tissue. The provided dot plot visualizes the expression patterns of these condition-specific surface markers across different fibroblast subpopulations (denoted as B_cac14, T_cac1, T_cac3, T_cac2). The size of each dot represents the fraction of cells within a given subpopulation that express a particular gene, while the color intensity indicates the mean expression level of that gene. Only surfaceome-associated genes were considered, focusing on their potential for therapeutic targeting.

Visual Summary

The dot plot clearly differentiates two major categories of fibroblast subpopulations based on their surface marker profiles and association with either 'normal' or 'tumor' conditions:

• Normal-Associated Fibroblasts (B_cac14): This fibroblast cluster is characterized by the strong and prevalent expression of a distinct set of surface markers exclusively in the 'normal' condition columns. Key markers for B_cac14 include PLPP3, CD9, PROCR, PRNP, ABCA8, SCARA5, CD74, GPNMB, TGFBR3, RNF13, ABCA6, CD34, PI16, and CADM3. These markers show high mean expression (dark red color) and are expressed in a large fraction of cells (large dot size) within the B_cac14 cluster, with negligible expression observed in the 'tumor' condition columns or in the other fibroblast clusters.
• Tumor-Associated Fibroblasts (T_cac1, T_cac3, T_cac2): Conversely, three distinct fibroblast clusters (T_cac1, T_cac3, T_cac2) show robust and specific expression of a separate set of surface markers predominantly in the 'tumor' condition columns. This set includes F2R, PTTG1IP, ITGA1, ANTXR1, CDH11, ITGAV, NECTIN2, TMEM204, TMEM30A, LTBR, ICAM1, PDLIM5, FAT1, IFNGR2, EDNRA, and LRRC32.
• While these markers collectively define tumor-associated fibroblasts, there is observable heterogeneity among the T_cac clusters. For instance, T_cac1 exhibits particularly high expression of F2R and PTTG1IP. Markers like ITGA1, ANTXR1, CDH11, and ITGAV are broadly and highly expressed across all three tumor-associated fibroblast clusters. These tumor-associated clusters show minimal to no expression of the 'normal' fibroblast markers.

The bar chart on the right indicates the total cell counts for each fibroblast subpopulation: B_cac14 (48 cells), T_cac1 (90 cells), T_cac3 (142 cells), and T_cac2 (239 cells), suggesting a larger proportion of fibroblasts adopt tumor-associated states.

Biological Interpretation

This analysis underscores the significant cellular and molecular reprogramming of fibroblasts within the tumor microenvironment (TME) in colon cancer. The distinct surface marker profiles clearly delineate normal-associated fibroblasts from multiple subtypes of cancer-associated fibroblasts (CAFs).

• Normal Fibroblast Signature (B_cac14): The B_cac14 cluster likely represents quiescent or homeostatic fibroblasts of the normal colon. Markers such as CD9 (a tetraspanin involved in cell adhesion, motility, and signaling, often linked to quiescent states [GeneCards]), PROCR (Endothelial Protein C Receptor, found on some mesenchymal stromal cells and involved in inflammation [GeneCards]), and CD34 (a general stromal/progenitor cell marker [GeneCards]) are consistent with a role in maintaining tissue integrity and modulating local immune responses in a healthy state. The presence of TGFBR3 (betaglycan), a TGF-β co-receptor, suggests a refined regulation of TGF-β signaling, crucial for normal tissue remodeling. [GeneCards]
• Tumor-Associated Fibroblast (CAF) Signatures (T_cac1, T_cac3, T_cac2): The robust emergence of distinct CAF subpopulations (T_cac1, T_cac3, T_cac2) with unique marker profiles highlights the remarkable plasticity and functional specialization of fibroblasts in supporting tumor progression.
• Markers like F2R (Protease-Activated Receptor 1, involved in pro-inflammatory and pro-coagulant responses often hijacked by tumors [GeneCards]), ITGA1 (Integrin alpha-1, forms part of VLA-1, crucial for adhesion to extracellular matrix components like collagen [GeneCards]), ANTXR1 (Anthrax toxin receptor 1, also known as TEM8, a known marker of tumor vasculature and CAFs, implicated in angiogenesis [GeneCards]), CDH11 (Osteoblast-cadherin, promoting cell-cell adhesion and tissue remodeling, often associated with aggressive tumor behavior and CAF activation [GeneCards]), and ITGAV (Integrin alpha-V, forming various heterodimers vital for cell adhesion, migration, and signaling in the TME [GeneCards]) collectively point towards highly activated, migratory, and pro-fibrotic fibroblast phenotypes. These are hallmarks of CAFs that contribute to extracellular matrix remodeling, immune suppression, and direct support for cancer cell proliferation and metastasis.
• ICAM1 (Intercellular Adhesion Molecule 1 [GeneCards]) expression can facilitate interactions with immune cells, influencing the tumor immune landscape.
• EDNRA (Endothelin receptor type A [GeneCards]) is frequently implicated in various cancers, promoting cell proliferation, survival, and fibrosis, further solidifying the pro-tumorigenic role of these activated fibroblasts.

The observed diversity among the CAF clusters (T_cac1, T_cac3, T_cac2) suggests that CAFs are not a single entity but comprise multiple functionally distinct subsets that may contribute differentially to tumor progression.

Clinical or Translational Implications

The identification of these condition-specific surface markers for fibroblast subpopulations offers significant clinical and translational opportunities in colon cancer.

• Biomarkers for Disease State: The robust and differential expression of these surface markers (e.g., ITGA1, ANTXR1, CDH11, ITGAV, EDNRA) provides excellent candidates for distinguishing normal tissue fibroblasts from CAFs. These could serve as novel diagnostic or prognostic biomarkers for colon cancer, potentially aiding in early detection, assessment of disease progression, or prediction of therapeutic response. Their surface localization makes them readily detectable in biopsies via immunohistochemistry or flow cytometry.
• Therapeutic Targets for CAF Depletion/Reprogramming: Surface proteins are highly attractive candidates for targeted therapies. The markers selectively expressed on tumor-associated fibroblast clusters (T_cac1, T_cac3, T_cac2) represent promising therapeutic targets for modulating the tumor microenvironment. Potential strategies include:
• Antibody-Drug Conjugates (ADCs): Antibodies against specific CAF surface markers could deliver cytotoxic payloads directly to CAFs, reducing their pro-tumorigenic effects without broadly affecting normal stromal cells.
• Chimeric Antigen Receptor (CAR) T-cell therapy: Developing CAR T-cells engineered to recognize and eliminate CAF subpopulations could effectively deplete these tumor-supporting cells.
• Small molecule inhibitors: Targeting receptor pathways like F2R or EDNRA with small molecule inhibitors could disrupt key pro-tumorigenic signaling in CAFs, altering the TME.
• Addressing CAF Heterogeneity: The presence of multiple distinct CAF subtypes (T_cac1, T_cac3, T_cac2) emphasizes the need for a nuanced approach to targeting CAFs. Future therapeutic strategies might benefit from combinatorial approaches targeting multiple CAF subsets or specific markers that are broadly expressed across critical CAF populations to achieve maximal efficacy.
• Experimental Validation: The next steps would involve validating the specificity and functional relevance of these surface markers at the protein level in a larger cohort of human colon cancer samples. This could include immunohistochemistry, multiplexed immunofluorescence, or flow cytometry to confirm their utility as bona fide biomarkers and therapeutic targets.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers in CD4+ T cells from human colon tissue, comparing normal and tumor conditions using single-cell RNA sequencing data. The plot_markers_and_expression_dot tool was used to visualize the expression patterns of up to 50 surfaceome markers per condition, providing insights into potential differential phenotypes of CD4+ T cells in the tumor microenvironment.

Visual Summary

The dot plot effectively visualizes the expression of various surfaceome genes across different samples, categorized by 'normal' and 'tumor' conditions. Each dot represents a gene's expression within a specific sample's CD4+ T cell population: its size indicates the fraction of cells expressing the gene, and its color intensity represents the mean expression level within that group.

Key observations from the plot include:

• Dominant Tumor-Associated Signature: There is a striking enrichment of highly expressed and prevalent surface markers in CD4+ T cells derived from tumor samples (e.g., T_cac16, T_cac9, T_cac6, etc.). This cluster of genes shows significantly larger and darker dots within the 'tumor' columns compared to the 'normal' columns.
• Sparse Normal-Associated Signature: In contrast, CD4+ T cells from 'normal' samples (e.g., B_cac14, B_cac15, B_cac11) show very few highly expressed surface markers. The dots in the 'normal' columns are generally smaller and lighter, indicating lower prevalence and mean expression. Some genes like AREG, SELPLG, CCR6, and CD82 show some basal or low-level expression in normal samples, but this pattern is not as pronounced as the tumor-specific upregulation.
• Specific Tumor Markers Highlighted: A broad panel of genes, spanning from CTLA4 to ENTPD1 (as indicated by the broad red box highlighting a significant portion of the x-axis markers), shows a clear upregulation pattern in tumor-associated CD4+ T cells. These markers include immune checkpoints, co-stimulatory molecules, adhesion molecules, and MHC class II genes.
• Cell Counts per Sample: The bar chart on the right indicates the number of CD4+ T cells analyzed per sample. While some samples have high cell counts (e.g., B_cac14: 2804 cells, T_cac16: 696 cells), the marker expression pattern is largely consistent across the tumor samples despite variability in cell numbers.

Biological Interpretation

The distinct surfaceome marker profiles observed in tumor-associated CD4+ T cells suggest a profound phenotypic shift compared to those in normal colon tissue. This shift reflects adaptation to the tumor microenvironment (TME) and engagement in specific immune responses.

Key Tumor-Associated Markers and Their Biological Roles:

Immune Checkpoints/Regulatory Molecules:

• CTLA4: A well-known inhibitory receptor highly expressed by activated T cells and regulatory T cells (Tregs). Its high expression in tumor CD4+ T cells suggests a role in immune regulation or potential exhaustion within the TME, contributing to tumor immune evasion PubMed: CTLA-4 cancer immunotherapy.
• ENTPD1 (CD39): An ectonucleotidase that, along with CD73, generates immunosuppressive adenosine from ATP. High expression on T cells, particularly Tregs, is associated with immunosuppression and T cell exhaustion in the TME GeneCards: ENTPD1.

Co-stimulatory/Activation Markers:

• TNFRSF4 (OX40): A co-stimulatory receptor expressed on activated CD4+ T cells, enhancing T cell proliferation, survival, and cytokine production GeneCards: TNFRSF4.
• TNFRSF18 (GITR): Another co-stimulatory receptor that enhances T cell effector functions and can abrogate Treg-mediated suppression GeneCards: TNFRSF18.
• IL2RB (CD122): A component of the high-affinity IL-2 receptor, crucial for T cell survival, proliferation, and differentiation. Its expression suggests active IL-2 signaling pathways are engaged in these cells GeneCards: IL2RB.
• CD58 (LFA-3): An adhesion molecule and ligand for CD2, involved in T cell activation and adhesion, promoting stable interactions with antigen-presenting cells GeneCards: CD58.

Tissue Residency/Homing/Effector Markers:

• ITGAE (CD103): Integrin alpha E, frequently marks tissue-resident memory T cells (TRM) and intraepithelial lymphocytes (IELs), especially in mucosal tissues like the colon. CD103+ T cells often exhibit cytotoxic functions GeneCards: ITGAE.
• CXCR6: A chemokine receptor involved in T cell trafficking to inflamed tissues and often found on effector and tissue-resident memory T cell subsets GeneCards: CXCR6.

MHC Class II Molecules:

• HLA-DPA1, HLA-DPB1: These are genes encoding components of MHC class II molecules. While primarily expressed on professional antigen-presenting cells (APCs), certain activated T cell subsets, particularly in inflammatory or tumor contexts, can upregulate MHC class II expression. This could indicate a unique functional state of CD4+ T cells with potential antigen-presenting capabilities or a role in modulating local immune responses within the TME.

The overall pattern suggests that CD4+ T cells in the tumor microenvironment are in a highly activated, yet potentially regulated or exhausted, state. They exhibit markers indicative of prolonged antigen exposure, co-stimulatory and co-inhibitory receptor engagement, tissue residency, and metabolic adaptation to an immunosuppressive environment.

Clinical or Translational Implications

The identified condition-specific surfaceome markers in CD4+ T cells hold significant clinical and translational potential:

• Biomarkers for Tumor-Associated T cell Subsets: These markers can serve as valuable tools for identifying and characterizing specific CD4+ T cell subsets within the tumor microenvironment. For example, co-expression of CTLA4 and ENTPD1 might indicate immunosuppressive or exhausted T cells, while OX40 and GITR could mark activated effector T cells. These could be useful for prognostic stratification or predicting response to immunotherapy.

Therapeutic Targets:

• Immune Checkpoint Modulation: The high expression of CTLA4 in tumor CD4+ T cells reinforces its role as a key immune checkpoint and a validated therapeutic target in cancer immunotherapy.
• Co-stimulatory Agonists: The presence of TNFRSF4 (OX40) and TNFRSF18 (GITR) suggests these receptors could be targeted with agonist antibodies to enhance anti-tumor CD4+ T cell responses, representing promising avenues for next-generation immunotherapies PubMed: OX40 and GITR agonists.
• Immunosuppressive Pathway Blockade: Targeting ENTPD1 (CD39) could be a strategy to reverse adenosine-mediated immunosuppression in the TME, thereby unleashing anti-tumor immunity.
• Experimental Validation and Functional Studies: These surfaceome markers warrant further experimental validation at the protein level using techniques such as flow cytometry or immunohistochemistry on patient tumor samples. Functional studies in vitro and in vivo could elucidate the precise roles of these markers in shaping CD4+ T cell responses and their contribution to tumor progression or control. The unexpected expression of MHC class II molecules on CD4+ T cells in the tumor context particularly warrants deeper investigation into their functional significance.

19. Dysregulation of Cell Cycle Pathways in Intestinal Epithelial Cells of Colorectal Tumors

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of a curated set of cell cycle pathway-related genes in Intestinal Epithelial cells, comparing normal colon tissue with tumor tissue. The goal is to identify genes that show statistically significant expression changes, which can shed light on cell cycle dysregulation contributing to colorectal tumorigenesis. The provided box plots visualize the distribution of gene expression for selected genes across normal and tumor conditions, highlighting significant differences.

Visual Summary

The box plots display the expression levels of 20 cell cycle-related genes in Intestinal Epithelial cells across normal and tumor conditions.

• Widespread Upregulation in Tumor: A striking pattern observed is the consistent upregulation of most genes in the tumor condition compared to normal. This includes genes such as MYC, CDK4, CCND1, SFN, YWHAE, YWHAB, YWHAQ, YWHAZ, YWHAH, ANAPC5, ANAPC11, RBX1, HDAC1, HDAC2, PRKDC, RAD21, and SKP1.
• Statistical Significance: The majority of these upregulated genes show statistically significant differences (indicated by *, , or *), with many reaching high levels of significance (e.g., MYC, CDK4, YWHAE).
• Exceptions/Differences:
• GADD45B shows a statistically significant *downregulation* in tumor samples compared to normal.
• GADD45G does not show a statistically significant difference in expression between the two conditions (labeled "ns").
• Expression Spread: The spread of expression (box height and whiskers) varies across genes and conditions, but generally, the tumor samples show a wider range of expression for many of the upregulated genes, suggesting heterogeneity in their overexpression within the tumor microenvironment.

Biological Interpretation

The observed gene expression patterns in Intestinal Epithelial cells provide strong evidence for extensive cell cycle dysregulation in colorectal tumors. Given that Intestinal Epithelial cells are identified as the "Tumor origin celltype" in the provided data context, these findings directly reflect the oncogenic transformation processes occurring within these cells.

• Accelerated Cell Cycle Progression: The pronounced upregulation of genes like MYC, CDK4, and CCND1 indicates a forceful push towards cell proliferation. MYC is a powerful proto-oncogene that orchestrates cell growth, metabolism, and proliferation. CDK4 (Cyclin-dependent kinase 4) and CCND1 (Cyclin D1) are crucial regulators of the G1-S phase transition, and their overexpression drives cells rapidly through the restriction point, overriding normal cell cycle checkpoints. GeneCards: MYC, GeneCards: CDK4, GeneCards: CCND1
• Dysregulation of Mitotic Machinery: Genes involved in the Anaphase-Promoting Complex/Cyclosome (APC/C), such as ANAPC5 and ANAPC11, as well as the cohesin complex component RAD21, are significantly upregulated. The APC/C is critical for progression through anaphase and exit from mitosis, while cohesin ensures proper sister chromatid segregation. Their increased expression suggests heightened mitotic activity and potentially altered chromosomal stability in tumor cells. GeneCards: ANAPC5, GeneCards: RAD21
• Ubiquitin-Proteasome System Overactivity: RBX1 and SKP1, components of E3 ubiquitin ligase complexes (SCF and APC/C), are also upregulated. These complexes are essential for degrading cell cycle inhibitors and promoting cell cycle progression. Their overexpression contributes to uncontrolled proliferation by accelerating the turnover of key regulatory proteins. GeneCards: RBX1, GeneCards: SKP1
• Epigenetic Modifications and DNA Repair: HDAC1 and HDAC2 (Histone Deacetylases) are upregulated, which are known to influence gene expression by modifying chromatin structure. Overexpression of HDACs is common in cancer and can contribute to oncogenesis by repressing tumor suppressor genes or promoting oncogenic pathways. PRKDC (DNA-PK catalytic subunit) is involved in DNA repair, and its upregulation may enhance the ability of tumor cells to repair DNA damage, thus tolerating genomic instability and surviving genotoxic stress. GeneCards: HDAC1, GeneCards: PRKDC
• Role of 14-3-3 Proteins: Several members of the 14-3-3 protein family (YWHAE, YWHAB, YWHAQ, YWHAZ, YWHAH) are significantly upregulated. These proteins are critical scaffolding proteins involved in various cellular processes including cell cycle control, apoptosis, and signal transduction. Their upregulation can promote tumor cell survival and proliferation by interacting with a wide range of client proteins involved in cell cycle progression and apoptosis inhibition. GeneCards: YWHAE
• Impaired Growth Arrest: Conversely, GADD45B, a gene typically involved in cell cycle arrest and DNA repair, is significantly *downregulated* in tumor cells. This downregulation aligns with a loss of growth control and a failure to induce appropriate cell cycle checkpoints in response to stress, further facilitating uncontrolled proliferation. GeneCards: GADD45B

Overall, the gene expression profile in Intestinal Epithelial cells from tumor samples paints a clear picture of rampant cell cycle activity, driven by oncogenic factors and impaired cell cycle checkpoints, which are fundamental characteristics of cancer development in the colon.

Clinical or Translational Implications

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

• Biomarker Potential: The consistently and significantly upregulated genes (e.g., MYC, CDK4, CCND1, YWHAE, HDAC1/2, PRKDC, RAD21, SKP1) could serve as valuable diagnostic or prognostic biomarkers for colorectal cancer. Their expression levels might correlate with disease stage, aggressiveness, or patient outcomes.
• Therapeutic Targets: Many of the identified upregulated genes represent promising therapeutic targets.
• CDK4 and CCND1 are key components of the cell cycle machinery, and CDK4/6 inhibitors are already approved for certain cancers, suggesting a potential strategy for colorectal cancer, especially in contexts where these genes are overexpressed. PubMed Search: CDK4/6 inhibitors colorectal cancer
• HDAC1 and HDAC2 are targets for HDAC inhibitors, a class of drugs with anticancer activity that are being explored in various malignancies, including colorectal cancer. PubMed Search: HDAC inhibitors colorectal cancer
• Targeting the ubiquitin-proteasome system via components like RBX1 or SKP1 could also disrupt cell cycle progression and induce cell death in highly proliferative tumor cells.
• Understanding Disease Pathogenesis: This analysis deepens our understanding of the molecular pathology of colorectal cancer by pinpointing specific cell cycle components that are dysregulated in the tumor-initiating Intestinal Epithelial cells. This mechanistic insight can guide the development of novel therapeutic strategies or personalized medicine approaches.

The downregulation of growth arrest genes like GADD45B further emphasizes the dual mechanism of cancer development: both promotion of growth and evasion of growth-inhibitory signals. These insights highlight the importance of targeting multiple facets of cell cycle dysregulation in colorectal cancer.

20. Intestinal Epithelial Cell Gene Ontology (GSA) Analysis: Insights into Ploidy Status and Tumor Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GO) enrichment results for Intestinal Epithelial cells, derived from single-cell RNA sequencing data. Specifically, it compares gene sets upregulated in two distinct contexts:

1. Diploid vs. others: Pathways enriched in Intestinal Epithelial cells inferred as Diploid (likely more normal-like) compared to cells with other ploidy statuses (e.g., Aneuploid).
2. Tumor vs. others: Pathways enriched in Intestinal Epithelial cells obtained from tumor tissue compared to those from normal tissue.

These analyses leverage Gene Set Analysis (GSA) to identify biological processes and pathways that are significantly altered under different ploidy and disease conditions within the colon epithelium.

Visual Summary

The provided bar plots illustrate the top significantly enriched Gene Ontology terms based on their statistical significance (-log(p-val) and -log(q-val)). Higher values on the x-axis indicate stronger enrichment.

GSA_up for Intestinal Epithelial cell: Diploid_vs_others

This plot shows pathways that are more active in Diploid Intestinal Epithelial cells. Key observations include:

• Significant enrichment of pathways associated with cellular regulation and maintenance: FoxO signaling pathway, p53 signaling pathway, and Cellular senescence.
• Pathways related to epithelial barrier function: Tight junction are prominently upregulated.
• Enrichment of some immune response pathways: C-type lectin receptor signaling pathway and Fc gamma R-mediated phagocytosis.
• Several terms related to metabolism and secretion: Aldosterone-regulated sodium reabsorption, Pancreatic secretion, Mineral absorption, Amino sugar and nucleotide sugar metabolism.
• Interestingly, some broad "cancer" related terms like Transcriptional misregulation in cancer and Proteoglycans in cancer also appear, suggesting that genes involved in the normal regulation or suppression of these processes are active in diploid cells.

GSA_up for Intestinal Epithelial cell: tumor_vs_others

This plot displays pathways that are more active in Intestinal Epithelial cells within the tumor microenvironment. This plot shows a much larger number of highly significant enriched terms, dominated by:

• High metabolic and biosynthetic activity: Ribosome, Protein processing in endoplasmic reticulum, Spliceosome, Proteasome, RNA transport, Oxidative phosphorylation, Citrate cycle (TCA cycle), Glycolysis / Gluconeogenesis, Pyruvate metabolism, and Cell cycle. These signify rapid cell growth and division.
• Cellular stress, survival, and recycling: Autophagy, Lysosome, Mitophagy, AMPK signaling pathway, and Endocytosis.
• Inflammation and infection-related pathways: Numerous terms like Salmonella infection, Pathogenic Escherichia coli infection, Vibrio cholerae infection, Epstein-Barr virus infection, Viral carcinogenesis, and Bacterial invasion of epithelial cells are highly enriched, pointing to an active inflammatory and potentially infection-driven tumor microenvironment.
• Direct cancer-related terms: Colorectal cancer, Endometrial cancer, and Viral carcinogenesis are explicitly enriched.
• Altered cell adhesion: Adherens junction and Tight junction also appear, suggesting remodeling or dysregulation of cell-cell contacts.
• A notable presence of pathways associated with neurodegenerative diseases: Amyotrophic lateral sclerosis, Parkinson disease, Huntington disease, Alzheimer disease, Pathways of neurodegeneration. This often indicates shared fundamental cellular stress, protein handling, or mitochondrial dysfunction mechanisms.

Biological Interpretation

The contrasting GO enrichment patterns provide valuable insights into the biological states of Intestinal Epithelial cells based on their ploidy and disease context.

Diploid Intestinal Epithelial Cells: Guardians of Homeostasis

The enrichment of pathways like FoxO signaling pathway GeneCards: FOXO Signaling Pathway and p53 signaling pathway GeneCards: TP53 Signaling Pathway in Diploid Intestinal Epithelial cells suggests an active role in maintaining cellular homeostasis, responding to stress, and potentially preventing malignant transformation. The Tight junction pathway's upregulation underscores the importance of barrier integrity in normal epithelial function. Even the "cancer" related terms (e.g., Transcriptional misregulation in cancer) appearing in the diploid context might indicate the active engagement of protective or regulatory mechanisms that prevent the onset of such pathologies, rather than promoting them. Cellular senescence can also act as a tumor-suppressive mechanism by permanently arresting the proliferation of damaged cells.

Tumor-Associated Intestinal Epithelial Cells: A Hyperactive and Inflammatory State

In contrast, Intestinal Epithelial cells from tumor samples exhibit hallmarks of aggressive cellular behavior. The overwhelming enrichment of pathways related to protein synthesis, processing, and degradation (Ribosome, ER protein processing, Proteasome, Spliceosome), coupled with high activity in energy metabolism (Oxidative phosphorylation, TCA cycle, Glycolysis) and Cell cycle, points to rapid proliferation and high metabolic demand characteristic of cancer cells. These are classic features of the Warburg effect and unchecked growth.

The significant presence of inflammation and infection-related pathways strongly indicates an immune-responsive or inflammatory tumor microenvironment. Chronic inflammation is a known risk factor and driver of colorectal cancer progression PubMed: Inflammation and Colorectal Cancer. The activation of pathways for various bacterial and viral infections suggests the involvement of the microbiota or host immune responses to pathogens, which can contribute to carcinogenesis.

The concurrent upregulation of Autophagy, Lysosome, and Mitophagy pathways suggests that tumor cells are actively engaging in cellular recycling and quality control mechanisms, likely to cope with metabolic stress and maintain survival under harsh tumor microenvironmental conditions PubMed: Autophagy in Cancer. The presence of Adherens junction and Tight junction pathways in tumor cells could reflect ongoing tissue remodeling, epithelial-to-mesenchymal transition (EMT) processes, or the emergence of new intercellular contacts that facilitate tumor growth and invasion, rather than maintaining normal barrier function.

The enrichment of Neurodegenerative diseases pathways is intriguing. While not directly linked to colon cancer pathogenesis, these pathways often involve common cellular processes like protein misfolding, mitochondrial dysfunction, and oxidative stress, which are also perturbed in cancer cells as they adapt to uncontrolled growth and metabolic reprogramming.

Clinical or Translational Implications

The findings highlight distinct biological programs operating in Intestinal Epithelial cells based on their genomic stability (ploidy) and disease state.

• Early Detection and Prevention: Understanding the active pathways in Diploid epithelial cells can shed light on mechanisms of tumor suppression and normal epithelial maintenance. Genes within pathways like FoxO and p53 signaling could serve as potential biomarkers for early detection of epithelial dysfunction or as targets for chemoprevention strategies.
• Therapeutic Targeting in CRC: The extensive upregulation of metabolic, protein synthesis, and cell cycle pathways in tumor-associated epithelial cells identifies crucial vulnerabilities of colorectal cancer. Targeting components of the ribosome, proteasome, or specific metabolic enzymes (e.g., those in glycolysis or oxidative phosphorylation) could offer new therapeutic avenues for colorectal cancer PubMed: Metabolic Reprogramming in Cancer Therapy.
• Immunomodulation: The strong inflammatory and infection-related signatures in tumor cells suggest that the tumor microenvironment is highly influenced by immune responses and potentially the gut microbiome. Therapies aimed at modulating the immune response or targeting specific microbial interactions could be beneficial, especially in inflammation-driven colorectal cancer.
• Biomarker Discovery: Specific genes within the highly enriched pathways unique to tumor epithelial cells could serve as diagnostic or prognostic biomarkers, or even as targets for personalized medicine approaches.

21. Gene Set Enrichment Analysis (GSEA) of Colon Single-Cell RNA-seq Data Across Key Cell Types

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results visualized as a dot plot. The purpose is to identify enriched biological pathways and processes across various cell types (B cell, Fibroblast, ILC, Intestinal Epithelial cell, Plasma cell, T cell CD4+, T cell CD8+) within the colon tissue, comparing distinct conditions: 'normal' versus 'others', 'tumor' versus 'others', and for Intestinal Epithelial cells, 'Diploid' versus 'others'. The analysis helps to understand cell-type-specific functional shifts associated with different states, particularly in the context of colon cancer.

Visual Summary

The dot plot visualizes the GSEA results, with each row representing a specific gene set/pathway and each column representing a cell type under a particular comparison condition (e.g., "B cell: normal_vs_others", "Intestinal Epithelial cell: tumor_vs_others").

• Dot Size: The size of each dot corresponds to the statistical significance of the enrichment, specifically the -log(P-value). Larger dots indicate more significant enrichment.
• Dot Color: The color of the dot indicates the Normalized Enrichment Score (NES). Red colors signify positive NES, indicating pathways that are upregulated or positively enriched in the test condition compared to the reference. Blue colors signify negative NES, indicating pathways that are downregulated or negatively enriched. A white/light grey color represents an NES close to zero.

Key Observations:

• There is a clear difference in enriched pathways and their directionality between 'normal_vs_others' and 'tumor_vs_others' comparisons for most cell types.
• The Intestinal Epithelial cells, being the tumor origin cell type, show distinct patterns in 'tumor_vs_others' and 'Diploid_vs_others' comparisons.
• Immune cell populations (T cells, B cells, Plasma cells, ILCs) exhibit significant enrichment in various immune-related and disease-specific pathways.
• Fibroblasts also show active participation in several pathways, suggesting their role in the tumor microenvironment.

Biological Interpretation

Intestinal Epithelial Cells: Insights into Tumorigenesis and Ploidy

The Intestinal Epithelial cells, identified as the tumor origin cell type, show critical pathway alterations:

• Tumor vs. Others (Intestinal Epithelial cell: tumor_vs_others): This comparison is crucial for understanding tumor-specific processes.
• Upregulated (Red NES): We observe strong upregulation of "Pathways in cancer" and "Transcriptional misregulation in cancer" (as expected, given they are tumor cells). "PI3K-Akt signaling pathway" and "MAPK signaling pathway" are highly enriched and upregulated, consistent with their well-established roles in cell proliferation, survival, and oncogenesis in colorectal cancer. PubMed Search: PI3K-Akt MAPK colorectal cancer "Viral carcinogenesis" is also upregulated, which can be relevant given the potential role of viruses in cancer development.
• Downregulated (Blue NES): "Tight junction" is significantly downregulated, which could indicate disrupted cell-cell adhesion and increased invasiveness in tumor cells.
• Diploid vs. Others (Intestinal Epithelial cell: Diploid_vs_others): This comparison likely highlights differences between diploid and potentially aneuploid epithelial cells, with aneuploidy being a hallmark of cancer.
• Upregulated (Red NES): Pathways such as "RNA transport", "Protein export", "Spliceosome", "mRNA surveillance pathway", "Oxidative phosphorylation", and "Pyruvate metabolism" are upregulated in diploid epithelial cells. This suggests that diploid cells might maintain more robust fundamental cellular machinery, including mRNA processing, energy metabolism, and protein synthesis/transport, compared to aneuploid cells which might have dysregulated processes due to genomic instability. "Proteoglycans in cancer" is also upregulated in diploid cells, which is interesting as proteoglycans can have dual roles in cancer progression. UniProt: Proteoglycans
• Downregulated (Blue NES): "TGF-beta signaling pathway" and "TNF signaling pathway" appear downregulated in diploid cells. These pathways are often involved in immune evasion and inflammation in cancer.

Immune Cell Responses in the Tumor Microenvironment

Immune cells demonstrate significant condition-specific pathway changes:

T cells (CD4+ and CD8+):

• Tumor vs. Others (T cell CD4+: tumor_vs_others, T cell CD8+: tumor_vs_others): Both CD4+ and CD8+ T cells show upregulation of "Th1 and Th2 cell differentiation", "IL-17 signaling pathway", and "TNF signaling pathway", indicating active immune responses and inflammation within the tumor. "IL-17 signaling" is particularly notable for its role in anti-tumor immunity and inflammation. PubMed Search: IL-17 signaling cancer immunity
• Normal vs. Others: In normal conditions, T cells (especially CD4+) show some downregulation of immune activation pathways.

B cells and Plasma cells:

• Tumor vs. Others (B cell: tumor_vs_others, Plasma cell: tumor_vs_others): "Intestinal immune network for IgA production" is significantly upregulated in Plasma cells in tumor conditions, suggesting active antibody production, possibly against tumor antigens or in response to altered microbiota. "Th1 and Th2 cell differentiation" is also upregulated in B cells and Plasma cells.
• Normal vs. Others: B cells in normal conditions show downregulation of pathways like "Epstein-Barr virus infection" and some other immune response pathways, indicating a more quiescent state or different immune context compared to tumor.

ILC (Innate Lymphoid Cells):

• Tumor vs. Others (ILC: tumor_vs_others): ILCs exhibit significant upregulation of "Th1 and Th2 cell differentiation", "IL-17 signaling pathway", and "TNF signaling pathway", mirroring the T cell response. This suggests a broad activation of innate immune responses in the tumor microenvironment.

Fibroblasts: Key Players in the Tumor Stroma

Fibroblasts show a prominent role in the tumor microenvironment:

Tumor vs. Others (Fibroblast: tumor_vs_others):

• Upregulated (Red NES): Fibroblasts in the tumor context strongly upregulate "Pathways in cancer", "PI3K-Akt signaling pathway", "MAPK signaling pathway", and "Focal adhesion". "Focal adhesion" upregulation is consistent with their role in extracellular matrix remodeling and cell migration, characteristic of Cancer-Associated Fibroblasts (CAFs). GeneCards: Focal Adhesion pathway "TGF-beta signaling pathway" is also upregulated, a critical pathway for CAF activation and fibrosis.
• Downregulated (Blue NES): In contrast, "Adrenergic signaling in cardiomyocytes" and "Aldosterone synthesis and secretion" are downregulated, which are less relevant to colon fibroblasts but indicate a shift away from certain physiological functions.

Other Notable Pathways

• Apoptosis and Autophagy: These pathways show mixed enrichment, often downregulated in tumor cells (Intestinal Epithelial cell: tumor_vs_others) but potentially altered in other cell types, reflecting complex cell death regulation in cancer.
• Metabolic Reprogramming: "Oxidative phosphorylation" and "Pyruvate metabolism" show varying enrichment across cell types and conditions, indicating metabolic shifts. For example, Intestinal Epithelial cells (Diploid_vs_others) show upregulation of these pathways, suggesting a potential metabolic preference in non-aneuploid epithelial cells.
• Immunosuppression and Inflammation: "TGF-beta signaling pathway" and "TNF signaling pathway" are broadly implicated across various immune and stromal cells, reflecting the intricate balance of pro-inflammatory and immunosuppressive signals within the tumor microenvironment.

Clinical or Translational Implications

1. Therapeutic Targets in Intestinal Epithelial Cells: The strong upregulation of "PI3K-Akt signaling pathway", "MAPK signaling pathway", and "Transcriptional misregulation in cancer" in tumor-associated Intestinal Epithelial cells highlights these as promising therapeutic targets for colorectal cancer. Strategies targeting these pathways, such as PI3K inhibitors or MEK inhibitors, could be investigated. PubMed Search: PI3K MAPK inhibitors colorectal cancer The downregulation of "Tight junction" could also indicate a target for improving cell-cell adhesion and reducing invasiveness.
2. Modulating Immune Responses: The widespread activation of immune signaling pathways (e.g., "Th1 and Th2 cell differentiation", "IL-17 signaling pathway", "TNF signaling pathway") in T cells, B cells, Plasma cells, and ILCs in tumor conditions suggests a robust but potentially dysfunctional immune response. Understanding the precise roles of these pathways (e.g., pro-tumorigenic vs. anti-tumorigenic subsets of Th1/Th2/Th17 cells) could guide immunotherapy strategies, potentially by enhancing anti-tumor immunity or dampening pro-tumor inflammation.
3. Targeting the Tumor Stroma: The activation of "Focal adhesion" and "TGF-beta signaling pathway" in tumor-associated Fibroblasts underscores their critical role in supporting tumor growth and metastasis. Targeting CAFs, perhaps by inhibiting TGF-beta signaling or modulating their interaction with the extracellular matrix, could be an effective anti-cancer strategy. PubMed Search: CAFs TGF-beta colorectal cancer
4. Ploidy as a Prognostic Marker/Therapeutic Indicator: The distinct metabolic and regulatory pathway enrichments in Intestinal Epithelial cells based on ploidy state (Diploid vs. Others) suggest that ploidy could be a significant factor influencing cellular behavior and therapeutic response. Further investigation into the "RNA transport", "Protein export", "Spliceosome", "Oxidative phosphorylation", and "Pyruvate metabolism" pathways in diploid vs. aneuploid tumor cells could reveal vulnerabilities or resistance mechanisms.
5. Biomarkers: Specific pathway enrichments (e.g., "PI3K-Akt signaling" activity in tumor epithelial cells, or specific immune pathway signatures in circulating immune cells) could serve as potential biomarkers for disease progression, prognosis, or response to therapy.

22. Discussion

The comprehensive single-cell analysis of human colon tissue reveals a striking reprogramming of both malignant cells and their microenvironment in colon cancer. Intestinal Epithelial cells, identified as the tumor origin, exhibit extensive genomic instability characterized by recurrent amplifications of oncogenes such as EGFR and ERBB2, along with widespread cell cycle dysregulation marked by upregulation of MYC, CDK4, CCND1, and various 14-3-3 proteins, and downregulation of tumor suppressor GADD45B. Gene Ontology analysis further supports a hyperactive metabolic and proliferative state, indicative of unchecked tumor growth.

The tumor microenvironment undergoes significant remodeling. Immune cell populations show notable shifts: an increase in immunosuppressive T regulatory (Treg) cells, pro-inflammatory Th17 and Th22 T cells, and a distinct repolarization of macrophages towards pro-tumorigenic M2B and M2D phenotypes. These changes suggest an active immune evasion strategy by the tumor, creating an environment that dampens effective anti-tumor responses. Fibroblasts also transform into distinct cancer-associated fibroblast (CAF) subsets, characterized by specific surface markers (e.g., ITGA1, ANTXR1, CDH11, EDNRA) and activated pathways (e.g., Focal Adhesion, TGF-beta signaling), underscoring their critical role in extracellular matrix remodeling and tumor support.

Cell-cell interaction analysis reveals a dramatic rewiring of intercellular communication. In normal tissue, diverse immune and epithelial crosstalk maintains homeostasis. In contrast, tumor tissue exhibits a more restricted, often immunosuppressive, interaction network. Notably, interactions involving immune checkpoint molecules like NECTIN2-TIGIT and VSIR-HLA-F are upregulated between tumor Intestinal Epithelial cells and T cells, directly contributing to T cell exhaustion. The loss of IFN-gamma-related signaling and specific B cell-T cell interactions in the tumor further indicates impaired adaptive immunity. Overall, these findings provide a high-resolution map of the cellular and molecular machinery driving colon cancer, highlighting a complex interplay between genetic alterations, immune dysregulation, and stromal support.

Hypotheses:

1. Aneuploid Intestinal Epithelial cells with upregulated oncogenes (EGFR, ERBB2) and dysregulated cell cycle pathways (MYC, CDK4, CCND1) are the primary drivers of aggressive tumor progression in colon cancer.
2. The observed shifts in T cell subsets (increased Tregs, Th17, Th22) and macrophage repolarization towards M2B/M2D phenotypes in the tumor microenvironment collectively establish an immunosuppressive milieu that actively promotes immune evasion and tumor growth.
3. Upregulated immune checkpoint interactions, particularly NECTIN2-TIGIT and VSIR-HLA-F, between tumor epithelial cells and T cells directly contribute to T cell exhaustion and render anti-tumor immune responses ineffective in colon cancer.
4. Cancer-associated fibroblasts (CAFs) expressing specific markers (e.g., ANTXR1, ITGAV, EDNRA) actively remodel the extracellular matrix and secrete factors that enhance tumor cell proliferation, survival, and metastasis in the colon cancer microenvironment.

Potential therapeutic targets:

1. NECTIN2-TIGIT pathway: This immune checkpoint interaction between tumor Intestinal Epithelial cells (NECTIN2) and T cells (TIGIT) is significantly upregulated in tumor tissue, suggesting a key mechanism of T cell suppression and immune evasion. Blocking this pathway could reactivate exhausted T cells. Evidence: Upregulation of NECTIN2-TIGIT interaction in tumor-associated Intestinal Epithelial cells and CD4+/CD8+ T cells (Sections 12, 15), confirmed by dot plot visualization of CCI patterns. TIGIT is a known inhibitory receptor on T cells. Validation: Test anti-TIGIT antibodies (alone or in combination with other immune checkpoint inhibitors) in preclinical colon cancer models. Measure T cell activation markers, cytokine production, and tumor growth inhibition. Validate NECTIN2 expression on tumor cells using IHC.
2. VSIR (VISTA)-HLA-F pathway: Similar to TIGIT, VSIR (VISTA) is an immune checkpoint molecule, and its interaction with HLA-F is enriched in tumor conditions, indicating another active immunosuppressive axis that tumors exploit to suppress T cell responses. Evidence: Significant upregulation of VSIR-HLA-F interactions in tumor samples (Sections 12, 13, 15) involving T cells and B cells/tumor epithelial cells. Validation: Develop and test anti-VISTA antibodies to block this interaction in *in vitro* co-culture systems and *in vivo* colon cancer models. Assess changes in T cell function and anti-tumor immunity. Validate VSIR and HLA-F expression on relevant cell types.
3. Macrophage M2D/M2B repolarization: The tumor microenvironment shows a significant increase in pro-tumorigenic M2B and M2D macrophage subsets, which contribute to immunosuppression, angiogenesis, and tumor growth. Repolarizing or inhibiting these subsets could enhance anti-tumor immunity. Evidence: Significant increase in M2B and M2D macrophage proportions in tumor samples compared to normal tissue (Sections 9, 10). M2D macrophages are strongly linked to angiogenesis, immune suppression (via IL-10, VEGF), and tumor growth. Validation: Investigate small molecule inhibitors or genetic approaches to block M2D/M2B macrophage recruitment or induce their repolarization towards an M1-like phenotype in *in vitro* macrophage polarization assays and *in vivo* colon cancer models. Assess changes in tumor growth, vascularization, and immune cell infiltration.
4. CDK4/CCND1 and HDAC1/2: Intestinal Epithelial cells from tumors exhibit widespread upregulation of cell cycle drivers like CDK4 and CCND1, and epigenetic regulators like HDAC1 and HDAC2, indicating unchecked proliferation and altered gene expression critical for tumorigenesis. Evidence: Statistically significant upregulation of CDK4, CCND1, HDAC1, and HDAC2 in tumor Intestinal Epithelial cells (Section 19), driving rapid cell cycle progression and epigenetic dysregulation. Validation: Evaluate the efficacy of existing CDK4/6 inhibitors and HDAC inhibitors (alone or in combination with other agents) in colon cancer cell lines and patient-derived organoids. Test *in vivo* efficacy in mouse models of colon cancer with relevant overexpression of these targets.
5. ERBB3 / EGFR: These receptor tyrosine kinases are critical for promoting cell proliferation and survival. ERBB3 is highly expressed on tumor-origin Intestinal Epithelial cells, and EGFR is amplified via CNVs, making them key drivers of oncogenic signaling. Evidence: High expression of ERBB3 as a tumor-specific surfaceome marker in Intestinal Epithelial cells (Section 16). EGFR amplification is a recurrent copy number variation in tumor samples (Section 4). PI3K-Akt and MAPK signaling pathways are highly activated in tumor epithelial cells (Section 21), where ERBB3 and EGFR are key upstream activators. Validation: Assess the efficacy of anti-ERBB3 or anti-EGFR antibodies, or small molecule inhibitors, in colon cancer cell lines and *in vivo* models. Focus on patient stratification based on ERBB3/EGFR expression levels and genomic amplifications.
6. ENTPD1 (CD39): ENTPD1 (CD39) is highly expressed on tumor-associated CD4+ T cells, particularly Tregs. It contributes to the immunosuppressive adenosine pathway by converting ATP to AMP, promoting T cell exhaustion and tumor immune evasion. Blocking CD39 can reverse this immunosuppression. Evidence: High expression of ENTPD1 (CD39) as a tumor-specific surfaceome marker in CD4+ T cells (Section 18). Validation: Test CD39 inhibitors or antibodies in preclinical colon cancer models, evaluating their impact on adenosine levels in the TME, T cell activation, and tumor growth. Combine with other immunotherapies.

Follow-up validation ideas:

1. Validate the protein expression of upregulated tumor-specific surface markers (e.g., CD44, GPRC5A, ITGB1, ERBB3) on Intestinal Epithelial cells via immunohistochemistry (IHC) or multiplex immunofluorescence (mIF) in a larger cohort of human colon cancer tissues.
2. Perform flow cytometry or mass cytometry to confirm the altered proportions of T cell subsets (Tfh, Th17, Treg, Th22) and macrophage subsets (M2A, M2B, M2D) in fresh tumor biopsies versus normal colon tissue.
3. Conduct *in vitro* co-culture experiments using colon cancer cell lines or patient-derived organoids with T cells and macrophages to functionally validate the NECTIN2-TIGIT and VSIR-HLA-F immune checkpoint interactions and their impact on T cell activation and exhaustion.
4. Utilize spatial transcriptomics or proteomics to precisely map the localization and interaction patterns of key cell types and their specific markers (e.g., CAFs and tumor cells, T cells and tumor cells) within the tumor microenvironment, confirming spatial proximity for observed cell-cell interactions.
5. Employ genetic perturbation assays (*in vitro* gene knockdown/overexpression or *in vivo* CRISPR/Cas9 in mouse models) to investigate the functional consequences of dysregulated cell cycle genes (e.g., MYC, CDK4, GADD45B) on colon cancer cell proliferation and survival.
6. Test the therapeutic efficacy of targeting specific CAF surface markers (e.g., ANTXR1, CDH11) using antibody-drug conjugates or CAR T-cell therapy in preclinical *in vivo* mouse models of colon cancer.

Limitations:

This report is based on single-cell RNA-seq data, providing correlative insights into cellular states and interactions. Causal relationships require further experimental validation. The identified therapeutic targets are candidates that need rigorous functional testing in relevant preclinical and clinical settings. Cell type annotation relies on marker expression and computational inference, which may not capture all cellular complexities or rare cell types. CNV inference from scRNA-seq can be subject to noise and technical limitations. The 'unassigned' cells represent a minor population whose precise identity and contribution to the tumor microenvironment remain unclear without further characterization.

23. 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 them by sample, show CNV heatmap, and also show a summary of significantly amplified copy number regions, and save it.
5. Show CNV patterns as UMAP. Include major cell type, minor cell type, ploidy results, condition, and sample in 2 columns and save it.
6. Show population bar plot for minor cell types and save it.
7. Show subset population bar plot for T cells and save it.
8. If there are significant differences between conditions in the T cell subset population, show box plots and save them. Set ncols appropriately considering the total number of panels.
9. Show subset population bar plot for macrophages and save it.
10. If there are significant differences between conditions in the macrophage subset population, show box plots and save them. Set ncols appropriately considering the total number of panels.
11. Select tumor origin cells and unassigned cells, and show their ploidy population as a bar plot and save it.
12. Show cell-cell interaction patterns involving Intestinal Epithelial cells (tumor origin), Fibroblast, Macrophage, and T cells by condition and save them. Select up to 80 cell-cell interactions per condition.
13. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
14. Select genes related to immune checkpoint pathways and cell cycle pathways, and show cell-cell interactions for these genes and save them.
15. Find statistically significant differences in cell-cell interactions between conditions for major immune cells and stromal cells and 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 Fibroblast cells and show them as a dot plot and save it. Only surfaceome markers, up to 50 per condition.
18. Extract condition-specific markers for T cell CD4+ cells and show them as a dot plot and save it. Only surfaceome markers, up to 50 per condition.
19. Among cell cycle pathway related genes, select those with statistically significant differences in expression between conditions for Intestinal Epithelial cells (tumor origin) and show box plots and save them. Set max_n_items_to_plot = 24, and set ncols appropriately so that the width x height ratio is about 2x3 considering the total number of panels.
20. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
21. Show Gene Set Enrichment Analysis results dot plot for B cell, Fibroblast, ILC, Intestinal Epithelial cell, Plasma cell, T cell CD4+, and T cell CD8+ cells and save it. Set color map to RdBu_r and n_pws_to_show = 80.