SCODiA Report by MLBI Lab

Single-Cell Landscape of Colorectal Cancer Reveals Genomic Instability, Dysregulated Microenvironment, and Targetable Pathways

This single-cell RNA-seq analysis of human colon tissue provides a comprehensive view of colorectal cancer (CRC), highlighting distinct cellular populations and altered genomic states in the tumor microenvironment. Key findings include widespread aneuploidy in tumor-origin Intestinal Epithelial cells, significant shifts in immune and stromal cell compositions, and dysregulated cell-cell communication networks. We identified numerous tumor-specific surface markers and pathways involved in proliferation, inflammation, and immune evasion, offering insights into CRC pathogenesis and potential therapeutic strategies.

Contents

  1. Dataset overview
  2. Single-Cell UMAP Embedding Overview of Colon Tissue
  3. UMAP Visualization of Major Cell Type Scores, Ploidy, and Cell Type Annotations
  4. Celltype Subtype Marker Gene Expression Overview
  5. Tumor-Origin and Unassigned Cell CNV Analysis: Sample-Level Patterns and Significant Amplifications
  6. CNV-Aware UMAP Embedding of Colon Tissue Single-Cell RNA-seq Data
  7. Minor Cell Type Population Analysis in Colon Tissue
  8. T 세포 아형 인구 분포 분석 (정상 및 종양 조직 비교)
  9. T Cell and ILC Subset Population Differences in Colon Tumor Microenvironment
  10. Macrophage Subset Population Dynamics in Colon Cancer Microenvironment
  11. Macrophage Subset Population Shifts in Colon Tumor Microenvironment
  12. Ploidy Population Analysis of Intestinal Epithelial and Unassigned Cells in Colorectal Tissue
  13. Cell-Cell Interaction Patterns in the Colon Tumor Microenvironment
  14. Condition-Specific Cell-Cell Interaction Patterns in Colon Cancer
  15. Immune Checkpoint and Cell Cycle Related Cell-Cell Interactions in Colorectal Tumor Microenvironment
  16. Condition-Specific Cell-Cell Interaction Patterns in Colon Tissue
  17. Intestinal Epithelial Cell Condition-Specific Surfaceome Markers in Colon Tissue
  18. Condition-Specific Surfaceome Markers in Macrophages from Colon Tissue
  19. Fibroblast Condition-Specific Surfaceome Markers in Colon Tissue
  20. CD4 T cell Condition-Specific Surfaceome Markers in Colon Tissue
  21. Dysregulation of Cell Cycle Genes in Intestinal Epithelial Cells of Colon Tumors
  22. 장 상피세포(Intestinal Epithelial cell)의 유전자 온톨로지(GSA) 분석 결과
  23. Gene Set Enrichment Analysis Reveals Condition-Specific Pathway Alterations Across Colon Cell Types
  24. Discussion
  25. Query List

0. Dataset overview

데이터셋 요약

주요 관측(obs) 컬럼:

사전 계산된 결과:

1. Single-Cell UMAP Embedding Overview of Colon Tissue

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a Uniform Manifold Approximation and Projection (UMAP) visualization of single-cell RNA-seq data from human colon tissue, comprising 96,642 cells and 27,779 genes. The UMAP plots are colored by several key metadata features: condition (tumor vs. adjacent_normal), sample, celltype_major, celltype_minor, ploidy_dec (aneuploidy status), and celltype_subset. This visualization aims to provide an overview of cellular heterogeneity, sample integration, and the distribution of biological states within the dataset.

Visual Summary

Condition

The UMAP colored by condition shows a clear separation between tumor (purple) and adjacent_normal (maroon) cells in several major clusters. The large cluster in the bottom-right quadrant and a smaller cluster on the far-right are predominantly composed of tumor cells. Other regions, particularly on the left and top-center, show a more mixed distribution or are dominated by adjacent_normal cells. This indicates distinct global transcriptional profiles associated with the tumor microenvironment compared to normal colon tissue.

Sample

The sample UMAP displays a broad distribution of cells from various samples (represented by many colors) across the entire embedding. This indicates that cells from different patients are largely well-mixed, suggesting effective integration of the dataset and minimal strong sample-specific batch effects that would otherwise lead to samples clustering independently of cell identity or condition.

Major Cell Type (celltype_major)

The Intestinal Epithelial cell (light yellow) population forms a prominent, large cluster, primarily localized in the bottom-right and central-right regions of the UMAP. This area largely overlaps with the tumor condition. T cell (cyan) and Myeloid cell (light green) populations form other substantial and distinct clusters, indicative of significant immune cell presence. Stromal cell (dark green) and Endothelial cell (orange) also form well-defined clusters. B cell (maroon) is present in smaller, distinct clusters. A relatively small fraction of cells remains unassigned (dark purple).

Minor Cell Type (celltype_minor)

At a higher resolution, Intestinal Epithelial cell (yellow) continues to define the clusters associated with the tumor. Macrophage (light yellow/green), T cell CD4+ (dark blue), and T cell CD8+ (purple) emerge as key immune cell populations, forming distinct and often large clusters. Fibroblast (orange) and Endothelial cell (red) populations are also clearly delineated. Other minor cell types like Plasma cell, DC, ILC, Mast cell, NK cell, and Smooth muscle cell form smaller but identifiable clusters.

Ploidy Status (ploidy_dec)

The ploidy_dec UMAP is particularly informative. Aneuploid cells (maroon) are strikingly concentrated within the large cluster in the bottom-right, which corresponds to the Intestinal Epithelial cell major type and predominantly to the tumor condition. This strong co-localization serves as a robust indicator of malignant epithelial cells. Diploid cells (yellow) are widely distributed across the rest of the UMAP, encompassing all non-malignant immune, stromal, and normal epithelial cell populations. A small number of cells are labeled as Unclear (purple).

Cell Type Subset (celltype_subset)

The most granular annotation, celltype_subset, reveals further heterogeneity within the major and minor cell types. Within the epithelial compartment, specific subtypes like Enterocyte, Crypt cell, Goblet cell, Paneth cell, Tuft cell, Microfold cell, Enteroendocrine cell, and Enterochromaffin cell are discernible. Immune cell clusters resolve into specialized populations such as T cell (Treg), T cell (Th17), T cell (Cytotoxic), different Macrophage subtypes (e.g., Mac_M1, Mac_M2a/b/c/d), and various B cell subsets (e.g., B cell (Memory), B cell (Follicular)). This level of detail provides fine-grained insights into the cellular composition of the colon tissue and its changes in disease.

Biological Interpretation

The UMAP analysis provides a comprehensive overview of the single-cell landscape of colon tissue in both tumor and adjacent normal conditions.

  1. Identification of Malignant Cells: The distinct clustering of Aneuploid cells, exclusively within the Intestinal Epithelial cell population that co-localizes with the tumor condition, strongly identifies the malignant epithelial cells forming the core of the colorectal tumor. This is a critical initial step for any downstream tumor-specific analysis.
  2. Tumor Microenvironment (TME) Complexity: The presence and distinct clustering of diverse immune (T cells, B cells, Myeloid cells, ILCs) and stromal (Fibroblasts, Endothelial cells, Smooth muscle cells) cell types highlight the complex cellular composition of the colon TME. The distribution of these cell types across tumor and adjacent normal regions indicates changes in cellular proportions and states driven by the disease.
  3. Cellular Heterogeneity: The hierarchical cell type annotations (major, minor, subset) effectively capture the considerable heterogeneity within both epithelial and non-epithelial compartments. For instance, the resolution of multiple macrophage and T cell subsets suggests a nuanced immune response and distinct functional states in different tissue environments. Similarly, the diverse epithelial cell subtypes reflect the intricate architecture of the colon lining.
  4. Dataset Quality and Integration: The general mixing of cells from different samples across the UMAP indicates successful integration of data from multiple patients, suggesting that patient-specific batch effects have been well-handled and do not obscure major biological distinctions.

Annotation Notes

The UMAP visualizations demonstrate high-quality cell type annotations that are consistent across different levels of granularity (major, minor, subset). The clear separation of cell populations and their biological relevance (e.g., aneuploidy indicating tumor cells) strongly support the reliability of the cell assignments. The relatively small fraction of "unassigned" cells across all annotation levels further indicates comprehensive coverage of the cellular landscape. The clear distinction between tumor and adjacent normal cells, together with the absence of significant sample-driven clustering, suggests that the dataset is well-structured for further in-depth biological investigations.

2. UMAP Visualization of Major Cell Type Scores, Ploidy, and Cell Type Annotations

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents UMAP visualizations that project cell type scores for various major cell types onto the single-cell RNA-seq embedding. These scores, derived from the HiCAT method, indicate the confidence of each cell belonging to a specific major cell type. For comparison and validation, the UMAP is also displayed with ploidy_dec (Aneuploid/Diploid status) and the final celltype_major annotations. This helps assess the quality of cell type identification and the overall structure of the single-cell dataset.

Visual Summary

The UMAP plots provide a comprehensive overview of cell populations and their characteristics within the dataset:

Major Cell Type Scores (HiCAT_major_score)

Ploidy Status (ploidy_dec)

Major Cell Type Annotation (celltype_major)

Biological Interpretation

  1. Robust Cell Type Segregation: The UMAP plots, particularly those showing major cell type scores, demonstrate excellent segregation of distinct cell populations. T cells, B cells, Myeloid cells, Intestinal Epithelial cells, Endothelial cells, and Stromal cells each occupy unique and well-defined regions in the embedding. This clear separation supports the high quality of the single-cell data processing and clustering.
  2. Concordance of Cell Type Scores and Annotations: There is a strong concordance between the continuous HiCAT major cell type scores and the discrete celltype_major annotations. For example, regions with high "HiCAT_major_score: T cell" precisely overlap with the cluster labeled "T cell" in the celltype_major plot. This consistency validates the accuracy of the cell type assignments.
  3. Identification of Tumor Cells via Ploidy: The ploidy_dec plot clearly identifies a large population of Aneuploid cells. Critically, this aneuploid population strongly overlaps with the region identified as "Intestinal Epithelial cell" by both the HiCAT score and the celltype_major annotation. Given the data context stating "Tumor origin celltype: Intestinal Epithelial cell" and "conditions: tumor, adjacent_normal," this strongly indicates that these aneuploid Intestinal Epithelial cells represent the malignant tumor cells. This is a common characteristic of many cancers, where genomic instability leads to widespread chromosomal abnormalities GeneCards: TP53.
  4. Normal/Stromal/Immune Cells are Diploid: Conversely, the vast majority of immune cells (T cells, B cells, Myeloid cells), stromal cells, and endothelial cells are identified as Diploid. This is expected for healthy, non-malignant cells and reinforces the distinction between the tumor compartment and the tumor microenvironment (TME) components.
  5. Rare Cell Types and Potential Ambiguity: The "Enteric neuron" scores are notably low across the UMAP, suggesting this cell type might be very rare, poorly captured, or the scoring model for it is less confident compared to other major types. The "unassigned" cells are likely those that exhibit mixed features or do not strongly express markers for any single major type, warranting further investigation if precise identification is required.

Annotation Notes

3. Celltype Subtype Marker Gene Expression Overview

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot visualizing the expression of marker genes across various celltype_subset populations identified from single-cell RNA-seq data of human colon tissue. The purpose of this plot is to assess the specificity and average expression levels of key genes for each cell type subset, thereby validating the quality and biological consistency of the cell type annotations. The size of each dot corresponds to the fraction of cells within a given group that express the gene, while the color intensity reflects the mean expression level of that gene in the group. Importantly, the marker discovery focused on surfaceome-only genes, and genes common to three or more cell groups were excluded to emphasize specificity.

Visual Summary

The dot plot generally shows distinct patterns of marker gene expression, with many celltype_subset populations exhibiting strong and specific expression of a limited set of genes (indicated by large, dark red dots primarily confined to a single row/cell type). Red boxes highlight groups of genes that are highly specific to certain cell type subsets, supporting their unique identities.

Notable observations include:

Biological Interpretation

The observed marker gene expression largely aligns with the known biological functions and identities of the annotated cell type subsets, providing strong evidence for the validity of most cell annotations.

Annotation Notes

While most cell type subsets are well-supported by their marker gene expression, some observations warrant further attention for refining the annotations:

4. Tumor-Origin and Unassigned Cell CNV Analysis: Sample-Level Patterns and Significant Amplifications

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates copy number variations (CNVs) in Intestinal Epithelial cells (identified as the tumor-origin cell type) and unassigned cells, grouped by individual samples. The primary goal is to visualize genomic gains (amplifications) and losses (deletions) across chromosomes in these cell populations and to identify significantly amplified regions, particularly focusing on those frequently observed across samples. The ploidy_dec annotation, indicating whether a cell population is predominantly Aneuploid or Diploid, helps in distinguishing tumor-like from potentially non-tumor or less malignant populations within samples.

Visual Summary

log2(CNR) Heatmap (Top Figure)

The heatmap displays the log2 ratio of copy number estimates (log2(CNR)) for genomic spots across all chromosomes (chr1-chr22) for selected cell populations (Intestinal Epithelial cells and unassigned cells) grouped by sample.

Significant Amplification Summary (Bottom Figure)

This heatmap and associated bar chart summarize the frequency of significant amplifications within specific cytogenetic bands across the analyzed samples.

Biological Interpretation

The analysis of tumor-origin Intestinal Epithelial cells and unassigned cells reveals distinct genomic profiles corresponding to their inferred ploidy status. Samples designated as "Diploid" largely maintain a normal copy number state, suggesting these may represent normal epithelial cells, adjacent normal tissue, or potentially well-differentiated tumors that haven't acquired widespread aneuploidy. In contrast, samples showing extensive amplifications and deletions are characteristic of transformed, aneuploid tumor cells.

The identification of recurrent genomic amplifications is particularly insightful:

Clinical or Translational Implications

The recurrent amplifications identified in tumor-origin Intestinal Epithelial cells have significant clinical and translational implications for colorectal cancer:

5. CNV-Aware UMAP Embedding of Colon Tissue Single-Cell RNA-seq Data

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the single-cell RNA-seq data on a UMAP embedding, which was generated using CNV estimates (obsm['X_cnv']), allowing for the assessment of chromosomal instability patterns. The UMAP plots are colored by various metadata: major cell types, minor cell types, ploidy inference (ploidy_dec), sample condition, and individual sample IDs. This helps to understand how different cellular identities, ploidy states, and experimental conditions are represented and spatially organized in a CNV-informed transcriptional landscape.

Visual Summary

The UMAP plots reveal distinct patterns and relationships between cell states, ploidy, and conditions:

Biological Interpretation

The CNV-aware UMAP embedding provides critical biological insights into the cellular composition and genomic integrity within the colon tissue dataset:

Annotation Notes

The provided UMAP visualization based on CNV estimates demonstrates excellent annotation quality and structural clarity. The distinct separation of cell clusters by major and minor cell types, combined with the clear demarcation of aneuploid (malignant) versus diploid cells, validates the cell type annotations and the reliability of the ploidy inference. The correlation between aneuploidy, tumor condition, and the expected tumor origin cell type (Intestinal Epithelial cell) further reinforces the biological accuracy of the dataset's annotations and the robustness of the CNV embedding method. The observation of good sample mixing in non-malignant populations, while retaining patient-specific patterns in malignant cells, suggests appropriate handling of batch effects during data integration.

6. Minor Cell Type Population Analysis in Colon Tissue

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents the cellular composition at the minor cell type level across individual samples, comparing adjacent normal colon tissue with tumor tissue. The stacked bar plots illustrate the proportional abundance of each identified cell type within each sample, providing insights into potential shifts in the cellular microenvironment associated with colon cancer.

Visual Summary

The visualization consists of two panels, one for "adjacent_normal" and one for "tumor," each displaying stacked bar plots representing the cell type composition of individual samples. Each bar sums to 100%, with different colors representing distinct minor cell types as indicated by the legend.

Key observations include:

Tumor-associated Shifts:

Biological Interpretation

The observed shifts in cell type proportions between adjacent normal and tumor colon tissue provide critical insights into the tumor microenvironment (TME) in human colorectal cancer.

Remodeling of the Immune Microenvironment:

Clinical or Translational Implications

Understanding the cellular composition of the tumor microenvironment has significant clinical and translational implications:

7. T 세포 아형 인구 분포 분석 (정상 및 종양 조직 비교)

Report figure

[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 사용하여 대장 조직에서 T 세포 주요 세포 유형 내의 다양한 T 세포 및 선천 림프구 세포(ILC) 아형의 상대적 인구 분포를 시각화한 것입니다. 각 샘플에 대해 인접 정상(adjacent_normal) 조직과 종양(tumor) 조직을 비교하여 세포 아형 구성의 변화를 탐색합니다. 이는 대장암 미세 환경에서 면역 세포 구성 변화를 이해하는 데 중요한 정보를 제공합니다.

Visual Summary

제공된 막대 그래프는 T 세포 주요 세포 유형 내 다양한 세포 아형의 상대적 비율을 보여줍니다. 각 막대는 개별 샘플을 나타내며, adjacent_normal 및 tumor 조건으로 분류되어 있습니다.

Biological Interpretation

이러한 시각적 분석 결과는 대장암 미세 환경에서 면역 억제성 T 세포의 잠재적 증강을 시사합니다.

Clinical or Translational Implications

이러한 T 세포 아형 분포 패턴은 대장암의 병태생리학적 이해를 심화하고 새로운 치료 전략을 개발하는 데 중요한 함의를 가집니다.

8. T Cell and ILC Subset Population Differences in Colon Tumor Microenvironment

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional representation of various T cell and Innate Lymphoid Cell (ILC) subsets, as identified by 'celltype_subset', between colon tumor tissue and adjacent normal tissue. The boxplots illustrate the distribution of celltype proportions for each subset across these two conditions, with statistical significance determined using a specified test configuration (p-value cutoff of 0.1 and log2 fold change cutoff of 0.1).

Visual Summary

The boxplots reveal several significant and near-significant differences in the proportions of T cell and ILC subsets between tumor and adjacent normal tissues:

Biological Interpretation

These observed shifts in T cell and ILC subsets provide insights into the immune landscape of colon cancer:

Clinical or Translational Implications

The findings underscore the significant immune dysregulation within the colon tumor microenvironment:

9. Macrophage Subset Population Dynamics in Colon Cancer Microenvironment

Report figure

[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱 데이터를 활용하여 대장 조직 내 대식세포(Macrophage)의 하위 유형(M1, M2A, M2B, M2C, M2D) 분포를 인접 정상 조직(adjacent_normal)과 종양 조직(tumor) 간에 비교한 결과입니다. 각 막대 그래프는 개별 샘플(patient ID)에서 대식세포가 차지하는 비율을 100%로 보았을 때, 각 하위 유형이 구성하는 상대적 비율을 보여줍니다. 이는 대장암 발생 및 진행 과정에서 대식세포의 면역 기능 변화를 이해하는 데 중요한 통찰력을 제공합니다.

Visual Summary

Biological Interpretation

대식세포는 종양 미세환경(TME)에서 중요한 역할을 하는 면역 세포로, 그들의 기능은 M1(고전적 활성화)과 M2(대안적 활성화)라는 두 가지 주요 극성으로 나눌 수 있습니다.

이 분석 결과에서 종양 조직 내 M2A 및 M2B 대식세포의 상대적인 증가는 대장암 미세환경이 면역억제적이고 종양 촉진적인 방향으로 변화하고 있음을 시사합니다. 이는 종양 세포가 대식세포를 M2형으로 재프로그래밍하여 자신의 성장에 유리한 환경을 조성하는 일반적인 현상과 일치합니다. 대장암 맥락에서 M2형 대식세포의 증가는 불량한 예후와 관련이 있는 경우가 많습니다.

Clinical or Translational Implications

10. Macrophage Subset Population Shifts in Colon Tumor Microenvironment

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigated the proportional representation of specific Macrophage subset populations (identified at the celltype_subset level) in single-cell RNA-seq data from human Colon tissue. The objective was to identify macrophage subsets that exhibit significant differences in their proportions when comparing tumor tissues to adjacent normal tissues. The comparison was performed using a statistical test with a p-value cutoff of 0.1 and a log2 fold change cutoff of 0.1.

Visual Summary

The boxplots illustrate the proportions of two Macrophage subsets, Mac (M1) and Mac (M2B), across 'tumor' and 'adjacent_normal' conditions. Each point represents a sample's proportion of that specific macrophage subset, with the box showing the interquartile range and the line indicating the median.

Biological Interpretation

Macrophages are highly plastic immune cells that polarize into distinct functional phenotypes, traditionally categorized as M1 (pro-inflammatory, anti-tumorigenic) and M2 (anti-inflammatory, pro-tumorigenic, tissue-repairing) subsets. However, this categorization is an oversimplification, and macrophages can exist on a spectrum with mixed phenotypes, particularly in complex environments like tumors [1, 2].

  1. Enrichment of Mac (M1) in Tumor: The observed higher proportion of Mac (M1) cells in tumor tissue is noteworthy. M1 macrophages are typically associated with robust anti-tumor immune responses, characterized by the production of pro-inflammatory cytokines (e.g., IL-12, TNF-α) and nitric oxide, leading to direct cytotoxicity against cancer cells [1]. Their increased presence could suggest an active host immune attempt to combat the tumor. However, it's also possible that these M1-like cells in the tumor microenvironment (TME) might be rendered dysfunctional or co-opted by the tumor to support aspects of its growth, or that their activation state is heterogeneous [3]. In some contexts, M1 macrophages can contribute to chronic inflammation that might paradoxically promote tumor progression over time, depending on the specific inflammatory milieu.
  2. Decreased Mac (M2B) in Tumor: The lower proportion of Mac (M2B) in tumor compared to adjacent normal tissue is an interesting finding. M2B macrophages are a distinct M2 subset, often induced by immune complexes and Toll-like receptor agonists. They are known to produce high levels of IL-6 and TNF-α, cytokines that can have both pro-inflammatory and pro-tumorigenic roles depending on the context [4]. A reduction in this specific M2 subset within the tumor might indicate a shift in the overall M2 polarization landscape, where other M2 subtypes (e.g., M2A, M2C, M2D, which are also present in celltype_subset) might be more dominant or actively recruited, or that M2B are less critical for tumor progression in this specific colon cancer context compared to their role in adjacent tissue homeostasis or early neoplastic changes.

These findings suggest a complex and dynamic macrophage landscape in the colon TME. The concurrent increase in M1 and decrease in M2B macrophages imply a nuanced immune response that deviates from a simple M1 depletion/M2 enrichment model often generalized in cancer. The specific balance and functional state of these macrophage subsets likely contribute to the overall immune contexture of colon cancer.

Clinical or Translational Implications

Understanding the precise shifts in macrophage subsets within the colon tumor microenvironment has significant clinical and translational implications:

These results highlight the necessity of deep phenotyping of immune cell subsets to unravel the intricate interactions within the tumor microenvironment and inform precision oncology approaches.

---

References

  1. M1/M2 Macrophage Polarization: Sica A, Mantovani A. Macrophage plasticity and polarization: in vivo experience with cytokines. *Semin Immunol*. 2012 Oct;24(5):367-75. PubMed search link
  2. Macrophages in Cancer: Rhee I. The Immune Landscape of Cancer: Macrophages. *Cancers (Basel)*. 2024 Jan 12;16(2):331. PubMed search link
  3. Tumor-associated Macrophages (TAMs) Plasticity: Guc E, Ozturk Z, Uysal-Sevimli N, Sivas H. The multifaceted roles of tumor-associated macrophages in cancer. *Cell Biosci*. 2023 Mar 1;13(1):37. PubMed search link
  4. M2B Macrophages: Italiani P, Boraschi D. From Monocytes to M1/M2 Macrophages: Phenotypical and Functional Differences. *Front Immunol*. 2014 Sep 9;5:514. PubMed search link
  5. Macrophage-targeted Immunotherapy: De Palma M, Lewis CE. Macrophage Regulation of Tumor Responses to Immunotherapy. *Cancer Cell*. 2020 Mar 16;37(3):323-336. PubMed search link

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

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a barplot showing the relative proportions of Aneuploid, Diploid, and Unclear cells within the 'Intestinal Epithelial cell' and 'unassigned' populations for individual samples. These proportions are separately visualized for 'adjacent_normal' and 'tumor' conditions. Given that 'Intestinal Epithelial cell' is identified as the tumor origin cell type, this analysis provides insight into the genomic stability of the presumed malignant cells and potentially difficult-to-classify cells across disease states.

Visual Summary

The visualization consists of two barplots, one for 'adjacent_normal' samples and another for 'tumor' samples. Each bar represents a distinct sample, and the bar segments indicate the percentage of cells classified as Aneuploid (dark red), Diploid (orange), or Unclear (light green) within the combined 'Intestinal Epithelial cell' and 'unassigned' populations.

Biological Interpretation

Aneuploidy, characterized by an abnormal number of chromosomes, is a hallmark of cancer and a direct indicator of genomic instability [1]. In this context, the observation of Aneuploid cells specifically within the 'Intestinal Epithelial cell' population—designated as the tumor origin cell type—strongly supports their malignant transformation and cancerous nature.

Clinical or Translational Implications

References

  1. Aneuploidy as a hallmark of cancer: PubMed search: aneuploidy cancer hallmark genomic instability
  2. Field Cancerization: PubMed search: field cancerization colorectal cancer
  3. Aneuploidy and Prognosis: PubMed search: aneuploidy cancer prognosis

12. Cell-Cell Interaction Patterns in the Colon Tumor Microenvironment

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the cell-cell interaction (CCI) landscape within the tumor microenvironment of human colon tissue using single-cell RNA-seq data. The CellPhoneDB method was applied to identify significant ligand-receptor interactions between specific cell types: Intestinal Epithelial cells (including the tumor origin population), Fibroblasts, Macrophages, and T cells (CD4+ and CD8+). The results are presented as a dot plot, highlighting the top 80 most significant interactions in the tumor condition, based on p-value and mean expression cutoffs. The expand_ploidy_from_tumor_origin parameter allowed for distinguishing between different ploidy states for the tumor origin cell type.

Visual Summary

The dot plot visualizes the strength and significance of cell-cell interactions in the tumor condition.

Biological Interpretation

The analysis highlights a dynamic and complex intercellular communication network within the colon tumor microenvironment, primarily driven by immune cells (Macrophages and T cells) and non-malignant (Diploid) Intestinal Epithelial cells.

  1. Dominance of Immune-Immune and Immune-Diploid Epithelial Interactions: The observed interactions suggest intense crosstalk among immune cells, crucial for orchestrating immune responses, and between immune cells and the resident non-malignant epithelial compartment. The frequent interactions involving Diploid Intestinal Epi cells indicate their active role in shaping the tumor microenvironment, even if they are not the malignant clone. These cells could be involved in maintaining tissue homeostasis, responding to inflammation, or providing signals that influence the adjacent tumor and immune cells.
  2. Key Immune Modulatory Interactions:
  1. Metabolic and Growth Factor Signaling: The APOE_TREM2_receptor interaction (Mac|Diploid Intestinal Epi) suggests lipid metabolism and sensing in the tumor microenvironment. TREM2 on macrophages is involved in lipid metabolism and has complex roles in cancer, sometimes promoting tumor growth by fostering an immunosuppressive environment [5]. HBEGF-ERBB2 interactions (Mac|Diploid Intestinal Epi) indicate potential growth factor signaling that could influence cell proliferation and survival.
  2. Implications of Absent Cell Types: The absence of interactions involving Fibroblast and specifically the Aneuploid Intestinal Epithelial cell (malignant tumor cells) within the top 80 most significant pairs is a notable finding. This does not mean these cells are inactive, but rather that their highly significant, high-expression interactions are less prominent than the immune-immune and immune-diploid epithelial cell interactions identified under the chosen criteria. It might imply that while malignant epithelial cells certainly interact, their strongest single ligand-receptor events might be diluted, less frequent, or have lower mean expression compared to the observed immune and non-malignant epithelial crosstalk. This could also suggest that the dominant influences on immune cells within the tumor microenvironment, at the level of specific ligand-receptor pairs, come from other immune cells and the non-malignant epithelial component.

Clinical or Translational Implications

The identified cell-cell interactions offer several avenues for clinical and translational exploration in colon cancer:

  1. Therapeutic Target Prioritization:
  1. Biomarker Discovery: The identified highly significant ligand-receptor pairs, particularly those involved in immune modulation, could serve as biomarkers for patient stratification, response to immunotherapy, or prognosis. For example, the expression levels of Galectin-9, TIM-3, VISTA, or their co-expression patterns in specific cell populations might predict patient outcomes.
  2. Experimental Validation: The specific cell-cell and ligand-receptor pairs identified provide clear hypotheses for further experimental validation. In vitro co-culture experiments, organoid models, or in vivo genetic manipulation in mouse models could be used to confirm the functional relevance of these interactions in colon cancer progression and immune response. For instance, investigating the impact of blocking LAGLS9 on T cell exhaustion in the context of T CD4+|Mac interactions could be a critical next step.
  3. Understanding Tumor Microenvironment Complexity: The emphasis on interactions between immune cells and non-malignant epithelial cells underscores the importance of the entire tumor microenvironment, not just the malignant cells, in disease progression. Therapeutic strategies might need to consider modulating the stromal and immune components that directly interact with both malignant and non-malignant tissue components.

---

References:

[1] Galectin-9/TIM-3 pathway: A PubMed search for "TIM-3 Galectin-9 cancer immunotherapy" will yield many relevant papers. https://pubmed.ncbi.nlm.nih.gov/?term=TIM-3+Galectin-9+cancer+immunotherapy

[2] VISTA (VSIR): GeneCards entry for VSIR. https://www.genecards.org/cgi-bin/carddisp.pl?gene=VSIR

[3] SPP1 (Osteopontin): GeneCards entry for SPP1. https://www.genecards.org/cgi-bin/carddisp.pl?gene=SPP1

[4] THBS1 (Thrombospondin-1): GeneCards entry for THBS1. https://www.genecards.org/cgi-bin/carddisp.pl?gene=THBS1

[5] TREM2: A PubMed search for "TREM2 cancer" can provide context. https://pubmed.ncbi.nlm.nih.gov/?term=TREM2+cancer

[6] TIM-3 inhibitors: A PubMed search for "TIM-3 inhibitor clinical trial" will show current developments. https://pubmed.ncbi.nlm.nih.gov/?term=TIM-3+inhibitor+clinical+trial

[7] Targeting TREM2 in cancer: A PubMed search for "targeting TREM2 cancer" can provide information. https://pubmed.ncbi.nlm.nih.gov/?term=targeting+TREM2+cancer

13. Condition-Specific Cell-Cell Interaction Patterns in Colon Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using CellPhoneDB results, comparing 'adjacent_normal' and 'tumor' conditions in human colon tissue. The visualization displays the standardized mean interaction strength (color intensity) and significance (-log10(p-value), dot size) for selected ligand-receptor pairs between specific cell types across individual samples, allowing for a detailed examination of how cell communication networks are altered in the tumor microenvironment. The plot_dot_for_cci_with_signif_difference tool was used to highlight interactions showing significant differences between conditions.

Visual Summary

The dot plot is organized into two main panels: 'adjacent_normal' (left) and 'tumor' (right). Each row represents a sample, and each column represents a unique cell-cell interaction (CCI Index), defined by a ligand-receptor pair and the interacting cell types.

Biological Interpretation

The observed differential CCI patterns provide critical insights into the distinct cellular crosstalk underlying colon tissue homeostasis versus tumor progression.

Clinical or Translational Implications

14. Immune Checkpoint and Cell Cycle Related Cell-Cell Interactions in Colorectal Tumor Microenvironment

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) using CellPhoneDB for a curated list of genes associated with immune checkpoint and cell cycle pathways. The CCI results are presented for two conditions: "adjacent_normal" and "tumor" tissue from human colon single-cell RNA-seq data. The goal is to identify specific ligand-receptor pairs and cellular interactions that are differentially active between normal and tumor microenvironments, with a focus on understanding immune regulation and cellular proliferation within colorectal cancer. The plot_cci_dots tool visualizes the significance (p-value, dot size) and interaction strength (mean expression, dot color) for identified ligand-receptor pairs across various cell-cell type combinations.

Visual Summary

The provided dot plots illustrate cell-cell interactions for selected immune checkpoint and cell cycle-related genes, comparing the adjacent normal tissue to the tumor microenvironment.

Adjacent Normal Tissue

Tumor Microenvironment

Heightened Immune Signaling:

Biological Interpretation

The analysis reveals a dynamic and drastically altered cell-cell communication landscape in the colorectal tumor microenvironment compared to adjacent normal tissue, particularly concerning immune checkpoint and T cell activation pathways.

  1. Shift in Macrophage-Epithelial Interactions: The disappearance of CD86-CD28 interactions between Macrophages and "Diploid Intestinal Epi" cells in the tumor context is highly significant. Given that the tumor's origin cell type is "Intestinal Epithelial cell" and ploidy inference labels (Aneuploid/Diploid) are available, this suggests that the normal diploid epithelial cells are either less prominent in the tumor or their interaction profile with macrophages changes drastically. Tumor epithelial cells are typically aneuploid and often evade immune recognition and costimulation.
  2. Dual Nature of Immune Activity:
  1. Role of Macrophages: Macrophages appear as central players, mediating both pro-inflammatory (via CD86-CD28) and predominantly immunosuppressive (via TGFB1-TGFbeta_receptor1) interactions with T cells and within their own population. The observed changes are consistent with the known plasticity of tumor-associated macrophages (TAMs), which often adopt an M2-like, pro-tumor phenotype in the TME.
  2. Limited Cell Cycle Direct Interactions: While many cell cycle genes were included in the target_genes list, only ligand-receptor pairs like HBEGF-EGFR, which can influence cell proliferation, were captured in the CCI plot. The low activity of HBEGF-EGFR suggests other growth factors or intracellular pathways are more dominant in driving proliferation or these interactions are not prominent in the displayed cell-cell pairs.

Clinical or Translational Implications

The findings have significant implications for understanding colorectal cancer progression and developing therapeutic strategies.

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

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates statistically significant differences in cell-cell interactions (CCI) between adjacent normal and tumor conditions in colon tissue, focusing on major immune and stromal cell types, as well as intestinal epithelial cells (including those inferred to be aneuploid, likely representing malignant cells). CellPhoneDB was used to infer ligand-receptor interactions, and these were aggregated and visualized to highlight condition-specific patterns. The dot plot displays interaction strength (standardized mean, color intensity) and statistical significance (-log10(p-value), dot size) for the top 25 differentially significant interactions per group.

Visual Summary

The dot plot clearly differentiates cell-cell interaction patterns between adjacent_normal and tumor conditions.

Biological Interpretation

The observed differential CCI patterns provide critical insights into the distinct microenvironments of normal and cancerous colon tissue.

In the adjacent_normal microenvironment:

In the tumor microenvironment:

The tumor microenvironment shows a dramatic shift towards interactions that support tumor growth, invasion, angiogenesis, and immune evasion.

Clinical or Translational Implications

The identified condition-specific CCI patterns have significant clinical and translational implications for colon cancer:

Therapeutic Targets:

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

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that distinguish Intestinal Epithelial cells in tumor conditions from those in adjacent normal colon tissue. Utilizing single-cell RNA sequencing data, a dot plot was generated to visualize the expression patterns of these surfaceome genes across individual samples, stratified by their inferred ploidy status and condition (adjacent normal vs. tumor). The selection of surfaceome markers ensures a focus on proteins accessible on the cell surface, which are of particular interest for therapeutic targeting and diagnostic applications.

Visual Summary

The dot plot effectively visualizes condition-specific surfaceome marker expression within Intestinal Epithelial cells across different samples.

Biological Interpretation

The identified surfaceome markers show a clear enrichment in Intestinal Epithelial cells from tumor samples, suggesting their involvement in the unique biology of colorectal cancer. Several prominent markers stand out:

The robust expression of these specific surfaceome and surface-interacting proteins in tumor-origin Intestinal Epithelial cells indicates a profound shift in their cellular identity and function in the cancerous state, involving changes in cell adhesion, growth factor signaling, metabolic reprogramming, and immune evasion.

Clinical or Translational Implications

The identified condition-specific surfaceome markers in Intestinal Epithelial cells have significant clinical and translational implications:

17. Condition-Specific Surfaceome Markers in Macrophages from Colon Tissue

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that are differentially expressed in macrophages within tumor tissues compared to adjacent normal tissues in the colon. The plot_markers_and_expression_dot tool was used to visualize the mean expression levels and the fraction of cells expressing these markers across individual samples, grouped by condition (tumor vs. adjacent normal). Only surfaceome genes were considered, and up to 50 markers were selected per condition based on differential expression metrics.

Visual Summary

The dot plot effectively visualizes the expression patterns of 31 selected surfaceome markers in macrophages across multiple patient samples. The samples are clearly divided into two major groups: a "tumor" group (upper section, labeled with "tumor") and an "adjacent_normal" group (lower section, separated by a red horizontal line).

Biological Interpretation

The observed differential expression patterns highlight distinct functional states of macrophages in the tumor microenvironment of colon tissue. Macrophages are highly plastic cells that can adopt various phenotypes, including pro-inflammatory (M1-like) and pro-tumoral (M2-like or Tumor-Associated Macrophages, TAMs) states. The prominent upregulation of specific surfaceome markers in tumor macrophages suggests a shift towards a TAM-like phenotype, which plays crucial roles in cancer progression.

Key tumor-enriched surfaceome markers and their biological implications include:

Immune Checkpoints & Evasion:

Tissue Remodeling & Metastasis:

Immunosuppression & Pro-Tumoral Signaling:

Macrophage Activation Markers:

The relatively lower expression of these markers in adjacent normal tissues suggests that these are not general macrophage activation markers but rather specific adaptations to the tumor environment.

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers in macrophages has significant clinical and translational implications, particularly in the context of colorectal cancer:

These findings strongly support further investigation into these specific surfaceome markers as potential targets for therapeutic intervention and as tools for understanding macrophage biology in colorectal cancer.

---

References:

  1. SIRPA-CD47 signaling: PubMed Search: SIRPA CD47 cancer immunotherapy
  2. HAVCR2 (TIM-3) in TAMs: PubMed Search: TIM3 TAM immunosuppression
  3. PLAUR (uPAR) in cancer: PubMed Search: PLAUR cancer invasion
  4. MMP14 (MT1-MMP) in cancer: PubMed Search: MMP14 cancer metastasis
  5. GPNMB in TAMs: PubMed Search: GPNMB tumor associated macrophages
  6. NRP1 in TAMs: PubMed Search: NRP1 TAM angiogenesis
  7. OLR1 (LOX-1) in cancer: GeneCards: OLR1
  8. TREM1 in cancer: PubMed Search: TREM1 cancer inflammation
  9. CD47-SIRPα blockade: PubMed Search: CD47 SIRPa cancer therapy clinical trials

18. Fibroblast Condition-Specific Surfaceome Markers in Colon Tissue

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that are specifically expressed by Fibroblasts in either "adjacent_normal" or "tumor" conditions within human colon tissue. Surfaceome markers are particularly interesting as they are accessible for cell-cell interactions and potential therapeutic targeting. The plot_markers_and_expression_dot tool was used to visualize the expression patterns and prevalence of these markers across different samples categorized by condition, enabling a direct comparison between normal and tumor-associated fibroblasts.

Visual Summary

The dot plot effectively illustrates the differential expression of fibroblast surfaceome markers between adjacent normal and tumor conditions.

Biological Interpretation

The differential surfaceome expression highlights the distinct functional roles and activation states of fibroblasts in normal colon tissue versus the tumor microenvironment.

The observed markers reflect the well-established role of CAFs in promoting chronic inflammation, immune evasion, angiogenesis, and metastatic dissemination in colorectal cancer.

Clinical or Translational Implications

The identification of condition-specific surfaceome markers for fibroblasts holds significant clinical and translational potential:

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

Report figure

[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱 데이터를 활용하여 대장 조직 내 CD4 T 세포에서 종양(tumor)과 인접 정상(adjacent_normal) 조직 간의 조건별 표면 단백질(surfaceome) 마커를 식별한 결과입니다. 표면 단백질에 초점을 맞춤으로써, 잠재적인 진단 바이오마커 또는 치료 표적을 발굴하는 데 유리합니다. 마커의 발현 수준(점의 색상 강도)과 해당 마커를 발현하는 세포의 비율(점의 크기)이 시각적으로 표현되어 있습니다.

Visual Summary

도트 플롯은 CD4 T 세포의 다양한 클러스터(Y축)에 걸쳐 인접 정상 및 종양 조건(X축 상단)에서 특정 유전자(X축 하단)의 발현 패턴을 보여줍니다.

Biological Interpretation

이 분석 결과는 대장 조직 미세환경에서 CD4 T 세포의 기능적 상태 및 아형 분포가 종양 발생에 따라 현저하게 변화함을 강력하게 시사합니다.

  1. 인접 정상 조직의 CD4 T 세포:
  1. 종양 미세환경의 CD4 T 세포 (TILs):

면역 관문 분자 (Immune Checkpoints):

활성화 및 MHC Class II 관련 분자:

전반적으로, 종양 미세환경 내 CD4 T 세포는 면역 억제성 면역 관문 분자(CTLA4, TIGIT, ENTPD1)와 활성화 및 기능 관련 분자(ICOS, MHC Class II, IL2RB, ITGB1)를 동시 발현하는 경향을 보입니다. 이는 종양 침윤 CD4 T 세포가 활성화되었지만, 동시에 면역 억제 경로에 의해 조절되거나 기능적으로 고갈되었을 가능성을 시사합니다.

Clinical or Translational Implications

이 분석에서 식별된 조건 특이적 표면 마커는 대장암 진단, 예후 및 치료에 중요한 임상적 의미를 가질 수 있습니다.

  1. 바이오마커 개발:
  1. 치료 표적 발굴:
  1. 질병 이해 및 환자 계층화:

20. Dysregulation of Cell Cycle Genes in Intestinal Epithelial Cells of Colon Tumors

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of genes involved in the Cell Cycle pathway within Intestinal Epithelial cells, comparing tumor tissue to adjacent normal tissue from colon samples. The aim is to identify specific cell cycle regulators that exhibit statistically significant expression changes in the tumor context, providing insights into the proliferative dynamics of colorectal cancer.

Visual Summary

The provided box plots display the gene expression levels (sample mean) for numerous cell cycle-related genes across Intestinal Epithelial cells, stratified by 'tumor' and 'adjacent_normal' conditions.

Biological Interpretation

The pervasive and significant upregulation of a broad spectrum of cell cycle genes in Intestinal Epithelial cells from colon tumors strongly points towards an enhanced proliferative state as a hallmark of the tumor cells. This aligns with the fundamental understanding of cancer, where uncontrolled cell division is a key characteristic.

Clinical or Translational Implications

21. 장 상피세포(Intestinal Epithelial cell)의 유전자 온톨로지(GSA) 분석 결과

Report figure

[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 활용하여 장 상피세포(Intestinal Epithelial cell)에서 유전자 온톨로지(Gene Ontology, GO) 분석(GSA)을 수행한 결과입니다. 이 분석은 특정 생물학적 상태에서 장 상피세포의 기능적 변화를 탐색하는 데 중점을 두었으며, 두 가지 주요 비교 그룹에 대한 결과를 제공합니다.

  1. Diploid_vs_others: 핵형(ploidy)이 이배체(Diploid)인 장 상피세포와 그 외의 세포(대부분 이수체(Aneuploid) 상피세포로 추정됨)를 비교하여 이배체 상피세포에서 상향 조절되는 GO 용어를 확인했습니다.
  2. tumor_vs_others: 대장암 종양(tumor) 조직 내 장 상피세포와 인접 정상(adjacent_normal) 조직 내 장 상피세포를 비교하여 종양 환경의 상피세포에서 상향 조절되는 GO 용어를 분석했습니다.

결과는 통계적 유의성(-log(p-val) 및 -log(q-val))을 기준으로 정렬된 막대 그래프로 시각화되어 있습니다.

Visual Summary

제공된 두 개의 막대 그래프는 장 상피세포에서 특정 조건과 비교하여 상향 조절된 유전자 온톨로지(GO) 용어들의 통계적 유의성(p-value 및 q-value의 -log 값)을 보여줍니다. 각 그래프는 왼쪽 패널에 -log(p-val) 값을, 오른쪽 패널에 -log(q-val) 값을 표시하며, 두 값 모두 높을수록 해당 용어가 통계적으로 더 유의미하게 상향 조절되었음을 의미합니다.

  1. "GSA_up for Intestinal Epithelial cell: Diploid_vs_others" 그래프: 이배체 장 상피세포에서 상향 조절된 GO 용어들을 보여줍니다. 최상위 용어들은 주로 Epstein-Barr virus infection, Intestinal immune network for IgA production, Antigen processing and presentation 등 면역 반응과 관련된 용어들이며, p-value는 약 1e-7 수준(p-val의 -log 값 7)의 유의성을 보입니다. 이는 이배체 상피세포가 특정 면역 관련 기능을 가질 수 있음을 시사합니다.
  2. "GSA_up for Intestinal Epithelial cell: tumor_vs_others" 그래프: 종양 조직 내 장 상피세포에서 상향 조절된 GO 용어들을 보여줍니다. 이 그래프에서는 Endocytosis, Protein processing in endoplasmic reticulum, Ubiquitin mediated proteolysis, Cell cycle 등과 같이 세포의 기본적인 기능 및 암과 관련된 핵심 경로들이 훨씬 더 높은 통계적 유의성(p-val의 -log 값 약 20 이상)으로 상향 조절된 것으로 나타납니다. 전반적으로 종양 상피세포의 변화가 이배체 상피세포의 변화보다 훨씬 더 광범위하고 통계적으로 강력하게 나타남을 알 수 있습니다.

Biological Interpretation

1. Diploid 장 상피세포의 생물학적 특성 (vs. Other 상피세포)

이배체(Diploid) 장 상피세포에서 상향 조절된 GO 용어들은 주로 면역 및 염증 반응, 그리고 정상적인 세포 기능과 관련이 깊습니다.

이러한 결과는 이배체 장 상피세포가 핵형 불안정성을 특징으로 하는 암세포에 비해 더욱 정상적인 생리적 기능, 특히 면역 항상성 유지에 중요한 역할을 할 수 있음을 시사합니다.

2. 종양 내 장 상피세포의 생물학적 특성 (vs. Adjacent normal 상피세포)

종양(tumor) 조직 내 장 상피세포에서 상향 조절된 GO 용어들은 대장암 세포의 특징적인 높은 증식률, 단백질 대사 이상, 세포 스트레스 반응, 그리고 세포 생존 메커니즘과 밀접하게 연관되어 있습니다.

이러한 결과는 종양 내 장 상피세포가 정상 상피세포와 비교하여 증식, 대사, 단백질 처리, 스트레스 반응 등 광범위한 세포 생물학적 과정에서 현저한 변화를 겪고 있으며, 이는 대장암 세포의 특징적인 악성 표현형을 형성하는 데 기여함을 명확히 보여줍니다.

Clinical or Translational Implications

  1. 암 진단 및 예후 마커 발굴: 종양 상피세포에서 강력하게 상향 조절되는 "Cell cycle" 관련 유전자, "mTOR signaling pathway" 구성 요소, "Ubiquitin mediated proteolysis" 관련 유전자 등은 대장암의 진행 정도나 공격성을 평가하는 바이오마커로 활용될 수 있습니다.
  2. 잠재적 치료 표적 발굴: mTOR 신호 전달 경로와 같은 핵심 암 관련 경로의 활성화는 해당 경로를 표적으로 하는 치료제(예: mTOR 억제제)의 임상적 유용성을 시사합니다. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754117/ 또한, 단백질 항상성(proteostasis) 관련 경로(ER stress, ubiquitin-proteasome system)의 이상 활성화는 이러한 시스템을 조절하는 약물을 통한 암 치료 전략 개발 가능성을 제시합니다.
  3. 면역 치료와의 연관성: Diploid 상피세포에서 면역 관련 경로(IgA 생산 네트워크, 항원 처리 및 제시)가 활성화되어 있다는 점은 종양 미세 환경 내 정상 또는 덜 변형된 상피세포가 면역 반응에 어떤 역할을 하는지 추가 연구할 필요성을 시사합니다. 이는 면역 체크포인트 억제제와 같은 면역 요법의 반응을 예측하거나 개선하는 데 중요한 단서를 제공할 수 있습니다.
  4. 세포 스트레스 및 대사 조절: 종양 상피세포의 대사 및 스트레스 반응 경로(Autophagy, Endocytosis 등)의 변화는 대장암 세포의 생존 전략을 이해하고, 이들 경로를 조절하여 암세포의 취약점을 공략하는 새로운 치료 접근법을 개발하는 데 기여할 수 있습니다.

22. Gene Set Enrichment Analysis Reveals Condition-Specific Pathway Alterations Across Colon Cell Types

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for various major cell types found in the colon tissue, comparing gene expression profiles under different conditions. The conditions include 'adjacent_normal_vs_others' (cells from adjacent normal tissue compared to all other cells), 'tumor_vs_others' (cells from tumor tissue compared to all other cells), and for Intestinal Epithelial cells, 'Diploid_vs_others' and 'Aneuploid_vs_others' (comparing cells based on their ploidy status). The results are visualized as a dot plot, where dot color indicates the Normalized Enrichment Score (NES) (red for positive enrichment, blue for negative enrichment) and dot size reflects the significance of enrichment (-log10(p-value)).

Visual Summary

The dot plot reveals distinct patterns of pathway enrichment and suppression across different cell types and conditions:

Immune Cells (Macrophages, T cells, Plasma cells, B cells)

Stromal Cells (Fibroblasts, Smooth Muscle cells)

Biological Interpretation

The GSEA results provide clear insights into the functional landscape of colon tissue in the context of cancer:

Clinical or Translational Implications

These GSEA findings have several clinical and translational implications for colon cancer:

References

  1. Oxidative phosphorylation in cancer metabolism:

PubMed search: "oxidative phosphorylation cancer metabolism"

  1. Cell cycle in cancer:

GeneCards: "Cell Cycle"

  1. Wnt signaling pathway in colorectal cancer:

PubMed search: "Wnt signaling colorectal cancer"

  1. Hedgehog signaling pathway in cancer:

PubMed search: "Hedgehog signaling pathway cancer"

  1. IL-17 signaling in tumor microenvironment:

PubMed search: "IL-17 signaling tumor microenvironment"

  1. TNF signaling in tumor microenvironment:

PubMed search: "TNF signaling tumor microenvironment"

  1. cGAS-STING pathway in cancer:

PubMed search: "cGAS-STING pathway cancer"

23. Discussion

This comprehensive single-cell analysis of human colon tissue provides a multi-faceted view of colorectal cancer (CRC), integrating cellular heterogeneity, genetic alterations, cell-cell interactions, and pathway dysregulation. A central finding is the robust identification of malignant Intestinal Epithelial cells through their distinct clustering, predominant presence in tumor samples, and, most critically, their widespread aneuploidy. The observation of aneuploid epithelial cells in some 'adjacent normal' samples is particularly noteworthy, suggesting a field cancerization effect or early oncogenic events prior to overt histological malignancy, which has significant implications for surveillance and risk assessment.

The tumor microenvironment (TME) undergoes profound remodeling. Macrophage populations show a complex shift with an increase in M1-like macrophages but a decrease in M2B, indicating a nuanced rather than a simple pro-tumor M2 polarization. T cell subsets demonstrate a clear increase in immunosuppressive regulatory T cells (Tregs) and regulatory innate lymphoid cells (ILCregs), alongside a trend for increased Th17 cells and a decrease in ILC2s. This points to a highly immunosuppressive TME that dampens anti-tumor immunity despite the presence of cytotoxic T cells. Fibroblasts also undergo significant transformation, adopting a cancer-associated fibroblast (CAF) phenotype with altered surface markers (e.g., CD44, Integrins) and increased Wnt signaling, contributing to ECM remodeling and tumor support.

Cell-cell interaction (CCI) analysis reveals a dramatic shift from normal tissue homeostasis to a pro-tumorigenic and immunosuppressive communication network. While diploid epithelial cells interact with immune cells in normal tissue (e.g., Mac-Diploid Intestinal Epi via CD86-CD28), aneuploid tumor epithelial cells become central to a new repertoire of interactions in the tumor. Prominent tumor-specific CCIs involve extensive extracellular matrix (ECM) remodeling (e.g., collagen-integrin, SPP1-integrin), angiogenesis (VEGFA-FLT1, PDGF, Notch), and immune evasion. Crucially, the tumor TME exhibits highly active immune checkpoint signaling, including PD-L2/PD-1 and CD80/CTLA4 interactions between macrophages and T cells, and significantly upregulated TGFB1-TGFbeta_receptor1 signaling. This intricate interplay underscores how tumor cells and their associated stroma actively co-opt immune cells to establish an immunosuppressive barrier. The absence of specific malignant epithelial cell interactions within the top 80 CellPhoneDB results (Image 13) may reflect that while they are active, their ligand-receptor events are perhaps more diffuse or less frequently highly ranked than the strong immune-immune and immune-stromal interactions under the selected statistical parameters.

Genetic alterations extend beyond global aneuploidy, with recurrent genomic amplifications identified on chromosomes 7 (including *EGFR*), 8q (including *MYC*), 17 (*ERBB2*), and 20q13. These amplifications are well-established oncogenic drivers in CRC and provide clear rationale for targeted therapies. Furthermore, tumor-derived Intestinal Epithelial cells exhibit a widespread and significant upregulation of a broad spectrum of cell cycle genes, directly reflecting their uncontrolled proliferation and metabolic reprogramming (e.g., oxidative phosphorylation, Wnt, Hedgehog signaling). These comprehensive findings provide robust evidence for the molecular and cellular drivers of colorectal cancer progression and offer a rich landscape for therapeutic intervention.

Hypotheses:

  1. The presence of aneuploid Intestinal Epithelial cells in histologically 'adjacent normal' tissue indicates a subclinical field cancerization effect, predisposing these areas to future malignant transformation.
  2. The observed increase in regulatory T cells (Tregs) and ILCregs, coupled with activated TGF-beta signaling from macrophages, establishes a robust immunosuppressive microenvironment that impedes effective anti-tumor immune responses in colorectal cancer.
  3. Tumor-associated fibroblasts (CAFs) actively contribute to an immunosuppressive and pro-metastatic niche through upregulated integrin-ECM interactions and Wnt signaling, driving tumor growth and resistance to therapy.
  4. The distinct surfaceome profiles of tumor-origin Intestinal Epithelial cells, tumor-associated macrophages, and CAFs, including molecules like CD47, CEACAM1, and specific integrins, facilitate immune evasion and provide novel avenues for targeted therapeutics.
  5. The widespread upregulation of cell cycle genes in tumor Intestinal Epithelial cells, along with specific recurrent genomic amplifications (e.g., EGFR, ERBB2, MYC), suggests a high dependency on these proliferative pathways, making them vulnerable to targeted cell cycle inhibitors.

Potential therapeutic targets:

  1. CD47-SIRPα axis: CD47 is highly expressed on tumor-origin Intestinal Epithelial cells and SIRPα on tumor-associated macrophages (TAMs). This interaction delivers a 'don't eat me' signal, allowing tumor cells to evade macrophage phagocytosis. Evidence: CD47 is a prominent surface marker in tumor Intestinal Epithelial cells (Section 16). SIRPα is a highly expressed surface marker in tumor macrophages (Section 17). Targeting this axis can unleash macrophage phagocytic activity (Section 17 References, PubMed Search: SIRPA CD47 cancer immunotherapy). Validation: In vitro phagocytosis assays using patient-derived macrophages and tumor cells, followed by in vivo efficacy studies with anti-CD47 or anti-SIRPα antibodies in xenograft models.
  2. TGFB1-TGFbeta_receptor1 pathway: TGF-beta signaling is significantly upregulated, particularly from macrophages to T cells and epithelial cells, and is a potent immunosuppressive and pro-tumorigenic pathway in the TME. Evidence: Strong upregulation of TGFB1-TGFbeta_receptor1 interactions in tumor CCIs (Section 14, 15). TGF-beta promotes T cell inhibition, Treg development, and M2-like macrophage phenotype, supporting tumor growth and metastasis (Section 14 Biological Interpretation). Validation: Evaluate the effect of TGF-beta inhibitors on T cell proliferation and cytokine production in co-culture with tumor macrophages, and assess tumor growth and immune infiltration in mouse models treated with TGF-beta blocking agents.
  3. EGFR (Epidermal Growth Factor Receptor): EGFR is frequently amplified in tumor-origin Intestinal Epithelial cells, driving cell proliferation and survival. Evidence: Significant amplification of the 7p12.3:7q21.12 region, containing the *EGFR* gene, is observed in tumor-origin cells across multiple samples (Section 4). EREG, an EGFR ligand, is also highly expressed in tumor Intestinal Epithelial cells (Section 16). Validation: Test response to anti-EGFR therapies (e.g., cetuximab, panitumumab) in tumor cell lines or patient-derived organoids with EGFR amplification. Correlate EGFR amplification status with clinical response in a validation cohort.
  4. CDK4/6 (Cyclin-Dependent Kinases 4 and 6): Core cell cycle regulators (CDK1, CDK4, CDK6) are significantly upregulated in tumor Intestinal Epithelial cells, indicating hyperactive proliferation. Evidence: Numerous cell cycle genes, including CDK4 and CDK6, show robust and widespread upregulation in Intestinal Epithelial cells from tumor samples compared to adjacent normal tissue (Section 20). Validation: Assess the anti-proliferative effects of CDK4/6 inhibitors on primary colorectal cancer cells or cell lines. Combine CDK4/6 inhibitors with other standard-of-care agents to evaluate synergistic effects in vitro and in vivo.
  5. Integrins (e.g., ITGAV, ITGA1, ITGA5): Various integrin subunits are highly expressed in cancer-associated fibroblasts (CAFs) and are involved in extensive ECM remodeling and tumor invasion, and in epithelial cells (ITGA2) contributing to cancer progression. Evidence: High expression of ITGA2 in tumor Intestinal Epithelial cells (Section 16). Strong upregulation of ITGAV, ITGA1, ITGA5 in tumor fibroblasts (CAFs) (Section 18). Collagen/Laminin-Integrin interactions are prominent in tumor CCIs (Section 15). Validation: Evaluate the impact of integrin-blocking antibodies or small molecule inhibitors on CAF-mediated ECM remodeling, tumor cell migration, and invasion using 3D cell culture models and in vivo metastasis assays.

Follow-up validation ideas:

  1. Validate the presence of aneuploid Intestinal Epithelial cells in adjacent normal tissue using FISH or targeted single-cell genomic sequencing on new patient cohorts to confirm field cancerization.
  2. Perform functional assays (e.g., co-culture experiments, organoids) to assess the impact of increased Tregs/ILCregs and TGF-beta signaling on CD8+ T cell cytotoxicity against primary colorectal cancer cells.
  3. Utilize spatial transcriptomics or multiplexed immunostaining to map the precise localization and interaction of specific CAF subsets (identified by CD44, ITGAV, PMEPA1) with tumor cells and immune cells, and evaluate their correlation with tumor invasion fronts.
  4. Conduct in vitro and in vivo studies (e.g., patient-derived xenografts) to test the efficacy of blocking newly identified surface markers (e.g., CEACAM1, ADGRE5, CD47 on epithelial cells; SIRPA, GPNMB on macrophages; ITGAV on fibroblasts) using antibodies or CAR T cells.
  5. Investigate the sensitivity of colorectal cancer cell lines and primary tumor cells with EGFR, ERBB2, or MYC amplifications to specific cell cycle inhibitors (e.g., CDK4/6 inhibitors, CHEK1 inhibitors) or pathway inhibitors (e.g., Wnt inhibitors).

Limitations:

This report is based on single-cell RNA sequencing data, which provides transcriptomic insights but does not directly measure protein expression or post-translational modifications. Cell-cell interaction inferences (CellPhoneDB) are computational predictions based on ligand and receptor expression, requiring functional validation. The identification of aneuploid cells relies on genomic inference from RNA-seq and should be orthogonally confirmed with DNA-based methods like FISH or WGS. The 'adjacent normal' samples, while histologically normal, may still harbor subtle molecular alterations or microscopic tumor cell contamination, as indicated by the presence of aneuploid cells in some cases. The patient cohort size and specific tumor stages or treatment histories were not fully detailed, which could influence the generalizability of findings. Furthermore, while numerous associations and dysregulated pathways were identified, direct causality cannot be established without further experimental perturbation studies.

24. Query List

  1. Show UMAP including condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset in 2 columns and save it.
  2. Show major cell type scores on UMAP and save it.
  3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
  4. Select tumor-origin cells and unassigned cells, group by sample, show CNV heatmap, and include a summary of significantly amplified regions and save it.
  5. Show CNV patterns on UMAP. Include major cell types, minor cell types, ploidy results, conditions, and samples in 2 columns and save it.
  6. Show population bar plot for minor cell types and save it.
  7. Show subset population barplot for T cells and save it.
  8. Show boxplot for T cell subset populations if there are significant differences between conditions, and save it. Set ncols appropriately based on the total number of panels.
  9. Show subset population barplot for Macrophages and save it.
  10. Show boxplot for Macrophage subset populations if there are significant differences between conditions, and save it. Set ncols appropriately based on the total number of panels.
  11. Select tumor-origin cells and unassigned cells, and show a barplot of their ploidy population and save it.
  12. Show cell-cell interaction patterns by condition, including Intestinal Epithelial cells (tumor origin), Fibroblasts, Macrophages, and T cells, and save it. Limit cell-cell interactions to a maximum of 80 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 and cell cycle pathways, show cell-cell interactions for these genes, and save it.
  15. Find statistically significant differences in cell-cell interactions between conditions for major immune and stromal cells, show as a dot plot, and save it. Set max_n_items_per_group = 25.
  16. Show the condition-specific markers for tumor-origin cells (Intestinal Epithelial cell) as a dot plot and save the result. Use only surfaceome markers, up to 50 markers per condition.
  17. Extract condition-specific markers for Macrophages, show as a dot plot, and save it. Show only surfaceome markers, up to 50 per condition.
  18. Extract condition-specific markers for Fibroblasts, show as a dot plot, and save it. Show only surfaceome markers, up to 50 per condition.
  19. Extract condition-specific markers for CD4 T cells, show as a dot plot, and save it. Show only surfaceome markers, up to 50 per condition.
  20. Select genes related to the Cell cycle pathway, find those with statistically significant expression differences between conditions in Intestinal Epithelial cells, show as a boxplot, and save it. Set max_n_items_to_plot = 24, and set ncols appropriately to achieve a 2x3 aspect ratio based on the total number of panels.
  21. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
  22. Show Gene set enrichment analysis results for major cell types as a dot plot and save it. Set color map to RdBu_r and n_pws_to_show = 80.
↑ Top