SCODiA Report by MLBI Lab

Single-Cell Transcriptomic and Genomic Landscape of Human Colorectal Cancer Reveals Key Drivers of Tumor Progression and Immune Evasion

This report details a single-cell analysis of human colon tissue, contrasting tumor samples with adjacent normal tissue. We identify significant shifts in cell population composition, characterized by an expansion of aneuploid intestinal epithelial cells and a distinct immune microenvironment. Key findings include widespread genomic instability, altered cell-cell interactions, and dysregulated gene expression pathways driving tumor proliferation and immune suppression, offering critical insights into colorectal cancer pathobiology.

Contents

  1. Dataset overview
  2. Single-cell UMAP Embedding Exploration of Colon Tissue
  3. Major Cell Type Score and Ploidy Distribution on UMAP
  4. Overall Celltype_subset Marker Expression Dot Plot Interpretation
  5. Copy Number Variation (CNV) Analysis in Intestinal Epithelial and Unassigned Cells
  6. CNV 기반 UMAP 분석을 통한 세포 유형, 이수성, 및 조건별 패턴 시각화
  7. Colon Tissue Minor Cell Type Population Analysis in Tumor vs. Adjacent Normal Conditions
  8. T 세포 아형 인구 분포 분석: 인접 정상 조직 및 종양 조직 비교
  9. Colon Tumor Microenvironment Shows Significant Shifts in T cell and Innate Lymphoid Cell Subpopulations
  10. Macrophage Subset Population Shifts in Colon Tumor Microenvironment
  11. Macrophage Subset Population Shifts in Colorectal Tumor Microenvironment
  12. Ploidy Population Analysis in Intestinal Epithelial and Unassigned Cells
  13. Colon Cancer Microenvironment: Cell-Cell Interaction Analysis by Condition
  14. Colon Tumor Microenvironment Cell-Cell Interaction Analysis
  15. Cell-Cell Interaction Analysis: Immune Checkpoint and Cell Cycle Pathways in Colon Tissue
  16. Differential Cell-Cell Interaction Patterns in Colon Tumor vs. Adjacent Normal Tissue
  17. Intestinal Epithelial Cell Condition-Specific Surfaceome Markers in Colon Cancer
  18. Macrophage Condition-Specific Surface Markers in 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 Pathways in Intestinal Epithelial Cells from Colon Tumor Tissue
  22. 장상피세포의 조건 및 배수성 상태에 따른 유전자 온톨로지(GSA) 분석 결과
  23. Gene Set Enrichment Analysis (GSEA) in Colon Tumor Microenvironment
  24. Discussion
  25. Query List

0. Dataset overview

Dataset Summary

1. Single-cell UMAP Embedding Exploration of Colon Tissue

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[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 기반으로 한 UMAP 임베딩을 통해, 대장 조직 내 세포들의 이질성 및 조건(종양 vs. 인접 정상), 샘플, 주요 세포 유형, 세부 세포 유형, 그리고 세포 핵형(ploidy)에 따른 분포를 시각화하고 해석합니다. 63,689개의 세포와 23,387개의 유전자를 포함하는 AnnData 객체를 사용하여, 세포 집단의 구조와 특성을 다차원적으로 이해하고자 합니다.

Visual Summary

  1. Condition (조건: Tumor vs. Adj_normal) UMAP
  1. Sample (샘플) UMAP
  1. celltype_major (주요 세포 유형) UMAP
  1. celltype_minor (세부 세포 유형) UMAP
  1. ploidy_dec (핵형 결정: Aneuploid/Diploid) UMAP
  1. celltype_subset (세포 아형) UMAP

Biological Interpretation

이번 UMAP 분석은 대장 조직의 단일 세포 데이터를 통해 여러 중요한 생물학적 통찰력을 제공합니다.

Clinical or Translational Implications

2. Major Cell Type Score and Ploidy Distribution on UMAP

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the UMAP (Uniform Manifold Approximation and Projection) embedding of single-cell RNA-seq data from Colon tissue. The UMAP plots serve two main purposes:

  1. Cell Type Score Visualization: Display the HiCAT_major_score for various major cell types across the UMAP, providing a quantitative assessment of cell identity for different regions of the embedding. This helps confirm the presence and localization of specific cell populations based on gene expression profiles characteristic of each major cell type.
  2. Ploidy and Annotation Visualization: Show the distribution of ploidy inference (ploidy_dec as Aneuploid/Diploid) and the final celltype_major annotations on the UMAP. This allows for validation of cell type assignments and investigation into the genomic stability (ploidy) of different cell populations within the tissue.

Visual Summary

The visualization consists of 10 UMAP plots. The first eight plots display the HiCAT_major_score for individual major cell types, with higher scores indicated by warmer colors (yellow/green) and lower scores by cooler colors (purple). The ninth plot shows the ploidy_dec (Aneuploid, Diploid, Unclear), and the tenth plot shows the celltype_major annotations.

HiCAT_major_score Plots

ploidy_dec Plot

celltype_major Plot

Biological Interpretation

Annotation Notes

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References

  1. Aneuploidy as a hallmark of cancer:

3. Overall Celltype_subset Marker Expression Dot Plot Interpretation

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[Analysis Visualization Results]...

Analysis Overview

This visualization presents a dot plot illustrating the expression of marker genes across various celltype_subset populations identified in the single-cell RNA-seq data from human colon tissue. The analysis aimed to identify and visualize specific marker genes for each cell subset, without focusing on a particular target cell type (as indicated by target_cell: None). The parameters used for finding markers (find_cfg) prioritized surfaceome-only genes, with specific cutoffs for expression score, fold change, and p-value. Notably, markers common in 3 or more groups were removed (rem_mkrs_common_in_N_groups_or_more: 3) to enhance specificity, making this plot ideal for assessing the distinctness and quality of the celltype_subset annotations.

Visual Summary

The dot plot is structured with celltype_subset groups on the y-axis and their corresponding marker genes on the x-axis. Each dot's size indicates the percentage of cells within a group that express a particular gene (fraction of cells in group), while its color intensity represents the mean expression level of that gene within the group (mean expression in group; darker red signifies higher expression).

Key visual patterns include:

Biological Interpretation

The marker gene expression patterns strongly support the biological identity of the assigned celltype_subset annotations in the colon tissue.

Intestinal Epithelial Cells

Stromal and Endothelial Cells

Immune Cells

Enteric Neurons

Annotation Notes

This "Overall Celltype_subset marker expression dot plot" serves as a highly effective and robust validation of the celltype_subset annotations within the AnnData object. The observation of distinct, highly specific marker gene sets for nearly all cell subsets, characterized by high mean expression and prevalence across cells within each group, strongly supports the accuracy and biological fidelity of the cell type assignments. The consistency of these markers with established biological knowledge for human colon tissue further reinforces the confidence in the current annotation schema. Minor overlaps or less distinct markers in some closely related cell types are expected and do not undermine the overall quality of the annotation, but rather highlight the continuous spectrum of cellular states.

4. Copy Number Variation (CNV) Analysis in Intestinal Epithelial and Unassigned Cells

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates copy number variations (CNVs) in Intestinal Epithelial cells (the identified tumor origin cell type) and unassigned cells from colon tissue, across both Tumor (T) and Adjacent Normal (N) samples. The goal is to visualize the genomic landscape of CNVs and identify significantly amplified or deleted regions, providing insights into genomic instability associated with the disease state. The results are presented as a heatmap of log2(Copy Number Ratio, CNR) and a summary plot highlighting recurrent cytogenetic band alterations.

Visual Summary

log2(CNR) Heatmap

The first heatmap visualizes the log2(CNR) values across genomic spots for individual cell groups, which are defined by their ploidy status and sample origin (e.g., "Diploid SMC01-T").

Summary of Significantly Amplified Copy Number Regions

The second heatmap and bar plot provide a summarized view of recurrent CNV events at the cytogenetic band level across samples.

Biological Interpretation

The analysis specifically targets Intestinal Epithelial cells, which are confirmed as the "Tumor origin celltype" in the data context, and unassigned cells. The observed CNV patterns are therefore highly relevant to the genomic instability underlying colon tumorigenesis.

Clinical or Translational Implications

The identified recurrent CNVs have several clinical implications:

5. CNV 기반 UMAP 분석을 통한 세포 유형, 이수성, 및 조건별 패턴 시각화

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[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱 데이터에서 추론된 체세포 유전체 복제수 변이(CNV)를 기반으로 UMAP 임베딩을 생성하고, 이를 다양한 세포 특성(celltype_major, celltype_minor, ploidy_dec, condition, sample)별로 시각화하여 데이터의 전반적인 구조, 세포 유형 분포, 이수성 상태, 그리고 종양 미세 환경의 특징을 탐색합니다. UMAP은 CNV 정보를 활용하여 세포 간의 유사성을 차원 축소된 공간에 표현하므로, 유전체 안정성 또는 불안정성과 관련된 세포 집단을 효과적으로 식별할 수 있습니다.

Visual Summary

celltype_major 및 celltype_minor 분포:

ploidy_dec (이수성 정도) 분포:

condition (조건) 분포:

sample (샘플) 분포:

Biological Interpretation

Annotation Notes

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

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Analysis Overview

This analysis visualizes the relative proportions of minor cell types across individual samples from both 'Adj_normal' (adjacent normal) and 'Tumor' conditions. The stacked bar plot allows for a direct comparison of cellular composition between healthy and cancerous colon tissue, highlighting potential shifts in the tumor microenvironment (TME) at the cellular population level. Each bar represents a single sample, and the segments within each bar show the percentage contribution of different celltype_minor populations, as defined in the AnnData object.

Visual Summary

The stacked bar plot presents the cellular heterogeneity within colon tissue samples, stratified by condition: 'Adj_normal' (left panel) and 'Tumor' (right panel).

Shifts in Tumor Tissue

Biological Interpretation

The observed shifts in cell type populations between adjacent normal and tumor colon tissue provide critical insights into the remodeling of the tumor microenvironment (TME) during colorectal cancer progression.

  1. Tumor Cell Dominance: The prominent increase in the relative proportion of Intestinal Epithelial cells in tumor samples is a direct reflection of neoplastic proliferation. As the 'Tumor origin celltype', their numerical expansion physically displaces or overshadows other resident cell populations within the tumor mass. This cellular dominance is a hallmark of cancer.
  2. Immune Landscape Alterations:
  1. Stromal Remodeling: The relative decrease in Fibroblasts in tumor samples is an interesting observation. While cancer-associated fibroblasts (CAFs) are known to be abundant and play critical roles in desmoplasia and tumor support in many cancers, their *relative* reduction here could be due to the vast expansion of epithelial cells overshadowing their absolute numbers, or a different balance of stromal components in colon cancer compared to other tumor types. Endothelial cells, although reduced in relative proportion, are vital for tumor angiogenesis, which might not be fully captured by proportional analysis.
  2. Inter-tumoral Heterogeneity: The variability in cellular composition among individual tumor samples underscores the high degree of inter-patient heterogeneity in colon cancer. This suggests that distinct TME profiles might exist, potentially influencing disease progression and therapeutic responses.

Clinical or Translational Implications

The distinct alterations in cell type proportions between normal colon tissue and colorectal tumors have significant clinical and translational implications:

  1. Biomarker Discovery: The relative abundance of specific immune cell populations (e.g., T cells, macrophages) or the ratio of tumor cells to immune cells (Tumor-Immune Cell Ratio) could serve as prognostic or predictive biomarkers for patient outcomes and response to therapies. For example, a lower T-cell proportion might indicate a "cold" tumor less responsive to immune checkpoint inhibitors.
  2. Therapeutic Targeting: Understanding the cellular makeup of the TME can inform the development of targeted therapies.
  1. Patient Stratification: The observed inter-tumoral heterogeneity suggests that patients could be stratified based on their tumor's cellular composition. This stratification might help guide personalized treatment decisions, matching specific therapies to patients most likely to benefit based on their unique TME profile.
  2. Diagnostic Potential: The distinct cellular landscape of tumor versus normal tissue can be leveraged for diagnostic purposes, potentially enhancing the accuracy of cancer detection and staging.

7. T 세포 아형 인구 분포 분석: 인접 정상 조직 및 종양 조직 비교

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[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱 데이터를 기반으로 대장(Colon) 조직에서 T 세포 및 ILC(선천성 림프구) 아형의 상대적 인구 분포를 인접 정상 조직(Adj_normal)과 종양(Tumor) 조직 간에 비교한 막대 그래프입니다. 각 막대는 개별 샘플을 나타내며, 각 세포 아형이 해당 샘플 내 T 세포 및 ILC 총 인구에서 차지하는 비율을 100% 기준으로 시각화합니다.

Visual Summary

제공된 막대 그래프는 인접 정상 대장 조직과 종양 조직 내 T 세포 및 ILC 아형의 인구 역학에서 뚜렷한 차이를 보여줍니다.

인접 정상 조직 (Adj_normal):

종양 조직 (Tumor):

Biological Interpretation

이 분석 결과는 대장암 종양 미세환경에서 T 세포 아형 구성의 중요한 재편을 시사합니다.

  1. 면역억제 환경으로의 전환: 종양 조직에서 조절 T 세포(Treg)의 현저한 증가는 종양 미세환경이 면역 관용 및 면역억제 특성을 강화하고 있음을 강력히 나타냅니다. Treg 세포는 항종양 면역 반응을 억제하여 종양이 면역 감시를 회피하고 성장을 촉진하는 데 기여하는 것으로 알려져 있습니다 [PubMed search: Regulatory T cells tumor microenvironment colon cancer].
  2. Naive T 세포의 분화 또는 고갈: Naive T 세포의 감소는 종양 특이적 항원에 의해 T 세포가 활성화되어 effector T 세포 또는 Treg 세포로 분화하거나, 종양 미세환경이 Naive T 세포의 침윤을 억제할 수 있음을 의미합니다.
  3. T helper 세포 아형의 균형 변화:

이러한 변화는 대장암에서 면역 회피 메커니즘이 활발하게 작동하고 있으며, 종양 미세환경이 염증 반응과 면역억제 사이의 복잡한 균형을 보이고 있음을 강조합니다.

Clinical or Translational Implications

이 분석 결과는 대장암의 진단, 예후 예측 및 치료 전략 개발에 중요한 시사점을 제공합니다.

  1. 면역관문억제제 반응 예측 및 개선: 종양 내 Treg 세포의 높은 비율은 면역관문억제제(예: anti-PD-1/PD-L1) 치료에 대한 반응률을 저해하는 요인이 될 수 있습니다. Treg 세포를 표적으로 하는 치료법을 병용하거나, Treg 세포를 제거하거나 기능을 억제하는 전략은 면역관문억제제의 효능을 향상시키는 데 기여할 수 있습니다 [PubMed search: Treg depletion immunotherapy cancer].
  2. 새로운 치료 표적 발굴: Th9 세포의 증가와 같은 다른 T 세포 아형의 변화는 대장암 특이적인 면역 조절 메커니즘을 밝히고, 이들 세포를 조절하여 항종양 면역 반응을 강화할 수 있는 새로운 치료 표적을 발굴하는 데 단서를 제공할 수 있습니다.
  3. 생체 지표 (Biomarker) 개발: 종양 내 T 세포 아형, 특히 Treg 세포의 비율은 대장암 환자의 예후를 예측하거나 특정 치료법에 대한 반응성을 예측하는 잠재적인 생체 지표로 활용될 수 있습니다. 예를 들어, 높은 종양 내 Treg/CD8+ T 세포 비율은 불량한 예후와 관련될 수 있습니다.

이러한 인구학적 변화는 대장암 종양 미세환경의 복잡성을 이해하는 데 필수적이며, 환자 맞춤형 면역 치료 전략을 개발하기 위한 중요한 기초 정보를 제공합니다.

8. Colon Tumor Microenvironment Shows Significant Shifts in T cell and Innate Lymphoid Cell Subpopulations

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional representation of various T cell and Innate Lymphoid Cell (ILC) subsets in human colon tissue, comparing tumor samples ('Tumor') to adjacent normal tissue ('Adj_normal'). The goal is to identify statistically significant shifts in these immune cell populations, which can provide insights into the immune landscape of colorectal cancer. The plot_box_for_celltype_population_with_signif_difference tool was used to visualize these differences with statistical annotations.

Visual Summary

The box plots display the cell type proportion for eight T cell and ILC subsets across 'Tumor' and 'Adj_normal' conditions. Several statistically significant differences were observed:

Decreased in Tumor Tissue:

Increased in Tumor Tissue:

These proportional changes highlight a distinct re-modeling of the immune cell composition within the colon tumor microenvironment.

Biological Interpretation

The observed shifts in immune cell proportions in colon tumors compared to adjacent normal tissue strongly suggest an immunosuppressive and pro-tumorigenic microenvironment.

  1. Immunosuppression by Treg and Th17 Cells:
  1. Reduced Innate Anti-Tumor Immunity:
  1. Compromised Adaptive Anti-Tumor Responses:

Collectively, these findings paint a picture of a colon tumor microenvironment that actively reshapes immune cell proportions to favor immune evasion and tumor progression. The enrichment of immunosuppressive (Treg) and pro-tumorigenic inflammatory (Th17) populations, coupled with a reduction in key innate (ILC1, LTI) and potentially adaptive (T_Cyto) anti-tumor cells, highlights critical mechanisms of tumor immune escape in colon cancer.

Clinical or Translational Implications

The distinct immunological profile identified in colon tumor tissue has several important clinical and translational implications:

Therapeutic Targets:

9. Macrophage Subset Population Shifts in Colon Tumor Microenvironment

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the relative proportions of different macrophage subsets (M1, M2A, M2B, M2C, M2D) within the total macrophage population across individual samples from both 'Adj_normal' (adjacent normal colon tissue) and 'Tumor' (colon tumor tissue) conditions. This helps to identify shifts in macrophage polarization that may be associated with the tumor microenvironment.

Visual Summary

The bar plots display the proportional distribution of macrophage subsets for each sample, grouped by condition.

Biological Interpretation

Macrophages are highly plastic immune cells that can differentiate into distinct functional phenotypes, often broadly categorized as M1 (pro-inflammatory, anti-tumorigenic) or M2 (anti-inflammatory, pro-tumorigenic). The observed shifts in macrophage subsets within the colon tumor microenvironment suggest a complex interplay between different macrophage polarization states.

  1. Shift towards M2B phenotype in tumors: The consistent expansion of Macrophage (M2B) in tumor samples is a significant finding. M2B macrophages are known to be involved in immune regulation and can be stimulated by immune complexes or specific cytokines. Their increased presence in the tumor microenvironment could contribute to an immunosuppressive environment, promoting tumor growth and progression through mechanisms such as antigen presentation, cytokine production, and fostering angiogenesis [NCBI].
  2. Concomitant increase in M1 phenotype: The increased proportion of Macrophage (M1) in tumor samples is somewhat counter-intuitive, as M1 macrophages are typically associated with anti-tumor immunity. However, the tumor microenvironment is highly heterogeneous. It is possible that while M2B macrophages contribute to immune suppression, there might also be concurrent pro-inflammatory signals driving M1 polarization, perhaps as an attempt by the host immune system to counteract the tumor, or as a response to necrotic cells or pathogen-associated molecular patterns (PAMPs) within the tumor. This coexistence highlights the complexity and potential for mixed signals within the tumor microenvironment [NCBI].
  3. Reduced M2A in tumors: The relative decrease in M2A macrophages, which are often involved in wound healing and fibrosis, suggests a possible shift away from these specific functions in the established tumor microenvironment, or that other M2 subtypes (like M2B) are preferentially recruited or polarized.
  4. Overall Macrophage Plasticity: The data strongly support the concept of macrophage plasticity within the colon, where the distinct microenvironments of normal tissue versus tumor drive different polarization states. The 'Adj_normal' samples display a more balanced or variable macrophage profile, whereas the 'Tumor' samples converge on a phenotype characterized by increased M1 and M2B, and decreased M2A.

Clinical or Translational Implications

The distinct shifts in macrophage populations, particularly the expansion of M2B and the concurrent significant presence of M1 macrophages in colon tumors, have several clinical implications:

  1. Biomarker Potential: The ratio or absolute abundance of specific macrophage subsets, particularly M2B relative to M2A, could serve as a potential prognostic or predictive biomarker for colon cancer progression or response to therapy.
  2. Therapeutic Targeting: The observed macrophage polarization suggests that therapeutic strategies aimed at re-educating tumor-associated macrophages (TAMs) or modulating their specific functions could be beneficial. For instance, targeting factors that promote M2B polarization might reduce immune suppression, while enhancing M1-like functions could boost anti-tumor immunity.
  3. Immunotherapy Design: Understanding the precise macrophage subset composition is crucial for designing effective immunotherapies. In a microenvironment with abundant M2B-like macrophages, therapies that overcome immunosuppression (e.g., checkpoint inhibitors) or directly reprogram these cells could be more effective. The presence of M1 macrophages also suggests potential avenues for therapies that amplify existing pro-inflammatory responses.

10. Macrophage Subset Population Shifts in Colorectal Tumor Microenvironment

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional changes of specific macrophage subsets (Mac (M1), Mac (M2A), and Mac (M2B)) between normal adjacent colon tissue ("Adj_normal") and colon tumor tissue ("Tumor"). The goal is to identify statistically significant shifts in these immune cell populations, which can provide insight into the immunological landscape of colorectal cancer.

Visual Summary

The box plots illustrate the celltype proportion of three macrophage subsets across "Adj_normal" and "Tumor" conditions:

Biological Interpretation

The observed shifts in macrophage subsets suggest a significant reprogramming of the macrophage compartment within the colorectal tumor microenvironment, consistent with the known plasticity of macrophages in response to pathological conditions, particularly cancer.

Overall, these findings indicate a shift towards a tumor-supportive macrophage phenotype, primarily driven by the accumulation of M2B macrophages, which can facilitate tumor progression and evade host immune surveillance in the colon.

Clinical or Translational Implications

The significant increase in M2B macrophages in colon tumors, coupled with a decrease in M2A, highlights a potential mechanism by which colorectal cancer establishes an immunosuppressive microenvironment conducive to its growth and spread.

11. Ploidy Population Analysis in Intestinal Epithelial and Unassigned Cells

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the ploidy status (Aneuploid, Diploid, or Unclear) within the combined population of "Intestinal Epithelial cell" and "unassigned" cells across individual samples from both "Adj_normal" and "Tumor" conditions. The Intestinal Epithelial cell type is noted as the tumor origin cell type, making its ploidy status particularly relevant to understanding tumor biology.

Visual Summary

The bar plots display the percentage of cells classified as Aneuploid (dark red), Diploid (light orange), or Unclear (light green) for each sample.

Biological Interpretation

The striking difference in ploidy distribution between "Adj_normal" and "Tumor" conditions highlights a fundamental genomic alteration characteristic of cancer: aneuploidy.

Clinical or Translational Implications

The ploidy status, particularly aneuploidy, holds significant clinical relevance:

12. Colon Cancer Microenvironment: Cell-Cell Interaction Analysis by Condition

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using CellPhoneDB results from single-cell RNA-seq data of human colon tissue, comparing "Adj_normal" (adjacent normal) and "Tumor" conditions. The focus is on critical cell populations within the tumor microenvironment: Intestinal Epithelial cells (the tumor origin cell type, considering both Diploid and Aneuploid states), Fibroblasts, Macrophages, and T cells (CD4+ and CD8+). The goal is to identify prominent and differentially regulated ligand-receptor interactions that may drive disease progression or offer therapeutic targets.

Visual Summary

The dot plots illustrate cell-cell interactions, where the y-axis represents interacting cell type pairs (Receiver:Sender or Sender:Receiver), and the x-axis lists specific ligand-receptor pairs. The size of each dot corresponds to the negative logarithm of the interaction's p-value (-log10(p)), indicating statistical significance, while the color intensity reflects the mean expression level of the ligand-receptor pair, indicating interaction strength. Only significant interactions (p-val < 0.05) with mean expression > 0.01 are displayed, up to 80 pairs per condition.

Adj_normal Condition

In the "Adj_normal" condition, interactions are broadly distributed across various cell-cell pairs. Prominent interactions involve:

Tumor Condition

The "Tumor" condition plot shows a distinct shift in interaction patterns, with a notable increase in interactions involving "Aneuploid Intestinal Epithelial cells" and Macrophages.

Key Differences Between Conditions

  1. Emergence of Aneuploid IEC-centric Interactions: The "Tumor" condition is characterized by a high number of significant and strong interactions involving Aneuploid Intestinal Epithelial cells, which are largely absent in the "Adj_normal" plot. This highlights the malignant epithelial cells as key orchestrators or recipients of signals within the tumor microenvironment.
  2. Increased Macrophage Activity: Macrophages display a marked increase in interaction diversity and strength in the "Tumor" environment, particularly with Aneuploid IECs and Fibroblasts, suggesting a crucial role in tumor progression.
  3. Shift in Dominant Ligand-Receptor Pairs: While integrin interactions remain important in both conditions, specific integrin complexes and their partners (e.g., SPP1, VEGFA, TGFB1) become more pronounced in the tumor. CDH1_integrin_aEb7_complex seems reduced in overall prominence involving epithelial cells in tumor, potentially indicating loss of epithelial integrity.
  4. Pro-tumorigenic Signaling: Interactions involving SPP1 (Osteopontin), TGFB1, and VEGFA are more prominent in the "Tumor" setting, indicating activation of pathways associated with angiogenesis, immune suppression, and tumor invasion.

Biological Interpretation

Intestinal Epithelial Cell Interactions (Diploid vs. Aneuploid)

The distinction between Diploid and Aneuploid Intestinal Epithelial cells in the tumor context is crucial. Diploid cells, even within the tumor microenvironment, likely retain some normal functional characteristics, as evidenced by interactions with CDH1_integrin_aEb7_complex in Adj_normal that are less prominent for Aneuploid cells in Tumor. Aneuploid cells, representing the cancer cells, actively engage with their microenvironment. Their strong interactions with Macrophages via SPP1-CD44 and SPP1-integrin_a5b1_complex suggest a role in recruiting and polarizing macrophages towards a pro-tumorigenic phenotype (e.g., M2-like TAMs), promoting tumor growth and immune evasion PMID: 29778749. Additionally, interactions with Fibroblasts and T cells via various integrin complexes underscore their involvement in ECM remodeling, migration, and modulating immune responses.

Fibroblast and Macrophage Interactions

Fibroblasts (CAFs): The continued and intensified interactions of Fibroblasts in the tumor context, particularly with Aneuploid IECs and Macrophages, point to their transformation into Cancer-Associated Fibroblasts (CAFs). CAFs are known to remodel the ECM via collagen and integrin interactions (COL1A1, FN1 with integrins) and produce growth factors and cytokines that promote tumor growth, invasion, and immunosuppression PMID: 31217696.

Macrophages (TAMs): Macrophages become highly interactive in the tumor, acting as Tumor-Associated Macrophages (TAMs). Their extensive interactions, especially with Aneuploid IECs and Fibroblasts, highlight their central role in shaping the tumor microenvironment. SPP1 (Osteopontin) produced by tumor cells or other stromal cells often interacts with CD44 and integrins on macrophages, driving their differentiation into pro-tumorigenic phenotypes that secrete factors promoting angiogenesis (VEGFA), metastasis, and immune suppression PMID: 31053744. The VEGFA-VEGFR1 interactions also suggest their contribution to tumor angiogenesis.

T Cell Interactions

T cells (CD4+ and CD8+) show distinct interaction patterns. While they engage with stromal and epithelial cells in both conditions, the specific context in the tumor is crucial. For instance, HLA interactions are present, reflecting antigen presentation potential. However, the prominent pro-tumorigenic signals from Aneuploid IECs, Macrophages, and Fibroblasts could lead to T cell exhaustion or anergy rather than effective anti-tumor immunity. The presence of TGFB1-TGFBR1 interactions in the tumor is concerning, as TGF-β is a potent immunosuppressive cytokine that inhibits T cell activation and promotes Treg differentiation, thereby dampening anti-tumor responses PMID: 34183863.

Notable Ligand-Receptor Systems

Clinical or Translational Implications

The identified cell-cell interaction patterns in the colon tumor microenvironment offer several potential clinical and translational avenues:

  1. Therapeutic Target Prioritization: Ligand-receptor pairs that are highly active and unique to the tumor condition, especially those driving pro-tumorigenic processes, represent promising therapeutic targets.
  1. Biomarker Discovery: The specific interacting cell types and ligand-receptor pairs prominent in the tumor (e.g., Aneuploid IEC-Macrophage interactions) could serve as novel biomarkers for disease progression, prognosis, or response to therapy.
  2. Combination Therapies: The complex interplay of multiple cell types and signaling pathways suggests that combination therapies, simultaneously targeting different components of the tumor microenvironment (e.g., inhibiting SPP1 to reprogram macrophages, while also blocking TGF-β to enhance T cell function), may yield superior outcomes in colon cancer patients.
  3. Understanding Immune Evasion: The differential interactions of T cells in the tumor context, particularly the presence of immunosuppressive signals (e.g., TGF-β), provide insights into mechanisms of immune evasion. Strategies to disrupt these interactions or reverse T cell exhaustion could enhance the efficacy of immunotherapies.

13. Colon Tumor Microenvironment Cell-Cell Interaction Analysis

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies significant cell-cell interactions (CCI) within the colon tumor microenvironment (TME) by analyzing single-cell RNA-seq data from the "Tumor" condition. Using CellPhoneDB, ligand-receptor pairs between various cell types, including Aneuploid and Diploid Intestinal Epithelial cells, Macrophages, and T cells (CD4+ and CD8+), were computed. The results are visualized as a dot plot, where dot size represents the statistical significance (-log10(p-value)) and dot color represents the mean expression level (log2(mean)) of the interacting ligand-receptor pair. The analysis was configured to focus on interactions involving tumor-originating (Intestinal Epithelial) cells by expanding their ploidy status, thus distinguishing between "Aneuploid Intestinal Epi" (presumptive tumor cells) and "Diploid Intestinal Epi" (normal-like epithelial cells).

Visual Summary

The dot plot displays up to 80 statistically significant (p-value < 0.05) and highly expressed (mean > 0.01) cell-cell interactions within the tumor condition.

Biological Interpretation

The observed cell-cell interactions provide insights into the complex communication networks driving the colon tumor microenvironment.

  1. Tumor Cell-Immune Cell Crosstalk:
  1. Tumor Cell Adhesion and Communication:
  1. Growth, Angiogenesis, and Invasion:
  1. Chemokine-Mediated Immune Cell Trafficking:

Clinical or Translational Implications

This analysis highlights several pathways and cell types that represent promising avenues for therapeutic intervention and biomarker development in colorectal cancer.

References:

  1. TIGIT in cancer immunotherapy: Wu X, Zhang R, Li Y, et al. TIGIT as an immune checkpoint in cancer immunotherapy. *Front Oncol*. 2021;11:665403. PubMed Search: TIGIT cancer immunotherapy
  2. Galectin-9 and TIM-3 in cancer: Li W, Li R, Liu D, et al. Galectin-9/Tim-3 Pathway as a Potential Target for Cancer Immunotherapy. *Front Immunol*. 2021;12:650972. PubMed Search: Galectin-9 TIM-3 cancer
  3. TREM2 in cancer: Cao D, Li R, Ding Y, et al. TREM2: a potential immunotherapeutic target for cancer. *J Exp Clin Cancer Res*. 2022;41(1):159. PubMed Search: TREM2 cancer immunotherapy
  4. CEACAMs in colorectal cancer: Kuckertz L, Leber J, Ostermann E, et al. The family of carcinoembryonic antigen-related cell adhesion molecules (CEACAMs) as a target for therapy and diagnosis of colorectal cancer. *Cells*. 2020;9(6):1460. PubMed Search: CEACAM colorectal cancer
  5. Ephrin-Eph signaling in cancer: Genna A, Miano S, Di Gregorio S, et al. Ephrin-Eph signaling as a target for cancer therapy. *Pharmacol Ther*. 2018;184:77-87. PubMed Search: Ephrin Eph cancer signaling
  6. VEGF in cancer angiogenesis: Ferrara N. VEGF as a therapeutic target in cancer. *Oncology*. 2004;67(1):11-15. PubMed Search: VEGF cancer angiogenesis

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) within human colon tissue using single-cell RNA sequencing data. The focus is on specific gene pairs involved in immune checkpoint regulation and cell cycle pathways. Interactions are compared between "Adj_normal" (adjacent normal tissue) and "Tumor" conditions, using CellPhoneDB to identify significant ligand-receptor pairs between various cell types. The results are visualized as dot plots, where dot size represents interaction significance (-log10(p-value)) and color intensity represents the mean expression level of the interacting ligand-receptor pair (log2(mean)).

Visual Summary

CCI for Adj_normal

The plot for adjacent normal tissue reveals diverse cell-cell interactions:

CCI for Tumor

The plot for tumor tissue shows a shift in interaction patterns:

Biological Interpretation

  1. Dynamic Remodeling of Immune Responses in the Tumor Microenvironment (TME):
  1. Epithelial-Stromal and Growth Factor Signaling Shifts:
  1. Role of Aneuploid Cells: The specific interaction of T CD8+ cells with Aneuploid Intestinal Epithelial cells via IFNG_IFNGR1 underscores the immune system's recognition and targeting of genetically unstable (likely malignant) cells in the tumor, a fundamental aspect of anti-tumor immunity.

Clinical or Translational Implications

  1. Immunotherapy Targets: The prominent IFNG_IFNGR1 and CD86_CD28 interactions in the tumor microenvironment point to active immune responses. Modulating these pathways could enhance anti-tumor immunity. For instance, strategies to sustain or amplify IFN-gamma signaling or to fine-tune CD28 co-stimulation could improve T cell effector functions in cancer patients.
  1. EGFR-targeted Therapies: The observed EREG_EGFR interaction in the tumor context suggests that EGFR signaling remains a critical pathway in colon cancer. Understanding the specific cell types involved in EREG-EGFR interactions within the TME could refine the application of existing EGFR inhibitors or guide the development of new strategies that target specific ligands or cellular contexts.
  1. Stromal Reprogramming: The strong TGF-beta signaling in normal tissue's epithelial-fibroblast interactions, coupled with its known role in promoting fibrosis and immunosuppression in cancer, suggests that targeting TGF-beta pathways could be beneficial. Even if not directly observed in the tumor CCI plot for the filtered genes, its foundational role in colon biology implies that dysregulation could be a therapeutic avenue for disrupting pro-tumorigenic stromal support.
  1. Biomarker Discovery: The distinct patterns of cell-cell interactions and specific ligand-receptor pairs in tumor versus normal tissue (e.g., changes in IFNG signaling partners or EGFR ligands) could serve as potential biomarkers for disease progression, response to therapy, or patient stratification.
  2. Combination Therapies: The complex interplay of immune checkpoint, co-stimulatory, and growth factor pathways suggests that combination therapies targeting multiple nodes—for example, combining immune checkpoint blockade with modulators of EGFR or TGF-beta signaling—might be more effective in overcoming resistance and achieving durable responses in colon cancer.

15. Differential Cell-Cell Interaction Patterns in Colon Tumor vs. Adjacent Normal Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) between 'Tumor' and 'Adj_normal' conditions in colon tissue, focusing on major immune and stromal cell types. Using CellPhoneDB results, a dot plot visualizes the strength (standardized sample mean, color intensity) and significance (-log10(p-value), dot size) of selected ligand-receptor pairs across individual samples. The aim is to highlight specific communication pathways that are distinctly active in either normal tissue homeostasis or the tumor microenvironment.

Visual Summary

The dot plot clearly delineates two major groups of cell-cell interactions, each predominantly active in one of the conditions:

The visualization effectively segregates interaction patterns, demonstrating a profound shift in cell communication landscape between healthy and cancerous colon tissue. The parameter max_n_items_per_group = 25 means the plot shows the top 25 most significant interactions enriched in each condition.

Biological Interpretation

The observed condition-specific CCI patterns reveal distinct biological processes operating in the adjacent normal colon tissue versus the tumor microenvironment.

Cell-Cell Interactions Prominent in Adjacent Normal Tissue:

The interactions enriched in adjacent normal tissue likely represent pathways critical for maintaining tissue homeostasis, barrier function, and basal immune surveillance.

Cell-Cell Interactions Prominent in Tumor Tissue:

The interactions upregulated in tumor tissue indicate pathways associated with tumor growth, altered stromal remodeling, immune modulation, and malignant cell behavior.

Clinical or Translational Implications

The distinct sets of cell-cell interactions identified in colon tumor versus adjacent normal tissue offer valuable insights for clinical applications:

Therapeutic Targets:

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers within Intestinal Epithelial cells, the presumed tumor-origin cell type in this colon cancer dataset. The dot plot visualizes the expression of up to 50 top surfaceome markers per condition (Adj_normal vs. Tumor) across individual patient samples. The goal is to uncover potential biomarkers that distinguish normal intestinal epithelial cells from their malignant counterparts, particularly highlighting differences within tumor samples based on ploidy status (Diploid vs. Aneuploid inferred).

Visual Summary

The dot plot effectively illustrates distinct patterns of surfaceome marker expression across Adj_normal and Tumor samples from Intestinal Epithelial cells.

Distinct Condition-Specific Clusters:

Heterogeneity within Tumor Samples (Ploidy-associated):

Expression and Prevalence:

Biological Interpretation

The differential expression of these surfaceome markers provides crucial insights into the biology of colon cancer and the functional changes occurring in Intestinal Epithelial cells during tumorigenesis.

Clinical or Translational Implications

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

Diagnostic and Prognostic Biomarkers:

Therapeutic Targets:

Experimental Validation:

17. Macrophage Condition-Specific Surface Markers in Colon Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surface markers uniquely expressed by Macrophages in human Colon tissue, distinguishing between 'Tumor' and 'Adj_normal' conditions. Using single-cell RNA sequencing data, the plot_markers_and_expression_dot tool was employed to find and visualize the top 50 condition-specific surfaceome markers for Macrophages, focusing on genes with significant differential expression (fold change > 1.5, p-value < 0.05) and high detection rates. The visualization highlights both the fraction of cells expressing a gene and its mean expression level across individual samples within each condition.

Visual Summary

The dot plot clearly delineates two distinct populations of macrophage surface markers, corresponding to the 'Adj_normal' and 'Tumor' conditions.

The plot demonstrates a clear transcriptional shift in the surfaceome of macrophages when transitioning from a homeostatic to a tumor-associated state. Dot sizes and color intensities robustly indicate both the proportion of cells expressing a marker and its average expression level, emphasizing the strength and specificity of these markers to their respective conditions.

Biological Interpretation

The observed condition-specific surface markers provide critical insights into the functional adaptation of macrophages within the colon cancer microenvironment.

Adj_normal Macrophage Markers: Reflecting Homeostasis and Basal Functions

The markers highly expressed in 'Adj_normal' macrophages suggest roles in maintaining tissue homeostasis and basal immune surveillance.

These markers collectively paint a picture of macrophages actively engaged in maintaining tissue integrity, nutrient homeostasis, and basal immune surveillance in the healthy colon.

Tumor Macrophage Markers: Signatures of Tumor-Associated Macrophages (TAMs)

The markers prominently expressed in 'Tumor' macrophages are highly indicative of Tumor-Associated Macrophages (TAMs), often characterized by an M2-like polarization that supports tumor progression.

These 'Tumor' specific markers collectively underscore a significant shift in macrophage phenotype towards a pro-tumorigenic and immunosuppressive state, actively participating in remodeling the tumor microenvironment and supporting disease progression in colorectal cancer.

Clinical or Translational Implications

The identification of condition-specific surface markers for macrophages in colon tissue has significant clinical and translational implications, particularly in the context of colorectal cancer.

  1. Biomarker Potential:
  1. Therapeutic Targets:

Further experimental validation in relevant *in vitro* and *in vivo* models, and patient cohorts, would be crucial to confirm the clinical utility of these macrophage-specific surface markers in colorectal cancer.

18. Fibroblast Condition-Specific Surfaceome Markers in Colon Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes surfaceome markers specifically expressed by Fibroblast cells in human colon tissue, distinguishing between "Adj_Normal" (adjacent normal) and "Tumor" conditions. The dot plot displays the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) across individual patient samples. By focusing on surfaceome markers, this analysis highlights potential candidates for cell-surface-targeted therapeutic strategies or flow cytometry-based characterization.

Visual Summary

The dot plot clearly delineates two distinct clusters of fibroblast surfaceome markers corresponding to the "Adj_Normal" and "Tumor" conditions.

Biological Interpretation

The identified surfaceome markers provide crucial insights into the altered biology of fibroblasts in colon cancer.

The shift from normal fibroblast markers to CAF-specific markers reflects the dynamic changes that occur in the stromal compartment during colon tumorigenesis, where fibroblasts acquire pro-tumorigenic functions.

Clinical or Translational Implications

The identified condition-specific surfaceome markers have significant clinical and translational implications, particularly in the context of colon cancer.

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in CD4+ T cells from human colon tissue, comparing Tumor samples to Adjacent Normal (Adj_normal) samples. The dot plot visualizes the expression of up to 50 surface markers per condition, showing the fraction of cells expressing each marker (dot size) and the mean expression level (dot color intensity) across individual samples, grouped by condition. The focus is on discovering cell-type-specific markers that can differentiate CD4+ T cell states between tumor and normal microenvironments.

Visual Summary

The dot plot clearly segregates genes into two distinct clusters based on their expression patterns in Adj_normal versus Tumor samples.

Biological Interpretation

The distinct surfaceome marker profiles highlight significant phenotypic and functional differences in CD4+ T cells residing in the tumor microenvironment (TME) compared to adjacent normal tissue.

Clinical or Translational Implications

These condition-specific surfaceome markers offer significant potential for biomarker development and therapeutic intervention strategies in colon cancer.

20. Dysregulation of Cell Cycle Pathways in Intestinal Epithelial Cells from Colon Tumor Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of a panel of cell cycle-related genes in Intestinal Epithelial cells, comparing samples from colon tumor tissue with adjacent normal tissue. The aim is to identify specific cell cycle regulators that are significantly altered in the tumor microenvironment, providing insights into the proliferative state and potential oncogenic mechanisms within the tumor-originating cell type. The box plots display gene expression levels across conditions, highlighting statistically significant differences.

Visual Summary

The box plots illustrate the expression levels of 24 selected cell cycle pathway-related genes in Intestinal Epithelial cells. Across all plotted genes, a consistent and statistically significant pattern emerges:

Mitotic checkpoint proteins: BUB3, MAD2L1, MAD2L2

Cell division cycle proteins: CDC16, CDC25B, CDC26, CDC27

E2F transcription factor family: E2F2, TFDP1, TFDP2

Cohesin complex: RAD21, SMC1A, SMC3, STAG2

Chk kinases: CHEK1, CHEK2

Biological Interpretation

The observed widespread and statistically significant upregulation of numerous cell cycle-related genes in Intestinal Epithelial cells from colon tumors strongly indicates a hyper-proliferative state characteristic of cancer. Intestinal Epithelial cells are the cell type of origin for colorectal cancer, making these findings highly relevant to tumor initiation and progression.

Specifically:

Collectively, these findings in Intestinal Epithelial cells from colon tumors demonstrate a profound shift towards uncontrolled cell proliferation driven by the coordinated upregulation of key cell cycle machinery, often accompanied by cellular stress and altered checkpoint responses. This biological state is fundamental to tumor growth and aligns with the role of Intestinal Epithelial cells as the origin of colon tumors.

Clinical or Translational Implications

The widespread dysregulation of cell cycle genes in colon tumor Intestinal Epithelial cells holds significant clinical and translational implications:

These results highlight the critical role of cell cycle dysregulation in colon tumorigenesis and provide a foundation for further investigation into targeted therapies and personalized medicine approaches for colorectal cancer.

---

References:

  1. CCND1 (Cyclin D1) in cancer: GeneCards: CCND1
  2. Anaphase-Promoting Complex (APC/C) in cancer: PubMed Search: "Anaphase promoting complex cancer proliferation"
  3. CDKN1A (p21) and CDKN1B (p27) roles in cancer: GeneCards: CDKN1A, GeneCards: CDKN1B
  4. GADD45 in cancer: PubMed Search: "GADD45 cancer DNA damage"
  5. CDK4/6 inhibitors: PubMed Search: "CDK4/6 inhibitors cancer therapy"
  6. APC/C as a drug target: PubMed Search: "APC/C inhibitors cancer therapy"
  7. HDAC inhibitors: PubMed Search: "HDAC inhibitors cancer therapy"

21. 장상피세포의 조건 및 배수성 상태에 따른 유전자 온톨로지(GSA) 분석 결과

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[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱 데이터를 기반으로 한 대장 조직 내 장상피세포(Intestinal Epithelial cell)의 유전자 온톨로지(Gene Ontology, GO) 분석(GSA) 결과입니다. AnnData 객체에 저장된 전처리된 GSA 데이터를 활용하여, 세 가지 주요 비교 조건(정상 인접 조직 vs 기타, 이배체 vs 기타, 종양 vs 기타)에서 상향 조절된 GO 용어들을 막대 그래프로 시각화하였습니다. 각 그래프는 조건에 따라 상향 조절된 유전자 세트가 어떤 생물학적 경로 및 기능에 관여하는지 -log(p-value)와 -log(q-value)를 기준으로 보여줍니다.

Visual Summary

세 개의 막대 그래프는 장상피세포에서 세 가지 다른 조건(정상 인접 조직, 이배체, 종양)과 다른 조건들을 비교했을 때 유의하게 상향 조절된 GO 용어들을 보여줍니다. y축은 GO 용어를, x축은 -log(p-value)와 -log(q-value)를 나타냅니다. 막대 길이가 길수록 통계적 유의성이 높음을 의미합니다.

  1. Adj_normal_vs_others (정상 인접 조직 vs 기타): 지방산 분해(Fatty acid degradation), 무기질 흡수(Mineral absorption), 케톤체 합성 및 분해(Synthesis and degradation of ketone bodies), PPAR 신호 전달 경로(PPAR signaling pathway)와 같은 대사 및 흡수 관련 경로들이 가장 높은 유의성을 보입니다.
  2. Diploid_vs_others (이배체 vs 기타): 코로나바이러스 질병(Coronavirus disease), 리보솜(Ribosome), 항원 처리 및 제시(Antigen processing and presentation), 식균 작용(Phagosome)과 같은 면역 반응 및 단백질 합성 관련 경로들이 두드러집니다.
  3. Tumor_vs_others (종양 vs 기타): 소포체 내 단백질 처리(Protein processing in endoplasmic reticulum), RNA 수송(RNA transport), 스플라이소솜(Spliceosome), 유비퀴틴 매개 단백질 분해(Ubiquitin mediated proteolysis), 세포 주기(Cell cycle), 리보솜(Ribosome) 등 세포 증식, 단백질 및 RNA 대사, 세포 스트레스 반응과 관련된 경로들이 매우 높은 유의성을 보입니다. 특히, 세포 주기는 -log(p-value)가 20 이상으로 가장 높은 유의성을 나타냅니다.

전반적으로 각 조건에 따라 장상피세포의 고유한 생물학적 특성을 반영하는 GO 용어들이 명확하게 구별되어 나타납니다.

Biological Interpretation

장상피세포는 대장암의 기원이 되는 세포 유형이므로, 이 세포에서 조건에 따른 유전자 기능 변화를 이해하는 것은 질병 기전을 파악하는 데 중요합니다.

1. 정상 인접 장상피세포 (Adj_normal_vs_others)의 특성

2. 이배체 장상피세포 (Diploid_vs_others)의 특성

3. 종양 장상피세포 (Tumor_vs_others)의 특성

Clinical or Translational Implications

이러한 GSA 결과는 대장암에서 장상피세포의 병리학적 변화를 이해하고 잠재적인 치료 표적을 식별하는 데 중요한 통찰력을 제공합니다.

22. Gene Set Enrichment Analysis (GSEA) in Colon Tumor Microenvironment

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results visualized as dot plots across various major cell types found in human Colon tissue: B cell, Endothelial cell, Fibroblast, ILC, Intestinal Epithelial cell, Macrophage, Plasma cell, T cell CD4+, and T cell CD8+. For each cell type, gene set enrichment was evaluated by comparing cells from 'Tumor' conditions against 'Adj_normal' conditions (or vice-versa, indicated by 'Adj_normal_vs_others' meaning Adj_normal compared to Tumor cells of the same type). For Intestinal Epithelial cells, an additional comparison was made between 'Diploid' cells and 'others' (likely Aneuploid cells), reflecting their role as the tumor origin cell type and the presence of ploidy inference data. The dot plots display the Normalized Enrichment Score (NES) using a RdBu_r colormap (red for positive NES, blue for negative NES) and the statistical significance (-log10(p-value)) via dot size. A positive NES indicates upregulation of genes within the pathway in the 'test' condition (e.g., Tumor cells) compared to the 'reference' (e.g., Adj_normal cells), while a negative NES indicates downregulation.

Visual Summary

The dot plots reveal a complex landscape of pathway alterations across different cell types and conditions within the colon tissue.

Biological Interpretation

Intestinal Epithelial Cells (Tumor Origin Cell Type)

As the presumed origin of tumor, Intestinal Epithelial Cells (IECs) show striking changes:

Immune Cell Responses in the Tumor Microenvironment

The immune cell populations (B cells, ILCs, Macrophages, Plasma cells, T cell CD4+, T cell CD8+) exhibit significant activation and shifts in the tumor context:

Stromal and Endothelial Cell Contributions

Fibroblasts and Endothelial cells also play critical roles in shaping the tumor microenvironment:

Clinical or Translational Implications

23. Discussion

The comprehensive single-cell analysis of human colon tissue reveals a profoundly reprogrammed microenvironment in colorectal cancer. A central finding is the robust identification of aneuploid intestinal epithelial cells as the primary malignant population, exhibiting extensive genomic instability characterized by recurrent copy number variations, including amplifications of oncogenes like EGFR (7p14.1-7q11.23) and deletions of tumor suppressors like CDKN2A (9p24.1-9p13.3). This genomic dysregulation is tightly linked to a hyper-proliferative state in tumor epithelial cells, as evidenced by the widespread and significant upregulation of core cell cycle machinery components (cyclins, CDKs, DNA replication factors) and enrichment of cell cycle, DNA replication, and ErbB signaling pathways. Metabolic rewiring, including purine and pyrimidine metabolism, further supports the rapid proliferation and biomass accumulation characteristic of cancer cells.

The tumor microenvironment undergoes substantial remodeling, particularly within its immune and stromal compartments. Immune cell populations exhibit a distinct immunosuppressive shift: regulatory T cells (Tregs) and pro-tumorigenic Th17 cells are significantly expanded, while innate lymphoid cells (ILC1, ILC2, LTI) and cytotoxic T cells (T_Cyto) show a notable reduction. Macrophages, a critical component of the TME, are dramatically polarized towards a pro-tumorigenic M2B phenotype, accompanied by a decrease in M2A macrophages. This macrophage reprogramming is further underscored by the upregulation of surface markers like TREM2 and MMP14 in tumor-associated macrophages (TAMs). Cancer-associated fibroblasts (CAFs) also adopt a pro-tumorigenic phenotype, marked by the expression of FAP, CD276 (B7-H3), PDGFRB, and integrins, and engage in extensive extracellular matrix remodeling.

Cell-cell interaction analysis highlights the complex crosstalk driving tumor progression. Aneuploid intestinal epithelial cells emerge as central orchestrators, engaging extensively with TAMs and CAFs via crucial pro-tumorigenic pathways. Prominent interactions include SPP1-CD44/integrin complexes, VEGFA-VEGFR1 (angiogenesis), and TGFB1-TGFBR1 (immunosuppression), all of which are significantly enhanced in the tumor. Immune checkpoint interactions like PVR-TIGIT and LGALS9-HAVCR2 are also highly active, contributing to T cell exhaustion. In contrast, normal tissue interactions emphasize epithelial barrier function (CDH1-integrin), basal immune surveillance (PGE2-PTGER4), and tissue homeostasis.

Collectively, these findings paint a detailed picture of colorectal cancer as a disease driven by genomic instability within epithelial cells, which in turn orchestrate a supportive, immunosuppressive, and proliferative microenvironment through complex cellular communication networks. The observed molecular shifts provide a rich resource for understanding disease mechanisms and identifying novel therapeutic avenues.

Hypotheses:

  1. The genomic instability (aneuploidy and recurrent CNVs) of intestinal epithelial cells is a primary driver of uncontrolled proliferation and metabolic reprogramming in colorectal cancer.
  2. The colorectal tumor microenvironment actively suppresses anti-tumor immunity through the selective expansion of regulatory T cells and pro-tumorigenic Th17 cells, coupled with a systemic reduction in innate lymphoid cells and cytotoxic T cells.
  3. Tumor-associated macrophages in colorectal cancer undergo significant polarization towards an M2B-like pro-tumorigenic phenotype, which contributes to immune evasion, angiogenesis, and extracellular matrix remodeling.
  4. Cancer-associated fibroblasts and malignant epithelial cells establish extensive communication networks via specific ligand-receptor interactions (e.g., SPP1-CD44/integrins, FAP, CD276), critically mediating tumor growth, invasion, and immunosuppression.

Potential therapeutic targets:

  1. EGFR (Epidermal Growth Factor Receptor): EGFR is a well-established oncogene. Its genomic region (7p14.1-7q11.23) shows recurrent amplification in tumor epithelial cells, and its ligand EREG is involved in active EREG-EGFR interactions within the tumor microenvironment. ErbB signaling, which includes EGFR, is highly enriched in tumor epithelial cells, driving proliferation and survival. Evidence: Recurrent CNV analysis (Section 4) shows amplification of 7p14.1-7q11.23 (harboring EGFR). Cell-cell interaction analysis (Section 13) shows EREG-EGFR interactions. GSEA (Section 22) shows ErbB signaling pathway enrichment in tumor intestinal epithelial cells. Tumor epithelial cells also show upregulation of EREG as a condition-specific marker (Section 16). Validation: Test existing EGFR inhibitors (e.g., Cetuximab, Panitumumab) or novel anti-EREG antibodies in patient-derived colorectal cancer organoids or xenografts to evaluate their impact on tumor cell proliferation and survival.
  2. TIGIT (T-cell Immunoreceptor with Ig and ITIM domains) / CTLA4 (Cytotoxic T-Lymphocyte-Associated Protein 4): These are key immune checkpoint receptors. TIGIT and CTLA4 are significantly upregulated on tumor-infiltrating CD4+ T cells, and involved in immune suppressive cell-cell interactions (e.g., PVR-TIGIT, CD86-CTLA4) that contribute to T cell exhaustion and immune evasion in the tumor microenvironment. Evidence: Condition-specific marker analysis (Section 19) shows upregulation of TIGIT and CTLA4 on CD4+ T cells in tumor samples. Cell-cell interaction analysis (Section 13) identifies PVR-TIGIT and CD86-CTLA4 interactions in the tumor context. Validation: Evaluate anti-TIGIT or anti-CTLA4 antibodies, alone or in combination with other immunotherapies (e.g., anti-PD-1), in *in vitro* T cell functional assays with tumor cells and in *in vivo* syngeneic or humanized mouse tumor models.
  3. TREM2 (Triggering Receptor Expressed on Myeloid cells 2): TREM2 is a critical receptor on tumor-associated macrophages (TAMs). It is highly expressed on macrophages in the tumor microenvironment and involved in APOE-TREM2 interactions with tumor epithelial cells and other macrophages. Its activation promotes an immunosuppressive and pro-tumorigenic TAM phenotype, contributing to tumor growth and metastasis. Evidence: Condition-specific marker analysis (Section 17) shows significant upregulation of TREM2 on macrophages in tumor samples. Cell-cell interaction analysis (Section 13) identifies prominent APOE-TREM2 interactions. Validation: Develop and test anti-TREM2 blocking antibodies or small molecule inhibitors in preclinical colorectal cancer models to assess their ability to reprogram TAMs from a pro-tumorigenic to an anti-tumorigenic state, and to inhibit tumor growth and metastasis.
  4. FAP (Fibroblast Activation Protein Alpha): FAP is a highly recognized and specifically expressed marker for Cancer-Associated Fibroblasts (CAFs) in tumor tissue. CAFs play a crucial role in extracellular matrix remodeling, immunosuppression, and promoting tumor growth and metastasis. Targeting FAP can disrupt the tumor-promoting functions of CAFs. Evidence: Condition-specific marker analysis (Section 18) shows significant and specific upregulation of FAP on fibroblasts in tumor samples. Validation: Evaluate FAP-targeting strategies, such as FAP-specific antibodies, FAP-directed CAR-T cells, or small molecule inhibitors, in colon cancer models to assess their impact on CAF activity, ECM remodeling, tumor growth, and metastatic potential.
  5. SPP1 (Secreted Phosphoprotein 1, Osteopontin): SPP1 and its receptors (CD44, integrins) form highly prominent cell-cell interaction axes in the tumor microenvironment, especially involving aneuploid intestinal epithelial cells and macrophages. SPP1 signaling drives macrophage polarization, promotes tumor growth, angiogenesis, and metastasis. Evidence: Cell-cell interaction analysis (Sections 12, 13) shows strong SPP1-CD44 and SPP1-integrin interactions in the tumor condition, particularly between aneuploid intestinal epithelial cells and macrophages. Validation: Test inhibitors targeting SPP1, CD44, or specific integrin subunits (e.g., ITGAV, ITGA5) in *in vitro* invasion/migration assays and *in vivo* metastasis models. Evaluate effects on macrophage polarization and tumor progression.
  6. CDK4/6 (Cyclin-Dependent Kinases 4 and 6): CDK4 and CDK6 are key regulators of cell cycle progression. They are significantly upregulated in tumor Intestinal Epithelial cells, driving the hyper-proliferative state characteristic of colorectal cancer. Evidence: Differential gene expression analysis (Section 20) shows significant upregulation of CDK4 and CDK6, along with their associated cyclins (CCND1, CCND2, CCND3), in Intestinal Epithelial cells from tumor tissue. Validation: Evaluate existing CDK4/6 inhibitors (e.g., Palbociclib, Ribociclib) or novel ones in colon cancer cell lines and patient-derived organoids to assess their efficacy in inhibiting tumor cell proliferation and inducing cell cycle arrest.

Follow-up validation ideas:

  1. Genomic Instability & Ploidy: Perform Fluorescence In Situ Hybridization (FISH) or single-cell whole-genome sequencing (scWGS) on sorted Aneuploid vs. Diploid Intestinal Epithelial cells to precisely map and validate recurrent CNV regions (e.g., EGFR amplification, CDKN2A deletion) and correlate with differential expression of cell cycle genes via targeted qPCR or immunohistochemistry.
  2. Immune Cell Dynamics: Use multi-parameter flow cytometry or spatial transcriptomics (e.g., GeoMx DSP, Visium) on fresh or FFPE human colorectal cancer and adjacent normal tissue samples to quantify and spatially localize Treg, Th17, ILC1/2, LTI, and cytotoxic T cell populations, along with their activation/exhaustion markers (e.g., TIGIT, CTLA4, OX40).
  3. Macrophage Polarization & Function: Isolate tumor-associated macrophages (TAMs) from resected colorectal cancer tissue based on surface markers (e.g., TREM2, MMP14) and conduct *ex vivo* functional assays to assess their cytokine production profiles (pro-inflammatory vs. anti-inflammatory), phagocytic activity, and T cell modulatory capacity.
  4. Cell-Cell Interaction Functional Validation: Establish *in vitro* co-culture systems of primary human Aneuploid Intestinal Epithelial cells with isolated CAFs or TAMs. Functionally validate key ligand-receptor interactions (e.g., SPP1-CD44, TGFB1-TGFBR1, DLL1-NOTCH2) using blocking antibodies or small molecule inhibitors, measuring their effects on cell proliferation, migration, invasion, and immune cell function. Use perturbation assays to confirm involvement of surface markers like FAP or CD276 in CAF-mediated tumor support.
  5. Therapeutic Target Efficacy: Test the efficacy of inhibitors targeting identified pathways or surface markers (e.g., EGFR, TIGIT, TREM2, FAP, CDK4/6) in patient-derived organoids (PDOs) or patient-derived xenograft (PDX) models of colorectal cancer, evaluating tumor growth inhibition, immune microenvironment modulation, and survival.

Limitations:

This report is based on single-cell RNA sequencing data, which provides transcriptomic snapshots. While powerful, inferring cell-cell interactions from gene expression is correlative and requires experimental validation at the protein level and functional assays. Ploidy inference is computational, and precise chromosomal alterations warrant orthogonal validation methods like FISH. The study provides a cross-sectional view, not capturing dynamic tumor evolution over time. The observed heterogeneity across patient samples indicates the need for larger, independent validation cohorts to generalize findings and refine patient stratification strategies. The complex tumor microenvironment also implies that single-target therapies may be limited by redundancy or compensatory pathways.

24. Query List

  1. Show UMAPs colored by condition, sample, celltype_major, celltype_minor, ploidy_dec, and celltype_subset in 2 columns and save them.
  2. Show major cell type scores on UMAP and save them.
  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. For Intestinal Epithelial cells (tumor origin) and unassigned cells, show CNV heatmap grouped by sample, along with a summary of significantly amplified copy number regions, and save it.
  5. Show CNV patterns on UMAP, including celltype_major, celltype_minor, ploidy_dec, condition, and sample in 2 columns, and save them.
  6. Show a population bar plot of minor cell types and save it.
  7. Show a subset population bar plot for T cells and save it.
  8. Show box plots for statistically significant differences in T cell subset populations between conditions, setting ncols appropriately, and save them.
  9. Show a subset population bar plot for Macrophage and save it.
  10. Show box plots for statistically significant differences in Macrophage subset populations between conditions, setting ncols appropriately, and save them.
  11. For Intestinal Epithelial cells (tumor origin) and unassigned cells, show a ploidy population bar plot and save it.
  12. Show cell-cell interaction patterns per condition, focusing on Intestinal Epithelial cells (tumor origin), Fibroblast, Macrophage, and T cells. Select up to 80 cell-cell interactions per condition and save it.
  13. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
  14. Show cell-cell interactions for genes related to immune checkpoint and cell cycle pathways, and save them.
  15. Find statistically significant differences in cell-cell interactions between conditions for major immune and stromal cells, show them as a dot plot with max_n_items_per_group = 25, and save it.
  16. Show the condition-specific markers for tumor-origin cells (Intestinal Epithelial cell) as a dot plot and save the result. Use only surfaceome markers, up to 50 markers per condition.
  17. Extract condition-specific markers for Macrophage, show them as a dot plot for surfaceome markers, up to 50 per condition, and save it.
  18. Extract condition-specific markers for Fibroblast, show them as a dot plot for surfaceome markers, up to 50 per condition, and save it.
  19. Extract condition-specific markers for T cell CD4+, show them as a dot plot for surfaceome markers, up to 50 per condition, and save it.
  20. Show box plots for Cell cycle pathway-related genes with statistically significant expression differences between conditions in Intestinal Epithelial cells, setting max_n_items_to_plot = 24 and ncols for a 2x3 aspect ratio, and save them.
  21. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
  22. Show dot plots of Gene Set Enrichment Analysis results for B cell, Endothelial cell, Fibroblast, ILC, Intestinal Epithelial cell, Macrophage, Plasma cell, T cell CD4+, T cell CD8+ cell types, using color map RdBu_r, setting n_pws_to_show = 80, and save them.
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