SCODiA Report by MLBI Lab

Single-Cell Deconvolution of the Lung Cancer Microenvironment: Immune Dysregulation, Stromal Remodeling, and Oncogenic Signaling

This single-cell RNA-seq analysis reveals profound cellular and molecular changes in the lung tumor microenvironment (TME) across normal, early, and advanced cancer stages. Malignant lung epithelial cells, characterized by extensive aneuploidy and specific oncogenic surface markers (e.g., MET, ERBB2), are identified as key drivers of tumor progression. The TME undergoes dynamic remodeling, marked by the emergence of pro-tumorigenic macrophage subsets (M2B), activated fibroblasts (CAFs expressing FAP), and a shift towards an immunosuppressive milieu with altered T cell and NK cell populations. Critical cell-cell interactions involving EGFR, TGF-beta, and prostaglandin E2 pathways, alongside integrin-ECM interactions, orchestrate this progression, offering multiple avenues for therapeutic intervention.

Contents

  1. Dataset overview
  2. scRNA-seq Data Overview: UMAP Visualization of Cell Populations, Conditions, and Ploidy
  3. Marker Gene Expression and Cell Type Annotation on UMAP
  4. Celltype Subset Marker Expression for Annotation Validation
  5. Analysis of Copy Number Variations in Tumor-Origin and Unassigned Lung Cells
  6. CNV 패턴 UMAP 시각화를 통한 세포 유형, 이수성 및 임상 조건 분석
  7. Minor Cell Type Population Analysis in Lung Tissues Across Normal and Tumor Conditions
  8. T 세포 및 관련 면역 세포 아형의 조건별 상대적 분포 분석
  9. Macrophage Population Analysis Across Lung Tissue Conditions
  10. 폐암 진행에 따른 T 세포 및 선천 림프구(ILC) 아형 집단 변화 분석
  11. Macrophage Subset Proportions Dynamically Shift Across Lung Cancer Progression
  12. Ploidy Population Analysis in Lung Tumor-Origin Cells Across Disease Stages
  13. Advanced Lung Cancer Cell-Cell Interaction Patterns
  14. Advanced Lung Tumor Cell-Cell Interaction Analysis
  15. Condition-Specific Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Related Genes in Lung Tissue
  16. Condition-specific Cell-Cell Interaction Patterns in Lung Tissue Progression
  17. Lung Epithelial Cell Condition-Specific Surfaceome Markers
  18. Macrophage Condition-Specific Surfaceome Markers in Lung Tissue
  19. Fibroblast Condition-Specific Surfaceome Markers in Lung Tissue
  20. Condition-Specific Surfaceome Markers in CD4 T Cells from Lung Tissue
  21. Differential Expression of Cell Cycle-Related Genes Across Lung Conditions
  22. Gene Ontology (GSA) Analysis of Lung Epithelial Cells Across Different Conditions
  23. Gene Set Enrichment Analysis (GSEA) of Lung Cell Types Across Tumor Conditions
  24. Discussion
  25. Query List

0. Dataset overview

Dataset Summary

1. scRNA-seq Data Overview: UMAP Visualization of Cell Populations, Conditions, and Ploidy

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents UMAP (Uniform Manifold Approximation and Projection) visualizations of a single-cell RNA-seq dataset from human lung tissue, comparing Normal, early-stage tumor (Tumor(early)), and advanced-stage tumor (Tumor(adv)) conditions. The UMAPs display the distribution of cells colored by various metadata features: experimental condition, individual sample, major cell type, minor cell type, ploidy status (ploidy_dec), and highly refined cell type subsets. These visualizations are crucial for understanding the overall data structure, assessing cell type annotation quality, identifying batch effects, and observing condition-specific cellular compositions and biological states.

Visual Summary

  1. Condition:

The UMAP colored by condition shows clear distinctions between the three conditions.

The separation of normal and tumor cells, and the partial overlap between early and advanced tumor cells, reflect expected biological differences in cellular composition and gene expression profiles during tumor progression.

  1. Sample:

The sample UMAP reveals that cells from individual samples are generally well-mixed within the major clusters, particularly within the tumor cell populations. This suggests that major batch effects, where cells from a single sample form isolated clusters, are not a dominant feature of the embedding, which is good for downstream comparative analyses. However, some samples (e.g., LUNG_T06, LUNG_T08, LUNG_T09, LUNG_T18, LUNG_T19, LUNG_T20, LUNG_T25, LUNG_T28, LUNG_T30, LUNG_T31, LUNG_T34) are primarily associated with tumor clusters, while others (e.g., BRONCHO_58, EBUS_06, EBUS_28, EBUS_49, LUNG_N01, LUNG_N06, LUNG_N08, LUNG_N09, LUNG_N18, LUNG_N19, LUNG_N20, LUNG_N28, LUNG_N30, LUNG_N31, LUNG_N34) represent a mix of normal and tumor-associated cells, or predominantly normal tissue, consistent with the expected sample origins.

  1. Celltype_major:

Major cell types exhibit well-defined and largely separated clusters, indicating successful clustering and annotation.

  1. Celltype_minor:

The celltype_minor UMAP provides a more granular view, showing further subdivisions within the major cell types.

  1. Ploidy_dec:

The ploidy_dec UMAP highlights a striking pattern:

This clear segregation of aneuploid cells to tumor clusters strongly supports the identification of malignant cell populations, likely corresponding to the tumor origin Lung Epithelial cells.

  1. Celltype_subset:

The most refined celltype_subset annotations show detailed subpopulations.

Biological Interpretation

The UMAP visualizations provide compelling biological insights into the cellular landscape of lung cancer progression.

  1. Tumor Microenvironment Heterogeneity: The large, diffuse nature of the tumor-associated clusters, especially when colored by condition, celltype_major, celltype_minor, and celltype_subset, highlights the profound cellular heterogeneity within both early and advanced lung tumors. This heterogeneity encompasses not only the malignant epithelial cells but also a diverse array of immune and stromal cells that constitute the tumor microenvironment (TME).
  2. Malignant Cell Identification: The strong correlation between Aneuploid status and the Lung Epithelial cell major cell type, particularly those forming the large tumor-associated cluster, robustly identifies the malignant cell populations. Aneuploidy is a hallmark of cancer, reflecting chromosomal instability that drives tumor evolution.
  1. Immune Cell Infiltration and States: The abundant presence of various immune cell types (T cells, Myeloid cells, B cells, NK cells, Mast cells, ILCs, DCs) within the tumor-associated regions signifies an active immune response in the TME. The resolution to celltype_minor and celltype_subset level allows for detailed investigation into the specific roles of T cell subsets (e.g., cytotoxic T cells vs. regulatory T cells), and macrophage polarization states (M1 vs. M2 subtypes), which are critical in modulating anti-tumor immunity and tumor progression. For example, the presence of specific macrophage M2 subtypes (e.g., Mac_M2A, Mac_M2B, Mac_M2C, Mac_M2D) often suggests an immunosuppressive or pro-tumorigenic microenvironment.
  1. Stromal Remodeling: The presence of distinct Fibroblast and Endothelial cell populations, often co-localizing with tumor cells, suggests active stromal remodeling and angiogenesis within the TME, which are essential processes for tumor growth and metastasis.
  2. Distinction between Early and Advanced Tumors: While both early and advanced tumor conditions show significant overlap in the UMAP space, the condition plot hints at subtle differences. The more distinct clustering of "Normal" cells, and the partial overlap of "Tumor(early)" with regions closer to normal cells, might indicate distinct stages of TME evolution or differences in the proportion of malignant versus non-malignant cells at different disease stages.

Annotation Notes

2. Marker Gene Expression and Cell Type Annotation on UMAP

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the expression of a panel of known marker genes across the UMAP embedding, alongside the pre-computed celltype_minor annotation. The primary goal is to assess the quality and consistency of cell type assignments by examining how well the expression of these canonical markers aligns with the identified cell populations. This provides an important validation step for the single-cell RNA-seq data annotation.

Visual Summary

The UMAP embedding displays the overall cellular landscape of the lung tissue, revealing several distinct clusters of cells. The celltype_minor annotation plot clearly delineates these clusters into specific cell types, including various immune cells (T cells, B cells, Macrophages, NK cells), stromal cells (Fibroblasts, Endothelial cells, Smooth muscle cells), and epithelial cells (Airway Epithelial, Alveolar Epithelial).

The expression patterns of the individual marker genes on the UMAPs show strong concordance with the celltype_minor annotations:

T Cell Lineage Markers (CD3D, CD4, CD8A):

B Cell and Plasma Cell Markers (CD79A, MS4A1, MZB1):

Myeloid Cell Markers (CD14, LYZ):

Stromal and Endothelial Cell Markers (FBLN1, NOTCH3, CD34):

Epithelial Cell Markers (EPCAM, MUC1):

Biological Interpretation

The strong co-localization of specific marker gene expression with their expected cell type annotations provides substantial biological validation for the celltype_minor assignments. This suggests that the single-cell RNA-seq data has been effectively clustered and annotated, capturing the distinct transcriptional profiles of major lung cell populations.

The observed cellular landscape includes a diverse array of immune cells (T cells, B cells, macrophages), which play critical roles in lung immunity and disease, particularly in the context of tumor and inflammation. The presence of both CD4+ helper T cells and CD8+ cytotoxic T cells, along with B cells and plasma cells, indicates an active adaptive immune response. Macrophages, as innate immune cells, are crucial for host defense and tissue homeostasis in the lung.

The identification of distinct epithelial cell populations (airway and alveolar) is fundamental for understanding lung structure and function, as these cells are directly involved in gas exchange and maintaining the airway barrier. Stromal components, including fibroblasts and endothelial cells, are essential for tissue architecture, angiogenesis, and providing a supportive microenvironment, which is particularly relevant in tumor development and progression.

The clear separation of clusters based on these known markers reinforces the robustness of the UMAP embedding and the underlying cell type identification. This well-annotated dataset forms a reliable basis for further downstream analyses, such as differential gene expression, pathway enrichment, and cell-cell interaction studies, which can be performed in a cell-type-specific manner across different conditions (Tumor, Normal).

Annotation Notes

The celltype_minor annotation appears to be of high quality, as evidenced by the clear and specific expression of canonical marker genes within the respective cell clusters. Each marker gene generally illuminates its expected cell population with minimal off-target expression in other major clusters. The distinction between closely related cell types, such as CD4+ and CD8+ T cells, and B cells versus Plasma cells, is also well-supported by their specific markers. This robust annotation ensures that subsequent analyses will be performed on accurately identified cell populations, enhancing the reliability of any biological conclusions drawn from this dataset.

3. Celltype Subset Marker Expression for Annotation Validation

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot visualizing the expression patterns of marker genes across various celltype_subset populations identified in the single-cell RNA-seq data from human lung tissue. The primary objective is to assess and validate the quality of the cell type annotations by examining the specificity and expression levels of known marker genes within each assigned cell subset. In the plot, each row represents a celltype_subset, and each column corresponds to a specific marker gene. The size of each dot is proportional to the fraction of cells within that subset expressing the gene, while the color intensity (from light to dark red) indicates the mean expression level of the gene in that group.

Visual Summary

The dot plot exhibits a highly organized and distinct diagonal pattern of marker gene expression. This pattern is a strong indicator that the identified marker genes are predominantly and specifically expressed within their corresponding celltype_subset. Red boxes visually emphasize these specific expression clusters, highlighting the unique molecular signatures that define each cell population.

Biological Interpretation

The observed marker expression profiles provide compelling evidence for the accuracy and biological relevance of the celltype_subset annotations within the human lung dataset.

Epithelial Cell Identity:

Immune Cell Identity:

Stromal and Endothelial Cell Identity:

Annotation Notes

This dot plot serves as strong validation for the robustness and accuracy of the celltype_subset annotations. The consistent and specific expression of canonical markers within each cell type, coupled with minimal off-target expression, confirms that the clustering and annotation process successfully captured distinct and biologically meaningful cell identities. Such robust annotations are foundational for all subsequent downstream analyses, including differential gene expression analysis, cell-cell interaction inference, and pathway enrichment studies, as they ensure that biological conclusions are drawn from accurately identified cellular populations. The clear transcriptional segregation observed reinforces confidence in the defined cell subsets.

4. Analysis of Copy Number Variations in Tumor-Origin and Unassigned Lung Cells

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a single-cell copy number variation (CNV) heatmap focusing on "Lung Epithelial cell" and "unassigned" cell types, grouped by sample. The visualization displays log2(CNR) (Copy Number Ratio) values across genomic spots, with a summary highlighting significantly amplified regions and their frequencies across selected samples. This approach allows for the identification of chromosomal gains (amplifications, indicated by red) and losses (deletions, indicated by blue), providing insights into genomic instability characteristic of cancer. The "Lung Epithelial cell" type is particularly relevant as the primary origin of most lung cancers, while "unassigned" cells may include highly aberrant or poorly characterized malignant cells.

Visual Summary

The top panel displays the CNV heatmap, where each row represents a group of cells from a specific sample (e.g., LUNG_N01, BRONCHO_58) and each column represents a genomic spot. The color intensity reflects the log2(CNR), with red indicating amplification and blue indicating deletion. The top of the heatmap annotates key cytogenetic bands and chromosome numbers.

Specific CNV Regions

The bottom panel provides a summary of significantly amplified copy number regions and their frequencies. The left heatmap shows the presence (intensity of blue) of amplifications in specific cytogenetic bands for each sample, while the right bar plot summarizes the frequency of these amplifications across all samples.

1q21.3:1q23.1 (Frequency ~0.70)

5q31.1:5q31.3 (Frequency ~0.70)

7p11.2:7q21.12 (EGFR) (Frequency ~0.60)

17q12:17q21.2 (ERBB2) (Frequency ~0.40)

Biological Interpretation

The observed CNV patterns provide critical insights into the genomic landscape of lung cancer and validate cell type annotations.

Clinical or Translational Implications

The identification of recurrent and specific CNVs in lung cancer cells has significant clinical and translational relevance:

Therapeutic Targeting

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

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[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱 데이터를 기반으로 추정된 염색체 수 변이(CNV) 패턴을 UMAP(Uniform Manifold Approximation and Projection)으로 시각화한 결과입니다. UMAP은 CNV 추정치를 사용하여 세포 간의 유사성을 2차원 공간에 투영하며, 이 공간에서 가까운 세포들은 유사한 CNV 프로파일을 가집니다. 각 UMAP 플롯은 celltype_major, celltype_minor, ploidy_dec (이수성 상태), condition (임상 조건), sample (환자 샘플) 별로 색상을 지정하여 CNV 패턴이 이러한 메타데이터와 어떻게 연관되는지 시각적으로 보여줍니다.

Visual Summary

세포 유형별 분포 (celltype_major, celltype_minor)

이수성 상태 분포 (ploidy_dec)

임상 조건별 분포 (condition)

샘플별 분포 (sample)

Biological Interpretation

CNV 기반 UMAP은 세포의 염색체 이상 유무를 효과적으로 구분하며, 이를 통해 정상 세포와 종양 세포 집단을 명확히 식별할 수 있습니다.

  1. 정상 세포와 종양 세포의 분리: Diploid 상태의 세포들이 Normal 조건과 주로 연관되어 왼쪽 클러스터를 형성하는 반면, Aneuploid 세포들은 Tumor 조건과 강하게 연관되어 오른쪽 클러스터를 형성합니다. 이는 Diploid 클러스터가 주로 정상적인 면역 및 기질 세포를 포함하고 있음을, 그리고 Aneuploid 클러스터가 종양성 특성을 가진 세포를 나타냄을 강력하게 시사합니다.
  2. 종양 세포의 기원: Aneuploid 클러스터에 Lung Epithelial cell (주요 세포 유형)과 Alveolar Epithelial cell, Airway Epithelial cell (세부 세포 유형)이 풍부하게 나타나는 것은, 데이터 컨텍스트에서 Tumor origin celltype이 Lung Epithelial cell로 지정된 것과 일치합니다. 이는 폐암이 주로 상피세포에서 기원하며 이들 세포가 상당한 CNV를 획득함을 뒷받침합니다.
  3. 종양 진행과 CNV: Tumor(adv) 세포가 Aneuploid 클러스터에 더욱 밀집되어 있는 것은, 종양 진행이 염색체 불안정성 증가 및 CNV 축적과 관련될 수 있음을 나타냅니다. Tumor(early) 세포가 Diploid 클러스터와 일부 겹치는 것은 초기 단계에서는 정상 세포의 오염이 있거나, 종양 세포가 아직 뚜렷한 이수성을 보이지 않을 수 있음을 시사합니다.
  4. 환자별 CNV 이질성: Aneuploid 클러스터 내에서 관찰되는 샘플별 클러스터링은 각 환자(샘플)의 종양이 고유한 CNV 프로파일을 가지고 있음을 보여줍니다. 이는 폐암의 종양 발생 및 진행이 환자마다 다른 유전적 경로를 따를 수 있다는 것을 시사하며, 개인 맞춤형 치료 전략의 필요성을 강조합니다.

Annotation Notes

이 CNV 기반 UMAP 시각화는 단일 세포 데이터에서 CNV 패턴을 성공적으로 분리하고, 이를 세포 유형, 이수성 상태 및 임상 조건과 연결하여 데이터의 기본적인 구조와 질병 관련성을 효과적으로 보여줍니다. 특히, ploidy_dec 정보가 CNV UMAP 공간에서 명확하게 분리되어 나타나는 것은 CNV 추정 및 이수성 분류의 신뢰성을 뒷받침합니다. unassigned 세포와 Unclear ploidy 세포의 분포를 추가로 분석하면 이들 세포 집단의 특성을 이해하는 데 도움이 될 수 있습니다.

6. Minor Cell Type Population Analysis in Lung Tissues Across Normal and Tumor Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative abundance of minor cell types (celltype_minor) across individual samples, stratified by three conditions: Normal, Tumor(adv) (advanced tumor), and Tumor(early) (early-stage tumor). Each bar represents a single sample, and the stacked segments indicate the percentage contribution of each identified minor cell type. This visualization provides critical insights into the cellular composition and heterogeneity of the lung microenvironment in health and disease.

Visual Summary

Biological Interpretation

The observed shifts in cell type populations reflect the dynamic changes occurring in the lung tissue during cancer development and progression.

Clinical or Translational Implications

7. T 세포 및 관련 면역 세포 아형의 조건별 상대적 분포 분석

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[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱 데이터를 활용하여 폐 조직 샘플에서 T 세포(CD4+ 및 CD8+), NK 세포, 그리고 ILC(Innate Lymphoid Cell) 아형의 상대적인 세포 집단 분포를 정량화한 결과를 보여줍니다. 샘플은 'Normal'(정상), 'Tumor(adv)'(진행성 종양), 'Tumor(early)'(초기 종양) 세 가지 조건으로 분류되었습니다. 이 막대 그래프는 각 샘플 내에서 T 세포 관련 주요 면역 세포 아형들이 전체 T 세포 집단 내에서 차지하는 비율을 나타내며, 각 조건에 따른 면역 미세환경의 변화를 시사합니다.

Visual Summary

제공된 스택형 막대 그래프는 정상, 초기 종양, 진행성 종양 조건에서 T 세포 하위 집단 (CD4+ T cell, CD8+ T cell), NK cell, ILC의 상대적 비율을 보여줍니다.

정상(Normal) 조건

진행성 종양(Tumor(adv)) 조건

초기 종양(Tumor(early)) 조건

Biological Interpretation

이러한 세포 아형 분포의 변화는 폐암의 발생 및 진행에 따른 종양 미세환경(TME) 내 면역 반응의 역동적인 조절을 시사합니다.

종양 미세환경의 변화와 면역 회피

Clinical or Translational Implications

이러한 면역 세포 집단 분석 결과는 폐암 환자의 진단, 예후 예측 및 면역치료 전략 수립에 중요한 함의를 가집니다.

8. Macrophage Population Analysis Across Lung Tissue Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the population distribution of Macrophages (specifically the celltype_minor category) across individual samples, grouped by different lung tissue conditions: Normal, Tumor(adv) (advanced tumor), and Tumor(early) (early tumor). The bar plot indicates the relative proportion of Macrophages within the selected Macrophage cell population for each sample.

Visual Summary

The visualization displays three bar plots, one for each condition: "Normal," "Tumor(adv)," and "Tumor(early)." Each plot contains multiple bars, with each bar representing a distinct biological sample (e.g., LUNG_N01, BRONCHO_58, LUNG_T06). All bars consistently reach 100% on the y-axis, and are uniformly colored, corresponding to "Macrophage" as indicated by the legend.

Biological Interpretation

The consistent 100% representation for "Macrophage" across all samples and conditions (Normal, Tumor(adv), Tumor(early)) confirms two key aspects:

  1. Presence and Annotation Consistency: Macrophages, as defined by the celltype_minor annotation, are present in all analyzed lung tissue samples from both healthy individuals and patients with early and advanced lung tumors. This plot demonstrates the consistent annotation of Macrophages within the dataset.
  2. Focus on a Single Cell Type: Since the analysis specifically targeted 'Macrophage' at the celltype_minor level, and no sub-classification within Macrophages (e.g., M1/M2 subtypes, which would typically fall under celltype_subset) was requested or visualized, the plot essentially confirms that 100% of the cells identified as 'Macrophage' in each sample are indeed 'Macrophage'.

It is important to note that this plot does not directly reflect the *absolute number* or the *proportional abundance* of Macrophages relative to the total number of all cells within each sample or condition. For instance, it doesn't show whether Macrophages are more or less abundant in tumor samples compared to normal tissue. It solely confirms their presence and the consistency of their classification at the specified taxonomic level. Macrophages are known to be a crucial component of the tumor microenvironment (TME) in lung cancer, often polarizing into tumor-associated macrophages (TAMs) that can promote tumor growth and metastasis [1]. Their consistent presence across all conditions is biologically expected given their ubiquitous role in tissue homeostasis and disease.

Clinical or Translational Implications

While this specific visualization confirms the presence and consistent annotation of Macrophages across the dataset, it does not provide direct clinical or translational insights into their differential roles or abundance in lung cancer progression. To derive such implications, further analyses are required, such as:

These subsequent analyses would be crucial for understanding the specific contributions of Macrophages to lung cancer pathobiology and for identifying potential therapeutic targets within the Macrophage population.

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References:

[1] Tumor-Associated Macrophages (TAMs) in Cancer. *GeneCards Human Gene Database*. https://www.genecards.org/Search/Keyword?query=tumor%20associated%20macrophages

9. 폐암 진행에 따른 T 세포 및 선천 림프구(ILC) 아형 집단 변화 분석

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[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱 데이터를 활용하여 폐암 환자의 종양 미세 환경에서 주요 면역 세포인 T 세포 및 선천 림프구(ILC) 아형들의 세포 비율 변화를 정량적으로 비교하였습니다. 폐암의 세 가지 조건(초기 종양(Tumor(early)), 정상 조직(Normal), 진행성 종양(Tumor(adv))) 간에 각 아형의 상대적 풍부도에서 통계적으로 유의미한 차이가 있는지 평가했습니다. 이를 통해 폐암의 발병 및 진행에 따른 면역 세포 구성 변화를 이해하고, 잠재적인 바이오마커 또는 치료 표적을 식별하고자 합니다.

Visual Summary

제공된 박스플롯은 Treg, NK, Th9, ILC1, ILC2, LTI, Th2, 그리고 Naive T 세포의 아형별 비율을 각 조건별로 보여주며, 통계적 유의성을 p-값으로 표시하고 있습니다. 주요 관찰 내용은 다음과 같습니다:

Biological Interpretation

이러한 면역 세포 아형 비율의 변화는 폐암의 면역 미세 환경(Immune Microenvironment, TME)이 질병 진행에 따라 역동적으로 변화함을 시사합니다.

  1. 면역 억제 환경의 강화:
  1. 항종양 면역 반응 관련 세포의 감소:
  1. 면역 반응의 질적 변화:

종합적으로 볼 때, 폐암의 종양 미세 환경은 질병이 진행됨에 따라 면역 억제 세포(Treg, Naive T cells)의 상대적 증가와 항종양 면역에 기여하는 세포(NK, Th9, ILC1, ILC2, LTI)의 감소를 특징으로 하는 경향이 있습니다.

Clinical or Translational Implications

이러한 발견은 폐암의 진단, 예후 예측 및 치료 전략 개발에 중요한 시사점을 제공합니다.

면역 치료 전략

이러한 세포 아형의 변화를 심층적으로 분석하고, 다른 임상 병리학적 정보와 통합하여 맞춤형 치료법 개발을 위한 기반을 마련할 수 있습니다.

10. Macrophage Subset Proportions Dynamically Shift Across Lung Cancer Progression

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional changes of specific macrophage subsets (M2A, M2C, M2B) in lung tissue across different conditions: Normal, Tumor (early stage), and Tumor (advanced stage). Boxplots are used to visualize these proportions, with statistical significance indicated for comparisons between conditions. The goal is to identify macrophage populations that are significantly altered during lung cancer progression.

Visual Summary

The boxplots illustrate the celltype proportion of Mac (M2A), Mac (M2C), and Mac (M2B) across the three conditions.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and can adopt diverse phenotypes, broadly categorized as M1 (pro-inflammatory, anti-tumorigenic) and M2 (anti-inflammatory, pro-tumorigenic). The subsets M2A, M2B, and M2C fall under the M2-like spectrum, but with distinct characteristics and roles.

  1. M2B Macrophages are Enriched in Advanced Lung Cancer: The most striking finding is the significant and progressive increase in Mac (M2B) populations from normal tissue to early-stage tumors and, most notably, to advanced-stage tumors. M2B macrophages are known for their dual roles, but in the context of cancer progression, their increase often signifies a shift towards an immunosuppressive and pro-tumorigenic TME. This suggests that M2B macrophages may be crucial drivers of disease progression in advanced lung cancer by contributing to immune evasion, angiogenesis, and tissue remodeling.
  1. M2A Macrophages Decrease with Tumor Progression: Conversely, Mac (M2A) populations are highest in normal lung tissue and significantly decrease with both early and advanced tumor development. M2A macrophages are typically associated with tissue repair, allergic responses, and immune regulation. Their decrease in the tumor context suggests that either these cells are not effectively recruited or maintained within the evolving TME, or that their functions are superseded by other macrophage subsets. This indicates M2A macrophages may not be the predominant pro-tumorigenic M2-like cells in this specific lung cancer progression model.
  2. M2C Macrophages Show a Trend of Decrease in Advanced Disease: While not as statistically robust as M2A and M2B, there is a trend of decreasing Mac (M2C) proportions in advanced tumors compared to normal and early-stage disease. M2C macrophages are generally linked to immune suppression and tissue remodeling, often promoting tumor growth. The observed trend here suggests that in this lung cancer dataset, while they might play a role, their relative abundance decreases in advanced disease, possibly indicating a shift in the specific M2-like macrophage polarization pathways that dominate as the tumor progresses, potentially in favor of M2B macrophages.

In summary, these results highlight a dynamic shift in macrophage subset composition during lung cancer progression, with a significant increase in M2B macrophages being a hallmark of advanced disease, while M2A and M2C proportions tend to decrease or stabilize.

Clinical or Translational Implications

The distinct shifts in macrophage subset populations observed in lung cancer have important clinical and translational implications:

11. Ploidy Population Analysis in Lung Tumor-Origin Cells Across Disease Stages

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[Analysis Visualization Results]...

Analysis Overview

이 분석은 폐 조직의 정상(Normal), 초기 종양(Tumor(early)), 진행성 종양(Tumor(adv)) 상태에서 'Lung Epithelial cell' 및 'unassigned' 세포 집단(종양 기원 세포로 간주됨)의 이수성(ploidy) 분포를 시각화한 것입니다. 각 샘플에 대한 이수체(Aneuploid), 정상 이배체(Diploid), 그리고 불분명(Unclear) 세포의 비율을 막대 그래프로 보여줍니다.

Visual Summary

Biological Interpretation

이수성(Aneuploidy)은 세포의 염색체 수가 비정상적인 상태를 의미하며, 암의 주요 특징 중 하나이자 암 발생 및 진행에 중요한 역할을 하는 것으로 알려져 있습니다. 이 분석 결과는 폐암의 진행 단계에 따른 종양 세포 집단의 이수성 변화를 명확히 보여줍니다.

  1. 정상 세포의 안정성: 정상 폐 조직의 상피 세포 및 미분류 세포가 거의 전적으로 정상 이배체 상태를 유지하는 것은 건강한 세포 집단의 유전적 안정성을 반영합니다.
  2. 종양 진행에 따른 이수성 증가: 초기 종양 단계에서 이수체 세포의 출현은 암의 시작과 함께 유전체 불안정성이 증가하고 있음을 시사합니다. 진행성 종양 단계에서는 이수체 세포가 압도적인 비율을 차지하는데, 이는 종양의 악성도가 증가함에 따라 유전체 재편성이 심화되고 불안정한 세포 클론이 선택적으로 증식했음을 강력히 나타냅니다 [1].
  3. 종양 내 이질성: 초기 종양 샘플에서 이수체 및 정상 이배체 세포의 비율이 샘플별로 다양하게 나타나는 것은 초기 단계 폐암의 유전적 이질성(heterogeneity)을 반영합니다. 이는 동일한 "초기 종양" 진단 내에서도 종양 세포의 진화 상태나 클론 구성이 다를 수 있음을 의미합니다.
  4. 'unassigned' 세포의 의미: 데이터 컨텍스트에서 'Lung Epithelial cell'과 함께 'unassigned' 세포가 "Tumor origin celltype"으로 분류된 점을 감안할 때, 이 'unassigned' 세포들에서도 관찰되는 이수성 증가는 이들이 실제로 종양 기원 세포의 일부이거나, 유전적 변이를 겪고 있는 세포일 가능성을 높여줍니다.

Clinical or Translational Implications

---

References:

  1. Aneuploidy as a hallmark of cancer:
  1. Aneuploidy in cancer prognosis:

12. Advanced Lung Cancer Cell-Cell Interaction Patterns

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns within the "Tumor(adv)" (advanced tumor) condition in lung single-cell RNA-seq data. The focus is on key cell types involved in the tumor microenvironment: Lung Epithelial cells (specifically distinguishing between Diploid and Aneuploid populations, with Aneuploid representing likely tumor cells), Macrophages, and T cells (CD4+ and CD8+). The analysis identifies prominent ligand-receptor pairs mediating communication between these cell types, limited to the top 80 most significant interactions.

Visual Summary

The dot plot visualizes the strength (mean expression, color intensity) and significance (p-value, dot size) of cell-cell interactions.

Dominant Interactions by Cell Type:

Key Ligand-Receptor Systems:

Biological Interpretation

The observed CCI landscape in advanced lung cancer reveals a highly interactive tumor microenvironment, particularly driven by Aneuploid Lung Epithelial cells and Macrophages.

  1. Tumor Cell Autonomy and Microenvironment Remodeling: The extensive homotypic interactions among Aneuploid Lung Epithelial cells, especially via EGFR signaling (TGFA, EREG, HBEGF-EGFR), underscore an autocrine/paracrine loop promoting tumor cell proliferation and survival, a hallmark of many cancers, including lung adenocarcinoma with activating EGFR mutations PubMed Search: EGFR signaling lung cancer. The strong integrin-ECM interactions (FN1, LAMA3, LAMC1, SPP1) indicate robust adhesion to and remodeling of the extracellular matrix, crucial for tumor growth, invasion, and metastasis GeneCards: SPP1. The uPA-uPAR system (PLAUR) further supports invasive potential by facilitating ECM degradation.
  2. Macrophage-Tumor Cell Crosstalk: A Pro-Tumor Axis: The numerous and strong interactions between Aneuploid Lung Epithelial cells and Macrophages highlight their critical role in the advanced tumor microenvironment. Macrophages, often polarizing towards M2-like phenotypes (tumor-associated macrophages, TAMs) in cancer, can promote angiogenesis, immune suppression, and metastasis. Ligand-receptor pairs like SPP1-integrin_a4b1_complex and ANXA1-FPR1/FPR3 could be key mediators of this pro-tumorigenic interaction PubMed Search: tumor associated macrophages lung cancer SPP1. Chemokines CCL3-CCR1 and CCL5-CCR1 might contribute to the recruitment of immune suppressive cell types or modulate their function within the TME. The PTPRC-MRC1 (CD45-CD206) interaction further suggests a role for M2-like macrophages.
  3. T Cell-Tumor Cell Interactions: Immune Evasion Potential: While less prominent than Macrophage-tumor interactions, the presence of CD58-CD2 suggests direct T cell-tumor cell contact and adhesion. However, the co-occurrence of CD52-SIGLEC10 is noteworthy. SIGLEC10, expressed on various immune cells, is an inhibitory receptor that can bind to CD52 expressed on tumor cells (though CD52 is also on immune cells), leading to immune suppression, similar to other immune checkpoints PubMed Search: SIGLEC10 immune checkpoint cancer. This indicates potential immune evasion mechanisms deployed by the Aneuploid Lung Epithelial cells in advanced cancer.
  4. Diploid Lung Epithelial Cells in the TME: Diploid Lung Epithelial cells, likely tumor-adjacent normal cells or other non-malignant epithelial cells, still engage in some interactions, particularly with Macrophages and Aneuploid Lung Epithelial cells. This suggests that the tumor microenvironment can influence even non-malignant cells, or these cells contribute to the overall complexity of the TME, for instance through EGFR signaling (EREG-EGFR).

Clinical or Translational Implications

The identified cell-cell interaction patterns present several avenues for therapeutic intervention and biomarker discovery in advanced lung cancer.

  1. Targeting Growth Factor Signaling: The prominent EGFR family signaling (TGFA, EREG, HBEGF-EGFR) in Aneuploid Lung Epithelial cells reinforces the continued relevance of EGFR inhibitors. For patients with acquired resistance to current EGFR TKIs, targeting these specific ligand-receptor interactions or downstream pathways could be beneficial.
  2. Disrupting ECM-Integrin Axis: The pervasive integrin-ECM interactions (involving FN1, LAMA3, LAMC1, SPP1) offer promising targets to inhibit tumor cell invasion, metastasis, and survival. Specifically, targeting SPP1 (Osteopontin), a well-known promoter of cancer progression and immune evasion, or its cognate integrin receptors, could be a valuable therapeutic strategy to impede tumor spread and modulate the immune suppressive microenvironment UniProt: SPP1.
  3. Modulating Macrophage-Tumor Crosstalk: The robust interactions between Aneuploid Lung Epithelial cells and Macrophages suggest that re-educating or depleting pro-tumorigenic TAMs could be a highly effective therapeutic approach. Targeting specific ligand-receptor pairs like ANXA1-FPR1/FPR3 or SPP1-integrin_a4b1_complex could disrupt the pro-tumor macrophage functions. Investigating the chemokine axes (CCL3-CCR1, CCL5-CCR1) to understand their specific role in TAM recruitment and polarization could also identify targets to reprogram the TME.
  4. Overcoming Immune Evasion: The presence of CD52-SIGLEC10 interactions suggests a potential immune checkpoint axis beyond PD-1/PD-L1. Developing therapies that block SIGLEC10 or its ligands could release T cells from suppression and enhance anti-tumor immunity, warranting further experimental validation of this specific pathway in lung cancer.
  5. Targeting Metastatic Pathways: The strong activity of the PLAUR (uPAR) system highlights its role in tumor invasion and metastasis. Inhibitors of uPAR or its associated pathways could be explored to prevent local invasion and distant dissemination of advanced lung cancer cells.

These findings highlight a complex network of interactions that drive advanced lung cancer progression. Further experimental validation of these specific ligand-receptor pairs in relevant in vitro and in vivo models is crucial for prioritizing them as therapeutic targets or diagnostic biomarkers.

13. Advanced Lung Tumor Cell-Cell Interaction Analysis

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[Analysis Visualization Results]...

Analysis Overview

이 분석은 진행성(advanced) 폐암 환경에서 세포-세포 상호작용(CCI)을 CellPhoneDB를 이용하여 식별합니다. 특히, Tumor(adv) 조건에서 가장 유의미하고 발현 수준이 높은 상위 80개 상호작용 쌍을 시각화하여, 종양 세포와 미세환경 세포 간의 통신 패턴을 파악하고 잠재적인 치료 표적을 발굴하는 데 중점을 둡니다.

Visual Summary

제공된 닷 플롯은 Tumor(adv) 조건에서 세포-세포 쌍(Y축)과 리간드-수용체 유전자 쌍(X축) 간의 상호작용을 보여줍니다. 점의 크기는 상호작용의 유의미성(-log10(p-value))을, 색상은 평균 발현 수준(log2(mean))을 나타냅니다.

주요 상호작용 세포 유형

주요 리간드-수용체 쌍

Biological Interpretation

이 분석 결과는 진행성 폐암의 종양 미세환경(TME) 내에서 복잡하고 역동적인 세포-세포 상호작용 네트워크를 강조합니다.

Clinical or Translational Implications

이러한 세포-세포 상호작용 분석 결과는 진행성 폐암 치료를 위한 새로운 전략 개발에 중요한 통찰력을 제공합니다.

잠재적 치료 표적

14. Condition-Specific Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Related Genes in Lung Tissue

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Analysis Overview

This analysis investigates cell-cell interactions (CCI) involving a curated list of immune checkpoint and cell cycle-related genes across normal lung tissue, early-stage lung tumors, and advanced lung tumors. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between various cell types, with a specific focus on distinguishing between Diploid and Aneuploid Lung Epithelial cells, the latter often indicative of malignancy. The analysis provides insights into how these critical signaling pathways evolve in the tumor microenvironment.

Visual Summary

The dot plots display the significance (-log10(p)) and interaction strength (log2(mean)) of specific gene-pair interactions between defined cell-cell pairs for Normal, Tumor(early), and Tumor(adv) conditions.

Biological Interpretation

The analysis highlights the dynamic changes in cell-cell communication related to immune checkpoint and cell cycle regulatory pathways during lung cancer progression.

  1. EGFR Pathway Activation in Tumor Progression: The marked increase in EGFR ligand-receptor interactions (AREG-EGFR, HBEGF-EGFR, BTC-EGFR, EREG-EGFR, TGFA-EGFR) involving Aneuploid Lung Epithelial cells in early and advanced tumors suggests an activated autocrine and paracrine growth loop. This is a hallmark of many cancers, driving cell proliferation and survival. The involvement of Macrophages in these interactions in advanced tumors implies that tumor-associated macrophages (TAMs) may contribute to EGFR activation and tumor growth by secreting EGFR ligands [1].
  2. TGF-beta Signaling as a Pro-Tumorigenic and Immunosuppressive Force: The consistent and strong TGFB1-TGFbeta_receptor2/3 interactions, particularly involving Aneuploid Lung Epithelial cells and Macrophages, underscore the critical role of TGF-beta signaling in lung cancer. TGF-beta is a potent immunosuppressive cytokine, promoting tumor evasion from immune surveillance, and also contributes to epithelial-mesenchymal transition (EMT) and fibrosis, crucial processes in cancer invasion and metastasis [2].
  3. Role of Integrin αvβ6: The strong interaction involving TGFB1_integrin_avb6_complex within Aneuploid Lung Epithelial cells in advanced tumors is significant. Integrin αvβ6 is known to activate latent TGF-beta, further amplifying the pro-tumorigenic effects of TGF-beta within the tumor microenvironment [3]. This suggests a self-reinforcing loop promoting tumor growth and stromal remodeling.
  4. Immune Cell Context and Modulation: While classic immune checkpoint inhibitors like PD-1/PD-L1 are not explicitly highlighted in these specific plots, the presence of CD86-CD28 (a co-stimulatory pathway) and IFNG_Type_IIFN_receptor interactions suggests ongoing immune activity. Macrophages, T cells, and NK cells are actively communicating within the tumor microenvironment. The significant macrophage involvement in EGFR and TGFB1 signaling points to their complex and often pro-tumorigenic roles in the advanced tumor setting.
  5. Ploidy as a Marker of Malignancy: The distinction between Diploid and Aneuploid Lung Epithelial cells is crucial. The shift of prominent interactions from Diploid to Aneuploid Lung Epithelial cells as the disease progresses highlights cancer-specific communication networks. Aneuploid cells, representing malignant cells, exhibit unique and amplified signaling events that drive tumor pathology.

Clinical or Translational Implications

The findings from this CCI analysis have several important clinical and translational implications, particularly concerning therapeutic targeting and understanding treatment resistance:

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References:

[1] Fang, W., et al. (2020). "Epidermal growth factor receptor (EGFR) signaling in lung cancer and drug resistance: From cell biology to novel therapeutic strategies." *Translational Oncology*, 13(8), 100808. PubMed search: EGFR ligands tumor microenvironment

[2] San-Miguel, S. M., et al. (2020). "TGF-β signaling in lung cancer: mechanisms of action and therapeutic strategies." *Frontiers in Oncology*, 10, 563503. PubMed search: TGF-beta lung cancer immunosuppression

[3] Al-Lamki, R. S., et al. (2017). "Integrin αvβ6: Biology, pathogenesis and potential as a therapeutic target in cancer and fibrosis." *Journal of Pathology*, 241(4), 512-523. GeneCards: ITGB6

15. Condition-specific Cell-Cell Interaction Patterns in Lung Tissue Progression

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Analysis Overview

This analysis investigates statistically significant differences in cell-cell interactions (CCIs) across Normal, Tumor(adv) (advanced tumor), and Tumor(early) (early tumor) conditions within human lung tissue. The focus is on major immune and stromal cells, as well as lung epithelial cells, including those identified as aneuploid (likely tumor cells) or diploid. The visualization highlights the strength (standardized mean expression, color intensity) and statistical significance (-log10(p-value), dot size) of various ligand-receptor pairs across individual samples, providing a detailed view of how cellular communication networks are reprogrammed during lung cancer development.

Visual Summary

The dot plot effectively delineates distinct patterns of cell-cell interactions specific to each condition:

Biological Interpretation

The dynamic shifts in cell-cell interactions observed across different conditions offer critical biological insights into lung cancer pathogenesis:

Reprogramming of Tumor-Immune and Tumor-Stromal Crosstalk:

Clinical or Translational Implications

The identified differential CCI patterns offer several compelling clinical and translational avenues:

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References:

[1] Prostaglandin E2 in cancer: For extensive reviews on the role of prostaglandin E2 in various cancers, including lung cancer, search PubMed: https://pubmed.ncbi.nlm.nih.gov/?term=prostaglandin+E2+cancer+lung

[2] TREM2 in cancer: To learn more about the role of Triggering Receptor Expressed on Myeloid cells 2 (TREM2) in the tumor microenvironment and its implications for cancer, search PubMed: https://pubmed.ncbi.nlm.nih.gov/?term=TREM2+cancer

16. Lung Epithelial Cell Condition-Specific Surfaceome Markers

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Analysis Overview

This analysis identifies condition-specific surfaceome markers in Lung Epithelial cells, focusing on distinguishing Normal tissue from Tumor (early and advanced stages) using single-cell RNA sequencing data. The dot plot visualizes the mean expression and fraction of cells expressing up to 50 top surfaceome markers per condition. This approach helps pinpoint cell surface proteins that are uniquely expressed or significantly altered in tumor-origin cells, providing crucial insights into the molecular changes associated with lung tumorigenesis.

Visual Summary

The dot plot clearly illustrates distinct surfaceome marker profiles between normal and tumor lung epithelial cells, although a strong differentiation between early and advanced tumor stages is less pronounced at this resolution.

Biological Interpretation

The observed shifts in surfaceome expression highlight critical biological changes underpinning lung tumorigenesis.

Clinical or Translational Implications

The identified condition-specific surfaceome markers in lung epithelial cells carry significant clinical and translational potential.

17. Macrophage Condition-Specific Surfaceome Markers in Lung Tissue

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Analysis Overview

This analysis identifies condition-specific surfaceome markers for Macrophages by comparing gene expression patterns between Normal lung tissue and Early-stage Lung Tumor tissue. The results are visualized using a dot plot, where dot size represents the fraction of cells expressing a gene and dot color intensity indicates the mean expression level within those cells. The markers are selected from differentially expressed genes (DEG) and are restricted to surfaceome proteins, which are particularly relevant for understanding cell-cell interactions, immune evasion, and potential therapeutic targeting.

Visual Summary

The dot plot clearly differentiates two major sets of macrophage surfaceome markers: those predominantly expressed in Normal lung samples and those enriched in Early-stage Lung Tumor samples.

Biological Interpretation

The identified condition-specific surfaceome markers provide insights into the distinct functional states of macrophages in normal lung homeostasis versus the early tumor microenvironment.

Clinical or Translational Implications

The identified surfaceome markers for macrophages hold significant clinical and translational potential for early lung cancer.

18. Fibroblast Condition-Specific Surfaceome Markers in Lung Tissue

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Analysis Overview

This analysis identifies condition-specific surfaceome markers in Fibroblast cells, comparing Normal lung tissue with early-stage Lung Tumor samples. The objective is to highlight surface-expressed genes that are differentially expressed between these conditions, offering insights into fibroblast phenotype changes during early tumorigenesis and potential targets for therapeutic intervention or diagnostic applications. The selection was limited to surfaceome markers to prioritize genes with direct accessibility for cell-surface interactions or drug targeting.

Visual Summary

The dot plot visualizes the expression of selected surfaceome markers in Fibroblast cells across different lung samples, grouped by condition (Normal vs. Tumor (early)).

Gene Expression Patterns

Biological Interpretation

The observed condition-specific surfaceome markers provide strong evidence of the functional specialization and activation of Fibroblasts in the lung tumor microenvironment, even at early stages.

These markers suggest a role in maintaining the quiescent state, extracellular matrix integrity, and normal physiological responses of fibroblasts in healthy lung tissue.

The presence of these markers collectively indicates that early-stage tumor fibroblasts undergo significant activation, adopting phenotypes that support extracellular matrix reorganization, inflammation, cell adhesion, and communication within the developing tumor.

Clinical or Translational Implications

The identification of condition-specific surfaceome markers in Fibroblasts offers several compelling clinical and translational opportunities for early lung cancer.

19. Condition-Specific Surfaceome Markers in CD4 T Cells from Lung Tissue

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Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers in CD4 T cells across normal lung tissue, early-stage lung tumors (Tumor(early)), and advanced lung tumors (Tumor(adv)). The plot_markers_and_expression_dot tool was used to visualize the expression of the top 50 highly variable surfaceome markers per condition, displaying both the fraction of cells expressing the marker and its mean expression level across individual samples. This approach helps to pinpoint cell surface proteins that differentiate CD4 T cell states in different disease contexts.

Visual Summary

The dot plot displays the expression profiles of selected surfaceome markers (x-axis) across various individual samples (y-axis), grouped by their condition: Normal, Tumor(adv), and Tumor(early).

Biological Interpretation

The distinct surfaceome marker profiles observed in CD4 T cells across normal, early, and advanced lung tumor conditions provide valuable insights into their functional states and roles in the tumor microenvironment.

Markers of Normal/Homeostatic CD4 T Cells:

Markers of Tumor-Associated CD4 T Cells:

Clinical or Translational Implications

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

Therapeutic Targets:

20. Differential Expression of Cell Cycle-Related Genes Across Lung Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of selected cell cycle pathway-related genes—TGFB1, CDKN1B, and CCND3—across different conditions: advanced tumor (Tumor(adv)), early tumor (Tumor(early)), and normal lung tissue. The genes were selected based on statistically significant expression differences in key disease-related cells (Lung Epithelial cell, Macrophage, T cell CD4+, Fibroblast). It is important to note that the provided boxplots display expression patterns per gene across conditions, but the specific cell type for which each plot was generated is not explicitly indicated in the visualization. Therefore, the biological interpretations will consider the general roles of these genes within the lung tumor microenvironment and how these patterns might manifest in one or more of the specified cell types.

Visual Summary

The visualization displays boxplots showing the distribution of gene expression (sample mean) for TGFB1, CDKN1B, and CCND3 across the three conditions, along with p-values indicating statistical significance of differences between groups.

  1. TGFB1 (Transforming Growth Factor Beta 1):
  1. CDKN1B (Cyclin Dependent Kinase Inhibitor 1B, p27Kip1):
  1. CCND3 (Cyclin D3):

Biological Interpretation

The observed differential expression patterns for these cell cycle-related genes indicate significant alterations in cellular regulation within the lung tumor microenvironment as the disease progresses from normal to early and advanced stages. The specific cell type context is crucial for a definitive interpretation, as these genes play diverse roles across different cell populations.

  1. TGFB1 (Transforming Growth Factor Beta 1):
  1. CDKN1B (p27Kip1):
  1. CCND3 (Cyclin D3):

Clinical or Translational Implications

The differential expression of these cell cycle regulators provides insights into the molecular mechanisms underlying lung cancer progression and could have potential clinical implications:

Further investigations are crucial to precisely delineate the cell-type-specific roles of these genes within the lung tumor microenvironment, including their localization, activation status, and downstream signaling, to fully unlock their clinical and translational potential.

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References

  1. TGFB1 (GeneCards): https://www.genecards.org/cgi-bin/carddisp.pl?gene=TGFB1
  2. TGFB1 in Cancer (PubMed Search): https://pubmed.ncbi.nlm.nih.gov/?term=TGFB1+cancer+progression
  3. CAFs TGFB1 (PubMed Search): https://pubmed.ncbi.nlm.nih.gov/?term=cancer+associated+fibroblasts+TGFB1
  4. CDKN1B (GeneCards): https://www.genecards.org/cgi-bin/carddisp.pl?gene=CDKN1B
  5. CCND3 (GeneCards): https://www.genecards.org/cgi-bin/carddisp.pl?gene=CCND3

21. Gene Ontology (GSA) Analysis of Lung Epithelial Cells Across Different Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GSA) results for Lung Epithelial cells, displayed as bar plots. The analysis compares gene set enrichment for upregulated genes (GSA_up) in four distinct conditions: "Diploid_vs_others", "Normal_vs_others", "Tumor(adv)_vs_others", and "Tumor(early)_vs_others". Each comparison highlights pathways and biological processes that are significantly enriched in the specified condition when compared to all other cells in the dataset (i.e., 'others'). This helps to identify condition-specific biological shifts in Lung Epithelial cells.

Visual Summary

The visualizations consist of four bar plots, each representing the top enriched Gene Ontology terms or pathways for Lung Epithelial cells under a specific condition. The terms are sorted by their statistical significance, with bar length indicating the -log(p-val) and -log(q-val).

  1. Diploid_vs_others: This plot primarily shows a strong enrichment of immune-related pathways and infection responses, such as "Staphylococcus aureus infection", "Phagosome", and "Antigen processing and presentation". Several autoimmune and inflammatory disease pathways are also prominent.
  2. Normal_vs_others: This plot highlights metabolic pathways like "PPAR signaling pathway", "Fatty acid degradation", and "Cholesterol metabolism", alongside cellular regulation processes such as "Ferroptosis". Some immune-related terms are also present but are less dominant than metabolic ones.
  3. Tumor(adv)_vs_others: This plot shows a significant enrichment in pathways associated with fundamental cellular processes like "Ribosome", "Protein processing in endoplasmic reticulum", "RNA transport", "Cell cycle", "DNA replication", and "Oxidative phosphorylation". Intriguingly, pathways linked to neurodegenerative diseases (e.g., "Amyotrophic lateral sclerosis", "Huntington disease", "Parkinson disease") are also highly enriched, as are several infection and cancer-related pathways (e.g., "p53 signaling pathway", "Human papillomavirus infection").
  4. Tumor(early)_vs_others: This plot presents a very similar profile to "Tumor(adv)_vs_others", with strong enrichment in ribosomal activity, protein processing, neurodegenerative disease pathways, and cell cycle/DNA replication-related terms. Cancer-specific pathways like "Chronic myeloid leukemia" and "Apoptosis" are also evident.

Biological Interpretation

Condition-Specific Functional Shifts in Lung Epithelial Cells

When compared to other cellular states (including aneuploid cells and tumor cells), diploid Lung Epithelial cells show a striking enrichment in pathways related to immune response and host defense. Terms like "Staphylococcus aureus infection," "Phagosome," and "Antigen processing and presentation" suggest an active role in sensing pathogens and initiating immune responses. This indicates that diploid epithelial cells, which include normal cells and potentially a subset of less transformed cells within tumors, retain or actively engage in critical immune functions. This could be a baseline function of healthy lung epithelium or an active participation in the tumor immune microenvironment, even when facing oncogenic stress.

In normal conditions, Lung Epithelial cells are characterized by enrichment in various metabolic pathways, notably "PPAR signaling pathway," "Fatty acid degradation," and "Cholesterol metabolism." These pathways are crucial for energy homeostasis, lipid processing, and maintaining cellular structure and function. The enrichment of "Ferroptosis" suggests a regulated cell death mechanism actively maintained in normal cells, possibly to remove damaged cells and preserve tissue integrity. This profile reflects a healthy, metabolically active, and well-regulated epithelial state.

Both early and advanced tumor stages in Lung Epithelial cells exhibit remarkably similar and profound alterations. There is a dominant signature of increased biosynthetic activity, indicated by the strong enrichment of "Ribosome," "Protein processing in endoplasmic reticulum," and "RNA transport" pathways. This reflects the high protein synthesis and processing demands necessary for rapid cell proliferation and growth characteristic of cancer cells.

Furthermore, pathways related to cell cycle progression, DNA replication, and metabolic reprogramming (e.g., "Oxidative phosphorylation," "Thermogenesis") are highly active, underscoring the shift towards oncogenic growth.

A notable observation is the consistent enrichment of pathways associated with neurodegenerative diseases (e.g., "Amyotrophic lateral sclerosis," "Huntington disease," "Parkinson disease"). While these are not lung-specific diseases, their underlying molecular mechanisms often involve severe cellular stress, protein misfolding, proteasome dysfunction, and mitochondrial damage. Their enrichment in tumor epithelial cells strongly suggests that cancer cells endure and adapt to high levels of cellular stress, particularly concerning proteostasis and energy metabolism, which are shared features with neurodegeneration. This indicates widespread cellular dysfunction and stress responses within tumor epithelial cells.

The presence of "p53 signaling pathway" and "Apoptosis" in tumor cells points to either attempts at tumor suppression or altered mechanisms of programmed cell death.

Progression of Lung Carcinogenesis at the Epithelial Level

The striking similarity in enriched pathways between early and advanced tumor stages suggests that the fundamental cellular reprogramming, including increased biosynthetic machinery and cellular stress responses, is largely established even at the initial phases of lung tumor development in epithelial cells. This indicates a rapid and extensive transformation process that profoundly alters epithelial cell biology from an early point.

Clinical or Translational Implications

22. Gene Set Enrichment Analysis (GSEA) of Lung Cell Types Across Tumor Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results visualized as a dot plot for key cell types found in the lung (B cell, Dendritic cell, Endothelial cell, Fibroblast, ILC, Lung Epithelial cell, Macrophage, Mast cell, NK cell, T cell CD4+, T cell CD8+). The comparisons shown are for each cell type within a specific condition (Normal, Tumor(early), Tumor(adv)) against all other conditions combined for that same cell type (e.g., "B cell: Normal_vs_others" compares B cells in Normal tissue to B cells across both early and advanced tumor stages).

The dot plot's features are:

This approach helps to identify condition-specific pathway shifts within each cell type, highlighting unique biological states associated with normal lung, early-stage tumor, and advanced-stage tumor environments.

Visual Summary

The GSEA dot plot reveals distinct and complex patterns of pathway activity across different cell types and disease conditions.

Biological Interpretation

  1. Malignant Epithelial Cell Transformation and Proliferation:
  1. Tumor Microenvironment (TME) Shaping by Stromal Cells:
  1. Dynamic and Diverse Immune Cell Responses:
  1. Widespread Metabolic Reprogramming:

Clinical or Translational Implications

  1. Targeting Key Oncogenic and Stromal Pathways:
  1. Reinforcing Immune Checkpoint Blockade:
  1. Exploiting Metabolic Vulnerabilities:
  1. Biomarkers for Disease Progression and Therapeutic Response:

23. Discussion

The single-cell analysis of human lung tissue provides a high-resolution view of the dynamic cellular and molecular landscape in lung cancer progression. UMAP visualizations clearly delineate distinct cellular clusters, with aneuploid lung epithelial cells forming the core of tumor-associated populations. This robust identification of malignant cells is further supported by recurrent copy number variations (CNVs) in EGFR and ERBB2, indicating their role as oncogenic drivers from early stages.

The immune microenvironment undergoes a significant and progressive shift towards immunosuppression. While early tumors may show variable immune infiltration, advanced tumors are characterized by a pronounced increase in regulatory T cells (Tregs) and naive T cells, alongside a notable decrease in anti-tumor innate immune cells like NK cells, Th9 cells, ILC1, ILC2, and LTI cells. This suggests an active immune evasion strategy by the tumor. CD4 T cells in tumor conditions upregulate both co-stimulatory (OX40, GITR) and inhibitory (TIGIT) receptors, implying a complex interplay of activation and exhaustion. Macrophages, critical components of the TME, exhibit a dramatic shift in their subset composition, with M2B macrophages significantly enriched in advanced tumors, strongly implicating their pro-tumorigenic and immunosuppressive roles.

Tumor-stromal interactions are fundamentally reprogrammed to support malignancy. Fibroblasts in early tumors transform into cancer-associated fibroblasts (CAFs), marked by the upregulation of FAP, CDH11, and VCAM1, contributing to extracellular matrix (ECM) remodeling, inflammation, and adhesion. Endothelial cells also show pathway enrichments indicative of active angiogenesis. Cell-cell interaction (CCI) analyses highlight critical communication axes. EGFR ligand-receptor interactions are highly active within aneuploid lung epithelial cells, forming autocrine/paracrine loops. TGF-beta signaling is consistently strong between tumor cells and macrophages, acting as a potent immunosuppressive and pro-fibrotic force, often amplified by integrin αvβ6. A pervasive finding is the widespread upregulation of prostaglandin E2 (PGE2) signaling, emanating from aneuploid lung epithelial cells to various immune and stromal components, acting as a central orchestrator of immunosuppression and angiogenesis. Integrin-ECM interactions (e.g., involving SPP1, FN1) are also robust, facilitating tumor invasion and metastasis.

Metabolic reprogramming is a hallmark of tumor epithelial cells, exhibiting a biosynthetic overdrive (ribosomal activity, protein processing, DNA replication) and profound cellular stress, reflected in the enrichment of neurodegenerative disease pathways. This suggests a high adaptive capacity to maintain proliferation under chronic stress. While TGFB1 (bulk expression) appears downregulated from normal to advanced tumor stages, this contrasts with its typical pro-tumorigenic role and is likely a cell-type specific effect, warranting further investigation into the precise cellular sources and activation states of TGF-beta signaling within the heterogeneous TME. Overall, the findings underscore a highly interactive and evolving TME that promotes lung cancer progression through coordinated changes in immune cell function, stromal support, and oncogenic signaling networks.

Hypotheses:

  1. Lung cancer progression is driven by a coordinated increase in immunosuppressive cell populations (e.g., Tregs, M2B macrophages, exhausted T cells) and a concurrent decrease in anti-tumor immune surveillance cells (e.g., NK cells, Th9 cells), allowing for immune evasion.
  2. Aneuploid Lung Epithelial cells establish and maintain a pro-tumorigenic microenvironment through pervasive autocrine/paracrine signaling via EGFR ligands and prostaglandin E2, which collectively promote tumor cell proliferation, survival, and active immune suppression.
  3. The activation of specific fibroblast phenotypes (e.g., FAP+ CAFs) and their subsequent interactions with tumor and immune cells are critical early events in lung cancer, contributing significantly to ECM remodeling, angiogenesis, and the establishment of an immunosuppressive niche.
  4. Lung cancer cells, even at early stages, exhibit substantial metabolic reprogramming and proteostasis stress, which are essential for their rapid proliferation and survival, and could represent novel therapeutic vulnerabilities.

Potential therapeutic targets:

  1. EGFR (Epidermal Growth Factor Receptor) and its ligands (AREG, BTC, EREG, HBEGF, TGFA): EGFR signaling is strongly activated in aneuploid Lung Epithelial cells in both early and advanced tumors, driving autocrine/paracrine proliferation and survival, and is a well-established oncogenic driver in NSCLC. Evidence: CNV analysis (Section 4) frequently identifies EGFR amplification. CCI analysis (Sections 12, 14) shows robust EGFR ligand-receptor interactions within Aneuploid Lung Epithelial cells and with Macrophages in tumor conditions. GSEA (Section 22) indicates enrichment of downstream pathways like HIF-1 and mTOR signaling in lung epithelial cells. Validation: Evaluate the efficacy of existing EGFR TKIs or novel inhibitors targeting specific EGFR ligands/receptors in patient-derived organoids or xenograft models. Monitor patient responses to EGFR-targeted therapies, correlating with expression levels of EGFR ligands and receptors in tumor cells.
  2. Prostaglandin E2 (PGE2) pathway (e.g., COX-2, PTGERs): PGE2 is a central orchestrator of the immunosuppressive and pro-tumorigenic tumor microenvironment, broadly upregulated in both early and advanced tumors by aneuploid Lung Epithelial cells, influencing immune and stromal cells. Evidence: CCI analysis (Sections 13, 15) consistently highlights widespread PGE2-PTGERx interactions from Aneuploid Lung Epithelial cells to macrophages, T cells, and NK cells across tumor conditions. Validation: Test COX-2 inhibitors or specific PTGER antagonists in preclinical lung cancer models to assess their ability to reverse immunosuppression, inhibit angiogenesis, and slow tumor progression. Consider clinical trials for combination therapies with immunotherapies.
  3. Integrin-Extracellular Matrix (ECM) interactions (e.g., SPP1-integrin α4β1 complex, TGFB1_integrin_avb6_complex): Robust integrin-ECM interactions mediate tumor cell adhesion, invasion, metastasis, and TME remodeling. These are prominent in aneuploid Lung Epithelial cells and their interactions with macrophages, contributing to tumor progression and immune modulation. Evidence: CCI analysis (Sections 12, 13, 15) shows strong interactions involving integrins with FN1, LAMA3, LAMC1, SPP1 in aneuploid Lung Epithelial cells and with macrophages. The SPP1-integrin_a4b1_complex is highly active, and TGFB1_integrin_avb6_complex is strong in advanced tumors. Validation: Develop blocking antibodies or small molecule inhibitors against specific integrins (e.g., α4β1, αvβ6) or their key ligands (e.g., SPP1). Test their efficacy in inhibiting tumor cell invasion, metastasis, and modulating macrophage polarization in in vitro and in vivo models.
  4. Fibroblast Activation Protein (FAP): FAP is a highly specific and prominent surface marker for activated fibroblasts (CAFs) in early lung tumors. CAFs play critical pro-tumorigenic roles in ECM remodeling, immunosuppression, and tumor growth from the outset of cancer development. Evidence: Condition-specific surfaceome marker analysis for Fibroblasts (Section 18) clearly shows FAP as a highly expressed and prevalent marker in early lung tumor samples, notably absent in normal fibroblasts. Validation: Test FAP-targeted therapies (e.g., FAP-antibody-drug conjugates, FAP inhibitors) in preclinical lung cancer models to assess their ability to deplete or reprogram CAFs, reduce ECM stiffness, and enhance anti-tumor immunity, particularly in early-stage disease.
  5. TIGIT (T cell immunoreceptor with Ig and ITIM domains): TIGIT is an immune checkpoint receptor upregulated in CD4 T cells in both early and advanced tumor conditions. Its expression suggests T cell exhaustion or a suppressive phenotype, contributing to immune evasion. Evidence: Condition-specific surfaceome marker analysis for CD4 T cells (Section 19) identifies TIGIT as a prominent marker in tumor conditions. Validation: Evaluate anti-TIGIT antibodies, potentially in combination with other immune checkpoint inhibitors (e.g., anti-PD-1/PD-L1), in lung cancer models to assess their ability to reverse T cell exhaustion, enhance T cell activation, and improve anti-tumor efficacy.

Follow-up validation ideas:

  1. Perform multi-parameter flow cytometry or immunostaining on larger cohorts of normal, early, and advanced lung cancer patient samples to validate the observed shifts in T cell (Treg, NK, Th9, T_Naive, OX40, GITR, TIGIT) and macrophage (M2A, M2B, M2C, GPR183, CD84, FCGR2B) subset proportions and surface marker expression.
  2. Utilize spatial transcriptomics or multiplex immunohistochemistry (IHC) to map the precise localization and physical interactions of key cell types (e.g., aneuploid Lung Epithelial cells, M2B macrophages, FAP+ fibroblasts, TIGIT+ T cells) and their ligand-receptor pairs (e.g., PGE2, integrins, EGFR ligands) within the tumor tissue architecture.
  3. Conduct in vitro co-culture experiments or patient-derived organoid models to functionally validate the identified cell-cell interactions. For example, perturbing PGE2-PTGER signaling or integrin-ECM interactions to assess their impact on tumor cell proliferation, invasion, and immune cell function.
  4. Employ in vivo preclinical models (e.g., genetically engineered mouse models or xenografts) to test the efficacy of targeting specific therapeutic candidates (e.g., FAP inhibitors, anti-TIGIT antibodies, EGFR inhibitors) on tumor growth, metastasis, and TME remodeling.
  5. Perform targeted CRISPR/Cas9 screens or RNA interference in lung cancer cell lines and primary TME cells to investigate the functional consequences of altering expression of key genes (e.g., MET, ERBB2, CEACAM6, FAP, TIGIT, SPP1) on tumor cell behavior and immune cell response.

Limitations:

This report is based on single-cell RNA sequencing data, providing correlative insights rather than definitive causal relationships, which would require functional perturbation experiments. The 'unassigned' cell populations in advanced tumors, while strongly suspected to be malignant based on ploidy and CNV, require further specific annotation. CNV estimates are inferred from gene expression, not direct genomic sequencing. The conclusions regarding therapeutic targets are based on observed molecular patterns and known biology, necessitating rigorous preclinical and clinical validation. The analysis focuses on specific cell types and pathways, and other important cellular interactions or molecular mechanisms may also contribute to lung cancer progression.

24. Query List

  1. Show UMAPs including condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset in 2 columns and save them.
  2. Show expression of CD3D CD4 CD8A CD79A MS4A1 MZB1 CD14 LYZ FBLN1 NOTCH3 EPCAM MUC1 CD34 genes on UMAPs along with minor cell type annotation. Set ncols=4 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. Select tumor-origin cells and unassigned cells, group them by sample, show a CNV heatmap, and include a summary of significantly amplified copy number regions, then save it.
  5. Show CNV patterns as UMAPs. Include major cell type, minor cell type, ploidy results, condition, and sample in 2 columns and save 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 a subset population bar plot for Macrophage and save it.
  9. For T cell subset population, if there are statistically significant differences between conditions, show them as boxplots and save them. Determine ncols appropriately based on the total number of panels.
  10. For Macrophage subset population, if there are statistically significant differences between conditions, show them as boxplots and save them. Determine ncols appropriately based on the total number of panels.
  11. Select tumor-origin cells and unassigned cells, show their ploidy population as a bar plot, and save it.
  12. Show cell-cell interaction patterns by condition, including Lung Epithelial cells (tumor origin), Fibroblast, Macrophage, and T cells, and save them. 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 only genes related to immune checkpoint and cell cycle pathways, show cell-cell interactions for these genes, 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, and save them. Set max_n_items_per_group = 25.
  16. Show the condition-specific markers for tumor-origin cells (Lung 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, and save them. Limit to surfaceome markers, max 50 per condition.
  18. Extract condition-specific markers for Fibroblast, show them as a dot plot, and save them. Limit to surfaceome markers, max 50 per condition.
  19. Extract condition-specific markers for CD4 T cells, show them as a dot plot, and save them. Limit to surfaceome markers, max 50 per condition.
  20. For key disease-related cells (Lung Epithelial cell, Macrophage, T cell CD4+, Fibroblast), select cell cycle pathway-related genes with statistically significant expression differences between conditions, show them as boxplots, and save them. Set max_n_items_to_plot = 24, and determine ncols such that the width-to-height ratio is about 2x3.
  21. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
  22. Show Gene set enrichment analysis results as a dot plot for B cell, Dendritic cell, Endothelial cell, Fibroblast, ILC, Lung Epithelial cell, Macrophage, Mast cell, NK cell, T cell CD4+, T cell CD8+ cell types and save them. Use the RdBu_r color map and set n_pws_to_show = 80.
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