SCODiA Report by MLBI Lab

Single-Cell Atlas of Renal Cell Carcinoma Reveals Genomic Instability, TME Remodeling, and Therapeutic Targets

This report presents a comprehensive single-cell analysis of human kidney tissue, revealing profound differences between renal cell carcinoma and adjacent normal tissue. Key findings include the identification of aneuploid renal epithelial cells as the malignant core of tumors, characterized by distinct genomic alterations and active pro-tumorigenic pathways. The tumor microenvironment exhibits significant remodeling, marked by altered immune cell infiltration, macrophage reprogramming towards immunosuppressive phenotypes, and an intricate network of cell-cell interactions driving angiogenesis and immune evasion. These insights highlight critical aspects of tumor biology and offer potential avenues for therapeutic intervention in kidney cancer.

Contents

0. Dataset overview
1. UMAP Visualization of Kidney scRNA-seq Data by Condition, Sample, Cell Type, and Ploidy
2. Major Cell Type Score and Annotation on UMAP
3. Celltype Subtype Marker Expression Dot Plot Analysis
4. Copy Number Variation (CNV) Landscape of Renal Epithelial and Unassigned Cells in Kidney Tissue
5. CNV Pattern Visualization Across Cell Types, Ploidy, Conditions, and Samples
6. Minor Cell Type Population Analysis in Kidney Tumor vs. Adjacent Normal Tissue
7. T Cell Subset Population Analysis in Kidney Tumor vs. Adjacent Normal Tissue
8. T cell Subset Population Differences in Kidney Tumor Microenvironment
9. Macrophage Population Subset Verification in Kidney Tissue
10. Macrophage (M2A) Subset Abundance Differs Significantly Between Kidney Tumor and Adjacent Normal Tissue
11. Ploidy Population Analysis of Tumor-Origin and Unassigned Cells in Kidney Cancer
12. Kidney Tumor Cell-Cell Interaction Analysis: Macrophage and T Cell Dynamics
13. Cell-Cell Interaction Analysis in Kidney Tumor vs. Adjacent Normal Tissues
14. Condition-Specific Cell-Cell Interaction Patterns in Kidney Cancer
15. Renal Epithelial Cell Condition-Specific Surfaceome Markers in Kidney Tissue
16. Macrophage Condition-Specific Surfaceome Markers in Kidney Tumor vs. Adjacent Normal Tissue
17. Condition-Specific Surface Markers for CD4+ T cells in Renal Tissue
18. 신장 상피세포의 유전자 온톨로지(GSA) 분석 결과
19. Gene Set Enrichment Analysis Reveals Pathway Dysregulation Across Kidney Tumor Microenvironment
20. Discussion
21. Query List

0. Dataset overview

데이터셋 요약

• 이 데이터셋은 49645개 세포와 19593개 유전자를 포함하는 단일 세포 RNA-seq AnnData 객체입니다.
• 인간 신장(Kidney) 조직에서 유래했으며, 'tumor'와 'adjacent_normal' 두 가지 조건으로 구성되어 있습니다.
• 주요 관측 컬럼으로는 library, sample, patient, condition, cell_type, 그리고 celltype_major, celltype_minor, celltype_subset과 같은 계층적 세포 타입 정보가 있습니다.
• 세포의 이수성(ploidy) 정보 (ploidy_dec: Aneuploid, Diploid)와 CNV(Copy Number Variation) 추정치 (X_cnv)가 포함되어 있습니다.
• 종양 유래 세포 타입은 'Renal Epithelial cell'입니다.
• 사전 계산된 주요 결과로는 조건 및 샘플별 세포-세포 상호작용 (CCI), 각 celltype_minor에 대한 차등 발현 유전자 (DEG), 유전자 세트 농축 분석 (GSEA), 그리고 유전자 온톨로지 (GSA/GO) 결과가 포함되어 있습니다.

1. UMAP Visualization of Kidney scRNA-seq Data by Condition, Sample, Cell Type, and Ploidy

[Analysis Visualization Results]...

Analysis Overview

This analysis presents six UMAP (Uniform Manifold Approximation and Projection) plots, visualizing the single-cell RNA sequencing data colored by key metadata features: condition (tumor vs. adjacent_normal), sample, celltype_major, celltype_minor, ploidy_dec (aneuploidy status), and celltype_subset. These visualizations provide an essential overview of the dataset's structure, cell type composition, sample integration, and condition-specific patterns, particularly in the context of kidney tissue and tumor.

Visual Summary

Condition and Sample Distribution

• Condition: The UMAP clearly distinguishes cells based on their condition. The 'tumor' cells (purple) and 'adjacent_normal' cells (maroon) primarily occupy distinct regions within the embedding, indicating significant transcriptional differences between these two conditions. Notably, there are some mixed regions, suggesting shared cell populations or transitional states. The top-right large cluster appears to be predominantly composed of 'tumor' cells.
• Sample: The sample UMAP displays a well-integrated dataset across different samples (N1-N9 for normal, T1-T9 for tumor). Cells from various samples are broadly distributed across the UMAP, forming a mosaic pattern rather than clustering strongly by individual sample. This suggests that technical batch effects between samples are not the primary driver of the overall UMAP structure, allowing for biologically meaningful cell type and condition separation.

Cell Type Hierarchy and Annotation Quality

• Cell Type Major, Minor, and Subset: The three UMAPs illustrating cell type annotations at different hierarchical levels (celltype_major, celltype_minor, celltype_subset) demonstrate a high resolution and consistency in cell type identification.
• Major Cell Types: Broad categories like 'Renal Epithelial cell', 'T cell', 'Myeloid cell', 'Endothelial cell', and 'Stromal cell' form distinct, well-separated clusters. This indicates robust identification of major cell lineages.
• Minor Cell Types: These plots further resolve the major clusters into more specific cell populations, such as 'Proximal Tubule', 'Collecting Duct Principal cell', 'Macrophage', 'T cell CD8+', and 'T cell CD4+', which maintain spatial coherence within the UMAP.
• Cell Type Subset: This level provides the most granular view, breaking down minor cell types into even finer subsets (e.g., 'Macrophage (M1)', 'Macrophage (M2A)', 'T cell (Cytotoxic)', 'T cell (Treg)', 'Proximal Convoluted Tubule S1_S2'). The consistent clustering and clear boundaries at this fine-grained level suggest high confidence in the cell type annotations and significant biological heterogeneity within the dataset. The presence of specific kidney epithelial cell subsets (e.g., PCT_S1S2, PCT_S3, PST_S3, DCT, TAL, CD-PC, IC, Podocyte) is particularly valuable for understanding kidney tissue architecture.
• Unassigned Cells: A small proportion of 'unassigned' cells are present but do not form a distinct large cluster, suggesting that most cells have been successfully classified.

Ploidy Status

• Ploidy Dec: The ploidy_dec UMAP shows a striking pattern. A distinct cluster of 'Aneuploid' cells (maroon) is highly concentrated in a specific region of the UMAP, which strongly overlaps with the predominant 'tumor' cell region and, very likely, the 'Renal Epithelial cell' cluster. The 'Diploid' cells (yellow) are widely distributed across the remaining UMAP space, encompassing most normal cell types. A small number of 'Unclear' cells (purple) are also visible.

Biological Interpretation

1. Tumor Microenvironment Heterogeneity: The distinct segregation of 'tumor' and 'adjacent_normal' conditions highlights the profound changes in cellular composition and gene expression profiles associated with renal cell carcinoma. The presence of mixed regions suggests the infiltration of immune or stromal cells into both conditions, or transitional states of epithelial cells.
2. Robust Cell Type Annotation: The hierarchical clustering from major to minor to subset cell types, with distinct and coherent clusters, indicates a high quality of cell type annotation for human kidney tissue. The identification of various renal epithelial subtypes (e.g., Proximal Tubule, Collecting Duct, Podocyte) is crucial for understanding kidney physiology and pathology, especially in the context of renal cell carcinoma which typically originates from renal tubular epithelial cells.
3. Cancer-Specific Ploidy and Cell Identity: The strong co-localization of 'Aneuploid' cells with the 'tumor' condition and likely the 'Renal Epithelial cell' cluster (the Tumor origin celltype is noted as Renal Epithelial cell in the DATA CONTEXT) is a critical finding. Aneuploidy, an abnormal number of chromosomes, is a hallmark of cancer cells and provides strong evidence for the malignant nature of these specific cell populations. This observation corroborates the distinction between tumor and normal tissue at a genomic level, further validating the tumor cell identification. PubMed Search: Aneuploidy in renal cell carcinoma
4. Immune Cell Infiltration: The presence of various T cell, Myeloid cell (Macrophage, Dendritic cell), B cell, ILC, and Mast cell populations in both tumor and normal regions implies an active immune microenvironment. The specific distribution of these immune cell subsets across the condition UMAPs would provide insights into the immune response in renal cancer and the composition of the tumor microenvironment (TME).
5. Stromal Contributions: Endothelial cells and Stromal cells (Fibroblasts, Smooth muscle cells) are also well-represented, indicating their significant roles in both normal kidney function and the tumor microenvironment, contributing to angiogenesis, extracellular matrix remodeling, and tumor progression.

Annotation Notes

The UMAPs collectively confirm the high quality and detailed nature of the cell annotations. The clear separation of conditions and cell types, coupled with good sample integration, suggests that the downstream differential gene expression, GSEA, and CCI analyses can be performed with confidence in the underlying cellular identities. The ploidy_dec annotation provides a strong biological anchor for identifying malignant cells, which is consistent with the Renal Epithelial cell origin of the tumor. The low number of 'unassigned' cells further supports the comprehensiveness of the current annotation scheme.

2. Major Cell Type Score and Annotation on UMAP

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the distribution of major cell type scores and their corresponding discrete annotations on a Uniform Manifold Approximation and Projection (UMAP) plot. Additionally, the ploidy inference for each cell (ploidy_dec) is displayed. The purpose is to assess the quality of cell type assignments, the distinctness of different cell populations in the gene expression space, and the distribution of aneuploid cells, which are critical for understanding tumor biology in the context of renal cancer.

Visual Summary

The UMAP visualizations reveal a well-structured embedding where most major cell types form distinct clusters, indicating robust separation based on their gene expression profiles.

Cell Type Score Distribution:

• T cells show high scores primarily in a well-defined cluster located in the upper-left region of the UMAP.
• B cells show high scores in a smaller, distinct cluster in the mid-left area.
• Myeloid cells are localized to a large, prominent cluster in the central-left region.
• Mast cells exhibit relatively lower overall scores and a more diffuse, less concentrated high-score region, suggesting they might be less abundant or transcriptionally more heterogeneous at this major cell type resolution.
• Endothelial cells form a clear, high-scoring cluster at the bottom of the UMAP.
• Stromal cells are characterized by high scores in a distinct cluster in the lower-right quadrant.
• Renal Epithelial cells display high scores predominantly in a large, somewhat spread-out cluster on the right side of the UMAP, showing a gradient of scores.

Ploidy Distribution (ploidy_dec):

• Aneuploid cells (maroon) are strikingly concentrated in the rightmost regions of the UMAP. This area largely overlaps with the clusters identified as having high Renal Epithelial cell scores.
• Diploid cells (yellow) are widely distributed across all other major cell type clusters, including immune cells, endothelial cells, and stromal cells.

Discrete Cell Type Annotation (celltype_major):

• The discrete celltype_major assignments (bottom-right panel) demonstrate excellent concordance with the regions showing high scores for each respective cell type. For example, the cluster annotated as "T cell" (teal) aligns perfectly with the region showing high "HiCAT_major_score: T cell".
• "Unassigned" cells (purple) are scattered sparsely, not forming a large distinct cluster, which suggests that most cells have been confidently assigned to a major cell type.

Biological Interpretation

1. Robust Cell Type Identification: The strong correspondence between the continuous HiCAT_major_score for each cell type and the discrete celltype_major labels confirms the high quality and confidence of the cell type annotations. This robust annotation is crucial for downstream analyses, ensuring that cell type-specific insights are based on accurately identified populations.
2. Tumor Cell Identification: The Renal Epithelial cell population is explicitly stated as the Tumor origin celltype. The UMAP shows a clear spatial overlap between the high Renal Epithelial cell scores/clusters and the Aneuploid cells. This strongly suggests that the tool has effectively identified the malignant (aneuploid) renal epithelial cells, which form the core of the tumor, and distinguished them from diploid non-malignant cells. Aneuploidy is a hallmark of cancer, indicating chromosomal instability and abnormal chromosome numbers [PMID: 29061803].
3. Tumor Microenvironment Composition: The UMAP clearly separates immune cells (T, B, Myeloid, Mast cells), stromal cells, and endothelial cells from the renal epithelial cells. This distinct clustering highlights the diverse cellular composition of the kidney tissue, including the tumor microenvironment. The presence and distinct clustering of various immune cell types are particularly important for understanding immune responses within the tumor.
4. Mast Cell Considerations: The relatively lower and more diffuse scores for Mast cells, compared to other major cell types, could indicate either a lower abundance of these cells in the dataset or a more heterogeneous transcriptional profile that makes them less distinct at this level of resolution.

Clinical or Translational Implications

The clear and confident identification of major cell types, particularly the malignant (aneuploid) renal epithelial cells, provides a strong foundation for future translational research. Accurately delineating tumor cells from the surrounding healthy tissue and immune infiltrate is essential for:

• Biomarker Discovery: Identifying molecular markers specific to tumor cells or critical stromal/immune components within the tumor microenvironment.
• Targeted Therapy Development: Understanding the specific cell types driving tumor progression or modulating immune responses can inform the development of more precise therapeutic strategies.
• Prognostic Assessment: The composition and state of different cell types could serve as prognostic indicators for disease progression and treatment response.

This initial robust cell type annotation and ploidy assessment are fundamental steps for deeper investigations into the cellular and molecular mechanisms of kidney cancer.

3. Celltype Subtype Marker Expression Dot Plot Analysis

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a marker expression dot plot, visualizing the expression of identified marker genes across various celltype_subset categories from the single-cell RNA-seq data. The primary goal of this visualization is to assess the quality and specificity of existing cell type annotations by examining whether each celltype_subset displays distinct and biologically relevant marker gene expression patterns. The plot depicts both the fraction of cells expressing a marker within each group (dot size) and the mean expression level of that marker (dot color intensity).

Visual Summary

The dot plot clearly illustrates a block-diagonal pattern, where distinct groups of marker genes exhibit high and specific expression within corresponding celltype_subset clusters.

• Marker Specificity: The vast majority of dots (representing marker expression) are concentrated along the diagonal, forming distinct red-bordered blocks. This indicates that the selected marker genes are highly specific to particular celltype_subset categories.
• Expression Level and Prevalence: Within these blocks, the dots are generally large and dark red, signifying both a high fraction of cells expressing the marker and a high mean expression level in the respective cell type.
• Cell Type Diversity: A broad range of celltype_subset categories, including various B cell, myeloid, T cell, endothelial, stromal, and renal epithelial cell populations, are represented on the y-axis.
• Cell Count: The bar plot on the right displays the total number of cells contributing to each celltype_subset, indicating sufficient cellular representation for robust marker analysis in most groups.

Biological Interpretation

The observed marker expression patterns strongly support the assigned celltype_subset annotations and confirm the presence of distinct cell populations within the Kidney tissue.

Immune Cell Populations

• B cells: Subsets like B cell (Breg) and B cell (Follicular) express canonical B cell markers such as POU2F2 and CD22, confirming their lineage.

Myeloid cells:

• Macrophage subsets (M1, M2A, M2B, M2C, M2D) display a core set of macrophage markers like CD68, MSR1, and SPP1, with some variations that might reflect their functional polarization (e.g., CD80/CD86 for M1/M2B).
• DC (Classical) cells are marked by specific dendritic cell markers such as CLEC9A and XCR1, characteristic of conventional type 1 dendritic cells (cDC1s).
• Mast cell identity is strongly supported by expression of KIT (CD117) and TPSAB1 (tryptase).

T cells:

• T cell (Cytotoxic) populations robustly express cytotoxic markers like CD8A, CD8B, and GZMB.
• T cell (Treg) cells are clearly identified by FOXP3 and IL2RA, essential for their regulatory function.
• Other T cell subsets (e.g., Th1, Th2, Th17) show expression of key lineage-defining transcription factors and surface markers such as TBX21, GATA3, and RORC, respectively, although some markers might overlap with other T cell or ILC subsets.
• ILCs and NK cells: NK cell populations are characterized by KLRD1 and NKG7. ILC1, ILC2, and ILC3 subsets show expression of their respective key transcription factors like TBX21, GATA3, and RORC. LTI cells, a subset of ILC3s, show TNFRSF14 expression.
• Plasma cells: Plasma cell annotations are validated by the expression of MZB1 and XBP1, consistent with their role in antibody production.

Stromal and Endothelial Cells

• Endothelial cells: Endothelial tip cell annotations are well-supported by markers such as DLL4 and ESM1, indicating active endothelial populations.
• Fibroblasts: Fibroblast populations demonstrate high expression of extracellular matrix components and stromal markers including COL1A1, COL1A2, COL3A1, DCN (decorin), and LUM (lumican).
• Smooth muscle cells: Smooth muscle cell identity is strongly confirmed by the expression of contractile proteins such as ACTA2 (alpha-SMA), MYH11, and TAGLN.

Renal Epithelial Cells

• Collecting Duct Principal cells: These cells show high expression of AQP2, SCNN1A, SCNN1B, and RHCG, which are critical for water and ion transport in the collecting duct.
• Intercalated cells: Markers like ATP6V0D2 and ATP6V1G3 (H+-ATPase subunits) confirm their role in acid-base regulation.
• Podocytes: NPHS1 (nephrin), MAFB, and WT1 are robustly expressed, validating these specialized glomerular cells.
• Proximal Tubule segments: Various Proximal Convoluted Tubule (S1_S2, S3) and Proximal Straight Tubule S3 segments express markers like SLC5A10, SLC5A3, EGF, and MFSD4A, reflecting their specific functions in reabsorption.
• Thick Ascending Limb cells: These are clearly identified by UMOD (uromodulin), SLC12A1 (NKCC2), and CLCNKA, CLCNKB (chloride channels), all critical for loop of Henle function.

Annotation Notes

The comprehensive display of highly specific marker gene expression across numerous celltype_subset categories provides strong evidence for the accuracy and robustness of the cell type annotations. The distinct clusters of markers, highlighted by the red boxes, serve as clear signatures for each annotated cell type, reaffirming their biological identities within the kidney tissue. Some broad lineage markers, such as CD44 and SPP1, show expression in multiple related cell types (e.g., various immune cells or macrophage subsets), which is biologically expected and does not detract from the overall specificity for annotation purposes. The removal of markers expressed in three or more groups helped to focus on highly specific markers, enhancing the clarity of the annotations.

4. Copy Number Variation (CNV) Landscape of Renal Epithelial and Unassigned Cells in Kidney Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes the plot_cnv_heatmap tool to visualize and summarize copy number variations (CNVs) within Renal Epithelial cell (identified as the tumor-origin cell type) and unassigned cell populations. These cells are grouped by sample, and the results are further stratified by inferred ploidy status (Diploid or Aneuploid) and condition (normal or tumor). The primary goal is to identify common chromosomal amplifications and deletions, providing insights into the genomic instability characteristic of renal tumors.

Visual Summary

CNV Heatmap (log2(CNR))

The heatmap displays the estimated log2 ratio of copy number (log2(CNR)) across genomic spots for the selected cell types, with red indicating gene amplification and blue indicating gene deletion.

• Stratification by Ploidy and Condition: The heatmap effectively separates samples into "Diploid N" (adjacent normal tissue samples with diploid ploidy), "Diploid T" (tumor tissue samples with diploid ploidy), and "Aneuploid T" (tumor tissue samples with aneuploid ploidy).
• Normal vs. Tumor Patterns: "Diploid N" samples generally exhibit a stable genomic landscape with minimal and sporadic CNVs, as expected for normal tissue. In contrast, "Aneuploid T" samples display widespread and pronounced CNVs, characterized by numerous large red (amplification) and blue (deletion) segments across various chromosomes. This extensive chromosomal instability is a hallmark of advanced tumor development. "Diploid T" samples show an intermediate level of CNVs, often with focal amplifications, which could represent early-stage tumor evolution or less genomically unstable tumor clones.

Key Visual Observations

• Chromosome 3 Deletions: Several "Aneuploid T" samples (e.g., T6, T8, T9) show prominent blue bands on chromosome 3p, indicating significant deletions.
• Recurrent Amplifications: Widespread amplifications (red bands) are visible on chromosomes 5q, 7q, 11q, 12q, 16q, 17q, and 20q in multiple "Aneuploid T" samples. These patterns are highly characteristic of renal cell carcinoma.
• Sample-Specific Patterns: While common patterns exist, there is also notable heterogeneity in the exact CNV profiles across individual tumor samples, reflecting inter-patient variability.

CNV Summary Plot (Significantly Amplified Regions)

This plot provides a quantitative summary of frequently amplified regions.

• Frequency Across Samples: The left heatmap shows the frequency of significant amplifications (darker blue indicating higher frequency) in specific cytogenetic bands for a subset of samples (N1, N4-N9, T5, T7, T9). This highlights that some amplifications are not exclusive to tumor samples, potentially indicating common variations or early events in adjacent normal tissue.
• Overall Amplification Frequency: The right bar chart summarizes the overall frequency of significant amplifications for each cytogenetic band across all analyzed samples. The most frequently amplified regions include:

11q12.2:11q13.1 (overall frequency ~0.7)

5q23.2:5q31.3 (overall frequency ~0.6)

11q23.1:11q23.3 (overall frequency ~0.5)

12q13.13:12q13.3 (overall frequency ~0.5)

16q12.2:16q22.1 (overall frequency ~0.4)

1q21.3:1q22 (overall frequency ~0.2)

Biological Interpretation

• Tumor-Specific Genomic Alterations: The CNV patterns observed in Renal Epithelial cell and unassigned populations strongly delineate tumor from normal tissues, particularly evident in the Aneuploid T samples. The extensive chromosomal instability, including specific recurrent gains and losses, is highly indicative of clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer.

Hallmark CNVs in ccRCC

• Loss of Chromosome 3p: The prominent deletion on chromosome 3p in several Aneuploid T samples is a canonical event in ccRCC, almost universally leading to the inactivation of the *VHL* tumor suppressor gene. https://www.genecards.org/cgi-bin/carddisp.pl?gene=VHL
• Recurrent Gains: The consistently observed amplifications on 5q, 7, 11q, 12q, 16q, 17, and 20q are frequently reported in ccRCC. These regions often harbor oncogenes or genes whose increased copy number contributes to tumor development and progression. For instance, gains on 7q can involve the *MET* oncogene, and gains on 12q can involve *MDM2*. https://www.genecards.org/cgi-bin/carddisp.pl?gene=MET
• Ploidy Status and Tumor Progression: The stark differences in CNV burden between "Diploid" and "Aneuploid" tumor samples underscore the biological significance of ploidy inference. Aneuploid T samples likely represent more advanced or aggressive tumors characterized by greater genomic instability. The "Diploid T" samples may represent earlier stages of tumor development or less aggressive clonal populations within the tumor.
• Role of Unassigned Cells: Since Renal Epithelial cell is the defined tumor-origin cell type, the analysis provides direct insight into tumor genomics. The inclusion of unassigned cells allows for a broader assessment; if these cells exhibit similar tumor-specific CNV patterns, it suggests they are likely unannotated tumor cells rather than benign or stromal cells.

Clinical or Translational Implications

• Biomarker Potential: The identified recurrent CNV patterns, particularly the loss of 3p and gains on 5q, 7, 11q, 12q, 16q, 17, and 20q, hold significant potential as diagnostic and prognostic biomarkers for renal cell carcinoma. The extent of aneuploidy, as revealed by widespread CNVs, could be correlated with tumor aggressiveness and patient outcomes.
• Therapeutic Target Identification: Genes residing within the frequently amplified regions could represent novel therapeutic targets. For example, oncogenes within the amplified regions (e.g., *MET* on 7q) could be explored for targeted drug development, although further functional validation would be necessary.
• Understanding Tumor Heterogeneity: The observed variability in CNV profiles across different tumor samples and ploidy states highlights the genomic heterogeneity within kidney cancer. This understanding is crucial for developing personalized treatment strategies, as different CNV profiles may predict differential responses to therapy.

5. CNV Pattern Visualization Across Cell Types, Ploidy, Conditions, and Samples

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes UMAP projections derived from Copy Number Variation (CNV) estimates to visualize the genomic landscape of single cells from kidney tissue, colored by major cell type, minor cell type, ploidy status, tissue condition (tumor vs. adjacent normal), and individual sample. The primary goal is to assess how these biological and technical annotations map onto the CNV-driven embedding space, particularly focusing on the segregation of malignant versus non-malignant cells.

Visual Summary

The UMAP plots clearly reveal distinct clusters formed based on CNV patterns.

• celltype_major and celltype_minor: A prominent, distinct cluster appears on the bottom-right of the UMAP, predominantly composed of "Renal Epithelial cell" (major type) and its subtypes such as "Proximal Tubule", "Distal Tubule", "Collecting Duct Principal cell", and "Thick Ascending Limb" (minor types). The remaining cell types (e.g., T cells, Myeloid cells, Endothelial cells, Stromal cells) are largely mixed in a broader, more diffuse region spanning the top and left parts of the UMAP.
• ploidy_dec: The distinct bottom-right cluster is almost exclusively labeled "Aneuploid" (dark red), while the large, diffuse region is overwhelmingly "Diploid" (yellow). This demonstrates a strong segregation based on inferred ploidy status.
• condition: The "Aneuploid" cluster maps directly to cells from the "tumor" condition (dark purple/blue). The "Diploid" cluster consists of cells from both "adjacent_normal" (dark red) and "tumor" conditions, indicating that "tumor" samples contain a significant proportion of diploid cells, likely representing tumor-infiltrating immune and stromal cells, or normal kidney cells captured within the tumor biopsy. Conversely, "adjacent_normal" cells are almost exclusively found within the diploid population.
• sample: The aneuploid population shows contributions from multiple "T" samples (T1-T9), indicating that this CNV-driven separation is consistent across different tumor individuals. The diploid population contains cells from both "N" (N1-N9) and "T" (T1-T9) samples, as expected.

Biological Interpretation

The UMAP analysis based on CNV estimates effectively segregates cells into distinct populations, primarily driven by ploidy status.

• Identification of Malignant Cells: The clear separation of an "Aneuploid" cell cluster, predominantly composed of "Renal Epithelial cells" (the tumor origin cell type), and originating almost exclusively from "tumor" conditions, strongly indicates that these cells represent the malignant cancer cell population. Aneuploidy, characterized by an abnormal number of chromosomes, is a well-established hallmark of cancer cells PubMed Search: "cancer aneuploidy definition".
• Distinction from Non-Malignant Cells: The large, diffuse "Diploid" cluster encompasses immune cells (T cells, B cells, Myeloid cells), endothelial cells, and stromal cells, as well as normal renal epithelial cells (from adjacent normal tissue or benign regions within tumors). This suggests that the CNV-based UMAP effectively differentiates between cells with significant chromosomal aberrations (malignant) and those with normal ploidy (non-malignant).
• Tumor Microenvironment Composition: The presence of both "tumor" and "adjacent_normal" cells within the "Diploid" cluster from the condition UMAP highlights the cellular heterogeneity of the tumor microenvironment, which includes infiltrating immune cells, stromal cells, and residual normal tissue, all typically diploid.

Clinical or Translational Implications

• Tumor Cell Identification: This CNV-based cellular classification provides a robust method to confidently identify and isolate true malignant cell populations within complex single-cell datasets, even from biopsies that include significant normal tissue contamination. This is critical for downstream analyses aimed at understanding tumor-specific biology and therapeutic vulnerabilities.
• Heterogeneity Assessment: The visualization helps to appreciate the genomic heterogeneity within tumor samples, distinguishing between cancer cells with gross chromosomal abnormalities and the surrounding or infiltrating normal cellular components.
• Quality Control and Annotation Refinement: The strong congruence between CNV-inferred ploidy, cell type of origin, and tissue condition serves as a powerful validation of cell type annotations and provides confidence in the distinction between malignant and non-malignant cells for subsequent analyses.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of different minor cell types across individual samples from both "adjacent_normal" kidney tissue and "tumor" kidney tissue. Each bar represents a single sample, and the stacked segments within the bar show the percentage contribution of each identified minor cell type. This visualization provides an overview of the cellular landscape shifts occurring in the tumor microenvironment compared to healthy kidney tissue.

Visual Summary

The stacked bar plots effectively demonstrate distinct differences in cell type composition between adjacent normal and tumor kidney samples.

• Adjacent Normal Tissue (Left Panel): Samples from adjacent normal tissue (N8, N9, N2, N4, N6, N5, N1, N7, N3) consistently show a high proportion of Proximal Tubule cells (light green), indicating the healthy epithelial architecture of the kidney. Other significant components include Distal Tubule (orange), Endothelial cell (light orange), and Macrophage (yellow). Immune cells like T cells (CD4+ and CD8+) are present but constitute a smaller fraction. The overall composition is relatively consistent across normal samples.
• Tumor Tissue (Right Panel): Tumor samples (T8, T9, T7, T2, T5, T4, T3, T6) exhibit a dramatic shift in cellular proportions:
• Profound reduction or near absence of Proximal Tubule cells: This is the most striking observation, with the light green segment being significantly diminished or completely missing in most tumor samples, replaced by other cell types.
• Increased Immune Infiltration: There is a clear increase in immune cell populations within the tumor microenvironment. Specifically, T cell CD8+ (teal) and T cell CD4+ (light teal) show a noticeable increase in proportion across multiple tumor samples. Macrophages (yellow) also appear expanded in many tumor samples compared to their proportions in adjacent normal tissue. Other immune cells like B cells, Plasma cells, Dendritic cells, and ILCs are also present and show variable proportions.
• Changes in Stromal Components: Endothelial cells (light orange) maintain a presence and appear relatively increased in some tumor samples (e.g., T8, T9, T7, T2), potentially indicative of angiogenesis. Fibroblasts (medium orange) are also variably present and seem slightly elevated in certain tumors (e.g., T6).
• Loss of Specialized Renal Epithelial Cells: Beyond Proximal Tubule cells, other normal renal epithelial cell types such as Distal Tubule, Thick Ascending Limb, Collecting Duct Principal cell, Intercalated cell, and Podocyte are largely reduced or absent in tumor samples.
• "Unassigned" Cells: A notable proportion of "unassigned" cells (dark blue) appears at the top of some tumor bars (e.g., T3, T4, T5, T6), which could represent highly aberrant tumor cells, novel cell states, or artifacts.

Biological Interpretation

The observed shifts in cell type populations provide critical biological insights into kidney tumor development and its interaction with the surrounding microenvironment.

1. Tumor-induced architectural disruption and cell displacement: The dramatic reduction of normal renal epithelial cells, particularly Proximal Tubule cells, in tumor samples is consistent with the Renal Epithelial cell being identified as the Tumor origin celltype. Malignant transformation and subsequent proliferation of these cells, often accompanied by aneuploidy (ploidy_dec: Aneuploid), would lead to the physical displacement and destruction of the normal kidney parenchyma. The loss of other specialized renal epithelial cells like Distal Tubule, Thick Ascending Limb, Collecting Duct Principal cell, Intercalated cell, and Podocyte further underscores this tissue remodeling process.
2. Immune Remodeling of the Tumor Microenvironment (TME): The significant increase in T cell CD4+ and T cell CD8+ populations, along with Macrophages, highlights a robust immune infiltration within the kidney tumors.

• T cells: An influx of CD8+ T cells often indicates an anti-tumor immune response, although their functional state (e.g., exhausted) needs further investigation. CD4+ T cells play diverse roles, including helper functions (Th1, Th2, Th17, Treg) that can either promote or suppress anti-tumor immunity.
• Macrophages: Tumor-associated macrophages (TAMs) are highly plastic and can adopt pro-tumor (e.g., M2-like, associated with immune suppression, angiogenesis, and matrix remodeling) or anti-tumor (e.g., M1-like) phenotypes. Their increased presence suggests active immune engagement, whose net effect requires deeper functional analysis (e.g., using celltype_subset annotations or specific marker expression).
• Other immune cells like B cells, plasma cells, and NK cells also contribute to the complex immune landscape, suggesting a multifaceted immune response.

3. Stromal Reprogramming: The presence and potential increase of Endothelial cells and Fibroblasts in tumors are indicative of angiogenesis (new blood vessel formation to supply the tumor) and desmoplasia (fibrotic reaction), respectively. These stromal components are critical for tumor growth, invasion, and metastasis, and they also contribute to immune evasion by forming physical barriers and secreting immunosuppressive factors https://pubmed.ncbi.nlm.nih.gov/30361596/.
4. Heterogeneity within Tumor Samples: The varying proportions of immune and stromal cells across different tumor samples (T8 vs. T3, for example) suggest inter-patient heterogeneity in the tumor microenvironment, which can influence disease progression and treatment response.

Clinical or Translational Implications

The distinct cellular landscape observed in kidney tumors compared to adjacent normal tissue has several clinical and translational implications:

1. Biomarker Discovery: The profound reduction of specific normal renal epithelial cell types (e.g., Proximal Tubule cells) could serve as a pathological hallmark of tumor presence. Conversely, the increased infiltration of specific immune cell subsets (e.g., CD8+ T cells, Macrophages) could be explored as potential prognostic biomarkers for kidney cancer progression or response to therapy https://pubmed.ncbi.nlm.nih.gov/31346083/.
2. Therapeutic Targeting: The observed immune infiltration points to the potential applicability of immunotherapies, such as immune checkpoint inhibitors, in kidney cancer. Understanding the precise phenotypes and functional states of these infiltrating immune cells (e.g., effector vs. exhausted T cells, M1 vs. M2 macrophages) is crucial for predicting response and developing combination therapies https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399225/. Targeting the pro-tumorigenic roles of stromal cells, such as angiogenesis (Endothelial cells) or fibrosis (Fibroblasts), could also be explored as complementary therapeutic strategies.
3. Diagnostic Insight: The "unassigned" cell population in some tumor samples warrants further investigation. These cells might represent highly dedifferentiated tumor cells, novel tumor-associated cell states, or cells that defy current annotation schemes. Characterizing these cells could lead to the identification of novel diagnostic markers or therapeutic targets.

This population analysis serves as a foundational step, highlighting significant compositional changes that warrant deeper functional investigation through differential gene expression (DEG), gene set enrichment (GSEA), and cell-cell interaction (CCI) analyses for specific cell types within this dataset.

7. T Cell Subset Population Analysis in Kidney Tumor vs. Adjacent Normal Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a stacked bar plot visualizing the relative proportions of T cell major subsets and related innate lymphoid cells (ILCs, NK cells) across individual samples, distinguishing between adjacent_normal kidney tissue and tumor tissue samples. The celltype_major category "T cell" was selected, and its celltype_minor populations (T cell CD8+, T cell CD4+, ILC, NK cell, unassigned) are displayed to provide a more granular view of immune cell composition.

Visual Summary

The stacked bar plots display the proportional distribution of different T cell subsets and related immune cells within each sample.

• Overall Composition Shift: There is a notable shift in the immune cell composition between adjacent normal and tumor tissues.
• CD8+ T cells (light green): This subset consistently represents the largest proportion of the T cell compartment in both adjacent normal and tumor samples. However, in tumor samples, CD8+ T cells appear to be even more dominant, often comprising over 60-70% of the total T cell population in many tumor samples (e.g., T4, T2, T8, T5, T7, T9). In contrast, in adjacent normal samples, while still dominant, their proportion is generally lower and more variable.
• CD4+ T cells (light yellow): CD4+ T cells show higher proportions in many adjacent normal samples compared to tumor samples. For instance, in normal samples N7, N6, N4, N3, N9, N5, N1, CD4+ T cells contribute significantly to the total T cell pool, often exceeding 10-20%. In tumor samples, their proportion generally appears reduced, becoming a smaller component relative to CD8+ T cells, especially in samples like T4, T2, T8, T5, T7, T9.
• ILC (dark red) and NK cell (orange): These innate lymphoid cell populations show higher and more variable proportions in adjacent normal samples. For example, sample N7 has a very high proportion of ILCs and NK cells combined (over 60%). Sample N6, N4, N3 also show substantial contributions from these cell types. In tumor samples, ILCs and NK cells are generally present at lower proportions, although some tumor samples (e.g., T6, T4, T8, T5) still contain notable fractions of these cells, particularly ILCs. The presence of NK cells appears particularly diminished in most tumor samples compared to normal.
• "unassigned" (light turquoise): This category represents a very minor fraction across all samples and conditions, indicating high confidence in the specific cell type assignments for the majority of cells within the T cell major group.
• Sample-to-Sample Variability: Within both conditions, there is considerable heterogeneity in the exact proportions of these cell subsets, reflecting inter-patient variability in immune infiltration.

Biological Interpretation

The observed shifts in immune cell populations between adjacent normal and tumor kidney tissues provide insights into the altered immune microenvironment in renal cell carcinoma (RCC).

• Dominance of CD8+ T cells in tumors: The increased relative abundance of CD8+ T cells in tumor samples is a common feature in many cancers. CD8+ T cells are cytotoxic T lymphocytes (CTLs) primarily responsible for directly killing cancer cells. Their high proportion suggests an ongoing anti-tumor immune response within the tumor microenvironment (TME) [1]. However, their functional status (e.g., activation, exhaustion) cannot be determined solely from population frequencies.
• Reduced CD4+ T cells in tumors: A decrease in the relative proportion of CD4+ T cells in tumor samples could reflect several phenomena. CD4+ T cells include helper T cells (Th1, Th2, Th17) that orchestrate immune responses and regulatory T cells (Tregs) that suppress immunity. A general reduction might indicate an altered balance of helper subsets or a proportional increase in CD8+ T cells masking their true frequency.
• Diminished ILCs and NK cells in tumors: The reduced presence of ILCs (including ILC1, ILC2, ILC3) and NK cells in tumor tissue, compared to adjacent normal tissue, is potentially significant. NK cells are crucial for innate anti-tumor immunity by directly killing tumor cells and producing cytokines [2]. A decrease in their relative proportion could suggest immune evasion mechanisms by the tumor or a less effective innate immune surveillance. ILCs also play diverse roles in immune responses and tissue homeostasis. Their lower proportion might indicate a shift from a tissue-resident protective immune state to a tumor-dominated suppressive environment.
• Implications for Immune Evasion: While CD8+ T cells are abundant in tumors, the relative reduction of CD4+ T cells, NK cells, and ILCs might point towards mechanisms of immune evasion that allow the tumor to thrive despite the presence of cytotoxic T cells. The balance between different immune cell types, rather than the absolute number of a single type, is often critical for effective anti-tumor immunity.

Clinical or Translational Implications

• Immunotherapy Response: The observed composition of T cell subsets and other immune cells could have implications for predicting response to immunotherapy. A higher proportion of CD8+ T cells, if functionally active, is often associated with better response rates to immune checkpoint inhibitors [3]. However, if these CD8+ T cells are exhausted, their mere presence may not translate to effective anti-tumor activity.
• Biomarker Potential: The relative proportions of CD4+ T cells, NK cells, and ILCs could serve as potential biomarkers for disease progression or prognosis in kidney cancer. For instance, a lower NK cell proportion in tumors might correlate with more aggressive disease or worse outcomes.
• Targeting the TME: Understanding these compositional shifts can inform strategies for therapeutic intervention. Restoring or augmenting NK cell and ILC populations, or re-activating dysfunctional CD8+ T cells, could be beneficial in combination with existing therapies.

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References

1. CD8+ T cell role in cancer immunity:

PubMed Search: CD8+ T cells cancer immunotherapy

2. NK cell role in cancer immunity:

GeneCards: NCAM1 (CD56, NK cell marker) related to cancer

PubMed Search: NK cells tumor immunity

3. T cell populations and immunotherapy response:

PubMed Search: T cell subsets immune checkpoint inhibitor response

8. T cell Subset Population Differences in Kidney Tumor Microenvironment

[Analysis Visualization Results]...

Analysis Overview

This analysis investigated the proportional changes of specific T cell subsets (T_Naive and T_Cytotoxic) between kidney tumor and adjacent normal tissues. The plot_box_for_celltype_population_with_signif_difference tool was utilized to visualize these differences, with statistical testing performed to identify significant changes, considering a p-value cutoff of 0.1 and a log2_FC cutoff of 0.1 for detection.

Visual Summary

The box plots display the cell type proportion of T_Naive and T_Cytotoxic (T_Cyto) cells across 'adjacent_normal' and 'tumor' conditions.

• T_Naive Cells: The proportion of T_Naive cells appears to be lower in tumor tissue compared to adjacent normal tissue. The median proportion in adjacent normal tissue is approximately 9-10%, while in tumor tissue it is around 4-5%. The statistical test indicates a p-value of 0.10, which is at the threshold of significance set by the pval_cutoff parameter.
• T_Cytotoxic Cells (T_Cyto): In contrast, the proportion of T_Cytotoxic cells is significantly higher in tumor tissue compared to adjacent normal tissue. The median proportion in adjacent normal tissue is around 50-55%, whereas in tumor tissue it is approximately 60-65%. This difference is statistically significant, with a p-value less than or equal to 0.05.

Biological Interpretation

These findings reveal distinct shifts in the T cell landscape within the kidney tumor microenvironment (TME) compared to normal tissue, which are critical for understanding anti-tumor immunity.

• Decreased T_Naive Cells in Tumor: The trend of reduced naive T cells in tumor tissue (p=0.10) suggests a potential decrease in the pool of undifferentiated T cells capable of responding to new antigens. This could indicate either the recruitment and differentiation of naive T cells into effector phenotypes within the TME, or an overall suppression of T cell influx, or migration into lymphoid organs. A lower proportion of naive T cells might also suggest an environment that is less permissive to the initial priming of new anti-tumor responses.
• Increased T_Cytotoxic Cells in Tumor: The significant increase in cytotoxic T cells (T_Cyto) within the tumor microenvironment is a common hallmark of immune-inflamed tumors. These cells are primary mediators of anti-tumor immunity, directly killing cancer cells. Their elevated presence suggests an ongoing immune response against the tumor. However, the efficacy of these infiltrating cytotoxic T cells in the TME can be highly variable; they may be functionally exhausted, anergic, or suppressed by other immune cells or inhibitory pathways present in the TME, despite their increased numbers. This observation prompts further investigation into their activation state and functional capacity.
• Reference: Cytotoxic T Lymphocytes in Cancer Therapy
• Reference: T Cell Exhaustion

Clinical or Translational Implications

The observed shifts in T cell populations have important implications for understanding kidney cancer immunology and potential therapeutic strategies.

• Immunotherapy Responsiveness: The heightened infiltration of cytotoxic T cells in kidney tumors could indicate a "hot" tumor microenvironment, which is often associated with better responses to immune checkpoint inhibitors (ICIs). However, as noted, their functional status is key. If these cells are exhausted, strategies to reverse T cell exhaustion could be beneficial.
• Prognostic Value: The balance between naive and cytotoxic T cells, and the functional state of the latter, can serve as prognostic markers for kidney cancer patients. A higher proportion of functional cytotoxic T cells may correlate with better patient outcomes, while signs of T cell exhaustion could predict resistance to certain therapies.
• Biomarker Development: Monitoring the proportions and states of T cell subsets, particularly cytotoxic T cells, could serve as a valuable biomarker for patient stratification, predicting response to immunotherapy, and assessing disease progression.
• Therapeutic Development: Understanding the mechanisms driving the depletion of naive T cells or the functional impairment of cytotoxic T cells in kidney cancer could lead to the development of novel immunotherapeutic approaches aimed at enhancing anti-tumor immunity.

9. Macrophage Population Subset Verification in Kidney Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis generated a bar plot visualizing the "subset population" for Macrophage cells, comparing them across 'adjacent_normal' and 'tumor' conditions in kidney tissue. The plot_celltype_population tool was used, specifically targeting cells annotated as 'Macrophage' in the celltype_minor column. This visualization serves primarily as a confirmation of the selection and grouping of the specified cell type for subsequent, more detailed analyses.

Visual Summary

The visualization displays two bar plots, one for the 'adjacent_normal' condition and one for the 'tumor' condition. Each subplot shows individual samples (N1-N9 for adjacent normal, T2-T9 for tumor). For every sample and both conditions, the bar corresponding to 'Macrophage' extends to 100% on the y-axis. This indicates that within the specific subset of cells selected for this plot (which were pre-filtered to be Macrophages), all cells are indeed identified as Macrophages.

Biological Interpretation

The consistent 100% population observed for Macrophages across all samples and conditions confirms that the data has been successfully subsetted and that the identified Macrophage cells are indeed annotated as such. This plot is not designed to show the *relative abundance* of Macrophages compared to other cell types within the overall tissue microenvironment; rather, it validates the internal composition of the selected macrophage cell population. This is an important initial step to ensure that downstream cell-type-specific analyses (such as differential gene expression, cell-cell interaction, or gene set enrichment analysis) are performed on a correctly identified and isolated cell population. The presence of Macrophages in both adjacent normal and tumor kidney tissue is biologically expected, as these immune cells are ubiquitous and play significant roles in both homeostasis and disease contexts, including cancer progression and immune surveillance PubMed Search: macrophages kidney tumor.

Annotation Notes

This plot functions as a quality control or annotation verification step. It successfully demonstrates the correct isolation and identification of the 'Macrophage' cell type based on the celltype_minor annotation. No differential abundance or cell-state shifts can be inferred from this specific plot, as its purpose is to confirm the target cell type selection. Further analyses would be required to investigate the relative abundance of macrophages, their specific subtypes (e.g., M1, M2A, M2D as noted in celltype_subset), or their functional states in tumor versus normal conditions.

10. Macrophage (M2A) Subset Abundance Differs Significantly Between Kidney Tumor and Adjacent Normal Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis investigated the relative proportions of specific macrophage subset populations, specifically Macrophage (M2A) cells, within kidney tumor tissues compared to adjacent normal kidney tissues. The goal was to identify significant shifts in immune cell composition between these two conditions, using single-cell RNA-seq data to quantify cell type proportions.

Visual Summary

The box plot illustrates a significant difference in the proportion of Macrophage (M2A) cells between tumor and adjacent normal kidney tissues.

• Condition Comparison: The proportion of Macrophage (M2A) cells is significantly lower in the 'tumor' condition compared to the 'adjacent_normal' condition (p ≤ 0.01).
• Proportion Estimates: In the tumor samples, the median proportion of Macrophage (M2A) cells is approximately 8-9%, with a range generally between 5% and 12%. In contrast, adjacent normal samples show a higher median proportion, approximately 14-15%, with a broader range, often extending from about 12% to over 20%, and one outlier reaching above 30%.
• Variability: Both conditions show some variability in cell proportions across samples, as indicated by the spread of individual data points (black dots) and the box heights.

Biological Interpretation

Macrophage (M2A) cells represent a specific subtype of M2-polarized macrophages, which are typically associated with wound healing, tissue repair, and anti-inflammatory responses. In the context of cancer, M2 macrophages, often broadly termed Tumor-Associated Macrophages (TAMs), frequently promote tumor growth, angiogenesis, and immunosuppression. However, the specific subtypes within the M2 spectrum (M2A, M2B, M2C, M2D) can have distinct roles and molecular signatures.

The observed significant decrease in Macrophage (M2A) cell proportion within kidney tumor tissue compared to adjacent normal tissue offers several biological insights:

• Shift in Macrophage Polarization: This finding suggests that the tumor microenvironment in kidney cancer may actively suppress the recruitment or differentiation of M2A-like macrophages, or that it favors the polarization towards other macrophage phenotypes (e.g., M1, M2B, M2C, M2D) that are more conducive to tumor progression. The adjacent normal kidney tissue, perhaps engaged in baseline tissue homeostasis or chronic low-grade inflammation/repair, may preferentially maintain a higher proportion of M2A cells.
• Altered Immune Landscape: The reduced presence of M2A cells in the tumor could indicate a specific immunological landscape where this particular pro-resolving/anti-inflammatory phenotype is either outcompeted, suppressed, or simply less relevant to the tumor's needs compared to other macrophage subsets.
• Kidney-Specific Context: Given that the tumor origin cell type is Renal Epithelial cell and the tissue is Kidney, this observation might reflect kidney-specific immune responses or adaptations within the tumor microenvironment that differ from those reported in other cancer types. It warrants further investigation into the functional roles of M2A macrophages in both normal kidney physiology and renal cell carcinoma progression.

Clinical or Translational Implications

• Immunotherapy Target Identification: Understanding the specific macrophage subsets present in the tumor microenvironment is crucial for designing effective immunotherapies. The relative paucity of M2A macrophages in kidney tumors, if this subtype possesses anti-tumorigenic properties or is critical for maintaining normal tissue barriers, could highlight specific vulnerabilities or drivers of tumor progression.
• Prognostic Marker: Differences in M2A macrophage infiltration could potentially serve as a prognostic biomarker for kidney cancer, though this would require correlation with patient outcomes.
• Therapeutic Repolarization: If M2A macrophages are generally considered beneficial or less pro-tumorigenic, strategies aimed at promoting their recruitment or differentiation into the tumor microenvironment might represent a novel therapeutic approach. Conversely, if M2A cells, despite their general anti-inflammatory roles, are found to contribute to tumor escape in kidney cancer, their depletion could be considered.
• Further Research: This finding necessitates further characterization of the precise roles of different macrophage subsets in kidney cancer, including their functional states, interactions with other immune and stromal cells, and their impact on patient response to existing treatments. This data provides a foundation for hypotheses regarding the specific immunological shifts occurring in renal cell carcinoma.
• For background on macrophage polarization and its role in cancer, see: PubMed search for "macrophage polarization cancer review"
• For general information on M2 macrophage subtypes: PubMed search for "M2 macrophage subsets"

11. Ploidy Population Analysis of Tumor-Origin and Unassigned Cells in Kidney Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the ploidy status (Aneuploid, Diploid, Unclear) of cells identified as 'Renal Epithelial cell' (which are the designated tumor-origin cell type) and 'unassigned' cells within both adjacent normal kidney tissue and tumor samples. The goal is to understand how the distribution of these ploidy states varies between conditions and individual samples, offering insights into genomic stability associated with malignancy.

Visual Summary

The stacked bar plots display the percentage of aneuploid (maroon), diploid (orange), and unclear (light green) cells for each sample, separated by 'adjacent_normal' and 'tumor' conditions.

• Adjacent Normal Samples: In the adjacent normal samples (N9, N7, N6, N8, N5, N4, N1, N2, N3), the majority of cells are diploid (orange), typically exceeding 70% in most samples. Aneuploid cells (maroon) are present in varying proportions, ranging from approximately 5% (e.g., N2, N3) to nearly 30% (e.g., N9). The proportion of 'Unclear' cells (light green) is consistently very low across all adjacent normal samples, generally below 2-3%.
• Tumor Samples: In contrast, tumor samples (T5, T6, T7, T9, T8, T2, T4, T3) show a distinct pattern. Several tumor samples, particularly T5, T6, and T7, exhibit a substantially higher proportion of aneuploid cells, with T5 showing nearly 50% aneuploid cells. Other tumor samples (T9, T8) also have a noticeable, though lower, percentage of aneuploid cells (around 15-20%). Samples T2, T4, and T3 are predominantly diploid, similar to many normal samples, but still appear under the 'tumor' condition. The 'Unclear' cell population remains low across tumor samples, similar to adjacent normal samples.
• Comparison between Conditions: There is a clear trend towards increased aneuploidy in tumor samples compared to adjacent normal samples, especially evident in samples T5, T6, and T7. While some adjacent normal samples show baseline aneuploidy, the highest proportions of aneuploid cells are observed in the tumor microenvironment.

Biological Interpretation

The observed shift in ploidy distribution between adjacent normal and tumor tissues provides strong biological insights into renal cell carcinoma development.

• Aneuploidy as a Hallmark of Cancer: Aneuploidy, defined as an abnormal number of chromosomes, is a well-established characteristic of many cancers, including renal cell carcinoma [1]. The significant increase in aneuploid cells within the 'Renal Epithelial cell' population (the tumor-origin cell type) in tumor samples is consistent with malignant transformation and genomic instability inherent to cancer cells.
• Tumor Heterogeneity: The variability in aneuploidy levels among different tumor samples (e.g., high aneuploidy in T5 vs. low in T2) suggests inter-tumor heterogeneity. This could reflect different stages of tumor progression, varying degrees of genomic instability, or distinct molecular subtypes within renal cell carcinoma [2].
• Presence of Aneuploidy in Adjacent Normal Tissue: The detection of a notable proportion of aneuploid cells in some adjacent normal samples is intriguing. This could represent early oncogenic events, clonal mosaicism, or "field cancerization," where morphologically normal tissue surrounding a tumor already harbors genomic alterations that predispose to cancer [3]. It might also reflect technical noise or minor contamination from tumor cells, although the systematic difference between adjacent normal and tumor samples suggests a biological basis.
• Contribution of 'Unassigned' Cells: The analysis includes 'unassigned' cells. While 'Renal Epithelial cells' are the primary tumor-origin cells, the ploidy profile shown for the combined population suggests that the major signal of aneuploidy in tumor is driven by the malignant 'Renal Epithelial cells'. The 'unassigned' population might include cells that are difficult to classify due to specific cell states, low RNA content, or novel cell types, and their individual ploidy contribution would be averaged into the overall population.

Clinical or Translational Implications

The findings have potential implications for understanding kidney cancer pathogenesis and could inform clinical strategies.

• Biomarker for Malignancy: The elevated proportion of aneuploid cells in tumor samples, particularly among renal epithelial cells, underscores the potential of aneuploidy as a diagnostic or prognostic biomarker for renal cell carcinoma. Quantifying aneuploidy could help differentiate benign from malignant lesions or identify aggressive tumors [4].
• Understanding Tumor Evolution: The heterogeneity in aneuploidy across tumor samples might correlate with different clinical outcomes or responses to therapy, suggesting that ploidy status could be a factor in personalized treatment strategies.
• Monitoring Field Cancerization: The presence of aneuploid cells in adjacent normal tissue could indicate an increased risk of recurrence or development of new lesions, suggesting a need for closer surveillance in these patients. Further research could explore if specific aneuploid patterns in the adjacent normal tissue predict disease progression.

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References:

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

[2] Cancer Genome Atlas Research Network. (2013). Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature, 499(7456), 43–49. PubMed Search: Clear cell renal cell carcinoma genomic instability

[3] Sidransky, D. (1998). Molecular biology of head and neck cancer. Current Opinion in Oncology, 10(3), 209-214. PubMed Search: Field cancerization molecular mechanisms

[4] Gerlinger, M., et al. (2012). Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. New England Journal of Medicine, 366(10), 883-892. PubMed Search: Renal cell carcinoma ploidy prognosis

12. Kidney Tumor Cell-Cell Interaction Analysis: Macrophage and T Cell Dynamics

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns within the kidney tumor microenvironment, specifically focusing on interactions between macrophages, CD8+ T cells, and CD4+ T cells. The CellPhoneDB tool was utilized to identify significant ligand-receptor interactions based on single-cell RNA-seq data from the tumor condition. The visualization highlights the most significant and strongly expressed interactions among the selected cell types. While the query requested interactions including Renal Epithelial cells and Fibroblasts, the provided visualization focuses on interactions solely between Macrophages and T cells (CD8+ and CD4+).

Visual Summary

The dot plot displays significant cell-cell interactions within the tumor condition, involving Macrophages, CD8+ T cells, and CD4+ T cells. The y-axis represents interacting cell pairs (e.g., T CD8+|Mac, Mac|T CD8+, Mac|T CD4+, Mac|Mac), and the x-axis shows specific ligand-receptor pairs or complexes. Dot size correlates with the statistical significance of the interaction (-log10(p-value)), while dot color represents the strength of the interaction (log2(mean expression)).

Key observations from the plot include:

• Dominant Role of Macrophages: Macrophages exhibit extensive interactions, both among themselves (Mac|Mac) and with T cells (Mac|T CD8+, Mac|T CD4+, T CD8+|Mac).
• Strong Autocrine/Paracrine Macrophage Interactions: Several highly significant and strongly expressed interactions are observed within macrophages (Mac|Mac). Notable examples include APOE_TREM2_receptor, TNF_TNFRSF1B, TYROBP_CD44, VSIR_HLA-E, and VSIR_HLA-F.

Macrophage-T Cell Interactions:

• Mac|T CD8+ and Mac|T CD4+ interactions are prominent for TNF_TNFRSF1B, TYROBP_CD44, and VSIR_HLA-E, showing high significance and expression.
• T CD8+|Mac interaction for CD99_PILRA is also highly significant.
• Immune Regulatory Pathways: A notable number of identified interactions involve known immune checkpoint molecules or regulatory pathways, such as VISTA (VSIR), TREM2, and components of the TNF superfamily (TNFRSF1B) and HLA complex (HLA-E, HLA-F).
• Missing Cell Types: The plot does not show interactions involving Renal Epithelial cells or Fibroblasts, despite these cell types being specified in the user query for investigation. This might be due to the n_pairs_to_show parameter limiting the output to the top 80 pairs for the given conditions and selected cell types, and other interactions may not have met the significance/mean expression cutoffs.

Biological Interpretation

The strong and significant cell-cell interactions observed in the kidney tumor microenvironment highlight a complex interplay, particularly involving macrophages and T cells, which are crucial components of anti-tumor immunity and immune evasion.

1. Macrophage-Mediated Immunosuppression:

• The prominent APOE_TREM2_receptor interaction within macrophages (Mac|Mac) suggests active TREM2 signaling. TREM2 (Triggering Receptor Expressed on Myeloid Cells 2) on tumor-associated macrophages (TAMs) is often linked to a pro-tumorigenic, immune-suppressive phenotype, promoting tumor growth, metastasis, and angiogenesis in various cancers [PubMed search: TREM2 tumor associated macrophages]. The APOE (Apolipoprotein E) ligand can activate TREM2.
• The significant interactions involving VSIR (VISTA) with HLA-E and HLA-F (Mac|Mac, Mac|T CD8+, Mac|T CD4+) are particularly noteworthy. VISTA is an immune checkpoint molecule that suppresses T cell responses. Its interaction with non-classical MHC class I molecules like HLA-E and HLA-F can contribute to immune evasion by inhibiting anti-tumor T cell activity [PubMed search: VISTA immune checkpoint HLA-E]. The widespread nature of these interactions suggests a robust immunosuppressive axis involving macrophages communicating with T cells, and potentially also influencing macrophage-macrophage behavior in the tumor.
• TNF_TNFRSF1B interactions (Mac|Mac, Mac|T CD8+, Mac|T CD4+) are also strong. TNFRSF1B (also known as TNFR2) signaling on macrophages can promote an M2-like phenotype and survival, while on T cells, it can contribute to the expansion and function of regulatory T cells (Tregs) or exhaustion of effector T cells, thus suppressing anti-tumor immunity [PubMed search: TNFR2 tumor immunity].

2. T Cell Modulation:

• CD99_PILRA interaction (T CD8+|Mac) indicates potential modulation of macrophage activity by CD8+ T cells or vice-versa. PILRA (Paired Immunoglobulin-like Type 2 Receptor Alpha) is an inhibitory receptor expressed on myeloid cells, and its engagement can suppress inflammatory responses. This interaction might contribute to shaping the immune landscape towards an immunosuppressive state.
• TYROBP_CD44 interactions (Mac|T CD8+, Mac|T CD4+, Mac|Mac) point to broader cellular communication. TYROBP (DAP12) is an adaptor protein crucial for activating various immune cell receptors, while CD44 is a cell surface glycoprotein involved in cell adhesion, migration, and signaling. These interactions could affect T cell activation, homing, or the overall inflammatory state within the tumor.

3. Renal Tumor Microenvironment Context: Given the tissue is Kidney and the condition is 'tumor', these macrophage-T cell interactions likely contribute to the unique immunosuppressive environment observed in renal cell carcinoma (RCC). The predominant immune suppressive signals (TREM2, VISTA, TNFR2) suggest that macrophages in this kidney tumor likely adopt pro-tumorigenic M2-like phenotypes, dampening the effector functions of CD8+ and CD4+ T cells.

Clinical or Translational Implications

The identified ligand-receptor interactions provide critical insights for understanding immune evasion mechanisms in kidney cancer and present potential avenues for therapeutic intervention.

1. Therapeutic Target Prioritization:

• TREM2 blockade: The strong APOE_TREM2_receptor interaction suggests that targeting TREM2 on tumor-associated macrophages could be a promising strategy to reprogram these cells towards an anti-tumor phenotype, enhancing overall immune responses in kidney cancer.
• VISTA checkpoint inhibition: The widespread and significant VSIR_HLA-E/F interactions make VISTA an attractive immune checkpoint target. Blocking VISTA could unleash anti-tumor T cell immunity, similar to other successful checkpoint inhibitors (e.g., PD-1/PD-L1).
• TNFR2 modulation: Given the strong TNF_TNFRSF1B interactions, particularly with CD4+ T cells, strategies to block TNFR2 signaling could potentially reduce Treg-mediated suppression or inhibit pro-tumorigenic macrophage functions.

2. Biomarker Discovery: The expression levels and specific interaction patterns of these ligand-receptor pairs (e.g., TREM2, VISTA, TNFRSF1B, HLA-E/F) could serve as prognostic biomarkers to predict patient response to immunotherapy or as diagnostic markers for the immune status of the tumor.
3. Experimental Validation and Combination Therapies:

• Further *in vitro* functional studies are warranted to validate the exact roles of these interactions in modulating T cell activation, proliferation, and macrophage polarization from kidney tumor samples.
• *In vivo* models using specific inhibitors or genetic manipulations of these pathways would be crucial to confirm their therapeutic potential.
• Given the complexity of the immune microenvironment, combining therapies targeting these novel pathways (e.g., VISTA or TREM2 blockade) with existing immunotherapies or conventional treatments might lead to synergistic anti-tumor effects.

13. Cell-Cell Interaction Analysis in Kidney Tumor vs. Adjacent Normal Tissues

[Analysis Visualization Results]...

This analysis investigates cell-cell interactions (CCIs) in kidney tissue, comparing tumor samples with adjacent normal tissue, using CellPhoneDB results. The goal is to identify prominent ligand-receptor interactions and the cell types involved, offering insights into disease mechanisms and potential therapeutic targets.

Analysis Overview

The plot_cci_dots tool was used to visualize the top 80 cell-cell interactions for both adjacent normal and tumor conditions. This provides a comparative view of the cellular communication landscape in healthy versus cancerous kidney environments. The analysis considers both major cell types and ploidy status (Aneuploid/Diploid) for Renal Epithelial cells, given their tumor origin.

Visual Summary

Adjacent Normal Tissue

The dot plot for adjacent normal tissue shows a relatively sparse interaction network.

• Dominant Cell Pairs: Interactions are primarily observed within Endothelial cells (Endo|Endo) and between Renal Epithelial cells with different ploidy statuses (Aneuploid Renal Epi|Diploid Renal Epi, Aneuploid Renal Epi|Aneuploid Renal Epi). The presence of Aneuploid Renal Epi interactions in "adjacent_normal" might indicate early genomic instability or field cancerization.
• Key Interactions: The most significant interactions include ESAM_ESAM (Endothelial cell-specific adhesion molecule) within Endothelial cells, and ALB_FcRn_complex, APP_CD74, and PPIA_BSG (Peptidylprolyl Isomerase A - Basigin) involving renal epithelial cells. These interactions generally suggest basic endothelial integrity, cell adhesion, and immune/stress response pathways.

Tumor Tissue

In stark contrast, the tumor tissue exhibits a significantly more complex and active interaction network, with a greater diversity of interacting cell types and ligand-receptor pairs.

Dominant Cell Pairs:

• Myeloid cells (Macrophages): Macrophages are central, forming numerous and strong interactions with T cells (CD8+, CD4+), Endothelial cells, ILCs, and self-interactions. This highlights their critical role in shaping the tumor microenvironment (TME).
• Endothelial cells: Engage in extensive self-interactions (Endo|Endo) and interactions with Macrophages, T cells, and Diploid Renal Epithelial cells, consistent with angiogenesis and immune cell trafficking.
• T cells (CD8+, CD4+): Primarily interact with Macrophages, indicating active immune modulation.

Key Interactions (High significance and mean expression):

• Integrin-Collagen interactions: Multiple COL_integrin_a1b1_complex interactions are highly prominent, especially between Macrophages, Endothelial cells, and sometimes T cells. These are crucial for cell-extracellular matrix adhesion, migration, and signaling. PubMed search: Integrin alpha1beta1 cancer
• Immune Checkpoint/Modulation: APOE_TREM2_receptor (Apolipoprotein E - TREM2) and LGALS9_P4HB (Galectin-9 - P4HB) interactions are notable, particularly involving Macrophages. TREM2 activation can influence myeloid cell function, while Galectin-9 is known to induce T-cell apoptosis and suppress anti-tumor immunity. GeneCards: TREM2, GeneCards: LGALS9
• Angiogenesis/Growth Factors: ADM_RAMP3 (Adrenomedullin - RAMP3) and IGFBP3_TMEM219 interactions are significant, indicating active roles in vascular regulation, inflammation, and cell proliferation within the TME. GeneCards: ADM
• Notch Signaling: JAG1_NOTCH4 points to active Notch signaling, a pathway crucial for cell fate, proliferation, and differentiation, often implicated in cancer progression. PubMed search: Notch signaling cancer
• Adhesion/Immune Evasion: CD99_PILRA, PPIA_BSG, THY1_ADGRE5, TYROBP_CD44, and VSIIR_HLA-E are also active, suggesting complex interactions related to cell adhesion, migration, and immune evasion mechanisms. HLA-E interactions, in particular, can modulate NK and T cell responses, contributing to immune escape. GeneCards: HLA-E

Biological Interpretation

The comparative analysis reveals a profound shift in cellular communication from a relatively homeostatic state in adjacent normal tissue to a highly active and complex network in the tumor microenvironment.

• Immune Landscape Remodeling: The dominant role of Macrophages in the tumor is striking. Their extensive interactions with T cells and other immune components via pathways like APOE-TREM2 and LGALS9-P4HB strongly suggest a re-programming of the immune environment towards immune suppression and inflammation, which are hallmarks of cancer progression. The presence of TNF_TNFRSF1B signaling further supports an inflammatory TME.
• Angiogenesis and Stromal Support: The high frequency and strength of various COL_integrin_a1b1_complex interactions, particularly involving endothelial cells and macrophages, point to active extracellular matrix remodeling and angiogenesis. These processes are essential for tumor growth, invasion, and metastasis. ADM-RAMP3 also contributes to vascular regulation.
• Tumor Cell-Stroma Interactions: Although Aneuploid Renal Epi cells are not explicitly prominent in the tumor plot's rows (perhaps due to being grouped within 'Diploid Renal Epi' for certain interactions or not being among the top 80 pairs), interactions involving 'Diploid Renal Epi' with endothelial cells still highlight critical cross-talk between tumor-associated cells and the stromal compartment. The JAG1-NOTCH4 pathway is vital for promoting tumor cell survival and proliferation.

Clinical or Translational Implications

The identified highly active cell-cell interactions in the kidney tumor microenvironment offer several potential avenues for clinical and translational applications.

Therapeutic Targeting:

• Macrophage Reprogramming: Inhibiting the APOE-TREM2 axis or LGALS9-P4HB interactions could potentially shift macrophages from a pro-tumorigenic to an anti-tumorigenic phenotype, making them attractive targets for immunotherapy.
• Anti-angiogenic/Anti-invasive Strategies: Targeting the specific COL_integrin_a1b1_complex interactions or ADM-RAMP3 could disrupt tumor blood supply and inhibit invasive capabilities, particularly relevant for kidney cancer, which is highly vascularized.
• Notch Pathway Inhibition: Modulating JAG1-NOTCH4 signaling could impact tumor cell stemness and proliferation.
• Immune Evasion Blockade: Disrupting VSIIR_HLA-E interactions might enhance immune recognition of tumor cells, potentially synergizing with existing immunotherapies.
• Biomarker Discovery: Components of these highly active ligand-receptor pairs (e.g., soluble Galectin-9, circulating ADM levels, or expression of specific integrins) could serve as prognostic biomarkers for disease aggressiveness or predictive markers for response to targeted therapies.
• Rational Combination Therapies: Given the intricate network of interactions, combination therapies targeting multiple interconnected pathways (e.g., immune checkpoint inhibitors + angiogenesis inhibitors + macrophage modulators) may offer more effective strategies for managing kidney cancer.

14. Condition-Specific Cell-Cell Interaction Patterns in Kidney Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) between kidney tumor and adjacent normal tissues, focusing on major immune cells (Myeloid, T, B, Mast cells) and Stromal cells, as well as Renal Epithelial cells (the tumor origin cell type). The dot plot visualizes the top 25 most significant CCIs in each condition, showing their standardized mean strength (color intensity) and statistical significance (-log10(p-value), dot size) across individual samples. This provides a detailed view of how the cellular communication landscape changes in the tumor microenvironment.

Visual Summary

The dot plot effectively illustrates distinct patterns of cell-cell communication in adjacent normal versus tumor samples.

• Differential Grouping: Samples are clearly separated by condition (adjacent\_normal vs. tumor) on the y-axis, and CCIs are grouped based on their prevalence. Two prominent blue boxes highlight these differences.
• Adjacent Normal Specificity (Left Blue Box): The left blue box encompasses a set of CCIs that are highly active (dark red, larger dots) primarily in the adjacent normal samples (N1-N9) and show minimal activity in tumor samples (T2-T9). These interactions largely involve Diploid Renal Epithelial cells, Macrophages, Endothelial cells, and T cells (e.g., SEMA4D-PTPRC_T CD8+-IT CD8+, CXCL14-CXCR4_Renal.Epi (Dipl)-Mac, ESAM-ESAM_Endo-Endo).
• Tumor Specificity (Right Blue Box): The right blue box highlights a substantially larger cluster of CCIs that are predominantly active and significant (dark red, large dots) in the tumor samples (T2-T9), with much weaker or absent activity in adjacent normal samples. These interactions frequently involve Aneuploid Renal Epithelial cells (the likely tumor cells), Macrophages, Endothelial cells, and Smooth Muscle cells. Examples include APP-CD74_Renal.Epi (Aneu)-Mac, CXCL14-CXCR4_Renal.Epi (Aneu)-Mac, VEGFA-FLT1_Mac-Endo, and various TNF family interactions.
• Macrophage Centrality: Macrophages (Mac) emerge as a central cell type, participating in numerous highly active interactions in both conditions, but particularly enriched in the tumor context, often engaging with Aneuploid Renal Epithelial cells, Endothelial cells, and Smooth Muscle cells.
• Ploidy Distinction: The inclusion of "(Dipl)" and "(Aneu)" labels for Renal Epithelial cells highlights a crucial distinction, showing Diploid Renal Epithelial cells interacting in normal tissue, while Aneuploid Renal Epithelial cells drive many tumor-specific interactions.

Biological Interpretation

The observed shifts in cell-cell interactions reveal fundamental changes in tissue biology and the establishment of a pro-tumorigenic microenvironment in kidney cancer.

1. Shift from Tissue Homeostasis to Tumor Progression:

• In adjacent normal tissue, interactions like ESAM-ESAM (Endothelial-Endothelial, Smooth muscle cell-Endothelial) and PGF-FLT1 (Smooth muscle cell-Endothelial, Endothelial-Endothelial) suggest maintenance of vascular integrity and normal stromal-vascular interactions. CXCL14-CXCR4 between Diploid Renal Epithelial cells and Macrophages/T CD8+ cells may play roles in normal immune surveillance or tissue organization.
• In tumor tissue, a new set of highly active interactions emerges, characteristic of tumor growth and immune evasion.

2. Macrophage Reprogramming and Tumor-Associated Macrophages (TAMs):

• Macrophages are key players in tumor-specific interactions. The strong presence of VEGFA-FLT1 (Macrophage-Endothelial) signaling in tumors indicates macrophage-driven angiogenesis, a hallmark of cancer progression. PubMed search: Macrophage angiogenesis tumor microenvironment
• Interactions such as APP-CD74 (Aneuploid Renal Epithelial cell/Smooth muscle cell-Macrophage) and LGALS9-P4HB (Macrophage-Aneuploid Renal Epithelial cell) suggest complex roles for macrophages in antigen presentation, immune modulation, and potentially tumor growth and invasion. CD74 can act as a signaling molecule in immune and cancer cells. GeneCards: CD74 LGALS9 (Galectin-9) is known to induce T cell apoptosis and promote immune tolerance, contributing to immune evasion in the tumor microenvironment. GeneCards: LGALS9

3. Immune Evasion and Inflammation:

• The upregulation of TNF-TNFRSF1A/B (Macrophage-Endothelial, Macrophage-T CD8+) interactions in tumors highlights heightened inflammatory signaling. While TNF can have anti-tumor effects, chronic inflammation is often pro-tumorigenic, contributing to immunosuppression and tumor survival. GeneCards: TNF
• VSIR-HLA-F (Macrophage-Smooth muscle cell) is noteworthy as VSIR (V-set immunoregulatory receptor, also known as B7-H5 or VISTA) is an immune checkpoint molecule that suppresses T cell responses. Its interaction with HLA-F (a non-classical MHC class I molecule) suggests a mechanism for immune evasion within the tumor stroma. GeneCards: VSIR
• CLEC2B-KLRF1 (Macrophage-T CD8+) also indicates specific immune cell interactions that warrant further investigation regarding their role in anti-tumor immunity or tolerance.

4. Stromal Remodeling and Tumor Support:

• Smooth Muscle Cells (SMC), representing a stromal component, engage in diverse interactions in the tumor. For example, CD99-PILRA (SMC-Macrophage) and THY1_ADGRE5/integrin_aXb2_complex (SMC-T CD8+/Macrophage) suggest complex crosstalk between stromal cells and immune cells, potentially contributing to extracellular matrix remodeling and creating an immune-suppressive environment conducive to tumor growth. GeneCards: THY1

5. Aneuploid Renal Epithelial Cells as Active Tumorigenic Drivers:

• The consistent involvement of "Renal.Epi (Aneu)" in multiple highly significant tumor-specific interactions (e.g., APP-CD74, CXCL14-CXCR4, LGALS9-P4HB, PPIA-BSG) confirms that the transformed renal epithelial cells are not merely passive entities but actively participate in and shape the tumor microenvironment through diverse signaling pathways.

Clinical or Translational Implications

The identified condition-specific CCI patterns offer several potential clinical and translational implications for kidney cancer:

• Biomarker Discovery: Specific CCI pairs highly enriched in tumor tissue, such as VEGFA-FLT1 (Macrophage-Endothelial) or APP-CD74 (Aneuploid Renal Epithelial cell-Macrophage), could serve as diagnostic or prognostic biomarkers. Their presence or intensity might correlate with disease stage, aggressiveness, or response to therapy.
• Therapeutic Targets: The upregulated interactions in tumors, particularly those involving immune checkpoints (e.g., VSIR-HLA-F) or pro-angiogenic pathways (e.g., VEGFA-FLT1), represent promising targets for novel therapies.
• Blocking VEGFA-FLT1 signaling, for instance, is a well-established strategy in cancer therapy to inhibit angiogenesis.
• Targeting immune checkpoint interactions like VSIR-HLA-F could potentially reinvigorate anti-tumor immune responses, similar to current PD-1/PD-L1 therapies. PubMed search: VISTA immune checkpoint therapy cancer
• Modulating macrophage function, which is central to many tumor-specific CCIs, could be an effective immunotherapeutic strategy.
• Understanding Treatment Resistance: Characterizing these tumor-specific interactions might also shed light on mechanisms of resistance to existing therapies, suggesting new combination approaches.
• Tumor Microenvironment (TME) Modulation: A deeper understanding of these complex intercellular communications, particularly involving macrophages, stromal cells, and tumor cells, is crucial for developing therapies that effectively modulate the TME to foster anti-tumor immunity and inhibit tumor progression.

15. Renal Epithelial Cell Condition-Specific Surfaceome Markers in Kidney Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Renal Epithelial cells, comparing tumor and adjacent normal tissue from kidney samples. The results are visualized as a dot plot, where dot size represents the fraction of cells expressing a gene and color intensity indicates the mean expression level. The cell groups on the y-axis are defined by patient identifier and ploidy status (Diploid or Aneuploid), and genes on the x-axis are restricted to surfaceome markers.

Visual Summary

The dot plot clearly delineates two major groups of Renal Epithelial cells based on their surfaceome marker expression: those from adjacent normal tissue and those from tumor tissue.

1. Adjacent Normal Specific Markers:

• A distinct cluster of genes, including *ATP1B1*, *CLTRN*, *SIGIRR*, *EPCAM*, *SLC3A1*, *TSPAN1*, *FOXP1*, *SLC6A10*, *CLDN10*, *SLC22A8*, *SLC22A6*, and *SLC22A2*, shows consistently high expression and prevalence across most diploid renal epithelial cells from adjacent normal samples.
• These markers are largely absent or expressed at very low levels in tumor cells.

2. Tumor Specific Markers:

• A different set of genes exhibits strong upregulation in renal epithelial cells from tumor samples.
• Prominent HLA Expression: Various *HLA* genes, including *HLA-B*, *HLA-A*, *HLA-C*, *HLA-E*, *HLA-DRA*, *HLA-DRB1*, *HLA-F*, and *HLA-DPA1*, are highly expressed in tumor cells. This pattern is particularly striking in the aneuploid renal epithelial cells from tumor samples (e.g., T5, T7, T9, T6).
• Other Tumor-Associated Markers: Genes such as *CD24*, *CD63*, *CD151*, *LY6E*, *TMEM219*, *PTTG1IP*, *APP*, *ATP1A1*, *CA12*, *SLC6A8*, *DPP4*, *ATRAID*, *BSG*, *ERBB3*, *VCAM1*, and *LRP2* are also significantly upregulated in tumor-derived renal epithelial cells.
• Ploidy-Associated Differences: A strong distinction is observed between diploid and aneuploid renal epithelial cells within the tumor condition. Aneuploid tumor cells generally show higher expression intensity and broader prevalence for most tumor-associated markers compared to diploid tumor cells. The aneuploid tumor cells (N6, N7, N5, N4, N8, N9, N2, N1, T5, T7, T9, T6) form a highly distinct expression group for tumor-specific markers.

Biological Interpretation

The analysis reveals clear molecular signatures distinguishing normal kidney epithelial cells from their malignant counterparts, with further stratification based on ploidy status.

• Normal Renal Epithelial Identity: The markers enriched in adjacent normal renal epithelial cells, such as *EPCAM* (a general epithelial cell adhesion molecule) and *SLC* family genes (*SLC3A1*, *SLC6A10*, *SLC22A8*, *SLC22A6*, *SLC22A2* involved in solute transport), are consistent with the known physiological functions of kidney epithelial cells, particularly in transport and maintaining epithelial integrity.
• Tumor Immune Microenvironment & Evasion: The striking upregulation of both MHC Class I (*HLA-A, -B, -C, -E, -F*) and MHC Class II (*HLA-DRA, -DRB1, -DPA1*) molecules in tumor-derived renal epithelial cells suggests significant immune modulation within the tumor microenvironment. While MHC Class I presentation is ubiquitous for immune surveillance, MHC Class II expression is typically restricted to professional antigen-presenting cells. Its presence on tumor cells can indicate:
• An inflammatory response (e.g., interferon-gamma signaling) driving promiscuous expression.
• An attempt by tumor cells to present antigens, which can sometimes be a precursor to immune escape through subsequent loss of HLA expression or presentation of tolerogenic antigens.
• *HLA-E* expression can be particularly relevant for immune evasion by inhibiting NK cells.
• Key Cancer-Associated Surface Markers: Several identified tumor-specific surface markers are well-established in various cancers:
• *CD24*: A mucin-like glycoprotein associated with cancer stem cell properties, immune evasion, and metastasis. Its upregulation often correlates with poor prognosis in multiple cancer types. GeneCards: CD24
• *CD63* and *CD151*: Members of the tetraspanin family, implicated in cell migration, invasion, and metastatic potential in various cancers. GeneCards: CD63, GeneCards: CD151
• *CA12* (Carbonic Anhydrase XII): An enzyme involved in pH regulation, frequently overexpressed in clear cell renal cell carcinoma (ccRCC), where it contributes to tumor growth and survival in hypoxic conditions. GeneCards: CA12
• *BSG* (Basigin / CD147): A glycoprotein that promotes tumor invasion and metastasis by inducing matrix metalloproteinases and regulating lactate transport. GeneCards: BSG
• *ERBB3* (HER3): A receptor tyrosine kinase often implicated in tumor cell proliferation, survival, and therapeutic resistance, particularly in combination with other HER family members. GeneCards: ERBB3
• Aneuploidy and Tumor Phenotype: The exacerbated expression of tumor-associated surface markers in aneuploid renal epithelial cells suggests a link between genomic instability (indicated by aneuploidy) and a more aggressive or profoundly transformed cellular phenotype. Aneuploidy is a hallmark of cancer and can drive changes in gene expression that contribute to tumor progression.

Clinical or Translational Implications

The identified condition-specific surfaceome markers hold significant potential for clinical applications in kidney cancer.

Diagnostic and Prognostic Biomarkers:

• The distinct panels of surface markers, especially those highly expressed in aneuploid tumor cells (*CD24, CD63, CD151, CA12, BSG, ERBB3*), could serve as powerful diagnostic tools to distinguish malignant renal epithelial cells from benign ones.
• Their expression patterns might also provide prognostic value, potentially identifying patients with more aggressive tumor subtypes (e.g., those driven by aneuploidy).

Therapeutic Targets:

• Given that these are surfaceome markers, they are highly accessible for targeted therapies. Genes like *CD24, CD63, CD151, CA12, BSG, ERBB3*, and *VCAM1* represent compelling candidates for antibody-drug conjugates (ADCs), chimeric antigen receptor (CAR)-T cell therapies, or bispecific antibodies.
• Targeting these markers could allow for selective elimination of tumor cells while sparing normal tissue. The observation that aneuploid tumor cells show particularly high expression of these markers suggests a strategy to specifically target these potentially more aggressive cell populations.
• Further investigation into the functional roles of these markers in kidney cancer progression and their suitability for specific therapeutic modalities is warranted.
• Immunotherapy Guiding: The broad upregulation of *HLA* molecules on tumor cells warrants further investigation into the immune context of these tumors. Understanding whether this HLA expression facilitates effective anti-tumor immunity or represents a mechanism of immune evasion could inform personalized immunotherapeutic strategies.

16. Macrophage Condition-Specific Surfaceome Markers in Kidney Tumor vs. Adjacent Normal Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers for macrophages derived from human kidney single-cell RNA-seq data, comparing tumor tissue to adjacent normal tissue. The plot_markers_and_expression_dot tool was used to visualize the expression of these markers across individual patients grouped by condition, focusing on surfaceome proteins with differential expression. A maximum of 50 markers per condition were considered, and the plot displays the most prominent ones based on expression and prevalence.

Visual Summary

The dot plot effectively illustrates distinct macrophage phenotypes in the adjacent normal versus tumor conditions.

• Patient Grouping: Patients are clearly separated into two main groups: 'adjacent_normal' (N1, N2, N4, N6, N8) and 'tumor' (T2, T3, T4, T5, T6, T7, T8, T9), highlighted by red boxes. The numbers next to each patient ID indicate the total macrophage cell counts contributing to the respective patient group (e.g., T7 has 679 cells, N6 has 59 cells).

Marker Distribution:

• Adjacent Normal Macrophages: A cluster of genes on the left side of the plot (e.g., CD52, EREG, HLA-DQB1, HLA-DQA1, CD63, MS4A6A, HLA-DMB, FCGR3A, TMEM219) shows consistently high mean expression (darker red color) and a high fraction of expressing cells (larger dot size) across most adjacent normal samples.
• Tumor Macrophages: In contrast, a different set of genes towards the middle and right (e.g., RNF130, TREM2, BST2, IGSF6, OLR1, CXCL16, PLXDC2, HLA-F, CD9, CPM, MSR1, GPNMB, SLC1A3) exhibits high expression and prevalence specifically in the tumor samples.
• Expression Patterns: The color intensity (mean expression) and dot size (fraction of cells) highlight the quantitative and qualitative differences. For instance, genes like TREM2 and GPNMB are highly expressed and prevalent in tumor macrophages, while showing minimal to no expression in adjacent normal macrophages. Conversely, MHC Class II genes (HLA-DQA1, HLA-DQB1, HLA-DMB) are strongly expressed in adjacent normal macrophages but largely absent or very low in tumor macrophages.
• Patient Heterogeneity: While clear condition-specific patterns emerge, there is also some variability among patients within each group. For example, within the tumor group, patients like T7, T9, T5, T8, T6 show generally higher expression of tumor-associated markers compared to T3, T2, T4, which also have lower macrophage cell counts.

Biological Interpretation

The differential expression of surfaceome markers reveals a profound phenotypic shift in macrophages within the kidney tumor microenvironment compared to homeostatic conditions in adjacent normal tissue.

• Antigen Presentation and Immune Surveillance in Normal Tissue: Macrophages in adjacent normal kidney tissue display high expression of MHC Class II molecules (HLA-DQB1, HLA-DQA1, HLA-DMB). This indicates an active role in antigen presentation, immune surveillance, and maintaining immune homeostasis. CD63 is a lysosomal protein that can be upregulated upon macrophage activation, while FCGR3A (CD16a) mediates antibody-dependent cellular cytotoxicity (ADCC) and phagocytosis, further supporting their role in baseline immune function. CD52 is a general leukocyte marker.
• Pro-tumorigenic and Immunosuppressive Phenotype in Tumor: Macrophages within kidney tumors exhibit a distinct signature, strongly indicative of a pro-tumorigenic and immunosuppressive phenotype, often referred to as Tumor-Associated Macrophages (TAMs).
• TREM2 (Triggering Receptor Expressed on Myeloid Cells 2) is a well-established marker for TAMs, associated with immune suppression, tissue repair, efferocytosis, and promoting tumor growth and metastasis [GeneCards: TREM2]. Its high expression suggests a crucial role in shaping the immune-evasive tumor microenvironment.
• MSR1 (Macrophage Scavenger Receptor 1 / CD204) is a key scavenger receptor often associated with M2-like, alternatively activated macrophages, which typically exert immunosuppressive functions and promote tumor progression [GeneCards: MSR1].
• GPNMB (Glycoprotein NMB) is a transmembrane glycoprotein that promotes tumor growth, angiogenesis, and metastasis in various cancers, including renal cell carcinoma [GeneCards: GPNMB]. Its strong expression in tumor macrophages further supports a pro-tumorigenic role.
• OLR1 (Oxidized Low-Density Lipoprotein Receptor 1 / LOX-1) is involved in inflammation and can contribute to pro-inflammatory and pro-tumorigenic responses in macrophages [GeneCards: OLR1].
• CD9 (a tetraspanin) is often associated with cell migration, invasion, and tumor progression, potentially facilitating TAM interactions with cancer cells or the extracellular matrix [GeneCards: CD9].
• BST2 (CD317/Tetherin) is an interferon-inducible protein, while CXCL16 is a chemokine. Their co-expression with other pro-tumorigenic markers might suggest complex immune regulatory roles within the tumor, potentially influencing immune cell recruitment or evasion.
• HLA-F is a non-classical MHC Class I molecule, which can be involved in immune evasion by tumor cells or stromal cells, including TAMs [GeneCards: HLA-F].
• SLC1A3 (a glutamate transporter) suggests altered metabolic activity in TAMs, which is a known characteristic of the tumor microenvironment.

This analysis highlights a clear phenotypic reprogramming of macrophages in the kidney tumor context, transitioning from antigen-presenting and immune surveillance roles in normal tissue to a distinct pro-tumorigenic and immunosuppressive state in the tumor.

Clinical or Translational Implications

The identified condition-specific surfaceome markers for macrophages hold significant clinical and translational potential for kidney cancer.

Biomarker Discovery:

• Diagnostic/Prognostic Markers: High expression of genes like TREM2, MSR1, GPNMB, OLR1, and CD9 on macrophages could serve as potential diagnostic biomarkers for kidney cancer or as prognostic indicators correlating with disease aggressiveness, stage, or risk of recurrence. These markers could be assessed in tumor biopsies or potentially through circulating macrophage populations.
• Therapeutic Response Prediction: The presence and specific phenotype of TAMs, as defined by these markers, might predict patient response to various cancer therapies, including immunotherapies.

Therapeutic Targeting Strategies:

• Direct Targeting of TAMs: Markers such as TREM2 and GPNMB are promising targets for therapeutic intervention aimed at depleting or reprogramming pro-tumorigenic TAMs in kidney cancer [PubMed search: TREM2 cancer therapy; PubMed search: GPNMB cancer therapy]. Antibodies or small molecules could be developed to specifically target macrophages expressing these surface proteins.
• Modulation of the Tumor Microenvironment: Understanding the unique surfaceome of TAMs can guide strategies to modulate the tumor microenvironment, for example, by interfering with their migratory cues (e.g., via CXCL16-CXCR6 axis) or their immunosuppressive functions.
• Combination Therapies: Targeting TAMs (e.g., through TREM2 or GPNMB inhibition) could be combined with existing standard-of-care treatments, such as immune checkpoint inhibitors or chemotherapy, to enhance therapeutic efficacy by overcoming macrophage-mediated immune suppression.

This analysis provides a strong foundation for further investigation into the functional roles of these macrophage subsets in kidney cancer and their potential as targets for novel therapeutic strategies.

17. Condition-Specific Surface Markers for CD4+ T cells in Renal Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that distinguish CD4+ T cells residing in 'adjacent_normal' kidney tissue from those infiltrating 'tumor' tissue. Using single-cell RNA sequencing data, differential gene expression analysis was performed on CD4+ T cells from various patient samples to pinpoint condition-specific surface markers. The plot_markers_and_expression_dot tool was employed to visualize the expression of the top 50 markers per condition, focusing exclusively on surface proteins.

Visual Summary

The provided dot plot effectively illustrates the expression patterns of identified surface markers for CD4+ T cells across different samples, categorized by 'adjacent_normal' (N1) and 'tumor' (T2, T3, T5, T6, T7, T8, T9) conditions.

• Dot Size and Color: The size of each dot corresponds to the fraction of cells within that sample expressing the gene, while the color intensity (ranging from white to dark red) represents the mean expression level of the gene in those cells.

Condition-Specific Patterns:

• Adjacent Normal Markers: A distinct set of genes, including KLRB1, CD69, GPR183, and AREG, are highly expressed and prevalent in CD4+ T cells from the 'adjacent_normal' sample (N1). These genes show minimal to no expression in the tumor samples.
• Tumor-Specific Markers: Conversely, genes such as PTPRC, HLA-DRA, HLA-DPB1, HLA-DPA1, HLA-DRB1, ITGB1, CD3G, and ATP1B3 exhibit strong and widespread expression across the majority of the 'tumor' samples. Their expression is very low or absent in the 'adjacent_normal' sample.
• Sample Variability: While general trends are observed, there is some variability in expression levels and cell fraction across individual tumor samples. For example, some tumor samples (e.g., T6) have fewer CD4+ T cells, as indicated by the bar chart on the right, but still show the general tumor-specific marker expression profile.

Biological Interpretation

The analysis reveals two distinct phenotypic signatures for CD4+ T cells, contingent on their tissue microenvironment in the kidney:

1. CD4+ T cells in Adjacent Normal Tissue:

• The prominent expression of KLRB1 (CD161) [UniProt: P26715] suggests the presence of innate-like T cells or T cells with regulatory functions.
• CD69 [UniProt: P32242], an early activation marker, when sustained, often denotes tissue-resident memory T cells (Trm), indicating an immune surveillance role in the healthy kidney.
• GPR183 (EBI2) [UniProt: O15589] is a chemoattractant receptor crucial for lymphocyte migration and positioning, further supporting a role in immune surveillance and homeostatic maintenance.
• AREG (Amphiregulin) [UniProt: P15514], an EGF family growth factor, can contribute to tissue repair and immune modulation, possibly indicating a protective or reparative function of these T cells in normal tissue.
• Collectively, these markers point to a CD4+ T cell population in normal kidney tissue that is likely involved in tissue homeostasis, immune surveillance, and potentially a resident memory phenotype.

2. CD4+ T cells in Tumor Tissue:

• The consistent expression of PTPRC (CD45) [UniProt: P08575] and CD3G [UniProt: P09693] serves to confirm the T cell identity within the tumor microenvironment.
• A striking observation is the widespread upregulation of MHC Class II molecules (HLA-DRA, HLA-DPB1, HLA-DPA1, HLA-DRB1) [UniProt: P01903 (HLA-DRA); UniProt: P04440 (HLA-DPB1); UniProt: P20022 (HLA-DPA1); UniProt: P01911 (HLA-DRB1)] on CD4+ T cells within the tumor. While typically expressed by antigen-presenting cells, T cells can express MHC Class II upon chronic activation, which can have complex implications, including self-presentation, modulation of T cell anergy, or interaction with other immune cells. This suggests a highly activated or chronically stimulated state of CD4+ T cells in the tumor, potentially linked to exhaustion or regulatory phenotypes.
• ITGB1 (CD29) [UniProt: P05556], an integrin subunit, indicates altered cell adhesion and migratory capabilities, which is consistent with T cell infiltration into the tumor and interactions with the extracellular matrix.
• ATP1B3 [UniProt: P54709], a subunit of Na+/K+-ATPase, implies changes in ion transport and cellular metabolism, which are often modified in activated or dysfunctional T cells within the tumor microenvironment.
• The robust expression of these markers suggests that CD4+ T cells in renal tumors undergo significant phenotypic changes, indicating an altered functional state often associated with chronic antigen exposure, inflammation, or immune suppression within the tumor microenvironment.

Clinical or Translational Implications

The identification of these condition-specific surface markers for CD4+ T cells holds several clinical and translational implications:

• Biomarker Discovery: The distinct surfaceome profiles could serve as valuable biomarkers for distinguishing between tumor-infiltrating and normal tissue-resident CD4+ T cells in renal cell carcinoma. This could be useful for diagnostic purposes, disease staging, or monitoring treatment response.

Therapeutic Targeting:

• The prominent expression of MHC Class II molecules on tumor-infiltrating CD4+ T cells presents an intriguing potential target. Modulating these molecules, or their downstream signaling, could alter the functional state of these T cells within the tumor, potentially shifting them from an immunosuppressive to an anti-tumorigenic phenotype.
• ITGB1 could be a target to influence T cell trafficking and their interaction with the tumor microenvironment, which is critical for effective anti-tumor immunity.
• Immunotherapy Stratification: Understanding these specific phenotypes might help stratify patients for immunotherapies. For instance, the high expression of MHC Class II on CD4+ T cells in the tumor could indicate a specific immune context that might respond differently to checkpoint blockade or other T cell-modulating therapies.
• Experimental Validation: These identified markers provide strong candidates for further experimental validation using techniques such as flow cytometry or immunohistochemistry on patient tissue samples. Such validation could confirm protein-level expression and correlate these findings with clinical outcomes, advancing our understanding of renal cancer immunology.

18. 신장 상피세포의 유전자 온톨로지(GSA) 분석 결과

[Analysis Visualization Results]...

분석 개요

본 분석은 단일 세포 RNA 시퀀싱 데이터에서 얻은 신장 상피세포(Renal Epithelial cell)의 유전자 온톨로지(Gene Ontology, GO) 경로 농축 분석(Gene Set Enrichment Analysis, GSA) 결과입니다. AnnData의 uns['GSA_up']에 사전 계산된 결과를 사용하여, 신장 상피세포를 다음 세 가지 조건에서 다른 세포들과 비교하여 유의하게 상향 조절된(up-regulated) GO 용어들을 시각화합니다.

1. 이배체(Diploid) 신장 상피세포 vs. 기타: 이배체 상태의 신장 상피세포에서 특이적으로 활성화된 경로를 보여줍니다.
2. 인접 정상(Adjacent Normal) 신장 상피세포 vs. 기타: 종양에 인접한 정상 신장 조직 내 상피세포의 특성을 나타냅니다.
3. 종양(Tumor) 신장 상피세포 vs. 기타: 신장 종양 조직 내 상피세포의 특성을 보여줍니다.

각 바 플롯은 GO 용어들의 -log(p-val)과 -log(q-val) 값을 기준으로 유의성을 나타내며, 높은 값이 더 큰 통계적 유의성을 의미합니다.

시각적 요약

세 개의 바 플롯은 각 비교 조건에서 상향 조절된 유전자 세트 또는 경로를 보여줍니다.

• 전반적인 패턴: 인접 정상 및 종양 신장 상피세포에서는 이배체 신장 상피세포에 비해 훨씬 더 많은 GO 용어들이 높은 통계적 유의성(특히 -log(q-val) 측면에서)으로 농축되어 있음을 보여줍니다. 이는 인접 정상 및 종양 세포가 전체 세포 집단 내에서 매우 독특하고 활발한 생물학적 활동을 가지고 있음을 시사합니다.
• 유의성 비교: 이배체 세포 분석 결과에서는 -log(p-val) 값이 상대적으로 낮고, -log(q-val) 값은 더욱 낮아 다중 검정 보정 후 유의미한 경로의 수가 적고 유의성도 약하다는 것을 알 수 있습니다. 반면, 인접 정상 및 종양 세포 분석에서는 최상위 용어들이 매우 높은 -log(p-val) 및 -log(q-val) 값을 보여주며, 이는 강력하고 신뢰할 수 있는 생물학적 신호를 나타냅니다.

생물학적 해석

1. 이배체 신장 상피세포 (Diploid_vs_others)

이배체 신장 상피세포는 주로 다음과 같은 경로들과 관련이 높습니다:

• 면역 및 염증 반응: "Staphylococcus aureus infection", "Asthma", "Systemic lupus erythematosus", "Rheumatoid arthritis", "Autoimmune thyroid disease", "Viral myocarditis", "Leishmaniasis", "Allograft rejection", "Graft-versus-host disease", "Th1 and Th2 cell differentiation", "Type I diabetes mellitus" 등 다양한 감염, 자가면역 질환 및 면역 세포 분화 관련 용어들이 상위에 있습니다. 이는 이배체 신장 상피세포가 일반적인 면역 반응, 염증 조절 또는 병원체 인식에 적극적으로 관여할 수 있음을 시사합니다. 또한, "Antigen processing and presentation", "Phagosome", "Cell adhesion molecules" 같은 용어들도 이 세포들이 면역 감시 또는 면역 세포와의 상호작용에 중요한 역할을 함을 지지합니다.
• 낮은 유의성: 다른 두 비교군에 비해 전반적인 유의성(특히 q-value)이 낮아, 이러한 면역 관련 특징이 다른 세포 유형이나 조건에 비해 아주 독보적이지 않을 수 있음을 시사합니다.

2. 인접 정상 신장 상피세포 (adjacent_normal_vs_others)

인접 정상 신장 상피세포는 높은 유의성으로 다음과 같은 경로들이 농축되어 있습니다:

• 에너지 대사 및 물질 수송: "Oxidative phosphorylation" (산화적 인산화), "Citrate cycle (TCA cycle)" (구연산 회로), "Valine, leucine and isoleucine degradation" (아미노산 분해), "Fatty acid degradation" (지방산 분해), "Protein processing in endoplasmic reticulum" (소포체 내 단백질 처리) 등 세포의 주요 에너지 생산 및 대사 경로가 두드러집니다. 이는 정상 신장 상피세포가 활발한 물질 대사를 통해 신장 기능(여과, 재흡수, 분비)을 수행하는 데 필요한 높은 에너지 요구량을 충족함을 반영합니다.
• 신장 특이적 기능: "Proximal tubule bicarbonate reabsorption" (근위세관 중탄산염 재흡수)과 같은 용어는 신장 상피세포의 특수화된 생리적 기능을 직접적으로 나타냅니다.
• 신경 퇴행성 질환 관련: "Parkinson disease", "Huntington disease", "Alzheimer disease", "Amyotrophic lateral sclerosis" 등 신경 퇴행성 질환 관련 용어들이 상위에 나타나는데, 이는 신장 상피세포와 신경 세포가 단백질 항상성, 미토콘드리아 기능 장애, 산화 스트레스 등과 같은 기본 세포 과정에서 공통적인 취약성 또는 조절 기전을 공유할 수 있음을 시사합니다 [PubMed search: protein misfolding mitochondrial dysfunction oxidative stress].

3. 종양 신장 상피세포 (tumor_vs_others)

종양 신장 상피세포는 인접 정상 세포와 유사하게 높은 유의성으로 에너지 대사 및 신경 퇴행성 질환 관련 용어들이 나타나지만, 종양 특이적인 중요한 경로들이 추가적으로 농축되어 있습니다:

• 암 관련 특징: "HIF-1 signaling pathway" (HIF-1 신호 전달 경로), "Angiogenesis" (혈관신생), "Ubiquitin mediated proteolysis" (유비퀴틴 매개 단백질 분해), "Mitophagy" (미토파지), "Apoptosis" (세포자멸사), "Tight junction" (밀착연접), "Adherens junction" (부착연접), "Viral carcinogenesis" (바이러스 발암), "Renal cell carcinoma" (신장 세포암)와 같은 용어들이 관찰됩니다.
• HIF-1 신호 전달 경로 및 혈관신생: 종양 미세환경의 저산소증에 대한 반응 및 종양 성장에 필수적인 혈관신생을 촉진하는 핵심 경로입니다 [PubMed search: HIF-1 signaling angiogenesis renal cell carcinoma].
• 단백질 항상성 조절: "Ubiquitin mediated proteolysis" 및 "Proteasome" 관련 경로는 종양 세포가 비정상적인 단백질을 처리하고 생존에 필요한 단백질 수준을 조절하는 데 중요함을 나타냅니다 [PubMed search: ubiquitin proteasome system cancer].
• 세포 생존 및 사멸 조절: "Mitophagy" (손상된 미토콘드리아 제거) 및 "Apoptosis" (세포자멸사) 관련 경로는 종양 세포가 세포 사멸 저항성을 획득하고 미토콘드리아 건강을 유지하는 기전을 반영합니다 [PubMed search: mitophagy apoptosis cancer].
• 세포-세포 접합 변화: "Tight junction"과 "Adherens junction" 관련 경로의 변화는 상피세포의 극성 상실 및 이동성 증가와 관련되어 종양 전이 가능성을 시사합니다 [PubMed search: cell junction renal cell carcinoma metastasis].
• 직접적인 암 연관성: "Renal cell carcinoma"와 "Viral carcinogenesis"는 신장암의 발생 및 진행에 대한 직접적인 관련성을 보여줍니다.
• 감염 및 염증: "Salmonella infection", "Pathogenic Escherichia coli infection" 등 감염 관련 용어들은 종양 미세환경 내의 면역 회피 또는 만성 염증 반응과 관련될 수 있습니다.

임상적 또는 중개적 함의

• 바이오마커 발굴: 종양 신장 상피세포에서 특이적으로 농축된 경로들(예: HIF-1 신호 전달, 혈관신생, 단백질 항상성 조절)은 신장암의 진단 및 예후 예측을 위한 잠재적인 바이오마커로 활용될 수 있습니다.
• 치료 표적: 이러한 경로들은 신장암 치료를 위한 새로운 약물 표적이 될 수 있습니다. 예를 들어, HIF-1 신호 전달 및 혈관신생 억제제는 이미 신장암 치료에 사용되거나 연구되고 있습니다 [PubMed search: kidney cancer targeted therapy HIF-1 angiogenesis].
• 질병 메커니즘 이해: 정상 세포와 종양 세포 간의 대사 및 세포 생존 기전의 차이를 명확히 이해함으로써, 신장암의 발생 및 진행 메커니즘에 대한 심층적인 통찰력을 얻을 수 있습니다. 또한, 이배체 신장 상피세포의 면역 관련 특징은 초기 종양 발생 또는 면역 회피 기전에 대한 연구에 기여할 수 있습니다.

19. Gene Set Enrichment Analysis Reveals Pathway Dysregulation Across Kidney Tumor Microenvironment

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results visualized as a dot plot. The goal was to identify significantly enriched (upregulated) or depleted (downregulated) biological pathways within specific cell types when comparing cells from the tumor microenvironment against all other cells, or adjacent normal tissue against all other cells. The analysis focused on key cell populations in kidney tissue: Endothelial cells, ILCs, Macrophages, Renal Epithelial cells (including diploid and aneuploid states), Smooth muscle cells, T cells CD4+, and T cells CD8+. The color intensity of each dot reflects the Normalized Enrichment Score (NES), where red indicates positive enrichment (pathway upregulated in the test condition) and blue indicates negative enrichment (pathway downregulated in the test condition). The size of the dot represents the significance, specifically the -log10(p-value), with larger dots indicating higher statistical significance.

Visual Summary

The dot plot effectively displays a complex landscape of pathway alterations across different cell types and conditions.

• Prevalence of Enrichment: Numerous pathways show significant enrichment or depletion (large dots) across multiple cell types and conditions, indicating widespread biological reprogramming in the kidney tumor microenvironment.
• Differential Regulation: Both positive (red, increased activity) and negative (blue, decreased activity) enrichment scores are observed, highlighting diverse cellular responses. The RdBu_r colormap clearly distinguishes between up- and down-regulated pathways.
• Condition-Specific Patterns: There is a clear distinction between "tumor_vs_others" and "adjacent_normal_vs_others" conditions. Tumor-associated cells generally exhibit a greater number of strongly enriched pathways, often related to cancer hallmarks, compared to adjacent normal cells.
• Cell Type Specificity: While some pathways are broadly affected, many show cell-type-specific enrichment patterns, reflecting the specialized roles of each cell type within the tumor and its surrounding tissue.
• Significance: Many dots are large, indicating highly significant enrichments (high -log(P) values, some reaching the maximum size corresponding to -log(P) > 30), particularly in tumor-associated cell populations.

Biological Interpretation

The GSEA results provide profound insights into the molecular mechanisms driving kidney cancer progression and the host response.

Renal Epithelial Cells: The Tumor Core

As the tumor origin cell type, Renal Epithelial cells show the most striking changes:

• Aneuploidy and Cancer Pathways: Renal Epithelial cell: Aneuploid_vs_others (representing the malignant population) exhibits strong positive enrichment for core cancer pathways such as "Pathways in cancer" (e.g., KEGG pathway hsa05200) and specific oncogenic signaling like "PI3K-Akt signaling pathway" and "MAPK signaling pathway" [PubMed search: PI3K-Akt pathway cancer]. This reflects uncontrolled proliferation, survival, and metabolic reprogramming.
• Metabolic Reprogramming: Aneuploid renal epithelial cells are highly enriched for "Purine metabolism", "Pyruvate metabolism", and "N-Glycan biosynthesis". This indicates a metabolic shift to support rapid growth and macromolecule synthesis, a hallmark of cancer cells [PubMed search: cancer metabolic reprogramming]. Conversely, "Drug metabolism" and "Mineral absorption" pathways are significantly downregulated, suggesting a loss of normal kidney epithelial function in tumor cells.
• Diploid vs. Aneuploid: Diploid renal epithelial cells in the tumor context (Renal Epithelial cell: Diploid_vs_others) show a different profile, with less pronounced enrichment in classical cancer pathways compared to their aneuploid counterparts, reinforcing the distinction between potentially pre-malignant/stressed diploid cells and fully transformed aneuploid cells.

Immune Cell Dynamics in the Tumor Microenvironment

The immune cell populations exhibit distinct adaptive responses within the tumor context:

• Macrophages: Macrophage: tumor_vs_others shows strong positive enrichment for "Fc gamma R-mediated phagocytosis", "MAPK signaling pathway", "PI3K-Akt signaling pathway", and "Autophagy". This suggests activated macrophages that are actively involved in phagocytosis, signaling, and cellular stress responses, potentially shifting towards pro-tumorigenic phenotypes (e.g., M2-like polarization) or an inflammatory M1-like state [PubMed search: tumor associated macrophages M1 M2]. The "Inflammatory bowel disease" pathway enrichment might reflect a general inflammatory gene signature in these cells.
• T Cells (CD4+ and CD8+): Both T cell CD4+: tumor_vs_others and T cell CD8+: tumor_vs_others show enrichment in "T cell receptor signaling pathway", "MAPK signaling pathway", and "PI3K-Akt signaling pathway", consistent with T cell activation and ongoing immune responses. Interestingly, "Cellular senescence" is also enriched, which could indicate T cell exhaustion, a common feature in the tumor microenvironment where T cells become dysfunctional after chronic antigen exposure [PubMed search: T cell exhaustion cancer]. The enrichment of pathways related to neurodegenerative diseases ("Huntington disease", "Alzheimer disease", "Prion disease") could point to shared cellular stress, protein quality control, or inflammatory mechanisms rather than direct relevance to the disease itself.
• ILCs: ILC: tumor_vs_others similarly shows enrichment in immune signaling pathways ("MAPK signaling pathway", "PI3K-Akt signaling pathway", "Fc gamma R-mediated phagocytosis"), suggesting their active participation in the innate immune response within the tumor.

Stromal and Vascular Remodeling

• Endothelial Cells: Endothelial cell: tumor_vs_others is highly enriched for "Pathways in cancer", "MAPK signaling pathway", "PI3K-Akt signaling pathway", and "Vascular smooth muscle contraction". This profile strongly indicates active angiogenesis, crucial for tumor growth, and highlights the endothelial cells' participation in tumor-associated signaling networks [PubMed search: tumor angiogenesis PI3K-Akt].
• Smooth Muscle Cells: Smooth muscle cell: tumor_vs_others also shows enrichment in "Vascular smooth muscle contraction", "MAPK signaling pathway", "PI3K-Akt signaling pathway", and "Rap1 signaling pathway". This suggests their involvement in vascular remodeling, regulation of blood flow, and extracellular matrix deposition, all critical components of the tumor stromal response.

Clinical or Translational Implications

• Therapeutic Targets: The consistent enrichment of pathways like PI3K-Akt and MAPK signaling across multiple tumor-associated cell types, particularly the malignant Renal Epithelial cells and supportive stromal/immune cells, suggests these pathways are central to kidney tumor biology. Targeting these pathways (e.g., with PI3K inhibitors or MEK inhibitors) could offer broad anti-tumor effects by simultaneously impacting cancer cells, angiogenesis, and immune suppression [GeneCards: PIK3CA, GeneCards: MAPK1].
• Biomarkers of Progression: The specific metabolic reprogramming signatures in aneuploid renal epithelial cells (e.g., Purine/Pyruvate metabolism, N-Glycan biosynthesis) could serve as potential biomarkers for aggressive disease or as metabolic vulnerabilities for therapeutic intervention.
• Immune Checkpoint Strategies: The signs of T cell activation alongside cellular senescence in tumor-infiltrating T cells underscore the complex interplay of anti-tumor immunity and exhaustion. Further investigation into specific senescence markers and their correlation with response to immune checkpoint blockade could refine patient stratification for immunotherapy.
• Tumor Microenvironment Modulation: Targeting angiogenesis-related pathways in endothelial and smooth muscle cells could be critical for starving tumors of nutrients and oxygen, complementing direct anti-cancer therapies.

20. Discussion

The single-cell RNA sequencing analysis of human kidney tissue provides a high-resolution view of the cellular and molecular landscape in Renal Cell Carcinoma (RCC), distinguishing it from adjacent normal tissue. A central and striking finding is the robust identification of aneuploid Renal Epithelial cells as the tumor-origin population, characterized by extensive chromosomal instability, including canonical 3p deletions and recurrent gains on 5q, 7, 11q, 12q, 16q, 17, and 20q. These genomic aberrations are a direct driver of malignancy and are closely linked to a highly transformed cellular phenotype, evident in their unique surfaceome markers and active cancer-related pathways.

The tumor microenvironment (TME) undergoes dramatic remodeling. Immune cell populations are significantly altered, with an increased infiltration of cytotoxic (CD8+) T cells, but a concurrent decrease in naive T cells, NK cells, ILCs, and M2A macrophage subsets. This suggests a complex immune response that, despite an influx of effector T cells, may be ultimately ineffective or suppressed. Macrophages, in particular, exhibit a profound phenotypic shift in the tumor, transitioning from antigen-presenting cells in normal tissue to a pro-tumorigenic and immunosuppressive state. This is evidenced by their distinct surface markers such as TREM2, MSR1, and GPNMB, and their prominent involvement in numerous cell-cell interactions.

Cell-cell interaction analysis reveals a highly active and interconnected communication network within the TME, profoundly distinct from the relatively quiescent state of normal tissue. Macrophages emerge as a central hub, interacting extensively with T cells, endothelial cells, and aneuploid renal epithelial cells. Critical pro-tumorigenic and immunosuppressive interactions include APOE-TREM2, VSIR-HLA-E/F, TNF-TNFRSF1B, and LGALS9-P4HB, suggesting active mechanisms of immune evasion and inflammation. Furthermore, significant interactions involving Integrins-Collagen and VEGFA-FLT1 highlight vigorous angiogenesis and extracellular matrix remodeling by endothelial and stromal cells, crucial for tumor growth and invasion.

Pathway enrichment analyses consistently underscore the activation of canonical cancer pathways such as PI3K-Akt, MAPK, and HIF-1 signaling across malignant renal epithelial cells, endothelial cells, and macrophages. Malignant epithelial cells also exhibit distinct metabolic reprogramming (e.g., purine, pyruvate metabolism), reflecting their high energetic demands. The presence of "cellular senescence" pathways in T cells suggests T cell exhaustion, a common mechanism of immune evasion in cancer, despite their increased presence.

These findings collectively paint a detailed picture of RCC progression, driven by intrinsic genomic instability in tumor cells and supported by a highly dynamic, immunosuppressive, and pro-angiogenic microenvironment. The multi-omic insights into cellular heterogeneity, altered cell states, and specific intercellular communication pathways provide a robust foundation for identifying novel diagnostic biomarkers and developing targeted therapeutic strategies for kidney cancer.

Hypotheses:

1. Aneuploidy drives specific metabolic vulnerabilities in RCC: The extensive aneuploidy in renal epithelial cells dictates a unique metabolic reprogramming (e.g., purine, pyruvate metabolism) that could be targeted to selectively inhibit tumor growth.
2. Macrophage plasticity is hijacked by RCC through specific ligand-receptor axes: The observed shift in macrophage phenotype (M2A decrease, TREM2/MSR1/GPNMB upregulation) and their central role in immunosuppressive CCIs (APOE-TREM2, VSIR-HLA-E/F) suggest that RCC cells actively reprogram macrophages to foster a pro-tumorigenic and immune-evasive microenvironment.
3. T cell exhaustion in RCC is mediated by chronic activation and specific TME signals: The increased cytotoxic T cell infiltration coupled with signs of cellular senescence and interactions involving immune checkpoints (e.g., VSIR-HLA-E/F) suggests that tumor-infiltrating T cells are functionally exhausted, contributing to immune evasion.
4. Novel surface markers on aneuploid renal epithelial cells correlate with aggressive disease and therapeutic resistance: Upregulated surfaceome markers like CD24, CD63, CD151, CA12, BSG, and ERBB3 on aneuploid tumor cells indicate a more aggressive phenotype and may mediate resistance to conventional therapies.

Potential therapeutic targets:

1. TREM2: Highly expressed on pro-tumorigenic macrophages (TAMs) in the tumor microenvironment. Associated with immunosuppression and promoting tumor growth and metastasis. Evidence: CCI analysis showed significant "APOE_TREM2_receptor" interactions involving macrophages in tumor. Macrophage condition-specific markers showed high TREM2 expression in tumor macrophages. Validation: *In vitro* functional assays (e.g., macrophage polarization, T cell suppression) using TREM2 inhibitors/agonists in RCC co-culture models. *In vivo* studies using genetic knockout or antibody blockade of TREM2 in murine RCC models, assessing tumor growth and immune infiltration.
2. VISTA (VSIR): An immune checkpoint molecule strongly involved in immunosuppressive cell-cell interactions within the tumor, known to suppress T cell responses and contribute to immune evasion. Evidence: CCI analysis highlighted significant and widespread "VSIR_HLA-E/F" interactions involving macrophages and T cells in tumor condition. GSEA explanation also notes VISTA as an immune checkpoint. Validation: Development and testing of VISTA-blocking antibodies in RCC pre-clinical models, alone or in combination with other immune checkpoint inhibitors (e.g., anti-PD-1), assessing anti-tumor immunity and T cell function.
3. CA12 (Carbonic Anhydrase XII): A tumor-specific surface marker highly upregulated in aneuploid renal epithelial cells. Involved in pH regulation and tumor survival in hypoxic conditions, frequently overexpressed in ccRCC. Evidence: Renal Epithelial cell condition-specific markers showed significant upregulation of CA12 in tumor samples, particularly in aneuploid cells. Explanation explicitly states CA12's role in ccRCC growth and survival. Validation: *In vitro* studies to assess the effect of CA12 inhibitors on RCC cell proliferation and survival under hypoxic conditions. *In vivo* xenograft models to evaluate the anti-tumor efficacy of CA12-targeted therapies (e.g., small molecule inhibitors or ADCs).
4. PI3K-Akt signaling pathway: Consistently enriched across multiple tumor-associated cell types, including malignant renal epithelial cells, macrophages, and endothelial cells, indicating its central role in driving tumor proliferation, survival, and microenvironment support. Evidence: GSEA showed strong positive enrichment for "PI3K-Akt signaling pathway" in Aneuploid Renal Epithelial cells, Macrophages, Endothelial cells, and T cells from tumor conditions. Validation: Clinical trials with existing PI3K/Akt pathway inhibitors in RCC patients, or development of novel inhibitors. Pre-clinical studies to investigate combination therapies that include PI3K/Akt inhibition alongside other targeted agents or immunotherapies.

Follow-up validation ideas:

1. Functional validation of metabolic targets: *In vitro* assays using RCC cell lines (aneuploid vs. diploid) and *in vivo* xenograft models to test the efficacy of inhibitors targeting purine or pyruvate metabolism. This could be coupled with stable isotope tracing to confirm metabolic shifts.
2. Reprogramming TAMs in RCC: *In vitro* co-culture experiments with RCC cells and macrophages, using specific inhibitors for TREM2 or VISTA, followed by functional assays (phagocytosis, cytokine secretion, T cell suppression) and RNA-seq to confirm phenotype reversal. Flow cytometry could validate surface marker changes.
3. Investigating T cell exhaustion mechanisms: Flow cytometry and mass cytometry (CyTOF) on patient tumor samples to characterize the co-expression of exhaustion markers (e.g., PD-1, TIM-3, LAG-3) with cellular senescence markers on CD8+ and CD4+ T cells. *In vitro* T cell activation assays in the presence of tumor-derived factors or cocultured with RCC cells to induce and reverse exhaustion using checkpoint inhibitors.
4. Therapeutic targeting of RCC surface markers: *In vivo* patient-derived xenograft (PDX) models or syngeneic models to test the efficacy of antibody-drug conjugates (ADCs) or CAR-T cells engineered against highly expressed surface markers like CD24, CA12, or ERBB3. Immunohistochemistry or multiplex immunofluorescence on clinical samples to validate marker expression and correlate with clinical outcomes.
5. Spatial transcriptomics/proteomics: Use spatial omics technologies to map the precise localization of identified cell types (e.g., M2A macrophages, exhausted T cells, aneuploid RCC cells) and key ligand-receptor interactions (e.g., APOE-TREM2, VSIR-HLA-E) within the kidney tumor microenvironment, confirming cellular proximity and interaction *in situ*.

Limitations:

This single-cell analysis provides comprehensive insights but has inherent limitations. The cross-sectional nature of the data does not establish causality; longitudinal studies are needed to understand temporal changes in tumor evolution. While CNV inference and ploidy assignment are robust, they are computational estimates and would benefit from orthogonal genomic validations like FISH. Cell-cell interaction predictions are based on ligand-receptor expression and do not directly demonstrate functional signaling; experimental validation is needed. Furthermore, while surfaceome markers are excellent therapeutic candidates, their specificity and internalization kinetics require experimental validation before clinical translation. The 'unassigned' cell populations, particularly in tumor samples, warrant further investigation to ensure complete cell type characterization and prevent potential bias.

21. Query List

1. Show UMAPs including condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset in 2 columns and save.
2. Show major celltype scores on UMAP and save.
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 regions. Save.
5. Show CNV patterns as UMAPs. Include major cell type, minor cell type, ploidy results, condition, and sample in 2 columns and save.
6. Show a population bar plot of minor cell types and save.
7. Show a subset population bar plot for T cells and save.
8. Show box plots for T cell subset populations if there are significant differences between conditions and save. Set ncols appropriately considering the total number of panels.
9. Show a subset population bar plot for macrophages and save.
10. Show box plots for macrophage subset populations if there are significant differences between conditions and save. Set ncols appropriately considering the total number of panels.
11. Select tumor-origin cells and unassigned cells, show a bar plot of their ploidy population, and save.
12. Show cell-cell interaction patterns by condition, including Renal Epithelial cells, fibroblasts, macrophages, and T cells, and save. For cell-cell interactions, select up to 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. Find statistically significant differences in cell-cell interactions between conditions for major immune cells and stromal cells, show them as a dot plot, and save. Set max_n_items_per_group = 25.
15. Show the condition-specific markers for tumor-origin cells (Renal Epithelial cell) as a dot plot and save the result. Use only surfaceome markers, up to 50 markers per condition.
16. Extract condition-specific markers for macrophages, show them as a dot plot, and save. Only surfaceome markers, up to 50 per condition.
17. Extract condition-specific markers for CD4 T cells, show them as a dot plot, and save. Only surfaceome markers, up to 50 per condition.
18. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
19. Show dot plots of Gene Set Enrichment Analysis results for Endothelial cells, ILC, Macrophages, Renal Epithelial cells, Smooth muscle cells, T cell CD4+, and T cell CD8+ and save. Set color map to RdBu_r and n_pws_to_show = 80.