Project2:Integrative AnalysisofSingle-Cell
RNASequencingData toUncoverCellular HeterogeneityinTumor
Microenvironment
ProjectDescription:
Thisprojectexploredsingle-cellRNAsequencing (scRNA-seq)todissectthecellulardiversitywithinthetumor microenvironment(TME)usingperipheralblood mononuclearcells(PBMCs)from10xGenomics.Thegoal wastoidentifyimmune,stromal,andrareregulatorycell typescontributingtotumorprogressionandresistance,andto uncovertherapeuticinsightsusingdimensionalityreduction, clustering,annotation,andpathwayenrichment.
AdvancedMethodology Pipeline:
1. Data Preprocessing and Normalization
• Data sourced from 10x Genomics PBMC dataset.
• Processed using Seurat in R and Scanpy in Python.
• Quality control removed low-quality cells and high mitochondrial reads.
• SCTransform normalization applied.
• Batch effects corrected with Harmony.
2. Dimensionality Reduction and Clustering
• PCA, t-SNE, and UMAP used for high-dimensional visualization.
• Louvain and Leiden algorithms used for clustering based on expression profiles.
3. Cell Type Annotation
• Clusters annotated using CellMarker and PanglaoDB reference databases.
• Identified major immune subtypes (T-cells, B-cells, macrophages), stromal cells, and rare populations (e.g., regulatory T-cells, myeloid-derived suppressor cells).
4. Functional and Pathway Analysis
• DEGs identified between clusters using FindMarkers().
• Gene set enrichment via clusterProfiler, ReactomePA, and KEGG.
• Mapped enriched pathways including immune regulation, antigen presentation, T-cell activation, and cancer-specific signaling (e.g., PD-L1 checkpoint, NF-kappa B, apoptotic pathways).
ProjectOutcomes:
• 11 distinct cell clusters identified, each corresponding to known immune or stromal subtypes.
• Successfully annotated regulatory T-cells and tumor-associated macrophages, showing upregulation of markers like CCR4, IL7R, GZMB, CD2.
• Enriched pathways included:
⚬ Cancer-related: PD-L1 checkpoint (hsa05235), Transcriptional misregulation in cancer (hsa05202)
⚬ Immune: B-cell and T-cell signaling, NK cell cytotoxicity
⚬ Metabolic/Cell cycle: Glycolysis, apoptosis, lysosome,
senescence
• Dimensionality plots (UMAP, t-SNE) showed clear separation of subpopulations.
Key Figures and Graphs:
UMAP Clustering Visualization
Cells colored by clusters. Rare cell types (e.g., regulatory T cells) highlighted.
Elbow Plot-Used to determine optimal PCs for clustering
PCA
Gene Contribution Table (PC1–PC5
Demonstrates genes contributing most positively/negatively to each principal component.
Genes: KYNU, SPI1, DOCK4 (PC1); GZMB, KLRD1, SLAMF7 (PC3)
KEGG Pathway Dot Plot
Displays enriched pathways for cluster-specific gene sets.
GO Enrichment Bar Plot
Top 10 biological processes for each cell type cluster (e.g., immune activation, antigen processing).
Conclusion
This project successfully implemented an advanced scRNA-seq analysis pipeline, identifying meaningful cellular heterogeneity in the TME and uncovering key regulatory cell types and pathways.
Key Achievements:
• High-resolution insight into PBMC heterogeneity using cutting-edge tools.
• Discovery of tumor-relevant immune signatures and enriched immune-cancer interaction pathways.
• Laid groundwork for precision oncology strategies targeting specific immune microenvironments.
