RITUPAM SARMA

Chronic stress has been linked to cancer progression, but its impact on the tumor microenvironment (TME) at a spatial level remains unclear. This project aims to determine how stress influences spatial gene expression in lung cancer and whether these effects differ between tumor and non-tumor regions.

Institutions: University of Glasgow, CRUK Scotland Institute, Medical University of Vienna

Key Contributors: Dr. Chrysoula Vraka (CRUK Scotland Institute & Medical University of Vienna), Mr. John Cole (University of Glasgow)

• Cohort: 19 lung cancer samples (9 high-stress, 10 low-stress), stress quantified via questionnaires and glucocorticoid levels
• Technology: 10x Genomics Visium spatial transcriptomics with tumor and non-tumor regions annotated by pathology
• Analysis Pipeline: Seurat workflow including QC (filtering low-quality spots), normalization, scaling, PCA/UMAP, clustering, differential expression, marker gene identification, and spatial visualization

• Built and executed the Seurat analysis pipeline from QC to clustering and visualization
• Conducted differential expression analysis to compare high- vs. low-stress groups
• Performed cluster marker identification and interpretation of top 10 genes per cluster
• Collaborated with supervisors to plan next steps, including validation and multi-omic integration

• High- vs. low-stress groups showed distinct expression differences, with low-stress samples generally having higher gene expression
• Median detected genes per spot: 400–5,500; average reads per spot: ~25,000
• Identified clear spatial clustering across tumor and non-tumor regions
• Top marker genes successfully characterized spatial domains of the tissue

Interpretation: Chronic stress significantly influences spatial gene expression patterns in lung cancer. Stress-related effects are evident in both tumor and non-tumor regions, suggesting a broader impact on TME interactions.

Variant surface antigens (VSAs) like rifin and stevor families in Plasmodium falciparum play key roles in malaria pathogenesis and immune evasion. This MSc project aimed to map sequence-similarity networks for these families to detect sub-groups and relate clusters to parasite life cycle stages and cell-line phenotypes.

Institutions: MSc Bioinformatics, University of Glasgow

Key Contributors: Project by Ritupam Sarma (GUID: 2734454s), supervised by Dr Virginia Howick, with contributions from Dr Priscilla Ngotho

• Sequence Analysis: All-vs-all BLASTn on rifin and stevor FASTA sets to identify sequence similarities
• Data Processing: Filtering and table integration using Python and R for network preparation
• Network Construction: Exported networks in GEXF format; community detection via Gephi modularity (Louvain algorithm)
• Visualization: Applied ForceAtlas2 and Fruchterman-Reingold layouts in Gephi for cluster representation
• Integration: Related clusters to expression data across parasite life cycle stages

• Led the entire MSc project, from data collection and BLASTn analysis to network construction and visualization
• Developed Python and R scripts for data filtering, integration, and initial cluster analysis
• Performed Gephi-based community detection and layout optimization to identify novel sub-groups
• Integrated expression data with networks to uncover co-expression patterns and their biological implications
• Collaborated with supervisors to interpret results and propose future functional studies

• Rifin Family: Identified four major and one minor clusters with clear separation of rifin A vs. rifin B subfamilies
• Isolate Structure: Minimal isolate-driven clustering, with ring-stage sequences dominating the networks
• Stevor Family: Revealed two major clusters, with unexpected isolate-specific grouping and limited correlation to life cycle stages
• Clinical Insights: Majority of stevor sequences originated from acute clinical samples, suggesting stage-specific roles

Interpretation: The clear intra-family clustering indicates underlying functional sub-structures within rifin and stevor families, potentially linked to immune evasion strategies. These findings deepen understanding of host-parasite interactions in malaria pathogenesis.