Uncovering immune biology through data-driven discovery

I am a biologist and biology educator working at the interface of experimental biology and data-driven analysis.
My research interests focus on human immunology, particularly T cell biology and immune cell functional states across disease contexts such as infection, autoimmunity, and cancer.

Through applied projects using bulk and single-cell transcriptomic data, I explore how immune cell states are shaped by their environment, with an emphasis on biological interpretation, reproducible analysis, and hypothesis-driven research.

• Immune cells biology
• Immune responses in infection, autoimmunity, and cancer
• Tumor microenvironment and immune dysfunction

• Bulk RNA-seq
• Single-cell RNA-seq
• Pseudobulk and comparative transcriptomic analyses

• Reproducible analysis workflows
• Data visualization and biological interpretation
• Python- and R-based analysis pipelines

Integrated single-cell and bulk RNA-seq analysis of immune cell states in non-small cell lung cancer patients treated with immune checkpoint inhibitors, with a focus on T cell functional programs associated with clinical response (PR vs SD).

Single-cell RNA-seq analysis of immune cells in human lung cancer, focusing on T cell states, tumor versus non-tumor compartments, and transcriptional programs associated with immune dysfunction within the tumor microenvironment.

Single-cell RNA-seq analysis of PBMC T cells in Sjögren’s disease, focusing on T cell state heterogeneity, disease-enriched and disease-depleted subclusters, and within-state differential expression (SjD vs healthy donors).

Single-cell RNA-seq analysis of peripheral immune cells from Influenza-infected patients, including quality control, cell-type annotation, T cell–focused analyses, pseudobulk differential expression, and interpretation of immune responses in clinical human samples.

Single-cell RNA-seq analysis of human T cells during Influenza infection, including quality control, CD4/CD8 scoring, pseudobulk construction, and critical assessment of dataset suitability and metadata limitations.

End-to-end host–virus RNA-seq analysis highlighting interferon-driven antiviral responses to Influenza A infection.

Reproducible dual RNA-seq analysis of host–pathogen transcriptomic responses during H. pylori infection, including WT vs KO strains and temporal effects.

Projects developed to build solid foundations in computational biology and reproducible analysis, using real biological datasets.

• Deepening biological interpretation of transcriptomic data in human disease
• Integrating single-cell analyses with immunological questions
• Developing reproducible and hypothesis-driven research workflows

“Understanding biology today means learning to listen to data.”
— Yasmina Soumahoro