Exploring Expressed Genetic Variants and Isoforms in Tumor Diversity Using Long-Read Single-Cell RNA Sequencing

Cancer remains a commonly diagnosed, diverse and complex disease. Tumor progression is believed to be driven in part by genome instability, which provides a selective advantage to cancer cells by altering key biological functions. Identifying expressed driving somatic mutations at the single-cell level is essential for understanding tumor heterogeneity and adaptation. However, traditional short-read sequencing lacks the resolution to fully capture entire transcripts, structural variants, and allele-specific expression, limiting insights into transcriptomic plasticity.The advent of long-read sequencing has transformed cancer transcriptomics by enabling the simultaneous detection of full-length isoforms, structural variants, and expressed mutations. When combined with single-cell RNA sequencing (scRNA-seq), this approach provides a detailed view of clonal dynamics and functional genetic alterations. Despite its potential, long-read scRNA-seq remains underutilized in cancer research, particularly in linking transcriptomic diversity to tumor evolution and therapy resistance. This study focuses on the development of computational workflows for processing and analyzing long-read single-cell cancer sequencing data to generate a comprehensive molecular profile, integrating gene expression, expressed driving somatic mutations, isoform diversity, and RNA modifications. By applying this framework to publicly available cancer samples, the aim is to demonstrate the power of long-read scRNA-seq data to characterize tumor heterogeneity at single-cell resolution.

Author

• Ballesteros Prieto, Vania

Language

• en

Keyword

• Computational workflow
• isoforms
• single-cell RNA sequencing
• single nucleotide variants
• long-read RNA sequencing
• Tumor heterogeneity

Date created

• 2025

Type of Work

• Master's Thesis

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Bioinformatics and Molecular Biochemistry

Degree

• M.S.

Advisor

• Horvath, Anelia

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/8p58pd979