Utilising Single-Cell Technology to Investigate Gene Regulation

Since the inception of single-cell sequencing in 2009, experimental and computational technologies have enabled the accurate quantification of gene expression on a cell-by-cell basis. By capturing the transcriptome of thousands of single cells within complex tissues, ranging from organoids to tumoroids, researchers can map individual gene expression profiles that correspond to distinct cellular identities and functions. This method has led to the discovery and classification of previously unrecognised cell types and dynamic cellular processes. This growing appreciation for cellular diversity and regulatory complexity at the single-cell level underscores a broader principle in biology: that functional complexity arises not merely from the number of genes, but from how genes are regulated and expressed across different cell types and contexts. Instead, increasing complexity is thought to result from a combination of molecular mechanisms, including epigenetic modifications, gene regulation, post-transcriptional processing, and protein modifications, each of which may contribute to varying degrees. The regulation of these molecular mechanisms is not limited to complexity; changes to these layers can also provoke disease. Since their inception, single-cell technologies have undergone rapid advancements, with adapted protocols and specialised bioinformatic tools now enabling analysis far beyond gene expression profiling. In addition to measuring expression, emerging methods can quantify the result of some of the molecular mechanisms previously mentioned. Explicitly, single-cell sequencing can quantify splicing, allele-specific expression, transposable elements, copy number variation, transcriptional velocity and more, each offering insight into mechanisms that drive complexity or disease. In my thesis, I contribute to this growing field by developing and applying computational approaches to extract, quantify, and interpret diverse layers of gene regulation and their impact on the phenotype from single-cell RNA-seq data.

Grantor dc:publisher

UNSW, Sydney

Year dc:date

2026

Author dc:creator

• King, Helen

• Bioinformatics
• RNA
• Gene Regulation
• CRISPR
• Transcriptomics
• anzsrc-for: 310204 Genomics and transcriptomics
• anzsrc-for: 3102 Bioinformatics and computational biology
• anzsrc-for: 310505 Gene expression (incl. microarray and other genome-wide approaches)
• anzsrc-for: 321103 Cancer genetics
• anzsrc-for: 310507 Genetic immunology

Statement dc:rights

• embargoed access
• CC BY 4.0

Licence

• http://purl.org/coar/access_right/c_f1cf
• https://creativecommons.org/licenses/by/4.0/

Language dc:language

en

Identifier

https://doi.org/10.26190/unsworks/32424

OAI identifier oai:identifier

oai:unsworks.library.unsw.edu.au:1959.4/108105