Understanding transcriptional regulation through computational analysis of single-cell transcriptomics

Gene expression is tightly regulated by complex transcriptional regulatory mechanisms to achieve specific expression patterns, which are essential to facilitate important biological processes such as embryonic development. Dysregulation of gene expression can lead to diseases such as cancers. A better understanding of the transcriptional regulation will therefore not only advance the understanding of fundamental biological processes, but also provide mechanistic insights into diseases. The earlier versions of high-throughput expression profiling techniques were limited to measuring average gene expression across large pools of cells. In contrast, recent technological improvements have made it possible to perform expression profiling in single cells. Single-cell expression profiling is able to capture heterogeneity among single cells, which is not possible in conventional bulk expression profiling. In my PhD, I focus on developing new algorithms, as well as benchmarking and utilising existing algorithms to study the transcriptomes of various biological systems using single-cell expression data. I have developed two different single-cell specific network inference algorithms, BTR and SPVAR, which are based on two different formalisms, Boolean and autoregression frameworks respectively. BTR was shown to be useful for improving existing Boolean models with single-cell expression data, while SPVAR was shown to be a conservative predictor of gene interactions using pseudotime-ordered single-cell expression data. In addition, I have obtained novel biological insights by analysing single-cell RNAseq data from the epiblast stem cells reprogramming and the leukaemia systems. Three different driver genes, namely Esrrb, Klf2 and GY118F, were shown to drive reprogramming of epiblast stem cells via different reprogramming routes. As for the leukaemia system, FLT3-ITD and IDH1-R132H mutations were shown to interact with each other and potentially predispose some cells for developing acute myeloid leukaemia.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2017

Author dc:creator

• Lim, Chee Yee

Advisors dc:contributor.advisor

• Gottgens, Berthold
• Fisher, Jasmin

• Bioinformatics
• Single-cell RNAseq
• Gene network inference
• Boolean model
• Autoregression model
• Epiblast stem cell
• Leukaemia
• Pseudotime inference

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/6ee39785-cdd1-4d0e-bc0f-74be28282845/download
• https://creativecommons.org/licenses/by-sa/4.0/

Language dc:language

en

Author Identifier

0000-0003-3553-3950

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/267786