Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.755469
Title: Computational approaches to study the immune system using gene expression and flow cytometry data
Author: Monaco, G.
ISNI:       0000 0004 7428 4632
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2017
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
The general mechanisms employed by the immune system have been widely understood; but we are still far from knowing how to support the immune system for all diseases and functional decline with age. Computational immunology is the promising field that uses high-throughput technologies to expand our holistic view. This study adopts bioinformatics methods to address questions of both technical and biological relevance using gene expression and flow cytometry. I used human and mouse co-expression maps to define evolutionary differences and similarities not only in the immune system, but also in other tissues, pathways and diseases. There is an overall conservation between the mouse and human immune system, however there are specific pathways that show signs of divergence, e.g. pathways related to the IFN alpha/beta, butyrophilins, defensins, prolactin and protein degradation for MHC class I antigen presentation. In addition, given the importance of flow cytometry to understanding the immune system, I developed the tool flowAI to perform quality control on flow cytometry data either automatically or interactively. flowAI detects and removes outliers and other anomalies from the aspects of flow cytometry: 1) flow rate, 2) signal acquisition, and 3) dynamic range. Finally, I analysed RNA-Seq data from 29 immune cell types to derive detailed insights on their transcriptional patterns, normalization and deconvolution. The cell subsets for which I found minimal gene expression specificity belong to memory cells. The transcriptomic composition was determined and expression values normalized for mRNA abundance were used to perform absolute deconvolution. In conclusion, the research areas that will mainly benefit from this thesis are related to translation from mouse models to human, standardization of flow cytometry analysis, and transcriptomic analysis of blood heterogeneous samples.
Supervisor: de Magalhães, João Pedro Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.755469  DOI:
Share: