Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763870
Title: Charting the single-cell transcriptional landscape of haematopoiesis
Author: Hamey, Fiona Kathryn
ISNI:       0000 0004 7653 6143
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2019
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Abstract:
High turnover in the haematopoietic system is sustained by stem and progenitor cells, which divide and mature to produce the range of cell types present in the blood. This complex system has long served as a model of differentiation in adult stem cell systems and its study has important clinical relevance. Maintaining a healthy blood system requires regulation of haematopoietic cell fate decisions, with severe dysregulation of these fate choices observed in diseases such as leukaemia. As transcriptional regulation is known to play a role in this regulation, the gene expression of many haematopoietic progenitors has been measured. However, many of the classic populations are actually extremely heterogeneous in both expression and function, highlighting the need for characterising the haematopoietic progenitor compartment at the level of individual cells. The first aim of this work was to chart the single-cell transcriptional landscape of the haematopoietic stem and progenitor cell (HSPC) compartment. To build a comprehensive map of this landscape, 1,654 HSPCs from mouse bone marrow were profiled using single-cell RNA-sequencing. Analysis of these data generated a useful resource, and reconstructed changes in gene expression, cell cycle and RNA content along differentiation trajectories to three blood lineages. To investigate how single-cell gene expression can be used to learn about regulatory relationships, data measuring the expression of 41 genes (including 31 transcription factors) in 2,167 stem and progenitor cells were used to construct Boolean gene regulatory network models describing the regulation of differentiation from stem cells to two different progenitor populations. The inferred relationships revealed positive regulation of Nfe2 and Cbfa2t3h by Gata2 that was unique to differentiation towards megakaryocyte-erythroid progenitors, which was subsequently experimentally validated. The next study focused on investigating the link between transcriptional and functional heterogeneity within blood progenitor populations. Single-cell profiles of human cord blood progenitors revealed a continuum of lympho-myeloid gene expression. Culture assays performed to assess the functional output of single cells found both unilineage and bilineage output and, by investigating the link between surface marker expression and function, a new sorting strategy was devised that was able to enrich for function within conventional lympho-myeloid progenitor sorting gates. The final project aimed to study changes to the HSPC compartment in a perturbed state. A droplet-based single-cell RNA-sequencing dataset of 44,802 cells was analysed to identify entry points to eight blood lineages and to characterise gene expression changes in this transcriptional landscape. Mapping single-cell data from W41/W41 Kit mutant mice highlighted quantitative shifts in progenitor populations such as a reduction in mast cell progenitors and an increase towards more mature progenitors along the erythroid trajectory. Differential gene expression identified upregulation of stress response and a reduction of apoptosis during erythropoiesis as potential compensatory mechanisms in the Kit mutant progenitors. Together this body of work characterises the HSPC compartment at single-cell level and provides methods for how single-cell data can be used to discover regulatory relationships, link expression heterogeneity to function, and investigate changes in the transcriptional landscape in a perturbed environment.
Supervisor: Göttgens, Berthold Sponsor: Medical Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.763870  DOI:
Keywords: Haematopoiesis ; Computational biology ; Bioinformatics ; Stem cell ; Single-cell
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