Defining Therapeutic Targets for Human Liver Fibrosis using Single -Cell Transcriptomics

Chronic liver disease is estimated to affect 844 million people worldwide, with over 2 million deaths per year. Currently it ranks as the 5th commonest cause of death in the UK, with rising incidence. Liver fibrosis is a feature of advanced chronic liver disease of any aetiology and predicts adverse patient outcomes. Despite this significant burden of liver fibrosis, there are currently no effective antifibrotic therapies available for patients. This is mainly due to the lack of a precision medicine-based approach, with a dearth of comparative data exploring the links between human disease and pre-clinical models. Hence, potential antifibrotic interventions have not been adequately targeted to pathways known to be active in fibrotic human liver tissue.
Macrophages are key regulators of liver fibrosis and represent an attractive therapeutic target. Recently, a single-cell RNA-sequencing approach has been used to define the pathogenic macrophage population in human liver cirrhosis for the first time. This method proceeds to define molecular interactions between these macrophages and other key cell types within the fibrotic niche. These data provide a unique opportunity, enabling the rational testing of antifibrotic therapies on pathways known to be present in human liver disease.
Macrophages in rodent models of liver fibrosis mirror several features of those identified in human liver disease. However, macrophages are highly heterogeneous and dynamic cells, meaning the precise corollary subpopulations between rodent models and human liver disease have not yet been defined. Hence, the main objectives of this project include generation of data from murine models of liver fibrosis, comprising simultaneous single-cell RNA-sequencing and multiplex protein marker detection, to resolve the fibrogenic macrophage subpopulations. We will then proceed to interrogate these data using cutting-edge computational approaches including unsupervised clustering, transcriptomic network analysis, pseudo-temporal dynamics and RNA velocity analysis. Subsequently we will map the transcriptomes of these cells to those identified in human cirrhotic liver tissue, enabling the identification of corollary macrophage populations between mouse and human and the detection of "core" fibrogenic pathways across species. Ultimately, we aim to test the functional relevance of identified pathways in order to define tractable therapeutic targets for liver fibrosis.
For the purpose of this project murine models of liver fibrosis including carbon tetrachloride, bile duct ligation and dietary models will be used for the analysis and interpretation of single cell transcriptomic data. In order to phenotype and spatially resolve macrophage subpopulations from the murine models of liver fibrosis, we will use multiparameter flow cytometry, histology, imaging and ex vivo cell culture analyses. Finally, once single cell transcriptomic data is mapped across species and conserved pathways are identified, antifibrotic interventional studies in mouse models both in vitro and in vivo will be designed, analysed and interpreted.