Multiomic Analysis of the Hepatic Fibrotic Niche to Define New Therapeutic Targets for Liver Scarring

肝纤维化微环境的多组学分析以确定肝脏疤痕的新治疗靶点

• 批准号: 2606192
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2606192
• 关键词: Multiomic; Analysis; Hepatic; Fibrotic; Niche

Liver disease accounts for an estimated 2 million deaths per year globally. One feature of advanced liver disease is liver scarring (fibrosis), which is associated with poor clinical outcome. There are, at present, no effective anti-fibrotic therapies thus highlighting an important need to develop novel therapeutic strategies in this area. Recent work in the Ramachandran lab has used single-cell RNA sequencing (scRNA-seq) to identify a pathogenic subpopulation of cells in the liver which reside in a distinct spatial environment, named the fibrotic niche. Discovery of this fibrotic niche provides an opportunity to target the more diseased areas of tissue without perturbing non-diseased regions, and as such this precision-medicine based strategy may be used for patients with liver fibrosis. However, the cellular composition and cell-to-cell interactions within the fibrotic niche have not yet been assessed. To investigate this, spatial transcriptomics can be used to identify which transcripts are enriched within specific areas of scarring, subsequently allowing for a more targeted approach in terms of therapeutic strategies. Within this project, we will utilise spatial transcriptomics to determine the composition of the fibrotic niche within diseased human liver. We aim to integrate spatial transcriptomic and single-cell RNA sequencing data, such that we can interrogate the cell-cell interactions involved in the fibrotic process. Furthermore, we aim to investigate the niche in mouse models to determine any conserved pro-fibrogenic pathways between human and mouse, allowing us to identify potential therapeutic targets. Spatial transcriptomics approach will use the 10X genomics visium platform. Analysis of scRNA-seq data and spatial transcriptomic data will be done using a variety of packages in the R and Python languages including Seurat, Harmony, Giotto, BayesSpace and scanpy. Multiomic analysis of the niche will be performed to compare human and mouse liver fibrosis, such that conserved cell types and pathways can be defined. Molecules of interest will be manipulated using a combination of genetic approaches and in vivo mouse models and in vitro cell culture models.

据估计，全球每年有 200 万人因肝病死亡。晚期肝病的特征之一是肝脏疤痕（纤维化），这与不良的临床结果相关。目前，尚无有效的抗纤维化疗法，因此迫切需要在该领域开发新的治疗策略。 Ramachandran 实验室最近的工作使用单细胞 RNA 测序 (scRNA-seq) 来识别肝脏中的致病细胞亚群，这些细胞位于一个独特的空间环境中，称为纤维化生态位。这种纤维化生态位的发现提供了一个机会，可以针对组织中患病较多的区域，而不干扰非患病区域，因此，这种基于精准医学的策略可用于肝纤维化患者。然而，纤维化微环境内的细胞组成和细胞间相互作用尚未得到评估。为了研究这一点，空间转录组学可用于识别哪些转录物在特定的疤痕区域中富集，从而在治疗策略方面采取更有针对性的方法。在这个项目中，我们将利用空间转录组学来确定患病人类肝脏内纤维化生态位的组成。我们的目标是整合空间转录组和单细胞 RNA 测序数据，以便我们能够探究纤维化过程中涉及的细胞间相互作用。此外，我们的目标是研究小鼠模型中的利基，以确定人和小鼠之间任何保守的促纤维化途径，使我们能够确定潜在的治疗靶点。空间转录组学方法将使用 10X 基因组学 visium 平台。 scRNA-seq 数据和空间转录组数据的分析将使用 R 和 Python 语言中的各种软件包完成，包括 Seurat、Harmony、Giotto、BayesSpace 和 scanpy。将进行生态位的多组学分析来比较人和小鼠的肝纤维化，从而可以定义保守的细胞类型和途径。将使用遗传方法和体内小鼠模型以及体外细胞培养模型的组合来操纵感兴趣的分子。