Dissecting Macrophage-Fibroblast Cell Circuits in Liver Fibrosis using Spatial Omics

Background

This project will use spatial -omics, computational biology, mathematical modelling and functional studies to study how macrophage-fibroblast circuits regulate fibrosis in chronic liver disease (CLD), how these can be therapeutically targeted and how they can improve approaches to stratify patients.

CLD is a global healthcare problem, affecting 1.5 billion people worldwide, with over 2 million deaths per year. Irrespective of cause, chronic liver damage can lead to scarring or fibrosis, ultimately progressing to cirrhosis and organ failure. The degree of fibrosis is the best-known predictor of adverse clinical outcomes in CLD, meaning there is huge interest in developing new antifibrotic treatments. No such therapies are currently available. Therefore, a more comprehensive understanding of the mechanisms orchestrating fibrosis is needed to inform precision medicine for CLD.

In liver fibrosis, scar-producing fibroblasts and monocyte-derived macrophages accumulate in areas of scarring, termed fibrotic niches. Single-cell RNAseq (scRNAseq) studies have identified disease-associated macrophages and fibroblasts (1). Interactions between these populations within the fibrotic niche might regulate fibroblast proliferation and activation (2). However, spatial information is lost in scRNAseq studies, meaning direct evidence of the molecular mechanisms regulating macrophage-fibroblast interactions in the liver fibrotic niche is lacking. High-resolution spatial -omics approaches promise to address this, for the first time enabling the in situ detection of which cell types, molecules and pathways are active within this niche.

Cell interactions in a tissue can be described as ‘cell circuits’ with dynamical systems theory using differential equations (3). Modelling of fibroblast-macrophage circuits has suggested that inflammatory fibrotic neighbourhoods, termed “hot” fibrosis, show greater scope for remodelling and therapeutic manipulation (3). However, these circuit models have been based on simplified data without the full understanding of which ligands and receptors are expressed by scar-associated macrophages and fibroblasts in in situ. Newer computational approaches based on tissue-level spatial cellular neighbourhood data (4) provide a framework to dissect the complexity of the fibrotic niche and link it to interpretable predictions of how macrophages can be targeted to abrogate fibroblast proliferation and activation.

Aims

1. Generate spatial -omics data (RNA and protein) on liver biopsy tissue from patients with different causes and stages of fibrosis.
2. Computational analysis of spatial -omics data to identify the cellular and molecular composition and of the liver fibrotic niche. 
   1. Detailed phenotyping of macrophages and fibroblasts within the fibrotic niche and integration with existing scRNAseq data
   2. Cellular neighbourhood analysis to define features of “hot” fibrosis in the human liver
3. Construct mathematical cell circuit models of macrophage-fibroblast interactions in the fibrotic niche, informed by spatial -omics data (e.g. using One-Shot Tissue Dynamics Reconstruction (4))
   1. Perturbation modelling of cell circuits to study effects of candidate therapeutic interventions on fibroblast proliferation and activation
4. Target identification, patient stratification and functional validation
   1. Collaborate with industry partner (Macomics  Ltd. to identify tractable targets to modulate macrophage-fibroblast interactions
   2. Assess which cell-circuits are enriched in CLD patients with adverse clinical outcomes using established disease cohorts
   3. Functional studies testing cell circuit modulation using in vitro macrophage-fibroblast co-culture models

Training Outcomes

The student will get interdisciplinary training comprising laboratory science, bioinformatics, mathematical modelling and statistics. They will also gain industry experience with Macomics Ltd., learning to translate research findings into potential therapeutics and biomarkers. 

Spatial -omics platforms will include CosMx/Xenium and PhenoCycler-Fusion, building on existing data from the Ramachandran lab. The Cole lab will provide expertise in bioinformatic analysis of spatial data. The Schumacher group will support the mathematical model. Target identification and functional validation will be done in collaboration with Macomics Ltd. and based on models, assays and samples available in the Ramachandran lab and/or at Macomics.

References

1. Ramachandran P, et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature. 2019;575(7783):512–518.

2. Bhattacharya M, Ramachandran P. Immunology of human fibrosis. Nat Immunol. 2023;24(9):1423–1433.

3. Adler M, et al. Principles of Cell Circuits for Tissue Repair and Fibrosis. iScience. 2020;23(2):100841.

4. Somer J, Mannor S, Alon U. Temporal tissue dynamics from a single snapshot. bioRxiv. 2024;2024.04.22.590503.