bioRxivDevelopmental BiologyPreprint
Cardiac-immune microniches programme macrophage states in the regenerating heart
Ehsan Razaghi, Selin Tüzüner, Trishalee Gungoosingh, Kerem ÇiliD, Wan Shern Wong, Rita AlonaizaniD, Rebecca J. RichardsoniD, Filipa Costa SimõesiD
Abstract
Adult zebrafish regenerate their hearts after injury, a process that requires macrophages, yet how local tissue microenvironments instruct macrophage states and function remains unclear. Here we combine single cell RNA sequencing with Visium and high-resolution MERFISH spatial transcriptomics to map the cardio-immune landscape of homeostatic and regenerating zebrafish hearts. We identify a mpeg1.1+ compartment comprising macrophages, dendritic, B and NK-like cells, and show that injury establishes a macrophage-centred immune environment with transcriptional programmes spanning resident surveillance, damage sensing, inflammation, antigen presentation, resolution and metabolic support. Communication-aware spatial modelling reveals that these states are not randomly distributed but organised into discrete structural-immune microniches across the injury region, each defined by stereotyped cellular compositions and ligand-receptor circuits. Within a fibroblast-macrophage microniche, we uncover an il34-csf1ra-egr1 axis in which col12a1a+ il34+ fibroblasts promote an egr1 pro-regenerative macrophage state that couples fibrosis, vascular integrity and epicardial signalling. We show that disruption of this axis by csf1ra loss of function reduces macrophage-, endothelial- and epicardial-rich microniches, amplifying fibroblast-driven domains that shift macrophages towards stress and long-sustained inflammatory programmes, thereby biasing early injury response towards a pro-fibrotic state. Our work establishes spatially defined cardio-immune microniches as key organisers of macrophage function and regenerative outcome, providing a mechanistic framework and actionable targets for reprogramming cardiac repair.
- organism: zebrafish
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: spatial domain detection
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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