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A, Schematic of the integrative analysis pipeline combining single cell RNA-seq, <t>Visium</t> spatial transcriptomics and ligand-receptor-target inference to identify niche-macrophage signalling programmes. B,C, Visium sections from homeostatic (B) and 5 dpi regenerating (C) hearts, with each white spot representing a 55µm capture area. D,D ′ , Spatial maps of key structural cell types identified (smooth muscle cells, cardiomyocytes, fibroblasts, epicardium and macrophages) in homeostatic (D) and regenerating (D ′ ) hearts according to inferred cell type composition after cell2location deconvolution. E, Representative MERFISH image illustrating single molecule transcript detection and cell segmentation. E ′ , Heat map summarising the biological categories included in designing the 500 gene MERFISH panel, grouped into structural cell markers, mpeg1.1 + subpopulation genes, injury-induced signatures, candidate niche-macrophage signalling mediators and other regeneration-related genes. F,G, Marker selection matrices used to distinguish structural cells (F) and mpeg1.1 + immune subpopulations (G) in MERFISH data. Detailed gene panel design included in Table S1. H-J, NicheNet-based prioritisation of ligand-receptor circuits, showing ligands upregulated after injury in structural “sender” populations (H), corresponding receptor activity in mpeg1.1 + “receiver” subsets (I) and predicted downstream target genes in mpeg1.1 + macrophages (J) that together define putative functional communication programmes. Detailed NicheNet output included in Supplementary Information 4.
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A, Schematic of the integrative analysis pipeline combining single cell RNA-seq, <t>Visium</t> spatial transcriptomics and ligand-receptor-target inference to identify niche-macrophage signalling programmes. B,C, Visium sections from homeostatic (B) and 5 dpi regenerating (C) hearts, with each white spot representing a 55µm capture area. D,D ′ , Spatial maps of key structural cell types identified (smooth muscle cells, cardiomyocytes, fibroblasts, epicardium and macrophages) in homeostatic (D) and regenerating (D ′ ) hearts according to inferred cell type composition after cell2location deconvolution. E, Representative MERFISH image illustrating single molecule transcript detection and cell segmentation. E ′ , Heat map summarising the biological categories included in designing the 500 gene MERFISH panel, grouped into structural cell markers, mpeg1.1 + subpopulation genes, injury-induced signatures, candidate niche-macrophage signalling mediators and other regeneration-related genes. F,G, Marker selection matrices used to distinguish structural cells (F) and mpeg1.1 + immune subpopulations (G) in MERFISH data. Detailed gene panel design included in Table S1. H-J, NicheNet-based prioritisation of ligand-receptor circuits, showing ligands upregulated after injury in structural “sender” populations (H), corresponding receptor activity in mpeg1.1 + “receiver” subsets (I) and predicted downstream target genes in mpeg1.1 + macrophages (J) that together define putative functional communication programmes. Detailed NicheNet output included in Supplementary Information 4.
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A, Schematic of the integrative analysis pipeline combining single cell RNA-seq, <t>Visium</t> spatial transcriptomics and ligand-receptor-target inference to identify niche-macrophage signalling programmes. B,C, Visium sections from homeostatic (B) and 5 dpi regenerating (C) hearts, with each white spot representing a 55µm capture area. D,D ′ , Spatial maps of key structural cell types identified (smooth muscle cells, cardiomyocytes, fibroblasts, epicardium and macrophages) in homeostatic (D) and regenerating (D ′ ) hearts according to inferred cell type composition after cell2location deconvolution. E, Representative MERFISH image illustrating single molecule transcript detection and cell segmentation. E ′ , Heat map summarising the biological categories included in designing the 500 gene MERFISH panel, grouped into structural cell markers, mpeg1.1 + subpopulation genes, injury-induced signatures, candidate niche-macrophage signalling mediators and other regeneration-related genes. F,G, Marker selection matrices used to distinguish structural cells (F) and mpeg1.1 + immune subpopulations (G) in MERFISH data. Detailed gene panel design included in Table S1. H-J, NicheNet-based prioritisation of ligand-receptor circuits, showing ligands upregulated after injury in structural “sender” populations (H), corresponding receptor activity in mpeg1.1 + “receiver” subsets (I) and predicted downstream target genes in mpeg1.1 + macrophages (J) that together define putative functional communication programmes. Detailed NicheNet output included in Supplementary Information 4.
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visium spatial tissue optimisation reagents kit  (10X Genomics)
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A, Schematic of the integrative analysis pipeline combining single cell RNA-seq, <t>Visium</t> spatial transcriptomics and ligand-receptor-target inference to identify niche-macrophage signalling programmes. B,C, Visium sections from homeostatic (B) and 5 dpi regenerating (C) hearts, with each white spot representing a 55µm capture area. D,D ′ , Spatial maps of key structural cell types identified (smooth muscle cells, cardiomyocytes, fibroblasts, epicardium and macrophages) in homeostatic (D) and regenerating (D ′ ) hearts according to inferred cell type composition after cell2location deconvolution. E, Representative MERFISH image illustrating single molecule transcript detection and cell segmentation. E ′ , Heat map summarising the biological categories included in designing the 500 gene MERFISH panel, grouped into structural cell markers, mpeg1.1 + subpopulation genes, injury-induced signatures, candidate niche-macrophage signalling mediators and other regeneration-related genes. F,G, Marker selection matrices used to distinguish structural cells (F) and mpeg1.1 + immune subpopulations (G) in MERFISH data. Detailed gene panel design included in Table S1. H-J, NicheNet-based prioritisation of ligand-receptor circuits, showing ligands upregulated after injury in structural “sender” populations (H), corresponding receptor activity in mpeg1.1 + “receiver” subsets (I) and predicted downstream target genes in mpeg1.1 + macrophages (J) that together define putative functional communication programmes. Detailed NicheNet output included in Supplementary Information 4.
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Image Search Results


A, Schematic of the integrative analysis pipeline combining single cell RNA-seq, Visium spatial transcriptomics and ligand-receptor-target inference to identify niche-macrophage signalling programmes. B,C, Visium sections from homeostatic (B) and 5 dpi regenerating (C) hearts, with each white spot representing a 55µm capture area. D,D ′ , Spatial maps of key structural cell types identified (smooth muscle cells, cardiomyocytes, fibroblasts, epicardium and macrophages) in homeostatic (D) and regenerating (D ′ ) hearts according to inferred cell type composition after cell2location deconvolution. E, Representative MERFISH image illustrating single molecule transcript detection and cell segmentation. E ′ , Heat map summarising the biological categories included in designing the 500 gene MERFISH panel, grouped into structural cell markers, mpeg1.1 + subpopulation genes, injury-induced signatures, candidate niche-macrophage signalling mediators and other regeneration-related genes. F,G, Marker selection matrices used to distinguish structural cells (F) and mpeg1.1 + immune subpopulations (G) in MERFISH data. Detailed gene panel design included in Table S1. H-J, NicheNet-based prioritisation of ligand-receptor circuits, showing ligands upregulated after injury in structural “sender” populations (H), corresponding receptor activity in mpeg1.1 + “receiver” subsets (I) and predicted downstream target genes in mpeg1.1 + macrophages (J) that together define putative functional communication programmes. Detailed NicheNet output included in Supplementary Information 4.

Journal: bioRxiv

Article Title: Cardiac-immune microniches programme macrophage states in the regenerating heart

doi: 10.64898/2026.03.05.709830

Figure Lengend Snippet: A, Schematic of the integrative analysis pipeline combining single cell RNA-seq, Visium spatial transcriptomics and ligand-receptor-target inference to identify niche-macrophage signalling programmes. B,C, Visium sections from homeostatic (B) and 5 dpi regenerating (C) hearts, with each white spot representing a 55µm capture area. D,D ′ , Spatial maps of key structural cell types identified (smooth muscle cells, cardiomyocytes, fibroblasts, epicardium and macrophages) in homeostatic (D) and regenerating (D ′ ) hearts according to inferred cell type composition after cell2location deconvolution. E, Representative MERFISH image illustrating single molecule transcript detection and cell segmentation. E ′ , Heat map summarising the biological categories included in designing the 500 gene MERFISH panel, grouped into structural cell markers, mpeg1.1 + subpopulation genes, injury-induced signatures, candidate niche-macrophage signalling mediators and other regeneration-related genes. F,G, Marker selection matrices used to distinguish structural cells (F) and mpeg1.1 + immune subpopulations (G) in MERFISH data. Detailed gene panel design included in Table S1. H-J, NicheNet-based prioritisation of ligand-receptor circuits, showing ligands upregulated after injury in structural “sender” populations (H), corresponding receptor activity in mpeg1.1 + “receiver” subsets (I) and predicted downstream target genes in mpeg1.1 + macrophages (J) that together define putative functional communication programmes. Detailed NicheNet output included in Supplementary Information 4.

Article Snippet: The ideal permeabilisation time was determined using the Visium Spatial Tissue Optimisation Kit (10X Genomics).

Techniques: Single Cell, RNA Sequencing, Spatial Transcriptomics, Marker, Selection, Activity Assay, Functional Assay