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Vizgen Inc merfish gene panels
Merfish Gene Panels, supplied by Vizgen Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/merfish+gene+panels/merscope+vizgen+%E2%80%A2/pm41872488-274-1-8
Average 86 stars, based on 1 article reviews

merfish gene panels - by Bioz Stars, 2026-10

86/100 stars

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Article Title: Single-cell spatial transcriptomic analysis of human skin anatomy.
Article Snippet: The MERFISH gene panels were designed through the Vizgen portal (portal.vizgen.com) using sun-exposed NS as total fragments per kilobase million reference.

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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: Using the integrated single cell transcriptomics dataset consisting of mpeg1.1 + cells as well as reference structural cells, a 500 gene MERFISH panel was designed for high-resolution spatial transcriptomics using the Merscope platform (Vizgen).

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

A,A ′ , MERFISH maps of homeostatic hearts with cells coloured by cell type (A) and with macrophages highlighted (A ′ ). A ′′ , MERFISH spatial expression of representative cell type markers (smooth muscle cells, endocardium/endothelium, cardiomyocytes, fibroblasts and epicardium) in homeostatic hearts. B,B ′ , Maps of 5 dpi regenerating hearts coloured by cell type (B, cell type key as in A), highlighting macrophage distribution (B ′ ). B ′′ , MERFISH spatial expression of cells type markers in regenerating hearts, illustrating depletion of cardiomyocytes and upregulation of fibroblast and macrophage signatures in the injury region. C, Proportion of structural and immune cell types per section (n=4 per condition) across biological replicates (n=2 per condition) in homeostatic and regenerating hearts, cell type key as in A. D,E, NicheCompass-derived multicellular niches in homeostatic (D) and regenerating (E) hearts, capturing anatomical domains including atrium, valves, myocardium, regenerating ventricle, border zone, injury core, injury epicardium, blood and outflow tract. F, Bar plots showing stereotyped cell type numbers and composition of each niche in homeostatic and regenerating conditions, cell type key as in A. Spatial distribution of macrophages in the homeostatic (G) and regenerating (H) hearts, with spatial expression of the macrophage markers mpeg1.1, marco, csf1ra, mrc1b and cxcr3.3 distributed along the injured heart, including the injury core and injury epicardium niches.

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,A ′ , MERFISH maps of homeostatic hearts with cells coloured by cell type (A) and with macrophages highlighted (A ′ ). A ′′ , MERFISH spatial expression of representative cell type markers (smooth muscle cells, endocardium/endothelium, cardiomyocytes, fibroblasts and epicardium) in homeostatic hearts. B,B ′ , Maps of 5 dpi regenerating hearts coloured by cell type (B, cell type key as in A), highlighting macrophage distribution (B ′ ). B ′′ , MERFISH spatial expression of cells type markers in regenerating hearts, illustrating depletion of cardiomyocytes and upregulation of fibroblast and macrophage signatures in the injury region. C, Proportion of structural and immune cell types per section (n=4 per condition) across biological replicates (n=2 per condition) in homeostatic and regenerating hearts, cell type key as in A. D,E, NicheCompass-derived multicellular niches in homeostatic (D) and regenerating (E) hearts, capturing anatomical domains including atrium, valves, myocardium, regenerating ventricle, border zone, injury core, injury epicardium, blood and outflow tract. F, Bar plots showing stereotyped cell type numbers and composition of each niche in homeostatic and regenerating conditions, cell type key as in A. Spatial distribution of macrophages in the homeostatic (G) and regenerating (H) hearts, with spatial expression of the macrophage markers mpeg1.1, marco, csf1ra, mrc1b and cxcr3.3 distributed along the injured heart, including the injury core and injury epicardium niches.

Techniques: Expressing, Derivative Assay

A, Heat map of NicheCompass-derived gene programmes enriched across microniches in the regenerating injury territory, highlighting microniche-specific combinations of signalling modules. B, MERFISH-based spatial co-localisation of il34 expression in fibroblasts and egr1 expression in macrophages across the injury zone. B ′ , Assignment of egr1 expression to macrophage subpopulations at single cell resolution.

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, Heat map of NicheCompass-derived gene programmes enriched across microniches in the regenerating injury territory, highlighting microniche-specific combinations of signalling modules. B, MERFISH-based spatial co-localisation of il34 expression in fibroblasts and egr1 expression in macrophages across the injury zone. B ′ , Assignment of egr1 expression to macrophage subpopulations at single cell resolution.

Techniques: Derivative Assay, Expressing, Single Cell

A,A ′ , MERFISH maps of the injured heart of csf1ra j4e1/j4e1 mutants at 5 dpi, coloured by cell type (A) and highlighting macrophages (A ′ ). B, Comparison of cell type proportions in the injury territory between wild type and csf1ra mutants. C,C ′ , NicheCompass-defined niches in the mutant heart (C) and injury-restricted niches (C ′ ). D, Microniches within the mutant injury region. E, Volcano plot of differentially expressed genes in macrophages in wild type compared to mutant injury territories. F, Heatmap of genes upregulated in mutant macrophages and ploted across macrophage clusters, showing preferential enrichment of cluster 5 “phagolysosomal” and cluster 12 “stress-adapted inflammatory” signatures in mutants. Spatial module scores for clusters 5 (G) and 12 (H) projected onto wild type and mutant hearts, illustrating selective upregulation of programmes and increase of stress-adapted inflammatory states, particularly cluster 12, in mutant heart. I, Spatial expression of il34, csf1ra and egr1 in wild type and mutant hearts within the injury zone. J, Volcano plot of differentially expressed genes within the injury territory of wild type versus csf1ra mutants. K-N, MERFISH spatial expression maps of fibrosis-related genes tgfbr2a, acta2, ccn2b, serpine1 (K), ECM remodelling genes dcn4, timp4.3, ctnnbip1 (L), endothelial factors itgae.1, cdh5, plvapb (M) and epicardial factors sema3e, mdkb, tbx18 (N) in the wild type and mutant injury area. O, Model summarising how disruption of il34-csf1ra signalling prevents egr1 induction in resident and pro-resolving macrophages, shifting macrophages towards stress-adapted inflammatory states, which impacts endothelial and epicardial compartments and biases repair towards fibrosis.

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,A ′ , MERFISH maps of the injured heart of csf1ra j4e1/j4e1 mutants at 5 dpi, coloured by cell type (A) and highlighting macrophages (A ′ ). B, Comparison of cell type proportions in the injury territory between wild type and csf1ra mutants. C,C ′ , NicheCompass-defined niches in the mutant heart (C) and injury-restricted niches (C ′ ). D, Microniches within the mutant injury region. E, Volcano plot of differentially expressed genes in macrophages in wild type compared to mutant injury territories. F, Heatmap of genes upregulated in mutant macrophages and ploted across macrophage clusters, showing preferential enrichment of cluster 5 “phagolysosomal” and cluster 12 “stress-adapted inflammatory” signatures in mutants. Spatial module scores for clusters 5 (G) and 12 (H) projected onto wild type and mutant hearts, illustrating selective upregulation of programmes and increase of stress-adapted inflammatory states, particularly cluster 12, in mutant heart. I, Spatial expression of il34, csf1ra and egr1 in wild type and mutant hearts within the injury zone. J, Volcano plot of differentially expressed genes within the injury territory of wild type versus csf1ra mutants. K-N, MERFISH spatial expression maps of fibrosis-related genes tgfbr2a, acta2, ccn2b, serpine1 (K), ECM remodelling genes dcn4, timp4.3, ctnnbip1 (L), endothelial factors itgae.1, cdh5, plvapb (M) and epicardial factors sema3e, mdkb, tbx18 (N) in the wild type and mutant injury area. O, Model summarising how disruption of il34-csf1ra signalling prevents egr1 induction in resident and pro-resolving macrophages, shifting macrophages towards stress-adapted inflammatory states, which impacts endothelial and epicardial compartments and biases repair towards fibrosis.

Techniques: Comparison, Mutagenesis, Expressing, Disruption

A,A ′ , MERFISH map of the homeostatic csf1raj4e1/j4e1 heart coloured by cell type (A) and highlighting macrophages (A ′ ). B, NicheCompass-derived niches in homeostatic csf1ra mutants, showing preserved global architecture. C, Change in microniche proportions within the injury territory of wild type versus csf1ra mutant. D,E, MERFISH spatial expression of col1a1a, il34, csf1ra and egr1 in wild type and csf1ra mutant hearts (D). Normalised fold change of captured col1a1a, il34, csf1ra and egr1 transcripts in mutant injury area compared to wild type. F,G, MERFISH whole-heart section of egr1 expression in wild type (F) and csf1ra mutant (G) hearts, showing that loss of egr1 is confined to the injury territory and not observed in remote myocardium. H, Normalised fold-change of egr1 transcripts in csf1ra mutants relative to wild type in regions outside the injury area.

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,A ′ , MERFISH map of the homeostatic csf1raj4e1/j4e1 heart coloured by cell type (A) and highlighting macrophages (A ′ ). B, NicheCompass-derived niches in homeostatic csf1ra mutants, showing preserved global architecture. C, Change in microniche proportions within the injury territory of wild type versus csf1ra mutant. D,E, MERFISH spatial expression of col1a1a, il34, csf1ra and egr1 in wild type and csf1ra mutant hearts (D). Normalised fold change of captured col1a1a, il34, csf1ra and egr1 transcripts in mutant injury area compared to wild type. F,G, MERFISH whole-heart section of egr1 expression in wild type (F) and csf1ra mutant (G) hearts, showing that loss of egr1 is confined to the injury territory and not observed in remote myocardium. H, Normalised fold-change of egr1 transcripts in csf1ra mutants relative to wild type in regions outside the injury area.

Techniques: Derivative Assay, Mutagenesis, Expressing

a. Log2 fold change (Log2FC) heatmap of marker genes related to glycosylation, per tissue and vascular bed subtype. Color scale: yellow, increased expression in aged versus young ECs; blue, decreased expression in aged versus young ECs. Asterisks indicate adjusted p-value of 0.1 or lower. b. Dot plot heatmap of predicted transcription factor (TF) activities of indicated markers involved in mechanoregulation, across tissue ECs. Color scale: yellow, high activity in aged ECs; blue, high activity in young ECs. c. Schematic of spatial transcriptomics analysis. d. Representative image of spatially resolved blood vascular (red) and lymphatic (yellow) ECs identified in human skin, as captured via MERFISH spatial transcriptomics. e. Hematoxylin and Eosin (H&E) staining of human skin tissue shown in (d). Scale = 500 μm. f. UMAP representation of human skin ECs, as captured via MERFISH spatial transcriptomics (n=2). Color-coding reflects vascular bed subtype. g-h. YAP1 (g) and TEAD1 (h) expression in human skin ECs, as captured via MERFISH spatial transcriptomics. Color scale: back, high expression; grey, low expression. i. Representative immunofluorescent images of human skin sections from young and aged subjects, immunostained for YAP (red), CD31 (green), and DAPI (blue). Smaller images are magnifications of the respective boxed areas. Arrowheads indicate nuclei with high YAP signal (within CD31 + areas). Scale = 200 μm. j. Quantification of the percentage of cells with a YAP nuclear/cytoplasmic ratio > 1 (i.e., indicative of increased mechanical signaling) in young and aged subgroups. Mean ±SEM, unpaired t-test, two-tailed, *p < 0.05, n = 10 and 8 for young and aged groups, respectively.

Journal: bioRxiv

Article Title: Uncovering the transcriptional hallmarks of endothelial cell aging via integrated single-cell analysis

doi: 10.1101/2025.08.18.669055

Figure Lengend Snippet: a. Log2 fold change (Log2FC) heatmap of marker genes related to glycosylation, per tissue and vascular bed subtype. Color scale: yellow, increased expression in aged versus young ECs; blue, decreased expression in aged versus young ECs. Asterisks indicate adjusted p-value of 0.1 or lower. b. Dot plot heatmap of predicted transcription factor (TF) activities of indicated markers involved in mechanoregulation, across tissue ECs. Color scale: yellow, high activity in aged ECs; blue, high activity in young ECs. c. Schematic of spatial transcriptomics analysis. d. Representative image of spatially resolved blood vascular (red) and lymphatic (yellow) ECs identified in human skin, as captured via MERFISH spatial transcriptomics. e. Hematoxylin and Eosin (H&E) staining of human skin tissue shown in (d). Scale = 500 μm. f. UMAP representation of human skin ECs, as captured via MERFISH spatial transcriptomics (n=2). Color-coding reflects vascular bed subtype. g-h. YAP1 (g) and TEAD1 (h) expression in human skin ECs, as captured via MERFISH spatial transcriptomics. Color scale: back, high expression; grey, low expression. i. Representative immunofluorescent images of human skin sections from young and aged subjects, immunostained for YAP (red), CD31 (green), and DAPI (blue). Smaller images are magnifications of the respective boxed areas. Arrowheads indicate nuclei with high YAP signal (within CD31 + areas). Scale = 200 μm. j. Quantification of the percentage of cells with a YAP nuclear/cytoplasmic ratio > 1 (i.e., indicative of increased mechanical signaling) in young and aged subgroups. Mean ±SEM, unpaired t-test, two-tailed, *p < 0.05, n = 10 and 8 for young and aged groups, respectively.

Article Snippet: A custom MERFISH gene panel targeting 453 genes (CP1623) ( Table S12 ), plus 15 blanks to control for unspecific binding of probes, was designed using the MERFISH gene panel design tool ( https://portal.vizgen.com ) according to the instructions listed in the Gene Panel Design Portal manual (Vizgen, 91600101).

Techniques: Marker, Glycoproteomics, Expressing, Activity Assay, Staining, Two Tailed Test

a. Log2 fold change (Log2FC) heatmap of marker genes related to ECM and glycoproteins, per tissue and vascular bed subtype. Color scale: yellow, increased expression in aged versus young ECs; blue, decreased expression in aged versus young ECs. Asterisks indicate adjusted p-value of 0.1 or lower. b. UMAP representation of human liver ECs. Color-coding reflects fine EC subclusters. c. Dot plot heatmap of capillary- and LSEC-specific marker genes in the liver dataset. The color intensity of each dot represents the average level of marker expression, the dot size reflects the percentage of liver ECs expressing the marker within the subcluster. d. Violin plots of the expression of marker genes representative of LSEC identity, basement membrane regulators, mechanosensors, and fibrosis-associated capillarization, in the indicated conditions and clusters in the liver dataset. e-f. Dot plot heatmap of global EC subcluster markers in the HEACA skin (e) and MERFISH skin (f) EC datasets. The color intensity of each dot represents the average level of marker expression, the dot size reflects the percentage of skin ECs expressing the marker within the subcluster. g. Violin plots of the expression of mechanoregulatory TFs YAP1 (left), TEAD1 (middle) and TEAD4 (right) in the HAECA skin dataset.

Journal: bioRxiv

Article Title: Uncovering the transcriptional hallmarks of endothelial cell aging via integrated single-cell analysis

doi: 10.1101/2025.08.18.669055

Figure Lengend Snippet: a. Log2 fold change (Log2FC) heatmap of marker genes related to ECM and glycoproteins, per tissue and vascular bed subtype. Color scale: yellow, increased expression in aged versus young ECs; blue, decreased expression in aged versus young ECs. Asterisks indicate adjusted p-value of 0.1 or lower. b. UMAP representation of human liver ECs. Color-coding reflects fine EC subclusters. c. Dot plot heatmap of capillary- and LSEC-specific marker genes in the liver dataset. The color intensity of each dot represents the average level of marker expression, the dot size reflects the percentage of liver ECs expressing the marker within the subcluster. d. Violin plots of the expression of marker genes representative of LSEC identity, basement membrane regulators, mechanosensors, and fibrosis-associated capillarization, in the indicated conditions and clusters in the liver dataset. e-f. Dot plot heatmap of global EC subcluster markers in the HEACA skin (e) and MERFISH skin (f) EC datasets. The color intensity of each dot represents the average level of marker expression, the dot size reflects the percentage of skin ECs expressing the marker within the subcluster. g. Violin plots of the expression of mechanoregulatory TFs YAP1 (left), TEAD1 (middle) and TEAD4 (right) in the HAECA skin dataset.

Techniques: Marker, Expressing, Membrane

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