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Spatial Transcriptomics Inc visium spatial transcriptomics sequencing

Single‐cell and spatial transcriptome landscape of healthy and fibrotic kidneys after unilateral ischemia‐reperfusion injury (UIRI). a) Schematic representation of single‐cell RNA <t>sequencing</t> (scRNA‐seq) and spatial <t>transcriptomics</t> (ST) of kidneys from the sham and 10‐day UIRI mice, graphically designed with Biorender ( https://www.biorender.com/ ). b) t‐SNE plot illustrating the intricate cellular diversity in fibrotic kidneys, demonstrating distinct clusters representing glomerular endothelial cells (GEC), podocytes (Podo), mesangial cells (Mesa), Bowman's capsule epithelium (BC), proximal tubules (PT), descending limbs of Henle (DLOH), ascending limbs of Henle (ALOH), distal tubules (DT), principal cells (PC), intercalated cells (IC), fibroblasts (Fib), smooth muscle cells (SMC), extraglomerular endothelial cells (EGEC), monocytes (Mono), dendritic cells (DC), macrophages (Mϕ), plasmacytoid dendritic cells (pDC), proliferating mononuclear lineage (Prolif mono_L), and neutrophils (Neu), B cells (B), T cells (T), proliferating T cells (prolif T), and natural killer cells (NK). These cell types were further categorized into four major compartments: Glomerular, Renal, Interstitium, and Immune, as indicated by color grouping in the plot. c) Bubble plot illustrating the relative proportions of major kidney cell types in sham and UIRI samples. Each dot represents the proportion of a given cell type in a specific sample group, with dot size corresponding to its relative proportion. d) A comprehensive heatmap depicting the unique marker gene signature of major renal cell types. e) UMAP plot illustrating the inferred renal cell region distribution based on integrated spatial transcriptomics data from normal (Sham) and UIRI 10D mouse kidneys, generated using the 10x Genomics <t>Visium</t> platform. The identified regions include glomerular cells (Glom), distinct segments of the proximal tubule (PTS1, PTS1S2, PTS2), injured proximal tubules (InjPT), ascending limbs of Henle in cortex (ALOH(C)), distal tubules (DT), connecting tubules and collecting ducts (CNT_CD), cells at the corticomedullary junction (CMJ), fibrogenic niche regions (Niche1, Niche2), the inner stripe of the outer medulla (IOM), inner medulla (IM), renal capsule (RC), and perirenal tissue (Perirenal). f) Spatial maps illustrating the anatomical distribution of renal cell regions in Sham and UIRI 10D mouse kidneys. Region colors correspond to the classifications defined in panel (e). g) Bubble plot illustrating the relative proportions of major renal cell regions in spatial transcriptomics data from sham and UIRI 10D mouse kidneys. h) Bubble plot depicting the expression patterns of marker genes across distinct renal cell regions in spatial transcriptomics data. Dot color indicates the average gene expression level within each region, while dot size represents the proportion of spatial spots expressing the gene. i) Schematic diagram of nephron segmentation by cell types. j) Comparison of kidney anatomical regions and spatial transcriptomic clusters, showing clusters in kidney tissue (top) and the corresponding Visium H&E‐stained section (bottom). k) Renal tissue structure alterations at the corticomedullary junction (CMJ) in UIRI samples, showing the formation of two distinct fibrogenic niches, Niche1 and Niche2. l) A heatmap showing the deconvolution scores of cell type compositions across different regions in Visium spatial transcriptomics data, obtained using the RCTD method. m) Spatial FeaturePlots of RCTD‐derived cell type scores in the sham (top) and UIRI (bottom) groups, with paired panels sharing a common legend.

Visium Spatial Transcriptomics Sequencing, supplied by Spatial Transcriptomics 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/spatial+transcriptomics+sequencing/sequencing+spatial+transcriptomics+visium/pmc12786295-261-6-7
Average 86 stars, based on 1 article reviews

visium spatial transcriptomics sequencing - by Bioz Stars, 2026-08

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1) Product Images from "Single Cell and Spatial Transcriptomics Define a Proinflammatory and Profibrotic Niche After Kidney Injury"

Article Title: Single Cell and Spatial Transcriptomics Define a Proinflammatory and Profibrotic Niche After Kidney Injury

Journal: Advanced Science

doi: 10.1002/advs.202503691

Figure Legend Snippet: Single‐cell and spatial transcriptome landscape of healthy and fibrotic kidneys after unilateral ischemia‐reperfusion injury (UIRI). a) Schematic representation of single‐cell RNA sequencing (scRNA‐seq) and spatial transcriptomics (ST) of kidneys from the sham and 10‐day UIRI mice, graphically designed with Biorender ( https://www.biorender.com/ ). b) t‐SNE plot illustrating the intricate cellular diversity in fibrotic kidneys, demonstrating distinct clusters representing glomerular endothelial cells (GEC), podocytes (Podo), mesangial cells (Mesa), Bowman's capsule epithelium (BC), proximal tubules (PT), descending limbs of Henle (DLOH), ascending limbs of Henle (ALOH), distal tubules (DT), principal cells (PC), intercalated cells (IC), fibroblasts (Fib), smooth muscle cells (SMC), extraglomerular endothelial cells (EGEC), monocytes (Mono), dendritic cells (DC), macrophages (Mϕ), plasmacytoid dendritic cells (pDC), proliferating mononuclear lineage (Prolif mono_L), and neutrophils (Neu), B cells (B), T cells (T), proliferating T cells (prolif T), and natural killer cells (NK). These cell types were further categorized into four major compartments: Glomerular, Renal, Interstitium, and Immune, as indicated by color grouping in the plot. c) Bubble plot illustrating the relative proportions of major kidney cell types in sham and UIRI samples. Each dot represents the proportion of a given cell type in a specific sample group, with dot size corresponding to its relative proportion. d) A comprehensive heatmap depicting the unique marker gene signature of major renal cell types. e) UMAP plot illustrating the inferred renal cell region distribution based on integrated spatial transcriptomics data from normal (Sham) and UIRI 10D mouse kidneys, generated using the 10x Genomics Visium platform. The identified regions include glomerular cells (Glom), distinct segments of the proximal tubule (PTS1, PTS1S2, PTS2), injured proximal tubules (InjPT), ascending limbs of Henle in cortex (ALOH(C)), distal tubules (DT), connecting tubules and collecting ducts (CNT_CD), cells at the corticomedullary junction (CMJ), fibrogenic niche regions (Niche1, Niche2), the inner stripe of the outer medulla (IOM), inner medulla (IM), renal capsule (RC), and perirenal tissue (Perirenal). f) Spatial maps illustrating the anatomical distribution of renal cell regions in Sham and UIRI 10D mouse kidneys. Region colors correspond to the classifications defined in panel (e). g) Bubble plot illustrating the relative proportions of major renal cell regions in spatial transcriptomics data from sham and UIRI 10D mouse kidneys. h) Bubble plot depicting the expression patterns of marker genes across distinct renal cell regions in spatial transcriptomics data. Dot color indicates the average gene expression level within each region, while dot size represents the proportion of spatial spots expressing the gene. i) Schematic diagram of nephron segmentation by cell types. j) Comparison of kidney anatomical regions and spatial transcriptomic clusters, showing clusters in kidney tissue (top) and the corresponding Visium H&E‐stained section (bottom). k) Renal tissue structure alterations at the corticomedullary junction (CMJ) in UIRI samples, showing the formation of two distinct fibrogenic niches, Niche1 and Niche2. l) A heatmap showing the deconvolution scores of cell type compositions across different regions in Visium spatial transcriptomics data, obtained using the RCTD method. m) Spatial FeaturePlots of RCTD‐derived cell type scores in the sham (top) and UIRI (bottom) groups, with paired panels sharing a common legend.

Techniques Used: RNA Sequencing, Marker, Generated, Expressing, Gene Expression, Comparison, Staining, Derivative Assay

High‐resolution spatial transcriptomics and immunostaining reveal the TNC‐enriched fibroblast‐macrophage niche organization in fibrotic kidneys. a) Schematic diagram of the Visium HD workflow applied to kidney tissues from sham and UIRI model mice. b) UMAP visualization of integrated Visium HD spatial transcriptomics data from control mice (obtained from the 10x Genomics public dataset) and UIRI mice (this study), processed using canonical correlation analysis (CCA). This dimensionality reduction visualization reveals distinct clusters representing various renal parenchymal and stromal cell populations, including: Glomerulus, Vasculature, PTS1, PTS2, PTS1S2, InjPT, ascending limbs of Henle in cortex [ALOH(Cortex)], distal tubule and connecting tubule (DT_CNT), connecting tubule and collecting duct (CNT_CD), collecting duct in cortex [CD(Cortex)], PTS3, injured PTS3 (InjPTS3), Fibrogenic Niche, Vasa recta, loop of Henle in outer medulla [LOH(IOM)], collecting duct in outer medulla [CD(IOM)], collecting duct in inner medulla [CD(IM)], thin ascending limbs of Henle in inner medulla [tALOH(IM)], renal capsule (RC), Perirenal Fibrous tissue, and Perirenal Adipose tissue. c) Bubble plot comparing the regional distribution in Control versus UIRI 10d kidneys (Visium HD). d) Bubble plot depicting the expression patterns of marker genes across distinct renal cell regions in Visium HD data. e) Spatial maps generated using Visium HD illustrate the inferred anatomical distribution of renal cell regions in kidney tissues from Control and UIRI mice. f) Spatial Feature Plots of Visium HD data showing the spatial distribution of selected renal cell types in controls (top) and UIRI mice (bottom), based on cell‐type deconvolution using RCTD. g) A heatmap showing the correlation between NMF factors and cell‐type deconvolution scores in standard Visium spatial transcriptomics data. h) Spatial distribution of gene scores associated with the NMF factors most correlated with the fibrogenic niche, along with the contribution of key genes to each factor. i) Spatial FeaturePlots showing the anatomical distribution of Tnc expression in standard Visium. j) A heatmap showing the correlation between NMF factors and cell type deconvolution scores in Visium HD spatial transcriptomics data. k) Spatial distribution of NMF factors (NMF3 and NMF11) associated with the fibrogenic niche in Visium HD data, along with their corresponding high‐contributing genes. l) Spatial FeaturePlots showing the anatomical distribution of Tnc expression in Visium HD datasets. m) Immunofluorescence staining demonstrates colocalization of TNC with macrophages (F4/80⁺) in the CMJ interstitial region. From top to bottom: an overview merged image (Merge), followed by magnified views of TNC, Vimentin, and F4/80 staining in the same region, and an enlarged merged image (Enlarged Merge) at the bottom.

Figure Legend Snippet: High‐resolution spatial transcriptomics and immunostaining reveal the TNC‐enriched fibroblast‐macrophage niche organization in fibrotic kidneys. a) Schematic diagram of the Visium HD workflow applied to kidney tissues from sham and UIRI model mice. b) UMAP visualization of integrated Visium HD spatial transcriptomics data from control mice (obtained from the 10x Genomics public dataset) and UIRI mice (this study), processed using canonical correlation analysis (CCA). This dimensionality reduction visualization reveals distinct clusters representing various renal parenchymal and stromal cell populations, including: Glomerulus, Vasculature, PTS1, PTS2, PTS1S2, InjPT, ascending limbs of Henle in cortex [ALOH(Cortex)], distal tubule and connecting tubule (DT_CNT), connecting tubule and collecting duct (CNT_CD), collecting duct in cortex [CD(Cortex)], PTS3, injured PTS3 (InjPTS3), Fibrogenic Niche, Vasa recta, loop of Henle in outer medulla [LOH(IOM)], collecting duct in outer medulla [CD(IOM)], collecting duct in inner medulla [CD(IM)], thin ascending limbs of Henle in inner medulla [tALOH(IM)], renal capsule (RC), Perirenal Fibrous tissue, and Perirenal Adipose tissue. c) Bubble plot comparing the regional distribution in Control versus UIRI 10d kidneys (Visium HD). d) Bubble plot depicting the expression patterns of marker genes across distinct renal cell regions in Visium HD data. e) Spatial maps generated using Visium HD illustrate the inferred anatomical distribution of renal cell regions in kidney tissues from Control and UIRI mice. f) Spatial Feature Plots of Visium HD data showing the spatial distribution of selected renal cell types in controls (top) and UIRI mice (bottom), based on cell‐type deconvolution using RCTD. g) A heatmap showing the correlation between NMF factors and cell‐type deconvolution scores in standard Visium spatial transcriptomics data. h) Spatial distribution of gene scores associated with the NMF factors most correlated with the fibrogenic niche, along with the contribution of key genes to each factor. i) Spatial FeaturePlots showing the anatomical distribution of Tnc expression in standard Visium. j) A heatmap showing the correlation between NMF factors and cell type deconvolution scores in Visium HD spatial transcriptomics data. k) Spatial distribution of NMF factors (NMF3 and NMF11) associated with the fibrogenic niche in Visium HD data, along with their corresponding high‐contributing genes. l) Spatial FeaturePlots showing the anatomical distribution of Tnc expression in Visium HD datasets. m) Immunofluorescence staining demonstrates colocalization of TNC with macrophages (F4/80⁺) in the CMJ interstitial region. From top to bottom: an overview merged image (Merge), followed by magnified views of TNC, Vimentin, and F4/80 staining in the same region, and an enlarged merged image (Enlarged Merge) at the bottom.

Techniques Used: Immunostaining, Control, Expressing, Marker, Generated, Immunofluorescence, Staining

TLR4 knockout in macrophages attenuates renal inflammation and renal fibrosis in vivo. a) The diagram shows the experimental protocol. Bone marrow chimera models were established by transplanting the WT bone marrow to WT mice, or TLR4 KO bone marrow to WT mice. Mice were irradiated at a single dose of 1100 Rads and then underwent bone marrow transplantation. After 8 weeks of successful transplantation, a unilateral ischemia‐reperfusion (UIRI) model was established. b) PCR‐based identification of kidney genotypes in the recipient mice of bone marrow transplantation models using TLR4 mutation site primers and wild‐type site primers, respectively. c,d) Graphic presentations show serum creatinine (Scr) (c) and blood urea nitrogen (BUN) (d) levels in different groups as indicated at 11 days after IRI. * p < 0.05 versus WT‐WT (n = 4–6). e,f) Western blot analyses show renal expression of TLR4, p‐P65, and P65 in different groups as indicated. Representative Western blot (e) and quantitative data (f) are shown. * p < 0.05 versus WT‐WT (n = 4–6). g) Representative micrographs show renal expression and co‐localization of TLR4 and F4/80 by immunofluorescence staining in different groups as indicated. The areas between the dashed lines represent the corticomedullary junction of the kidney. h,i) Western blot analyses show renal expression of MR, Arg‐1, iNOS, TNF‐α, and CCL2 in different groups as indicated. Representative Western blot (h) and quantitative data (i) are shown. * p < 0.05 versus WT‐WT (n = 4–6). j,k) Western blot analyses show renal expression of TNC, FN, and α‐SMA in different groups as indicated. Representative Western blot (j) and quantitative data (k) are shown. * p < 0.05 versus WT‐WT (n = 4–6). l) A schematic diagram shows a crucial role of TNC in organizing the proinflammatory and profibrotic niche. By integrating single‐cell RNA sequencing and spatial transcriptomics, we unveil TNC as a central organizer of the proinflammatory and profibrotic niche in kidney fibrosis. TNC promotes macrophage activation through TLR4/NF‐κB signaling, leading to macrophage activation, proliferation, and cytokine production.

Figure Legend Snippet: TLR4 knockout in macrophages attenuates renal inflammation and renal fibrosis in vivo. a) The diagram shows the experimental protocol. Bone marrow chimera models were established by transplanting the WT bone marrow to WT mice, or TLR4 KO bone marrow to WT mice. Mice were irradiated at a single dose of 1100 Rads and then underwent bone marrow transplantation. After 8 weeks of successful transplantation, a unilateral ischemia‐reperfusion (UIRI) model was established. b) PCR‐based identification of kidney genotypes in the recipient mice of bone marrow transplantation models using TLR4 mutation site primers and wild‐type site primers, respectively. c,d) Graphic presentations show serum creatinine (Scr) (c) and blood urea nitrogen (BUN) (d) levels in different groups as indicated at 11 days after IRI. * p < 0.05 versus WT‐WT (n = 4–6). e,f) Western blot analyses show renal expression of TLR4, p‐P65, and P65 in different groups as indicated. Representative Western blot (e) and quantitative data (f) are shown. * p < 0.05 versus WT‐WT (n = 4–6). g) Representative micrographs show renal expression and co‐localization of TLR4 and F4/80 by immunofluorescence staining in different groups as indicated. The areas between the dashed lines represent the corticomedullary junction of the kidney. h,i) Western blot analyses show renal expression of MR, Arg‐1, iNOS, TNF‐α, and CCL2 in different groups as indicated. Representative Western blot (h) and quantitative data (i) are shown. * p < 0.05 versus WT‐WT (n = 4–6). j,k) Western blot analyses show renal expression of TNC, FN, and α‐SMA in different groups as indicated. Representative Western blot (j) and quantitative data (k) are shown. * p < 0.05 versus WT‐WT (n = 4–6). l) A schematic diagram shows a crucial role of TNC in organizing the proinflammatory and profibrotic niche. By integrating single‐cell RNA sequencing and spatial transcriptomics, we unveil TNC as a central organizer of the proinflammatory and profibrotic niche in kidney fibrosis. TNC promotes macrophage activation through TLR4/NF‐κB signaling, leading to macrophage activation, proliferation, and cytokine production.

Techniques Used: Knock-Out, In Vivo, Irradiation, Transplantation Assay, Mutagenesis, Western Blot, Expressing, Immunofluorescence, Staining, RNA Sequencing, Activation Assay

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(A) Schematic showing the sequencing chip of stereo-seq technology. (B) Visualization of the spatial transcriptome of the coronal brain slice containing RSG region. Scale bars, 300 μm. (C) Clustering analysis of RSG cells visualized by Uniform manifold approximation and projection (UMAP) dimensional reduction. (D) Spatial distribution of different clusters of glutamatergic and GABAergic neurons in RSG. (E) Dotplot showing the Cckbr mRNA expression in different clusters of RSG glutamatergic and GABAergic neurons. (F) Representative image showing the expression of Cckbr protein in RSG. Scale bars, 100 μm. (G) Normalized fluorescence intensity of Cckbr protein across the different layers of RSG. (H) Area under curve of the fluorescence intensity of Cckbr protein in different layers of RSG ( n = 5). One-way ANOVA (F (3, 16) = 72.32, p < 0.0001) followed by Tukey’s post hoc test, **** p < 0.0001. (I) Left: representative images showing the expression of Cckbr protein in RSG layer 5 of rats in Saline SA and Heroin SA groups. Scale bars, 50 μm. Right: average expression level of Cckbr protein in RSG layer 5 of Saline SA ( n = 3) vs Heroin SA ( n = 3) rats. Mann-Whitney test, * p < 0.05. (J) Recognition and separation of different layers in RSG. Scale bars, 200 μm. (K) Heatmap showing the differential IEGs expression in RSG layer 2/3, layer 5 and layer 6. Multiple Mann-Whitney test followed by False Discovery Rate (FDR) post test, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 vs L2/3, #### p < 0.0001 vs L6. (L) Left: schematic of the viral strategy for chemogenetic inhibition of ZI neurons and the representative image showing the hM4Di expression in ZI. Scale bars, 100 μm. Right: number of responses of rats in EGFP control group ( n = 10) vs hM4Di group ( n = 9). Two-way ANOVA (F (1,34) = 1.635, p = 0.2096) followed by Sidak’s post hoc test, * p < 0.05. (M) Left: representative images showing the expression of TH and c-fos (top) or Gad and c-fos (bottom) in ZI of rats in ABB group and ABA group. Scale bars, 100 μm. Right: number of c-fos-positive cells in TH + or Gad + neurons in ZI of ABB group ( n = 4) vs ABA group ( n = 5). Two-way ANOVA (F (1,14) = 25.68, p < 0.001) followed by Sidak’s post hoc test, **** p < 0.0001, ns, no significant difference. (N) Left: representative image showing the co-localization of Cckbr and the mCherry-labeled ZI-projecting neurons in RSG layer 5. Scale bars, 50 μm. Right: percentage of Cckbr + and Cckbr - cells in mCherry + neurons in RSG layer 5 ( n = 3). (O) Left: representative images showing the expression of mCherry and c-fos in RSG layer 5 of rats in ABB group and ABA group. Scale bars, 50 μm. Right: number of mCherry + c-fos + neurons in RSG layer 5 of ABB group ( n = 5) vs ABA group ( n = 3) and percentage of Fos + and Fos - nuclei in mCherry + cells in RSG layer 5 in ABA group. Unpaired t test, ** p < 0.01, **** p < 0.0001. (P) Top: representative images showing the co-localization of Vgat , Vglut2 and mCherry in ZI, and percentage of mCherry-positive cells in Vglut2 + and Vgat + neurons in ZI ( n = 4). Unpaired t test, **** p < 0.0001. Bottom: representative images showing the co-localization of Vgat , mCherry and Fos in ZI after context-induced relapse, and percentage of Fos + and Fos - nuclei in Vgat + mCherry + cells in ZI after context-induced relaspe ( n = 4). Scale bars, 100 μm and 50 μm. Unpaired t test, **** p < 0.0001. (Q) Schematic showing the training and perfusion schedule, the viral strategy for Cckbr knockout and chemogenetic activation of RSG glutamatergic neurons and the representative image of RSG axon terminals in ZI. Scale bars, 500 μm. (R) Left: representative images showing the c-fos expression in ZI adjacent to the axon terminals of RSG glutamatergic neurons in rats of control, Cckbr knockdown and Cckbr knockdown with hM3Dq groups after context-induced relapse. Scale bars, 50 μm. Right: number of c-fos-positive neurons in ZI of rats in control ( n = 4), Cckbr knockdown ( n = 5) and Cckbr knockdown with hM3Dq ( n = 6) groups after context-induced relapse. One-way ANOVA (F( 2, 12) = 12.30, p < 0.01) followed by Tukey’s post hoc test, ** p < 0.01, ns, no significant difference. (S) Schematic of the viral strategy for chemogenetic inhibition of RSG Glu-Cckbr -ZI GABA circuit. (T) Number of responses in mCherry control group ( n = 8) and hM4Di group ( n = 9) during test 1 with clozapine injection (i.p.). Two-way ANOVA (F (1,30) = 6.145, p < 0.05) followed by Sidak’s post hoc test, * p < 0.05. (U) Number of responses in mCherry control group ( n = 8) and hM4Di group ( n = 9) during test 2 with vehicle injection. Two-way RM ANOVA (F (1,30) = 0.3091, p = 0.5824) followed by Sidak’s post hoc test. ns, no significant difference.

Journal: bioRxiv

Article Title: A non-canonical top-down pathway regulating relapse to opioid

doi: 10.1101/2025.11.27.691060

Figure Lengend Snippet: (A) Schematic showing the sequencing chip of stereo-seq technology. (B) Visualization of the spatial transcriptome of the coronal brain slice containing RSG region. Scale bars, 300 μm. (C) Clustering analysis of RSG cells visualized by Uniform manifold approximation and projection (UMAP) dimensional reduction. (D) Spatial distribution of different clusters of glutamatergic and GABAergic neurons in RSG. (E) Dotplot showing the Cckbr mRNA expression in different clusters of RSG glutamatergic and GABAergic neurons. (F) Representative image showing the expression of Cckbr protein in RSG. Scale bars, 100 μm. (G) Normalized fluorescence intensity of Cckbr protein across the different layers of RSG. (H) Area under curve of the fluorescence intensity of Cckbr protein in different layers of RSG ( n = 5). One-way ANOVA (F (3, 16) = 72.32, p < 0.0001) followed by Tukey’s post hoc test, **** p < 0.0001. (I) Left: representative images showing the expression of Cckbr protein in RSG layer 5 of rats in Saline SA and Heroin SA groups. Scale bars, 50 μm. Right: average expression level of Cckbr protein in RSG layer 5 of Saline SA ( n = 3) vs Heroin SA ( n = 3) rats. Mann-Whitney test, * p < 0.05. (J) Recognition and separation of different layers in RSG. Scale bars, 200 μm. (K) Heatmap showing the differential IEGs expression in RSG layer 2/3, layer 5 and layer 6. Multiple Mann-Whitney test followed by False Discovery Rate (FDR) post test, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 vs L2/3, #### p < 0.0001 vs L6. (L) Left: schematic of the viral strategy for chemogenetic inhibition of ZI neurons and the representative image showing the hM4Di expression in ZI. Scale bars, 100 μm. Right: number of responses of rats in EGFP control group ( n = 10) vs hM4Di group ( n = 9). Two-way ANOVA (F (1,34) = 1.635, p = 0.2096) followed by Sidak’s post hoc test, * p < 0.05. (M) Left: representative images showing the expression of TH and c-fos (top) or Gad and c-fos (bottom) in ZI of rats in ABB group and ABA group. Scale bars, 100 μm. Right: number of c-fos-positive cells in TH + or Gad + neurons in ZI of ABB group ( n = 4) vs ABA group ( n = 5). Two-way ANOVA (F (1,14) = 25.68, p < 0.001) followed by Sidak’s post hoc test, **** p < 0.0001, ns, no significant difference. (N) Left: representative image showing the co-localization of Cckbr and the mCherry-labeled ZI-projecting neurons in RSG layer 5. Scale bars, 50 μm. Right: percentage of Cckbr + and Cckbr - cells in mCherry + neurons in RSG layer 5 ( n = 3). (O) Left: representative images showing the expression of mCherry and c-fos in RSG layer 5 of rats in ABB group and ABA group. Scale bars, 50 μm. Right: number of mCherry + c-fos + neurons in RSG layer 5 of ABB group ( n = 5) vs ABA group ( n = 3) and percentage of Fos + and Fos - nuclei in mCherry + cells in RSG layer 5 in ABA group. Unpaired t test, ** p < 0.01, **** p < 0.0001. (P) Top: representative images showing the co-localization of Vgat , Vglut2 and mCherry in ZI, and percentage of mCherry-positive cells in Vglut2 + and Vgat + neurons in ZI ( n = 4). Unpaired t test, **** p < 0.0001. Bottom: representative images showing the co-localization of Vgat , mCherry and Fos in ZI after context-induced relapse, and percentage of Fos + and Fos - nuclei in Vgat + mCherry + cells in ZI after context-induced relaspe ( n = 4). Scale bars, 100 μm and 50 μm. Unpaired t test, **** p < 0.0001. (Q) Schematic showing the training and perfusion schedule, the viral strategy for Cckbr knockout and chemogenetic activation of RSG glutamatergic neurons and the representative image of RSG axon terminals in ZI. Scale bars, 500 μm. (R) Left: representative images showing the c-fos expression in ZI adjacent to the axon terminals of RSG glutamatergic neurons in rats of control, Cckbr knockdown and Cckbr knockdown with hM3Dq groups after context-induced relapse. Scale bars, 50 μm. Right: number of c-fos-positive neurons in ZI of rats in control ( n = 4), Cckbr knockdown ( n = 5) and Cckbr knockdown with hM3Dq ( n = 6) groups after context-induced relapse. One-way ANOVA (F( 2, 12) = 12.30, p < 0.01) followed by Tukey’s post hoc test, ** p < 0.01, ns, no significant difference. (S) Schematic of the viral strategy for chemogenetic inhibition of RSG Glu-Cckbr -ZI GABA circuit. (T) Number of responses in mCherry control group ( n = 8) and hM4Di group ( n = 9) during test 1 with clozapine injection (i.p.). Two-way ANOVA (F (1,30) = 6.145, p < 0.05) followed by Sidak’s post hoc test, * p < 0.05. (U) Number of responses in mCherry control group ( n = 8) and hM4Di group ( n = 9) during test 2 with vehicle injection. Two-way RM ANOVA (F (1,30) = 0.3091, p = 0.5824) followed by Sidak’s post hoc test. ns, no significant difference.

Article Snippet: The spatial transcriptome sequencing was conducted by Novogene Co. Ltd (Beijing, China).

Techniques: Sequencing, Slice Preparation, Expressing, Fluorescence, Saline, MANN-WHITNEY, Inhibition, Control, Labeling, Knock-Out, Activation Assay, Knockdown, Injection

Journal: Journal of Translational Medicine

Article Title: SGMS2+ macrophages enhance NR4A3hi NK cell infiltration to improve prognosis and PD-1 treatment efficacy in hepatocellular carcinoma

doi: 10.1186/s12967-025-07040-x

Figure Lengend Snippet: Investigations on SGMS2—related Cellular and Molecular Interactions in Hepatocellular Carcinoma. a Western blotting analysis of SGMS2 expression in THP—1 cells and differentiated macrophages. Each experiment was independently repeated three times. b , c Apoptosis levels of Huh7 tumor cells co—cultured with control macrophages and SGMS2—overexpressing macrophages were detected by flow cytometry (FCM). d Expression of SGMS2 in spatial transcriptomics sequencing data. e Abundance estimation of the CD56dimCD16highNR4A3high NK cell population by single—sample gene—set enrichment analysis (ssGSEA). f Multiplex immunofluorescence (mIF) images of SGMS2, CD68, CD16, CD56, and NR4A3 markers in 6 human HCC tissue samples. “Zoom macrophage” indicates the aggregation area of SGMS2—positive macrophages, and “Zoom NK cell” represents the CD56dimCD16highNR4A3high NK cells. The scale bar is 50 um or 20 um. g Scatter plots showing the density of CD56dimCD16highNR4A3high NK cells between patients with high and low infiltration of SGMS2—positive macrophages. Statistical analysis was performed using the Mann—Whitney U test. h Pearson correlation analysis of the density of CD56dimCD16highNR4A3high NK cells and the density of SGMS2—positive macrophages. i Kaplan—Meier analysis of OS, RFS, and early RFS in HCC patients with different infiltration densities of SGMS2—positive macrophages and CD56dimCD16highNR4A3high NK cells. Survival distributions were compared using the log—rank test. Statistical significance is indicated as follows: * P < 0.05, ** P < 0.01, *** P < 0.001; ns indicates no significant difference

Article Snippet: Spatial transcriptomics sequencing data were obtained from http://lifeome.net/supp/livercancer-st/data.htm and analyzed using Seurat in R. Subsequently, SCTtransform normalization was performed.

Techniques: Western Blot, Expressing, Cell Culture, Control, Flow Cytometry, Sequencing, Multiplex Assay, Immunofluorescence, MANN-WHITNEY