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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
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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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Integration of Single-cell transcriptomics datasets. a Diagram depicting the single-cell transcriptomics dataset utilized. b Highlighted transcriptional states selected from each single-cell transcriptomics dataset, demarcated with dotted lines. c UMAP plot showing 14 distinct integrated clusters labeled 0–13, comprising a total of 222,822 cells. d Quantification of individual cell state contributions to the integrated transcriptional state. e – f Gene expression analysis within each transcriptional state, referencing studies by Yun Chen et al. and Sun Victor et al. . Cluster numbers and gene names are highlighted with the same color code to indicate enrichment. Note: Xenografted-mic term used for Xenografted-microglia

Journal: Alzheimer's Research & Therapy

Article Title: Exploring cellular heterogeneity: single-cell and spatial transcriptomics of Alzheimer's disease brains and iPSC-derived microglia

doi: 10.1186/s13195-025-01944-y

Figure Lengend Snippet: Integration of Single-cell transcriptomics datasets. a Diagram depicting the single-cell transcriptomics dataset utilized. b Highlighted transcriptional states selected from each single-cell transcriptomics dataset, demarcated with dotted lines. c UMAP plot showing 14 distinct integrated clusters labeled 0–13, comprising a total of 222,822 cells. d Quantification of individual cell state contributions to the integrated transcriptional state. e – f Gene expression analysis within each transcriptional state, referencing studies by Yun Chen et al. and Sun Victor et al. . Cluster numbers and gene names are highlighted with the same color code to indicate enrichment. Note: Xenografted-mic term used for Xenografted-microglia

Article Snippet: Further, the Visium SD spatial transcriptomics platform is limited by its resolution, as each capture spot (< 55 μm) often contains transcripts from multiple cells, which is improved from first report (capture > 10,000 transcripts per spot) [ ].

Techniques: Single-cell Transcriptomics, Labeling, Gene Expression

$Microglial transcriptional shift in response to AD pathology. a Spatial transcriptomics (SRT) of the Middle Temporal Gyrus (MTG) in Alzheimer's disease (AD), with each section being 10 µm thick. b Visium spots highlighting the top 25% highest probability for Homeostatic, DAM, MHCII, Neuronal Surveillance and Inflammatory-I states. c Heatmap illustrating the fraction of predicted transcriptional states within each cortical layer. d Overview of spatial transcriptomics Aβ localization. Aβ-proximal spots refer to those directly overlapping Aβ plaques, while all others are considered Aβ-distal. e Upper: Quantification of transcriptional states around proximal and distal Aβ spots for Combined II-VI, External II-III, and Internal IV-VI cortical layers. f - j SRT sample from AD frontal cortex from van Olst et al. . f Spatially resolved clusters based on gene expression from van Olst et al. AD sample. g Cortical layers identified based on main layer markers reported in van Olst et al., shown in panel h . The grey matter layers were identified as External (Layers I-III) and Internal (Layers IV-VI). Meninges and white mater were not considered in the analysis. i The Homeostatic and DAM enriched spots identified across the grey matter. j Proportion of Homeostatic and DAM enriched spots in each Internal and External layers. Chi-square significance tests were used to calculate p-values (refer Fig. S9 for other transcriptional states). Note: Xenografted-mic term used for Xenografted-microglia$

Journal: Alzheimer's Research & Therapy

Article Title: Exploring cellular heterogeneity: single-cell and spatial transcriptomics of Alzheimer's disease brains and iPSC-derived microglia

doi: 10.1186/s13195-025-01944-y

Figure Lengend Snippet: Microglial transcriptional shift in response to AD pathology. a Spatial transcriptomics (SRT) of the Middle Temporal Gyrus (MTG) in Alzheimer's disease (AD), with each section being 10 µm thick. b Visium spots highlighting the top 25% highest probability for Homeostatic, DAM, MHCII, Neuronal Surveillance and Inflammatory-I states. c Heatmap illustrating the fraction of predicted transcriptional states within each cortical layer. d Overview of spatial transcriptomics Aβ localization. Aβ-proximal spots refer to those directly overlapping Aβ plaques, while all others are considered Aβ-distal. e Upper: Quantification of transcriptional states around proximal and distal Aβ spots for Combined II-VI, External II-III, and Internal IV-VI cortical layers. f - j SRT sample from AD frontal cortex from van Olst et al. . f Spatially resolved clusters based on gene expression from van Olst et al. AD sample. g Cortical layers identified based on main layer markers reported in van Olst et al., shown in panel h . The grey matter layers were identified as External (Layers I-III) and Internal (Layers IV-VI). Meninges and white mater were not considered in the analysis. i The Homeostatic and DAM enriched spots identified across the grey matter. j Proportion of Homeostatic and DAM enriched spots in each Internal and External layers. Chi-square significance tests were used to calculate p-values (refer Fig. S9 for other transcriptional states). Note: Xenografted-mic term used for Xenografted-microglia

Techniques: Gene Expression

Spatial distribution of microglial activation across cortical layers in Alzheimer’s disease (AD) brain. a Immunofluorescence (IF) staining of P2RY12 and Aβ on adjacent Sects. (10 µm interval) from the Middle Temporal Gyrus of an AD donor, aligned to 10X Genomics Visium spatial transcriptomics spots (color-coded) across cortical layers II–VI (Chen et al., ANC, 2022) . High-magnification images show nuclei (DAPI, gray), homeostatic microglia (P2RY12, magenta), and Aβ plaques (blue) in external layers II–III (top) and internal layers IV–VI (bottom). b Quantification of IF-stained P2RY12⁺ cells and Aβ⁺ plaques across cortical layers II–III and IV–VI in AD samples. Bar plots display normalized counts for: Upper Left—P2RY12⁺ cells; Upper Right—Aβ⁺ plaques; Lower Left—P2RY12⁺/Aβ⁺ overlap; Lower Right—P2RY12⁺/Aβ⁻ plaques. Counts were normalized to the total number within layers II–VI. c IF co-staining of Aβ (red) and phosphorylated tau (pTAU, green) in frontal cortex sections with AD pathology (Section A). Nuclei stained with DAPI (blue). Adjacent section (Section B) stained for CD68 (red), a marker of activated microglia. d Quantification of CD68⁺ cells across cortical layers in AD frontal cortex. Graph shows distribution of CD68⁺ and CD68⁻ cells in external versus internal layers

Journal: Alzheimer's Research & Therapy

Article Title: Exploring cellular heterogeneity: single-cell and spatial transcriptomics of Alzheimer's disease brains and iPSC-derived microglia

doi: 10.1186/s13195-025-01944-y

Figure Lengend Snippet: Spatial distribution of microglial activation across cortical layers in Alzheimer’s disease (AD) brain. a Immunofluorescence (IF) staining of P2RY12 and Aβ on adjacent Sects. (10 µm interval) from the Middle Temporal Gyrus of an AD donor, aligned to 10X Genomics Visium spatial transcriptomics spots (color-coded) across cortical layers II–VI (Chen et al., ANC, 2022) . High-magnification images show nuclei (DAPI, gray), homeostatic microglia (P2RY12, magenta), and Aβ plaques (blue) in external layers II–III (top) and internal layers IV–VI (bottom). b Quantification of IF-stained P2RY12⁺ cells and Aβ⁺ plaques across cortical layers II–III and IV–VI in AD samples. Bar plots display normalized counts for: Upper Left—P2RY12⁺ cells; Upper Right—Aβ⁺ plaques; Lower Left—P2RY12⁺/Aβ⁺ overlap; Lower Right—P2RY12⁺/Aβ⁻ plaques. Counts were normalized to the total number within layers II–VI. c IF co-staining of Aβ (red) and phosphorylated tau (pTAU, green) in frontal cortex sections with AD pathology (Section A). Nuclei stained with DAPI (blue). Adjacent section (Section B) stained for CD68 (red), a marker of activated microglia. d Quantification of CD68⁺ cells across cortical layers in AD frontal cortex. Graph shows distribution of CD68⁺ and CD68⁻ cells in external versus internal layers

Techniques: Activation Assay, Immunofluorescence, Staining, Marker

The timeline of technological developments in exploring musculoskeletal diseases spans multiple biological levels, including transcriptomics, epigenomics, proteomics, and metabolomics

Journal: Bone Research

Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

doi: 10.1038/s41413-025-00442-z

Figure Lengend Snippet: The timeline of technological developments in exploring musculoskeletal diseases spans multiple biological levels, including transcriptomics, epigenomics, proteomics, and metabolomics

Article Snippet: Using the Visium CytAssist spatial transcriptomics platform, researchers successfully mapped genes associated with hard callus (e.g., Dmp1 and Sost ) and soft callus (e.g., Acan and Col2a1 ) while preserving the spatial integrity of the tissue.

Techniques:

Application of advanced epigenomics, transcriptomics, proteomics, and metabolomics in cartilage

Journal: Bone Research

Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

doi: 10.1038/s41413-025-00442-z

Figure Lengend Snippet: Application of advanced epigenomics, transcriptomics, proteomics, and metabolomics in cartilage

Techniques:

Application of advanced epigenomics, transcriptomics, proteomics, and metabolomics in synovium

Journal: Bone Research

Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

doi: 10.1038/s41413-025-00442-z

Figure Lengend Snippet: Application of advanced epigenomics, transcriptomics, proteomics, and metabolomics in synovium

Techniques:

Application of advanced transcriptomics and metabolomics in bone cells and bony callus

Journal: Bone Research

Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

doi: 10.1038/s41413-025-00442-z

Figure Lengend Snippet: Application of advanced transcriptomics and metabolomics in bone cells and bony callus

Techniques:

Application of advanced transcriptomics, proteomics, and metabolomics in intervertebral disc

Journal: Bone Research

Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

doi: 10.1038/s41413-025-00442-z

Figure Lengend Snippet: Application of advanced transcriptomics, proteomics, and metabolomics in intervertebral disc

Techniques:

Application of single-cell RNA-seq and spatial transcriptomics in Tendon

Journal: Bone Research

Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

doi: 10.1038/s41413-025-00442-z

Figure Lengend Snippet: Application of single-cell RNA-seq and spatial transcriptomics in Tendon

Techniques: RNA Sequencing

Application of single-cell RNA-seq, single-nucleus RNA-seq, spatial transcriptomics, and metabolomics in muscle

Journal: Bone Research

Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

doi: 10.1038/s41413-025-00442-z

Figure Lengend Snippet: Application of single-cell RNA-seq, single-nucleus RNA-seq, spatial transcriptomics, and metabolomics in muscle

Techniques: RNA Sequencing

(A) Overview of our systematic approach to identify microglial and/or astrocytic cell-cell signals regulating Astrocyte 10 (Ast10). (1) NicheNet prioritizes ligand-receptor pairs based on their expression and how well their downstream signaling activities recapitulating the Ast10 transcriptional signature. (2) Partial Least Squares Regression (PLSR) models predict Ast10 frequency per donor using expression patterns of prioritized ligands or receptors. (3) Validation includes replication in independent datasets, spatial transcriptomics to confirm ligand-Ast10 colocalization, immunohistochemistry for coexpression of an Ast10 marker with a top receptor, and genetic depletion of the top receptor in iPSC-derived and murine astrocytes, followed by scRNA-seq. (B) Ligand activity z-scores from NicheNet for the top 100 sender-ligand-receptor interactions. A high z-score indicates that a ligand’s predicted target genes are enriched for Ast10 signature genes. A positive z-score reflects above-average activity relative to all other ligands analyzed. (C) Differential expression of the top ligands across all analyzed astrocytic and microglial sender states. Color indicates log fold-change (logFC) in ligand expression relative to other sender populations; circle size represents the percentage of cells expressing each ligand. (D) Differential expression of the receptors for top-ranked ligands from (B). Color denotes logFC of receptor expression in Ast10 compared to other astrocytic and microglial subsets.

Journal: bioRxiv

Article Title: PLXNB1 and other signaling drives a pathologic astrocyte state contributing to cognitive decline in Alzheimer’s Disease

doi: 10.1101/2025.02.24.639868

Figure Lengend Snippet: (A) Overview of our systematic approach to identify microglial and/or astrocytic cell-cell signals regulating Astrocyte 10 (Ast10). (1) NicheNet prioritizes ligand-receptor pairs based on their expression and how well their downstream signaling activities recapitulating the Ast10 transcriptional signature. (2) Partial Least Squares Regression (PLSR) models predict Ast10 frequency per donor using expression patterns of prioritized ligands or receptors. (3) Validation includes replication in independent datasets, spatial transcriptomics to confirm ligand-Ast10 colocalization, immunohistochemistry for coexpression of an Ast10 marker with a top receptor, and genetic depletion of the top receptor in iPSC-derived and murine astrocytes, followed by scRNA-seq. (B) Ligand activity z-scores from NicheNet for the top 100 sender-ligand-receptor interactions. A high z-score indicates that a ligand’s predicted target genes are enriched for Ast10 signature genes. A positive z-score reflects above-average activity relative to all other ligands analyzed. (C) Differential expression of the top ligands across all analyzed astrocytic and microglial sender states. Color indicates log fold-change (logFC) in ligand expression relative to other sender populations; circle size represents the percentage of cells expressing each ligand. (D) Differential expression of the receptors for top-ranked ligands from (B). Color denotes logFC of receptor expression in Ast10 compared to other astrocytic and microglial subsets.

Article Snippet: Fresh-frozen dorsolateral prefrontal cortex (DLPFC) samples from ROSMAP participants were processed using the Visium Spatial Transcriptomics (ST) platform, coupled with immunofluorescence.

Techniques: Expressing, Biomarker Discovery, Immunohistochemistry, Marker, Derivative Assay, Activity Assay, Quantitative Proteomics

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