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10X Genomics visium spatial transcriptome

( a-b ) Uniform Manifold Approximation and Projection (UMAP) visualization of cell populations from single-cell RNA-seq of HS lesional and normal skin. We integrated the NYU dataset and two publicly available datasets containing normal skin samples (GSE173706 and GSE195452). Cell types were annotated using known marker genes. The UMAP plots are colored by cell types ( a ) and conditions, i.e., HS lesion vs. normal ( b ). ( c ) Comparisons of PiHSM module scores between HS lesions and normal controls across major cell populations. For each cell type, module scores were calculated based on the PiHSM genes that were expressed in ≥ 2% of cells and upregulated in HS lesional skins. ( d ) Heatmap of differentially expressed genes (HS lesion vs. normal) across cell types. Genes upregulated in HS are shown in red, and downregulated genes are shown in blue. *FDR < 0.05; **FDR < 0.01; ***FDR < 0.001. ( e-h ) We integrated two 10x <t>Visium</t> spatial transcriptomics including one normal axilla and one HS patient sample containing tunnel. Spatial feature plots for multiple genes are shown with the same expression scale. Corresponding immunofluorescence staining was performed to evaluate if the protein expression pattern matches its transcription ( f-h ). Designated proteins are visualized as red, with references of keratinocytes by K14 (green) and nuclei with DAPI staining (grey). Scale bars: 25 µm.

Visium Spatial Transcriptome, supplied by 10X Genomics, 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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1) Product Images from "A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases"

Article Title: A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases

Journal: medRxiv

doi: 10.64898/2026.05.13.26353128

Figure Legend Snippet: ( a-b ) Uniform Manifold Approximation and Projection (UMAP) visualization of cell populations from single-cell RNA-seq of HS lesional and normal skin. We integrated the NYU dataset and two publicly available datasets containing normal skin samples (GSE173706 and GSE195452). Cell types were annotated using known marker genes. The UMAP plots are colored by cell types ( a ) and conditions, i.e., HS lesion vs. normal ( b ). ( c ) Comparisons of PiHSM module scores between HS lesions and normal controls across major cell populations. For each cell type, module scores were calculated based on the PiHSM genes that were expressed in ≥ 2% of cells and upregulated in HS lesional skins. ( d ) Heatmap of differentially expressed genes (HS lesion vs. normal) across cell types. Genes upregulated in HS are shown in red, and downregulated genes are shown in blue. *FDR < 0.05; **FDR < 0.01; ***FDR < 0.001. ( e-h ) We integrated two 10x Visium spatial transcriptomics including one normal axilla and one HS patient sample containing tunnel. Spatial feature plots for multiple genes are shown with the same expression scale. Corresponding immunofluorescence staining was performed to evaluate if the protein expression pattern matches its transcription ( f-h ). Designated proteins are visualized as red, with references of keratinocytes by K14 (green) and nuclei with DAPI staining (grey). Scale bars: 25 µm.

Techniques Used: Single Cell, RNA Sequencing, Marker, Spatial Transcriptomics, Expressing, Immunofluorescence, Staining

Microscopy:

Article Title: A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases
Article Snippet: .. Further, Visium spatial transcriptome (10X Genomics) and immunofluorescent (IF) microscopy were performed to confirm their differential expression with spatial information at the RNA and protein level, respectively ( , and ). ..

Quantitative Proteomics:

Gene Expression:

Article Title: DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax.
Article Snippet: Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data.. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed.. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach.

Image Search Results

Journal: medRxiv

Article Title: A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases

doi: 10.64898/2026.05.13.26353128

Figure Lengend Snippet: ( a-b ) Uniform Manifold Approximation and Projection (UMAP) visualization of cell populations from single-cell RNA-seq of HS lesional and normal skin. We integrated the NYU dataset and two publicly available datasets containing normal skin samples (GSE173706 and GSE195452). Cell types were annotated using known marker genes. The UMAP plots are colored by cell types ( a ) and conditions, i.e., HS lesion vs. normal ( b ). ( c ) Comparisons of PiHSM module scores between HS lesions and normal controls across major cell populations. For each cell type, module scores were calculated based on the PiHSM genes that were expressed in ≥ 2% of cells and upregulated in HS lesional skins. ( d ) Heatmap of differentially expressed genes (HS lesion vs. normal) across cell types. Genes upregulated in HS are shown in red, and downregulated genes are shown in blue. *FDR < 0.05; **FDR < 0.01; ***FDR < 0.001. ( e-h ) We integrated two 10x Visium spatial transcriptomics including one normal axilla and one HS patient sample containing tunnel. Spatial feature plots for multiple genes are shown with the same expression scale. Corresponding immunofluorescence staining was performed to evaluate if the protein expression pattern matches its transcription ( f-h ). Designated proteins are visualized as red, with references of keratinocytes by K14 (green) and nuclei with DAPI staining (grey). Scale bars: 25 µm.

Article Snippet: Further, Visium spatial transcriptome (10X Genomics) and immunofluorescent (IF) microscopy were performed to confirm their differential expression with spatial information at the RNA and protein level, respectively ( , and ).

Techniques: Single Cell, RNA Sequencing, Marker, Spatial Transcriptomics, Expressing, Immunofluorescence, Staining

Spatial transcriptomics reveals pHL tumor microenvironment comparments and checkpoint blockade targets: A. Characterisation of Hodgkin samples by spatial transcriptomics. (Left) Integration, dimension reduction and unsupervised clustering of Visium spatial dots. (Middle) module scores distribution across clusters. (violin plot from left: Hodgkin, Histiocyte, M2 macrophage-like, B cell). (Right) cell distribution across each sample for different clusters visualised as heat map B. Representative Visium slide highlighting locations of clusters of interest (left, cluster3, 10 and 11) and contour plot of module scores calculated by pathologist curated gene sets. Hodgkin inclusive (red contour): TNFRSF8 (CD30), FUT4 (CD15), IRF4 ; Hodgkin exclusive (green contour): MS4A1 (CD20), CD79A , CD19 , SLC22A2 , POU2AF1 ; Exhaustion (blue contour): CTLA4 , LAG3 , TIGIT . C. Selected spatial gene expression of Hodgkin sample. D. Differently expressed gene (DEG) focused on extracellular signalling. (Top row) ligand (in red) or receptor (in blue) transcript upregulated in cluster of interest (from left: cluster 3, 10 & 11). (Bottom row) CellChat visualisation of selected top ligand-receptor pair from above. E. Selected gene expression of Visium clusters categorized into surface expression (cluster 3 only), progenitor B, Hodgkin markers, selected T cell immune check point expression and metalloproteins.

Journal: bioRxiv

Article Title: Single cell analysis reveals molecular traits of pediatric lymphoma resistant subclones

doi: 10.64898/2026.04.21.719850

Figure Lengend Snippet: Spatial transcriptomics reveals pHL tumor microenvironment comparments and checkpoint blockade targets: A. Characterisation of Hodgkin samples by spatial transcriptomics. (Left) Integration, dimension reduction and unsupervised clustering of Visium spatial dots. (Middle) module scores distribution across clusters. (violin plot from left: Hodgkin, Histiocyte, M2 macrophage-like, B cell). (Right) cell distribution across each sample for different clusters visualised as heat map B. Representative Visium slide highlighting locations of clusters of interest (left, cluster3, 10 and 11) and contour plot of module scores calculated by pathologist curated gene sets. Hodgkin inclusive (red contour): TNFRSF8 (CD30), FUT4 (CD15), IRF4 ; Hodgkin exclusive (green contour): MS4A1 (CD20), CD79A , CD19 , SLC22A2 , POU2AF1 ; Exhaustion (blue contour): CTLA4 , LAG3 , TIGIT . C. Selected spatial gene expression of Hodgkin sample. D. Differently expressed gene (DEG) focused on extracellular signalling. (Top row) ligand (in red) or receptor (in blue) transcript upregulated in cluster of interest (from left: cluster 3, 10 & 11). (Bottom row) CellChat visualisation of selected top ligand-receptor pair from above. E. Selected gene expression of Visium clusters categorized into surface expression (cluster 3 only), progenitor B, Hodgkin markers, selected T cell immune check point expression and metalloproteins.

Article Snippet: The experimental slide with Hodgkin Reed-Stenberg cells and Hodgkin cells was fixed and stained with hematoxylin and eosin (H&E) and the sequence libraries were then processed according to manufacturer’s instructions (10x Genomics, Visium Spatial Transcriptomic).

Techniques: Spatial Transcriptomics, Gene Expression, Expressing

Overview of the single-cell and spatial data generated from TB-diseased and control lungs. (A) Schematic showing the experimental flow for the isolation of cells from human lung tissues, generation of single-cell libraries using Seq-Well S 3 . Four TB-negative and nine TB-positive lung samples were processed through scRNA-seq. Shown adjacent to the process flow is a low-dimensional embedding (UMAP) of the 19,632 cells passing quality control annotated with high-level cell types (middle) or detailed cell subtype (right). (B) 10x Visium platform workflow for spatial transcriptomics profiling on FFPE samples from TB-diseased lung resections. 21 of these samples come from current TB patients with detectable M.tb ; 9 came from post-TB patient, where bacteria are no longer detected in BAL TB culture after infection. Samples contain either granulomas, iBALTs, or lung LNs, representing different pathological states.

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Overview of the single-cell and spatial data generated from TB-diseased and control lungs. (A) Schematic showing the experimental flow for the isolation of cells from human lung tissues, generation of single-cell libraries using Seq-Well S 3 . Four TB-negative and nine TB-positive lung samples were processed through scRNA-seq. Shown adjacent to the process flow is a low-dimensional embedding (UMAP) of the 19,632 cells passing quality control annotated with high-level cell types (middle) or detailed cell subtype (right). (B) 10x Visium platform workflow for spatial transcriptomics profiling on FFPE samples from TB-diseased lung resections. 21 of these samples come from current TB patients with detectable M.tb ; 9 came from post-TB patient, where bacteria are no longer detected in BAL TB culture after infection. Samples contain either granulomas, iBALTs, or lung LNs, representing different pathological states.

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: Generated, Control, Isolation, Bacteria, Infection

Spatial transcriptomics on TB-infected human lung samples and single-cell deconvolution. (A) H&E staining on all 30 lung samples from patients previously infected with TB. Scale bars: 800 μm. Identical images for pid_0037, pid_177, pid_0186, pid_187, pid_0192, pid_199, pid_0209, and pid_304. (B) Examples of manual annotation on granuloma structures on H&E staining images. Scale bars: 800 μm.

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Spatial transcriptomics on TB-infected human lung samples and single-cell deconvolution. (A) H&E staining on all 30 lung samples from patients previously infected with TB. Scale bars: 800 μm. Identical images for pid_0037, pid_177, pid_0186, pid_187, pid_0192, pid_199, pid_0209, and pid_304. (B) Examples of manual annotation on granuloma structures on H&E staining images. Scale bars: 800 μm.

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: Infection, Staining

Single-cell transcriptomic reveals heterogeneity within neutrophil populations with disease-specific difference. (A) Neutrophil ( n = 2,963) subclustering reveals three subclusters (left), also colored by patient ID (middle) and disease condition (right). (B) Volcano plot of differential gene expression results of each neutrophil subcluster compared with the rest. Y axis shows −log10 (BH-adjusted P value); x axis shows log2 fold change between cells in subcluster and outside the subcluster. (C) Heatmap of subtype top 10 differentially expressed (DE) genes in each of the neutrophil subcluster. (D) Expression of marker genes in neutrophil subclusters by disease conditions. (E) Fisher’s exact test on abundance of detailed neutrophil subclusters between TB conditions. Statistical annotations: fold-change >2 (ΔΔ). (F) Cell2loc imputed neutrophil abundance distribution on the Visium dataset grouped by TB and granuloma status (Materials and methods). The 5% quantile of the estimated posterior distribution of cell abundance at each Visium spot is displayed, representing the value of cell abundance that the model has high confidence in. Two-sided Mann–Whitney U test without correction were used for statistical testing. ****: P < 0.0001.

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Single-cell transcriptomic reveals heterogeneity within neutrophil populations with disease-specific difference. (A) Neutrophil ( n = 2,963) subclustering reveals three subclusters (left), also colored by patient ID (middle) and disease condition (right). (B) Volcano plot of differential gene expression results of each neutrophil subcluster compared with the rest. Y axis shows −log10 (BH-adjusted P value); x axis shows log2 fold change between cells in subcluster and outside the subcluster. (C) Heatmap of subtype top 10 differentially expressed (DE) genes in each of the neutrophil subcluster. (D) Expression of marker genes in neutrophil subclusters by disease conditions. (E) Fisher’s exact test on abundance of detailed neutrophil subclusters between TB conditions. Statistical annotations: fold-change >2 (ΔΔ). (F) Cell2loc imputed neutrophil abundance distribution on the Visium dataset grouped by TB and granuloma status (Materials and methods). The 5% quantile of the estimated posterior distribution of cell abundance at each Visium spot is displayed, representing the value of cell abundance that the model has high confidence in. Two-sided Mann–Whitney U test without correction were used for statistical testing. ****: P < 0.0001.

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: Gene Expression, Expressing, Marker, MANN-WHITNEY

Single-cell transcriptomic reveals heterogeneity within monocyte and macrophage populations with disease-specific difference. (A) Monocyte/macrophage ( n = 8,318) subclustering reveals 10 subclusters (left), also colored by patient ID (middle) and disease condition (right). (B) Heatmap of subtype top 10 DE genes in each of the monocyte/macrophage subcluster. (C) Expression of marker genes in monocyte/macrophage subclusters by disease conditions. (D) Two-sided Fisher’s exact test on abundance of detailed macrophage (left) and monocyte (right) subclusters between TB conditions. Holm’s method was applied to adjust P values for multiple-testing correction. Statistical annotations: P value < 0.05 (*), P value < 0.01 (**), P value < 0.001 (***), fold-change >1 (Δ), fold-change >2 (ΔΔ), and fold-change <1 (∇). (E) Cell2loc imputed macrophage (left) and monocyte (right) abundance distribution on the Visium dataset grouped by TB and granuloma status (Materials and methods). The 5% quantile of the estimated posterior distribution of cell abundance at each Visium spot is displayed, representing the value of cell abundance that the model has high confidence in. Two-sided Mann–Whitney U test without correction were used for statistical testing. Statistical annotations: P value < 0.0001 (****). (F) Similar to E, but grouped by TB status and HIV status.

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Single-cell transcriptomic reveals heterogeneity within monocyte and macrophage populations with disease-specific difference. (A) Monocyte/macrophage ( n = 8,318) subclustering reveals 10 subclusters (left), also colored by patient ID (middle) and disease condition (right). (B) Heatmap of subtype top 10 DE genes in each of the monocyte/macrophage subcluster. (C) Expression of marker genes in monocyte/macrophage subclusters by disease conditions. (D) Two-sided Fisher’s exact test on abundance of detailed macrophage (left) and monocyte (right) subclusters between TB conditions. Holm’s method was applied to adjust P values for multiple-testing correction. Statistical annotations: P value < 0.05 (*), P value < 0.01 (**), P value < 0.001 (***), fold-change >1 (Δ), fold-change >2 (ΔΔ), and fold-change <1 (∇). (E) Cell2loc imputed macrophage (left) and monocyte (right) abundance distribution on the Visium dataset grouped by TB and granuloma status (Materials and methods). The 5% quantile of the estimated posterior distribution of cell abundance at each Visium spot is displayed, representing the value of cell abundance that the model has high confidence in. Two-sided Mann–Whitney U test without correction were used for statistical testing. Statistical annotations: P value < 0.0001 (****). (F) Similar to E, but grouped by TB status and HIV status.

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: Expressing, Marker, MANN-WHITNEY

Deconvolution of bulk human LN dataset and fibroblast in spatial and single-cell dataset. (A) Dot plot showing distribution of cell type proportion from deconvolution results on each bulk RNA-seq human LN TB granuloma sample, separated by cell type and colored by TB conditions. Only cell types with significant difference between TB conditions are shown. Two-sided T test with Bonferroni correction was used to compare the means. Statistical annotations: P value < 0.05 (*) and P value < 0.01 (**). (B) Cell2loc imputed fibroblast abundance distribution on the Visium dataset group by TB and granuloma status (Materials and methods). The 5% quantile of the estimated posterior distribution of cell abundance per Visium spot is displayed, representing the value of cell abundance that the model has high confidence in. Two-sided Mann–Whitney U test without correction were used for statistical testing. P value < 0.0001 (****); P value > 0.05 (ns). (C) Same as B, but grouped by HIV and TB status. (D) Bar plot of patient distribution in each fibroblast subcluster. (E) UMAP embedding of fibroblasts colored by HIV status of the sample.

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Deconvolution of bulk human LN dataset and fibroblast in spatial and single-cell dataset. (A) Dot plot showing distribution of cell type proportion from deconvolution results on each bulk RNA-seq human LN TB granuloma sample, separated by cell type and colored by TB conditions. Only cell types with significant difference between TB conditions are shown. Two-sided T test with Bonferroni correction was used to compare the means. Statistical annotations: P value < 0.05 (*) and P value < 0.01 (**). (B) Cell2loc imputed fibroblast abundance distribution on the Visium dataset group by TB and granuloma status (Materials and methods). The 5% quantile of the estimated posterior distribution of cell abundance per Visium spot is displayed, representing the value of cell abundance that the model has high confidence in. Two-sided Mann–Whitney U test without correction were used for statistical testing. P value < 0.0001 (****); P value > 0.05 (ns). (C) Same as B, but grouped by HIV and TB status. (D) Bar plot of patient distribution in each fibroblast subcluster. (E) UMAP embedding of fibroblasts colored by HIV status of the sample.

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: RNA Sequencing, MANN-WHITNEY

Spatial transcriptomics analysis on post- and current TB lung resections. (A) Heatmap showing the expression of human TB-myofibroblast gene signature and SPP1 + CHI3L1 + macrophage markers on selective tissue slides from patients who are post-TB (top) or current TB (bottom), alongside paired H&E staining (these H&E stains are also shown in together with those other samples used for spatial transcriptomics not shown here). (B) Distribution of human TB-myofibroblast signature expression on the spatial cohort. HIV statuses are shown in different shades of blue for positive or negative. Two-sided Mann–Whitney U test without correction was used for statistical testing. Statistical annotation: P value < 0.0001 (****). (C) Distribution of SPP1 + CHI3L1 + macrophage markers and human TB-myofibroblast signature on the spatial data across all Visium spots. Left two panels: Manual segmentation of the granuloma structure was done to allow separation of the Visium slide into three different regions: in granuloma, on granuloma border (cuff), and outside of granuloma (Materials and methods). Right two panels: The same as left panels with the exception that “on border” = True means on granuloma cuff and False means the rest. Two-sided Mann–Whitney U test without correction was used for statistical testing. Statistical annotation: P value < 0.0001 (****). (D) Correlation between human TB-myofibroblast signature and all macrophage subpopulations’ markers. Each circle represents a Visium sample. Boxplot of the Pearson’s r distribution is shown for each macrophage subtype. Mann–Whitney U test without correction were used for statistical testing. Statistical annotation: P value < 0.0001 (****). (E) Spatially informed ligand–receptor (L–R) analysis using LIANA+ on Visium samples. Examples are shown where SPP1(L)–CD44(R) interactions are being nominated as top L–R pairs. H&E overlaid with pathology annotation for granuloma structures are shown next to heatmap of L–R interaction scores, which are calculated at each Visium spot using spatially weighted Cosine similarity (Materials and methods).

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Spatial transcriptomics analysis on post- and current TB lung resections. (A) Heatmap showing the expression of human TB-myofibroblast gene signature and SPP1 + CHI3L1 + macrophage markers on selective tissue slides from patients who are post-TB (top) or current TB (bottom), alongside paired H&E staining (these H&E stains are also shown in together with those other samples used for spatial transcriptomics not shown here). (B) Distribution of human TB-myofibroblast signature expression on the spatial cohort. HIV statuses are shown in different shades of blue for positive or negative. Two-sided Mann–Whitney U test without correction was used for statistical testing. Statistical annotation: P value < 0.0001 (****). (C) Distribution of SPP1 + CHI3L1 + macrophage markers and human TB-myofibroblast signature on the spatial data across all Visium spots. Left two panels: Manual segmentation of the granuloma structure was done to allow separation of the Visium slide into three different regions: in granuloma, on granuloma border (cuff), and outside of granuloma (Materials and methods). Right two panels: The same as left panels with the exception that “on border” = True means on granuloma cuff and False means the rest. Two-sided Mann–Whitney U test without correction was used for statistical testing. Statistical annotation: P value < 0.0001 (****). (D) Correlation between human TB-myofibroblast signature and all macrophage subpopulations’ markers. Each circle represents a Visium sample. Boxplot of the Pearson’s r distribution is shown for each macrophage subtype. Mann–Whitney U test without correction were used for statistical testing. Statistical annotation: P value < 0.0001 (****). (E) Spatially informed ligand–receptor (L–R) analysis using LIANA+ on Visium samples. Examples are shown where SPP1(L)–CD44(R) interactions are being nominated as top L–R pairs. H&E overlaid with pathology annotation for granuloma structures are shown next to heatmap of L–R interaction scores, which are calculated at each Visium spot using spatially weighted Cosine similarity (Materials and methods).

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: Expressing, Staining, MANN-WHITNEY

Mast cells from IBD patients with HαT demonstrate increased MRGPRX2 expression. Spatial transcriptomics (10x Xenium) was performed on 8 descending colon biopsies from the University of Pennsylvania IBD biobank (4 HαT, 4 non-HαT; balanced UC/CD). (A) UMAP embedding showing major cellular populations. (B) Mast cells (MCs), defined as TPSAB1 + MS4A2 + KIT + , are more abundant in HαT samples. (C) Feature map of isolated MCs demonstrating increased MRGPRX2 transcript levels in HαT. (D) Digital droplet PCR (ddPCR) of representative tissues from the same cohort confirms upregulated MRGPRX2 expression in HαT vs. non-HαT. (E) Spatial transcriptomics images showing increased MRGPRX2 transcripts (red dots) in HαT-positive IBD tissue compared with non-HαT tissue. (F) ddPCR validation on matched samples (HαT: n = 4; non-HαT: n = 4) showing elevated MRGPRX2 mRNA. (G) Pseudobulk differential expression demonstrates significantly increased MRGPRX2 in HαT samples. For transcriptomic analyses, differential expression was calculated using DESeq2 with Benjamini–Hochberg FDR correction (FDR < 0.05). Effect sizes are shown as log₂ fold-change with 95% CIs. For ddPCR comparisons, Welch's t -test was used with Cohen's d reported.

Journal: Frontiers in Allergy

Article Title: MRGPRX2-expressing mast cells are increased in the GI tract of individuals with active inflammatory bowel disease and hereditary α-tryptasemia

doi: 10.3389/falgy.2025.1726096

Figure Lengend Snippet: Mast cells from IBD patients with HαT demonstrate increased MRGPRX2 expression. Spatial transcriptomics (10x Xenium) was performed on 8 descending colon biopsies from the University of Pennsylvania IBD biobank (4 HαT, 4 non-HαT; balanced UC/CD). (A) UMAP embedding showing major cellular populations. (B) Mast cells (MCs), defined as TPSAB1 + MS4A2 + KIT + , are more abundant in HαT samples. (C) Feature map of isolated MCs demonstrating increased MRGPRX2 transcript levels in HαT. (D) Digital droplet PCR (ddPCR) of representative tissues from the same cohort confirms upregulated MRGPRX2 expression in HαT vs. non-HαT. (E) Spatial transcriptomics images showing increased MRGPRX2 transcripts (red dots) in HαT-positive IBD tissue compared with non-HαT tissue. (F) ddPCR validation on matched samples (HαT: n = 4; non-HαT: n = 4) showing elevated MRGPRX2 mRNA. (G) Pseudobulk differential expression demonstrates significantly increased MRGPRX2 in HαT samples. For transcriptomic analyses, differential expression was calculated using DESeq2 with Benjamini–Hochberg FDR correction (FDR < 0.05). Effect sizes are shown as log₂ fold-change with 95% CIs. For ddPCR comparisons, Welch's t -test was used with Cohen's d reported.

Article Snippet: Spatial transcriptomics cohort , 8 , 4 , 4 , Severe IBD: UC ( n = 4), CD ( n = 4)—balanced across HαT and non-HαT , Descending colon , 10x Visium Spatial Transcriptomics + scRNA-binning , Selected from genotyped cohort; used to evaluate MC abundance and MRGPRX2 expression patterns..

Techniques: Expressing, Isolation, Biomarker Discovery, Quantitative Proteomics

Individuals with IBD and HαT exhibit increased SIGLEC8 expression in colon tissue. Spatial transcriptomics and pseudobulk analysis were performed on 8 representative descending colon samples (4 HαT, 4 non-HαT; balanced UC/CD). (A) Pseudobulk counts aggregated by sample show higher SIGLEC8 expression in the HαT group (Wilcoxon test; Cohen's d and 95% CI reported). (B) Volcano plot of DESeq2 pseudobulk differential expression analysis contrasting non-HαT (blue) and HαT (red) samples. Genes surpassing FDR < 0.05 (Benjamini–Hochberg correction) are highlighted. SIGLEC8 is prominently upregulated in HαT, consistent with findings from CyTOF and ddPCR validation.

Journal: Frontiers in Allergy

Article Title: MRGPRX2-expressing mast cells are increased in the GI tract of individuals with active inflammatory bowel disease and hereditary α-tryptasemia

doi: 10.3389/falgy.2025.1726096

Figure Lengend Snippet: Individuals with IBD and HαT exhibit increased SIGLEC8 expression in colon tissue. Spatial transcriptomics and pseudobulk analysis were performed on 8 representative descending colon samples (4 HαT, 4 non-HαT; balanced UC/CD). (A) Pseudobulk counts aggregated by sample show higher SIGLEC8 expression in the HαT group (Wilcoxon test; Cohen's d and 95% CI reported). (B) Volcano plot of DESeq2 pseudobulk differential expression analysis contrasting non-HαT (blue) and HαT (red) samples. Genes surpassing FDR < 0.05 (Benjamini–Hochberg correction) are highlighted. SIGLEC8 is prominently upregulated in HαT, consistent with findings from CyTOF and ddPCR validation.

Techniques: Expressing, Quantitative Proteomics, Biomarker Discovery

Comparative spatial multi-omics analysis of acute myeloid leukemia patients’ bone marrow and extramedullary tissues (A) Schematic representation of the study workflow. Paired bone marrow (BM) samples (BM1 and BM2) and extramedullary (EM) samples (EM1, from skin; and EM2, from lymph node) from 2 newly diagnosed patients with acute myeloid leukemia (AML) (PT1 and PT2) were fixed in formalin and embedded in paraffin (FFPE) and then sectioned for use in Visium assays (v1 and v2), and Opal multiplex fluorescent immunohistochemistry (mfIHC). The Visium spatial transcriptomics (ST) results were validated using GeoMx digital spatial profiling (DSP) with tissue microarrays (TMAs) of samples from 3 newly diagnosed patients with AML (PT3, PT4, and PT5). An additional 4 AML bone marrow samples that performed the Visium gene and protein expression assay are used as a validational cohort (PT6, PT7, PT8, and PT9). Image created with BioRender ( https://biorender.com ). (B) Uniform manifold approximation and projection (UMAP) plot showing our reference map consisted of 79,029 cells collected from 9 healthy BM donors and 7 patients with AML with diploid cytogenetics to match the patient cytogenetic profiles, and included both newly generated scRNA data and previous works. This map consisted of 21 cell types, including T cells (CD4 + and CD8 + naive, effector, and memory T cells, T regulatory [Treg] cells, and unconventional T cells), other immune cells (Natural killer [NK] cells, B cells and plasma cells), hematopoietic progenitors (Hematopoietic stem cells [HSCs], common lymphoid progenitors [CLPs], granulocyte-monocyte progenitors [GMPs]), myeloid cells (megakaryocytes/platelets, monocytes, early and late erythroid cells, conventional and plasmacytoid dendritic cells) and leukemic (AML) cell populations. (C) Immunohistochemical staining of CD11c, MPO, and CD3e on BM1 sections that were used for histopathological annotation. The scale bar for the main tissue panels represents 1 mm. The scale bar for the zoomed-in panels, corresponding to the boxed regions, represents 100 μm. (D) Unsupervised clustering and pathology annotation for the projected spatial map of BM1, revealing 3 distinct regions with an adjusted rand index (ARI) of 0.46. (E) Spatial deconvolution of BM1 tissue, showing erythroid and AML cell populations, with CD11c immunohistochemistry (IHC) overlaid on an image of hematoxylin and eosin (H&E)-staining. The dotted red lines represent regions enriched for the erythroid cell population; dotted black lines, regions enriched for the AML cell population; and solid lines, regions that overlapped with other tissue sections. (F) Heatmap of Z score normalized canonical markers in pathology annotations, with matching unsupervised cluster distributions represented as a pie chart. HBB, HBD, HBA2, GATA1/2 are erythroid genes and S100A12, FCGR3A, CD14, MS4A7, and , CD33 are monocyte/leukemic genes. (G) Representative overlay of Visium H&E staining with Opal mfIHC and the generated spot-level data for CD33, CD71, CXCL12, CXCR4, CD68, and IL-6. Boxes illustrate magnified regions showing concordance between transcript-level (Visium) and protein-level (Opal) signals at the spot level. (H) Phenotype staining on near-adjacent tissue sections for markers of leukemic (CD33), monocytic (CD68), and erythroid (CD71) populations. DAPI was used as a nuclear counterstain. The spatial distribution of these markers corroborates ST-based spot deconvolution. Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). (I) Box and spatial plots of mfIHC staining intensities for phenotypic markers across ST-defined clusters in BM1, highlighting the enrichment of leukemic and monocytic populations in cluster 3 and that of erythroid populations in cluster 2 at BM1. Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). ns, not significant. ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test.

Journal: iScience

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML

doi: 10.1016/j.isci.2025.114289

Figure Lengend Snippet: Comparative spatial multi-omics analysis of acute myeloid leukemia patients’ bone marrow and extramedullary tissues (A) Schematic representation of the study workflow. Paired bone marrow (BM) samples (BM1 and BM2) and extramedullary (EM) samples (EM1, from skin; and EM2, from lymph node) from 2 newly diagnosed patients with acute myeloid leukemia (AML) (PT1 and PT2) were fixed in formalin and embedded in paraffin (FFPE) and then sectioned for use in Visium assays (v1 and v2), and Opal multiplex fluorescent immunohistochemistry (mfIHC). The Visium spatial transcriptomics (ST) results were validated using GeoMx digital spatial profiling (DSP) with tissue microarrays (TMAs) of samples from 3 newly diagnosed patients with AML (PT3, PT4, and PT5). An additional 4 AML bone marrow samples that performed the Visium gene and protein expression assay are used as a validational cohort (PT6, PT7, PT8, and PT9). Image created with BioRender ( https://biorender.com ). (B) Uniform manifold approximation and projection (UMAP) plot showing our reference map consisted of 79,029 cells collected from 9 healthy BM donors and 7 patients with AML with diploid cytogenetics to match the patient cytogenetic profiles, and included both newly generated scRNA data and previous works. This map consisted of 21 cell types, including T cells (CD4 + and CD8 + naive, effector, and memory T cells, T regulatory [Treg] cells, and unconventional T cells), other immune cells (Natural killer [NK] cells, B cells and plasma cells), hematopoietic progenitors (Hematopoietic stem cells [HSCs], common lymphoid progenitors [CLPs], granulocyte-monocyte progenitors [GMPs]), myeloid cells (megakaryocytes/platelets, monocytes, early and late erythroid cells, conventional and plasmacytoid dendritic cells) and leukemic (AML) cell populations. (C) Immunohistochemical staining of CD11c, MPO, and CD3e on BM1 sections that were used for histopathological annotation. The scale bar for the main tissue panels represents 1 mm. The scale bar for the zoomed-in panels, corresponding to the boxed regions, represents 100 μm. (D) Unsupervised clustering and pathology annotation for the projected spatial map of BM1, revealing 3 distinct regions with an adjusted rand index (ARI) of 0.46. (E) Spatial deconvolution of BM1 tissue, showing erythroid and AML cell populations, with CD11c immunohistochemistry (IHC) overlaid on an image of hematoxylin and eosin (H&E)-staining. The dotted red lines represent regions enriched for the erythroid cell population; dotted black lines, regions enriched for the AML cell population; and solid lines, regions that overlapped with other tissue sections. (F) Heatmap of Z score normalized canonical markers in pathology annotations, with matching unsupervised cluster distributions represented as a pie chart. HBB, HBD, HBA2, GATA1/2 are erythroid genes and S100A12, FCGR3A, CD14, MS4A7, and , CD33 are monocyte/leukemic genes. (G) Representative overlay of Visium H&E staining with Opal mfIHC and the generated spot-level data for CD33, CD71, CXCL12, CXCR4, CD68, and IL-6. Boxes illustrate magnified regions showing concordance between transcript-level (Visium) and protein-level (Opal) signals at the spot level. (H) Phenotype staining on near-adjacent tissue sections for markers of leukemic (CD33), monocytic (CD68), and erythroid (CD71) populations. DAPI was used as a nuclear counterstain. The spatial distribution of these markers corroborates ST-based spot deconvolution. Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). (I) Box and spatial plots of mfIHC staining intensities for phenotypic markers across ST-defined clusters in BM1, highlighting the enrichment of leukemic and monocytic populations in cluster 3 and that of erythroid populations in cluster 2 at BM1. Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). ns, not significant. ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test.

Article Snippet: Visium Spatial Transcriptomics Data , This paper , GEO: GSE279576.

Techniques: Biomarker Discovery, Multiplex Assay, Immunohistochemistry, Expressing, Generated, Clinical Proteomics, Immunohistochemical staining, Staining

Spatial multi-omics profiling identifies leukemic infiltration and tissue composition in extramedullary acute myeloid leukemia samples (A) Unsupervised clustering of the extramedullary sample EM1 into 3 spatial clusters (left) compared against the pathology-based annotation (right; indicating a composition of leukemia, dermis, epidermis, and gland). The adjusted rand index (ARI; 0.51) reflects moderate agreement between the clusters and pathology annotations. (B) Spatial deconvolution scores obtained using the SpaCET algorithm show EM1’s malignant cell distribution overlaid on the hematoxylin and eosin (H&E) image. (C) Heatmap of canonical marker expression in EM1 regions, validating transcriptional segregation and matching pathologist-defined regions. Markers of leukemic populations and dermis regions show shared expression profiles. Unsupervised cluster overlap is represented as pie charts, with pathology annotation. (D) Phenotypic staining (Opal multiplex immunofluorescent) on near-adjacent sections validating the spatial distribution of CD33 (malignant cells), CD68, and CD71, which is consistent with the Visium malignant signature (spot-level) and CD68 and CD71 expression patterns. Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels).

Journal: iScience

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML

doi: 10.1016/j.isci.2025.114289

Figure Lengend Snippet: Spatial multi-omics profiling identifies leukemic infiltration and tissue composition in extramedullary acute myeloid leukemia samples (A) Unsupervised clustering of the extramedullary sample EM1 into 3 spatial clusters (left) compared against the pathology-based annotation (right; indicating a composition of leukemia, dermis, epidermis, and gland). The adjusted rand index (ARI; 0.51) reflects moderate agreement between the clusters and pathology annotations. (B) Spatial deconvolution scores obtained using the SpaCET algorithm show EM1’s malignant cell distribution overlaid on the hematoxylin and eosin (H&E) image. (C) Heatmap of canonical marker expression in EM1 regions, validating transcriptional segregation and matching pathologist-defined regions. Markers of leukemic populations and dermis regions show shared expression profiles. Unsupervised cluster overlap is represented as pie charts, with pathology annotation. (D) Phenotypic staining (Opal multiplex immunofluorescent) on near-adjacent sections validating the spatial distribution of CD33 (malignant cells), CD68, and CD71, which is consistent with the Visium malignant signature (spot-level) and CD68 and CD71 expression patterns. Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels).

Article Snippet: Visium Spatial Transcriptomics Data , This paper , GEO: GSE279576.

Techniques: Biomarker Discovery, Marker, Expressing, Staining, Multiplex Assay

Inflammatory microenvironment analysis reveals region-specific signatures in bone marrow and extramedullary tissues from patients with acute myeloid leukemia (A) Distribution of spatial inflammation classes in BM1 and EM1, based on composite inflammation scores from inflammation-related hallmark pathways (Inflammatory response, IL6/JAK/STAT3 signaling, TNF-α/NF-κB signaling, IFN-γ response, IFN-α response, Complement, IL2/STAT5 signaling). Classes were defined using Jenks' natural breaks optimization. (B) Mean activity comparison of individual inflammatory related pathways in spots with high-inflammatory activity revealed the highest activity of IFN-γ response in EM tissue. Complement pathway activity is higher in BM1 when compared with the EM1 inflammatory niche. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test. (C) Boxplots of inflammation scores across the 3 clusters in BM1 (left) and EM1 (right). Each cluster displays significantly different levels of inflammatory activity; leukemia-enriched cluster 3 in BM1 and cluster 1 in EM1 have higher inflammation scores. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test. (D) IL-6 staining (Opal multiplex fluorescent immunohistochemistry [mfIHC]) in whole-slide images (left) of BM1 (top) and EM1 (bottom) and corresponding magnified regions (center), aligned with Visium spot-level composite inflammation score (right). Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). (E) Scatterplots showing the correlation of IL-6 protein staining intensity (mfIHC IL-6) with the composite inflammation score in BM1 (left) and EM1 (right). IL-6 levels are higher in high-inflammation regions in both BM1 and EM1. (F) Dot plot showing the localization of T cell subtypes (exhausted, CD8 + dysfunction, senescence, regulatory T cells [Treg]) based on inflammation class in BM1 and EM1.

Journal: iScience

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML

doi: 10.1016/j.isci.2025.114289

Figure Lengend Snippet: Inflammatory microenvironment analysis reveals region-specific signatures in bone marrow and extramedullary tissues from patients with acute myeloid leukemia (A) Distribution of spatial inflammation classes in BM1 and EM1, based on composite inflammation scores from inflammation-related hallmark pathways (Inflammatory response, IL6/JAK/STAT3 signaling, TNF-α/NF-κB signaling, IFN-γ response, IFN-α response, Complement, IL2/STAT5 signaling). Classes were defined using Jenks' natural breaks optimization. (B) Mean activity comparison of individual inflammatory related pathways in spots with high-inflammatory activity revealed the highest activity of IFN-γ response in EM tissue. Complement pathway activity is higher in BM1 when compared with the EM1 inflammatory niche. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test. (C) Boxplots of inflammation scores across the 3 clusters in BM1 (left) and EM1 (right). Each cluster displays significantly different levels of inflammatory activity; leukemia-enriched cluster 3 in BM1 and cluster 1 in EM1 have higher inflammation scores. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test. (D) IL-6 staining (Opal multiplex fluorescent immunohistochemistry [mfIHC]) in whole-slide images (left) of BM1 (top) and EM1 (bottom) and corresponding magnified regions (center), aligned with Visium spot-level composite inflammation score (right). Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). (E) Scatterplots showing the correlation of IL-6 protein staining intensity (mfIHC IL-6) with the composite inflammation score in BM1 (left) and EM1 (right). IL-6 levels are higher in high-inflammation regions in both BM1 and EM1. (F) Dot plot showing the localization of T cell subtypes (exhausted, CD8 + dysfunction, senescence, regulatory T cells [Treg]) based on inflammation class in BM1 and EM1.

Article Snippet: Visium Spatial Transcriptomics Data , This paper , GEO: GSE279576.

Techniques: Activity Assay, Comparison, Staining, Multiplex Assay, Immunohistochemistry

Chemokine signaling through the CXCL12-CXCR4 axis is linked to inflammatory niches and trans-differentiation in acute myeloid leukemia (A) Spatial and chord diagrams of the strength of interactions among acute myeloid leukemia (AML) cells, granulocyte-monocyte progenitors (GMP), and monocytes through the CXCL12-CXCR4 axis, as predicted by CellChat. (B) Boxplots of the expression levels of CXCL12 and CXCR4 in BM1, stratified by inflammation class (top), and corresponding spot-level expression maps (bottom) for the bone marrow sample BM1. Red spots indicate higher expression levels. (C and D) Whole-slide images of Opal multiplex fluorescent immunohistochemistry (mfIHC; left) for CXCR4 (turquoise) and CXCL12 (magenta) overlaid with DAPI (blue), alongside magnified Opal regions and Visium-based gene expression maps (right) in BM1 (C) and the extramedullary sample EM1 (D). Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). (E) Scatterplot shows the positive correlation of the PI3K/Akt/mTOR pathway score with the combined CXCL12-CXCR4 co-expression score (R = 0.50, p < 2.2e-16). Colors denote inflammation class. (F) Relationship between CXCR4 expression and inflammation score in EM1 (R = 0.19, p < 2.2e-16). Spatial maps show the distribution of CXCR4 expression. (G) Boxplots comparing CXCR4 protein signal intensity (mfIHC) across inflammation classes in BM1 (left) and EM1 (right). Spot-level images illustrate higher CXCR4 signal intensities in high-inflammation areas. (H) Sections 1 and 2 represent adjacent serial sections of the same EM1 biopsy embedded on a single Visium capture area. Visium ST visualization of PI3K/Akt/mTOR pathway (left) and trans -differentiation pathway (right) activity in these EM1 sections, revealing elevated pathway scores in high-inflammation and leukemic regions.

Journal: iScience

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML

doi: 10.1016/j.isci.2025.114289

Figure Lengend Snippet: Chemokine signaling through the CXCL12-CXCR4 axis is linked to inflammatory niches and trans-differentiation in acute myeloid leukemia (A) Spatial and chord diagrams of the strength of interactions among acute myeloid leukemia (AML) cells, granulocyte-monocyte progenitors (GMP), and monocytes through the CXCL12-CXCR4 axis, as predicted by CellChat. (B) Boxplots of the expression levels of CXCL12 and CXCR4 in BM1, stratified by inflammation class (top), and corresponding spot-level expression maps (bottom) for the bone marrow sample BM1. Red spots indicate higher expression levels. (C and D) Whole-slide images of Opal multiplex fluorescent immunohistochemistry (mfIHC; left) for CXCR4 (turquoise) and CXCL12 (magenta) overlaid with DAPI (blue), alongside magnified Opal regions and Visium-based gene expression maps (right) in BM1 (C) and the extramedullary sample EM1 (D). Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). (E) Scatterplot shows the positive correlation of the PI3K/Akt/mTOR pathway score with the combined CXCL12-CXCR4 co-expression score (R = 0.50, p < 2.2e-16). Colors denote inflammation class. (F) Relationship between CXCR4 expression and inflammation score in EM1 (R = 0.19, p < 2.2e-16). Spatial maps show the distribution of CXCR4 expression. (G) Boxplots comparing CXCR4 protein signal intensity (mfIHC) across inflammation classes in BM1 (left) and EM1 (right). Spot-level images illustrate higher CXCR4 signal intensities in high-inflammation areas. (H) Sections 1 and 2 represent adjacent serial sections of the same EM1 biopsy embedded on a single Visium capture area. Visium ST visualization of PI3K/Akt/mTOR pathway (left) and trans -differentiation pathway (right) activity in these EM1 sections, revealing elevated pathway scores in high-inflammation and leukemic regions.

Article Snippet: Visium Spatial Transcriptomics Data , This paper , GEO: GSE279576.

Techniques: Expressing, Multiplex Assay, Immunohistochemistry, Gene Expression, Activity Assay

Bone proximity analysis reveals the spatial distribution of acute myeloid leukemia cells in different differentiation states (A) Representative spatial map of SpatialTime calculated distances from trabeculae overlaid with hematoxylin and eosin (H&E) image. (B) Boxplots show deconvolution scores of primitive-like, granulocyte-monocyte progenitor (GMP)-like, and committed-like acute myeloid leukemia (AML) cells relative to their distance from bone in Visium data. ∗ p < 0.05, ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test. (C) GeoMx analysis of AML deconvolution in bone marrow regions from 3 patients with AML (PT3, PT4, PT5). D, distal (dark red); P, proximal (dark blue); B, bone (white). Stacked bar plots represent cell type deconvolution within distal and proximal regions. Scale bars: 250 μm. (D) Line graphs show proportions of primitive-like and GMP-like cells relative to distance from bone.

Journal: iScience

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML

doi: 10.1016/j.isci.2025.114289

Figure Lengend Snippet: Bone proximity analysis reveals the spatial distribution of acute myeloid leukemia cells in different differentiation states (A) Representative spatial map of SpatialTime calculated distances from trabeculae overlaid with hematoxylin and eosin (H&E) image. (B) Boxplots show deconvolution scores of primitive-like, granulocyte-monocyte progenitor (GMP)-like, and committed-like acute myeloid leukemia (AML) cells relative to their distance from bone in Visium data. ∗ p < 0.05, ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test. (C) GeoMx analysis of AML deconvolution in bone marrow regions from 3 patients with AML (PT3, PT4, PT5). D, distal (dark red); P, proximal (dark blue); B, bone (white). Stacked bar plots represent cell type deconvolution within distal and proximal regions. Scale bars: 250 μm. (D) Line graphs show proportions of primitive-like and GMP-like cells relative to distance from bone.

Article Snippet: Visium Spatial Transcriptomics Data , This paper , GEO: GSE279576.

Techniques:

(A) Schematic overview of WoundScape, the organ-scale spatial transcriptomic wound atlas. High-resolution 2 μm Visium HD data were generated for UW, D7PW, D15PW, and D30PW skin, totaling 532,066 spatial barcoded spots. These data were merged with the OWHA omnibus, yielding a comprehensive tetra-modal spatially resolved database encompassing 725,590 total cells and spots organized across all anatomical compartments of the skin. Histological H&E staining was combined with WoundScape spatial profiling to precisely align Visium HD-identified neighborhoods within defined cutaneous wound anatomical regions. (B–E ) Spatial transcriptomic mapping of Banksy neighborhoods (bottom), and corresponding H&E sections (above), in 8 μm Visium HD sections from unwounded ( B ) (UW), ( C ) D4PW, ( D ) D7PW, and ( E ) D30PW. Middle insets show magnified views of local BANKSY neighborhoods clustering (clustering resolution = 0.5). Right insets depict corresponding RCTD-derived metacluster annotations for all discrete spatial domains within each section. Each section represents a technical replicate from the same biological specimen (UW = 113,587 spots; D4PW = 104,247; D7PW = 176,256; D30PW = 137,976). Red arrowheads indicate the initial wound edge in the suprabasal layer. White guidelines mark the initial subcutis wound boundaries. Scale bars represent 500 um or 1 mm as indicated. (F–I) Stacked bar plots showing the proportional metacluster composition within each BANKSY cluster for unwounded skin and wounded mouse skin (D4PW, D7PW, D30PW). Data represents two technical replicates derived from the same biological sample. Statistical similarity of metacluster compositions between replicates was assessed using a chi-square test with Monte Carlo permutation (100,000 simulations). BANKSY clusters that show significant concordance between technical replicates ( P < 0.05) are highlighted in red font. (J) Visium HD localization of Dominant Signalers and Central Orchestrators, including Basal IV, Papillary II, HF I, Myofibroblasts II, and Muscle Progenitor clusters, along BANKSY neighborhoods proximal to the wound bed at D7PW. Red outlines mark wound-associated regions of interest, while yellow guidelines and red arrowheads denote suprabasal and subcutis wound edge boundaries, respectively. Scale bars, 1 mm. (K) Anatomical distance quantifications of wound emergent BANKSY clusters along the anterior-posterior transverse plane at D7PW. The x-axis represents arbitrary spatial units (1 a.u. = 9 mm) corresponding to anterior-posterior distance across the entire tissue section. Vertical redline demarcates the wound center. Data represents two technical replicates from the same biological timepoints. A variance-based localization test was used, and multiple testing correction was applied to p-values using the Benjamini–Hochberg procedure. (p < 0.05 = *, p < 0.01 = **, p < .001 = ***). (L) Schematic illustration summarizing the spatial geometry of the wound edge (anterior and posterior margins) as visualized at D7PW. Red arrowheads indicate typical location of wound boundaries from the adjacent wound bed. (M-N) High-magnification 20x H&E of the D7PW wound edge regions of interest (ROI). Red arrowheads indicate wound boundaries. Scale bars, 500 μm. (O-T) BANKSY spatial clustering of respective Dominant Signalers populations within the posterior wound edge ROI: ( O ) BANKSY cluster positions, ( P ) merged overlay of selected Dominant Signaler populations: ( Q ) Spinous I, ( R ) Proliferative Endothelial Cells, ( S ) Pericyte I, and ( T ) HF I. Each overlay highlights discrete but spatially organized domains at the wound front where reparative signaling networks converge. Scale bars, 100 μm. (U) Stacked bar plots showing fine cell-type composition of BANKSY clusters localized at the D7PW wound front. Data represents two technical replicates from the same biological timepoints. (V) Numbering and classification legend of fine cell types corresponding to panel (U) .

Journal: bioRxiv

Article Title: 4D multimodal wound healing atlas reveals organ-level controls of repair phase transitions

doi: 10.64898/2026.01.15.699736

Figure Lengend Snippet: (A) Schematic overview of WoundScape, the organ-scale spatial transcriptomic wound atlas. High-resolution 2 μm Visium HD data were generated for UW, D7PW, D15PW, and D30PW skin, totaling 532,066 spatial barcoded spots. These data were merged with the OWHA omnibus, yielding a comprehensive tetra-modal spatially resolved database encompassing 725,590 total cells and spots organized across all anatomical compartments of the skin. Histological H&E staining was combined with WoundScape spatial profiling to precisely align Visium HD-identified neighborhoods within defined cutaneous wound anatomical regions. (B–E ) Spatial transcriptomic mapping of Banksy neighborhoods (bottom), and corresponding H&E sections (above), in 8 μm Visium HD sections from unwounded ( B ) (UW), ( C ) D4PW, ( D ) D7PW, and ( E ) D30PW. Middle insets show magnified views of local BANKSY neighborhoods clustering (clustering resolution = 0.5). Right insets depict corresponding RCTD-derived metacluster annotations for all discrete spatial domains within each section. Each section represents a technical replicate from the same biological specimen (UW = 113,587 spots; D4PW = 104,247; D7PW = 176,256; D30PW = 137,976). Red arrowheads indicate the initial wound edge in the suprabasal layer. White guidelines mark the initial subcutis wound boundaries. Scale bars represent 500 um or 1 mm as indicated. (F–I) Stacked bar plots showing the proportional metacluster composition within each BANKSY cluster for unwounded skin and wounded mouse skin (D4PW, D7PW, D30PW). Data represents two technical replicates derived from the same biological sample. Statistical similarity of metacluster compositions between replicates was assessed using a chi-square test with Monte Carlo permutation (100,000 simulations). BANKSY clusters that show significant concordance between technical replicates ( P < 0.05) are highlighted in red font. (J) Visium HD localization of Dominant Signalers and Central Orchestrators, including Basal IV, Papillary II, HF I, Myofibroblasts II, and Muscle Progenitor clusters, along BANKSY neighborhoods proximal to the wound bed at D7PW. Red outlines mark wound-associated regions of interest, while yellow guidelines and red arrowheads denote suprabasal and subcutis wound edge boundaries, respectively. Scale bars, 1 mm. (K) Anatomical distance quantifications of wound emergent BANKSY clusters along the anterior-posterior transverse plane at D7PW. The x-axis represents arbitrary spatial units (1 a.u. = 9 mm) corresponding to anterior-posterior distance across the entire tissue section. Vertical redline demarcates the wound center. Data represents two technical replicates from the same biological timepoints. A variance-based localization test was used, and multiple testing correction was applied to p-values using the Benjamini–Hochberg procedure. (p < 0.05 = *, p < 0.01 = **, p < .001 = ***). (L) Schematic illustration summarizing the spatial geometry of the wound edge (anterior and posterior margins) as visualized at D7PW. Red arrowheads indicate typical location of wound boundaries from the adjacent wound bed. (M-N) High-magnification 20x H&E of the D7PW wound edge regions of interest (ROI). Red arrowheads indicate wound boundaries. Scale bars, 500 μm. (O-T) BANKSY spatial clustering of respective Dominant Signalers populations within the posterior wound edge ROI: ( O ) BANKSY cluster positions, ( P ) merged overlay of selected Dominant Signaler populations: ( Q ) Spinous I, ( R ) Proliferative Endothelial Cells, ( S ) Pericyte I, and ( T ) HF I. Each overlay highlights discrete but spatially organized domains at the wound front where reparative signaling networks converge. Scale bars, 100 μm. (U) Stacked bar plots showing fine cell-type composition of BANKSY clusters localized at the D7PW wound front. Data represents two technical replicates from the same biological timepoints. (V) Numbering and classification legend of fine cell types corresponding to panel (U) .

Article Snippet: Spatial Transcriptomics data was generated using 10x Genomics Visium V2 CytAssist Spatial Gene Expression Mouse Transcriptome Assay (#1000445) for FFPE tissue as per user’s guide.

Techniques: Generated, Staining, Derivative Assay

( A ) Schematic illustration of cross-tissue wound signaling between epidermal Central Orchestrators and deep tissue population populations. ( B–E ) Visium HD spatial localization of Basal IV keratinocytes (blue) and proliferative endothelial cells (yellow) across the healing time course ( B ) UW, ( C ) D4PW, ( D ) D7PW, ( E ) D30PW. Images represent one biological replicate, with one technical replicate shown for UW. Red arrowheads indicate the suprabasal wound edge; red guidelines denote subcutis wound boundaries. Yellow boxes indicate magnified insets highlighting Basal IV–endothelial interactions at the wound edge. Scale bar, 1 mm. ( F–I ) Quantification of anatomical distance between Basal IV and proliferative endothelial cells along the anterior–posterior wound axis (1 a.u. = 9 mm) at ( F ) UW, ( G ) D4PW, ( H ) D7PW, ( I ) D30PW. Data represent two technical replicates from one biological sample per timepoint. Significance was assessed using a variance-based localization test with Benjamini–Hochberg correction (* p < 0.05; ** p < 0.01; *** p < 0.001). ( J–K ) CellChat-inferred signaling network ( J ) and corresponding ligand–receptor pathways ( K ) transmitted between Basal IV keratinocytes and endothelial cells. Edge width denotes interaction strength. ( L ) Visium HD spatial transcriptomic expression of Sema3c pathway components ( Sema3c , Nrp1 , Nrp2 , Plxna4 ) overlaid on corresponding H&E sections at D7PW. Scale bar, 1 mm. ( M–N ) Single-molecule RNA FISH (smFISH) immunofluorescence showing Sox6 (magenta) and Sema3c (yellow) mRNA transcripts with DAPI (white) in ( M ) UW and ( N ) D4PW skin. Boxed regions highlight an Sox6-high epidermal zone. (Right) zoomed in images of the Sox6-high zone with single-channel panels shown. Wound edge (w.e.) indicated by red arrowheads and subcutis wound boundaries indicated by red lines. Scale bars, 100 µm (full images) or 10 µm (zoomed insets). ( O–P ) Boxplots quantifying mean fluorescence intensity of ( O ) Sox6 and ( P ) Sema3c smFISH signals across timepoints (UW, D4PW, D7PW), comparing unwounded, distal, and proximal wound regions. UW includes three biological replicates, D4PW two biological replicates, and D7PW one biological replicate . Statistical testing performed using a two-sided Wilcoxon test. Variability is represented using the interquartile range (IQR). Statistical significance was determined using a Wilcoxon rank-sum test (p < 0.05 = *, p < 0.01 = **). ( Q ) UMAP visualizations and corresponding pseudotime ordering of UW IFE keratinocyte subclusters from OWHA snRNA-seq. n = two biological replicates. ( R ) Heatmap of top 100 pseudotime-associated genes in UW snRNA-seq keratinocytes ordered by cluster and pseudotime. Arrowheads mark genes enriched in early (purple) vs. late (yellow) pseudotime. ( S ) UMAP visualizations and corresponding pseudotime ordering of D4PW–D7PW IFE keratinocyte subclusters showing branching into re-epithelization (red) and neurovasculogenesis (green) lineage trajectories. n = two biological replicates per timepoint. ( T ) Heatmap of the top 100 pseudotime-associated genes expressed along the neurovasculogenesis pseudotime trajectories in D4PW–D7PW keratinocytes, ordered by cluster and timepoint. Arrows indicate early (purple) and late (yellow) pseudotime gene signatures. ( U–W ) Mean pseudotime expression profiles of ( U ) Sox6 , ( V ) Sema3c , and ( W ) Krt6a along the D4PW–D7PW proliferative pseudotime trajectory. ( X ) Gene Ontology terms among genes upregulated at early pseudotime stages of the neurovasculogenesis trajectory, subdivided into molecular function (MF), biological process (BP), and cellular component (CC) categories.

Journal: bioRxiv

Article Title: 4D multimodal wound healing atlas reveals organ-level controls of repair phase transitions

doi: 10.64898/2026.01.15.699736

Figure Lengend Snippet: ( A ) Schematic illustration of cross-tissue wound signaling between epidermal Central Orchestrators and deep tissue population populations. ( B–E ) Visium HD spatial localization of Basal IV keratinocytes (blue) and proliferative endothelial cells (yellow) across the healing time course ( B ) UW, ( C ) D4PW, ( D ) D7PW, ( E ) D30PW. Images represent one biological replicate, with one technical replicate shown for UW. Red arrowheads indicate the suprabasal wound edge; red guidelines denote subcutis wound boundaries. Yellow boxes indicate magnified insets highlighting Basal IV–endothelial interactions at the wound edge. Scale bar, 1 mm. ( F–I ) Quantification of anatomical distance between Basal IV and proliferative endothelial cells along the anterior–posterior wound axis (1 a.u. = 9 mm) at ( F ) UW, ( G ) D4PW, ( H ) D7PW, ( I ) D30PW. Data represent two technical replicates from one biological sample per timepoint. Significance was assessed using a variance-based localization test with Benjamini–Hochberg correction (* p < 0.05; ** p < 0.01; *** p < 0.001). ( J–K ) CellChat-inferred signaling network ( J ) and corresponding ligand–receptor pathways ( K ) transmitted between Basal IV keratinocytes and endothelial cells. Edge width denotes interaction strength. ( L ) Visium HD spatial transcriptomic expression of Sema3c pathway components ( Sema3c , Nrp1 , Nrp2 , Plxna4 ) overlaid on corresponding H&E sections at D7PW. Scale bar, 1 mm. ( M–N ) Single-molecule RNA FISH (smFISH) immunofluorescence showing Sox6 (magenta) and Sema3c (yellow) mRNA transcripts with DAPI (white) in ( M ) UW and ( N ) D4PW skin. Boxed regions highlight an Sox6-high epidermal zone. (Right) zoomed in images of the Sox6-high zone with single-channel panels shown. Wound edge (w.e.) indicated by red arrowheads and subcutis wound boundaries indicated by red lines. Scale bars, 100 µm (full images) or 10 µm (zoomed insets). ( O–P ) Boxplots quantifying mean fluorescence intensity of ( O ) Sox6 and ( P ) Sema3c smFISH signals across timepoints (UW, D4PW, D7PW), comparing unwounded, distal, and proximal wound regions. UW includes three biological replicates, D4PW two biological replicates, and D7PW one biological replicate . Statistical testing performed using a two-sided Wilcoxon test. Variability is represented using the interquartile range (IQR). Statistical significance was determined using a Wilcoxon rank-sum test (p < 0.05 = *, p < 0.01 = **). ( Q ) UMAP visualizations and corresponding pseudotime ordering of UW IFE keratinocyte subclusters from OWHA snRNA-seq. n = two biological replicates. ( R ) Heatmap of top 100 pseudotime-associated genes in UW snRNA-seq keratinocytes ordered by cluster and pseudotime. Arrowheads mark genes enriched in early (purple) vs. late (yellow) pseudotime. ( S ) UMAP visualizations and corresponding pseudotime ordering of D4PW–D7PW IFE keratinocyte subclusters showing branching into re-epithelization (red) and neurovasculogenesis (green) lineage trajectories. n = two biological replicates per timepoint. ( T ) Heatmap of the top 100 pseudotime-associated genes expressed along the neurovasculogenesis pseudotime trajectories in D4PW–D7PW keratinocytes, ordered by cluster and timepoint. Arrows indicate early (purple) and late (yellow) pseudotime gene signatures. ( U–W ) Mean pseudotime expression profiles of ( U ) Sox6 , ( V ) Sema3c , and ( W ) Krt6a along the D4PW–D7PW proliferative pseudotime trajectory. ( X ) Gene Ontology terms among genes upregulated at early pseudotime stages of the neurovasculogenesis trajectory, subdivided into molecular function (MF), biological process (BP), and cellular component (CC) categories.

Techniques: Expressing, Immunofluorescence, Fluorescence

( A ) UMAP embedding of the fully integrated cross-species wound-healing atlas (COWA) containing 236,930 single cells. COWA integrates the multimodal OWHA dataset with the human wound healing dataset (GSE241132), generating a total of 40 sequencing runs (28 murine, 12 human). ( B ) Annotation legend displaying all 107 fine-grained subcluster identities represented in COWA. ( C–D ) UMAP projections showing ( C ) human (n=12) and ( D ) mouse (n=28) wound datasets downsampled to 43,406 cells each for comparable visualization of interspecies cell capture differences. ( E ) Sankey plot showcasing proportional representation of major cell types between mouse and human samples across metaclusters, highlighting shared and species-specific tissue compositions. Percentages display proportion of each respective metacluster assigned to integrated cross-species Harmony clusters. ( F ) MILO differential abundance analysis of select IFE keratinocyte, endothelial, pericyte, and Schwann cell subclusters states significantly enriched in mouse (blue) or human (red) samples (FDR = 0.15). Mouse-enriched (>0 LFC) states represent populations preferentially detected in by multimodal murine atlasing. ( G–H ) UMAP feature plots depicting ( G ) mouse Sox6 and ( H ) human SOX6 expression. ( I–J ) UMAP feature plots of (I) mouse Sema3c and (J) human SEMA3C , showing substantially reduced SEMA3C detection in human wound-healing scRNA-seq datasets. ( K–L ) Dot plots of Basal IV–associated markers ( Sox6 , Cdh13 , Sema3c ) and the wound-induced keratinocyte gene Krt16 in IFE keratinocytes from ( K ) the murine OWHA dataset and ( L ) the human wound dataset. Dot size indicates the percentage of expressing cells; color intensity denotes mean expression level. ( M ) Beeswarm plot of MILO differential abundance comparing whole-cell (blue) versus single-nucleus (red) RNA-seq, highlighting modality-specific biases in cell type capture (FDR = 0.15). ( N ) Visium spatial transcriptomic feature plot of D7PW human wound tissue (GSE241132), annotated by regional labels as defined Liu et al. 2024. Red arrowheads mark the wound edge. ( O–P ) Quantification of Basal IV module scores in ( O ) unwounded and ( P ) D7PW human tissue sections (calculated using AddModuleScore() ). Red box highlights wound edge region zoomed-in in the right panel. Red arrowheads denote the wound edge; white dashed lines outline the epidermal-dermal boundary. ( Q–R ) Quantification of SEMA3C pathway signature scores in ( Q ) unwounded and ( R ) D7PW human tissue sections. Red box highlights wound edge region zoomed-in in the right panel. Red arrowheads denote the wound front; white dashed lines indicate the epidermal-dermal boundary. ( S–T ) Mean expression of ( S ) SEMA3C in keratinocytes and ( T ) NRP1 and NRP2 in endothelial cells at the human wound edge across healing timepoints (D0PW, D1PW, D7PW, D30PW). Each dot represents the mean signal per sequencing run. Statistical significance was assessed using a Wilcoxon rank-sum test relative to D0/UW (* p < 0.05; ** p < 0.01). n = 4.

Journal: bioRxiv

Article Title: 4D multimodal wound healing atlas reveals organ-level controls of repair phase transitions

doi: 10.64898/2026.01.15.699736

Figure Lengend Snippet: ( A ) UMAP embedding of the fully integrated cross-species wound-healing atlas (COWA) containing 236,930 single cells. COWA integrates the multimodal OWHA dataset with the human wound healing dataset (GSE241132), generating a total of 40 sequencing runs (28 murine, 12 human). ( B ) Annotation legend displaying all 107 fine-grained subcluster identities represented in COWA. ( C–D ) UMAP projections showing ( C ) human (n=12) and ( D ) mouse (n=28) wound datasets downsampled to 43,406 cells each for comparable visualization of interspecies cell capture differences. ( E ) Sankey plot showcasing proportional representation of major cell types between mouse and human samples across metaclusters, highlighting shared and species-specific tissue compositions. Percentages display proportion of each respective metacluster assigned to integrated cross-species Harmony clusters. ( F ) MILO differential abundance analysis of select IFE keratinocyte, endothelial, pericyte, and Schwann cell subclusters states significantly enriched in mouse (blue) or human (red) samples (FDR = 0.15). Mouse-enriched (>0 LFC) states represent populations preferentially detected in by multimodal murine atlasing. ( G–H ) UMAP feature plots depicting ( G ) mouse Sox6 and ( H ) human SOX6 expression. ( I–J ) UMAP feature plots of (I) mouse Sema3c and (J) human SEMA3C , showing substantially reduced SEMA3C detection in human wound-healing scRNA-seq datasets. ( K–L ) Dot plots of Basal IV–associated markers ( Sox6 , Cdh13 , Sema3c ) and the wound-induced keratinocyte gene Krt16 in IFE keratinocytes from ( K ) the murine OWHA dataset and ( L ) the human wound dataset. Dot size indicates the percentage of expressing cells; color intensity denotes mean expression level. ( M ) Beeswarm plot of MILO differential abundance comparing whole-cell (blue) versus single-nucleus (red) RNA-seq, highlighting modality-specific biases in cell type capture (FDR = 0.15). ( N ) Visium spatial transcriptomic feature plot of D7PW human wound tissue (GSE241132), annotated by regional labels as defined Liu et al. 2024. Red arrowheads mark the wound edge. ( O–P ) Quantification of Basal IV module scores in ( O ) unwounded and ( P ) D7PW human tissue sections (calculated using AddModuleScore() ). Red box highlights wound edge region zoomed-in in the right panel. Red arrowheads denote the wound edge; white dashed lines outline the epidermal-dermal boundary. ( Q–R ) Quantification of SEMA3C pathway signature scores in ( Q ) unwounded and ( R ) D7PW human tissue sections. Red box highlights wound edge region zoomed-in in the right panel. Red arrowheads denote the wound front; white dashed lines indicate the epidermal-dermal boundary. ( S–T ) Mean expression of ( S ) SEMA3C in keratinocytes and ( T ) NRP1 and NRP2 in endothelial cells at the human wound edge across healing timepoints (D0PW, D1PW, D7PW, D30PW). Each dot represents the mean signal per sequencing run. Statistical significance was assessed using a Wilcoxon rank-sum test relative to D0/UW (* p < 0.05; ** p < 0.01). n = 4.

Techniques: Sequencing, Expressing, RNA Sequencing

Review

visium spatial transcriptome (10X Genomics)

Structured Review

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1) Product Images from "A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases"

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