visium probe  (Spatial Transcriptomics Inc)

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 Human Transcriptome Probe Panel v1 , 10x Genomics , PN-1000364.

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

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 Human Transcriptome Probe Panel v1 , 10x Genomics , PN-1000364.

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

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 Human Transcriptome Probe Panel v1 , 10x Genomics , PN-1000364.

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

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 Human Transcriptome Probe Panel v1 , 10x Genomics , PN-1000364.

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

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 Human Transcriptome Probe Panel v1 , 10x Genomics , PN-1000364.

Techniques:

Journal: Gut

Article Title: Targeting Treg–fibroblast interaction to enhance immunotherapy in steatotic liver disease-related hepatocellular carcinoma

doi: 10.1136/gutjnl-2025-335084

Figure Lengend Snippet: Spatial architecture of SLD-HCC and non-SLD-HCC. ( A ) Data analysis workflow for Visium spatial transcriptomics (ST). ( B ) UMAP plot illustrating the distribution of ST data after Harmony integration. n=126 099 spots from n=7 SLD-HCCs and n=5 non-SLD-HCCs. ( C ) UMAP plots showing gene signature scorings for major immune and non-immune subsets from all Visium data. ( D ) Representative H&E images on FFPE tissues comprising tumours, non-tumours and margin areas from patients with SLD-HCC and non-SLD-HCC (left); spatial deconvolution using SpaCET, indicating tumour and non-tumour regions (right). Box, field of view (FOV)=6.5 x 6.5 mm. ( E ) Spatially aware clustering using Banksy, showing the different domains within each tissue sample. ( F ) Heatmap showing relative gene signature scoring used to estimate the cellular composition within each domain. DC, dendritic cell; FFPE, formalin-fixed, paraffin-embedded; NK, natural killer; scRNA seq, single-cell RNA sequencing; SLD-HCC, steatotic liver disease-related hepatocellular carcinoma; Treg, regulatory T cell; UMAP, uniform manifold approximation and projection.

Article Snippet: Spatial RNA library preparation was performed using the Visium Human Transcriptome Probe Kit V.2 (10x Genomics, California, USA).

Techniques: Formalin-fixed Paraffin-Embedded, RNA Sequencing

Journal: Gut

Article Title: Targeting Treg–fibroblast interaction to enhance immunotherapy in steatotic liver disease-related hepatocellular carcinoma

doi: 10.1136/gutjnl-2025-335084

Figure Lengend Snippet: Cellular interaction network in SLD-HCC and non-SLD-HCC. ( A ) Representative immunofluorescence (IF) image matched with ST data generated from CosMx unsupervised clustering. Each cluster is colour-coded. PanCK, pan-cytokeratin. Each FOV=0.7 x 0.9mm. ( B ) UMAP plot illustrating all clusters from CosMx with n=152 214 total cells, n=50 FOVs from two SLD-HCCs and two non-SLD-HCCs. ( C ) Heatmap showing relative expression levels of selected genes representing each CosMx cluster. ( D ) Bar graphs showing the absolute number (top) and proportion within total cells (bottom) of each identified cell type across the two SLD-HCC and two non-SLD-HCC tissue samples. ( E ) Neighbourhood enrichment scores showing interaction strength between Tregs and other cell types from CosMx ST data. Two-sided p values calculated by pairwise Mann-Whitney test. ( F ) Neighbourhood enrichment scores showing interaction strength between Tregs and fibroblasts at margin domains from deconvoluted Visium ST data (n=7 SLD-HCCs and n=5 non-SLD-HCCs). Two-sided p values calculated by pairwise Mann-Whitney test. ( G ) Representative IF images of margin areas from SLD-HCC and non-SLD-HCC tissues stained for CD4, FoxP3 (Treg) and αSMA (fibroblast). DAPI was used for nuclear staining. Scale bar denotes 20 µm. ( H ) Comparison of mean number of Tregs between SLD-HCC and non-SLD-HCC, quantified from three to five randomly selected FOVs per tissue at tumour margin. ( I ) The distance between Treg and nearest fibroblast in the same FOVs from ( H ) was compared between SLD-HCCs and non-SLD-HCCs. ( F, H and I ) Boxplots show median and the whiskers represent minimum and maximum values with the box edges showing the first and third quartiles. ( H and I ) mIF data was obtained from six SLD-HCCs and six non-SLD-HCCs, and analysis was performed using Mann-Whitney U test. Graphs show mean±SEM. CAFs, cancer-associated fibroblasts; DAPI, 4',6-diamidino-2-phenylindole; DC, dendritic cell; EC, endothelial cell; FOVs, fields of views; LSEC, Liver sinusoidal endothelial cells; mIF, multiplex immunofluorescence; NK, natural killer; pDC, plasmacytoid dendritic cell; SLD-HCC, steatotic liver disease-related hepatocellular carcinoma; ST, spatial transcriptomic; Treg, regulatory T cell; UMAP, uniform manifold approximation and projection.

Article Snippet: Spatial RNA library preparation was performed using the Visium Human Transcriptome Probe Kit V.2 (10x Genomics, California, USA).

Techniques: Immunofluorescence, Generated, Expressing, MANN-WHITNEY, Staining, Comparison, Multiplex Assay

Journal: Gut

Article Title: Targeting Treg–fibroblast interaction to enhance immunotherapy in steatotic liver disease-related hepatocellular carcinoma

doi: 10.1136/gutjnl-2025-335084

Figure Lengend Snippet: Ligand–receptor interactomes in SLD-HCC. ( A ) Heatmap showing relative COMMOT scores of enriched L–R pathways from CosMx data. ( B ) Heatmap showing relative expression levels of L–R pairs, determined by NICHES analysis. A representative Visium map highlighting the tumour margin domains was shown (upper right). Specific enriched L–R pairs from clusters enriched at the tumour margin domains were shown (boxed, bottom right). ( C ) Representative images from Visium data showing tumour fraction scoring, tissue segmentation into tumour, margin and non-tumour regions as well as relative expression of TNFSF14-TNFRSF14 in SLD-HCCs versus non-SLD-HCCs. ( D ) COMMOT analysis on Visium data showing distinct TNFSF14-TNFRSF14 strength and directionality in SLD-HCCs versus non-SLD-HCCs. Gene expression intensity is marked by size and directionality by the pointed end of the arrows. Tumour (T) and non-tumour (NT) regions are separated by dashed red lines. ( E ) Representative IF images showing expression of TNFSF14-TNFRSF14 at tumour margins in SLD-HCCs and non-SLD-HCCs. Scale bar denotes 100 µm. ( F ) COMMOT scores comparing the strength of the TNFSF14-TNFRSF14 interaction between SLD-HCCs (n=7) versus non-SLD-HCCs (n=5) on Visium data. Treg–CAFs and CAFs–Treg interactions from margin domains were analysed. ( G ) COMMOT scores comparing the strength of the TNFSF14-TNFRSF14 interaction between three responders and five non-responders to immunotherapy in SLD-HCC (n=5) versus non-SLD-HCC (n=3). Treg–CAFs and CAFs–Treg interactions from margin domains were analysed. ( B–D ) Visium FOV=6.5 x 6.5 mm. ( F and G ) Boxplots show median and the whiskers represent minimum and maximum values with the box edges showing the first and third quartiles. P value determined by two-tailed Mann-Whitney test. CAFs, cancer-associated fibroblasts; COMMOT, COMMunication analysis by Optimal Transport; DAPI, 4',6-diamidino-2-phenylindole;FOV, fields of view; IF, immunofluorescence; L–R, ligand–receptor; NA, not applicable; NICHES, Niche Interactions and Communication Heterogeneity in Extracellular Signaling; SLD-HCC, steatotic liver disease-related hepatocellular carcinoma; TNFSF14, tumour necrosis factor superfamily member 14; TNFRSF14, tumour necrosis factor receptor superfamily member 14; Treg, regulatory T cell.

Article Snippet: Spatial RNA library preparation was performed using the Visium Human Transcriptome Probe Kit V.2 (10x Genomics, California, USA).

Techniques: Expressing, Gene Expression, Two Tailed Test, MANN-WHITNEY, Immunofluorescence

Journal: bioRxiv

Article Title: A Foundational Generative Model for Cross-platform Unified Enhancement of Spatial Transcriptomics

doi: 10.64898/2025.12.23.696267

Figure Lengend Snippet: a , Model overview. FOCUS is a diffusion-based generative model that leverages ST and H&E encoders , pretrained on large-scale, cross-tissue data to extract robust multimodal features. It integrates multimodal conditions as inputs, including under-refined ST maps, paired H&E images with cell segmentation masks, scRNA-seq references, and spatial gene co-expression matrices. Each challenge is addressed through tailored modules, with a cross-challenge coordination strategy enabling module interaction for coherent improvement across challenges. b , Large-scale, cross-platform multimodal dataset. In total, we assemble 6,876 paired ST-H&E images (corresponding to over 1.7 million patches) with matched cell segmentation masks, referenced scRNA-seq from public resources (over 5.8 million scRNA-seq cell profiles; Table S1), and precomputed spatial gene co-expression matrices. The data collection spans ten ST platforms, including eight sequencing-based (Visium (probe-based and polyA-based), Visium Cytassist, VisiumHD, Spatial Transcriptomics, Stereo-seq, BMK S1000, and Open-ST) and two imaging-based (Xenium and CosMx) platforms, and two species (human and mouse), comprising 17 normal and 17 cancer tissues, with whole-transcriptome profiles available for both species. c, Benchmarking and validation across challenges and downstream tasks, including spatial domain characterization, cell-cell communications, and cell-cell co-localization.

Article Snippet: FOCUS consistently performed the best in all comparisons, with SSIM gains of 0.80 and 0.28, and RMSE reductions of 0.20 and 0.18 (all P < 0.001) on Visium (probe) and Spatial Transcriptomics, respectively, highlighting real-world utility.

Techniques: Diffusion-based Assay, Expressing, Sequencing, Imaging, Biomarker Discovery