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

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a , The C-G2P framework. Mice are HDTV injected with a pool of barcoded perturbation plasmids leading to sleeping beauty (SB)-transposon-mediated stable integration into the genome of hepatocytes. Higher-order combinatorial perturbations drive mosaic liver tumour development in a conceptual 2 n combination space for clonal selection (RUBIX). Direct barcode identification is achieved by linking perturbations to 50-nt barcode sequences that are captured and identified by RTL probes as embedded in the 10X Visium for FFPE platform (PERTURB-CAST). Endogenous transcripts are captured alongside barcodes, hence enabling simultaneous mapping of genotypes (as defined by the presence of perturbations) and phenotypes (as defined by transcriptional signatures) on the same tissue section. b , PERTURB-CAST barcode selection. Transcripts not expressed in murine liver are identified using public databases. Their respective 50-nt RTL-probe capture sequences are used as barcodes detected by redeployed commercially available RTL probes provided with the 10X Visium for FFPE mouse kit . Barcodes derived from chemosensory receptor transcripts are embedded in perturbation plasmids as triplet arrays.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: Injection, Selection, Derivative Assay

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a , Frequency of liver tumour samples with at least k potential driver mutations per sample in The Cancer Genome Atlas Program (TCGA) HCC dataset. Potential drivers were defined as either amplification or fusion of known COSMIC oncogenes, or homozygous deletion, nonsense mutation, splice site mutation or frameshift deletion/insertion in tumour-suppressor genes. b , Frequent alterations observed in human liver cancer (The Cancer Genome Atlas Program HCC dataset) are ‘geno-copied’ in a C-G2P mouse model (oncoprint based on https://www.cbioportal.org/study/summary?id=lihc_tcga ). ORF, open reading frame; RNAi, RNA interference. c , RUBIX mouse model generated in this study. Schematic overview of sleeping beauty transposon perturbation plasmids to ectopically overexpress genes of interest (oncogenic-driver perturbations) or shRNA to enable gene knockdown (tumour-suppressor perturbations). Functional elements are highlighted. BC, barcode in which three redeployed RTL-probe capture sequences (as indicated) are embedded; EF1, polymerase II promoter; IR, inverted/direct repeats of sleeping beauty transposon; pA: polyadenylation signal; sh, shRNA embedded in miRE context. Note that we used Visium mouse transcriptome probe set v1 to derive barcodes. Each 50-nt barcode is separated and flanked by spacer sequences of approximately 20 nt to avoid potential steric hindrance during hybridization. Further information in .

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: Amplification, Mutagenesis, Generated, shRNA, Knockdown, Functional Assay, Hybridization

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: Schematic overview of Sleeping Beauty transposon perturbation plasmids to ectopically overexpress genes-of-interest (oncogenic-driver perturbations) or shRNA to enable gene knockdown (tumor-suppressor perturbations). Functional elements are highlighted. IR: Inverted/direct repeats of sleeping beauty transposon; GFP: green fluorescent protein, mK2: mKate2 red fluorescent protein, EF1: Polymerase II promoter; U6: Polymerase III promoter; pA: polyadenylation signal; sh: short hairpin RNA embedded in mir-E context; BC: a barcode in which 3 redeployed RTL-probe capture sequences (as indicated) are embedded. Note that we used Visium Mouse Transcriptome Probe Set v1 to derive barcodes. Each 50 nt barcode is separated and flanked by ca. 20 nt spacer sequences to avoid potential steric hindrance during hybridization. Spacer sequences used were derived from T7 and T3 promoters and/or AsCas12a-DR sequences and/or 10X Capture sequences cs1 and cs2 (not shown). Functionality of spacer sequences was not tested in this study. Note that plasmids were equipped with multiple orthogonal barcodes at varying positions. See Methods for further information.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: shRNA, Knockdown, Functional Assay, Hybridization, Derivative Assay

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a , RUBIX with a pool-of-8 plasmid mix results in rapid liver tumour development. Injection of a shRNA targeting Renilla (shRen; matching total plasmid concentration for pool-of-8 mix) served as control, n = 2 each group. Representative H&E-stained samples revealing multiple tumour nodules from the two individual animals are shown. Absence of tumours in the control group (shRen only) indicates that random integration of transposon plasmids itself is unlikely to contribute to tumorigenesis. b , Tissue preprocessing. Following liver tumour development, livers were extracted, divided and processed to FFPE as well as fresh frozen specimens. FFPE samples were initially sectioned to enable sample selection. c , C-G2P liver samples. Overview of three representative FFPE samples used in this study. A total of 513 tumour nodules (red outline) were identified based on histopathological examination (based on H&E). d , Overview of ROIs selected for 10X Visium. 6 segregated regions were selected across 3 FFPE samples. Squares indicate approximate position of ROIs selected for ST. Orange: first 10X Visium run, light orange: first 10X Visium run, replicate ROI; blue: second 10X Visium run; black: 10X Visium CytAssist run. Note overlap between ROIs, where serial sections are used for 10X Visium. e , 10X Visium workflow. Samples for ST are derived directly from FFPE blocks and mounted on 10X Visium slides. f , 10X Visium CytAssist workflow. Samples for ST are derived from sections already mounted on glass slides and transferred to 10X Visium slides using the 10X CytAssist instrument. g , Overview of all samples used for ST. 12 samples from a single RUBIX experiment with two animals were used for 10X Visium in this study. Respective H&E stainings are depicted. Note that the utility of sample ML-II_B_2Cyt is constrained by tissue detachment of the sample during the processing for 10X Visium CytAssist and was not included for further analyses (asterisk). QC summary stats for each sample related to Visium runs performed are provided.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: Plasmid Preparation, Injection, shRNA, Concentration Assay, Control, Staining, Selection, Derivative Assay

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a , RUBIX establishes hundreds of coexisting tumours in the context of native tissue. Respective H&E-stained tissue samples for six topographically separated regions (approximately 6 × 6 mm) that were used for 10X Visium for FFPE-spatial transcriptomics analysis. A total of 324 nodules (colour-coded and numbered) were annotated. Colours were chosen arbitrarily. b , PERTUB-CAST allows perturbation-specific barcode identification. Average log(1 p )-transformed expression of all 38 barcode-associated transcripts used (left). Combined data for the reference control liver datasets from ref. and the six main spatial transcriptomics samples (C-G2P). Spatially resolved expression of triplet barcodes (as indicated in Fig. ) for each of the eight perturbations (top right). Aggregated log(1 p )-transformed and quantile-rescaled expression per 10X Visium spot. A representative sample is shown. Average log(1 p )-transformed expression of individual barcodes in each triplet array for each perturbation averaged across the six spatial transcriptomics samples (bottom right). c , Conversion of PERTURB-CAST barcode signals to perturbation maps. Spatially resolved visualization of the inferred probabilities indicating the presence or absence of each of the eight perturbations associated with annotated tumour nodules . A representative sample is displayed. b , c , Both the quantitative barcode expression ( b ) and probabilities ( c ) of all samples can be explored through the interactive web browser https://chocolat-g2p.dkfz.de/ . d , Validation of inferred perturbation integration. A GLM model predicts the phenotype expression signals based on the estimated probabilities of perturbation presence using Bayesian modelling . Phenotypes are defined as direct target transcripts associated with perturbations such as shPten– Pten and NICD– Notch1 . Expression data were log(1 p )-transformed. GS, a well-established marker for active WNT signalling in murine livers, was used to infer mtCtnnb1-GS-positive phenotype via IHC on a corresponding serial section. Baseline depicts background phenotype marker expression. Data are presented as feature coefficients shown as mean and error bars depict 3σ confidence intervals (CIs). Data are derived from 324 nodules across six topographically separated regions used for 10X Visium from a single RUBIX experiment with two animals. Mapping GS IHC data are derived from three corresponding sections from a single RUBIX experiment with two animals. A representative sample section is displayed.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: Staining, Transformation Assay, Expressing, Control, Biomarker Discovery, Marker, Derivative Assay

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a , Quantitative reproducibility. Scatterplots of inferred perturbation probabilities for nodules on the primary section to those on the corresponding replica sections, with Pearson’s correlation values displayed. In total, 136 nodule pairs were analysed. b , Matching nodules across samples. Spatial maps of the inferred probabilities (as in Fig. ) indicating the presence or absence of each of the 8 perturbations associated with annotated tumour nodules for all samples that have matching ROIs from a single RUBIX experiment with two animals. Matching nodules were manually annotated (Methods). Addition of “_Cyt” in sample name indicates use of 10X CytAssist. Number of matching nodules is indicated.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques:

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a – c , Perturbation–phenotype association. Spatially resolved visualization of the inferred probabilities indicating the presence or absence of each of the 8 perturbations associated with annotated tumour nodules (Methods). a , A representative sample is displayed. A generalized linear model (GLM) predicts phenotype expression signals based on the estimated probabilities of perturbation presence (Methods). b , Phenotypes are defined as direct target transcripts associated with perturbations such as shKmt2c-Kmt2c and shTrp53-Trp53. Expression data are log1p-transformed. Note that shTrp53 is linked to a GFP peptide barcode and shKmt2c is linked to a RFP barcode (Extended Data Fig. ). c , Hence we infer shTrp53-GFP-positive phenotype and shKmt2c-RFP-positive phenotype. Representative IHCs for a corresponding ROI on a serial section. Baseline depicts background phenotype marker expression. Data are presented as feature coefficients shown as mean and error-bars depict 3σ confidence intervals. As in Fig. , and d . d , H&E and IHC for GFP, RFP, GS. Three representative FFPE samples from a single RUBIX experiment with two animals were sectioned and stained for H&E (see Extended Data Fig. ). GFP and RFP IHC staining was performed on two individual serial sections. GS IHC staining was performed on serial sections. GFP and RFP were embedded in perturbation plasmids as orthogonal barcodes (see Methods and Extended Data Fig. ). GS is a well-known marker for liver WNT/mtCtnnb1-signalling activity (see Fig. ). e , GFP and RFP detection for the same hepatocellular tumour nodules to spatially map peptide barcode combinations associated with introduced perturbations. Left: H&E. Zoom-in: GFP and RFP respectively. Note that one tumour nodule is positive for RFP alone whereas the other tumour nodule is positive for both GFP as well as RFP peptide barcodes. A representative example is shown. Data is derived from 324 nodules across 6 topographically separated regions used for 10X Visium from a single RUBIX experiment with two animals. Mapping GFP and RFP IHC data is derived from 3 corresponding sections from a single RUBIX experiment with two animals.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: Expressing, Transformation Assay, Marker, Staining, Immunohistochemistry, Activity Assay, Derivative Assay

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a , Genotype maps (right); 2 8 powerset embedding of spatially mapped perturbations encompassing 324 nodules across six topographically separated regions. Each of the 256 combinations is colour-coded. Perturbation probabilities for a representative nodule are depicted (left; black text highlights present perturbations, while grey text highlights absent perturbations). Nodules sharing representative similar genotypes are encircled and indicated in b (solid lines, Myc + mtCtnnb1 + NICD; dashed lines, Myc + mtCtnnb1 + shKmt2c + NICD + shRen + shTrp53 + shPten). b , Clonal selection. Observed occurrences of genotypically defined tumour clones (median and 95% CI) across 2 8 powerset embedding (top). Grey text indicates combinatorial complexity. The highlighted genotypes are encircled in a . Probability p (O > E) that observed occurrences (O) deviate from the expected baseline distribution (E) ( ; bottom). Deviations of >0.5 indicate increased tumorigenic potential (orange), whereas values of <0.5 suggest potentially disadvantageous combinations (blue). c , Combinatorial order distribution. Observed distribution of the perturbation integration order (mean and 95% CI). A binomial distribution with p = 0.5 is included as a reference of a random unbiased integration rate (red line). d , Ranking of cancer-driving perturbations. Marginal frequencies of individual perturbations in descending order (mean and 95% CI). e , Pairwise co-occurrence and mutual exclusivity patterns. An OR > 1 suggests co-occurrence, whereas OR < 1 indicates mutual exclusivity . Perturbations are ordered according to d . f , Identification of pairwise genetic interactions. Comparison of observed versus expected frequencies (median and 95% CI) for selected gene pairs, calculated using multiplicative models of gene interaction. We simulated the expected probabilities for the pairwise groups under the assumption of no interaction OR, which indicates the direction of the gene interaction effect (arrows), are reported along with the corresponding P values. OR values were estimated from 5,000 posterior samples. A softmax GLM with interaction fixed at one defined the null. P values reflect two-tailed deviations of observed double-positive proportions from the null based on 5,000 draws . Data are derived from 324 nodules across six topographically separated regions used for 10X Visium from a single RUBIX experiment with two animals. Bayesian modelling of perturbation probabilities was used to infer the occurrence of individual perturbation combinations per nodule. From the inferred Bayesian posterior, we sampled 5,000 points and computed the median and CI for the frequencies of individual perturbations as well as individual genotypes and calculated the OR . H0, null hypothesis.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: Selection, Clone Assay, Comparison, Two Tailed Test, Derivative Assay

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a , The tumour ecosystem. Spatial maps of tumour-intrinsic phenotypes (top) and TME phenotypes (bottom) across six topographically separated regions. Colour shade depicts aggregated log(1 p )-transformed expression of phenotype-associated transcripts (colour-code as in b and d ). Nodule borders are highlighted (grey). The aggregated values for all samples and underlying quantitative data of individual transcript expression can be explored through the interactive web browser interface ( https://chocolat-g2p.dkfz.de/ ). b , Co-clustering of tumour-intrinsic phenotypes by associated transcripts. Tumour phenotypes are colour-coded. c , Associations between tumour-intrinsic and TME phenotypes. Pearson’s correlation coefficient for each pair of tumour-intrinsic and TME phenotype-associated transcripts across all nodules. d , Co-clustering of TME phenotypes by associated transcripts. TME phenotypes are colour-coded. b , d , Clustering based on Spearman correlations. Phenotypes are subdivided using hierarchical clustering. Scaled ( p 10 ) estimated plasmid probabilities per nodule are indicated ( b (bottom) and d (left) . Data are derived from 324 nodules across six topographically separated regions used for 10X Visium from a single RUBIX experiment with two animals.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: Transformation Assay, Expressing, Plasmid Preparation, Derivative Assay

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a , Identification of genotype–phenotype relationships. Comparison of the prevalence of perturbations between phenotypic groups and the remainder of the nodules (total n = 324) for tumour-intrinsic phenotypes (top) and TME (bottom) using ORs. OR > 1 indicates enrichment of perturbations within the phenotypic group; OR < 1 indicates depletion . The number of nodules with a given phenotype ( n ) is indicated. Note that groups are not mutually exclusive. The median and 90% CI are reported. Significant relationships are indicated (exact P values are provided); two-tailed deviations from one, computed with 20,000 samples from the posterior ; *** P < 0.001, ** P < 0.01, * P < 0.05. b , Identification of genotype–phenotype relationships for genes associated with cholangiocytes. A GLM model predicts gene expression signals at each 10X Visium spot using estimated probabilities of perturbation presence . Feature coefficients, shown as the mean and 3σ CIs, indicate associations between gene expression and perturbations for representative transcripts. Bayesian modelling of perturbation probabilities was used to infer the occurrence of individual perturbations per nodule . Data are derived from 324 nodules across six topographically separated regions used for 10X Visium from a single RUBIX experiment with two animals.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: Comparison, Two Tailed Test, Gene Expression, Derivative Assay

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a , Tumour-intrinsic genotype–phenotype relations. A generalized linear model (GLM) predicts gene expression signals at each 10X Visium spot, using estimated probabilities of perturbation presence (Methods). Data are presented as feature coefficients shown as mean and error bars depict 3σ confidence intervals. Feature coefficients indicate associations between gene expression and perturbations for representative transcripts of four tumour-intrinsic phenotypes. b , TME-related genotype–phenotype relations. As in a for representative transcripts of two exemplary TME phenotypes. c , GLM-inferred genotype–phenotype associations. Top: heatmap of 1,283 genes with at least one significant (3σ) feature weight, ordered by 1D UMAP embedding. Bottom: Detailed views of four representative clusters linked to marker genes of known phenotypic groups. Data is derived from 324 nodules across 6 topographically separated regions used for 10X Visium from a single RUBIX experiment with two animals.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: Gene Expression, Marker, Derivative Assay

Journal: Nature Biomedical Engineering

Article Title: Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships

doi: 10.1038/s41551-025-01437-1

Figure Lengend Snippet: a , Spatially resolved co-occurence of VEGFA and mutual exclusivity of mtCtnnb1 for the CCA tumour subtype as revealed by C-G2P. Magnified views of three representative nodules ((i)–(iii)) identified as CCA. Nodules identified as CCA (left; the area covered by the tumour nodule is indicated as 10X Visium spots in yellow) as well as the mtCtnnb1 (middle; as in Fig. ) and VEGFA (as in Fig. ) perturbation probabilities are shown. Bayesian modelling of perturbation probabilities is used to infer the occurrence of individual perturbations per nodule . Data are derived from 324 nodules across six topographically separated regions used for 10X Visium from a single RUBIX experiment with two animals. Perturbation probabilities for all samples can be explored through the interactive web browser https://chocolat-g2p.dkfz.de/ . b , Experimental design. Parallel RUBIX mouse models were performed using the leave-one-out experimental design. c , Time to tumour occurrence. Animals were palpated twice weekly to monitor tumour development. d , Histological quantification of liver tumour subtypes. H&E images were analysed, and tumour nodules were counted and classified as either HCC (top) or CCA (bottom); two independent liver-tissue sections per animal. The median ± s.d. alongside individual tumour counts are indicated. Group comparisons used a two-sided Kruskal–Wallis test with Dunn’s post-hoc test (Holm–Bonferroni correction). Exact adjusted P values are shown. e , Abundance of CCA. CK19 IHC was used as a cholangiocyte marker. Representative samples from a total of two separate sections per animal are depicted. b – e , n = 4 animals per group.

Article Snippet: The endogenous transcripts associated with the REDPRO-BCs used in this study are illustrated in Extended Data Fig. and the respective nucleotide sequences for 10X Visium RTL-probe capture barcodes (reverse complement to RTL-probe sequence provided by 10X Genomics) are listed under the section ‘Molecular cloning’.

Techniques: Derivative Assay, Marker