geomx dsp instrument  (Spatial Transcriptomics Inc)

Journal: STAR Protocols

Article Title: Protocol for dual spatial transcriptomic profiling of infected tissues

doi: 10.1016/j.xpro.2025.104282

Figure Lengend Snippet: Spatial transcriptomics workflow using GeoMx DSP instrument The procedure covers the following steps: Day1 - Slide processing using Bond RxV pipeline and probe library hybridization, Day 2 - GeoMx run consisting of staining of slides with morphology markers, GeoMx imaging, ROI selection, and sample acquisition, Day 3 - PCR-based library preparation, quantification, and sequencing, Day 4 - Data analytics. Created in BioRender. G, M. (2025) https://BioRender.com/p7q0gem .

Article Snippet: The pathogen-specific probes were custom-designed using P. aeruginosa PA14 genomic information , , with a computational in silico preselection filters., Spatial transcriptomics workflow using GeoMx DSP instrument The procedure covers the following steps: Day1 - Slide processing using Bond RxV pipeline and probe library hybridization, Day 2 - GeoMx run consisting of staining of slides with morphology markers, GeoMx imaging, ROI selection, and sample acquisition, Day 3 - PCR-based library preparation, quantification, and sequencing, Day 4 - Data analytics.

Techniques: Hybridization, Staining, Imaging, Selection, Sequencing

Journal: STAR Protocols

Article Title: Protocol for dual spatial transcriptomic profiling of infected tissues

doi: 10.1016/j.xpro.2025.104282

Figure Lengend Snippet: Representative images of GeoMx ROI selection on infected corneas Cross-sections of murine eyes at 24 h (left) and 48 h (right) post-infection. Fluorescent morphology markers visualize tissue architecture and immune cell infiltration (pink), guiding the placement of geometric Regions of Interest (ROIs, outlined in white) for spatial transcriptomic analysis.

Article Snippet: The pathogen-specific probes were custom-designed using P. aeruginosa PA14 genomic information , , with a computational in silico preselection filters., Spatial transcriptomics workflow using GeoMx DSP instrument The procedure covers the following steps: Day1 - Slide processing using Bond RxV pipeline and probe library hybridization, Day 2 - GeoMx run consisting of staining of slides with morphology markers, GeoMx imaging, ROI selection, and sample acquisition, Day 3 - PCR-based library preparation, quantification, and sequencing, Day 4 - Data analytics.

Techniques: Selection, Infection

Journal: EMBO Molecular Medicine

Article Title: Spatial transcriptomics elucidates localized immune responses in atherosclerotic coronary artery

doi: 10.1038/s44321-025-00280-w

Figure Lengend Snippet: ( A ) Representative haematoxylin and eosin (H&E) stained sections showing arterial lesions of varying severity (mild, moderate and severe). Specific regions are highlighted at a higher magnification to reveal morphological differences across lesion severity, indicating progressive changes from near normal to severe. ( B ) Workflow for spatial transcriptomics using GeoMx® DSP: slide preparation with morphology markers and ~18,000 oligo-conjugated RNA probes; selection of ROIs in each sample analysed; cleavage with UV light of barcodes from RNA probes; collection and release of collected barcodes onto a 96-well plate for all selected ROIs; generation of a cDNA library for next generation sequencing and upload of resulting sequencing data onto the GeoMx® DSP. ( C ) Fluorescent imaging of arterial lesions with varying severities, reflecting those shown at higher magnification in ( A ). Fluorescent imaging is a prerequisite for choosing regions of interest (ROIs) for downstream profiling by GeoMx. Samples were stained with SYTO13 (nuclear dye, blue), CD45 (pan-leucocyte marker, yellow) and CD4 (T cell subset marker, red). Representative ROIs chosen for downstream spatial profiling are indicated by white circles. .

Article Snippet: Spatial Transcriptomics GeoMx® DSP and CosMxTM SMI analyses of human coronary arteries with different stages of atherosclerosis progression provide state-of-the-art datasets to interrogate immune pathways involved in disease establishment and progression.

Techniques: Staining, Selection, cDNA Library Assay, Next-Generation Sequencing, Sequencing, Imaging, Marker

Journal: EMBO Molecular Medicine

Article Title: Spatial transcriptomics elucidates localized immune responses in atherosclerotic coronary artery

doi: 10.1038/s44321-025-00280-w

Figure Lengend Snippet: ( A ) In situ expression of ANXA2 in a representative severe lesion in GeoMx® (top panel) and CosMx ™ (bottom panel). Higher magnification boxes show ANXA2 expression in an ATLO. Figure was reused to create this panel. ( B ) NanoString-provided workflow for spatial deconvolution of GeoMx® data. ( C ) Proposed workflow for spatial deconvolution utilising a CosMx-generated single cell matrix (from the same tissue) to inform cell estimates. .

Article Snippet: Spatial Transcriptomics GeoMx® DSP and CosMxTM SMI analyses of human coronary arteries with different stages of atherosclerosis progression provide state-of-the-art datasets to interrogate immune pathways involved in disease establishment and progression.

Techniques: In Situ, Expressing, Generated

Journal: EMBO Molecular Medicine

Article Title: Spatial transcriptomics elucidates localized immune responses in atherosclerotic coronary artery

doi: 10.1038/s44321-025-00280-w

Figure Lengend Snippet: ( A ) Heatmap of spatial deconvolution estimates using the inbuilt GeoMx® reference matrix 'safeTME'. Scaled abundances are shown as a ratio to the maximum value are displayed across five tissue localisations (adventitia, plaque, negative control, muscle and infiltrated muscle layer). 'Subsets' correspond to ROIs segmented on the GeoMx® platform and are highlighted. Cell types present in the safeTME matrix are indicated as rows. ( B ) Heatmap of the CosMx™-derived cell signature matrix. Census-annotated cell populations from the CosMx™ are represented as columns with rows representing genes on the CosMx™ platform. Genes are scaled from red to white, with red indicating a higher expression. ( C ) Heatmap of the genes present in the GeoMx® dataset from the CosMx™-derived cell signature matrix. ( D ) Heatmap of spatial deconvolution estimates using the CosMx™-derived matrix. Cell types present in the CosMx™-matrix are indicated as rows.

Article Snippet: Spatial Transcriptomics GeoMx® DSP and CosMxTM SMI analyses of human coronary arteries with different stages of atherosclerosis progression provide state-of-the-art datasets to interrogate immune pathways involved in disease establishment and progression.

Techniques: Negative Control, Derivative Assay, Expressing

Journal: Cancer Medicine

Article Title: CXCL16 Producing Tumor Clones Are Shaping Immunosuppressive Microenvironment in Squamous Cell Carcinoma via CXCR6 Regulatory T Cell

doi: 10.1002/cam4.71060

Figure Lengend Snippet: Digital spatial profiling reveals transcriptional programs and tumor–immune interactions in SCC. (A) Example of a representative SCC sample stained with H&E. Inset shows a higher magnification of a selected ROI. (B) Representative GeoMx spatial transcriptomics images from squamous cell carcinoma (SCC), and pemphigus vulgaris (PV) skin lesions. In SCC samples ( n = 2), regions of interest (ROIs) were selected to capture PanCK + tumor areas in close proximity to immune cell infiltrates. ROIs were segmented into SCC_Tumor and SCC_TME based on PanCK expression. In PV and PSO lesions, epithelial and immune compartments were delineated using CD45/CD31 and PanCK markers. (C) UMAP plot displaying spatial transcriptomic profiles from SCC ( n= 8 tumor, n = 8 TME), and PV ( n = 10 epithelial, n = 9 immune) regions. Each point represents an individual area of interest (AOI), color‐coded by lesion type and tissue compartment. (D) Boxplots illustrating genes associated with SCC progression and previously identified in the Carcinoma 3 cluster, the majority of which were significantly upregulated in SCC_Tumor compared to PV and PSO epithelial regions. (E) Heatmap showing paired Spearman correlation analysis of ligand–receptor gene pairs in SCC. Each row represents a ligand expressed in SCC_Tumor regions, and each column represents its corresponding receptor in SCC_TME regions. Ligand–receptor pairs were pre‐selected based on top‐ranked interactions predicted from Carcinoma 3 in Figure , and only those with Spearman correlation coefficient ≥ 0 are shown. Cell color represents the strength of correlation, and values within each cell indicate the associated p value. The red‐highlighted ligand–receptor pairs were previously identified as key components of the Carcinoma 3—Treg interaction network. (F) Scatter plots showing the expression of selected ligands (CXCL16, TNFSF9) and their corresponding receptors (CXCR6, TNFRSF9) in SCC and PV samples. Ligands were measured in SCC_Tumor and PV epithelial regions, while receptors were measured in SCC_TME and PV immune regions. Among these, CXCL16 and TNFRSF9 showed significant upregulation in SCC samples. p < 0.05 suggested significant differences. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001 and ns, not significant.

Article Snippet: Spatial transcriptomics was performed using the GeoMx DSP platform of the whole‐transcriptome atlas (NanoString Technologies, Seattle, WA, USA).

Techniques: Staining, Expressing

Journal: bioRxiv

Article Title: Tissue transcriptomics of endomyocardial biopsies reveals widespread molecular perturbations independent of leukocyte-rich foci in human myocarditis

doi: 10.1101/2025.07.11.664335

Figure Lengend Snippet: (A) Proportional Venn diagram showing the overlap of DEGs between Visium (FindAllMarkers) and GeoMx datasets (all compartments). Fourteen DEGs were consistently differentially regulated in myocarditis relative to controls across both platforms. Corresponding fold changes for these overlapping genes are shown in the heatmap below. (B) Proportional Venn diagram comparing DEGs identified only in cardiomyocyte-stained segments (TNNI3⁺CD45⁻) and leukocyte depleted, cardiomyocyte-enriched genes (Visium), revealing ten shared DEGs between both datasets. Fold change values for these overlapping genes are shown in the heatmap. Color intensity in the heatmaps reflects the magnitude of absolute fold change values for each gene. Genes shown were filtered based on adjusted p-value of at least < 0.01 and exhibited consistent directionality of effect across platforms. Heatmap values for upregulated genes with FC higher than 4 were capped to this maximum value to aid visualization (see Supplementary Table 10 for values). (C) Chord plot illustrating inferred ligand– receptor interactions derived from differentially expressed genes in cardiomyocyte-enriched regions from both experimental techniques, focusing on overlapping antigen presentation–related genes, weighted by expression confidence. Arcs represent predicted interactions between ligands and immune receptors. Interactions were inferred using the OmniPath ligand–receptor database, and visualized using network-based filtering of curated, directional signaling interactions. Bolded genes represent overlapped genes present in OmniPath, between the two orthogonal experimental techniques.

Article Snippet: GeoMx Digital Spatial Profiling (DSP) was subsequently performed in the Spatial Cancer Research Immunobiology & Therapeutics (SCRIPT) Laboratory and the Johns Hopkins Experimental and Computational Genomics Core to validate transcriptional findings and enable spatially resolved whole-transcriptome gene expression profiling in cardiac tissue.

Techniques: Staining, Derivative Assay, Immunopeptidomics, Expressing

Journal: bioRxiv

Article Title: Tissue transcriptomics of endomyocardial biopsies reveals widespread molecular perturbations independent of leukocyte-rich foci in human myocarditis

doi: 10.1101/2025.07.11.664335

Figure Lengend Snippet: (A) Representative immunohistochemical (IHC) micrograph of endomyocardial biopsy (EMBx) tissue highlighting cardiomyocytes (TNNI3⁺, yellow), leukocytes (CD45⁺, red), and nuclei (Syto83, green). (B) Representative segmentation overlay into three compartments: cardiomyocytes (TNNI3⁺CD45⁻, yellow), leukocytes (TNNI3⁻CD45⁺, red), and non-myocytes (TNNI3⁻CD45⁻, blue), for IHC-guided transcriptomics (GeoMx DSP). (C) Volcano plot showing all DEGs between controls and myocarditis in all segments, (D) TNNI3 + CD45 - cardiomyocytes, (E) TNNI3 - CD45 + leukocytes, and (F) TNNI3 - CD45 - non-myocytes/stromal cells. DEGs were computed using Q3 normalization followed by linear mixed-effects modeling with a FC threshold > 1.5 and an adjusted p < 0.05. For non-myocyte comparisons (F), unadjusted p -values were used due to lower segment counts and limited detection sensitivity.

Article Snippet: GeoMx Digital Spatial Profiling (DSP) was subsequently performed in the Spatial Cancer Research Immunobiology & Therapeutics (SCRIPT) Laboratory and the Johns Hopkins Experimental and Computational Genomics Core to validate transcriptional findings and enable spatially resolved whole-transcriptome gene expression profiling in cardiac tissue.

Techniques: Immunohistochemical staining