Journal: Frontiers in Neuroscience

Article Title: Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics

doi: 10.3389/fnins.2022.1086168

Figure Lengend Snippet: Global Moran’s index revealing three spatial autocorrelation patterns. Clustered, z > 1.65 and P < 0.1; random, –1.65 < z < 1.65 and P > 0.1; dispersed, z < –1.65 and P < 0.1. ST, spatial transcriptomic; CI, confidence interval.

Article Snippet: Spatial transcriptomic data of normal brain section was obtained from the 10X Genomics dataset.

Techniques:

Journal: Frontiers in Neuroscience

Article Title: Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics

doi: 10.3389/fnins.2022.1086168

Figure Lengend Snippet: Differentially expressed genes (DEGs) in the different brain regions. (A) Brain region. ROI indicates sensorimotor cortex. (B) Yellow area indicates sensorimotor cortex in HE images. (C) Arpp19 of ST map. (D) Hotspot of Arpp19 . (E) DEGs in the sensorimotor cortex. Ratio = The number of H-H spots in sensorimotor cortex/The total number of spots in the section. (F) Septicity of DEGs in the sensorimotor cortex. Ratio = The number of H-H spots in each region/The total number of H-H spots. (G) Marker genes of each brain region. ST, spatial transcriptomic.

Article Snippet: Spatial transcriptomic data of normal brain section was obtained from the 10X Genomics dataset.

Techniques: Marker

Journal: Frontiers in Neuroscience

Article Title: Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics

doi: 10.3389/fnins.2022.1086168

Figure Lengend Snippet: Downregulated DEGs revealed by saSpatial. (A) Bubble plots of top20 DEGs. P -values are indicated by circle size; scale adjacent to the plot. The rate difference are indicated by color. NC, normal control. P, penumbra. Typical DEGs of (B) ischemic core and (C) penumbra. N = 3. ST, spatial transcriptomic; CI, confidence interval.

Article Snippet: Spatial transcriptomic data of normal brain section was obtained from the 10X Genomics dataset.

Techniques: Control

Journal: Frontiers in Neuroscience

Article Title: Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics

doi: 10.3389/fnins.2022.1086168

Figure Lengend Snippet: Differentially expressed genes (DEGs) in the cortex layers. (A) Top 3 DEGs in the layers. (B) Typical DEGs. ST, spatial transcriptomic.

Article Snippet: Spatial transcriptomic data of normal brain section was obtained from the 10X Genomics dataset.

Techniques:

Journal: Frontiers in Neuroscience

Article Title: Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics

doi: 10.3389/fnins.2022.1086168

Figure Lengend Snippet: Heterogenicity of DEGs within the ischemic cortex. (A) Six layers of cortex in the ischemic cortex. (B) Typical DEGs. (C) ST map and Hotspot of typical DEGs. ST, spatial transcriptomic.

Article Snippet: Spatial transcriptomic data of normal brain section was obtained from the 10X Genomics dataset.

Techniques:

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

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

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

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

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:

Journal: Small Methods

Article Title: Robust Spatial Cell‐Type Deconvolution with Qualitative Reference for Spatial Transcriptomics

doi: 10.1002/smtd.202401145

Figure Lengend Snippet: Analysis of mouse olfactory bulb data. a) H&E staining of the olfactory bulb (top) and the deconvolution results of all candidate methods displayed by the spatial scatter pie plot of cell‐type composition on each spatial location. The examined cell types were granule cells (GC), olfactory sensory neurons (OSNs), periglomerular cells (PGC), mitral/tufted cells (M‐TC), and external plexiform layer interneurons (EPL‐IN) b) Manual annotation of anatomic layers (top), including the granule cell layer (GCL), the mitral cell layer (MCL), the glomerular layer (GL), and the nerve layer (ONL), and the spatial domains of different deconvolution methods visualized by spatial scatters of specific domain types. c) Performance comparison between candidate deconvolution methods, including QR‐SIDE, STdeconvolve, CARDfree, RCTD, CARD, SPOTlight, and spatialDWLS in terms of NMI (left) and ARI (right). d) UMAP plots of gene expression for Topic 1, 2, 3 identified by QR‐SIDE. The color scheme of each topic domain was the same as in (b). e) The heatmap of normalized expression level for the top 10 DE genes for topic domain 1, 2, 3. f) The correlation between DE genes of identified domains and marker genes of each cell type. g) The mean expression level of an example marker gene list for QR‐SIDE, where Tyro3 was included as the interference marker gene of cell type GC. h) Left and middle panels: The estimated spot‐separable η scores of correct marker Penk and misclassified marker Tyro3 . Right panel: The line plots of mean η of all markers across all spatial spots and the RMSE between estimated cell‐type composition by varying the inclusion of top 3‐7 marker genes of each cell type as the input gene list and the deconvolution results using high‐quality marker genes (as shown in a).

Article Snippet: These include the mouse olfactory bulb ST data from Spatial Transcriptomics v1.0 ( https://www.spatialresearch.org ), the four human hepatocellular carcinoma Visium datasets ( https://www.ncbi.nlm.nih.gov/sra?linkname=bioproject_sra_all&from_uid=858545 ), mouse anterior brain 10x Visium data ( https://support.10xgenomics.com/spatial‐gene‐expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Anterior ), and mouse posterior brain 10x Visium data ( https://support.10xgenomics.com/spatial‐gene‐expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Posterior ).

Techniques: Staining, Comparison, Gene Expression, Expressing, Marker

Journal: bioRxiv

Article Title: Web engine for tumor pathology image retrievals on massive scales

doi: 10.1101/2025.10.25.684566

Figure Lengend Snippet: The framework comprises two modules: Development and Retrieval. a, The engine development has two stages: model training and index generation. Stage 1 trains a prediction model composed of an image encoder, attention map, and decoder using TCGA data from 32 tumor types. The input to the image encoder is H&E whole-slide images, and the decoder output includes a binary indication of gene mutation functions in cancer progression and average expression of pathway gene sets. Stage 2 indexes patch vectors encoded from the whole slide images from the NCI Lab of Pathology, TCGA, CPTAC, and spatial transcriptomics studies. b, Two retrieval applications. For a query H&E image patch or region, the Image-to-Image retrieval returns similar image patches and associated whole slides from the indexed database. If users select images with spatial transcriptomics (ST) data, HERE returns images that closely match the query and the corresponding gene expression profiles from ST detection spots. The Transcriptomics-to-Image retrieval finds H&E slides with paired ST data where the query gene has high expression levels in ST detection spots associated with specific image feature clusters from those slides.

Article Snippet: Spatial transcriptomics (ST) data paired with H&E images also makes it possible to implement a Transcriptomics-to-Image module, which returns image features associated with the high expression level of a query gene name (e.g., SERPING1 ) ( ).

Techniques: Mutagenesis, Expressing, Gene Expression

Journal: bioRxiv

Article Title: Web engine for tumor pathology image retrievals on massive scales

doi: 10.1101/2025.10.25.684566

Figure Lengend Snippet: a , An example query image from a tumor with hypoxia indicated by anti-Pimonidazole staining . The right side shows examples of matched image patches (green squares) within an H&E slide with spatial transcriptomics (ST) data. b , Transcriptomics heatmap of ST detection spots from the top three ST profiles, each representing a distinct cancer type. Expression values are variance-stabilizing transformed values relative to levels in all ST spots and sequencing depth per slide . Only genes with expression values greater than 5 in at least three spots across all match patches are shown, and genes are ranked by mean values across all spots. Within each cancer type, columns (spots) are organized by hierarchical clustering with correlation distances. Only spots with expression values greater than 5 in at least three genes are shown. c , Glycolysis gene set enrichment. Along the x-axis, all genes are ranked from high to low by mean expression value (lower y-axis) among all ST detection spots returned by the image query. Members of the glycolysis hallmark gene set are indicated by horizontal blue lines in the middle of the plot. The top y-axis plots the glycolysis enrichment score at each gene rank. The p-value was computed through the one-sided permutation test with 1000 randomizations. d , Gene sets with higher enrichment scores than hypoxia.

Article Snippet: Spatial transcriptomics (ST) data paired with H&E images also makes it possible to implement a Transcriptomics-to-Image module, which returns image features associated with the high expression level of a query gene name (e.g., SERPING1 ) ( ).

Techniques: Staining, Expressing, Transformation Assay, Sequencing

Journal: bioRxiv

Article Title: Web engine for tumor pathology image retrievals on massive scales

doi: 10.1101/2025.10.25.684566

Figure Lengend Snippet: a , Screenshot of the 3D UMAP of patch encoding vectors from H&E images paired with spatial transcriptomics. The full interactive UMAP is available at https://hereapp.ccr.cancer.gov/ST_CONCH_umap3d.html . Some image patch clusters or local regions comprise image patches from mostly one or two ST profiles (circle highlights), typical of batch effects. b , Statistical links between gene expression and image feature clusters. For each ST cluster, we counted the number of genes with FDR < 0.05 (Benjamini-Hochberg corrected from the two-sided Wilcoxon rank sum test). The histogram of gene count values across image clusters from all ST profiles is shown. The total number of clusters evaluated was 1,039, and the total number of genes was 11,137. c , Gene set enrichment analysis. For Cohen’s d profile for each ST cluster, we performed gene set enrichment analysis. The X-axis presents the fraction of ST clusters above which a GO_BP term is enriched (GSEA q-value < 0.05). The left Y-axis presents the fraction of GO_BP terms enriched above the threshold on the X-axis for real and randomly permuted data. The right Y-axis presents the False Discovery Rate, computed as the (Random GO_BP term fraction) / (Real GO_BP term fraction). d , The H&E image from the human lung tumor region shown in , which has high expression of C1R , C1S , and SERPING1 . e , In-vitro growth of B16F10 cancer cells in culture, measured by XTT assay. Dots and error bars represent mean and standard deviations (n = 3 cell culture replicates). Metabolic activity is measured as optical density at 492 nm (read) divided by the value at 620 nm (reference)

Article Snippet: Spatial transcriptomics (ST) data paired with H&E images also makes it possible to implement a Transcriptomics-to-Image module, which returns image features associated with the high expression level of a query gene name (e.g., SERPING1 ) ( ).

Techniques: Gene Expression, Expressing, In Vitro, XTT Assay, Cell Culture, Activity Assay

Journal: bioRxiv

Article Title: Web engine for tumor pathology image retrievals on massive scales

doi: 10.1101/2025.10.25.684566

Figure Lengend Snippet: a , Associations between gene expression and image features. Hierarchical clustering was applied to Spatial transcriptomics (ST) data from a human lung tumor to organize image patches around ST detection spots into eight clusters (left panel; each cluster is a different color). For a given query gene (e.g., C1R and SERPING1, center panel) and each image cluster (for example, cluster #2 (blue) in left panel), the expression difference among ST detection spots within the image cluster region and spots outside the cluster region is quantified using the Cohen’s d value (right panel). Testing each of all possible query genes against each of every ST profile cluster will generate the result matrix (bottom panel). b , Complement Activation gene set enrichment. Along the x-axis, all genes are ranked from high to low by Cohen’s d values (bottom Y-axis) computed for cluster #2 of the ST profile in panel a. Members of the “complement activation” pathway from Gene Ontology biological processes (GO_BP) are indicated by horizontal blue lines in the middle of the plot. The top y-axis plots “complement activation” enrichment scores at each gene rank. The P -value is computed through the one-sided permutation test (1000 randomizations). c , Top 20 GO_BP terms associated with image features. For each term, the Y-axis presents the fraction of ST profile clusters whose Cohen’s d gene scores are significantly enriched (False Discovery Rate < 0.05). Multiple GO_BP terms related to similar biological processes are grouped with the y-axis presenting mean values across all merged terms. The star marks the complement activation pathway discussed in the main text. d , Cohen’s d heatmap of complement activation genes. Columns are labeled with each ST profile’s cancer type and the cluster index. e , In-vivo effects of Serping1 overexpression on tumor volume. Left panel: B16-mhgp100 cells with Serping1 and vector-only overexpression were inoculated subcutaneously into mice treated by immune checkpoint blockade. Right panel: The tumor sizes on day 28, the day before the first tumor reached an endpoint (tumor volume ≥ 2000 mm or length ≥ 2 cm). Box plots are shown as in . Group values were compared through the two-sided Wilcoxon rank-sum test. f , Serping1 overexpression in tumors extended survival. B16-mhgp100 cells express immunogenic antigen hgp100, while the B16F10 cell line is less immunogenic. On the Y-axis, the fraction of mice with endpoint-free survival is plotted against days since tumor inoculation (X-axis). The two-sided log-rank test compared group survival differences.

Article Snippet: Spatial transcriptomics (ST) data paired with H&E images also makes it possible to implement a Transcriptomics-to-Image module, which returns image features associated with the high expression level of a query gene name (e.g., SERPING1 ) ( ).

Techniques: Gene Expression, Expressing, Activation Assay, Labeling, In Vivo, Over Expression, Plasmid Preparation

Journal: Nature Communications

Article Title: MultiGATE: integrative analysis and regulatory inference in spatial multi-omics data via graph representation learning

doi: 10.1038/s41467-025-63418-x

Figure Lengend Snippet: a Bright-field image and manually annotated segmentation of hippocampus layers and white matter (WM) in the human hippocampus. b Spatial clustering of hippocampal regions using MultiGATE, SpatialGlue, and Seurat WNN. Clustering performance is assessed using the Adjusted Rand Index (ARI), with higher values indicating greater clustering accuracy. c Box plots representing attention scores for peak–gene pairs across different genomic distances, grouped based on whether they are supported by expression quantitative trait loci (eQTL) evidence. The box plots indicate the medians (centerlines), means (triangles), first and third quartiles (bounds of boxes), and 1.5 × interquartile range (whiskers). Sample sizes per bin (False/True): 0–25 kb (621/222), 25–50 kb (479/88), 50–75 kb (461/78), 75–100 kb (469/44), 100–125 kb (446/30), 125–150 kb (405/29). d Receiver operating characteristic (ROC) curves comparing the performance of MultiGATE and other methods in predicting eQTL-associated regulatory interactions. e Visualization of MultiGATE-predicted cis-regulatory interactions for the target genes CA12 and PRKD3 along with eQTL evidence. Source data are provided as a Source Data file.

Article Snippet: For the adult human hippocampus dataset (spatial ATAC–RNA–seq), the mouse brain dataset (spatial transcriptomics + metabolomics), the breast cancer-patterned spatial ATAC + RNA dataset (Supplementary Fig. ), which have ground truth spatial domain annotations, we computed the following metrics to assess clustering agreement: Rand Index (RI), ARI, Adjusted Mutual Information, Normalized Mutual Information, Homogeneity, Completeness, V-measure, Fowlkes-Mallows Index.

Techniques: Expressing

Journal: Frontiers in Cell and Developmental Biology

Article Title: Acetylcholine in the gingival epithelium drives the pathogenesis of periodontitis

doi: 10.3389/fcell.2025.1701252

Figure Lengend Snippet: Acetylcholine signaling and receptor distribution in the periodontal epithelium. (A) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of epithelial neurotransmitter signaling-related DEGs. Left: upregulated pathways in PD vs. HC; right: downregulated pathways in TP vs. PD. (B) Expression profiles of acetylcholine receptors across different cell types. Receptors not detected in any cells were excluded. (C) Transcript counts of acetylcholine receptor genes from HOKs RNA-seq data. Five receptors detected in all 12 samples (n = 3 per group), with colored shapes representing groups and black bars indicating the mean. Receptors with low expression (zero counts in some samples) were excluded. (D) Expression of acetylcholine receptors CHRNB1 , CHRNA5 , and CHRNA7 in gingival spatial transcriptomics data.

Article Snippet: Utilizing single-cell RNA sequencing (scRNA-seq) data (205,334 cells, 40 human gingival samples) and gingival spatial transcriptomics data (46,230–25 μm 2 spots), we revealed that the gingival epithelium exhibits the most significant functional reprogramming of neural signaling pathways in the periodontitis state.

Techniques: Expressing, RNA Sequencing

Journal: Frontiers in Cell and Developmental Biology

Article Title: Acetylcholine in the gingival epithelium drives the pathogenesis of periodontitis

doi: 10.3389/fcell.2025.1701252

Figure Lengend Snippet: Distribution of tight junction genes in the periodontal gingival epithelium and their regulation by acetylcholine. (A) Expressions of OCLN, CLDN1, and CDH1 in spatial transcriptomics data. (B) Differential comparison of OCLN, CLDN1, and CDH1 in the epithelial subpopulation of HC versus PD groups (***, p < 0.001). (C) Heatmap showing the expressions of OCLN, CLDN1, and CDH1 for each cluster in HC and PD gingiva. (D) Quantitative polymerase chain reaction (qPCR) validation in HOKs. Data are presented as the mean ± standard error of the mean (SEM). ***, p < 0.001.

Article Snippet: Utilizing single-cell RNA sequencing (scRNA-seq) data (205,334 cells, 40 human gingival samples) and gingival spatial transcriptomics data (46,230–25 μm 2 spots), we revealed that the gingival epithelium exhibits the most significant functional reprogramming of neural signaling pathways in the periodontitis state.

Techniques: Comparison, Real-time Polymerase Chain Reaction, Biomarker Discovery