Journal: Frontiers in Genetics

Article Title: Deciphering the role of CNIH4 in pan-cancer landscapes and its significance in breast cancer progression

doi: 10.3389/fgene.2025.1536620

Figure Lengend Snippet: (A) Expression of CNIH4 gene in each microdomain in BRCA spatial transcriptome sections; (B–E) The cell type with the largest proportion in each microdomain at BRCA idle resolution and the spatial transcriptome localization of the CNIH4 gene. Each dot is a spot for spatial transcriptome sequencing, and different colors represent different cell types. The darker the color (red) in the same spot, the higher the expression of the CNIH4 gene in the spot; (F–I) Spearman correlation of CNIH4 gene expression with each cell type in microdomains at idle resolution. The red line indicates a positive correlation, the green line denotes a negative correlation, the gray line signifies no statistical significance, and the thickness of the line reflects the absolute value of the correlation coefficient.

Article Snippet: Spatial transcriptome datasets were obtained from the 10x Genomics server ( https://www.10xgenomics.com/ ) and previous studies ( ; ). provide details of the spatial transcriptome dataset and detailed abbreviations for the 33 tumors.

Techniques: Expressing, Sequencing, Gene Expression

Journal: Nucleic Acids Research

Article Title: SPathDB: a comprehensive database of spatial pathway activity atlas

doi: 10.1093/nar/gkae1041

Figure Lengend Snippet: Overview of data content and functions of SPathDB. The left panel contains the database content, which includes the spatial transcriptome dataset and pathway data content, and construction of spatial pathway activity profiles. The right panel contains the tools of SPathDB to retrieve, analyze and visualize spatial pathway activity.

Article Snippet: Spatial transcriptome datasets of human and mouse were collected from the Gene Expression Omnibus (GEO) database , 10× Genomics ( https://www.10xgenomics.com/ ), Mendeley Data ( https://data.mendeley.com/ ) and CROST ( https://ngdc.cncb.ac.cn/crost/ ).

Techniques: Activity Assay

Journal: Science immunology

Article Title: Spatial transcriptomics stratifies psoriatic disease severity by emergent cellular ecosystems

doi: 10.1126/sciimmunol.abq7991

Figure Lengend Snippet: (A) Schematic of spatial transcriptomics study workflow. Table S1 contains metadata for each sample. (B) Schematic of skin, representative hematoxylin-eosin (H&E) image and corresponding ST plot (left-to-right). Scale bar = 440μm (C) UMAP visualization of 3,815 spots colored by cluster obtained from healthy skin samples (N=3, n=5). (D) Composition plots displaying relative abundance of each cluster by sample. Note up to two samples (labeled S) were collected from each Healthy Volunteer (HV). Replicate arrays are labeled “R” along the X axis. (E) Integration with a publicly-sourced single cell RNA-seq data set (dataset 1) with a representative ST spatial feature plot. See Figure S4 for UMAP of annotated cell type clusters. SMC=smooth muscle cell. Scale bar = 520μm (F) Multimodal intersection analysis (MIA) of overlap between data from datasets 1 and 2 and our ST-generated clusters. A sample hypergeometric distribution of keratinocyte cluster from dataset 1 and our epidermis cluster (cluster 6). MIA enrichment heatmaps of non-immune cell types in dataset 1 (G) and dataset 2 (H) and ST clusters from healthy skin. The X axis denotes the scRNA seq-identified cell types while the Y axis represents the ST-generated clusters. Differentiated keratinocytes (Diff KC) lymphatic endothelium (LE), proliferating keratinocytes (Prolif KC), vascular endothelium (VE), keratinocyte (KC). (I) MIA heatmap showing the enrichment of scRNA-seq-identified adipose- cell types from Hildreth et al. within pooled healthy skin ST clusters. (J) KEGG pathway analysis of the adipose cluster (cluster 2).

Article Snippet: Spatial Transcriptomics (ST) faithfully maps gene expression in healthy human skin We developed a human skin specific protocol for 10X Genomics Visium platform and performed ST on 25 skin samples collected from 3 healthy controls and 11 patients with PsO/PsA ( , figs. S1, A to C , S14A and table S1 ).

Techniques: Labeling, RNA Sequencing, Generated

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: Nature Communications

Article Title: SpaIM: single-cell spatial transcriptomics imputation via style transfer

doi: 10.1038/s41467-025-63185-9

Figure Lengend Snippet: SpaIM comprises an ST autoencoder and an ST generator. Both the ST autoencoder and the ST generator are built on the multilayer recursive style transfer (ReST) layers.

Article Snippet: Spatial transcriptomics (ST) autoencoder The ST autoencoder (Fig. ) comprises multilayer ReST encoders.

Techniques:

Journal: Nature Communications

Article Title: SpaIM: single-cell spatial transcriptomics imputation via style transfer

doi: 10.1038/s41467-025-63185-9

Figure Lengend Snippet: a Benchmarking results on the NanoString CosMx spatial transcriptomics dataset (Lung5–rep3), using evaluation metrics including structural similarity index measure (SSIM) and Jaccard similarity (JS). Data are presented as mean values ± 95% confidence intervals across predicted genes ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document} n = 2,038). b Spatial visualization of cell types in the whole slide. c Spatial visualization of cell types in specific field of views (FOVs).

Article Snippet: Spatial transcriptomics (ST) autoencoder The ST autoencoder (Fig. ) comprises multilayer ReST encoders.

Techniques:

Journal: Nature Communications

Article Title: SpaIM: single-cell spatial transcriptomics imputation via style transfer

doi: 10.1038/s41467-025-63185-9

Figure Lengend Snippet: a Benchmarking results on the NanoString CosMx spatial transcriptomics dataset (Lung5–rep3), using evaluation metrics including structural similarity index measure (SSIM) and Jaccard similarity (JS). Data are presented as mean values ± 95% confidence intervals across predicted genes ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document} n = 2,038). b Spatial visualization of cell types in the whole slide. c Spatial visualization of cell types in specific field of views (FOVs).

Article Snippet: Spatial transcriptomics (ST) generator A similar architecture (Fig. ) is used to generate ST data from SC data.

Techniques:

Journal: Computational and Structural Biotechnology Journal

Article Title: Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning

doi: 10.1016/j.csbj.2024.10.029

Figure Lengend Snippet: GRAS4T improved the accuracy of identifying layer structures within the DLPFC dataset compared to other methods. (a) Boxplot of ARI values across all sections of the DLPFC dataset for six methods. (b) The H&E image and manual annotation of slice 151672. (c) The spatial domains in six methods for slice 151672. (d) UMAP visualizations and PAGA graphs in six methods for slice 151672.

Article Snippet: The ST datasets supporting the findings of this study are all publicly available. (1) The DLPFC dataset is available at http://research.libd.org/spatialLIBD/ . (2) The HER2+ dataset generated by spatial transcriptomics platform is accessed at https://github.com/almaan/her2st . (3) The mouse visual cortex dataset generated by STARmap is available at https://www.dropbox.com/sh/f7ebheru1lbz91s/AADm6D54GSEFXB1feRy6OSASa/visual_1020/20180505_BY3_1kgenes?dl=0&subfolder_nav_tracking=1 . (4) The adult mouse brain dataset is accessed at https://www.10xgenomics.com/resources/datasets . (5) The Stereo-seq mouse olfactory bulb dataset is available at https://github.com/JinmiaoChenLab/SEDR_analyses/ . (6) The MERFISH dataset is accessed at https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248 . (7) The human breast cancer dataset is available at https://www.10xgenomics.com/resources/datasets . (8) The anterior and posterior sections of the mouse brain are accessed at https://www.10xgenomics.com/resources/datasets and the Allen Brain Atlas reference is available at https://mouse.brain-map.org/static/atlas .

Techniques: