Journal: PLOS Computational Biology

Article Title: Network models for bridging denoising and identifying spatial domains of spatially resolved transcriptomics

doi: 10.1371/journal.pcbi.1013867

Figure Lengend Snippet: (A) Visium SRT data of breast cancer annotated by pathologists consists of IDC (invasive ductal carcinoma), DCIS (ductal carcinoma in situ), LCIS (lobular carcinoma in situ), tumor edge, and healthy region. (B) Spatial domains identified by stACN (left), stLearn (middle), and stACN-Con (right). (C) Heatmap of Pearson correlation coefficient among domains (domain=13). (D) Visualization of topological structure of spatial domains for breast cancer data in cell affinity graph learned by stACN, where thickness of edges is proportional to edge weights. (E) Distribution density estimation of cells in IDC, DCIS/LCIS and Healthy domain in terms of the learned cell features, where x-axis denotes cell features, and Kolmogorov-Smirnov test is for significance (left), and Distributions of degree, betweennessand eigenvector of cells in IDC, DCIS/LCIS and Healthy domains identified by stACN (right), where p-value is calculated with Student’s t-test. (F) UMAP visualization of spatial domains identified by stACN (left) and stLearn (right), where dashed circle denotes mixed domains. (G) Hierarchical structure of domain 3 and 14 in SRT data (left), and topological structure of subnetwork induced by domain 3 and 14 in cell affinity graph (right). (H) Spatial distribution of expression of GSTM3 and TFF1 with regional annotation (left), and Violin plots of gene expression (right).

Article Snippet: For the sake of convenience, we use cells to represent measurement units in spatial transcriptomics, such as spots in the 10× Visium platform, which can be interchanged freely. stACN models and characterizes the structure of noisy SRT data by learning compatible cell features from clean cell networks with graph denoising.

Techniques: In Situ, Expressing, Gene Expression

Journal: PLOS Computational Biology

Article Title: Network models for bridging denoising and identifying spatial domains of spatially resolved transcriptomics

doi: 10.1371/journal.pcbi.1013867

Figure Lengend Snippet: (A) H&E images of mouse anterior and posterior brain datasets of 10 × Visium, which are horizontally aligned (left). The zoomed in region consists of cornu ammonis(CA) and dentate gyrus(DG) domain. The corresponding anatomical Allen Mouse Brain Atlas (right). (B) Spatial domains identified by stACN (left) and STAGATE (right), where CA and DG across different slices. (C) 3D coordinates of MERFISH data for mouse hypothalamic preoptic region with slice 4, 9, and 14 (left), and spatial domains identified by stACN for each slice (right). (D) Visualization of SRT data for mouse breast cancer, where slice S1 and S3 are from different batches (first two columns), visualization of slice S1 and S3 with and without removing batch effect (the third column), and spatial domains identified by stACN with and without removing batch effect (last two columns), respectively.

Article Snippet: For the sake of convenience, we use cells to represent measurement units in spatial transcriptomics, such as spots in the 10× Visium platform, which can be interchanged freely. stACN models and characterizes the structure of noisy SRT data by learning compatible cell features from clean cell networks with graph denoising.

Techniques:

Journal: NAR Genomics and Bioinformatics

Article Title: stPipe: a flexible and streamlined R/Bioconductor pipeline for preprocessing sequencing-based spatial transcriptomics data

doi: 10.1093/nargab/lqaf167

Figure Lengend Snippet: Interactive R-shiny web app created by the Run_Interactive function and visualization of QC metrics created by the Run_Visualization function for data quality assessment. ( a ) Visualization of Slide-seq mouse brain sample Puck_200115_08. The Run_Interactive function offers flexible options for selecting a ROI through four intuitive buttons. The ‘Add Selection’ button allows users to add spatial coordinates along with corresponding metadata, such as UMI count and spatial barcode sequences, each time an ROI is selected. The ‘Clear Last Selection’ button removes the most recently selected ROI from the current selection list. The ‘Reset All Selections’ button resets both the spatial heatmap and clustering plot, providing a clean slate for a new selection. Finally, the ‘Save All Selected ROI’ button saves the finalized selection as ‘selected_ROI’ object in the user’s R global environment, streamlining data management and export. In this example, the selection of cluster 7, highlighted in purple on the t-SNE plot, is found to mostly correspond to the choroid plexus region in the spatial UMI count plot. ( b ) Barplot showing spatial barcode demultiplexing information between 10× Visium probe-based (left) and polyA-based (right) protocols to assess sequencing accuracy. ( c ) Stacked bar plots showing the mapping rate, separated into reads that map to exons, introns, and those that are ambiguously mapped or map elsewhere in the genome (ordered by exon mapping rate) between 10× Visium probe-based (left) and polyA-based (right) protocols. ( d ) UMI duplication plot between a probe-based sample (left) with a higher UMI duplication number than a polyA-based one (right). A distribution skewed toward lower duplication values indicates higher library complexity and minimal redundancy, suggesting that the sequencing depth is well-matched to the diversity of the transcriptome. In contrast, a pronounced tail toward higher duplication values suggests substantial over-sequencing or PCR amplification biases, as many reads may originate from the same underlying transcript molecule. (e) UMI count distribution between sample 709 with two protocols, the first and last two are plotted as distribution of raw UMI count per spot and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\log _{10}$\end{document} UMI count per gene respectively.

Article Snippet: Spatial transcriptomics technology has developed rapidly in recent years, with various sequencing-based platforms such as 10× Visium, Slide-seq, and Stereo-seq becoming widely used by researchers.

Techniques: Selection, Sequencing, Amplification

Journal: bioRxiv

Article Title: Infusible Extracellular Matrix Biomaterial Enhances Cell-Specific Pro-Repair Responses Following Acute Myocardial Infarction

doi: 10.1101/2025.11.08.687255

Figure Lengend Snippet: A) Overview of iECM manufacturing, where porcine left ventricular myocardium is chopped into small pieces (1). The resulting ECM is washed with sodium dodecyl sulfate (SDS) (2) followed by rinsing, milled into a fine powder (3) and digested (4). High speed centrifugation is then performed to separate out large particulate matter (5) and is finally reconstituted for infusion for MI treatment (6). B) SDS-PAGE of iECM and collagen. C) Overall timeline for iECM bioactivity studies in acute MI. Simulated intracoronary infusion of iECM or saline was performed after MI and reperfusion. Hearts were harvested 1-, 3-, and 7-days post infusion. Samples were analyzed via single nucleus RNA sequencing (snRNAseq) and spatial transcriptomics.

Article Snippet: Odd slices were frozen in TissueTek OCT TM and sectioned into 10 μm thick slices and placed onto a 10X Visium Spatial Transcriptomics Slide or a regular histology slide.

Techniques: Centrifugation, SDS Page, Saline, RNA Sequencing

Journal: bioRxiv

Article Title: Infusible Extracellular Matrix Biomaterial Enhances Cell-Specific Pro-Repair Responses Following Acute Myocardial Infarction

doi: 10.1101/2025.11.08.687255

Figure Lengend Snippet: A) 10X Visium sections indicate the spatial difference between iECM treated infarcts (red) to saline treated infarcts (cyan). B) Volcano plot displays differences between iECM and saline treated spatial transcriptomic sections at 1 day post infusion. C) Volcano plot displays differences between iECM and saline treated spatial transcriptomic sections at 3 days post infusion. D) Volcano plot displays differences between iECM and saline treated spatial transcriptomic sections at 7 days post infusion. E) Volcano plot displays differences between iECM at 1-day vs. 3-day post infusion. F) Volcano plot displays differences between iECM at 1-day vs. 7-day post infusion.

Article Snippet: Odd slices were frozen in TissueTek OCT TM and sectioned into 10 μm thick slices and placed onto a 10X Visium Spatial Transcriptomics Slide or a regular histology slide.

Techniques: Saline

Journal: Discover Oncology

Article Title: Dissecting PTPN7‐driven aggressiveness in IDH‐wildtype astrocytomas: multi‐omics, clinical validation, and spatial transcriptomics for prognostic insights

doi: 10.1007/s12672-025-02662-5

Figure Lengend Snippet: Spatial transcriptomic profiling of an adult glioblastoma via the 10 × Genomics Visium platform. A H&E image showing the tissue section. B A pseudocolor map of the PTPN7 expression is overlaid in the same section. Key tumor subregions—leading edge, infiltrating border, cellular tumor, peri‐necrotic area, and necrotic core—are delineated by dashed lines

Article Snippet: To examine PTPN7 expression at subregional resolution in glioblastoma, we utilized the 10 × Genomics Visium Whole Transcriptome platform on a human GBM sample (Parent_Visium_Human_Glioblastoma, https://www.10xgenomics.com/datasets/human-glioblastoma-whole-transcriptome-analysis-1-standard-1-2-0 ).

Techniques: Expressing

Journal: mBio

Article Title: A spatial transcriptomic atlas of the host response to oropharyngeal candidiasis

doi: 10.1128/mbio.00849-25

Figure Lengend Snippet: Major cell type annotation. Spatially resolved transcriptome ( A ) and t-SNE plots ( B ) showing representation of five major cell types in the tongue tissue. ( C ) Cell Chat software revealed the number and strength of inferred cellular signaling interactions from the spatial transcriptomics data.

Article Snippet: To analyze the microenvironment during OPC, we employed the 10× Genomics Visium spatial transcriptomics technology on frozen tissue sections ( n = 4) from tongues of normal and C. albicans -infected Balb/c mice at 60 h of OPC (hereby referred to as day 2, for ease of representation).

Techniques: Software

Journal: Frontiers in Immunology

Article Title: Targeting the immune microenvironment for ovarian cancer therapy

doi: 10.3389/fimmu.2023.1328651

Figure Lengend Snippet: Overview and applications of spatial technologies.

Article Snippet: Visium , 10X Genomics , Near cellular (1-10 cells per spot; 55uM) , FF/FFPE , Whole Transcriptome (~18000+) , , ( , ) .

Techniques: Imaging, Mass Cytometry