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: Nature Communications

Article Title: Spatially-restricted inflammation-induced senescent-like glia in multiple sclerosis and patient-derived organoids

doi: 10.1038/s41467-025-63371-9

Figure Lengend Snippet: a Overview of the study design. Multimodal data were imputed in each spot, including gene expression, definition of microenvironments/niches (unsupervised and/or manual clustering), snRNA-seq informed spot’s cell-type deconvolution, and ligand-receptor pair-based cell communication. Created in BioRender. Absinta, M. (2025) https://BioRender.com/d0hxq2g . b Pie chart showing the percentage of spots by anatomo-pathological niches (total number of spots = 45,335; Supplementary Data for a detailed description of each sample and identification number). c Violin plots showing the number of gene counts by niches for each location and pathological stage. d Heatmap showing the deconvoluted cell-type proportion by niches (asterisks identify conditions with more than 15% of total deconvoluted cells). e Representative example of the comprehensive spatial transcriptomic analysis of a CAL (woman with progressive MS in her 40 s). By histology, CAL are demyelinated lesions with an inflammatory edge of MHCII + myeloid cells. A 6.5-mm 2 of tissue (white square) was processed for spatial transcriptomics using the 10× Visium platform. Both manual and unsupervised clustering are shown. The unsupervised clustering identified transcriptional microenvironments (CAL edge, core, WM periplaque, and cortex; red spots) overlaid onto the LFB-stained tissue. These microenvironments are in line with the pathological tissue staging. f Enhanced spatial gene expression of SPP1, CHIT1, FTL, APOE, and C1QB that are the among top differentially expressed genes at the CAL edge. g Spatially-resolved microglia subclustering (red spots) after deconvolution of the snRNAseq dataset from Absinta et al. are overlaid onto the LFB-stained tissue. The previously described MIMS-iron and MIMS-foamy are microglia clusters located at the CAL edge. Stressed microglia are sparsely seen in the CAL core, edge, and periplaque. Homeostatic microglia are rarely located in the periplaque WM only. WM white matter, CAL chronic active lesion, CI chronic inactive lesion, LFB-PAS luxol fast blue-periodic acid-Schiff.

Article Snippet: The integration of all the Visium slide datasets was performed following the recommendation provided by 10× Genomics [ https://www.10xgenomics.com/analysis-guides/correcting-batch-effects-in-visium-data ].

Techniques: Gene Expression, Staining

Journal: Nature Communications

Article Title: Spatially-restricted inflammation-induced senescent-like glia in multiple sclerosis and patient-derived organoids

doi: 10.1038/s41467-025-63371-9

Figure Lengend Snippet: a Representative expression of inflammatory ( SPP1 , C3 ) and senescence-associated genes ( SERPINA3, HMGB1 , IGFBP5, and CDKN1A ) in different MS pathological tissue (i.e., active lesion, CAL, chronic inactive lesion, remyelinated lesion, and non-lesional MS tissue). b Gradients of senescence-associated astrocyte signature (55 genes) was observed extending from the lesional areas toward the periplaque in Visium slides with CAL and chronic inactive lesions. c , d Spatial differential expression analysis of genes along a linear trajectory identified non-random gene gradients (SPARKX p < 0.01). Error bands of line plots indicate the confidence interval (level 0.95). Cellular senescence-related genes and SenMayo genelist exhibited non-random gradients not only within lesional areas but also in the surrounding periplaque in chronic active lesion slide V01, whereas such pattern was not observed in the active lesion slide V05. e SenMayo genelists spatial gradient for Visium slide, including a remyelinated lesion. Error bands of line plots indicate the confidence interval (level 0.95). Remyelination was assessed by both lipid staining (LFB) and myelin PLP immunostaining. The asterisk indicates the postcapillary central venule. CAL chronic active lesion, MBP myelin basic protein, LFB luxol fast blue, WM white matter, PLP myelin proteolipid protein.

Article Snippet: The integration of all the Visium slide datasets was performed following the recommendation provided by 10× Genomics [ https://www.10xgenomics.com/analysis-guides/correcting-batch-effects-in-visium-data ].

Techniques: Expressing, Quantitative Proteomics, Staining, Immunostaining

Journal: Nature Communications

Article Title: Spatially-restricted inflammation-induced senescent-like glia in multiple sclerosis and patient-derived organoids

doi: 10.1038/s41467-025-63371-9

Figure Lengend Snippet: a Workflow schematic of the cell-cell communication analysis using both snRNAseq and spatial transcriptomic datasets (CellChat, NICHES tools). Created in BioRender. Absinta, M. (2025) https://BioRender.com/d0hxq2g . b Circle plot representing the comparison between the cellular communication (ligand-receptors [LR] pairs) between senescent-like microglia and other cell populations vs. non-senescent microglia and other cell populations using CellChat on snRNAseq data. Red lines indicate increased number of interaction ligand-receptors pairs, while blue line reduced number of interactions. c Chord diagram showing detailed communication through upregulated pathways and providing insights into the autocrine- vs . paracrine-acting pathways for senescent microglia using CellChat. Significant upregulation was defined by an interaction with p < 0.05 and logFC for ligand expression >0.2. Colours correspond to the different cell-types. d Spatially mapping of prioritized ligand-receptor pairs involving senescent-like microglia ( SPP1-CD44, TREM2-APOE, HLADB1-CD4, NAMPT-ITGAV5, PSAP-GPR37 ) within the Visium spot and their relative quantification (mean ± SD) by location and pathological stage ( n = 7 locations/pathological stages from 12 Visium slides, ANOVA p < 0.0001 for all pairs, except PSAP-GPR37 , post-hoc multiple comparison analysis * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001). Representative icons with ligand-receptors pairs created in BioRender. Absinta, M. (2025) https://BioRender.com/d0hxq2g .

Article Snippet: The integration of all the Visium slide datasets was performed following the recommendation provided by 10× Genomics [ https://www.10xgenomics.com/analysis-guides/correcting-batch-effects-in-visium-data ].

Techniques: Comparison, Expressing, Quantitative Proteomics

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: Spatial Proximity Sequencing Maps Developmental Dynamics in the Germinal Center

doi: 10.1101/2025.10.27.684659

Figure Lengend Snippet: (A) Overview of the Sprox-seq experimental workflow. Human tonsil tissue sections are fixed onto Visium slides and incubated with AOC pairs. Upon spatial proximity, the probes are ligated and treated with USER enzyme to expose a poly (A) tail, enabling co-capture of PLA products and mRNAs via spatial barcodes. (B) Computational pipeline for RNA and PLA data analysis. RNA expression is analyzed using standard spatial transcriptomics workflows. PLA UMI counts are processed to infer protein expression and identify protein complexes using Fisher’s Exact Test. Protein fractional overlap analysis quantifies interaction strength. Cluster-specific interaction network was constructed using protein complexes identified within each cluster. Integration of RNA data enables functional annotation of spatially enriched protein interactions. Part of panel A and B were created using Biorender. (C) Histological reference and imaging. H&E staining of an adjacent tissue section (left) and brightfield image of the Visium slide used for capture (right). (D) Total mRNA UMI counts and number of detected genes per spot for sample A1. (E) Total PLA UMI counts and number of detected PLA pairs per spot for sample A1. (F) Total protein UMI counts and number of detected proteins per spot for sample A1. (G) UMAP plot of the clustering analysis based on RNA data (left), spatial distribution of the clusters (middle) and cluster annotation based on H&E morphology and RNA expression (right). (H) UMAP plot of the clustering analysis based on normalized protein data (left) and spatial distribution of the clusters (right). Protein data was derived from PLA UMI counts. (I) UMAP plot of the clustering analysis based on normalized PLA data (left) and spatial distribution of the clusters (right). See also - and Tables S1-S4.

Article Snippet: Briefly, 10-μm-thick human tonsil tissue sections were mounted onto spatial-transcriptomics (10x Visium) slides and fixed with 1% paraformaldehyde (PFA).

Techniques: Incubation, RNA Expression, Expressing, Construct, Functional Assay, Imaging, Staining, Derivative Assay

Journal: bioRxiv

Article Title: Spatial Proximity Sequencing Maps Developmental Dynamics in the Germinal Center

doi: 10.1101/2025.10.27.684659

Figure Lengend Snippet: (A) Histological reference and imaging for sample B1. H&E staining of an adjacent tissue section (left) and the brightfield image of the Visium slide used for spatial capture (right). (B– D) Quality measurement data for sample B1. Total mRNA UMI counts and number of detected genes per spot (B). Total PLA UMI counts and number of detected PLA pairs per spot (C). Total protein UMI counts and number of detected proteins per spot (D). € Histological reference and imaging for sample D1. H&E staining of an adjacent tissue section (left) and the brightfield image of the Visium slide used for spatial capture (right). (F– H) Quality measurement data for sample D1. Total mRNA UMI counts and number of detected genes per spot (F). Total PLA UMI counts and number of detected PLA pairs per spot (G). Total protein UMI counts and number of detected proteins (derived from PLA data) per spot (H).

Article Snippet: Briefly, 10-μm-thick human tonsil tissue sections were mounted onto spatial-transcriptomics (10x Visium) slides and fixed with 1% paraformaldehyde (PFA).

Techniques: Imaging, Staining, Derivative Assay