Journal: bioRxiv

Article Title: St2cell: Reconstruction of in situ single-cell spatial transcriptomics by integrating high-resolution histological image

doi: 10.1101/2022.10.13.512059

Figure Lengend Snippet: (A) Co-registered HR histological images of adult mouse brain and its manually annotated tissue regions. (B) Clusters of St2cell-reconstructed in situ single-cell gene expression profiles. (C) The partial enlarged figure of areas including the CTX, WM, HPC. Left panel: original FFPE H&E stained histological image. Middle panel: The size and position of the spots, with different colors representing different categories after clustering of the measured spots’ transcriptomics. Right panel: The locations and types of cells inferred by St2cell, with different colors representing different categories after clustering of the cellular-level gene expression profiles obtained by our proposed method. (D) Spatial gene expression patterns of regional highly expressed markers. (E) Spatial gene expression patterns of six markers of layer-specific cortex pyramidal cells. (F) Spatial distributions of layer-specific expressed genes at different locations.

Article Snippet: A Visium FFPE human breast tissue from BioIVT Asterand Human Tissue Specimens annotated as “ductal carcinoma in situ, invasive carcinoma” was used here (see also Data availability).

Techniques: In Situ, Gene Expression, Staining

Journal: Nature

Article Title: Cells of the adult human heart

doi: 10.1038/s41586-020-2797-4

Figure Lengend Snippet: a – d , Spatial expression (log 2 FC) of CDH5 (pan-EC marker), SEMA3G and GJA5 (arterial EC markers) ( a ), ACKR1 and PLVAP (venous EC markers) ( b ), MYH11 and ACTA2 (pan-SMC markers) ( c ), and JAG1 and NOTCH2 ( d ) on publicly available 10X Visium section of human left ventricle. JAG1 and NOTCH2 are the predicted interaction partners for arterial ECs and SMCs, respectively.

Article Snippet: Extended Data Fig. 5 Vascular markers visualized on 10X Genomics Visium data. a – d , Spatial expression (log 2 FC) of CDH5 (pan-EC marker), SEMA3G and GJA5 (arterial EC markers) ( a ), ACKR1 and PLVAP (venous EC markers) ( b ), MYH11 and ACTA2 (pan-SMC markers) ( c ), and JAG1 and NOTCH2 ( d ) on publicly available 10X Visium section of human left ventricle.

Techniques: Expressing, Marker

Journal: Nature

Article Title: Cells of the adult human heart

doi: 10.1038/s41586-020-2797-4

Figure Lengend Snippet: a , Visualization of transcriptional signatures from published studies. The score values represent the likelihood of the external transcriptional signature to be present when comparing it against the transcriptional background of a cardiac immune population. Bajpai_2018 = CCR2 - MERTK + tissue-resident macrophages from ref. . Dick_2019 = self-renewing tissue macrophages from ref. . Bian_2020 = yolk sac-derived macrophages from ref. . The complete signature can be found in Supplementary Table . b , Expression (log 2 FC) of LYVE1 , FOLR2 and TIMD4 characteristic of the self-renewing tissue-resident murine macrophages previously described , as well as MERTK as previously described and the TREM2 expression associated to lipid-associated macrophages (LAM) previously described . Complete signatures can be found in Supplementary Table . c , Scaled expression (log 2 FC) of genes differentiating DOCK4 + MP1 from DOCK4 + MP2: IL4R , ITGAM , STAT3 , DOCK1 , HIF1A and RASA2 . d , Predicted cell–cell interactions calculated for 69,295 cardiomyocytes, fibroblasts and myeloid cells from 14 donors ( n = 14) and enriched for ‘extracellular matrix organization’. Mean of combined gene expression of interacting pairs (log 2 FC). Data are available in Supplementary Table . e , Spatial mapping of the CD74 – MIF interaction between LYVE1 + MP and FB4 on a publicly available 10X Genomics Visium dataset for left ventricular myocardium. We identified four spots where we observe co-expression of FN1 , LYVE1 , CD74 and MIF , as predicted from the cell–cell interactions. The bar represents the log 2 FC. f , Confusion matrix for the logistic regression model trained on cardiac immune cells. This model reached an accuracy score of 0.6862, showing a stronger accuracy with lymphoid cells, compared with the myeloid ones.

Article Snippet: Extended Data Fig. 5 Vascular markers visualized on 10X Genomics Visium data. a – d , Spatial expression (log 2 FC) of CDH5 (pan-EC marker), SEMA3G and GJA5 (arterial EC markers) ( a ), ACKR1 and PLVAP (venous EC markers) ( b ), MYH11 and ACTA2 (pan-SMC markers) ( c ), and JAG1 and NOTCH2 ( d ) on publicly available 10X Visium section of human left ventricle.

Techniques: Derivative Assay, Expressing

Journal: STAR Protocols

Article Title: Protocol to evaluate mouse brain spatial cell type-resolved transcriptomic discoveries using 10× Visium spatial transcriptomics and FLEX scRNA-seq

doi: 10.1016/j.xpro.2025.104277

Figure Lengend Snippet: Web summary metrics generated from 10× Genomics SpaceRanger Web summaries generated from 10× Genomics Space Ranger pipeline after receiving raw data for P0 mouse tissue in Visium spatial transcriptomics step. The summary page will provide detailed information regarding data quality including “Fraction Reads in Spots Under Tissue”. To determine localization of diffused RNA and confirm that RNA is “leaking” from tissue section, rerun Space Ranger on all spots in the Visium capture area. If Fraction Reads in Spots Under Tissue is below 50%, optimization is required. (A) Unsuccessful reads in spots are most likely due to over permeabilization when releasing RNA. (B) Successful processing of P0 tissue with 10× Visium.

Article Snippet: Permeabilization time is determined with Visium Spatial Tissue Optimization (protocol CG000238) which needs to be performed before starting Spatial Transcriptomics Tissue Processing.

Techniques: Generated