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10X Genomics quantitative whole transcriptome rna sequencing
Quantitative Whole Transcriptome Rna Sequencing, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/visium+spatial+sequencing+data/visium/pmc10245699-26-16-20
Average 86 stars, based on 1 article reviews

quantitative whole transcriptome rna sequencing - by Bioz Stars, 2026-11

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Spatial Transcriptomics:

Article Title: GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.
Article Snippet: .. The typical evolution of spatial transcriptomics technologies is characterized by continuous improvement in spatial resolution, ranging from 10x Genomics Visium (55 μm spot resolution, AR TIC LE IN PR ES S capturing approximately 1-10 cells per spot) [5], to Slide-seq (~10 μm, near-cellular resolution) [6], and finally to Stereo-seq (0.22 μm, subcellular resolution) [7]. ..

Article Title: Understanding nerve–tumor interactions: From basic biology to therapeutic innovation
Article Snippet: .. 2D spatial transcriptomics, such as Visium (10x Genomics) and MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization), enable the precise mapping of transcriptomes to their corresponding locations in a 2D plane., , Compared with high-plex protein labeling, 2D spatial transcriptomics offers high-resolution spatial gene expression data and facilitates the discovery of novel spatially regulated genes within the TME. ..

Article Title: GLP1-E2 therapy delays autoimmune diabetes in late-stage prediabetic NOD mice and potentiates low-dose anti-CD3 therapy for enhanced disease protection.
Article Snippet: .. Analytically, we employed 10X Genomics Visium for spatial transcriptomics, which lacks true single-cell resolution compared with newer platforms such as Visium HD or Xenium. ..

Article Title: Analysis of unmapped RNA-seq data from cancer spatial transcriptome toward characterizing cancer microbiome.
Article Snippet: .. Recently, spatial transcriptomics, especially Visium (10X genomics, USA), has been garnering significant attention in cancer research. ..

Fluorescence:

In Situ Hybridization:

Labeling:

Gene Expression:

other:

Article Title: Integrative single-cell and spatial multi-Omics analyses identify NETs-driven colon cancer subtypes with distinct metabolic features and prognostic implications.
Article Snippet: ACC, Adrenocortical Carcinoma; ARRDC1, Arrestin Domain Containing 1; AUC, Area Under the Curve; AUCell, Area Under the Curve Cell-level scoring; BH, Benjamini–Hochberg; BLCA, Bladder Urothelial Carcinoma; AR TIC LE IN PR ES S BRCA, Breast Cancer; CESC, Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma; CHOL, Cholangiocarcinoma; CI, Confidence Interval; CNV, Copy Number Variation; COAD, Colon Adenocarcinoma; CRC, Colorectal Cancer; CSS, Cancer-Specific Survival; CXCR4, C-X-C Chemokine Receptor Type 4; DEGs, Differentially Expressed Genes; DUSP5, Dual-Specificity Phosphatase 5; ELISA, Enzyme-Linked Immunosorbent Assay; eQTL, Expression Quantitative Trait Locus; ESCA, Esophageal Carcinoma; FDR, False Discovery Rate; GDSC, Genomics of Drug Sensitivity in Cancer; GEO, Gene Expression Omnibus; GO, Gene Ontology; GSEA, Gene Set Enrichment Analysis; GWAS, Genome-Wide Association Study; HNSC, Head and Neck Squamous Cell Carcinoma; HPA, Human Protein Atlas; HR, Hazard Ratio; IC50, Half-Maximal Inhibitory Concentration; ICIs, Immune Checkpoint Inhibitors; KEGG, Kyoto Encyclopedia of Genes and Genomes; KICH, Kidney Chromophobe; KIRC, Kidney Renal Clear Cell Carcinoma; KM, Kaplan–Meier; AR TIC LE IN PR ES S LASSO, Least Absolute Shrinkage and Selection Operator; LGG, Low-Grade Glioma; LUAD, Lung Adenocarcinoma; MAPK, Mitogen-Activated Protein Kinase; MD, Molecular Dynamics; MESO, Mesothelioma; MIA, Multimodal Intersection Analysis; MIF, Macrophage Migration Inhibitory Factor; NETs, Neutrophil Extracellular Traps; OS, Overall Survival; PCA, Principal Component Analysis; READ, Rectal Adenocarcinoma; RMSD, Root Mean Square Deviation; ROC, Receiver Operating Characteristic; ROS, Reactive Oxygen Species; SARC, Sarcoma; scRNA-seq, Single-Cell RNA Sequencing; SLC2A3, Solute Carrier Family 2 Member 3; SNV, Single Nucleotide Variant; ssGSEA, Single-Sample Gene Set Enrichment Analysis; STAD, Stomach Adenocarcinoma; TAMs, Tumor-Associated Macrophages; TANs, Tumor-Associated Neutrophils; TCGA, The Cancer Genome Atlas; THCA, Thyroid Carcinoma; THYM, Thymoma; TIDE, Tumor Immune Dysfunction and Exclusion; TME, Tumor Microenvironment; TPM, Transcripts Per Million; AR TIC LE IN PR ES S Tregs, Regulatory T Cells; TUBB2A, Tubulin Beta Class 2A; UCEC, Uterine Corpus Endometrial Carcinoma; UMI, Unique Molecular Identifier; UVM, Uveal Melanoma; Visium, 10x Genomics Visium

Single Cell:

Expressing:

Article Title: SpatialBench : Comparative cross-platform benchmarking of high-resolution spatial transcriptomics using matched mouse lymphoid tissue
Article Snippet: .. The original Visium (10x Genomics) became the most widely adopted sST platform ( ) , offering transcriptome-wide coverage while aggregating expression from multiple cells per 55 μm resolution that aggregates expression from multiple cells per spot ( , ) . ..

Image Search Results

A HE-stained image of the Visium tissue section and adjacent Xenium section, alongside their alignment. One repeat is performed for the publicly available data. B Pearson correlation between FineST and iStar for all input genes, calculated after aggregating super-resolution data to spot resolution. C Spatial expression plots for OPRPN from left to right: Visium, Xenium, FineST and iStar. FineST enhances the signal relative to Visium and yields results more comparable to Xenium. D Pearson correlation for OPRPN in FineST, corresponding to panel ( C ). Each dot represents a Visium spot ( n = 4992) or overlapping Xenium pseudo-spot ( n = 3958). E Ground-truth cell type annotations at spot (Visium) and single-cell (Xenium) resolution, as reported previously . F FineST's predicted cell types at single-nucleus and sub-spot levels. G FineST accurately identifies the DCIS 2 cell type in a triple-positive receptor ROI. H Pearson correlation for cell type abundance and mean gene expression across each cell type, comparing FineST and iStar. Each dot represents a cell type (Left, n = 19) or gene (Right, n = 65), lines connect matched pairs. Diamond indicates the mean. Statistical significance was assessed by a paired two-sided t -test. I Three marked regions (ROI 1, ROI 2 and ROI 3) dominated by DCIS 1, DCIS 2 and Invasive tumor cells. J Cell type deconvolution from FineST, compared with Xenium ground truth, demonstrates FineST's results visually match the ground truth and outperform Visium’s lower resolution (see Supplementary Fig. ). Alongside, single-cell resolved CCC patterns identified using SparseAEH (cluster number 2) and pathway enrichment analysis for Pattern 0 correspond to interesting cell distributions. K Venn plot of significant LR pairs (FDR < 0.05) interacting in >25% (ROI 1, 5589 cells) or >20% (ROI 2, 3330 cells; ROI 3, 5853 cells) of cells. In total, 103, 146 and 159 pairs were selected for spatial clustering analysis in the three ROIs, respectively. L Comparative analysis of region- and cell-specific LR pairs reveals two unique pairs specific to DCIS 2 and Invasive tumor cells. For panels ( B ), ( H – J ), source data are provided in the Source Data file. Scare bars, 1 mm.

Journal: Nature Communications

Article Title: FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis

doi: 10.1038/s41467-026-70528-7

Figure Lengend Snippet: A HE-stained image of the Visium tissue section and adjacent Xenium section, alongside their alignment. One repeat is performed for the publicly available data. B Pearson correlation between FineST and iStar for all input genes, calculated after aggregating super-resolution data to spot resolution. C Spatial expression plots for OPRPN from left to right: Visium, Xenium, FineST and iStar. FineST enhances the signal relative to Visium and yields results more comparable to Xenium. D Pearson correlation for OPRPN in FineST, corresponding to panel ( C ). Each dot represents a Visium spot ( n = 4992) or overlapping Xenium pseudo-spot ( n = 3958). E Ground-truth cell type annotations at spot (Visium) and single-cell (Xenium) resolution, as reported previously . F FineST's predicted cell types at single-nucleus and sub-spot levels. G FineST accurately identifies the DCIS 2 cell type in a triple-positive receptor ROI. H Pearson correlation for cell type abundance and mean gene expression across each cell type, comparing FineST and iStar. Each dot represents a cell type (Left, n = 19) or gene (Right, n = 65), lines connect matched pairs. Diamond indicates the mean. Statistical significance was assessed by a paired two-sided t -test. I Three marked regions (ROI 1, ROI 2 and ROI 3) dominated by DCIS 1, DCIS 2 and Invasive tumor cells. J Cell type deconvolution from FineST, compared with Xenium ground truth, demonstrates FineST's results visually match the ground truth and outperform Visium’s lower resolution (see Supplementary Fig. ). Alongside, single-cell resolved CCC patterns identified using SparseAEH (cluster number 2) and pathway enrichment analysis for Pattern 0 correspond to interesting cell distributions. K Venn plot of significant LR pairs (FDR < 0.05) interacting in >25% (ROI 1, 5589 cells) or >20% (ROI 2, 3330 cells; ROI 3, 5853 cells) of cells. In total, 103, 146 and 159 pairs were selected for spatial clustering analysis in the three ROIs, respectively. L Comparative analysis of region- and cell-specific LR pairs reveals two unique pairs specific to DCIS 2 and Invasive tumor cells. For panels ( B ), ( H – J ), source data are provided in the Source Data file. Scare bars, 1 mm.

Article Snippet: The raw and processed Visium spatial sequencing data of human hepatocellular carcinoma (HCC) tissues were downloaded from Mendeley Data under accession number skrx2fz79n [ https://data.mendeley.com/datasets/skrx2fz79n/1 ], while the cell type annotations and high-resolution HE-stained images were provided by the original corresponding author.

Techniques: Staining, Expressing, Single Cell, Gene Expression

$A , B Spatial plot of seven cell types estimated by cell2location (spot resolution) and FineST (single-cell resolution). A' , B' Zoon-in views of A and B . B'' Zoon-in view of the region marked in B' , where FineST increases resolution from 8 spots to 869 single cells. C Cell type composition from reference scRNA-seq and deconvolution at Visium spot and FineST single-cell levels. D Distribution and PCC of cell type proportions ( n = 7) for Visium spot (PCC = 0.24) and FineST single-cell (PCC = 0.64) resolutions vs reference scRNA-seq. E Pathologists identified 36 spots (of 1331) co-localized with tertiary lymphoid structure (TLS), important for antigen presentation and T cell activation. FineST's single-cell views validate TLS by T and B cell co-localization (see B'' ). F Three example spatially co-expressed LR pairs detected at FineST's single-cell resolution. G Visualization of PVR - TIGIT interaction among local single cells ( z -score FDR < 0.05). H Clustering of 633 significant LR pairs, from single-cell resolution, within selected ROI into three spatial patterns using SpatialDE. I Spatial co-localization of tumor and Treg cells, estimated by cell2location . J Spatially co-expressed LR pairs detected at FineST's single-cell resolution. Scatter plot of global Moran’s R and one-sided z -score p -values (orange: significant, FDR < 0.05, Benjamini-Hochberg correction), with examples highlighted. K Communication strength of CD70 - CD27 at single-cell resolution (color: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1-{p}_{{{{\rm{local}}}}{z}_{p}}$$\end{document} 1 − p local z p ; mean strength: 0.45, interacting cells: 5961, occupancy: 49.2%). L Detection of local CCC patterns by clustering significant LR pairs. Top: original spot resolution (332 pairs, SpatialDE); Middle: sub-spot resolution (957 pairs, SparseAEH) and Bottom: single-nucleus resolution (931 pairs, SparseAEH). M Dot plots of enriched pathways in Pattern 0 and Pattern 1 (from L , Bottom), 2-by-2 contingency tables for MHC-I and WNT. Statistical significance was assessed using a one-sided Fisher’s exact test; dot size indicates p -value. N Sankey plot of selected L-R-TF-TG communication pathways. For panels (D , K ), box plots show median (center), IQR (box), whiskers at 1.5 × IQR, and points for seven cell types; violin plots show density, median (white line), and IQR (thick bar). For panels ( A ), ( J , L ), source data are provided in the Source Data file.$

Journal: Nature Communications

Article Title: FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis

doi: 10.1038/s41467-026-70528-7

Figure Lengend Snippet: A , B Spatial plot of seven cell types estimated by cell2location (spot resolution) and FineST (single-cell resolution). A' , B' Zoon-in views of A and B . B'' Zoon-in view of the region marked in B' , where FineST increases resolution from 8 spots to 869 single cells. C Cell type composition from reference scRNA-seq and deconvolution at Visium spot and FineST single-cell levels. D Distribution and PCC of cell type proportions ( n = 7) for Visium spot (PCC = 0.24) and FineST single-cell (PCC = 0.64) resolutions vs reference scRNA-seq. E Pathologists identified 36 spots (of 1331) co-localized with tertiary lymphoid structure (TLS), important for antigen presentation and T cell activation. FineST's single-cell views validate TLS by T and B cell co-localization (see B'' ). F Three example spatially co-expressed LR pairs detected at FineST's single-cell resolution. G Visualization of PVR - TIGIT interaction among local single cells ( z -score FDR < 0.05). H Clustering of 633 significant LR pairs, from single-cell resolution, within selected ROI into three spatial patterns using SpatialDE. I Spatial co-localization of tumor and Treg cells, estimated by cell2location . J Spatially co-expressed LR pairs detected at FineST's single-cell resolution. Scatter plot of global Moran’s R and one-sided z -score p -values (orange: significant, FDR < 0.05, Benjamini-Hochberg correction), with examples highlighted. K Communication strength of CD70 - CD27 at single-cell resolution (color: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1-{p}_{{{{\rm{local}}}}{z}_{p}}$$\end{document} 1 − p local z p ; mean strength: 0.45, interacting cells: 5961, occupancy: 49.2%). L Detection of local CCC patterns by clustering significant LR pairs. Top: original spot resolution (332 pairs, SpatialDE); Middle: sub-spot resolution (957 pairs, SparseAEH) and Bottom: single-nucleus resolution (931 pairs, SparseAEH). M Dot plots of enriched pathways in Pattern 0 and Pattern 1 (from L , Bottom), 2-by-2 contingency tables for MHC-I and WNT. Statistical significance was assessed using a one-sided Fisher’s exact test; dot size indicates p -value. N Sankey plot of selected L-R-TF-TG communication pathways. For panels (D , K ), box plots show median (center), IQR (box), whiskers at 1.5 × IQR, and points for seven cell types; violin plots show density, median (white line), and IQR (thick bar). For panels ( A ), ( J , L ), source data are provided in the Source Data file.

Techniques: Single Cell, Immunopeptidomics, Activation Assay

A , B HE staining and cell type annotation of spatial transcriptomic spots in tumor tissues from an ICB non-responder (P1_T) and responder (P7_T) from the original study . In the non-responder (P1_T), a tumor immune barrier (TIB) structure is formed by SPP1 + macrophages and cancer-associated fibroblasts (CAFs). C , D Spatial signature score of SPP1 + macrophages and CAFs, spatial gene expression of CCR1 , and Pearson correlation between SPP1 + macrophage score and CCR1 expression across all spots (Visium vs FineST). The red line represents the fitted linear regression, and the shaded area corresponds to the 95% confidence interval. Statistical significance was assessed using a two-sided Pearson correlation test. E Scatterplot of global Moran’s R and one-sided z -score p -values for spatially co-expressed LR pairs detected using FineST-enhanced gene expression at spot-level resolution in ROI. Significant pairs (orange) were identified using FDR < 0.05 (Benjamini-Hochberg correction). The CD274-PDCD1 interaction was detected in P1_T only. F Spatial gene expression of ligand CD274 and receptor PDCD1 across all spots (Visium vs FineST) for P1_T (non-responder) and P7_T (responder), respectively. For panels ( A , B , E ), source data are provided in the Source Data file. Part of the panels ( A , B ) is created in BioRender. Huang, Y. (2026) https://BioRender.com/fv0byvk .

Journal: Nature Communications

Article Title: FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis

doi: 10.1038/s41467-026-70528-7

Figure Lengend Snippet: A , B HE staining and cell type annotation of spatial transcriptomic spots in tumor tissues from an ICB non-responder (P1_T) and responder (P7_T) from the original study . In the non-responder (P1_T), a tumor immune barrier (TIB) structure is formed by SPP1 + macrophages and cancer-associated fibroblasts (CAFs). C , D Spatial signature score of SPP1 + macrophages and CAFs, spatial gene expression of CCR1 , and Pearson correlation between SPP1 + macrophage score and CCR1 expression across all spots (Visium vs FineST). The red line represents the fitted linear regression, and the shaded area corresponds to the 95% confidence interval. Statistical significance was assessed using a two-sided Pearson correlation test. E Scatterplot of global Moran’s R and one-sided z -score p -values for spatially co-expressed LR pairs detected using FineST-enhanced gene expression at spot-level resolution in ROI. Significant pairs (orange) were identified using FDR < 0.05 (Benjamini-Hochberg correction). The CD274-PDCD1 interaction was detected in P1_T only. F Spatial gene expression of ligand CD274 and receptor PDCD1 across all spots (Visium vs FineST) for P1_T (non-responder) and P7_T (responder), respectively. For panels ( A , B , E ), source data are provided in the Source Data file. Part of the panels ( A , B ) is created in BioRender. Huang, Y. (2026) https://BioRender.com/fv0byvk .

Techniques: Staining, Gene Expression, Expressing

Spatial Transcriptomics in horizontally sectioned E14.5 mouse diaphragm identifies distinct tissues and muscle domains (A) Schematic representation of embryonic mouse diaphragm in which relevant anatomical regions are indicated. NMJ: Neuromuscular junction, MTJ: Myotendinous junction. (B) SpatialFeaturePlots demonstrating expression levels and distributions of representative genes for muscle, NMJ, MTJ and tendon. (C) SpatialDimPlot demonstrating the distribution of all the identified clusters within the diaphragm tissue. (D) Spatial distribution of representative Seurat clusters differentiating genetically specific domains in developing mouse diaphragm. (E) Uniform Manifold Approximation and Projection (UMAP) diagram of identified clusters of spatial RNA sequencing with muscle and endothelial clusters at the left and tendon and erythrocyte clusters at right. (F) FeaturePlots showing expression of muscle ( Ttn , Myh3 , Myh8 ), crural diaphragm ( Crlf1 ), NMJ ( Chrna1 , Chrng , Musk, Etv5 ), MTJ ( Col22a1 , Ankrd1 , Rxrg , Csrp3 ) and tendon ( Col12a1 , Antxr1, Tnmd , Tnc ) markers displayed by UMAP.

Journal: iScience

Article Title: Spatial transcriptomics in embryonic mouse diaphragm muscle reveals regional gradients and subdomains of developmental gene expression

doi: 10.1016/j.isci.2024.110018

Figure Lengend Snippet: Spatial Transcriptomics in horizontally sectioned E14.5 mouse diaphragm identifies distinct tissues and muscle domains (A) Schematic representation of embryonic mouse diaphragm in which relevant anatomical regions are indicated. NMJ: Neuromuscular junction, MTJ: Myotendinous junction. (B) SpatialFeaturePlots demonstrating expression levels and distributions of representative genes for muscle, NMJ, MTJ and tendon. (C) SpatialDimPlot demonstrating the distribution of all the identified clusters within the diaphragm tissue. (D) Spatial distribution of representative Seurat clusters differentiating genetically specific domains in developing mouse diaphragm. (E) Uniform Manifold Approximation and Projection (UMAP) diagram of identified clusters of spatial RNA sequencing with muscle and endothelial clusters at the left and tendon and erythrocyte clusters at right. (F) FeaturePlots showing expression of muscle ( Ttn , Myh3 , Myh8 ), crural diaphragm ( Crlf1 ), NMJ ( Chrna1 , Chrng , Musk, Etv5 ), MTJ ( Col22a1 , Ankrd1 , Rxrg , Csrp3 ) and tendon ( Col12a1 , Antxr1, Tnmd , Tnc ) markers displayed by UMAP.

Article Snippet: Visium Spatial Transcriptomics sequencing data were aligned using the default SpaceRanger (2.0.1) pipeline for FFPE slides in a Singularity Container running Ubuntu 22.04 on a high-performance cluster (Medical University of Innsbruck).

Techniques: Expressing, RNA Sequencing

Spatial Transcriptomics reveals distinct myogenic processes in the muscle center and periphery in E14.5 mouse diaphragm (A) SpatialFeaturePlots demonstrating expression levels and distributions of myogenic differentiation markers. (B) VlnPlots of expression levels of genes in clusters identified as muscle center, default muscle, and muscle periphery show an overall increase or decrease in expression of genes involved in muscle development from the center to the periphery. Y axis indicates expression levels. (C) Cnetplots showing GO terms in biological processes (red nodes) and their associated genes (blue nodes) for the upregulated genes in clusters annotated as muscle center compared to muscle periphery (top) or muscle periphery compared to muscle center (bottom). (D) Dotplots of expression of genes involved in myogenesis displaying a declining (right) or increasing (left) gradient in clusters from the muscle center, over default muscle (muscle middle), to the muscle periphery.

Journal: iScience

Article Title: Spatial transcriptomics in embryonic mouse diaphragm muscle reveals regional gradients and subdomains of developmental gene expression

doi: 10.1016/j.isci.2024.110018

Figure Lengend Snippet: Spatial Transcriptomics reveals distinct myogenic processes in the muscle center and periphery in E14.5 mouse diaphragm (A) SpatialFeaturePlots demonstrating expression levels and distributions of myogenic differentiation markers. (B) VlnPlots of expression levels of genes in clusters identified as muscle center, default muscle, and muscle periphery show an overall increase or decrease in expression of genes involved in muscle development from the center to the periphery. Y axis indicates expression levels. (C) Cnetplots showing GO terms in biological processes (red nodes) and their associated genes (blue nodes) for the upregulated genes in clusters annotated as muscle center compared to muscle periphery (top) or muscle periphery compared to muscle center (bottom). (D) Dotplots of expression of genes involved in myogenesis displaying a declining (right) or increasing (left) gradient in clusters from the muscle center, over default muscle (muscle middle), to the muscle periphery.

Techniques: Expressing

Spatial Transcriptomics in horizontally sectioned E18.5 mouse diaphragm identifies specific functional muscle domains and fiber types (A) SpatialFeaturePlots demonstrating expression levels and distributions of NMJ genes ( Chrna1 , Musk , Etv5 , Ache , Chrng, Chrne ), neonatal ( Myh8 ) and embryonic ( Myh3 ) myosin heavy chains, developmental troponin Tnnt2 , and ventral diaphragm markers ( Myog , Flnc , Csrp3 ). (B) Spatial distribution of Seurat clusters of distinct muscle and tendon domains. (C) UMAP representation of identified clusters of spatial RNA sequencing indicates spatially and functionally divergent differentiation of diaphragm muscle. (D) FeaturePlots demonstrating expression of muscle ( Ttn , Myh3 , Myh8 ), type I muscle ( Myh7, Myl2, Myl3 ), type IIb muscle ( Myh4 , Pvalb , Mybpc2 ), NMJ ( Chrna1 , Musk , Etv5 ), MTJ ( Col22a1 , Ankrd1 , Uchl1 ) and tendon ( Tnmd , Col11a1 , Scx ) markers displayed by UMAP.

Journal: iScience

Article Title: Spatial transcriptomics in embryonic mouse diaphragm muscle reveals regional gradients and subdomains of developmental gene expression

doi: 10.1016/j.isci.2024.110018

Figure Lengend Snippet: Spatial Transcriptomics in horizontally sectioned E18.5 mouse diaphragm identifies specific functional muscle domains and fiber types (A) SpatialFeaturePlots demonstrating expression levels and distributions of NMJ genes ( Chrna1 , Musk , Etv5 , Ache , Chrng, Chrne ), neonatal ( Myh8 ) and embryonic ( Myh3 ) myosin heavy chains, developmental troponin Tnnt2 , and ventral diaphragm markers ( Myog , Flnc , Csrp3 ). (B) Spatial distribution of Seurat clusters of distinct muscle and tendon domains. (C) UMAP representation of identified clusters of spatial RNA sequencing indicates spatially and functionally divergent differentiation of diaphragm muscle. (D) FeaturePlots demonstrating expression of muscle ( Ttn , Myh3 , Myh8 ), type I muscle ( Myh7, Myl2, Myl3 ), type IIb muscle ( Myh4 , Pvalb , Mybpc2 ), NMJ ( Chrna1 , Musk , Etv5 ), MTJ ( Col22a1 , Ankrd1 , Uchl1 ) and tendon ( Tnmd , Col11a1 , Scx ) markers displayed by UMAP.

Techniques: Functional Assay, Expressing, RNA Sequencing

Spatial transcriptomics reveals aberrant regulation of myogenic genes in Ca V 1.1 −/− mice (A) FeaturePlots showing expression of Ttn (muscle), Chrna1 and Musk (NMJ) displayed by UMAP in control and Ca V 1.1 −/− integrated dataset at E14.5 (left) and E18.5 (right). (B) Violin plots showing expression of representative genes differentially expressed in control and Ca V 1.1 −/− samples at E14.5 and E18.5. Y axis indicates expression level. (C) FeaturePlots of module scores of muscle differentiation markers displayed by UMAP in E18.5 control and Ca V 1.1 −/− integrated dataset show increased expression of early markers (top) and a decreased expression of late markers (bottom) in Ca V 1.1 −/− muscles. (D) Venn diagrams of top 200 DEGs genes and GO terms for these genes between E14.5 control and E18.5 control and between E18.5 Ca V 1.1 −/− and E18.5 control muscles indicate more shared genes and GO terms for upregulated genes in E18.5 Ca V 1.1 −/− with E14.5 control muscle and for downregulated genes in E18.5 Ca V 1.1 −/− with E18.5 control muscle. (E) FeaturePlots showing expression of Klf5 and Tead4 displayed by UMAP in control and Ca V 1.1 −/− integrated dataset at E14.5 (left) and E18.5 (right). (F) Violin plots of Klf5 and Tead4 expression in muscle clusters of E14.5 and E18.5 control and Ca V 1.1 −/− spatial datasets. Y axis indicates expression level.

Journal: iScience

Article Title: Spatial transcriptomics in embryonic mouse diaphragm muscle reveals regional gradients and subdomains of developmental gene expression

doi: 10.1016/j.isci.2024.110018

Figure Lengend Snippet: Spatial transcriptomics reveals aberrant regulation of myogenic genes in Ca V 1.1 −/− mice (A) FeaturePlots showing expression of Ttn (muscle), Chrna1 and Musk (NMJ) displayed by UMAP in control and Ca V 1.1 −/− integrated dataset at E14.5 (left) and E18.5 (right). (B) Violin plots showing expression of representative genes differentially expressed in control and Ca V 1.1 −/− samples at E14.5 and E18.5. Y axis indicates expression level. (C) FeaturePlots of module scores of muscle differentiation markers displayed by UMAP in E18.5 control and Ca V 1.1 −/− integrated dataset show increased expression of early markers (top) and a decreased expression of late markers (bottom) in Ca V 1.1 −/− muscles. (D) Venn diagrams of top 200 DEGs genes and GO terms for these genes between E14.5 control and E18.5 control and between E18.5 Ca V 1.1 −/− and E18.5 control muscles indicate more shared genes and GO terms for upregulated genes in E18.5 Ca V 1.1 −/− with E14.5 control muscle and for downregulated genes in E18.5 Ca V 1.1 −/− with E18.5 control muscle. (E) FeaturePlots showing expression of Klf5 and Tead4 displayed by UMAP in control and Ca V 1.1 −/− integrated dataset at E14.5 (left) and E18.5 (right). (F) Violin plots of Klf5 and Tead4 expression in muscle clusters of E14.5 and E18.5 control and Ca V 1.1 −/− spatial datasets. Y axis indicates expression level.

Techniques: Expressing, Control, Muscles

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quantitative whole transcriptome rna sequencing (10X Genomics)

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Spatial Transcriptomics:

Fluorescence:

In Situ Hybridization:

Labeling:

Gene Expression:

other:

Single Cell:

Expressing:

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