cell transcriptomic sequencing dataset (10X Genomics)

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10X Genomics cell transcriptomic sequencing dataset

<t>Transcriptomic</t> and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Cell Transcriptomic Sequencing Dataset, 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/and+transcriptomics/cell+copy+genomic+number+profiling+single+technologies/pmc13197639-319-1-9
Average 86 stars, based on 1 article reviews

cell transcriptomic sequencing dataset - by Bioz Stars, 2026-09

86/100 stars

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1) Product Images from "Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis"

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

Journal: iScience

doi: 10.1016/j.isci.2026.115982

Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Figure Legend Snippet: Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Techniques Used: Activity Assay, Protein-Protein interactions, Labeling

$Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.$
Figure Legend Snippet: Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.

Techniques Used: Single Cell, RNA Sequencing, Expressing, Marker

Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Figure Legend Snippet: Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Techniques Used: Marker, Expressing

Single-cell Transcriptomics:

Article Title: Sex differences in adenosine deaminase activity associate with disparities in SARS-CoV-2 innate immunity
Article Snippet: .. Cell isolates were processed for 10x single cell transcriptomics by 10x Chromium Single Cell 5’ library for barcoding and Chromium Single Cell 3’ v2, v3, and v5 reagent kits (10x Genomics), with a target of 300-14,000 single cells per reaction. .. Sequencing was performed on either DIPSEQ, BGISEQ, or Illumina platforms.

Single Cell:

Spatial Transcriptomics:

Article Title: Tissue-specific fibroblast lipid cues impose the rate of epithelial cancer invasion.
Article Snippet: .. To explore this further, we performed spatial transcriptomics on two cSCC tumours (Visium 10x Genomics), identifying regions of epithelial tumours and fibroblasts (Supplementary Table 14), and testing whether the tissue-specific pathways that drive oral and lung SCC (Fig. 8a–h) are absent in cSCC. ..

Article Title: The U-method: Leveraging expression probability for robust biological marker detection
Article Snippet: The Visium HD colorectal cancer samples (CRC1, CRC2, CRC5) and the corresponding normal tissues (NAT3, NAT5) were obtained from the 10x Genomics Human Colon HD Dataset and the Visium HD Human Colon Cancer Dataset. .. Raw spatial transcriptomics data, including count matrices and spatial coordinates for the 8 μm resolution captures ( https://www.10xgenomics.com/platforms/visium/product-family/dataset-human-crc ). ..

Article Title: GatorST: A Versatile Contrastive Meta-Learning Framework for Spatial Transcriptomic Data Analysis.
Article Snippet: .. Data Availability Statement The spatial transcriptomics datasets analyzed in this study are publicly available from the following sources: the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset, which was obtained using the 10x Visium platform ( http://research.libd.org/spatialLIBD/ ); human lymph node Visium dataset acquired from tissue containing germinal centers (GCs) and obtained from GEO (accession no. GSE263617); the human breast cancer dataset ( https://www.10xgenomics.com/datasets/ human- breast- cancer- block- a- section- 1- 1- standard- 1- 1- 0 ) and the mouse brain tissue dataset ( https://www.10xgenomics.com/datasets/mousebrain- serial- section- 1- sagittal- anterior- 1- standard- 1- 1- 0 ), both obtained Small Methods, 2026 e C om m ons L icense f E l a J p s R a 2 2 C N s 3 D d 4 M 0 5 t 1 6 T R 1 7 T B 0 8 A C 0 9 o ( 1 o 3 1 S ( 1 S D o 1 R 1 1 T ( 1 S V 1 1 S S nloaded from https://onlinelibrary.w iley.com /doi/10.1002/sm td.202600006 by IN A SP - N E PA L , W iley O nline L ibrary on [10/04/2026]. ..

Article Title: GatorST: A Versatile Contrastive Meta‐Learning Framework for Spatial Transcriptomic Data Analysis
Article Snippet: .. The spatial transcriptomics datasets analyzed in this study are publicly available from the following sources: the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset, which was obtained using the 10x Visium platform ( http://research.libd.org/spatialLIBD/ ); human lymph node Visium dataset acquired from tissue containing germinal centers (GCs) and obtained from GEO (accession no. GSE263617 ); the human breast cancer dataset ( https://www.10xgenomics.com/datasets/human‐breast‐cancer‐block‐a‐section‐1‐1‐standard‐1‐1‐0 ) and the mouse brain tissue dataset ( https://www.10xgenomics.com/datasets/mouse‐brain‐serial‐section‐1‐sagittal‐anterior‐1‐standard‐1‐1‐0 ), both obtained from the 10x Genomics Data Repository. .. In addition, we used an E9.5 mouse embryo dataset generated with Stereo‐seq and downloaded from the MOSTA resource ( https://db.cngb.org/stomics/mosta/ ), a Stereo‐seq dataset of mouse olfactory bulb ( https://github.com/JinmiaoChenLab/SEDR_analyses ), and a mouse hippocampus dataset profiled with Slide‐seqV2 ( https://portals.broadinstitute.org/single_cell/study/slide‐seq‐study ).

Article Title: L-RNA aptamer-based CXCL12 inhibition combined with radiotherapy and bevacizumab in newly-diagnosed glioblastoma: expansion of the phase I/II GLORIA trial
Article Snippet: .. Spatial transcriptomics data were processed and visualized using Xenium Explorer (version 4.1.0, 10x Genomics). ..

Article Title: Non-variable RNA deletion using the CRISPR-Cas9 technique demonstrated improved outcomes in human intestine single-cell RNA sequencing data, even at half sequencing depths.
Article Snippet: .. Furthermore, this cost-efficient approach could be applied to other areas, such as bulk RNA sequencing and spatial transcriptomics, including technologies like 10X Visium (10X Genomics). ..

Blocking Assay:

RNA Sequencing:

Sequencing:

Article Title: Multiomic Analysis Reveals an IFN-driven Cellular Landscape Effectively Targeted by Ruxolitinib in Hailey-Hailey Disease
Article Snippet: Libraries were sequenced on Illumina NovaSeq 6000 using S2 flow cell with a 28-10-10-90 cycle configuration (paired-end: R1 = 28 bp, i7 = 10 bp, i5 = 10 bp, R2 = 90 bp). .. Sequencing reads were demultiplexed and aligned to the human reference transcriptome (GRCh38-2020-A from 10x Genomics). ..

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