Review




Structured Review

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
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cell transcriptomic sequencing dataset - by Bioz Stars, 2026-11
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Images

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

Related Articles

RNA Sequencing:

Article Title: Unravelling early hematoendothelial development through the chick model: Insights and future perspectives.
Article Snippet: .. As a result of the emergence of single-cell RNA sequencing (scRNA-seq) technologies, notably from technologies such as 10X Genomics Chromium and Smart-Seq2 (Wang et al., 2021), several papers have already been published using the chicken, each targeting different biological systems within the developing embryo. ..

Single Cell:

Article Title: Single-cell sequencing and machine learning-based prediction of spliceosome-associated factor 2 may represent potential targets for osteoarthritis
Article Snippet: RNA purification kit (Cat. K0731) purchased from Thermo Fisher Scientific (USA), reverse transcription kit (Cat. RT203) purchased from YUNBIO (Kunming, China), SYBR qPCR Master mix (Cat. 22,204-1) purchased from TOLOBIO (Shanghai, China), neutral tissue fixative (Cat. G1101) purchased from Servicebio (Wuhan, China), SYF2 Rabbit pAb purchased from Bioss (Beijing, China). .. Download the publicly available 10X Genomics single-cell RNA transcriptomic map GSE220243 [ ] from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). ..

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis
Article Snippet: .. The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository. .. The spatial transcriptomic sequencing dataset using the 10X Genomics Visium platform was under accession GEO: GSE194329 .

Article Title: Single-cell sequencing and machine learning-based prediction of spliceosome-associated factor 2 may represent potential targets for osteoarthritis
Article Snippet: RNA purification kit (Cat. K0731) purchased from Thermo Fisher Scientific (USA), reverse transcription kit (Cat. RT203) purchased from YUNBIO (Kunming, China), SYBR qPCR Master mix (Cat. 22,204-1) purchased from TOLOBIO (Shanghai, China), neutral tissue fixative (Cat. G1101) purchased from Servicebio (Wuhan, China), SYF2 Rabbit pAb purchased from Bioss (Beijing, China). .. Download the publicly available 10X Genomics single-cell RNA transcriptomic map GSE220243 [21] from the GEO database (http s://www.ncbi.nlm.nih.gov/geo/). ..

Article Title: Comparative benchmarking of single-cell transcriptomes and immune repertoires across technologies
Article Snippet: .. This study presents a head-to-head benchmarking comparison of two single-cell immune profiling technologies: droplet-based microfluidics from 10x Genomics (10x) and combinatorial barcoding from Parse Biosciences (Parse). ..

Article Title: Prominent role of gut dysbiosis in the pathogenesis of cystic fibrosis-related liver disease in mice
Article Snippet: .. We performed single cell transcriptomic profiling using 10X Genomics. .. We performed single cell transcriptomic profiling using 10X Genomics.

other:

Article Title: Single-cell copy number calling and event history reconstruction.
Article Snippet: Inferred copy number profiles for 2053 cells from a breast cancer (10x Genomics). (a) Inferred tree on the clustered data, with the genes which are affected [among the 33 associated with breast cancer in the COSMIC Cancer Gene Census Sondka et al. 2018] displayed at each node. (b) Counts per cell for the different clusters. (c) Normalized counts per bin. (d) Copy number profiles estimated jointly with the CNA tree. ow nloaded from https://academ ic.oup.com /bioinform atics/article/41/3/btaf072/8011370 by guest on 17 July 2025 et al. (2019)] and no overdispersion (Supplementary Fig. S6).

Sequencing:

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis
Article Snippet: .. The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository. .. The spatial transcriptomic sequencing dataset using the 10X Genomics Visium platform was under accession GEO: GSE194329 .

Gene Expression:

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis
Article Snippet: .. The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository. .. The spatial transcriptomic sequencing dataset using the 10X Genomics Visium platform was under accession GEO: GSE194329 .

Immune Cell:

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML
Article Snippet: Dual Index Kit TS Set A , 10x Genomics , PN-1000251. .. Visium Human Immune Cell Profiling Panel , 10x Genomics , PN-1000607. .. GeoMx Human Whole Transcriptome Atlas , NanoString , 121401102.

Profiling:

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML
Article Snippet: Dual Index Kit TS Set A , 10x Genomics , PN-1000251. .. Visium Human Immune Cell Profiling Panel , 10x Genomics , PN-1000607. .. GeoMx Human Whole Transcriptome Atlas , NanoString , 121401102.

Comparison:

Article Title: Comparative benchmarking of single-cell transcriptomes and immune repertoires across technologies
Article Snippet: .. This study presents a head-to-head benchmarking comparison of two single-cell immune profiling technologies: droplet-based microfluidics from 10x Genomics (10x) and combinatorial barcoding from Parse Biosciences (Parse). ..



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Image Search Results


Comprehensive comparison of scRNA-seq and spatial omics technologies in oncology.

Journal: Frontiers in Immunology

Article Title: Decoding the spatial logic of immune evasion in hepatocellular carcinoma: from single-cell profiling to spatial omics

doi: 10.3389/fimmu.2026.1935648

Figure Lengend Snippet: Comprehensive comparison of scRNA-seq and spatial omics technologies in oncology.

Article Snippet: Sequencing-based spatial transcriptomics , Visium (10x Genomics); Stereo-seq (BGI); Slide-seq , Whole-transcriptome mapping across large tissue areas; resolution ranges from multi-cellular spots to subcellular/nanoscale platforms. , Maps tissue-scale architecture, spatial gene modules, subclonal competition zones and tumor-stroma boundaries. , Resolution-sensitivity trade-off; multi-cellular spots require deconvolution; high sequencing cost..

Techniques: Comparison, Single Cell, Profiling, Sample Throughput, Sequencing, Spatial Transcriptomics, Tissue, Imaging, Sampling, In Situ, Transcriptomics, Functional Assay, Multiplexing, Biomarker Discovery