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stereo seq spatiotemporal single cell transcriptome 352 reagent kit  (Complete Genomics Inc)


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    Structured Review

    Complete Genomics Inc stereo seq spatiotemporal single cell transcriptome 352 reagent kit
    Stereo Seq Spatiotemporal Single Cell Transcriptome 352 Reagent Kit, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 99/100, based on 451 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/transcriptome/Stereo-seq+Transcriptomics+Set+for+FFPE/pm41770024-161-16-24
    Average 99 stars, based on 451 article reviews
    stereo seq spatiotemporal single cell transcriptome 352 reagent kit - by Bioz Stars, 2026-09
    99/100 stars

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    Related Articles

    Transcriptomics:

    Article Title: A spatial code governs olfactory receptor choice and aligns sensory maps in the nose and brain
    Article Snippet: MERSCOPE Cell Boundary Staining Kit , Vizgen , Cat# 10400118. .. Stereo-seq Transcriptomics T kit v1.3 , STOmics , Cat# 211KT13114-CG. .. Stereo-seq 16 Barcode Library Preparation Kit , STOmics , Cat # 111KL160-CG.

    Expressing:

    Article Title: Functional and Genetic Analyses Unveil the Implication of hoxa4a in Zebrafish Craniofacial Development
    Article Snippet: .. Analysis of hoxa4a expression patterns during early zebrafish embryogenesis was conducted using the Spatial Transcript Omics Database (STOmics DB) [20], incorporating both single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic (stereo-seq) datasets. ..

    Single Cell:

    Article Title: Functional and Genetic Analyses Unveil the Implication of hoxa4a in Zebrafish Craniofacial Development
    Article Snippet: .. Analysis of hoxa4a expression patterns during early zebrafish embryogenesis was conducted using the Spatial Transcript Omics Database (STOmics DB) [20], incorporating both single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic (stereo-seq) datasets. ..

    RNA Sequencing:

    Article Title: Functional and Genetic Analyses Unveil the Implication of hoxa4a in Zebrafish Craniofacial Development
    Article Snippet: .. Analysis of hoxa4a expression patterns during early zebrafish embryogenesis was conducted using the Spatial Transcript Omics Database (STOmics DB) [20], incorporating both single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic (stereo-seq) datasets. ..

    other:

    Article Title: Artificial Intelligence in Transcriptomics: From Human-in-the-Loop to Agentic AI.
    Article Snippet: Abbreviations: Serial Analysis of Gene Expression (SAGE); Expressed Sequence Tag (EST); In Situ Hybridization (ISH); Gene Expression Omnibus (GEO); Database for Gene Expression Evolution (Bgee); Sequence Read Archive (SRA); SPAtial transcriptomics annotation at Single-CEll Resolution (SPASCER); Panglao Database (PanglaoDB); Cancer Genome Anatomy Project (CGAP) uses SAGE; Human Cell Landscape (HCL); Genomic Data Commons (GDC) Data Portal; The Cancer Genome Atlas (TCGA); CellMiner Cross-DataBase (CDB); Chinese Glioma Genome Atlas (CGGA); IVY Glioblastoma Atlas Project (GAP); Open Pediatric Brain Tumor Atlas (OpenPBTA); Open Pediatric Cancer (OpenPedCan) Project; Single-Cell Pediatric Cancer Atlas (ScPCA); The Spinal Cord Injury (SCI) Myeloid Cell Atlas; Spatio-Temporal Cell Atlas of Brain (STAB2); Human Tumor Atlas Network (HTAN); Spatial Omics Resource of Cancer (SORC) Database; Comprehensive Repository of Spatial Transcriptomics (CROST); Spatial Transcript Omics DataBase (STOmics DB); Human Brain Transcriptome (HBT); National Institutes of Health (NIH) Blueprint Non-Human Primate (NHP) Atlas; Mouse Genome Informatics Gene Expression Database (MGI GXD); Adult Genotype–Tissue Expression (GTEx) Project; Brain Transcriptome (BrainTx) Database; Brain Initiative Cell Census Network (BICCN); Integrative Library of Integrated Network-Based Cellular Signatures (iLINCS); Database of Genotypes and Phenotypes (dbGaP); Alzheimer’s Disease (AD) Knowledge Portal; Aging, Dementia and Traumatic Brain Injury (TBI) Study; Common Metabolic Diseases Genome Atlas (CMDGA); Therapeutically Applicable Research to Generate Effective Treatments (TARGET); Single-Cell and Spatial RNA-Seq Database for Alzheimer’s Disease (ssREAD).

    Article Title: RegFormer: a single-cell foundation model powered by gene regulatory hierarchies.
    Article Snippet: We acknowledge the Stomics Cloud platform (https://cloud.stomics.tech/) for providing GPU computational resources.

    Sequencing:

    Article Title: Adult regenerative defects arise from discordant scaling of signal dependent growth and patterning
    Article Snippet: .. All subsequent steps, including cryosectioning, Stereo-seq library preparation, sequencing, and raw data processing, were performed at the BGI facility (Riga, Latvia) in collaboration with BGI, supported by a STOmics Grant awarded to E. Tanaka. ..



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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.
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    <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.
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    10X Genomics transcriptomic rna quantification in situ
    <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.
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    Image Search Results


    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.

    Journal: iScience

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

    doi: 10.1016/j.isci.2026.115982

    Figure Lengend 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.

    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.

    Techniques: 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.

    Journal: iScience

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

    doi: 10.1016/j.isci.2026.115982

    Figure Lengend 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.

    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.

    Techniques: 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.

    Journal: iScience

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

    doi: 10.1016/j.isci.2026.115982

    Figure Lengend 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.

    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.

    Techniques: Marker, Expressing