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spatial transcriptome analysis platform sdas  (Complete Genomics Inc)


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    Complete Genomics Inc spatial transcriptome analysis platform sdas
    Spatial Transcriptome Analysis Platform Sdas, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 99/100, based on 459 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/transcriptome+analysis+platform/Stereo-seq+Transcriptomics+Set+for+FFPE/pm41527855-113-26-34
    Average 99 stars, based on 459 article reviews
    spatial transcriptome analysis platform sdas - 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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    Representative H&E images of granulomas from A) Case 3, B) Case 2, and C) Case 4 showing a range of granuloma morphology with D) necrotizing granulomas, E) cellular non-necrotizing granulomas, and F) small, lymphocyte rich granulomas as assessed by a pulmonary pathologist using traditional morphological annotation. Regions of interest (ROIs) ( G-K ) were selected based on morphological and fluorescence staining for DNA (blue), CD3 (green), CD68/CD163 (red), and vimentin (yellow). ROIs bordering necrotic (G) granulomas and (H) solid or cellular granulomas were selected in addition to tertiary lymphoid structures (I), lymph node (J), and uninvolved lung (K) for spatial transcriptional profiling using the human whole <t>transcriptome</t> probe set using GeoMx (Bruker).
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    DEGs from <t>transcriptome</t> sequencing across four fruit developmental stages in C. drupifera . (A) Summary of DEGS in all combinations of developmental stage comparisons; (B) PCA from July to October. Samples from the same development stage were grouped together within the same circle; The Venn diagram illustrates the DEGs between Jul. vs Aug., Jul. vs Sep., and Jul. vs Oct. (C) All DEGs; (D) Up-regulated DEGs; (E) Down- regulated DEGs.
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    Image Search Results


    An analysis of transcriptome data derived from mice immunized with K. pneumoniae _OMVs. ( A ) A Venn diagram illustrating the co-expressed genes between the K. pneumoniae _OMVs and control groups. ( B ) A volcano plot depicting the DEGs. ( C ) A clustering heat map of the significant DEGs. ( D ) A bar graph representing the GO enrichment of the DEGs. ( E ) Summary plot of KEGG classification of DEGs. An online tool for data analysis and visualization, https://www.bioinformatics.com.cn (last viewed on 10 December 2024), created the heatmap .

    Journal: Microorganisms

    Article Title: Outer Membrane Vesicles Attenuate Klebsiella pneumoniae Infection Injury by Affecting Macrophage Polarisation and Helper T Cell Differentiation

    doi: 10.3390/microorganisms13122849

    Figure Lengend Snippet: An analysis of transcriptome data derived from mice immunized with K. pneumoniae _OMVs. ( A ) A Venn diagram illustrating the co-expressed genes between the K. pneumoniae _OMVs and control groups. ( B ) A volcano plot depicting the DEGs. ( C ) A clustering heat map of the significant DEGs. ( D ) A bar graph representing the GO enrichment of the DEGs. ( E ) Summary plot of KEGG classification of DEGs. An online tool for data analysis and visualization, https://www.bioinformatics.com.cn (last viewed on 10 December 2024), created the heatmap .

    Article Snippet: RNA-seq data analysis: The Norwood Biomedical Reference Transcriptome Cloud Analysis platform ( https://magic.novogene.com/customer/main#/homeNew (accessed on 29 May 2025)) was used to analyze differentially expressed genes (DEGs).

    Techniques: Derivative Assay, Control

    Representative H&E images of granulomas from A) Case 3, B) Case 2, and C) Case 4 showing a range of granuloma morphology with D) necrotizing granulomas, E) cellular non-necrotizing granulomas, and F) small, lymphocyte rich granulomas as assessed by a pulmonary pathologist using traditional morphological annotation. Regions of interest (ROIs) ( G-K ) were selected based on morphological and fluorescence staining for DNA (blue), CD3 (green), CD68/CD163 (red), and vimentin (yellow). ROIs bordering necrotic (G) granulomas and (H) solid or cellular granulomas were selected in addition to tertiary lymphoid structures (I), lymph node (J), and uninvolved lung (K) for spatial transcriptional profiling using the human whole transcriptome probe set using GeoMx (Bruker).

    Journal: bioRxiv

    Article Title: Multiomic analysis identifies suppressive myeloid cell populations in human TB granulomas

    doi: 10.1101/2025.03.10.642376

    Figure Lengend Snippet: Representative H&E images of granulomas from A) Case 3, B) Case 2, and C) Case 4 showing a range of granuloma morphology with D) necrotizing granulomas, E) cellular non-necrotizing granulomas, and F) small, lymphocyte rich granulomas as assessed by a pulmonary pathologist using traditional morphological annotation. Regions of interest (ROIs) ( G-K ) were selected based on morphological and fluorescence staining for DNA (blue), CD3 (green), CD68/CD163 (red), and vimentin (yellow). ROIs bordering necrotic (G) granulomas and (H) solid or cellular granulomas were selected in addition to tertiary lymphoid structures (I), lymph node (J), and uninvolved lung (K) for spatial transcriptional profiling using the human whole transcriptome probe set using GeoMx (Bruker).

    Article Snippet: Due to the limited data on the functional role and significance of MDSCs in TB infected lung tissues, we used an unbiased spatial whole transcriptome analysis (WTA) platform (GeoMx, Bruker, Inc) integrated with single-cell immunophenotyping via highly multiplexed tissue cyclic immunofluorescence (CyCIF) to initiate this study of human TB.

    Techniques: Fluorescence, Staining

    DEGs from transcriptome sequencing across four fruit developmental stages in C. drupifera . (A) Summary of DEGS in all combinations of developmental stage comparisons; (B) PCA from July to October. Samples from the same development stage were grouped together within the same circle; The Venn diagram illustrates the DEGs between Jul. vs Aug., Jul. vs Sep., and Jul. vs Oct. (C) All DEGs; (D) Up-regulated DEGs; (E) Down- regulated DEGs.

    Journal: Frontiers in Plant Science

    Article Title: Combination of transcriptomic, biochemical, and physiological analyses reveals sugar metabolism in Camellia drupifera fruit at different developmental stages

    doi: 10.3389/fpls.2024.1424284

    Figure Lengend Snippet: DEGs from transcriptome sequencing across four fruit developmental stages in C. drupifera . (A) Summary of DEGS in all combinations of developmental stage comparisons; (B) PCA from July to October. Samples from the same development stage were grouped together within the same circle; The Venn diagram illustrates the DEGs between Jul. vs Aug., Jul. vs Sep., and Jul. vs Oct. (C) All DEGs; (D) Up-regulated DEGs; (E) Down- regulated DEGs.

    Article Snippet: Furthermore, transcriptome sequencing, quality assessment, transcriptome assembly, functional annotation of Unigenes, and differential gene expression analysis were conducted using a no-reference genome transcriptome analysis platform provided by Beijing Biomarker Technologies Co., Ltd.

    Techniques: Sequencing

    Clusters of total genes and EuKaryotic Orthologous Group (KOG) annotations of profiles. (A) All genes were clustered based on their FPKM data with an FDR ≤ 0.05. Profile numbers, p-values, and gene numbers are in the upper left corner, lower left corner, and upper right corner; (B) Six clusters were obtained by short time-series expression miner (STEM) software, analyzing gene expression profiles. The y-axis of each cluster indicates the log2FC value. KOG analysis utilized a p-value ≤ 0.05 and a minimum fold change of two. The y−axis of each KOG analysis represents the number of genes.

    Journal: Frontiers in Plant Science

    Article Title: Combination of transcriptomic, biochemical, and physiological analyses reveals sugar metabolism in Camellia drupifera fruit at different developmental stages

    doi: 10.3389/fpls.2024.1424284

    Figure Lengend Snippet: Clusters of total genes and EuKaryotic Orthologous Group (KOG) annotations of profiles. (A) All genes were clustered based on their FPKM data with an FDR ≤ 0.05. Profile numbers, p-values, and gene numbers are in the upper left corner, lower left corner, and upper right corner; (B) Six clusters were obtained by short time-series expression miner (STEM) software, analyzing gene expression profiles. The y-axis of each cluster indicates the log2FC value. KOG analysis utilized a p-value ≤ 0.05 and a minimum fold change of two. The y−axis of each KOG analysis represents the number of genes.

    Article Snippet: Furthermore, transcriptome sequencing, quality assessment, transcriptome assembly, functional annotation of Unigenes, and differential gene expression analysis were conducted using a no-reference genome transcriptome analysis platform provided by Beijing Biomarker Technologies Co., Ltd.

    Techniques: Expressing, Software, Gene Expression