visium spatial gene expression (10X Genomics)

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10X Genomics visium spatial gene expression

The patient characteristics ( a ) of the <t>six</t> <t>ccRCC</t> patients ( n = 3 LG and n = 3 HG) include: patient LG_2 with a vena cava thrombus (VCT) for which we collected primary tumour microenvironment (TME) and thrombi separately but processed in the one capture array for ST-seq; patient HG_1 that we collected and processed tissues from para-TME (pTME) and TME; and patient HG_3 that we collected tissues from pTME and TME. For this experimental workflow ( b ), ten tissue regions were sampled from pTME, TME and VCT that excluded fibrotic and necrotic regions. ST-seq was completed using 10x Genomics <t>Visium</t> Gene Expression microarrayed glass slides with unique spatially barcoded ST-spots that captured the mRNA released from the overlaying thin ccRCC tissue sections. Annotation of immune ST-spots was completed with data integration of six published single-cell RNA-sequencing (scRNA-seq) datasets. Further immune cell sub-typing was completed with a scRNA and T-cell receptor (TCR) sequencing dataset. Integrated analysis was completed on CD8 + T cells, TAM and monocytes.

Visium Spatial Gene Expression, 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/spatial+transcriptomics+sequencing+st-seq/expression+gene+slides+spatial+visium/pmc10495390-126-12-21
Average 86 stars, based on 1 article reviews

visium spatial gene expression - by Bioz Stars, 2026-09

86/100 stars

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1) Product Images from "High risk clear cell renal cell carcinoma microenvironments contain protumour immunophenotypes lacking specific immune checkpoints"

Article Title: High risk clear cell renal cell carcinoma microenvironments contain protumour immunophenotypes lacking specific immune checkpoints

Journal: NPJ Precision Oncology

doi: 10.1038/s41698-023-00441-5

The patient characteristics ( a ) of the six ccRCC patients ( n = 3 LG and n = 3 HG) include: patient LG_2 with a vena cava thrombus (VCT) for which we collected primary tumour microenvironment (TME) and thrombi separately but processed in the one capture array for ST-seq; patient HG_1 that we collected and processed tissues from para-TME (pTME) and TME; and patient HG_3 that we collected tissues from pTME and TME. For this experimental workflow ( b ), ten tissue regions were sampled from pTME, TME and VCT that excluded fibrotic and necrotic regions. ST-seq was completed using 10x Genomics Visium Gene Expression microarrayed glass slides with unique spatially barcoded ST-spots that captured the mRNA released from the overlaying thin ccRCC tissue sections. Annotation of immune ST-spots was completed with data integration of six published single-cell RNA-sequencing (scRNA-seq) datasets. Further immune cell sub-typing was completed with a scRNA and T-cell receptor (TCR) sequencing dataset. Integrated analysis was completed on CD8 + T cells, TAM and monocytes.

Figure Legend Snippet: The patient characteristics ( a ) of the six ccRCC patients ( n = 3 LG and n = 3 HG) include: patient LG_2 with a vena cava thrombus (VCT) for which we collected primary tumour microenvironment (TME) and thrombi separately but processed in the one capture array for ST-seq; patient HG_1 that we collected and processed tissues from para-TME (pTME) and TME; and patient HG_3 that we collected tissues from pTME and TME. For this experimental workflow ( b ), ten tissue regions were sampled from pTME, TME and VCT that excluded fibrotic and necrotic regions. ST-seq was completed using 10x Genomics Visium Gene Expression microarrayed glass slides with unique spatially barcoded ST-spots that captured the mRNA released from the overlaying thin ccRCC tissue sections. Annotation of immune ST-spots was completed with data integration of six published single-cell RNA-sequencing (scRNA-seq) datasets. Further immune cell sub-typing was completed with a scRNA and T-cell receptor (TCR) sequencing dataset. Integrated analysis was completed on CD8 + T cells, TAM and monocytes.

Techniques Used: Gene Expression, RNA Sequencing, Sequencing

Gene Expression:

Article Title: Mapping neuro-vascular unit communications reveals distinct angiogenic programs across developing mouse brain regions.
Article Snippet: .. Reactions were carried out with the Visium Spatial Gene Expression (GEX) and Tissue Optimization (TO) Slide & Reagent Kits according to the manufacturer’s protocol with recommended reagents (10X Genomics, 1000193 and 1000187). ..

Article Title: A glycan-based adjuvant expands the breadth and duration of protection of mRNA-based vaccines.
Article Snippet: .. 5′ gene expression (GEX) libraries, single-cell V(D)J libraries (10x Genomics, 1000255) and cell surface protein libraries were prepared according to the manufacturer’s protocols. .. Library quality was evaluated using an Agilent 2200 TapeStation, and final libraries were quantified by qPCR using the KAPA Library Quantification Kit for Illumina platforms.

Article Title: WNT signaling in human pluripotent stem cells promotes HDAC2-dependent epigenetic programs and development of retinoic acid-responsive mesoderm.
Article Snippet: 10,000 nuclei per sample were processed and isolated using the Nuclei EZ Prep kit (Sigma-Aldrich) according to the manufacturer’s protocol. .. Libraries for both ATAC-seq and RNA-seq were prepared using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle (10x Genomics, PN-1000283). ..

Single Cell:

Article Title: Multilayer network approaches to omics data integration in digital twins for cancer research
Article Snippet: .. This process requires the non-trivial development of new methods that capture salient information from either estimation of cell type proportions from bulk RNA-seq , or from single-cell spatial (Visium/Xenium) ( ) and non-spatial datasets (10x Genomics scRNA-seq/scATAC-seq) ( ). ..

RNA Sequencing:

other:

Article Title: Melanoma cell states shape spatial tumor-immune ecosystems to dictate the efficacy of anti-PD1 immunotherapy.
Article Snippet: Spatial transcriptomic data were generated by the CRCL (Cancer Research Center Of Lyon) Cancer Genomics Platform using Visium CytAssist Spatial gene expression for FFPE, Human Transcriptome, kit (10X Genomics), according to the manufacturer’s guidelines.

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