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Spatial Transcriptomics Inc spatial transcriptomics arrays
Spatial Transcriptomics Arrays, supplied by Spatial Transcriptomics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/spatial+transcriptomics+arrays/microarray+based+spatial+transcriptomics/us12110541-6-0-0
Average 90 stars, based on 1 article reviews
spatial transcriptomics arrays - by Bioz Stars, 2026-09
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Spatial Transcriptomics:

Article Title: Microarray integrated spatial transcriptomics (MIST) for affordable and robust digital pathology.
Article Snippet: .. To summarize,Microarray Integrated Spatial Transcriptomics (MIST) allows flexible selection of diverse regions of interest (ROIs), allowing judicious use of precious tissue biopsies and also reduces the cost of Visium assay by fivefold. ..

Article Title: Methods for preparing high-resolution spatial arrays
Article Snippet: .. Spatial transcriptomics arrays can be designed a priori so that the position of each oligonucleotide (e.g., capture probe) is predetermined, with known X-Y positions for each oligonucleotide. .. However, resolution of printed spatial transcriptomics arrays can be increased.

Article Title: DNA microarray chips: Fabrication and cutting-edge applications
Article Snippet: For decades, DNA chips, also known as DNA microarrays, have been extensively utilized for high-throughput gene expression analysis and single nucleotide polymorphism (SNP) genotyping.. However, its utility extends far beyond these applications.. Its use as a chip for Next Generation Sequencing (NGS) has brought about a revolutionary impact on many aspects of life science research.

Selection:

Article Title: Microarray integrated spatial transcriptomics (MIST) for affordable and robust digital pathology.
Article Snippet: .. To summarize,Microarray Integrated Spatial Transcriptomics (MIST) allows flexible selection of diverse regions of interest (ROIs), allowing judicious use of precious tissue biopsies and also reduces the cost of Visium assay by fivefold. ..

other:

Article Title: Multi-omics integration and machine learning uncover molecular basal-like subtype of pancreatic cancer and implicate A2ML1 in promoting tumor epithelial-mesenchymal transition.
Article Snippet: Integrating microarray-based Spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas.

Article Title: Decoding Spatial Complexity of Diverse RNA Species in Archival Tissues
Article Snippet: Notably, microfluidic-based spatial transcriptomics technologies provide distinguished cost-effectiveness, flexible designs, and widespread accessibility within the community [ , ].

Article Title: ImSpiRE: image feature-aided spatial resolution enhancement method.
Article Snippet: Spatial transcriptomics (ST), for instance, contains ~1,000 spots in a microarray (Ståhl et al., 2016).

Article Title: The burgeoning spatial multi-omics in human gastrointestinal cancers
Article Snippet: Gastric cancer , Microarray-based spatial transcriptomics (ST), in addition to the mass spectrometry imaging-based spatial metabolomics (SM) and lipidomics (SL) , Postoperative cancer tissue from seven male patients diagnosed with gastric adenocarcinoma , 2023 ( ) .

Article Title: Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications
Article Snippet: Gastric cancer , Mass spectrometry imaging-based spatial metabolomics and microarray-based spatial transcriptomics , Distinct tumor-neighboring tissue junction interface called cluster9 , [ ] .

Microarray:

Article Title: DNA microarray chips: Fabrication and cutting-edge applications
Article Snippet: For decades, DNA chips, also known as DNA microarrays, have been extensively utilized for high-throughput gene expression analysis and single nucleotide polymorphism (SNP) genotyping.. However, its utility extends far beyond these applications.. Its use as a chip for Next Generation Sequencing (NGS) has brought about a revolutionary impact on many aspects of life science research.



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a Schematic representation of the experimental design of this study. Livers were collected at 12, 24, or 38 h post infection (hpi) with P. berghei parasites or salivary gland lysate of uninfected mosquitoes (SGC) (left). Immunofluorescence (IF) staining of the parasite, spatial <t>transcriptomics</t> (ST) or 10X visium spatial technology protocols, and droplet-based single-nuclei RNA sequencing (snRNA-seq) were performed (center). Both data were further analyzed computationally (right). IHS stands for immune hotspot. b After dimensionality reduction, the normalized and batch-corrected data were embedded in UMAP space and split by the original condition for visualization. Data from SGC sections are shown on the top from 12 to 38 hpi (left to right) and data from P. berghei - infected sections are shown on the bottom from 12 to 38 hpi (left to right). Clusters with an obvious association to infection conditions are highlighted with gray boxes. c For identified clusters ST10 and ST11, differential gene expression analysis (DGEA) was performed, followed by functional enrichment analysis for each cluster (see Methods for details). Overrepresented pathways of the KEGG database for ST10 are shown in rose and for ST11 in aquamarine. Scales for expression values of overrepresented genes belonging to the individual KEGG pathways are shown for ST11 (left) or ST10 (right), from high expression (dark) to lower expression (light). Selected gene names are shown at the bottom. Enrichment scores for the pathways are shown on the right. d Interaction analysis of clusters was performed to evaluate spatial enrichment expression programs as suggested by clustering analysis in space. Positive enrichment values (orange) indicate spots belonging to these clusters are more likely to be neighboring, while negative enrichment values (blue) indicate spots associated with these expression programs are less likely to be neighboring. Clusters without significant enrichment in each other’s neighborhoods are shown in white. e Visium clusters were imposed on spatial positions and annotated according to spatial expression features. Sections of the investigated conditions are divided for ease of inspection as in ( b ), with the top panel comprising SGC sections across 12–38 hpi and the bottom panel comprising P. berghei infected sections across 12–38 hpi.
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a Schematic representation of the experimental design of this study. Livers were collected at 12, 24, or 38 h post infection (hpi) with P. berghei parasites or salivary gland lysate of uninfected mosquitoes (SGC) (left). Immunofluorescence (IF) staining of the parasite, spatial <t>transcriptomics</t> (ST) or 10X visium spatial technology protocols, and droplet-based single-nuclei RNA sequencing (snRNA-seq) were performed (center). Both data were further analyzed computationally (right). IHS stands for immune hotspot. b After dimensionality reduction, the normalized and batch-corrected data were embedded in UMAP space and split by the original condition for visualization. Data from SGC sections are shown on the top from 12 to 38 hpi (left to right) and data from P. berghei - infected sections are shown on the bottom from 12 to 38 hpi (left to right). Clusters with an obvious association to infection conditions are highlighted with gray boxes. c For identified clusters ST10 and ST11, differential gene expression analysis (DGEA) was performed, followed by functional enrichment analysis for each cluster (see Methods for details). Overrepresented pathways of the KEGG database for ST10 are shown in rose and for ST11 in aquamarine. Scales for expression values of overrepresented genes belonging to the individual KEGG pathways are shown for ST11 (left) or ST10 (right), from high expression (dark) to lower expression (light). Selected gene names are shown at the bottom. Enrichment scores for the pathways are shown on the right. d Interaction analysis of clusters was performed to evaluate spatial enrichment expression programs as suggested by clustering analysis in space. Positive enrichment values (orange) indicate spots belonging to these clusters are more likely to be neighboring, while negative enrichment values (blue) indicate spots associated with these expression programs are less likely to be neighboring. Clusters without significant enrichment in each other’s neighborhoods are shown in white. e Visium clusters were imposed on spatial positions and annotated according to spatial expression features. Sections of the investigated conditions are divided for ease of inspection as in ( b ), with the top panel comprising SGC sections across 12–38 hpi and the bottom panel comprising P. berghei infected sections across 12–38 hpi.
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a Schematic representation of the experimental design of this study. Livers were collected at 12, 24, or 38 h post infection (hpi) with P. berghei parasites or salivary gland lysate of uninfected mosquitoes (SGC) (left). Immunofluorescence (IF) staining of the parasite, spatial <t>transcriptomics</t> (ST) or 10X visium spatial technology protocols, and droplet-based single-nuclei RNA sequencing (snRNA-seq) were performed (center). Both data were further analyzed computationally (right). IHS stands for immune hotspot. b After dimensionality reduction, the normalized and batch-corrected data were embedded in UMAP space and split by the original condition for visualization. Data from SGC sections are shown on the top from 12 to 38 hpi (left to right) and data from P. berghei - infected sections are shown on the bottom from 12 to 38 hpi (left to right). Clusters with an obvious association to infection conditions are highlighted with gray boxes. c For identified clusters ST10 and ST11, differential gene expression analysis (DGEA) was performed, followed by functional enrichment analysis for each cluster (see Methods for details). Overrepresented pathways of the KEGG database for ST10 are shown in rose and for ST11 in aquamarine. Scales for expression values of overrepresented genes belonging to the individual KEGG pathways are shown for ST11 (left) or ST10 (right), from high expression (dark) to lower expression (light). Selected gene names are shown at the bottom. Enrichment scores for the pathways are shown on the right. d Interaction analysis of clusters was performed to evaluate spatial enrichment expression programs as suggested by clustering analysis in space. Positive enrichment values (orange) indicate spots belonging to these clusters are more likely to be neighboring, while negative enrichment values (blue) indicate spots associated with these expression programs are less likely to be neighboring. Clusters without significant enrichment in each other’s neighborhoods are shown in white. e Visium clusters were imposed on spatial positions and annotated according to spatial expression features. Sections of the investigated conditions are divided for ease of inspection as in ( b ), with the top panel comprising SGC sections across 12–38 hpi and the bottom panel comprising P. berghei infected sections across 12–38 hpi.
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a Schematic representation of the experimental design of this study. Livers were collected at 12, 24, or 38 h post infection (hpi) with P. berghei parasites or salivary gland lysate of uninfected mosquitoes (SGC) (left). Immunofluorescence (IF) staining of the parasite, spatial <t>transcriptomics</t> (ST) or 10X visium spatial technology protocols, and droplet-based single-nuclei RNA sequencing (snRNA-seq) were performed (center). Both data were further analyzed computationally (right). IHS stands for immune hotspot. b After dimensionality reduction, the normalized and batch-corrected data were embedded in UMAP space and split by the original condition for visualization. Data from SGC sections are shown on the top from 12 to 38 hpi (left to right) and data from P. berghei - infected sections are shown on the bottom from 12 to 38 hpi (left to right). Clusters with an obvious association to infection conditions are highlighted with gray boxes. c For identified clusters ST10 and ST11, differential gene expression analysis (DGEA) was performed, followed by functional enrichment analysis for each cluster (see Methods for details). Overrepresented pathways of the KEGG database for ST10 are shown in rose and for ST11 in aquamarine. Scales for expression values of overrepresented genes belonging to the individual KEGG pathways are shown for ST11 (left) or ST10 (right), from high expression (dark) to lower expression (light). Selected gene names are shown at the bottom. Enrichment scores for the pathways are shown on the right. d Interaction analysis of clusters was performed to evaluate spatial enrichment expression programs as suggested by clustering analysis in space. Positive enrichment values (orange) indicate spots belonging to these clusters are more likely to be neighboring, while negative enrichment values (blue) indicate spots associated with these expression programs are less likely to be neighboring. Clusters without significant enrichment in each other’s neighborhoods are shown in white. e Visium clusters were imposed on spatial positions and annotated according to spatial expression features. Sections of the investigated conditions are divided for ease of inspection as in ( b ), with the top panel comprising SGC sections across 12–38 hpi and the bottom panel comprising P. berghei infected sections across 12–38 hpi.
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a Schematic representation of the experimental design of this study. Livers were collected at 12, 24, or 38 h post infection (hpi) with P. berghei parasites or salivary gland lysate of uninfected mosquitoes (SGC) (left). Immunofluorescence (IF) staining of the parasite, spatial <t>transcriptomics</t> (ST) or 10X visium spatial technology protocols, and droplet-based single-nuclei RNA sequencing (snRNA-seq) were performed (center). Both data were further analyzed computationally (right). IHS stands for immune hotspot. b After dimensionality reduction, the normalized and batch-corrected data were embedded in UMAP space and split by the original condition for visualization. Data from SGC sections are shown on the top from 12 to 38 hpi (left to right) and data from P. berghei - infected sections are shown on the bottom from 12 to 38 hpi (left to right). Clusters with an obvious association to infection conditions are highlighted with gray boxes. c For identified clusters ST10 and ST11, differential gene expression analysis (DGEA) was performed, followed by functional enrichment analysis for each cluster (see Methods for details). Overrepresented pathways of the KEGG database for ST10 are shown in rose and for ST11 in aquamarine. Scales for expression values of overrepresented genes belonging to the individual KEGG pathways are shown for ST11 (left) or ST10 (right), from high expression (dark) to lower expression (light). Selected gene names are shown at the bottom. Enrichment scores for the pathways are shown on the right. d Interaction analysis of clusters was performed to evaluate spatial enrichment expression programs as suggested by clustering analysis in space. Positive enrichment values (orange) indicate spots belonging to these clusters are more likely to be neighboring, while negative enrichment values (blue) indicate spots associated with these expression programs are less likely to be neighboring. Clusters without significant enrichment in each other’s neighborhoods are shown in white. e Visium clusters were imposed on spatial positions and annotated according to spatial expression features. Sections of the investigated conditions are divided for ease of inspection as in ( b ), with the top panel comprising SGC sections across 12–38 hpi and the bottom panel comprising P. berghei infected sections across 12–38 hpi.
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a Schematic representation of the experimental design of this study. Livers were collected at 12, 24, or 38 h post infection (hpi) with P. berghei parasites or salivary gland lysate of uninfected mosquitoes (SGC) (left). Immunofluorescence (IF) staining of the parasite, spatial transcriptomics (ST) or 10X visium spatial technology protocols, and droplet-based single-nuclei RNA sequencing (snRNA-seq) were performed (center). Both data were further analyzed computationally (right). IHS stands for immune hotspot. b After dimensionality reduction, the normalized and batch-corrected data were embedded in UMAP space and split by the original condition for visualization. Data from SGC sections are shown on the top from 12 to 38 hpi (left to right) and data from P. berghei - infected sections are shown on the bottom from 12 to 38 hpi (left to right). Clusters with an obvious association to infection conditions are highlighted with gray boxes. c For identified clusters ST10 and ST11, differential gene expression analysis (DGEA) was performed, followed by functional enrichment analysis for each cluster (see Methods for details). Overrepresented pathways of the KEGG database for ST10 are shown in rose and for ST11 in aquamarine. Scales for expression values of overrepresented genes belonging to the individual KEGG pathways are shown for ST11 (left) or ST10 (right), from high expression (dark) to lower expression (light). Selected gene names are shown at the bottom. Enrichment scores for the pathways are shown on the right. d Interaction analysis of clusters was performed to evaluate spatial enrichment expression programs as suggested by clustering analysis in space. Positive enrichment values (orange) indicate spots belonging to these clusters are more likely to be neighboring, while negative enrichment values (blue) indicate spots associated with these expression programs are less likely to be neighboring. Clusters without significant enrichment in each other’s neighborhoods are shown in white. e Visium clusters were imposed on spatial positions and annotated according to spatial expression features. Sections of the investigated conditions are divided for ease of inspection as in ( b ), with the top panel comprising SGC sections across 12–38 hpi and the bottom panel comprising P. berghei infected sections across 12–38 hpi.

Journal: Nature Communications

Article Title: Host-pathogen interactions in the Plasmodium -infected mouse liver at spatial and single-cell resolution

doi: 10.1038/s41467-024-51418-2

Figure Lengend Snippet: a Schematic representation of the experimental design of this study. Livers were collected at 12, 24, or 38 h post infection (hpi) with P. berghei parasites or salivary gland lysate of uninfected mosquitoes (SGC) (left). Immunofluorescence (IF) staining of the parasite, spatial transcriptomics (ST) or 10X visium spatial technology protocols, and droplet-based single-nuclei RNA sequencing (snRNA-seq) were performed (center). Both data were further analyzed computationally (right). IHS stands for immune hotspot. b After dimensionality reduction, the normalized and batch-corrected data were embedded in UMAP space and split by the original condition for visualization. Data from SGC sections are shown on the top from 12 to 38 hpi (left to right) and data from P. berghei - infected sections are shown on the bottom from 12 to 38 hpi (left to right). Clusters with an obvious association to infection conditions are highlighted with gray boxes. c For identified clusters ST10 and ST11, differential gene expression analysis (DGEA) was performed, followed by functional enrichment analysis for each cluster (see Methods for details). Overrepresented pathways of the KEGG database for ST10 are shown in rose and for ST11 in aquamarine. Scales for expression values of overrepresented genes belonging to the individual KEGG pathways are shown for ST11 (left) or ST10 (right), from high expression (dark) to lower expression (light). Selected gene names are shown at the bottom. Enrichment scores for the pathways are shown on the right. d Interaction analysis of clusters was performed to evaluate spatial enrichment expression programs as suggested by clustering analysis in space. Positive enrichment values (orange) indicate spots belonging to these clusters are more likely to be neighboring, while negative enrichment values (blue) indicate spots associated with these expression programs are less likely to be neighboring. Clusters without significant enrichment in each other’s neighborhoods are shown in white. e Visium clusters were imposed on spatial positions and annotated according to spatial expression features. Sections of the investigated conditions are divided for ease of inspection as in ( b ), with the top panel comprising SGC sections across 12–38 hpi and the bottom panel comprising P. berghei infected sections across 12–38 hpi.

Article Snippet: In this study, we use a combination of the original Spatial Transcriptomics 2K arrays , (henceforth referred to as ST) and Visium arrays (10X Genomics Inc.) .

Techniques: Infection, Immunofluorescence, Staining, RNA Sequencing, Gene Expression, Functional Assay, Expressing