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salus sts high resolution spatial transcriptomics  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc salus sts high resolution spatial transcriptomics
    <t>Salus-STS</t> <t>high-resolution</t> spatial <t>transcriptomics</t> enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.
    Salus Sts High Resolution Spatial Transcriptomics, supplied by Spatial Transcriptomics Inc, 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/resolution+spatial+transcriptomics+st/highresolution+spatial+technologies+transcriptomics/pmc12832764-4-5-7
    Average 86 stars, based on 1 article reviews
    salus sts high resolution spatial transcriptomics - by Bioz Stars, 2026-09
    86/100 stars

    Images

    1) Product Images from "Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes"

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

    Journal: Frontiers in Reproductive Health

    doi: 10.3389/frph.2025.1747902

    Salus-STS high-resolution spatial transcriptomics enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.
    Figure Legend Snippet: Salus-STS high-resolution spatial transcriptomics enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.

    Techniques Used:

    Cellbin-based analysis enables accurate identification of distinct cell types in the mouse testis. (A) RCTD-annotated distinct cell types and their proportions. (B) UMAP visualization of the Salus-STS Cellbin data with scRNA-Seq data. (C) Spatial distribution of distinct cell types in the mouse testis. (D) Integrated distribution map of cell distributions in the mouse testis. (E) Markers of distinct cell types and their expression levels. Scaled expression: the average expression level scaled across genes to eliminate the effect of total expression level differences among genes. Percentage: for each cell type, the percentage of cellbins that express the specific gene out of all cellbins of the same type.
    Figure Legend Snippet: Cellbin-based analysis enables accurate identification of distinct cell types in the mouse testis. (A) RCTD-annotated distinct cell types and their proportions. (B) UMAP visualization of the Salus-STS Cellbin data with scRNA-Seq data. (C) Spatial distribution of distinct cell types in the mouse testis. (D) Integrated distribution map of cell distributions in the mouse testis. (E) Markers of distinct cell types and their expression levels. Scaled expression: the average expression level scaled across genes to eliminate the effect of total expression level differences among genes. Percentage: for each cell type, the percentage of cellbins that express the specific gene out of all cellbins of the same type.

    Techniques Used: Expressing

    High-resolution spatial transcriptomics uncovers spatiotemporal markers of spermatogenesis. (A) Pseudotime trajectory analysis. (B) Randomly selected seminiferous tubules. (C,D) Top 6 genes with expression levels positively (C) and negatively (D) correlated with the axis from the tubule basement membrane (epithelium) to the lumen center respectively.
    Figure Legend Snippet: High-resolution spatial transcriptomics uncovers spatiotemporal markers of spermatogenesis. (A) Pseudotime trajectory analysis. (B) Randomly selected seminiferous tubules. (C,D) Top 6 genes with expression levels positively (C) and negatively (D) correlated with the axis from the tubule basement membrane (epithelium) to the lumen center respectively.

    Techniques Used: Expressing, Membrane

    Related Articles

    Spatial Transcriptomics:

    Article Title: Bridging viral hepatitis and liver cancer: Emerging concepts in pathogenesis and therapeutic innovation
    Article Snippet: .. 5.4.1 Deciphering spatiotemporal heterogeneity of the HCC tumour microenvironment using high-resolution spatial transcriptomics High-resolution spatial transcriptomics technologies, such as spatial transcriptomics and in situ sequencing, enable the acquisition of single-cell level gene expression data while preserving precise spatial information of cells within tissues, thus revealing the spatial distribution and functional heterogeneity of different cell populations in the HCC tumour microenvironment. .. The application of these advanced spatial transcriptomics technologies to investigate the heterogeneity of the HCC tumour microenvironment can elucidate the precise spatial distribution and interaction networks of various cell types within the tumour (such as tumour cells, immune cells, vascular endothelial cells and stromal cells).

    Article Title: ELLA: modeling subcellular spatial variation of gene expression within cells in high-resolution spatial transcriptomics.
    Article Snippet: .. Together, these highresolution spatial transcriptomics technologies have enabled expressionmeasurement at subcellular resolution, providing unprecedented opportunities to interrogate the intracellular localization and distribution of mRNAs within cells. ..

    Article Title: Microdissection: Insights and progress in the era of precision medicine.
    Article Snippet: .. In a basic research project, recent advances in high-resolution spatial transcriptomics technologies that are compatible with FFPE tissues have enabled us to gain unprecedented access to spatial information [133, 134]. ..

    Article Title: Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics.
    Article Snippet: .. Despite being in their infancy, high-resolution ST methods have been already successfully employed for building atlases of complex healthy and diseased tissues (Supplemental Table 1). www.annualreviews.org • Clinical Translation of Spatial Transcriptomics 409 D ow nl oa de d fr om w w w .a nn ua lre vi ew s. or g. G ue st ( gu es t) IP : 13 6. ..

    Article Title: Variable Schwann cell merlin inactivation is targetable with TEAD1 inhibition in schwannomas
    Article Snippet: .. To overcome the limitations of protein-level , , and spot-aggregated spatial transcriptomics , we investigated VS tissues with high-resolution spatial transcriptomics platform (stRNAish). ..

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes
    Article Snippet: .. In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level. ..

    Article Title: Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics.
    Article Snippet: .. 10 2 O n: W ed , 1 4 Ja n 20 26 1 6: 10 :3 3 Next-generation sequencing (NGS): high-throughput DNA sequencing methods for rapid, cost-effective analysis of nucleotide sequences Omics: large-scale studies of biological molecules or systems, such as genomics, transcriptomics, proteomics, or metabolomics Tissue dissociation: process of breaking down tissue into single cells while preserving molecular integrity for analysis high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology. ..

    Article Title: Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics.
    Article Snippet: .. At this early stage, however, high-resolution ST technologies have been mostly limited to the research space.While their compatibility with routinely collected www.annualreviews.org • Clinical Translation of Spatial Transcriptomics 421 D ow nl oa de d fr om w w w .a nn ua lre vi ew s. or g. G ue st ( gu es t) IP : 13 6. ..

    In Situ:

    Article Title: Bridging viral hepatitis and liver cancer: Emerging concepts in pathogenesis and therapeutic innovation
    Article Snippet: .. 5.4.1 Deciphering spatiotemporal heterogeneity of the HCC tumour microenvironment using high-resolution spatial transcriptomics High-resolution spatial transcriptomics technologies, such as spatial transcriptomics and in situ sequencing, enable the acquisition of single-cell level gene expression data while preserving precise spatial information of cells within tissues, thus revealing the spatial distribution and functional heterogeneity of different cell populations in the HCC tumour microenvironment. .. The application of these advanced spatial transcriptomics technologies to investigate the heterogeneity of the HCC tumour microenvironment can elucidate the precise spatial distribution and interaction networks of various cell types within the tumour (such as tumour cells, immune cells, vascular endothelial cells and stromal cells).

    Sequencing:

    Article Title: Bridging viral hepatitis and liver cancer: Emerging concepts in pathogenesis and therapeutic innovation
    Article Snippet: .. 5.4.1 Deciphering spatiotemporal heterogeneity of the HCC tumour microenvironment using high-resolution spatial transcriptomics High-resolution spatial transcriptomics technologies, such as spatial transcriptomics and in situ sequencing, enable the acquisition of single-cell level gene expression data while preserving precise spatial information of cells within tissues, thus revealing the spatial distribution and functional heterogeneity of different cell populations in the HCC tumour microenvironment. .. The application of these advanced spatial transcriptomics technologies to investigate the heterogeneity of the HCC tumour microenvironment can elucidate the precise spatial distribution and interaction networks of various cell types within the tumour (such as tumour cells, immune cells, vascular endothelial cells and stromal cells).

    Single Cell:

    Article Title: Bridging viral hepatitis and liver cancer: Emerging concepts in pathogenesis and therapeutic innovation
    Article Snippet: .. 5.4.1 Deciphering spatiotemporal heterogeneity of the HCC tumour microenvironment using high-resolution spatial transcriptomics High-resolution spatial transcriptomics technologies, such as spatial transcriptomics and in situ sequencing, enable the acquisition of single-cell level gene expression data while preserving precise spatial information of cells within tissues, thus revealing the spatial distribution and functional heterogeneity of different cell populations in the HCC tumour microenvironment. .. The application of these advanced spatial transcriptomics technologies to investigate the heterogeneity of the HCC tumour microenvironment can elucidate the precise spatial distribution and interaction networks of various cell types within the tumour (such as tumour cells, immune cells, vascular endothelial cells and stromal cells).

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes
    Article Snippet: .. In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level. ..

    Gene Expression:

    Article Title: Bridging viral hepatitis and liver cancer: Emerging concepts in pathogenesis and therapeutic innovation
    Article Snippet: .. 5.4.1 Deciphering spatiotemporal heterogeneity of the HCC tumour microenvironment using high-resolution spatial transcriptomics High-resolution spatial transcriptomics technologies, such as spatial transcriptomics and in situ sequencing, enable the acquisition of single-cell level gene expression data while preserving precise spatial information of cells within tissues, thus revealing the spatial distribution and functional heterogeneity of different cell populations in the HCC tumour microenvironment. .. The application of these advanced spatial transcriptomics technologies to investigate the heterogeneity of the HCC tumour microenvironment can elucidate the precise spatial distribution and interaction networks of various cell types within the tumour (such as tumour cells, immune cells, vascular endothelial cells and stromal cells).

    Preserving:

    Article Title: Bridging viral hepatitis and liver cancer: Emerging concepts in pathogenesis and therapeutic innovation
    Article Snippet: .. 5.4.1 Deciphering spatiotemporal heterogeneity of the HCC tumour microenvironment using high-resolution spatial transcriptomics High-resolution spatial transcriptomics technologies, such as spatial transcriptomics and in situ sequencing, enable the acquisition of single-cell level gene expression data while preserving precise spatial information of cells within tissues, thus revealing the spatial distribution and functional heterogeneity of different cell populations in the HCC tumour microenvironment. .. The application of these advanced spatial transcriptomics technologies to investigate the heterogeneity of the HCC tumour microenvironment can elucidate the precise spatial distribution and interaction networks of various cell types within the tumour (such as tumour cells, immune cells, vascular endothelial cells and stromal cells).

    Article Title: Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics.
    Article Snippet: .. 10 2 O n: W ed , 1 4 Ja n 20 26 1 6: 10 :3 3 Next-generation sequencing (NGS): high-throughput DNA sequencing methods for rapid, cost-effective analysis of nucleotide sequences Omics: large-scale studies of biological molecules or systems, such as genomics, transcriptomics, proteomics, or metabolomics Tissue dissociation: process of breaking down tissue into single cells while preserving molecular integrity for analysis high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology. ..

    Functional Assay:

    Article Title: Bridging viral hepatitis and liver cancer: Emerging concepts in pathogenesis and therapeutic innovation
    Article Snippet: .. 5.4.1 Deciphering spatiotemporal heterogeneity of the HCC tumour microenvironment using high-resolution spatial transcriptomics High-resolution spatial transcriptomics technologies, such as spatial transcriptomics and in situ sequencing, enable the acquisition of single-cell level gene expression data while preserving precise spatial information of cells within tissues, thus revealing the spatial distribution and functional heterogeneity of different cell populations in the HCC tumour microenvironment. .. The application of these advanced spatial transcriptomics technologies to investigate the heterogeneity of the HCC tumour microenvironment can elucidate the precise spatial distribution and interaction networks of various cell types within the tumour (such as tumour cells, immune cells, vascular endothelial cells and stromal cells).

    Formalin-fixed Paraffin-Embedded:

    Article Title: Microdissection: Insights and progress in the era of precision medicine.
    Article Snippet: .. In a basic research project, recent advances in high-resolution spatial transcriptomics technologies that are compatible with FFPE tissues have enabled us to gain unprecedented access to spatial information [133, 134]. ..

    Next-Generation Sequencing:

    Article Title: Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics.
    Article Snippet: .. 10 2 O n: W ed , 1 4 Ja n 20 26 1 6: 10 :3 3 Next-generation sequencing (NGS): high-throughput DNA sequencing methods for rapid, cost-effective analysis of nucleotide sequences Omics: large-scale studies of biological molecules or systems, such as genomics, transcriptomics, proteomics, or metabolomics Tissue dissociation: process of breaking down tissue into single cells while preserving molecular integrity for analysis high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology. ..

    DNA Sequencing:

    Article Title: Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics.
    Article Snippet: .. 10 2 O n: W ed , 1 4 Ja n 20 26 1 6: 10 :3 3 Next-generation sequencing (NGS): high-throughput DNA sequencing methods for rapid, cost-effective analysis of nucleotide sequences Omics: large-scale studies of biological molecules or systems, such as genomics, transcriptomics, proteomics, or metabolomics Tissue dissociation: process of breaking down tissue into single cells while preserving molecular integrity for analysis high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology. ..

    Transcriptomics:

    Article Title: Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics.
    Article Snippet: .. 10 2 O n: W ed , 1 4 Ja n 20 26 1 6: 10 :3 3 Next-generation sequencing (NGS): high-throughput DNA sequencing methods for rapid, cost-effective analysis of nucleotide sequences Omics: large-scale studies of biological molecules or systems, such as genomics, transcriptomics, proteomics, or metabolomics Tissue dissociation: process of breaking down tissue into single cells while preserving molecular integrity for analysis high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology. ..



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    Image Search Results


    Salus-STS high-resolution spatial transcriptomics enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.

    Journal: Frontiers in Reproductive Health

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

    doi: 10.3389/frph.2025.1747902

    Figure Lengend Snippet: Salus-STS high-resolution spatial transcriptomics enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.

    Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

    Techniques:

    Cellbin-based analysis enables accurate identification of distinct cell types in the mouse testis. (A) RCTD-annotated distinct cell types and their proportions. (B) UMAP visualization of the Salus-STS Cellbin data with scRNA-Seq data. (C) Spatial distribution of distinct cell types in the mouse testis. (D) Integrated distribution map of cell distributions in the mouse testis. (E) Markers of distinct cell types and their expression levels. Scaled expression: the average expression level scaled across genes to eliminate the effect of total expression level differences among genes. Percentage: for each cell type, the percentage of cellbins that express the specific gene out of all cellbins of the same type.

    Journal: Frontiers in Reproductive Health

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

    doi: 10.3389/frph.2025.1747902

    Figure Lengend Snippet: Cellbin-based analysis enables accurate identification of distinct cell types in the mouse testis. (A) RCTD-annotated distinct cell types and their proportions. (B) UMAP visualization of the Salus-STS Cellbin data with scRNA-Seq data. (C) Spatial distribution of distinct cell types in the mouse testis. (D) Integrated distribution map of cell distributions in the mouse testis. (E) Markers of distinct cell types and their expression levels. Scaled expression: the average expression level scaled across genes to eliminate the effect of total expression level differences among genes. Percentage: for each cell type, the percentage of cellbins that express the specific gene out of all cellbins of the same type.

    Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

    Techniques: Expressing

    High-resolution spatial transcriptomics uncovers spatiotemporal markers of spermatogenesis. (A) Pseudotime trajectory analysis. (B) Randomly selected seminiferous tubules. (C,D) Top 6 genes with expression levels positively (C) and negatively (D) correlated with the axis from the tubule basement membrane (epithelium) to the lumen center respectively.

    Journal: Frontiers in Reproductive Health

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

    doi: 10.3389/frph.2025.1747902

    Figure Lengend Snippet: High-resolution spatial transcriptomics uncovers spatiotemporal markers of spermatogenesis. (A) Pseudotime trajectory analysis. (B) Randomly selected seminiferous tubules. (C,D) Top 6 genes with expression levels positively (C) and negatively (D) correlated with the axis from the tubule basement membrane (epithelium) to the lumen center respectively.

    Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

    Techniques: Expressing, Membrane

    a Ground-truth segmentation of 6 cortical layers and white matter (WM) for simulated spatial transcriptomics data based on the annotation of the human dorsolateral prefrontal cortex (DLPFC) section 151673. b Spot clustering performance on the raw data and the imputed data by CoSTCo, DTD, FIST ( λ = 0 or 0.01), and GNTD ( λ = 0 or 0.1) at different ranks in the simulated spatial transcriptomics data with 40% or 80% zero inflation rate. c Visualization of the spatial domains detected by spot clustering on the raw data and the imputed data of the simulated spatial transcriptomics data with 40% and 80% zero inflation rates. The imputed data with the best rank by each tensor decomposition method was used in the visualization. d Spatially variable genes detection comparison. The plot shows the percentage of correctly detected spatially variable genes by the AUC thresholds of the recovered highly expressed spots in the more sparse simulated spatial transcriptomics data with 80% zero inflation rate. e Spatial patterns visualization of three example genes by their expression in the ground-truth data, raw data, and the imputation data of the simulated spatial transcriptomics data with 80% zero inflation rate. Note that in ( d ) and ( e ), a higher AUC indicates a better consistency between the imputed or raw expressions and the ground-truth expression over the spots for the gene. Source data for ( b ) and ( d ) are provided as a Source Data file.

    Journal: Nature Communications

    Article Title: GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations

    doi: 10.1038/s41467-023-44017-0

    Figure Lengend Snippet: a Ground-truth segmentation of 6 cortical layers and white matter (WM) for simulated spatial transcriptomics data based on the annotation of the human dorsolateral prefrontal cortex (DLPFC) section 151673. b Spot clustering performance on the raw data and the imputed data by CoSTCo, DTD, FIST ( λ = 0 or 0.01), and GNTD ( λ = 0 or 0.1) at different ranks in the simulated spatial transcriptomics data with 40% or 80% zero inflation rate. c Visualization of the spatial domains detected by spot clustering on the raw data and the imputed data of the simulated spatial transcriptomics data with 40% and 80% zero inflation rates. The imputed data with the best rank by each tensor decomposition method was used in the visualization. d Spatially variable genes detection comparison. The plot shows the percentage of correctly detected spatially variable genes by the AUC thresholds of the recovered highly expressed spots in the more sparse simulated spatial transcriptomics data with 80% zero inflation rate. e Spatial patterns visualization of three example genes by their expression in the ground-truth data, raw data, and the imputation data of the simulated spatial transcriptomics data with 80% zero inflation rate. Note that in ( d ) and ( e ), a higher AUC indicates a better consistency between the imputed or raw expressions and the ground-truth expression over the spots for the gene. Source data for ( b ) and ( d ) are provided as a Source Data file.

    Article Snippet: These methods range from lower resolution Spatial Transcriptomics (ST) (commercialized as 10x Genomics Visium ), to higher resolution Slide-seq , or even sub-cellular resolution technologies such as high-definition spatial transcriptomics (HDST) and Spatio-temporal enhanced resolution omics-sequencing (Stereo-seq) .

    Techniques: Comparison, Expressing