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Spatial Transcriptomics Inc high-resolution spatial transcriptomics (st)
High Resolution Spatial Transcriptomics (St), 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/resolution+spatial+transcriptomics+st/spatial+transcriptomics++st+/pm39097166-7-24-25
Average 90 stars, based on 1 article reviews
high-resolution spatial transcriptomics (st) - by Bioz Stars, 2026-09
90/100 stars

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Related Articles

Gene Expression:

Article Title: STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition
Article Snippet: Spatial transcriptomics (ST) technologies have enabled gene expression profiling in the native spatial context of tissues.

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the epithelial cell states present in the human endometrium we considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 2a & Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 2b-f and Extended Data Fig. 7).

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) technologies have revolutionized the understanding of spatially informed gene expression, which provide invaluable insights into the molecular architecture of complex diseases such as Alzheimer disease (AD).

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the stromal cell states present in the human endometrium, we used the same approach described in Supplementary Note 3 for the annotation of epithelial cells which considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 3b).

Article Title: Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings
Article Snippet: Additionally, the size of features in image-based spatial transcriptomics technologies has increased from 30 to 10,000 , making it increasingly feasible to use deep learning models.

Article Title: Centrosome-, mitotic spindle- and cytokinetic bridge-specific compartmentalization of AGO2 protein in human liver cells undergoing mitosis: Non-canonical, RNAi-dependent, control of local homeostasis
Article Snippet: Spatial transcriptomics-mediated profiling of the mRNA/siRNA species populating either TRBP2 +/+ or TRBP2 −/− MEF-residing centrosomes in dividing/mitotic vs. interphase cells may indicate TRBP2-independent centrosome-specific RNAi machinery that functions non-canonically during mitosis.

Article Title:
Article Snippet: B266/P1991 Imaging-based spatial transcriptomics technology identifies predictive biomarkers for relapse in colon cancer stage II.

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) data provide spatially informed gene expression profiles.

Histopathology:

Article Title: STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition
Article Snippet: Spatial transcriptomics (ST) technologies have enabled gene expression profiling in the native spatial context of tissues.

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the epithelial cell states present in the human endometrium we considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 2a & Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 2b-f and Extended Data Fig. 7).

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) technologies have revolutionized the understanding of spatially informed gene expression, which provide invaluable insights into the molecular architecture of complex diseases such as Alzheimer disease (AD).

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the stromal cell states present in the human endometrium, we used the same approach described in Supplementary Note 3 for the annotation of epithelial cells which considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 3b).

Article Title: Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings
Article Snippet: Additionally, the size of features in image-based spatial transcriptomics technologies has increased from 30 to 10,000 , making it increasingly feasible to use deep learning models.

Article Title: Centrosome-, mitotic spindle- and cytokinetic bridge-specific compartmentalization of AGO2 protein in human liver cells undergoing mitosis: Non-canonical, RNAi-dependent, control of local homeostasis
Article Snippet: Spatial transcriptomics-mediated profiling of the mRNA/siRNA species populating either TRBP2 +/+ or TRBP2 −/− MEF-residing centrosomes in dividing/mitotic vs. interphase cells may indicate TRBP2-independent centrosome-specific RNAi machinery that functions non-canonically during mitosis.

Article Title:
Article Snippet: B266/P1991 Imaging-based spatial transcriptomics technology identifies predictive biomarkers for relapse in colon cancer stage II.

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) data provide spatially informed gene expression profiles.

Comparison:

Article Title: STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition
Article Snippet: Spatial transcriptomics (ST) technologies have enabled gene expression profiling in the native spatial context of tissues.

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the epithelial cell states present in the human endometrium we considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 2a & Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 2b-f and Extended Data Fig. 7).

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) technologies have revolutionized the understanding of spatially informed gene expression, which provide invaluable insights into the molecular architecture of complex diseases such as Alzheimer disease (AD).

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the stromal cell states present in the human endometrium, we used the same approach described in Supplementary Note 3 for the annotation of epithelial cells which considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 3b).

Article Title: Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings
Article Snippet: Additionally, the size of features in image-based spatial transcriptomics technologies has increased from 30 to 10,000 , making it increasingly feasible to use deep learning models.

Article Title: Centrosome-, mitotic spindle- and cytokinetic bridge-specific compartmentalization of AGO2 protein in human liver cells undergoing mitosis: Non-canonical, RNAi-dependent, control of local homeostasis
Article Snippet: Spatial transcriptomics-mediated profiling of the mRNA/siRNA species populating either TRBP2 +/+ or TRBP2 −/− MEF-residing centrosomes in dividing/mitotic vs. interphase cells may indicate TRBP2-independent centrosome-specific RNAi machinery that functions non-canonically during mitosis.

Article Title:
Article Snippet: B266/P1991 Imaging-based spatial transcriptomics technology identifies predictive biomarkers for relapse in colon cancer stage II.

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) data provide spatially informed gene expression profiles.

Expressing:

Article Title: STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition
Article Snippet: Spatial transcriptomics (ST) technologies have enabled gene expression profiling in the native spatial context of tissues.

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the epithelial cell states present in the human endometrium we considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 2a & Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 2b-f and Extended Data Fig. 7).

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) technologies have revolutionized the understanding of spatially informed gene expression, which provide invaluable insights into the molecular architecture of complex diseases such as Alzheimer disease (AD).

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the stromal cell states present in the human endometrium, we used the same approach described in Supplementary Note 3 for the annotation of epithelial cells which considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 3b).

Article Title: Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings
Article Snippet: Additionally, the size of features in image-based spatial transcriptomics technologies has increased from 30 to 10,000 , making it increasingly feasible to use deep learning models.

Article Title: Centrosome-, mitotic spindle- and cytokinetic bridge-specific compartmentalization of AGO2 protein in human liver cells undergoing mitosis: Non-canonical, RNAi-dependent, control of local homeostasis
Article Snippet: Spatial transcriptomics-mediated profiling of the mRNA/siRNA species populating either TRBP2 +/+ or TRBP2 −/− MEF-residing centrosomes in dividing/mitotic vs. interphase cells may indicate TRBP2-independent centrosome-specific RNAi machinery that functions non-canonically during mitosis.

Article Title:
Article Snippet: B266/P1991 Imaging-based spatial transcriptomics technology identifies predictive biomarkers for relapse in colon cancer stage II.

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) data provide spatially informed gene expression profiles.

Marker:

Article Title: STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition
Article Snippet: Spatial transcriptomics (ST) technologies have enabled gene expression profiling in the native spatial context of tissues.

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the epithelial cell states present in the human endometrium we considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 2a & Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 2b-f and Extended Data Fig. 7).

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) technologies have revolutionized the understanding of spatially informed gene expression, which provide invaluable insights into the molecular architecture of complex diseases such as Alzheimer disease (AD).

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the stromal cell states present in the human endometrium, we used the same approach described in Supplementary Note 3 for the annotation of epithelial cells which considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 3b).

Article Title: Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings
Article Snippet: Additionally, the size of features in image-based spatial transcriptomics technologies has increased from 30 to 10,000 , making it increasingly feasible to use deep learning models.

Article Title: Centrosome-, mitotic spindle- and cytokinetic bridge-specific compartmentalization of AGO2 protein in human liver cells undergoing mitosis: Non-canonical, RNAi-dependent, control of local homeostasis
Article Snippet: Spatial transcriptomics-mediated profiling of the mRNA/siRNA species populating either TRBP2 +/+ or TRBP2 −/− MEF-residing centrosomes in dividing/mitotic vs. interphase cells may indicate TRBP2-independent centrosome-specific RNAi machinery that functions non-canonically during mitosis.

Article Title:
Article Snippet: B266/P1991 Imaging-based spatial transcriptomics technology identifies predictive biomarkers for relapse in colon cancer stage II.

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) data provide spatially informed gene expression profiles.

Single-cell Transcriptomics:

Article Title: STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition
Article Snippet: Spatial transcriptomics (ST) technologies have enabled gene expression profiling in the native spatial context of tissues.

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the epithelial cell states present in the human endometrium we considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 2a & Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 2b-f and Extended Data Fig. 7).

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) technologies have revolutionized the understanding of spatially informed gene expression, which provide invaluable insights into the molecular architecture of complex diseases such as Alzheimer disease (AD).

Article Title: An integrated single-cell reference atlas of the human endometrium
Article Snippet: To annotate the stromal cell states present in the human endometrium, we used the same approach described in Supplementary Note 3 for the annotation of epithelial cells which considered: (i) the distinctive expression of genes, including bona fide markers (Fig. 3a), (ii) the menstrual stage at which these cells appear (Fig. 1f), and (iii) their spatial coordinates, as inferred by integrating single-cell transcriptomics with Spatial Transcriptomics (Visium) (Fig. 3b).

Article Title: Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings
Article Snippet: Additionally, the size of features in image-based spatial transcriptomics technologies has increased from 30 to 10,000 , making it increasingly feasible to use deep learning models.

Article Title: Centrosome-, mitotic spindle- and cytokinetic bridge-specific compartmentalization of AGO2 protein in human liver cells undergoing mitosis: Non-canonical, RNAi-dependent, control of local homeostasis
Article Snippet: Spatial transcriptomics-mediated profiling of the mRNA/siRNA species populating either TRBP2 +/+ or TRBP2 −/− MEF-residing centrosomes in dividing/mitotic vs. interphase cells may indicate TRBP2-independent centrosome-specific RNAi machinery that functions non-canonically during mitosis.

Article Title:
Article Snippet: B266/P1991 Imaging-based spatial transcriptomics technology identifies predictive biomarkers for relapse in colon cancer stage II.

Article Title: Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Article Snippet: Spatial transcriptomics (ST) data provide spatially informed gene expression profiles.



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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
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Spatial Transcriptomics Inc high-resolution spatial transcriptomics (st)
<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.
High Resolution Spatial Transcriptomics (St), 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/resolution+spatial+transcriptomics+st/spatial+transcriptomics++st+/pm39097166-7-24-25
Average 90 stars, based on 1 article reviews
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Spatial Transcriptomics Inc resolution spatial transcriptomics st
a Ground-truth segmentation of 6 cortical layers and white matter (WM) for simulated spatial <t>transcriptomics</t> 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.
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a Ground-truth segmentation of 6 cortical layers and white matter (WM) for simulated spatial <t>transcriptomics</t> 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.
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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