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111cp118 stereo seq transcriptomics t kit bgi stomics  (Complete Genomics Inc)


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    Complete Genomics Inc 111cp118 stereo seq transcriptomics t kit bgi stomics
    111cp118 Stereo Seq Transcriptomics T Kit Bgi Stomics, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 99/100, based on 484 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stereo-seq+transcriptomics+t+kit/Stereo-seq+Transcriptomics+Set+for+FFPE/pm39111312-188-19-24
    Average 99 stars, based on 484 article reviews
    111cp118 stereo seq transcriptomics t kit bgi stomics - by Bioz Stars, 2026-10
    99/100 stars

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    Related Articles

    Spatial Transcriptomics:

    Article Title: A pan cancer multi omics analysis of OLR1 reveals its association with tumor immunity metabolic pathways and clinical prognosis
    Article Snippet: .. Spatial transcriptomics datasets were obtained from the STOmics database (https://db.cngb.org/stomics/) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/), including STDS0000027, STDS0000033, STDS0000046, GSM5924031, and GSM6505133. ..

    Article Title: Spi1-Soat1 Transcriptional axis drives microglial lipid droplet accumulation and secondary brain injury after intracerebral hemorrhage
    Article Snippet: .. Cholesterol metabolic pathway activation and microglia-specific upregulation of Soat1 are tightly associated with lipid droplet accumulation after ICH To define the core metabolic pathways and key regulators driving microglial LD accumulation after ICH, we analyzed the day 7 perihematomal tissue subset of a spatial transcriptomics dataset (STOmics DB, ID:STT0000047), with a focus on lipid metabolic cascades underlying LD biogenesis (Fig. 3A). ..

    Analysis:

    Article Title: Diverse infections transcriptionally reprogram the intestinal epithelium and epithelial-immune cell interactions.
    Article Snippet: .. Indeed, using Stereo-seq (STOmics) spatial transcriptomic analysis, which profiles host and microbial RNA, we found that Yersinia transcripts were localized to the basal region of the PG and did not extend to the enterocytes overlaying the PG (Supplementary Fig. 10g). ..

    Sequencing:

    Article Title: Multi-omics integration and machine learning identify an ACKR1 + endothelial cell-based lactate-immune signature associated with carotid plaque vulnerability and progression.
    Article Snippet: .. The internal singlecell and spatial transcriptomes were deposited with our previous study (Lai et al., Nat Cardiovasc Res, 2025) in the Genome Sequence Archive for Human at the National Genomics Data Center under accession HRA006030 (https://ngdc.cncb.ac.cn/gsa-human), with backups in the China National GeneBank: spatial transcriptomes in the STOmics Database (STT0000052, https://db.cngb.org/stomics/) and single-cell data in the China National Sequence Archive (CNP0004894, https:// db.cngb.org/cnsa/). ..

    Single Cell:

    Article Title: Multi-omics integration and machine learning identify an ACKR1 + endothelial cell-based lactate-immune signature associated with carotid plaque vulnerability and progression.
    Article Snippet: .. The internal singlecell and spatial transcriptomes were deposited with our previous study (Lai et al., Nat Cardiovasc Res, 2025) in the Genome Sequence Archive for Human at the National Genomics Data Center under accession HRA006030 (https://ngdc.cncb.ac.cn/gsa-human), with backups in the China National GeneBank: spatial transcriptomes in the STOmics Database (STT0000052, https://db.cngb.org/stomics/) and single-cell data in the China National Sequence Archive (CNP0004894, https:// db.cngb.org/cnsa/). ..

    Activation Assay:

    Article Title: Spi1-Soat1 Transcriptional axis drives microglial lipid droplet accumulation and secondary brain injury after intracerebral hemorrhage
    Article Snippet: .. Cholesterol metabolic pathway activation and microglia-specific upregulation of Soat1 are tightly associated with lipid droplet accumulation after ICH To define the core metabolic pathways and key regulators driving microglial LD accumulation after ICH, we analyzed the day 7 perihematomal tissue subset of a spatial transcriptomics dataset (STOmics DB, ID:STT0000047), with a focus on lipid metabolic cascades underlying LD biogenesis (Fig. 3A). ..

    Tissue:

    Article Title: Spi1-Soat1 Transcriptional axis drives microglial lipid droplet accumulation and secondary brain injury after intracerebral hemorrhage
    Article Snippet: .. Cholesterol metabolic pathway activation and microglia-specific upregulation of Soat1 are tightly associated with lipid droplet accumulation after ICH To define the core metabolic pathways and key regulators driving microglial LD accumulation after ICH, we analyzed the day 7 perihematomal tissue subset of a spatial transcriptomics dataset (STOmics DB, ID:STT0000047), with a focus on lipid metabolic cascades underlying LD biogenesis (Fig. 3A). ..

    H&E Stain:

    Article Title: Spatial transcriptomic profiling of porcine tissue microarray detecting subclinical circovirus infection
    Article Snippet: .. The first section was subjected to H&E staining for histological evaluation, while the second was mounted directly onto a Stereo-seq chip N Slide V1.0 (STOmics, Cat. No. 210CN114). .. Subsequent processing was performed according to the Stereo-seq Transcriptomics Set for FFPE User Manual (Document No. STUM-TT004) using the Stereo-seq Transcriptomics N Kit V1.0 (Cat. No. 211KN114) and Stereo-seq 16 Barcode Library Preparation Kit V1.0 (Cat. No. 111KL160).



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    a , Visualization of the five Bregmata selected to study different regions of the aging brain. b , From left to right: Two-dimensional UMAP representation of colored spot clusters computationally integrated by brain slice (top to bottom), pie chart showing the proportion of annotated clusters across all brain samples, one representative annotated Visium sample with the spot cluster identities plotted over the H&E-stained tissue image. c , Number of differentially expressed genes per aging brain bregma (old vs. young) and direction of dysregulation. Total DEG counts were derived across all organ clusters, without removing duplicates. d , Five-dimensional Venn diagram comparing the brain DEG sets from ( c ). e , Heatmap showing scaled expression of the 17 aging DEGs (rows) shared between all five brain slices, using the Brain1 pseudobulk samples and spot clusters for visualization (columns). All genes except Rbm3 are also significant SVGs in at least one of the five brain bregmata. f , Sketch of 10x Visium and <t>STOmics</t> <t>Stereo-seq</t> examples comparing the features of both spatial transcriptomics technology platforms. g , Examples for binned (bin200) and annotated spot clusters of the aging brain (top to bottom; young, middle, old) at Bregma#1 sequenced with Stereo-seq. h , Dot plot showing the top 3 most significant marker genes per cell type annotated spot cluster using the Stereo-seq cellbin resolution of Brain1 samples. i , Normalized spatial expression of Trem2 across all 15 STOmics Stereo-seq brain samples using the near-cellular resolution bin20 (from left to right: young, middle, old; from top to bottom: Brain1-5). For visualization spot sizes were rescaled into the point interval [0.1, 1.5] according to their expression of Trem2.
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    a , Visualization of the five Bregmata selected to study different regions of the aging brain. b , From left to right: Two-dimensional UMAP representation of colored spot clusters computationally integrated by brain slice (top to bottom), pie chart showing the proportion of annotated clusters across all brain samples, one representative annotated Visium sample with the spot cluster identities plotted over the H&E-stained tissue image. c , Number of differentially expressed genes per aging brain bregma (old vs. young) and direction of dysregulation. Total DEG counts were derived across all organ clusters, without removing duplicates. d , Five-dimensional Venn diagram comparing the brain DEG sets from ( c ). e , Heatmap showing scaled expression of the 17 aging DEGs (rows) shared between all five brain slices, using the Brain1 pseudobulk samples and spot clusters for visualization (columns). All genes except Rbm3 are also significant SVGs in at least one of the five brain bregmata. f , Sketch of 10x Visium and <t>STOmics</t> Stereo-seq examples comparing the features of both spatial transcriptomics technology platforms. g , Examples for binned (bin200) and annotated spot clusters of the aging brain (top to bottom; young, middle, old) at Bregma#1 sequenced with Stereo-seq. h , Dot plot showing the top 3 most significant marker genes per cell type annotated spot cluster using the Stereo-seq cellbin resolution of Brain1 samples. i , Normalized spatial expression of Trem2 across all 15 STOmics Stereo-seq brain samples using the near-cellular resolution bin20 (from left to right: young, middle, old; from top to bottom: Brain1-5). For visualization spot sizes were rescaled into the point interval [0.1, 1.5] according to their expression of Trem2.
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    Complete Genomics Inc 111cp118 stereo seq transcriptomics t kit bgi stomics
    a , Visualization of the five Bregmata selected to study different regions of the aging brain. b , From left to right: Two-dimensional UMAP representation of colored spot clusters computationally integrated by brain slice (top to bottom), pie chart showing the proportion of annotated clusters across all brain samples, one representative annotated Visium sample with the spot cluster identities plotted over the H&E-stained tissue image. c , Number of differentially expressed genes per aging brain bregma (old vs. young) and direction of dysregulation. Total DEG counts were derived across all organ clusters, without removing duplicates. d , Five-dimensional Venn diagram comparing the brain DEG sets from ( c ). e , Heatmap showing scaled expression of the 17 aging DEGs (rows) shared between all five brain slices, using the Brain1 pseudobulk samples and spot clusters for visualization (columns). All genes except Rbm3 are also significant SVGs in at least one of the five brain bregmata. f , Sketch of 10x Visium and <t>STOmics</t> Stereo-seq examples comparing the features of both spatial transcriptomics technology platforms. g , Examples for binned (bin200) and annotated spot clusters of the aging brain (top to bottom; young, middle, old) at Bregma#1 sequenced with Stereo-seq. h , Dot plot showing the top 3 most significant marker genes per cell type annotated spot cluster using the Stereo-seq cellbin resolution of Brain1 samples. i , Normalized spatial expression of Trem2 across all 15 STOmics Stereo-seq brain samples using the near-cellular resolution bin20 (from left to right: young, middle, old; from top to bottom: Brain1-5). For visualization spot sizes were rescaled into the point interval [0.1, 1.5] according to their expression of Trem2.
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    a , Visualization of the five Bregmata selected to study different regions of the aging brain. b , From left to right: Two-dimensional UMAP representation of colored spot clusters computationally integrated by brain slice (top to bottom), pie chart showing the proportion of annotated clusters across all brain samples, one representative annotated Visium sample with the spot cluster identities plotted over the H&E-stained tissue image. c , Number of differentially expressed genes per aging brain bregma (old vs. young) and direction of dysregulation. Total DEG counts were derived across all organ clusters, without removing duplicates. d , Five-dimensional Venn diagram comparing the brain DEG sets from ( c ). e , Heatmap showing scaled expression of the 17 aging DEGs (rows) shared between all five brain slices, using the Brain1 pseudobulk samples and spot clusters for visualization (columns). All genes except Rbm3 are also significant SVGs in at least one of the five brain bregmata. f , Sketch of 10x Visium and <t>STOmics</t> Stereo-seq examples comparing the features of both spatial transcriptomics technology platforms. g , Examples for binned (bin200) and annotated spot clusters of the aging brain (top to bottom; young, middle, old) at Bregma#1 sequenced with Stereo-seq. h , Dot plot showing the top 3 most significant marker genes per cell type annotated spot cluster using the Stereo-seq cellbin resolution of Brain1 samples. i , Normalized spatial expression of Trem2 across all 15 STOmics Stereo-seq brain samples using the near-cellular resolution bin20 (from left to right: young, middle, old; from top to bottom: Brain1-5). For visualization spot sizes were rescaled into the point interval [0.1, 1.5] according to their expression of Trem2.
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    Image Search Results


    a , Visualization of the five Bregmata selected to study different regions of the aging brain. b , From left to right: Two-dimensional UMAP representation of colored spot clusters computationally integrated by brain slice (top to bottom), pie chart showing the proportion of annotated clusters across all brain samples, one representative annotated Visium sample with the spot cluster identities plotted over the H&E-stained tissue image. c , Number of differentially expressed genes per aging brain bregma (old vs. young) and direction of dysregulation. Total DEG counts were derived across all organ clusters, without removing duplicates. d , Five-dimensional Venn diagram comparing the brain DEG sets from ( c ). e , Heatmap showing scaled expression of the 17 aging DEGs (rows) shared between all five brain slices, using the Brain1 pseudobulk samples and spot clusters for visualization (columns). All genes except Rbm3 are also significant SVGs in at least one of the five brain bregmata. f , Sketch of 10x Visium and STOmics Stereo-seq examples comparing the features of both spatial transcriptomics technology platforms. g , Examples for binned (bin200) and annotated spot clusters of the aging brain (top to bottom; young, middle, old) at Bregma#1 sequenced with Stereo-seq. h , Dot plot showing the top 3 most significant marker genes per cell type annotated spot cluster using the Stereo-seq cellbin resolution of Brain1 samples. i , Normalized spatial expression of Trem2 across all 15 STOmics Stereo-seq brain samples using the near-cellular resolution bin20 (from left to right: young, middle, old; from top to bottom: Brain1-5). For visualization spot sizes were rescaled into the point interval [0.1, 1.5] according to their expression of Trem2.

    Journal: bioRxiv

    Article Title: Spatiotemporal transcriptomic niches of complement pathway and serine protease inhibitor activation in aging and infection

    doi: 10.1101/2024.11.04.621811

    Figure Lengend Snippet: a , Visualization of the five Bregmata selected to study different regions of the aging brain. b , From left to right: Two-dimensional UMAP representation of colored spot clusters computationally integrated by brain slice (top to bottom), pie chart showing the proportion of annotated clusters across all brain samples, one representative annotated Visium sample with the spot cluster identities plotted over the H&E-stained tissue image. c , Number of differentially expressed genes per aging brain bregma (old vs. young) and direction of dysregulation. Total DEG counts were derived across all organ clusters, without removing duplicates. d , Five-dimensional Venn diagram comparing the brain DEG sets from ( c ). e , Heatmap showing scaled expression of the 17 aging DEGs (rows) shared between all five brain slices, using the Brain1 pseudobulk samples and spot clusters for visualization (columns). All genes except Rbm3 are also significant SVGs in at least one of the five brain bregmata. f , Sketch of 10x Visium and STOmics Stereo-seq examples comparing the features of both spatial transcriptomics technology platforms. g , Examples for binned (bin200) and annotated spot clusters of the aging brain (top to bottom; young, middle, old) at Bregma#1 sequenced with Stereo-seq. h , Dot plot showing the top 3 most significant marker genes per cell type annotated spot cluster using the Stereo-seq cellbin resolution of Brain1 samples. i , Normalized spatial expression of Trem2 across all 15 STOmics Stereo-seq brain samples using the near-cellular resolution bin20 (from left to right: young, middle, old; from top to bottom: Brain1-5). For visualization spot sizes were rescaled into the point interval [0.1, 1.5] according to their expression of Trem2.

    Article Snippet: One brain sample of each age was processed at the MGI Tech Co., Ltd. (Riga, Latvia) using the STOmics Stereo-seq Transcriptomics T Kit (MGI).

    Techniques: Slice Preparation, Staining, Derivative Assay, Expressing, Marker

    a , Illustration of the five different brain bregma used for STOmics Stereo-seq in accordance with the Visium data set. Representative H&E stains are shown for each Bregma. Since Stereo-seq does not support H&E stains directly from the sequenced tissue slices, an adjacent (directly before or after) tissue slice was prepared and stained before running the spatial transcriptomics experiments. b , From left to right and per brain bregma (top to bottom): integrated UMAP representation of all cleaned Stereo-seq spot clusters using the bin200 resolution, pie charts and per replicate spatial projections of the final annotated spot clusters. Cluster names and colors were assigned in accordance with the Visium data set (cf. Methods). c , Distribution of four main quality control features across the cleaned spots and per Stereo-seq brain replicate at bin200 resolution.

    Journal: bioRxiv

    Article Title: Spatiotemporal transcriptomic niches of complement pathway and serine protease inhibitor activation in aging and infection

    doi: 10.1101/2024.11.04.621811

    Figure Lengend Snippet: a , Illustration of the five different brain bregma used for STOmics Stereo-seq in accordance with the Visium data set. Representative H&E stains are shown for each Bregma. Since Stereo-seq does not support H&E stains directly from the sequenced tissue slices, an adjacent (directly before or after) tissue slice was prepared and stained before running the spatial transcriptomics experiments. b , From left to right and per brain bregma (top to bottom): integrated UMAP representation of all cleaned Stereo-seq spot clusters using the bin200 resolution, pie charts and per replicate spatial projections of the final annotated spot clusters. Cluster names and colors were assigned in accordance with the Visium data set (cf. Methods). c , Distribution of four main quality control features across the cleaned spots and per Stereo-seq brain replicate at bin200 resolution.

    Article Snippet: One brain sample of each age was processed at the MGI Tech Co., Ltd. (Riga, Latvia) using the STOmics Stereo-seq Transcriptomics T Kit (MGI).

    Techniques: Staining, Control

    a , Scatter plot showing the average log2-scaled fold-change between aging and infection for all brain DEGs matched and colored by spot cluster. b , Normalized spatial expression of C4b in four representative brain samples (Bregma: - 2.06), two from aging (top left: young, top right: old) and infection (bottom left: control, bottom right: infected) cohort. c , Dot plot showing adjusted and log-scaled Hypergeometric test p-values and the number of gene hits for the enriched categories from the Reactome pathway database across the four organs shared between aging and infection cohort. For each row of results a different list of genes was used as input (cf. Methods). d , Relative (scored) complement pathway activity in pseudobulk samples across all aging and infection cohort organs, split and colored by the five experimental groups (young, middle, old, healthy controls, infected). e , STRING network for Serpina3n in Mus musculus after performing one level of network expansion and removing edges from text mining and gene fusion. Edges are colored according to the type of association or interaction: curated databases (light blue), experientially determined (purple), gene neighborhood (green), gene co-occurrence (dark blue), co-expression (black), or protein homology (light purple). Nodes are colored according to their shell of interactions towards Serpina3n. f , Scatter plot showing normalized pseudobulk expression of C4b (x-axis) against Serpina3n (y-axis) across all samples and colored by the five experimental groups (young, middle, old, healthy controls, infected). g , Row and column clustered heatmap showing the scaled pseudobulk expression for all members of the serine protease inhibitor (Serpin*) gene family. h , Demonstration of spatially connected (sub-)cellular activity of the complement pathway (C4b+) and activated Astrocytes (Gfap+) in the aging brain (Bregma -2.06). Normalized expression values from one young representative on the top and one old representative on the bottom originating from the Stereo-seq samples at bin20 resolution are displayed.

    Journal: bioRxiv

    Article Title: Spatiotemporal transcriptomic niches of complement pathway and serine protease inhibitor activation in aging and infection

    doi: 10.1101/2024.11.04.621811

    Figure Lengend Snippet: a , Scatter plot showing the average log2-scaled fold-change between aging and infection for all brain DEGs matched and colored by spot cluster. b , Normalized spatial expression of C4b in four representative brain samples (Bregma: - 2.06), two from aging (top left: young, top right: old) and infection (bottom left: control, bottom right: infected) cohort. c , Dot plot showing adjusted and log-scaled Hypergeometric test p-values and the number of gene hits for the enriched categories from the Reactome pathway database across the four organs shared between aging and infection cohort. For each row of results a different list of genes was used as input (cf. Methods). d , Relative (scored) complement pathway activity in pseudobulk samples across all aging and infection cohort organs, split and colored by the five experimental groups (young, middle, old, healthy controls, infected). e , STRING network for Serpina3n in Mus musculus after performing one level of network expansion and removing edges from text mining and gene fusion. Edges are colored according to the type of association or interaction: curated databases (light blue), experientially determined (purple), gene neighborhood (green), gene co-occurrence (dark blue), co-expression (black), or protein homology (light purple). Nodes are colored according to their shell of interactions towards Serpina3n. f , Scatter plot showing normalized pseudobulk expression of C4b (x-axis) against Serpina3n (y-axis) across all samples and colored by the five experimental groups (young, middle, old, healthy controls, infected). g , Row and column clustered heatmap showing the scaled pseudobulk expression for all members of the serine protease inhibitor (Serpin*) gene family. h , Demonstration of spatially connected (sub-)cellular activity of the complement pathway (C4b+) and activated Astrocytes (Gfap+) in the aging brain (Bregma -2.06). Normalized expression values from one young representative on the top and one old representative on the bottom originating from the Stereo-seq samples at bin20 resolution are displayed.

    Article Snippet: One brain sample of each age was processed at the MGI Tech Co., Ltd. (Riga, Latvia) using the STOmics Stereo-seq Transcriptomics T Kit (MGI).

    Techniques: Infection, Expressing, Control, Activity Assay, Protease Inhibitor

    a , Combined analysis of C4b and Serpina3n by per-spot multiplication of normalized expression values across four groups of samples (young, old, control, infected). Shown are the combined expression values for Astrocyte assigned spots from the cell binning resolved brain1 Stereo-seq samples (left), and the Astrocyte marker enriched spots from the aging (middle) and malaria disease (right) mouse brain Visium samples. b , As in ( a ) but for the Oligodendrocyte assigned spots (Stereo-seq) and Oligodendrocyte marker enriched spots (Visium). c , Normalized protein expression intensities of C4b and Serpina3n in young (3 month), adult middle aged (15 month), and old (24 month) mouse brain cortex (left) and hippocampus (right), as originally obtained by Tsumagari et al .

    Journal: bioRxiv

    Article Title: Spatiotemporal transcriptomic niches of complement pathway and serine protease inhibitor activation in aging and infection

    doi: 10.1101/2024.11.04.621811

    Figure Lengend Snippet: a , Combined analysis of C4b and Serpina3n by per-spot multiplication of normalized expression values across four groups of samples (young, old, control, infected). Shown are the combined expression values for Astrocyte assigned spots from the cell binning resolved brain1 Stereo-seq samples (left), and the Astrocyte marker enriched spots from the aging (middle) and malaria disease (right) mouse brain Visium samples. b , As in ( a ) but for the Oligodendrocyte assigned spots (Stereo-seq) and Oligodendrocyte marker enriched spots (Visium). c , Normalized protein expression intensities of C4b and Serpina3n in young (3 month), adult middle aged (15 month), and old (24 month) mouse brain cortex (left) and hippocampus (right), as originally obtained by Tsumagari et al .

    Article Snippet: One brain sample of each age was processed at the MGI Tech Co., Ltd. (Riga, Latvia) using the STOmics Stereo-seq Transcriptomics T Kit (MGI).

    Techniques: Expressing, Control, Infection, Marker

    a , Visualization of the five Bregmata selected to study different regions of the aging brain. b , From left to right: Two-dimensional UMAP representation of colored spot clusters computationally integrated by brain slice (top to bottom), pie chart showing the proportion of annotated clusters across all brain samples, one representative annotated Visium sample with the spot cluster identities plotted over the H&E-stained tissue image. c , Number of differentially expressed genes per aging brain bregma (old vs. young) and direction of dysregulation. Total DEG counts were derived across all organ clusters, without removing duplicates. d , Five-dimensional Venn diagram comparing the brain DEG sets from ( c ). e , Heatmap showing scaled expression of the 17 aging DEGs (rows) shared between all five brain slices, using the Brain1 pseudobulk samples and spot clusters for visualization (columns). All genes except Rbm3 are also significant SVGs in at least one of the five brain bregmata. f , Sketch of 10x Visium and STOmics Stereo-seq examples comparing the features of both spatial transcriptomics technology platforms. g , Examples for binned (bin200) and annotated spot clusters of the aging brain (top to bottom; young, middle, old) at Bregma#1 sequenced with Stereo-seq. h , Dot plot showing the top 3 most significant marker genes per cell type annotated spot cluster using the Stereo-seq cellbin resolution of Brain1 samples. i , Normalized spatial expression of Trem2 across all 15 STOmics Stereo-seq brain samples using the near-cellular resolution bin20 (from left to right: young, middle, old; from top to bottom: Brain1-5). For visualization spot sizes were rescaled into the point interval [0.1, 1.5] according to their expression of Trem2.

    Journal: bioRxiv

    Article Title: Spatiotemporal transcriptomic niches of complement pathway and serine protease inhibitor activation in aging and infection

    doi: 10.1101/2024.11.04.621811

    Figure Lengend Snippet: a , Visualization of the five Bregmata selected to study different regions of the aging brain. b , From left to right: Two-dimensional UMAP representation of colored spot clusters computationally integrated by brain slice (top to bottom), pie chart showing the proportion of annotated clusters across all brain samples, one representative annotated Visium sample with the spot cluster identities plotted over the H&E-stained tissue image. c , Number of differentially expressed genes per aging brain bregma (old vs. young) and direction of dysregulation. Total DEG counts were derived across all organ clusters, without removing duplicates. d , Five-dimensional Venn diagram comparing the brain DEG sets from ( c ). e , Heatmap showing scaled expression of the 17 aging DEGs (rows) shared between all five brain slices, using the Brain1 pseudobulk samples and spot clusters for visualization (columns). All genes except Rbm3 are also significant SVGs in at least one of the five brain bregmata. f , Sketch of 10x Visium and STOmics Stereo-seq examples comparing the features of both spatial transcriptomics technology platforms. g , Examples for binned (bin200) and annotated spot clusters of the aging brain (top to bottom; young, middle, old) at Bregma#1 sequenced with Stereo-seq. h , Dot plot showing the top 3 most significant marker genes per cell type annotated spot cluster using the Stereo-seq cellbin resolution of Brain1 samples. i , Normalized spatial expression of Trem2 across all 15 STOmics Stereo-seq brain samples using the near-cellular resolution bin20 (from left to right: young, middle, old; from top to bottom: Brain1-5). For visualization spot sizes were rescaled into the point interval [0.1, 1.5] according to their expression of Trem2.

    Article Snippet: One brain sample of each age was processed at the MGI Tech Co., Ltd. (Riga, Latvia) using the STOmics Stereo-seq Transcriptomics T Kit (MGI).

    Techniques: Slice Preparation, Staining, Derivative Assay, Expressing, Marker

    a , Illustration of the five different brain bregma used for STOmics Stereo-seq in accordance with the Visium data set. Representative H&E stains are shown for each Bregma. Since Stereo-seq does not support H&E stains directly from the sequenced tissue slices, an adjacent (directly before or after) tissue slice was prepared and stained before running the spatial transcriptomics experiments. b , From left to right and per brain bregma (top to bottom): integrated UMAP representation of all cleaned Stereo-seq spot clusters using the bin200 resolution, pie charts and per replicate spatial projections of the final annotated spot clusters. Cluster names and colors were assigned in accordance with the Visium data set (cf. Methods). c , Distribution of four main quality control features across the cleaned spots and per Stereo-seq brain replicate at bin200 resolution.

    Journal: bioRxiv

    Article Title: Spatiotemporal transcriptomic niches of complement pathway and serine protease inhibitor activation in aging and infection

    doi: 10.1101/2024.11.04.621811

    Figure Lengend Snippet: a , Illustration of the five different brain bregma used for STOmics Stereo-seq in accordance with the Visium data set. Representative H&E stains are shown for each Bregma. Since Stereo-seq does not support H&E stains directly from the sequenced tissue slices, an adjacent (directly before or after) tissue slice was prepared and stained before running the spatial transcriptomics experiments. b , From left to right and per brain bregma (top to bottom): integrated UMAP representation of all cleaned Stereo-seq spot clusters using the bin200 resolution, pie charts and per replicate spatial projections of the final annotated spot clusters. Cluster names and colors were assigned in accordance with the Visium data set (cf. Methods). c , Distribution of four main quality control features across the cleaned spots and per Stereo-seq brain replicate at bin200 resolution.

    Article Snippet: One brain sample of each age was processed at the MGI Tech Co., Ltd. (Riga, Latvia) using the STOmics Stereo-seq Transcriptomics T Kit (MGI).

    Techniques: Staining, Control