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stereo seq transcriptomics t kit v1 3  (Complete Genomics Inc)


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    Complete Genomics Inc stereo seq transcriptomics t kit v1 3
    Stereo Seq Transcriptomics T Kit V1 3, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 99/100, based on 472 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/pmc13134484-1369-0-6
    Average 99 stars, based on 472 article reviews
    stereo seq transcriptomics t kit v1 3 - by Bioz Stars, 2026-10
    99/100 stars

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    Related Articles

    Transcriptomics:

    Article Title: A spatial code governs olfactory receptor choice and aligns sensory maps in the nose and brain
    Article Snippet: MERSCOPE Cell Boundary Staining Kit , Vizgen , Cat# 10400118. .. Stereo-seq Transcriptomics T kit v1.3 , STOmics , Cat# 211KT13114-CG. .. Stereo-seq 16 Barcode Library Preparation Kit , STOmics , Cat # 111KL160-CG.

    Expressing:

    Article Title: Functional and Genetic Analyses Unveil the Implication of hoxa4a in Zebrafish Craniofacial Development
    Article Snippet: .. Analysis of hoxa4a expression patterns during early zebrafish embryogenesis was conducted using the Spatial Transcript Omics Database (STOmics DB) [20], incorporating both single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic (stereo-seq) datasets. ..

    Single Cell:

    Article Title: Functional and Genetic Analyses Unveil the Implication of hoxa4a in Zebrafish Craniofacial Development
    Article Snippet: .. Analysis of hoxa4a expression patterns during early zebrafish embryogenesis was conducted using the Spatial Transcript Omics Database (STOmics DB) [20], incorporating both single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic (stereo-seq) datasets. ..

    RNA Sequencing:

    Article Title: Functional and Genetic Analyses Unveil the Implication of hoxa4a in Zebrafish Craniofacial Development
    Article Snippet: .. Analysis of hoxa4a expression patterns during early zebrafish embryogenesis was conducted using the Spatial Transcript Omics Database (STOmics DB) [20], incorporating both single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic (stereo-seq) datasets. ..

    other:

    Article Title: Artificial Intelligence in Transcriptomics: From Human-in-the-Loop to Agentic AI.
    Article Snippet: Abbreviations: Serial Analysis of Gene Expression (SAGE); Expressed Sequence Tag (EST); In Situ Hybridization (ISH); Gene Expression Omnibus (GEO); Database for Gene Expression Evolution (Bgee); Sequence Read Archive (SRA); SPAtial transcriptomics annotation at Single-CEll Resolution (SPASCER); Panglao Database (PanglaoDB); Cancer Genome Anatomy Project (CGAP) uses SAGE; Human Cell Landscape (HCL); Genomic Data Commons (GDC) Data Portal; The Cancer Genome Atlas (TCGA); CellMiner Cross-DataBase (CDB); Chinese Glioma Genome Atlas (CGGA); IVY Glioblastoma Atlas Project (GAP); Open Pediatric Brain Tumor Atlas (OpenPBTA); Open Pediatric Cancer (OpenPedCan) Project; Single-Cell Pediatric Cancer Atlas (ScPCA); The Spinal Cord Injury (SCI) Myeloid Cell Atlas; Spatio-Temporal Cell Atlas of Brain (STAB2); Human Tumor Atlas Network (HTAN); Spatial Omics Resource of Cancer (SORC) Database; Comprehensive Repository of Spatial Transcriptomics (CROST); Spatial Transcript Omics DataBase (STOmics DB); Human Brain Transcriptome (HBT); National Institutes of Health (NIH) Blueprint Non-Human Primate (NHP) Atlas; Mouse Genome Informatics Gene Expression Database (MGI GXD); Adult Genotype–Tissue Expression (GTEx) Project; Brain Transcriptome (BrainTx) Database; Brain Initiative Cell Census Network (BICCN); Integrative Library of Integrated Network-Based Cellular Signatures (iLINCS); Database of Genotypes and Phenotypes (dbGaP); Alzheimer’s Disease (AD) Knowledge Portal; Aging, Dementia and Traumatic Brain Injury (TBI) Study; Common Metabolic Diseases Genome Atlas (CMDGA); Therapeutically Applicable Research to Generate Effective Treatments (TARGET); Single-Cell and Spatial RNA-Seq Database for Alzheimer’s Disease (ssREAD).

    Article Title: RegFormer: a single-cell foundation model powered by gene regulatory hierarchies.
    Article Snippet: We acknowledge the Stomics Cloud platform (https://cloud.stomics.tech/) for providing GPU computational resources.

    Sequencing:

    Article Title: Adult regenerative defects arise from discordant scaling of signal dependent growth and patterning
    Article Snippet: .. All subsequent steps, including cryosectioning, Stereo-seq library preparation, sequencing, and raw data processing, were performed at the BGI facility (Riga, Latvia) in collaboration with BGI, supported by a STOmics Grant awarded to E. Tanaka. .. All subsequent steps, including cryosectioning, Stereo-seq library preparation, sequencing, and raw data processing, were performed at the BGI facility (Riga, Latvia) in collaboration with BGI, supported by a STOmics Grant awarded to E. Tanaka.



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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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    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