Review



stereo seq technology  (Complete Genomics Inc)


Bioz Verified Symbol Complete Genomics Inc is a verified supplier
Bioz Manufacturer Symbol Complete Genomics Inc manufactures this product  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
  • 98

    Structured Review

    Complete Genomics Inc stereo seq technology
    Stereo Seq Technology, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 98/100, based on 33 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stereo+seq+spatial+transcriptomics+data/Stereo-seq+Transcriptomics+Set/pmc11379900-421-36-39
    Average 98 stars, based on 33 article reviews
    stereo seq technology - by Bioz Stars, 2026-09
    98/100 stars

    Images

    Related Articles

    Spatial Transcriptomics:

    Article Title: Single-cell Stereo-seq reveals regulatory mechanisms driving regeneration of injured proximal tubules during AKI.
    Article Snippet: .. Stereo-seq spatial transcriptomics data (BGI) were normalized using SCTransform in Seurat Spots with <1,000 total UMIs were filtered. ..

    Article Title: Somatic structural variants drive upper tract urothelial carcinoma muscle invasiveness via activation of TPX2 transcription.
    Article Snippet: .. 6 www.thelancet.com Vol 125 ▪, 2026 Spatial transcriptomics data generation and analysis Formalin-fixed paraffin-embedded (FFPE) urothelial carcinoma tissue sections were profiled using the Stereo-seq spatial transcriptomics platform (MGI, China) according to the manufacturer’s protocols. ..

    Article Title: A Foundational Generative Model for Cross-platform Unified Enhancement of Spatial Transcriptomics
    Article Snippet: .. These include sequencing-based: Visium (probe-based and polyA-based) , Visium CytAssist , and VisiumHD (all Visium platforms are products of 10X Genomics), Spatial Transcriptomics (Spatial Transcriptomics AB), Stereo-seq (BGI), BMK S1000 (BMKGENE), and Open-ST , and image-based platforms: Xenium (10X Genomics) and CosMx (Nanostring). ..

    Article Title: Somatic structural variants drive upper tract urothelial carcinoma muscle invasiveness via activation of TPX2 transcription
    Article Snippet: .. Formalin-fixed paraffin-embedded (FFPE) urothelial carcinoma tissue sections were profiled using the Stereo-seq spatial transcriptomics platform (MGI, China) according to the manufacturer's protocols. ..

    Formalin-fixed Paraffin-Embedded:

    Article Title: Somatic structural variants drive upper tract urothelial carcinoma muscle invasiveness via activation of TPX2 transcription.
    Article Snippet: .. 6 www.thelancet.com Vol 125 ▪, 2026 Spatial transcriptomics data generation and analysis Formalin-fixed paraffin-embedded (FFPE) urothelial carcinoma tissue sections were profiled using the Stereo-seq spatial transcriptomics platform (MGI, China) according to the manufacturer’s protocols. ..

    Article Title: Somatic structural variants drive upper tract urothelial carcinoma muscle invasiveness via activation of TPX2 transcription
    Article Snippet: .. Formalin-fixed paraffin-embedded (FFPE) urothelial carcinoma tissue sections were profiled using the Stereo-seq spatial transcriptomics platform (MGI, China) according to the manufacturer's protocols. ..

    Article Title: Mutational Signatures and Clonal Hematopoiesis in Intestinal Metaplasia across Countries with Varying Stomach Cancer Incidence
    Article Snippet: .. We used the STOmics Stereo-seq Transcriptomics Set for FFPE, which uses random primers to capture and sequence RNAs in situ . ..

    Article Title: Mutational Signatures and Clonal Hematopoiesis in Intestinal Metaplasia across Countries with Varying Stomach Cancer Incidence
    Article Snippet: across Countries with Varying Stomach Cancer Incidence Kie Kyon Huang1, Takeshi Hagihara1, Benedict Shi Xiang Lian1, Zhi Xuan Ong1, Shen Kiat Lim1, Roxanne Hui Heng Chong2, Supriya Srivastava2, Jason Xing Kang3, May Yin Lee4, Angie Lay-Keng Tan1, Minghui Lee1, Shamaine Wei Ting Ho4, Siti Aishah Binte Abdul Ghani1, Clara Shi Ya Ng1, Ruanyi Liang1, Lin Liu2, Su Ting Tay1, Xuewen Ong1, Feng Zhu2, Hui Chen5, Zhen Li5, Tiing Leong Ang6, Takuji Gotoda7,8, Robert J. Huang9, Christopher J.L.. Khor1,10, Hyun-Soo Kim11, Louis Ho Shing Lau12,13, Yi-Chia Lee14, Ayaka Takasu7,8, Ming Teh15, Mann Yie Thian16, Wai Leong Tam4,17,18,19, Xin Lu20, Sunny H. Wong3, Jimmy B.Y.. So21, Hyunsoo Chung22, Jonathan Lee2,23,24,25, Khay Guan Yeoh2,25, and Patrick Tan1,4,26,27,28; for the Singapore Gastric Cancer Consortium D ow nloaded from http://aacrjournals.org/cancerdiscovery/article-pdf/doi/10.1158/2159-8290.C D -25-0778/3726001/cd-25-0778.pdf by guest on 17 January 2026 AACRJournals.orgOF2 | CANCER DISCOVERY XXX 2026 intRoduction Gastric cancer is the fifth most common malignancy worldwide and the fourth leading cause of cancer death, accounting for 769,000 deaths globally in 2020 (1).

    Sequencing:

    Article Title: A Foundational Generative Model for Cross-platform Unified Enhancement of Spatial Transcriptomics
    Article Snippet: .. These include sequencing-based: Visium (probe-based and polyA-based) , Visium CytAssist , and VisiumHD (all Visium platforms are products of 10X Genomics), Spatial Transcriptomics (Spatial Transcriptomics AB), Stereo-seq (BGI), BMK S1000 (BMKGENE), and Open-ST , and image-based platforms: Xenium (10X Genomics) and CosMx (Nanostring). ..

    Article Title: Mutational Signatures and Clonal Hematopoiesis in Intestinal Metaplasia across Countries with Varying Stomach Cancer Incidence
    Article Snippet: .. We used the STOmics Stereo-seq Transcriptomics Set for FFPE, which uses random primers to capture and sequence RNAs in situ . ..

    Article Title: Mutational Signatures and Clonal Hematopoiesis in Intestinal Metaplasia across Countries with Varying Stomach Cancer Incidence
    Article Snippet: across Countries with Varying Stomach Cancer Incidence Kie Kyon Huang1, Takeshi Hagihara1, Benedict Shi Xiang Lian1, Zhi Xuan Ong1, Shen Kiat Lim1, Roxanne Hui Heng Chong2, Supriya Srivastava2, Jason Xing Kang3, May Yin Lee4, Angie Lay-Keng Tan1, Minghui Lee1, Shamaine Wei Ting Ho4, Siti Aishah Binte Abdul Ghani1, Clara Shi Ya Ng1, Ruanyi Liang1, Lin Liu2, Su Ting Tay1, Xuewen Ong1, Feng Zhu2, Hui Chen5, Zhen Li5, Tiing Leong Ang6, Takuji Gotoda7,8, Robert J. Huang9, Christopher J.L.. Khor1,10, Hyun-Soo Kim11, Louis Ho Shing Lau12,13, Yi-Chia Lee14, Ayaka Takasu7,8, Ming Teh15, Mann Yie Thian16, Wai Leong Tam4,17,18,19, Xin Lu20, Sunny H. Wong3, Jimmy B.Y.. So21, Hyunsoo Chung22, Jonathan Lee2,23,24,25, Khay Guan Yeoh2,25, and Patrick Tan1,4,26,27,28; for the Singapore Gastric Cancer Consortium D ow nloaded from http://aacrjournals.org/cancerdiscovery/article-pdf/doi/10.1158/2159-8290.C D -25-0778/3726001/cd-25-0778.pdf by guest on 17 January 2026 AACRJournals.orgOF2 | CANCER DISCOVERY XXX 2026 intRoduction Gastric cancer is the fifth most common malignancy worldwide and the fourth leading cause of cancer death, accounting for 769,000 deaths globally in 2020 (1).

    Transcriptomics:

    Article Title: Mutational Signatures and Clonal Hematopoiesis in Intestinal Metaplasia across Countries with Varying Stomach Cancer Incidence
    Article Snippet: .. We used the STOmics Stereo-seq Transcriptomics Set for FFPE, which uses random primers to capture and sequence RNAs in situ . ..

    Article Title: Mutational Signatures and Clonal Hematopoiesis in Intestinal Metaplasia across Countries with Varying Stomach Cancer Incidence
    Article Snippet: across Countries with Varying Stomach Cancer Incidence Kie Kyon Huang1, Takeshi Hagihara1, Benedict Shi Xiang Lian1, Zhi Xuan Ong1, Shen Kiat Lim1, Roxanne Hui Heng Chong2, Supriya Srivastava2, Jason Xing Kang3, May Yin Lee4, Angie Lay-Keng Tan1, Minghui Lee1, Shamaine Wei Ting Ho4, Siti Aishah Binte Abdul Ghani1, Clara Shi Ya Ng1, Ruanyi Liang1, Lin Liu2, Su Ting Tay1, Xuewen Ong1, Feng Zhu2, Hui Chen5, Zhen Li5, Tiing Leong Ang6, Takuji Gotoda7,8, Robert J. Huang9, Christopher J.L.. Khor1,10, Hyun-Soo Kim11, Louis Ho Shing Lau12,13, Yi-Chia Lee14, Ayaka Takasu7,8, Ming Teh15, Mann Yie Thian16, Wai Leong Tam4,17,18,19, Xin Lu20, Sunny H. Wong3, Jimmy B.Y.. So21, Hyunsoo Chung22, Jonathan Lee2,23,24,25, Khay Guan Yeoh2,25, and Patrick Tan1,4,26,27,28; for the Singapore Gastric Cancer Consortium D ow nloaded from http://aacrjournals.org/cancerdiscovery/article-pdf/doi/10.1158/2159-8290.C D -25-0778/3726001/cd-25-0778.pdf by guest on 17 January 2026 AACRJournals.orgOF2 | CANCER DISCOVERY XXX 2026 intRoduction Gastric cancer is the fifth most common malignancy worldwide and the fourth leading cause of cancer death, accounting for 769,000 deaths globally in 2020 (1).

    Article Title: Single-cell spatiotemporal dissection of the human maternal-fetal interface.
    Article Snippet: .. 10 μm cryosections were dissected using a Leica cryostat and mounted onto Stereo-seq transcriptomics T chips (Complete Genomics). ..

    In Situ:

    Article Title: Mutational Signatures and Clonal Hematopoiesis in Intestinal Metaplasia across Countries with Varying Stomach Cancer Incidence
    Article Snippet: .. We used the STOmics Stereo-seq Transcriptomics Set for FFPE, which uses random primers to capture and sequence RNAs in situ . ..

    Article Title: Mutational Signatures and Clonal Hematopoiesis in Intestinal Metaplasia across Countries with Varying Stomach Cancer Incidence
    Article Snippet: across Countries with Varying Stomach Cancer Incidence Kie Kyon Huang1, Takeshi Hagihara1, Benedict Shi Xiang Lian1, Zhi Xuan Ong1, Shen Kiat Lim1, Roxanne Hui Heng Chong2, Supriya Srivastava2, Jason Xing Kang3, May Yin Lee4, Angie Lay-Keng Tan1, Minghui Lee1, Shamaine Wei Ting Ho4, Siti Aishah Binte Abdul Ghani1, Clara Shi Ya Ng1, Ruanyi Liang1, Lin Liu2, Su Ting Tay1, Xuewen Ong1, Feng Zhu2, Hui Chen5, Zhen Li5, Tiing Leong Ang6, Takuji Gotoda7,8, Robert J. Huang9, Christopher J.L.. Khor1,10, Hyun-Soo Kim11, Louis Ho Shing Lau12,13, Yi-Chia Lee14, Ayaka Takasu7,8, Ming Teh15, Mann Yie Thian16, Wai Leong Tam4,17,18,19, Xin Lu20, Sunny H. Wong3, Jimmy B.Y.. So21, Hyunsoo Chung22, Jonathan Lee2,23,24,25, Khay Guan Yeoh2,25, and Patrick Tan1,4,26,27,28; for the Singapore Gastric Cancer Consortium D ow nloaded from http://aacrjournals.org/cancerdiscovery/article-pdf/doi/10.1158/2159-8290.C D -25-0778/3726001/cd-25-0778.pdf by guest on 17 January 2026 AACRJournals.orgOF2 | CANCER DISCOVERY XXX 2026 intRoduction Gastric cancer is the fifth most common malignancy worldwide and the fourth leading cause of cancer death, accounting for 769,000 deaths globally in 2020 (1).



    Similar Products

    98
    Complete Genomics Inc stereo seq spatial transcriptomics data
    Stereo Seq Spatial Transcriptomics Data, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 98/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stereo+seq+spatial+transcriptomics+data/Stereo-seq+Transcriptomics+Set/pm42091869-364-0-4
    Average 98 stars, based on 1 article reviews
    stereo seq spatial transcriptomics data - by Bioz Stars, 2026-09
    98/100 stars
      Buy from Supplier

    99
    Complete Genomics Inc spatial transcriptomics data
    Single-nucleus transcriptome and spatial <t>transcriptomics</t> landscape of the ileal tissue of SAP and CON group rats. (A) Schematic illustration of the workflow for this study. (B) Representative Hematoxylin and Eosin (H&E)–stained ileal sections from CON and SAP rats. (C) UMAP plot of single-nucleus transcriptome profiles of SAP and CON group samples. Colors indicate groups, clusters and cell types. (D) Heatmap plot of marker genes for cell annotation. (E) Bar plot showing cell-type proportions (mean ± SEM) in snRNA-seq data. (F) Spatial transcriptomics profiles of SAP and CON group samples. Colors indicate cell types. (G) Bar plot showing cell-type proportions (mean ± SEM) in spatial transcriptomics (Stereo-seq) data. Statistical significance: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
    Spatial Transcriptomics Data, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stereo+seq+spatial+transcriptomics+data/Stereo-seq+Transcriptomics+Set+for+FFPE/pmc12901460-62-1-11
    Average 99 stars, based on 1 article reviews
    spatial transcriptomics data - by Bioz Stars, 2026-09
    99/100 stars
      Buy from Supplier

    99
    Complete Genomics Inc spatial transcriptomic data
    Single-nucleus transcriptome and spatial <t>transcriptomics</t> landscape of the ileal tissue of SAP and CON group rats. (A) Schematic illustration of the workflow for this study. (B) Representative Hematoxylin and Eosin (H&E)–stained ileal sections from CON and SAP rats. (C) UMAP plot of single-nucleus transcriptome profiles of SAP and CON group samples. Colors indicate groups, clusters and cell types. (D) Heatmap plot of marker genes for cell annotation. (E) Bar plot showing cell-type proportions (mean ± SEM) in snRNA-seq data. (F) Spatial transcriptomics profiles of SAP and CON group samples. Colors indicate cell types. (G) Bar plot showing cell-type proportions (mean ± SEM) in spatial transcriptomics (Stereo-seq) data. Statistical significance: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
    Spatial Transcriptomic Data, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stereo+seq+spatial+transcriptomics+data/Stereo-seq+Transcriptomics+Set+for+FFPE/pm41449269-436-2-7
    Average 99 stars, based on 1 article reviews
    spatial transcriptomic data - by Bioz Stars, 2026-09
    99/100 stars
      Buy from Supplier

    98
    Complete Genomics Inc stereo seq spatial transcriptomic data
    Single-nucleus transcriptome and spatial <t>transcriptomics</t> landscape of the ileal tissue of SAP and CON group rats. (A) Schematic illustration of the workflow for this study. (B) Representative Hematoxylin and Eosin (H&E)–stained ileal sections from CON and SAP rats. (C) UMAP plot of single-nucleus transcriptome profiles of SAP and CON group samples. Colors indicate groups, clusters and cell types. (D) Heatmap plot of marker genes for cell annotation. (E) Bar plot showing cell-type proportions (mean ± SEM) in snRNA-seq data. (F) Spatial transcriptomics profiles of SAP and CON group samples. Colors indicate cell types. (G) Bar plot showing cell-type proportions (mean ± SEM) in spatial transcriptomics (Stereo-seq) data. Statistical significance: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
    Stereo Seq Spatial Transcriptomic Data, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 98/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stereo+seq+spatial+transcriptomics+data/Stereo-seq+Transcriptomics+Set/pm41125446-326-1-13
    Average 98 stars, based on 1 article reviews
    stereo seq spatial transcriptomic data - by Bioz Stars, 2026-09
    98/100 stars
      Buy from Supplier

    99
    Complete Genomics Inc cell level resolution spatial data
    (a) Simplified cross-section of the human epidermis, highlighting squamous cells, melanocytes and basal cells. Coloured regions represent cSCC (green), which originates from squamous cells, melanoma (orange), which originates from melanocytes, and BCC (blue), which originates from basal cells. Two orange melanocytes are shown in the dermal region as occurs in invasive melanoma; other cells in the lower dermis layer are not depicted. (b) Overview of sample design and technologies used to generate data for this project. ROI - region of interest; FOV - field of view; S - cSCC; B - BCC; M - melanoma; HC - healthy (cancer patient); HNC - healthy (non-cancer patient donor). Technologies included are <t>single</t> <t>cell</t> RNA sequencing for fresh samples, single nuclei sequencing for formalin-fixed samples, Visium, Xenium, CosMX, GeoMX DSP for whole transcriptome, GeoMX DSP for proteins, Polaris, RNAscope, the proximal ligation assay, spatial glycomics and CODEX.
    Cell Level Resolution Spatial Data, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stereo+seq+spatial+transcriptomics+data/Stereo-seq+Transcriptomics+Set+for+FFPE/bio_rxiv__2025__07__25__666708-218-8-14
    Average 99 stars, based on 1 article reviews
    cell level resolution spatial data - by Bioz Stars, 2026-09
    99/100 stars
      Buy from Supplier

    Image Search Results


    Single-nucleus transcriptome and spatial transcriptomics landscape of the ileal tissue of SAP and CON group rats. (A) Schematic illustration of the workflow for this study. (B) Representative Hematoxylin and Eosin (H&E)–stained ileal sections from CON and SAP rats. (C) UMAP plot of single-nucleus transcriptome profiles of SAP and CON group samples. Colors indicate groups, clusters and cell types. (D) Heatmap plot of marker genes for cell annotation. (E) Bar plot showing cell-type proportions (mean ± SEM) in snRNA-seq data. (F) Spatial transcriptomics profiles of SAP and CON group samples. Colors indicate cell types. (G) Bar plot showing cell-type proportions (mean ± SEM) in spatial transcriptomics (Stereo-seq) data. Statistical significance: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

    Journal: Frontiers in Immunology

    Article Title: Single-nucleus and spatial transcriptomics reveal intestinal cellular heterogeneity, differentiation, and cell communication mechanisms in SAP-induced intestinal injury

    doi: 10.3389/fimmu.2026.1719902

    Figure Lengend Snippet: Single-nucleus transcriptome and spatial transcriptomics landscape of the ileal tissue of SAP and CON group rats. (A) Schematic illustration of the workflow for this study. (B) Representative Hematoxylin and Eosin (H&E)–stained ileal sections from CON and SAP rats. (C) UMAP plot of single-nucleus transcriptome profiles of SAP and CON group samples. Colors indicate groups, clusters and cell types. (D) Heatmap plot of marker genes for cell annotation. (E) Bar plot showing cell-type proportions (mean ± SEM) in snRNA-seq data. (F) Spatial transcriptomics profiles of SAP and CON group samples. Colors indicate cell types. (G) Bar plot showing cell-type proportions (mean ± SEM) in spatial transcriptomics (Stereo-seq) data. Statistical significance: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

    Article Snippet: The spatial transcriptomics data were obtained according to the protocol of STOmics Gene Expression Set-S1 on the website ( https://www.stomics.tech/ ), which is an improved version of initial procedures.

    Techniques: Spatial Transcriptomics, Staining, Marker

    (a) Simplified cross-section of the human epidermis, highlighting squamous cells, melanocytes and basal cells. Coloured regions represent cSCC (green), which originates from squamous cells, melanoma (orange), which originates from melanocytes, and BCC (blue), which originates from basal cells. Two orange melanocytes are shown in the dermal region as occurs in invasive melanoma; other cells in the lower dermis layer are not depicted. (b) Overview of sample design and technologies used to generate data for this project. ROI - region of interest; FOV - field of view; S - cSCC; B - BCC; M - melanoma; HC - healthy (cancer patient); HNC - healthy (non-cancer patient donor). Technologies included are single cell RNA sequencing for fresh samples, single nuclei sequencing for formalin-fixed samples, Visium, Xenium, CosMX, GeoMX DSP for whole transcriptome, GeoMX DSP for proteins, Polaris, RNAscope, the proximal ligation assay, spatial glycomics and CODEX.

    Journal: bioRxiv

    Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

    doi: 10.1101/2025.07.25.666708

    Figure Lengend Snippet: (a) Simplified cross-section of the human epidermis, highlighting squamous cells, melanocytes and basal cells. Coloured regions represent cSCC (green), which originates from squamous cells, melanoma (orange), which originates from melanocytes, and BCC (blue), which originates from basal cells. Two orange melanocytes are shown in the dermal region as occurs in invasive melanoma; other cells in the lower dermis layer are not depicted. (b) Overview of sample design and technologies used to generate data for this project. ROI - region of interest; FOV - field of view; S - cSCC; B - BCC; M - melanoma; HC - healthy (cancer patient); HNC - healthy (non-cancer patient donor). Technologies included are single cell RNA sequencing for fresh samples, single nuclei sequencing for formalin-fixed samples, Visium, Xenium, CosMX, GeoMX DSP for whole transcriptome, GeoMX DSP for proteins, Polaris, RNAscope, the proximal ligation assay, spatial glycomics and CODEX.

    Article Snippet: Cells expressing the two genes are visualized on single-cell level resolution spatial data from STOmics and Curio-Seeker (Takara Bio, USA) melanoma samples and appear to be in spatial proximity ( ).

    Techniques: RNA Sequencing, Sequencing, RNAscope, Ligation

    (a) Gene specificity score (GSS) and association of spatial spots with skin cancer heritability. GSS score for each gene in a spot/cell represents the enrichment of the gene as a top rank most abundant gene in the spot/cell and its neighbour spots/cells in an anatomical region, a spatial domain, or a cell type. The p-value shows the spatial heritability enrichment significance of a spot with a trait based on SNPs mapped to the genes with high GSS scores (one-sided Z-test for stratified coefficient different to 0). The p-value is more significant if the SNPs that are mapped to the high GSS genes explain a higher proportion of heritability for the trait. (b) Cell types with the highest enrichment of heritability explained by SNPs tagged to GSS genes of cells in a cell type. The white asterisks indicate the most enriched cell-type for heritability of cutaneous melanoma, cSCC and BCC traits. (c) gsMAP significance spatial heritability enrichment is shown at single-cell resolution across the tissue (upper tissue plots) or per annotated skin regions (lower violin plots) from the cosMx data of the sample mel48974. (d) LR pairs with significant association with SNP heritability explained by the corresponding cell types. The rectangles show cases where both L and R genes had PCC >0.3 between GSS of the gene and the gsMAP P-values (the significance level for the LD stratified coefficients for the spot bigger than 0). The results suggest which LR pairs are related with the heritability of a cell type pairs. (e) GSS of two LR pairs showing specificity of the L and R genes to tissue regions at the immune-rich dermal layers and the epidermis of the skin. (f) Manhattan plot showing top significant GWAS SNPs co-localizing with genes in melanocytes (red) and T cells (blue) that had the highest Pearson correlation between GSS and the gsMAP trait association P-value or associated with SNPs with genome-wide significance. The Y-axis shows the -log(P-value) from GWAS analysis.

    Journal: bioRxiv

    Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

    doi: 10.1101/2025.07.25.666708

    Figure Lengend Snippet: (a) Gene specificity score (GSS) and association of spatial spots with skin cancer heritability. GSS score for each gene in a spot/cell represents the enrichment of the gene as a top rank most abundant gene in the spot/cell and its neighbour spots/cells in an anatomical region, a spatial domain, or a cell type. The p-value shows the spatial heritability enrichment significance of a spot with a trait based on SNPs mapped to the genes with high GSS scores (one-sided Z-test for stratified coefficient different to 0). The p-value is more significant if the SNPs that are mapped to the high GSS genes explain a higher proportion of heritability for the trait. (b) Cell types with the highest enrichment of heritability explained by SNPs tagged to GSS genes of cells in a cell type. The white asterisks indicate the most enriched cell-type for heritability of cutaneous melanoma, cSCC and BCC traits. (c) gsMAP significance spatial heritability enrichment is shown at single-cell resolution across the tissue (upper tissue plots) or per annotated skin regions (lower violin plots) from the cosMx data of the sample mel48974. (d) LR pairs with significant association with SNP heritability explained by the corresponding cell types. The rectangles show cases where both L and R genes had PCC >0.3 between GSS of the gene and the gsMAP P-values (the significance level for the LD stratified coefficients for the spot bigger than 0). The results suggest which LR pairs are related with the heritability of a cell type pairs. (e) GSS of two LR pairs showing specificity of the L and R genes to tissue regions at the immune-rich dermal layers and the epidermis of the skin. (f) Manhattan plot showing top significant GWAS SNPs co-localizing with genes in melanocytes (red) and T cells (blue) that had the highest Pearson correlation between GSS and the gsMAP trait association P-value or associated with SNPs with genome-wide significance. The Y-axis shows the -log(P-value) from GWAS analysis.

    Article Snippet: Cells expressing the two genes are visualized on single-cell level resolution spatial data from STOmics and Curio-Seeker (Takara Bio, USA) melanoma samples and appear to be in spatial proximity ( ).

    Techniques: Genome Wide