5d0fc3d3-e1be-418d-83fe-e59cd943311a - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001010140 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Angiosarcomas are rare malignant tumors of the endothelium, arising commonly from the head and neck region (AS-HN) and recently associated with ultraviolet (UV) exposure and human herpesvirus-7 (HHV-7) infection. We examined 81 cases of angiosarcomas, including 47 cases of AS-HN, integrating information from whole genome sequencing, gene expression profiling and spatial transcriptomics (10X Visium). In this dataset spatial transcriptomics revealed topological profiles of the tumor microenvironment, identifying dominant but tumor-excluded inflammatory signals in “immune-hot” cases and immune foci even in otherwise “cold” cases. In conclusion, spatial transcriptomics reveal the tumor immune landscape of angiosarcoma, and in combination with multi-omic information, may improve implementation of treatment strategies.
2023-03-19 | GSE227469 | GEO
Project description:These data were used in the spatial transcriptomics analysis of the article titled \\"Single-Cell and Spatial Transcriptomics Analysis of Human Adrenal Aging\\".
2024-04-22 | E-MTAB-13991 | biostudies-arrayexpress
Project description:Angiosarcomas are rare malignant tumors of the endothelium, arising commonly from the head and neck region (AS-HN) and recently associated with ultraviolet (UV) exposure and human herpesvirus-7 (HHV-7) infection. We examined 81 cases of angiosarcomas, including 47 cases of AS-HN, integrating information from whole genome sequencing, gene expression profiling and spatial transcriptomics (10X Visium). In the AS-HN cohort, we observed recurrent somatic mutations in CSMD3 (18%), LRP1B (18%), MUC16 (18%), POT1 (16%) and TP53 (16%). UV-positive AS-HN harbored significantly higher tumor mutation burden (TMB) than UV-negative cases (p=0.0294). NanoString profiling identified three clusters with distinct tumor inflammation signature (TIS) scores (p<0.001). Spatial transcriptomics revealed topological profiles of the tumor microenvironment, identifying dominant but tumor-excluded inflammatory signals in “immune-hot†cases and immune foci even in otherwise “cold†cases. In conclusion, spatial transcriptomics reveal the tumor immune landscape of angiosarcoma, and in combination with multi-omic information, may improve implementation of treatment strategies.
| EGAS00001007083 | EGA
Project description:Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular resolution data, TopACT is able to pinpoint the spatial locations of individual sparsely dispersed cells without prior knowledge of cell boundaries. In extant mouse brain data, TopACT locates previously undetectable macrophages. Pairing TopACT with multiparameter persistent homology landscapes predicts immune cells forming a peripheral ring structure within kidney glomeruli in a murine model of lupus nephritis, which we experimentally validate with multiplex imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene expression to multicellular tissue organization.
2024-04-24 | GSE265819 | GEO
Project description:Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular resolution data, TopACT is able to pinpoint the spatial locations of individual sparsely dispersed cells without prior knowledge of cell boundaries. In extant mouse brain data, TopACT locates previously undetectable macrophages. Pairing TopACT with multiparameter persistent homology landscapes predicts immune cells forming a peripheral ring structure within kidney glomeruli in a murine model of lupus nephritis, which we experimentally validate with multiplex imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene expression to multicellular tissue organization.
2024-04-23 | GSE264334 | GEO
Project description:Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular resolution data, TopACT is able to pinpoint the spatial locations of individual sparsely dispersed cells without prior knowledge of cell boundaries. In extant mouse brain data, TopACT locates previously undetectable macrophages. Pairing TopACT with multiparameter persistent homology landscapes predicts immune cells forming a peripheral ring structure within kidney glomeruli in a murine model of lupus nephritis, which we experimentally validate with multiplex imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene expression to multicellular tissue organization.
2024-04-23 | GSE264321 | GEO
Project description:Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular resolution data, TopACT is able to pinpoint the spatial locations of individual sparsely dispersed cells without prior knowledge of cell boundaries. In extant mouse brain data, TopACT locates previously undetectable macrophages. Pairing TopACT with multiparameter persistent homology landscapes predicts immune cells forming a peripheral ring structure within kidney glomeruli in a murine model of lupus nephritis, which we experimentally validate with multiplex imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene expression to multicellular tissue organization.
2024-04-23 | GSE264393 | GEO
Project description:CRISPR/Cas9 lethality screens in a set of Asian head and neck cancer cell lines to identify novel targets.
| EGAS00001002204 | EGA
Project description:Sage performed on microdissection of Head and Neck tumor, and Head and Neck normal tissue comparative analysis of gene expression profiles of head and neck squamous cell carcinoma and Head and Neck normal tissue
2008-09-30 | E-GEOD-11986 | biostudies-arrayexpress
Project description:Sage performed on microdissection of Head and Neck tumor, and Head and Neck normal tissue
2008-09-30 | GSE11986 | GEO