An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus [spatial-visium]
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1103809 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR28784700_1.fastq.gz | Fastqsanger.gz | |||
| SRR28784700_2.fastq.gz | Fastqsanger.gz | |||
| SRR28784701_1.fastq.gz | Fastqsanger.gz | |||
| SRR28784701_2.fastq.gz | Fastqsanger.gz | |||
| SRR28784702_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 318 |
Similar Datasets
Project description:The hippocampus contains many unique cell types, which serve the structure’s specialized functions, including learning, memory and cognition. These cells have distinct spatial topography, morphology, physiology, and connectivity, highlighting the need for transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. We defined molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization and transfer learning, we integrated these data to define gene expression patterns within the snRNA-seq data and probe their expression in the SRT data. Using this approach, we leveraged existing rodent datasets that feature information on circuit connectivity and neural activity induction to make predictions about axonal projection targets and likelihood of ensemble recruitment in spatially-defined cellular populations of the human hippocampus. Finally, we integrated genome-wide association studies with transcriptomic data to identify enrichment of genetic components for neurodevelopmental, neuropsychiatric, and neurodegenerative disorders across cell types, spatial domains, and gene expression patterns of the human hippocampus. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.
2024-04-27 | GSE264692 | GEO
Project description:The hippocampus contains many unique cell types, which serve the structure’s specialized functions, including learning, memory and cognition. These cells have distinct spatial topography, morphology, physiology, and connectivity, highlighting the need for transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. We defined molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization and transfer learning, we integrated these data to define gene expression patterns within the snRNA-seq data and probe their expression in the SRT data. Using this approach, we leveraged existing rodent datasets that feature information on circuit connectivity and neural activity induction to make predictions about axonal projection targets and likelihood of ensemble recruitment in spatially-defined cellular populations of the human hippocampus. Finally, we integrated genome-wide association studies with transcriptomic data to identify enrichment of genetic components for neurodevelopmental, neuropsychiatric, and neurodegenerative disorders across cell types, spatial domains, and gene expression patterns of the human hippocampus. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.
2024-04-27 | GSE264624 | GEO
Project description:Multiple distinct cell types of the human lung and airways have been defined by single cell RNA sequencing (scRNAseq). Here we present a multi-omics spatial lung atlas to define novel cell types which we map back into the macro- and micro-anatomical tissue context to define functional tissue microenvironments. Firstly, we have generated single cell and nuclei RNA sequencing, VDJ-sequencing and Visium Spatial Transcriptomics data sets from 5 different locations of the human lung and airways. Secondly, we define additional cell types/states, as well as spatially map novel and known human airway cell types, such as adult lung chondrocytes, submucosal gland (SMG) duct cells, distinct pericyte and smooth muscle subtypes, immune-recruiting fibroblasts, peribronchial and perichondrial fibroblasts, peripheral nerve associated fibroblasts and Schwann cells. Finally, we define a survival niche for IgA-secreting plasma cells at the SMG, comprising the newly defined epithelial SMG-Duct cells, and B and T lineage immune cells. Using our transcriptomic data for cell-cell interaction analysis, we propose a signalling circuit that establishes and supports this niche. Overall, we provide a transcriptional and spatial lung atlas with multiple novel cell types that allows for the study of specific tissue microenvironments such as the newly defined gland-associated lymphoid niche (GALN).
2022-01-06 | E-MTAB-11640 | biostudies-arrayexpress
Project description:We developed cell2location, a principled and versatile Bayesian model that is designed to resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. To validate cell2location in real tissue, we applied the model to data from the mouse brain, which features diverse neural cell types organised in a well characterised spatial architecture across brain areas, thus presenting a canonical use case to test spatial genomics. We generated matched single nucleus (sn, this submission) and Visium spatial RNA-seq (10X Genomics) profiles of adjacent mouse brain sections that contain multiple regions from the telencephalon and diencephalon. To assess the biological and intra-organ technical variation in spatial mapping, we assayed two mouse brains and serial tissue sections from each brain (total of 3 and 2 matched sections from two animals, respectively, and an extra section for snRNA-seq), creating a rich multi-modal and replicated transcriptomic dataset. Tissue processing. Brains of wild-type adult C57BL/6 mice (postnatal day 56, 1 female and 1 male) were dissected, snap frozen, embedded in optimal cutting temperature compound (Tissue-Tek) and stored at -80oC. Brain hemispheres were cryosectioned at -20oC using a cryostat (Leica, CM3050S). To assess tissue quality, RNA was extracted from test tissue sections using the RNeasy Pico Kit (Qiagen) and yielded high RIN values (9.6 and 9.7) on an Agilent Bioanalyser, indicating high RNA quality. For matched single nuclei and Visium RNA-seq experiments, brain hemispheres were cryosectioned to adjacent thick (200 µm) and thin (10 µm) coronal sections, respectively, and processed the same day. In total, four consecutive sets of thick and thin tissue sections were collected from each brain. Five sets of tissue sections yielded both good quality single nuclei and Visium data (three adjacent sections from mouse 1 and two sections from mouse 2) while one additional section from mouse 2 yielded good single nuclei; these were considered for analysis in this study. Visium spatial transcriptomics. Thin (10 µm) mouse brain sections were cryosectioned and mounted directly onto separate capture areas on 10X Visium Spatial Gene Expression slides (beta product version). Processing was done per manufacturer’s protocols. Briefly, sections were methanol-fixed, hematoxylin and eosin (H&E)-stained, and imaged on a NanoZoomer 2.0 slide scanner (Hamamatsu). Sections were then permeabilized and further processed to obtain cDNA libraries that were quality controlled using the Agilent Bioanalyser. The cDNA libraries were sequenced on the Illumina HiSeq 4000 system, aiming at 300 million raw reads per section with read lengths 28cy R1, 8cy i7 index, 0cy i5 index, 91cy read 2. 10X Visium spatial sequencing data was aligned to mouse pre-mRNA genome reference version mm10 using 10X SpaceRanger and mRNA count matrices were generated by adding intronic and exonic reads for each gene in each location. The paired histology H&E images were processed using 10X SpaceRanger to select locations covered by tissue by aligning pre-recorded spot locations with fiducial border spots in the histology image. This allows evaluating the correspondence between cell maps produced using our method and the known brain anatomy. This also allows identifying the number of nuclei in each spot using nuclear segmentation as described in Suppl. Methods and reported in Fig S8A-D. The histology image was used to manually annotate cortical layers in the primary somatosensory cortex (SSp) region using the lasso tool in the 10X Loupe browser.
2021-11-12 | E-MTAB-11114 | biostudies-arrayexpress
Project description:We report a bulk transcriptomic dataset of mouse dorsal hippocampus to identify gene expression changes of spatial object recognition training. We report a spatial transcriptomics dataset of mouse brain tissue generated with the 10x Genomics Visium platform to identify gene expression profiles of spatial object recognition training. We report a single nuclei ATAC-seq and a Single nuclei RNA-sequencing using SPLiT-pool barcoding datasets of mouse dorsal hippocampus to identify chromatin and gene expression changes of spatial object recognition training.
2023-01-18 | GSE223066 | GEO
Project description:We developed cell2location, a principled and versatile Bayesian model that is designed to resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. To validate cell2location in real tissue, we applied the model to data from the mouse brain, which features diverse neural cell types organised in a well characterised spatial architecture across brain areas, thus presenting a canonical use case to test spatial genomics. We generated matched single nucleus (sn, this submission) and Visium spatial RNA-seq (10X Genomics) profiles of adjacent mouse brain sections that contain multiple regions from the telencephalon and diencephalon. To assess the biological and intra-organ technical variation in spatial mapping, we assayed two mouse brains and serial tissue sections from each brain (total of 3 and 2 matched sections from two animals, respectively, and an extra section for snRNA-seq), creating a rich multi-modal and replicated transcriptomic dataset. Tissue processing. Brains of wild-type adult C57BL/6 mice (postnatal day 56, 1 female and 1 male) were dissected, snap frozen, embedded in optimal cutting temperature compound (Tissue-Tek) and stored at -80oC. Brain hemispheres were cryosectioned at -20oC using a cryostat (Leica, CM3050S). To assess tissue quality, RNA was extracted from test tissue sections using the RNeasy Pico Kit (Qiagen) and yielded high RIN values (9.6 and 9.7) on an Agilent Bioanalyser, indicating high RNA quality. For matched single nuclei and Visium RNA-seq experiments, brain hemispheres were cryosectioned to adjacent thick (200 µm) and thin (10 µm) coronal sections, respectively, and processed the same day. In total, four consecutive sets of thick and thin tissue sections were collected from each brain. Five sets of tissue sections yielded both good quality single nuclei and Visium data (three adjacent sections from mouse 1 and two sections from mouse 2) while one additional section from mouse 2 yielded good single nuclei; these were considered for analysis in this study. Single nucleus RNA-sequencing. Thick (200 µm) mouse brain sections were cryosectioned, dissected from OCT and kept in a tube on dry ice until subsequent processing. Nuclei were extracted from each section as described previously. Briefly, nuclei were released from sections via Dounce homogenisation, Hoechst-stained, and isolated via fluorescence-activated cell sorting (FACS). Nuclei were then loaded into the 10X Chromium Single Cell 3′ Kit (v3) to obtain 3,000-7,000 nuclei per well, and library preparation was done per manufacturer’s protocol. Libraries were sequenced on an Illumina NovaSeq S4 system. Sequencing data were processed using 10X CellRanger version 3.0.2, aligned to mouse pre-mRNA genome reference version mm10 and mRNA count matrices were generated by adding intronic and exonic unique molecular identifier (UMI) counts for each gene in each cell. Initially, snRNA-seq counts were processed using standard Seurat V3 workflow without correcting batch effects between 6 individual samples.
2021-11-12 | E-MTAB-11115 | biostudies-arrayexpress
Project description:Liver fibrosis is a major cause of death worldwide. As a progressive step in chronic liver disease, fibrosis is almost always diagnosed too late with limited treatment options. Here, we uncover the spatial transcriptional landscape driving human liver fibrosis using single nuclei RNA and Assay for Transposase-Accessible Chromatin (ATAC) sequencing to deconvolute multi-cell spatial transcriptomic profiling in human liver cirrhosis.Through multi-modal data integration,we define molecular signatures driving cell state transitions in liver disease and define an impaired cellular response and directional trajectory from hepatocytes to cholangiocytes associated with disease remodelling.We identifypro-fibrogenic signatures in non-parenchymal cell subpopulations co-localised within the fibrotic niche and localise transitional cell states at the scar interface. This combined approach providesa spatial atlas of gene regulation and defines molecular signatures associated liver diseasefor targeted therapeutics or as early diagnostic markers of progressive liver disease. This is the 10X Visium data for the work.
2025-03-14 | E-MTAB-13132 | biostudies-arrayexpress
Project description:Spatial transcriptomics facilitates the understanding of gene expression within complex tissue contexts. Among the array of spatial capture technologies available is 10x Genomics’ Visium which provides whole tissue section profiling, enabling whole transcriptome spatial analysis. Our dataset comprises spleen tissue from mice infected with malaria, spanning multiple experiments and sample preparation protocols for tissue preservation, either as fresh frozen at optimal cutting temperature (OCT) or formalin-fixed paraffin-embedded (FFPE). Tissue placement was also considered, comparing direct tissue placement on the slide with the use of CytAssist (CA), which expands the Visium platform’s capabilities by allowing for the pre-selection of tissue sections and genes through a set of probes. We also include a matching scRNA-seq dataset that can be integrated with the spatial data.
2024-02-26 | GSE254652 | GEO
Project description:Liver fibrosis is a major cause of death worldwide. As a progressive step in chronic liver disease, fibrosis is almost always diagnosed too late with limited treatment options. Here, we uncover the spatial transcriptional landscape driving human liver fibrosis using single nuclei RNA and Assay for Transposase-Accessible Chromatin (ATAC) sequencing to deconvolute multi-cell spatial transcriptomic profiling in human liver cirrhosis.Through multi-modal data integration,we define molecular signatures driving cell state transitions in liver disease and define an impaired cellular response and directional trajectory from hepatocytes to cholangiocytes associated with disease remodelling.We identifypro-fibrogenic signatures in non-parenchymal cell subpopulations co-localised within the fibrotic niche and localise transitional cell states at the scar interface. This combined approach providesa spatial atlas of gene regulation and defines molecular signatures associated liver diseasefor targeted therapeutics or as early diagnostic markers of progressive liver disease. This is the 10X Visium data for the work.
2025-04-04 | E-MTAB-14960 | biostudies-arrayexpress