Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA427695 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6424940.fastq.gz | Fastqsanger.gz | |||
| SRR6424950.fastq.gz | Fastqsanger.gz | |||
| SRR6424931.fastq.gz | Fastqsanger.gz | |||
| SRR6424941.fastq.gz | Fastqsanger.gz | |||
| SRR6424951.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 28 |
Similar Datasets
Project description:We molecularly characterized thousands of liver endothelial cells and immune cells using scRNAseq. We next sequenced pairs of hepatocytes and attached endothelial cells, and used the hepatocyte genes to infer the liver lobule coordinates of these pairs, thus obtaining the global zonation patterns of endothelial genes.
2018-09-17 | GSE108561 | GEO
Project description:Spatially resolved single-cell RNA sequencing (scRNAseq) is a powerful approach for inferring connections between a cell's identity and its position in a tissue. We recently combined scRNAseq with spatially mapped landmark genes to infer the expression zonation of hepatocytes. However, determining zonation of small cells with low mRNA content, or without highly expressed landmark genes, remains challenging. Here we used paired-cell sequencing, in which mRNA from pairs of attached mouse cells were sequenced and gene expression from one cell type was used to infer the pairs' tissue coordinates. We applied this method to pairs of hepatocytes and liver endothelial cells (LECs). Using the spatial information from hepatocytes, we reconstructed LEC zonation and extracted a landmark gene panel that we used to spatially map LEC scRNAseq data. Our approach revealed the expression of both Wnt ligands and the Dkk3 Wnt antagonist in distinct pericentral LEC sub-populations. This approach can be used to reconstruct spatial expression maps of non-parenchymal cells in other tissues.
| S-EPMC6546596 | biostudies-literature
Project description:HCC is a highly vascular tumor, and many effective drug regimens target the tumor blood vessels. Prior bulk HCC subtyping data used bulk transcriptomes, which contained a mixture of parenchymal and stromal contributions. Using cell type–specific spatial transcriptomics techniques to separate cancer cells and endothelial cells applied to a set of 41 resected HCC tissue specimens, we report that the prior Hoshida bulk transcriptional subtyping schema is driven largely by an endothelial fraction, show an alternative tumor-specific schema has potential prognostic value, and use spatially paired ligand-receptor analyses to identify known and novel (LGALS9 tumor-HAVCR2 vessel) signaling relationships that drive HCC biology in a subtype-specific and potentially targetable manner. Our study leverages spatial gene expression profiling technologies to dissect HCC heterogeneity and identify heterogeneous sig- naling relationships between cancer cells and their endothelial cells. Future validation and expansion of these findings may validate novel cancer- endothelial cell interactions and related drug targets.
2024-09-23 | GSE277104 | GEO
Project description:Spatial RNA-seq were successfully constructed through expression profiling of a total of 18,694 genes in non-small cell lung cancer (NSCLC) cases with paired brain metastases.
2022-04-25 | GSE200563 | GEO
Project description:Here, we have developed a novel methodology called IRIS (Imaging Reconstruction using Indexed Sequencing) that enables cost-effective spatial transcriptomics profiling without relying on optical imaging. Through neighborhood interaction-based reconstruction, IRIS allows extensive analysis of large tissue sections and many replicates with adjustable mapping resolution at only a fraction of the cost of other commercial platforms. With the IRIS platform, we reconstructed a large area spatial area with two whole mouse brain coronal sections. Moreover, we also created a spatially resolved transcriptome atlas of the mouse brain and identified aging-associated changes in gene expression and spatial organization across various brain cell types. Further analysis of cell-cell interaction changes identified aging-associated foci in white matter regions enriched with inflammatory subtypes of microglia and oligodendrocytes. Overall, the IRIS methodology cost-effective and ease-of-use approach makes it broadly applicable to the studies of spatial gene expression changes in various systems.
2024-09-12 | GSE270383 | GEO
Project description:We generated a paired snRNA-seq (n= 15) and spatial transcriptomics (n=19) dataset from subcortical chronic active and chronic inactive MS lesions, identifying spatial niches and key cell interactions driving inflammation and disease progression at the lesion rim. This repository offers access to all the trancriptomics data that was used in the paper. It includes, all FASTQ files for both transcriptomics, along with the necessary files for running spatial transcriptomic samples (H&E images and JSON files), as well as the curated atlas, all derived cell subtype atlases from the main atlas and all curated ST slides.
2024-10-21 | GSE279181 | 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. 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 previously identified somatic activating KRAS mutations in a majority of human arteriovenous malformations (AVMs), using whole exome sequencing, which were enhanced in AVM endothelial cell fractions. We have now performed whole genome sequencing on AVM endothelial and non-endothelial cell fractions, as well as paired blood samples, in order to identify further somatic mutations.
| EGAS00001006719 | EGA
Project description:To study the spatial localisations of the cell populations in an early haematopoietic tissue and lymphoid organs critical for T and B cell development, we profiled fetal liver, thymus and spleen from 3 donors at 18 PCW with sequencing-based spatial transcriptomics (10x Genomics Visium).
2022-05-12 | E-MTAB-11341 | biostudies-arrayexpress
Project description:The mammalian liver is composed of repeating hexagonal units termed lobules. Spatially resolved single-cell transcriptomics revealed that about half of hepatocyte genes are differentially expressed across the lobule, yet technical limitations impeded reconstructing similar global spatial maps of other hepatocyte features. Here, we show how zonated surface markers can be used to sort hepatocytes from defined lobule zones with high spatial resolution. We apply transcriptomics, miRNA array measurements and mass spectrometry proteomics to reconstruct spatial atlases of multiple zonated features. We demonstrate that protein zonation largely overlaps with mRNA zonation, with the periportal HNF4? as an exception. We identify zonation of miRNAs such as miR-122, and inverse zonation of miRNAs and their hepatocyte target genes, highlighting potential regulation of protein levels through zonated mRNA degradation. Among the targets we find the pericentral Wnt receptors Fzd7 and Fzd8 and the periportal Wnt inhibitors Tcf7l1 and Ctnnbip1. Our approach facilitates reconstructing spatial atlases of multiple cellular features in the liver and other structured tissues.
| S-EPMC6751089 | biostudies-literature