Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1314867 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Total abdominal colectomy (TAC) with a staged ileal pouch-anal anastomosis (IPAA) is a common surgical treatment for ulcerative colitis (UC). However, a significant percentage of patients experience pouch failure, leading to considerable morbidity. This retrospective case-control study aimed to identify histopathological features of the TAC specimen associated with subsequent pouch failure and to investigate the underlying molecular mechanisms of this susceptibility using single-cell spatial transcriptomics.
2025-02-26 | GSE283625 | GEO
Project description:Alternative splicing significantly contributes to transcriptome complexity and has critical implications for cellular functions. Recent advancements in single-cell isolation and capture techniques have enabled high-throughput quantification of gene expression at single-cell resolution. Long-read sequencing technologies can further be combined with single-cell technologies and enable an unambiguous identification of complete exon structures. Several computational methods have been developed to specifically address bioinformatics challenges associated with the processing of long read scRNA-seq data. Evaluating and comparing these computational methods becomes crucial. The goal of this study was to benchmark state-of-the-art computational tools for single-cell and spatial long-read transcriptomics. The scRNA-seq data were generated from two tumors developed by a mouse model, and designated as MPNST1 and MPNST2. Data were obtained by using the 10X Genomics technology, then generating sequencing libraries using either Illumina, Oxford Nanopore Technology (ONT) or scNaUmi-Seq protocols. Raw data were obtained after sequencing the libraries on Illumina, MinION or PromethION sequencing platforms. The two Illumina data were uploaded as part of the related submission E-MTAB-14222, with sample MPNST1 corresponding to 2020_23 and MPNST2 to 2022_26. This current submission contains the four long-read raw data et the data processed using the wf-single-cell pipeline. For the additional processed data, please refer to https://github.com/GenomiqueENS/scKenver.
2025-08-26 | E-MTAB-15190 | biostudies-arrayexpress
Project description:The pathophysiology of Crohn’s-like disease of the pouch (CDP) that develops after restorative proctocolectomy with ileal pouch-anal anastomosis (IPAA) for ulcerative colitis (UC) is unknown. We examined mucosal cells from patients with and without CDP using single cell analyses.
2024-08-02 | GSE246987 | GEO
Project description:This repository contains raw and processed targeted spatial transcriptomics data generated from cerebral cortex sections of APP23^het transgenic and wild-type mice at 13 and 24 months of age, as well as human Alzheimer’s disease cortical tissue. Spatial profiling was performed using the Molecular Cartography™ platform (Resolve Biosciences), an imaging-based highly multiplexed single-molecule fluorescence in situ hybridization (smFISH) technology. The dataset includes raw transcript coordinate files, as well as cell segmentation outputs generated by the platform’s segmentation algorithm. Both segmentation-free (transcript-based) and segmentation-based spatial analyses were performed. These data were used to investigate age-dependent immune cell composition and spatial enrichment of T-cell phenotypes in amyloid plaque-associated regions, as described in the associated manuscript. Detailed experimental procedures, gene panels, preprocessing steps, normalization, clustering, and spatial analysis methods are provided in the Methods section of the publication.
2026-04-01 | GSE325911 | GEO
Project description:Single-cell single-molecule spatial transcriptomics using CosMx on colectomy specimens collected during Pouch surgery in IBD patients.
| PRJNA1194636 | ENA
Project description:Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems. We generated single-cell RNA-seq profiles from dissociated cells from developing zebrafish embryos (late blastula stage - 50% epiboly)
2015-04-13 | E-GEOD-66688 | biostudies-arrayexpress