E331a5e6-7268-4152-b6cc-99c819195f21 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001011114 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Animal R.401 was infused with ex-vivo expanded T-regulatory cells. After adoptive transfer, PBMC were obtained and stained with surface antibodies. Single cell libraries from sorted populations were generated using a UMI-based droplet-partitioning platform (10X Genomics) and sequenced using a NextSeq 500 (Illumina). Resulting reads were processed using Cellranger software (10X Genomics) and downstream analysis was performed using Monocle6,7 using a negative binomial model of distribution with fixed variance.
2019-10-24 | GSE139200 | GEO
Project description:Human synovial Single cell RNA-seq was performed on three tissue samples from healthy donors. This experiment was done to explore the heterogeneity of cells in healthy human synovial joint and enabled the comparison of cellular states and composition to those of publicly available single cell RNA-seq datasets from psoriatic arthritis and rheumatoid arthritis patients. Human synovial cells were loaded immediately after tissue dissociation with up to 25,000 cells in a single well of a Chromium chip G (10x Genomics). 3’ gene expression libraries were generated using Chromium Next GEM Single Cell 3' Kit 3.1 with 3' Feature Barcode Kit and dual indexing (10x Genomics protocol CG000316 Rev C). Libraries were sequenced as paired end (PE) 150 bp by Illumina sequencing to 65-80% saturation. Reads were mapped to the GRCh38 human genome (GENCODE) using the 10x Genomics Cell Ranger pipeline (7.2.0).
2024-08-06 | E-MTAB-14339 | biostudies-arrayexpress
Project description:Purpose: The 10x Genomics Visium platform allows us to define the spatial topography of gene expression and provides detailed molecular maps that overcome limitations associated with sn/scRNA-seq and microscopy-based spatial transcriptomics methods. The goals of this study are to compare and identify unique transcriptome profiling (RNA-seq) signature between unfavorable and favorable Wilms Tumors and against human fetal kidney. Methods: Human fetal kidney and Wilms tumor spatial topography of gene expression were generated using the 10X Visium platform Results: Using an optimized data analysis workflow, we mapped the reads to the hg38 genome build and grouped the spots into 9 clusters based on gene expression profiles. Conclusion: Our study represents the first implement of Visium technology in human fetal kidney and Wilms Tumor tissue, providing a number of important functional insights about the spatial and molecular definitions of cell populations across human fetal kidney and different subtypes of Wilms Tumor through analyzing gene expression within the intact spatial organization of the human samples.
2023-07-01 | GSE178349 | 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 thus isolated hippocampus from rhesus macaques and performed single-cell RNA-sequencing analysis. The scRNA-Seq libraries were generated using the 10X Genomics Chromium Controller Instrument and Chromium Single Cell 3’ V2 Reagent Kits (10X Genomics, Pleasanton, USA).
2023-06-12 | GSE153598 | GEO
Project description:The goals of this study are to determine tissue composition of human lung organoids (hLOs) when maintained long-term (day 230). hLOs consist of alveolar epithelial cells, mesenchymal/endothelial cells, smooth muscle cells and immune cells. After dissociated hLOs, a target capture of 1x 104 cells was performed using the 10X Genomics Chromium Single Cell RNA sequencing. Briefly, single cell gel bead-in-emulsions(GEMs) are generated by passing cells with enzyme mix, partitioning oil, and barcoded gel beads by 10X Chromium chip. After GEM formation, the gel bead is dissolved and co-partitioned cell is lysed. Subsequently, reverse transcription (RT) occurs inside GEMs and barcoded full-length cDNA is generated. After RT, amplified cDNAs with barcode sequences (cell index and UMI) are pooled and single cell library is constructed using the Single Cell 3` Reagent Kit (v3 chemistry). The resulting library was sequenced on illumina HiseqX platform with 150bp paired end. Raw base calling files generated by illumine sequencing were demultiplexed based on the sample index read to generate FASTQ files using the 10X Genomics Cell Ranger (v3.0.2) pipeline. The raw reads were trimmed from the 3’ end to get the recommended number of cycle for read pairs (read1: 28bp; read2 : 91bp). The trimmed reads were mapped to the hg38 reference human genome with subsequent analysis, including filtering, barcode counting, and UMI counting. The resulting data were used to generate the multidimensional feature-barcode matrix using the CellRanger count with default parameters.
2020-01-03 | GSE142838 | GEO
Project description:Purpose: The goal of this study is to determine alterations in heart endothelial cell transcripts and cell populations associated with aging. Methods: Endothelial single cell mRNA profiles of 3 month and 24 month old wild-type mice were generated by 10X Genomics Results: Using an optimized data analysis workflow, we mapped about 50 thousand reads per cell Conclusions: Our study represents the first analysis of aged cardiac endothelial cell transcriptomes using single cell RNA-seq technology, identifiying specific transcripts and cell populations affected by age.
2021-05-28 | GSE163822 | GEO
Project description:We thus isolated Anterior cingulate cortex (ACC) and Retro splenial cortex (RSC) from rhesus macaques and mice and performed single-cell RNA-sequencing analysis.The scRNA-Seq libraries were generated using the 10X Genomics Chromium Controller Instrument and Chromium Single Cell 3’ V2 Reagent Kits (10X Genomics, Pleasanton, USA).
2024-05-30 | GSE183052 | GEO