Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Cross-species single-cell transcriptomic analysis reveals divergence of cell composition and functions in mammalian ileum epithelium [scRNA-seq_Rat]

ABSTRACT: Purpose: To reveal the differences on epithelial cell composition, functional assignments and pharmacokinetics among mammals intestine, we use single cell sequencing to examine the conserved and divergent features among species, providing a cross-species single-cell transcriptomic atlases of ileal epithelium Methods:Alive ileum epithelial cells were sorted from three adult rats (3 months old). ScRNA-seq libraries were generated using the Chromium Single Cell 3’ Reagent Ki V3 (10X Genomics). The libraries were sequenced as paired-end with Illumina Novaseq 6000. Raw reads were aligned to the Rnor_6.0 Rat genome, and Cell Ranger (v3.1.0) was used to estimate unique molecular identifiers (UMIs). Raw aligned features were loaded and processed using the Seurat package (v4.0.2). Low-quality cells were filtered if they expressed no more than 200 genes or with more than 20% of mitochondrial genes. Results: After cells with low information content and a high fraction of mitochondrial RNAs were excluded, 3040 cells were analyzed. Conclusions: Seven cell types were identified in rat ileum based on reported markers, including enterocytes, transient-amplifying (TA) cells, goblet cells, goblet progenitor cells, stem cells, enteroendorcine cells and tuft cells.

ORGANISM(S): Rattus norvegicus

PROVIDER: GSE196660 | GEO | 2022/05/27

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Cross-species single-cell transcriptomic analysis reveals divergence of cell composition and functions in mammalian ileum epithelium [scRNA-seq_Mouse]

Project description:Purpose: To reveal the differences on epithelial cell composition, functional assignments and pharmacokinetics among mammals intestine, we use single cell sequencing to examine the conserved and divergent features among species, providing a cross-species single-cell transcriptomic atlases of ileal epithelium Methods:Alive ileum epithelial cells were sorted from three adult mice (10 weeks). ScRNA-seq libraries were generated using the Chromium Single Cell 3’ Reagent Ki V3 (10X Genomics). The libraries were sequenced as paired-end with Illumina Novaseq 6000. Raw reads were aligned to the GRCm38/mm10 Mouse genome, and Cell Ranger (v3.1.0) was used to estimate unique molecular identifiers (UMIs). Raw aligned features were loaded and processed using the Seurat package (v4.0.2). Low-quality cells were filtered if they expressed no more than 200 genes or with more than 20% of mitochondrial genes. Results: After cells with low information content and a high fraction of mitochondrial RNAs were excluded, 1060 cells were analyzed. Conclusions: Eight cell types were identified in mouse ileum based on reported markers, including enterocytes, transient-amplifying (TA) cells, goblet cells, goblet progenitor cells, stem cells, Paneth cells, enteroendorcine cells and tuft cells.

2022-05-27 | GSE196662 | GEO

Cross-species single-cell transcriptomic analysis reveals divergence of cell composition and functions in mammalian ileum epithelium [scRNA-seq_Pig]

Project description:Purpose: To reveal the differences on epithelial cell composition, functional assignments and pharmacokinetics among mammals intestine, we use single cell sequencing to examine the conserved and divergent features among species, providing a cross-species single-cell transcriptomic atlases of ileal epithelium Methods:Alive ileum epithelial cells were sorted from three adult pigs (6 months old). ScRNA-seq libraries were generated using the Chromium Single Cell 3’ Reagent Ki V3 (10X Genomics). The libraries were sequenced as paired-end with Illumina Novaseq 6000. Raw reads were aligned to the Sscrofa11.1 Pig genome, and Cell Ranger (v3.1.0) was used to estimate unique molecular identifiers (UMIs). Raw aligned features were loaded and processed using the Seurat package (v4.0.2). Low-quality cells were filtered if they expressed no more than 200 genes or with more than 20% of mitochondrial genes. Results: After cells with low information content and a high fraction of mitochondrial RNAs were excluded, 730 cells were analyzed. Conclusions: Eight cell types were identified in pig ileum based on reported markers, including enterocytes, transient-amplifying (TA) cells, goblet cells, goblet progenitor cells, stem cells, enteroendorcine cells, CA7+ cells and tuft cells.

2022-05-27 | GSE196659 | GEO

Cross-species single-cell transcriptomic analysis reveals divergence of cell composition and functions in mammalian ileum epithelium [scRNA-seq_Macaque]

Project description:Purpose: To reveal the differences on epithelial cell composition, functional assignments and pharmacokinetics among mammals intestine, we use single cell sequencing to examine the conserved and divergent features among species, providing a cross-species single-cell transcriptomic atlases of ileal epithelium Methods:Alive ileum epithelial cells were sorted from two cynomolgus monkeys (14 years old). ScRNA-seq libraries were generated using the Chromium Single Cell 3’ Reagent Ki V3 (10X Genomics). The libraries were sequenced as paired-end with Illumina Novaseq 6000. Raw reads were aligned to the Mmul_10 Macaque genome, and Cell Ranger (v3.1.0) was used to estimate unique molecular identifiers (UMIs). Raw aligned features were loaded and processed using the Seurat package (v4.0.2). Low-quality cells were filtered if they expressed no more than 200 genes or with more than 20% of mitochondrial genes. Results: After cells with low information content and a high fraction of mitochondrial RNAs were excluded, 886 cells were analyzed. Conclusions: Eight cell types were identified in macaque ileum based on reported markers, including enterocytes, transient-amplifying (TA) cells, goblet cells, goblet progenitor cells, stem cells, enteroendorcine cells, CA7+ cells and tuft cells.

2022-05-27 | GSE196658 | GEO

m6A modification segregates the stemness program from proliferation in ISCs [scRNA-seq]

Project description:Purpose: To monitor the different of ISCs and cell fate alteration upon Mettl3 deletion, we use single cell sequencing to examine the sequence information from individual cells with optimized next-generation sequencing (NGS) technologies, providing a single-cell resolution of cellular differences. Methods:Alive intestinal epithelial cells were sorted from WT and Mettl3-KO mice at 4 dpt. ScRNA-seq libraries were generated using the Chromium Single Cell 3’ Reagent Ki V3 (10X Genomics). The libraries were sequenced as paired-end with Illumina Novaseq 6000. Raw reads were aligned to the GRCm38/mm10 mouse genome, and Cell Ranger (v3.1.0) was used to estimate unique molecular identifiers (UMIs). Raw aligned features were loaded and processed using the Seurat package (v4.0.2). Low-quality cells were filtered if they expressed no more than 200 genes or with more than 20% of mitochondrial genes. Results: After cells with low information content and a high fraction of mitochondrial RNAs were excluded, 7865 cells (4467 WT and 3398 KO) were analyzed. Conclusions: Mettl3 deletion reduced the expression of stem cell signature genes but unchanged proliferation profile in ISCs. Thus, we conclude that Mettl3 deletion leads to loss of stemness but not proliferation, and enhanced regeneration and apoptosis.

2023-04-13 | GSE186913 | GEO

Next Generation Sequencing measuring Nod-like receptors mRNA expression in intestinal epithelial cells

Project description:Intestinal epithelisal cells were obtained by EDTA isolation of ileum from WT mice followed by facs sorting (EpCAM + CD45 – 7AAD – ). Raw sequencing reads were aligned to the mouse genome (mm10) with Tophat , and gene expression levels were measured by Cufflinks .

2017-06-29 | GSE98388 | GEO

Tcells_CD_scRNAseq

Project description:This set features unfiltered, aligned, UMI-based single cell RNA sequencing count data for 5290 Blood, intraepithelial ileum (IEL) and lamina propria ileum (LPL) T cells from Crohn's disease patients as published in Uniken Venema et al, Gastroenterology 2019

| EGAD00010001649 | EGA

High resolution single cell transcriptomics reveals heterogeneity of self-renewing hair follicle stem cells

Project description:Purpose: To dissect the transcriptomic profiles and unravel population-specific transcriptional heterogeneity of self renewing hair follicle stem cells in vivo Methods: We performed 10x genomics single-cell RNA sequencing (scRNA-seq) of FACS sorted CD34+/K14-H2BGFP+ hair follicle stem cells from mouse skin at mid-anagen. FACS purified CD34+/K14-H2BGFP+ single-cell suspension was processed for the barcoded single-cell 3′ cDNA libraries generation using Chromium Single Cell 3′ gel bead and library Kit v3. The final libraries were quantified using Agilent Bioanalyzer high sensitivity DNA chip and sequenced using an Illumina NextSeq-500. The raw data files were demultiplexed to generate the sample-specific FASTQ files, which were aligned to the mouse reference genome (mm10-3.0.0) using the 10x Genomics Cell Ranger pipeline (v3.1.0). The raw scRNA-seq data was processed using Cell Ranger from the 10x platform to generate an expression matrix that was further analyzed in R using the Seurat package version 3.1. Only high-quality cells that had between 200 and 5000 genes expressed and had under 10% of the UMIs mapped to mitochondrial genes were retained. Results: We obtained a total of 6736 high quality cells from two datasets for further analysis by Seurat workflow Conclusions: Obtained high quality single cell transcriptomic data to dissect molecular heterogeneity of hair follicle stem cells

2021-04-14 | GSE162333 | GEO

BRIN-BD11 pancreatic beta cell mRNA profile upon treatment with Class1 HDAC inhibitor MS-275

Project description:Methods: We performed mRNA sequence analysis by deep sequencing, in triplicate, using Illumina HiSeq to assess the impact of Class1 HDAC inhibitor MS-275 in regulating gene expression in BRIN-BD11 pancreatic beta cells. The transcriptome libraries were constructed using the NEB adapters and were sequenced on at 150 nucleotide read length using the paired-end chemistry. The raw reads were subjected to Adapter and low quality reads removal by Trimmomatic -0.36v. The sequence reads that passed quality filters was mapped to the Rattus_norvegicus Rnor_6.0 using STAR , counted using feature count module of sub reads package and was normalized in DESeq2-3. Differentially expressed genes were selected based on log2-ratio change with p-value <0.05 (Student‘s t test, unpaired). Hierarchical clustering was performed with the programs Cluster (uncentered correlation; average linkage clustering) and Treeview. Results: We mapped 136,002,940 (136 million reads) reads per sample to rat genome (Rnor_6.0) and observed 97% alignment to the reference rat genome. We found that 1858 genes were up regulated and 624 genes got down regulated on MS-275 treatment (log fold change ≥±2 p value < 0.05). Inference: Gene ontology reveals that the upregulated pathways upon MS-275 treatment include endocytosis, cAMP signaling, insulin secretion, Wnt signaling PI3K-Akt signaling and thermogenesis while the downregulated genes include histone modification, chromosome organization and cell cycle regulation. The study represents first detailed mRNA profiling of BRIN-BD11 pancreatic beta cell in context of modulation of expression upon treatment with Class1 HDAC inhibitor MS-275.

2019-10-22 | GSE139147 | GEO

Epithelial cell diversity in small intestine of pigs

Project description:To establish better understanding of specific epithelial cells found across different regions of the small intestine in pigs, we utilized single-cell RNA sequencing (scRNA-seq) to recover and analyze epithelial cells from duodenum, jejunum, and ileum. Cells identified included crypt cells, enterocytes, BEST4 enterocytes, goblet cells, and enteroendocrine (EE) cells. Overall, results provide new information on regional localization and transcriptional profiles of epithelial cells in the pig small intestine.

2023-01-18 | GSE208613 | GEO

Epidermal basal domains organization highlights skin robustness to environmental exposure [scRNA-seq]

Project description:Purpose: To unravel the molecular heterogeneity and characterise cell states of LRC/Non-LRC domains in interfollicular epidermis in vivo Methods: We performed 10x genomics single-cell RNA sequencing (scRNA-seq) of FACS purified basal (Sca1+/alpha6-integrin+) IFE cells as H2BGFP LRCs, mid-LRCs, and non-LRCs from K5-tTA x pTRE-H2BGFP mice. Single-cell suspension from back (2-week chase) and tail skin (3-week chase) was processed for the barcoded single-cell 3′ cDNA libraries generation using Chromium Single Cell 3′ gel bead and library Kit v3. The final libraries were quantified using Agilent Bioanalyzer high sensitivity DNA chip and sequenced using an Illumina NextSeq-500. The raw data files were demultiplexed to generate the sample-specific FASTQ files, which were aligned to the mouse reference genome (mm10-3.0.0) using the 10x Genomics Cell Ranger pipeline (v3.1.0). The raw scRNA-seq data was processed using Cell Ranger from the 10x platform to generate an expression matrix that was further analyzed in R using the Seurat package version 3.1. Only high-quality cells that had between 200 and 5000 genes expressed and had under 10% of the UMIs mapped to mitochondrial genes were retained. Results: We obtained a total of 13484 and 14884 high quality cells from two different skin types after combining two datasets each from LRCs/mid-LRCs/Non-LRCs for further analysis by Seurat workflow Conclusions: Obtained high quality single cell transcriptomic data to dissect molecular and proliferative heterogeneity of interfollicular epidermis (IFE)

2022-07-05 | GSE205745 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data