Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Smart-seq2 single-cell RNA sequencing of mouse liver non-parenchymal cells

ABSTRACT: Smart-seq2 scRNA-seq of liver non-parenchymal cells from 3 high-fat diet and 3 control mice.

ORGANISM(S): Mus musculus

PROVIDER: GSE230440 | GEO | 2023/04/29

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Single cell RNA-sequencing of Spike-ins and mESC using Smart-Seq2 on C1 System

2017-03-29 | E-MTAB-5485 | biostudies-arrayexpress

Single cell RNA-sequencing of Spike-ins and mESC using Smart-Seq2 on C1 system (Replicate run)

2017-03-29 | E-MTAB-5484 | biostudies-arrayexpress

Single cell RNA-sequencing of Spike-ins and mESC using Smart-Seq2 on 96-well plates

Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we assess the RNA-degradation and decay rates by subjecting both spike-in molecules to range of repeated freezing and thawing (freeze-thaw) cycles. We manually add spike-in molecules across a 96-well plate (containing cells and reagents), perform Smart-Seq2 method manually and generate single-cell libraries using Nextera XT kit

2017-03-14 | E-MTAB-5481 | biostudies-arrayexpress

In situ transcriptome characteristics are lost following culture adaptation of adult cardiac stem cells (Smart-Seq2 scRNA-seq)

Project description:Single-cell RNA-seq (Smart-Seq2) to profile of cardiac progenitor cells

2018-09-12 | GSE114279 | GEO

[Smart-seq2] scRNA-seq of mouse Plasmodium-specific CD4+ T cells

Project description:[Smart-seq2] scRNA-seq of mouse Plasmodium-specific CD4+ T cells

| PRJEB40000 | ENA

Single-cell RNAseq (SMART-seq2) of wild-type (TLAB) and MZoep (tz57) zebrafish embryos at 50% epiboly stage

Project description:SMART-seq2 was performed on single cells isolated from visually staged zebrafish embryos.

2018-04-27 | GSE106466 | GEO

Smart-seq2 analysis of P17 FACS sorted retinal cells from the Kcng4-cre;stop-YFP X Thy1-stop-YFP Line#1 mice

Project description:Four Kcng4-cre;stop-YFP mouse retinas from two mice were dissected, dissociated and FACS sorted, and single cell RNA-seq libraries were generated for 384 single cells using Smart-seq2. Aligned bam files are generated for 383 samples as one failed to align. Four mouse retinas (labeled 1la, 1Ra, and 2la, 2Ra respective from the two mice) were used, and 96 single cells from each were processed using Smart-seq2. Total 384 cells Smart-seq2 analysis of P17 FACS sorted retinal cells from the Kcng4-cre;stop-YFP mice (Kcng4tm1.1(cre)Jrs mice [Duan et al., Cell 158, 793-807, 2015] crossed to the cre-dependent reporter Thy1-stop-YFP Line#1 [Buffelli et al., Nature 424, 430-434, 2003])

2016-08-25 | E-GEOD-81903 | biostudies-arrayexpress

Smart-seq2 analysis of P17 FACS sorted retinal cells from the Tg(Chx10-EGFP/cre,-ALPP)2Clc or Vsx2-GFP transgenic line

Project description:Three Vsx2-GFP mouse retinas were dissected, dissociated and FACS sorted, and single cell RNA-seq libraries were generated for 288 single cells and 3 bulk libraries using Smart-seq2 (~10,000 cells each)

2016-08-25 | GSE80232 | GEO

Smart-seq2 analysis of P17 FACS sorted retinal cells from the Kcng4-cre;stop-YFP X Thy1-stop-YFP Line#1 mice

2016-08-25 | GSE81903 | GEO

Direct Comparative Analysis of 10X Genomics Chromium and Smart-seq2

Project description:Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study.

| EGAS00001003973 | EGA

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