Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

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

PROVIDER: PRJNA416939 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

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

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

2023-04-29 | GSE230440 | GEO

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 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

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

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

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

Laser-capture microscopy coupled with Smart-seq2 (LCM-seq) for robust and efficient transcriptomic profiling of mouse and human tissues

Project description:Generally, when LCM is used in diverse transcriptomic analyses, several hundred, if not thousands, of cells are needed to obtain high quality of RNA-seq data. As some cellular populations are very small and tissue often in scarcity, we aimed to carefully document the lowest number of cells needed to retrieve sequencable libraries. We started with capturing 120 cells and subsequently scaled down to 50 cells, 30 cells, 10 cells, 5 cells, 2 cells and finally 1 cell. By optimizing multiple steps in the procedure, including direct lysis of cells without performing RNA isolation, we developed LCM-seq that couples LCM with Smart-seq2 for robust and efficient polyA-based RNA sequencing. We applied LCM-seq to mouse and human neuron samples, and demonstrated that LCM-seq can allow us to acquire high quality RNA-seq data from mouse and human tissues to conduct various transcriptomic studies. Developing new sequencing technology LCM-seq to efficiently sequence mouse and human tissues

2016-07-05 | E-GEOD-76514 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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