Single cell RNA-sequencing of Spike-ins and mESC using Smart-Seq2 on C1 system (Replicate run)
Ontology highlight
ABSTRACT: 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 perform a replicate of Smart-Seq2 method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit
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
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 perform Smart-Seq2 method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit
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 perform SMARTer method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit
Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. In this dataset, we assess the RNA detection rates using high-throughput 10x Genomics Chromium system. We mix equal volume of Control Brain RNA (3ul; FirstChoice Total Brain RNA; #AM7962) and ERCC spikes (3ul 1:4 dilution; #4456653) to make a '2x Control RNA+ERCC' master mix. The '2x Control RNA+ERCC' master mix is diluted with equal volume nuclease-free water to make '1x Control RNA+ERCC' master mix. 3ul of '1x Control RNA+ERCC' master mix is added to Chromium single cell suspension and processed as per Chromium guidelines. The sample-data relationship format (SDRF) file for this submission contains only a high-level representation of the sample, library and run information per flow cell, and not per cell. For meta-data at the level of individual cells, please refer to the supplementary file called single_cells_list.txt, which is included as part of this ArrayExpress submission.
Project description:In this study, we aimed to study the gene expression patterns at single cell level across the different cell cycle stages in mESC. We performed single cell RNA-Seq experiment on mESC that were stained with Hoechst 33342 and Flow cytometry sorted for G1, S and G2M stages of cell cycle. Single cell RNA-Seq was performed using Fluidigm C1 system and libraries were generated using Nextera XT (Illumina) kit.
Project description:In this study, we develop computational tools for assignment of cell-cycle stages from single cell and bulk transcriptome data. We perform bulk RNA-Sequencing for mouse embryonic stem cells with known cell cycle stage.
Project description:Sall4 is a stem cell factor which is important for embryogenesis. We have genetically modified Sall4 in mouse embryonic stem cells to access the transcriptional changes. There are three different genetic modifications for the ES cells in the form of Sall4 Knockout (KO), Sall4 Zinc Finger Cluster 4 Mutation (ZFC4mut) and Sall4 Zinc Finger Cluster 4 Deletion (ZFC4Δ) respectively that we have considered for our study.
Project description:In this study, we perform comparative analysis of Illumina HiSeq and BGISEQ-500 sequencing platforms for single-cell transcriptomics data. We performed scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules and sequenced across both sequencing platforms. The matched Illumina platform datasets can be found with accession numbers (E-MTAB-5483, E-MTAB-5484, E-MTAB-5485). The additional data comparison performed in the study can be found (BioProject# PRJNA430491, SRA# SRP132313 and CNBG# CNP0000075).