GEO Accession viewer

NCBI > GEO > Accession Display

Not logged in | Login

GEO help: Mouse over screen elements for information.

Sample GSM2891363

Query DataSets for GSM2891363

Status

Public on Jun 21, 2018

Title

single cell RNA-seq batch 7

Sample type

SRA

Source name

batch AB383

Organism

Mus musculus

Characteristics

cell type: HSC
selection markers: LSK, CD150+, CD48-
cfp status: mixed
mouse #: batch 7
strain: -60/DR5-TA-RA-CFP

Extracted molecule

total RNA

Extraction protocol

Single HSCs were index sorted into cold 384-well capture plates containing 2 ul of cell lysis solution and barcoded poly(T) RT primers for scRNA-seq. cDNA from barcoded single cells was pooled using an automated pipeline (Jaitin, D.A., et al. Science 2014). The pooled sample was amplified in-vitro and the resulting RNA was fragmented and converted into a sequencing-ready library by tagging the samples with pool barcodes and Illumina sequences during subsequent steps (ligation, RT, and PCR) (Jaitin, D.A., et al. Science 2014)

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NextSeq 500

Description

facs_indexing.txt
HSCi_umitab.txt.gz

Data processing

Bulk RNA seq data: Sequencing reads were processed using the blue collar bioinformatics (BcBio, https://github.com/chapmanb/bcbio-nextgen) RNA-seq pipeline. Briefly, after removal of Illumina adapter sequences with cutadapt, RNA-seq reads were mapped to the mouse genome (UCSC mm10) assembly using STAR with default parameters. Post alignment quantification was performed with featureCount. Read count data were then transformed with the variance stabilizing method from DESeq2.
WGBS data: (1) Quality control: quality trimming and adapter trimming of bisulfite sequencing data were done using Bismark v.0.19.0 with default parameters. Alignment and methylation calling of reads were also done using Bismark v.0.19.0. (2) Mapping: Methylation data were analysed using BiSeq v.1.18.0, CpG clusters were defined by 20 CpG sites covered in the majority of samples (3 out of 4), with a max. distance of 100 base pairs to nearest CpG cluster. (3) Differentially methylated regions: If methylation differences exceeded over 30% between the samples, differentially methylated regions were identified. (4) SuperDMRs: were defined by merging DMR together with at maximum distance of 100 base pairs to their nearest DMR neighbour. SuperDMRs were annotated to genes and enhancers by means of Granges object. In case of any overlaps the superDMRs were identified in genes/enhancers
sc RNA seq data: bcl2fastq/2.15.0.4 Sequences with RMT of low quality (defined as RMT with minimum Phred score of less than 27) were filtered out. Pool-barcode and well-barcode-RMT were extracted from the first and second end of the read (respectively) and concatenated to the fastq header, delimited by a underscore i.e. POOL_BARCODE_WELL_BARCODE_RMT while "NNNNNN" was used as a place holders if plate barcode was not used. Reads were separated by POOL_BARCODE_WELL_BARCODE header data, allowing 1 sequencing error. This process created a single fastq file for each source well. Genome_build: mm9 Supplementary_files_format_and_content: tab-delimited text files include mRNA molecule count values for each Sample
Genome_build: mm10

Submission date

Dec 15, 2017

Last update date

Jun 21, 2018

Contact name

Nicolas Rapin

E-mail(s)

[email protected]

Organization name

Copenhagen University

Department

Finsen Laboratory