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Sample GSM2891348

Query DataSets for GSM2891348

Status

Public on Jun 21, 2018

Title

Bulk RNA-seq HSC CFP Intermediate sample 4

Sample type

SRA

Source name

HSC CFP Intermediate sample 4

Organism

Mus musculus

Characteristics

cell type: HSC
selection markers: LSK, CD150+, CD48-
cfp status: intermediate
mouse #: 1731
strain: -60/DR5-TA-RA-CFP

Extracted molecule

total RNA

Extraction protocol

HSCs were separated into three groups depending on their CFP levels (~20% brightest, dimmest and an intermediate group) and 400-1000 cells were sorted into 50 ul of LiDS lysis/binding buffer (Life Technologies). mRNA was captured with 12 ul of Dynabeads oligo(dT) (Life Technologies), washed and eluted with 10 ul of 10 mM Tris-HCl buffer (pH 7.5)
To produce gene expression libraries, a derivation of the protocol used for generating the single cell expression libraries was used (Jaitin, D.A., et al. Science 2014).

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NextSeq 500

Description

Bulk_limma.xlsx

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