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Sample GSM3763094

Query DataSets for GSM3763094

Status

Public on Oct 19, 2019

Title

Scid.adh.2c2_aGATA3_ChIP-rep1

Sample type

SRA

Source name

Scid.adh.2c2 cells

Organism

Mus musculus

Characteristics

cell type: Scid.adh.2c2
chip (antibody): anti-GATA3 Ab (a mixture of sc-268, Santa Cruz Biotechnology and MAB26051, R&D Systems)

Growth protocol

Scid.adh.2c2 cells (Dionne et al., 2005) were cultured in RPMI1640 with 10% fetal bovine serum (FBS, Sigma-Aldrich), sodium pyruvate (Gibco), non-essential amino acids (Gibco), Pen-Strep-Glutamine (Gibco) and 50 mM b-mercaptoethanol (Sigma-Aldrich). An ILC2 cell line, ILC2/b6 (Zhang et al., 2017) was cultured in OP9 medium (a-MEM, 20% FBS, 50 mM b-mercaptoethanol, Pen-Step-Glutamine) supplemented with 10 ng/ml of IL-2, IL-7 and IL-33 (Pepro Tech Inc.).

Extracted molecule

genomic DNA

Extraction protocol

ChIP-seq; Ten million of ILC2/b6 or Schd.adh.2c2 cells were fixed with 1 mg/ml DSG (Thermo Scientific) in PBS for 30 min at RT followed by additional 10 min with addition of formaldehyde up to 1%. The reaction was quenched by addition of 1/10 volume of 0.125M glycine and the cells were washed with HBSS (Gibco). Pelleted nucleus were dissolved in lysis buffer (0.5% SDS, 10 mM EDTA, 0.5 mM EGTA, 50 mM Tris-HCl (pH 8) and PIC) and sonicated on a Bioruptor (Diagenode) for 18 cycles of 30sec sonication followed by 30sec rest, with max power. Five μg per 107 cells of anti-Bcl11b Abs (a mixture of A300-383A (Bethyl), A300-385A (Bethyl), ab18465 (Abcam) and 12120 (CST)), anti-Runx3 (kindly provided from Dr. Ditsa Levanon and Dr. Yoram Groner, Weizmann Institute of Science), anti-GATA3 (a mixture of sc-268, Santa Cruz and MAB26051, R&D) or anti-Runx1 (ab23980, Abcam) were hybridized to Dynabeads anti-Rabbit, Dynabeads anti-Mouse or Dynabeads Protein A/G (Invitrogen) and added to the diluted chromatin complex. They were incubated over night at 4°C, then washed and eluted for 6hr at 65°C in ChIP elution buffer (20 mM Tris-HCl, pH 7.5, 5 mM EDTA 50 mM NaCl, 1% SDS, and 50 μg/ml proteinase K). Precipitated chromatin fragments were cleaned up using Zymo ChIP DNA Clean & Concentrator. ChIP-seq libraries were constructed using NEBNext ChIP-Seq Library Preparation Kit (E6240, NEB) and sequenced on Illumina HiSeq2500 in single read mode with the read length of 50 nt.
RNA-seq; Total RNA was isolated from 300,000 cells using an RNAeasy MicroKit (Qiagen). Libraries were constructed using NEBNext Ultra RNA Library Prep Kit for Illumina (E7530, NEB) from ~1 μg of total RNA following manufacturer’s instructions. Libraries were sequenced on Illumina HiSeq2500 in single read mode with the read length of 50 nt.

Library strategy

ChIP-Seq

Library source

genomic

Library selection

ChIP

Instrument model

Illumina HiSeq 2500

Data processing

ChIP-seq data were mapped to the mouse genome build NCBI37/mm9 using Bowtie (v1.1.1; http://bowtie-bio.sourceforge.net/index.shtml) with “-v 3 -k 11 -m 10 -t --best –strata” settings and HOMER tagdirectories were created with makeTagDirectory and visualized in the UCSC-genome browser (http://genome.ucsc.edu) (Speir et al., 2016). ChIP peaks were identified with findPeaks.pl against a matched control sample using the settings “-P .1 -LP .1 -poisson .1 -style factor”. Peak reproducibility was determined by a HOMER adaptation of the IDR (Irreproducibility Discovery Rate) package according to ENCODE guidelines (https://sites.google.com/site/anshulkundaje/projects/idr). Only reproducible high quality peaks, with a normalized peak score ≥ 15, were considered for further analysis. RNA-sequenced reads were trimmed with Trimmomatic (v.033; http://www.usadellab.org/cms/?page=trimmomatic) (Bolger et al., 2014) for removal of adapter –and low quality sequences (settings: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36). Resulting reads were then mapped onto the mouse genome build NCBI37/mm9 with STAR (v2.4.0) (Dobin et al., 2013) and post-processed with RSEM (v1.2.25; http://deweylab.github.io/RSEM/) (Li and Dewey, 2011) according to the settings in the ENCODE long-rna-seq-pipeline (https://github.com/ENCODE-DCC/long-rna-seq-pipeline/blob/master/DAC/STAR_RSEM.sh) with the minor modifications that settings “--output-genome-bam --sampling-for-bam” was added to rsem-calculate-expression. STAR and RSEM reference libraries were created from genome build NCBI37/mm9 together with the Ensembl gene model file Mus_musculus.NCBIM37.66.gtf. The resulting bam-files were used to create HOMER (Heinz et al., 2010) tag directories (makeTagDirectory with –keepAll setting). For analysis of statistical significance among differentially expressed genes the raw gene counts were derived from each tag directory with analyzeRepeats.pl with the –noadj -condenseGenes options followed by the getDiffExpression.pl command using EdgeR (v3.6.8; http://bioconductor.org/packages/release/bioc/html/edgeR.html) (Robinson et al., 2010).
Genome_build: mm9
Supplementary_files_format_and_content: Processed files include BED-files All genomic coordinates are relative to mm9 mouse assembly.

Submission date

May 13, 2019

Last update date

Oct 19, 2019

Contact name

Hiroyuki Hosokawa

E-mail(s)

[email protected]

Organization name

Tokai University School of Medicine

Department

Department of Immunology