 |
 |
GEO help: Mouse over screen elements for information. |
|
| Status |
Public on Apr 16, 2024 |
| Title |
Bafetinib_LPS_30 |
| Sample type |
SRA |
| |
|
| Source name |
mouse bone marrow derived macrophages
|
| Organism |
Mus musculus |
| Characteristics |
cell type: BMDM
treatment: 1h pre-treatment with Bafetinib followed by 30 LPS 10ng/mL
genotype: wild type
antibody: H3K27ac_ABCAM_ab4729
|
| Treatment protocol |
Individual samples have been pre-treated with each compound separately for 1h before stimulating the cells with either LPS (10ng/mL) or IL4 (10 ng/mL) for 30minutes, 1h, 2h or 4h.
|
| Extracted molecule |
genomic DNA |
| Extraction protocol |
genomic DNA
ChIPseq was carried out as previously described (XXXXXXX). Briefly, 2 x 106 BMDM cells were fixed with 1% of formaldehyde (Merck cod. 252549) for 10 min at RT and lysed to prepare nuclear extracts. After chromatin shearing by sonication with a Bioruptor® Pico, lysates were incubated overnight at 4°C with protein G Dynabeads (Invitrogen, cod.10009D) previously coupled with 2 μg of H3K27ac antibody (ab4729). After immunoprecipitation, beads were recovered using a magnetic stand and washed six times with FA/SDS buffer (50mM Hepes pH 8, 1% Triton-X, 0.1% Na-deoxycholate, 0.1% SDS, 1mM EDTA, 650mM Nacl), four times with Wash buffer (10mM Tris pH 8, 0.25M LiC, 1mM EDTA, 0.5% NP40, 0.5% Na-deoxycholate), followed by two washes with TEbuffer plus 50mM NaCl. Immunoprecipitated chromatin was eluted and cross-link reverted overnight at 65°C. DNA was purified with QIAquick PCR Purification Kit (QIAGEN) and then quantified with QuantiFluor (Cod.E2670, Promega. DNA libraries were prepared for NextSeq500 sequencing as described (Gualdrini et al 2022). The purified DNA libraries were quantified with the Quantifluor reagent (Cod.E2670, Promega) and the quality of the size was controlled with the Tapestation instrument (Agilent) using the High Sensitivity Assay HD5000 (Agilent cod.5067-5592). Library DNA were diluted to a working concentration of 4 nM and Single Reads (76bp) sequenced on an Illumina NextSeq500 platform.
|
| |
|
| Library strategy |
ChIP-Seq |
| Library source |
genomic |
| Library selection |
ChIP |
| Instrument model |
Illumina NovaSeq 6000 |
| |
|
| Description |
ChIPseq
|
| Data processing |
Reads were quality filtered according to the illumina pipeline
Single end reads were mapped to the mouse mm10 genome (Illumina's iGenomes reference annotation downloaded from UCSC http://support.illumina.com/sequencing/sequencing_software/igenome.html) by exploiting the mapping steps of ShortStack (Johnson et al. 2016).Briefly, ShortStack exploits bowtie to identify all best-matched alignments for each read. It will compute a probability for each alignment to different loci in the genome. To do so the frequency of uniquely aligned reads mapping within the vicinity of multi-mapping reads is used to redistribute multi-mapping reads accordingly.
Mapped reads were filtered in order to remove randomly placed multi-mappers and unmapped reads using SAMtools standard procedure (version 1.9)(Li et al. 2009).
In order to identify high confidence genome wide site of enrichment per antibody we implemented three strategies accordingly to the type of signal distribution evaluated after visual inspection of bigwig tracks on IGV (Robinson et al. 2011): For H2K27ac we used peaks identified in ATACseq samples (see below) expanding the ATAC peaks by 1.5 kb on both sides.
Read counts per samples at the identified loci were retrieved using the R/Bioconductor package GenomicRanges and GenomicAlignment (Lawrence et al. 2013). Sample normalization was achieved by selecting invariant peaks across samples (Gualdrini et al. 2016). Briefly, we modelled the frequency distribution of the differences in reads counts across samples with a log-normal distribution. Peaks laying within 1σ of the best fitted mean difference were considered as invariant and used to normalize the samples.
Assembly: mm10
Supplementary files format and content: .bw are BigWig normalised files
|
| |
|
| Submission date |
Dec 02, 2022 |
| Last update date |
Apr 16, 2024 |
| Contact name |
Francesco Gualdrini |
| E-mail(s) |
Francesco.gualdrini@ieo.it
|
| Organization name |
European Institute of Oncology
|
| Department |
Department of Experimental Oncology
|
| Lab |
Transcriptional Control in Inflammation and Cancer
|
| Street address |
Via Adamello, 16
|
| City |
Milan |
| ZIP/Postal code |
20139 |
| Country |
Italy |
| |
|
| Platform ID |
GPL24247 |
| Series (2) |
| GSE219239 |
Unbiased profiling of clinical kinase inhibitors’ effects in activated macrophages using chromatin modifications as high-content readouts [ChIP-Seq] |
| GSE219240 |
Unbiased profiling of clinical kinase inhibitors’ effects in activated macrophages using chromatin modifications as high-content readouts |
|
| Relations |
| BioSample |
SAMN31996980 |
| SRA |
SRX18467812 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM6778579_Bafetinib_LPS_30.scaled.bw |
76.0 Mb |
(ftp)(http) |
BW |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
| Processed data provided as supplementary file |
|
|
|
|
 |
