|
| Status |
Public on Jul 24, 2024 |
| Title |
Microglia_H3K4me3_BCG_rep2 |
| Sample type |
SRA |
| |
|
| Source name |
Brain
|
| Organism |
Mus musculus |
| Characteristics |
tissue: Brain
cell type: Cd11b+ microglia
strain: C57Bl/6J
age: 12 months
treatment: BCG
Sex: Male
chip antibody: H3K4me3 (Diagenode, #C15410003)
|
| Treatment protocol |
200ul of reconstituted BCG containing 8x105-12x106 CFU was injected i.v in aged mice whereas the control mice received Saline
|
| Extracted molecule |
genomic DNA |
| Extraction protocol |
Cells were fixed with 1% formaldehyde for 10 minutes. After quenching and cell lysis, chromatin was sheared using Bioruptor® Pico sonication device (Diagenode Cat# B01060001)
ChIP-seq libraries were prepared using IP-Star® Compact Automated System (Diagenode Cat# B03000002) from input and ChIP’d DNA using MicroPlex Library Preparation Kit v3 /96 rxns (Diagenode Cat# C05010002) with 24 UDI for MicroPlex v3 - Set I (Diagenode Cat# C05010008). 94 ng of DNA was used for ip samples. Chromatin corresponsing to 1% was set as an input. Optimal library amplification was assessed by qPCR using KAPA SYBR® FAST (Sigma-Aldrich) on Light Cycler® 96 System (Roche) and by using High Sensitivity NGS Fragment Analysis Kit (DNF-474) on a Fragment Analyzer™ (Agilent). Libraries were then purified using Agencourt® AMPure® XP (Beckman Coulter) and quantified using Qubit™ dsDNA HS Assay Kit (Thermo Fisher Scientific, Q32854). Finally, their fragment size was analyzed by High Sensitivity NGS Fragment Analysis Kit (DNF-474) on a Fragment Analyzer™ (Agilent).
|
| |
|
| Library strategy |
ChIP-Seq |
| Library source |
genomic |
| Library selection |
ChIP |
| Instrument model |
Illumina NovaSeq 6000 |
| |
|
| Data processing |
FastQC was used to check the quality of raw fastq files
The raw reads with a phred score > 30 were further used to align to the mm10 genome using bowtie2 using default parameters . Duplicate reads were removed using the Picard tools.
Peak calling was performed using MACS2 (create_model - - mfold (5-50) - -bw 300’) in Galaxy normalised to input . Detection of differential binding sites was done using csaw .
Bigwig files were generated from the bedgraph treatment files generated by MACS2 using the command wigtoBigWig in Galaxy. The replicates in each condition were merged using the Bigwig merge to generate a combined bigwig file for each condition.. NarrowPeak files were generated using MACS 2 .Merged and sorted narrow peak file were used as regions to plot and merged bigwig files were used as score file to compute matrix for coverage heatmap.
Assembly: mm10
Supplementary files format and content: bigWig, narrowPeak (except for Input sample)
|
| |
|
| Submission date |
Apr 20, 2023 |
| Last update date |
Jul 24, 2024 |
| Contact name |
Vini Tiwari |
| E-mail(s) |
v.tiwari@tum.de
|
| Organization name |
Technical University Munich, (TUM), Deutsches Zentrum für Neurodegenerative Erkrankungen
|
| Street address |
Feodor Lynen Straße 17
|
| City |
Munich |
| ZIP/Postal code |
81377 |
| Country |
Germany |
| |
|
| Platform ID |
GPL24247 |
| Series (1) |
| GSE230190 |
Immune training in aged microglia [ChIP-seq] |
|
| Relations |
| BioSample |
SAMN34268897 |
| SRA |
SRX20029292 |
