|
| Status |
Public on Aug 11, 2023 |
| Title |
RNAseq for LSK cells from Tet2 KO2 old mouse |
| Sample type |
SRA |
| |
|
| Source name |
LSK cells from bone marrow
|
| Organism |
Mus musculus |
| Characteristics |
cell type: LSK
genotype: Tet2 KO
age: old
|
| Treatment protocol |
mxCre_Dnmt3af/f mice were injected with 300 ul pIpC every other day for total 5 times
|
| Growth protocol |
6-8 weeks WT mice were used as WT-young; 12-24 months WT mice were used as WT-old; 6-8 weeks Tet2KO mice were used as Tet2KO-young; 12-24 months Tet2KO mice were used as Tet2KO-old;
|
| Extracted molecule |
total RNA |
| Extraction protocol |
RNA and DNA were purified using Qiagen AllPrep DNA/RNA micro kit.
1. Single-cell ATAC-seq libraries were generated by Chromium Single Cell ATAC Reagent Kits (10× Genomics); 2. Low amount RNA seq libraries were generated using Takara SMART-Seq Single Cell Kit; 3. CMSIP: 5hmC-containing genomic DNA were immunoprecipitated by CMS antibody after being bisulfite treated. Immunoprecipitated DNA and input were proceed for Library generation by using zymo pico methyl kit. 4. CHIP seq: H3K9me2/3 binded chromatins were immunoprecipitated by H3K9me3 (ab8898) and H3K9me2 (ab1220). immunoprecipitated DNA and input were proceeded for library generation by using Takara ThruPLEX DNA library kit. 5. WGBS: Bisulfite conversions were performed by using Zymo lighting bisulfite kit and libraries were generated by using Swift Methyl kit for BS converted DNA.
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
| |
|
| Description |
WTy-WTo-T2KOy-T2KOo.normalize.txt
|
| Data processing |
Chromium 10x v2 was processed using Cell Ranger (v6.0.0) with default parameters to generated single cell RNA sequencing data. Single cell RNAseq was sequenced on Novaseq S4 platform. Raw fastq files were demultiplexed using cellranger mkfastq from 10x Genomics. Then, the demultiplexed fastq files were aligned on mouse genome (refdata-gex-mm10-2020-A) provided by 10x Genomics. Scanpy (https://scanpy.readthedocs.io/en/stable/) was used to filter out cells with low UMI (<1,000) and cells with mitochondria reads percentage > 5%. Cells with less than 200 genes detected and genes expressed in less than 10 cells were also filtered out. Normalization and clustering analysis were using scanpy packages with default settings.
scATACseq data was prepared using Chromium 10x v2 kits and was demultiplexed using cellranger-atac mkfastq (cellranger-atac-1.2.0). The fastq files were aligned to mm10 (refdata-cellranger-atac-mm10-1.2.0) using Cellranger-atac count. ArchR (https://www.archrproject.com) was used to do quality control and data analysis. Specifically, the size of fragments and TSS enrichment were estimated, and doublet was removed. Clustering and gene scores calculation were performed based on the tutorial (https://www.archrproject.com/bookdown/index.html).
Bulk RNAseq data was mapped to mm10 using STAR (version=2.7.7a). Uniquely mapped reads was used to calculate raw counts for each gene and sample were inputted to DESeq2 R package to identify significantly differential expressed genes among groups.
For the WGBS analysis, raw FASTQ files were mapped to the NCBI Human Reference Genome Build GRCh37 (hg19) using BSMAP. Fastp was used to trim adaptor sequences from fastq data and remove 15 bp from the two ends of the reads based on Swift protocol. MCALL module in MOABS software was used to calculate DNA methylation ratio and coverage for each CpG site. Differentially methylated CpGs and regions were identified using the MCOMP module considering variance among samples (--withVariacne 1) in the MOABS software. Bisulfite conversion efficiencies were estimated using spike-in unmethylated lambda phage DNA. The output bedGraph files from MCALL include single base resolution DNA methylation ratios, which were transformed to a bigwig file format. The bigwig files were uploaded to the UCSC genome browser for visualization.
For CMS-IP-seq analysis, raw FASTQ files were mapped to mm10 using BSMAP . Only uniquely mapped non duplicated reads were kept as input for MACS2 to call 5hmC enriched regions (peaks). Differential hydroxymethylation regions (DHMRs) were identified using DESeq2 with default parameters. Bam files were transformed to bigwig format, which can be used to visualize 5hmC signals in the UCSC genome browser.. H3K9me3 and H3K9me2 ChIPseq data was aligned on mm10 using bowtie2. The bam files were inputted to EDD (https://github.com/CollasLab/edd) to identify ChIPseq enriched regions.
Genome_build: mm10
Supplementary_files_format_and_content: bigwig; csv; txt
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| |
|
| Submission date |
Sep 08, 2021 |
| Last update date |
Aug 11, 2023 |
| Contact name |
Jia Li |
| E-mail(s) |
jiali@tamu.edu
|
| Organization name |
Texas A&M U Health Science Center
|
| Department |
CEDP
|
| Lab |
Jia Li Lab
|
| Street address |
2121 W HOLCOMBE BLVD
|
| City |
Houston |
| State/province |
Texas |
| ZIP/Postal code |
77030 |
| Country |
USA |
| |
|
| Platform ID |
GPL24247 |
| Series (1) |
| GSE183675 |
Tet2 deficiency altered hematopoietic stem and progenitor cells ageing process |
|
| Relations |
| BioSample |
SAMN21356723 |
| SRA |
SRX12102686 |
