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GEO help: Mouse over screen elements for information. |
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| Status |
Public on Oct 25, 2017 |
| Title |
7.22.16.No.Dox_S40 |
| Sample type |
SRA |
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| Source name |
OT-II, day 3 post immunization
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| Organism |
Mus musculus |
| Characteristics |
cell type: OT-II CD4+ (Rag1-/-) TCR transgenic cells
condition: Irf4+/+
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| Treatment protocol |
Each OT-II group represented a different genotype of Irf4. For the Irf4-inducible group, such mice were administered DOX in the drinking water for the first two days of the response and switch to normal water thereafter.
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| Growth protocol |
OT-II CD4+ (Rag1-/-) TCR transgenic cells from lymph nodes of Irf4-/-, Irf4+/+, or Irf4-inducible donor mice were quantitated by measuring the frequency of CD4+Vβ5+ cells and 5x105 OT-II cells were adoptively transferred into CD45.1 congenic recipients. For all transfers involving Irf4-inducible OT-II cells, donor cells were enriched by negative selection involving a cocktail of biotinylated antibodies (CD8, CD11b, GR-1, DX5, B220, CD19, IgM, CD25, CD44) followed by anti-biotin magnetic beads (Miltenyi) and magnetic separation; cells were typically ≥90% CD4+ after the procedure. The next day, transplanted mice were immunized with RFR-OVA emulsified in CFA. Three days after immunization, responding cells OT-II cells were sorted (CD45.1-B220-MHCII-CD45.2+CD4+) on a FACSAria and cells were processed for RNA-seq and ATAC-seq. Each replicate sample was derived by pooling responding OT-II cells from multiple immunized mice.
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| Extracted molecule |
genomic DNA |
| Extraction protocol |
ATAC-seq libraries were constructed using the Nexterra kit (Illumina) and then subjected to 8 rounds of amplification with index-specific primers. The ATAC-seq libraries were quantified by both Bioanalyzer and Qubit measurements and pooled in equimolar ratios. They were sequenced on an Illumina HiSeq 4000 sequencer with paired-end 100bp reads on 6 lanes.
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| Library strategy |
ATAC-seq |
| Library source |
genomic |
| Library selection |
other |
| Instrument model |
Illumina HiSeq 4000 |
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| Data processing |
Raw reads were mapped to reference genome mm9 using BWA MEM (Li and Durbin, 2010), and apparent PCR duplicates were removed using Picard MarkDuplicates (Wysoker, et al., 2013). Read alignment positions were adjusted to account for the transposase binding motif: alignments to the + strand were adjusted by +4 bp, and reads on the – strand by -5bp. Open chromatin peak calls were called using Macs2 (Zhang, et al., 2008) with a q-value threshold of 1e-5 and --nomodel selected. Bedgraph tracks quantifying open chromatin were created using bedtools genomecov (Quinlan and Hall, 2010) on the adjusted alignments, followed by conversion to bigWig using UCSC tool bedGraphToBigWig (Kent, et al., 2010). Open chromatin enrichment tracks were normalized to counts per billion bases, based on the sum of read lengths in the library. To quantify ATAC enrichment, a union of peak calls from all ATAC-seq samples was created using bedtools merge (Quinlan and Hall, 2010). Enrichment was quantified as raw counts against this annotation of merged peak calls using featureCounts (Liao, et al., 2014). We computed differential expression statistics in the same manner as for RNA-seq with one exception: using principle component analysis,, we noticed a batch effect among the ATAC samples that separated the samples into three batches, correlated with their processing dates (batches: batch 1 – 11-16-15-KO_S56, 11-16-15-WT_S39; batch 2 – 7-22-16-DOX_S41, 10-30-15-KO_S34, 10-30-15-WT_S32, 12-11-15-WT_S31, 7-22-16-No-Dox_S40; batch 3 – 1-12-16-Dox_S37, 12-18-15-Dox_S38, 12-11-15-KO_S33, 10-23-15-KO_S55, 10-23-15-WT_S38, 1-12-16-No-Dox_S35, 12-18-15-No-Dox_S36). We controlled for the batch effect in the edgeR processing by including the batch as a factor in the differential enrichment analysis and using removeBatchEffect to subtract it from the log-scaled normalized enrichment levels.
Genome_build: mm9
Supplementary_files_format_and_content: bigWig: Open chromatin enrichment tracks for ATAC-seq data. Units are reads per billion bases.
Supplementary_files_format_and_content: bed: Peak calls for ATAC-seq data. Scores are -log10 q-value from Macs2.
Supplementary_files_format_and_content: normalized expression/enrichment: Normalized expression levels (RNA-seq) or open chromatin enrichment levels (ATAC-seq). RNA expression are in counts per million (CPM). ATAC enrichment are in log2 CPM, and have been corrected for batch effect observed among ATAC samples using edgeR; batches are: batch 1 – 11-16-15-KO_S56, 11-16-15-WT_S39; batch 2 – 7-22-16-DOX_S41, 10-30-15-KO_S34, 10-30-15-WT_S32, 12-11-15-WT_S31, 7-22-16-No-Dox_S40; batch 3 – 1-12-16-Dox_S37, 12-18-15-Dox_S38, 12-11-15-KO_S33, 10-23-15-KO_S55, 10-23-15-WT_S38, 1-12-16-No-Dox_S35, 12-18-15-No-Dox_S36.
Supplementary_files_format_and_content: raw expression/enrichment: Raw counts for RNA-seq and ATAC seq samples. Regions in merged.bed were used for quantification of ATAC-seq samples.
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| Submission date |
Dec 12, 2016 |
| Last update date |
May 15, 2019 |
| Contact name |
Mark Maienschein-Cline |
| E-mail(s) |
mmaiensc@uic.edu
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| Organization name |
University of Illinois at Chicago
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| Department |
Research Resources Center
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| Lab |
Center for Research Informatics
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| Street address |
1819 W Polk Ave, Rm 336 M/C 789
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| City |
Chicago |
| State/province |
IL |
| ZIP/Postal code |
60612 |
| Country |
USA |
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| Platform ID |
GPL21103 |
| Series (1) |
| GSE92272 |
Read-write integration by the IRF4 gene regulatory module dynamically controls T helper cell fate |
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| Relations |
| BioSample |
SAMN06131932 |
| SRA |
SRX2415993 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM2424828_7-22-16-No-Dox_S40_open.bw |
541.5 Mb |
(ftp)(http) |
BW |
| GSM2424828_7-22-16-No-Dox_S40_open_hiqual.bed.gz |
502.5 Kb |
(ftp)(http) |
BED |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
| Processed data provided as supplementary file |
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