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Sample GSM4041763

Query DataSets for GSM4041763

Status

Public on Oct 08, 2019

Title

ATAC_IL33_2

Sample type

SRA

Source name

Lung Innate lymphoid cells

Organism

Mus musculus

Characteristics

strain: C57BL/6
tissue: lung
cell type: Innate lymphoid cells
cgrp-treated: No
il33-treated: Yes
timepoint: 6 hours
in vitro vs. in vivo: In vitro
protocol: ATAC-seq

Treatment protocol

Sort-purified lung ILC2s were cultured overnight with IL-7 and for additional 6 hours with IL-7+IL-33 or IL-7+IL-33+CGRP.

Extracted molecule

genomic DNA

Extraction protocol

Lungs were digested with the lung dissociation kit (Miltenyi Biotec) and subsequently enriched for CD90.2+ cells using CD90.2 MicroBeads (Miltenyi Biotec). Lung ILCs were isolated by fluorescence-activated cell sorting as 7AAD- CD45+ CD90.2+ CD127+ Lineage (CD3, CD4, CD8, CD11b, CD11c, CD19, NK1.1, TCRβ, TCRγδ)- cells.
6,000 viable ILC2s were sorted into DPBS supplemented with 2% FCS. Cells were pelleted by centrifugation and stored in BambankerTM cell freezing media (LYMPHOTEC Inc.) at -80 °C. For ATAC-seq library preparation, cells were thawed at 37 °C, washed once with PBS, and lysed and tagmented in 1X TD Buffer, 0.2ul TDE1 (Illumina), 0.01% digitonin, and 0.3X PBS in 40ul reaction volume following the protocol described in Corces at al. (Corces et al., 2016). Transposition was performed at 37 °C for 30 min at 300 rpm. The DNA was purified immediately with the MinElute PCR purification kit (Qiagen). The complete eluate was then amplified with PCR, as follows. First, 5 cycles of pre-amplification were performed using indexed primers with NEBNext High-Fidelity 2X PCR Master Mix (NEB). The number of additional cycles was assessed by SYBR Green quantitative PCR. After purifying the final library with the MinElute PCR purification kit (Qiagen), the library was quantified with the Kapa Library Quantification Kit (Kapa Biosystems) and a Qubit dsDNA HS Assay kit (Invitrogen). Libraries were sequenced on an Illumina NextSeq 550 system with paired-end reads of 37 base pairs in length.

Library strategy

ATAC-seq

Library source

genomic

Library selection

other

Instrument model

NextSeq 550

Description

ATAC_all.rawcounts.tab.gz
ATAC_all.normalized_counts.txt

Data processing

For bulk RNA-seq (in vitro samples) data, the raw data was converted to fastq files using bcl2fastq 2.17.1.14 with options “--minimum-trimmed-read-length 10 --mask-short-adapter-reads 10”. Transcript quantification was done using Kallisto 0.42.3 (Bray et al., 2016) with the mm10 mouse genome annotation, and transcript counts were then converted to gene counts and normalized TPM values using the R package tximport (Soneson et al., 2015).
For scRNA-seq data data, gene counts were obtained by aligning reads to the mm10 genome using CellRanger software (v1.3) (10x Genomics). To remove doublets and poor-quality cells, cells were excluded from subsequent analysis if they were outliers in their sample or condition of origin in terms of number of genes or number of unique molecular identifiers (UMIs), which left 83.1% and 84.2% of cells from CGRP and PBS, respectively, and 90.3% and 93% of cells from IL-33 and IL-33+CGRP conditions. Sample-specific minimum and maxium cut-offs per cell were 900–5,000 genes for IL-33 and IL-33+CGRP conditions and 900–3,900 genes for CGRP and PBS. Another 2.1% of the remaining cells were excluded for having greather than 10% of mitochondrial gene counts.
For scRNA-seq data, normalization of gene counts was performed using regularized negative binomial regression via the SCTransform() function from Seurat v3, with the “batch_var” parameter set to the replicate indicator variable and the “vars_to_regress” parameter set to the variable capturing the percentage of mitochondrial gene counts in each cell (Butler et al., 2018; Hafemeister and Satija, 2019). This step takes the place of the typical steps of log-normalization, variable gene selection, and scaling. We used log1p of the corrected counts to compute principle components analysis (PCA).
ATAC-seq: Read alignment, filtering, visualization of signal tracks, and measurement of quality control metrics was performed using a publicly available ATAC-seq pipeline (Lee et al., 2016). Briefly, reads were aligned to the mm10 genome using Bowtie2 and filtered to remove duplicates and mitochondrial reads. Alignment files were merged for biological replicates for peak-calling using MACS2 (Zhang et al., 2008). Read counts per peak were compiled using the bedtools multicov tool. Counts were normalized and processed for differential peak accessibility between conditions using DESeq2 (Love et al., 2014). Gene annotation with putative regulatory elements was performed using GREAT with default parameters (McLean et al., 2010).
Genome_build: mm10

Submission date

Aug 21, 2019

Last update date

Oct 08, 2019

Contact name

Samantha Riesenfeld

Organization name

Broad Institute

Lab

Regev

Street address

415 Main St