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Sample GSM4041744 Query DataSets for GSM4041744
Status Public on Oct 08, 2019
Title Invitro_Tpt6H_IL7_IL33_CGRP_A1
Sample type SRA
Source name Lung Innate lymphoid cells
Organism Mus musculus
Characteristics strain: C57BL/6
tissue: lung
cell type: Innate lymphoid cells
batch: A
cgrp-treated: Yes
il33-treated: Yes
timepoint: 6 hours
in vitro vs. in vivo: In vitro
protocol: Bulk RNA-seq (Smart-Seq2)
Treatment protocol Sort-purified lung ILC2s were cultured overnight with IL-7 and for additional 6 hours with IL-7, IL-7+CGRP, IL-7+IL-33 or IL-7+IL-33+CGRP.
Extracted molecule polyA RNA
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.
Two replicates from each batch of distinct experiments were sequenced. RNA was quantified using a Qubit RNA HS Assay kit (Invitrogen), and 2ng of RNA were used as input for a modified SMART-Seq2 protocol (Picelli et al., 2014) entailing RNA secondary structure denaturation (72˚C for three minutes), reverse transcription with Maxima Reverse Transcriptase (Life Technologies), and whole transcriptome amplification (WTA) with KAPA HiFi HotStart ReadyMix 2X (Kapa Biosystems) for 12 cycles. WTA products were purified with Ampure XP beads (Beckman Coulter), quantified with a Qubit dsDNA HS Assay Kit (Invitrogen), and quality accessed with a High Sensitivity DNA Chip run on a Bioanalyzer 2100 system (Agilent). 0.2 ng of purified WTA product was used as input for Nextera XT DNA Library Preparation Kit (Illumina). Uniquely barcoded libraries were pooled and sequenced with a NextSeq 500 sequencer using a high output V2 75 cycle kit (Illumina) and 2x38 paired end reads.
Library strategy RNA-Seq
Library source transcriptomic
Library selection cDNA
Instrument model Illumina NextSeq 500
Description Bulk_all.matrix.counts.txt
Data processing For bulk RNA-seq (in vitro samples) data, the raw data was converted to fastq files using bcl2fastq 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
City Cambridge
State/province MA
ZIP/Postal code 02142
Country USA
Platform ID GPL19057
Series (1)
GSE136154 Calcitonin gene related peptide negatively regulates alarmin-driven type 2 innate lymphoid cell responses
BioSample SAMN12617286
SRA SRX6749251

Supplementary data files not provided
SRA Run SelectorHelp
Raw data are available in SRA
Processed data are available on Series record

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