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Sample GSM4041732

Query DataSets for GSM4041732

Status

Public on Oct 08, 2019

Title

Invitro_Tpt3D_IL7_CGRP_B1

Sample type

SRA

Source name

Lung Innate lymphoid cells

Organism

Mus musculus

Characteristics

strain: C57BL/6
tissue: lung
cell type: Innate lymphoid cells
batch: B
cgrp-treated: Yes
il33-treated: No
timepoint: 3 days
in vitro vs. in vivo: In vitro
protocol: Bulk RNA-seq (Smart-Seq2)

Treatment protocol

Sort-purified lung ILCs were cultured for 3 days 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
Bulk_all.matrix.abundance_TPM.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