Computational protocol: Innate lymphoid cell development requires TOX-dependent generation of a common ILC progenitor

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Protocol publication

[…] We performed a customized low input RNA-seq protocol to allow whole transcriptome measurements with as few as 250 cells. Isolated bone marrow cell populations were sorted into a 96-well plate containing lysis buffer (SMARTer® Ultra™ Low Input RNA v3 kit, Takara) for sequencing which was also used for constructing cDNA libraries per manufacturer’s instructions. The protocol for the Ion Torrent Proton was modified by enzymatically fragmenting the resulting double-stranded cDNA libraries and ligating sequencing adapters from the Ion Xpress™ Plus Fragment Library Kit (Life Technologies). cDNA libraries were amplified onto Ion Sphere Particles using Ion PI™ template OT2 200 Kit v3 (Life Technologies) and then sequenced on the Ion Proton™. Samples were sequenced with the Ion PI™ Sequencing 200 v3 kit (Life Technologies) to a depth of 12 to 25 million reads with less than 2.5% of reads coming from ribosomal RNA and over 90% of reads mapping to the mouse genome. The raw reads were filtered and trimmed by FASTX toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) then aligned to mouse reference genome (mm10) using TMAP with mouse reference Gencode version 19 reference genome annotation (http://www.gencodegenes.org). FPKM (fragment per kilobase of gene per million reads sequenced) values were calculated for 23,847 genes using Cufflinks 2.0.8. Poorly measured raw FPKM values less than 1 were increased to a floor threshold of 1. Raw FPKM values were then log2 transformed. Genes with an average of 1 or less in addition to miRNAs and SNORDs where removed from further analysis. Unsupervised analysis was performed by filtering FPKM values for any three of eight samples that demonstrated at least five-fold greater expression than any other sample. For visualization of coordinated gene expression in samples, we performed 2-way hierarchical clustering with samples and genes. Genes were mean centered and dendrograms were calculated using hclust and plotted using heatmap.2 (v2.12) in gplots (v 2.14.2) in R (v 3.0.2). A two-tailed t-test was used to assess the significance of gene expression differences and then corrected for multiple hypotheses by calculating the q value using the Benjamini and Hochberg method. Data from well-annotated genes that had significant gene expression differences with a false discovery rate below 5% (defined as a q value < 0.05) are presented (). Data were deposited in Gene Expression Omnibus at NCBI (http://www.ncbi.nlm.nih.gov/geo/) and can be obtained using accession code GSE65850. […]

Pipeline specifications

Software tools FASTX-Toolkit, Cufflinks, Hclust, gplots
Databases GEO
Application RNA-seq analysis
Diseases Congenital Abnormalities, Deficiency Diseases