Computational protocol: Cytoplasmic chromatin triggers inflammation in senescence and cancer

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Protocol publication

[…] RNA-seq was performed as previously described, with modifications. Total RNA was isolated from IMR90 cells using RNeasy kit (Qiagen) and 1 ug of RNA was used as input in the Scriptseq Complete kit (Epicentre). Briefly, total RNA was ribo-depleted using a Ribozero magnetic protocol (Epicentre). The ribo-depleted RNA was then ethanol-precipitated, fragmented, and tagged at both ends for stranded library preparation, using the Scriptseq v2 library preparation protocol. The PCR amplification step was used to index the libraries and multiplexed libraries were quantified by Bioanalyzer (Agilent) and qPCR (Kapa Biosystems). The RNA-seq run was performed on the NextSeq 500 platform (Illumina).Single-ended, 75 bp reads were mildly trimmed using Trimmomatic (version 0.32) to remove leading or trailing nucleotides whose sequencing quality was below 3. Reads whose length fell below 30 bp after trimming were also removed from downstream analysis. STAR (version 2.3.0e) was used for mapping reads to reference genome (hg19), requiring a minimum alignment score of 10. The expression level of RefSeq genes was quantified using featureCounts (version 1.5.0) and normalized using DESeq2.Gene expression of RNA-seq data were compared in log2-CPM (i.e. log2 read per million mapped reads) as reported by DESeq2. The average log2-CPM values were used for biological replicates. Genes with over three fold change in expression between etoposide-treated sh-NTC and sh-cGAS were uploaded to DAVID for GO analysis. Genes contributed to the top four down-regulated GO terms were combined with known SASP genes for heatmap visualization, where expression of each gene was scaled to between 0 and 1 based on its minimum and maximum values in proliferating, etoposide-treated sh-NTC and sh-cGAS. SASP genes that are induced less than three-fold (comparing etoposide-treated sh-NTC and proliferating) and those that are not induced in IMR90 DNA damage conditions are not included for the heatmap.RNA-sequencing data were uploaded to GEO under accession number GSE99028. [...] The results of TCGA were based upon data generated by the TCGA Research Network: For TCGA analyses, RNA sequencing datasets were obtained from cBioportal ( For a given cancer type, tumor samples were ranked based on their targeted gene expression values, and were evenly divided into four groups accordingly. Statistical comparisons were then performed between the first group (samples with the lowest 25% expression) and the last group (samples with the highest 25% expression) for inflammatory genes or the house-keeping gene (GAPDH), as denoted. Similarly, for analyses of the CCLE samples, RNA sequencing datasets were obtained from the Broad Institute data portal ( The samples were ranked as described above for TCGA samples, and likewise, comparisons were carried out between the first group and the last group for inflammatory genes or GAPDH. For box plots displayed in this study, the central rectangle spans a range from the first quartile to the third quartile (this range is also known as the interquartile range, IQR). A line inside the rectangle shows the median. Outliers were defined as data points that are either 1.5×IQR or more above the third quartile or 1.5×IQR or more below the first quartile. If either type of outlier is present, the whisker on the appropriate side is taken to 1.5×IQR from the quartile rather than the maximum or minimum. Outliers were not displayed in the box plots, but all data points were included in P-value calculations. One-sided Wilcoxon rank sum test were used to compute statistical significance. P values less than 0.05 were considered significant. […]

Pipeline specifications

Software tools Trimmomatic, STAR, Subread, DESeq2, DAVID, cBioPortal
Databases TCGA Data Portal CCLE
Application RNA-seq analysis
Organisms Homo sapiens, Mus musculus
Diseases Neoplasms
Chemicals Ampicillin, Cyclic GMP