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Good vs poor responder RNAseq transcriptome profiles in DBA/2J mice

Citation

Herzog, David P. (2020), Good vs poor responder RNAseq transcriptome profiles in DBA/2J mice, Dryad, Dataset, https://doi.org/10.5061/dryad.5mkkwh74d

Abstract

Major depressive disorder is the most prevalent mental illness worldwide, still its pharmacological treatment is limited by various challenges, such as the large heterogeneity in treatment response and the lack of insight into the neurobiological pathways underlying this phenomenon. To decode the molecular mechanisms shaping antidepressant response and to distinguish those from general paroxetine effects, we used a previously established approach targeting extremes (i.e. good vs. poor responder mice). Transcriptome profiling on micro-dissected DG granule cells as well as on peripheral blood samples was performed to i) reveal celltype specific changes in paroxetine-induced gene expression (paroxetine vs. vehicle) and ii) to identify molecular signatures of treatment response within a cohort of paroxetine-treated animals. In this datasheet, we provide the mapped and norm-counted RNAseq results of our experiments in an user-friendly excel file.

Methods

NGS library prep was performed with Illumina's TruSeq stranded Total RNA LT Sample Prep Kit following Illumina’s standard protocol (Part #15031048 Rev. E). Libraries were prepared with a starting amount of 114ng and amplified in 15 PCR cycles. Libraries were profiled in a DNA 1000 chip on a 2100 Bioanalyzer (Agilent technologies) and quantified using the Qubit dsDNA HS Assay Kit, in a Qubit 2.0 Fluorometer (Life technologies). All libraries were pooled together in equimolar ratio and sequenced on 8 NextSeq 500 Highoutput FC, SR for 1x 75 cycles plus 16 cycles for the index reads (8 + 8), obtaining on average 40 million reads per library. RNAseq raw fastq files were aligned to the mouse reference genome (mm9) using TopHat and the transcript count was calculated using HTSeq. A principle component analysis was applied to exclude gene expression outliers. Based on transcript count, differentially expressed genes (DEGs) across the conditions were identified using DESeq package. The expression cutoff of the genes were defined based on the density plot of log2 normalized read count. The criteria for the significant differential expression was defined as the padj < 0.05 and the absolute log2-fold change more than 0.58, and the average normalized read count larger than the expression cutoff.

Usage Notes

The provided excel file contains the mapping statistics as well as the norm count data of the complete RNAseq dataset.