RNA extraction and RNA sequencing
Total RNA of 12 WT and 11 Plcb1+/- mice from mPFC and HPC were
isolated using the RNeasy Lipid Tissue Mini Kit (Qiagen Düsseldorf,
Germany) according to the manufacturer’s protocol. RNA concentration was
determined using the NanoDrop ND‐1000 spectrophotometer (NanoDrop
Technologies, Wilmington, DE, USA), and integrity was evaluated using
the Bioanalyzer2100 platform (Agilent Technologies, Santa Clara, CA,
USA). RNA samples were grouped in 4 pools consisting of 3 mice per pool
for each experimental group, except for 1 group in the Plcb1+/-mice were only 2 animals were pooled. The pools were organized to
homogenize the average number of nose pokes in the different pools. The
pooled individuals were the same for both mPFC and HPC.RNA
sequencing
RNA sequencing (RNAseq) was performed by the Centre de Regulació
Genòmica (CRG, Barcelona, Spain). Libraries were prepared using the
TruSeq Stranded mRNA Sample Prep Kit_v2 (Illumina, San Diego, CA, USA)
according to the manufacturer’s protocol and sequenced 2x75 on
Illumina’s HiSeq3000 system for both mPFC and HPC. The Bioinformatics
service of CRG carried out the analysis of RNAseq. Briefly, FastQC
v0.11.5(Andrews S., 2010) was used to inspect the reads quality and
CutAdapt 1.7.1 (Martin, 2011) to clean the data of adapters and
low-quality reads. Then, reads were mapped to the Mus musculusgenome of reference (GRCm38/mm10) with STAR 2.5.3a (Dobin et al., 2013),
and the differential expression analysis was done by DESeq2 (Love et
al., 2014a) to compare WT and Plcb1+/- . Corrections for multiple
testing were applied by adjusting the p-values with a 5% False
Discovery Rate.
RNAseq data of mPFC and HPC were explored on a principal component
analysis (PCA) plot using the ”plotPCA” method from the DESeq2 package
(Love et al., 2014b) and log2 gene expression data. The PCAs were
performed with the 500 genes showing the highest variance among the
samples to calculate the distance among them. The heatmaps were
performed using the ”heatmap” function on R, and the hierarchical
clustering considered the euclidean distance between the samples
considering all the genes or only those with corrected p-value
< 1e-05.