DNA methylation in neurons from post-mortem brains in schizophrenia and bipolar disorder (RNA-Seq)

We fine-mapped DNA methylation in neuronal nuclei (NeuN+) isolated by flow cytometry from post-mortem frontal cortex of the brain of individuals diagnosed with schizophrenia, bipolar disorder, and controls (n=29, 26, and 28 individuals). Overall design: Brain tissue samples (n=34 human samples, 17 case and 17 control) were lysed using QIAzol Lysis Reagent (Qiagen) and homogenized with a TissueLyser (Qiagen). Total RNA from each sample was isolated using the RNeasy Plus Universal Mini kit (Qiagen) according to manufacturer's instructions and included an enzymatic DNase (Qiagen) digestion step. RNA quality was measured on a 2100 Bioanalyzer (Agilent) and quantity was determined with a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific). Only RNA samples with a RIN quality score >7 proceeded to RNA-seq library preparation (RIN between 7.1 to 9.4 for all samples). Libraries were prepared by the Van Andel Genomics Core from 300 ng of total RNA using the KAPA RNA HyperPrep Kit with RiboseErase (v1.16) (Kapa Biosystems). RNA was sheared to 300-400 bp. Prior to PCR amplification, cDNA fragments were ligated to Bio Scientific NEXTflex Adapters (Bioo Scientific). Quality and quantity of the finished libraries were assessed using a combination of Agilent DNA High Sensitivity chip (Agilent Technologies, Inc.), QuantiFluor® dsDNA System (Promega Corp.), and Kapa Illumina Library Quantification qPCR assays (Kapa Biosystems). Individually indexed libraries were pooled, and 75 bp paired-end sequencing was performed on an Illumina NextSeq 500 sequencer, with all libraries run across 3 flowcells. Base calling was done by Illumina NextSeq Control Software (NCS) v2.0 and output of NCS was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v1.9.0. Trimgalore (v0.11.5) was used for adapter removal prior to genome alignment. STAR33 (v2.3.5a) index was generated using Ensemble GRCh38 p10 primary assembly genome and the Gencode v26 primary assembly annotation. Read alignment was performed using a STAR two-pass mode. Gene counts matrix was imported into R (3.4.1) and low expressed genes (counts per million (CPM) < 1 in all samples) were removed prior to differential expression in EdgeR. Gene counts were normalized using the trimmed mean of M-values, fitted in a generalized linear model and differentially tested using a likelihood ratio test. The generalized linear model contained covariates age, sex, post mortem interval and neuronal cell composition. Cell-type compositions for each sample was accessed using CIBERSORT34 on normalized sample counts against cell-type specific markers, identifying the proportion of neurons in each samples. Benjamini Hochberg correction was used to adjust for multiple testing.

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Pai S, Li P, Killinger B, Marshall L, Jia P, Liao J, Petronis A, Szabó PE, Labrie V