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Sample GSM6043248

Query DataSets for GSM6043248

Status

Public on May 11, 2022

Title

mRNA-seq PFC Cocaine Rep2

Sample type

SRA

Source name

prefrontal cortex (PFC)

Organism

Rattus norvegicus

Characteristics

strain: heterogeneous stock (HS)
treatment: prolonged abstinence after cocaine intravenous self-administration
tissue: prefrontal cortex (PFC)

Growth protocol

Male Heterogenous Stock (HS) rats were trained to self-administer drugs in short access conditions (2 h/day for 4 days for oxycodone or 2 h/day for 10 days for cocaine) followed by long access conditions (12 h/day for oxycodone and 6 h /day for cocaine) for 14 days to develop escalation of drug intake. Age-matched naive male rats that were not exposed to any drug were used as control. Lastly, brain punches of PFC and NAc tissues were collected after 5 weeks of abstinence.

Extracted molecule

polyA RNA

Extraction protocol

Brain punches of PFC and NAc tissues were extracted and snap-frozen (at -30°C). Cryosections of approximately 500 microns were used to dissect PFC and NAc punches on a –20°C frozen stage. Total RNA was extracted using the TRIZOL protocol.
csRNA-seq: csRNA-seq was performed as described previously (Duttke et al., 2019). Briefly, small RNAs of 15–60 nt were size selected from 0.3–1.0 microgram of total RNA by denaturing gel electrophoresis. A 10% input sample was taken aside, and the remainder enriched for 5’-capped RNAs. Monophosphorylated RNAs were selectively degraded by Terminator 5’-phosphate-dependent exonuclease (Lucigen). Subsequent 5’ dephosphorylation by quickCIP (NEB) followed by decapping with RppH (NEB) augments Cap-specific 5’ adapter ligation by T4 RNA ligase 1 (NEB)(Hetzel et al., 2016). Thermostable quickCIP was used instead of rSAP and hence the bead clean-up step was skipped before heat denaturation prior to the second round of CIP treatment. The 3’ adapter was ligated using truncated T4 RNA ligase 2 (NEB) prior 3’ repair to select against degraded RNA fragments. Following cDNA synthesis, libraries were amplified for 11–14 cycles and sequenced SE75 on the Illumina NextSeq 500 sequencer. RNA-seq: RNA sequencing libraries were generated using the Illumina Stranded mRNA Prep (Illumina, San Diego, CA). Samples were processed following manufacturer’s instructions. Resulting libraries were multiplexed and sequenced with 100 basepair (bp) Paired End reads (PE100) to a depth of approximately 25 million reads per sample on an Illumina NovaSeq 6000. Hi-C: PFC tissue as pulverized in liquid nitrogen. The Arima-Hi-C kit was used to construct the Hi-C libraries (#A410231, Arima Genomics). Sequencing of the libraries was conducted on an Illumina Novaseq S4 instrument. snATAC-seq: Nuclei were isolated from brain tissue as previously described (Corces et al., 2018). Briefly, frozen tissue was homogenized using a 2 ml glass dounce with 1 ml cold 1x Homogenization Buffer (HB). Cell suspension was filtered using a 70 µm Flowmi strainer (BAH136800070, Millipore Sigma) and centrifuged at 350g for 5 min at 4°C. Nuclei were isolated by iodixanol (D1556, Millipore Sigma) density gradient. The nuclei iodixanol solution (25%) was layered on top of 40% and 30% iodixanol solutions. Samples were centrifuged in a swinging bucket centrifuge at 3,000g for 20 min at 4°C. Nuclei were isolated from the 30%-40% interface. Library preparation targeting the capture of ~6000 nuclei was carried out as detailed in the Chromium Next GEM Single Cell ATAC v1.1 manual (10x Genomics). Library sequencing was performed using the Illumina NovaSeq.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NovaSeq 6000

Description

mRNA transcripts from PFC Cocaine Rep1

Data processing

csRNA-seq: Sequencing reads were trimmed for 3’ adapter sequences using HOMER (“homerTools trim −3 AGATCGGAAGAGCACACGTCT -mis 2 -minMatchLength 4 -min 20”) and aligned to the rat mRatBN7.2/rn7 genome assembly using STAR (Dobin et al., 2013) with default parameters. Only reads with a single, unique alignment (MAPQ >=10) were considered in the downstream analysis. Furthermore, reads with spliced or soft clipped alignments were discarded (the latter often removes erroneous alignments from abundant snRNA species). Transcription Start Regions (TSRs), representing 150 bp sized loci with significant transcription initiation activity (i.e. ‘peaks’ in csRNA-seq), were defined using HOMER’s findPeaks tool using the ‘-style tss’ option, which uses short input RNA-seq to eliminate loci with csRNA-seq signal arising from non-initiating, high abundance RNAs that nonetheless are captured and sequenced by the method (full description is available in Duttke et al. (Duttke et al., 2019)). To lessen the impact of outlier samples across the data collected for this study, csRNA-seq samples were first pooled into a single META-experiment per brain tissue region to collectively identify TSRs in each tissue.
Hi-C: Sequencing reads were first trimmed for sequences downstream of the restriction/ligation site (“GATCGATC”) and aligned to the rat genome using STAR with default parameters. Normalized interaction contact maps were then generated using HOMER. Juicer_tools was then used to generate the hic file.
snATAC-seq: Sequencing reads were processed using Cell Ranger ATAC 2.0 with a custom reference for Rattus norvegicus, built from the Ensembl Rnor 6.0 release 103 genome and annotation. The filtered results were subsequently analyzed using Signac 1.4.0 (Stuart et al., 2021).
RNA-seq: Sequencing reads were aligned to the rat mRatBN7.2/rn7 genome assembly using STAR (Dobin et al., 2013) with default parameters. Gene expression counts were calculated using featureCounts.
Assembly: rn7 (csRNA-seq, RNA-seq, Hi-C), rn6 (snATAC-seq)
Supplementary files format and content: BED (genomic locations of transcription start regions - TSRs), gene_counts.txt (tab-delimited text file with counts per gene from RNA-seq experiments), H5 (compressed sparse matrix file), TSV (tab delimited), CSV (comma separated file), hic (chromatin contact binary file)

Submission date

Apr 13, 2022

Last update date

May 11, 2022

Contact name

Christopher Benner

E-mail(s)

cbenner@ucsd.edu

Organization name

University of California, San Diego (UCSD)

Department

Medicine