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Sample GSM4128634

Query DataSets for GSM4128634

Status

Public on Oct 18, 2019

Title

RNA_ECKO_rep3

Sample type

SRA

Source name

RNA_ECKO_MAECs

Organism

Mus musculus

Characteristics

strain background: C57BL/6
genotype/variation: S1PR1-GS (GFPlow)
age: young adult (8-12 weeks)
Sex: male
cell type: endothelial cell
cell type: FACS-sorted adult mouse aortic endothelial cells (MAECs)

Treatment protocol

"WT" and "ECKO" mice were given intraperitoneal injections of tamoxifen (2 mg/day) at 5-6 weeks of age for five consecutive days. Tamoxifen treated mice were rested for a minimum of 2 weeks prior to experiments.

Growth protocol

Mice were housed in our animal facility with food ad libitum and 12 hr light/dark cycles. Animals were used in accordance with protocols approved by the Institutional Animal Care and Use Committees (IACUC) of Boston Children’s Hospital and the French Department of Education.

Extracted molecule

polyA RNA

Extraction protocol

bulk RNA-seq: Cells were sorted into either 0.1% FAF-BSA/PBS or buffer RLT (Qiagen) supplemented with beta-mercaptoethanol for ATAC-seq and RNA-seq, respectively. Cells sorted into buffer RLT were subjected to total RNA extraction using the RNeasy Micro Kit (Qiagen).
scRNA-seq: Library preparation from single cells was performed as previously described (Vanlandewijck et al., 2018). Briefly, cells were deposited into individual wells of 384-well plates containing 2.3 µL of lysis buffer (0.2% Triton-X (Sigma, T9284), 2U/μL RNase inhibitor (ClonTech, 2313B), 2 mM dNTP’s (ThermoFisher Scientific, R1122), 1 μM Smart-dT30VN (Sigma), ERCC 1:4 × 107 dilution (Ambion, # 4456740)) prior to library preparation using the Smart-seq2 protocol (Picelli et al., 2014).
ATAC-seq: ATAC-seq libraries were prepared according to the previously described fast-ATAC protocol (Corces et al., 2016). Briefly, 800-4,000 FACS-isolated cells in 0.1% FAF-BSA/PBS were pelleted by centrifugation at 400  g at 4 ˚C for 5 min. Supernatant was carefully removed to leave the cell pellet undisturbed, then cells were washed once with 1 mL ice-cold PBS. The transposition mix [25 µL buffer TD, 2.5 µL TDE1 (both from Illumina FC-121-1030), 1 µL of 0.5% digitonin (Promega, G9441) and 16 µL nuclease-free water] was prepared and mixed by pipetting, then added to the cell pellet. Pellets were disrupted by gently flicking the tubes, followed by incubation at 37 ˚C for 30 minutes in an Eppendorf ThermoMixer with constant agitation at 300 rpm. Tagmented DNA was purified using the MinElute Reaction Cleanup Kit (Qiagen, 28204), and subjected to cycle-limiting PCR as previously described (Buenrostro et al., 2013). Transposed fragments were purified using the MinElute PCR Purification Kit (Qiagen, 28004) and Agilent DNA Tapestation D1000 High Sensitivity chips (Agilent) were used to quantify libraries.
bulk RNA-seq: Double-stranded cDNA was synthesized from 5-10 ng RNA using the SMART-Seq2 v4 Ultra Low RNA Kit for Sequencing (Takara) according to the manufacturer’s instructions.
single-cell RNA-seq: mRNA was converted to cDNA by a reverse transcription (RT) reaction using oligo(dT) primers and the SuperScript II RT enzyme (ThermoFisher Scientific, cat: 18064-071), as previously described (Picelli et al., 2014). This cDNA was amplified with 22 cycles of PCR, then cDNA was fragmented and tagged (i.e. tagmented) with the Tn5 transposase (Nextera XT library kit, Illumina, cat: FC-131-1096)8, and individual wells indexed using the Illumina Nextera XT indexing kits (Illumina, FC-131-2001).

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NextSeq 500

Description

processed data file:
RNAseq.aorta.GEO.edgeR.xls
Supplementary.File.1.Bulk.RNA-seq.GFP.and.ECKO.xls

Data processing

ATAC-seq: Read alignment to the MGSCv37 (mm9) genome assembly was performed with bowtie2 (Langmead & Salzberg, 2012) and the options: --very-sensitive –X 2000 –no-mixed –no-discordant. Duplicated fragments were removed using the Picard “MarkDuplicates” script with the options: Remove_Duplicates=true Validation_stringency=lenient (http://broadinstitute.github.io/picard/). Paired-end reads were separated, centered on Tn5 cut sites, and trimmed to 10 bp using a custom in-house script. Peaks were called using the MACS2 “callpeak” script (Zhang et al., 2008) with options: -B –keep-dup all –nomodel –nolambda –shift -75 –extsize 150. Reads mapping to murine blacklisted regions and mitochondrial DNA were masked out of peak lists using the Bedtools “intersect -v” script (Quinlan & Hall, 2010). Replicates from each biological group were merged using the bedops “merge” script to generate one high-confidence peak set for each of the four biological groups (GFPhigh, GFPlow, S1pr1-ECKO, S1pr1-WT) (Neph et al., 2012). These four peak sets were then merged to generate a merged, consensus peak set of 123,473 peaks. For each replicate, reads covering consensus peak intervals were counted using the Bedtools “coverage” script with the “-counts” option (Quinlan & Hall, 2010). The resultant count table was input to edgeR (M. D. Robinson et al., 2010) to determine differentially accessible peaks (DAPs).
Base calls were performed using Illumina CASAVA 1.8.2
bigWig files: Nucleotide-resolution coverage (bigWig) tracks were generated by first combining trimmed reads from each replicate, then inputting the resultant .bam files to the DeepTools (Ramirez et al., 2016) “bamCoverage” script with options “—normalizeUsing RPGC –binSize 1”.
bulk RNA-seq: Reads from each sample were aligned to the MGSCv37 (mm9) genome assembly using STAR (Dobin et al., 2013) with the options: --runMode alignReads --outFilterType BySJout --outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterMismatchNmax 999 --alignIntronMin 10 --alignIntronMax 1000000 --alignMatesGapMax 1000000 --outSAMtype BAM SortedByCoordinate --quantMode TranscriptomeSAM. Gene-level counts over UCSC annotated exons were calculated using the Rsubread package and “featureCounts” script (Liao, Smyth, & Shi, 2013) with options: -M –O –p –d 30 –D 50000. The resultant count table was input to edgeR (M. D. Robinson, McCarthy, & Smyth, 2010) for differential gene expression analysis.
bulk RNA-seq: The .bam files from STAR were input to the RSEM (B. Li & Dewey, 2011) script “rsem-calculate-expression” with default parameters to generate FPKMs for each replicate. FPKMs are located in the Supplementary File called "Supplementary.File.1.Bulk.RNA-seq.GFP.and.ECKO.xls"
single-cell RNA-seq: 1152 Fastq files (one per cell) were aligned to the GRCm38 (mm10) genome assembly using STAR with options –runThreadN 4 –outSAMstrandField intronMotif –twopassmode Basic. Bam files were input to the velocyto (http://velocyto.org/velocyto.py/) command-line script “run-smartseq2” (La Manno et al., 2018). Expressed repetitive elments were downloaded from the UCSC genome browser and masked from analysis using the “-m” option of the “run-smartseq2” script. The resultant table of read counts per transcript (“loom” file) was input to the PAGODA2 (https://github.com/hms-dbmi/pagoda2) R package for further analysis (Fan et al., 2016; La Manno et al., 2018).
Genome_build: mm9 (all bulk sequencing experiments); mm10 (single-cell RNA-seq only)
Supplementary_files_format_and_content: Excel files include edgeR outputs from bulk RNA-seq and ATAC-seq experiments. the bigWig files were generated as described in "data processing". The file "Supplementary.File.1.Bulk.RNA-seq.GFP.and.ECKO.xls" includes FPKMs and lists of differentially expressed genes, in addition to the edgeR output. The file "aorta.GFP.scRNAseq.cell.info.updated.xlsx" includes information about individual sorted cells, including GFP fluorescence (GFP+ or GFP-) and cluster annotation among the 9 clusters identified by Pagoda2 clustering. The loom file (scRNAseq.Aorta_GFP_EC.loom) was generated using the velocyto package (http://velocyto.org/velocyto.py/) and the “run-smartseq2” script. This files contains a matrix of transcript counts for each single cell.

Submission date

Oct 17, 2019

Last update date

Jan 06, 2020

Contact name

Eric Engelbrecht

E-mail(s)

eric.g.engelbrecht@gmail.com

Phone

9143917079

Organization name

Boston Children's Hospital