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| Status |
Public on May 08, 2023 |
| Title |
iPSC-neurons, 5 µM NegA, rep1 |
| Sample type |
SRA |
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| Source name |
CHOP-WT10
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| Organism |
Homo sapiens |
| Characteristics |
cell line: CHOP-WT10
cell type: iPSC-neurons
genotype: WT
treatment: 5 microM NegA LNA gapmer
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| Treatment protocol |
LNA gapmer treatments in mature iPSC-neurons were performed by gymnotic delivery of the gapmer for 7 days.
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| Growth protocol |
For generation of mature iPSC-neurons, we used a protocol, modified from a combination of previous studies (Telezhkin et al., 2016; Yan et al., 2013). Experiments were performed when neurons matured to day 40-50 of differentiation.
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| Extracted molecule |
total RNA |
| Extraction protocol |
iPSC-derived neurons were lysed with TRIzol Reagent (Invitrogen 15596-026). Chloroform was added to the lysate, and samples were centrifuged at 4°C to separate phases. After addition of ethanol, the aqueous phase was applied to an RNeasy Mini column (Qiagen #74104). On-column DNAse treatment was performed using Qiagen DNAse Set (Qiagen #79254) at room temperature for 15 minutes.
Purified total RNA was analyzed on Agilent TapeStation systems, using D1000 Screen Tape & Reagents, and then quantified with a Qubit BR kit (Thermo Fisher #Q10210). Poly(A)+ RNA transcript was isolated from 1 ug purified total RNA (RNA integrity number >9.0) with NEBNext poly(A) mRNA magnetic isolation module (New England Biolabs #E7490). RNA-seq libraries were prepared with NEBNext Ultra Directional RNA library preparation kit for Illumina (New England Biolabs #E7420S) according to the manufacturer's instruction.
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
Neg_1
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| Data processing |
For differential gene expression analysis, RNA-seq libraries were demultiplexed, and adapter sequences were removed with Cutadapt v1.18.
Transcriptome indices were prepared for Salmon v1.5.2 using a decoy-aware transcriptome file (gencode.v38.transcripts.fa with GRCh38.primary_assembly.genome.fa genome as decoy). Transcripts were quantified from paired-end reads in mapping-based mode (selective alignment, --libType ISR --gcBias --validateMappings).
Salmon transcript counts were imported into R and aggregated to the gene level using the tximeta (Love et al., 2020) and SummarizedExperiment (Martin Morgan et al., 2021) packages in Bioconductor.
For splicing analysis, demultiplexed, paired-end reads were aligned to the human genome (GRCh38.primary_assembly.genome.fa) with STAR v2.7.1a in two-pass mode. For first-pass alignment, GENCODE basic gene annotation (gencode.v38.basic.annotation.gtf) was used (--outSAMstrandField intronMotif --outFilterType BySJout --alignSJoverhangMin 8 --alignSJDBoverhangMin 3 --outFilterMismatchNoverReadLmax 0.04 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000 --scoreGenomicLengthLog2scale 0). The output splice junction files were concatenated and filtered (to remove junctions on chrM, non-canonical junctions, junctions supported by multi-mappers or by too few reads) then used in on-the-fly second-pass alignment (--outSAMstrandField intronMotif --outSAMattributes NH HI AS NM MD --outFilterType BySJout --alignSJoverhangMin 8 --alignSJDBoverhangMin 3 --outFilterMismatchNoverReadLmax 0.04 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000 --scoreGenomicLengthLog2scale 0 --quantMode TranscriptomeSAM GeneCounts).
Alignments were filtered with samtools v0.1.19 to remove unmapped reads and reads mapping to 10 or more locations.
Differential splicing analysis was performed using rMATS v4.1.1 with bam files as input (group 1 Untreated, group 2 PTBP2 KD). Prep and post steps were run (--gtf gencode.v38.primary_assembly.annotation.gtf -t paired --libType fr-firststrand --readLength 151 --variable-read-length --novelSS --allow-clipping) followed by the statistical model (--cstat 0.01).
Bigwig files were prepared using Yeo lab’s makebigwigfiles [https://github.com/YeoLab/makebigwigfiles].
Assembly: hg38
Supplementary files format and content: summarizedExperiment counts: tab-delimited text file includes gene-summarized counts for each sample
Supplementary files format and content: salmon_quant tar: entire sample output directory from Salmon
Supplementary files format and content: rMATS output: entire output directory from rMATS, sample information specified in README.txt
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| Submission date |
Jun 22, 2022 |
| Last update date |
May 08, 2023 |
| Contact name |
Jennine M Dawicki-McKenna |
| E-mail(s) |
[email protected]
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| Organization name |
University of Pennsylvania
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| Department |
Physiology
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| Street address |
700 CRB, 415 Curie Blvd
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| City |
Philadelphia |
| State/province |
PA |
| ZIP/Postal code |
19104 |
| Country |
USA |
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| Platform ID |
GPL24676 |
| Series (2) |
| GSE206660 |
PTBP2 knock-down RNA-seq from human iPSC-neurons |
| GSE206661 |
human iPSC-neurons and human cortex |
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| Relations |
| BioSample |
SAMN29248164 |
| SRA |
SRX15826313 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM6260207_STAR_GRCh38_Neg_1.tar.gz |
321.9 Mb |
(ftp)(http) |
TAR |
| GSM6260207_salmon_quant_Neg_1.tar.gz |
3.6 Mb |
(ftp)(http) |
TAR |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
| Processed data are available on Series record |
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