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| Status |
Public on Jul 19, 2021 |
| Title |
Healthy2_DSRSF1_WCT |
| Sample type |
SRA |
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| Source name |
Heathy SFRSF1RNAi WCT
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| Organism |
Homo sapiens |
| Characteristics |
cell type: iPs derived neurons
group: Healthy
fraction: Total - WCT
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| Treatment protocol |
In order to evaluate the neuroprotective potential of NS6180 (NS), a potent calcium-activated potassium channel blocker, motor neurons derived from iPSC cells from unaffected controls and C9ORF72 patients were exposed to NS (0.3-10 µM) diluted in neuronal medium for 72 hours. Cells treated with dimethyl sulfoxide (DMSO), the vehicle of dilution of the NS, was used as an untreated control.
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| Growth protocol |
Human induced pluripotent stem cells iPSCs were maintained in Matrigel-coated plates according to the manufacturer’s recommendations in complete mTeSR™-Plus™ Medium. Cultures were replenished with fresh medium every day. Cells were passaged every four to six days as clumps using ReLeSR™ an enzyme-free reagent for dissociation according to the manufacturer’s recommendations. For all the experiments in this study, iPSCs were used between passage 20 and 35, all iPSCs were cultured in 5% O2, 5% CO2 at 37°C. Neural differentiation of iPSCs was performed using the modified version dual SMAD inhibition protocol (Du et al., 2015).
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| Extracted molecule |
total RNA |
| Extraction protocol |
Total sample extracts were added to PureZOL™ to extract the RNA. Briefly, lysates was cleared by centrifugation for 10 min at 12,000 g at 4 °C. One fifth the volume of chloroform was added and tubes were vigorously shaken for 15 s. After 10 min incubation at room temperature, tubes were centrifuged 12,000 g, 10 min, 4 °C and supernatants collected. RNA was precipitated for 30 min at room temperature with equal volume isopropanol and 2 μl Glycogen and subsequently pelleted at 12,000 g, 20 min, 4 °C. Pellets were washed with 70% DEPC ethanol and re-suspended in DEPC water. All PureZol™ extracted RNA samples were treated with DNaseI and quantified using a Nanodrop (NanoDropTechnologies). Fractionated extracts were subjected to RNA extraction using Direct-zol™ RNA microprep kits following the manufacturer’s protocol, including the recommended in-column DNase I treatment and quantified using a Nanodrop. RNA quality was then assessed using a eukaryote total RNA Nano 6000 Kit prior to high depth RNA sequencing
RNA libraries were prepared for sequencing using standard Illumina protocols
Preparation of dual-indexed, strand-specific RNASeq library from submitted total RNA, using RiboZero rRNA depletion and the NEBNext Ultra Directional RNA library preparation kits
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 4000 |
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| Description |
H2_DSRSF1_WCT
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| Data processing |
RNASeq reads were QC and trimmed and post processing of read data including removal of low-quality bases and sequencing adapters
After alignment, quantification of the transcripts abundance was done using aligned the aligned .bam files with the RSEM, a methods to accurately quantify transcripts from RNA-Seq data with or without a reference genome (Li, B & Dewey, CN, 2011). Transcripts counts were then analysed with EdgeR (Robinson et al., 2009), to normalise the data and to perform Differential Expression. Data normalisation was performed using the relative log expression (RLE) implemented in EdgeR. After normalisation, transcripts abundance was filtered for low and no reads, we retained those transcripts with which counts per million (cmp) was greater or equal than 2 in at least two samples. Transcripts sequenced in each group were compared to one another to evaluate transcriptome coverage across our experimental conditions. For each comparison we evaluated the biological variation using the maximisation of the negative binomial dispersion using the empirical Bayes likelihood function, as implemented in EdgeR. Differentially-expressed transcript isoforms were computed for fold change FC>2 and p-value p<0.05, which were evaluated using the quasi-likelihood (QL) methods with empirical-Bayes Test in EdgeR. Differentially expressed transcripts were annotated using BioMart (Kasprzyk, 2011) based on their Ensembl transcript Id to recover gene symbol, gene description and biotype.
For the splicing analysis, sequencing reads were aligned to the GRCh38.79 genome build through STAR two-pass mode (v2.5.4b) (Dobin et al. 2013). The DEXSeq module of Bioconductor was used to identify differential exon usage (Anders et al. 2012). We used the python scripts provided by the package to annotate the genome and to count the reads overlapping the exons. The significance thresholds for differential exon usage were set at a Benjamini–Hochberg false discovery rate of 5%.
Genome_build: Human Genome GRCh38
Supplementary_files_format_and_content: two tab-delimited text files: 1) RPKM values of transcripts for each sample and 2) exon counts for each sample
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| Submission date |
Nov 04, 2019 |
| Last update date |
Jul 19, 2021 |
| Contact name |
Ilaria Granata |
| E-mail(s) |
ilaria.granata@icar.cnr.it
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| Organization name |
National Research Council of Italy (CNR)
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| Department |
Institute for high performance computing and networking (ICAR)
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| Street address |
Via Pietro Catellino, 111
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| City |
Napoli |
| ZIP/Postal code |
80131 |
| Country |
Italy |
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| Platform ID |
GPL20301 |
| Series (1) |
| GSE139900 |
Therapeutic manipulation of SRSF1 mitigates genome-wide transcriptome alterations and neuronal hyperexcitability in C9ORF72-linked amyotrophic lateral sclerosis |
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| Relations |
| BioSample |
SAMN13191836 |
| SRA |
SRX7099164 |
| Supplementary data files not provided |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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