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GEO help: Mouse over screen elements for information. |
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Status |
Public on Nov 26, 2019 |
Title |
Mcph1 KO1 |
Sample type |
SRA |
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Source name |
Neural progenitor cells
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Organism |
Mus musculus |
Characteristics |
cell type: Neuroephitelial cells strain: C57BL/6J genotype: Mcph1 KO age: E 12.5
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Growth protocol |
Mouse dorsal telencephalon was dissected from E12.5 brains in DMEM/F12 medium (Invitrogen). Cell suspension was obtained by mechanical dissociation. NPCs from each telencephalon were grown individually as neurospheres in 6 well plates and in DMEM/F12 medium supplemented with 1xN2 and 0.5xB27, 10 ng/ml FGF (Invitrogen), 10 μg/ml Insulin and 10 ng/ml bFGF (Sigma Aldrich). Two days after the culture start, wild type (WTNPCs) or Mcph1 KO (KONPCs) primary cultures were pooled by two or three in a T75 flask for further expansion during two to three days. At this stage, neurospheres were pelleted for protein extraction or RNA extraction, or mechanically dissociated for seeding on gelatin-coated dishes, glass coverslips in 24-well plates or IBIDI slides (IBIDI). Attached cells were cultured in DMEM medium (Invitrogen) containing 10% fetal bovine serum, and containing either i) 4,5 g/L Glucose, 1mM sodium pyruvate and 1mM Glutamine (GPGln complete medium); ii) 2mM sodium pyruvate and 1mM Glutamine (PGln medium); iii) 1mM Glutamine only (Gln medium). Cells were then maintained in culture for four days.
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Extracted molecule |
total RNA |
Extraction protocol |
Total RNA was extracted from WTNPCs and WTNPCs. Sample quality and quantification were assessed on Experion chips (Bio-Rad). Twin samples were pooled according to genotype and matching amounts. Library preparation and Illumina sequencing were performed on wild type and Mcph1 KO samples, two of each, at the Genomic Platform of the Ecole Normale Superieure (Paris, France). Ribosomal RNA depletion was performed with the Ribo-Zero kit (Epicentre), using 1.5 μg of total RNA. Libraries were prepared using the strand specific RNA-Seq library preparation ScriptSeq V2 kit (Epicentre). Libraries were multiplexed by 4 on 1 flow cell lane. A 75bp read sequencing was performed on a HiSeq 1500 device (Illumina). A mean of 39.3 ± 7 million passing Illumina quality filter reads was obtained for each of the 4 samples.
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Library strategy |
RNA-Seq |
Library source |
transcriptomic |
Library selection |
cDNA |
Instrument model |
Illumina HiSeq 1500 |
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Data processing |
RNA-Seq data analysis was performed by GenoSplice technology (www.genosplice.com). Sequencing, data quality, reads repartition (e.g., for potential ribosomal contamination), and insert size estimation were performed using FastQC (v0.10.1), Picard-Tools (v1.119), Samtools (v0.1.19) and rseqc (v2.4). Reads were mapped using STAR (v2.3.0) (Dobin et al., 2013). Gene expression was estimated using Mouse FAST DB v2013_1 annotations. Genes were considered as expressed if their rpkm value was greater than 97.5% of the background rpkm value based on intergenic regions. Raw count data normalization and differential expression analysis were then performed using Deseq (v1.12.1). Results were considered statistically significant for unadjusted p-values ≤ 0.05 and fold-changes ≥ 1.5. The mouse dataset was annotated with human Ensembl gene ID using BiomaRt Bioconductor R package (Naro et al., 2017; Noli et al., 2015) and selecting human genes that were ‘one-to-one’ orthologues with mouse genes. As previously described (Delahaye-Duriez et al., 2016), we generated a gene-level score reflecting the significance and the intensity of differential expression (multiplying the –log10 of p value by the log-transformed fold change). This score was used as a metric to ‘rank’ genes to test using Gene set enrichment analysis (GSEA) if gene sets defined in the Molecular Signatures database (MSigDB v5.2) occupy higher (or lower) positions in the ranked gene list than what it would be expected by chance (Subramanian et al., 2005). The enrichment was tested for several sets of genes of the MSigDB: Gene ontology (GO) biological process, cellular component and molecular functions, Reactome pathways, and Hallmark gene sets. The MSigDB hallmark gene sets is a collection of 50 gene sets that provide coherently expressed signatures derived by aggregating many MSigDB gene sets (Molecular Signatures Database v5.2) to represent well-defined biological states or processes (Liberzon et al., 2015). Gene set enrichment scores (NES, p value, FDR and FWER) were provided in the GSEA output format developed by Broad Institute of MIT and Harvard (permutations = 10,000). Supplementary_files_format_and_content: normalized counts
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Submission date |
Nov 25, 2019 |
Last update date |
Nov 27, 2019 |
Contact name |
Javier Gilabert-Juan |
E-mail(s) |
javier.gilabert@college-de-france.fr
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Phone |
0033768805677
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Organization name |
College de France
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Department |
CIRB
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Street address |
11, Marcelin Berthelot
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City |
Paris |
ZIP/Postal code |
75321 |
Country |
France |
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Platform ID |
GPL18480 |
Series (1) |
GSE140976 |
Mcph1 inactivation affected cell cycle progression and survival of neocortical progenitors at the molecular level |
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Relations |
BioSample |
SAMN13384187 |
SRA |
SRX7213833 |
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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