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Sample GSM5883838

Query DataSets for GSM5883838

Status

Public on Mar 22, 2022

Title

Ythdc2YTHmut/YTHmut_4N_rep2 [RR963; RNA]

Sample type

SRA

Source name

4N cells

Organism

Mus musculus

Characteristics

tissue: 4N cells
age: adult (P60)
genotype: KI
experiment: gene expression profiling

Growth protocol

Mutant mice were generated at the Transgenic Mouse Facility, University of Geneva. The mice were bred in the Animal Facility of Sciences III, University of Geneva. The generation of Ythdc2 knockout mouse line is described previously (Wojtas et al., 2017). The exon 7 encodes for parts of the N-terminal RecA domain of the RNA helicase module in YTHDC2, and specifically the ATPase motif DEVH. YTH domain is encoded in exon 27-29. We targeted the endogenous Ythdc2 locus in mouse embryos of the hybrid background B6D2F1 (50% C57BL/6 and 50% DBA/2) with specific guide RNAs and homology repair templates to create Ythdc2 mutant lines inactivating the RNA helicase domain and the m6A-binding capacity of the YTH domain.

Extracted molecule

total RNA

Extraction protocol

RNA extract: Total RNA was isolated using the Trizol reagent (ThermoFisher Scientific; Cat. No. 15596026) from the required mouse testes samples (whole testes or FACS purified germ cells). Biological replicates were used. RNA concentration was measured with a Qubit fluorimeter (Life Technologies) and RNA integrity assessed with a Bioanalyzer (Agilent Technologies).
RNA-seq library: The TruSeq Stranded Total RNA kit with Ribo-Zero Gold was used for library preparation with 500 ng of total RNA as input. Library molarity and quality was assessed with the Qubit and Tapestation using a DNA High sensitivity chip (Agilent Technologies). Libraries were diluted at 2 nM and pooled before the clustering process on a HiSeq 4000 Single Read flow cell. Reads of 50 bases were generated using the TruSeq SBS reagents on the Illumina HiSeq 4000 sequencer (iGE3 Genomics Platform, University of Geneva).

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 4000

Description

gene expression profiling

Data processing

For gene expression profiling the reads were sorted into individual libraries based on the barcodes and mapped to the mouse genome (Ensembl release 102) either using salmon v1.3.0 (salmon quant with options -l A --validateMappings --gcBias)(Patro et al., 2017) or kallisto (quant v0.46.2 with options --plaintext)(Bray et al., 2016). The DESeq function of DESeq2_1.25.10 bioconductor package (Love et al., 2014) was used to obtain log2 fold changes of gene expression between wild-type and knock-in/mutant samples and the adjusted p-values. Log2 fold change of 1.5 and adjusted p-value 0.05 was used as a threshold for statistical significance. The individual mouse tissues were analyzed separately.
The whole single-cell RNAseq (scRNA-seq) analysis strategy was based on a previously published approach (Luecken and Theis, 2019). Demultiplexed raw sequencing data were processed by cellranger pipeline (v3.0.2) developed by 10x Genomics. Reads were aligned to the custom mm10 reference by cellranger count. Cell count matrix, gene and barcode tables were taken as final output for further processing. Scanpy scRNA-seq toolkit (v1.4.5) was mainly used to integrate, normalize, and process the data. Once integrated, scRNA-seq dataset was filtered based on the following criteria: 1) transcript counts per cell (cells containing less than 1500 or more than 40000 transcripts were discarded) using filter_cells function; 2) genes expressed per cell (cells with less than 2000 genes expressed were discarded) using filter_cells function; 3) mitochondrial gene fraction (cells with more that 20% of mt genes per total number of genes expressed were discarded); 4) gene occurrence (genes which were found in less than 20 cells were discarded) with filter_genes function. Ythdc2+/+ sample was removed from the analysis because of very low sequencing quality, only Ythdc2+/- and Ythdc2-/- samples were processed. Finally, 4528 cells were retained for further analysis. Scran bioconductor package was used for the normalization with default parameters on count matrices before constructing single-cell maps. To reduce the dimentionality of the data and construct single-cell maps UMAP algorithm was applied to the normalized count matrices based on 3000 highly variable genes identified. Batch correction was done by MNN algorithm with mnnpy package. Leiden clustering algorithm (leiden function of the scanpy package) was used to identify cell populations. MAST algorithm was used to perform differential gene expression analysis (diffxpy.api python package). Pseudotime variable for the developmental trajectory analyses was inferred with the Slingshot software (v1.4.0).
Reads were QC-checked with FastQC (v0.11.5) and adapters were removed with Flexbar (v3.5.0, SeqAn v2.4.0). Trimmed reads were aligned to the mouse genome reference (GRCm38) by STAR (v2.7.9a) with the following iCLIP-specific parameters: --outFilterMismatchNoverReadLmax 0.04 --outFilterMismatchNmax 999 --outFilterMultimapNmax 1 --alignEndsType Extend5pOfRead1. BAM files were further deduplicated with umi_tools dedup (v1.1.1). The 2 replicas were merged together with samtools merge (v1.7). PureCLIP was used to identify binding sites from the merged library.
Genome_build: Ensembl release 102
Supplementary_files_format_and_content: ScRNA-seq files contain either gene expression matrix per cell, or cell-related parameters (mitochondrial gene fraction per cell, cell type annotated, etc.), or gene-related parameters (highly variable gene label, total gene expression counts in cells, etc.). YTH mutant RNA-seq supplementary file contains tables with DESeq2-specific differential gene expression parameters (baseMean, log2 fold change, adjusted p-value, etc.), as well as DESeq2-normalized gene counts per replica per condition both for 2N and for 4N cells. Ythdc2 cat-dead mutant RNA-seq supplementary file contains DESeq2-specific differential gene expression parameters table (baseMean, log2 fold change, adjusted p-value, etc.), as well as DESeq2-normalized gene counts matrix per replica per condition. ICLIP supplementary file contains a summary table with the total number of filtered binding sites (0<log2(BS coverage)<4.5) per 5’UTR, CDS, and 3’UTR of the YTHDC2 binding target, a table of the ubiquitination pathway genes, as well as a BED file of all the PureCLIP-defined and annotated binding sites.

Submission date

Feb 09, 2022

Last update date

Mar 22, 2022

Contact name

Ramesh Pillai

E-mail(s)

ramesh.pillai@unige.ch

Organization name

University of Geneva

Department

Department of Molecular Biology