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Sample GSM8215979

Query DataSets for GSM8215979

Status

Public on Aug 08, 2024

Title

CD19+ Splenocytes, Nemo3SAΔT, 4hDMSO, rep3

Sample type

SRA

Source name

CD19+ Splenocyte

Organism

Mus musculus

Characteristics

cell type: CD19+ Splenocyte
genotype: Nemo3SA{delta}T
treatment: 4h DMSO

Treatment protocol

CD19+ splenocytes were resuspended to ~2million cells per milliliter in IL7- media and ~2million cells were treated with DMSO or 10 μg/mL etoposide (Sigma-Aldrich, E2600000). The cells were collected after four hours of treatment in 250 μL of Trizol (Life Technologies, 15596018) for RNAseq analysis.

Extracted molecule

polyA RNA

Extraction protocol

CD19+ splenocytes were isolated from single-cell suspensions of spleens using the EasySep™ Mouse CD19 Positive Selection Kit II (STEMCELL Cat. No. 18954)
Strand-specific RNA sequencing libraries was prepared by using NEBNext Ultra II Directional RNA Library Prep Kit for Illumina following manufacturer’s instructions (NEB, Ipswich, MA, USA). Briefly, the enriched RNAs were fragmented for 8 minutes at 94 °C. First strand and second strand cDNA were subsequently synthesized. The second strand of cDNA was marked by incorporating dUTP during the synthesis. cDNA fragments were adenylated at 3’ends, and indexed adapter was ligated to cDNA fragments. Limited cycle PCR was used for library enrichment. The incorporated dUTP in second strand cDNA quenched the amplification of second strand, which helped to preserve the strand specificity. The sequencing library was validated on the Agilent TapeStation (Agilent Technologies, Palo Alto, CA, USA), and quantified by using Qubit 2.0 Fluorometer (ThermoFisher Scientific, Waltham, MA, USA) as well as by quantitative PCR (KAPA Biosystems, Wilmington, MA, USA).

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 2500

Description

3SA3-4hDMSO

Data processing

The RNA-seq fastq files were analyzed using the snakemake pipeline:https://github.com/khayer/rna_seq_standard_pipeline. In brief, raw RNA-seq reads were trimmed using bbduk as part of bbmap (version 38.92:https://sourceforge.net/projects/bbmap/).The trimmed reads were then aligned to mm10 using STAR (version 2.7.9a) (37). Next, bamCoverage from the deeptools package (version 3.5.1) was employed to generate bigWig coverage files, applying flags to differentiate coverage tracks by strand and to normalize using counts per million (cpm) (38). The normalized bigWig coverage files were thenuploaded to the UCSC genome browser (39).
Gene expression levels were quantified using TPMcalculator (version 0.0.3) with the GENCODE annotation for the mm10 genome (version M23_GRCm38.p6) (40). For normalization and identification of significantly differentially expressed genes, the raw read counts were analyzed with the limma-voom package from Bioconductor (version 3.54.2) (41). From this data, we defined three subsets of genes. The first subset was etoposide-dependent genes, which had a log2(Fold-Change Etoposide/DMSO) > 1 or < -1 and ap-value< 0.05 when comparing etoposide and DMSO treated CD19+splenocytes fromWTmice. Within the etoposide-dependent genes that were expressed above the 20thquantile in average expression, we defined the Nemo-dependent ones as genes with a log2(Fold-Change Etoposide/DMSO) < 1 or > -1 inNemo-CD19+splenocytes and ap-value< 0.05 when comparing the log2(Fold-Change Etoposide/DMSO) betweenWTandNemo-splenocytes. We intorduced the qualification that the genes be expressed above the 20th quantile of average expression in this group to ensure observed changes in fold-change values were not an artifact of low expression. Finally, within the etoposide and Nemo-dependent genes, we defined a subset of genes as Nemo3SAΔT-dependent, meaning when the log2(Fold-Change Etoposide/DMSO) betweenWTandNemo3SAΔTsamples were compares, thep-value< 0.05. To visually depict expression patterns of these subsets of genes inWTCD19+splenocytes relative to eitherNemo-orNemo3SAΔTCD19+splenocytes, scatterplots of the log2(Fold-Change Etoposide/DMSO) values were plotted using the R package ggplot2 (42).
37. Dobin, A., C. A. Davis, F. Schlesinger, J. Drenkow, C. Zaleski, S. Jha, P. Batut, M. Chaisson, and T. R. Gingeras. 2013. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29: 15–21. 38. Ramírez, F., D. P. Ryan, B. Grüning, V. Bhardwaj, F. Kilpert, A. S. Richter, S. Heyne, F. Dündar, and T. Manke. 2016. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44: W160–W165. 39. Kent, W. J., C. W. Sugnet, T. S. Furey, K. M. Roskin, T. H. Pringle, A. M. Zahler, and and D. Haussler. 2002. The Human Genome Browser at UCSC. Genome Res. 12: 996–1006. 40. Vera Alvarez, R., L. S. Pongor, L. Mariño-Ramírez, and D. Landsman. 2019. TPMCalculator: one-step software to quantify mRNA abundance of genomic features. Bioinformatics 35: 1960–1962. 41. Law, C. W., Y. Chen, W. Shi, and G. K. Smyth. 2014. Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15: 1–17. 42. Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis,. Springer-Verlag New York.
Assembly: mm10
Supplementary files format and content: Raw read counts as produced by TPMCalculator

Submission date

Apr 18, 2024

Last update date

Aug 08, 2024

Contact name

Craig Bassing

E-mail(s)

bassing@email.chop.edu

Organization name

Children's Hospital of Philadelphia

Street address

3501 Civic Center Blvd