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Sample GSM5035297

Query DataSets for GSM5035297

Status

Public on Aug 08, 2022

Title

D3_5359_mLN

Sample type

SRA

Source name

CD4 T cells from mesenteric LNs

Organism

Macaca mulatta

Characteristics

cell type: CD4 T cells
tissues: mesenteric LNs
days post siv infection: D3

Treatment protocol

N/A

Growth protocol

N/A

Extracted molecule

total RNA

Extraction protocol

CD4 T cells from mesenteric LNs and pelvic LNs in monkeys necropsied on day 0, 3, 7 and 10 following infection were enriched by negative selection through EasySep™ Non-Human Primate Custom Enrichment Kit (STEMCELL Technologies). The enriched cells were further stained with viability dyes and flow cytometric antibodies including CD45, CD3, CD4, CD8, and CD4 T cells were sorted by BD FACSAria flow cytometer. RNA extraction was carried out using RNeasy Mini Kit (QIAGEN) according to manufacturer’s instructions. A Low-Input mRNA library (Clontech SMARTer) v4 was applied and samples were sequenced on Illumina NS500 Paired-End 75 bp (PE75) at the Molecular Biology Core Facility at Dana-Farber Cancer Institute.
RNA libraries were prepared for sequencing using standard Illumina protocols

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NextSeq 500

Description

TMP matrix.csv

Data processing

All samples were processed using an RNA-Seq pipeline implemented in the bcbio-nextgen project (https://bcbio-nextgen.readthedocs.org/en/latest/). Raw reads were examined for quality issues using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to ensure library generation and sequencing are suitable for further analysis. Adapter sequences, other contaminant sequences such as polyA tails and low-quality sequences with PHRED quality scores less than five were trimmed from reads using atropos (https://github.com/jdidion/atropos).
Trimmed reads were aligned to the Macaca mulatta genome (https://www.unmc.edu/rhesusgenechip/index.htm) using STAR v2.7.3. Alignments will be checked for evenness of coverage, rRNA content, genomic context of alignments (for example, alignments in known transcripts and introns), complexity and other quality checks using a combination of FastQC, Qualimap and other custom tools.
Counts of reads aligning to known genes are generated by featureCounts. In parallel, Transcripts Per Million (TPM) measurements per isoform were generated using Salmon (Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference). We used DESeq2 (Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2) internal filtering algorithm called "independentFiltering" that sets a threshold on the mean of normalized counts of all samples (baseMean > 10) in order to maximize the number of tests that pass multiple test correction.
A corrected P value cut-off of 0.05 was used to assess significant genes that were up-regulated or down-regulated on days 3, 7, and 10 using an adjusted P of<0.05 and the Benjamini-Hochberg (BH) correction method.
Genome_build: Macaca mulatta genome (https://www.unmc.edu/rhesusgenechip/index.htm)
Supplementary_files_format_and_content: TMP matrix.csv

Submission date

Jan 26, 2021

Last update date

Aug 08, 2022

Contact name

Xuan He

E-mail(s)

[email protected]

Phone

6177354551

Organization name

BIDMC