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Sample GSM4041410

Query DataSets for GSM4041410

Status

Public on Aug 22, 2019

Title

RJj10_Gard_2

Sample type

SRA

Source name

Whole blood

Organism

Macaca mulatta

Characteristics

cell type: White blood cells
subject id: RJj10
study phase: phase 2
study group: WXC_SGE2
condition: Gard
age: 10.69
current rank (elo): 1098
past rank (elo): 1583
flowcell: run0316
pmn cells (%): 59.27
class. mon.: cd14+ cd16- (%): 5.93
cd14+ act. mon.: cd14+ cd16+ (%): 0.42
cd14-act. mon.: cd14-cd16+ (%): 0.03
helper t cells: cd3+ cd4+ (%): 16.07
cytotoxic t cells: cd3+ cd8+ (%): 10.9
double+ t cells: cd3+ cd4+ cd8+ (%): 1.09
cd8- b cells: cd3- cd20+ cd8- (%): 4.01
cd8+ b cells: cd3- cd20+ cd8+ (%): 0.56
nk t cells: cd3+ cd16+ (%): 0.1
nk cells cd3- cd16+ (%): 1.62

Treatment protocol

We drew 1 mL of whole blood from each female (in Phase 2 only) directly into a TruCulture tube (Myriad RBM) that contained cell culture media plus 1 μg/ml of the TLR7 agonist Gardiquimod (Gard), a synthetic ligand that mimics infection with a single-stranded RNA virus. Paired samples treated with either media only (control) or Lipopolysaccharide (1 μg/mL ultra pure LPS from the E. coli 0111:B4 strain) were processed in two additional tubes per subject (see GSE83304). Samples in the three conditions were incubated in parallel for 4 hours at 37°C.

Extracted molecule

polyA RNA

Extraction protocol

We separated the serum and cellular fractions, lysed and discarded the red cells from the cell pellet with red blood cell lysis buffer (RBC lysis solution, 5 Prime Inc.), and lysed the remaining white blood cell fraction in Qiazol for storage at -80°C. We extracted total RNA from each sample using the Qiagen miRNAEASY kit.
Each RNA-sequencing library was prepared from 200 ng of total RNA using the NEBNext Poly(A) mRNA Magnetic Isolation Module and the NEBNext Ultra RNA Library Prep Kit (New England Biolabs), following the manufacturer’s instructions and selecting for ~350 bp size fragments. Libraries were amplified via PCR for 13 cycles, barcoded, and pooled into sets of 10-12 samples for sequencing on an Illumina HiSeq 2500.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 2500

Data processing

Illumina adapters and low-quality score (<20) bases were removed from the raw reads using TrimGalore! (V.0.2.7). Trimmed reads were mapped to the rhesus macaque genome (MacaM v7.6.8) using the STAR 2-pass method, and collated the number of reads that mapped uniquely to each annotated MacaM gene using HTSeq-count (v0.6.1) with the option “intersection-nonempty”.
Prior to RNA-seq data analysis, we first filtered out genes that were very lowly or not detectably expressed in our samples. Specifically, we removed genes that exhibited low median RPKM (≤ 2) in all three conditions (Gard, reported here, and control and LPS, available in GSE83304), which resulted in a final set of read counts for 9,088 genes.
We then normalized gene expression levels across samples using the TMM algorithm (weighted trimmed mean of M-values), implemented in the R package edgeR. Finally, we log-transformed the data using the voom function in R package limma.
Genome_build: MacaM
Supplementary_files_format_and_content: SGE_Gard_read_counts.txt

Submission date

Aug 21, 2019

Last update date

Nov 11, 2021

Contact name

Luis B Barreiro

E-mail(s)

[email protected]

Organization name

University of Chicago

Lab

Barreiro Lab