|
| Status |
Public on Aug 07, 2023 |
| Title |
IL-10, 2 H, biol rep 2 |
| Sample type |
SRA |
| |
|
| Source name |
primary macrophages
|
| Organism |
Mus musculus |
| Characteristics |
strain: C57Bl/6J
Sex: Female
age: 8-12 weeks
cell type: BMDM
cell type: primary macrophages
genotype: WT
treatment: IL-10
time: 2 H
|
| Treatment protocol |
On day 7, BMDM were stimulated with the appropriate dose of IL-6 or IL-10 at the specified time and lysed with TRIzol Reagent (Ambion) for RNA isolation.Fedratinib-treated samples were pre-treated with 1 nM of inhibitor 20 minutes prior to cytokine stimulation.
|
| Growth protocol |
Cells were cultured for six days in DMEM with 10% fetal bovine serum (FBS), penicillin (100 U/ml), streptomycin (100 U/ml), 2 mM l-glutamine, and 20 mM Hepes supplemented with recombinant mouse M-CSF (60 ng/ml) at 37C.
|
| Extracted molecule |
total RNA |
| Extraction protocol |
RNA was extracted with the Direct-zol RNA MicroPrep Kit (Zymo Research).
RNA libraries for RNA-seq were prepared using Illumina Stranded mRNA Prep protocol.
Final libraries were sequenced on the NextSeq2000 (Illumina)
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
NextSeq 2000 |
| |
|
| Data processing |
Rsubread was used for the file alignment and read counting
DESeq2 was used for normalization
Assembly: mm10
Supplementary files format and content: JAK2i_DESeq2_normalizedCounts.csv
|
| |
|
| Submission date |
Apr 30, 2023 |
| Last update date |
Aug 07, 2023 |
| Contact name |
Rachel A. Gottschalk |
| E-mail(s) |
[email protected]
|
| Organization name |
University of Pittsburgh
|
| Department |
Immunology
|
| Street address |
200 Lothrop St., W1047 BST
|
| City |
Pittsburgh |
| State/province |
Pennsylvania |
| ZIP/Postal code |
15261 |
| Country |
USA |
| |
|
| Platform ID |
GPL30172 |
| Series (2) |
| GSE231344 |
Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework II |
| GSE231345 |
Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework |
|
| Relations |
| BioSample |
SAMN34472689 |
| SRA |
SRX20149496 |
