 |
 |
GEO help: Mouse over screen elements for information. |
|
| Status |
Public on Mar 26, 2020 |
| Title |
A1-2 cells, EtOH_rep2, Bulk RNAseq |
| Sample type |
SRA |
| |
|
| Source name |
A1-2 breast cancer cells
|
| Organism |
Homo sapiens |
| Characteristics |
cell type: A1-2 breast cancer cells
treatment: 18hr EtOH
|
| Treatment protocol |
For Dexamethasone treatments, cells were initially cultured for 24 hours in reduced-serum, hormone-stripped media (Phenol-red free MEM (Gibco 51200) with 5% Charcoal/Dextran treated FBS (Atlanta S11650), 1X Penicillin/Streptomycin (Sigma P0781), 1% HEPES (Sigma H0887), 1X Glutamax (Gibco 35050), and 250 ug/ml G418 (Gibco 10131)). Subsequently, Dexamethasone (Sigma D4902) was added to the media at 100 nM for all treatments timepoints and 9.5 x 10e-4% ethanol was used as vehicle control. For both RNAseq experiments, control cells were treated with ethanol for 18 hours.
|
| Growth protocol |
Cells were cultured in modified Eagle medium (MEM) containing 10% fetal bovine serum, 1X Penicillin/Streptomycin (Sigma P0781), 1% HEPES (Sigma H0887), 1X Glutamax (Gibco 35050), and 250 ug/ml G418 (Gibco 10131).
|
| Extracted molecule |
total RNA |
| Extraction protocol |
For bulk RNAseq, Qiagen RNeasy kits with on-column DNase treatment were used to isolate total RNA. For single-cell RNAseq, cells were dissociated using 0.25% Trypsin-EDTA (Gibco 25200) into single cell suspension and the BioRad ddSeq Single-Cell Isolator was used to create single cell emulsions
Bulk RNAseq libraries were generated using Ribo-Zero Gold and the Illumina TruSeq RNA Stranded kit. Single-cell RNAseq libraries were generated using the Illumina SureCell WTA 3’ Library Prep kit
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
| |
|
| Description |
bulkRNAseq_normalized_counts.txt
|
| Data processing |
For bulk RNAseq, adapter sequences were trimmed from RNA-seq reads using Cutadapt (Martin, 2011) and low quality reads were removed from analysis using Sickle (Joshi NA et al., 2011). Alignment was performed using STAR (Dobin et al., 2013) to generate coverage tracks and using Salmon (Patro et al., 2017) and to obtain gene counts for differential expression analysis using limma-voom (Law et al., 2014) with cutoffs of Fold Change > 1.5, p-value<0.01, and False Discovery Rate < 0.05
For single-cell RNAseq, read 1 was technical and read 2 was biological. Barcode and UMI sequence positions are not fixed, so Bio-Rad ddSEQ reads were parsed for cell barcode and UMI information. Reads were filtered out according to the following constraints: no 6nt barcode segment is more than a Hamming distance of 1 from a valid barcode block; all linker segments are within 1nt length of the expected value; the UMI has a length of 8nt; and only 1 mismatch is allowed within the ACG-GAC segments flanking the UMI. Reads with cell barcode information passing these filters were then aligned to the hg19 reference genome using STAR v2.5.2b (Dobin et al., 2013) and annotated with the Gencode comprehensive v28lift37. Uniquely-mapped reads were subsequently assigned to genes using featureCounts v1.5.3 (Liao et al., 2013). PCR duplicates were removed using umi_tools (Smith et al., 2017) in per-gene mode. The knee-calling algorithm from umi_tools was used to identify an appropriate total transcript count cut-off for cells in each sample, yielding a total of 4001 cells. Cells with mitochondrial gene percentage greater than 5% were removed. Biological replicates of 4hr and 8hr treatments were combined and cell counts were balanced between all time points by randomly sub-setting each time point to 400 cells. Cyclone from the scran package (Lun et al., 2016; Scialdone et al., 2015) was used to determine cell-cycle scores for all cells, and Seurat v3 (Stuart et al., 2019) was then use to normalize and scale the data such that the impacts of transcript counts, mitochondrial percent, and cell-cycle scores were regressed-out.
Genome_build: hg19
Supplementary_files_format_and_content: raw Counts and normalized Counts
bigwigs were generated from STAR coverage files using bedGraphToBigWig.
|
| |
|
| Submission date |
Dec 11, 2019 |
| Last update date |
Mar 28, 2020 |
| Contact name |
Jackson Andrew Hoffman |
| E-mail(s) |
jackson.hoffman@nih.gov
|
| Organization name |
NIEHS
|
| Department |
DIR
|
| Lab |
ESCBL
|
| Street address |
111 TW Alexander Dr, Building 101 Room C432
|
| City |
Research Triangle Park |
| State/province |
North Carolina |
| ZIP/Postal code |
27709 |
| Country |
USA |
| |
|
| Platform ID |
GPL24676 |
| Series (1) |
| GSE141834 |
Transcriptional Heterogeneity within the Glucocorticoid Response |
|
| Relations |
| BioSample |
SAMN13541124 |
| SRA |
SRX7347917 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM4213968_EtOH_rep2-NOVA0058.STAR_hg19.neg.bw |
158.8 Mb |
(ftp)(http) |
BW |
| GSM4213968_EtOH_rep2-NOVA0058.STAR_hg19.pos.bw |
162.9 Mb |
(ftp)(http) |
BW |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
| Processed data provided as supplementary file |
|
|
|
|
 |
