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| Status |
Public on Jan 27, 2021 |
| Title |
Forskolin 2 |
| Sample type |
SRA |
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| Source name |
Human Primary Adipocytes
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| Organism |
Homo sapiens |
| Characteristics |
vendor: PromoCell
cell type: differentiated stromal vascular fraction preadipocytes
passage: Passages 3 - 5
agent: forskolin
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| Treatment protocol |
Human white primary adipocytes were treated with 1 μM forskolin on days 10 - 12 of differentiation or remained untreated (control).
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| Growth protocol |
Human white primary preadipocytes (PromoCell, Heidelberg, Germany) were seeded (10,000-15,000 cells/cm2) and grown to confluence (37°C, 5% CO2) in preadipocyte growth medium (0.05ml/ml fetal calf serum, 0.004ml/ml endothelial cell growth supplement, 10ng/ml epidermal growth factor, 1µg/ml hydrocortisone, 90µg/ml heparin). Preadipocytes were differentiated according to supplier’s instructions. Briefly, growth medium was replaced by differentiation media (8µg/ml d-biotin, 0.5µg/ml insulin, 400ng/ml dexamethasone, 44µg/ml isobutylmethylxanthine, 9ng/ml L-thyroxine, 3µg/ml ciglitazone) for 48 hours. Differentiation medium was replaced with adipocyte nutrition media (0.03ml/ml fetal calf serum, 8µg/ml d-biotin, 0.5µg/ml insulin, 400ng/ml dexamethasone) for 12 days until cells were fully differentiated. Media was changed every 48 hours.
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| Extracted molecule |
total RNA |
| Extraction protocol |
Total RNA was isolated using the Qiagen Lipis Tissue Rneasy kit according to the manufacturers instructions.
RNA libraries were prepared using the Lexogen QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina according to the manufacturers' instructions
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NextSeq 500 |
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| Description |
replicate 2
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| Data processing |
Data was collected as Bcl files and converted to Fastq files. The quality control of the reads was done using FastQC [1] v0.11.4. Reads were trimmed using TrimGalore [2] v0.4.1. TrimGalore trimms off low-quality base calls from the 3’ end of the reads before the adapter removal. Next, it finds and removes adapter sequences from the 3’ end of reads. If reads after trimming are less than 20 bases long, they are discarded from the dataset. Additionally to the default trimming, QuantSeq 3’ mRNA-Seq Libraries require trimming of the first 12 bases of all reads.
The tool used for mapping in this analysis is STAR. STAR does local alignment and clipping of 3’-end of reads in case that such an alignment gives a better score. This means that the majority of polyA sequences are clipped from reads during the alignment step. For this reason it is not strictly necessary to trim polyA sequences from the reads. However polyA sequences were trimmed before the mapping with the tool prinseq.
Reads were mapped using STAR [3] v2.5.2a. Ensembl Homo_sapiens.GRCh38.dna.primary_assembly.fa (release 85) reference genome file was used to do the mapping of reads, using the annotated transcripts from the ensembl Homo_sapiens.GRCh38.85.gtf. The number of reads that map to genomic features is calculated using HTSeq[6] v0.6.0. A feature is considered as the union of all gene’s exons whose genomic coordinates are determined from the ensembl Homo_sapiens.GRCh38.85.gtf. Reads with a mapping quality less than 10, that map to multiple loci or to overlapping gene regions are discarded to avoid ambiguity and false positives
Differential Gene Expression Analysis was done using the counted reads and the R package edgeR [8] v3.8.6. The standard pipeline follows the next steps: Filter genes with low count of reads. Genes having more than 1 cpm (i. e. counts per million) in at least 1 sample were kept. Calculate normalization factors. EdgeR computes effective library sizes using TMM (i.e trimmed mean of M-values) normalization. The normalization factors account for sequencing depth and RNA composition. Data exploration. MDS and scatter plots to show sample relationships. MDS plots show distances in terms of BCV (i. e. biological coefficient of variation, which represents the square root of the common dispersion described in step 4 below). For both plots, data is filtered and normalised within each comparison. Scatter plots use normalised and log-2 transformed cpm values. Estimate dispersion of each gene across samples and common dispersion. The common dispersion is the average dispersion across genes. A plot is shown for each comparison. Each gene’s average log-counts per million across samples (x-axis) is plotted against its coefficient of variation (BCV), so each point (refered to as tagwise in the plot legend) corresponds to a gene. The red line is the common dispersion. It indicates the overall variability in the dataset. Perform differential gene expression. We used the exact test edgeR approach to make pairwise comparisons between groups. This involves adjusting for multiple testing. EdgeR uses FDR approach for multiple testing correction. Please refer to the edgeR documentation to know more about statistical principles of the procedure. A table of the top 10 genes is shown for each comparison with the following information: Ensembl ID Gene name logFC, which indicates the log difference between the two groups. The baseline group is always the control group, which means that negative values indicate higher expression in control samples. logCPM, which is the log-average abundance across samples Pvalue for the differential expression test FDR, which is the adjusted p-value for multiple testing. Following columns show the normalised non-log cpm value for each gene for each sample.
Parameters were as follows: TrimGalore - --gzip; --fastqc; --fastqc_args '--nogroup --extract'; --clip_R1 12; prinseq -trim_right 8; STAR --runThreadN 4; --outSAMtype BAM SortedByCoordinate; --readFilesCommand zcat; HTSeq HTSeq -a 10; -m union; -s yes; -t exon
Genome_build: Ensembl Homo_sapiens.GRCh38.dna.primary_assembly.fa (release 85) reference genome file was used to do the mapping of reads, using the annotated transcripts from the ensembl Homo_sapiens.GRCh38.85.gtf.
Supplementary_files_format_and_content: abundance
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| Submission date |
Apr 01, 2019 |
| Last update date |
Jan 27, 2021 |
| Contact name |
Lee Roberts |
| E-mail(s) |
L.D.Roberts@leeds.ac.uk
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| Organization name |
University of Leeds
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| Department |
LICAMM
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| Street address |
Level 7 LIGHT Laboratories, University of Leeds
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| City |
Leeds |
| State/province |
Yorkshire |
| ZIP/Postal code |
LS2 9JT |
| Country |
United Kingdom |
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| Platform ID |
GPL18573 |
| Series (1) |
| GSE129153 |
RNA-seq analysis of the effects of forskolin treatment on human primary adipocytes |
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| Relations |
| BioSample |
SAMN11309508 |
| SRA |
SRX5620912 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM3701301_F2.txt.gz |
170.0 Kb |
(ftp)(http) |
TXT |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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