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Sample GSM4862233 Query DataSets for GSM4862233
Status Public on Oct 28, 2020
Title PitNET12
Sample type SRA
Source name PitNET_untreated
Organism Homo sapiens
Characteristics subject status: patient with somatotrophinomas
Sex: female
treatment: untreated; naïve
tissue: Pituitary neuroendocrine tumor (PitNET)
Extracted molecule total RNA
Extraction protocol Twenty to 30 mg of PitNET tissues was homogenised using a FastPrep-24™ homogenizer in RLT Plus buffer in Lysing Matrix D 1.5 ml tubes (MP Biomedicals, USA), and total RNA was extracted with AllPrep DNA/RNA Mini Kit (Qiagen, Germany) and stored at -80 °C. Total RNA quality control was carried out using Agilent 2100 bioanalyzer and an Agilent RNA 6000 Pico Kit (Agilent Technologies, USA) and concentration was measured using Qubit 2.0 fluorometer and Qubit™ RNA HS Assay Kit (Thermo Scientific, USA).
Transcriptome libraries were prepared with the Low Input RiboMinus™ Eukaryote System v2 and Ion Total RNA-Seq Kit v2 (Thermo Fisher Scientific, USA) following the manufacturer’s instructions. Input amount for the transcriptome library preparation was 500 ng. The preparation of transcriptome libraries was carried out in relation to 1:1 based on patient samples, who had, or had no medical therapy before PitNET resection, to reduce any batch effects. Libraries were sequenced using the Ion Proton™ System for Next-Generation Sequencing (Ion OneTouch and Ion PI™ Hi-Q™ OT2 Reagents 200 Kit for emulsion PCR and Ion PI™ Hi-Q™ Sequencing 200 Solutions kit and Ion PI™ Chip V3 chips for sequencing, all from Thermo Fisher Scientific, USA). Libraries were sequenced 3 - 4 times to acquire at least 20 M reads per sample and at least 10 M would represent uniquely mapped reads, so that the rRNA content would not reach 50 % of the sample.
Library strategy RNA-Seq
Library source transcriptomic
Library selection cDNA
Instrument model Ion Torrent Proton
Data processing Torrent Suite 5.10.1 was used for basecalling
Adapter removal performed with Cutadapt (Galaxy Version 1.16.5)
Quality trimming performed with Trimmomatic (Galaxy Version 0.36.5) with sliding window with average quality across 5 bases set to 15
Alignment was performed using RNA-STAR (Galaxy Version 2.6.0b-1)
To account for batch effects, surrogate variables (SVA) were calculated using the sva (v3.34.0) package (Bioconductor project) and added to the design matrix.
Read counts were first filtered based on read count frequency in all samples. Genes with at least ten reads in at least three samples were included in the analysis. To detect sample outliers, read count data were transformed with variant stabilizing transformations (VST), considering the design of the experiment, and visualized with sample distance heat mapping, PCA and MDS methods. Read count density visualization was used to check for problematic samples. Counts were replaced with trimmed mean values for genes which were marked as outliers based on their dispersion Cook`s distance values, which were calculated as the .99 percentile of the F (p, m - p) distribution for each gene.
Differentially expressed genes were obtained using the DESeq2 (v1.26.0) package from the Bioconductor (v3.10) project and R (v3.6.1) software.
Genome_build: GRCh38.p12
Supplementary_files_format_and_content: Matrix table with raw gene counts for every gene and every sample
Submission date Oct 27, 2020
Last update date Oct 28, 2020
Contact name Raitis Peculis
Organization name Latvian Biomedical Research and Study Centre
Street address Ratsupites str 1 k-1
City Riga
ZIP/Postal code LV-1067
Country Latvia
Platform ID GPL17303
Series (1)
GSE160195 Medication for acromegaly reduces expression of MUC16, MACC1 and GRHL2 in pituitary neuroendocrine tumour tissue
BioSample SAMN16562510
SRA SRX9370580

Supplementary data files not provided
SRA Run SelectorHelp
Raw data are available in SRA
Processed data are available on Series record

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