RNA seq of liver cancer cells treated with different concentration gradients of Thapsigargin (TG), Tunicamycin (TM), and FCCP
RNA-seq (gene expression) · human · 44 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 44 samples: 22 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE281511_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1184164 and SRA study SRP544241. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 44 samples.
How we read this: 22 replicated groups read from 44 sample titles; they account for 44 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8622278 FCCP_1.25_1 FCCP_1.25
- GSM8622279 FCCP_1.25_2 FCCP_1.25
- GSM8622280 FCCP_2.5_1 FCCP_2.5
- GSM8622281 FCCP_2.5_2 FCCP_2.5
- GSM8622282 FCCP_5_1 FCCP_5
- GSM8622283 FCCP_5_2 FCCP_5
- GSM8622284 FCCP_DMSO_1 FCCP_DMSO
- GSM8622285 FCCP_DMSO_2 FCCP_DMSO
- GSM8622286 FCCP_MG132_1 FCCP_MG132
- GSM8622287 FCCP_MG132_2 FCCP_MG132
- GSM8622288 FCCP_3_MA_1 FCCP_3_MA
- GSM8622289 FCCP_3_MA_2 FCCP_3_MA
- GSM8622290 TG_0.5_1 TG_0.5
- GSM8622291 TG_0.5_2 TG_0.5
- GSM8622292 TG_1_1 TG_1
- GSM8622293 TG_1_2 TG_1
- GSM8622294 TG_2_1 TG_2
- GSM8622295 TG_2_2 TG_2
- GSM8622296 TG_DMSO_1 TG_DMSO
- GSM8622297 TG_DMSO_2 TG_DMSO
- GSM8622298 TG_MG132_1 TG_MG132
- GSM8622299 TG_MG132_2 TG_MG132
- GSM8622300 TG_ISRIB_1 TG_ISRIB
- GSM8622301 TG_ISRIB_2 TG_ISRIB
- GSM8622302 TG_melatonin_1 TG_melatonin
- GSM8622303 TG_melatonin_2 TG_melatonin
- GSM8622304 TG_4μ8C_1 TG_4μ8C
- GSM8622305 TG_4μ8C_2 TG_4μ8C
- GSM8622306 TM_0.5_1 TM_0.5
- GSM8622307 TM_0.5_2 TM_0.5
- GSM8622308 TM_1_1 TM_1
- GSM8622309 TM_1_2 TM_1
- GSM8622310 TM_2_1 TM_2
- GSM8622311 TM_2_2 TM_2
- GSM8622312 TM_DMSO_1 TM_DMSO
- GSM8622313 TM_DMSO_2 TM_DMSO
- GSM8622314 TM_MG132_1 TM_MG132
- GSM8622315 TM_MG132_2 TM_MG132
- GSM8622316 TM_ISRIB_1 TM_ISRIB
- GSM8622317 TM_ISRIB_2 TM_ISRIB
+ 4 more — browse all 44 samples with per-sample file links →
- GSE281194 RNA seq of breast cancer cells treated with different concentration gradients of Thapsigargin (TG), Tunicamycin (TM), and FCCP. 44 samples shares fccp, concentration, thapsigargin, tunicamycin, different, gradients, treated
- GSE232322 Differential gene expression in breast cancer cell lines treated with a Baicalein at 100 uM concentration 12 samples shares concentration, different, treated
- GSE249009 RNA sequencing analysis of isoginkgetin and thapsigargin treated HCT116 and HCT116 ATF4 deficient cells 48 samples shares thapsigargin, treated
- GSE106724 Profiles for long noncoding RNAs in ovarian granulosa cells from polycystic ovary syndrome patients with different serum concentrations of androgen 12 samples shares concentration, different
- GSE300610 Growing iPSC-derived neuronal precursor cells (NPCs) of MT-ATP6 patients and of normal controls under physiological glucose concentrations of 4.5 mM with and without 1 µM or 10 µM Sildenafil for 6 or 24 hours. 192 samples shares concentration
- GSE273600 Transcriptomics of specific activation of the Integrated Stress Response compared to Arsenite and Thapsigargin treatment 120 samples shares thapsigargin
- GSE277467 First multiomics association study of trace element and mineral concentration and RNA sequencing profiles in human cancers 85 samples shares concentration
- GSE327797 Prolonged duration induces divergent transcriptomic responses to manganese, distinct from concentration effects, in an SH-SY5Y neurotoxicity model 60 samples shares concentration
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.