GEO series
Integrating Gene Expression and Splicing Dynamics Across Dose-Response Oxidative Modulators
GSE241672
Homo sapiens (human)
RNA-seq (gene expression)
400 samples
Submitted 2024/05/15
Platform GPL24676
At a glance
RNA-seq (gene expression) · human · 400 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
- 400 samples: 100 conditions, mostly with about 4 replicates eachGroups 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.
- Paper
- Integrating gene expression and splicing dynamics across dose-response oxidative modulatorsFrontiers in genetics · 2024
Summary (written by the submitters)
Toxicological risk assessment increasingly utilizes transcriptomics to derive point of departure (POD) and modes of action (MOA) for chemicals. One essential biological process that allows a single gene to generate several different RNA isoforms is called alternative splicing. To comprehensively assess the role of splicing dysregulation in toxicological evaluation and elucidate its potential as a complementary endpoint, we performed RNA-seq on A549 cells treated with five oxidative stress modulators across a wide dose range.
Read the full summary ↓
Differential gene expression (DGE) showed limited pathway enrichment except at high concentrations. However, alternative splicing analysis revealed variable intron retention events affecting diverse pathways for all chemicals in the absence of significant expression changes. For instance, diazinon elicited negligible gene expression changes but progressive increase in the number of intron retention events, suggesting splicing alterations precede expression responses. Benchmarkdose modeling of intron retention data highlighted relevant pathways overlooked by expression analysis. Systematic integration of splicing datasets should be a useful addition to the toxicogenomic toolkit. Combining both modalities paint amore complete picture of transcriptomic dose-responses. Overall, evaluating intron retention dynamics afforded by toxicogenomics may provide biomarkers that can enhance chemical risk assessment and regulatory decision making. This work highlights splicing-aware toxicogenomics as a possible additional tool for examining cellular responses.
Published in
Barutcu AR, Black MB, Samuel R et al.
· Frontiers in genetics 2024
· PMID 38846964
· doi:10.3389/fgene.2024.1389095
This dataset
Download · which file do I need?
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.
1 · Just have a look
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.
2 · Re-analyse it
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE241672_RAW.tar (it can run to tens of gigabytes).
3 · Start from the raw reads
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 PRJNA1009099 and SRA study SRP457471. Searching any of these in the dataset finder brings you back here.
Study design
100 conditions, mostly with about 4 replicates each
0.01uM_Paraquat_A549, A ×4
0uM_Paraquat_A549, A ×4
0.0316uM_Paraquat_A549, A ×4
0.1uM_Paraquat_A549, A ×4
0.316uM_Paraquat_A549, A ×4
1uM_Paraquat_A549, A ×4
3.16uM_Paraquat_A549, A ×4
10uM_Paraquat_A549, A ×4
+92 more
Supports a between-group comparison across 400 samples.
How we read this: 100 replicated groups read from 400 sample titles; they account for 400 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.
Samples in this study
- GSM7733165 0.01uM_Paraquat_A549, A_201 0.01uM_Paraquat_A549, A
- GSM7733166 0uM_Paraquat_A549, A_202 0uM_Paraquat_A549, A
- GSM7733167 0.0316uM_Paraquat_A549, A_203 0.0316uM_Paraquat_A549,…
- GSM7733168 0.1uM_Paraquat_A549, A_204 0.1uM_Paraquat_A549, A
- GSM7733169 0.316uM_Paraquat_A549, A_205 0.316uM_Paraquat_A549, A
- GSM7733170 0uM_Paraquat_A549, A_206 0uM_Paraquat_A549, A
- GSM7733171 0.01uM_Paraquat_A549, A_207 0.01uM_Paraquat_A549, A
- GSM7733172 0.0316uM_Paraquat_A549, A_208 0.0316uM_Paraquat_A549,…
- GSM7733173 0.1uM_Paraquat_A549, A_209 0.1uM_Paraquat_A549, A
- GSM7733174 0.316uM_Paraquat_A549, A_210 0.316uM_Paraquat_A549, A
- GSM7733175 0uM_Paraquat_A549, A_211 0uM_Paraquat_A549, A
- GSM7733176 0.01uM_Paraquat_A549, A_212 0.01uM_Paraquat_A549, A
- GSM7733177 0.0316uM_Paraquat_A549, A_213 0.0316uM_Paraquat_A549,…
- GSM7733178 0.1uM_Paraquat_A549, A_214 0.1uM_Paraquat_A549, A
- GSM7733179 0.316uM_Paraquat_A549, A_215 0.316uM_Paraquat_A549, A
- GSM7733180 0uM_Paraquat_A549, A_216 0uM_Paraquat_A549, A
- GSM7733181 0.01uM_Paraquat_A549, A_217 0.01uM_Paraquat_A549, A
- GSM7733182 0.0316uM_Paraquat_A549, A_218 0.0316uM_Paraquat_A549,…
- GSM7733183 0.1uM_Paraquat_A549, A_219 0.1uM_Paraquat_A549, A
- GSM7733184 0.316uM_Paraquat_A549, A_220 0.316uM_Paraquat_A549, A
- GSM7733185 1uM_Paraquat_A549, A_221 1uM_Paraquat_A549, A
- GSM7733186 3.16uM_Paraquat_A549, A_222 3.16uM_Paraquat_A549, A
- GSM7733187 10uM_Paraquat_A549, A_223 10uM_Paraquat_A549, A
- GSM7733188 31.6uM_Paraquat_A549, A_224 31.6uM_Paraquat_A549, A
- GSM7733189 100uM_Paraquat_A549, A_225 100uM_Paraquat_A549, A
- GSM7733190 1uM_Paraquat_A549, A_226 1uM_Paraquat_A549, A
- GSM7733191 3.16uM_Paraquat_A549, A_227 3.16uM_Paraquat_A549, A
- GSM7733192 10uM_Paraquat_A549, A_228 10uM_Paraquat_A549, A
- GSM7733193 31.6uM_Paraquat_A549, A_229 31.6uM_Paraquat_A549, A
- GSM7733194 100uM_Paraquat_A549, A_230 100uM_Paraquat_A549, A
- GSM7733195 1uM_Paraquat_A549, A_231 1uM_Paraquat_A549, A
- GSM7733196 3.16uM_Paraquat_A549, A_232 3.16uM_Paraquat_A549, A
- GSM7733197 10uM_Paraquat_A549, A_233 10uM_Paraquat_A549, A
- GSM7733198 31.6uM_Paraquat_A549, A_234 31.6uM_Paraquat_A549, A
- GSM7733199 100uM_Paraquat_A549, A_235 100uM_Paraquat_A549, A
- GSM7733200 1uM_Paraquat_A549, A_236 1uM_Paraquat_A549, A
- GSM7733201 3.16uM_Paraquat_A549, A_237 3.16uM_Paraquat_A549, A
- GSM7733202 10uM_Paraquat_A549, A_238 10uM_Paraquat_A549, A
- GSM7733203 31.6uM_Paraquat_A549, A_239 31.6uM_Paraquat_A549, A
- GSM7733204 100uM_Paraquat_A549, A_240 100uM_Paraquat_A549, A
+ 360 more — browse all 400 samples with per-sample file links →
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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.