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Assessing developmental neurotoxicity of emerging environmental chemicals using multiple in vitro models: A comparative analysis.

GSE213302 Rattus norvegicus (rat) RNA-seq (gene expression) 78 samples Submitted 2025/09/13 Platform GPL30103Platform GPL28038
At a glance

RNA-seq (gene expression) · rat · 78 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
Rattus norvegicus (rat)
Samples
78 samples: 78 samples, each with a distinct title — no replicated groupsGroups 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.
Summary (written by the submitters)
Newly synthesized chemicals are being introduced into the environment without undergoing proper toxicological evaluation, particularly in terms of their effects on the vulnerable neurodevelopment. Thus, it is important to carefully assess the developmental neurotoxicity of these novel environmental contaminants using methods that are closely relevant to human physiology.
Read the full summary ↓
This study comparatively evaluated the potential developmental neurotoxicity of 19 prevalent environmental chemicals including neonicotinoids (NEOs), organophosphate esters (OPEs), and synthetic phenolic antioxidants (SPAs) at environment-relevant doses (100 nM and 1 μM), using three commonly employed in vitro neurotoxicity models: human neural stem cells (NSCs), as well as the SK-N-SH and PC12 cell lines. Our results showed that NSCs were more sensitive than SK-N-SH and PC12 cell lines. Among all the chemicals tested, the two NEOs imidaclothiz (IMZ) and cycloxaprid (CYC), as well as the OPE tris(1,3dichloro-2-propyl) phosphate (TDCIPP), generated the most noticeable perturbation by impairing NSC maintenance and neuronal differentiation, as well as promoting the epithelial-mesenchymal transition process, likely via activating NF-κB signaling. Our data indicate that novel NEOs and OPEs, particularly IMZ, CYC, and TDCIPP, may not be safe alternatives as they can affect NSC maintenance and differentiation, potentially leading to neural tube defects and neuronal differentiation dysplasia in fetuses.
This dataset
Open GSE213302 in finder → Find more RNA-seq datasets →
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.

Series matrix ↓
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 GSE213302_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA880361 and SRA study SRP397028. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
78 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 78 sample titles: 78 distinct titles with little repetition. 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

+ 38 more — browse all 78 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.