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Effect of IVNS1ABP mutation, knockout on gene expression during iPSCs differentiation to Neural Progenitors

GSE270946 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/01/23 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 18 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
18 samples: 6 conditions, mostly in triplicateGroups 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)
A homozygous variant in IVNS1ABP was identified in three siblings, displaying progeroid features with severe neuropathy. The association between IVNS1ABP and aging has never been reported before. By generating isogenic iPSCs from the patients’ fibroblasts and differentiating the iPSCs into neural progenitor cells (NPCs), we found that mutant IVNS1ABP fibroblasts, iPSCs, and NPCs exhibited disrupted cytokinesis, DNA damage, and cellular senescence.
Read the full summary ↓
Transcription analysis of isogenic iPSCs and iPSCs derived neural progenitors (NPCs) also showed impaired cytokinesis and senescence alteration. Correspondingly, cerebral organoids displayed premature differentiation of NPCs to neurons. Molecular profiling as well as biochemical and cellular analysis revealed altered binding of mutant IVNS1ABP to actin and actin-associated proteins and dysregulated actin dynamics during cytokinesis. Taken together, we propose that IVNS1ABP mutation dysregulates actin polymerization and organization which is at least partly responsible for the cellular senescence phenotypes in this undiagnosed disease.
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 GSE270946_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 PRJNA1129086 and SRA study SRP516566. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in triplicate
MT ips cells, ×3 Correctted ips cells, ×3 KO ips cells, ×3 MT iPSCs derived NPCs, ×3 Correctted iPSCs derived NPCs, ×3 KO iPSCs derived NPCs, ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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
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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.