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Transcriptional regulation of temporal dynamics in human neurogenesis and neuronal maturation [RNA-seq]

GSE285570 Homo sapiens (human) RNA-seq (gene expression) 50 samples Submitted 2025/12/30 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 50 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
50 samples: 25 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.
Paper
Temporal Transcriptional Regulation of Human Neuronal Differentiation via Forward ProgrammingAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
Summary (written by the submitters)
Human pluripotent stem cells (hPSCs) provide a valuable platform for studies of human neuronal differentiation, but the mechanisms regulating the timing of this process remain poorly understood. This study compares two differentiation systems with distinct timing of differentiation, transcription factor (TF)-induced forward programming and stepwise cellular differentiation.
Read the full summary ↓
Our analyses reveal that divergent cellular trajectories drive distinct neurogenesis timing. Multi-omic analysis identifies crucial gene regulatory networks (GRNs) that govern cell fate determination and timing control. Modulation of these GRNs modulates neurogenesis and timing of neuronal maturation. Specifically, OLIG family TFs, enriched in the TF-induced system, promoted cell cycle exit through NOTCH signaling regulation; conversely, ablation of these TFs delayed neurogenesis. In contrast, NEUROD2 overexpression accelerated neuronal maturation through precocious activation of maturation gene modules. Collectively, these findings illuminate characteristics of the cell-intrinsic mechanisms that govern timing of differentiation; the studies also offer a framework for investigation into, and the rational design of, timing-controlled in vitro differentiation strategies.
Published in
Temporal Transcriptional Regulation of Human Neuronal Differentiation via Forward Programming
Zhu L, Wang W, Zhang J et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2026 · PMID 41276918 · doi:10.1002/advs.202510641
This dataset
Open GSE285570 in finder → Find more human 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 GSE285570_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 PRJNA1204812 and SRA study SRP554631. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41276918) ↗
Study design
25 conditions, mostly in duplicate
3N_TF 0h ×2 3N_TF 2h ×2 3N_TF 4h ×2 3N_TF 6h ×2 3N_TF 8h ×2 3N_TF 10h ×2 3N_TF 12h ×2 3N_TF 14h ×2 +17 more

Supports a between-group comparison across 50 samples.

How we read this: 25 replicated groups read from 50 sample titles; they account for 50 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

+ 10 more — browse all 50 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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