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

GSE286357 Homo sapiens (human) Single-cell RNA-seq 35 samples Submitted 2025/12/30 Platform GPL34281Platform GPL30209
At a glance

Single-cell RNA-seq · human · 35 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
35 samples: 35 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.
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 GSE286357 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 GSE286357_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1208971 and SRA study SRP556496. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41276918) ↗
Study design
35 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 35 sample titles: 35 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
Datasets on a similar topic

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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.