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Single-cell analysis of human iPSC-derived midbrain organoids generated with triphasic Wnt modulation combined with a dynamic bioreactor

GSE301365 Homo sapiens (human) Single-cell RNA-seq 30 samples Submitted 2026/06/03 Platform GPL34281

At a glance

Single-cell RNA-seq · human · 30 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: 30 samples: 10 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)

Human midbrain organoids (hMOs) derived from induced pluripotent stem cells offer a promising platform to model Parkinson’s disease (PD), but current systems often fall short in generating mature, regionally specified dopaminergic (DA) neurons that reflect disease-relevant vulnerabilities. Here, we present a refined differentiation strategy that combines tri-phasic WNT signaling modulation with dynamic bioreactor cultivation to enhance DA neuron yield, specification, and maturation within hMOs.

Read the full summary ↓

This approach generated DA neurons with enriched substantia nigra-like identity (GIRK2⁺, ALDH1A1⁺), elevated dopamine release, and robust functional activity. Single cell transcriptomic profiling revealed upregulation of synaptic, metabolic, and neuroprotective pathways within the DA neuron cluster, while stress-related and pro-apoptotic programs were suppressed. Importantly, these hMOs exhibited selective vulnerability upon challenge with α-synuclein preformed fibrils (aSyn-PFFs), modeling PD-relevant neurodegeneration. Together, our findings establish a scalable, physiologically relevant platform for investigating PD mechanisms, therapeutic screening and advancing the development of cell replacement strategies.

This dataset

Open GSE301365 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 GSE301365_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 PRJNA1284858 and SRA study SRP596419. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

10 conditions, mostly in triplicate

CTRL_DIV25 n ×3 IWP2_DIV25 n ×3 CTRL_DIV40 n ×3 IWP2_DIV40 n ×3 CTRL_BR_DIV40 n ×3 IWP2_BR_DIV40 n ×3 CTRL_DIV65 n ×3 IWP2_DIV65 n ×3 +2 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 30 sample titles; they account for 30 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

GSM9081636 CTRL_DIV25 n1 CTRL_DIV25 n
GSM9081637 CTRL_DIV25 n2 CTRL_DIV25 n
GSM9081638 CTRL_DIV25 n3 CTRL_DIV25 n
GSM9081639 IWP2_DIV25 n1 IWP2_DIV25 n
GSM9081640 IWP2_DIV25 n2 IWP2_DIV25 n
GSM9081641 IWP2_DIV25 n3 IWP2_DIV25 n
GSM9081642 CTRL_DIV40 n1 CTRL_DIV40 n
GSM9081643 CTRL_DIV40 n2 CTRL_DIV40 n
GSM9081644 CTRL_DIV40 n3 CTRL_DIV40 n
GSM9081645 IWP2_DIV40 n1 IWP2_DIV40 n
GSM9081646 IWP2_DIV40 n2 IWP2_DIV40 n
GSM9081647 IWP2_DIV40 n3 IWP2_DIV40 n
GSM9081648 CTRL_BR_DIV40 n1 CTRL_BR_DIV40 n
GSM9081649 CTRL_BR_DIV40 n2 CTRL_BR_DIV40 n
GSM9081650 CTRL_BR_DIV40 n3 CTRL_BR_DIV40 n
GSM9081651 IWP2_BR_DIV40 n1 IWP2_BR_DIV40 n
GSM9081652 IWP2_BR_DIV40 n2 IWP2_BR_DIV40 n
GSM9081653 IWP2_BR_DIV40 n3 IWP2_BR_DIV40 n
GSM9081654 CTRL_DIV65 n1 CTRL_DIV65 n
GSM9081655 CTRL_DIV65 n2 CTRL_DIV65 n
GSM9081656 CTRL_DIV65 n3 CTRL_DIV65 n
GSM9081657 IWP2_DIV65 n1 IWP2_DIV65 n
GSM9081658 IWP2_DIV65 n2 IWP2_DIV65 n
GSM9081659 IWP2_DIV65 n3 IWP2_DIV65 n
GSM9081660 CTRL_BR_DIV65 n1 CTRL_BR_DIV65 n
GSM9081661 CTRL_BR_DIV65 n2 CTRL_BR_DIV65 n
GSM9081662 CTRL_BR_DIV65 n3 CTRL_BR_DIV65 n
GSM9081663 IWP2_BR_DIV65 n1 IWP2_BR_DIV65 n
GSM9081664 IWP2_BR_DIV65 n2 IWP2_BR_DIV65 n
GSM9081665 IWP2_BR_DIV65 n3 IWP2_BR_DIV65 n

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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.