GEO series

A temporal factorial dissection of kidney organoid differentiation identifies the impact of WNT, FGF, BMP and GDF signaling on ‘off-target’ patterning

GSE269904 Homo sapiens (human) Single-cell RNA-seq 30 samples Submitted 2026/03/18 Platform GPL18573Platform GPL24676

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. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 30 samples: 30 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)

Human pluripotent stem cell-derived organoids are multicellular models of developing tissues proposed to recapitulate developmental stages of lineage commitment across time. Assessing how accurately protocols recapitulate development is challenged by the lack of accurate reference data sets for early human development, the presence of ‘off target’ states and the potential to form cellular states not presentin vivo.

Read the full summary ↓

This study addresses these challenges with respect to differentiation of human pluripotent stem cells to kidney organoids. Based on a factorial single-cell transcriptomic analysis across a 27-day differentiation protocol, including 150,957 cells collected at five time points, we present a comprehensive classification of predicted identity, representing anin vitrotemporal transcriptional atlas. A significant component of all timepoints were neuromesodermal ‘off target’ populations which resulted in a variety of neural/neural crest and muscle endpoints. Transitional mesodermal populations with no clearin vivoequivalent were also present and varied with duration of initial mesodermal commitment. The analysis of cell trajectory across the timecourse identified distinct asynchrony of cell responses duringin vitrodifferentiation, with this driving ‘off target’ patterning.

This dataset

Open GSE269904 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 GSE269904_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 PRJNA1124094 and SRA study SRP514034. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

30 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 30 sample titles: 30 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

GSM8329901 Stage1, sample capture 1, day 0 to 5, scRNAseq GEX
GSM8329902 Stage2, sample capture 2, day 7, scRNAseq GEX
GSM8329903 Stage2, sample capture 3, day8, scRNAseq GEX
GSM8329904 Stage2, sample capture 4, day9, scRNAseq GEX
GSM8329905 Stage3, sample capture 5, day12, scRNAseq GEX
GSM8329906 Stage3, sample capture 6, day13, scRNAseq GEX
GSM8329907 Stage3, sample capture 7, day14, scRNAseq GEX
GSM8329908 Stage4, sample capture 8, day 27, scRNAseq GEX
GSM8329909 Stage4, sample capture 9, day 27, scRNAseq GEX
GSM8329910 Stage4, sample capture 10, day 27, scRNAseq GEX
GSM8329911 Stage4, sample capture 11, day 27, scRNAseq GEX
GSM8329912 Stage4, sample capture 12, day 27, scRNAseq GEX
GSM8329913 Stage4, sample capture 13, day 27, scRNAseq GEX
GSM8329914 All samples, scRNAseq HTO
GSM8329915 Stage 2, 3c4F, day 7, RNASeq
GSM8329916 Stage2, 4c4F, day8, RNASeq
GSM8329917 Stage2, 5c4F, day9, RNASeq
GSM8329918 Stage2, 3c4FA, day7, RNASeq
GSM8329919 Stage2, 5c4FA, day9, RNASeq
GSM8329920 Stage2, 3c4FC, day7, RNASeq
GSM8329921 Stage2, 4c4FC, day8, RNASeq
GSM8329922 Stage2, 5c4FC, day9, RNASeq
GSM8329923 Stage2, 3c4F200, day7, RNASeq
GSM8329924 Stage2, 4c4F200, day8, RNASeq
GSM8329925 Stage2, 3c4FL, day7, RNASeq
GSM8329926 Stage2, 4c4FL, day8, RNASeq
GSM8329927 Stage2, 5c4FL, day9, RNASeq
GSM8329928 Stage2, 3c4E, day7, RNASeq
GSM8329929 Stage2, 4c4E, day8, RNASeq
GSM8329930 Stage2, 5c4E, day9, RNASeq

Datasets on a similar topic

GSE279593 BMP, MEK and WNT modulation with NGN2 expression for rapid neuron differentiation from hiPSC amenable to regional patterning 158 samples shares wnt, bmp, patterning
GSE253508 BMP, MEK and WNT modulation with NGN2 expression for rapid neuron differentiation from hiPSC amenable to regional patterning [BMWi] 69 samples shares wnt, bmp, patterning
GSE279590 BMP, MEK and WNT modulation with NGN2 expression for rapid generation of hiPSC-derived neurons amenable to regional patterning [bulk RNA-seq] 61 samples shares wnt, bmp, patterning
GSE275686 GATA6 regulates WNT and BMP programs to pattern precardiac mesoderm during the earliest stages of human cardiogenesis (RNA-Seq) 22 samples shares wnt, bmp
GSE245117 Circular RNA profiling identifies circ5078 as a BMPR2-derived regulator of endothelial translation 24 samples shares bmp, identifies
GSE266683 Patterning effects of FGF17 and cAMP on generation of dopaminergic progenitors for cell replacement therapy in Parkinson’s disease. [RNA-Seq] 24 samples shares fgf, patterning
GSE259318 Human Pluripotent Stem Cell Derived Organoids Reveal a Role for WNT Signaling in Dorsal-Ventral Patterning of the Hindgut 19 samples shares wnt, patterning
GSE273244 BMP4 induces a p21-dependent cell state shift in glioblastoma linking mesenchymal transition to senescence 80 samples shares bmp

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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.