GEO series

Comparative single-cell analyses identify shared and divergent features of human and mouse kidney development [human snRNA-seq]

GSE232479 Homo sapiens (human) Single-nucleus RNA-seq 10 samples Submitted 2024/06/21 Platform GPL20301Platform GPL24676Platform GPL28038

At a glance

Single-nucleus RNA-seq · human · 10 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism: Homo sapiens (human)
Samples: 10 samples: 9 distinct titles, 1 of them repeatedGroups 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: Dmrt2 and Hmx2 direct intercalated cell diversity in the mammalian kidney through antagonistic and supporting regulatory processesProceedings of the National Academy of Sciences of the United States of America · 2025

Summary (written by the submitters)

Mammalian kidneys maintain fluid homeostasis through the cellular activity of nephrons and the conjoined collecting system. Each epithelial network originates from distinct progenitor cell populations that reciprocally interact during development. To extend our understanding of human and mouse kidney development, we profiled chromatin organization (ATAC-seq) and gene expression (RNA-seq) in developing human and mouse kidneys.

Read the full summary ↓

Data were analyzed at a species level and then integrated into a common, cross-species multimodal data set. Comparative analysis of cell types and developmental trajectories identified conserved and divergent features of chromatin organization and linked gene activity, revealing species- and cell-type specific regulatory programs. Identification of human-specific enhancer regions linked through GWAS studies to kidney disease highlights the potential of developmental modeling to provide clinical insight.

Published in

Dmrt2 and Hmx2 direct intercalated cell diversity in the mammalian kidney through antagonistic and supporting regulatory processes

Parvez RK, Kim DK, Csipán RL et al. · Proceedings of the National Academy of Sciences of the United States of America 2025 · PMID 40354537 · doi:10.1073/pnas.2418471122

This dataset

Open GSE232479 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 GSE232479_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 PRJNA972295 and SRA study SRP561333. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40354537) ↗

Study design

9 distinct titles, 1 of them repeated

15.6 Wk human fetal, snRNA-seq, rep F ×2

How we read this: Read from 10 sample titles: 9 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

GSM7337016 10.6 Wk human fetal, snRNA-seq, rep M1
GSM7337017 11.1 Wk human fetal, snRNA-seq, rep M2
GSM7337018 11.6 Wk human fetal, snRNA-seq, rep M3
GSM7337019 11.3 Wk human fetal, snRNA-seq, rep F1
GSM7337020 15.6 Wk human fetal, snRNA-seq, rep F2
GSM7337021 15.6 Wk human fetal, snRNA-seq, rep F3
GSM7337022 17.1 Wk human fetal, snRNA-seq, rep M4
GSM7337023 17.5 Wk human fetal, snRNA-seq, rep M5
GSM7337024 17.1 Wk human fetal, snRNA-seq, rep F5
GSM7337025 17.6 Wk human fetal, snRNA-seq, rep F6

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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.