GEO series

A MEF2C transcription factor network regulates proliferation of glomerular endothelial cells in diabetic kidney disease

GSE307817 Homo sapiens (human) Other 12 samples Submitted 2026/05/21 Platform GPL24676

At a glance

human · 12 samples

Organism: Homo sapiens (human)
Samples: 12 samples: 12 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.

Summary (written by the submitters)

The maintenance of a healthy epithelial-endothelial juxtaposition requires cross-talk within glomerular cellular niches. We sought to understand the spatially-anchored regulation and transition of endothelial and mesangial cells from health to injury in DKD. From 74 human kidney samples, an integrated multi-omics approach was leveraged to identify cellular niches, cell-cell communication, cell injury trajectories, and regulatory transcription factor (TF) networks in glomerular capillary endothelial (EC-GC) and mesangial cells.

Read the full summary ↓

Data were culled from single nucleus RNA and ATAC sequencing and three orthogonal spatial transcriptomic technologies for correlation with histopathological and clinical trial data. We identified a cellular niche in diabetic glomeruli enriched in a proliferative endothelial cell subtype (prEC) and altered vascular smooth muscle cells (VSMCs). Cellular communication within this niche maintained pro-angiogenic signaling with loss of anti-angiogenic factors. We identified a TF network of MEF2C, MEF2A, and TRPS1 which regulated SEMA6A and PLXNA2, a receptor-ligand pair opposing angiogenesis. It was observed in this Visium FFPE dataset that glomeruli enriched in the prEC niche had histologic evidence of neovascularization, and MEF2C activity was increased in diabetic glomeruli with nodular mesangial sclerosis.

This dataset

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Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗

3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE307817_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

Also filed as BioProject PRJNA1327363. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

12 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 12 sample titles: 12 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

GSM9231917 V10S15-103_XY01_IU-21-015F
GSM9231918 V10S15-103_XY03_IU-21-019F
GSM9231919 V42N07-395_XY04_IU94
GSM9231920 V42D20-364_XY04_IU92
GSM9231921 V42N07-395_XY01_IU98
GSM9231922 V42D20-364_XY01_IU103
GSM9231923 V42N07-339_XY04_F44
GSM9231924 V10S15-103_XY02_IU-21-016F
GSM9231925 V10S15-103_XY04_IU-21-020F
GSM9231926 V42N07-339_XY01_3781
GSM9231927 V42N07-399_XY01_3723
GSM9231928 V42N07-399_XY04_3775

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