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Placental gene signatures associated with high neonatal adiposity: role for immune cell activation

GSE318865 Homo sapiens (human) RNA-seq (gene expression) 79 samples Submitted 2026/04/15 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 79 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
79 samples
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
Placental gene signatures associated with high neonatal adiposity: role for immune cell activationJournal of the Endocrine Society · 2026
Summary (written by the submitters)
Fetal fat accumulation is an important indicator of the nutritional environment in pregnancy and placental function. Excessive fat accretion leading to high adiposity at birth, however, can increase a child’s long-term risk for obesity and metabolic disease. While maternal body mass index is associated with neonatal adiposity, there is a wide variation in body composition among babies born to both women with and without obesity.
Read the full summary ↓
The placenta orchestrates a complex exchange of nutrients and signals between the mother and baby. To better understand the molecular mechanisms that govern fetal fat accumulation, we profiled the transcriptomics of 79 placentas collected from mothers with and without obesity. We identified a set of 18 neonatal adiposity-associated genes, common to pregnancies with and without obesity. A co-expressed cluster of these genes are involved in innate immune responses, particularly neutrophil activation. We also identified neonatal adiposity-associated genes unique to mothers with or without obesity, suggesting different biological pathways support high newborn adiposity, and/or are responsive to a common initial immune signal. These findings suggest that placental inflammation may influence fetal fat accumulation. Understanding these pathways may help identify novel ways to support healthy fetal growth and reduce the risk of long-term disease.
Published in
Placental gene signatures associated with high neonatal adiposity: role for immune cell activation
Laird J, Venkataraman D, Yen H et al. · Journal of the Endocrine Society 2026 · PMID 41958865 · doi:10.1210/jendso/bvag071
This dataset
Open GSE318865 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 GSE318865_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1420737 and SRA study SRP675743. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41958865) ↗
Study design
A cohort of 79 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run s1 … s9 and carry no group information.

Samples in this study

+ 39 more — browse all 79 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.