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Placental transcriptome profiles in late-onset preeclampsia: a cross-sectional observational analysis stratified by fetal sex

GSE306864 Homo sapiens (human) Single-cell RNA-seq 58 samples Submitted 2025/10/07 Platform GPL16791
At a glance

Single-cell RNA-seq · human · 58 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
58 samples: 30 × STP vs 28 × SCPGroups 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
Sex-specific placental transcriptome alterations in late-onset preeclampsia reveal male-biased immune and metabolic dysregulationBiology of sex differences · 2025
Summary (written by the submitters)
Background Preeclampsia is a hypertensive disorder of pregnancy with significant maternal and fetal health implications. While the molecular mechanisms underlying early-onset preeclampsia have been studied extensively, less is known about late-onset preeclampsia, particularly with regard to fetal sex-specific effects. Understanding transcriptional changes in the placenta may provide insight into persistent mechanisms or downstream consequences of the disease, thereby contributing to our understanding of its pathophysiology.
Read the full summary ↓
While the initiating events of late-onset preeclampsia occur earlier in gestation, analysis of term placental tissue can reveal cumulative effects of disease processes and identify biological pathways that remain altered at delivery. Methods We conducted a cross-sectional observational analysis of placental gene expression using RNA sequencing in a subset of 58 term placentas (21 male-bearing and 37 female-bearing pregnancies) drawn from two large prospective birth cohorts. Pregnancies were classified based on clinical diagnosis of late-onset preeclampsia (diagnosed ≥20 weeks’ gestation according to ISSHP criteria) or uncomplicated and assessed for differential gene expression. Cell type proportions were estimated using CIBERSORTx from a placenta-specific reference single-cell dataset. Weighted gene co-expression network analysis identified modules of co-expressed genes associated with late-onset preeclampsia and fetal sex. Results Differential gene expression analysis identified 150 genes with altered expression in male-bearing placentas from pregnancies with late-onset preeclampsia compared to those from uncomplicated pregnancies. No differentially expressed genes were identified in female-bearing placentas. Cell type deconvolution revealed increased abundance of CD14+ monocytes and CD8+ activated T cells (log odds of 1.42 and 1.44 respectively) and reduced fetal GZMK natural killer cells (log odds of 0.60) in male-bearing placentas from affected pregnancies. In female-bearing placentas, late-onset preeclampsia was associated with increased fetal nucleated red blood cells and maternal plasma cells (log odds of 1.33 and 1.40 respectively). Male-specific co-expression analysis identified gene modules enriched for biological processes including RNA processing, immune regulation, and metabolism. Conclusions Placental transcription and cellular responses to late-onset preeclampsia differ by fetal sex. Evidence of altered immune cell composition and gene co-expression in male-bearing placentas suggests a sex-specific vulnerability. These findings highlight the importance of considering fetal sex in molecular investigation and clinical management of preeclampsia.
Published in
Sex-specific placental transcriptome alterations in late-onset preeclampsia reveal male-biased immune and metabolic dysregulation
Smith MD, Plaisier S, Breen J et al. · Biology of sex differences 2025 · PMID 41444673 · doi:10.1186/s13293-025-00781-w
This dataset
Open GSE306864 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 GSE306864_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 PRJNA816267 and SRA study SRP364071. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41444673) ↗
Study design
30 × STP vs 28 × SCP

Supports a between-group comparison across 58 samples.

How we read this: 2 replicated groups read from 58 sample titles; they account for 58 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

+ 18 more — browse all 58 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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