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Searching for the influencers among placental immune cells in preeclampsia.

GSE290578 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2025/05/26 Platform GPL20795
At a glance

Single-cell RNA-seq · human · 16 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
16 samples: 4 × Placenta, Maternal-Fetal interface, normal, vs 4 × Placenta, Deep placenta, normal, vs 4 × Placenta, Maternal-Fetal interface, preeclampsia preterm parturitions, vs 4 × Placenta, Deep placenta, preeclampsia preterm parturitions,Groups 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)
It has been posited that cells of the maternal and fetal immune systems communicate to lead to immune tolerance during pregnancy however been inconclusive. Here, we profiled single-cell transcriptional signatures by placental layers consisting of maternal-fetal interface (MF) and deep-placenta (Pla) regions, then searched influencer gene for preeclampsia (PE).
Read the full summary ↓
As underpin principle of the failure of immune tolerance, we start by clarifying the systemic framework, which consists of immune interactions frequency (IIF) and specific trigger (i.e., influencer) tolerance (IT) models. We elaborate single-cell transcriptional profiles of normal term (Norm) and preeclampsia preterm parturitions (PePT). Fetal and maternal cells admixed across placenta, in both of Norms and PePTs, rejecting IIF model of immune failure of pregnancy posed by exceed interactions of fetomaternal cells. Whereases placental layers are well mixed with maternal cells, we identified conserved gradual immune transition of fetal T cells in both of PePT and Norm, disapproving IIF model. To search the influencer of PePT in IT model, we established and validated a classification model for PePT and Norm immune cells including T cells, then prioritized major contributors for classifier model. Interestingly, those prioritized genes are highly enriched with ligands and receptors (p-value 5.98e-05). Out of prioritized ligand-receptors, SPP1 and CD44 suggested as influencer of inflammation signatures and experimentally validated by exclusive colocalization of SPP1 and CD44 expressed cells in placentas of PePT. Different IL4 and IFN-g levels in the serum and urine of PePTs further support contribution of SPP1 in associated pathways, including allograft rjection pathway. Collectively, our finding provide insight into the influence of specific immune interactions between cells in human placenta and these influencer-derived impact the immune harmonization of PePT.
This dataset
Open GSE290578 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 GSE290578_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 PRJNA1228650 and SRA study SRP566530. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × Placenta, Maternal-Fetal interface, normal, vs 4 × Placenta, Deep placenta, normal, vs 4 × Placenta, Maternal-Fetal interface, preeclampsia preterm parturitions, vs 4 × Placenta, Deep placenta, preeclampsia preterm parturitions,

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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
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