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Discrete placental gene expression signatures of diabetes during pregnancy

GSE249311 Homo sapiens (human) Single-nucleus RNA-seq 54 samples Submitted 2025/05/05 Platform GPL11154
At a glance

Single-nucleus RNA-seq · human · 54 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). 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
54 samples: 24 × X vs 21 × NCS vs 9 × DMGroups 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)
BACKGROUND: Although gestational diabetes affects up to 10% of all pregnancies, there is ambiguity as to whether the disease subtype classifications are biologically significant at the maternal-fetal interface, or more reflective of an extended spectrum of normal pregnancy endocrine physiology. OBJECTIVE: Bulk RNA-sequencing (RNA-seq), single-cell RNA-sequencing (scRNA-seq), and spatial transcriptomics have been integrated to reveal gene signatures of disease in subsets of cells and microenvironments.
Read the full summary ↓
We aimed to combine these high-resolution technologies with rigorous classification of diabetes subtypes in pregnancy. We hypothesized that differences between pre-existing and gestational diabetes subtypes would be associated with altered gene expression profiles in specific placental cell populations. STUDY DESIGN: In a two-phase case-cohort design, we clinically validated gestational diabetes mellitus type 1 (GDMA1), GDMA2, and type 2 diabetes (T2DM) cases within a cohort of placentae and compared them to healthy controls by bulk RNA-seq (n=54). Quantitative analysis with RT-qPCR of presumptive genes of significant interest were undertaken in an independent and non-overlapping validation cohort of similarly well-characterized cases and controls (n=122). Additional integrated analyses of term placenta single-cell, single-nuclei, and spatial transcriptomics data to determine the cellular subpopulations and niches that aligned with the GDMA1, GDMA2, and T2DM gene expression signatures. RESULTS: Dimension reduction of the bulk RNA-seq data revealed that the most common source of placental gene expression variation was diabetic disease subtype. Relative to controls, we found 2,052 unique significantly differentially expressed transcripts (-2<Log2(fold-change)>2 threshold; q<0.05 Wald Test) among GDMA1 placental specimens, 267 among GDMA2, and 1,520 among T2DM. Several candidate marker genes (CSH1, PER1, PIK3CB, FOXO1, EGFR, IL2RB, SOD3, DOCK5, and SOGA1) were validated in an independent and non-overlapping validation cohort (q<0.05 Tukey). Functional enrichment revealed the pathways and genes most impacted for each diabetes subtype, and degree of proximal similarity to other subclassifications. Surprisingly, GDMA1 and T2DM were more proximal by virtue of increased expression of chromatin remodeling and epigenetic regulation genes, while albumin was the top marker for GDMA2 with increased expression of genes in the wound healing pathway. Assessment of these gene signatures in the single-cell, single-nuclei, and spatial transcriptomics data revealed expression of these genes were highly variable by placental cell and microarchitecture type. For example, at the cellular and spatial (e.g., microarchitectural) levels, distinguishing features were observed in in extravillous trophoblasts (GDMA1) and macrophages (GDMA2). Lastly, we utilized these data to generate machine learning models to predict participants diabetes status and observed greater proximity of placental gene expression among GDMA1 and T2DM participants relative to GDMA2. CONCLUSION: Consistent with their distinct risks of perinatal outcomes, placentae from GDMA1, GDMA2, and T2DM affected pregnancies harbor gene signatures which can be further distinguished by placental microarchitecture and cellular subtypes.
This dataset
Open GSE249311 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 GSE249311_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 PRJNA1048494 and SRA study SRP475786. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 × X vs 21 × NCS vs 9 × DM

Supports a between-group comparison across 54 samples.

How we read this: 3 replicated groups read from 54 sample titles; they account for 54 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

+ 14 more — browse all 54 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.