Trophectoderm-like cells from EPS cells enable generating EPS cell-derived post-implantation embryoids that complete gastrulation [bulk RNA-seq]
Single-cell RNA-seq · mouse · 34 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
- Mus musculus (mouse)
- Samples
- 34 samples: 16 conditions, mostly in duplicateGroups 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
- Trophectoderm-like cells from EPS cells enable generating EPS cell-derived post-implantation embryoids that complete gastrulationProtein & cell · 2026
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE291068_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1231499 and SRA study SRP567879. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 34 samples.
How we read this: 16 replicated groups read from 34 sample titles; they account for 34 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.
- GSM8828537 EPS rep1 EPS
- GSM8828538 EPS rep2 EPS
- GSM8828539 EPS rep3 EPS
- GSM8828540 EPS rep4 EPS
- GSM8828541 EPS-N rep1 EPS-N
- GSM8828542 EPS-N rep2 EPS-N
- GSM8828543 EPS-C3 rep1 EPS-C3
- GSM8828544 EPS-C3 rep2 EPS-C3
- GSM8828545 EPS-C6 rep1 EPS-C6
- GSM8828546 EPS-C6 rep2 EPS-C6
- GSM8828547 EPS-C10 rep1 EPS-C10
- GSM8828548 EPS-C10 rep2 EPS-C10
- GSM8828549 EPS-C20 rep1 EPS-C20
- GSM8828550 EPS-C20 rep2 EPS-C20
- GSM8828551 FAXY-TS rep1 FAXY-TS
- GSM8828552 FAXY-TS rep2 FAXY-TS
- GSM8828553 FBS-TS rep1 FBS-TS
- GSM8828554 FBS-TS rep2 FBS-TS
- GSM8828555 OG-BLES rep1 OG-BLES
- GSM8828556 OG-BLES rep2 OG-BLES
- GSM8828557 OG-EPS rep1 OG-EPS
- GSM8828558 OG-EPS rep2 OG-EPS
- GSM8828559 OG-PE rep1 OG-PE
- GSM8828560 OG-PE rep2 OG-PE
- GSM8828561 TES1 rep1 TES1
- GSM8828562 TES1 rep2 TES1
- GSM8828563 TES2-P1 rep1 TES2-P1
- GSM8828564 TES2-P1 rep2 TES2-P1
- GSM8828565 TES2-P3 rep1 TES2-P3
- GSM8828566 TES2-P3 rep2 TES2-P3
- GSM8828567 TES2-P6 rep1 TES2-P6
- GSM8828568 TES2-P6 rep2 TES2-P6
- GSM8828569 TES2-P21 rep1 TES2-P21
- GSM8828570 TES2-P21 rep2 TES2-P21
- GSE176506 Synergistic behavioral and neuroinflammatory effects of amyloid neuropathology and sepsis survival in a murine model of Alzheimer’s disease 112 samples shares eps
- GSE176505 Synergistic behavioral and neuroinflammatory effects of amyloid neuropathology and sepsis survival in a murine model of Alzheimer’s disease II 72 samples shares eps
- GSE226649 The immune landscape of the “deregulated host response” in sepsis: newly emerging MDSCs with high expression of CD47 leads to B cell suppression via App-CD74 signaling 64 samples shares eps
- GSE222996 The immune landscape of the “deregulated host response” in sepsis: newly emerging MDSCs with high expression of CD47 leads to B cell suppression via App-CD74 signaling [RNA-seq] 61 samples shares eps
- GSE311155 Age- and Sex- Driven Transcriptional and Metabolic Diversity in Myeloid-Derived Suppressor Cells After Mouse Sepsis 57 samples shares eps
- GSE305103 RNA-seq of liver, kidney, and quadriceps muscle from corylin-treated and control C57BL/6J mice 54 samples shares eps
- GSE313149 ACBP: a poor-prognosis biomarker in sepsis and a target for disease mitigation [Lung] 50 samples shares eps
- GSE292277 Temporal impact of sepsis on Alzheimer's disease pathology and neuroinflammation 48 samples shares eps
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.