ZNHIT3 Regulates Translation to Ensure Cell Lineage Differentiation in Mouse Preimplantation Development [scRNA-Seq]
Single-cell RNA-seq · mouse · 96 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Mus musculus (mouse)
- Samples
- 96 samples: 12 conditions, mostly with about 8 replicates eachGroups 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
- ZNHIT3 Regulates Translation to Ensure Cell Lineage Differentiation in Mouse Preimplantation DevelopmentAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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 GSE267993_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 PRJNA1114016 and SRA study SRP508958. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 96 samples.
How we read this: 12 replicated groups read from 96 sample titles; they account for 96 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.
- GSM8283480 scRNAseq_8cell_R33_1 scRNAseq_8cell_R33
- GSM8283481 scRNAseq_8cell_R33_2 scRNAseq_8cell_R33
- GSM8283482 scRNAseq_8cell_R33_3 scRNAseq_8cell_R33
- GSM8283483 scRNAseq_8cell_R33_4 scRNAseq_8cell_R33
- GSM8283484 scRNAseq_8cell_R33_5 scRNAseq_8cell_R33
- GSM8283485 scRNAseq_8cell_R33_6 scRNAseq_8cell_R33
- GSM8283486 scRNAseq_8cell_R33_7 scRNAseq_8cell_R33
- GSM8283487 scRNAseq_8cell_R33_8 scRNAseq_8cell_R33
- GSM8283488 scRNAseq_8cell_R34_1 scRNAseq_8cell_R34
- GSM8283489 scRNAseq_8cell_R34_2 scRNAseq_8cell_R34
- GSM8283490 scRNAseq_8cell_R34_3 scRNAseq_8cell_R34
- GSM8283491 scRNAseq_8cell_R34_4 scRNAseq_8cell_R34
- GSM8283492 scRNAseq_8cell_R34_5 scRNAseq_8cell_R34
- GSM8283493 scRNAseq_8cell_R34_6 scRNAseq_8cell_R34
- GSM8283494 scRNAseq_8cell_R34_7 scRNAseq_8cell_R34
- GSM8283495 scRNAseq_8cell_R34_8 scRNAseq_8cell_R34
- GSM8283496 scRNAseq_8cell_R35_1 scRNAseq_8cell_R35
- GSM8283497 scRNAseq_8cell_R35_2 scRNAseq_8cell_R35
- GSM8283498 scRNAseq_8cell_R35_3 scRNAseq_8cell_R35
- GSM8283499 scRNAseq_8cell_R35_4 scRNAseq_8cell_R35
- GSM8283500 scRNAseq_8cell_R35_5 scRNAseq_8cell_R35
- GSM8283501 scRNAseq_8cell_R35_6 scRNAseq_8cell_R35
- GSM8283502 scRNAseq_8cell_R35_7 scRNAseq_8cell_R35
- GSM8283503 scRNAseq_8cell_R35_8 scRNAseq_8cell_R35
- GSM8283504 scRNAseq_8cell_R39_1 scRNAseq_8cell_R39
- GSM8283505 scRNAseq_8cell_R39_2 scRNAseq_8cell_R39
- GSM8283506 scRNAseq_8cell_R39_3 scRNAseq_8cell_R39
- GSM8283507 scRNAseq_8cell_R39_4 scRNAseq_8cell_R39
- GSM8283508 scRNAseq_8cell_R39_5 scRNAseq_8cell_R39
- GSM8283509 scRNAseq_8cell_R39_6 scRNAseq_8cell_R39
- GSM8283510 scRNAseq_8cell_R39_7 scRNAseq_8cell_R39
- GSM8283511 scRNAseq_8cell_R39_8 scRNAseq_8cell_R39
- GSM8283512 scRNAseq_8cell_R40_1 scRNAseq_8cell_R40
- GSM8283513 scRNAseq_8cell_R40_2 scRNAseq_8cell_R40
- GSM8283514 scRNAseq_8cell_R40_3 scRNAseq_8cell_R40
- GSM8283515 scRNAseq_8cell_R40_4 scRNAseq_8cell_R40
- GSM8283516 scRNAseq_8cell_R40_5 scRNAseq_8cell_R40
- GSM8283517 scRNAseq_8cell_R40_6 scRNAseq_8cell_R40
- GSM8283518 scRNAseq_8cell_R40_7 scRNAseq_8cell_R40
- GSM8283519 scRNAseq_8cell_R40_8 scRNAseq_8cell_R40
+ 56 more — browse all 96 samples with per-sample file links →
- GSE267192 PPARγ-dependent Remodeling of Translational Machinery in Adipose Progenitors is Impaired in Obesity [scRNA-seq] 12 samples shares translation, scrna-seq
- GSE319598 Lineage identity governs oncogene dependence in murine NSCLC models of KRAS inhibitor resistance [scRNA-Seq] 9 samples shares scrna-seq, lineage
- GSE314859 The degradation of maternal RNA-binding protein 4E-T regulates translational activation to ensure maternal-to-zygotic transition in mouse embryos 67 samples shares translation, ensure
- GSE241770 ACSS2 mediates an epigenetic pathway to regulate maternal Beta-cell adaptation and progression of gestational diabetes (scRNA-Seq) 7899 samples shares scrna-seq
- GSE277228 Changing dynamics of the fibroblast differentiation hierarchy during mammary gland morphogenesis and tumorigenesis [scRNA-Seq I] 13 samples shares scrna-seq
- GSE286398 Lysine-specific demethylase 1 regulates hematopoietic stem cell expansion and myeloid cell differentiation [scRNA-seq] 13 samples shares scrna-seq
- GSE264105 Plate-based scRNA-seq of plasma-positive microglia and plasma-negative microglia in young mice 1111 samples shares scrna-seq
- GSE271100 Stem cells tightly regulate dead cell clearance to maintain tissue fitness [scRNA-seq] 1037 samples shares scrna-seq
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.