Single embryo RNA-seq analysis in RIF1 depleted preimplantation mouse embryos
Single-cell RNA-seq · mouse · 40 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
- 40 samples: 13 × siRif1_4C vs 12 × siRif1_8C vs 8 × siCon_8C vs 7 × siCon_4CGroups 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
- RIF1 controls replication timing in early mouse embryos independently of lamina-associated nuclear organizationDevelopmental cell · 2025
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 GSE262789_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 PRJNA1093389 and SRA study SRP498634. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 40 samples.
How we read this: 4 replicated groups read from 40 sample titles; they account for 40 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.
- GSM8178915 siCon_4C_1 siCon_4C
- GSM8178916 siCon_4C_2 siCon_4C
- GSM8178917 siCon_4C_3 siCon_4C
- GSM8178918 siCon_4C_4 siCon_4C
- GSM8178919 siCon_4C_5 siCon_4C
- GSM8178920 siCon_4C_6 siCon_4C
- GSM8178921 siCon_4C_7 siCon_4C
- GSM8178922 siRif1_4C_1 siRif1_4C
- GSM8178923 siRif1_4C_2 siRif1_4C
- GSM8178924 siRif1_4C_3 siRif1_4C
- GSM8178925 siRif1_4C_4 siRif1_4C
- GSM8178926 siRif1_4C_5 siRif1_4C
- GSM8178927 siRif1_4C_6 siRif1_4C
- GSM8178928 siRif1_4C_7 siRif1_4C
- GSM8178929 siRif1_4C_8 siRif1_4C
- GSM8178930 siRif1_4C_9 siRif1_4C
- GSM8178931 siRif1_4C_10 siRif1_4C
- GSM8178932 siRif1_4C_11 siRif1_4C
- GSM8178933 siRif1_4C_12 siRif1_4C
- GSM8178934 siRif1_4C_13 siRif1_4C
- GSM8178935 siCon_8C_1 siCon_8C
- GSM8178936 siCon_8C_2 siCon_8C
- GSM8178937 siCon_8C_3 siCon_8C
- GSM8178938 siCon_8C_4 siCon_8C
- GSM8178939 siCon_8C_5 siCon_8C
- GSM8178940 siCon_8C_6 siCon_8C
- GSM8178941 siCon_8C_7 siCon_8C
- GSM8178942 siCon_8C_8 siCon_8C
- GSM8178943 siRif1_8C_1 siRif1_8C
- GSM8178944 siRif1_8C_2 siRif1_8C
- GSM8178945 siRif1_8C_3 siRif1_8C
- GSM8178946 siRif1_8C_4 siRif1_8C
- GSM8178947 siRif1_8C_5 siRif1_8C
- GSM8178948 siRif1_8C_6 siRif1_8C
- GSM8178949 siRif1_8C_7 siRif1_8C
- GSM8178950 siRif1_8C_8 siRif1_8C
- GSM8178951 siRif1_8C_9 siRif1_8C
- GSM8178952 siRif1_8C_10 siRif1_8C
- GSM8178953 siRif1_8C_11 siRif1_8C
- GSM8178954 siRif1_8C_12 siRif1_8C
- GSE222656 RNA-seq and Cut & Tag analyses of mouse 2-cell embryos in which lamin B1 dissociation from the nuclear envelope is inhibited 40 samples shares rna-seq, embryos, embryo
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- GSE300288 Single-cell and spatial transcriptomics analysis of tobacco-associated lung adenocarcinoma development, and in response to targeting inflammation alone or in combination with immune checkpoint blockade in precancerous phase [scRNA-seq] 31 samples shares rna-seq
- GSE264105 Plate-based scRNA-seq of plasma-positive microglia and plasma-negative microglia in young mice 1111 samples shares rna-seq
- GSE227108 Integrated single-cell analysis defines the epigenetic basis of castration-resistant prostate luminal cells (scRNA-seq) 18 samples shares rna-seq
- GSE264278 TIme-cource single-cell RNA-seq analysis of bleomycin and LPS-induced lung injury and fibrosis 18 samples shares rna-seq
- GSE326100 scRNA-seq of mouse cochlea organoid cells 768 samples shares rna-seq
- GSE98969 Single cell RNA-seq identifies a unique microglia type associated with Alzheimer’s disease [RNA] 97 samples shares rna-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.