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Temporal recording of mammalian development and precancer

GSE235119 Mus musculus (mouse) Single-cell RNA-seq 25 samples Submitted 2024/07/07 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 25 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
25 samples: 25 samples, each with a distinct title — no replicated groupsGroups 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)
Insights into many biological phenomena requires knowing the temporal order of cellular events, which is traditionally achieved through continuous direct observations [1, 2]. An alternative solution leverages irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8].
Read the full summary ↓
Using Native sgRNA Capture and sequencing (NSC-seq), a newly devised and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo while incorporating cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development, unconventional developmental relationships between cell types, and new epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas coupled to multi-omic and single-cell profiling of human precancers, with clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Our study presents a multimodal framework that lays the foundation for in vivo recording, integrating synthetic or natural indelible genetic changes with single-cell analyses to explore the origins and timing of development and tumorigenesis in mammalian systems. This SuperSeries is composed of the SubSeries listed below.
This dataset
Open GSE235119 in finder → Find more mouse 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 GSE235119_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 PRJNA984615 and SRA study SRP443476. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
25 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 1 samples read as cases, 2 as controls.

How we read this: Read from 25 sample titles: 25 distinct titles with little repetition. 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
Datasets on a similar topic

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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.