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High-Resolution Lineage Tracing in Native Tissue Contexts [SPACE-seq]

GSE327687 Mus musculus (mouse) Single-cell RNA-seq 39 samples Submitted 2026/06/08 Platform GPL32159Platform GPL16417
At a glance

Single-cell RNA-seq · mouse · 39 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
39 samples: 39 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)
High-resolution lineage tracing has provided essential insights into key biological processes, yet existing methods are limited in tracing diverse cell populations at single-cell resolution while preserving spatial context. Here, we present SPACE-seq (SPatial trAcing enabled by CRISPR-based barcodEs and Slide-seq), a versatile tracing platform that integrates spatial transcriptomics with Cas9 barcoding in mice, to allow simultaneous analysis of cell lineages, transcriptomic states, and spatial organization at near-cellular resolution.
Read the full summary ↓
Using this approach, we generated lineage maps that reveal novel interactions during liver cancer progression within the tumor microenvironment and uncovered distinct clonal architecture patterns in the developing cerebral cortex. Additionally, we identified a developmentally restricted window for hepatoblast migration and spatially confined compartments involved in liver lobe formation. With its broad applicability and adaptability, SPACE-seq offers new potential to explore previously inaccessible questions in cellular organization and lineage dynamics.
This dataset
Open GSE327687 in finder → Find more mouse 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 GSE327687_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 PRJNA1451866 and SRA study SRP690926. Searching any of these in the dataset finder brings you back here.

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

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 39 sample titles: 39 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

Search all mouse 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.