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The temporal progression of lung immune remodeling during breast cancer metastasis

GSE263963 Mus musculus (mouse) Single-cell RNA-seq 38 samples Submitted 2024/04/22 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 38 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
38 samples: 9 × PyMT Cryo vs 6 × PyMT Fresh vs 4 × 4T1 CryoGroups 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
The temporal progression of lung immune remodeling during breast cancer metastasisCancer cell · 2024
Summary (written by the submitters)
The dynamics of immune reprogramming in the breast cancer lung metastastic niche remains incomplete. We used MULTI-seq (McGinnis, Patterson, et al., Nature Methods, 2019) to perform multiplexed single-cell RNA-sequencing analysis of cryopreserved lung immune cells isolated from PyMT mice, freshly-isolated lung immune cells isolated from PyMT mice, and cryopreserved lung immune cells isolated from 4T1 tumor-bearing mice from the onset of primary tumorigenesis through the formation of macrometastatic lesions.
Read the full summary ↓
Cryopreserved PyMT samples were collected from 6-14 weeks of age, fresh PyMT samples were collected at 6, 10 and 13 weeks of age (with age-matched wild-type controls)
Published in
The temporal progression of lung immune remodeling during breast cancer metastasis
McGinnis CS, Miao Z, Superville D et al. · Cancer cell 2024 · PMID 38821060 · doi:10.1016/j.ccell.2024.05.004
This dataset
Open GSE263963 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 GSE263963_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 PRJNA1100478 and SRA study SRP501963. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38821060) ↗
Study design
9 × PyMT Cryo vs 6 × PyMT Fresh vs 4 × 4T1 Cryo

Supports a between-group comparison across 19 samples.

How we read this: 3 replicated groups read from 38 sample titles; they account for 19 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.

Samples in this study
Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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