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Clinical outcome is unlinked to injection of adipose-derived regenerative cells in the axilla of breast cancer-related lymphedema patients

GSE276533 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2024/11/20 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 10 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
Homo sapiens (human)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
10 samples: 10 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.
Paper
Clinical outcome is unlinked to injection of adipose-derived regenerative cells in the axilla of breast cancer-related lymphedema patientsStem cell research & therapy · 2024
Summary (written by the submitters)
Background: Injection of autologous adipose-derived regenerative cells (ADRCs) combined with lipotransfer has been suggested to alleviate symptoms in diseases including breast cancer-related lymphedema (BCRL). We recently performed a randomized controlled trial injecting lipoaspirate with ADRCs into the axilla of BCRL patients, and here we aimed in the intervention group to define in an unbiased fashion whether ADRC injection was linked to the clinical outcome.
Read the full summary ↓
Results: Unbiased multifactorial analysis ranked and defined the clinical outcomes (Sf36 physical change, L-Dex Lymph Change, ICG mdanderson change) with the highest effect on BRCL patients. The 10 patients with the highest- and lowest effect (five responders and five non-responders) were included in the study. No difference between non-responders and responders were observed for injected ADRC number/size/viability (p>0.05). In scRNAseq, we did not find any major difference (p>0.05) between groups in ADRC composition regarding adipose derived stem cells, endothelial-, smooth muscle-, T-, B-, mast cells as well as macrophages, which was verified by flow cytometry. Differential subcluster gene expression between groups were for 92.5% of genes below the threshold of 1.5, and thus neglible. Together this suggested that the ADRC phenotype was indistinguishable between BRCL responders and non-responders to the intervention.
Published in
Clinical outcome is unlinked to injection of adipose-derived regenerative cells in the axilla of breast cancer-related lymphedema patients
Andersen DC, Bjerre FA, Jørgensen MG et al. · Stem cell research & therapy 2024 · PMID 39543743 · doi:10.1186/s13287-024-04037-z
This dataset
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.

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 GSE276533_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1157795 and SRA study SRP531054. Searching any of these in the dataset finder brings you back here.

Study design
10 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 10 sample titles: 10 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
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