GEO series

Profling of breast cancer cells using single-cell triomics analysis of DNA, RNA, and proteins with Tapestri chemistry

GSE291751 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2025/12/15 Platform GPL24676Platform GPL21697

At a glance

Single-cell RNA-seq · human · 24 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Topic: Breast cancerThis series is part of our curated briefing on it.
Samples: 24 samples: 24 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: Single-Cell Triomics Analysis of Tumor Cells Infiltrating Patient-Derived Breast Cancer ScaffoldsThe American journal of pathology · 2026

Summary (written by the submitters)

Cancer displays large heterogeneity both between and within tumors where intratumor heterogeneity can occur at genetical, transcriptional and translational level. The variability results in tumor cell subpopulations with distinct molecular and cellular characteristics, some of which can have therapy-resistant properties. To date it has been experimentally challenging to study tumor cell heterogeneity.

Read the full summary ↓

Here, we present a single-cell triomics approach to assess DNA, RNA and protein simultaneously in thousands of single cells using microfluidics. First, we profiled two breast cancer cell lines, MCF7 and T-47D, cultured in monolayer with and without 5-fluorouracil treatment. Next, we generated cell-free patient-derived scaffolds (PDS) that we repopulated with MCF7 cells for 21 days. This experimental model system mimics in vivo tumor cell growth and is suitable for drug testing, such as chemotherapies. We profiled individual MCF7 cells cultured in different PDSs with and without 5-fluorouracil treatment using triomics. Our approach and data demonstrate that single-cell triomics is feasible, which opens up new means to decipher tumor heterogeneity at different layers.

Published in

Single-Cell Triomics Analysis of Tumor Cells Infiltrating Patient-Derived Breast Cancer Scaffolds

Filges S, Jonasson E, Leiva Arrabal MDC et al. · The American journal of pathology 2026 · PMID 41580235 · doi:10.1016/j.ajpath.2025.12.013

This dataset

Open GSE291751 in finder → Find more human RNA-seq datasets → Breast cancer landscape →

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 GSE291751_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 PRJNA1235193 and SRA study SRP569879. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41580235) ↗

Study design

24 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 24 sample titles: 24 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

GSM8841690 2d_control_dna
GSM8841691 2d_control_rna
GSM8841692 2d_control_protein
GSM8841693 2d_5fu_dna
GSM8841694 2d_5fu_rna
GSM8841695 2d_5fu_protein
GSM8841696 pds1_control_dna
GSM8841697 pds1_control_rna
GSM8841698 pds1_control_protein
GSM8841699 pds1_5fu_dna
GSM8841700 pds1_5fu_rna
GSM8841701 pds1_5fu_protein
GSM8841702 pds2_control_dna
GSM8841703 pds2_control_rna
GSM8841704 pds2_control_protein
GSM8841705 pds2_5fu_dna
GSM8841706 pds2_5fu_rna
GSM8841707 pds2_5fu_protein
GSM8841708 pds3_control_dna
GSM8841709 pds3_control_rna
GSM8841710 pds3_control_protein
GSM8841711 pds3_5fu_dna
GSM8841712 pds3_5fu_rna
GSM8841713 pds3_5fu_protein

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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.