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Co-mapping Clonal and Transcriptional Heterogeneity in Somatic Evolution via GoT-Multi.

GSE294045 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2025/09/03 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 18 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)
Samples
18 samples: 18 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
Co-mapping clonal and transcriptional heterogeneity in somatic evolution via GoT-MultiCell genomics · 2026
Summary (written by the submitters)
Somatic evolution leads to clonal heterogeneity, which fuels cancer progression and therapy resistance. To decipher the consequences of clonal heterogeneity, we require a method that deconvolutes complex clonal architectures and their downstream transcriptional states. We developed Genotyping of Transcriptomes for multiple targets and sample types (GoT-Multi), a high-throughput, formalin-fixed paraffin-embedded (FFPE) tissue-compatible single-cell multi-omics for co-detection of multiple somatic genotypes and whole transcriptomes.
Read the full summary ↓
We developed an ensemble-based machine learning pipeline to optimize genotyping. We applied GoT-Multi to frozen or FFPE samples of Richter transformation, a progression of chronic lymphocytic leukemia to therapy-resistant large B cell lymphoma. GoT-Multi detected heterogeneous cancer cell states with genotypic data of 27 mutations, enabling clonal architecture reconstruction linked with their transcriptional programs. Distinct subclonal genotypes, including therapy-resistant mutations, converged on an inflammatory state. Other subclones displayed enhanced proliferation and/or MYC program. Thus, GoT-Multi revealed that distinct genotypic identities may converge on similar transcriptional states to mediate therapy resistance.
Published in
Co-mapping clonal and transcriptional heterogeneity in somatic evolution via GoT-Multi
Pak M, Saurty-Seerunghen MS, Wise K et al. · Cell genomics 2026 · PMID 41075791 · doi:10.1016/j.xgen.2025.101036
This dataset
Open GSE294045 in finder → Find more human 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 GSE294045_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 PRJNA1247954. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41075791) ↗
Study design
18 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 18 sample titles: 18 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.