Multidimensional single-cell sequencing profiles the bone marrow immune microenvironment in chronic myeloid leukemia
Single-cell RNA-seq · human · 23 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
- Chronic myeloid leukemiaThis series is part of our curated briefing on it.
- Samples
- 23 samples: 23 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 sequencing reveals the expansion and diversity of T cell subsets in the bone marrow microenvironment of chronic myeloid leukemiaGenes & diseases · 2025
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE241842_RAW.tar (it can run to tens of gigabytes).
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 PRJNA1010221 and SRA study SRP457329. Searching any of these in the dataset finder brings you back here.
Supports a case/control comparison: 15 samples read as cases, 8 as controls.
How we read this: Read from 23 sample titles: 23 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.
- GSM7744314 CML patient 1 BM
- GSM7744315 CML patient 2 BM
- GSM7744316 CML patient 3 BM
- GSM7744317 CML patient 5 BM
- GSM7744318 CML patient 6 BM
- GSM7744319 Normal 2 BM
- GSM7744320 Normal 3 BM
- GSM7744321 CML patient 1 CD3
- GSM7744322 CML patient 2 CD3
- GSM7744323 CML patient 3 CD3
- GSM7744324 CML patient 5 CD3
- GSM7744325 CML patient 6 CD3
- GSM7744326 Normal 2 CD3
- GSM7744327 Normal 3 CD3
- GSM7744328 CML patient 1 TCR
- GSM7744329 CML patient 2 TCR
- GSM7744330 CML patient 3 TCR
- GSM7744331 CML patient 5 TCR
- GSM7744332 CML patient 6 TCR
- GSM7744333 Normal 2 TCR
- GSM7744334 Normal 3 TCR
- GSM8157893 Normal 1 CD3
- GSM8157894 Normal 1 TCR
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- GSE198142 Selective translational control by PABPC1 phase separation regulates blast crisis and target therapy resistance of chronic myeloid leukemia 8 samples same disease briefing · shares leukemia, chronic, myeloid
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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.