Single cell transcriptomic landscape of pediatric B-cell acute lymphoblastic leukemia: dissection of transcriptional heterogeneity and B-cell developmental state
Single-cell RNA-seq · human · 89 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
- Acute lymphoblastic leukemiaThis series is part of our curated briefing on it.
- Samples
- 89 samples: 89 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
- Multipotent lineage potential in B cell acute lymphoblastic leukemia is associated with distinct cellular origins and clinical featuresNature cancer · 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 GSE241405_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 PRJNA1008084. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 89 sample titles: 89 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.
- GSM7728352 SJALL040053_D1 scRNA-seq
- GSM7728353 SJALL040066_D1 scRNA-seq
- GSM7728354 SJALL040069_D1 scRNA-seq
- GSM7728355 SJALL040070_D1 scRNA-seq
- GSM7728356 SJALL040099_D1 scRNA-seq
- GSM7728357 SJALL040100_D1 scRNA-seq
- GSM7728358 SJALL040103_D1 scRNA-seq
- GSM7728359 SJALL040119_D1 scRNA-seq
- GSM7728360 SJALL040137_D1 scRNA-seq
- GSM7728361 SJALL040137_R1 scRNA-seq
- GSM7728362 SJBALL004097_D2 scRNA-seq
- GSM7728363 SJBALL006_D scRNA-seq
- GSM7728364 SJBALL006_R scRNA-seq
- GSM7728365 SJBALL014876_D1 scRNA-seq
- GSM7728366 SJBALL021901_D1 scRNA-seq
- GSM7728367 SJBALL021947_D1 scRNA-seq
- GSM7728368 SJBALL021964_D1 scRNA-seq
- GSM7728369 SJBALL021968_D1 scRNA-seq
- GSM7728370 SJBALL021973_D1 scRNA-seq
- GSM7728371 SJBALL022020_D1 scRNA-seq
- GSM7728372 SJBALL022035_D1 scRNA-seq
- GSM7728373 SJBALL022052_D1 scRNA-seq
- GSM7728374 SJBALL030036_D1 scRNA-seq
- GSM7728375 SJBALL030040_D1 scRNA-seq
- GSM7728376 SJBALL030059_D1 scRNA-seq
- GSM7728377 SJBALL030072_D1 scRNA-seq
- GSM7728378 SJBALL030090_D1 scRNA-seq
- GSM7728379 SJBALL030095_D1 scRNA-seq
- GSM7728380 SJBALL030123_D1 scRNA-seq
- GSM7728381 SJBALL030123_R1 scRNA-seq
- GSM7728382 SJBALL030127_D1 scRNA-seq
- GSM7728383 SJBALL030145_D1 scRNA-seq
- GSM7728384 SJBALL030216_D1 scRNA-seq
- GSM7728385 SJBALL030247_D1 scRNA-seq
- GSM7728386 SJBALL030254_D1 scRNA-seq
- GSM7728387 SJBALL030276_D1 scRNA-seq
- GSM7728388 SJBALL030285_D1 scRNA-seq
- GSM7728389 SJBALL030313_D1 scRNA-seq
- GSM7728390 SJBALL030344_D1 scRNA-seq
- GSM7728391 SJBALL030370_D1 scRNA-seq
+ 49 more — browse all 89 samples with per-sample file links →
- GSE115656 Alternative splicing analysis of pediatric B-cell acute lymphoblastic leukemia compared to normal bone marrow derived pro-B cells 38 samples same disease briefing · shares lymphoblastic, pediatric, leukemia, b-cell, acute
- GSE12995 Expression data for diagnosis acute lymphoblastic leukemia samples 175 samples same disease briefing · shares lymphoblastic, leukemia, acute
- GSE160298 The chromatin accessibility landscape of T-Cell Acute Lymphoblastic Leukemia 80 samples same disease briefing · shares lymphoblastic, leukemia, acute
- GSE193713 Genome-wide maps of chromatin state for histone modifications and chromatin remodeling factors in WT and PHF6-KO conditions in Jurkat T-ALL cells. 20 samples same disease briefing · shares state
- GSE13425 Expression data from ALL patients included in the set used to construct a classification signature (COALL cohort) 190 samples same disease briefing
- GSE154675 PHF6 Interacts with LMO2 During Normal Haematopoiesis and in Leukaemia and Regulates the Level of Gene Expression and Genome Integrity 41 samples same disease briefing
- GSE18198 Expression profile of human T-ALL cell lines treated with DMSO or SAHM1 12 samples same disease briefing
- GSE272023 CpG island methylator phenotype classification improves risk assessment in pediatric T-cell Acute Lymphoblastic Leukemia [RNA-seq] 111 samples shares lymphoblastic, pediatric, leukemia, acute
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.