GEO series

Single-cell Transcriptomic and Proteomic Analysis of Acute Myeloid Leukemia (AML) Patients with Abnormalities on Chromosome 7

GSE271741 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2026/07/01 Platform GPL24676

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: Acute myeloid leukemiaThis 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.

Summary (written by the submitters)

Chromosome 7 (chr7) abnormalities are commonly seen in patients with acute myeloid leukemia (AML) or myelodysplastic syndromes and are associated with poor prognosis. Flow cytometry (FCM) is typically used clinically to quantify residual malignancy during treatment but the relationship of cell surface immunophenotype with genetic features is incompletely defined.

Read the full summary ↓

Single-cell RNA sequencing (scRNA-seq) with oligonucleotide-conjugated antibodies may be able to integrate cytogenetic genotypes found within leukemic clones with specific transcriptomic and immunophenotypic signatures. Bone marrow (BM) aspirate was collected from 12 AML patients with known abnormalities on chr7 according to cytogenetics studies. Among all 12 patients, 3 of them only had monosomy 7 as the sole genetic aberration, while 9 patients had abnormalities on additional chromosomes. By scRNA-seq with oligonucleotide-conjugated antibodies, we identified the correlation between cell types showed distinct features among patients, and cells in different groups had different protein expression profiles. By comparing the expression profile of AML patients with abnormalities on chr7 to HDs, this study provides evidence that leukemic immunophenotype is correlated with chromosomal structural changes. The experiments on a single-cell level were able to identify clones in higher resolution and revealed potential cell surface protein markers that could be used clinically to identify specific malignant populations in patients with myeloid malignancies.

This dataset

Open GSE271741 in finder → Find more human RNA-seq datasets → Acute myeloid leukemia 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 GSE271741_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 PRJNA1133012 and SRA study SRP518566. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

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

GSM8383843 MN1_GEX
GSM8383844 MN2_GEX
GSM8383845 MN3_GEX
GSM8383846 MN4_GEX
GSM8383847 MN5_GEX
GSM8383848 MN6_GEX
GSM8383849 MN7_GEX
GSM8383850 MN8_GEX
GSM8383851 MN9_GEX
GSM8383852 MN10_GEX
GSM8383853 MN11_GEX
GSM8383854 MN12_GEX
GSM8383855 MN1_ADT
GSM8383856 MN2_ADT
GSM8383857 MN3_ADT
GSM8383858 MN4_ADT
GSM8383859 MN5_ADT
GSM8383860 MN6_ADT
GSM8383861 MN7_ADT
GSM8383862 MN8_ADT
GSM8383863 MN9_ADT
GSM8383864 MN10_ADT
GSM8383865 MN11_ADT
GSM8383866 MN12_ADT

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Search all human RNA-seq datasets in GEO →

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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.