GEO series

Hierarchical Lineage Tracing Reveals Diverse Pathways of AML Treatment Resistance [scRNA-seq]

GSE289454 Homo sapiens (human) Single-cell RNA-seq 11 samples Submitted 2025/02/12 Platform GPL30173Platform GPL30172

At a glance

Single-cell RNA-seq · human · 11 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: 11 samples: 11 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: Hierarchical Lineage Tracing Reveals Diverse Pathways of AML Treatment ResistancebioRxiv : the preprint server for biology · 2025

Summary (written by the submitters)

Cancer cells adapt to treatment, leading to the emergence of clones that are more aggressive and resistant to anti-cancer therapies. We have a limited understanding of the evolution of treatment resistance as we have lacked technologies to map the evolution of cancer under the selective pressures. To address this, we developed a hierarchical, dynamic lineage tracing method called FLARE (Following Lineage Adaptation and Resistance Evolution).

Read the full summary ↓

We use this technique to track the progression of acute myeloid leukemia (AML) cell lines through exposure to Cytarabine (AraC), a front-line treatment in AML, in vitro and in vivo. We map distinct cellular lineages in murine and human AML cell lines that are predisposed to AraC persistence and/or resistance via upregulation of cell adhesion and motility pathways. Additionally, we highlight heritable increased expression of immunoproteasome 11S regulatory cap subunits as a potential mechanism aiding AML cell survival, proliferation, and immune escape in vivo. Finally, we validate the clinical relevance of these signatures in the TARGET-AML cohort, with a bisected response in blood and bone marrow. Our findings reveal a broad spectrum of resistance signatures attributed to significant cell transcriptional changes, and we expect this high-resolution profiling of treatment response to be a useful tool to dissect the evolution of treatment response in a wide range of tumor types.

Published in

Hierarchical Lineage Tracing Reveals Diverse Pathways of AML Treatment Resistance

Saxe R, Stuart H, Marshall A et al. · bioRxiv : the preprint server for biology 2025 · PMID 40093111 · doi:10.1101/2025.02.27.640600

This dataset

Open GSE289454 in finder → Find more 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 GSE289454_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 PRJNA1222993 and SRA study SRP563551. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40093111) ↗

Study design

11 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 11 sample titles: 11 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

GSM8791693 C1498 founder pool, multiplexed control, resistant, and persistent flasks
GSM8791694 MULTIseq barcodes for demultiplexing sample1_scRNA_c1498_pool
GSM8791695 FLARE founder tag and clone tag sequencing associated with sample1_scRNA_c1498_pool
GSM8791696 PBMCs from mouse M1 collected at endpoint
GSM8791697 Bone marrow from mouse M1 collected at endpoint
GSM8791698 FLARE founder tag and clone tag sequencing associated with sample2 blood and bone marrow and sample 3
GSM8791699 HL60, TF1, and OCI-AML3 pre-AraC treatment multiplexed
GSM8791700 MULTIseq barcodes for demultiplexing sample3_humanAML_lines
GSM8791701 HL60 and TF1 post-AraC, control and treated flasks multiplexed
GSM8791702 MULTIseq barcodes for demultiplexing sample4_scRNA_humanAML_lines_postAraC
GSM8791703 FLARE founder tag and clone tag sequencing associated with sample4_scRNA_humanAML_lines_postAraC

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