GEO series

AML induces TTCR-C4 NK-like transcriptional skewing associated with low persistence and dysfunction

GSE285214 Homo sapiens (human) Single-cell RNA-seq 20 samples Submitted 2025/04/16 Platform GPL16791Platform GPL29480

At a glance

Single-cell RNA-seq · human · 20 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)
Topic: Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples: 20 samples: 20 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)

This dataset includes single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data generated to investigate T cell responses in the context of acute myeloid leukemia (AML) and WT1-specific TCR-T cell therapy. The scRNA-seq dataset comprises peripheral blood (PB) and bone marrow (BM) samples from AML patients treated with WT1-specific TCR-engineered T cells (TCR-T).

Read the full summary ↓

We specifically profiled both endogenous CD8⁺ T cells and TCR-T cells to characterize their transcriptional states and assess the impact of AML on T cell differentiation and function. In-depth analysis of samples from a patient with a durable clinical response provided insights into the transcriptional profiles of TCR-T cells over time, including after Azacitidine treatment. The bulk RNA-seq dataset was generated using an in vitro model of AML-induced T cell dysfunction. CD8⁺ transgenic WT1-specific T cells (TTCR37-45) recognizing the HLA-A0201-restricted WT137-45 epitope were repeatedly challenged with WT1-expressing HLA-A0201-transduced K562 AML cells. Loss of tumor control by day 13 was associated with a transition from a naïve/stem-like transcriptional state to one enriched in effector, activation/exhaustion, and NK-like/Temra gene signatures. By day 23, activation/exhaustion markers declined while NK-like/Temra-associated genes persisted or increased, highlighting a phenotypic shift in dysfunctional T cells.

This dataset

Open GSE285214 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 GSE285214_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 PRJNA1202029 and SRA study SRP553333. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

20 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 20 sample titles: 20 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

GSM8698445 PBMC 100 days after TCR-T infusion
GSM8698446 PBMC 581 days after TCR-T infusion
GSM8698447 PBMC 49 days after TCR-T infusion
GSM8698448 PBMC 256 days after TCR-T infusion
GSM8698449 PBMC 405 days after TCR-T infusion
GSM8698450 PBMC 1322 days after TCR-T infusion
GSM8698451 PBMC 1343 days after TCR-T infusion
GSM8698452 BMMC 1322 days after TCR-T infusion
GSM8698453 BMMC 1344 days after TCR-T infusion
GSM8698454 PBMC 68 days after TCR-T infusion
GSM8698455 PBMC 7 days after TCR-T infusion
GSM8698456 PBMC 28 days after TCR-T infusion
GSM8698457 PBMC 21 days after TCR-T infusion
GSM8698458 PBMC 58 days after TCR-T infusion
GSM8698459 PBMC 178 days after TCR-T infusion
GSM8917389 Sorted TTCR37-45 after 13 days of coculture with K562 cell line (replicate 1)
GSM8917390 Sorted TTCR37-45 after 23 days of coculture with K562 cell line (replicate 1)
GSM8917391 Sorted TTCR37-45 after 13 days of coculture with K562 cell line (replicate 2)
GSM8917392 Sorted TTCR37-45 after 23 days of coculture with K562 cell line (replicate 2)
GSM8917393 Sorted TTCR37-45 at day 0

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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.