GEO series

Selective insertion of a payload into NK cell checkpoint NKG2A, as a new platform for treating AML.

GSE334320 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2026/06/10 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 10 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)
Samples: 10 samples: 7 × Relapsed AML Patient vs 3 × Normal DonoGroups 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)

Natural killer (NK) cells are promising effectors for immunotherapy due to their intrinsic ability to recognize and eliminate malignant cells without prior sensitization. In acute myeloid leukaemia (AML), NK cell function is impaired, in part through inhibitory receptor signalling. We profiled NK cells from healthy donors and patients with relapsed AML using single-cell RNA sequencing, revealing broad alteration of activating and inhibitory receptor programs in patient NK cells.

Read the full summary ↓

KLRC1, encoding the inhibitory receptor NKG2A, was co-expressed with effector genes such as granzyme A, granzyme B, and granulysin in NK cells, suggesting its relevance in cytotoxic competence and highlighting it as a targetable locus. Using CRISPR-Cas9, we disrupted KLRC1 and simultaneously integrated either an EGFRt reporter or an anti-CD33 CAR via AAV6-mediated homology-directed repair. KLRC1-deficient (KLRC1 KO ) and CAR-KLRC1 KO NK cells exhibited enhanced cytotoxicity against AML cell lines and primary AML blasts, particularly under limiting effector-to-target conditions. However, KLRC1 disruption imposed proliferative constraints and limited in vivo persistence. To overcome this, we implemented a cis-targeted IL-2 delivery strategy, selectively expanding edited NK cells and restoring long- term persistence in vivo without inducing bystander activation. This dual-editing strategy take advantage of KLRC1 as a versatile genomic platform for inhibitory checkpoint removal and precise transgene insertion, combining enhanced antitumor activity with controlled cytokine support. Collectively, our findings establish a physiologically grounded and adaptable NK cell engineering framework, providing a potent, persistent, and lineage-specific platform for AML immunotherapy.

This dataset

Open GSE334320 in finder → Find more human RNA-seq datasets →

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 GSE334320_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 PRJNA1474615 and SRA study SRP706641. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

7 × Relapsed AML Patient vs 3 × Normal Dono

Supports a case/control comparison: 7 samples read as cases, 3 as controls.

How we read this: 2 replicated groups read from 10 sample titles; they account for 10 of them. 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

GSM9785363 Relapsed AML Patient 1 Relapsed AML Patient
GSM9785364 Relapsed AML Patient 2 Relapsed AML Patient
GSM9785365 Relapsed AML Patient 3 Relapsed AML Patient
GSM9785366 Relapsed AML Patient 4 Relapsed AML Patient
GSM9785367 Relapsed AML Patient 5 Relapsed AML Patient
GSM9785368 Relapsed AML Patient 6 Relapsed AML Patient
GSM9785369 Relapsed AML Patient 7 Relapsed AML Patient
GSM9785370 Normal Donor 1 Normal Dono
GSM9785371 Normal Donor 2 Normal Dono
GSM9785372 Normal Donor 3 Normal Dono

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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.