GEO series

Bulk and single-cell transcriptomics identify gene signatures of stem cell-derived NK cell donors with superior anti-tumor activity [Bulk RNA-seq]

GSE249991 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2024/10/10 Platform GPL16791

At a glance

Single-cell RNA-seq · human · 16 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)
Samples: 16 samples: 9 × I20-1188 vs 4 × I19-1290 vs 3 × I19-1278Groups 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: Bulk and single-cell transcriptomics identify gene signatures of stem cell-derived NK cell donors with superior cytolytic activityMolecular therapy. Oncology · 2024

Summary (written by the submitters)

Allogeneic natural killer (NK) cell therapies are a valuable treatment option for cancer, given their remarkable safety and favorable efficacy profile. Although the use of allogeneic donors allows for off-the-shelf and timely patient treatment, intrinsic interindividual differences put clinical efficacy at risk. The identification of donors with superior anti-tumor activity is essential to ensure the success of adoptive NK cell therapies.

Read the full summary ↓

Here, we investigated the heterogeneity of 10 umbilical cord blood stem cell-derived NK cell batches. First, we evaluated the donors’ cytotoxic potential against tumor cell lines from solid and hematological cancer indications, to distinguish a group of superior, “excellent” killers (4/10), compared to “good” killers (6/10). Next, bulk and single-cell RNA-Sequencing, performed at different stages of NK differentiation, revealed distinct transcriptomic features of the two groups. Excellent donors showed an enrichment in cytotoxicity pathways and a depletion of myeloid traits, linked to the presence of a larger population of effector-like NK cells early on during differentiation. Consequently, we defined a multi-factorial gene expression signature able to predict the donors’ anti-tumor potential. Our study contributes to the identification of key traits of superior NK cell batches, supporting the development of efficacious NK therapeutics and the achievement of durable anti-tumor responses

Published in

Bulk and single-cell transcriptomics identify gene signatures of stem cell-derived NK cell donors with superior cytolytic activity

van Vliet AA, van den Hout MGCN, Steenmans D et al. · Molecular therapy. Oncology 2024 · PMID 39346765 · doi:10.1016/j.omton.2024.200870

This dataset

Open GSE249991 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 GSE249991_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 PRJNA1051573 and SRA study SRP477565. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39346765) ↗

Study design

9 × I20-1188 vs 4 × I19-1290 vs 3 × I19-1278

Supports a between-group comparison across 16 samples.

How we read this: 3 replicated groups read from 16 sample titles; they account for 16 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

GSM7968664 I20-1188-36 I20-1188
GSM7968665 I20-1188-34 I20-1188
GSM7968666 I20-1188-32 I20-1188
GSM7968667 I20-1188-35 I20-1188
GSM7968668 I20-1188-33 I20-1188
GSM7968669 I20-1188-37 I20-1188
GSM7968670 I20-1188-40 I20-1188
GSM7968671 I20-1188-38 I20-1188
GSM7968672 I20-1188-39 I20-1188
GSM7968673 I19-1290-05 I19-1290
GSM7968674 I19-1290-04 I19-1290
GSM7968675 I19-1290-02 I19-1290
GSM7968676 I19-1278-42 I19-1278
GSM7968677 I19-1278-44 I19-1278
GSM7968678 I19-1278-40 I19-1278
GSM7968679 I19-1290-06 I19-1290

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