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Precisely controlled cell-cell interactions with single-cell transcriptome readout identify a time-demand threshold response of human oncolytic NK cells

GSE214129 Homo sapiens (human) Single-cell RNA-seq 447 samples Submitted 2025/09/24 Platform GPL21290
At a glance

Single-cell RNA-seq · human · 447 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
447 samples: 337 distinct titles, 29 of them repeatedGroups 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)
Cell communication via cell-cell interactions is a fundamental biological process shared by all multicellular organisms and broadly relevant to human health and disease. Activation and function of cells of the im-mune system particularly depends on direct cell-cell interactions, which are fast, transient and hard to study.
Read the full summary ↓
To characterize NK-cell intrinsic gene expression programs that are activated upon target cell en-counter, we develop a cell interaction profiling system (CIPS) and investigate the genome-wide transcrip-tional responses of individual interacting cells with precise temporal control over the first minutes of inter-action. We utilize di-electrophoretic fields to initiate cell-cell contact of fluorescence microscopy-informed, selected single cells and separate them again with precise control of interaction time followed by single-cell transcriptomics as readout. Applying CIPS to the interaction of NK cells with cancer cells, we find that in-teractions shorter than 15 minutes lead to downregulation of a gene set including the tyrosine kinases TYK2 and LCK, required for NK-cell effector function and tumor surveillance. Dissecting the molecular effects associated with a rare genetic disease that compromises NK cell function, we show that this re-sponse is independent of target cell lysis. Exploiting our method’s ability to orchestrate sequential series of cell-cell interactions, we further demonstrate that the downregulation of the identified gene signature is overcome by repeatedly challenging NK cells with cancer cells. In conclusion, we introduce a method for precise dissection of cell-cell interactions with unprecedented temporal resolution; our observations sug-gest a model where NK cells downregulate genes relevant for engagement of lytic function if the interaction with a potential target cell is too short, but allows responsiveness to further interactions, representing a “time-demand threshold (TDT)”.
This dataset
Open GSE214129 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 GSE214129_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 PRJNA883987 and SRA study SRP399453. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
337 distinct titles, 29 of them repeated
CD19Jurkat ×4 CD4pos_aCD19CA ×4 CD8pos_aCD19CA ×4 Jurkat ×4 primNKSG_CD56dim ×4 primNKSG_CD56hi ×4 primNKWG_CD56dim ×4 primNKWG_CD56hi ×4 +21 more

How we read this: Read from the first 400 of 447 sample titles: 337 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

+ 407 more — browse all 447 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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