GEO series

Single-cell heterogeneity in interferon induction potential is heritable and governed by variation in cell state IV

GSE313071 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2025/12/13 Platform GPL34284

At a glance

Single-cell RNA-seq · human · 24 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: 24 samples: 8 conditions, mostly in triplicateGroups 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: Single-cell heterogeneity in interferon induction potential is heritable and governed by variation in cell stateCell systems · 2026

Summary (written by the submitters)

Type I and III interferons (IFNs) are among the first lines of defense against viral infections, yet they are generally only produced by a tiny fraction of infected cells. Here, we show that variability in tonic cell signaling significantly influences cells’ ability to produce IFN upon stimulation with the synthetic double-stranded RNA, polyinosinic:polycytidylic acid (pIC).

Read the full summary ↓

Using single-cell approaches, we found that members of the activator protein (AP)-1 transcription factor were implicated in IFNL1 expression predisposition. This guided us to investigate the role of the mitogen-activated protein kinase (MAPK) pathway, specifically the c-Jun N-terminal kinase (JNK), in IFNL1 production. We found that inhibition of JNK signaling severely altered the nature of the innate antiviral response upon pIC stimulation, independently of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. Together, our study emphasizes the influence of intrinsic stochasticity in cell state on heterogeneity in IFN expression.

Published in

Single-cell heterogeneity in interferon induction potential is heritable and governed by variation in cell state

Thayer EA, Shipman G, Rivera-Cardona J et al. · Cell systems 2026 · PMID 42462718 · doi:10.1016/j.cels.2026.101676

This dataset

Open GSE313071 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE313071_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 PRJNA1378215 and SRA study SRP653813. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42462718) ↗

Study design

8 conditions, mostly in triplicate

Parental (WT), A549, ×3 ET1, A549, ×3 ET2, A549, ×3 ET3, A549, ×3 ET7, A549, ×3 ET8, A549, ×3 ET9, A549, ×3 ET10, A549, ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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

GSM9360054 Parental (WT), A549, rep1 Parental (WT), A549,
GSM9360055 Parental (WT), A549, rep2 Parental (WT), A549,
GSM9360056 Parental (WT), A549, rep3 Parental (WT), A549,
GSM9360057 ET1, A549, rep1 ET1, A549,
GSM9360058 ET1, A549, rep2 ET1, A549,
GSM9360059 ET1, A549, rep3 ET1, A549,
GSM9360060 ET2, A549, rep1 ET2, A549,
GSM9360061 ET2, A549, rep2 ET2, A549,
GSM9360062 ET2, A549, rep3 ET2, A549,
GSM9360063 ET3, A549, rep1 ET3, A549,
GSM9360064 ET3, A549, rep2 ET3, A549,
GSM9360065 ET3, A549, rep3 ET3, A549,
GSM9360066 ET7, A549, rep1 ET7, A549,
GSM9360067 ET7, A549, rep2 ET7, A549,
GSM9360068 ET7, A549, rep3 ET7, A549,
GSM9360069 ET8, A549, rep1 ET8, A549,
GSM9360070 ET8, A549, rep2 ET8, A549,
GSM9360071 ET8, A549, rep3 ET8, A549,
GSM9360072 ET9, A549, rep1 ET9, A549,
GSM9360073 ET9, A549, rep2 ET9, A549,
GSM9360074 ET9, A549, rep3 ET9, A549,
GSM9360075 ET10, A549, rep1 ET10, A549,
GSM9360076 ET10, A549, rep2 ET10, A549,
GSM9360077 ET10, A549, rep3 ET10, A549,

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