GEO series

Systematic transcriptomic comparison of the time-serial interferon responses in two widely used human cell lines

GSE336141 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2026/07/20 Platform GPL29480

At a glance

RNA-seq (gene expression) · human · 30 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 30 samples: 15 conditions, mostly in duplicateGroups 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)

Here, we systematically compared the transcriptomic dynamics of IFN‑γ stimulation in HEK293T and HeLa cells across a time series (0–24 h) in duplicate. Our analysis uncovered both shared and cell‑type‑specific transcriptional responses, providing insights into the temporal regulation of interferon signaling in distinct human epithelial backgrounds.

This dataset

Open GSE336141 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 GSE336141_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1480749 and SRA study SRP711558. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

15 conditions, mostly in duplicate

HeLa, IFNg_0m, RNA-seq, ×2 HeLa, IFNg_15m, RNA-seq, ×2 HeLa, IFNg_30m, RNA-seq, ×2 HeLa, IFNg_1h, RNA-seq, ×2 HeLa, IFNg_3h, RNA-seq, ×2 HeLa, IFNg_6h, RNA-seq, ×2 HeLa, IFNg_12h, RNA-seq, ×2 HeLa, IFNg_24h, RNA-seq, ×2 +7 more

Supports a between-group comparison across 30 samples.

How we read this: 15 replicated groups read from 30 sample titles; they account for 30 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

GSM9828599 HeLa, IFNg_0m, RNA-seq, replicate 1 HeLa, IFNg_0m, RNA-seq,
GSM9828600 HeLa, IFNg_0m, RNA-seq, replicate 2 HeLa, IFNg_0m, RNA-seq,
GSM9828601 HeLa, IFNg_15m, RNA-seq, replicate 1 HeLa, IFNg_15m, RNA-seq,
GSM9828602 HeLa, IFNg_15m, RNA-seq, replicate 2 HeLa, IFNg_15m, RNA-seq,
GSM9828603 HeLa, IFNg_30m, RNA-seq, replicate 1 HeLa, IFNg_30m, RNA-seq,
GSM9828604 HeLa, IFNg_30m, RNA-seq, replicate 2 HeLa, IFNg_30m, RNA-seq,
GSM9828605 HeLa, IFNg_1h, RNA-seq, replicate 1 HeLa, IFNg_1h, RNA-seq,
GSM9828606 HeLa, IFNg_1h, RNA-seq, replicate 2 HeLa, IFNg_1h, RNA-seq,
GSM9828607 HeLa, IFNg_3h, RNA-seq, replicate 1 HeLa, IFNg_3h, RNA-seq,
GSM9828608 HeLa, IFNg_3h, RNA-seq, replicate 2 HeLa, IFNg_3h, RNA-seq,
GSM9828609 HeLa, IFNg_6h, RNA-seq, replicate 1 HeLa, IFNg_6h, RNA-seq,
GSM9828610 HeLa, IFNg_6h, RNA-seq, replicate 2 HeLa, IFNg_6h, RNA-seq,
GSM9828611 HeLa, IFNg_12h, RNA-seq, replicate 1 HeLa, IFNg_12h, RNA-seq,
GSM9828612 HeLa, IFNg_12h, RNA-seq, replicate 2 HeLa, IFNg_12h, RNA-seq,
GSM9828613 HeLa, IFNg_24h, RNA-seq, replicate 1 HeLa, IFNg_24h, RNA-seq,
GSM9828614 HeLa, IFNg_24h, RNA-seq, replicate 2 HeLa, IFNg_24h, RNA-seq,
GSM9828615 HEK293T, IFNg_0m, RNA-seq, replicate 1 HEK293T, IFNg_0m, RNA-s…
GSM9828616 HEK293T, IFNg_0m, RNA-seq, replicate 2 HEK293T, IFNg_0m, RNA-s…
GSM9828617 HEK293T, IFNg_30m, RNA-seq, replicate 1 HEK293T, IFNg_30m, RNA-…
GSM9828618 HEK293T, IFNg_30m, RNA-seq, replicate 2 HEK293T, IFNg_30m, RNA-…
GSM9828619 HEK293T, IFNg_1h, RNA-seq, replicate 1 HEK293T, IFNg_1h, RNA-s…
GSM9828620 HEK293T, IFNg_1h, RNA-seq, replicate 2 HEK293T, IFNg_1h, RNA-s…
GSM9828621 HEK293T, IFNg_3h, RNA-seq, replicate 1 HEK293T, IFNg_3h, RNA-s…
GSM9828622 HEK293T, IFNg_3h, RNA-seq, replicate 2 HEK293T, IFNg_3h, RNA-s…
GSM9828623 HEK293T, IFNg_6h, RNA-seq, replicate 1 HEK293T, IFNg_6h, RNA-s…
GSM9828624 HEK293T, IFNg_6h, RNA-seq, replicate 2 HEK293T, IFNg_6h, RNA-s…
GSM9828625 HEK293T, IFNg_12h, RNA-seq, replicate 1 HEK293T, IFNg_12h, RNA-…
GSM9828626 HEK293T, IFNg_12h, RNA-seq, replicate 2 HEK293T, IFNg_12h, RNA-…
GSM9828627 HEK293T, IFNg_24h, RNA-seq, replicate 1 HEK293T, IFNg_24h, RNA-…
GSM9828628 HEK293T, IFNg_24h, RNA-seq, replicate 2 HEK293T, IFNg_24h, RNA-…

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