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Transcriptomic profiling of interferon responses induced by IVT sgRNAs in CRISPRi HEK293T cells

GSE317890 Homo sapiens (human) RNA-seq (gene expression) 360 samples Submitted 2026/06/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 360 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
360 samples: 300 distinct titles, 12 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)
In this study, we investigated the transcriptional responses induced by in vitro–transcribed (IVT) sgRNAs in a CRISPR interference (CRISPRi) system. A total of 906 sgRNAs were generated using a commercially available IVT workflow and delivered to HEK293T cells stably expressing dCas9-KRAB-MECP2. RNA sequencing was performed to profile genome-wide transcriptional changes following sgRNA transfection.
Read the full summary ↓
Compared with mock-transfected controls, sgRNA-transfected cells exhibited a strong and consistent upregulation of interferon-stimulated genes (ISGs), with prominent enrichment of interferon-α response pathways across the majority of sgRNAs. While most sgRNAs induced robust interferon signaling, a small subset showed a more limited transcriptional response. IFIT1 and IFIT2, two canonical markers of interferon activation, were consistently upregulated and were selected as representative genes for downstream validation. This dataset provides a systematic resource for assessing innate immune activation triggered by IVT sgRNAs in CRISPRi-based perturbation experiments.
This dataset
Open GSE317890 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 GSE317890_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 PRJNA1415764 and SRA study SRP668995. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
300 distinct titles, 12 of them repeated
DPH1_A_LP ×6 DPH1_B_LP ×6 NEAT1_A_LP ×6 NEAT1_B_LP ×6 GAL4_A_LP ×6 GAL4_B_LP ×6 PoolCTL_A_LP ×6 PoolCTL_B_LP ×6 +4 more

How we read this: Read from 360 sample titles: 300 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

+ 320 more — browse all 360 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.