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An arrayed CRISPR screen identifies knockout combinations improving antibody productivity in HEK293 cells

GSE288118 Homo sapiens (human) RNA-seq (gene expression) 28 samples Submitted 2025/02/27 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 28 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
28 samples: 28 samples, each with a distinct title — no replicated groupsGroups 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
An Arrayed CRISPR Screen Identifies Knockout Combinations Improving Antibody Productivity in HEK293 CellsACS synthetic biology · 2025
Summary (written by the submitters)
Mammalian cells are used to express complex biologics such as multispecific antibodies. While multispecifics enable promising new strategies for treating human disease, their production at high expression titer and purity can be challenging. To understand how cells respond to antibody and multispecific expression, five molecules were selected for bulk RNA-seq early after transfection of a HEK293 host.
Read the full summary ↓
All five molecules shared a differential expression signature of secretory and protein folding stress, but this signature was stronger for molecules with low titer. We then designed an arrayed CRISPR knockout screen of 206 differentially-expressed target genes and 223 literature-motivated targets to identify knockouts that impact antibody productivity. Eight novel knockout targets were identified that increased expression titers by 20 to 80%. Notably, seven of these top eight hits were from the differentially- expressed set of candidate-gene knockouts. The top knockout target, HIST2H3C, showed evidence for additivity with five other hits, including a knockout combination that increased the titer of a difficult-to-express antibody by up to 100%. Findings for both HIST2H3C and INHBE knockout targets generalized to an alternate HEK293 host expressing an additional antibody and a multispecific with no meaningful impact on product purity. Thus, we propose HIST2H3C and INHBE disruption to be a promising and novel strategy for host-cell engineering to improve antibody and multispecific productivity.
Published in
An Arrayed CRISPR Screen Identifies Knockout Combinations Improving Antibody Productivity in HEK293 Cells
Bryant EE, Gong D, Guo C et al. · ACS synthetic biology 2025 · PMID 40014422 · doi:10.1021/acssynbio.4c00772
This dataset
Open GSE288118 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 GSE288118_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 PRJNA1216223 and SRA study SRP560133. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40014422) ↗
Study design
28 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 28 sample titles: 28 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
Datasets on a similar topic

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