Evidence map›Paper›PMID 42031757›Full record

ArticleNature communications2026

Genome-wide screening reveals producer-cell modifications that improve virus-like particle production and delivery potency.

Diana Ly, Hyewon Jang, Adhiraj Goel, Arnav Singh, Aditya Raguram

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Diana LyWhitehead Institute for Biomedical Research, Cambridge, MA, USA.
Hyewon JangWhitehead Institute for Biomedical Research, Cambridge, MA, USA.
Adhiraj GoelWhitehead Institute for Biomedical Research, Cambridge, MA, USA.
Arnav SinghWhitehead Institute for Biomedical Research, Cambridge, MA, USA.
Aditya RaguramWhitehead Institute for Biomedical Research, Cambridge, MA, USA. araguram@wi.mit.edu.ORCID 0000-0002-5749-7308

Funding

Leveraging cell-derived bioparticles for macromolecular deliveryDP5OD037342 · OD · WHITEHEAD INSTITUTE FOR BIOMEDICAL RES · PI Aditya Raguram · 2024 to 2026
$2.4M
NIH HHS DP5 OD037342U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OD) DP5OD037342
6 · The paper itself

Abstract

Engineered virus-like particles (eVLPs) are promising vehicles for transient delivery of gene editing agents. While extensive particle engineering has yielded efficient eVLPs, it remains underexplored whether engineering the cells used to produce eVLPs could further improve eVLP properties. We report an unbiased genome-wide screening approach to systematically investigate how genetic perturbations in producer cells influence eVLP production. This approach generates eVLPs loaded with guide RNAs that identify the genetic perturbation in the cell that produced a particular particle; the abundance of each guide RNA in eVLPs therefore reflects how the corresponding genetic perturbation influences eVLP production or cargo loading. We apply this approach to identify several genes that regulate eVLP cargo expression and loading into particles during the production process. Leveraging these insights, we engineer producer cells that support increased eVLP cargo packaging and a 2- to 9-fold increase in eVLP delivery potency across several cargo, particle, and target-cell types in cultured cells and in mice. Our findings suggest the potential of producer-cell engineering as a useful strategy for improving the utility of eVLPs and related delivery methods.

Indexed as

Cell EngineeringVirionAnimalsGene EditingHEK293 CellsHumansMiceRNA, Guide, CRISPR-Cas SystemsRNA, Guide, CRISPR-Cas Systems

Identifiers

PMID42031757
PMCPMC13109392

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.