Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery

Natalie Boehnke, Joelle P. Straehla, Hannah C. Safford, Mustafa Kocak, Matthew G. Rees, Melissa Ronan, Danny Rosenberg, Charles H. Adelmann, Raghu R. Chivukula, Namita Nabar, Adam G. Berger, Nicholas G. Lamson, Jaime H. Cheah, Hojun Li, Jennifer A. Roth, Angela N. Koehler, Paula T. Hammond

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

Abstract

To accelerate the translation of cancer nanomedicine, we used an integrated genomic approach to improve our understanding of the cellular processes that govern nanoparticle trafficking. We developed a massively parallel screen that leverages barcoded, pooled cancer cell lines annotated with multiomic data to investigate cell association patterns across a nanoparticle library spanning a range of formulations with clinical potential. We identified both materials properties and cell-intrinsic features that mediate nanoparticle-cell association. Using machine learning algorithms, we constructed genomic nanoparticle trafficking networks and identified nanoparticle-specific biomarkers. We validated one such biomarker: gene expression of SLC46A3, which inversely predicts lipid-based nanoparticle uptake in vitro and in vivo. Our work establishes the power of integrated screens for nanoparticle delivery and enables the identification and utilization of biomarkers to rationally design nanoformulations.

Original languageEnglish (US)
Article numbereabm5551
JournalScience
Volume377
Issue number6604
DOIs
StatePublished - Jul 22 2022
Externally publishedYes

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