Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery

• Published in: Science (American Association for the Advancement of Science) Vol. 377; no. 6604; p. eabm5551
• Main Authors: Boehnke, Natalie, Straehla, Joelle P, Safford, Hannah C, Kocak, Mustafa, Rees, Matthew G, Ronan, Melissa, Rosenberg, Danny, Adelmann, Charles H, Chivukula, Raghu R, Nabar, Namita, Berger, Adam G, Lamson, Nicholas G, Cheah, Jaime H, Li, Hojun, Roth, Jennifer A, Koehler, Angela N, Hammond, Paula T
• Format: Journal Article
• Language: English
• Published: United States The American Association for the Advancement of Science 22-07-2022
• Subjects: Algorithms; Animals; Annotations; Antineoplastic Agents - administration & dosage; Antineoplastic Agents - metabolism; Biocompatibility; Biological products; Biological properties; Biomarkers; Biomarkers, Pharmacological; Cancer; Cargo; Cell interactions; Cell Line, Tumor; Cell size; Cellular communication; Cellular structure; Chemical compounds; Chemotherapy; Cluster analysis; Clustering; Composition; Copper Transport Proteins - genetics; Drug Compounding; Drug delivery; Epidermal growth factor; Epidermal growth factor receptors; Fluorescence; Gene Expression; Genomics; Heterogeneity; High-throughput screening; Humans; Interrogation; Lipids; Liposomes; Machine learning; Mice; Nanomedicine; Nanoparticle Drug Delivery System; Nanoparticles; Nanoparticles - administration & dosage; Nanoparticles - metabolism; Neoplasms - drug therapy; Neoplasms - genetics; Neoplasms - metabolism; Nucleic acids; Organic Chemistry; Pharmacology; Precision medicine; Protein interaction; Proteins; Screening; Structure-function relationships; Surface chemistry; Toxicity; Transfection; Tumor cell lines
• ISSN: 0036-8075; 1095-9203
• DOI: 10.1126/science.abm5551

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
, 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.