Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery
Name
2021.04.05.438521v3.full.pdf
Description
Submitted version
Size
24.07 MB
Format
Adobe PDF
Checksum (MD5)
dc3cc3888971e74f5c92cd8a954889bf
Author(s)
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
Date Issued
2022
Journal
Science
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Boehnke, Natalie, Straehla, Joelle P, Safford, Hannah C, Kocak, Mustafa, Rees, Matthew G et al. 2022. "Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery." Science, 377 (6604).
Version
Original manuscript
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.
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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1126/SCIENCE.ABM5551
