巨量平行
纳米颗粒
计算生物学
纳米技术
计算机科学
生物
材料科学
并行计算
作者
Natalie Boehnke,Joelle P. Straehla,Hannah C. Safford,Mustafa Kocak,Matthew G. Rees,Melissa M. 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
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2022-07-21
卷期号:377 (6604)
被引量:127
标识
DOI:10.1126/science.abm5551
摘要
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
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