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Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery
Science ( IF 56.9 ) Pub Date : 2022-07-21 , DOI: 10.1126/science.abm5551
Natalie Boehnke 1, 2 , Joelle P Straehla 1, 2, 3, 4 , Hannah C Safford 1 , Mustafa Kocak 2 , Matthew G Rees 2 , Melissa Ronan 2 , Danny Rosenberg 2 , Charles H Adelmann 5, 6, 7 , Raghu R Chivukula 6, 8 , Namita Nabar 1, 9 , Adam G Berger 1, 10, 11 , Nicholas G Lamson 1 , Jaime H Cheah 1 , Hojun Li 1, 3, 4 , Jennifer A Roth 2 , Angela N Koehler 1, 2, 12 , Paula T Hammond 1, 9, 10
Affiliation  

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.

中文翻译:

大规模平行合并筛选揭示了纳米颗粒递送的基因组决定因素

为了加速癌症纳米医学的转化,我们使用了一种综合基因组方法来提高我们对控制纳米颗粒运输的细胞过程的理解。我们开发了一个大规模平行筛选,利用条形码化、合并的癌细胞系,并用多组学数据注释,以研究跨越一系列具有临床潜力的制剂的纳米粒子库的细胞关联模式。我们确定了介导纳米颗粒-细胞结合的材料特性和细胞固有特征。使用机器学习算法,我们构建了基因组纳米粒子贩运网络并确定了纳米粒子特异性生物标志物。我们验证了一种这样的生物标志物:SLC46A3,它反向预测基于脂质的纳米颗粒在体外和体内的摄取。我们的工作建立了用于纳米颗粒输送的集成屏幕的功能,并使生物标志物的识别和利用能够合理地设计纳米制剂。
更新日期:2022-07-21
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