美国麻省理工学院Paula T. Hammond、Natalie Boehnke、Joelle P. Straehla课题组近日取得一项新成果。经过不懈努力，他们通过大规模平行汇集筛选揭示了纳米颗粒传递基因组的决定因素。2022年7月22日，国际学术期刊《科学》发表了这一成果。

为了加速癌症纳米医学转化，研究人员使用了一种综合基因组方法来提高人们对细胞如何摄入纳米粒子过程的理解。研究人员研发了一个大规模平行筛选平台，利用带有多组数据注释的条形码、汇集的癌细胞系在具有临床潜力一系列制剂纳米粒子库中揭示细胞关联模式。研究确定了介导纳米粒子与细胞结合的材料特性和细胞内在特征。

使用机器学习算法，研究人员构建了基因组纳米粒子运输网络并确定了纳米粒子特异性生物标志物。研究人员验证了其中一种生物标志物：SLC46A3的基因表达，它在体外和体内反向预测了基于脂质纳米颗粒的摄取。这项工作揭示了集成筛选在纳米颗粒递送筛选中的能力，并且通过生物标志物的识别和利用能够更合理地设计纳米制剂。

附：英文原文

Title: Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery

Author: 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

Issue&Volume: 2022-07-22

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.

DOI: abm5551

Source: https://www.science.org/doi/10.1126/science.abm5551