平行免疫磁性纳米孔排序的建模与优化:针对复杂介质中细胞外囊泡的特定表面标记分离
Modeling and optimization of parallelized immunomagnetic nanopore sorting for surface marker specific isolation of extracellular vesicles from complex media.
发表日期:2023 Aug 16
作者:
Andrew A Lin, Hanfei Shen, Griffin Spychalski, Erica L Carpenter, David Issadore
来源:
Disease Models & Mechanisms
摘要:
基于表面标记物表达的特异亚群胞外囊泡(EVs)的分离面临着重大挑战,因为其纳米级尺寸(< 800 nm),其异质性表面标记物表达和临床标本中存在的大量背景 EVs(血液中每毫升存在1010-1012 EVs)。
使用径迹刻蚀磁性纳米孔(TENPO)芯片的高度并行纳米磁性分离已实现高通量、抗堵塞的精确免疫特异分选。然而,目前还没有对控制通量、目标 EVs 回收和丢弃背景 EVs 能力的折衷设计参数进行系统研究。我们结合有限元模拟和TENPO芯片的实验表征来阐明从血液中分离 EV 亚群的设计规则。我们通过选择孔径、串联膜片数量和流速,在不牺牲目标 EVs 回收的情况下,将装置背景降低>10倍,展示了该方法的实用性。我们将TENPO分离的 EVs 与 EV 分离的黄金标准方法进行比较,并通过针对肺癌、胰腺癌和肝癌等多种疾病模型的 EVs 亚群进行展示,证明了其广泛应用和模块化的实用性。© 2023. Springer Nature Limited.
The isolation of specific subpopulations of extracellular vesicles (EVs) based on their expression of surface markers poses a significant challenge due to their nanoscale size (< 800 nm), their heterogeneous surface marker expression, and the vast number of background EVs present in clinical specimens (1010-1012 EVs/mL in blood). Highly parallelized nanomagnetic sorting using track etched magnetic nanopore (TENPO) chips has achieved precise immunospecific sorting with high throughput and resilience to clogging. However, there has not yet been a systematic study of the design parameters that control the trade-offs in throughput, target EV recovery, and ability to discard background EVs in this approach. We combine finite-element simulation and experimental characterization of TENPO chips to elucidate design rules to isolate EV subpopulations from blood. We demonstrate the utility of this approach by reducing device background > 10× relative to prior published designs without sacrificing recovery of the target EVs by selecting pore diameter, number of membranes placed in series, and flow rate. We compare TENPO-isolated EVs to those of gold-standard methods of EV isolation and demonstrate its utility for wide application and modularity by targeting subpopulations of EVs from multiple models of disease including lung cancer, pancreatic cancer, and liver cancer.© 2023. Springer Nature Limited.