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一种用于连续高通量分离多尺寸颗粒的径向柱装置(RAPID)。

A Radial Pillar Device (RAPID) for continuous and high-throughput separation of multi-sized particles.

作者信息

Mehendale Ninad, Sharma Oshin, D'Costa Claudy, Paul Debjani

机构信息

Indian Institute of Technology Bombay, Mumbai, India.

出版信息

Biomed Microdevices. 2017 Nov 28;20(1):6. doi: 10.1007/s10544-017-0246-4.

DOI:10.1007/s10544-017-0246-4
PMID:29185049
Abstract

Pillar-based microfluidic sorting devices are preferred for isolation of rare cells due to their simple designs and passive operation. Dead-end pillar filters can efficiently capture large rare cells, such as, circulating tumor cells (CTCs), nucleated red blood cells (NRBCs), CD4 cells in HIV patients, etc., but they get clogged easily. Cross flow filters are preferred for smaller rare particles (e.g. separating bacteria from blood), but they need additional buffer inlets and a large device footprint for efficient operation. We have designed a new microparticle separation device i.e. Ra dial Pi llar D evice (RAPID) that combines the advantages of dead-end and cross flow filters. RAPID can simultaneously isolate both large and small rare particles from a mixed population, while functioning for several hours without clogging. We have achieved simultaneous separation of 10 μ m and 2 μ m polystyrene particles from a mixture of 2 μ m, 7 μ m and 10 μ m particles. RAPID achieved average separation purity and recovery in excess of ∼90%. The throughput of our device (∼3ml/min) is 10 and 100 times higher compared to cross flow and dead-end filters respectively, thereby justifying the name RAPID.

摘要

基于柱体的微流控分选装置因其设计简单且操作被动,在分离稀有细胞方面备受青睐。死端柱式过滤器能够高效捕获大型稀有细胞,如循环肿瘤细胞(CTC)、有核红细胞(NRBC)、HIV患者的CD4细胞等,但容易堵塞。错流过滤器则更适用于较小的稀有颗粒(如从血液中分离细菌),不过它们需要额外的缓冲液入口且为实现高效操作需要较大的设备占地面积。我们设计了一种新型微粒分离装置,即径向柱式装置(RAPID),它结合了死端过滤器和错流过滤器的优点。RAPID能够同时从混合群体中分离大型和小型稀有颗粒,并且能够连续运行数小时而不堵塞。我们已实现从2μm、7μm和10μm颗粒的混合物中同时分离出10μm和2μm的聚苯乙烯颗粒。RAPID的平均分离纯度和回收率超过约90%。我们装置的通量(约3ml/分钟)分别比错流过滤器和死端过滤器高10倍和100倍,因此“快速”(RAPID)这个名字名副其实。

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Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.流式细胞术检测稀有目标:成像、细胞分选和深度学习方法。
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