A design and optimization of a high throughput valve based microfluidic device for single cell compartmentalization and analysis

用于单细胞区室化和分析的高通量基于阀门的微流体装置的设计和优化

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作者:Jonathan Briones, Wilfred Espulgar, Shohei Koyama, Hyota Takamatsu, Eiichi Tamiya, Masato Saito

Abstract

The need for high throughput single cell screening platforms has been increasing with advancements in genomics and proteomics to identify heterogeneity, unique cell subsets or super mutants from thousands of cells within a population. For real-time monitoring of enzyme kinetics and protein expression profiling, valve-based microfluidics or pneumatic valving that can compartmentalize single cells is advantageous by providing on-demand fluid exchange capability for several steps in assay protocol and on-chip culturing. However, this technique is throughput limited by the number of compartments in the array. Thus, one big challenge lies in increasing the number of microvalves to several thousand that can be actuated in the microfluidic device to confine enzymes and substrates in picoliter volumes. This work explores the design and optimizations done on a microfluidic platform to achieve high-throughput single cell compartmentalization as applied to single-cell enzymatic assay for protein expression quantification. Design modeling through COMSOL Multiphysics was utilized to determine the circular microvalve's optimized parameters, which can close thousands of microchambers in an array at lower sealing pressure. Multiphysical modeling results demonstrated the relationships of geometry, valve dimensions, and sealing pressure, which were applied in the fabrication of a microfluidic device comprising of up to 5000 hydrodynamic traps and corresponding microvalves. Comparing the effects of geometry, actuation media and fabrication technique, a sealing pressure as low as 0.04 MPa was achieved. Applying to single cell enzymatic assay, variations in granzyme B activity in Jurkat and human PBMC cells were observed. Improvement in the microfluidic chip's throughput is significant in single cell analysis applications, especially in drug discovery and treatment personalization.

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