Synthesis of a Cyberphysical Hybrid Microfluidic Platform for Single-Cell Analysis

Abstract

Single-cell genomics is used to advance our understanding of diseases such as cancer. Microfluidic solutions have recently been developed to classify cell types or perform single-cell biochemical analysis on pre-isolated types of cells. However, new techniques are needed to efficiently classify cells and conduct biochemical experiments on multiple cell types concurrently. Nondeterministic cell-type identification, system integration, and design automation are major challenges in this context. To overcome these challenges, we present a hybrid microfluidic platform that enables complete single-cell analysis on a heterogeneous pool of cells. We combine this architecture with an associated design-automation and optimization framework, referred to as Co-Synthesis (CoSyn). The proposed framework employs real-time resource allocation to coordinate the progression of concurrent cell analysis. Besides this framework, a probabilistic model based on a discrete-time Markov chain (DTMC) is also deployed to investigate protocol settings where experimental conditions, such as sonication time, vary probabilistically among cell types. Simulation results show that CoSyn efficiently utilizes platform resources and outperforms baseline techniques.