文献类型：期刊文献

英文题名：A Sparse Constrained Optimization Method for Resolving Coincident Single-Cell Events in Microfluidic-Based Impedance Sensing

作者：Xia, Yucheng[1]; Guo, Jiahao[1]; Shi, Yifan[2]; Jiang, Guojun[1]; Gu, Zhen[1]; Wang, Huifeng[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education, State Key Laboratory of Bioreactor Engineering, Shanghai, 200237, China; [2] Fudan University, Department of Digestive Diseases, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Shanghai, 200040, China

年份：2025

外文期刊名：IEEE Transactions on Biomedical Engineering

收录：EI(收录号：20255219794232)

语种：英文

外文关键词：Additives - Biochips - Cell proliferation - Electric impedance measurement - Flow cytometry - Fluidic devices - Microfluidics - Screening - Suspensions (fluids)

摘要：Objective: Label-free electrical impedance-based single-cell detection has been widely applied in cell sorting, electrical phenotyping, and monitoring of cell growth status. However, when high-concentration cell suspensions pass through the sensing region simultaneously, coincident events frequently occur, which leads to inaccurate segmentation of cell events and distorted identification of single-cell waveforms. As a result, statistical errors in electrical phenotyping are introduced. Methods: In this work, we propose a two-step sparse-constrained optimization algorithm based on 1-norm regularization, which addresses this challenge without requiring any structural modification to the microfluidic chip. The raw signal is processed using this two-step framework: first, a waveform detection dictionary is constructed to segment the signal; subsequently, a de-coincidence dictionary is applied to resolve coincident waveforms. Results: Experimental validation on synthetic data streams demonstrates robust counting accuracy from 2×105 to 5×106 particles/ml (99.9%–98.4%), with only a 5.1% reduction under five levels of additive noise at 2×106 particles/ml. Analysis of polystyrene beads of two sizes and T cells at three concentrations demonstrates enhanced size discrimination, improved statistical accuracy, and consistent counting performance compared with conventional algorithms. Conclusion: The proposed method effectively segments and decomposes coincident signals into individual cell events by employing sparse optimization techniques. Significance: This algorithm is well suited for applications that demand accurate counting and classification of cell/particle suspensions across a wide concentration range. ? 1964-2012 IEEE.