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piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties

文献类型：期刊论文

作者Luan, Xiaofeng1,4; Liu, Pengbin1,4; Huang, Di2; Zhao, Haiping3; Li, Yuang1,4; Sun, Sheng1,4; Zhang, Wenchang1; Zhang, Lingqian1; Li, Mingxiao1; Zhi, Tian2
刊名MICROSYSTEMS & NANOENGINEERING
出版日期2023-06-08
卷号9期号:1页码:10
ISSN号2055-7434
DOI10.1038/s41378-023-00545-9
英文摘要Real-time transformation was important for the practical implementation of impedance flow cytometry. The major obstacle was the time-consuming step of translating raw data to cellular intrinsic electrical properties (e.g., specific membrane capacitance C-sm and cytoplasm conductivity s(cyto)). Although optimization strategies such as neural network-aided strategies were recently reported to provide an impressive boost to the translation process, simultaneously achieving high speed, accuracy, and generalization capability is still challenging. To this end, we proposed a fast parallel physical fitting solver that could characterize single cells' C-sm and s(cyto) within 0.62 ms/cell without any data preacquisition or pretraining requirements. We achieved the 27000-fold acceleration without loss of accuracy compared with the traditional solver. Based on the solver, we implemented physics-informed real-time impedance flow cytometry (piRT-IFC), which was able to characterize up to 100,902 cells' C-sm and s(cyto) within 50 min in a real-time manner. Compared to the fully connected neural network (FCNN) predictor, the proposed real-time solver showed comparable processing speed but higher accuracy. Furthermore, we used a neutrophil degranulation cell model to represent tasks to test unfamiliar samples without data for pretraining. After being treated with cytochalasin B and N-Formyl-Met-Leu-Phe, HL-60 cells underwent dynamic degranulation processes, and we characterized cell's C-sm and s(cyto) using piRT-IFC. Compared to the results from our solver, accuracy loss was observed in the results predicted by the FCNN, revealing the advantages of high speed, accuracy, and generalizability of the proposed piRT-IFC.
资助项目National Key Research and Development Program of China[2018YFC2001100] ; National Natural Science Foundation of China[62171441] ; State Key Laboratory of Computer Architecture (ICT, CAS)[CARCH202122]
WOS研究方向Science & Technology - Other Topics ; Instruments & Instrumentation
语种英语
出版者SPRINGERNATURE
WOS记录号WOS:001003561700001
源URL[http://119.78.100.204/handle/2XEOYT63/21205]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhi, Tian; Zhao, Yang; Huang, Chengjun
作者单位1.Chinese Acad Sci, Inst Microelect, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China
3.Capital Med Univ, Xuanwu Hosp, Cerebrovasc Dis Res Inst, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Luan, Xiaofeng,Liu, Pengbin,Huang, Di,et al. piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties[J]. MICROSYSTEMS & NANOENGINEERING,2023,9(1):10.
APA Luan, Xiaofeng.,Liu, Pengbin.,Huang, Di.,Zhao, Haiping.,Li, Yuang.,...&Huang, Chengjun.(2023).piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties.MICROSYSTEMS & NANOENGINEERING,9(1),10.
MLA Luan, Xiaofeng,et al."piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties".MICROSYSTEMS & NANOENGINEERING 9.1(2023):10.

入库方式： OAI收割

来源：计算技术研究所

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