Three-dimensional inertial focusing based impedance cytometer enabling high-accuracy characterization of electrical properties of tumor cells†

IF 6.1 2区 工程技术 Q1 BIOCHEMICAL RESEARCH METHODS
Lab on a Chip Pub Date : 2024-08-06 DOI:10.1039/D4LC00523F
Chen Ni, Mingqi Yang, Shuai Yang, Zhixian Zhu, Yao Chen, Lin Jiang and Nan Xiang
{"title":"Three-dimensional inertial focusing based impedance cytometer enabling high-accuracy characterization of electrical properties of tumor cells†","authors":"Chen Ni, Mingqi Yang, Shuai Yang, Zhixian Zhu, Yao Chen, Lin Jiang and Nan Xiang","doi":"10.1039/D4LC00523F","DOIUrl":null,"url":null,"abstract":"<p >The differences in the cross-sectional positions of cells in the detection area have a severe negative impact on achieving accurate characterization of the impedance spectra of cells. Herein, we proposed a three-dimensional (3D) inertial focusing based impedance cytometer integrating sheath fluid compression and inertial focusing for the high-accuracy electrical characterization and identification of tumor cells. First, we studied the effects of the particle initial position and the sheath fluid compression on particle focusing. Then, the relationship of the particle height and the signal-to-noise ratio (SNR) of the impedance signal was explored. The results showed that efficient single-line focusing of 7–20 μm particles close to the electrodes was achieved and impedance signals with a high SNR and a low coefficient of variation (CV) were obtained. Finally, the electrical properties of three types of tumor cells (A549, MDA-MB-231, and UM-UC-3 cells) were accurately characterized. Machine learning algorithms were implemented to accurately identify tumor cells based on the amplitude and phase opacities at multiple frequencies. Compared with traditional two-dimensional (2D) inertial focusing, the identification accuracy of A549, MDA-MB-231, and UM-UC-3 cells using our 3D inertial focusing increased by 57.5%, 36.4% and 36.6%, respectively. The impedance cytometer enables the detection of cells with a wide size range without causing clogging and obtains high SNR signals, improving applicability to different complex biological samples and cell identification accuracy.</p>","PeriodicalId":85,"journal":{"name":"Lab on a Chip","volume":" 18","pages":" 4333-4343"},"PeriodicalIF":6.1000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lab on a Chip","FirstCategoryId":"5","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2024/lc/d4lc00523f","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

The differences in the cross-sectional positions of cells in the detection area have a severe negative impact on achieving accurate characterization of the impedance spectra of cells. Herein, we proposed a three-dimensional (3D) inertial focusing based impedance cytometer integrating sheath fluid compression and inertial focusing for the high-accuracy electrical characterization and identification of tumor cells. First, we studied the effects of the particle initial position and the sheath fluid compression on particle focusing. Then, the relationship of the particle height and the signal-to-noise ratio (SNR) of the impedance signal was explored. The results showed that efficient single-line focusing of 7–20 μm particles close to the electrodes was achieved and impedance signals with a high SNR and a low coefficient of variation (CV) were obtained. Finally, the electrical properties of three types of tumor cells (A549, MDA-MB-231, and UM-UC-3 cells) were accurately characterized. Machine learning algorithms were implemented to accurately identify tumor cells based on the amplitude and phase opacities at multiple frequencies. Compared with traditional two-dimensional (2D) inertial focusing, the identification accuracy of A549, MDA-MB-231, and UM-UC-3 cells using our 3D inertial focusing increased by 57.5%, 36.4% and 36.6%, respectively. The impedance cytometer enables the detection of cells with a wide size range without causing clogging and obtains high SNR signals, improving applicability to different complex biological samples and cell identification accuracy.

Abstract Image

基于三维惯性聚焦技术的阻抗细胞计可高精度鉴定肿瘤细胞的电特性
细胞在检测区域横截面位置的差异会对准确表征细胞阻抗谱产生严重的负面影响。在此,我们提出了一种基于三维(3D)惯性聚焦的阻抗细胞仪,它集成了鞘液压缩和惯性聚焦,可用于高精度的肿瘤细胞电学表征和识别。首先,我们研究了粒子初始位置和鞘液压缩对粒子聚焦的影响。然后,探讨了粒子高度与阻抗信号信噪比(SNR)的关系。结果表明,7-20 μm 的粒子在靠近电极处实现了高效的单线聚焦,并获得了高信噪比和低变异系数(CV)的阻抗信号。最后,三种肿瘤细胞(A549、MDA-MB-231 和 UM-UC-3 细胞)的电特性得到了准确表征。通过机器学习算法,可以根据多频率下的振幅和相位不透明度准确识别肿瘤细胞。与传统的二维(2D)惯性聚焦相比,使用我们的三维惯性聚焦技术识别 A549、MDA-MB-231 和 UM-UC-3 细胞的准确率分别提高了 57.5%、36.4% 和 36.6%。该阻抗细胞仪能检测大尺寸范围的细胞,不会造成堵塞,并能获得高信噪比信号,提高了对不同复杂生物样本的适用性和细胞鉴定的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Lab on a Chip
Lab on a Chip 工程技术-化学综合
CiteScore
11.10
自引率
8.20%
发文量
434
审稿时长
2.6 months
期刊介绍: Lab on a Chip is the premiere journal that publishes cutting-edge research in the field of miniaturization. By their very nature, microfluidic/nanofluidic/miniaturized systems are at the intersection of disciplines, spanning fundamental research to high-end application, which is reflected by the broad readership of the journal. Lab on a Chip publishes two types of papers on original research: full-length research papers and communications. Papers should demonstrate innovations, which can come from technical advancements or applications addressing pressing needs in globally important areas. The journal also publishes Comments, Reviews, and Perspectives.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信