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

电阻抗 惯性参考系 信号(编程语言) 粒子(生态学) 材料科学 生物医学工程 工程类 声学 物理 计算机科学 电气工程 量子力学 海洋学 地质学 程序设计语言
作者
Chen Ni,Mingqi Yang,Shuai Yang,Zhixian Zhu,Chen Yao,Lin Jiang,Nan Xiang
出处
期刊:Lab on a Chip [Royal Society of Chemistry]
卷期号:24 (18): 4333-4343 被引量:4
标识
DOI:10.1039/d4lc00523f
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI6.1应助三斤采纳,获得10
2秒前
2秒前
ding应助Youyou采纳,获得10
3秒前
华仔应助程程程哇采纳,获得10
3秒前
小猫疯完成签到 ,获得积分10
4秒前
香蕉觅云应助小土豆采纳,获得10
4秒前
4秒前
molihuakai应助大意的念梦采纳,获得10
5秒前
5秒前
5秒前
rc7完成签到,获得积分10
6秒前
6秒前
万能图书馆应助jack采纳,获得10
6秒前
南屿完成签到,获得积分10
7秒前
滕祥发布了新的文献求助10
7秒前
霍则风发布了新的文献求助10
7秒前
7秒前
共享精神应助lll采纳,获得10
7秒前
乐乐应助七七采纳,获得10
8秒前
shmily完成签到,获得积分10
8秒前
dqq完成签到 ,获得积分20
9秒前
gu完成签到,获得积分10
9秒前
9秒前
9秒前
超帅孱完成签到,获得积分10
9秒前
sun孫发布了新的文献求助10
9秒前
10秒前
wangzihao1995应助liao采纳,获得50
10秒前
pxh完成签到,获得积分10
10秒前
123发布了新的文献求助10
10秒前
spmt完成签到,获得积分10
10秒前
轻松戎发布了新的文献求助10
11秒前
huohuo发布了新的文献求助30
11秒前
12秒前
12秒前
12秒前
pxh发布了新的文献求助10
13秒前
14秒前
碎觉觉发布了新的文献求助10
14秒前
14秒前
高分求助中
Adhesion Science: Principles & Practice 1234
Signals, Systems, and Signal Processing 610
Burger's Medicinal Chemistry and Drug Discovery 400
A Step-by-Step Guide to Qualitative Data Coding 2nd Edition 400
Impact of Storage Orientation and Duration on Prefilled Syringe Performance: Break-Loose and Glide Forces, and Injection Time Across Multiple Time Points 360
Programming for Chemical Engineers Using C, C++, and MATLAB 320
Birth of Twins After Genome Editing for HIV Resistance 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6673395
求助须知:如何正确求助?哪些是违规求助? 8421026
关于积分的说明 18001721
捐赠科研通 5885259
什么是DOI,文献DOI怎么找? 2978598
邀请新用户注册赠送积分活动 1954459
关于科研通互助平台的介绍 1884519