Classification of cell types using a microfluidic device for mechanical and electrical measurement on single cells

Jian Chen, Yi Zheng, Qingyuan Tan, Ehsan Shojaei-Baghini, Yan Liang Zhang, Jason Li, Preethy Prasad, Lidan You, Xiao Yu Wu, Yu Sun

Research output: Contribution to journalArticlepeer-review

145 Scopus citations

Abstract

This paper presents a microfluidic system for cell type classification using mechanical and electrical measurements on single cells. Cells are aspirated continuously through a constriction channel with cell elongations and impedance profiles measured simultaneously. The cell transit time through the constriction channel and the impedance amplitude ratio are quantified as cell's mechanical and electrical property indicators. The microfluidic device and measurement system were used to characterize osteoblasts (n = 206) and osteocytes (n = 217), revealing that osteoblasts, compared with osteocytes, have a larger cell elongation length (64.51 ± 14.98 μm vs. 39.78 ± 7.16 μm), a longer transit time (1.84 ± 1.48 s vs. 0.94 ± 1.07 s), and a higher impedance amplitude ratio (1.198 ± 0.071 vs. 1.099 ± 0.038). Pattern recognition using the neural network was applied to cell type classification, resulting in classification success rates of 69.8% (transit time alone), 85.3% (impedance amplitude ratio alone), and 93.7% (both transit time and impedance amplitude ratio as input to neural network) for osteoblasts and osteocytes. The system was also applied to test EMT6 (n = 747) and EMT6/AR1.0 cells (n = 770, EMT6 treated by doxorubicin) that have a comparable size distribution (cell elongation length: 51.47 ± 11.33 μm vs. 50.09 ± 9.70 μm). The effects of cell size on transit time and impedance amplitude ratio were investigated. Cell classification success rates were 51.3% (cell elongation alone), 57.5% (transit time alone), 59.6% (impedance amplitude ratio alone), and 70.2% (both transit time and impedance amplitude ratio). These preliminary results suggest that biomechanical and bioelectrical parameters, when used in combination, could provide a higher cell classification success rate than using electrical or mechanical parameter alone.

Original languageEnglish (US)
Pages (from-to)3174-3181
Number of pages8
JournalLab on a Chip
Volume11
Issue number18
DOIs
StatePublished - Sep 21 2011
Externally publishedYes

ASJC Scopus subject areas

  • Bioengineering
  • Biochemistry
  • Chemistry(all)
  • Biomedical Engineering

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