Cellular subpopulations identified using an ensemble average of multiple dielectrophoresis measurements

Seungyeop Choi; Sung-Hun Woo; Insu Park; Sena Lee; Kang In Yeo; Sang Hyun Lee; Sei Young Lee; Sejung Yang; Gyudo Lee; Woo-Jin Chang; Rashid Bashir; Yoon Suk Kim; Sang Woo Lee

doi:10.1016/j.compbiomed.2024.108011

Cellular subpopulations identified using an ensemble average of multiple dielectrophoresis measurements

Comput Biol Med. 2024 Mar:170:108011. doi: 10.1016/j.compbiomed.2024.108011. Epub 2024 Jan 18.

Authors

Seungyeop Choi¹, Sung-Hun Woo², Insu Park³, Sena Lee⁴, Kang In Yeo⁵, Sang Hyun Lee⁵, Sei Young Lee⁶, Sejung Yang⁴, Gyudo Lee⁷, Woo-Jin Chang⁸, Rashid Bashir⁹, Yoon Suk Kim¹⁰, Sang Woo Lee¹¹

Affiliations

¹ Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Republic of Korea; School of Biomedical Engineering, Korea University, Seoul, 02481, Republic of Korea; BK21 Four Institute of Precision Public Health, Korea University, Seoul, 02841, Republic of Korea.
² Department of Biomedical Laboratory Science, Yonsei University, Wonju, 26493, Republic of Korea.
³ Holonyak Micro and Nanotechnology Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; Department of Biomedical Engineering, Konyang University, Daejeon, 35365, Republic of Korea.
⁴ Department of Precision Medicine, Wonju College of Medicine, Yonsei University, Wonju, 26426, Republic of Korea.
⁵ Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Republic of Korea.
⁶ Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Republic of Korea; Department of Medical Informatics and Biostatistics, Graduate School, Yonsei University, Wonju, 26426, Republic of Korea.
⁷ Department of Biotechnology and Bioinformatics, Korea University, Sejong, 30019, Republic of Korea; Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, 30019, Republic of Korea.
⁸ Mechanical Engineering Department, University of Wisconsin-Milwaukee, Milwaukee, WI, 53211, USA.
⁹ Holonyak Micro and Nanotechnology Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
¹⁰ Department of Biomedical Laboratory Science, Yonsei University, Wonju, 26493, Republic of Korea. Electronic address: yoonsukkim@yonsei.ac.kr.
¹¹ Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Republic of Korea. Electronic address: yusuklee@yonsei.ac.kr.

PMID: 38271838
DOI: 10.1016/j.compbiomed.2024.108011

Abstract

While the average value measurement approach can successfully analyze and predict the general behavior and biophysical properties of an isogenic cell population, it fails when significant differences among individual cells are generated in the population by intracellular changes such as the cell cycle, or different cellular responses to certain stimuli. Detecting such single-cell differences in a cell population has remained elusive. Here, we describe an easy-to-implement and generalizable platform that measures the dielectrophoretic cross-over frequency of individual cells by decreasing measurement noise with a stochastic method and computing ensemble average statistics. This platform enables multiple, real-time, label-free detection of individual cells with significant dielectric variations over time within an isogenic cell population. Using a stochastic method in combination with the platform, we distinguished cell subpopulations from a mixture of drug-untreated and -treated isogenic cells. Furthermore, we demonstrate that our platform can identify drug-treated isogenic cells with different recovery rates.

Keywords: DEP crossover frequency; Dielectrophoresis; Ensemble average; Multiple measurements; Subpopulation discrimination.