An Automated High-throughput Mechanoelectrical Cell Cytometry for Cell Biophysical Screening

自動化高通量地識別細胞生物物理特性的機電能量傳導式細胞儀

Student thesis: Doctoral Thesis

View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Awarding Institution
Supervisors/Advisors
Award date2 Sep 2021

Abstract

The Cell biophysical phenotype is a natural biophysical marker of cell state and function with a wide range of fundamental and applied biology applications. The microfluidic technology-based approach has facilitated the determination of a broad range of biophysical characteristics in single cells, with a throughput equivalent to commercial flow cytometry. Many different types of equipment and methods for measuring cell stiffness have been published in the literature. Still, only a few of them properly employ mechanical models that account for cell diameter and stiffness. At the same time, it can be noted that almost no electrical method that can eliminate the microscope meets the requirements of automatic measurement. I developed a high-throughput cell stiffness cytometer in the first part. I initially presented a dynamic model that might more correctly describe cell stress and strain during cell movement in a microfluidic channel. In addition, I suggested an innovative and clever image processing technique for immediately detecting parameters from the recorded video and calculating cell stiffness; I further explored how CD 133 and CD 44 alter the stiffness of cell lines using this device I built. The results of the measurements revealed that MCF-7 cell lines exhibit different trends than other cell lines. These significant findings also shed insight into the relationship between CSC markers and cancer metastasis.

After constructing the robust mechanics model, I investigated using an electrical-based technique to automate cell stiffness detection. I created a cell motion sensor based on the triboelectric effect between moving cells and metal electrodes within a single cell scale, which could be utilized to monitor the motion characteristics of a single cell in real-time. The cell motion characteristics could finally be translated into the elastic modulus via the stress-strain analysis by the physical model after integrating with the microfluidic device in the first portion, which cell could be deformed and moved in the channel under hydraulic pressure. The motion parameters of three cell lines were successfully qualified by the bio-triboelectric sensor and eventually converted to the elastic modulus.

After the first two parts of the work, I decided to expand the measurement throughput to more than 100 times that of the original device. I used a ‘4-core’ microfluidic detection channel, each with parallel measurement channels. The electrical signals of multiple cells can be detected in parallel and converted into biophysical signals in real-time. The delicate microfluidic channel design can avoid overlapping the measurement signals in the superposition state of multiple cells, which could help improve the measurement accuracy. Besides, the measurement accuracy could up to more than 95% by adding a signal measurement channel as an auxiliary sensor through a clever geometric and microfluidic scheme design. Eventually, I could monitor cell stiffness and surface charge ability after I correct the bio-triboelectric model simultaneously.