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Download fileSingle-Cell Stretching in Viscoelastic Fluids with Electronically Triggered Imaging for Cellular Mechanical Phenotyping
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posted on 2021-03-04, 18:09 authored by Minhui Liang, Dahou Yang, Yinning Zhou, Peixian Li, Jianwei Zhong, Ye AiCellular mechanical phenotypes in
connection to physiological and
pathological states of cells have become a promising intrinsic biomarker
for label-free cell analysis in various biological research and medical
diagnostics. In this work, we present a microfluidic system capable
of high-throughput cellular mechanical phenotyping based on a rapid
single-cell hydrodynamic stretching in a continuous viscoelastic fluid
flow. Randomly introduced single cells are first aligned into a single
streamline in viscoelastic fluids before being guided to a flow splitting
junction for consistent hydrodynamic stretching. The arrival of individual
cells prior to the flow splitting junction can be detected by an electrical
sensing unit, which produces a triggering signal to activate a high-speed
camera for on-demand imaging of the cell motion and deformation through
the flow splitting junction. Cellular mechanical phenotypes, including
cell size and cell deformability, are extracted from the analysis
of these captured single-cell images. We have evaluated the sensitivity
of the developed microfluidic mechanical phenotyping system by measuring
the synthesized hydrogel microbeads with known Young’s modulus.
With this microfluidic cellular mechanical phenotyping system, we
have revealed the statistical difference in the deformability of microfilament
disrupted, normal, and fixed NIH 3T3 fibroblast cells. Furthermore,
with the implementation of a machine-learning-based classification
of MCF-10A and MDA-MB-231 mixtures, our label-free cellular phenotyping
system has achieved a comparable cell analysis accuracy (0.9:1, 5.03:1)
with respect to the fluorescence-based flow cytometry results (0.97:1,
5.33:1). The presented microfluidic mechanical phenotyping technique
will open new avenues for high-throughput and label-free single-cell
analysis in diverse biomedical applications.