Development of microfluidic flow cytometry capable of characterization of single-cell intrinsic structural and electrical parameters

Abstract

Although single-cell intrinsic structural and electrical parameters (e.g., Dc of cell diameter, Dn of nuclear diameter, σcy of cytoplasmic conductivity and Csm of specific membrane capacitance) are promising for cell-type classification, they cannot be obtained simultaneously due to structural limitations of previously reported flow cytometry. This paper presented a microfluidic flow cytometry made of a double T-type constriction channel plus a predefined fluorescence detection domain, capable of high-throughput characterizing single-cell Dc, Dn, σcy and Csm leveraging a home-developed impedance-fluorescence model. As a demonstration, the microfluidic platform quantified Dc, Dn, σcy and Csm from ~10,000 individual cells of three well-established tumor cell lines of A549, SW620 and HeLa where successful rates of cell-type classification were estimated as 54.5% ± 1.3% (Dc), 68.9% ± 6.8% (Dc + Dn) and 84.8% ± 4.4% (Dc, Dn, σcy + Csm) based on neural pattern recognition. Then Dc, Dn, σcy and Csm derived from ~10,000 single cells of K562 vs. Jurkat of leukemia and SACC-LM vs. CAL 27 of oral tumor were quantified and compared, where successful rates of cell-type classification were estimated as 87.3% (K562 vs. Jurkat) and 79.5% (SACC-LM vs. CAL 27), respectively. In summary, the microfluidic platform reported in this study could quantify single-cell intrinsic structural and electrical parameters simultaneously, leading to significant increases in successful rates of cell-type classification.

Computer vision meets microfluidics: a label-free method for high-throughput cell analysis

“…It integrates multiple detection methods into a chip to measure multiple parameters of the target, such as simultaneously measuring the size of a single cell, the polarizability under multiple frequencies and the deformability of cells in the process of cell tracking 112 for more comprehensively characterized cells. Convolutional networks have proven to be effective in analyzing the impedance spectra generated by electrical impedance detection 113 , and the synergistic detection and cross-validation of optical imaging and electrical impedance is very promising 114 .…”

Developments of Conventional and Microfluidic Flow Cytometry Enabling High-Throughput Characterization of Single Cells

“…For instance, in DxH 900 of In 2022, Chen@CAS reported the simultaneous characterization of Single-cell impedance and imaging based on constriction microchannels with a microfabricated metal window. Based on an equivalent bioelectrical model for a cell squeezing through the microchannel, impedance profiles of the whole cell were translated into cell diameter, specific membrane capacitance and cytoplasmic conductivity where fluorescent signals of cell nucleus captured by a PMT were translated into nuclear diameter based on time spatial translation, producing high rates of classifying K562 and Jurkat cells of leukemia [40]. However, in this approach, the nuclear diameter was calculated by the signal detected by PMT where nuclear morphologies were still missing (see Figure 6d).…”

“…However, due to the issue of out of focus, high-speed images of travelling single cells have seldom been used to obtain geometrical information and the following cell type classification of WBC. Recently, Chen@CAS reported the development of microfluidic impedance and imaging flow cytometry of capturing both electrical and geometrical parameters of single cells (e.g., cell diameter, nuclear diameter, specific membrane capacitance and cytoplasmic conductivity), producing high classification rates of two leukemia cell lines, although nuclear morphologies were missing [ 40 ]. Further developments in this direction are highly demanded where intrinsic biophysical parameters of blood cells can be quantitatively rather than qualitatively measured, and thus, function as the gold standard approach of blood analysis by replacing microscopic smear screening in the near future.…”

“… Key developments of microfluidic imaging flow cytometry, ( a ) inertial focusing for differentiation of MCF-7 vs. WBC [ 24 ]; ( b ) viscoelastic focusing for imaging yeast and 293T [ 35 ]; ( c ) spatial filter with microfabricated slits and pinholes for differentiation of tumor cells [ 39 ]; ( d ) constriction microchannel with microfabricated window for differentiation of tumor cells [ 40 ]. Figures were reprinted with permissions from ( a ) Proceedings of the National Academy Sciences, copyright 2012; ( b ) copyright 2021, the author(s); ( c ) copyright 2022, the author(s) and ( d ) copyright 2022, the author(s).…”

A microfluidic microalgae detection system for cellular physiological response based on an object detection algorithm

“…Image-activated microfluidic cell detection methods are able to visualize the phenotypic features and even the internal structure of each cell and enable automated cell classification or status assessment through machine learning models. 19,20 Most of these classification tasks are commonly binary or ternary, few models can handle multi-classification with more than five classes. Cell state assessment typically involves sheath flow or narrow microchannel structure that induce cell deformation, 21,22 allowing for the extraction of cell deformation features for clustering and variance analysis, and to identify the state of the cells.…”