Inherent bioelectrical parameters of hundreds of thousands of single leukocytes based on impedance flow cytometry

Abstract

As label‐free biomarkers, bioelectrical properties of single cells have been widely used in hematology analyzers for 3‐part differential of leukocytes, in which, however, instrument dependent bioelectrical parameters (e.g., DC/AC impedance values) rather than inherent bioelectrical parameters (e.g., diameter Dc, specific membrane capacitance Csm and cytoplasmic conductivity σcy) were used, leading to poor comparisons among different instruments. In order to address this issue, this study collected inherent bioelectrical parameters from hundreds of thousands of white blood cells based on a home‐developed impedance flow cytometry with corresponding 3‐part differential of leukocytes realized. More specifically, leukocytes were separated into three major subtypes of granulocytes, monocytes and lymphocytes based on density gradient centrifugation. Then these separated cells were aspirated through a constriction‐microchannel based impedance flow cytometry where inherent bioelectrical parameters of Dc, Csm and σcy were quantified as 9.8 ± 0.7 μm, 2.06 ± 0.26 μF/cm2, and 0.34 ± 0.05 S/m for granulocytes (ncell = 134,829); 10.4 ± 1.0 μm, 2.45 ± 0.48 μF/cm2, and 0.42 ± 0.08 S/m for monocytes (ncell = 40,226); 8.0 ± 0.5 μm, 2.23 ± 0.34 μF/cm2, and 0.35 ± 0.08 S/m for lymphocytes (ncell = 129,193). Based on these inherent bioelectrical parameters, neural pattern recognition was conducted, producing a high “classification accuracy” of 93.5% in classifying these three subtypes of leukocytes. These results indicate that as inherent bioelectrical parameters, Dc, Csm, and σcy can be used to electrically phenotype white blood cells in a label‐free manner.

Label-free microfluidic cell sorting and detection for rapid blood analysis

“…Extracted features from single-cell images or signals can be used for classification of leukocytes. 215,216 For example, Singh et al employed features extracted from holographics for classification of the PBMC and tumor cell line using decision tree. 217 Another interesting work is from Zeming et al in which the authors employed DLD extracted biophysical features of patient blood for classification of sepsis infection using support vector machine (SVM) classification using radial basis function kernel (AUROC 0.97).…”

“…219 Tan et al used neural network to classify leukocytes based on single electrical features (diameter, specific membrane capacitance and cytoplasmic conductivity) derived from a constriction-based impedance cytometer. 216…”

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

“…Meanwhile, Chen@CAS reported the microfluidic impedance flow cytometry based on constriction microchannels where, based on an equivalent circuit model for a cell squeezing through the microchannel, impedance profiles were translated into intrinsic bioelectrical markers of cell diameter, specific membrane capacitance and cytoplasmic conductivity [ 25 ]. Based on these intrinsic bioelectrical markers, three-part differential of white blood cells were realized by the same group [ 36 ]. Furthermore, by integrating preliminary impedance profiles and intrinsic bioelectrical markers of single cells travelling through the constriction microchannels, five-part differential of white blood cells were also reported by Chen@CAS [ 37 ] (see Figure 5 d).…”

“…For instance, intrinsic electrical parameters of blood cells such as membrane capacitance and cytoplasmic conductivity have been measured by microfluidic impedance flow cytometry reported by Chen@CAS [ 25 ] and Morgan@Southampton [ 34 ], respectively. However, based on these intrinsic bioelectrical parameters (e.g., cell diameter, specific membrane capacitance and cytoplasmic conductivity), only 3-part differential of WBC can be realized while classifications of granulocyte subgroups based on these intrinsic bioelectrical parameters cannot be effectively realized [ 36 , 37 ].…”

Toward five‐part differential of leukocytes based on electrical impedances of single cells and neural network

“…In order to address this issue, recently, constriction microchannels were included in impedance flow cytometry where a cell under measurements deformed through the microchannel and effectively blocked electrical lines, producing much higher impedance variations [16,17]. This approach was used to classify NEU, EOS, and BAS for the first time, although a relatively low classification accuracy of 80% was reported [18,19]. Thus, the five-part differential of leukocytes based on electrical impedances of single cells without chemical treatments has never been reported previously.…”