Advance of microfluidic flow cytometry enabling high-throughput characterization of single-cell electrical and structural properties.

This paper reported a micro flow cytometer capable of high-throughput characterization of single-cell electrical and structural features based on constrictional microchannels and deep neural networks. When single cells traveled through microchannels with constricted cross-sectional areas, they effectively blocked concentrated electric field lines, producing large impedance variations. Meanwhile, the traveling cells were confined within the cross-sectional areas of the constrictional microchannels, enabling the capture of high-quality images without losing focuses. Then single-cell features from impedance profiles and optical images were extracted from customized recurrent and convolution networks (RNN and CNN), which were further fused for cell-type classification based on support vector machines (SVM). As a demonstration, two leukemia cell lines (e.g., HL60 vs. Jurkat) were analyzed, producing high-classification accuracies of 99.3% based on electrical features extracted from Long Short-Term Memory (LSTM) of RNN, 96.7% based on structural features extracted from Resnet18 of CNN and 100.0% based on combined features enabled by SVM. The microfluidic flow cytometry developed in this study may provide a new perspective for the field of single-cell analysis.

Leukocyte differential based on an imaging and impedance flow cytometry of microfluidics coupled with deep neural networks.

The differential of leukocytes functions as the first indicator in clinical examinations. However, microscopic examinations suffered from key limitations of low throughputs in classifying leukocytes while commercially available hematology analyzers failed to provide quantitative accuracies in leukocyte differentials. A home-developed imaging and impedance flow cytometry of microfluidics was used to capture fluorescent images and impedance variations of single cells traveling through constrictional microchannels. Convolutional and recurrent neural networks were adopted for data processing and feature extractions, which were then fused by a support vector machine to realize the four-part differential of leukocytes. The classification accuracies of the four-part leukocyte differential were quantified as 95.4% based on fluorescent images plus the convolutional neural network, 90.3% based on impedance variations plus the recurrent neural network, and 99.3% on the basis of fluorescent images, impedance variations, and deep neural networks. Based on single-cell fluorescent imaging and impedance variations coupled with deep neural networks, the four-part leukocyte differential can be realized with almost 100% accuracy.

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

The five-part differential of leukocytes plays key roles in the diagnosis of a variety of diseases and is realized by optical examinations of single cells, which is prone to various artifacts due to chemical treatments. The classification of leukocytes based on electrical impedances without cell treatments has not been demonstrated because of limitations in approaches of impedance acquisition and data processing. In this study, based on treatment-free single-cell impedance profiles collected from impedance flow cytometry leveraging constriction microchannels, two types of neural pattern recognition were conducted for comparisons with the purpose of realizing the five-part differential of leukocytes. In the first approach, 30 features from impedance profiles were defined manually and extracted automatically, and then a feedforward neural network was conducted, producing a classification accuracy of 84.9% in the five-part leukocyte differential. In the second approach, a customized recurrent neural network was developed to process impedance profiles directly and based on deep learning, a classification accuracy of 97.5% in the five-part leukocyte differential was reported. These results validated the feasibility of the five-part leukocyte differential based on label-free impedance profiles of single cells and thus provide a new perspective of differentiating white blood cells based on impedance flow cytometry.

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

This article first reviews scientific meanings of single-cell analysis by highlighting two key scientific problems: landscape reconstruction of cellular identities during dynamic immune processes and mechanisms of tumor origin and evolution. Secondly, the article reviews clinical demands of single-cell analysis, which are complete blood counting enabled by optoelectronic flow cytometry and diagnosis of hematologic malignancies enabled by multicolor fluorescent flow cytometry. Then, this article focuses on the developments of optoelectronic flow cytometry for the complete blood counting by comparing conventional counterparts of hematology analyzers (e.g., DxH 900 of Beckman Coulter, XN-1000 of Sysmex, ADVIA 2120i of Siemens, and CELL-DYN Ruby of Abbott) and microfluidic counterparts (e.g., microfluidic impedance and imaging flow cytometry). Future directions of optoelectronic flow cytometry are indicated where intrinsic rather than dependent biophysical parameters of blood cells must be measured, and they can replace blood smears as the gold standard of blood analysis in the near future.

Inherent single-cell bioelectrical parameters of thousands of neutrophils, eosinophils and basophils derived from impedance flow cytometry.

Single-cell bioelectrical properties are commonly used for blood cell phenotyping in a label-free manner. However, previously reported inherent single-cell bioelectrical parameters (e.g., diameter Dc , specific membrane capacitance Csm and cytoplasmic conductivity σcy ) of neutrophils, eosinophils and basophils were obtained from only tens of individual cells with limited statistical significance. In this study, granulocytes were separated into neutrophils, eosinophils and basophils based on fluorescent flow cytometry, which were further aspirated through a constriction-microchannel impedance flow cytometry for electrical property characterization. Based on this microfluidic impedance flow cytometry, single-cell values of Dc , Csm and σcy were measured as 10.25 ± 0.66 µm, 2.17 ± 0.30 µF/cm2 , and 0.37 ± 0.05 S/m for neutrophils (ncell  = 9442); 9.73 ± 0.51 µm, 2.07 ± 0.19 µF/cm2 , and 0.30 ± 0.04 S/m for eosinophils (ncell  = 2982); 9.75 ± 0.49 µm, 2.06 ± 0.17 µF/cm2 , and 0.31 ± 0.04 S/m for basophils (ncell  = 5377). Based on these inherent single-cell bioelectrical parameters, neural pattern recognition was conducted, producing classification rates of 80.8% (neutrophil vs. eosinophil), 77.7% (neutrophil vs. basophil) and 59.3% (neutrophil vs. basophil). These results indicate that as inherent single-cell bioelectrical parameters, Dc , Csm and σcy can be used to classify neutrophils from eosinophils or basophils to some extent while they cannot be used to effectively distinguish eosinophils from basophils.

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

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.

A Novel Self-Calibration Method and Experiment of MEMS Gyroscope Based on Virtual Coriolis Force.

This paper proposes an effective method to calibrate the microelectromechanical systems (MEMS) vibratory gyroscope based on the virtual Coriolis force. This method utilizes a series of voltage signals to simulate the Coriolis force input, and the gyroscope output is monitored to obtain the scale factor characteristics of the gyroscopes. The scale factor and bias parameters of the gyroscope can be calibrated conveniently and efficiently in the sense-mode open loop. The calibration error of the scale factor based on the turntable and the virtual Coriolis force method is only 1.515%, which proves the correction of the method proposed in this paper. Meanwhile, the non-linearity and bias value of the turntable and the virtual Coriolis force method are 742 ppm and 42.04 mV and 3389 ppm and 0.66 mV, respectively.