A microfluidic cytometer for white blood cell analysis

Computational Hyperspectral Microflow Cytometry

Miniaturized flow cytometry has significant potential for portable applications, such as cell-based diagnostics and the monitoring of therapeutic cell manufacturing, however, the performance of current techniques is often limited by the inability to resolve spectrally-overlapping fluorescence labels. Here, the study presents a computational hyperspectral microflow cytometer (CHC) that enables accurate discrimination of spectrally-overlapping fluorophores labeling single cells. CHC employs a dispersive optical element and an optimization algorithm to detect the full fluorescence emission spectrum from flowing cells, with a high spectral resolution of ≈3 nm in the range from 450 to 650 nm. CHC also includes a dedicated microfluidic device that ensures in-focus imaging through viscoelastic sheathless focusing, thereby enhancing the accuracy and reliability of microflow cytometry analysis. The potential of CHC for analyzing T lymphocyte subpopulations and monitoring changes in cell composition during T cell expansion is demonstrated. Overall, CHC represents a major breakthrough in microflow cytometry and can facilitate its use for immune cell monitoring.

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.

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.

Ultraportable Flow Cytometer Based on an All-Glass Microfluidic Chip

The flow cytometer has become a powerful and widely accepted measurement device in both biological studies and clinical diagnostics. The application of the flow cytometer in emerging point-of-care scenarios, such as instant detection in remote areas and emergency diagnosis, requires a significant reduction in physical dimension, cost, and power consumption. This requirement promotes studies to develop portable flow cytometers, mostly based on the utilization of polymer microfluidic chips. However, due to the relatively poor optical performance of polymer materials, existing microfluidic flow cytometers are incapable of accurate blood analysis, such as the four-part leukocyte differential count, which is necessary to monitor the immune system and to assess the risk of allergic inflammation or viral infection. To address this issue, an ultraportable flow cytometer based on an all-glass microfluidic chip (AG-UFCM) has been developed in this study. Compared with that of a typical commercial flow cytometer (BD FACSAria III), the volume of the AG-UFCM was reduced by 90 times (from 720 to 8 L). A two-step laser processing was employed to fabricate an all-glass microfluidic chip with a surface roughness of less than 1 nm, significantly improving the optical performance of on-chip micro-lens. The signal-to-noise ratio was enhanced by 3 dB, compared with that of polymer materials. For the first time, a four-part leukocyte differential count based on single fluorescence staining was realized using a miniaturized flow cytometer, laying a foundation for the point-of-care testing of miniaturized flow cytometers.

Microfluidic Systems for Blood and Blood Cell Characterization

A laboratory blood test is vital for assessing a patient's health and disease status. Advances in microfluidic technology have opened the door for on-chip blood analysis. Currently, microfluidic devices can reproduce myriad routine laboratory blood tests. Considerable progress has been made in microfluidic cytometry, blood cell separation, and characterization. Along with the usual clinical parameters, microfluidics makes it possible to determine the physical properties of blood and blood cells. We review recent advances in microfluidic systems for measuring the physical properties and biophysical characteristics of blood and blood cells. Added emphasis is placed on multifunctional platforms that combine several microfluidic technologies for effective cell characterization. The combination of hydrodynamic, optical, electromagnetic, and/or acoustic methods in a microfluidic device facilitates the precise determination of various physical properties of blood and blood cells. We analyzed the physical quantities that are measured by microfluidic devices and the parameters that are determined through these measurements. We discuss unexplored problems and present our perspectives on the long-term challenges and trends associated with the application of microfluidics in clinical laboratories. We expect the characterization of the physical properties of blood and blood cells in a microfluidic environment to be considered a standard blood test in the future.

A Study on the Uniform Distribution and Counting Method of Raw Cow's Milk Somatic Cells

The somatic cell count (SCC) in raw milk is an important basis for determining whether a cow is suffering from mastitis. To address the problem of an uneven distribution of somatic cells due to cell-adherent sedimentation, among other reasons, during milk sampling, which in turn results in unrepresentative somatic cell counting, a method is proposed for obtaining a uniform distribution of somatic cells and improving the counting accuracy based on a nine-cell grid microfluidic chip. Firstly, a simulation was performed to verify the uniformity of the somatic cell distribution within the chip observation cavities. Secondly, a nine-cell grid microfluidic chip was prepared and a negative-pressure injection system integrating staining and stirring was developed to ensure that the somatic cells were uniformly distributed and free from air contamination during the injection process. As well as the structure of the chip, a microscopic imaging system was developed, and the nine chip observation cavities were photographed. Finally, the somatic cells were counted and the uniformity of the somatic cell distribution was verified using image processing. The experimental results show that the standard deviation coefficient of the SCC in each group of nine images was less than 1.61%. The automatic counting accuracy of the system was between 97.07% and 99.47%. This research method lays the foundation for the detection and prevention of mastitis in cows.

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 D_c , specific membrane capacitance C_sm and cytoplasmic conductivity σ_cy ) 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 D_c , C_sm and σ_cy were measured as 10.25 ± 0.66 μm, 2.17 ± 0.30 μF/cm² , and 0.37 ± 0.05 S/m for neutrophils (n_cell = 9 442); 9.73 ± 0.51 μm, 2.07 ± 0.19 μF/cm² , and 0.30 ± 0.04 S/m for eosinophils (n_cell = 2 982); 9.75 ± 0.49 μm, 2.06 ± 0.17 μF/cm² , and 0.31 ± 0.04 S/m for basophils (n_cell = 5 377). 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, D_c , C_sm and σ_cy can be used to classify neutrophils from eosinophils or basophils to some extent while they cannot be used to effectively distinguish eosinophils from basophils.