Inherent Bioelectrical Parameters of Hundreds of Thousands of Single Leukocytes Based on Impedance Flow Cytometry

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.

Electrical micro flow cytometry with LSTM and its application in leukocyte differential

This paper developed an electrical micro flow cytometry to realize leukocyte differentials leveraging a constrictional microchannel and a deep neural network. Firstly, purified granulocytes, lymphocytes or monocytes traveled through the constrictional microchannel with a cross-sectional area marginally larger than individual cells and produced large impedance variations by blocking focused electric field lines. By optimizing key elements (e.g., normalization, learning rate, batch size and neuron number) of the recurrent neural network (RNN), electrical results of purified leukocytes were analyzed to establish a leukocyte differential system with a classification accuracy of 95.2%. Then the leukocyte mixtures were forced to travel through the same constrictional microchannel, producing mixed impedance profiles which were classified into granulocytes, lymphocytes and monocytes based on the aforementioned differential system. As to the classification results, two leukocyte mixtures from the same donor were processed, producing comparable classification results, which were 57% versus 59% of granulocytes, 37% versus 34% of lymphocytes and 6% versus 7% of monocytes. These results validated the established classification system based on the constrictional microchannel and the recurrent neural network, providing a new perspective of differentiating white blood cells by electrical flow cytometry.

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

Blood tests are considered as standard clinical procedures to screen for markers of diseases and health conditions. However, the complex cellular background (>99.9% RBCs) and biomolecular composition often pose significant technical challenges for accurate blood analysis. An emerging approach for point-of-care blood diagnostics is utilizing "label-free" microfluidic technologies that rely on intrinsic cell properties for blood fractionation and disease detection without any antibody binding. A growing body of clinical evidence has also reported that cellular dysfunction and their biophysical phenotypes are complementary to standard hematoanalyzer analysis (complete blood count) and can provide a more comprehensive health profiling. In this review, we will summarize recent advances in microfluidic label-free separation of different blood cell components including circulating tumor cells, leukocytes, platelets and nanoscale extracellular vesicles. Label-free single cell analysis of intrinsic cell morphology, spectrochemical properties, dielectric parameters and biophysical characteristics as novel blood-based biomarkers will also be presented. Next, we will highlight research efforts that combine label-free microfluidics with machine learning approaches to enhance detection sensitivity and specificity in clinical studies, as well as innovative microfluidic solutions which are capable of fully integrated and label-free blood cell sorting and analysis. Lastly, we will envisage the current challenges and future outlook of label-free microfluidics platforms for high throughput multi-dimensional blood cell analysis to identify non-traditional circulating biomarkers for clinical diagnostics.

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.