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.

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.

Development of constrictional microchannels and the recurrent neural network in single-cell protein analysis

Introduction: As the golden approach of single-cell analysis, fluorescent flow cytometry can estimate single-cell proteins with high throughputs, which, however, cannot translate fluorescent intensities into protein numbers. Methods: This study reported a fluorescent flow cytometry based on constrictional microchannels for quantitative measurements of single-cell fluorescent levels and the recurrent neural network for data analysis of fluorescent profiles for high-accuracy cell-type classification. Results: As a demonstration, fluorescent profiles (e.g., FITC labeled β-actin antibody, PE labeled EpCAM antibody and PerCP labeled β-tubulin antibody) of individual A549 and CAL 27 cells were firstly measured and translated into protein numbers of 0.56 ± 0.43 × 10⁴, 1.78 ± 1.0⁶ × 10⁶ and 8.11 ± 4.89 × 10⁴ of A549 cells (n_cell = 10232), and 3.47 ± 2.45 × 10⁴, 2.65 ± 1.19 × 10⁶ and 8.61 ± 5.25 × 10⁴ of CAL 27 cells (n_cell = 16376) based on the equivalent model of the constrictional microchannel. Then, the feedforward neural network was used to process these single-cell protein expressions, producing a classification accuracy of 92.0% for A549 vs. CAL 27 cells. In order to further increase the classification accuracies, as a key subtype of the recurrent neural network, the long short-term memory (LSTM) neural network was adopted to process fluorescent pulses sampled in constrictional microchannels directly, producing a classification accuracy of 95.5% for A549 vs. CAL 27 cells after optimization. Discussion: This fluorescent flow cytometry based on constrictional microchannels and recurrent neural network can function as an enabling tool of single-cell analysis and contribute to the development of quantitative cell biology.