Neuromorphic-enabled video-activated cell sorting

Weihua He, Junwen Zhu, Yongxiang Feng, Fei Liang, Kaichao You, Huichao Chai, Zhipeng Sui, Haiqing Hao, Guoqi Li, Jingjing Zhao, Lei Deng, Rong Zhao and Wenhui Wang ()
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Weihua He: Tsinghua University
Junwen Zhu: Tsinghua University
Yongxiang Feng: Tsinghua University
Fei Liang: Tsinghua University
Kaichao You: Tsinghua University
Huichao Chai: Tsinghua University
Zhipeng Sui: Tsinghua University
Haiqing Hao: Tsinghua University
Guoqi Li: Chinese Academy of Sciences
Jingjing Zhao: Huazhong University of Science and Technology
Lei Deng: Tsinghua University
Rong Zhao: Tsinghua University
Wenhui Wang: Tsinghua University

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature dimensions in cellular morphology, structure, and composition. However, existing IACS frameworks suffer from the challenges of 3D information loss and processing latency dilemma in real-time sorting operation. Herein, we establish a neuromorphic-enabled video-activated cell sorter (NEVACS) framework, designed to achieve high-dimensional spatiotemporal characterization content alongside high-throughput sorting of particles in wide field of view. NEVACS adopts event camera, CPU, spiking neural networks deployed on a neuromorphic chip, and achieves sorting throughput of 1000 cells/s with relatively economic hybrid hardware solution (~$10 K for control) and simple-to-make-and-use microfluidic infrastructures. Particularly, the application of NEVACS in classifying regular red blood cells and blood-disease-relevant spherocytes highlights the accuracy of using video over a single frame (i.e., average error of 0.99% vs 19.93%), indicating NEVACS’ potential in cell morphology screening and disease diagnosis.

Date: 2024
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DOI: 10.1038/s41467-024-55094-0

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