Neuromorphic-enabled video-activated cell sorting.

Journal: Nature communications
PMID: 39737963

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.

Authors

  • Weihua He
    Department of Precision Instrument, Tsinghua University, Beijing 100084, China; Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106, USA. Electronic address: hewh16@mails.tsinghua.edu.cn.
  • Junwen Zhu
    State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
  • Yongxiang Feng
    State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China. wwh@tsinghua.edu.cn.
  • Fei Liang
    Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Room 603, No. 6 Tiantan Xili, Dongcheng District, Beijing, China.
  • Kaichao You
    Software School, Tsinghua University, Beijing, China.
  • Huichao Chai
    State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China. wwh@tsinghua.edu.cn.
  • Zhipeng Sui
    State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
  • Haiqing Hao
    State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
  • Guoqi Li
    University of Chinese Academy of Sciences, Beijing 100049, China.
  • Jingjing Zhao
    Department of Medical Oncology, Shanghai Key Laboratory of Medical Epigenetics, Fudan University Shanghai Cancer Center, Institutes of Biomedical Sciences, Fudan University, 270 Dong An Rd, Shanghai, 200032, China.
  • Lei Deng
    1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
  • Rong Zhao
    Pinggu District Center for Disease Control and Prevention, Beijing 101200, China.
  • Wenhui Wang
    Department of Pathology, Hangzhou Women's Hospital, Hangzhou, 310008, Zhejiang, China.

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