He Weihua, Zhu Junwen, Feng Yongxiang, Liang Fei, You Kaichao, Chai Huichao, Sui Zhipeng, Hao Haiqing, Li Guoqi, Zhao Jingjing, Deng Lei, Zhao Rong, Wang Wenhui
State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
Software School, Tsinghua University, Beijing, China.
Nat Commun. 2024 Dec 30;15(1):10792. doi: 10.1038/s41467-024-55094-0.
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.
成像流式细胞术允许进行图像激活细胞分选（IACS），并在细胞形态、结构和组成方面增强特征维度。然而，现有的IACS框架在实时分选操作中面临三维信息丢失和处理延迟困境的挑战。在此，我们建立了一个基于神经形态的视频激活细胞分选仪（NEVACS）框架，旨在实现高维时空表征内容以及在宽视野中对粒子进行高通量分选。NEVACS采用事件相机、中央处理器、部署在神经形态芯片上的脉冲神经网络，并通过相对经济的混合硬件解决方案（控制成本约1万美元）和易于制作和使用的微流体基础设施实现了1000个细胞/秒的分选通量。特别是，NEVACS在对正常红细胞和与血液疾病相关的球形红细胞进行分类中的应用突出了使用视频而非单帧的准确性（即平均误差为0.99%对19.93%），表明NEVACS在细胞形态筛选和疾病诊断方面的潜力。
