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Intelligent image-activated cell sorting 2.0.
Lab on a Chip ( IF 6.1 ) Pub Date : 2020-05-18 , DOI: 10.1039/d0lc00080a Akihiro Isozaki 1 , Hideharu Mikami , Hiroshi Tezuka , Hiroki Matsumura , Kangrui Huang , Marino Akamine , Kotaro Hiramatsu , Takanori Iino , Takuro Ito , Hiroshi Karakawa , Yusuke Kasai , Yan Li , Yuta Nakagawa , Shinsuke Ohnuki , Tadataka Ota , Yong Qian , Shinya Sakuma , Takeichiro Sekiya , Yoshitaka Shirasaki , Nobutake Suzuki , Ehsen Tayyabi , Tsubasa Wakamiya , Muzhen Xu , Mai Yamagishi , Haochen Yan , Qiang Yu , Sheng Yan , Dan Yuan , Wei Zhang , Yaqi Zhao , Fumihito Arai , Robert E Campbell , Christophe Danelon , Dino Di Carlo , Kei Hiraki , Yu Hoshino , Yoichiroh Hosokawa , Mary Inaba , Atsuhiro Nakagawa , Yoshikazu Ohya , Minoru Oikawa , Sotaro Uemura , Yasuyuki Ozeki , Takeaki Sugimura , Nao Nitta , Keisuke Goda
Lab on a Chip ( IF 6.1 ) Pub Date : 2020-05-18 , DOI: 10.1039/d0lc00080a Akihiro Isozaki 1 , Hideharu Mikami , Hiroshi Tezuka , Hiroki Matsumura , Kangrui Huang , Marino Akamine , Kotaro Hiramatsu , Takanori Iino , Takuro Ito , Hiroshi Karakawa , Yusuke Kasai , Yan Li , Yuta Nakagawa , Shinsuke Ohnuki , Tadataka Ota , Yong Qian , Shinya Sakuma , Takeichiro Sekiya , Yoshitaka Shirasaki , Nobutake Suzuki , Ehsen Tayyabi , Tsubasa Wakamiya , Muzhen Xu , Mai Yamagishi , Haochen Yan , Qiang Yu , Sheng Yan , Dan Yuan , Wei Zhang , Yaqi Zhao , Fumihito Arai , Robert E Campbell , Christophe Danelon , Dino Di Carlo , Kei Hiraki , Yu Hoshino , Yoichiroh Hosokawa , Mary Inaba , Atsuhiro Nakagawa , Yoshikazu Ohya , Minoru Oikawa , Sotaro Uemura , Yasuyuki Ozeki , Takeaki Sugimura , Nao Nitta , Keisuke Goda
Affiliation
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The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software–hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology. Specifically, it provides a high throughput of ∼2000 events per second and a high sensitivity of ∼50 molecules of equivalent soluble fluorophores (MESFs), both of which are 20 times superior to those achieved in previous reports. This is made possible by employing (i) an image-sensor-based optomechanical flow imaging method known as virtual-freezing fluorescence imaging and (ii) a real-time intelligent image processor on an 8-PC server equipped with 8 multi-core CPUs and GPUs for intelligent decision-making, in order to significantly boost the imaging performance and computational power of the iIACS machine. We characterize the iIACS machine with fluorescent particles and various cell types and show that the performance of the iIACS machine is close to its achievable design specification. Equipped with the improved capabilities, this new generation of the iIACS technology holds promise for diverse applications in immunology, microbiology, stem cell biology, cancer biology, pathology, and synthetic biology.
中文翻译：
智能图像激活细胞分选2.0。
智能图像激活细胞分选（iIACS）的出现已实现了对来自异质群体的单个活细胞进行高通量基于智能图像的分选。iIACS是一种片上微流体技术，它基于高通量荧光显微镜，细胞聚焦仪，细胞分选仪和深层神经网络在混合软件-硬件数据管理架构上的无缝集成，从而提供了光学显微镜的综合优点，荧光激活细胞分选（FACS）和深度学习。在这里，我们报告一台iIACS机器，其系统性能远远超过了最新的iIACS机器，从而扩大了该技术支持的应用范围和发现。特别，它提供了每秒约2000个事件的高通量，以及约50个分子的等效可溶性荧光团（MESF）的高灵敏度，两者均比以前的报道要高20倍。通过（i）基于图像传感器的光机流成像方法（称为虚拟冻结荧光成像）和（ii）在配备8个多核CPU的8-PC服务器上使用实时智能图像处理器，可以实现这一点以及用于智能决策的GPU，以显着提高iIACS机器的成像性能和计算能力。我们用荧光颗粒和各种细胞类型对iIACS机器进行了表征，并表明iIACS机器的性能接近其可达到的设计规格。配备了改进的功能，
更新日期：2020-06-30
中文翻译：
智能图像激活细胞分选2.0。
智能图像激活细胞分选（iIACS）的出现已实现了对来自异质群体的单个活细胞进行高通量基于智能图像的分选。iIACS是一种片上微流体技术，它基于高通量荧光显微镜，细胞聚焦仪，细胞分选仪和深层神经网络在混合软件-硬件数据管理架构上的无缝集成，从而提供了光学显微镜的综合优点，荧光激活细胞分选（FACS）和深度学习。在这里，我们报告一台iIACS机器，其系统性能远远超过了最新的iIACS机器，从而扩大了该技术支持的应用范围和发现。特别，它提供了每秒约2000个事件的高通量，以及约50个分子的等效可溶性荧光团（MESF）的高灵敏度，两者均比以前的报道要高20倍。通过（i）基于图像传感器的光机流成像方法（称为虚拟冻结荧光成像）和（ii）在配备8个多核CPU的8-PC服务器上使用实时智能图像处理器，可以实现这一点以及用于智能决策的GPU，以显着提高iIACS机器的成像性能和计算能力。我们用荧光颗粒和各种细胞类型对iIACS机器进行了表征，并表明iIACS机器的性能接近其可达到的设计规格。配备了改进的功能，
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