.png){kind=icon}
返回
Intelligent image-activated cell sorting 2.0
.png){kind=icon}
OA
AI
DOI：10.1039/d0lc00080a.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 similar to 2000 events per second and a high sensitivity of similar to 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.
Keyword:
FLOW-CYTOMETRY
IDENTIFICATION
FLUORESCENT
MICROSCOPY
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
L
IF:
5.4
论文数:
9.0K
被引数:
3.3W
机构
引用论文
分享收藏分享收藏Genomic Profiling of Isolated Circulating Tumor Cells from Metastatic Breast Cancer Patients.png)
CANCER RESEARCH
OAAI
CANCER RESEARCHIF16.6
OAAI分享收藏Flow cytometry and FISH to measure the average length of telomeres (flow FISH)NATURE PROTOCOLS
NATURE PROTOCOLSIF16
PREAI
分享收藏De novo synthesized Min proteins drive oscillatory liposome deformation and regulate FtsA-FtsZ cytoskeletal patternsNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏On-chip cell sorting by high-speed local-flow control using dual membrane pumpsLAB ON A CHIP
LAB ON A CHIPIF5.4
PREAI
分享收藏
查看更多内容