.png){kind=icon}
返回
User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm
.png){kind=icon}
PRE
AI
DOI：10.1039/d0lc00747a.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Image-activated cell sorting is an essential biomedical research technique for understanding the unique characteristics of single cells. Deep learning algorithms can be used to extract hidden cell features from high-content image information to enable the discrimination of cell-to-cell differences in image-activated cell sorters. However, such systems are challenging to implement from a technical perspective due to the advanced imaging and sorting requirements and the long processing times of deep learning algorithms. Here, we introduce a user-friendly image-activated microfluidic sorting technique based on a fast deep learning model under the TensorRT framework to enable sorting decisions within 3 ms. The proposed sorter employs a significantly simplified operational procedure based on the use of a syringe connected to a piezoelectric actuator. The sorter has a 2.5 ms latency. The utility of the sorter was demonstrated through real-time sorting of fluorescent polystyrene beads and cells. The sorter achieved 98.0%, 95.1%, and 94.2% sorting purities for 15 mu m and 10 mu m beads, HL-60 and Jurkat cells, and HL-60 and K562 cells, respectively, with a throughput of up to 82.8 events per second (eps).
Keyword:
SINGLE-CELL
GENE-EXPRESSION
FLOW-CYTOMETRY
CHIP
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
Characterization and Transplantation of CD73-Positive Photoreceptors Isolated from Human iPSC-Derived Retinal Organoids.png)
STEM CELL REPORTS
OAAI
STEM CELL REPORTSIF5.1
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Intelligent image-based deformation-assisted cell sorting with molecular specificityNATURE METHODS
OAAI
NATURE METHODSIF32.1
OAAI分享收藏
查看更多内容