Intelligent sort‐timing prediction for image‐activated cell sorting

分类 分类 计算机科学 排序算法 图像(数学) 人工智能 模式识别(心理学) 计算生物学 生物 情报检索 算法
作者
Yaqi Zhao,Akihiro Isozaki,Maik Herbig,Mika Hayashi,K. Hiramatsu,Sota Yamazaki,Naoko Kondo,Shinsuke Ohnuki,Yoshikazu Ohya,Nao Nitta,Keisuke Goda
出处
期刊:Cytometry Part A [Wiley]
卷期号:103 (1): 88-97 被引量:5
标识
DOI:10.1002/cyto.a.24664
摘要

Abstract Intelligent image‐activated cell sorting (iIACS) has enabled high‐throughput image‐based sorting of single cells with artificial intelligence (AI) algorithms. This AI‐on‐a‐chip technology combines fluorescence microscopy, AI‐based image processing, sort‐timing prediction, and cell sorting. Sort‐timing prediction is particularly essential due to the latency on the order of milliseconds between image acquisition and sort actuation, during which image processing is performed. The long latency amplifies the effects of the fluctuations in the flow speed of cells, leading to fluctuation and uncertainty in the arrival time of cells at the sort point on the microfluidic chip. To compensate for this fluctuation, iIACS measures the flow speed of each cell upstream, predicts the arrival timing of the cell at the sort point, and activates the actuation of the cell sorter appropriately. Here, we propose and demonstrate a machine learning technique to increase the accuracy of the sort‐timing prediction that would allow for the improvement of sort event rate, yield, and purity. Specifically, we trained an algorithm to predict the sort timing for morphologically heterogeneous budding yeast cells. The algorithm we developed used cell morphology, position, and flow speed as inputs for prediction and achieved 41.5% lower prediction error compared to the previously employed method based solely on flow speed. As a result, our technique would allow for an increase in the sort event rate of iIACS by a factor of ~2.
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