Intelligent sort‐timing prediction for image‐activated cell sorting
Intelligent sort‐timing prediction for image‐activated cell sorting
复制标题
图像激活细胞分选的智能分选时间预测
DOI：
10.1002/cyto.a.24664
复制
发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Goda Keisuke
中科院分区：
文献类型：
--
作者：
Zhao Yaqi;Isozaki Akihiro;Herbig Maik;Hayashi Mika;Hiramatsu Kotaro;Yamazaki Sota;Kondo Naoko;Ohnuki Shinsuke;Ohya Yoshikazu;Nitta Nao;Goda Keisuke
智能图像激活细胞分选（iIACS）利用人工智能（AI）算法实现了单细胞的高通量图像分选。这种人工智能芯片技术结合了荧光显微镜，基于人工智能的图像处理，排序时间预测和细胞分选。排序时间预测是特别重要的，因为在图像采集和排序驱动之间有毫秒级的延迟，在此期间执行图像处理。长潜伏期放大了细胞流速波动的影响，导致细胞到达微流控芯片上排序点的时间存在波动和不确定性。为了补偿这种波动，iIACS测量每个细胞上游的流速，预测细胞到达排序点的时间，并适当地激活细胞分选器的驱动。在这里，我们提出并演示了一种机器学习技术，以提高排序时间预测的准确性，从而提高排序事件率、产量和纯度。具体来说，我们训练了一个算法来预测形态异质芽殖酵母细胞的排序时间。我们开发的算法使用细胞形态、位置和流速作为预测输入，与之前仅基于流速的方法相比，预测误差降低了41.5%。因此，我们的技术将允许将iIACS的排序事件率提高约2倍。
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.