Although label-free cell sorting is desirable for providing pristine cells for further analysis or use, current approaches lack molecular specificity and speed. Here, we combine real-time fluorescence and deformability cytometry with sorting based on standing surface acoustic waves and transfer molecular specificity to image-based sorting using an efficient deep neural network. In addition to general performance, we demonstrate the utility of this method by sorting neutrophils from whole blood without labels.

中文翻译：

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具有分子特异性的基于图像的智能形变辅助细胞分选。

尽管无标记细胞分选对于提供原始细胞用于进一步分析或使用是合乎需要的，但是当前的方法缺乏分子特异性和速度。在这里，我们将实时荧光和可变形细胞术与基于立面声波的分类相结合，并使用高效的深度神经网络将分子特异性转移至基于图像的分类。除一般性能外，我们还通过无标记的全血中性粒细胞分选展示了该方法的实用性。