德国德累斯顿工业大学分子与细胞生物工程中心Jochen Guck研究组在研究中取得进展。他们开发出具有分子特异性的智能图像变形工具可辅助细胞分选。相关论文于2020年5月25日发表在《自然—方法学》杂志上。

他们将实时荧光和可变形细胞术与基于立面声波的分选相结合，并使用高效的深度神经网络将分子特异性转换成基于图像的分选。除一般性能外，他们通过从无标记的全血中分选嗜中性粒细胞来证明该方法的实用性。

据介绍，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。

附：英文原文

Title: Intelligent image-based deformation-assisted cell sorting with molecular specificity

Author: Ahmad Ahsan Nawaz, Marta Urbanska, Maik Herbig, Martin Ntzel, Martin Krter, Philipp Rosendahl, Christoph Herold, Nicole Toepfner, Markta Kubnkov, Ruchi Goswami, Shada Abuhattum, Felix Reichel, Paul Mller, Anna Taubenberger, Salvatore Girardo, Angela Jacobi, Jochen Guck

Issue&Volume: 2020-05-25

Abstract: 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.

DOI: 10.1038/s41592-020-0831-y

Source: https://www.nature.com/articles/s41592-020-0831-y