Fast Label-Free Nanoscale Composition Mapping of Eukaryotic Cells Via Scanning Dielectric Force Volume Microscopy and Machine Learning
Fast Label-Free Nanoscale Composition Mapping of Eukaryotic Cells Via Scanning Dielectric Force Volume Microscopy and Machine Learning
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DOI：
10.1002/smtd.202100279
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发表时间：
2021-05-16
期刊：
影响因子：
12.4
通讯作者：
Gomila, Gabriel
中科院分区：
文献类型：
--
作者：
Checa, Marti;Millan-Solsona, Ruben;Gomila, Gabriel
在不使用外源标记的情况下绘制真核细胞的生化组成图是细胞生物学长期追求的目标。最近，研究表明，可以从扫描介电显微镜获得的定量纳米级介电常数图推断出具有纳米级空间分辨率的干燥单个细菌细胞的成分图。在这里，研究表明，这种方法也可以应用于更具挑战性的固定和干燥真核细胞的情况，这些细胞具有高度异质性并显示出微米形貌变化。更重要的是，事实证明，该技术的主要瓶颈（提取纳米级介电常数图所需的长计算时间）可以通过使用监督神经网络来缩短，将工作站计算机中的计算时间从几周缩短到几秒钟。这种易于使用的数据驱动方法为利用扫描介电显微镜对真核细胞进行原位和动态无标记纳米级成分图谱打开了大门。
Mapping the biochemical composition of eukaryotic cells without the use of exogenous labels is a long-sought objective in cell biology. Recently, it has been shown that composition maps on dry single bacterial cells with nanoscale spatial resolution can be inferred from quantitative nanoscale dielectric constant maps obtained with the scanning dielectric microscope. Here, it is shown that this approach can also be applied to the much more challenging case of fixed and dry eukaryotic cells, which are highly heterogeneous and show micrometric topographic variations. More importantly, it is demonstrated that the main bottleneck of the technique (the long computation times required to extract the nanoscale dielectric constant maps) can be shortcut by using supervised neural networks, decreasing them from weeks to seconds in a wokstation computer. This easy-to-use data-driven approach opens the door for in situ and on-the-fly label free nanoscale composition mapping of eukaryotic cells with scanning dielectric microscopy.