Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning
Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning
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DOI：
10.1038/s41556-021-00802-x
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发表时间：
2021-12-07
影响因子：
21.3
通讯作者：
Park, YongKeun
中科院分区：
文献类型：
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作者：
Jo, YoungJu;Cho, Hyungjoo;Park, YongKeun
在多个时空尺度上同时成像完整生物系统的各个方面是生物学和医学的长期目标，这方面的进展受到传统成像方式的限制。在这里，我们建议使用折射率（RI），一个控制光-物质相互作用的固有量，作为这种测量的手段。我们展示了主要的内源性亚细胞结构，通常通过外源性荧光标记进入，在三维（3D） RI层析图中编码。我们以数据驱动的方式解码这些信息，使用基于深度学习的模型，从相应亚细胞目标的RI测量推断出多个3D荧光层析图，从而实现多路微层析成像。这种方法被称为RI2FL（荧光折射率），它继承了高特异性荧光成像和无标记RI成像的优点。重要的是，绝对无偏RI的全3D建模提高了泛化，使得该方法适用于大范围的新样本，而无需重新训练，以促进立即适用性。该技术的性能、可靠性和可扩展性得到了广泛的表征，并在前所未有的规模（可以产生新的实验可测试的假设）的单细胞分析中展示了其各种应用。Jo等人开发了一种广泛适用的深度学习方法，基于无标记折射率（RI）测量来预测荧光（FL）， ‘RI2FL’ （RI to FL）。经过训练的模型可以跨细胞类型使用，而无需再训练。
Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is hindered by limits of conventional imaging modalities. Here we propose using the refractive index (RI), an intrinsic quantity governing light-matter interaction, as a means for such measurement. We show that major endogenous subcellular structures, which are conventionally accessed via exogenous fluorescence labelling, are encoded in three-dimensional (3D) RI tomograms. We decode this information in a data-driven manner, with a deep learning-based model that infers multiple 3D fluorescence tomograms from RI measurements of the corresponding subcellular targets, thereby achieving multiplexed microtomography. This approach, called RI2FL for refractive index to fluorescence, inherits the advantages of both high-specificity fluorescence imaging and label-free RI imaging. Importantly, full 3D modelling of absolute and unbiased RI improves generalization, such that the approach is applicable to a broad range of new samples without retraining to facilitate immediate applicability. The performance, reliability and scalability of this technology are extensively characterized, and its various applications within single-cell profiling at unprecedented scales (which can generate new experimentally testable hypotheses) are demonstrated.Jo et al. develop a broadly applicable deep-learning approach to predict fluorescence (FL) based on label-free refractive index (RI) measurements, 'RI2FL' (RI to FL). The trained model can be used across cell types without retraining.