Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning

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.

Automated summary

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.