YoungJu Jo 1,2,3,8,9, Hyungjoo Cho3,9, Wei Sun Park1,2,9, Geon Kim1,2, DongHun Ryu1,2, Young Seo Kim1,2,4, Moosung Lee1,2, Sangwoo Park5, Mahn Jae Lee2,4, Hosung Joo3, HangHun Jo3, Seongsoo Lee5, Sumin Lee 3, Hyun-seok Min3,10,*, Won Do Heo 6,7,10,* and YongKeun Park 1,2,3,10,*
1Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. 2KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea. 3Tomocube, Daejeon, Republic of Korea. 4Graduate School of Medial Science and Engineering, KAIST, Daejeon, Republic of Korea. 5Gwangju Center, Korea Basic Science Institute (KBSI), Gwangju, Republic of Korea. 6Department of Biological Sciences, KAIST, Daejeon, Republic of Korea. 7KAIST Institute for the BioCentury, KAIST, Daejeon, Republic of Korea. 8Present address: Departments of Applied Physics and of Biology, Stanford University, Stanford, CA, USA. 9These authors contributed equally:YoungJu Jo, Hyungjoo Cho, Wei Sun Park. 10These authors jointly supervised this work: Hyun-seok Min, Won Do Heo, YongKeun Park.
*Correspondence to Hyun-seok Min, Won Do Heo or YongKeun Park.
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.