Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning.
Journal:
Nature cell biology
PMID:
34876684
Abstract
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.
Authors
Keywords
3T3 Cells
Actins
Animals
Cell Line, Tumor
Cell Membrane
Cell Nucleolus
Cell Nucleus
Chlorocebus aethiops
COS Cells
Deep Learning
Electron Microscope Tomography
HEK293 Cells
HeLa Cells
Humans
Imaging, Three-Dimensional
Lipid Droplets
Mice
Mitochondria
Optical Imaging
Refractometry
Single-Cell Analysis
Subcellular Fractions