Abstract
We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue.
Highlights
The function of living systems emerges from dynamic interaction of components over spatial and temporal scales that range many orders of magnitude
We previously developed Stokes representation of fluorescence polarization for simultaneous recovery of concentration, alignment, and orientation of fluorophores imaged with instantaneous fluorescence polarization microscope [42]
Our approach enables simultaneous measurement of phase, retardance, orientation, and degree of polarization contrasts with diffractionlimited spatial resolution
Summary
The function of living systems emerges from dynamic interaction of components over spatial and temporal scales that range many orders of magnitude. Molecular components of biological systems can be identified with scalable genomic and proteomic technologies, these technologies cannot capture dynamic interactions of components and are destructive measurements. Used fluorescence microscopy methods report on labeled molecules within the crowded environment of biological system. It is difficult to visualize more than 7 components in a live sample [1] due to the stochastic nature of labeling, photo-toxicity, and the broad spectra of fluorescent proteins. Label-free imaging enables simultaneous and reproducible visualization of many biological structures with minimal photo-toxicity by measuring intrinsic physical properties of the sample. Label-free microscopy with phase contrast [2], differential interference contrast (DIC) [3], and polarization contrast [4, 5], has enabled discoveries of biological processes for almost a century
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