Revealing architectural order with quantitative label-free imaging and deep learning

Author:

Guo Syuan-Ming1,Yeh Li-Hao1ORCID,Folkesson Jenny1ORCID,Ivanov Ivan E1,Krishnan Anitha P1,Keefe Matthew G2,Hashemi Ezzat3,Shin David2,Chhun Bryant B1,Cho Nathan H1,Leonetti Manuel D1,Han May H3,Nowakowski Tomasz J2,Mehta Shalin B1ORCID

Affiliation:

1. Chan Zuckerberg Biohub, San Francisco, United States

2. Department of Anatomy, University of California, San Francisco, San Francisco, United States

3. Department of Neurology, Stanford University, Stanford, United States

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.

Funder

Chan Zuckerberg Biohub

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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