Paper

uPTI: uniaxial permittivity tensor imaging of intrinsic density and anisotropy

Li-Hao, Yeh, Ivanov, Ivan E, Byrum, Janie R, Conklin, Bruce R, Pérez-Bermejo, Juan A, Wang, Huijun, Yu, Yenhao, Han, May H, Chhun, Bryant B, Guo, Syuan-Ming, Hashemi, Ezzat, Foltz, Cameron, Mehta, Shalin B, Kazansky, Peter G

2021

Overview

Biological architecture is intrinsically tensorial. The permittivity tensor (PT) of biological material reports the density, angular anisotropy, symmetry, and 3D orientation of biomolecules. High-resolution measurement of PT can enable quantitative and label-free analysis of organelle, cell, and tissue architecture, but remains challenging. We report uniaxial permittivity tensor imaging (uPTI), a label-free computational imaging method for volumetric measurement of PT with diffraction-limited resolution. uPTI encodes the components of PT into intensity modulations using oblique illumination and polarization-resolved imaging. The high-dimensional data is decoded with a vectorial image formation model and a multi-channel convex optimization, assuming that the molecular distribution in each voxel has uniaxial symmetry. We describe a modular implementation of uPTI that can be multiplexed with complementary imaging modalities. We report volumes of uPT in mouse brain tissue, SARS-CoV-2 infected cardiomyocytes, RSV infected A549 cells, H&E stained tissue sections, isotropic beads, and anisotropic glass targets. uPTI enabled volumetric imaging of the 3D orientation and symmetry of organelles, cells, and tissue components with higher spatio-angular resolution than current vectorial tomography, ptychography, and light-field microscopy methods. We provide an open source implementation of the image formation model and reconstruction algorithms. Competing Interest Statement A patent filed by the Chan Zuckerberg Biohub with S.B.M, L.H, and I.E.I as inventors is pending and describes the uPTI method reported in this paper. B.R.C. is a founder of Tenaya Therapeutics (https://www.tenayatherapeutics.com/), a company focused on finding treatments for heart failure, including genetic cardiomyopathies. Other authors declare no competing interests. Footnotes * Main figures and text are revised to clarify the image formation, reconstruction, benchmarking, and applications. New applications of the technology are reported in Figs. 4, 5 and 6. Multiple supplemental videos and figures are updated or added. * https://github.com/mehta-lab/waveorder

• Publisher: Cold Spring Harbor Laboratory Press,Cold Spring Harbor Laboratory
• Subject: Algorithms / Anisotropy / Biophysics / Cardiomyocytes / Congestive heart failure / Diffraction / Image processing / Neuroimaging / Organelles / Polystyrene / Spatial discrimination