🔬🤖Excited to share permittivity tensor imaging (PTI), a label-free computational imaging module for high-resolution 3D imaging of dry mass and 3D orientation of organelles, cells, and tissues! 🧬🧫 Just published in Nature Methods Shout out to Li-Hao Yeh , Talon Chandler, Ivan Ivanov, Janie Byrum, Bryant , Syuan-Ming Guo,... Cameron Foltz at Chan Zuckerberg Biohub Network, Peter Kazansky lab ORC Southampton, BruceConklin lab at Gladstone Institutes, and May Han lab at Stanford Medicine. 🧵1/nshow more

Permittivity tensor is a physical property of soft matter that reports the distribution and alignment of biomolecules. For most biological materials, it is a square of the refractive index tensor. Here is a neat illustration, in collaboration with @DrLachie, that shows how a cylindrical lipid bilayer maps to the 3D distribution of the permittivity tensor when it scatters light. If you're curious about why the refractive index is a tensor, check out Feynman's lecture on the origin of the refractive index: 2/n

PTI acquires all of these label-free channels using a modular imaging path. 🔧 Here is a build video illustrating all the components involved in integrating PTI into an existing research microscope. 3/n

PTI encodes the invisible permittivity tensor into visible images using oblique illumination and a polarization-resolved camera. The permittivity tensor is decoded from the volumes with a physics-based image formation model and an inverse algorithm. The combined design of the optical path and algorithms lets us shift the design complexity from the atoms to the bits. 💡🙌 4/n

We share the image formation models and reconstruction algorithms through our vector diffraction library, waveOrder, implemented using PyTorch. Check it out here: Kudos to @talonchandler for his work in making this a performant and readable library that we use every day. 5/n

We are thrilled about the biological data we can obtain with PTI: it enabled high-resolution mapping of the cytopathic effects of SARS-CoV-2 infection on iPSC-derived cardiomyocytes in collaboration with the @BruceConklin lab. We see the 3D distribution of sarcomeres more accurately with PTI than with antibody labeling. 6/n

PTI enabled label-free imaging of changes in the nucleus, nucleoli, cytoskeleton, and cell membrane of epithelial cells (A549) caused by respiratory syncytial virus (RSV) in collaboration with our colleagues @czbiohub. 7/n

The state-of-the-art optical sectioning of PTI allowed 3D imaging of axons in mouse brain tissue sections, capturing architectural details from single axons to whole slices. 8/n

The 3D resolution of PTI is high enough to visualize the complex 3D distribution of axons. Some axons oriented along the z-axis are not visible with many other label-free imaging technologies. Here is an XY, XZ, and YZ fly-through of a volume. 9/n

PTI is easy to multiplex with fluorescence and H&E imaging, allowing label-agnostic mapping of cell and tissue architecture. In this movie, we image a standard H&E stained cardiac tissue slice. 10/n

As I mentioned before, the design complexity lies in the algorithms and calibration. @LiHao_Yeh validated the wave optical model of the microscope and the reconstruction algorithm with methodical simulations of isotropic beads and an anisotropic patterns. 🌊💡 11/n

We needed a calibration target to evaluate the measurements of 3D dry mass and 3D orientation made with PTI. In addition to biological structures of known architecture and isotropic beads, we used anisotropic glass targets built by the Peter Kazansky lab @orctweets. Intriguing side note: anisotropic glass is emerging as a solution for high-density storage. Learn more here: 12/n

@LiHao_Yeh @orctweets This is one of those projects that has taken several iterations and years to finish - look at the timestamps! Special thanks to @rita_strack for being an amazing editor throughout this process. 13/n

Successful execution required the integration of ideas across optics, algorithms, automation, high-performance computing, cell biology, and neuropathology. I am thankful to my colleagues for their expertise and for placing this bet with me. 14/n

Finally, thanks to Priscilla Chan and Mark Zuckerberg for their support, and to the leadership at @czbiohub for creating an environment where partnerships between technologists and biologists are the norm. /end