Nanocubic · Nanoscale Isotropic 3D Resolution using Omni-view Structured Light Sheet Microscopy

After spectacular advances in microscopy, a next leap is needed to enable biological studies at the deep sub-cellular scale of cells in their native multi-cellular tissue environment. Currently, it is impossible to image in 3D with isotropic resolutions below 100 nm over volumes larger than (100 micron)^3. I want to disprove the paradigm that there should always be a trade-off between resolution, imaging volume and sample longevity. Building on my track record in super-resolution microscopy, I propose a revolutionary optical architecture where the volume is illuminated and scanned sequentially using three tilted 2D light sheets that form three mutually orthogonal planes. These light sheets are created by illumination from both sides of the sample to form interference-based light patterns with fine 200 nm scale pitch. Together with new structured illumination image reconstructions this enables a 3D fully isotropic resolution twice as good as conventional light sheet microscopy. In a second conceptual advance, the patterned light sheets are applied in single-molecule imaging to achieve 3D isotropic enhanced localization precision at the 1 nm level, an order of magnitude better than current methods. The omni-directional illumination and imaging also enables refractive index tomography, leading to time and space-variant point spread functions. These are used in-silico in first-ever super-resolution image reconstructions that are fully adapted to volumetric sample heterogeneity. In a final step, the tomographic refractive index data is projected to spatio-temporal aberration maps that are fed into adaptive optics for dynamic correction of sample-induced aberrations to boost super-resolution imaging of live cells over extended times. The envisioned suite of information-optimal computational microscopy modalities, rooted in fundamental physics, not only overcomes imaging barriers but also paves the way to break open black box approaches in image reconstruction.