One major reason is the distortion and blurring of single-molecule emission patterns (that is, point spread functions (PSFs)) caused by the inhomogeneous refractive indices within the tissue. SMLM in tissues, however, is challenging. Localization precision as low as 1–10 nm can be achieved in fixed and living cells 13, 14, 15, 16. The unique advantage of SMLM lies in measuring individual molecules without ensemble averaging and, therefore, its potential in molecular counting and ultra-high resolution in both live and fixed specimens 11, 12. In particular, SMLM detects individual molecules using photo-switchable or convertible fluorescent dyes or proteins, pinpoints the centers of probes from their emission patterns and reconstructs the molecular centers into a super-resolution image. Super-resolution microscopies such as stimulated emission depletion microscopy 2, structured illumination microscopy 3 and SMLM 4, 5, 6 have overcome this barrier, allowing biological observations 7, 8, 9, 10 well beyond this fundamental limit of light. Molecular features smaller than this limit cannot be resolved. We demonstrated that our method simultaneously estimates and compensates 28 wavefront deformation shapes and improves the resolution and fidelity of three-dimensional SMLM through >130-µm-thick brain tissue specimens.įluorescence microscopy is an indispensable tool in visualizing cellular and tissue machinery with molecular specificity however, in its conventional form, the resolution is limited to 250–700 nm laterally and axially due to the diffraction of light 1. Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. ![]() To bypass iterative trial-then-evaluate processes, we developed deep learning-driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM).
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