| First Author | Zhang P | Year | 2023 |
| Journal | Nat Methods | Volume | 20 |
| Issue | 11 | Pages | 1748-1758 |
| PubMed ID | 37770712 | Mgi Jnum | J:350132 |
| Mgi Id | MGI:7661190 | Doi | 10.1038/s41592-023-02029-0 |
| Citation | Zhang P, et al. (2023) Deep learning-driven adaptive optics for single-molecule localization microscopy. Nat Methods 20(11):1748-1758 |
| abstractText | 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). Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. 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. 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. 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-microm-thick brain tissue specimens. |
