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-µm-thick brain tissue specimens.

We present a method for the fabrication of an arrayed one-axis scale grating for a two-probe optical linear encoder using a dual-beam interference lithography (IL) system with a compact diode laser source. We employ a multiple-exposure stitching method to form an arrayed scale grating. This allows a scale grating with small line spacing to be achieved over a large width. This stitched scale grating integrates well with a newly developed two-probe optical encoder, allowing the measurement results to be numerically connected. Since neither the gap width nor the grating phase of the two adjacent gratings must be controlled, the fabrication process is both simplified and made more robust. This flexible and cost-effective fabrication technique can benefit many precision measurement applications. Experiments are carried out to demonstrate the feasibility of this technology.

Single-molecule localization microscopy (SMLM) surpasses the diffraction limit by randomly switching fluorophores between fluorescent and dark states, precisely pinpointing the resulted isolated emission patterns, thereby reconstructing the super-resolution images based on the accumulated locations of thousands to millions of single molecules. This technique achieves a ten-fold improvement in resolution, unveiling the intricate details of molecular activities and structures in cells and tissues. Multicolor SMLM extends this capability by imaging distinct protein species labeled with various fluorescent probes, providing insights into structural intricacies and spatial relationships among different targets. This review explores recent advancements in multicolor SMLM, evaluates the strengths and limitations of each variant, and discusses the future prospects.

Assessing the quality of localisation microscopy images is highly challenging due to the difficulty in reliably detecting errors in experimental data. The most common failure modes are the biases and errors produced by the localisation algorithm when there is emitter overlap. Also known as the high density or crowded field condition, significant emitter overlap is normally unavoidable in live cell imaging. Here we use Haar wavelet kernel analysis (HAWK), a localisation microscopy data analysis method which is known to produce results without bias, to generate a reference image. This enables mapping and quantification of reconstruction bias and artefacts common in all but low emitter density data. By avoiding comparisons involving intensity information, we can map structural artefacts in a way that is not adversely influenced by nonlinearity in the localisation algorithm. The HAWK Method for the Assessment of Nanoscopy (HAWKMAN) is a general approach which allows for the reliability of localisation information to be assessed.