Single molecule localization microscopy (SMLM) has matured into one of the most powerful and widely used super-resolution imaging methods. In this talk, we'll highlight recent developments of our lab to push the limits of SMLM using computational approaches.
One long-standing challenge is to visualize cells at high resolution and with high throughput. SMLM delivers exquisite spatial resolution, but at the price of very low throughput. Previous approaches to accelerate SMLM typically trade off spatial resolution. We present ANNA-PALM, a computational technique based on deep learning that can reconstruct high resolution views from strongly under-sampled SMLM data and widefield images, enabling considerable speed-ups without any compromise on spatial resolution. We illustrate ANNA-PALM's robustness and potential for high throughput super-resolution imaging and highlight a dedicated web platform (annapalm.pasteur.fr). We will also discuss limitations and perspectives of ANNA-PALM.
Another challenge is to extend SMLM to 3D imaging of entire cells. While many approaches for 3D SMLM have been proposed, the need remains for a more accessible and flexible technique. We present ZOLA-3D a combined optical and computational method that enables versatile 3D super-resolution imaging over up to ~5 um depth. Software and sample data are freely available from github.com/imodpasteur/ZOLA-3D.
Finally, the microscopy field could greatly benefit from easier access to SMLM data generated by the community, especially to train machine learning models. We will briefly highlight shareloc.xyz, an online platform to facilitate the sharing and reanalysis of SMLM data.