AutoSTED: An automated workflow for STED super-resolution imaging of cell nuclei

Abstract

The traditional microscopy workflow involves manual selection of objects one after the other. This is not only time consuming, but inevitably introduces human bias. Here, we describe a workflow, AutoSTED for identification of cell nuclei on wide-field fluorescence images and subsequent automated imaging of these nuclei at super-resolution by Stimulated Emission Depletion (STED) microscopy. We demonstrate that in an overnight run we are able to image hundreds of cells selected without bias. The resolution afforded by STED allowed straightforward identification and counting of individual nuclear pore complexes (NPCs), and enabled us to automatically extract parameters such as NPC density under different experimental conditions. Together, we illustrate how automation enables optimal use of instrument time to produce large unbiased datasets.