CLEMSite, a software for automated phenotypic screens using light microscopy and FIB-SEM

Abstract

Correlative light and electron microscopy (CLEM) combines two imaging modalities, balancing out the limits of one technique with the other. In recent years, Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) has emerged as a flexible method that enables semi-automated volume acquisition at the ultrastructural level. We present a toolset for adherent cultured cells that enables tracking and finding cell regions previously identified in light microscopy, in the FIB-SEM along with automatic acquisition of high-resolution volume datasets. We detect a grid pattern in both modalities (LM and EM), which identifies common reference points. The novel combination of these techniques enables complete automation of the workflow. This includes setting the coincidence point of both ion and electron beams, automated evaluation of the image quality and constantly tracking the sample position with the microscopes field of view reducing or even eliminating operator supervision. We show the ability to target the regions of interest in EM within 5 {micro}m accuracy, while iterating between different targets and implementing unattended data acquisition. Our results demonstrate that executing high throughput volume acquisition in electron microscopy is possible.