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

José M. Serra Lleti, Anna M. Steyer*, Nicole L. Schieber, Beate Neumann, Christian Tischer, Volker Hilsenstein, Mike Holtstrom, David Unrau, Robert Kirmse, John M. Lucocq, Rainer Pepperkok, Yannick Schwab*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

In recent years, Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) has emerged as a flexible method that enables semi-automated volume ultrastructural imaging. We present a toolset for adherent cells that enables tracking and finding cells, previously identified in light microscopy (LM), in the FIB-SEM, along with the automatic acquisition of high-resolution volume datasets. We detect the underlying grid pattern in both modalities (LM and EM), to identify common reference points. A combination of computer vision 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 microscope’s field of view reducing or even eliminating operator supervision. We show the ability to target the regions of interest in EM within 5 µm accuracy while iterating between different targets and implementing unattended data acquisition. Our results demonstrate that executing volume acquisition in multiple locations autonomously is possible in EM.
Original languageEnglish
Article numbere202209127
Number of pages26
JournalJournal of Cell Biology
Volume222
Issue number3
Early online date23 Dec 2022
DOIs
Publication statusPublished - 6 Mar 2023

Keywords

  • Cell biology

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