A modular platform for automated cryo-FIB workflows
- Max Planck Institute of Biochemistry, Germany
- European Molecular Biology Laboratory, Germany
- Max Planck Institute of Biophysics, Germany
Abstract
Lamella micromachining by focused ion beam milling at cryogenic temperature (cryo-FIB) has matured into a preparation method widely used for cellular cryo-electron tomography. Due to the limited ablation rates of low Ga+ ion beam currents required to maintain the structural integrity of vitreous specimens, common preparation protocols are time-consuming and labor intensive. The improved stability of new generation cryo-FIB instruments now enables automated operations. Here, we present an open-source software tool, SerialFIB, for creating automated and customizable cryo-FIB preparation protocols. The software encompasses a graphical user interface for easy execution of routine lamellae preparations, a scripting module compatible with available Python packages, and interfaces with 3-dimensional correlative light and electron microscopy (CLEM) tools. SerialFIB enables the streamlining of advanced cryo-FIB protocols such as multi-modal imaging, CLEM-guided lamella preparation and in situ lamella lift-out procedures. Our software therefore provides a foundation for further development of advanced cryogenic imaging and sample preparation protocols.
Data availability
All code developed in this work is available on GitHub. SerialFIB and a written tutorial can be obtained on: https://github.com/sklumpe/SerialFIB. A comprehensive video tutorial session is uploaded to YouTube: https://www.youtube.com/watch?v=QR7ngJ0apBk. The documentation is available on GitHub (https://github.com/sklumpe/SerialFIB/blob/main/documentation/SFIB.pdf). A Python script for post-processing of cryo-FIB-SEM volume imaging data as described in the method section is available in the same repository. The Python 3-ported 3DCT is available on: https://github.com/hermankhfung/3dct. New 3DCT functions described in this work, scripts for cryo-FLM virtual slice series creation, 3D point-based registration and transformation of cryo-FLM data with respect to cryo-FIB-SEM data, and bUnwarpJ-based analysis of FIB images before and after milling are available on: https://github.com/hermankhfung/tools3dct. All tomograms and subtomogram averages depicted in the figures are deposited to EMDB under accession codes EMD-13832, EMD-13833, EMD-13836, EMD-13837, EMD-13838, EMD-13834, EMD-13835. FIB-SEM volume imaging data for the Sum159 cells and Chlamydomonas reinhardtii are deposited in EMPIAR under accession codes EMPIAR-10847, EMPIAR-10870. FIB, SEM, TEM and fluorescence images used for 3D correlation are available on the BioImage Archive (S-BSST730, S-BSST729). Numerical data for lamella widths and thicknesses presented in Figure 2-figure supplement 4 are tabulated in Figure 2-figure supplement 4-Source Data 1 and 2.
Article and author information
Author details
Christoph W Müller
Funding
European research council (760067)
- Julia Mahamid
European research council (871037)
- Julia Mahamid
EMBL Interdisciplinary Postdoctoral Program under Marie Curie Actions COFUND (664726)
- Herman K H Fung
Max Planck Institute for Biochrmistry
- Sven Klumpe
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Klumpe et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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A modular platform for automated cryo-FIB workflows
eLife 10:e70506.
https://doi.org/10.7554/eLife.70506
Further reading
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Images taken by transmission electron microscopes are usually affected by lens aberrations and image defocus, among other factors. These distortions can be modeled in reciprocal space using the contrast transfer function (CTF). Accurate estimation and correction of the CTF is essential for restoring the high-resolution signal in cryogenic electron microscopy (cryoEM). Previously, we described the implementation of algorithms for this task in the cisTEM software package (Grant et al., 2018). Here we show that taking sample characteristics, such as thickness and tilt, into account can improve CTF estimation. This is particularly important when imaging cellular samples, where measurement of sample thickness and geometry derived from accurate modeling of the Thon ring pattern helps judging the quality of the sample. This improved CTF estimation has been implemented in CTFFIND5, a new version of the cisTEM program CTFFIND. We evaluated the accuracy of these estimates using images of tilted aquaporin crystals and eukaryotic cells thinned by focused ion beam milling. We estimate that with micrographs of sufficient quality CTFFIND5 can measure sample tilt with an accuracy of 3° and sample thickness with an accuracy of 5 nm.
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