OkapiEM
OkapiEM is a tool for processing serial-FIB/SEM data that can do linear stack alignment, charge artifact suppression and Fourier ring correlation estimation.
Dual-beam focused ion beam scanning electron microscopy (FIB/SEM) builds volumetric representation of the specimen by cyclic FIB-milling (to remove the freshly imaged surface) and SEM imaging of the specimen. Typically hundreds of SEM images are obtained, corresponding to decreasing heights of the specimen, which can be computationally stacked to obtain a volumetric representation of the sample being measured. This technique is called serial-FIB/SEM and Okapi-EM was developed to help process this type of data.
As with many experimental techniques which produce 3D data, it is often desirable to annotate biological features and to visualise the structure in three-dimensions (3D), but important pre-processing steps are needed before data is suitable as input for these segmentation tasks. For instance, small translational movements between images within the stack caused by stage and/or sample movement are often observed as misalignment between images, which must be compensated for, otherwise volumetric segmentation [1,2].
Additionally, SEM images of biological samples often contain artifacts caused by charging around insulating substances such as lipids [3,4]. Automatic and semi-automatic segmentation tools require aligned datasets and data that can be effectively normalised to remove any strong features generated by sample charging. Finally, having quantitative tools that assess the quality of the data under certain imaging (e.g., optimal focus, voltage, etc) and milling conditions (e.g., focusing, curtaining, milling accuracy) will assist in the generation of data that best mitigate these factors, resulting in optimal further processing and optimisation of future data acquisition strategies. These necessary pre-segmentation tasks can be time-consuming and often require use of multiple pieces of software or bespoke code.
OkapiEM provides a selection of tools which address some of these needs in a single software package (5). In OkapiEM, there are three tools available:
- Stack Alignment. This tool provides the user with appropriate transformation options for alignment of stacks of slices.
- Charge mitigation. This tool requires pre-segmented “charge centres”, then applies filters to mitigate the charging artifacts found nearby.
- Resolution estimation. This tool requires microscope calibration and provides a measure of the mean resolution and standard deviation for individual slices.
Screenshot of Okapi-EM plugin (right) running inside napari visualiser.
Install OkapiEM via Napari interface here – https://pypi.org/project/okapi-em/.
Visit GitHub to find the open source OkapiEM software here –
https://github.com/rosalindfranklininstitute/okapi-em
References
[1] A. Pennington et al., ‘SuRVoS 2: Accelerating Annotation and Segmentation for Large Volumetric Bioimage Workflows Across Modalities and Scales’, Front. Cell Dev. Biol., vol. 10, p. 842342, Apr. 2022, doi: 10.3389/fcell.2022.842342.
[2] M. C. Darrow et al., ‘Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench’, JoVE (Journal of Visualized Experiments), no. 126, p. e56162, Aug. 2017, doi: 10.3791/56162.
[3] D. Spehner et al., ‘Cryo-FIB-SEM as a promising tool for localizing proteins in 3D’, Journal of Structural Biology, vol. 211, no. 1, p. 107528, Jul. 2020, doi: 10.1016/j.jsb.2020.107528.
[4] L. Mendonça et al., ‘Correlative multi-scale cryo-imaging unveils SARS-CoV-2 assembly and egress’, Nat Commun, vol. 12, no. 1, Art. no. 1, Jul. 2021, doi: 10.1038/s41467-021-24887-y.
[5] L. M. A. Perdigão, E. M. L. Ho, Z. C. Cheng, N. B.-Y. Yee, T. Glen, L. Wu, M. Grange, M. Dumoux, M. Basham, M. C. Darrow, Okapi-EM: A napari plugin for processing and analyzing cryogenic serial focused ion beam/scanning electron microscopy images. Biological Imaging 3, 2023., doi: 10.1017/S2633903X23000119
Project Leadership
Michele Darrow
Mark Basham
Project Members at the Franklin
Elaine Ho
Neville Yee
Chloe Cheng
Maud Dumoux
Tom Glen
Michael Grange
Collaborating Institutes
Thermofisher Scientific
Funded by
Wellcome Trust (Electrifying Life Sciences)