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Electron cryo-tomography (cryo-ET) is an imaging technique for probing 3D structures with at the nanometre scale. This technique has been used extensively in the biomedical field to study the complex structures of proteins and other macromolecules. With the advancement in technology, microscopes are currently capable of producing images amounting to terabytes of data per day, posing great challenges for scientists as the speed of processing of the images cannot keep up with the ever-higher throughput of the microscopes. Therefore, automation is an essential and natural pathway on which image processing – from individual micrographs to full tomograms – is developing. In this paper, we present Ot2Rec, an open-source pipelining tool which aims to enable scientists to build their own processing workflows in a flexible and automatic manner. The basic building blocks of Ot2Rec are plugins which follow a unified API structure, making it simple for scientists to contribute to Ot2Rec by adding features which are not already available. In this paper, we also present three case studies of image processing using Ot2Rec, through which we demonstrate the speedup of using a semi-automatic workflow over a manual one, the possibility of writing and using custom (prototype) plugins, and the flexibility of Ot2Rec which enables the mix-and-match of plugins. We also demonstrate, in the supplementary information, a built-in reporting feature in Ot2Rec which aggregates the metadata from all process being run, and output them in the Jupyter Notebook and/or HTML formats for quick review of image processing quality. Ot2Rec can be found athttps://github.com/rosalindfranklininstitute/ot2rec.Impact StatementThe field of cryo electron tomography has grown substantially in recent years, bringing about new advances in hardware and software which enable visualisation of cell and tissue architecture and proteins found in their native context. These same advances have, in some ways, stratified the field into those with access and those without. On the software side, this has emphasised the need for open-source options that do not require high levels of computational literacy to access. Additionally, it has highlighted the need for ways to both mix-and-match software for easy prototyping and comparisons between parameters and methods. Ot2Rec addresses these needs through a simple, unified plugin structure allowing the addition of existing software or the development of new and does so in a way which democratises access.

An emergent volume electron microscopy (vEM) technique called cryogenic serial plasma focused ion beam milling scanning electron microscopy (pFIB/SEM) can decipher complex biological structures by building a three-dimensional picture of biological samples at mesoscale resolution. This is achieved by collecting consecutive SEM images after successive rounds of FIB milling that expose a new surface after each milling step. Due to instrumental limitations, some image processing is necessary before 3D visualisation and analysis of the data is possible. SEM images are affected by noise, drift, and charging effects, that can make precise 3D reconstruction of biological features difficult. This paper presents Okapi-EM, an open-source Napari plugin(1)developed to process and analyse cryogenic serial FIB/SEM images. Okapi-EM enables automated image registration of slices, evaluation of image quality metrics specific to FIB-SEM imaging, and mitigation of charging artefacts. Implementation of Okapi-EM within the Napari framework ensures that the tools are both user- and developer-friendly, through provision of a graphical user interface and access to Python programming. Napari also hosts a variety of other image processing plugins so Okapi-EM tools can be integrated into and combined with other workflows. Okapi-EM can be downloaded freely athttps://github.com/rosalindfranklininstitute/okapi-em, or installed from Python package index (PyPI).Impact statementCryogenic serial pFIB/SEM is an emerging microscopy technique that is used to visualise 3D structures of biological features at mesoscale resolutions(2). This technique requires common post processing of data such as alignment and charge mitigation to enable robust segmentation and analysis. In addition, approaches are needed to quantify data quality to enable an assessment of features and tune data acquisition parameters to enable optimal image acquisition. This article presents Okapi-EM, a combination of software tools designed to facilitate these important initial steps in assessing and processing images from these experiments. These tools have been assembled as a plugin for a popular 3D biological image visualiser called Napari, making their usage user-friendly and readily accessible.

Serial focussed ion beam scanning electron microscopy (FIB/SEM) enables imaging and assessment of sub-cellular structures on the mesoscale (10 nm to 10 μm). When applied to vitrified samples, serial FIB/SEM is also a means to target specific structures in cells and tissues while maintaining constituents’ hydration shells for in-situ structural biology downstream. However, the application of serial FIB/SEM imaging of non-stained cryogenic biological samples is limited due to low contrast, curtaining and charging artefacts. We address these challenges using a cryogenic plasma FIB/SEM (cryo-pFIB/SEM). We evaluated the choice of plasma ion source and imaging regimes to produce high quality SEM images of a range of different biological samples. Using an automated workflow we produced three dimensional volumes of bacteria, human cells, and tissue, and calculated estimates for their resolution, typically achieving 20 to 50 nm. Additionally, a tag-free tool is needed to drive the application of in situ structural biology towards tissue. The combination of serial FIB/SEM with plasmabased ion sources promises a framework for targeting specific features in bulk-frozen samples (>100 μm) to produce lamella for cryogenic electron tomography.

As sample preparation and imaging techniques have expanded and improved to include a variety of options for larger sized and numbers of samples, the bottleneck in volumetric imaging is now data analysis. Annotation and segmentation are both common, yet difficult, data analysis tasks which are required to bring meaning to the volumetric data. The SuRVoS application has been updated and redesigned to provide access to both manual and machine learning-based segmentation and annotation techniques, including support for crowd sourced data. Combining adjacent, similar voxels (supervoxels) provides a mechanism for speeding up segmentation both in the painting of annotation and by training a segmentation model on a small amount of annotation. The support for layers allows multiple datasets to be viewed and annotated together which, for example, enables the use of correlative data (e.g. crowd-sourced annotations or secondary imaging techniques) to guide segmentation. The ability to work with larger data on high-performance servers with GPUs has been added through a client-server architecture and the Pytorch-based image processing and segmentation server is flexible and extensible, and allows the implementation of deep learning-based segmentation modules. The client side has been built around Napari allowing integration of SuRVoS into an ecosystem for open-source image analysis while the server side has been built with cloud computing and extensibility through plugins in mind. Together these improvements to SuRVoS provide a platform for accelerating the annotation and segmentation of volumetric and correlative imaging data across modalities and scales.