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Maud
Dumoux
,
Thomas
Glen
,
Jake L. R.
Smith
,
Elaine M. L.
Ho
,
Luis Ma
Perdigão
,
Avery
Pennington
,
Sven
Klumpe
,
Neville B. Y.
Yee
,
David A.
Farmer
,
Pui Y. A.
Lai
,
William
Bowles
,
Ron
Kelley
,
Jürgen M
Plitzko
,
Liang
Wu
,
Mark
Basham
,
Daniel K.
Clare
,
C. Alistair
Siebert
,
Michele C.
Darrow
,
James H.
Naismith
,
Michael
Grange
Open Access
Abstract: 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 localisation tool for regions of interest is needed to drive the application of in-situ structural biology towards tissue. The combination of serial FIB/SEM with plasma-based ion sources promises a framework for targeting specific features in bulk-frozen samples (>100 µm) to produce lamellae for cryogenic electron tomography.
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Feb 2023
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Abstract: Recent developments in experimental microscopy techniques have led to improvements in the way we visualize various biological phenomena. Presently, state-of-art microscopy involves cryogenic sample preparation, 3D correlative microscopy and milling, followed by tilt series acquisition of biological volumes leading to datasets with nanometer scale information. While this workflow is technically possible, it is still challenging to collect, process, and analyze these large datasets, especially when the workflow includes correlative imaging and segmentation steps. In the Artificial Intelligence and Informatics group (AI&I) at The Rosalind Franklin Institute we are automating these workflow steps to solve computationally difficult and time-intensives problems by developing open-source software tools. Here, we present some notable examples.
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Aug 2022
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Open Access
Abstract: The specimen preparation process is a key determinant in the success of any cryo electron microscopy (cryoEM) structural study and until recently had remained largely unchanged from the initial designs of Jacques Dubochet and others in the 1980s. The process has transformed structural biology, but it is largely manual and can require extensive optimisation for each protein sample. The chameleon instrument with its self-wicking grids and fast-plunge freezing represents a shift towards a robust, automated, and highly controllable future for specimen preparation. However, these new technologies require new workflows and an understanding of their limitations and strengths. As early adopters of the chameleon technology, we report on our experiences and lessons learned through case studies. We use these to make recommendations for the benefit of future users of the chameleon system and the field of cryoEM specimen preparation generally.
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Jun 2022
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B24-Cryo Soft X-ray Tomography
I13-2-Diamond Manchester Imaging
Krios I-Titan Krios I at Diamond
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Open Access
Abstract: 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.
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Apr 2022
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I13-2-Diamond Manchester Imaging
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Diamond Proposal Number(s):
[12538, 14907, 23866]
Open Access
Abstract: Thousands of soft tissue microtomography experiments are conducted at synchrotrons around the world each year, and the quality of results varies widely. Soft biological tissues pose a particular challenge for synchrotron tomography, owing to poor contrast and susceptibility to deformation and beam damage artefacts. The rationale behind the choice of sample preparation methods, imaging parameters and reconstruction strategy is not always reported in articles, and so we conducted a systematic investigation of these aspects of experimental design for central nervous system samples. Computational segmentation can be particularly challenging for soft-tissue tomograms, and so we demonstrate the use of supervoxel-based machine-learning segmentation of our data.
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Jul 2021
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I13-2-Diamond Manchester Imaging
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W. M.
Tun
,
G.
Poologasundarampillai
,
H.
Bischof
,
G.
Nye
,
O. N. F.
King
,
M.
Basham
,
Y.
Tokudome
,
R. M.
Lewis
,
E. D.
Johnstone
,
P.
Brownbill
,
M.
Darrow
,
I. L.
Chernyavsky
Diamond Proposal Number(s):
[23941, 22562]
Open Access
Abstract: Multi-scale structural assessment of biological soft tissue is challenging but essential to gain insight into structure–function relationships of tissue/organ. Using the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced imaging and robust, validated machine-learning segmentation techniques to provide the first massively multi-scale and multi-domain information that enables detailed morphological and functional analyses of both maternal and fetal placental domains. Finally, we quantify the scale-dependent error in morphological metrics of heterogeneous placental tissue, estimating the minimal tissue scale needed in extracting meaningful biological data. The developed protocol is beneficial for high-throughput investigation of structure–function relationships in both normal and diseased placentas, allowing us to optimize therapeutic approaches for pathological pregnancies. In addition, the methodology presented is applicable in the characterization of tissue architecture and physiological behaviours of other complex organs with similarity to the placenta, where an exchange barrier possesses circulating vascular and avascular fluid spaces.
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Jun 2021
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Ugis
Sarkans
,
Wah
Chiu
,
Lucy M.
Collinson
,
Michele C.
Darrow
,
Jan
Ellenberg
,
David
Grunwald
,
Jean-Karim
Hériché
,
Andrii
Iudin
,
Gabriel G.
Martins
,
Terry
Meehan
,
Kedar
Narayan
,
Ardan
Patwardhan
,
Matthew Robert Geoffrey
Russell
,
Helen R.
Saibil
,
Caterina
Strambio-De-Castillia
,
Jason R.
Swedlow
,
Christian
Tischer
,
Virginie
Uhlmann
,
Paul
Verkade
,
Mary
Barlow
,
Omer
Bayraktar
,
Ewan
Birney
,
Cesare
Catavitello
,
Christopher
Cawthorne
,
Stephan
Wagner-Conrad
,
Elizabeth
Duke
,
Perrine
Paul-Gilloteaux
,
Emmanuel
Gustin
,
Maria
Harkiolaki
,
Pasi
Kankaanpää
,
Thomas
Lemberger
,
Jo
Mcentyre
,
Josh
Moore
,
Andrew W.
Nicholls
,
Shuichi
Onami
,
Helen
Parkinson
,
Maddy
Parsons
,
Marina
Romanchikova
,
Nicholas
Sofroniew
,
Jim
Swoger
,
Nadine
Utz
,
Lenard M.
Voortman
,
Frances
Wong
,
Peijun
Zhang
,
Gerard J.
Kleywegt
,
Alvis
Brazma
Abstract: Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. We propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. We hope this publication and the proposed Recommended Metadata for Biological Images (REMBI) will stimulate discussions about their implementation and future extension.
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May 2021
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I13-2-Diamond Manchester Imaging
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Diamond Proposal Number(s):
[12538, 14907]
Open Access
Abstract: Synchrotron radiation microtomography (SRμCT) is a nominally non-destructive 3D imaging technique which can visualise the internal structures of whole soft tissues. As a multi-stage technique, the cumulative benefits of optimising sample preparation, scanning parameters and signal processing can improve SRμCT imaging efficiency, image quality, accuracy and ultimately, data utility. By evaluating different sample preparations (embedding media, tissue stains), imaging (projection number, propagation distance) and reconstruction (artefact correction, phase retrieval) parameters, a novel methodology (combining reversible iodine stain, wax embedding and inline phase contrast) was optimised for fast (~12 minutes), high-resolution (3.2–4.8 μm diameter capillaries resolved) imaging of the full diameter of a 3.5 mm length of rat spinal cord. White-grey matter macro-features and micro-features such as motoneurons and capillary-level vasculature could then be completely segmented from the imaged volume for analysis through the shallow machine learning SuRVoS Workbench. Imaged spinal cord tissue was preserved for subsequent histology, establishing a complementary SRμCT methodology that can be applied to study spinal cord pathologies or other nervous system tissues such as ganglia, nerves and brain. Further, our ‘single-scan iterative downsampling’ approach and side-by-side comparisons of mounting options, sample stains and phase contrast parameters should inform efficient, effective future soft tissue SRμCT experiment design.
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Aug 2018
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B24-Cryo Soft X-ray Tomography
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Abstract: Cryo soft-X-ray tomography (Cryo–SXT) is a 3D imaging technique that allows us to image whole vitrified biological cells and their contents in a near native state. Cryo-SXT utilises the natural contrast of cellular structures by imaging in an energy range termed the ‘water window’ The water window is between the K absorption edges of Carbon 284 eV and Oxygen 543 eV. Imaging between these energies results in higher X-ray absorption by carbon rich cellular structures than oxygen containing molecules such as vitrified water. The resulting contrast allows us to see cellular structures (Figure 1A) at a resolution of ~40nm, depending on the sample and microscope components.
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Aug 2018
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B24-Cryo Soft X-ray Tomography
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Open Access
Abstract: Cryo-soft X-ray tomography is an imaging technique that addresses the need for mesoscale imaging of cellular ultrastructure of relatively thick samples without the need for staining or chemical modification. It allows the imaging of cellular ultrastructure to a resolution of 25–40 nm and can be used in correlation with other imaging modalities, such as electron tomography and fluorescence microscopy, to further enhance the information content derived from biological samples. An overview of the technique, discussion of sample suitability and information about sample preparation, data collection and data analysis is presented here. Recent developments and future outlook are also discussed.
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Mar 2018
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