Biology subjects

Peddie, C. J.

Publications and source records attributed to Peddie, C. J..

10 recordsLinked to original sources

bioRxiv2023

3D-Imaging of synapses in neuronal tissues with synchrotron X-ray ptychography

Maps of dense subcellular features in biological tissue are the key to understanding the structural basis of organ function. Electron microscopy provides the necessary resolution, yet - as electrons penetrate samples for only a few 100s of nm - requires physical sectioning or ablation, which strongly challenges anatomical investigations of entire organs such as mammalian brains. As demonstrated for the engineering and physical sciences, X-ray nanotomography represents a promising alternative for ultrastructural 3d imaging without physical sectioning1-15. Leveraging the high brilliance of 4th generation synchrotron X-ray sources, it has the potential to non-destructively image mm3-sized samples at ultrastructural resolution within a few days16. A fundamental barrier to application to the life sciences is that, when irradiated with high-intensity X-rays, biological samples deform and ultimately disintegrate, prohibiting reaching sufficient resolution. Here, we introduce a combination of engineering solutions which defeat this barrier for X-ray ptychography17, a coherent diffractive X-ray imaging technique. The solutions include a cryogenic sample stage with high stability, high-precision interferometric positioners and tailored non-rigid tomographic reconstruction algorithms18. Furthermore, adapting an epoxy resin developed for the nuclear and aerospace industry, we demonstrate radiation resistance to X-ray doses exceeding 1010 Gy. The resulting sub-40 nm isotropic resolution makes it possible to densely resolve axon bundles, boutons, dendrites and reliably identify synapses without physical sectioning. Moreover, we validated the X-ray technique using the current gold standard, namely focused ion beam scanning electron microscopy (FIB-SEM)19,20 to demonstrate intact ultrastructure in tissue volumes first imaged by X-rays. This unlocks the potential of X-ray tomography for high-resolution tissue imaging, coinciding with the transformative advancements of next-generation synchrotrons worldwide21.

neuroscience↗

bioRxiv2023

CLEM-Reg: An automated point cloud based registration algorithm for correlative light and volume electron microscopy

Correlative light and volume electron microscopy (vCLEM) is a powerful imaging technique that enables the visualisation of fluorescently labelled proteins within their ultrastructural context on a subcellular level. Currently, expert microscopists align vCLEM acquisitions using time-consuming and subjective manual methods. This paper presents CLEM-Reg, an algorithm that automates the 3D alignment of vCLEM datasets by leveraging probabilistic point cloud registration techniques. These point clouds are derived from segmentations of common structures in each modality, created by state-of-the-art open-source methods, with the option to leverage alternative tools from other plugins or platforms. CLEM-Reg drastically reduces the time required to register vCLEM datasets to a few minutes and achieves correlation of fluorescent signal to sub-micron target structures in EM on three newly acquired vCLEM benchmark datasets (fluorescence microscopy combined with FIB-SEM or SBF-SEM). CLEM-Reg was then used to automatically obtain vCLEM overlays to unambiguously identify TGN46-positive transport carriers involved in the trafficking of proteins between the trans-Golgi network and plasma membrane. The datasets are available in the EMPIAR and BioStudies public image archives for reuse in testing and developing multimodal registration algorithms by the wider community. A napari plugin integrating the algorithm is also provided to aid end-user adoption.

cell biology↗

bioRxiv2023

Femtosecond laser preparation of resin embedded samples for correlative microscopy workflows in life sciences

Correlative multimodal imaging is a useful approach to investigate complex structural relations in life sciences across multiple scales. For these experiments, sample preparation workflows that are compatible with multiple imaging techniques must be established. In one such implementation, a fluorescently-labelled region of interest in a biological soft tissue sample can be imaged with light microscopy before staining the specimen with heavy metals, enabling follow-up higher resolution structural imaging at the targeted location, bringing context where it is required. Alternatively, or in addition to fluorescence imaging, other microscopy methods such as synchrotron X-ray computed tomography with propagation-based phase contrast (SXRT) or serial blockface scanning electron microscopy (SBF-SEM) might also be applied. When combining imaging techniques across scales, it is common that a volumetric region of interest (ROI) needs to be carved from the total sample volume before high resolution imaging with a subsequent technique can be performed. In these situations, the overall success of the correlative workflow depends on the precise targeting of the ROI and the trimming of the sample down to a suitable dimension and geometry for downstream imaging. Here we showcase the utility of a novel femtosecond laser device to prepare microscopic samples (1) of an optimised geometry for synchrotron X-ray microscopy as well as (2) for subsequent volume electron microscopy applications, embedded in a wider correlative multimodal imaging workflow (Fig. 1). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/523473v1_fig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1c72db6org.highwire.dtl.DTLVardef@18a234forg.highwire.dtl.DTLVardef@142bd0org.highwire.dtl.DTLVardef@4894f0_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Correlative microscopy workflows incorporating samples processed using a femtosecond laser. C_FIG

neuroscience↗

bioRxiv2022

Crosshair, semi-automated targeting for electron microscopy with a motorised ultramicrotome

Volume electron microscopy (EM) is a time consuming process - often requiring weeks or months of continuous acquisition for large samples. In order to compare the ultrastructure of a number of individuals or conditions, acquisition times must therefore be reduced. For resin-embedded samples, one solution is to selectively target smaller regions of interest by trimming with an ultramicrotome. This is a difficult and labour-intensive process, requiring manual positioning of the diamond knife and sample, and much time and training to master. Here, we have developed a semi-automated workflow for targeting with a modified ultramicrotome. We adapted two recent commercial systems to add motors for each rotational axis (and also each translational axis for one system), allowing precise and automated movement. We also developed a user-friendly software to convert X-ray images of resin-embedded samples into angles and cutting depths for the ultramicrotome. This is provided as an open-source Fiji plugin called Crosshair. This workflow is demonstrated by targeting regions of interest in a series of Platynereis dumerilii samples.

cell biology↗

bioRxiv2021

Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron micro-tomography and volume electron microscopy

Attributing in vivo neurophysiology to the brains ultrastructure requires a large field of view containing contextual anatomy. Electron microscopy (EM) is the gold standard technique to identify ultrastructure, yet acquiring volumes containing full mammalian neural circuits is challenging and time consuming using EM. Here, we show that synchrotron X-ray computed tomography (SXRT) provides rapid imaging of EM-prepared tissue volumes of several cubic millimetres. Resolution was sufficient for distinguishing cell bodies as well as for tracing apical dendrites in olfactory bulb and hippocampus, for up to 350 m. Correlating EM with SXRT allowed us to associate dendritic spines on pyramidal cell apical dendrites in the stratum radiatum to their corresponding soma locations. Superficial pyramidal neurons had larger spine apparatus density compared to deeper ones, implying differential synaptic plasticity for superficial and deeper cells. Finally, we show that X-ray tomography and volume EM can be reliably correlated to prior in vivo imaging. Thus, combining functional measurements with multiscale X-ray microscopy and volume EM establishes a correlative workflow that enables functional and structural investigation of subcellular features in the context of cellular morphologies, tissues and ultimately whole organs.

neuroscience↗

bioRxiv2020

Citizen science, cells and CNNs - deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.

cell biology↗

bioRxiv2020

Geometric differences between nuclear envelopes of Wild-type and Chlamydia trachomatis-infected HeLa cells

In this work, the geometrical characteristics of two different types of cells observed with Electron Microscopy were analysed. The nuclear envelope of Wild-type HeLa cells and Chlamydia trachomatis-infected HeLa cells were automatically segmented and then modelled against a spheroid and converted to a two-dimensional surface. Geometric measurements from this surface and the volumetric nuclear envelope were extracted to compare the two types of cells. The measurements included the nuclear volume, the sphericity of the nucleus, its flatness or spikiness. In total 13 different cells were segmented: 7 Wild-type and 6 Chlamydia trachomatis-infected. The cells were statistically different in the following measurements. Wild-type HeLa cells have greater volumes than that of Chlamydia trachomatis-infected HeLa cells and they are more spherical as Jaccard index suggests. Standard deviation ({sigma}), and range of values for the nuclear envelope, which shows the distance of the highest peaks and deepest valleys from the spheroid, were also extracted from the modelling against a spheroid and these metrics were used to compare two different data sets in order to draw conclusions.

cell biology↗

bioRxiv2020

Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures

In this work, images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and three deep learning architectures: VGG16, ResNet18 and Inception-ResNet-v2. Three hundred slices, each 2000 x 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%).

cancer biology↗

bioRxiv2020

Enteric glia as a source of neural progenitors in adult zebrafish

The presence and identity of neural progenitors in the enteric nervous system (ENS) of vertebrates is a matter of intense debate. Here we demonstrate that the non-neuronal ENS cell compartment of teleosts shares molecular and morphological characteristics with mammalian enteric glia but cannot be identified by the expression of canonical glia markers. However, unlike their mammalian counterparts, which are generally quiescent and do not undergo neuronal differentiation during homeostasis, we show that a relatively high proportion of zebrafish enteric glia proliferate under physiological conditions giving rise to progeny that differentiate into enteric neurons. We also provide evidence that, similar to brain neural stem cells, the activation and neuronal differentiation of enteric glia are regulated by Notch signalling. Our experiments reveal remarkable similarities between enteric glia and brain neural stem cells in teleosts and open new possibilities for use of mammalian enteric glia as a potential source of neurons to restore the activity of intestinal neural circuits compromised by injury or disease.

neuroscience↗

bioRxiv2018

Modelling the nuclear envelope of HeLa cells

This paper describes a framework for the automatic segmentation of the nuclear envelope of cancerous HeLa cells and the modelling of the volumetric shape against an ellipsoid. The framework is automatic and unsupervised and reported a Jaccard Similarity Index of 0.968 against a manual segmentation. The modelling of the surface provides a visual display of the variations, both smooth and rugged over the surface, and measurements can be extracted with the expectation that they can correlate with the biological characteristics of the cells.

cancer biology↗