Digital capture of the histological microarchitecture in the myometrium and its implications for the propagation of electrophysiological excitation.

Coordination of uterine contractions during labour is critical for successful delivery. The mechanisms underlying this coordination are not fully understood. Propagation of contraction signals has previously been observed to occur through transmission of electrical excitation waves. This thesis aims to examine the histological microarchitecture of the muscular layer of the uterus (myometrium) and determine how this structure affects the propagation of excitation by means of in silico three-dimensional reconstruction of the myometrium and numerical simulations of a spatially structured excitation-relaxation model. A key aim of the in silico reconstruction of the smooth muscle architecture of the myometrium is to identify structural features that correspond to the control of excitation behaviour in the myometrium. This examination is aided by analysis of excitation patterns observed in multi-electrode array recordings. The reconstruction is subsequently used as a basis for simulating electrical activity in the myometrium. Novel structural features are identified here that are located at the initiation points of electrical activity and are proposed to be the pacemaker sites in rat myometrium. Furthermore, boundary of low connectivity across the mesometrial border was observed in the rat, which corresponds to the termination of excitation waves observed in multielectrode array recordings. In addition, bridges of smooth muscle cells connecting the inner and outer layers of the myometrium were observed in both rat and human myometrium. Taken together these three features suggest a novel mechanism for control of contraction in the rat myometrium; an analogous mechanism is proposed for the human myometrium. The results presented in this thesis could provide an explanation for the patterns of excitation propagation observed in human and rat uteri. Further refinements of the methods used here are outlined and expected to generate a more detailed visualisation of the structures underpinning these mechanisms

A curvature-enhanced random walker segmentation method for detailed capture of 3D cell surface Membranes

High-resolution 3D microscopy is a fast advancing field and requires new techniques in image analysis to handle these new datasets. In this work, we focus on detailed 3D segmentation of Dictyostelium cells undergoing macropinocytosis captured on an iSPIM microscope. We propose a novel random walker-based method with a curvature-based enhancement term, with the aim of capturing fine protrusions, such as filopodia and deep invaginations, such as macropinocytotic cups, on the cell surface. We tested our method on both real and synthetic 3D image volumes, demonstrating that the inclusion of the curvature enhancement term can improve the segmentation of the aforementioned features. We show that our method performs better than other state of the art segmentation methods in 3D images of Dictyostelium cells, and performs competitively against CNN-based methods in two Cell Tracking Challenge datasets, demonstrating the ability to obtain accurate segmentations without the requirement of large training datasets. We also present an automated seeding method for microscopy data, which, combined with the curvature-enhanced random walker method, enables the segmentation of large time series with minimal input from the experimenter

Generative adversarial networks for augmenting training data of microscopic cell images

Generative adversarial networks (GANs) have recently been successfully used to create realistic synthetic microscopy cell images in 2D and predict intermediate cell stages. In the current paper we highlight that GANs can not only be used for creating synthetic cell images optimized for different fluorescent molecular labels, but that by using GANs for augmentation of training data involving scaling or other transformations the inherent length scale of biological structures is retained. In addition, GANs make it possible to create synthetic cells with specific shape features, which can be used, for example, to validate different methods for feature extraction. Here, we apply GANs to create 2D distributions of fluorescent markers for F-actin in the cell cortex of Dictyostelium cells (ABD), a membrane receptor (cAR1), and a cortex-membrane linker protein (TalA). The recent more widespread use of 3D lightsheet microscopy, where obtaining sufficient training data is considerably more difficult than in 2D, creates significant demand for novel approaches to data augmentation. We show that it is possible to directly generate synthetic 3D cell images using GANs, but limitations are excessive training times, dependence on high-quality segmentations of 3D images, and that the number of z-slices cannot be freely adjusted without retraining the network. We demonstrate that in the case of molecular labels that are highly correlated with cell shape, like F-actin in our example, 2D GANs can be used efficiently to create pseudo-3D synthetic cell data from individually generated 2D slices. Because high quality segmented 2D cell data are more readily available, this is an attractive alternative to using less efficient 3D networks

MiCellAnnGELo: Annotate microscopy time series of complex cell surfaces with 3D Virtual Reality

Summary: Advances in 3D live cell microscopy are enabling high-resolution capture of previously unobserved processes. Unleashing the power of modern machine learning methods to fully benefit from these technologies is, however, frustrated by the difficulty of manually annotating 3D training data. MiCellAnnGELo virtual reality software offers an immersive environment for viewing and interacting with 4D microscopy data, including efficient tools for annotation. We present tools for labelling cell surfaces with a wide range of applications, including cell motility, endocytosis, and transmembrane signalling. Availability and implementation: MiCellAnnGELo employs the cross platform (Mac/Unix/Windows) Unity game engine and is available under the MIT licence at https://github.com/CellDynamics/MiCellAnnGELo.git, together with sample data and demonstration movies. MiCellAnnGELo can be run in desktop mode on a 2D screen or in 3D using a standard VR headset with compatible GPU.Comment: For associated code and sample data, see https://github.com/CellDynamics/MiCellAnnGELo.gi

Cell membrane feature detection using graph neural networks

Many cellular processes involve complex deformations of the cell surface, which are difficult to automatically detect and analyse in 3D microscopy images. One issue faced by modern machine learning methods is that 3D microscopy images are large and require a high computational load to analyse. To simplify this problem, we propose a graph convolutional neural network applied to a triangulated mesh of the cell surface, where nodes are associated with geometric and intensity features of biomarkers on or near the surface. Here, we focus on identification of macropinocytic cups on the surface of Dictyostelium cells, structures involved in the uptake of extracellular fluid. The network classifies each node into belonging to a cup or not, enabling subsequent studies of the detailed distribution of molecules regulating fluid uptake in cells. We show that a simple network architecture can identify key features of the surface, suggesting that these methods have strong potential for advancing the analysis of cell surface dynamics

MiCellAnnGELo : annotate microscopy time series of complex cell surfaces with 3D virtual reality

Summary: Advances in 3D live cell microscopy are enabling high-resolution capture of previously unobserved processes. Unleashing the power of modern machine learning methods to fully benefit from these technologies is, however, frustrated by the difficulty of manually annotating 3D training data. MiCellAnnGELo virtual reality software offers an immersive environment for viewing and interacting with 4D microscopy data, including efficient tools for annotation. We present tools for labelling cell surfaces with a wide range of applications, including cell motility, endocytosis and transmembrane signalling. Availability and implementation: MiCellAnnGELo employs the cross-platform (Mac/Unix/Windows) Unity game engine and is available under the MIT licence at https://github.com/CellDynamics/MiCellAnnGELo.git, together with sample data. MiCellAnnGELo can be run in desktop mode on a 2D screen or in 3D using a standard VR headset with a compatible GPU. Supplementary information: Supplementary data are available at Bioinformatics online

Identification of uterine pacemaker regions at the myometrial-placental interface in the rat

Coordinated uterine contractions are essential for delivering viable offspring in mammals. In contrast to other visceral smooth muscles, it is not known where excitation within the uterus is initiated, and no defined pace‐making region has hitherto been identified. Using multi‐electrode array recordings and high‐resolution computational reconstruction of the three‐dimensional micro‐structure of late pregnant rat uterus, we demonstrate that electrical potentials are initiated in distinct structures within the placental bed of individual implantation sites. These previously unidentified structures represent modified smooth muscle bundles that are derived from bridges between the longitudinal and circular layers. Coordinated implantation and encapsulation by invading trophoblast give rise to isolated placental/myometrial interface bundles that directly connect to the overlying longitudinal smooth muscle layer. Taken together, these observations imply that the anatomical structure of the uterus, combined with site‐specific implantation, gives rise to emergent patterns of electrical activity that drive effective contractility during parturition

A computational method for three-dimensional reconstruction of the microarchitecture of myometrial smooth muscle from histological sections.

BACKGROUND:The fibrous structure of the myometrium has previously been characterised at high resolutions in small tissue samples (< 100 mm3) and at low resolutions (∼500 μm per voxel edge) in whole-organ reconstructions. However, no high-resolution visualisation of the myometrium at the organ level has previously been attained. METHODS AND RESULTS:We have developed a technique to reconstruct the whole myometrium from serial histological slides, at a resolution of approximately 50 μm per voxel edge. Reconstructions of samples taken from human and rat uteri are presented here, along with histological verification of the reconstructions and detailed investigation of the fibrous structure of these uteri, using a range of tools specifically developed for this analysis. These reconstruction techniques enable the high-resolution rendering of global structure previously observed at lower resolution. Moreover, structures observed previously in small portions of the myometrium can be observed in the context of the whole organ. The reconstructions are in direct correspondence with the original histological slides, which allows the inspection of the anatomical context of any features identified in the three-dimensional reconstructions. CONCLUSIONS AND SIGNIFICANCE:The methods presented here have been used to generate a faithful representation of myometrial smooth muscle at a resolution of ∼50 μm per voxel edge. Characterisation of the smooth muscle structure of the myometrium by means of this technique revealed a detailed view of previously identified global structures in addition to a global view of the microarchitecture. A suite of visualisation tools allows researchers to interrogate the histological microarchitecture. These methods will be applicable to other smooth muscle tissues to analyse fibrous microarchitecture

Computational physiology of uterine smooth muscle.

Pregnancy can be accompanied by serious health risks to mother and child, such as pre-eclampsia, premature birth and postpartum haemorrhage. Understanding of the normal physiology of uterine function is essential to an improved management of such risks. Here we focus on the physiology of the smooth muscle fibres which make up the bulk of the uterine wall and which generate the forceful contractions that accompany parturition. We survey computational methods that integrate mathematical modelling with data analysis and thereby aid the discovery of new therapeutic targets that, according to clinical needs, can be manipulated to either stop contractions or cause the uterine wall muscle to become active

Fibrous structures in rat and human myometrium.

<p>Stereo pairs showing the fibrous structure in the rat (<b>A</b>) and human (<b>B</b>). Images were generated by highlighting portions of tissue along the bundle direction from randomly selected seed points in the reconstruction. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173404#pone.0173404.s019" target="_blank">S1 Appendix</a> describes the methods used to generate these visualisations. The rat uterus is shown from the exterior with the cervical end at the top. Longitudinal bundles can be seen lying on top, with some distortion due to the pinning of the tissue, while circular bundles are positioned underneath. The human tissue is shown with fundus at the top, interior to the left of the image and exterior to the right. The bundles in the human do not show the same distinctive layers as the rat, with the central portion of the tissue showing no apparent preferred orientation. Toward the interior, circular bundles can be observed as horizontal from this viewing plane, while an inner layer of longitudinal bundles can be observed toward the cervix, in accordance with Weiss <i>et al.</i> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173404#pone.0173404.ref003" target="_blank">3</a>]. The larger structures toward the exterior are in fact sheet-like structures, as previously observed by Young and Hession [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173404#pone.0173404.ref002" target="_blank">2</a>]. Scale bars represent 5 mm.</p