Bio21 - Research Publications
Permanent URI for this collection
Search Results
1 - 5 of 5
-
ItemEffects of altered cellular ultrastructure on energy metabolism in diabetic cardiomyopathy: an in silico study.(The Royal Society, 2022-11-21)Diabetic cardiomyopathy is a leading cause of heart failure in diabetes. At the cellular level, diabetic cardiomyopathy leads to altered mitochondrial energy metabolism and cardiomyocyte ultrastructure. We combined electron microscopy (EM) and computational modelling to understand the impact of diabetes-induced ultrastructural changes on cardiac bioenergetics. We collected transverse micrographs of multiple control and type I diabetic rat cardiomyocytes using EM. Micrographs were converted to finite-element meshes, and bioenergetics was simulated over them using a biophysical model. The simulations also incorporated depressed mitochondrial capacity for oxidative phosphorylation (OXPHOS) and creatine kinase (CK) reactions to simulate diabetes-induced mitochondrial dysfunction. Analysis of micrographs revealed a 14% decline in mitochondrial area fraction in diabetic cardiomyocytes, and an irregular arrangement of mitochondria and myofibrils. Simulations predicted that this irregular arrangement, coupled with the depressed activity of mitochondrial CK enzymes, leads to large spatial variation in adenosine diphosphate (ADP)/adenosine triphosphate (ATP) ratio profile of diabetic cardiomyocytes. However, when spatially averaged, myofibrillar ADP/ATP ratios of a cardiomyocyte do not change with diabetes. Instead, average concentration of inorganic phosphate rises by 40% owing to lower mitochondrial area fraction and dysfunction in OXPHOS. These simulations indicate that a disorganized cellular ultrastructure negatively impacts metabolite transport in diabetic cardiomyopathy. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.
-
ItemCardioVinci: building blocks for virtual cardiac cells using deep learning(ROYAL SOC, 2022-11-21)Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimensional views of cardiac ultrastructures within sample volumes ranging from hundreds of nanometres to hundreds of micrometres. The datasets from these samples are typically large, with file sizes ranging from gigabytes to terabytes and the number of image slices within the three-dimensional stack in the hundreds. A significant bottleneck with these large datasets is the time taken to extract and statistically analyse three-dimensional changes in cardiac ultrastructures. This is because of the inherently low contrast and the significant amount of structural detail that is present in EM images. These datasets often require manual annotation, which needs substantial person-hours and may result in only partial segmentation that makes quantitative analysis of the three-dimensional volumes infeasible. We present CardioVinci, a deep learning workflow to automatically segment and statistically quantify the morphologies and spatial assembly of mitochondria, myofibrils and Z-discs with minimal manual annotation. The workflow encodes a probabilistic model of the three-dimensional cardiomyocyte using a generative adversarial network. This generative model can be used to create new models of cardiomyocyte architecture that reflect variations in morphologies and cell architecture found in EM datasets. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.
-
ItemAutomated segmentation of cardiomyocyte Z-disks from high-throughput scanning electron microscopy data.(BioMed Central, 2019-12-19)BACKGROUND: With the advent of new high-throughput electron microscopy techniques such as serial block-face scanning electron microscopy (SBF-SEM) and focused ion-beam scanning electron microscopy (FIB-SEM) biomedical scientists can study sub-cellular structural mechanisms of heart disease at high resolution and high volume. Among several key components that determine healthy contractile function in cardiomyocytes are Z-disks or Z-lines, which are located at the lateral borders of the sarcomere, the fundamental unit of striated muscle. Z-disks play the important role of anchoring contractile proteins within the cell that make the heartbeat. Changes to their organization can affect the force with which the cardiomyocyte contracts and may also affect signaling pathways that regulate cardiomyocyte health and function. Compared to other components in the cell, such as mitochondria, Z-disks appear as very thin linear structures in microscopy data with limited difference in contrast to the remaining components of the cell. METHODS: In this paper, we propose to generate a 3D model of Z-disks within single adult cardiac cells from an automated segmentation of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The proposed fully automated segmentation scheme is comprised of three main modules including "pre-processing", "segmentation" and "refinement". We represent a simple, yet effective model to perform segmentation and refinement steps. Contrast stretching, and Gaussian kernels are used to pre-process the dataset, and well-known "Sobel operators" are used in the segmentation module. RESULTS: We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. The results show that our model correctly detects Z-disks with 90.56% accuracy. We also compare and contrast the accuracy of the proposed algorithm in segmenting a FIB-SEM dataset against the accuracy of segmentations from a machine learning program called Ilastik and discuss the advantages and disadvantages that these two approaches have. CONCLUSIONS: Our validation results demonstrate the robustness and reliability of our algorithm and model both in terms of validation metrics and in terms of a comparison with a 3D visualisation of Z-disks obtained using immunofluorescence based confocal imaging.
-
ItemMultimodal analysis of Plasmodium knowlesi-infected erythrocytes reveals large invaginations, swelling of the host cell, and rheological defects(WILEY-HINDAWI, 2019-05)The simian parasite Plasmodium knowlesi causes severe and fatal malaria infections in humans, but the process of host cell remodelling that underpins the pathology of this zoonotic parasite is only poorly understood. We have used serial block-face scanning electron microscopy to explore the topography of P. knowlesi-infected red blood cells (RBCs) at different stages of asexual development. The parasite elaborates large flattened cisternae (Sinton Mulligan's clefts) and tubular vesicles in the host cell cytoplasm, as well as parasitophorous vacuole membrane bulges and blebs, and caveolar structures at the RBC membrane. Large invaginations of host RBC cytoplasm are formed early in development, both from classical cytostomal structures and from larger stabilised pores. Although degradation of haemoglobin is observed in multiple disconnected digestive vacuoles, the persistence of large invaginations during development suggests inefficient consumption of the host cell cytoplasm. The parasite eventually occupies ~40% of the host RBC volume, inducing a 20% increase in volume of the host RBC and an 11% decrease in the surface area to volume ratio, which collectively decreases the ability of the P. knowlesi-infected RBCs to enter small capillaries of a human erythrocyte microchannel analyser. Ektacytometry reveals a markedly decreased deformability, whereas correlative light microscopy/scanning electron microscopy and python-based skeleton analysis (Skan) reveal modifications to the surface of infected RBCs that underpin these physical changes. We show that P. knowlesi-infected RBCs are refractory to treatment with sorbitol lysis but are hypersensitive to hypotonic lysis. The observed physical changes in the host RBCs may underpin the pathology observed in patients infected with P. knowlesi.
-
ItemInsights on the impact of mitochondrial organisation on bioenergetics in high-resolution computational models of cardiac cell architecture(PUBLIC LIBRARY SCIENCE, 2018-12)Recent electron microscopy data have revealed that cardiac mitochondria are not arranged in crystalline columns but are organised with several mitochondria aggregated into columns of varying sizes spanning the cell cross-section. This raises the question-how does the mitochondrial arrangement affect the metabolite distributions within cardiomyocytes and what is its impact on force dynamics? Here, we address this question by employing finite element modeling of cardiac bioenergetics on computational meshes derived from electron microscope images. Our results indicate that heterogeneous mitochondrial distributions can lead to significant spatial variation across the cell in concentrations of inorganic phosphate, creatine (Cr) and creatine phosphate (PCr). However, our model predicts that sufficient activity of the creatine kinase (CK) system, coupled with rapid diffusion of Cr and PCr, maintains near uniform ATP and ADP ratios across the cell cross sections. This homogenous distribution of ATP and ADP should also evenly distribute force production and twitch duration with contraction. These results suggest that the PCr shuttle and associated enzymatic reactions act to maintain uniform force dynamics in the cell despite the heterogeneous mitochondrial organization. However, our model also predicts that under hypoxia activity of mitochondrial CK enzymes and diffusion of high-energy phosphate compounds may be insufficient to sustain uniform ATP/ADP distribution and hence force generation.