Automated segmentation of cardiomyocyte Z-disks from high-throughput scanning electron microscopy data

Exploring developmental MeHg impact on extraembryonic and cardiac vessels and its effect on cardiomyocyte contractility

The toxicity of methylmercury (MeHg) during embryonic development is a relevant issue that remains unclear and deserves investigation. In this sense, there is evidence that links the intake of contaminated food with cardiovascular pathologies in human adults and children. Thus, this study aimed to verify the impact of MeHg on the structure and integrity of extraembryonic and cardiac blood vessels and the contractile function of cardiomyocytes, also evaluating embryonic weight and the cardiosomatic index (CSI). Thus, chicken embryos, used as an experimental model, were exposed to a single dose of 0.1 μg MeHg/50 μl saline at E1.5 and analyzed at E10. After exposure, an increase in the number of extraembryonic blood vessels and the veins of the cardiac tissue was observed. These increases were accompanied by a reduction in the content of VEGF and VCAM proteins related to vessel growth and adhesiveness. Together, these results were related to reduced nitrite (NOx) levels. Furthermore, MeHg reduces the number of sarcomeres and increases the content of cardiac troponin I (cTnI), a protein that regulates contraction. In general, exposure to MeHg affected the integrity of extraembryonic and cardiac vessels and the contractile function of cardiomyocytes, which had a systemic impact evidenced by the reduction in embryonic weight gain and CSI.

CardioVinci: building blocks for virtual cardiac cells using deep learning

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’.

The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?

Modern biology and biomedicine are undergoing a big data explosion, needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome project: a framework and approach for creating, sharing, and using biophysics-based computational models of single-cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date toward creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and reusability of computational models of cells at scale. We conclude with a look forward at steps that will help fully realize this exciting new field of mechanistic biomedical data science. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Application of plasma polymerized pyrrole nanoparticles to prevent or reduce de-differentiation of adult rat ventricular cardiomyocytes

Cardiovascular diseases are the leading cause of death in the world, cell therapies have been shown to recover cardiac function in animal models. Biomaterials used as scaffolds can solve some of the problems that cell therapies currently have, plasma polymerized pyrrole (PPPy) is a biomaterial that has been shown to promote cell adhesion and survival. The present research aimed to study PPPy nanoparticles (PPPyN) interaction with adult rat ventricular cardiomyocytes (ARVC), to explore whether PPPyN could be employed as a nanoscaffold and develop cardiac microtissues. PPPyN with a mean diameter of 330 nm were obtained, the infrared spectrum showed that some pyrrole rings are fragmented and that some fragments of the ring can be dehydrogenated during plasma synthesis, it also showed the presence of amino groups in the structure of PPPyN. PPPyN had a significant impact on the ARVC´s shape, delaying dedifferentiation, necrosis, and apoptosis processes, moreover, the cardiomyocytes formed cell aggregates up to 1.12 mm² with some aligned cardiomyocytes and generated fibers on its surface similar to cardiac extracellular matrix. PPPyN served as a scaffold for adult ARVC. Our results indicate that PPPyN-scaffold is a biomaterial that could have potential application in cardiac cell therapy (CCT).

EM-stellar: benchmarking deep learning for electron microscopy image segmentation

MOTIVATION

The inherent low contrast of electron microscopy (EM) datasets presents a significant challenge for rapid segmentation of cellular ultrastructures from EM data. This challenge is particularly prominent when working with high resolution big-datasets that are now acquired using electron tomography and serial block-face imaging techniques. Deep learning (DL) methods offer an exciting opportunity to automate the segmentation process by learning from manual annotations of a small sample of EM data. While many DL methods are being rapidly adopted to segment EM data no benchmark analysis has been conducted on these methods to date.

RESULTS

We present EM-stellar, a platform that is hosted on Google Colab that can be used to benchmark the performance of a range of state-of-the-art DL methods on user-provided datasets. Using EM-Stellar we show that the performance of any DL method is dependent on the properties of the images being segmented. It also follows that no single DL method performs consistently across all performance evaluation metrics.

AVAILABILITY

EM-stellar (code and data) is written in Python and is freely available under MIT license on GitHub (https://github.com/cellsmb/em-stellar).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.