Isolation and reconstruction of cardiac mitochondria from SBEM images using a deep learning-based method

Beyond the surface: Investigation of tumorsphere morphology using volume electron microscopy

The advent of volume electron microscopy (vEM) has provided unprecedented insights into cellular and subcellular organization, revolutionizing our understanding of cancer biology. This study presents a previously unexplored comparative analysis of the ultrastructural disparities between cancer cells cultured as monolayers and tumorspheres. By integrating a robust workflow that incorporates high-pressure freezing followed by freeze substitution (HPF/FS), serial block face scanning electron microscopy (SBF-SEM), manual and deep learning-based segmentation, and statistical analysis, we have successfully generated three-dimensional (3D) reconstructions of monolayer and tumorsphere cells, including their subcellular organelles. Our findings reveal a significant degree of variation in cellular morphology in tumorspheres. We observed the increased prevalence of nuclear envelope invaginations in tumorsphere cells compared to monolayers. Furthermore, we detected a diverse range of mitochondrial morphologies exclusively in tumorsphere cells, as well as intricate cellular interconnectivity within the tumorsphere architecture. These remarkable ultrastructural differences emphasize the use of tumorspheres as a superior model for cancer research due to their relevance to in vivo conditions. Our results strongly advocate for the utilization of tumorsphere cells in cancer research studies, enhancing the precision and relevance of experimental outcomes, and ultimately accelerating therapeutic advancements.

Mitochondria morphometry in 3D datasets obtained from mouse brains with serial block-face scanning electron microscopy

The dysfunction of mitochondria is linked with many diseases. In the nervous system, evidence of their implication in neurodegenerative disease is growing. Mitochondria health is assessed by their impact on cellular metabolism but alterations in their morphologies and locations in the cells can also be markers of dysfunctions. Light microscopy techniques allow us to look at mitochondria in vivo in cells or tissue. But in the case of the nervous system, in order to assess the precise location of mitochondria in different cell types and neuronal compartments (cell bodies, dendrites or axons), electron microscopy is required. While the percentage of volume occupied by mitochondria can be assessed on 2D images, alterations in length, branching, and interactions with other organelles require three-dimensional (3D) segmentation of mitochondria in volumes imaged at ultrastructural level. Nowadays three-dimensional volume electron microscopy (vEM) imaging techniques such as serial block face scanning electron microscopy (SBF-SEM) enable us to image 3D volumes of tissue at ultrastructural level and can be done routinely. Segmentation of all the neuropil is also successfully achieved at a large scale in the nervous system. Here, we show a workflow based on open access resources, which allows us to image, segment, and analyze mitochondria in 3D volumes of regions of interest in the mouse brain. Taking advantage of recent developments, e.g., pre-trained models for mitochondria, we speed up the reconstruction and analysis. We also critically assess the impact on the results of the different reconstruction methods chosen and the level of manual corrections invested.

Electron microscopy of cardiac 3D nanodynamics: form, function, future

The 3D nanostructure of the heart, its dynamic deformation during cycles of contraction and relaxation, and the effects of this deformation on cell function remain largely uncharted territory. Over the past decade, the first inroads have been made towards 3D reconstruction of heart cells, with a native resolution of around 1 nm³, and of individual molecules relevant to heart function at a near-atomic scale. These advances have provided access to a new generation of data and have driven the development of increasingly smart, artificial intelligence-based, deep-learning image-analysis algorithms. By high-pressure freezing of cardiomyocytes with millisecond accuracy after initiation of an action potential, pseudodynamic snapshots of contraction-induced deformation of intracellular organelles can now be captured. In combination with functional studies, such as fluorescence imaging, exciting insights into cardiac autoregulatory processes at nano-to-micro scales are starting to emerge. In this Review, we discuss the progress in this fascinating new field to highlight the fundamental scientific insight that has emerged, based on technological breakthroughs in biological sample preparation, 3D imaging and data analysis; to illustrate the potential clinical relevance of understanding 3D cardiac nanodynamics; and to predict further progress that we can reasonably expect to see over the next 10 years.