Origins of Enterovirus Replication Organelles Established by Whole-Cell Electron Microscopy

ABSTRACTEnterovirus genome replication occurs at virus-induced structures derived from cellular membranes and lipids. However, the origin of these replication organelles (ROs) remains uncertain. Ultrastructural evidence of the membrane donor is lacking, suggesting that the sites of its transition into ROs are rare or fleeting. To overcome this challenge, we combined live-cell imaging and serial block-face scanning electron microscopy of whole cells to capture emerging enterovirus ROs. The first foci of fluorescently labeled viral protein correlated with ROs connected to the endoplasmic reticulum (ER) and preceded the appearance of ROs stemming from thetrans-Golgi network. Whole-cell data sets further revealed striking contact regions between ROs and lipid droplets that may represent a route for lipid shuttling to facilitate RO proliferation and genome replication. Our data provide direct evidence that enteroviruses use ER and then Golgi membranes to initiate RO formation, demonstrating the remarkable flexibility with which enteroviruses usurp cellular organelles.IMPORTANCEEnteroviruses are causative agents of a range of human diseases. The replication of these viruses within cells relies on specialized membranous structures termed replication organelles (ROs) that form during infection but whose origin remains elusive. To capture the emergence of enterovirus ROs, we use correlative light and serial block-face scanning electron microscopy, a powerful method to pinpoint rare events in their whole-cell ultrastructural context. RO biogenesis was found to occur first at ER and then at Golgi membranes. Extensive contacts were found between early ROs and lipid droplets (LDs), which likely serve to provide LD-derived lipids required for replication. Together, these data establish the dual origin of enterovirus ROs and the chronology of their biogenesis at different supporting cellular membranes.

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Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy

Journal of Imaging ◽

10.3390/jimaging5090075 ◽

2019 ◽

Vol 5 (9) ◽

pp. 75 ◽

Cited By ~ 5

Author(s):

Cefa Karabağ ◽

Martin L. Jones ◽

Christopher J. Peddie ◽

Anne E. Weston ◽

Lucy M. Collinson ◽

...

Keyword(s):

Electron Microscopy ◽

Scanning Electron Microscopy ◽

Nuclear Envelope ◽

Hela Cells ◽

Nuclear Region ◽

Pixel Intensity ◽

Whole Cell ◽

Face Scanning ◽

Block Face ◽

Scanning Electron

This paper describes an unsupervised algorithm, which segments the nuclear envelope of HeLa cells imaged by Serial Block Face Scanning Electron Microscopy. The algorithm exploits the variations of pixel intensity in different cellular regions by calculating edges, which are then used to generate superpixels. The superpixels are morphologically processed and those that correspond to the nuclear region are selected through the analysis of size, position, and correspondence with regions detected in neighbouring slices. The nuclear envelope is segmented from the nuclear region. The three-dimensional segmented nuclear envelope is then modelled against a spheroid to create a two-dimensional (2D) surface. The 2D surface summarises the complex 3D shape of the nuclear envelope and allows the extraction of metrics that may be relevant to characterise the nature of cells. The algorithm was developed and validated on a single cell and tested in six separate cells, each with 300 slices of 2000 × 2000 pixels. Ground truth was available for two of these cells, i.e., 600 hand-segmented slices. The accuracy of the algorithm was evaluated with two similarity metrics: Jaccard Similarity Index and Mean Hausdorff distance. Jaccard values of the first/second segmentation were 93%/90% for the whole cell, and 98%/94% between slices 75 and 225, as the central slices of the nucleus are more regular than those on the extremes. Mean Hausdorff distances were 9/17 pixels for the whole cells and 4/13 pixels for central slices. One slice was processed in approximately 8 s and a whole cell in 40 min. The algorithm outperformed active contours in both accuracy and time.

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