scholarly journals The Replication Cycle of Varicella-Zoster Virus: Analysis of the Kinetics of Viral Protein Expression, Genome Synthesis, and Virion Assembly at the Single-Cell Level

2009 ◽  
Vol 83 (8) ◽  
pp. 3904-3918 ◽  
Author(s):  
Mike Reichelt ◽  
Jennifer Brady ◽  
Ann M. Arvin

ABSTRACT Varicella-zoster virus (VZV) is a human alphaherpesvirus that is highly cell associated in cell culture. Because cell-free virus yields are too low to permit the synchronous infections needed for time-resolved analyses, information is lacking about the sequence of events during the VZV replication cycle. To address this challenge, we differentially labeled VZV-infected inoculum cells (input) and uninfected (output) cells with fluorescent cell dyes or endocytosed nanogold particles and evaluated newly infected cells by confocal immunofluorescence or electron microscopy (EM) at the single-cell level at defined intervals. We demonstrated the spatiotemporal expression of six major VZV proteins, ORF61, IE62, IE63, ORF29, ORF23, and gE, representing all putative kinetic classes, for the first time. Newly synthesized ORF61, as well as IE62, the major VZV transactivator, appeared within 1 h, and they were targeted to different subnuclear compartments. The formation of VZV DNA replication compartments started between 4 and 6 h, involved recruitment of ORF29 to putative IE62 prereplication sites, and resulted in large globular nuclear compartments where newly synthesized viral DNA accumulated. Although considered a late protein, gE accumulated in the Golgi compartment at as early as 4 h. ORF23 capsid protein was present at 9 h. The assembly of viral nucleocapsids and mature enveloped VZ virions was detected by 9 to 12 h by time-resolved EM. Although syncytium formation is a hallmark of VZV infection, infection of neighboring cells did not require cell-cell fusion; its occurrence from 9 h is likely to amplify VZV replication. Our results define the productive cycle of VZV infection in a single cell as occurring in 9 to 12 h.

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model,we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.


RSC Advances ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 5384-5392
Author(s):  
Abd Alaziz Abu Quba ◽  
Gabriele E. Schaumann ◽  
Mariam Karagulyan ◽  
Doerte Diehl

Setup for a reliable cell-mineral interaction at the single-cell level, (a) study of the mineral by a sharp tip, (b) study of the bacterial modified probe by a characterizer, (c) cell-mineral interaction, (d) subsequent check of the modified probe.


2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.


2021 ◽  
Author(s):  
Cecile COURREGES ◽  
Mélanie Bonnecaze ◽  
Delphine Flahaut ◽  
Sophie Nolivos ◽  
Regis Grimaud ◽  
...  

A chemical fingerprint of Escherichia coli cells surface labeled by gelatin coated gold nanoparticles was obtained by combining Auger Electron Spectroscopy (AES) for single cell level chemical images, and Time-of-Flight...


2021 ◽  
Vol 56 (3) ◽  
pp. 253-254
Author(s):  
Mary Mohrin ◽  
Heinrich Jasper

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