Cell counting is a vital practice in the maintenance and manipulation of cell cultures. It is a crucial aspect of assessing cell viability and determining proliferation rates, which are integral to maintaining the health and functionality of a culture. Additionally, it is critical for establishing the time of infection in bioreactors and monitoring cell culture response to targeted infection over time. However, when cell counting is performed manually, the time involved can become substantial, particularly when multiple cultures need to be handled in parallel. Automated cell counters, which enable significant time reduction, are commercially available but remain relatively expensive. Here, we present a machine learning (ML) model based on YOLOv4 that is able to perform cell counts with a high accuracy (>95%) for Trypan blue-stained insect cells. Images of two distinctly different cell lines, Trichoplusia ni (High FiveTM; Hi5 cells) and Spodoptera frugiperda (Sf9), were used for training, validation, and testing of the model. The ML model yielded F1 scores of 0.97 and 0.96 for alive and dead cells, respectively, which represents a substantially improved performance over that of other cell counters. Furthermore, the ML model is versatile, as an F1 score of 0.96 was also obtained on images of Trypan blue-stained human embryonic kidney (HEK) cells that the model had not been trained on. Our implementation of the ML model comes with a straightforward user interface and can image in batches, which makes it highly suitable for the evaluation of multiple parallel cultures (e.g. in Design of Experiments). Overall, this approach for accurate classification of cells provides a fast, bias-free alternative to manual counting.@en

Vaccination is the most effective strategy in humanity’s fight against viruses. The concept of vaccination was first proposed by dr. Edward Jenner in the 18th Century, and its efficacy has been proven over time, providing unparalleled protection against viral infections. Large-scale vaccination campaigns have been successful in eradicating diseases such as smallpox in 1980. In recent times, the coronavirus pandemic has brought vaccines to the forefront of public, academic, and industry interest. Apart from conventional live vaccines, which were originally designed and employed by Jenner, there has been a significant expansion in vaccine types, including mRNA, viral vector, attenuated, and inactivated vaccines, each with their own advantages and limitations. Furthermore, virus-like particles (VLPs), a novel class of vaccines, have emerged as a promising alternative to traditional vaccine design strategies, potentially offering improved efficacy and safety. VLPs are multimeric nanoparticles derived from one or more viral structures. They are typically empty or devoid of genetic material, rendering them non-replicative and non-infectious. Despite the proposed absence of genetic material, VLPs possess immune-inducing surface patterns resembling those of the native virus, making them recognizable to the immune system. This unique feature can be exploited for vaccine purposes. VLP-based vaccines offer a safer alternative to traditional vaccines and present an opportunity to create vaccines against viruses that recognize specific viral surface structures instead of a single protein. Large-scale production of VLPs can potentially mitigate many of the drawback of current vaccines, such as extreme storage conditions (mRNA), vector immunity (vector), reversion (attenuated), and high production costs (inactivated). Therefore, VLP-based vaccines hold great promise in the fight against viral infection, by providing a safer and superior product. The safety and efficacy of VLP-based vaccines have been established through extensive research. At present, the market has numerous VLP vaccines available, with HPV VLP vaccines being the most prominent. Its success has set the standard and paved the way for the development of other VLP-based vaccines. In this work, we demonstrated the feasibility of producing and purifying virus-like particles that closely resemble enterovirus A71 (EV71) and coxsackievirus A6 (CVA6). Both viruses have a positive sense RNA genome of ~7.4 kilobase pairs (kbp), which encodes a 260 kDa polyprotein that is stepwise cleaved into eleven viral proteins. Enteroviruses, and most prominently EV71 and CVA6, are the main causative agents of hand, foot, and mouth disease (HFMD). HFMD is named after the characteristic lesions that develop on the hands, feet, mouth, and buttocks of infected individuals. In severe cases, especially among children, the disease can spread to the central nervous system (CNS), resulting in complications such as aseptic meningitis and encephalitis. By employing VLPs, a multivalent vaccine can be developed to target multiple viral strains simultaneously, providing an opportunity for the prevention and control of HFMD. We utilized the baculovirus expression vector system (BEVS) to produce the enterovirus-like particles. For the insect cell lines employed in the BEVS, cell counting is crucial for the maintenance and manipulation of cell cultures. It is a vital aspect of assessing cell viability and determining proliferation rates, which are critical to maintaining the health and functionality of the culture. In Chapter 2, we introduce a machine learning (ML) model based on YOLOv4, capable of performing cell counts with high accuracy (>95%) for Trypan blue-stained insect cells. The model was trained, validated, and tested using images of two distinctly different insect cell lines, Trichoplusia ni (High FiveTM; Hi5 cells) and Spodoptera frugiperda (Sf9). The model achieved F1 scores of 0.97 and 0.96 for alive and dead cells respectively, demonstrating substantially improved performance over other cell counters. Furthermore, the ML model is versatile, as an F1 score of 0.96 was also obtained on images of Trypan blue-stained human embryonic kidney (HEK) cells that the model had not been trained on. Our implementation of the ML model comes with a straightforward user interface and can image in batches, which makes it highly suitable for the evaluation of multiple parallel cultures (e.g., in Design of Experiments). Overall, this approach for accurate classification of cells provides a fast, bias-free alternative to manual counting. Previous studies have shown that the expression of the viral P1 structural proteins and the 3CD protease is sufficient to produce enterovirus-like particles in various organisms. However, there has been a lack of optimization based on the interplay between the three most commonly altered infection parameters, namely multiplicity of infection (MOI), viable cell density at the time of infection (VCD), and the infection period (tinf). In Chapter 3 we addressed this point by using Design of Experiments (DoE) to optimize the production of both EV71 and CVA6 VLPs. Our results indicated distinctively different preferences for infection parameters between the two types of VLPs, with EV71 VLP production preferring low MOI, low VCD, and long infection period, while CVA6 VLP production preferring for high MOI, high VCD and long infection period. Additionally, we developed a purification process for both VLPs, resulting in yields of 158 mg/l and 38 ml/l of culture volume for purified EV71 and CVA6 VLPs, respectively. These concentrations translate into thousands to tens of thousands of vaccines, highlighting the economic potential of enterovirus-like particles for vaccine purposes. Virus-like particles have been identified as a promising approach for the development of a multivalent vaccine. However, their stability is a major issue due to the significantly lower particle integrity lifetimes compared to inactivated vaccines. In Chapter 4, the VLPs produced using the optimized protocols described in Chapter 3 were subjected to biophysical characterization. We employed multiple biophysical techniques such as transmission electron microscopy and atomic force microscopy, to elucidate the origins of the reduced VLP stability (on average 1.5-2-fold lower) in comparison to native virions. Contrary to previous work on enterovirus VLPs, this study demonstrates that a substantial portion (31%) of the produced VLPs were able to encapsidate viral RNA (vRNA). Additionally, this work shows that the presence of vRNA in the capsids may not be the primary factor in enterovirus capsid stability. Furthermore, vRNA may not be the sole factor responsible for triggering the stabilizing viral maturation, and other underlying mechanisms may be at play. To achieve stability comparable to that of virions, artificial methods of inducing viral maturation or alternative means of stabilizing the capsids are of the utmost important to ensure success of VLPs as vaccine candidates. In Chapter 5, we present a protocol for the simultaneous investigation of RNA synthesis dynamics of hundreds of single polymerases with magnetic tweezers (MT). The protocol encompasses the entire process, starting from RNA construct preparation to quantitative and statistical analysis of the MT measurements of RNA synthesis kinetics. The protocol enables the measurement of hundreds of RNA tethers simultaneously, resulting in the characterization of single-molecule dynamics, which is presented in the subsequent chapter. Chapter 6 of this dissertation showcases the potential of magnetic tweezers (MT) for the detailed mechanistic characterization of the viral RNA-dependent RNA polymerase (RdRp). By examining the pause dynamics and probabilities of each viral polymerase, we were able to decipher their individual mechanistic properties. In particular, we investigated the effects of the T-1106 triphosphate, a pyrazine-carboxamide ribonucleotide with antiviral properties, on the enterovirus A71 RdRp. Our result indicated that T-1106 incorporation into nascent RNA led to increased pauses and backtracking by the RdRp. Additionally, we identified the backtracked state as an intermediate used by the RdRp for copy-back RNA synthesis and homologous recombination, suggesting that pyrazine-carboxamide ribonucleotides function by promoting template switching and formation of defective genomes. Finally, we demonstrated that MT can scan promising antiviral candidates and indicate the most propitious ones for further development. The detailed mechanistic characterization of viral RdRp dynamics afforded by MT is a promising avenue for identifying and optimizing antiviral therapeutics. Chapter 7 of this dissertation provides concluding remarks and aims to illuminate potential avenues for subsequent studies. This work can serve as a basis for future investigations, not only from a biophysical perspective but also from a biochemical standpoint. @en

Multiplexed single-molecule magnetic tweezers (MT) have recently been employed to probe the RNA synthesis dynamics of RNA-dependent RNA polymerases (RdRp). Here, we present a protocol for simultaneously probing the RNA synthesis dynamics of hundreds of single polymerases with MT. We describe the preparation of a dsRNA construct for probing single RdRp kinetics. We then detail the measurement of RdRp RNA synthesis kinetics using MT. The protocol is suitable for high-throughput probing of RdRp-targeting antiviral compounds for mechanistic function and efficacy. For complete details on the use and execution of this protocol, please refer to Janissen et al. (2021).

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Viral RNA-dependent RNA polymerases (RdRps) are a target for broad-spectrum antiviral therapeutic agents. Recently, we demonstrated that incorporation of the T-1106 triphosphate, a pyrazine-carboxamide ribonucleotide, into nascent RNA increases pausing and backtracking by the poliovirus RdRp. Here, by monitoring enterovirus A-71 RdRp dynamics during RNA synthesis using magnetic tweezers, we identify the “backtracked” state as an intermediate used by the RdRp for copy-back RNA synthesis and homologous recombination. Cell-based assays and RNA sequencing (RNA-seq) experiments further demonstrate that the pyrazine-carboxamide ribonucleotide stimulates these processes during infection. These results suggest that pyrazine-carboxamide ribonucleotides do not induce lethal mutagenesis or chain termination but function by promoting template switching and formation of defective viral genomes. We conclude that RdRp-catalyzed intra- and intermolecular template switching can be induced by pyrazine-carboxamide ribonucleotides, defining an additional mechanistic class of antiviral ribonucleotides with potential for broad-spectrum activity.

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Although the spatiotemporal structure of the genome is crucial to its biological function, many basic questions remain unanswered on the morphology and segregation of chromosomes. Here, we experimentally show in Escherichia coli that spatial confinement plays a dominant role in determining both the chromosome size and position. In non-dividing cells with lengths increased to 10 times normal, single chromosomes are observed to expand > 4-fold in size. Chromosomes show pronounced internal dynamics but exhibit a robust positioning where single nucleoids reside robustly at mid-cell, whereas two nucleoids self-organize at 1/4 and 3/4 positions. The cell-size-dependent expansion of the nucleoid is only modestly influenced by deletions of nucleoid-associated proteins, whereas osmotic manipulation experiments reveal a prominent role of molecular crowding. Molecular dynamics simulations with model chromosomes and crowders recapitulate the observed phenomena and highlight the role of entropic effects caused by confinement and molecular crowding in the spatial organization of the chromosome. Imaging chromosomes in E. coli within a broad length range, Wu et al. observe that chromosome size and position strongly depend on cell size. They provide evidence that this arises from a confinement-modulated entropic repulsion between chromosome and cytosolic crowders, highlighting the importance of confinement effects in cellular organization.

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