Genomics of Vero cells: Understanding this cell line and its virus-host interactions for improved vaccine production

The Vero cell line is the most used continuous cell line for viral vaccine manufacturing with more than 40 years of accumulated experience in the vaccine industry. Additionally, the Vero cell line has shown high affinity for infection by MERS-CoV, SARS-CoV and recently SARS-CoV-2, emerging as an important discovery and screening tool to support the global research and development efforts in this COVID-19 pandemic. Furthermore, Vero cells anchorage-dependent use renders scaling-up challenging and operations very labor intensive which affects cost effectiveness. Thus, efforts to adapt Vero cells to suspension cultures have been invested but hurdles such as the long doubling time and low cell viability remain to be addressed. However, the lack of a reference genome for the Vero cell line has limited our understanding of host-virus interactions underlying such affinity of the Vero cell towards key emerging pathogens, and more importantly our ability to re-design high-yield vaccine production processes using Vero genome editing. In this study, we present an annotated highly contiguous 2.9 Gb assembly of the Vero cell genome. Please click Download on the upper right corner to see the full abstract

De novo assembly of the olive fruit fly (Bactrocera oleae) genome with linked-reads and long-read technologies minimizes gaps and provides exceptional Y chromosome assembly

Background: The olive fruit fly, Bactrocera oleae, is the most important pest in the olive fruit agribusiness industry. This is because female flies lay their eggs in the unripe fruits and upon hatching the larvae feed on the fruits thus destroying them. The lack of a high-quality genome and other genomic and transcriptomic data has hindered progress in understanding the fly’s biology and proposing alternative control methods to pesticide use. Results: Genomic DNA was sequenced from male and female Demokritos strain flies, maintained in the laboratory for over 45 years. We used short-, mate-pair-, and long-read sequencing technologies to generate a combined male-female genome assembly (GenBank accession GCA_001188975.2). Genomic DNA sequencing from male insects using 10x Genomics linked-reads technology followed by mate-pair and long-read scaffolding and gap-closing generated a highly contiguous 489 Mb genome with a scaffold N50 of 4.69 Mb and L50 of 30 scaffolds (GenBank accession GCA_001188975.4). RNA-seq data generated from 12 tissues and/or developmental stages allowed for genome annotation. Short reads from both males and females and the chromosome quotient method enabled identification of Y-chromosome scaffolds which were extensively validated by PCR. Conclusions: The high-quality genome generated represents a critical tool in olive fruit fly research. We provide an extensive RNA-seq data set, and genome annotation, critical towards gaining an insight into the biology of the olive fruit fly. In addition, elucidation of Y-chromosome sequences will advance our understanding of the Y-chromosome’s organization, function and evolution and is poised to provide avenues for sterile insect technique approaches

The living microarray: a high-throughput platform for measuring transcription dynamics in single cells

<p>Abstract</p> <p>Background</p> <p>Current methods of measuring transcription in high-throughput have led to significant improvements in our knowledge of transcriptional regulation and Systems Biology. However, endpoint measurements obtained from methods that pool populations of cells are not amenable to studying time-dependent processes that show cell heterogeneity.</p> <p>Results</p> <p>Here we describe a high-throughput platform for measuring transcriptional changes in real time in single mammalian cells. By using reverse transfection microarrays we are able to transfect fluorescent reporter plasmids into 600 independent clusters of cells plated on a single microscope slide and image these clusters every 20 minutes. We use a fast-maturing, destabilized and nuclear-localized reporter that is suitable for automated segmentation to accurately measure promoter activity in single cells. We tested this platform with synthetic drug-inducible promoters that showed robust induction over 24 hours. Automated segmentation and tracking of over 11 million cell images during this period revealed that cells display substantial heterogeneity in their responses to the applied treatment, including a large proportion of transfected cells that do not respond at all.</p> <p>Conclusions</p> <p>The results from our single-cell analysis suggest that methods that measure average cellular responses, such as DNA microarrays, RT-PCR and chromatin immunoprecipitation, characterize a response skewed by a subset of cells in the population. Our method is scalable and readily adaptable to studying complex systems, including cell proliferation, differentiation and apoptosis.</p

The transcriptional landscape of Shh medulloblastoma

© The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.Sonic hedgehog medulloblastoma encompasses a clinically and molecularly diverse group of cancers of the developing central nervous system. Here, we use unbiased sequencing of the transcriptome across a large cohort of 250 tumors to reveal differences among molecular subtypes of the disease, and demonstrate the previously unappreciated importance of non-coding RNA transcripts. We identify alterations within the cAMP dependent pathway (GNAS, PRKAR1A) which converge on GLI2 activity and show that 18% of tumors have a genetic event that directly targets the abundance and/or stability of MYCN. Furthermore, we discover an extensive network of fusions in focally amplified regions encompassing GLI2, and several loss-of-function fusions in tumor suppressor genes PTCH1, SUFU and NCOR1. Molecular convergence on a subset of genes by nucleotide variants, copy number aberrations, and gene fusions highlight the key roles of specific pathways in the pathogenesis of Sonic hedgehog medulloblastoma and open up opportunities for therapeutic intervention.info:eu-repo/semantics/publishedVersio

3D tracking of non-rigid articulated objects

Articulated objects can be found in many living beings. Tracking is essential if we want to interpret the behavior of such objects. This thesis describes a framework for learning the relationship between the state and the appearance of the object. It will also show how to use this representation to track the state of the articulated object.The learning phase of the method results in populations of models that describe the appearance of small regions of the object for small regions of the state space. To efficiently train off-line, it is necessary to model the appearance of the object as function of the state. The local models use Principal Component Analysis (PCA) on windowed regions of the projected object. Manifolds in PCA subspace represent the appearance of the small local regions as they undergo deformations.The tracking algorithm recursively matches the link appearances while searching in the state space of the articulated object. To match the object appearance to the model, a coarse search finds the models that are active. The error of the projected object image is then minimized (at the new unknown state) in model subspace by fine-tuning the state.Algorithm performance is evaluated on real and synthetic data of a 4 d.o.f. finger following arbitrary 3-D paths. The results show that the local PCA models capture the deformations successfully even after discarding some of the bases. These deformations account for key features that are essential to the matching process. Also, the way in which the appearance data is partitioned allows for a fast and efficient caching strategy, thus allowing the algorithm to meet real-time constraints. Finally the merging of predictions and observations makes the algorithm very robust to outliers

Multilevel Space-Time Aggregation for Bright Field Cell Microscopy Segmentation and Tracking

A multilevel aggregation method is applied to the problem of segmenting live cell bright field microscope images. The method employed is a variant of the so-called “Segmentation by Weighted Aggregation” technique, which itself is based on Algebraic Multigrid methods. The variant of the method used is described in detail, and it is explained how it is tailored to the application at hand. In particular, a new scale-invariant “saliency measure” is proposed for deciding when aggregates of pixels constitute salient segments that should not be grouped further. It is shown how segmentation based on multilevel intensity similarity alone does not lead to satisfactory results for bright field cells. However, the addition of multilevel intensity variance (as a measure of texture) to the feature vector of each aggregate leads to correct cell segmentation. Preliminary results are presented for applying the multilevel aggregation algorithm in space time to temporal sequences of microscope images, with the goal of obtaining space-time segments (“object tunnels”) that track individual cells. The advantages and drawbacks of the space-time aggregation approach for segmentation and tracking of live cells in sequences of bright field microscope images are presented, along with a discussion on how this approach may be used in the future work as a building block in a complete and robust segmentation and tracking system