The VRNetzer platform enables interactive network analysis in Virtual Reality

A metaverse laboratory setup for interactive atom visualization and manipulation with scanning probe microscopy

We present a metaverse laboratory system that integrates mixed reality (MR) technologies with scanning probe microscopy (SPM) for interactive atomic-scale visualization and manipulation. In order to accommodate both the visualization and input of SPM data in a virtual environment and the physical interaction with SPM-related equipment in the laboratory, the system incorporates a virtual reality (VR) and augmented reality (AR) framework to enable seamless switching between these two environments. Utilizing the pose-tracking capabilities in AR, users can intuitively interact with virtual interface elements and three-dimensional objects through physical hand gesture input to control SPM parameters and probe positioning. The system provides real-time visualization of scanned surfaces at the atomic scale in the virtual environment, enabling immediate feedback during experiments. To demonstrate the system's capabilities, we performed atomic manipulation experiments using hand gestures for lateral probe positioning, showing how MR-enhanced SPM can simplify nanoscale operations and improve experimental efficiency. Our integrated MR-SPM system allows users to conduct experiments via the metaverse platform while enhancing the human-instrument interaction experience. It extends the practical utility required for both real-time physical and virtual environment SPM operations in the laboratory, making nanoscale research more accessible and intuitive.

Blending space and time to talk about cancer in extended reality

We introduce a proof-of-concept extended reality (XR) environment for discussing cancer, presenting genomic information from multiple tumour sites in the context of 3D tumour models generated from CT scans. This tool enhances multidisciplinary discussions. Clinicians and cancer researchers explored its use in oncology, sharing perspectives on XR's potential for use in molecular tumour boards, clinician-patient communication, and education. XR serves as a universal language, fostering collaborative decision-making in oncology.

Advancements in risk stratification and management strategies in primary cardiovascular prevention

Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality worldwide, highlighting the urgent need for advancements in risk assessment and management strategies. Although significant progress has been made recently, identifying and managing apparently healthy individuals at a higher risk of developing atherosclerosis and those with subclinical atherosclerosis still poses significant challenges. Traditional risk assessment tools have limitations in accurately predicting future events and fail to encompass the complexity of the atherosclerosis trajectory. In this review, we describe novel approaches in biomarkers, genetics, advanced imaging techniques, and artificial intelligence that have emerged to address this gap. Moreover, polygenic risk scores and imaging modalities such as coronary artery calcium scoring, and coronary computed tomography angiography offer promising avenues for enhancing primary cardiovascular risk stratification and personalised intervention strategies. On the other hand, interventions aiming against atherosclerosis development or promoting plaque regression have gained attention in primary ASCVD prevention. Therefore, the potential role of drugs like statins, ezetimibe, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, omega-3 fatty acids, antihypertensive agents, as well as glucose-lowering and anti-inflammatory drugs are also discussed. Since findings regarding the efficacy of these interventions vary, further research is still required to elucidate their mechanisms of action, optimize treatment regimens, and determine their long-term effects on ASCVD outcomes. In conclusion, advancements in strategies addressing atherosclerosis prevention and plaque regression present promising avenues for enhancing primary ASCVD prevention through personalised approaches tailored to individual risk profiles. Nevertheless, ongoing research efforts are imperative to refine these strategies further and maximise their effectiveness in safeguarding cardiovascular health.

Visualization and exploration of linked data using virtual reality

In this report, we analyse the use of virtual reality (VR) as a method to navigate and explore complex knowledge graphs. Over the past few decades, linked data technologies [Resource Description Framework (RDF) and Web Ontology Language (OWL)] have shown to be valuable to encode such graphs and many tools have emerged to interactively visualize RDF. However, as knowledge graphs get larger, most of these tools struggle with the limitations of 2D screens or 3D projections. Therefore, in this paper, we evaluate the use of VR to visually explore SPARQL Protocol and RDF Query Language (SPARQL) (construct) queries, including a series of tutorial videos that demonstrate the power of VR (see Graph2VR tutorial playlist: https://www.youtube.com/playlist?list=PLRQCsKSUyhNIdUzBNRTmE-_JmuiOEZbdH). We first review existing methods for Linked Data visualization and then report the creation of a prototype, Graph2VR. Finally, we report a first evaluation of the use of VR for exploring linked data graphs. Our results show that most participants enjoyed testing Graph2VR and found it to be a useful tool for graph exploration and data discovery. The usability study also provides valuable insights for potential future improvements to Linked Data visualization in VR.

Transomics2cytoscape: an automated software for interpretable 2.5-dimensional visualization of trans-omic networks

Biochemical network visualization is one of the essential technologies for mechanistic interpretation of omics data. In particular, recent advances in multi-omics measurement and analysis require the development of visualization methods that encompass multiple omics data. Visualization in 2.5 dimension (2.5D visualization), which is an isometric view of stacked X-Y planes, is a convenient way to interpret multi-omics/trans-omics data in the context of the conventional layouts of biochemical networks drawn on each of the stacked omics layers. However, 2.5D visualization of trans-omics networks is a state-of-the-art method that primarily relies on time-consuming human efforts involving manual drawing. Here, we present an R Bioconductor package 'transomics2cytoscape' for automated visualization of 2.5D trans-omics networks. We confirmed that transomics2cytoscape could be used for rapid visualization of trans-omics networks presented in published papers within a few minutes. Transomics2cytoscape allows for frequent update/redrawing of trans-omics networks in line with the progress in multi-omics/trans-omics data analysis, thereby enabling network-based interpretation of multi-omics data at each research step. The transomics2cytoscape source code is available at https://github.com/ecell/transomics2cytoscape .

An architecture for collaboration in systems biology at the age of the Metaverse

As the current state of the Metaverse is largely driven by corporate interests, which may not align with scientific goals and values, academia should play a more active role in its development. Here, we present the challenges and solutions for building a Metaverse that supports systems biology research and collaboration. Our solution consists of two components: Kosmogora, a server ensuring biological data access, traceability, and integrity in the context of a highly collaborative environment such as a metaverse; and ECellDive, a virtual reality application to explore, interact, and build upon the data managed by Kosmogora. We illustrate the synergy between the two components by visualizing a metabolic network and its flux balance analysis. We also argue that the Metaverse of systems biology will foster closer communication and cooperation between experimentalists and modelers in the field.

Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico

With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.

Metaverse Wearables for Immersive Digital Healthcare: A Review

The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all-encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high-quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post-pandemic era.

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.

Digital Phenotyping: A Game Changer for the Broiler Industry

In response to escalating global demand for poultry, the industry grapples with an array of intricate challenges, from enhancing productivity to improving animal welfare and attenuating environmental impacts. This comprehensive review explores the transformative potential of digital phenotyping, an emergent technological innovation at the cusp of dramatically reshaping broiler production. The central aim of this study is to critically examine digital phenotyping as a pivotal solution to these multidimensional industry conundrums. Our investigation spotlights the profound implications of 'digital twins' in the burgeoning field of broiler genomics, where the production of exact digital counterparts of physical entities accelerates genomics research and its practical applications. Further, this review probes into the ongoing advancements in the research and development of a context-sensitive, multimodal digital phenotyping platform, custom-built to monitor broiler health. This paper critically evaluates this platform's potential in revolutionizing health monitoring, fortifying the resilience of broiler production, and fostering a harmonious balance between productivity and sustainability. Subsequently, the paper provides a rigorous assessment of the unique challenges that may surface during the integration of digital phenotyping within the industry. These span from technical and economic impediments to ethical deliberations, thus offering a comprehensive perspective. The paper concludes by highlighting the game-changing potential of digital phenotyping in the broiler industry and identifying potential future directions for the field, underlining the significance of continued research and development in unlocking digital phenotyping's full potential. In doing so, it charts a course towards a more robust, sustainable, and productive broiler industry. The insights garnered from this study hold substantial value for a broad spectrum of stakeholders in the broiler industry, setting the stage for an imminent technological evolution in poultry production.

Extended reality for biomedicine

Extended reality (XR) refers to an umbrella of methods that allows users to be immersed in a three-dimensional (3D) or a 4D (spatial + temporal) virtual environment to different extents, including virtual reality (VR), augmented reality (AR), and mixed reality (MR). While VR allows a user to be fully immersed in a virtual environment, AR and MR overlay virtual objects over the real physical world. The immersion and interaction of XR provide unparalleled opportunities to extend our world beyond conventional lifestyles. While XR has extensive applications in fields such as entertainment and education, its numerous applications in biomedicine create transformative opportunities in both fundamental research and healthcare. This Primer outlines XR technology from instrumentation to software computation methods, delineating the biomedical applications that have been advanced by state-of-the-art techniques. We further describe the technical advances overcoming current limitations in XR and its applications, providing an entry point for professionals and trainees to thrive in this emerging field.

Extended reality gives digital agricultural biotechnology a new dimension

Facing up to the global challenges of designing climate-resilient biotech crops involves a great deal of out-of-the-box thinking. Extended reality is coming of age in digital agricultural biotechnology. Here, we seek to stimulate technological innovation by empowering future innovators, researchers, academics, and startups to think and partner creatively.

Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Bioinformatics and the Metaverse: Are We Ready?

COVID-19 forced humanity to think about new ways of working globally without physically being present with other people, and eXtended Reality (XR) systems (defined as Virtual Reality, Augmented Reality and Mixed Reality) offer a potentially elegant solution. Previously seen as mainly for gaming, commercial and research institutions are investigating XR solutions to solve real world problems from training, simulation, mental health, data analysis, and studying disease progression. More recently large corporations such as Microsoft and Meta have announced they are developing the Metaverse as a new paradigm to interact with the digital world. This article will look at how visualization can leverage the Metaverse in bioinformatics research, the pros and cons of this technology, and what the future may hold.

Network cartographs for interpretable visualizations

Networks offer an intuitive visual representation of complex systems. Important network characteristics can often be recognized by eye and, in turn, patterns that stand out visually often have a meaningful interpretation. In conventional network layout algorithms, however, the precise determinants of a node's position within a layout are difficult to decipher and to control. Here we propose an approach for directly encoding arbitrary structural or functional network characteristics into node positions. We introduce a series of two- and three-dimensional layouts, benchmark their efficiency for model networks, and demonstrate their power for elucidating structure-to-function relationships in large-scale biological networks.

CellexalVR: A virtual reality platform to visualize and analyze single-cell omics data

Single-cell RNAseq is a routinely used method to explore heterogeneity within cell populations. Data from these experiments are often visualized using dimension reduction methods such as UMAP and tSNE, where each cell is projected in two or three dimensional space. Three-dimensional projections can be more informative for larger and complex datasets because they are less prone to merging and flattening similar cell-types/clusters together. However, visualizing and cross-comparing 3D projections using current software on conventional flat-screen displays is far from optimal as they are still essentially 2D, and lack meaningful interaction between the user and the data. Here we present CellexalVR (www.cellexalvr.med.lu.se), a feature-rich, fully interactive virtual reality environment for the visualization and analysis of single-cell experiments that allows researchers to intuitively and collaboratively gain an understanding of their data.

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Single-cell experiments are often visualized when embedded into three dimensions

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CellexalVR is a virtual reality environment to visualize all data simultaneously

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Teams can analyze single-cell experiments together in VR regardless of location

The VRNetzer platform enables interactive network analysis in Virtual Reality

Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods.

Data-rich networks can be difficult to interpret beyond a certain size. Here, the authors introduce a platform that uses virtual reality to allow the visual exploration of large networks, while interfacing with data repositories and other analytical methods to improve the interpretation of big data.