Virtual reality for 3D histology: multi-scale visualization of organs with interactive feature exploration

Virtual reality (VR) enables data visualization in an immersive and engaging manner, and it can be used for creating ways to explore scientific data. Here, we use VR for visualization of 3D histology data, creating a novel interface for digital pathology. Our contribution includes 3D modeling of a whole organ and embedded objects of interest, fusing the models with associated quantitative features and full resolution serial section patches, and implementing the virtual reality application. Our VR application is multi-scale in nature, covering two object levels representing different ranges of detail, namely organ level and sub-organ level. In addition, the application includes several data layers, including the measured histology image layer and multiple representations of quantitative features computed from the histology. In this interactive VR application, the user can set visualization properties, select different samples and features, and interact with various objects. In this work, we used whole mouse prostates (organ level) with prostate cancer tumors (sub-organ objects of interest) as example cases, and included quantitative histological features relevant for tumor biology in the VR model. Due to automated processing of the histology data, our application can be easily adopted to visualize other organs and pathologies from various origins. Our application enables a novel way for exploration of high-resolution, multidimensional data for biomedical research purposes, and can also be used in teaching and researcher training

Integrating light-sheet imaging with virtual reality to recapitulate developmental cardiac mechanics

Currently, there is a limited ability to interactively study developmental cardiac mechanics and physiology. We therefore combined light-sheet fluorescence microscopy (LSFM) with virtual reality (VR) to provide a hybrid platform for 3D architecture and time-dependent cardiac contractile function characterization. By taking advantage of the rapid acquisition, high axial resolution, low phototoxicity, and high fidelity in 3D and 4D (3D spatial + 1D time or spectra), this VR-LSFM hybrid methodology enables interactive visualization and quantification otherwise not available by conventional methods, such as routine optical microscopes. We hereby demonstrate multiscale applicability of VR-LSFM to (a) interrogate skin fibroblasts interacting with a hyaluronic acid–based hydrogel, (b) navigate through the endocardial trabecular network during zebrafish development, and (c) localize gene therapy-mediated potassium channel expression in adult murine hearts. We further combined our batch intensity normalized segmentation algorithm with deformable image registration to interface a VR environment with imaging computation for the analysis of cardiac contraction. Thus, the VR-LSFM hybrid platform demonstrates an efficient and robust framework for creating a user-directed microenvironment in which we uncovered developmental cardiac mechanics and physiology with high spatiotemporal resolution

A Study of Mental Maps in Immersive Network Visualization

The visualization of a network influences the quality of the mental map that the viewer develops to understand the network. In this study, we investigate the effects of a 3D immersive visualization environment compared to a traditional 2D desktop environment on the comprehension of a network's structure. We compare the two visualization environments using three tasks--interpreting network structure, memorizing a set of nodes, and identifying the structural changes--commonly used for evaluating the quality of a mental map in network visualization. The results show that participants were able to interpret network structure more accurately when viewing the network in an immersive environment, particularly for larger networks. However, we found that 2D visualizations performed better than immersive visualization for tasks that required spatial memory.Comment: IEEE Pacific Visualization Symposium 202

Experimental and Computational Methods for the Study of Cerebral Organoids: A Review

Cerebral (or brain) organoids derived from human cells have enormous potential as physiologically relevant downscaled in vitro models of the human brain. In fact, these stem cell-derived neural aggregates resemble the three-dimensional (3D) cytoarchitectural arrangement of the brain overcoming not only the unrealistic somatic flatness but also the planar neuritic outgrowth of the two-dimensional (2D) in vitro cultures. Despite the growing use of cerebral organoids in scientific research, a more critical evaluation of their reliability and reproducibility in terms of cellular diversity, mature traits, and neuronal dynamics is still required. Specifically, a quantitative framework for generating and investigating these in vitro models of the human brain is lacking. To this end, the aim of this review is to inspire new computational and technology driven ideas for methodological improvements and novel applications of brain organoids. After an overview of the organoid generation protocols described in the literature, we review the computational models employed to assess their formation, organization and resource uptake. The experimental approaches currently provided to structurally and functionally characterize brain organoid networks for studying single neuron morphology and their connections at cellular and sub-cellular resolution are also discussed. Well-established techniques based on current/voltage clamp, optogenetics, calcium imaging, and Micro-Electrode Arrays (MEAs) are proposed for monitoring intra- and extra-cellular responses underlying neuronal dynamics and functional connections. Finally, we consider critical aspects of the established procedures and the physiological limitations of these models, suggesting how a complement of engineering tools could improve the current approaches and their applications

Ele- ja ohjainkäyttökokemus virtuaalitodellisuudessa

Virtuaalitodellisuuden (VR) viimeaikainen yleistyminen on nostanut esille kysymyksiä myös uudenlaisista vuorovaikutustavoista. Käsielekäyttöliittymät tarjoavat potentiaalisesti intuitiivisen ja monipuolisen vuorovaikutustavan virtuaaliympäristöihin. Käsieleiden käytettävyyttä on kuitenkin tutkittu vasta vähän virtuaaliympäristössä. Tutkielmassa tarkastelen, miten elepohjainen käyttöliittymä ja ohjainkäyttöliittymä vaikuttavat käyttökokemukseen virtuaalitodellisuudessa. Käytettävyystestien pohjalta selvisi että perinteinen VR-ohjain on eleitä tarkempi ja nopeampi kappaleidenlajittelutehtävässä. Eleet tarjoavat kuitenkin luonnollisemman tavan käsitellä todellisen maailman esineitä. Käyttäjät arvioivat ohjaimet käytettävämmiksi kuin eleet. Testissä käytetyn Leap Motion Controller:n eleiden tunnistuksen epätarkkuus aiheutti testihenkilöille ongelmia erityisesti nopeutta ja tarkkuutta vaativassa tehtävässä. Eleiden tunnistuksen ongelmien ratkaiseminen on oleellista eleiden käytettävyyden kehittämisessä

Experimental and Computational Methods for the Study of Cerebral Organoids: A Review

Cerebral (or brain) organoids derived from human cells have enormous potential as physiologically relevant downscaled in vitro models of the human brain. In fact, these stem cell-derived neural aggregates resemble the three-dimensional (3D) cytoarchitectural arrangement of the brain overcoming not only the unrealistic somatic flatness but also the planar neuritic outgrowth of the two-dimensional (2D) in vitro cultures. Despite the growing use of cerebral organoids in scientific research, a more critical evaluation of their reliability and reproducibility in terms of cellular diversity, mature traits, and neuronal dynamics is still required. Specifically, a quantitative framework for generating and investigating these in vitro models of the human brain is lacking. To this end, the aim of this review is to inspire new computational and technology driven ideas for methodological improvements and novel applications of brain organoids. After an overview of the organoid generation protocols described in the literature, we review the computational models employed to assess their formation, organization and resource uptake. The experimental approaches currently provided to structurally and functionally characterize brain organoid networks for studying single neuron morphology and their connections at cellular and sub-cellular resolution are also discussed. Well-established techniques based on current/voltage clamp, optogenetics, calcium imaging, and Micro-Electrode Arrays (MEAs) are proposed for monitoring intra- and extra-cellular responses underlying neuronal dynamics and functional connections. Finally, we consider critical aspects of the established procedures and the physiological limitations of these models, suggesting how a complement of engineering tools could improve the current approaches and their applications

A Modular and Open-Source Framework for Virtual Reality Visualisation and Interaction in Bioimaging

Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. The advent of new imaging technologies, such as lightsheet microscopy, has resulted in the users being confronted with an ever-growing amount of data, with even terabytes of imaging data created within a day. With the possibility of gentler and more high-performance imaging, the spatiotemporal complexity of the model systems or processes of interest is increasing as well. Visualisation is often the first step in making sense of this data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualisations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we present scenery, a modular and extensible visualisation framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features, and discuss its use with VR/AR hardware and in distributed rendering. In addition to the visualisation framework, we present a series of case studies, where scenery can provide tangible benefit in developmental and systems biology: With Bionic Tracking, we demonstrate a new technique for tracking cells in 4D volumetric datasets via tracking eye gaze in a virtual reality headset, with the potential to speed up manual tracking tasks by an order of magnitude. We further introduce ideas to move towards virtual reality-based laser ablation and perform a user study in order to gain insight into performance, acceptance and issues when performing ablation tasks with virtual reality hardware in fast developing specimen. To tame the amount of data originating from state-of-the-art volumetric microscopes, we present ideas how to render the highly-efficient Adaptive Particle Representation, and finally, we present sciview, an ImageJ2/Fiji plugin making the features of scenery available to a wider audience.:Abstract Foreword and Acknowledgements Overview and Contributions Part 1 - Introduction 1 Fluorescence Microscopy 2 Introduction to Visual Processing 3 A Short Introduction to Cross Reality 4 Eye Tracking and Gaze-based Interaction Part 2 - VR and AR for System Biology 5 scenery — VR/AR for Systems Biology 6 Rendering 7 Input Handling and Integration of External Hardware 8 Distributed Rendering 9 Miscellaneous Subsystems 10 Future Development Directions Part III - Case Studies C A S E S T U D I E S 11 Bionic Tracking: Using Eye Tracking for Cell Tracking 12 Towards Interactive Virtual Reality Laser Ablation 13 Rendering the Adaptive Particle Representation 14 sciview — Integrating scenery into ImageJ2 & Fiji Part IV - Conclusion 15 Conclusions and Outlook Backmatter & Appendices A Questionnaire for VR Ablation User Study B Full Correlations in VR Ablation Questionnaire C Questionnaire for Bionic Tracking User Study List of Tables List of Figures Bibliography Selbstständigkeitserklärun

Dopamine : a versatile player in development, regeneration and disease

The dopamine neurotransmitter is present in all multicellular organisms. In the brain, the dopaminergic system orchestrates reward-motivation pathways and is involved in the control of voluntary movements and endocrine hormone secretion. Dysfunction of dopamine signalling may lead to pathological conditions such as Parkinson’s disease, where dopaminergic neurons of the midbrain degenerate. Moreover, modulation of dopamine receptor signalling influences tumour growth. The aim of this work was to explore the regeneration capacity of the dopaminergic system in the vertebrate brain and to test whether dopamine may control the growth of brain tumours. To this end we performed two sets of studies. Initially, we investigated the development and regeneration of the dopaminergic system in newts, which are aquatic salamanders capable of complete regeneration of the dopaminergic system in the brain. Thereafter, we investigated how dopaminergic ligands impinge specifically on brain tumour cells. In paper I, we screened a library of dopaminergic ligands for their ability to stimulate or to inhibit glioblastoma cell growth and survival. We identified the dopamine receptor 2 antagonist, trifluoperazine, as an inhibitor of glioblastoma growth. We also showed that susceptibility to trifluoperazine correlates with the dopamine receptor expression profile of the investigated glioblastoma cell lines. We concluded that dopamine receptor signalling pathways are promising targets for pharmacological interventions to inhibit glioblastoma growth. In paper II, we characterized the cellular basis of brain development and stereotyped behaviour in two regeneration model salamander species. These data provide insight into the maturation of neural stem cells that are found in the adult salamander brain. Furthermore, we showed how lesioning of the dopaminergic innervation affects neurogenesis in the forebrain and behavioural performance. This study provides a new evolutionary perspective on the genesis and dynamics of brain cells in the salamander brain, including dopaminergic cells. In paper III, we developed a tissue clearing method, CUBICe, to extend our study of dopaminergic neurite outgrowth during development as well as regeneration. We demonstrated that CUBICe is compatible for high resolution imaging of whole salamander brains. It is also a faster and more robust method, which allows to maintain a better sample integrity of embryonic brains in general, compared to Advanced CUBIC and Advanced CLARITY. In addition, using CUBICe we achieved tracing of genetically marked cells with neurite outgrowth of over 3600 µm. Ultimately, we showed that our method is ideal for tracing genetically marked dopaminergic cells in the salamander brain and for quantifying dopaminergic neurite density and regeneration in whole brain regions. In summary, this thesis provides insight into the versatile role of dopamine in both normal and pathological conditions of the vertebrate brain, as well as offers innovative tools for studying the regeneration of the dopaminergic system