Visualization of image data from cells to organisms

Cross-sectional and skeletal anatomy of long-tailed gorals (Naemorhedus caudatus) using imaging evaluations

BACKGROUND

Accurate diagnosis of diseases in animals is crucial for their treatment, and imaging evaluations such as radiographs, computed tomography (CT), and magnetic resonance imaging (MRI) are important tools for this purpose. However, a cross-sectional anatomical atlas of normal skeletal and internal organs of long-tailed gorals (Naemorhedus caudatus) has not yet been prepared for diagnosing their diseases.

OBJECTIVES

The objective of this study was to create an anatomical atlas of gorals using CT and MRI, which are imaging techniques that have not been extensively studied in this type of wild animal in Korea.

METHODS

The researchers used CT and MRI to create an anatomical atlas of gorals, and selected 37 cross-sections from the head, thoracic, lumbar, and sacrum parts of gorals to produce an average cross-sectional anatomy atlas.

RESULTS

This study successfully created an anatomical atlas of gorals using CT and MRI.

CONCLUSIONS

The atlas provides valuable information for the diagnosis of diseases in gorals, which can improve their treatment and welfare. The study highlights the importance of developing cross-sectional anatomical atlases of gorals to diagnose and treat their diseases effectively.

Deep Low-Shot Learning for Biological Image Classification and Visualization From Limited Training Samples

Predictive modeling is useful but very challenging in biological image analysis due to the high cost of obtaining and labeling training data. For example, in the study of gene interaction and regulation in Drosophila embryogenesis, the analysis is most biologically meaningful when in situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. However, labeling training data with precise stages is very time-consuming even for developmental biologists. Thus, a critical challenge is how to build accurate computational models for precise developmental stage classification from limited training samples. In addition, identification and visualization of developmental landmarks are required to enable biologists to interpret prediction results and calibrate models. To address these challenges, we propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images. Specifically, to enable accurate model training on limited training samples, we formulate the task as a deep low-shot learning problem and develop a novel two-step learning approach, including data-level learning and feature-level learning. We use a deep residual network as our base model and achieve improved performance in the precise stage prediction task of ISH images. Furthermore, the deep model can be interpreted by computing saliency maps, which consists of pixel-wise contributions of an image to its prediction result. In our task, saliency maps are used to assist the identification and visualization of developmental landmarks. Our experimental results show that the proposed model can not only make accurate predictions but also yield biologically meaningful interpretations. We anticipate our methods to be easily generalizable to other biological image classification tasks with small training datasets. Our open-source code is available at https://github.com/divelab/lsl-fly.

Sex-Specific Microglial Responses to Glucocerebrosidase Inhibition: Relevance to GBA1-Linked Parkinson's Disease

Microglia are heterogenous cells characterized by distinct populations each contributing to specific biological processes in the nervous system, including neuroprotection. To elucidate the impact of sex-specific microglia heterogenicity to the susceptibility of neuronal stress, we video-recorded with time-lapse microscopy the changes in shape and motility occurring in primary cells derived from mice of both sexes in response to pro-inflammatory or neurotoxic stimulations. With this morpho-functional analysis, we documented distinct microglia subpopulations eliciting sex-specific responses to stimulation: male microglia tended to have a more pro-inflammatory phenotype, while female microglia showed increased sensitivity to conduritol-B-epoxide (CBE), a small molecule inhibitor of glucocerebrosidase, the enzyme encoded by the GBA1 gene, mutations of which are the major risk factor for Parkinson's Disease (PD). Interestingly, glucocerebrosidase inhibition particularly impaired the ability of female microglia to enhance the Nrf2-dependent detoxification pathway in neurons, attenuating the sex differences observed in this neuroprotective function. This finding is consistent with the clinical impact of GBA1 mutations, in which the 1.5-2-fold reduced risk of developing idiopathic PD observed in female individuals is lost in the GBA1 carrier population, thus suggesting a sex-specific role for microglia in the etiopathogenesis of PD-GBA1.

IMAGE-IN: Interactive web-based multidimensional 3D visualizer for multi-modal microscopy images

Advances in microscopy hardware and storage capabilities lead to increasingly larger multidimensional datasets. The multiple dimensions are commonly associated with space, time, and color channels. Since "seeing is believing", it is important to have easy access to user-friendly visualization software. Here we present IMAGE-IN, an interactive web-based multidimensional (N-D) viewer designed specifically for confocal laser scanning microscopy (CLSM) and focused ion beam scanning electron microscopy (FIB-SEM) data, with the goal of assisting biologists in their visualization and analysis tasks and promoting digital workflows. This new visualization platform includes intuitive multidimensional opacity fine-tuning, shading on/off, multiple blending modes for volume viewers, and the ability to handle multichannel volumetric data in volume and surface views. The software accepts a sequence of image files or stacked 3D images as input and offers a variety of viewing options ranging from 3D volume/surface rendering to multiplanar reconstruction approaches. We evaluate the performance by comparing the loading and rendering timings of a heterogeneous dataset of multichannel CLSM and FIB-SEM images on two devices with installed graphic cards, as well as comparing rendered image quality between ClearVolume (the ImageJ open-source desktop viewer), Napari (the Python desktop viewer), Imaris (the closed-source desktop viewer), and our proposed IMAGE-IN web viewer.

HeartCV: a tool for transferrable, automated measurement of heart rate and heart rate variability in transparent animals

Heart function is a key component of whole-organismal physiology. Bioimaging is commonly, but not exclusively, used for quantifying heart function in transparent individuals, including early developmental stages of aquatic animals, many of which are transparent. However, a central limitation of many imaging-related methods is the lack of transferability between species, life-history stages and experimental approaches. Furthermore, locating the heart in mobile individuals remains challenging. Here, we present HeartCV: an open-source Python package for automated measurement of heart rate and heart rate variability that integrates automated localization and is transferrable across a wide range of species. We demonstrate the efficacy of HeartCV by comparing its outputs with measurements made manually for a number of very different species with contrasting heart morphologies. Lastly, we demonstrate the applicability of the software to different experimental approaches and to different dataset types, such as those corresponding to longitudinal studies.

Recent Advances in Sugarcane Genomics, Physiology, and Phenomics for Superior Agronomic Traits

Advances in sugarcane breeding have contributed significantly to improvements in agronomic traits and crop yield. However, the growing global demand for sugar and biofuel in the context of climate change requires further improvements in cane and sugar yields. Attempts to achieve the desired rates of genetic gain in sugarcane by conventional breeding means are difficult as many agronomic traits are genetically complex and polygenic, with each gene exerting small effects. Unlike those of many other crops, the sugarcane genome is highly heterozygous due to its autopolyploid nature, which further hinders the development of a comprehensive genetic map. Despite these limitations, many superior agronomic traits/genes for higher cane yield, sugar production, and disease/pest resistance have been identified through the mapping of quantitative trait loci, genome-wide association studies, and transcriptome approaches. Improvements in traits controlled by one or two loci are relatively easy to achieve; however, this is not the case for traits governed by many genes. Many desirable phenotypic traits are controlled by quantitative trait nucleotides (QTNs) with small and variable effects. Assembling these desired QTNs by conventional breeding methods is time consuming and inefficient due to genetic drift. However, recent developments in genomics selection (GS) have allowed sugarcane researchers to select and accumulate desirable alleles imparting superior traits as GS is based on genomic estimated breeding values, which substantially increases the selection efficiency and genetic gain in sugarcane breeding programs. Next-generation sequencing techniques coupled with genome-editing technologies have provided new vistas in harnessing the sugarcane genome to look for desirable agronomic traits such as erect canopy, leaf angle, prolonged greening, high biomass, deep root system, and the non-flowering nature of the crop. Many desirable cane-yielding traits, such as single cane weight, numbers of tillers, numbers of millable canes, as well as cane quality traits, such as sucrose and sugar yield, have been explored using these recent biotechnological tools. This review will focus on the recent advances in sugarcane genomics related to genetic gain and the identification of favorable alleles for superior agronomic traits for further utilization in sugarcane breeding programs.

ShapoGraphy: A User-Friendly Web Application for Creating Bespoke and Intuitive Visualisation of Biomedical Data

Effective visualisation of quantitative microscopy data is crucial for interpreting and discovering new patterns from complex bioimage data. Existing visualisation approaches, such as bar charts, scatter plots and heat maps, do not accommodate the complexity of visual information present in microscopy data. Here we develop ShapoGraphy, a first of its kind method accompanied by an interactive web-based application for creating customisable quantitative pictorial representations to facilitate the understanding and analysis of image datasets (www.shapography.com). ShapoGraphy enables the user to create a structure of interest as a set of shapes. Each shape can encode different variables that are mapped to the shape dimensions, colours, symbols, or outline. We illustrate the utility of ShapoGraphy using various image data, including high dimensional multiplexed data. Our results show that ShapoGraphy allows a better understanding of cellular phenotypes and relationships between variables. In conclusion, ShapoGraphy supports scientific discovery and communication by providing a rich vocabulary to create engaging and intuitive representations of diverse data types.

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

BACKGROUND

In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look?

QUESTIONS

Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term.

RESULTS

We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information-a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept.

CONCLUSIONS

We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.

Application of Advanced Imaging Modalities in Veterinary Medicine: A Review

Veterinary anatomy has traditionally relied on detailed dissections to produce anatomical illustrations, but modern imaging modalities, now represent an enormous resource that allows for fast non-invasive visualizations in living animals for clinical and research purposes. In this review, advanced anatomical imaging modalities and their applications, safety issues, challenges, and future prospects of the techniques commonly employed for animal imaging would be highlighted. The quality of diagnostic imaging equipment in veterinary practice has greatly improved. Recent advances made in veterinary advanced imaging specifically about cross-sectional modalities (CT and MRI), nuclear medicine (PET, SPECT), and dual imaging modalities (PET/CT, PET/MR, and SPECT/CT) have become widely available, leading to greater demands and expectations from veterinary clients. These modalities allow for the creation of three-dimensional representations that can be of considerable value in the dissemination of clinical diagnosis and anatomical studies. Despite, the modern imaging modalities well established in developed countries across the globe, it is yet to remain in its infancy stage in veterinary practice in developing countries due to heavy initial investment and maintenance costs, lack of expert interpretation, a requirement of specialized technical staff and need of adjustable machines to accommodate the different range of animal sizes. Therefore, veterinarians should take advantage of these imaging techniques in designing future experiments by considering the availability of these varied imaging modalities and the creation of three-dimensional graphical representations of internal structures.

DICOMization of Proprietary Files Obtained from Confocal, Whole-Slide, and FIB-SEM Microscope Scanners

The evolution of biomedical imaging technology is allowing the digitization of hundreds of glass slides at once. There are multiple microscope scanners available in the market including low-cost solutions that can serve small centers. Moreover, new technology is being researched to acquire images and new modalities are appearing in the market such as electron microscopy. This reality offers new diagnostics tools to clinical practice but emphasizes also the lack of multivendor system’s interoperability. Without the adoption of standard data formats and communications methods, it will be impossible to build this industry through the installation of vendor-neutral archives and the establishment of telepathology services in the cloud. The DICOM protocol is a feasible solution to the aforementioned problem because it already provides an interface for visible light and whole slide microscope imaging modalities. While some scanners currently have DICOM interfaces, the vast majority of manufacturers continue to use proprietary solutions. This article proposes an automated DICOMization pipeline that can efficiently transform distinct proprietary microscope images from CLSM, FIB-SEM, and WSI scanners into standard DICOM with their biological information maintained within their metadata. The system feasibility and performance were evaluated with fifteen distinct proprietary modalities, including stacked WSI samples. The results demonstrated that the proposed methodology is accurate and can be used in production. The normalized objects were stored through the standard communications in the Dicoogle open-source archive.

Loon: Using Exemplars to Visualize Large-Scale Microscopy Data

Which drug is most promising for a cancer patient? A new microscopy-based approach for measuring the mass of individual cancer cells treated with different drugs promises to answer this question in only a few hours. However, the analysis pipeline for extracting data from these images is still far from complete automation: human intervention is necessary for quality control for preprocessing steps such as segmentation, adjusting filters, removing noise, and analyzing the result. To address this workflow, we developed Loon, a visualization tool for analyzing drug screening data based on quantitative phase microscopy imaging. Loon visualizes both derived data such as growth rates and imaging data. Since the images are collected automatically at a large scale, manual inspection of images and segmentations is infeasible. However, reviewing representative samples of cells is essential, both for quality control and for data analysis. We introduce a new approach for choosing and visualizing representative exemplar cells that retain a close connection to the low-level data. By tightly integrating the derived data visualization capabilities with the novel exemplar visualization and providing selection and filtering capabilities, Loon is well suited for making decisions about which drugs are suitable for a specific patient.

Cross-Modality Imaging of Murine Tumor Vasculature-a Feasibility Study

Tumor vasculature and angiogenesis play a crucial role in tumor progression. Their visualization is therefore of utmost importance to the community. In this proof-of-principle study, we have established a novel cross-modality imaging (CMI) pipeline to characterize exactly the same murine tumors across scales and penetration depths, using orthotopic models of melanoma cancer. This allowed the acquisition of a comprehensive set of vascular parameters for a single tumor. The workflow visualizes capillaries at different length scales, puts them into the context of the overall tumor vessel network and allows quantification and comparison of vessel densities and morphologies by different modalities. The workflow adds information about hypoxia and blood flow rates. The CMI approach includes well-established technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and ultrasound (US), and modalities that are recent entrants into preclinical discovery such as optical coherence tomography (OCT) and high-resolution episcopic microscopy (HREM). This novel CMI platform establishes the feasibility of combining these technologies using an extensive image processing pipeline. Despite the challenges pertaining to the integration of microscopic and macroscopic data across spatial resolutions, we also established an open-source pipeline for the semi-automated co-registration of the diverse multiscale datasets, which enables truly correlative vascular imaging. Although focused on tumor vasculature, our CMI platform can be used to tackle a multitude of research questions in cancer biology.

Review: Application of Artificial Intelligence in Phenomics

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.

Cross-Modality Imaging of Murine Tumor Vasculature-a Feasibility Study

Tumor vasculature and angiogenesis play a crucial role in tumor progression. Their visualization is therefore of utmost importance to the community. In this proof-of-principle study, we have established a novel cross-modality imaging (CMI) pipeline to characterize exactly the same murine tumors across scales and penetration depths, using orthotopic models of melanoma cancer. This allowed the acquisition of a comprehensive set of vascular parameters for a single tumor. The workflow visualizes capillaries at different length scales, puts them into the context of the overall tumor vessel network and allows quantification and comparison of vessel densities and morphologies by different modalities. The workflow adds information about hypoxia and blood flow rates. The CMI approach includes well-established technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and ultrasound (US), and modalities that are recent entrants into preclinical discovery such as optical coherence tomography (OCT) and high-resolution episcopic microscopy (HREM). This novel CMI platform establishes the feasibility of combining these technologies using an extensive image processing pipeline. Despite the challenges pertaining to the integration of microscopic and macroscopic data across spatial resolutions, we also established an open-source pipeline for the semi-automated co-registration of the diverse multiscale datasets, which enables truly correlative vascular imaging. Although focused on tumor vasculature, our CMI platform can be used to tackle a multitude of research questions in cancer biology.

Underwater photogrammetry for close-range 3D imaging of dry-sensitive objects: The case study of cephalopod beaks

Technical advances in 3D imaging have contributed to quantifying and understanding biological variability and complexity. However, small, dry-sensitive objects are not easy to reconstruct using common and easily available techniques such as photogrammetry, surface scanning, or micro-CT scanning. Here, we use cephalopod beaks as an example as their size, thickness, transparency, and dry-sensitive nature make them particularly challenging. We developed a new, underwater, photogrammetry protocol in order to add these types of biological structures to the panel of photogrammetric possibilities.We used a camera with a macrophotography mode in a waterproof housing fixed in a tank with clear water. The beak was painted and fixed on a colored rotating support. Three angles of view, two acquisitions, and around 300 pictures per specimen were taken in order to reconstruct a full 3D model. These models were compared with others obtained with micro-CT scanning to verify their accuracy.The models can be obtained quickly and cheaply compared with micro-CT scanning and have sufficient precision for quantitative interspecific morphological analyses. Our work shows that underwater photogrammetry is a fast, noninvasive, efficient, and accurate way to reconstruct 3D models of dry-sensitive objects while conserving their shape. While the reconstruction of the shape is accurate, some internal parts cannot be reconstructed with photogrammetry as they are not visible. In contrast, these structures are visible using reconstructions based on micro-CT scanning. The mean difference between both methods is very small (10-5 to 10-4 mm) and is significantly lower than differences between meshes of different individuals.This photogrammetry protocol is portable, easy-to-use, fast, and reproducible. Micro-CT scanning, in contrast, is time-consuming, expensive, and nonportable. This protocol can be applied to reconstruct the 3D shape of many other dry-sensitive objects such as shells of shellfish, cartilage, plants, and other chitinous materials.

Digital imaging and vision analysis in science project improves the self-efficacy and skill of undergraduate students in computational work

In many areas of science, the ability to use computers to process, analyze, and visualize large data sets has become essential. The mismatch between the ability to generate large data sets and the computing skill to analyze them is arguably the most striking within the life sciences. The Digital Image and Vision Applications in Science (DIVAS) project describes a scaffolded series of interventions implemented over the span of a year to build the coding and computing skill of undergraduate students majoring primarily in the natural sciences. The program is designed as a community of practice, providing support within a network of learners. The program focus, images as data, provides a compelling 'hook' for participating scholars. Scholars begin the program with a one-credit spring semester seminar where they are exposed to image analysis. The program continues in the summer with a one-week, intensive Python and image processing workshop. From there, scholars tackle image analysis problems using a pair programming approach and can finish the summer with independent research. Finally, scholars participate in a follow-up seminar the subsequent spring and help onramp the next cohort of incoming scholars. We observed promising growth in participant self-efficacy in computing that was maintained throughout the project as well as significant growth in key computational skills. DIVAS program funding was able to support seventeen DIVAS over three years, with 76% of DIVAS scholars identifying as women and 14% of scholars identifying as members of an underrepresented minority group. Most scholars (82%) entered the program as first year students, with 94% of DIVAS scholars retained for the duration of the program and 100% of scholars remaining a STEM major one year after completing the program. The outcomes of the DIVAS project support the efficacy of building computational skill through repeated exposure of scholars to relevant applications over an extended period within a community of practice.

Tissue clearing and imaging methods for cardiovascular development

Tissue imaging in 3D using visible light is limited and various clearing techniques were developed to increase imaging depth, but none provides universal solution for all tissues at all developmental stages. In this review, we focus on different tissue clearing methods for 3D imaging of heart and vasculature, based on chemical composition (solvent-based, simple immersion, hyperhydration, and hydrogel embedding techniques). We discuss in detail compatibility of various tissue clearing techniques with visualization methods: fluorescence preservation, immunohistochemistry, nuclear staining, and fluorescent dyes vascular perfusion. We also discuss myocardium visualization using autofluorescence, tissue shrinking, and expansion. Then we overview imaging methods used to study cardiovascular system and live imaging. We discuss heart and vessels segmentation methods and image analysis. The review covers the whole process of cardiovascular system 3D imaging, starting from tissue clearing and its compatibility with various visualization methods to the types of imaging methods and resulting image analysis.

Holistic description of new deep sea megafauna (Cephalopoda: Cirrata) using a minimally invasive approach

BACKGROUND

In zoology, species descriptions conventionally rely on invasive morphological techniques, frequently leading to damage of the specimens and thus only a partial understanding of their structural complexity. More recently, non-destructive imaging techniques have successfully been used to describe smaller fauna, but this approach has so far not been applied to identify or describe larger animal species. Here, we present a combination of entirely non-invasive as well as minimally invasive methods that permit taxonomic descriptions of large zoological specimens in a more comprehensive manner.

RESULTS

Using the single available representative of an allegedly novel species of deep-sea cephalopod (Mollusca: Cephalopoda), digital photography, standardized external measurements, high-field magnetic resonance imaging, micro-computed tomography, and DNA barcoding were combined to gather all morphological and molecular characters relevant for a full species description. The results show that this specimen belongs to the cirrate octopod (Octopoda: Cirrata) genus Grimpoteuthis Robson, 1932. Based on the number of suckers, position of web nodules, cirrus length, presence of a radula, and various shell characters, the specimen is designated as the holotype of a new species of dumbo octopus, G. imperator sp. nov. The digital nature of the acquired data permits a seamless online deposition of raw as well as derived morphological and molecular datasets in publicly accessible repositories.

CONCLUSIONS

Using high-resolution, non-invasive imaging systems intended for the analysis of larger biological objects, all external as well as internal morphological character states relevant for the identification of a new megafaunal species were obtained. Potentially harmful effects on this unique deep-sea cephalopod specimen were avoided by scanning the fixed animal without admixture of a contrast agent. Additional support for the taxonomic placement of the new dumbo octopus species was obtained through DNA barcoding, further underlining the importance of combining morphological and molecular datasets for a holistic description of zoological specimens.

webTDat: A Web-Based, Real-Time, 3D Visualization Framework for Mesoscopic Whole-Brain Images

The popularity of mesoscopic whole-brain imaging techniques has increased dramatically, but these techniques generate teravoxel-sized volumetric image data. Visualizing or interacting with these massive data is both necessary and essential in the bioimage analysis pipeline; however, due to their size, researchers have difficulty using typical computers to process them. The existing solutions do not consider applying web visualization and three-dimensional (3D) volume rendering methods simultaneously to reduce the number of data copy operations and provide a better way to visualize 3D structures in bioimage data. Here, we propose webTDat, an open-source, web-based, real-time 3D visualization framework for mesoscopic-scale whole-brain imaging datasets. webTDat uses an advanced rendering visualization method designed with an innovative data storage format and parallel rendering algorithms. webTDat loads the primary information in the image first and then decides whether it needs to load the secondary information in the image. By performing validation on TB-scale whole-brain datasets, webTDat achieves real-time performance during web visualization. The webTDat framework also provides a rich interface for annotation, making it a useful tool for visualizing mesoscopic whole-brain imaging data.

Bioimage Analysis and Cell Motility

Bioimage analysis (BIA) has historically helped study how and why cells move; biological experiments evolved in intimate feedback with the most classical image processing techniques because they contribute objectivity and reproducibility to an eminently qualitative science. Cell segmentation, tracking, and morphology descriptors are all discussed here. Using ameboid motility as a case study, these methods help us illustrate how proper quantification can augment biological data, for example, by choosing mathematical representations that amplify initially subtle differences, by statistically uncovering general laws or by integrating physical insight. More recently, the non-invasive nature of quantitative imaging is fertilizing two blooming fields: mechanobiology, where many biophysical measurements remain inaccessible, and microenvironments, where the quest for physiological relevance has exploded data size. From relief to remedy, this trend indicates that BIA is to become a main vector of biological discovery as human visual analysis struggles against ever more complex data.

4D Widefield Fluorescence Imaging of Appressorium Morphogenesis by Magnaporthe oryzae

Fluorescence microscopy has become a widely used and indispensable tool for the M. oryzae research community, providing unique insight into appressorium formation and function. A common practice within the field is to acquire and present images of a number of different conidia, expressing a fluorescent fusion protein of interest, at various stages of infectious development, therein providing a representative "snapshot" of the population at a given point in time. Furthermore, these images typically show only a single focal plane through the specimen (2D) and therefore lack, often valuable, volumetric information. While this approach has its advantages, the continuous imaging of (multiple) single conidia in three dimensions (3D), and over time (4D), can provide additional insight into the spatial and temporal dynamics of fluorescent fusion proteins, and the subcellular structures and compartments they label, in living cells. Here we describe our typical workflow for the 4D live-cell imaging of appressorium morphogenesis in vitro using two-color widefield fluorescence microscopy and briefly outline some important considerations for strain construction, and downstream image processing and visualization.

Introducing Biomedisa as an open-source online platform for biomedical image segmentation

We present Biomedisa, a free and easy-to-use open-source online platform developed for semi-automatic segmentation of large volumetric images. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. Biomedisa is particularly valuable when little a priori knowledge is available, e.g. for the dense annotation of the training data for a deep neural network. The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters, thus addressing the needs of scientists without substantial computational expertise. We demonstrate that Biomedisa can drastically reduce both the time and human effort required to segment large images. It achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data. Biomedisa can be used for different 3D imaging modalities and various biomedical applications.

Radiological characterization of gilthead seabream (Sparus aurata) by X-ray computed tomography

In recent years, the increasing use of fish as new animal models in scientific research and the growth of fish farming (mainly for human consumption) have highlighted the need for advanced technology to deepen our knowledge of fish biology. Hence, the present study was carried out to radiologically analyse the whole body of gilthead seabream (Sparus aurata) specimens using X-ray computed tomography (CT). Images were acquired in an Albira SPECT/PET/CT tri-modal preclinical-scanner. Segmentation, measurements and three-dimensional reconstruction were made using the Carestream Molecular imaging Albira CT system in conjunction with Pmod, AMIDE and Amira software packages. The results showed that the density values of gilthead seabream are in the range -700 to +2500 HU for the whole body. We also determined the density ranges that topographically coincide with the swim bladder, soft tissues, fat, skin and skeleton. This work describes, validates and demonstrates the application of a fully automated image analysis technique to study and quantify fish body composition, whether segmented or as a whole. In addition, the basis for applying this image technique in other in vivo studies is established.

A bird's-eye view of deep learning in bioimage analysis

Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

Evaluating fidelity of CT based 3D models for Zebrafish conductive hearing system

The zebrafish Weberian apparatus is an emerging model for human conductive hearing system. Their Weberian apparatus comprises minute bones and ligamentary links, and conducts sound pressure transmission from the gas bladder to inner ear through four pairs of Weberian ossicles along the vertebral column. We herein present a methodological study using MicroCT to image the Weberian apparatus for biomechanical and morphological analysis. The aim of this work is to evaluate computational models generated from multiple MicroCT scans with different parameters, to identify the most feasible scan combination for practical (minimized scan time) yet accurate (relative to highest resolution) biomechanical simulations. We segmented and created 3D models from CT scan image stacks at 4.64 μm, 5.05 μm, 9.30 μm and 13.08 μm voxel resolutions, respectively. Then, we used geometric morphometrics analysis to quantify inter-model shape differences, as well as a series of finite element modal and harmonic analyses to simulate auditory signal vibrations. Relative to the highest resolution and most accurate model, the Model 9.30 is closest in overall geometry and biomechanical behavior of all lower resolution models. The differences in resolution and quality of the CT substantially affect the segmentation and reconstruction process of the three-dimensional model of the ossicles, and the subsequent analyses. We conclude that scan voxel resolution is a key factor influencing outcomes of biomechanical simulations of delicate and minute structures, especially when studying the harmonic response of minute ossicles connected by ligaments using finite element modeling. Furthermore, contrast variations in CT images as determined by x-ray power and scan speed, also affect fidelity in 3D models and simulation outcomes.

Fluorescence microscopy tensor imaging representations for large-scale dataset analysis

Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail.

A Practical Guide to the Automated Analysis of Vascular Growth, Maturation and Injury in the Brain

The distinct organization of the brain's vasculature ensures the adequate delivery of oxygen and nutrients during development and adulthood. Acute and chronic pathological changes of the vascular system have been implicated in many neurological disorders including stroke and dementia. Here, we describe a fast, automated method that allows the highly reproducible, quantitative assessment of distinct vascular parameters and their changes based on the open source software Fiji (ImageJ). In particular, we developed a practical guide to reliably measure aspects of growth, repair and maturation of the brain's vasculature during development and neurovascular disease in mice and humans. The script can be used to assess the effects of different external factors including pharmacological treatments or disease states. Moreover, the procedure is expandable to blood vessels of other organs and vascular in vitro models.

Enhanced translation expands the endo-lysosome size and promotes antigen presentation during phagocyte activation

The mechanisms that govern organelle adaptation and remodelling remain poorly defined. The endo-lysosomal system degrades cargo from various routes, including endocytosis, phagocytosis, and autophagy. For phagocytes, endosomes and lysosomes (endo-lysosomes) are kingpin organelles because they are essential to kill pathogens and process and present antigens. During phagocyte activation, endo-lysosomes undergo a morphological transformation, going from a collection of dozens of globular structures to a tubular network in a process that requires the phosphatidylinositol-3-kinase-AKT-mechanistic target of rapamycin (mTOR) signalling pathway. Here, we show that the endo-lysosomal system undergoes an expansion in volume and holding capacity during phagocyte activation within 2 h of lipopolysaccharides (LPS) stimulation. Endo-lysosomal expansion was paralleled by an increase in lysosomal protein levels, but this was unexpectedly largely independent of the transcription factor EB (TFEB) and transcription factor E3 (TFE3), which are known to scale up lysosome biogenesis. Instead, we demonstrate a hitherto unappreciated mechanism of acute organelle expansion via mTOR Complex 1 (mTORC1)-dependent increase in translation, which appears to be mediated by both S6Ks and 4E-BPs. Moreover, we show that stimulation of RAW 264.7 macrophage cell line with LPS alters translation of a subset but not all of mRNAs encoding endo-lysosomal proteins, thereby suggesting that endo-lysosome expansion is accompanied by functional remodelling. Importantly, mTORC1-dependent increase in translation activity was necessary for efficient and rapid antigen presentation by dendritic cells. Collectively, we identified a previously unknown and functionally relevant mechanism for endo-lysosome expansion that relies on mTORC1-dependent translation to stimulate endo-lysosome biogenesis in response to an infection signal.

Activation of phagocytes rapidly expands the endo-lysosomal system and promotes antigen presentation. Endo-lysosome expansion was driven by mTORC1-dependent enhanced translation, revealing regulated translation as a mechanism to remodel membrane organelles in response to external signals and stresses.

Integrating Imaging and Omics: Computational Methods and Challenges

Fluorescence microscopy imaging has long been complementary to DNA sequencing- and mass spectrometry–based omics in biomedical research, but these approaches are now converging. On the one hand, omics methods are moving from in vitro methods that average across large cell populations to in situ molecular characterization tools with single-cell sensitivity. On the other hand, fluorescence microscopy imaging has moved from a morphological description of tissues and cells to quantitative molecular profiling with single-molecule resolution. Recent technological developments underpinned by computational methods have started to blur the lines between imaging and omics and have made their direct correlation and seamless integration an exciting possibility. As this trend continues rapidly, it will allow us to create comprehensive molecular profiles of living systems with spatial and temporal context and subcellular resolution. Key to achieving this ambitious goal will be novel computational methods and successfully dealing with the challenges of data integration and sharing as well as cloud-enabled big data analysis.

A novel framework for MR image segmentation and quantification by using MedGA

BACKGROUND AND OBJECTIVES

Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks.

METHODS

In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram.

RESULTS

The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evaluation metrics.

CONCLUSIONS

Thanks to this framework, MR image segmentation accuracy is considerably increased, allowing for measurement repeatability in clinical workflows. The proposed computational solution could be well-suited for other clinical contexts requiring MR image analysis and segmentation, aiming at providing useful insights for differential diagnosis and prognosis.

A primer on resolving the nanoscale structure of the plasma membrane with light and electron microscopy

Taraska reviews the imaging methods that are being used to understand the structure of the plasma membrane at the molecular level.

The plasma membrane separates a cell from its external environment. All materials and signals that enter or leave the cell must cross this hydrophobic barrier. Understanding the architecture and dynamics of the plasma membrane has been a central focus of general cellular physiology. Both light and electron microscopy have been fundamental in this endeavor and have been used to reveal the dense, complex, and dynamic nanoscale landscape of the plasma membrane. Here, I review classic and recent developments in the methods used to image and study the structure of the plasma membrane, particularly light, electron, and correlative microscopies. I will discuss their history and use for mapping the plasma membrane and focus on how these tools have provided a structural framework for understanding the membrane at the scale of molecules. Finally, I will describe how these studies provide a roadmap for determining the nanoscale architecture of other organelles and entire cells in order to bridge the gap between cellular form and function.

Mitochondrial Morphofunction in Mammalian Cells

Significance: In addition to their classical role in cellular ATP production, mitochondria are of key relevance in various (patho)physiological mechanisms including second messenger signaling, neuro-transduction, immune responses and death induction. Recent Advances: Within cells, mitochondria are motile and display temporal changes in internal and external structure ("mitochondrial dynamics"). During the last decade, substantial empirical and in silico evidence was presented demonstrating that mitochondrial dynamics impacts on mitochondrial function and vice versa. Critical Issues: However, a comprehensive and quantitative understanding of the bidirectional links between mitochondrial external shape, internal structure and function ("morphofunction") is still lacking. The latter particularly hampers our understanding of the functional properties and behavior of individual mitochondrial within single living cells. Future Directions: In this review we discuss the concept of mitochondrial morphofunction in mammalian cells, primarily using experimental evidence obtained within the last decade. The topic is introduced by briefly presenting the central role of mitochondria in cell physiology and the importance of the mitochondrial electron transport chain (ETC) therein. Next, we summarize in detail how mitochondrial (ultra)structure is controlled and discuss empirical evidence regarding the equivalence of mitochondrial (ultra)structure and function. Finally, we provide a brief summary of how mitochondrial morphofunction can be quantified at the level of single cells and mitochondria, how mitochondrial ultrastructure/volume impacts on mitochondrial bioreactions and intramitochondrial protein diffusion, and how mitochondrial morphofunction can be targeted by small molecules.

MeshMonk: Open-source large-scale intensive 3D phenotyping

Dense surface registration, commonly used in computer science, could aid the biological sciences in accurate and comprehensive quantification of biological phenotypes. However, few toolboxes exist that are openly available, non-expert friendly, and validated in a way relevant to biologists. Here, we report a customizable toolbox for reproducible high-throughput dense phenotyping of 3D images, specifically geared towards biological use. Given a target image, a template is first oriented, repositioned, and scaled to the target during a scaled rigid registration step, then transformed further to fit the specific shape of the target using a non-rigid transformation. As validation, we use n = 41 3D facial images to demonstrate that the MeshMonk registration is accurate, with 1.26 mm average error, across 19 landmarks, between placements from manual observers and using the MeshMonk toolbox. We also report no variation in landmark position or centroid size significantly attributable to landmarking method used. Though validated using 19 landmarks, the MeshMonk toolbox produces a dense mesh of vertices across the entire surface, thus facilitating more comprehensive investigations of 3D shape variation. This expansion opens up exciting avenues of study in assessing biological shapes to better understand their phenotypic variation, genetic and developmental underpinnings, and evolutionary history.

A Revised View on Growth and Remodeling in the Retinal Vasculature

The mouse retina provides an excellent model for studying angiogenesis. Recent advancements in high-throughput microscopy and image analysis provide great tools to visualize and describe the complexity of the retinal vascular architecture in a detailed and comprehensive way. Most developmental studies have focused on only a few parameters mostly in the inner-most layers that do not describe the entirety of the three-dimensional vascular network. Here, we analyzed the entire three-dimensional retinal vascular architecture and its growth and remodeling starting from the age of postnatal day 3 to 4 months in mice. We show plexus specific characteristics of the vasculature in terms of vascular tissue fraction, branching and length of the blood vessels, and distance and distribution between single capillaries. Such detailed knowledge is of particular interest, as it has become apparent that disease-specific mechanisms and treatments affect the retinal vasculature often in a plexus specific way.

Towards Advanced Interactive Visualization for Virtual Atlases

An atlas is generally defined as a bound collection of tables, charts or illustrations describing a phenomenon. In an anatomical atlas for example, a collection of representative illustrations and text describes anatomy for the purpose of communicating anatomical knowledge. The atlas serves as reference frame for comparing and integrating data from different sources by spatially or semantically relating collections of drawings, imaging data, and/or text. In the field of medical image processing, atlas information is often constructed from a collection of regions of interest, which are based on medical images that are annotated by domain experts. Such an atlas may be employed, for example, for automatic segmentation of medical imaging data. The combination of interactive visualization techniques with atlas information opens up new possibilities for content creation, curation, and navigation in virtual atlases. With interactive visualization of atlas information, students are able to inspect and explore anatomical atlases in ways that were not possible with the traditional method of presenting anatomical atlases in book format, such as viewing the illustrations from other viewpoints. With advanced interaction techniques, it becomes possible to query the data that forms the basis for the atlas, thus empowering researchers to access a wealth of information in new ways. So far, atlas-based visualization has been employed mainly for medical education, as well as biological research. In this survey, we provide an overview of current digital biomedical atlas tasks and applications and summarize relevant visualization techniques. We discuss recent approaches for providing next-generation visual interfaces to navigate atlas data that go beyond common text-based search and hierarchical lists. Finally, we reflect on open challenges and opportunities for the next steps in interactive atlas visualization.

Streamlining volumetric multi-channel image cytometry using hue-saturation-brightness-based surface creation

Image cytometry is the process of converting image data to flow cytometry-style plots, and it usually requires computer-aided surface creation to extract out statistics for cells or structures. One way of dealing with structures stained with multiple markers in three-dimensional images, is carrying out multiple rounds of channel co-localization and image masking before surface creation, which is cumbersome and laborious. We propose the application of the hue-saturation-brightness color space to streamline this process, which produces complete surfaces, and allows the user to have a global view of the data before flexibly defining cell subsets. Spectral compensation can also be performed after surface creation to accurately resolve different signals. We demonstrate the utility of this workflow in static and dynamic imaging datasets of a needlestick injury on the mouse ear, and we believe this scalable and intuitive approach will improve the ease of performing histocytometry on biological samples.

Yingrou Tan et al. present a method streamlining surface creation in 3D imaging by applying the hue-saturation-brightness transformed channels simultaneously. They show the utility of this approach by imaging ear skin following needlestick injury, observing immune cell infiltration.

Visualization of Biomedical Data

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.

Laboratory x-ray micro-computed tomography: a user guideline for biological samples

Laboratory x-ray micro–computed tomography (micro-CT) is a fast-growing method in scientific research applications that allows for non-destructive imaging of morphological structures. This paper provides an easily operated “how to” guide for new potential users and describes the various steps required for successful planning of research projects that involve micro-CT. Background information on micro-CT is provided, followed by relevant setup, scanning, reconstructing, and visualization methods and considerations. Throughout the guide, a Jackson's chameleon specimen, which was scanned at different settings, is used as an interactive example. The ultimate aim of this paper is make new users familiar with the concepts and applications of micro-CT in an attempt to promote its use in future scientific studies.

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.

Quantitative imaging of plants: multi-scale data for better plant anatomy

This article comments on: Staedler YM, Kreisberger T, Manafzadeh S, Chartier M, Handschuh S, Pamperl S, Sontag S, Paun O, Schönenberger J. 2017. Novel computed tomography-based tools reliably quantify plant reproductive investment. Journal of Experimental Botany 69, 525–535.

Visualization Multi-Pipeline for Communicating Biology

We propose a system to facilitate biology communication by developing a pipeline to support the instructional visualization of heterogeneous biological data on heterogeneous user-devices. Discoveries and concepts in biology are typically summarized with illustrations assembled manually from the interpretation and application of heterogenous data. The creation of such illustrations is time consuming, which makes it incompatible with frequent updates to the measured data as new discoveries are made. Illustrations are typically non-interactive, and when an illustration is updated, it still has to reach the user. Our system is designed to overcome these three obstacles. It supports the integration of heterogeneous datasets, reflecting the knowledge that is gained from different data sources in biology. After pre-processing the datasets, the system transforms them into visual representations as inspired by scientific illustrations. As opposed to traditional scientific illustration these representations are generated in real-time - they are interactive. The code generating the visualizations can be embedded in various software environments. To demonstrate this, we implemented both a desktop application and a remote-rendering server in which the pipeline is embedded. The remote-rendering server supports multi-threaded rendering and it is able to handle multiple users simultaneously. This scalability to different hardware environments, including multi-GPU setups, makes our system useful for efficient public dissemination of biological discoveries.

Strategies and Solutions to Maintain and Retain Data from High Content Imaging, Analysis, and Screening Assays

Data analysis and management in high content screening (HCS) has progressed significantly in the past 10 years. The analysis of the large volume of data generated in HCS experiments represents a significant challenge and is currently a bottleneck in many screening projects. In most screening laboratories, HCS has become a standard technology applied routinely to various applications from target identification to hit identification to lead optimization. An HCS data management and analysis infrastructure shared by several research groups can allow efficient use of existing IT resources and ensures company-wide standards for data quality and result generation. This chapter outlines typical HCS workflows and presents IT infrastructure requirements for multi-well plate-based HCS.

Investigating dye performance and crosstalk in fluorescence enabled bioimaging using a model system

Detailed imaging of biological structures, often smaller than the diffraction limit, is possible in fluorescence microscopy due to the molecular size and photophysical properties of fluorescent probes. Advances in hardware and multiple providers of high-end bioimaging makes comparing images between studies and between research groups very difficult. Therefore, we suggest a model system to benchmark instrumentation, methods and staining procedures. The system we introduce is based on doped zeolites in stained polyvinyl alcohol (PVA) films: a highly accessible model system which has the properties needed to act as a benchmark in bioimaging experiments. Rather than comparing molecular probes and imaging methods in complicated biological systems, we demonstrate that the model system can emulate this complexity and can be used to probe the effect of concentration, brightness, and cross-talk of fluorophores on the detected fluorescence signal. The described model system comprises of lanthanide (III) ion doped Linde Type A zeolites dispersed in a PVA film stained with fluorophores. We tested: F18, MitoTracker Red and ATTO647N. This model system allowed comparing performance of the fluorophores in experimental conditions. Importantly, we here report considerable cross-talk of the dyes when exchanging excitation and emission settings. Additionally, bleaching was quantified. The proposed model makes it possible to test and benchmark staining procedures before these dyes are applied to more complex biological systems.

Time-lapsed, large-volume, high-resolution intravital imaging for tissue-wide analysis of single cell dynamics

Pathologists rely on microscopy to diagnose disease states in tissues and organs. They utilize both high-resolution, high-magnification images to interpret the staining and morphology of individual cells, as well as low-magnification overviews to give context and location to these cells. Intravital imaging is a powerful technique for studying cells and tissues in their native, live environment and can yield sub-cellular resolution images similar to those used by pathologists. However, technical limitations prevent the straightforward acquisition of low-magnification images during intravital imaging, and they are hence not typically captured. The serial acquisition, mosaicking, and stitching together of many high-resolution, high-magnification fields of view is a technique that overcomes these limitations in fixed and ex vivo tissues. The technique however, has not to date been widely applied to intravital imaging as movements caused by the living animal induce image distortions that are difficult to compensate for computationally. To address this, we have developed techniques for the stabilization of numerous tissues, including extremely compliant tissues, that have traditionally been extremely difficult to image. We present a novel combination of these stabilization techniques with mosaicked and stitched intravital imaging, resulting in a process we call Large-Volume High-Resolution Intravital Imaging (LVHR-IVI). The techniques we present are validated and make large volume intravital imaging accessible to any lab with a multiphoton microscope.

Membrane remodeling during embryonic abscission in Caenorhabditis elegans

Abscission is the final step of cytokinesis and results in the physical separation of two daughter cells. In this study, we conducted a time-resolved series of electron tomographic reconstructions to define the steps required for the first embryonic abscission in Caenorhabditis elegans Our findings indicate that membrane scission occurs on both sides of the midbody ring with random order and that completion of the scission process requires actomyosin-driven membrane remodeling, but not microtubules. Moreover, continuous membrane removal predominates during the late stages of cytokinesis, mediated by both dynamin and the ESCRT (endosomal sorting complex required for transport) machinery. Surprisingly, in the absence of ESCRT function in C. elegans, cytokinetic abscission is delayed but can be completed, suggesting the existence of parallel membrane-reorganizing pathways that cooperatively enable the efficient severing of cytoplasmic connections between dividing daughter cells.

MORPHOMUSEUM: AN ONLINE PLATFORM FOR PUBLICATION AND STORAGE OF VIRTUAL SPECIMENS

Since the early 1990s, methods for the acquisition of three-dimensional (3-D) data and computer-assisted techniques for the visualization of such data have grown increasingly popular among biologists, paleontologists, and paleoanthropologists. However, thus far no standardized repository for complex virtual models based on 3-D digital data of specimens has emerged, whereas the need for researchers to provide access to 3-D models of specimens as well as the pressure imposed on authors by scientific journals to make original 3-D morphological data publicly available have increased. MorphoMuseuM (M3) aims to fill this gap. M3 is both a peer-reviewed scientific journal (M3 Journal) and a virtual specimen repository (M3 Repository). All scientific articles and their associated 3-D models deposited in M3 go through a formal review process. Each published model is given a DOI and a unique identifier code, which should be cited by researchers using this model in their scientific publications. In this paper, we describe the place of M3 among other online repositories for 3-D data, and explain how the growing community of biologists working with 3-D data can benefit from using M3.