I-CARE-An Interaction System for the Individual Activation of People with Dementia

I-CARE is a hand-held activation system that allows professional and informal caregivers to cognitively and socially activate people with dementia in joint activation sessions without special training or expertise. I-CARE consists of an easy-to-use tablet application that presents activation content and a server-based backend system that securely manages the contents and events of activation sessions. It tracks various sources of explicit and implicit feedback from user interactions and different sensors to estimate which content is successful in activating individual users. Over the course of use, I-CARE's recommendation system learns about the individual needs and resources of its users and automatically personalizes the activation content. In addition, information about past sessions can be retrieved such that activations seamlessly build on previous sessions while eligible stakeholders are informed about the current state of care and daily form of their protegees. In addition, caregivers can connect with supervisors and professionals through the I-CARE remote calling feature, to get activation sessions tracked in real time via audio and video support. In this way, I-CARE provides technical support for a decentralized and spontaneous formation of ad hoc activation groups and fosters tight engagement of the social network and caring community. By these means, I-CARE promotes new care infrastructures in the community and the neighborhood as well as relieves professional and informal caregivers.

Enhancing deep-learning training for phase identification in powder X-ray diffractograms

Within the domain of analyzing powder X-ray diffraction (XRD) scans, manual examination of the recorded data is still the most popular method, but it requires some expertise and is time consuming. The usual workflow for the phase-identification task involves software for searching databases of known compounds and matching lists of d spacings and related intensities to the measured data. Most automated approaches apply some iterative procedure for the search/match process but fail to be generally reliable yet without the manual validation step of an expert. Recent advances in the field of machine and deep learning have led to the development of algorithms for use with diffraction patterns and are producing promising results in some applications. A limitation, however, is that thousands of training samples are required for the model to achieve a reliable performance and not enough measured samples are available. Accordingly, a framework for the efficient generation of thousands of synthetic XRD scans is presented which considers typical effects in realistic measurements and thus simulates realistic patterns for the training of machine- or deep-learning models. The generated data set can be applied to any machine- or deep-learning structure as training data so that the models learn to analyze measured XRD data based on synthetic diffraction patterns. Consequently, we train a convolutional neural network with the simulated diffraction patterns for application with iron ores or cements compounds and prove robustness against varying unit-cell parameters, preferred orientation and crystallite size in synthetic, as well as measured, XRD scans.

Parkinson's disease with restless legs syndrome-an in vivo corneal confocal microscopy study

Small fiber neuropathy (SFN) has been suggested as a trigger of restless legs syndrome (RLS). An increased prevalence of peripheral neuropathy has been demonstrated in Parkinson's disease (PD). We aimed to investigate, in a cross-sectional manner, whether SFN is overrepresented in PD patients with concurrent RLS relative to PD patients without RLS, using in vivo corneal confocal microscopy (IVCCM) and quantitative sensory testing (QST) as part of small fiber assessment. Study participants comprised of age- and sex-matched PD patients with (n = 21) and without RLS (n = 21), and controls (n = 13). Diagnosis of RLS was consolidated with the sensory suggested immobilization test. Assessments included nerve conduction studies (NCS), Utah Early Neuropathy Scale (UENS), QST, and IVCCM, with automated determination of corneal nerve fiber length (CNFL) and branch density (CNBD) from wide-area mosaics of the subbasal nerve plexus. Plasma neurofilament light (p-NfL) was determined as a measure of axonal degeneration. No significant differences were found between groups when comparing CNFL (p = 0.81), CNBD (p = 0.92), NCS (p = 0.82), and QST (minimum p = 0.54). UENS scores, however, differed significantly (p = 0.001), with post-hoc pairwise testing revealing higher scores in both PD groups relative to controls (p = 0.018 and p = 0.001). Analysis of all PD patients (n = 42) revealed a correlation between the duration of L-dopa therapy and CNBD (ρ = -0.36, p = 0.022), and p-NfL correlated with UENS (ρ = 0.35, p = 0.026) and NCS (ρ = -0.51, p = 0.001). Small and large fiber neuropathy do not appear to be associated with RLS in PD. Whether peripheral small and/or large fiber pathology associates with central neurodegeneration in PD merits further longitudinal studies.

epiTracker: A Framework for Highly Reliable Particle Tracking for the Quantitative Analysis of Fish Movements in Tanks

Behavioral analysis of moving animals relies on a faithful recording and track analysis to extract relevant parameters of movement. To study group behavior and social interactions, often simultaneous analyses of individuals are required. To detect social interactions, for example to identify the leader of a group as opposed to followers, one needs an error-free segmentation of individual tracks throughout time. While automated tracking algorithms exist that are quick and easy to use, inevitable errors will occur during tracking. To solve this problem, we introduce a robust algorithm called epiTracker for segmentation and tracking of multiple animals in two-dimensional (2D) videos along with an easy-to-use correction method that allows one to obtain error-free segmentation. We have implemented two graphical user interfaces to allow user-friendly control of the functions. Using six labeled 2D datasets, the effort to obtain accurate labels is quantified and compared to alternative available software solutions. Both the labeled datasets and the software are publicly available.

Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy

The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in images with a low signal-to-noise-ratio is still a challenging problem. In this paper, we present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. Furthermore, this representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included cell types. For the prediction of the proposed neighbor distances, an adapted U-Net convolutional neural network (CNN) with two decoder paths is used. In addition, we adapt a graph-based cell tracking algorithm to evaluate our proposed method on the task of cell tracking. The adapted tracking algorithm includes a movement estimation in the cost function to re-link tracks with missing segmentation masks over a short sequence of frames. Our combined tracking by detection method has proven its potential in the IEEE ISBI 2020 Cell Tracking Challenge (http://celltrackingchallenge.net/) where we achieved as team KIT-Sch-GE multiple top three rankings including two top performances using a single segmentation model for the diverse data sets.

BeadNet: deep learning-based bead detection and counting in low-resolution microscopy images

MOTIVATION

An automated counting of beads is required for many high-throughput experiments such as studying mimicked bacterial invasion processes. However, state-of-the-art algorithms under- or overestimate the number of beads in low-resolution images. In addition, expert knowledge is needed to adjust parameters.

RESULTS

In combination with our image labeling tool, BeadNet enables biologists to easily annotate and process their data reducing the expertise required in many existing image analysis pipelines. BeadNet outperforms state-of-the-art-algorithms in terms of missing, added and total amount of beads.

AVAILABILITY AND IMPLEMENTATION

BeadNet (software, code and dataset) is available at https://bitbucket.org/t_scherr/beadnet. The image labeling tool is available at https://bitbucket.org/abartschat/imagelabelingtool.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MondoA regulates gene expression in cholesterol biosynthesis-associated pathways required for zebrafish epiboly

The glucose-sensing Mondo pathway regulates expression of metabolic genes in mammals. Here, we characterized its function in the zebrafish and revealed an unexpected role of this pathway in vertebrate embryonic development. We showed that knockdown of mondoa impaired the early morphogenetic movement of epiboly in zebrafish embryos and caused microtubule defects. Expression of genes in the terpenoid backbone and sterol biosynthesis pathways upstream of pregnenolone synthesis was coordinately downregulated in these embryos, including the most downregulated gene nsdhl. Loss of Nsdhl function likewise impaired epiboly, similar to MondoA loss of function. Both epiboly and microtubule defects were partially restored by pregnenolone treatment. Maternal-zygotic mutants of mondoa showed perturbed epiboly with low penetrance and compensatory changes in the expression of terpenoid/sterol/steroid metabolism genes. Collectively, our results show a novel role for MondoA in the regulation of early vertebrate development, connecting glucose, cholesterol and steroid hormone metabolism with early embryonic cell movements.

Evaluation of semi-supervised learning using sparse labeling to segment cell nuclei

The analysis of microscopic images from cell cultures plays an important role in the development of drugs. The segmentation of such images is a basic step to extract the viable information on which further evaluation steps are build. Classical image processing pipelines often fail under heterogeneous conditions. In the recent years deep neuronal networks gained attention due to their great potentials in image segmentation. One main pitfall of deep learning is often seen in the amount of labeled data required for training such models. Especially for 3D images the process to generate such data is tedious and time consuming and thus seen as a possible reason for the lack of establishment of deep learning models for 3D data. Efforts have been made to minimize the time needed to create labeled training data or to reduce the amount of labels needed for training. In this paper we present a new semisupervised training method for image segmentation of microscopic cell recordings based on an iterative approach utilizing unlabeled data during training. This method helps to further reduce the amount of labels required to effectively train deep learning models for image segmentation. By labeling less than one percent of the training data, a performance of 90% compared to a full annotation with 342 nuclei can be achieved.

GPU-accelerated ray-casting for 3D fiber orientation analysis

Orientation analysis of fibers is widely applied in the fields of medical, material and life sciences. The orientation information allows predicting properties and behavior of materials to validate and guide a fabrication process of materials with controlled fiber orientation. Meanwhile, development of detector systems for high-resolution non-invasive 3D imaging techniques led to a significant increase in the amount of generated data per a sample up to dozens of gigabytes. Though plenty of 3D orientation estimation algorithms were developed in recent years, neither of them can process large datasets in a reasonable amount of time. This fact complicates the further analysis and makes impossible fast feedback to adjust fabrication parameters. In this work, we present a new method for quantifying the 3D orientation of fibers. The GPU implementation of the proposed method surpasses another popular method for 3D orientation analysis regarding accuracy and speed. The validation of both methods was performed on a synthetic dataset with varying parameters of fibers. Moreover, the proposed method was applied to perform orientation analysis of scaffolds with different fibrous micro-architecture studied with the synchrotron μCT imaging setup. Each acquired dataset of size 600x600x450 voxels was analyzed in less 2 minutes using standard PC equipped with a single GPU.

Pax6 organizes the anterior eye segment by guiding two distinct neural crest waves

Cranial neural crest (NC) contributes to the developing vertebrate eye. By multidimensional, quantitative imaging, we traced the origin of the ocular NC cells to two distinct NC populations that differ in the maintenance of sox10 expression, Wnt signalling, origin, route, mode and destination of migration. The first NC population migrates to the proximal and the second NC cell group populates the distal (anterior) part of the eye. By analysing zebrafish pax6a/b compound mutants presenting anterior segment dysgenesis, we demonstrate that Pax6a/b guide the two NC populations to distinct proximodistal locations. We further provide evidence that the lens whose formation is pax6a/b-dependent and lens-derived TGFβ signals contribute to the building of the anterior segment. Taken together, our results reveal multiple roles of Pax6a/b in the control of NC cells during development of the anterior segment.

Segregation of Dispersed Silica Nanoparticles in Microfluidic Water-in-Oil Droplets: A Kinetic Study

Dispersed negatively charged silica nanoparticles segregate inside microfluidic water-in-oil (W/O) droplets that are coated with a positively charged lipid shell. We report a methodology for the quantitative analysis of this self-assembly process. By using real-time fluorescence microscopy and automated analysis of the recorded images, kinetic data are obtained that characterize the electrostatically-driven self-assembly. We demonstrate that the segregation rates can be controlled by the installment of functional moieties on the nanoparticle's surface, such as nucleic acid and protein molecules. We anticipate that our method enables the quantitative and systematic investigation of the segregation of (bio)functionalized nanoparticles in microfluidic droplets. This could lead to complex supramolecular architectures on the inner surface of micrometer-sized hollow spheres, which might be used, for example, as cell containers for applications in the life sciences.

The effect of lipidation and glycosylation on short cationic antimicrobial peptides

The global health threat surrounding bacterial resistance has resulted in antibiotic researchers shifting their focus away from 'traditional' antibiotics and concentrating on other antimicrobial agents, including antimicrobial peptides. These low molecular weight "mini-proteins" exhibit broad-spectrum activity against bacteria, including multi-drug resistant strains, viruses, fungi and protozoa and constitute a major element of the innate-immune system of many multicellular organisms. Some naturally occurring antimicrobial peptides are lipidated and/or glycosylated and almost all antimicrobial peptides in clinical use are either lipopeptides (Daptomycin and Polymyxin E and B) or glycopeptides (Vancomycin). Lipidation, glycosylation and PEGylation are an option for improving stability and activity in serum and for reducing the rapid clearing via the kidneys and liver. Two broad-spectrum antimicrobial peptides NH₂-RIRIRWIIR-CONH₂ (A1) and NH₂-KRRVRWIIW-CONH₂ (B1) were conjugated via a linker, producing A2 and B2, to individual fatty acids of C₈, C₁₀, C₁₂ and C₁₄ and in addition, A2 was conjugated to either glucose, N-acetyl glucosamine, galactose, mannose, lactose or polyethylene glycol (PEG). Antimicrobial activity against two Gram-positive strains (methicillin resistant Staphylococcus aureus (MRSA) and vancomycin resistant Enterococcus faecalis (VRE)) and three Gram-negative strains (Salmonella typhimurium, E. coli and Pseudomonas aeruginosa) were determined. Activity patterns for the lipidated versions are very complex, dependent on sequence, bacteria and fatty acid. Two reciprocal effects were measured; compared to the parental peptides, some combinations led to a 16-fold improvement whereas other combinations let to a 32-fold reduction in antimicrobial activity. Glycosylation decreased antimicrobial activity by 2 to 16-fold in comparison to A1, respectively on the sugar-peptide combination. PEGylation rendered the peptide inactive. Antimicrobial activity in the presence of 25% human serum of A1 and B1 was reduced 32-fold and 8-fold, respectively. The longer chain fatty acids almost completely restored this activity; however, these fatty acids increased hemolytic activity. B1 modified with C8 increased the therapeutic index by 2-fold for four bacterial strains. Our results suggest that finding the right lipid-peptide combination can lead to improved activity in the presence of serum and potentially more effective drug candidates for animal studies. Glycosylation with the optimal sugar and numbers of sugars at the right peptide position could be an alternative route or could be used in addition to lipidation to counteract solubility and toxicity issues.

Variability of running coordination in experts and novices: A 3D uncontrolled manifold analysis

The uncontrolled manifold (UCM) approach has been widely used in recent studies to examine variability in daily tasks; however, it has not yet been used to study running or the effects of expertise. Therefore, the aim of this study was to analyse the synergy structure stabilizing the centre of mass (CoM) trajectory in experts compared to novices during running at two different speeds using a subject-specific 3D model. A total of 25 healthy young adults (13 experts, 12 novices) participated in the study. All subjects ran at 10 and 15 km h^-1 on a treadmill. In each case, kinematics of 20 consecutive gait cycles were recorded and the effects of expertise and gait cycle phase on the synergy structure were investigated at both speeds. Specifically, the variance affecting the CoM ( U C M ⊥ ) , the variance not affecting the CoM ( U C M ∥ ) , and their ratio ( U C M R a t i o ) were analysed. Descriptively, in both groups there was a synergy stabilizing the CoM trajectory in running. However, the ANOVA showed no differences in U C M R a t i o between the two groups. In novices, U C M ⊥ and U C M ∥ were significantly higher compared to experts at the 15 km h^-1 condition. In both groups, there was more variability in the stance phase compared to the flight phase in the majority of cases. The results indicate that experts adopted a more consistent running style. The stride-to-stride variability was diminished but not abolished. This difference was only visible at the 15 km h^-1 condition. Furthermore, variability was less constrained in the stance phase compared to the flight phase.

Motion prediction enables simulated MR-imaging of freely moving model organisms

Magnetic resonance tomography typically applies the Fourier transform to k-space signals repeatedly acquired from a frequency encoded spatial region of interest, therefore requiring a stationary object during scanning. Any movement of the object results in phase errors in the recorded signal, leading to deformed images, phantoms, and artifacts, since the encoded information does not originate from the intended region of the object. However, if the type and magnitude of movement is known instantaneously, the scanner or the reconstruction algorithm could be adjusted to compensate for the movement, directly allowing high quality imaging with non-stationary objects. This would be an enormous boon to studies that tie cell metabolomics to spontaneous organism behaviour, eliminating the stress otherwise necessitated by restraining measures such as anesthesia or clamping. In the present theoretical study, we use a phantom of the animal model C. elegans to examine the feasibility to automatically predict its movement and position, and to evaluate the impact of movement prediction, within a sufficiently long time horizon, on image reconstruction. For this purpose, we use automated image processing to annotate body parts in freely moving C. elegans, and predict their path of movement. We further introduce an MRI simulation platform based on bright field videos of the moving worm, combined with a stack of high resolution transmission electron microscope (TEM) slice images as virtual high resolution phantoms. A phantom provides an indication of the spatial distribution of signal-generating nuclei on a particular imaging slice. We show that adjustment of the scanning to the predicted movements strongly reduces distortions in the resulting image, opening the door for implementation in a high-resolution NMR scanner.

Magnetic resonance imaging (MRI) requires its subjects not to move, since movement will cause image artifacts. This is hard to achieve for adult humans, whom we can ask to comply, but can currently only be achieved by sedation for other freely moving biological specimens. Because of the importance of non-invasive MRI as a technique to also capture metabolic information during activity, this is a huge deficiency of the methodology that is hampering progress. In our paper we ask the question whether it is possible to computationally combine optical information on specimen movement with MRI. Our approach is to predict the future movement and position of the specimen and thereby anticipate where it will be so as to specify correct MRI parameters. Our computer simulations show, for a freely moving worm, that a reasonable prediction is already possible for a short time window, and that we can control the amount of error of the resulting MRI image. Importantly, with the continuous speedup of computation, our simulations suggest that it is opportune now to implement such a system in hardware.

Synergy Pattern of Short Cationic Antimicrobial Peptides Against Multidrug-Resistant Pseudomonas aeruginosa

With the rise of various multidrug-resistant (MDR) pathogenic bacteria, worldwide health care is under pressure to respond. Conventional antibiotics are failing and the development of novel classes and alternative strategies is a major priority. Antimicrobial peptides (AMPs) cannot only kill MDR bacteria, but also can be used synergistically with conventional antibiotics. We selected 30 short AMPs from different origins and measured their synergy in combination with polymyxin B, piperacillin, ceftazidime, cefepime, meropenem, imipenem, tetracycline, erythromycin, kanamycin, tobramycin, amikacin, gentamycin, and ciprofloxacin. In total, 403 unique combinations were tested against an MDR Pseudomonas aeruginosa isolate (PA910). As a measure of the synergistic effects, fractional inhibitory concentrations (FICs) were determined using microdilution assays with FICs ranges between 0.25 and 2. A high number of combinations between peptides and polymyxin B, erythromycin, and tetracycline were found to be synergistic. Novel variants of indolicidin also showed a high frequency in synergist interaction. Single amino acid substitutions within the peptides can have a very strong effect on the ability to synergize, making it possible to optimize future drugs toward synergistic interaction.

[Digital Image Processing and Deep Neural Networks in Ophthalmology - Current Trends]

The use of deep neural networks ("deep learning") creates new possibilities in digital image processing. This approach has been widely applied and successfully used for the evaluation of image data in ophthalmology. In this article, the methodological approach of deep learning is examined and compared to the classical approach for digital image processing. The differences between the approaches are discussed and the increasingly important role of training data for model generation is explained. Furthermore, the approach of transfer learning for deep learning is presented with a representative data set from the field of corneal confocal microscopy. In this context, the advantages of the method and the specific problems when dealing with medical microscope data will be discussed.

Machine Learning Methods for Automated Quantification of Ventricular Dimensions

Medaka (Oryzias latipes) and zebrafish (Danio rerio) contribute substantially to our understanding of the genetic and molecular etiology of human cardiovascular diseases. In this context, the quantification of important cardiac functional parameters is fundamental. We have developed a framework that segments the ventricle of a medaka hatchling from image sequences and subsequently quantifies ventricular dimensions.

BioSAXS Measurements Reveal That Two Antimicrobial Peptides Induce Similar Molecular Changes in Gram-Negative and Gram-Positive Bacteria

Two highly active short broad-spectrum AMPs (14D and 69D) with unknown mode of action have been investigated in regards to their effect against the Gram-negative bacteria Escherichia coli and the Gram-positive bacteria methicillin-resistant Staphylococcus aureus (MRSA). Minimal inhibitory concentration (MIC) measurements using a cell density of 10⁸ cfu/ml resulted in values between 16 and 32 µg/ml. Time-kill experiments using 10⁸ cfu/ml revealed complete killing, except for 69D in combination with MRSA, where bacterial load was reduced a million times. Small-angle X-ray scattering of biological samples (BioSAXS) at 10⁸ cfu/ml was applied to investigate the ultrastructural changes in E. coli and MRSA in response to these two broad-spectrum AMPs. In addition, electron microscopy (EM) was performed to visualize the treated and non-treated bacteria. As expected, the scattering curves generated using BioSAXS show the ultrastructure of the Gram-positive and Gram-negative bacteria to be very different (BioSAXS is not susceptible to the outer shape). After treatment with either peptide, the scattering curves of E. coli and MRSA cells are much more alike. Whereas in EM, it is notoriously difficult to observe changes for spherical Gram-positives; the BioSAXS results are superior and reveal strongly similar effects for both peptides induced in Gram-positive as well as Gram-negative bacteria. Given the high-throughput possibility and robust statistics, BioSAXS can support and speed up mode of action research in AMPs and other antimicrobial compounds, making a contribution toward the development of urgently needed drugs against resistant bacteria.

Microfluidic Chips for Life Sciences-A Comparison of Low Entry Manufacturing Technologies

Microfluidic water-in-oil droplets are a versatile tool for biological and biochemical applications due to the advantages of extremely small monodisperse reaction vessels in the pL-nL range. A key factor for the successful dissemination of this technology to life science laboratory users is the ability to produce microfluidic droplet generators and related accessories by low-entry barrier methods, which enable rapid prototyping and manufacturing of devices with low instrument and material costs. The direct, experimental side-by-side comparison of three commonly used additive manufacturing (AM) methods, namely fused deposition modeling (FDM), inkjet printing (InkJ), and stereolithography (SLA), is reported. As a benchmark, micromilling (MM) is used as an established method. To demonstrate which of these methods can be easily applied by the non-expert to realize applications in topical fields of biochemistry and microbiology, the methods are evaluated with regard to their limits for the minimum structure resolution in all three spatial directions. The suitability of functional SLA and MM chips to replace classic SU-8 prototypes is demonstrated on the basis of representative application cases.

Quanfima: An open source Python package for automated fiber analysis of biomaterials

Hybrid 3D scaffolds composed of different biomaterials with fibrous structure or enriched with different inclusions (i.e., nano- and microparticles) have already demonstrated their positive effect on cell integration and regeneration. The analysis of fibers in hybrid biomaterials, especially in a 3D space is often difficult due to their various diameters (from micro to nanoscale) and compositions. Though biomaterials processing workflows are implemented, there are no software tools for fiber analysis that can be easily integrated into such workflows. Due to the demand for reproducible science with Jupyter notebooks and the broad use of the Python programming language, we have developed the new Python package quanfima offering a complete analysis of hybrid biomaterials, that include the determination of fiber orientation, fiber and/or particle diameter and porosity. Here, we evaluate the provided tensor-based approach on a range of generated datasets under various noise conditions. Also, we show its application to the X-ray tomography datasets of polycaprolactone fibrous scaffolds pure and containing silicate-substituted hydroxyapatite microparticles, hydrogels enriched with bioglass contained strontium and alpha-tricalcium phosphate microparticles for bone tissue engineering and porous cryogel 3D scaffold for pancreatic cell culturing. The results obtained with the help of the developed package demonstrated high accuracy and performance of orientation, fibers and microparticles diameter and porosity analysis.

The HMG box transcription factors Sox1a and b specify a new class of glycinergic interneurons in the spinal cord of zebrafish embryos

Specification of neurons in the spinal cord relies on extrinsic and intrinsic signals, which in turn are interpreted by expression of transcription factors. V2 interneurons develop from the ventral aspects of the spinal cord. We report here a novel neuronal V2 subtype, named V2s, in zebrafish embryos. Formation of these neurons depends on the transcription factors sox1a and sox1b. They develop from common gata2a/gata3 dependent precursors co-expressing markers of V2b and V2s interneurons. Chemical blockage of Notch signaling causes a decrease of V2s and an increase of V2b cells. Our results are consistent with the existence of at least two types of precursors arranged in a hierarchical manner in the V2 domain. V2s neurons grow long ipsilateral descending axonal projections with a short branch at the ventral midline. They acquire a glycinergic neurotransmitter type during the second day of development. Unilateral ablation of V2s interneurons causes a delay in touch-provoked escape behavior suggesting that V2s interneurons are involved in fast motor responses.

[In vivo imaging of the corneal nerve plexus : From single image to large scale map]

The sub-basal nerve plexus (SNP) of the cornea provides the possibility of in vivo and non-invasive examination of peripheral nerve structures by corneal confocal microscopy (CCM). Thus morphological alterations of the SNP can be directly detected and quantified. A single CCM image is insufficient for a well-founded diagnosis because of the inhomogeneous distribution of the nerve fibers; therefore, there is a demand for techniques for large area imaging of the SNP. This article provides an overview of published approaches to the problem. Current developmental work at the Karlsruhe Institute of Technology and the University of Rostock Eye Clinic is expected to lead to a simplified handling of the technology and a further improvement in the image quality.

Wnt/PCP controls spreading of Wnt/β-catenin signals by cytonemes in vertebrates

Signaling filopodia, termed cytonemes, are dynamic actin-based membrane structures that regulate the exchange of signaling molecules and their receptors within tissues. However, how cytoneme formation is regulated remains unclear. Here, we show that Wnt/planar cell polarity (PCP) autocrine signaling controls the emergence of cytonemes, and that cytonemes subsequently control paracrine Wnt/β-catenin signal activation. Upon binding of the Wnt family member Wnt8a, the receptor tyrosine kinase Ror2 becomes activated. Ror2/PCP signaling leads to the induction of cytonemes, which mediate the transport of Wnt8a to neighboring cells. In the Wnt-receiving cells, Wnt8a on cytonemes triggers Wnt/β-catenin-dependent gene transcription and proliferation. We show that cytoneme-based Wnt transport operates in diverse processes, including zebrafish development, murine intestinal crypt and human cancer organoids, demonstrating that Wnt transport by cytonemes and its control via the Ror2 pathway is highly conserved in vertebrates.

Communication helps the cells that make up tissues and organs to work together as a team. One way that cells share information with each other as tissues grow and develop is by exchanging signaling proteins. These interact with receptors on the surface of other cells; this causes the cell to change how it behaves.

The Wnt family of signaling proteins orchestrate organ development. Wnt proteins influence which types of cells develop, how fast they divide, and how and when they move. Relatively few cells, or small groups of cells, in developing tissues produce Wnt proteins, while larger groups nearby respond to the signals.

We do not fully understand how Wnt proteins travel between cells, but recent work revealed an unexpected mechanism – cells seem to hand-deliver their messages. Finger-like structures called cytonemes grow out of the cell membrane and carry Wnt proteins to their destination. If the cytonemes do not form properly the target cells do not behave correctly, which can lead to severe tissue malformation.

Mattes et al. have now investigated how cytonemes form using a combination of state-of-the-art genetic and high-resolution imaging techniques. In initial experiments involving zebrafish cells that were grown in the laboratory, Mattes et al. found that the Wnt proteins kick start their own transport; before they travel to their destination, they act on the cells that made them. A Wnt protein called Wnt8a activates the receptor Ror2 on the surface of the signal-producing cell. Ror2 then triggers signals inside the cell that begin the assembly of the cytonemes. The more Ror2 is activated, the more cytonemes the cell makes, and the more Wnt signals it can send out.

This mechanism operates in various tissues: Ror2 also controls the cytoneme transport process in living zebrafish embryos, the mouse intestine and human stomach tumors. This knowledge will help researchers to develop new ways to control Wnt signaling, which could help to produce new treatments for diseases ranging from cancers (for example in the stomach and bowel) to degenerative diseases such as Alzheimer’s disease.

Cellular in vivo 3D imaging of the cornea by confocal laser scanning microscopy

We present an in vivo confocal laser scanning microscopy based method for large 3D reconstruction of the cornea on a cellular level with cropped volume sizes up to 266 x 286 x 396 µm3. The microscope objective used is equipped with a piezo actuator for automated, fast and precise closed-loop focal plane control. Furthermore, we present a novel concave surface contact cap, which significantly reduces eye movements by up to 87%, hence increasing the overlapping image area of the whole stack. This increases the cuboid volume of the generated 3D reconstruction significantly. The possibility to generate oblique sections using isotropic volume stacks opens the window to slit lamp microscopy on a cellular level.