The Cell Tracking Challenge: 10 years of objective benchmarking.

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

Surface-Patterned DNA Origami Rulers Reveal Nanoscale Distance Dependency of the Epidermal Growth Factor Receptor Activation.

The nanoscale arrangement of ligands can have a major effect on the activation of membrane receptor proteins and thus cellular communication mechanisms. Here we report on the technological development and use of tailored DNA origami-based molecular rulers to fabricate "Multiscale Origami Structures As Interface for Cells" (MOSAIC), to enable the systematic investigation of the effect of the nanoscale spacing of epidermal growth factor (EGF) ligands on the activation of the EGF receptor (EGFR). MOSAIC-based analyses revealed that EGF distances of about 30-40 nm led to the highest response in EGFR activation of adherent MCF7 and Hela cells. Our study emphasizes the significance of DNA-based platforms for the detailed investigation of the molecular mechanisms of cellular signaling cascades.

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 TGFß 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.

Characterization of the pace-and-drive capacity of the human sinoatrial node: A 3D in silico study.

The sinoatrial node (SAN) is a complex structure that spontaneously depolarizes rhythmically ("pacing") and excites the surrounding non-automatic cardiac cells ("drive") to initiate each heart beat. However, the mechanisms by which the SAN cells can activate the large and hyperpolarized surrounding cardiac tissue are incompletely understood. Experimental studies demonstrated the presence of an insulating border that separates the SAN from the hyperpolarizing influence of the surrounding myocardium, except at a discrete number of sinoatrial exit pathways (SEPs). We propose a highly detailed 3D model of the human SAN, including 3D SEPs to study the requirements for successful electrical activation of the primary pacemaking structure of the human heart. A total of 788 simulations investigate the ability of the SAN to pace and drive with different heterogeneous characteristics of the nodal tissue (gradient and mosaic models) and myocyte orientation. A sigmoidal distribution of the tissue conductivity combined with a mosaic model of SAN and atrial cells in the SEP was able to drive the right atrium (RA) at varying rates induced by gradual If block. Additionally, we investigated the influence of the SEPs by varying their number, length, and width. SEPs created a transition zone of transmembrane voltage and ionic currents to enable successful pace and drive. Unsuccessful simulations showed a hyperpolarized transmembrane voltage (-66 mV), which blocked the L-type channels and attenuated the sodium-calcium exchanger. The fiber direction influenced the SEPs that preferentially activated the crista terminalis (CT). The location of the leading pacemaker site (LPS) shifted toward the SEP-free areas. LPSs were located closer to the SEP-free areas (3.46 ± 1.42 mm), where the hyperpolarizing influence of the CT was reduced, compared with a larger distance from the LPS to the areas where SEPs were located (7.17± 0.98 mm). This study identified the geometrical and electrophysiological aspects of the 3D SAN-SEP-CT structure required for successful pace and drive in silico.

The HMG box transcription factors Sox1a and Sox1b specify a new class of glycinergic interneuron 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- and gata3-dependent precursors co-expressing markers of V2b and V2s interneurons. Chemical blockage of Notch signalling causes a decrease in V2s and an increase in V2b cells. Our results are consistent with the existence of at least two types of precursor 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 behaviour, suggesting that V2s interneurons are involved in fast motor responses.

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.

An objective comparison of cell-tracking algorithms.

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

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.

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.

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.

EmbryoMiner: A new framework for interactive knowledge discovery in large-scale cell tracking data of developing embryos.

State-of-the-art light-sheet and confocal microscopes allow recording of entire embryos in 3D and over time (3D+t) for many hours. Fluorescently labeled structures can be segmented and tracked automatically in these terabyte-scale 3D+t images, resulting in thousands of cell migration trajectories that provide detailed insights to large-scale tissue reorganization at the cellular level. Here we present EmbryoMiner, a new interactive open-source framework suitable for in-depth analyses and comparisons of entire embryos, including an extensive set of trajectory features. Starting at the whole-embryo level, the framework can be used to iteratively focus on a region of interest within the embryo, to investigate and test specific trajectory-based hypotheses and to extract quantitative features from the isolated trajectories. Thus, the new framework provides a valuable new way to quantitatively compare corresponding anatomical regions in different embryos that were manually selected based on biological prior knowledge. As a proof of concept, we analyzed 3D+t light-sheet microscopy images of zebrafish embryos, showcasing potential user applications that can be performed using the new framework.

[Digital Image Processing and Deep Neural Networks in Ophthalmology - Current Trends]. / Digitale Bildverarbeitung und Tiefe Neuronale Netze in der Augenheilkunde ­ aktuelle 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.

Improving short antimicrobial peptides despite elusive rules for activity.

Antimicrobial peptides (AMPs) can effectively kill a broad range of life threatening multidrug-resistant bacteria, a serious threat to public health worldwide. However, despite great hopes novel drugs based on AMPs are still rare. To accelerate drug development we studied different approaches to improve the antibacterial activity of short antimicrobial peptides. Short antimicrobial peptides seem to be ideal drug candidates since they can be synthesized quickly and easily, modified and optimized. In addition, manufacturing a short peptide drug will be more cost efficient than long and structured ones. In contrast to longer and structured peptides short AMPs seem hard to design and predict. Here, we designed, synthesized and screened five different peptide libraries, each consisting of 600 9-mer peptides, against Pseudomonas aeruginosa. Each library is presenting a different approach to investigate effectiveness of an optimization strategy. The data for the 3000 peptides were analyzed using models based on fuzzy logic bioinformatics and plausible descriptors. The rate of active or superior active peptides was improved from 31.0% in a semi-random library from a previous study to 97.8% in the best new designed library. This article is part of a Special Issue entitled: Antimicrobial peptides edited by Karl Lohner and Kai Hilpert.

XPIWIT--an XML pipeline wrapper for the Insight Toolkit.

UNLABELLED: The Insight Toolkit offers plenty of features for multidimensional image analysis. Current implementations, however, often suffer either from a lack of flexibility due to hard-coded C++ pipelines for a certain task or by slow execution times, e.g. caused by inefficient implementations or multiple read/write operations for separate filter execution. We present an XML-based wrapper application for the Insight Toolkit that combines the performance of a pure C++ implementation with an easy-to-use graphical setup of dynamic image analysis pipelines. Created XML pipelines can be interpreted and executed by XPIWIT in console mode either locally or on large clusters. We successfully applied the software tool for the automated analysis of terabyte-scale, time-resolved 3D image data of zebrafish embryos. AVAILABILITY AND IMPLEMENTATION: XPIWIT is implemented in C++ using the Insight Toolkit and the Qt SDK. It has been successfully compiled and tested under Windows and Unix-based systems. Software and documentation are distributed under Apache 2.0 license and are publicly available for download at https://bitbucket.org/jstegmaier/xpiwit/downloads/. CONTACT: johannes.stegmaier@kit.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clinical and molecular implications of MED15 in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) progression depends on various dysregulated pathways. Regulation of diverse pathways is mediated by the mediator complex. The mediator subunit MED15 is essential for transforming growth factor (TGF)-ß signaling and involved in breast and prostate cancers. We investigated the implication of MED15 in HNSCC. IHC for MED15 was performed on 324 tissue samples, and TGF-ß assessed the use of Ki-67 and pSMAD3 as markers. MED15 knockdown followed by proliferation and migration assays, as well as TGF-ß1 treatment followed by MED15 analysis, was also performed. MED15 was overexpressed in 35% of primary tumors, 30% of lymph node metastases, and 70% of recurrences in contrast to no or low expression in benign tumors. MED15 overexpression in primary tumors from patients who developed recurrences was associated with higher mortality rates and occurred at highest frequency in oral cavity or oropharyngeal tumors. Furthermore, MED15 expression correlated between primary tumors and corresponding lymph node metastases. MED15 correlated with proliferation in tissues, and MED15 knockdown reduced proliferation and migration. We observed an association between MED15 and TGF-ß activity in tissues because TGF-ß activation led to increased MED15 expression and reduced pSMAD3 on MED15 knockdown. Taken together, our results implicate MED15 in HNSCC and hint that MED15 overexpression is a clonal event during HNSCC progression. MED15 may serve as a prognostic marker for recurrence and as a therapeutic target.

Optimization of oncocin for antibacterial activity using a SPOT synthesis approach: extending the pathogen spectrum to Staphylococcus aureus.

The identification of lead molecules against multidrug-resistant bacteria ensuing the development of novel antimicrobial drugs is an urgent task. Proline-rich antimicrobial peptides are highly active in vitro and in vivo, but only against a few Gram-negative human pathogens, with rather weak activities against Pseudomonas aeruginosa and Staphylococcus aureus. This reduced level of efficacy could be related to inadequate uptake mechanisms or structural differences of the intracellular target proteins, i.e., the 70S ribosome or chaperone DnaK. Here we synthesized peptide arrays on cellulose membranes using cleavable linkers to release the free individual peptides for further antimicrobial tests. Thus, a library of singly substituted oncocin analogs was produced by replacing each residue by all other 19 canonical amino acids yielding a set of 361 individual peptides to be evaluated against a luminescent P. aeruginosa strain. Thirteen substitutions appeared promising and their improved antibacterial activities were confirmed for different bacteria after larger scale synthesis of these analogs. By combining two favorable substitutions into one peptide, we finally obtained an oncocin analog that was ten times more active against P. aeruginosa and even 100-fold more active against S. aureus than the original oncocin, providing minimal inhibitory concentrations of 4-8 and 0.5 µg/mL, respectively.

Automated prior knowledge-based quantification of neuronal patterns in the spinal cord of zebrafish.

MOTIVATION: To reliably assess the effects of unknown chemicals on the development of fluorescently labeled sensory-, moto- and interneuron populations in the spinal cord of zebrafish, automated data analysis is essential. RESULTS: For the evaluation of a high-throughput screen of a large chemical library, we developed a new method for the automated extraction of quantitative information from green fluorescent protein (eGFP) and red fluorescent protein (RFP) labeled spinal cord neurons in double-transgenic zebrafish embryos. The methodology comprises region of interest detection, intensity profiling with reference comparison and neuron distribution histograms. All methods were validated on a manually evaluated pilot study using a Notch inhibitor dose-response experiment. The automated evaluation showed superior performance to manual investigation regarding time consumption, information detail and reproducibility. AVAILABILITY AND IMPLEMENTATION: Being part of GNU General Public Licence (GNU-GPL) licensed open-source MATLAB toolbox Gait-CAD, an implementation of the presented methods is publicly available for download at http://sourceforge.net/projects/zebrafishimage/.

Identification of nonvisual photomotor response cells in the vertebrate hindbrain.

Nonvisual photosensation enables animals to sense light without sight. However, the cellular and molecular mechanisms of nonvisual photobehaviors are poorly understood, especially in vertebrate animals. Here, we describe the photomotor response (PMR), a robust and reproducible series of motor behaviors in zebrafish that is elicited by visual wavelengths of light but does not require the eyes, pineal gland, or other canonical deep-brain photoreceptive organs. Unlike the relatively slow effects of canonical nonvisual pathways, motor circuits are strongly and quickly (seconds) recruited during the PMR behavior. We find that the hindbrain is both necessary and sufficient to drive these behaviors. Using in vivo calcium imaging, we identify a discrete set of neurons within the hindbrain whose responses to light mirror the PMR behavior. Pharmacological inhibition of the visual cycle blocks PMR behaviors, suggesting that opsin-based photoreceptors control this behavior. These data represent the first known light-sensing circuit in the vertebrate hindbrain.

Genome-wide, whole mount in situ analysis of transcriptional regulators in zebrafish embryos.

Transcription is the primary step in the retrieval of genetic information. A substantial proportion of the protein repertoire of each organism consists of transcriptional regulators (TRs). It is believed that the differential expression and combinatorial action of these TRs is essential for vertebrate development and body homeostasis. We mined the zebrafish genome exhaustively for genes encoding TRs and determined their expression in the zebrafish embryo by sequencing to saturation and in situ hybridisation. At the evolutionary conserved phylotypic stage, 75% of the 3302 TR genes encoded in the genome are already expressed. The number of expressed TR genes increases only marginally in subsequent stages and is maintained during adulthood suggesting important roles of the TR genes in body homeostasis. Fewer than half of the TR genes (45%, n=1711 genes) are expressed in a tissue-restricted manner in the embryo. Transcripts of 207 genes were detected in a single tissue in the 24h embryo, potentially acting as regulators of specific processes. Other TR genes were expressed in multiple tissues. However, with the exception of certain territories in the nervous system, we did not find significant synexpression suggesting that most tissue-restricted TRs act in a freely combinatorial fashion. Our data indicate that elaboration of body pattern and function from the phylotypic stage onward relies mostly on redeployment of TRs and post-transcriptional processes.

A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids.

Brain organoids represent a useful tool for modeling of neurodevelopmental disorders and can recapitulate brain volume alterations such as microcephaly. To monitor organoid growth, brightfield microscopy images are frequently used and evaluated manually which is time-consuming and prone to observer-bias. Recent software applications for organoid evaluation address this issue using classical or AI-based methods. These pipelines have distinct strengths and weaknesses that are not evident to external observers. We provide a dataset of more than 1,400 images of 64 trackable brain organoids from four clones differentiated from healthy and diseased patients. This dataset is especially powerful to test and compare organoid analysis pipelines because of (1) trackable organoids (2) frequent imaging during development (3) clone diversity (4) distinct clone development (5) cross sample imaging by two different labs (6) common imaging distractors, and (6) pixel-level ground truth organoid annotations. Therefore, this dataset allows to perform differentiated analyses to delineate strengths, weaknesses, and generalizability of automated organoid analysis pipelines as well as analysis of clone diversity and similarity.